51 results about "Supply chain scheduling" patented technology

The invention relates to the field of supply chain scheduling decisions, in particular to a supply chain intelligent scheduling decision optimization method and system based on big data driving. The method comprises the following steps: collecting supply chain heterogeneous data streams, extracting abnormal disturbance events, carrying out nonlinear disturbance response mining, and constructing a supply chain disturbance response field; identifying dynamic state parameters of all nodes of the supply chain, analyzing correlation characteristics among the parameters, performing multi-node logistics state transfer evolution, and generating logistics state transfer characteristics of each node; and multi-node delay evaluation is carried out based on the supply chain heterogeneous data flow, global distribution mapping is carried out, and an upstream and downstream logistics state delay characteristic spectrum is constructed. According to the invention, adaptive optimization collaboration between multiple nodes and multiple targets is realized, so that the stability, flexibility and economic benefit of the whole supply chain system are effectively improved.

The invention discloses a supply chain production scheduling optimization method and system based on multi-source data. According to the method, supplier purchase data is extracted through a data crawler, raw material sharing is analyzed in combination with a knowledge graph, and a raw material sharing list is generated; on the basis of delivery records and product attributes, a quality stable value is predicted through linear regression, process similarity is calculated through a text similarity algorithm, and a process similarity matrix is constructed; in combination with historical sales data, a market demand prediction model is constructed by applying multiple linear regression and time sequence analysis. A complex relation among raw material sharing, process similarity and market demands is described through a graph neural network, a product association network graph is generated, an optimal balance point is found between delivery time and a resource utilization rate through Pareto optimization, and a production scheduling scheme is formed; and finally, integrating the performance score, the quality stable value, the raw material sharing list and the production scheduling scheme through an integer programming algorithm, and optimizing a supplier combination. According to the invention, the production scheduling efficiency and precision are improved, and complex business requirements are met.

The application provides a virtual commodity supply chain scheduling system and method based on artificial intelligence, relates to the technical field of computers, and is applied to a commodity supply system, which comprises a plurality of supply warehouses; the scheduling system comprises: a data acquisition module for acquiring user behavior data, external supply data and inventory state data of each supply warehouse; an intelligent decision module for obtaining user demand prediction data according to the user behavior data based on a preset prediction model; determining the corresponding risk level according to each inventory state data, and obtaining the corresponding weight adjustment coefficient based on each risk level; and a decision execution module for performing inventory supply scheduling on each supply warehouse according to the user demand prediction data and each weight adjustment coefficient. The application collects various types of data through the data acquisition module and performs deep analysis and processing by using the intelligent decision module, realizes accurate prediction of user demand and automatic intelligent evaluation of inventory risk, and reduces the uncertainty of manual intervention.

The present invention relates to the technical field of supply chain scheduling, and discloses a multiple supply chain scheduling method and system based on a global Critic multi-agent algorithm. The method includes: constructing a multi-agent supply chain environment; introducing a global Critic network, and using the multi-agent deep deterministic policy gradient algorithm to perform reinforcement learning on the multi-agent supply chain environment to construct a multi-agent decision-making model; inputting supply chain demand data into the multi-agent decision-making model for optimized scheduling processing to generate a scheduling strategy that maximizes the overall profit of the supply chain. The present invention introduces a global Critic network to reduce multi-agent competition and optimize the overall performance. The multi-agent deep deterministic policy gradient algorithm is used to construct a decision-making model to adapt to complex supply chain environments. With the goal of maximizing the overall profit, it solves the problems of low efficiency and low returns. The environment has strong adaptability and can handle scheduling problems of different scales. By continuously learning to optimize decisions, the reliability is improved, and the efficiency and returns are significantly enhanced.

