110 results about "Inventory optimization" patented technology

The invention provides a multi-warehouse demand management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the exchange rate sensitivity correlation analysis processing of demand fluctuation data in a cross-border e-commerce multi-warehouse network, and obtaining the exchange rate elasticity coefficient early warning information of each warehouse node; performing distributed coordination decision processing on the logistics constraint condition of each warehouse node according to the early warning information to obtain a decision scheme of resource allocation between warehouses; performing block chain trusted measurement processing on inventory distribution information in the multi-warehouse logistics alliance chain according to the decision scheme to obtain a multi-warehouse resource reconfiguration execution instruction based on the smart contract; and performing adaptive exchange rate risk avoidance processing on the inventory configuration strategy of each warehouse node according to the execution instruction to obtain a multi-warehouse collaborative inventory optimization management result. According to the method, the problem of influence of exchange rate fluctuation on multi-warehouse demand management is effectively solved through exchange rate sensitivity analysis and distributed coordination decision.

The application relates to the technical field of supply chain management, in particular to a supply chain management system based on big data, which comprises a data integration module, a demand prediction module, an inventory optimization module, a collaborative scheduling module and a feedback optimization module. The system realizes supply chain global state analysis and demand prediction through multi-source heterogeneous data collection and a deep learning model, combines dynamic inventory adjustment and cross-regional resource scheduling optimization, and improves the supply chain efficiency. The feedback optimization module further generates optimized control instructions by analyzing and intervening in the time delay of historical records. The application can improve the response speed and resource utilization efficiency of the supply chain and reduce the operation cost.

The invention relates to an AI-based supply chain demand prediction and inventory optimization method and system, and the method comprises the following steps: calling a sales record from a supply chain, recognizing a periodic demand rule of a corresponding commodity, and predicting an estimated demand quantity in a future time period according to the periodic demand rule; carrying out supply and demand difference analysis by combining the current warehouse commodity stock margin, and judging the stock state; if the commodity stock surplus exists, making a corresponding stock digestion strategy; if a shortage quantification result appears, carrying out key degree evaluation and urgency sorting based on a shortage amount in combination with market factors, and generating a key commodity list and an out-of-stock urgency index; and finally, in combination with a preset commodity constraint condition, according to the key commodity list and the urgency index, an optimal replenishment scheme meeting resource and time limit is formulated, and the technical problem of how to realize conversion from passive response to active pre-judgment in a supply chain environment with large demand fluctuation and complex influence factors is solved.

The invention relates to a supply room consumable intelligent inventory management method and device, equipment and a medium. The method comprises the steps that multi-source sensing data of all consumables in a supply room are acquired, multi-source information fusion processing is carried out according to the multi-source sensing data, and inventory state posterior distribution of all the consumables is obtained; performing event correction processing on the inventory state posterior distribution, and performing demand prediction processing according to the inventory state distribution subjected to event correction and the historical consumption data of the consumables to obtain a multi-step demand prediction result of each consumable in the target prediction period; performing inventory optimization calculation processing according to the multi-step demand prediction result and the inventory state distribution subjected to event correction to obtain the replenishment order quantity of each consumable in the target prediction period; and according to the replenishment order quantity and the batch validity period information of each consumable, performing consumable scheduling and risk early-warning processing, and generating an immediate consumable priority use strategy and inventory risk early-warning information. By adopting the method, the consumable stock can be identified, and the replenishment order can be generated.

