84 results about "Manufacturing enterprises" patented technology

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 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 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 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 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 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 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.

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 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.

The utility model relates to the field of data information acquisition, transmission and control, and particularly discloses an environment-friendly online data acquisition and transmission instrument. The environment-friendly online data acquisition and transmission instrument comprises a shell and an upper cover of which one side edge is connected with each other through a hinge, and is characterized in that a cable fixing unit, a data receiving and transmitting unit and a display screen unit are sequentially arranged in the shell from bottom to top; a shell cable outlet is formed in the shell, and the cable fixing unit corresponds to the shell cable outlet in position; the data receiving and transmitting unit comprises a circuit board, a switching power supply, a battery, an RJ45 network port and a spring type wrench wiring terminal; the display screen unit comprises a display screen support, and a circuit board baffle and a display screen module electrically connected with a circuit board are installed on the display screen support. The device is small in size, light in weight, convenient to carry, high in protection level, capable of being directly installed and used in most industrial environments, low in maintenance cost, accurate in data detection and especially suitable for small and medium-sized production enterprises.

The application relates to a cluster equipment cross-region operation database construction method and device and a storage medium. The method comprises the following steps: acquiring cross-region operation business requirements; processing the cross-region operation business requirements; dividing cross-region operation database subject domains according to processing results; and constructing a cross-region operation database according to the cross-region operation business requirements and the cross-region operation database subject domains. The cluster equipment cross-region operation database construction method and device and the storage medium provided by the application combine manufacturing enterprises and actual service business characteristics, construct a complete database warehouse system architecture, effectively improve the informatization and intelligent level of cluster equipment cross-region operation, and provide decision support for cluster equipment cross-region operation service.

The invention relates to the technical field of supply chain information management, in particular to an AI-based supplier purchase management method, which comprises the following steps of: acquiring a commodity image in commodity information in advance, acquiring a contour recognition result of the scaled commodity image, screening a quality confidence feature image according to the coincidence condition of the contour recognition result, and performing supplier purchase management according to the quality confidence feature image. Comparing the quality confidence feature image with an image in an existing commodity library, judging that commodity information of a supplier is stored or cleared, and determining a comprehensive feature index of a commodity corresponding to the commodity image in production; and determining whether to compress the commodity image to a plurality of data packets according to a comparison result of the comprehensive feature index and the comprehensive feature index reference value, and transmitting the data packets to an auditing terminal. According to the invention, the information processing process of a manufacturing enterprise in a purchase scene with complex purchase information is optimized, so that the supplier information content is extracted in a targeted manner, and the scientificity of supplier information management is improved.

This invention discloses an intelligent method for pushing safety training courseware to manufacturing enterprises, a computer device, and a computer-readable storage medium. This intelligent push method, based on the job identifier bound to the upload node and combined with a developed video push model, pushes high-quality training video courseware tailored to job requirements and individual preferences to each upload node, improving the relevance and effectiveness of safety training. Furthermore, it performs quality and effectiveness evaluation and optimization on the safety training short videos, autonomously judging the quality of training courseware, prioritizing the push of excellent video courseware, and autonomously eliminating inferior video courseware. Simultaneously, the system calculates the effective inventory of training courseware based on its quality and autonomously collects missing training courseware based on the effective inventory, supplemented by a series of incentive measures to encourage upload nodes to specifically supplement the system with missing training courseware, ensuring the integrity of the training courseware system.

The invention provides a manufacturing cluster production scheduling method considering energy cost and carbon emission, and relates to the technical field of industrial automation and intelligent manufacturing, and the method comprises the steps: constructing a mixed integer linear programming model used for job shop scheduling under peak power constraint, the model takes minimization of tardiness penalty, total completion time, energy cost and indirect carbon emission as optimization targets, and comprises constraint conditions related to a process processing sequence, machine distribution, energy consumption and carbon emission in a manufacturing enterprise cluster; and solving the mixed integer linear programming model by adopting an optimization algorithm based on NSGA-II so as to obtain a Pareto optimal solution set, wherein the solving process comprises coding and decoding based on a project-order-process-machine multilayer structure, selection operation based on non-dominated sorting and congestion degree calculation, and crossover and mutation operation designed for different coded character strings; and according to the Pareto optimal solution set, outputting production scheduling schemes corresponding to different optimization target preferences.

The invention belongs to the technical field of man-machine interaction, and particularly discloses a manufacturing industry man-machine interaction dialogue method and system based on deep collaborative evaluation, and the system comprises a front-end interaction module, a rear-end processing module, a multi-dimensional evaluation module, a collaborative evaluation module and a data storage module. And the multi-dimensional evaluation module comprises a task completion evaluation unit, a terminology understanding evaluation unit, a multi-round dialogue continuity evaluation unit and a user intention recognition evaluation unit. According to the manufacturing industry man-machine interaction dialogue method and system based on deep collaborative evaluation, the term recognition accuracy, the context coherence accuracy and the intention recognition accuracy are greatly improved, and evaluation results of four dimensions of task completion, term understanding, dialogue coherence and intention recognition are fused; quantitative evaluation of the manufacturing scene task completion degree can be realized, the blank that an existing evaluation method cannot measure the specific task completion condition is filled up, and comprehensive data support and decision basis are provided for a manufacturing enterprise to optimize a dialogue system.