The invention relates to the technical field of medical consumable supply chain scheduling, and discloses a medical consumable supply chain intelligent scheduling method. The method comprises the following steps: firstly, acquiring real-time orders and inventory data of medical consumables, and generating a demand prediction graph and an inventory state graph after standardized preprocessing; constructing a multi-objective optimization scheduling model of a double-layer decision network based on linear programming and dynamic programming, and initializing model parameters; and inputting the preprocessed data into the model, obtaining a scheduling path planning graph and an inventory distribution graph through forward calculation, then calculating deviation loss and matching loss, updating model parameters through back propagation, and outputting a scheduling scheme and an inventory dynamic adjustment strategy after multiple iterative optimization. According to the method, multiple targets such as transportation time and cost can be comprehensively considered, and through real-time feedback correction and multi-dimensional evaluation, the scheduling accuracy and the supply chain response speed are improved, inventory management is optimized, and the overall benefits of the medical consumable supply chain are improved.

The invention discloses an industrial software platform industrial cluster supply chain super network model construction and optimization method, and aims to better research an industrial cluster supply chain and enable the industrial cluster supply chain to obtain the minimum economic cost and time cost and the maximum supply chain anti-risk capability. Dividing the whole industrial cluster into a supply chain sub-network, a value self-network and a technical service sub-network to systematically optimize supply chain resource configuration; aiming at the problem that a traditional optimization modeling method is difficult to accurately evaluate the influence of a technical service network on factors such as the production value of a whole industrial cluster supply chain, a deep learning method is adopted to construct an input and output model of a technical service sub-network, and parameters output by the model are predicted to obtain supply chain parameters required by optimization; and in combination with a multi-objective optimization algorithm, the cost of the supply chain is minimized, the production cycle is minimized, the anti-risk capability is maximized, and an optimal supply chain scheduling strategy is sought.

The invention provides a virtual commodity supply chain scheduling system and method based on artificial intelligence, relates to the technical field of computers, is applied to a commodity supply system, and comprises a plurality of supply libraries; the scheduling system comprises a data acquisition module used for acquiring user behavior data, external supply data and inventory state data of each supply library; the intelligent decision module is used for obtaining user demand prediction data according to the user behavior data based on a preset prediction model; determining a corresponding risk level according to each piece of inventory state data, and obtaining a corresponding weight adjustment coefficient based on each risk level; and the decision execution module is used for carrying out inventory supply scheduling on each supply library according to the user demand prediction data and each weight adjustment coefficient. According to the invention, various data are widely collected through the data acquisition module, and deep analysis and processing are carried out through the intelligent decision-making module, so that accurate prediction of user demands and automatic intelligent assessment of inventory risks are realized, and the uncertainty of manual intervention is reduced.

The invention discloses an agricultural product wholesale market transaction management system and method, and the method comprises the steps: a commercial tenant and a user register real-name authentication on a platform, a seller uploads and updates the information of agricultural products, and a buyer selects a commodity and fills in an order; multiple payment channels are integrated, a settlement bill is automatically generated for fund settlement, sales and price data are collected, and the market trend is analyzed by using big data; the purchase trend is predicted according to the transaction record and the buyer preference, and replenishment suggestions are automatically generated; transaction data is encrypted by SSL and protected by multiple identity authentication, and transaction monitoring abnormity is recorded by the block chain; an APP and a Web end are provided, and personalized setting and 24 / 7AI customer service are supported; the performance of the system is evaluated in each quarter, functions are optimized according to feedback, and a modular framework is adopted for flexible expansion; according to the agricultural product wholesale market transaction management system and method, an AI technology is introduced, demand changes are automatically identified, inventory and supply chain scheduling are optimized, and pricing and customer prediction intelligent suggestions are provided.

