1268 results about "Production planning" patented technology

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

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 discloses a full-process packaging management and control system and method based on a digital twinborn model, relates to the technical field of packaging management, and forms a full-process closed-loop management and control system from external demand perception to internal packaging process optimization through digital twinborn model and multi-source data fusion. By integrating data such as an order system, market feedback, a production plan and an equipment state, the problem of data islands in traditional packaging production is solved, and real-time fusion of external requirements and internal data is realized; a multi-source data weighting and cleaning fusion mechanism is adopted, and a packaging scheme is ensured to be closer to an actual production environment through digital twinborn simulation and evaluation; through a multi-target optimization algorithm, conflicts and coupling effects among a plurality of targets are accurately captured, and the decision accuracy is improved. Through rapid deployment and a closed-loop feedback mechanism, the production line is monitored and adjusted in real time, the production efficiency and quality are improved, the production cost is reduced, the efficiency and the product quality are improved, and meanwhile the environmental influence is reduced.

The invention discloses a pipe production task management method and system, and belongs to the technical field of pipe production, and in the pipe production task management method and system, factors such as order priority, equipment state and raw material supply are analyzed through an intelligent algorithm, and the production schedule is automatically optimized, so that production resources are utilized more reasonably and efficiently. Resource waste can be avoided, idle time in the production process is shortened, and therefore production efficiency and the productivity utilization rate are improved. The urgency degree and the delivery date of the order are analyzed through an intelligent algorithm, and the enterprise can preferentially arrange the production of the urgent order to ensure that the customer demand is met in time. Meanwhile, production tasks are reasonably distributed according to the running state of the equipment and the raw material supply condition, and the use efficiency of the equipment and the utilization rate of raw materials are improved. The dynamic adjustment mechanism monitors the production progress in real time and automatically adjusts the production plan according to the actual situation, so that the system can rapidly adjust the production plan and distribute the tasks to other devices, and continuity and on-time delivery of the production tasks are ensured.

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 relates to the technical field of industrial image recognition, in particular to an automatic visual inspection system applied to charging pile production. The method comprises the following steps: the image enhancement module is used for acquiring an omnibearing production monitoring image; carrying out image detail convolution enhancement on the omnibearing production monitoring image, and carrying out topological structure fitting to generate a component topological structure diagram; the twinborn model module is used for performing production process action recognition on the assembly topological structure diagram based on the omnibearing production monitoring image, performing three-dimensional simulation modeling and constructing a dynamic production twinborn model; and the deviation identification module is used for identifying the position deviation of the dynamic production twin model assembly according to a preset production planning log, then carrying out reference positioning deviation calculation, and generating assembly position deviation parameters. According to the invention, efficient and accurate charging pile production diagnosis detection is realized, so that the production process of the charging pile is optimized.

The invention relates to the technical field of production task decision making, in particular to a furniture production task automatic matching method and system based on intelligent manufacturing. And extracting personalized demand information corresponding to the production task, converting the personalized demand information into standard design parameters, and generating a customized task. The production task is disassembled into a plurality of sub-tasks, and each sub-task has independent attributes including process requirements, resource requirements and time requirements; and performing one-to-one mapping on the customized tasks and the subtasks to generate an execution plan. Performing resource matching by using a constraint algorithm based on the execution plan and the existing production resource information to obtain a production plan; and obtaining an optimal scheduling plan by using a constraint algorithm according to the production plan. And based on the real-time production data and the burst information, adjusting the optimal scheduling plan through the data analysis model to obtain a final scheduling plan. The method can cope with emergencies or real-time data changes, and manual decisions are reduced.

The invention discloses a tobacco enterprise composite scheduling method based on multi-agent collaborative optimization, and the method comprises the steps: obtaining the system data of a tobacco enterprise, and obtaining a plurality of corresponding decision-making schemes based on a sales prediction agent, a production plan agent, a purchase stock agent, and an inventory management agent; performing iterative optimization on the decision scheme based on a Nash equilibrium game and a Lupinstein bargaining model, and calculating a system utility value to obtain an optimal decision scheme; according to the optimal decision-making scheme, based on an adaptive mechanism, adaptive optimization is carried out, and an adjusted decision-making scheme of each agent is obtained, so that tobacco enterprise composite scheduling of multi-agent collaborative optimization is realized; the sales prediction agent, the production plan agent, the purchase stock agent and the inventory management agent perform information interaction through an attention mechanism. Interactive cooperation among multiple agents is realized by introducing an attention communication mechanism and a game theory cooperation mechanism, and overall optimization of tobacco enterprise scheduling is promoted.

The invention provides a supply chain prediction method based on a knowledge graph and an industrial model, and the method comprises the steps: collecting the features needed by supply chain prediction, and the needed features comprise historical order data and production plan data, industrial product purchasing data and collection information, logistics data and storage data; performing data cleaning, standardization and correlation analysis on the historical order data, the production plan data, the industrial product purchasing data, the collection information, the logistics data and the storage data to obtain target features; key entities and relationships in the target features are identified, and a knowledge graph is constructed based on the key entities and the relationships; the industrial model is used for identifying supply chain key nodes and node relations in the knowledge graph, the supply chain key nodes and the node relations are converted into numerical feature vectors used for supply chain prediction so as to carry out supply chain prediction, the relations among the nodes in the supply chain can be better captured by introducing the knowledge graph and the industrial model, and the supply chain prediction accuracy is improved. Therefore, the supply chain prediction precision is effectively improved.