A metering equipment warehouse division demand prediction and scheduling method based on spatio-temporal feature fusion comprises the steps of firstly obtaining and collecting warehouse division data, then establishing a spatio-temporal feature fusion model based on the warehouse division data, and then establishing a spatio-temporal diagram convolution prediction network, the spatio-temporal diagram convolution comprises a diagram convolution layer and a time convolution layer, through extraction and fusion of the two layered features, dynamic weight fusion is obtained, joint modeling of space-time dynamics is realized, and finally, prediction and allocation decision are implemented based on the dynamic weight fusion. According to the method, the multi-modal graph structure fusing the space-time association and the replacement rule is constructed, and a space-time joint modeling architecture and a dynamic feedback mechanism are designed, so that high-precision demand prediction and global inventory optimization are realized; the method systematically solves the core problems of insufficient spatial correlation modeling, dynamic event response lagging, low efficiency in multi-source data utilization and the like of a traditional method, and provides an efficient solution for electric power material management.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention belongs to the technical field of medicine inventory management, and discloses a Western medicine intelligent inventory optimization and automatic replenishment decision-making method and a Western medicine intelligent inventory optimization and automatic replenishment decision-making system. Comprising the steps of collecting environment data, inventory information and historical inventory consumption data of inventory drugs, and integrating the environment data and the inventory information to obtain a health degree index of each drug; based on the drug health degree index and the consumption prediction result, generating a replenishment decision matrix, and outputting replenishment opportunity, replenishment amount and replenishment priority; in combination with an operation audit result and inventory area division, differential inventory management scheme configuration is realized; and after external information is obtained, a replenishment strategy is dynamically adjusted, and a target inventory optimization decision is formed. According to the method, accurate evaluation of the inventory state and intelligent decision of replenishment response are realized, and the safety, efficiency and refinement level of medicine inventory management are improved.

The invention discloses a multi-target inventory optimization method based on dynamic demand prediction and an intelligent decision-making system, and the method comprises the steps: firstly collecting a cache library inventory state of a cache library system, including considering production order data and AGV transportation capability data, and carrying out the standardization processing of the cache library inventory state; inputting the data into an LSTM neural network model of a dynamic demand prediction module to predict raw and auxiliary material demands in a future time period; the replenishment amount and the replenishment period are solved based on an improved multi-objective optimization algorithm NSGA-II, and the optimization objectives are the lowest total cost, the lowest stockout rate and the lowest inventory fluctuation; issuing a replenishment instruction to the cache library system through the production management system, and scheduling the AGV to execute a transportation task; and the inventory state is monitored in real time in the production process, strategy adjustment or model retraining is triggered according to the fluctuation coefficient, and finally a zero-stockout, low-fluctuation and optimal-cost replenishment strategy is output, so that the method is suitable for a high-dynamic manufacturing environment.

The invention relates to the technical field of inventory optimization, in particular to a multilevel guarantee-based maintenance resource optimization method, which comprises the following steps of: determining a resource allocation quantity based on a guarantee system and a guarantee resource total number, and determining a maintenance function of each base station in combination with spare part historical use data; determining the annual average demand quantity of the superior replacement unit based on the base-level replacement unit, and further determining a superior supply channel mean value and a superior expected stockout number; determining a basic-level supply channel mean value and a basic-level expected stockout number based on the annual average demand quantity, the maintenance function and the superior expected stockout number so as to determine the availability of each basic-level equipment and the total availability, and constructing a resource allocation optimization model based on the total availability and the resource allocation quantity. And performing calculation based on the model to obtain the maximum total availability, the maintenance resource allocation quantity and the inventory configuration. According to the method, dynamic allocation of resources can be guaranteed, and the problem of marginal effect decline existing in single-way improvement is solved, so that the availability level is improved.

The invention relates to the technical field of logistics data processing and artificial intelligence, and discloses an intelligent warehouse management system based on logistics data processing, and the system comprises a multi-source heterogeneous data fusion preprocessing module which carries out the real-time collection of the data of a global distributed storage center, and outputs a time-space standardized multi-dimensional feature data set; the spatio-temporal correlation demand prediction module is used for constructing a spatio-temporal model and carrying out depth prediction by adopting a long-short-term memory network and an attention mechanism; the multi-constraint global inventory optimization module is used for constructing a multi-objective optimization model and solving an inventory configuration scheme through a decomposition coordination method; the reinforcement learning intelligent scheduling module is used for establishing an environment state space and performing strategy optimization by adopting a deep Q network; the digital twinborn visual decision support module is used for realizing real-time synchronization of physical storage and a digital model and outputting decision suggestions and risk early warning; according to the method, multi-source heterogeneous data can be effectively processed, intelligent prediction of cultural perception is realized, and global collaborative optimization is carried out.