The invention relates to the technical field of supply chain management, and discloses a smart logistics and supply chain collaborative management method based on the Internet of Things, which comprises the following steps: 1, in logistics transportation and storage links, collecting logistics-related multi-source data by using Internet of Things equipment arranged on a transportation vehicle, a storage facility and a distribution terminal, and storing the logistics-related multi-source data in the logistics transportation and storage links; the collected data are sent to an edge computing node for preprocessing through a wireless communication network; and 2, performing formatting conversion and anomaly detection on the received multi-source data at an edge computing node. The supply chain scheduling model of reinforcement learning optimization is introduced, the transportation and inventory strategies can be adaptively adjusted in a complex logistics network, dynamic optimization is achieved, compared with a traditional logistics scheduling method based on a static rule, the limitation of fixed threshold setting is broken through, the problem that the response speed is low under sudden orders and inventory fluctuation is solved, and the logistics scheduling method is suitable for large-scale popularization and application. The flexibility of the supply chain is improved.

The invention provides an intelligent vaccine supply chain collaborative optimization system based on multi-source data fusion, and relates to the technical field of vaccine supply chains, and the system comprises a data acquisition and processing module which is used for collecting and preprocessing multi-source heterogeneous data in real time; the intelligent prediction and optimization module is used for constructing a hybrid prediction model and predicting vaccine demands; the storage and logistics management module is used for constructing a virtual warehouse model; the collaborative decision module is used for generating a multi-dimensional supply chain scheduling scheme and evaluating the feasibility and efficiency of the scheme; and the dynamic priority marking module is used for carrying out priority ranking on vaccines. By constructing the hybrid prediction model, the future vaccine demand can be accurately predicted, the prediction accuracy and reliability are improved, vaccine shortage or excess can be avoided, and the response speed and flexibility of a supply chain are improved; through accurate priority ranking of vaccines, when emergency situations such as epidemic situations and the like are coped, resources can be quickly allocated, and the inoculation requirement is met to the maximum extent.

The invention relates to the technical field of supply chain management, in particular to a CBAM-based product full-life-cycle dynamic supply chain optimization method and management system, and the method comprises the steps: obtaining materials and numbers required by an outlet, building the association between raw materials and transportation links, collecting production carbon release details, and screening compliance paths to form an operation list. And judging the position overlap ratio to generate a resource path atlas, and outputting a supply chain scheduling scheme according to time consumption and span sorting. The method comprises the following steps: quantifying a geographic distance and a transportation mode in a raw material path, establishing mapping between transportation and a carbon source, identifying a carbon release path by combining an equipment operation section sequence, rejecting non-compliant paths according to target market limitation, associating a screening result with a carrying node space position, and constructing a connection combination according to a position overlap ratio. The path is optimized through time consumption and span sorting, and collaborative optimization of path screening and job scheduling is achieved.

The invention relates to the technical field of supply chain scheduling, and discloses an AI-based supply chain scheduling method and system, and the method comprises the steps: obtaining the order, resource, logistics topological data and historical performance data of a supply chain; generating a delivery uncertainty embedding vector by using the graph neural network; determining a central moment and a slope of an asymmetric trapezoidal function and a mean value and a variance of a Gaussian function according to the delivery uncertainty embedded vector, and constructing a fuzzy time window for a scheduling task of each destination node; obtaining a reinforcement learning model, and outputting a weight combination of a cost item, a time item and a robustness item in the scheduling objective function; determining a scheduling objective function according to the weight combination, and solving the scheduling objective function; the crossover operation of the genetic algorithm is key sub-graph exchange, and is realized by identifying and exchanging key path sub-graphs of two parent scheduling schemes. According to the method, complex performance risk distribution can be accurately described, real-time changes can be adapted, and meanwhile the calculation amount is reduced.

The invention provides an agricultural intelligent supply chain scheduling method and system, and the method comprises the steps: generating supply chain comprehensive research and judgment data through various types of deployed sensors and Internet of Things equipment; time sequence modeling is carried out through a convolutional neural network CNN, and feature extraction and importance evaluation are carried out on comprehensive research and judgment data; screening out key scheduling feature vectors; constructing a scheduling demand space based on the key scheduling feature vector, and forming a linkage scheduling strategy; all linkage scheduling strategies are uniformly coded into a structured scheduling unit, an initial resource scheduling scheme is generated, and accurate resource matching and efficiency optimization are achieved. According to the invention, through the Internet of Things sensors deployed on the agricultural production base, the storage center and the transport vehicle, the five types of modal data are collected in real time, and the reference data are constructed based on the historical periodic data, so that the availability of the data is significantly improved.

