986 results about "Production planning" patented technology

The invention relates to the technical field of digital factory production management, and discloses a digital factory-oriented multi-target production plan optimization method and system, and the method comprises the steps: decomposing an enterprise order into a plurality of subtasks through a production task decomposition module, and extracting related information; the resource dynamic evaluation module collects data in real time to generate a resource state matrix; the multi-objective optimization algorithm module constructs a model and adopts an improved non-dominated sorting genetic algorithm to solve; the dynamic priority distribution module adjusts the priorities of the sub-tasks according to the real-time data; and the conflict resolution unit is used for solving resource allocation conflicts. The method can accurately decompose tasks, evaluate resources in real time, collaboratively optimize multiple targets, flexibly deal with abnormities, efficiently resolve conflicts, comprehensively improve the production efficiency and benefits of the digital factory, and effectively solve the complex problems in the production plan making and optimizing process of the digital factory.

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention relates to the technical field of industrial production scheduling optimization, and discloses an industrial production process intelligent scheduling and optimization method and system.The industrial production process intelligent scheduling and optimization method comprises the steps that resource competition hotspots are identified by establishing a resource time two-dimensional matrix; according to the method, the resource competition relationship between the emergency order and the existing order can be accurately identified, global re-planning is replaced by local fine tuning, the optimal insertion position is determined by constructing an order interference degree evaluation model, the hotspot intensity is reduced by implementing a resource competition mitigation strategy, and optimal embedding of the emergency order is realized by adopting a progressive order insertion algorithm. The disturbance to an existing production plan is minimized, the emergency order processing efficiency and the system stability are remarkably improved, and the technical problem of intelligent scheduling of emergency orders in industrial production is solved.

The invention discloses a simulation optimization method for a digital twin factory. The simulation optimization method comprises the steps of obtaining workshop equipment data and performing classified storage; generating a task priority and resource configuration rule based on the basic operation data of the equipment; constructing a three-dimensional space model by using a 3D modeling tool, and creating a digital twinborn simulation factory model in combination with real-time production data; constructing a dynamic planning state space and a decision variable, and determining the resource use time, the resource allocation position and the specific quantity of each task; according to the resource allocation scheme and the task chain dependency relationship, evaluating a risk value of a task node in combination with historical equipment fault data; and based on the influence range report of the production fault chain, evaluating a task execution sequence and adjusting resource allocation and task time arrangement. According to the method, the influence of faults on a production plan and resource waste are reduced, the stability and adaptability of a factory system are remarkably enhanced, and the reliability and fine management level of overall production are improved.

The invention provides a food information tracing method and system based on Internet data, and relates to the technical field of industrial Internet, and the method comprises the following steps: generating an initial batch identifier of a to-be-produced product according to a product production plan, obtaining real-time external data of an associated region, and storing the real-time external data in a database; when it is judged that a batch adjustment event occurs in the production process of the to-be-produced product, based on real-time state data and real-time external data of a production line, dynamic batch decision is carried out to generate at least two sub-batch identifiers, and a tree-shaped association relationship is established between the sub-batch identifiers and the initial batch identifier; and receiving sub-batch detailed data of each sub-batch identifier, uploading the sub-batch detailed data to a block chain network, storing the batch topological relation in a block chain main chain, storing the sub-batch detailed data in a corresponding sub-chain node, responding to a tracing request of a user terminal, and according to the tree-shaped association relation, tracing the sub-batch data of each sub-batch identifier. And retrieving the full life cycle data of the corresponding target batch in the block chain network. The method overcomes the problem of tracing information splitting caused by batch splitting.

The invention relates to the technical field of intelligent warehousing management, in particular to an intelligent warehousing system optimization method based on production equipment linkage. The method comprises the following steps: carrying out multi-source data acquisition on a sales system and a production system, and carrying out dynamic demand prediction to obtain a predicted demand quantity; obtaining a product attribute list; performing safe inventory calculation according to the predicted demand quantity and the product attribute list to obtain a safe inventory benchmark; according to the predicted demand quantity, the product attribute list and the safety inventory benchmark, carrying out maximum inventory quantity rough calculation to obtain a maximum inventory limit; performing dynamic threshold adjustment according to the highest inventory limit to obtain a dynamic inventory threshold; and carrying out production demand decomposition on the predicted demand quantity and the dynamic inventory threshold value to obtain a to-be-produced list. According to the method, collaborative optimization of warehouse management and production plans is realized through data driving, prediction driving and intelligent optimization, and the warehouse operation efficiency and enterprise benefits are remarkably improved.