The invention provides a test data modeling method and system, and the method comprises the following steps: a structural model building step: building a structural model through a module definition graph; a behavior model establishment step: constructing a behavior model through the activity graph; a demand model building step: building a demand model through the demand graph; and a parameter model building step: building a parameter model through the parameter diagram. The method has good universality and expandability, is suitable for manufacturing enterprises or test-intensive organizations of different scales and types, and has wide application prospects in the fields of enterprise data modeling, digital test management, intelligent manufacturing and the like. Through the test data model constructed by the invention, an enterprise can provide an efficient and visual data sharing platform for employees, the operation efficiency and problem solving ability of the employees are improved, the test data is converted from individual experience to organization assets, experience loss is effectively prevented, and inheritance, innovation and continuous improvement of enterprise tests are promoted.

The application discloses a kind of based on deep learning's sewing equipment work hour intelligent detection method and system, through real-time acquisition sewing equipment motor start-stop signal and cloth tension change data by sensor, and synchronous acquisition job image sequence, after pre-processing, fusion construction multimodal dataset;The multimodal dataset is input into target detection model, accurately identifies equipment operating state and operator on-site key state, forms continuous state sequence label;Further, extract the feature vector related to cutting line action from the state sequence, use deep learning algorithm to deeply mine its time series correlation law, to intelligently judge and divide effective sewing interval and invalid time interval, realize the accurate segmentation of real production time;On this basis, the duration of each effective sewing interval is accumulated to form a single-process work hour value;The application greatly improves the work hour accounting precision and management refinement level of garment manufacturing enterprises.

This utility model discloses a detachable pot handle, including an upper handle body and a lower handle body. The lower handle body has a gripper mechanism at its front end for clamping the pot body. The gripper mechanism and the lower handle body move in a lever-like motion on the upper handle body. A switch assembly is provided on the upper handle body, and a transmission and fixing mechanism is provided inside the upper handle body. The switch assembly includes a button and a fastener. The transmission and fixing mechanism includes a transmission arm that can move back and forth inside the upper handle body, a reset member on the transmission arm, and a locking member on the lower handle body for locking the transmission arm. The fastener fixes the transmission arm to the button. This utility model allows for a more convenient installation structure, reduces manufacturing costs, and allows for push-to-unlock functionality using different structural methods, meeting the needs of modern manufacturing enterprises.

The invention discloses a manufacturing industry channel collaborative decision-making method based on industrial-level multi-agent, and relates to the technical field of manufacturing industry intelligent decision-making and multi-agent collaborative control, and the method comprises the steps: collecting internal data of a manufacturing enterprise, carrying out the time alignment and consistency verification, and generating a collaborative basic data set; constructing a collaborative semantic structure reflecting a product, a channel, a contract and a performance relationship, and outputting channel collaborative state description data; a plurality of business agents respectively extract demand fluctuation, inventory pressure and performance risk states based on the channel cooperation state description data, and convert the demand fluctuation, the inventory pressure and the performance risk states into channel constraint conditions and production and supply constraint conditions to form a cooperation strategy input set; and performing parallel deduction evaluation on the collaborative strategy input set in a digital twin environment, screening strategy results of which the stability and consistency meet preset conditions, and outputting a channel allocation and production scheduling decision. According to the method, the actual execution risk is remarkably reduced, and the order satisfaction rate, the inventory turnover efficiency and the performance stability are improved.

The invention discloses a new energy automobile charging mode analysis method based on an evolutionary game theory, and the method comprises the steps: firstly, determining a government, an automobile manufacturing enterprise and a consumer as game participants, and defining a strategy set and influence factor variables of the game participants; based on this, a payment matrix coupling benefits of three parties is constructed, and then a replication dynamic equation is established by taking the strategy selection probability of each party as a state variable; a system equilibrium point is obtained by solving an equation, stability analysis is carried out by using a Jacobian matrix eigenvalue method, and a stability condition for enabling the equilibrium point to become an evolution stability strategy point is deduced; and finally, based on the condition analysis, factor variables needing to be adjusted for realizing the expected stable strategy are analyzed, and a decision support result is generated. According to the method, the defect that the prior art lacks a dynamic view angle, a system framework and a quantitative model is overcome, the three-party strategy interaction evolution process can be quantitatively depicted, the stability condition is determined, and a scientific and quantitative basis is provided for industrial policy making and enterprise strategic decision making of a battery charging and replacing mode.