The invention provides an intelligent storage demand prediction scheduling method and system combined with an AIAgent, and the method comprises the steps: obtaining a historical storage data set of a target storage scene, carrying out the demand feature extraction processing through the AIAgent, and generating a storage demand fluctuation feature and an inventory dynamic distribution feature; the method comprises the following steps: constructing an inventory-demand matching model based on warehousing demand fluctuation characteristics and preset market trend prediction data, calling a dynamic path optimization algorithm to generate a warehousing operation sequence according to inventory dynamic distribution characteristics and inventory adjustment priorities, and carrying out collaborative scheduling processing on the warehousing operation sequence based on replenishment trigger conditions, and generating an optimized warehousing operation instruction set, and feeding back the warehousing operation instruction set to the warehousing control system to execute inventory optimization operation. The method can effectively reduce the inventory redundancy caused by the demand prediction deviation and the operation cost caused by path repeated planning, and improves the real-time response capability of the warehousing system to the market fluctuation and the resource utilization rate.

The invention provides a method and system for constructing a multi-source time series data fusion prediction model in BI analysis, and relates to the technical field of enterprise-level business intelligent analysis, and the method comprises the steps: obtaining multi-source original time series data, and carrying out the standardization preprocessing; obtaining a multi-dimensional feature set through time sequence feature extraction and business feature extraction; dynamically fusing the time sequence features and the service features by adopting an attention mechanism, and constructing an LSTM and XGBoost combined prediction model for training; performing parameter optimization based on the independent verification set to obtain a final prediction model; the system comprises a data acquisition and preprocessing unit, a multi-dimensional feature extraction unit, a fusion prediction model construction unit and a model verification and optimization unit. According to the method, the problems that multi-source time series data fusion is insufficient, the model is difficult to capture complex spatial-temporal characteristics and the dynamic adaptive ability is lacked are solved, the accuracy and the real-time performance of enterprise-level BI analysis such as sales trend prediction and inventory optimization are improved, and the prediction precision is improved.

The invention provides an AI-driven supply chain sales prediction and intelligent inventory optimization system and method, and the system comprises a data preprocessing module which is used for docking with a supply chain order system; the sales prediction module adopts a deep neural network with an adaptive sparseness evolution mechanism; the inventory optimization decision-making module adopts a qualification trace-based streaming reinforcement learning architecture and abandons experience playback cache and a target network structure; the expert model auxiliary module adopts a confidence-based three-level intervention mechanism; and the strategy feedback updating module adopts an incremental parameter updating strategy to realize prediction-optimization-feedback closed-loop iteration. According to the method, a streaming reinforcement learning method is adopted, a traditional experience playback structure is abandoned, and real-time decision making and continuous optimization in a high-frequency order scene are achieved; a sparse sensing network and a confidence regulation mechanism are introduced, and the adaptability of the system to cold start, small samples and jump scenes is enhanced.

The invention discloses a military strength resource scheduling method based on multi-objective optimization, which comprises the following steps: analyzing a strategy selection problem in a confrontation environment through a game theory model according to a resource demand matching degree and an enemy action prediction result, and if an enemy threat mode changes, triggering a self-adaptive adjustment mechanism specified by scheduling, dynamically correcting resource allocation strategy parameters by adopting a reinforcement learning algorithm to obtain an optimal scheduling rule adapted to the current confrontation situation; and calculating a resource consumption rate and supplement demand prediction according to an accurate resource allocation result, predicting a consumption trend of various resources in a future time period through a time sequence analysis algorithm, if the predicted consumption rate exceeds the supplement capability, triggering a resource allocation plan in advance, and determining an optimal resource reserve and supplement strategy by adopting an inventory optimization model. According to the method, the military strength resource scheduling efficiency and the combat effectiveness in a complex confrontation environment are effectively improved.

A system and method for closed-loop tracking of advertising events through offer redemption with artificial intelligence feedback. A session identifier links device, pass, content, and contextual data throughout a content playback session. Advertisements dynamically inserted into the content stream inherit the session identifier. An offer platform generates offers linked to the session identifier and pass identifier, enabling tracking through distinct lifecycle states from push to redemption. A database captures timestamped and geo-referenced data for each event. An artificial intelligence engine uses redemption and avail events as verified ground-truth outcomes for training machine learning models, enabling supervised learning that establishes causal relationships between ad exposure and purchasing activity. An inventory gateway receives merchant product inventory data. An optimization engine employs demand forecasting and reinforcement learning to generate inventory-driven triggers and offer recommendations. A merchant interface presents analytics for inventory optimization.