The present invention discloses a supply chain production scheduling optimization method and system based on multi-source data. The method extracts supplier procurement data through a data crawler, analyzes the commonality of raw materials in combination with a knowledge graph, and generates a raw material commonality list; based on delivery records and product attributes, a linear regression is used to predict the quality stability value, and the process similarity is calculated through a text similarity algorithm to construct a process similarity matrix; in combination with historical sales data, a multivariate linear regression and time series analysis are used to construct a market demand prediction model. The complex relationships among raw material commonality, process similarity, and market demand are characterized through a graph neural network to generate a product association network diagram, and the Pareto optimization is used to find the optimal balance point between delivery time and resource utilization rate to form a production scheduling plan; finally, an integer programming algorithm is used to comprehensively evaluate the performance score, quality stability value, raw material commonality list, and production scheduling plan to optimize the supplier portfolio. The present invention improves the production scheduling efficiency and accuracy and meets complex business requirements.

The invention relates to the technical field of supply chain scheduling, and discloses a supply chain production-transportation-sales cooperative scheduling method and system, and a storage medium. The system comprises a production data acquisition module, a transportation data acquisition module and a sales data acquisition module which are respectively used for acquiring plans, schedules, inventory, routes, states, costs, orders, prediction and market demand data of corresponding links; the production, transportation and sales association modeling module is used for establishing an association model based on the collected data and generating a collaborative scheduling prediction result; the dynamic feature extraction module combines historical and real-time data to extract dynamic features and identify a scheduling demand spatial-temporal change rule; the resource optimization scoring module calculates the resource adjustment priority and the allocation proportion according to the dynamic characteristics and the prediction result, and generates an optimization scheduling scheme; and the self-adaptive scheduling execution module adjusts production, transportation and sales scheduling in real time based on the optimization scheme to realize collaborative adaptive change. The system can break data islands and improve supply chain cooperation efficiency and strain capability.

The application relates to the technical field of supply chain scheduling, and discloses a supply chain scheduling method and system based on AI, which comprises the following steps: acquiring order, resource, logistics topology data and historical performance data of a supply chain; generating a delivery uncertainty embedding vector by using a graph neural network; determining the center time and slope of an asymmetric trapezoidal function and the mean and variance of a Gaussian function according to the delivery uncertainty embedding vector, and constructing a fuzzy time window for a scheduling task of each destination node; acquiring a reinforcement learning model, and outputting a weight combination of a cost term, a time term and a robustness term in a scheduling target function; determining the scheduling target function according to the weight combination, and solving the scheduling target function; and performing cross operation of a genetic algorithm, wherein the cross operation is key subgraph exchange, and the key path subgraphs of two parent scheduling schemes are identified and exchanged to realize the cross operation. The application can not only accurately depict complex performance risk distribution, but also adapt to real-time changes, and the calculation amount is reduced.

The invention discloses an intelligent supply chain collaborative platform intelligent scheduling system based on multi-source data fusion, and relates to the field of supply chain scheduling, and the system comprises an acquisition module which is used for collecting historical supply product association information of each supply chain in real time; the analysis module is used for traversing the supply chain supply product associated information acquired by the acquisition module, and analyzing each supply chain trust index based on the supply chain supply product associated information; the receiving module is used for receiving supply chain service target product supply demand information; according to the invention, through deep mining and analysis of massive historical supply data in the operation process, the credibility of each supply chain can be accurately evaluated, a powerful basis is provided for an enterprise to screen partners, the scientificity of supply chain selection is greatly improved, and the supply chain selection efficiency is greatly improved in the face of product supply demands. According to the method, options meeting requirements can be quickly matched from a plurality of supply chains and are sorted and recommended according to trust indexes, the supply response speed is greatly increased in the process, and the timeliness of production and operation of enterprises is guaranteed.