The invention provides a coal preparation plant production data full-process management system and method, and the method comprises the steps: a data collection subsystem collects equipment data and production data of different data sources of each plant through a preset data collection interface; the data transmission subsystem encrypts and transmits equipment data and production data acquired by the data acquisition subsystem to the data cleaning subsystem for cleaning, and the data storage subsystem stores the data cleaned by the data cleaning subsystem to a plurality of databases of different types in a mixed storage mode. And the data application subsystem obtains the target data from the data storage subsystem and performs production plan optimization, equipment fault early warning and quality control and optimization based on the target data and preset data. By the adoption of the method, efficient processing and deep application of the whole process of production data of the coal preparation plant are achieved, powerful support is provided for intelligent and refined production management of the coal preparation plant, and the overall operation level and economic benefits of the coal preparation plant can be effectively improved.

The invention relates to the technical field of industrial control, and discloses a dry-wet combined cooling tower operation state prediction and diagnosis system comprising a load prediction module used for obtaining production plan data and environment information, constructing a basic heat load prediction model, and outputting a total prediction heat load; the thermal modeling module is used for establishing a heat transfer model and an energy consumption model, the heat transfer model outputs cooling amounts in different operation modes, and the energy consumption model outputs total predicted power; the dry-wet decision module is used for constructing a multi-objective optimization function and solving the multi-objective optimization function to obtain a dry-wet switching strategy; and the control execution module is used for executing the dry-wet switching strategy. According to the method, the basic thermal load prediction model is established, so that the prediction precision of the thermal load is improved, and differentiated cooling strategies are provided for different production stages; on the basis of real-time load requirements and environmental conditions, a dry mode or a wet mode is intelligently selected for operation, and a multi-objective optimization function is constructed, so that the balance between cooling requirement meeting and system energy consumption minimization is realized.

The invention discloses a furniture production informatization management system based on data analysis, and relates to the technical field of production management, and the system comprises a production plan optimization module, a raw material cutting optimization module and a quality prediction center module. The production plan optimization module collects data by deploying an RFID sensing network, establishes a multi-dimensional evaluation matrix to determine order priority, optimizes a production plan by using a mixed integer programming model and an improved algorithm, and generates a production scheduling scheme after digital twinborn verification; the raw material cutting optimization module analyzes order data to construct a raw material model, optimizes component layout by applying a self-adaptive algorithm, and generates a cutting scheme with process constraint and an excess material map in combination with stress analysis; through an intelligent production management means, the production efficiency, the raw material utilization rate and the product quality stability are remarkably improved, and remarkable economic benefits are brought to enterprises.

The invention discloses a quick-freezing equipment fault prediction and health management system based on digital twinning, and particularly relates to the field of quick-freezing equipment operation and maintenance. The theoretical fault index generation module is used for generating a multi-dimensional theoretical fault index matrix; the real-time fault index calculation module generates a real-time fault index; the deviation threshold value library establishing unit forms a dynamically updated deviation threshold value library; the residual life prediction module calculates the residual running time of the equipment from the current state to the fault; the health management module calculates equipment health scores and divides equipment health states into four grades; the visual display module provides a visual interface for a user; compared with the traditional operation and maintenance that the residual life cannot be estimated, the method can enable the operation and maintenance personnel to plan maintenance and replace parts in advance and reasonably arrange a production plan, reduces the risk of sudden failure, and enables the equipment operation and maintenance to be changed from passive response to active prevention.

