118 results about "Manufacturing enterprises" patented technology

The invention provides a manufacturing logistics intelligent matching recommendation method and system based on multi-modal data, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining the logistics demand data of a manufacturing enterprise and the service capability data of a logistics supplier; constructing a multi-level feature attention network to perform feature extraction on the logistics demand data and the service capability data, and performing screening to obtain multi-modal feature representation; constructing a positive and negative sample pair based on the multi-modal feature representation, obtaining an inter-modal semantic mapping matrix through a hierarchical contrast learning mode, and projecting the multi-modal feature representation to a unified semantic space to obtain a fusion feature representation; the fusion feature representation and the historical cooperation record are constructed into a state vector, an instant reward function and a long-term reward function are constructed, and a matching degree score between the manufacturing enterprise and the logistics supplier is obtained through optimization of a deep reinforcement learning algorithm; and recommending a logistics supplier to the manufacturing enterprise based on the matching degree score.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

The invention discloses a production and manufacturing enterprise cluster chain type interaction carbon emission reduction method based on dynamic electrical carbon factors, and relates to the technical field of industrial carbon emission reduction and energy management systems. According to the method, a four-layer decision-making framework of a global coordination layer, a cluster coordination layer, an enterprise execution layer and an equipment control layer is constructed, and dynamically updated electric carbon factors are introduced, so that accurate identification, carbon emission accounting and collaborative optimization of enterprise clusters on a product chain are realized. The core of the method is that carbon quota allocation, production scheduling and resource allocation are carried out among hierarchies by utilizing a multi-objective and double-layer optimization model, so that the total carbon emission of a cluster is minimized while the economic benefit is ensured. A dynamic monitoring and feedback mechanism is also established, the power carbon factor weight can be adjusted and a resource allocation plan can be triggered according to real-time data such as the proportion of clean energy of the power grid, closed-loop regulation from a macroscopic strategy to microscopic operation is formed, the overall carbon footprint of an industrial chain is effectively reduced finally, and the participation barrier of small and medium-sized enterprises is broken.

The embodiment of the invention discloses a project scheduling method and device based on reinforcement learning. The method comprises the following steps: acquiring a current state vector of a target project; wherein the target project comprises engineering and service projects of a tobacco manufacturing enterprise; inputting the current state vector into a preset project scheduling model to output an execution action corresponding to the current state vector; wherein the preset project scheduling model is a convergent preset project scheduling model obtained by training by adopting a reinforcement learning algorithm and adjusting model parameters by adopting a pre-constructed project reward function; and performing dynamic project scheduling on the target project according to the execution action. By adopting the technical scheme of the embodiment of the invention, the plan and task arrangement of the target project can be dynamically adjusted according to the real-time progress of the target project, the risk factor and the external environment change, and various changes can be timely coped with, so that the project delay and failure risks are reduced, and the success rate of the project is improved.

The invention discloses a manufacturing capacity trend prediction method based on AI visual analysis, and relates to the technical field of industrial big data analysis, and the method comprises the following steps: building a cross-time scale alignment baseline, generating a phase consistency evidence set of a continuous time structure through timestamp reconstruction and sampling calibration, and obtaining a phase consistency evidence set; extracting a high-confidence time anchor point as a unified time reference; and constructing a nonlinear disturbance spectrum diagram based on the time anchor points, and carrying out hierarchical decomposition on energy mutation, slow drift and pseudo-stable fragments in a continuous time period to generate a traceable disturbance fingerprint set. Through time sequence alignment, disturbance modeling, weight repair and dynamic regulation and control mechanisms, stable convergence and trend consistency prediction of the artificial intelligence model under nonlinear disturbance are realized, the accuracy of productivity prediction and the reliability of decision making are improved, and a manufacturing enterprise is assisted to realize intelligent productivity management.

The application provides a multi-objective robust optimization method, system, storage medium and electronic equipment for production task planning of a multi-agent collaborative platform, and relates to the technical field of production task planning of a multi-agent collaborative platform. First, according to the production task planning resources of the multi-agent collaborative platform and a single-batch overseas order, a production task planning model with a manufacturing enterprise agent as the core is constructed under the premise of determining the sea transportation time. Then, an infinity norm ball uncertain set is designed for the sea transportation time of a large ship, a robust quadratic multi-objective model for cross-regional multi-agent production task planning is constructed, and the total delivery time deviation and total delivery cost multi-objective of the single-batch overseas order are optimized. The model considers the sea transportation plan prepared in advance by a shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, a two-stage hybrid heuristic algorithm is designed for solving, and a reasonable and feasible production task planning suggestion is given for the single-batch overseas order.