The present invention relates to the technical field of supply chain planning, and specifically relates to an optimization method for cross-border trade supply chain scheduling based on the ant colony algorithm. This method first highlights the situation among all initial fit degrees according to the initial fit degree corresponding to the company nodes, adjusts the initial fit degree, and obtains the adjusted fit degree; according to the adjusted fit degree of adjacent company node combinations, adjusts the initial transaction quality parameters to obtain the adjusted transaction quality parameters of adjacent company node combinations; according to the adjusted transaction quality parameters of adjacent company node combinations in each supply chain, screens out the optimal supply chain from all the supply chains in the supply chain network. The present invention can better screen out the optimal supply chain by deeply analyzing the transportation cost and the fit degree between companies, and ensure the long-term stable income during the product supply process.

The present invention relates to the technical field of port dispatching management, and discloses a supply chain dispatching method based on port digital supply chain cloud platform management; the method comprises calculating the loading and unloading cycle of the port, dividing the loading and unloading cycle into continuous sub-periods, obtaining the comprehensive navigation data of the ship in the sub-period, predicting the vehicle demand for the next sub-period through a machine learning model, identifying the available vehicles from the vehicles to be dispatched, and performing supply chain dispatching; compared with the prior art, the present invention can refine and split the overall cargo loading and unloading process of the port terminal, reduce the amount of data in each sub-period, and combine with the prediction method of the machine learning model to evaluate and judge in advance whether the dispatching vehicles in the port terminal meet the supply chain dispatching needs, so that the ship cargo in the port terminal and the dispatching vehicles can achieve an efficient linkage effect, avoiding the phenomenon of accumulation of cargo at the port terminal due to the inability to transfer cargo in time.

The invention discloses a cross-border import supply chain intelligent scheduling system and method based on big data analysis, and the method comprises the following steps: forming an original scheduling data set through a multi-source data fusion collection module; performing structured processing on the original scheduling data set, extracting market emotion features by adopting an emotion analysis algorithm based on an ELCTRA model, constructing scheduling feature vectors, and generating a scheduling feature vector set; inputting the scheduling feature vector set into a demand prediction model, and fusing and outputting a demand prediction result; generating a plurality of groups of scheduling candidate solutions by adopting an improved dominant Pareto evolutionary algorithm, and selecting a scheduling execution scheme according to scheduling target preference; in the scheduling execution process, execution data is collected through the Internet of Things equipment, execution state monitoring and three-dimensional visualization of a scheduling result are carried out, and risk early warning information is pushed to a high-risk scheduling event. According to the invention, intelligent closed-loop optimization of cross-border import supply chain scheduling is realized, and the accuracy and efficiency of demand prediction and risk response are improved.

The invention discloses a supply chain collaborative scheduling management method and system, and relates to the technical field of data management. The method comprises the following steps: in response to a received supply chain scheduling demand, constructing a dynamic virtual resource pool based on schedulable resources of each supply chain node, obtaining a digital mirror image and an intelligent cooperation carrier of each supply chain node on a supply chain, constructing a joint intelligence wedge, carrying out collaborative decision game training on the joint intelligence wedge through a collaborative gain co-molding device, and obtaining a joint intelligence model; the method comprises the following steps: establishing a co-evolutionary co-frequency joint intelligence wedge group, searching a candidate co-frequency joint intelligence wedge group with matched resource granularity from the co-frequency joint intelligence wedge group based on a dynamic virtual resource pool, establishing a temporary co-scheduling chain, dynamically executing a co-scheduling task, and obtaining an execution result of each co-frequency joint intelligence wedge. And reconstructing the same-frequency joint intelligence wedge group to execute the scheduling task. According to the method, the overall operation efficiency, the anti-risk capability and the sustainable development capability of the supply chain can be comprehensively improved, and powerful support is provided for efficient, agile and stable operation of the supply chain.