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention belongs to the technical field of factory production scheduling, and discloses an MES (Manufacturing Execution System)-based capacity balance type intelligent factory scheduling system, which comprises a preprocessing module for realizing the function of intelligently selecting a production strategy according to a current production mode, and improving the flexibility and accuracy of production decision making. The data execution module ensures the stable execution of the production plan, and can timely discover and respond to the change of the production plan, thereby avoiding the confusion and delay in the production process. And secondly, through introduction of the change scheduling module and the plan change module, the production plan change process is more scientific and efficient. By storing and calling a change algorithm, the system can automatically analyze the relation between order performance indexes and reasonably adjust the weight of each performance index of production, so that a new production plan better meeting actual requirements is formulated.

The invention relates to an impregnated paper production process multi-parameter collaborative optimization control method based on reinforcement learning, and the method comprises the following steps: S1, building an omnibearing data collection network, achieving the panoramic perception of a production process, and obtaining a real-time data flow; s2, based on a data fusion technology, performing intelligent integration on different sources and different types of data to obtain a fused data set; s3, according to the fused data set and based on a process mechanism, constructing a knowledge graph of the impregnated paper production process; s4, constructing a digital twinborn model of the impregnated paper production line in combination with the fused data set and the knowledge graph; and S5, designing a multi-objective optimization decision framework according to the digital twinborn model, the production plan and the constraint condition, and realizing collaborative optimization of different time scales. According to the invention, multi-parameter collaborative optimization control of the impregnated paper production process is realized.

The invention relates to the technical field of report generation, in particular to an enterprise business report generation method and system based on a large model. The method comprises the following steps: acquiring component operation state data and production plan data of industrial equipment; performing master-slave relation division on internal components of the equipment according to the component operation state data, designating a driving device as a master component, designating a transmission component and an execution component as slave components, and constructing a component transfer chain relation graph; monitoring operation state parameters of the main component in the component transmission chain relation graph; extracting the master component of which the fluctuation of the operation state parameter exceeds a preset threshold value, and measuring the response parameter change condition of the connected slave component; and generating abnormal transmission confirmation data according to the response parameter change condition. According to the invention, based on abnormal transmission characteristics between components, early fault identification and propagation path tracking are realized, and a problem source is found before fault expansion.

Disclosed in the present invention is a material picking method based on a mobile robot, comprising the following steps: step S1, acquiring a real-time production plan and a bill of materials, wherein the bill of materials comprises material codes, material names, material quantity and lead time; step S2, on the basis of the material codes, dividing the bill of materials into a plurality of task units suitable for single operation of the mobile robot, and assigning corresponding picking stations; step S3, performing picking state labeling on the task units on the basis of the lead time of materials in the task units, and assigning corresponding mobile robots on the basis of the labeled picking states; and step S4, the mobile robots performing path planning on the basis of the assigned task units, and executing the assigned task units on the basis of the planned path, so that racks to which the materials in the assigned task units belong are transported to specified picking stations for operators to pick the required materials.

The invention relates to the technical field of data processing, in particular to a multi-agent collaborative calling method and system based on big data, by sending supply chain data to a decision-making agent, the real-time state of supply chain operation can be comprehensively captured, data support is provided for decision-making of production plans and resource allocation, and the decision-making efficiency is improved. Through introduction of conflict detection of allocated resources and analysis of task completion degree, reference data is fed back and updated, so that a decision-making agent performs iterative optimization on first control data according to data dimensions and reference data with richer content, the resource conflict condition between execution agents is eliminated, and the decision-making efficiency of the decision-making agent is improved. The completion degree of the production condition of the supply chain in the dimensions of efficiency, energy consumption and capacity is optimized, so that the second control data output by the decision-making agent can realize multi-target collaborative optimal calling, and more efficient and reliable control data is provided for the management of the supply chain; therefore, the production efficiency and the productivity of the supply chain are improved, and the production energy consumption is reduced.