The invention relates to a decision recommendation system and method for preferentially processing key data of a supply chain of a manufacturing enterprise. According to the decision recommendation system and method, through a data fusion module, a priority evaluation module, a decision recommendation module and a self-adaptive feedback module, supply chain key data management realizes collection, analysis and decision; the invention discloses an optimized decision recommendation system and method for an automatic closed-loop process. The system is characterized by comprising a data fusion module, a priority evaluation module, a decision recommendation module and a self-adaptive feedback module, the data fusion module outputs structured data to the priority evaluation module, and the decision recommendation module generates a scheme according to the priority and feeds back an actual effect to the self-adaptive feedback module after execution. The self-adaptive feedback module reversely updates the priority rule; the data fusion module is composed of a data acquisition unit, a data preprocessing unit and a data storage unit, and the priority evaluation module is composed of a rule engine unit, a machine learning model unit and a dynamic weight adjustment unit.

The invention discloses a manufacturing enterprise supply chain digital transformation maturity evaluation method driven by multi-dimensional indexes. The method comprises the following steps: widely collecting internal and external multi-source data of an enterprise through a data acquisition module, storing preprocessed data in a graph structure through a knowledge graph construction module, and optimizing query efficiency; potential association and abnormal modes among multi-modal data mining indexes are fused through a semantic understanding and relation reasoning module, field adaptability parameter adjustment and optimization are performed on each key model through a model training optimization module, and a hierarchical evaluation system is established and a quantitative improvement scheme is generated through an intelligent evaluation and improvement suggestion module. And finally, a complete closed loop from data acquisition to decision optimization is formed, and accurate evaluation and continuous improvement of the digital transformation maturity of the supply chain of the manufacturing enterprise are realized. The method is characterized in that a manufacturing enterprise supply chain digital transformation maturity evaluation system driven by a multi-dimensional index is used for evaluation.

The invention relates to the technical field of energy management, in particular to a multi-source heterogeneous energy load prediction method and system for a manufacturing enterprise cluster, and the method comprises the steps: collecting original energy data from a multi-source heterogeneous system in the manufacturing enterprise cluster and external environment information related to the energy load change, preprocessing the original energy data and the external environment information to obtain standardized energy time sequence data and final external environment information; based on the standardized energy time sequence data, the final external environment information and a pre-established equipment information model, constructing a triple heterogeneous atlas, and extracting structural semantic features of each node in the triple heterogeneous atlas; based on the structure semantic features, constructing a coupling sequence diagram, modeling the coupling sequence diagram, and identifying key factors; and inputting the structure semantic features and the key factors into a multi-scale prediction model, and outputting hourly load prediction curves of various energy media in a future prediction period.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent product life cycle management system and method based on a large model, a data layer is used for receiving multi-modal heterogeneous data input of different stages of a product full life cycle and is connected with a domain knowledge base; the model layer comprises a basic model and a task model, wherein the basic model is formed by fusing a large language model subjected to efficient parameter fine adjustment with a visual Transform hybrid architecture, and the task model is supported and driven by the basic model to run, and the model layer outputs a result based on the data layer; the data analysis module is used for analyzing heterogeneous data, constructing a knowledge graph for cross-source data intelligent retrieval and reasoning, and performing user demand analysis, semantic intelligent material classification, BOM full-life-cycle management, demand-driven optimization product design, supply chain risk prediction, dynamic process response and quality control and knowledge precipitation; and the application layer outputs a result based on the task model, and carries out real-time collaboration and key information visualization. According to the invention, automatic, intelligent and collaborative management of product life cycles of research, development and manufacturing enterprises is realized.