The invention relates to the technical field of coal transportation and sales management, and discloses a coal transportation and sales management system based on deep learning. The system comprises a multi-source data acquisition and processing module which collects and preprocesses coal quality, a transportation state and market sales data; the coal demand prediction module predicts a market demand trend based on the preprocessed data; the inventory optimization scheduling module optimizes the inventory level according to the demand trend; the transportation task dynamic allocation module allocates transportation tasks in combination with the optimized inventory; the abnormity monitoring and analyzing module monitors transportation abnormity; the constraint evaluation and optimization module evaluates transportation resource constraints and optimizes task allocation; the real-time response adjustment module dynamically adjusts the transportation task according to the abnormality and constraint evaluation result; the life cycle prediction module predicts the aging trend of inventory equipment; the energy efficiency evaluation module evaluates the transportation energy efficiency; and the maintenance decision optimization module optimizes a maintenance strategy in combination with the aging trend and the energy efficiency evaluation result. The system realizes intelligent management of the whole coal transportation and sale process.

The invention provides a self-adaptive inventory management method and system based on real-time anomaly detection driving in the technical field of internet product inventory management and artificial intelligence crossing, and the method comprises the steps: S1, obtaining a large amount of historical multi-dimensional heterogeneous data to construct a supply chain data set, training and deploying the created multi-dimensional anomaly detection model and the self-adaptive inventory decision optimization model; s2, the server obtains real-time multi-dimensional heterogeneous data of each supply node of the supply chain and inputs the real-time multi-dimensional heterogeneous data into the multi-dimensional anomaly detection model to obtain a real-time anomaly detection report; and S3, inputting the real-time anomaly detection report, the real-time multi-dimensional heterogeneous data and the historical strategy execution record into the adaptive inventory decision optimization model to obtain a real-time inventory optimization strategy, and executing an adaptive inventory management operation based on the real-time inventory optimization strategy. The method has the advantages that the timeliness, the accuracy, the reliability and the robustness of inventory management are greatly improved.

The invention relates to the technical field of inventory management, and discloses a safety tool dynamic inventory optimization system based on operation data fusion, which comprises an operation data fusion module, an operation step table generation module, a tool mapping module, an inventory attribute analysis module and a dynamic optimization strategy generation module. According to the method, deep binding of the inventory and the operation time sequence is realized through a collaborative architecture generated by operation data fusion, step time sequence analysis, tool dynamic mapping, multi-dimensional inventory attribute analysis and a scenarized optimization strategy, so that the requirements of tools in each step are accurately matched to reduce resource waste; and the compliance and the emergency capability are guaranteed through multi-dimensional analysis of effectiveness, safety and position, and efficient and reliable inventory guarantee is provided for safe operation.

The invention provides a demand degree and inventory optimization-based medicine data dynamic management method and system, and relates to the technical field of medicine data management. The method comprises the following steps: firstly, acquiring consumption time series data, inventory time series data and environmental factor time series data of a target medicine set, and fusing the data to generate medicine dynamic collaboration data; secondly, calculating a corresponding real-time demand degree in a target medicine set based on the medicine dynamic collaboration data; according to the real-time demand degree, dynamically generating a dynamic safety inventory benchmark; the real-time demand degree is coupled with the real-time safety inventory benchmark of the corresponding target medicine, and a dynamic inventory control line is generated; and finally, updating and managing the inventory data of the target medicine set according to the dynamic inventory management and control line. According to the technical scheme provided by the invention, closed-loop intelligent inventory management from environment perception to automatic replenishment execution is realized.

The invention discloses a WMS inventory optimization method and system based on digital twinning, and relates to the technical field of warehouse logistics management, and the method comprises the steps: collecting cargo inventory state data, refrigerated transport resource data and seasonal operation progress data from each cold chain warehouse management system, converting the data into different data formats through employing a standardized protocol, and carrying out the optimization of the data formats; the method comprises the following steps: acquiring a real-time inventory data set in a unified format, synchronizing the real-time inventory data set to a central processing node by adopting a distributed database mechanism according to the acquired real-time inventory data set, determining current cargo inventory levels and available refrigerated transport capacities of all cold-chain warehouse nodes, and if the cargo inventory level of a certain cold-chain warehouse node is determined to be lower than a preset threshold value, sending the cold-chain warehouse node to the central processing node; if yes, comparing the cargo inventory level and the transportation capacity available evaluation of adjacent warehouse nodes through an inventory level threshold; according to the WMS inventory optimization method and system based on digital twinning, efficient circulation of cold chain products such as goods is guaranteed.