The invention, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention discloses a smart factory energy efficiency optimization method, system and device based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things, and the method comprises the steps: collecting production energy consumption data based on equipment operation parameters and production plan information; constructing a factory digital twinborn model, and determining an equipment start-stop time sequence and an energy distribution strategy; deploying edge computing nodes, monitoring the vibration spectrum of the equipment, generating a state prediction map in a time sliding window, and performing adaptive compensation optimization; and meanwhile, an energy flow network is drawn up based on the equipment start-stop time sequence and the energy distribution strategy, and energy dynamic adjustment is carried out. The technical problems of low energy utilization efficiency and insufficient equipment operation stability of a smart factory in the prior art are solved, and the technical effects of realizing smart factory energy efficiency optimization based on the industrial Internet of Things and improving the energy utilization efficiency and the equipment operation stability are achieved.

The invention relates to the technical field of power equipment supply chain management, and discloses a power equipment supply chain collaborative management system based on an alliance chain, comprising an alliance chain network layer: a distributed network composed of a manufacturer node, a supplier node, a logistics node, a detection node and a grid company node, each node maintaining account book consistency through a consensus algorithm; the intelligent contract execution layer comprises an order processing contract, a logistics tracking contract, a quality acceptance contract and a settlement management contract, and supports automatic execution of a business process; and the data management platform is provided with a distributed storage unit, an encryption transmission unit and an authority control unit and is used for realizing encryption storage and hierarchical access of key data. Full-process automation of order matching, production plan generation and logistics tracking is realized through an intelligent contract execution layer, an AI prediction module can pre-judge a demand peak value in advance based on historical data, a node processing priority is optimized in combination with a dynamic consensus algorithm, and manufacturer order response time is compressed.

This disclosure describes, in part, systems and methods for dynamic allocation, monitoring, and reallocation of work machines (108) at a worksite (100) by using a controller (112) to monitor ranked production circuits (102) and dynamically reallocate work machines (108) based on their energy consumption, energy availability, machine operating parameters, flow rates, delays, wait times, and other such factors to provide compliance with the ranked production plan (204). The controller (112) is used to determine slow-downs, delays, queues, or other potential delays, including insufficient energy availability for a particular work machine allocation (110), and to dynamically adjust to provide for work machines (108) to reduce empty travel time and to maintain material flow rates or other metrics of the production plan (204).

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 relates to the technical field of collaborative operation of a production system and a logistics system in an intelligent manufacturing scene, and discloses an order distribution system for multi-bin supply of production raw materials. A data analysis module of the system extracts data from data sources such as a production plan and warehouse information, and generates a production raw material demand order; the scheme solving module is based on a multi-objective optimization model, comprehensively considers factors such as inventory, conveying time and equipment load rate, and calculates an optimal delivery warehouse and quantity; the distribution execution module dispatches the logistics equipment according to an optimization result to complete a warehouse-out task; the monitoring prediction module learns and predicts historical and real-time order data by using a long short-term memory network, and dynamically adjusts an allocation strategy; and the user interaction module provides a human-computer interaction interface. According to the system, efficient and economical production raw material JIT supply is achieved through an intelligent means, the logistics efficiency and the resource utilization rate are remarkably improved, and reliable technical support is provided for production and logistics collaboration in an intelligent manufacturing scene.

The invention relates to the technical field of data management, in particular to an intelligent flow scheduling method for a high-performance oil way of a metal plate pipe network. The specific implementation process comprises the following steps: establishing a distributed intelligent executor and a resource optimization model based on a sheet metal pipe network comprising a schedulable unit and production management information comprising a historical performance baseline and a production plan; the distributed intelligent executor obtains and evaluates the operation data of the schedulable unit and generates real-time state data; combining the real-time state data and the historical performance baseline to analyze the drift trend and generate performance degradation data; based on the production plan and the performance degradation data, quantifying the comprehensive operation cost as a target function to calculate and generate a resource allocation strategy and an operation instruction; and the distributed intelligent executor combines the operation instruction, the real-time state data and the performance degradation data to execute dynamic compensation management to generate a scheduling instruction. According to the method, resource allocation is optimized by evaluating the real-time and long-term states of the equipment, so that risk avoidance and cost dynamic optimization are realized.