The invention discloses a large model plan scheduling and simulation rehearsal agent platform, belongs to the field of advanced planning and scheduling, belongs to the field of intelligent scheduling, and solves the problem of intelligence of an advanced planning and scheduling system, the problem of association between the advanced planning and scheduling system and multiple systems and the problem of advanced simulation rehearsal of plan scheduling. The problem of efficient application of a large model under different resource scene schedules is solved, a large model LLMs and AI model set is designed and fused, automatic and intelligent advanced planning and scheduling are achieved, full unmanned chemical plant application scene scheduling is supported, highly structured resource data is designed, manufacturing enterprise resources and rules can be understood, and the method is suitable for large-scale industrial production. Accurate quantitative scheduling is achieved in an enhanced tool mode, large model training of purchase plans, inventory plans, production scheduling and order delivery plans is achieved in an auxiliary enhanced training and tool enhancement mode, the system is associated with a robot and mechanical arm system, and direct cluster scheduling, AI simulation preview scheduling results and ERP system associated viewing are achieved.

A task-level cooperative optimization dispatching method supporting multi-cloud disconnection disaster recovery is provided. Since cooperative dispatching is required for different resource requirements in the abnormal stage of a network, a cooperative computing system is formed by nodes of a production shop, production tasks are transmitted and processed between the nodes according to technological requirements, and task-level cooperative optimization dispatching is conducted through a CoopDispatch method in case of disconnection to ensure continuous production. The CoopDispatch method has the characteristic of high dependence on specific resources for manufacturing tasks. Data transmission, storage and calculation are achieved through a LocalAny mechanism, which can reduce task delay, improve the cooperation efficiency among the tasks and satisfy the needs of manufacturing enterprises for disconnection disaster recovery in the multi-cloud mode.

The utility model discloses a kind of worm processing comprehensive machine tools, including bed, the upper surface both sides of bed are fixedly installed with two symmetrical distribution primary bearing seat, one number screw rod is rotatably connected between two primary bearing seats, the outer thread of one number screw rod is connected with horizontal slide, the output shaft of one number motor is fixedly connected to one end of one number screw rod, symmetrically distributed secondary bearing seat is fixedly installed in the front and back two ends of horizontal slide, two secondary bearing seats are rotatably connected with two number screw rod, two number screw rod and one number screw rod are perpendicularly arranged between each other, the outer thread of two number screw rod is connected with longitudinal slide, the output shaft of two number motor is fixedly connected to one end of two number screw rod, equipment seat is fixedly installed on the upper surface of longitudinal slide, the machine tool of the utility model whole structure is simple, floor area is small, manufacturing difficulty is low, effectively reduce the enterprise's early purchase and later use cost, with good use economy, suitable for small and micro manufacturing enterprises development use.

This application discloses a method and system for information-based diagnosis of production batches in pharmaceutical manufacturing enterprises, belonging to the interdisciplinary field of artificial intelligence and pharmaceutical information technology. The method includes: unifying the time base of multi-source equipment; collecting equipment operating status, process parameters, and event data to construct a multi-dimensional array; establishing process constraints in the dimensions of product, process, and operational equipment flow; using a binning method to logically combine equipment status parameters into different operating status categories; training a random forest model using labeled historical batch data to obtain a batch diagnosis model; inputting the start time of the batch to be diagnosed into the model to derive the equipment operating time period, matching it with the operating status category, and statistically analyzing the classified operating time of each piece of equipment. This application solves the problem of statistical efficiency and validity in calculating the net operating time of each piece of equipment within a batch in pharmaceutical enterprises.

The invention discloses an aeronautical manufacturing enterprise-oriented multi-dimensional business index management system, which comprises a data source management module for managing data source information required by the multi-dimensional business index management system and executing data source connection and data acquisition; the business domain management module is used for setting business domains, business classifications, index sources and index classification information of to-be-managed indexes; the calculation dimension management module is used for maintaining and performing mutual exclusion group management on each calculation dimension; the index calculation model configuration module is used for configuring a business domain, business classification, an index source, index classification, calculation parameters, a calculation formula, calculation frequency and calculation dimension of an index to be maintained; and the automatic calculation module is used for calculating an index result according to the content configured by the index calculation model configuration module and the calculation frequency. According to the method, the problems of complex flow, large workload, extremely low index calculation and utilization efficiency and the like of the existing calculation mode of the business indexes in the aeronautical manufacturing enterprise are solved.