The invention relates to the field of reverse supply chain inventory management, and discloses an inventory optimization method and system based on evolution-assisted multi-agent reinforcement learning, and the method comprises the steps: carrying out the processing of the real-time operation data of a supply chain through a trained evolution-assisted multi-agent reinforcement learning model, the method comprises the steps of obtaining real-time operation data of a supply chain, inputting the real-time operation data of the supply chain into the evolution-assisted multi-agent reinforcement learning model for processing, and then outputting a supply chain inventory optimal strategy. According to the invention, through combination of the evolutionary assisted multi-agent reinforcement learning algorithm EAMARL and the online adaptability of the multi-agent reinforcement learning algorithm MARL and the global search capability of the evolutionary algorithm EA, the strategy has stronger robustness for complex dynamic states such as recovered product quantity fluctuation, customized order demand uncertainty and processing delay in the reverse supply chain; and the inventory management efficiency is obviously improved.

The invention relates to the technical field of data processing, and provides an equipment material full life cycle-oriented big data platform management system and method, and the system comprises a multi-source data collection module which is used for collecting material data of equipment materials in each stage of the full life cycle to form a material data set; the data fusion module is used for carrying out data fusion on different material data in the material data set based on different application scenes so as to form a fused data group corresponding to the application scenes; and the decision module is used for generating at least one decision of an inventory optimization decision and a fault alarm decision based on the fused data set. According to the invention, the problems of information lagging and data islanding in the material management method in the prior art are effectively solved, the inventory optimization decision and the fault alarm decision are generated through the fusion data set, and the function of effective decision making according to the corresponding fusion data set in different application scenarios is realized. The beneficial effects of optimizing inventory or finding faults and giving an alarm are achieved.

The invention relates to a supply chain demand prediction and dynamic inventory optimization method based on big data, and relates to the technical field of inventory scheduling prediction, and the optimization steps are as follows: S1, collecting data, preprocessing the collected data, and uploading the preprocessed data to a data analysis module; s2, a data analysis module which performs grid division on different areas of the supply chain, analyzes supply demands of different grid division areas based on historical data, and judges factors which influence the demands of the supply chain of the divided areas; and S3, based on the obtained factors, obtaining the size degree of each influence factor existing in different divided areas in real time, and carrying out weighted comprehensive calculation to obtain an influence value. Complex multi-factor influences are presented in a quantitative form, the comprehensive effect of different factors on supply chain demands is scientifically measured, quantitative index support is provided for decision making, the warehouse stock amount is intelligently adjusted based on the influence value, and the stock level can be better matched with actual demands.

The application discloses a kind of inventory optimization methods of charging facility spare parts replacement cycle and failure rate analysis, and the core process of this method includes building data layer, multidimensional data is cleaned and optimized, to realize standardized storage;Based on the LSTM algorithm, a multidimensional linkage prediction model is built, the loss effect of design defects and environmental factors is quantified to optimize the model, and the accurate spare parts replacement cycle threshold and equipment failure rate are output;According to the flow properties of spare parts, a differentiated inventory management mode is designed, and the optimal management mode of slow-moving parts is selected;A full-cost quantification model is built to calculate the total cost and service level under each mode;Finally, with the goal of minimizing cost, an intelligent inventory control model is built, which dynamically outputs inventory warning thresholds, replenishment quantities and times;Through the algorithmization and parameter quantification of the whole process, the application realizes fine and intelligent management of inventory, significantly reduces operating costs and stockout risk, and improves the stability of charging facilities.

The application relates to a supply room consumable intelligent inventory management method, device, equipment and medium. The method comprises the following steps: acquiring multi-source sensing data of each consumable in a supply room, performing multi-source information fusion processing according to the multi-source sensing data, obtaining an inventory state posterior distribution of each consumable, performing event correction processing on the inventory state posterior distribution, performing demand prediction processing according to the inventory state distribution after event correction and consumable historical consumption data, obtaining multi-step demand prediction results of each consumable in a target prediction period, performing inventory optimization calculation processing according to the multi-step demand prediction results and the inventory state distribution after event correction, obtaining replenishment order quantities of each consumable in the target prediction period, performing consumable scheduling and risk early warning processing according to the replenishment order quantities and batch validity period information of each consumable, and generating an expired consumable priority use strategy and inventory risk early warning information. The method can identify consumable inventory and generate replenishment orders.