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 discloses a ship block construction process-level production scheduling method and system based on a particle swarm-genetic hybrid algorithm. The method comprises the following steps: constructing a standardized process-level process design data model; based on an operation plan decomposition rule, decomposing the segmented production task into a part-level operation production task matched with the process; establishing an optimization target model with the shortest delivery time, the least overdue tasks, the most balanced load and the like, and constraint rules such as lane and line separation and customization station rules, process route and process constraint rules, material and capacity constraint, priority and the like; optimizing the segmented production plan through a particle swarm-genetic hybrid algorithm until an optimization result meeting the requirement is obtained; and according to the feedback of the actual execution condition of the plan, the orders are classified based on a clustering algorithm, and the operation cycle parameters of different processes are dynamically compensated. The method has the advantages of being high in plan fineness, small in operation progress estimation deviation and the like, is high in calculation speed and low in algorithm complexity, fully considers support of process design data of a design end on algorithm implementation, and is very suitable for engineering application.

The invention discloses a scheduling optimization method for an aviation composite material manufacturing hot-press forming process, which comprises the following steps of: 1, establishing a hot-press forming production plan scheduling objective function by taking the minimum total delay time of a workpiece as an objective according to an aviation composite material manufacturing hot-press forming production plan; 2, establishing a two-dimensional hot press molding process batch scheduling model considering a workpiece release time window and machine applicability; and step 3, taking the objective function as an optimization objective, taking the two-dimensional hot-press forming process batch scheduling model as an optimization model, and utilizing a genetic algorithm embedded with a self-adaptive domain search strategy to solve and obtain a hot-press forming production plan scheduling scheme. According to the method, the total delay time of workpieces can be remarkably shortened while the machining batch of the workpieces is minimized, the occurrence of prepreg deterioration conditions is reduced, and conditions are created for improving the hot press molding production efficiency and promoting digital and intelligent transformation and upgrading of composite material manufacturing enterprises.

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 relates to an oil refinery production and purchase collaborative optimization method based on multi-agent reinforcement learning, and the method comprises the following steps: S1, constructing a production agent, a purchase agent and a production plan model, setting an action space, a state space and a reward function of a production agent and a purchasing agent and an environment updated based on a production plan model after the agents output actions; s2, setting action interaction, state interaction and reward interaction between the production agent and the purchasing agent; and S3, training a production agent and a purchasing agent by adopting an asynchronous MAPPO method based on the interaction set in the S2, obtaining actual oil refinery production data, and inputting the actual oil refinery production data into the trained production agent and purchasing agent to obtain an actual production and purchasing plan. Compared with the prior art, the method has the advantages of improving the production efficiency, reducing the operation cost, keeping the stock balance of crude oil and the like.

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

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 relates to the technical field of production design optimization and verification, in particular to a medical auxiliary sock production plan generation method and system based on artificial intelligence, and the method comprises the steps: obtaining the expected order data of medical auxiliary socks, including order demand information, product specification information and delivery requirement information, dividing the expected order data into a plurality of subclasses, obtaining M groups of order subclass data; obtaining a production plan constraint condition, and obtaining M groups of to-be-produced order data according to the inventory data and the M groups of order subclass data; generating a plurality of candidate production strategies according to the M groups of to-be-produced order data and the production plan constraint conditions; establishing a medical auxiliary sock production digital twinborn model; and simulating and verifying different candidate production strategies through the medical auxiliary sock production digital twin model to obtain virtual delivery time of each order to be produced, and selecting an optimal production plan according to the virtual delivery time. According to the method, through simulation verification of the digital twinborn model, the decision transparency and predictability of the production plan can be enhanced.

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 discloses a steel rolling equipment optimization system capable of reducing energy consumption, and relates to the technical field of steel rolling, and the optimization system comprises a load monitoring module, a dynamic load balancing module, a load adjusting module and an intelligent control module. Load prediction is performed in combination with an advanced machine learning model, and dynamic load balance adjustment is performed based on the load prediction, so that overload of part of rolling mill stations and light-load idle states of other rolling mill stations can be effectively avoided, the whole rolling equipment always runs in an optimal load state, energy waste is greatly reduced, the energy utilization efficiency is remarkably improved, and the economic benefit is increased. Moreover, the intelligent control module adjusts the frequency and precision of load monitoring according to the production plan and the real-time load condition, and further optimizes the energy management.

The invention provides an industrial field production decision-making system and method based on an intelligent decision-making model, and the system comprises an IBP consistency plan module which is used for supporting the generation of an integrated business plan group; a plurality of different strategy plans are set for comparison, and plan decision making is assisted; the intelligent main plan decision-making module is used for supporting constraint configuration at least including productivity and materials, generating different target-oriented main plan versions and making a decision on an optimized production plan; the intelligent production planning module is used for supporting configuration of demand plans of different levels and generating a production line scheduling plan with limited capability; the system is also used for supporting visual display of a production line scheduling plan result; and the order full life cycle management module is used for measuring and calculating an order delivery plan and generating an order delivery time report by taking the order demand as input and the capacity and the material as constraints. According to the invention, an intelligent decision technology is introduced to solve the production decision problem in the industrial field.

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.