This invention provides an intelligent supply chain scheduling method and system that integrates a large-scale model, belonging to the field of intelligent supply chain scheduling technology. The method includes first collecting production line sensor signals, supplier engineering change notifications, and market commentary text. Implicit quality characteristics of component batches are extracted by analyzing the signals, and a technology transmission network with the impact of changes as weights is constructed based on this. User sentiment in the market commentary is quantified as tolerance constraints on specific technical parameters and injected back into the network to form a constraint network. Supply chain disturbance events are simulated on this constraint network, and the strength of their transmission path's impact on performance constraint boundaries is evaluated, thereby generating preventative scheduling plans. Finally, with the goal of minimizing inter-stage switching costs, a full-cycle, staged scheduling instruction sequence is optimized. This invention achieves a closed loop from market perception to preventative mobilization, enhancing the supply chain's proactive immunity and overall resilience to internal and external uncertainties.

The invention discloses an integrated coal supply chain scheduling decision-making method and system, and the method comprises the steps: constructing a plurality of basic scheduling optimization algorithms covering the railway transportation of a coal supply chain to the port shipping, comprising a coal train loading optimization algorithm, a coal train delivery backspace optimization algorithm, a railway downstream heavy train shunting optimization algorithm, a port ship arrangement and shipping plan optimization algorithm and a ship replacement optimization algorithm. And according to a typical scene in a scheduling process, carrying out combined calling on the basic optimization algorithm, and constructing a decision optimization model in the typical scene. According to the method, multiple scheduling optimization algorithms are automatically combined and called for multiple scenes such as insufficient loading, transport capacity reduction, port abnormity and power plant inventory emergency, rapid response and stable operation of the whole coal transportation supply chain are achieved, and the overall scheduling efficiency of the supply chain is remarkably improved.

The invention provides a fresh food supply chain scheduling management system. The system comprises an intelligent prediction mechanism which is used for predicting and presetting fresh food material supply data which needs to be supplied to a target company by a fresh food material supply enterprise on the same day by adopting a production quantity prediction model; and the production issuing mechanism is used for issuing the intelligent prediction result to each downstream fresh food material production enterprise for logistics transmission of the fresh food materials. The invention further provides a fresh food supply chain scheduling management method. According to the invention, for the technical problem that the fresh food materials are not easy to store, so that the resource saving of the fresh food materials and the meeting of the catering demand of the company are difficult to consider, the production quantity prediction model is adopted to predict the quantity of various types of fresh food materials consumed by the target company in the day; and the intelligent prediction result is used as production data to be published to each fresh food material production enterprise, so that the technical problem is solved.

This specification provides a method and apparatus for optimizing natural gas pipeline network scheduling based on multi-agent systems. The method includes constructing a physical model of the natural gas pipeline network, representing the nodes, pipelines between nodes, and constraints within the network; constructing a reward strategy for the target agent during reinforcement learning, with at least one of the following as optimization objectives: lowest operating cost, maximum pipeline inventory, or minimum gas consumption fluctuation. The target agent includes at least a gas source, compressor, and gas storage unit within the nodes. Based on the behavioral rules of the target agent and the constraints, the physical model is simulated for supply chain scheduling. The behavior of the target agent during the supply chain scheduling simulation is then reinforced and adjusted based on the reward strategy to obtain optimized natural gas pipeline network scheduling information. This method can accurately simulate optimized natural gas pipeline network scheduling information, promoting scientific scheduling and cost reduction / efficiency improvement in the natural gas supply chain.