This invention relates to a data-driven intelligent logistics supply chain digital management system. The system includes a demand forecasting and analysis unit, an inventory optimization unit, a supplier management unit, a logistics route optimization unit, a system integration and feedback unit, and a real-time logistics marketing unit. By analyzing historical orders and market data through recurrent neural networks and long short-term memory networks, accurate forecasting of short-term and medium-to-long-term demand is achieved. The inventory optimization unit combines a Bayesian dynamic linear model and a convolutional neural network to achieve dynamic inventory management. The supplier management unit optimizes supplier selection using a multilayer perceptron and fuzzy clustering algorithm. The logistics route optimization unit achieves optimal scheduling of logistics routes based on genetic algorithms and an adaptive large-scale neighborhood search algorithm (ALNS). By integrating multiple functional units, this invention effectively improves the operational efficiency and response speed of the logistics supply chain.

The invention is suitable for the field of computers, and provides a supply chain inventory optimization method, device and equipment, and the method comprises the steps: obtaining supply chain graph data; inputting the supply chain graph data into a pre-trained supply chain dynamic cascade network, and encoding to obtain a node state vector; inputting the node state vector into a pre-trained spatio-temporal joint prediction function to obtain a prediction result; and if the current mode is not the training mode, obtaining a planned delivery quantity vector and a service constraint rule corresponding to the supply chain network, and inputting the predicted delivery quantity vector, the planned delivery quantity vector and the service constraint rule corresponding to the supply chain network into a preset decision constraint optimization function to obtain an executable delivery decision vector. According to the method, the problems of inaccurate dynamic prediction, lack of flexibility of replenishment strategies and high optimization complexity in the prior art are effectively solved, and an efficient, accurate and dynamically adaptive solution is provided.

This invention relates to the field of inventory cost analysis, addressing the problems of lagging information updates and lack of predictive warnings in cost accounting, which prevent the automatic triggering of targeted inventory optimization strategies. Specifically, it is a rolling analysis method for spot inventory costs with full-process tracking, including a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost prediction statistics module, and an inventory optimization decision-making module. Based on complete inventory cost accounting, this invention endows cost management with real-time and forward-looking capabilities through rolling analysis and dynamic prediction. Ultimately, through intelligent linkage between cost and profit and a multi-level decision-making mechanism, it achieves an intelligent closed loop from data to action, transforming inventory cost management from traditional recording into a core decision-making tool for profit protection and risk prevention, effectively improving the enterprise's inventory operation efficiency and overall profitability.

The invention provides a quality evaluation method for a single ordnance equipment item and a complete set of goods and materials. According to the method, ordnance equipment materials are divided into six categories of metal equipment, non-metal equipment and the like according to materials, quantitative evaluation is carried out according to six indexes of technical performance, storage time and the like, the quality of the materials is scientifically divided into seven grades of new products, workable products and the like, and unification of evaluation standards and standardization of processes are achieved; according to the method for determining the quality grade of the complete set of materials according to the proportion of the to-be-scraped products, the accuracy and reliability of evaluation are remarkably improved. According to the method, through comprehensive evaluation of multi-dimensional indexes, the influence of human factors is effectively reduced, the problems of high subjectivity and different standards in the prior art are solved, and meanwhile, potential safety hazards are reduced by standardizing a dangerous goods evaluation process; the evaluation result based on quantitative data can accurately reflect the material quality condition, a scientific basis is provided for inventory optimization and resource allocation, and the management cost is remarkably reduced.

The application relates to the technical field of supply chain management, and discloses a supply chain data information analysis platform system, which comprises a dynamic data acquisition condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module and an information analysis platform management output module. The first stage of a time series cycle and the second stage of the time series cycle are taken as constraint conditions of a condition constraint layer, the commodity delivery quantity of the first stage and the second stage in the time series cycle is acquired, the demand prediction of supply chain data is completed through a time series prediction model, the difference between the demand prediction result and the current inventory quantity is compared with a preset judgment threshold value, a single-level optimization scheme based on an inventory redundancy risk and a multi-level optimization scheme based on an inventory shortage risk are generated, and an optimization instruction is sent, so that the continuity and stability of the supply chain are ensured.