The utility model relates to hydraulic clothes compression equipment which comprises a rack, a working bin is arranged in the rack, a front side opening communicated with the working bin is formed in the front side of the rack, a lower end door and an upper end door which are used for opening and closing the front side opening are installed on the front side of the rack, and the upper end door is located on the upper side of the lower end door. A discharging port communicated with the working bin is formed in the left side of the rack, limiting sliding grooves formed in the height direction of the rack are formed in the two sides of the discharging port, a side door is installed on the left side of the rack, protrusions are arranged on the two sides of the side door and arranged in the limiting sliding grooves, and the discharging port is opened and closed by moving the side door up and down along the limiting sliding grooves; a compression hydraulic cylinder and a side door hydraulic cylinder are installed on the top of the rack, and the side door hydraulic cylinder is located on the left side of the top of the rack. The compression device is suitable for compression of soft materials such as clothes, is particularly suitable for the fields of clothes manufacturing enterprises, logistics storage and the like, can effectively save space and improve transportation efficiency, and has great market application and popularization value.

The invention discloses a knowledge extraction and intelligent management and control method and system based on a large language model, and the method comprises the steps: obtaining a text-recognizable manufacturing knowledge document through preprocessing, recognizing a manufacturing knowledge entity-attribute pair from the manufacturing knowledge document based on the large language model, and carrying out the recognition of the manufacturing knowledge entity-attribute pair. Extracting relationships between manufacturing knowledge entities and between entities and attributes by combining entity-attribute pairs to obtain a manufacturing knowledge triple, optimizing the manufacturing knowledge triple, storing the triple and visualizing the triple into a manufacturing knowledge graph; fusing the plurality of knowledge maps by using a large language model, identifying potential manufacturing knowledge triads between the maps by combining a relationship prediction tool, merging the fused triads and the potential triads, and then obtaining a refined manufacturing knowledge map through visualization; a manufacturing knowledge graph is used as a knowledge base of a large language model in a triple form, and the model retrieves related manufacturing knowledge according to user problems and feeds back the manufacturing knowledge to users in a natural language form, so that the automation and intelligence level of knowledge management and control of manufacturing enterprises is effectively improved.

The invention discloses a low-value consumable full-life-cycle tracking management method based on a full-process voucher chain, and belongs to the technical field of enterprise resource management. According to the method, a management system is constructed based on an inventory overview table (ZDZYHZL), an inventory mobile detail table (ZDZYHMX) and a configuration table, the whole process of basic data configuration, warehousing, getting (borrowing), returning, scrapping, inventory adjustment and waste sales is covered, operation traceability is achieved through a certificate chain closed loop, data consistency is ensured through atomic affairs and logic linkage, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. And unauthorized operation is completely eradicated through fine-grained permission configuration. The method can be seamlessly adapted to the SAP ERP system, solves the problems of flow splitting, data distortion, operation non-traceability and the like in traditional management, is particularly suitable for fine management of low-value and high-consumption products of precision manufacturing enterprises, and improves the management efficiency and the compliance level.

The present invention provides a dual-agent collaborative scheduling method for a high-end equipment supply chain based on flexible production line opening, which relates to the technical field of production scheduling. The scheduling method sets input parameters and operating parameters, constructs an initial solution set on the premise of giving priority to agent 2, selects the optimal solution as the initial solution x0; performs a shaking operation on the initial solution x0 to obtain a new solution x1, performs a local search on x1 to obtain a new solution x2, and determines whether the fitness value of x2 is better than the fitness value of x1. If so, x2 is used as the initial solution and iterates continuously until the global optimal solution x0 is found. best , according to x best The provided solution assigns production tasks to suppliers to maximize the resource utilization and operational efficiency of high-end equipment manufacturing companies and reduce the total cost of the supply chain.

The invention provides a wide-area cloud manufacturing industrial service system and method based on an Internet of Things large model. The system comprises a service cloud platform and a user platform, wherein the service cloud platform is configured to obtain a constraint index; determining first service data from historical service data corresponding to the user based on the constraint index; responding to the condition that the first service data meets user requirements, and displaying the first service data to the user through a user platform; and in response to the condition that the first service data does not meet the user demand, taking one or more candidate service data of which the matching degree meets a preset matching condition as one or more second service data, and presenting the one or more second service data to the user through the user platform. Through the system, real demands of a manufacturing enterprise can be obtained, and accurately matched service data is provided for the manufacturing enterprise, so that high-quality operation of wide-area cloud manufacturing industrial services is supported.