The invention discloses a supply chain demand prediction method based on pattern clustering, and the method comprises the following steps: S1, collecting multi-source data, and constructing a multi-source original data set; s2, preprocessing the original data set to generate a standard data set; s3, based on improved HDBSCAN clustering analysis, identifying a potential demand trend and dividing an output demand mode group; s4, extracting a key feature vector of each group, inputting the key feature vector into a DeepAR model for autoregressive training and cross validation, and outputting a prediction model; s5, loading the trained model, and combining real-time sales and market data to generate a current key feature vector; and S6, inputting the current feature vector into a DeepAR model, predicting a future demand, and outputting the future demand to a supply chain scheduling system. According to the invention, automatic identification and high-precision prediction of the demand trend under the driving of multi-source data are realized, and the supply chain response, efficiency and inventory decision intelligent level are improved.

The present application relates to the field of supply chain scheduling and digital twin technology, and specifically provides a supply chain order scheduling method, system, device and storage medium based on digital twin, the method comprising: acquiring the basic attributes and topological association relationship of each scheduling node of the supply chain link, and constructing a digital twin model; configuring the data synchronization frequency according to the scene, collecting the real-time running state data of the node and dynamically updating the model; analyzing the order information to obtain the order priority, constraint condition and optimization target weight; generating a candidate scheduling scheme that meets the constraints based on the twin model, simulating execution to obtain scheduling evaluation indexes, and selecting the optimal scheme through multi-objective optimization algorithm sorting; collecting the execution state of the optimal scheme, adjusting the algorithm weight and iteratively optimizing the digital twin model, so as to improve the efficiency and emergency adaptation capability of the supply chain order scheduling, and realize the adaptive operation and continuous iterative optimization of the whole process of the supply chain order scheduling.

This invention relates to the field of intelligent supply chain scheduling technology, specifically disclosing an intelligent supply chain optimization scheduling method and system for carbon fiber materials. The method acquires process data during the production of carbon fiber materials and evaluates the performance of carbon fiber products based on this data to obtain performance evaluation results. Simultaneously, it acquires order delivery information for current carbon fiber production orders and, combined with the performance evaluation results and order delivery information, generates a delivery confidence index. This index is used to determine whether there is any order delivery risk. Finally, by responding to order delivery risks, a response strategy is output. This method, through real-time process data monitoring and performance evaluation, can identify potential quality risks early on and combine them with order delivery information to form a quantified delivery confidence index. This avoids resource waste and delivery risks caused by scheduling downstream high-value resources according to a predetermined plan even when product quality risks exist.

The invention discloses a business process risk dynamic identification system based on multi-modal data analysis, relates to the technical field of business process risk identification, and aims to solve the technical problem that full-process identification monitoring cannot be performed according to a business process front end, a business process middle end and a business process rear end in the prior art. Specifically, on the basis of multi-modal data acquisition, full-process dynamic identification is taken as a core, closed-loop management and control is taken as a target, information islands of capacity planning, production execution and supply chain delivery are broken through according to business process characteristics of the manufacturing industry, and a full-link risk monitoring system is constructed; and through quantitative index and trend analysis, the accuracy and foresight of risk identification are improved, a closed loop of identification-early warning-disposal is realized, landing measures such as equipment upgrading, process optimization and supply chain scheduling can be directly linked after a risk signal is triggered, and core risks such as capacity overload, equipment failure and supply chain delay in the manufacturing industry are effectively reduced.

The present application relates to the technical field of supply chain management, in particular to a product whole life cycle dynamic supply chain optimization method and management system based on CBAM, which comprises the following steps: obtaining materials and numbers required for export, establishing correlation between raw materials and transportation links, collecting production carbon release details, screening compliance paths to form operation lists, judging position coincidence degree to generate resource path atlas, and outputting supply chain scheduling scheme according to time consumption and span sorting. The present application quantifies the geographical distance and transportation mode in the raw material path, establishes the mapping between transportation and carbon source, identifies the carbon release path in combination with the equipment operation section sequence, eliminates non-compliant paths according to the target market restrictions, associates the screening results with the spatial position of the carrying node, constructs the connection combination according to the position coincidence degree, optimizes the path through time consumption and span sorting, and realizes the collaborative optimization of path screening and operation scheduling.