The invention relates to a platform-based intelligent manufacturing MES system, and the system comprises a plan management module which is used for generating a production plan; the planning and scheduling module decomposes the production plan into specific operation tasks and generates an operation plan scheduling instruction; the document management module is responsible for managing and maintaining various documents involved in the production process; the operation management module converts an operation plan scheduling instruction of the planning and scheduling module into a specific production operation instruction; the automatic acquisition module acquires data of production equipment in real time and transmits the data to the plan management module in real time; the operation information module sends a control instruction to the automatic acquisition module, automatically acquires equipment working information, and feeds back the equipment working information acquired by the automatic acquisition module to the plan management module; and the material management module performs data interaction with the planning and scheduling module. Intelligent, automatic and fine management of the production process can be realized, and the system has important significance for improving the competitiveness of lithium battery copper foil production enterprises.

The invention relates to a plastic cup production equipment control method and system, and relates to the field of cup production technologies, and the method comprises the steps: obtaining a production schedule and a production capacity quantity; determining the number of production plans in the production plan table; randomly selecting any number of production lines to form a single operation combination, and determining the number of combined productivity according to the single operation combination; after one production line is randomly excluded from the single-body operation combination, the second-order capacity quantity is determined according to the remaining production lines; defining an effective operation combination according to the combined capacity quantity, the production plan quantity and the inferior capacity quantity; an effective operation combination is randomly selected in each time period to form an overall production combination, and the continuous operation duration is determined according to the overall production combination; and defining effective production combinations according to the continuous operation duration, and selecting a unique use production combination from the effective production combinations to control each production line to operate. The plastic cup production device has the effect of facilitating production of plastic cups.

The invention discloses an intelligent cable production management and control method and system based on feature data analysis, and relates to the technical field of cable production management and control, and the method comprises the steps: firstly obtaining cable structure information to determine a type, and synchronously collecting historical production whole-process data of a corresponding type, including equipment operation, technological parameters and other data; key features are extracted from historical data, and a cable production quality index prediction model and a production equipment energy consumption prediction model are constructed; obtaining real-time production data, extracting key features, and inputting the key features into the model to obtain quality and energy consumption indexes; and finally, the production plan is dynamically adjusted based on the two types of indexes, and a set threshold value, multi-scene early warning and a plan maintaining or adjusting strategy are covered. The method has the advantages that intelligent control over the whole production process is achieved through data driving, the quality prediction accuracy can be improved, energy consumption management can be optimized, the production plan flexibility can be enhanced, and technical support is provided for efficient and accurate management of cable production.

The invention relates to a multi-dimensional and multi-level capability modeling method for production plan scheduling. The method comprises the following steps: defining process basic data based on a time model of each process stage in a production process; defining product basic data based on the material archive and the bill of material; defining resource basic data based on the device capability and the personnel capability; classifying the products based on the characteristics of the product families and the raw materials to obtain each product group; classifying processes based on product processes and process requirements to obtain each process group; classifying the resources based on the grouping of the equipment and the personnel to obtain each resource group; a production plan target is determined, capability modeling is carried out based on the production plan target and constraint conditions to obtain a production plan scheduling model, and the constraint conditions comprise constraints of basic data of products / product groups, working procedures / working procedure groups and products / product groups, so that the data processing efficiency is improved, and formulation of a production plan is optimized.

The invention provides an MES-based solar cell production and manufacturing execution method and system, and the method comprises the steps: collecting equipment states and technological parameters in real time, combining a bar code system to achieve the single-chip-level metadata association, constructing a standardized data architecture, building a multi-dimensional index, converting the expert experience into reasonable rules, and carrying out the calculation of the reasonable rules. The method comprises the following steps: simulating the influence of parameter combination on product performance, triggering an edge computing node to call a knowledge graph to perform parameter optimization and production simulation based on a dynamic threshold early warning mechanism, identifying process improvement opportunities through a batch tracing report and clustering analysis, forming knowledge base iteration update, predicting inventory risks in combination with a production plan and equipment load, and performing production management. According to the method, intelligent purchasing is automatically triggered, raw material distribution is optimized, technological parameter self-optimization, abnormity self-healing, quality traceability and supply chain collaboration are achieved through data driving, the production efficiency, the product consistency and the resource utilization rate are improved, and a whole-process digital solution is provided for photovoltaic manufacturing.