The present invention provides a rapid development framework and method for inventory management software of process manufacturing enterprises. The development framework includes an interface layer, a service layer, and a data layer. The interface layer has built-in basic components, the service layer has a built-in microservice architecture, and the data layer includes a MySQL database and an object model management module. Both the basic components and the microservice architecture access the database through the object model management module. The development method includes: inventory management business analysis of process manufacturing enterprises, data modeling, rapid construction of the interface, and rapid construction of the business process model. The present invention uses a microservice architecture to make the inside of the development framework loosely coupled, distinguishes the front-end interface logic from the back-end process business logic, and improves the flexibility and scalability of the development framework through a development method of drag-and-drop and attribute configuration, effectively reducing the software development difficulty, development cost, and development technical threshold of inventory management in process manufacturing enterprises.

This invention discloses an integrated management and control method for the entire sales settlement process based on table-valued parameters, belonging to the field of sales settlement management technology for industrial enterprises. This invention adopts a back-end-heavy, front-end-light architecture, using custom table-valued parameters as a unified data transmission carrier to batch transmit core information such as settlement order numbers, finished product roll numbers, and final unit prices. A single stored procedure centrally manages all business operations, including settlement order binding, unit price modification, and status updates. The front-end is only responsible for interface display and unified interface calls, without handling complex business logic. The back-end incorporates centralized data verification and database atomic transaction control to ensure data consistency, while supporting multi-dimensional combination filtering, batch selection of roll numbers, and automatic weight and amount statistics. This invention solves the pain points of traditional settlement systems, such as fragmented logic, high coupling, inefficient batch operations, and data error susceptibility. It has been fully implemented and is running in actual industrial MES / ERP integrated systems, possessing advantages such as stable architecture, efficient operation, long-term iterative capability, and strong scalability, making it suitable for the standardized and intelligent management needs of sales settlement in manufacturing enterprises. This technical solution is not a theoretical model, but a mature technical solution that has been fully engineered, developed, and stably run in actual industrial manufacturing enterprise MES / ERP integrated systems.

The invention belongs to the technical field of intelligent manufacturing capability maturity evaluation, and discloses an intelligent manufacturing capability maturity evaluation method and system based on industry characteristics and semantic understanding. The evaluation method comprises the steps of constructing an industry feature knowledge base, generating a personalized evaluation information packet, performing semantic processing on evidence materials, calculating matching degree and similarity, calculating the maturity of the capability subdomains, performing comprehensive rating and the like, and finally integrating maturity scores of all the capability subdomains to form an intelligent manufacturing capability maturity grade result of an evaluated enterprise. Through industry feature matching, semantic vector analysis and dynamic weight calculation, objective, efficient and accurate evaluation of the intelligent manufacturing capability maturity is realized, and the method can be widely applied to manufacturing enterprises and has remarkable economic benefits and popularization values.

To provide a technique capable of adding value to a product by giving a special benefit without imposing a burden on a manufacturing company. [Solution] The reward granting system 1 includes a user device 3 used by a user and a reward granting device 2 that communicates with the user device. The reward granting device stores information indicating multiple rewards provided by multiple reward providing companies that provide rewards, and when a user purchases a specific product from a specific manufacturing company among the multiple manufacturing companies, determines by lottery whether or not the user will be granted a desired reward selected from the multiple rewards using the user device. If the user wins the lottery, the reward granting system arranges for the desired reward to be provided by the specific reward providing company that provides the desired reward, and bills the specific manufacturing company for the costs incurred in arranging the desired reward.

The application discloses a kind of multi-level progressive parameter optimization methods for complex process industry, comprising: the correlation between M process parameters and quality indicators is analyzed one by one to determine the importance of each process parameter to quality indicators;According to the importance of process parameters to quality indicators, descending order is sorted, and the process parameters after importance degree sorting are obtained;According to the process parameters after sorting, the process parameter layering method based on correlation analysis is constructed, the hierarchical division of process parameters is realized, and the multi-level process parameter combination is obtained by C times division;Modeling optimization is sequentially carried out from the Cth layer process parameter combination to the 1st layer process parameter combination in order, and the 1st layer process parameter combination optimal solution to the Cth layer process parameter combination optimal solution is obtained.The application effectively solves the problems of low modeling prediction accuracy and optimization difficulty caused by numerous process parameters in complex process manufacturing, which is of great significance for process manufacturing enterprises to ensure process quality and improve processing efficiency.

The invention provides a cabin part two-stage mixed job shop scheduling method considering beat difference, and aims to improve the efficiency and accuracy of productivity evaluation. The method comprises the following steps: acquiring workpiece information and resource information of cabin part production and manufacturing; obtaining a production constraint and a target function according to the production characteristics of the two-stage hybrid job shop, and constructing a two-stage hybrid job shop scheduling model; designing an improved genetic algorithm to solve the model; and carrying out production resource scheduling according to an optimization result. According to the method, by considering the beat difference of cabin part production, a two-stage hybrid job shop scheduling method which is more suitable for the complex production task requirements of modern manufacturing enterprises is provided, the enterprises can quickly and effectively obtain a better scheduling scheme, and the workshop production efficiency is improved.

According to the factory management system data access method and system based on double-entrance hybrid deployment provided by the invention, through a double-entrance hybrid deployment architecture and a dynamic perception control mechanism, deep decoupling of client-side independent deployment sense and actual cloud storage is effectively realized; users of small and medium-sized enterprises can perceive a traditional local deployment system in a use process, so that psychological obstacles on data cloud are eliminated; meanwhile, all core production data are centrally stored in the cloud, the professional safety protection capability and the low-cost storage advantage of a cloud service provider are fully utilized, a company headquarters can directly access production operation data of customer factories in real time through an independent public network entrance, and efficient remote management and control and unification and coordination of multiple factories are achieved. According to the scheme, on the premise that local hardware burdens of clients are not increased and data consistency is not sacrificed, contradictions in three aspects of safety, cost and management efficiency are balanced, and the digital transformation requirements of vast small and medium-sized manufacturing enterprises in China at present are particularly met.

The pot handle comprises a handle body and a raking and grabbing part arranged on the handle body, a pressing assembly for promoting the raking and grabbing part to move towards one side of the end face of the handle body is arranged on the handle body, and an unlocking assembly for unlocking the pressing assembly is arranged on the handle body. When the unlocking assembly is pressed, the unlocking assembly is promoted to unlock the pressing assembly, and at the moment, under the condition that the pressing assembly returns, the raking and grabbing piece moves away from the end face of the handle body. Comprising a pot body, and the pot handle is arranged on the pot body. The design is more reasonable, the structure is more optimized, the production and manufacturing complexity and difficulty are greatly reduced, the production and manufacturing cost is further reduced, batch production and manufacturing of production and manufacturing enterprises are facilitated, meanwhile, operation is extremely easy and convenient for users, and large-scale application and popularization are facilitated.

The invention discloses a machine needle receiving and supplementing management system based on artificial intelligence multi-permission grading, which relates to the technical field of consumable intelligent management and comprises a permission management module, a receiving management module, a needle supplementing prediction module, an inventory management module and a data interaction module. The authority management module calculates and grades comprehensive authority values through an authority quantification model on the basis of post basic weights, historical taking compliance and production task association degrees; the needle supplementing prediction module fuses the historical consumption data, the production plan, the equipment state and the inventory, and obtains the needle supplementing demand through a consumption prediction model; the receiving management module calculates a single-time receiving limit through a dynamic limit adjustment model; the inventory management module executes needle supplement and updates the inventory; and the data interaction module realizes real-time data synchronization between the modules and an external system. The system solves the problems of authority stiffness, needle supplement lag, fixed limit and the like in traditional management, and meets the requirements of manufacturing enterprises.

This invention relates to the field of supply chain information management technology, and more particularly to an AI-based supplier procurement management method. The invention pre-acquires product images from product information, obtains contour recognition results from scaled product images, filters quality confidence feature images based on the overlap of contour recognition results, compares these quality confidence feature images with images in an existing product database to determine whether to store or delete the supplier's product information, determines the comprehensive characteristic indicators of the product corresponding to the product image in production, and determines whether to compress the product image into several data packets for transmission to the review terminal based on the comparison results of the comprehensive characteristic indicators and their reference values. This invention optimizes the information processing process in complex procurement scenarios for manufacturing enterprises, enabling targeted extraction of supplier information content and improving the scientific nature of supplier information management.