587 results about "Inventory data" patented technology

The invention relates to the technical field of project management, in particular to an intelligent management method and system for a civil engineering project, and the method comprises the following steps: obtaining personnel distribution, equipment state and material inventory data, extracting a key task node and an execution state, recognizing a progress lagging task, screening a key path node, analyzing a time difference, and marking a progress trend. And identifying an equipment influence section, judging resource allocation imbalance, and generating a progress monitoring index. According to the invention, through multi-source data acquisition of personnel distribution, equipment state and material inventory, difference identification between a task state and a plan node is enhanced, progress lag early warning efficiency is improved, progress fluctuation dynamic classification and trend identification are realized, construction rhythm optimization is assisted, and through coincidence analysis of equipment operation and a task fluctuation area, the progress lag early warning efficiency is improved. The construction efficiency bottleneck is identified, the scheduling accuracy is improved, fine progress monitoring is realized through investment and period difference comparison, and the progress control capability and the collaboration of on-site resource allocation are comprehensively enhanced.

The invention relates to the technical field of logistics supply chain optimization, and provides an order dynamic distribution optimization method and system based on a user portrait, so as to improve the precision service capability and resource scheduling efficiency in a community retail scene. The method comprises the steps that user behavior data and community merchant inventory data are collected, and the user behavior data comprise an access behavior sequence recorded by intelligent cat eye equipment and a parcel access event triggered by an Internet of Things lock; generating a dynamic demand prediction parameter based on the user behavior data, and constructing a user portrait including consumption cycle characteristics and instant demand intensity; according to the user portrait and the community merchant inventory data, a replenishment order generation operation of a target community merchant is triggered, and a replenishment order comprises a delivery time limit constraint and a category priority identifier; and in combination with the real-time distribution load parameters and the community inventory distribution topology, generating a dynamic distribution path planning result and updating a time sequence control instruction of the distribution carrier.

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

A real-time inventory management system that includes: a centralized control platform module configured to receive, consolidate, and process inventory data from multiple sources, including warehouses, retail stores, and distribution centers; an IoT and sensor module with RFID tags, barcode scanners, and smart sensors configured to track inventory items, monitor environmental conditions, and update inventory records in real time; a machine learning and demand forecasting module configured to analyze historical inventory data, market trends, and external factors to predict future demand and optimize inventory levels; an automatic replenishment and replenishment module configured to dynamically generate and execute replenishment orders based on real-time inventory levels and forecasted demand; a data integration and synchronization module configured to enable seamless communication and data synchronization with external systems, including Enterprise Resource Planning (ERP) platforms and supplier networks; a user interface and dashboard module configured to display real-time inventory status, actionable insights, and key performance indicators (KPIs) for decision-making.

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention discloses a logistics center intelligent optimization scheduling system based on data analysis, and relates to the technical field of intelligent optimization scheduling, during operation of the system, key data in logistics center operation is collected and processed, after preprocessing, the inventory state of a logistics center is managed and optimized, and based on real-time inventory data and order demand information, the logistics center intelligent optimization scheduling system based on data analysis is obtained. Based on real-time traffic data, order demands and vehicle positions, calculation is carried out to obtain a comprehensive optimization index SOF, according to the order demands, the cargo capacity, the vehicle capacity and the real-time positions, vehicles are intelligently distributed, the loading sequence and the loading rate are optimized, and through the Internet of Things technology and a data analysis tool, the real-time traffic data and the real-time traffic data are analyzed. And the vehicle position, the transportation state, the environment change and the order processing progress are monitored in real time, the scheduling optimization level of the logistics system is evaluated in real time by comparing the comprehensive optimization index SOF with a preset threshold value, and an abnormal early warning and feedback mechanism is provided.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention discloses a privacy protection and verifiable product carbon footprint evaluation method based on a block chain. The method mainly comprises the steps that a chain owner enterprise defines a purpose and a range; a chain owner enterprise requests carbon emission list data from an upstream raw material supplier according to a product material list (BOM), the raw material supplier performs homomorphic encryption on the carbon emission list data and then sends the data to a cloud computing platform of the chain owner enterprise, and identity verification is performed on a data source in combination with an RSA digital signature algorithm; the cloud platform completes carbon footprint calculation in a ciphertext state, and finally, a ciphertext result is returned to an upstream supplier; and the chain owner enterprise integrates the raw material supplier data through the cloud platform, completes final product carbon footprint calculation, verifies the validity of the calculation process based on a zero-knowledge proof technology, realizes automatic verification in combination with an intelligent contract, and finally explains and explains the whole process. According to the method, privacy protection of the carbon emission list data is realized, the evaluation process is verifiable, and the evaluation result is more accurate.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

The embodiment of the invention relates to the technical field of data processing and AI vision, and provides a warehouse inventory intelligent management method and device based on AI vision monitoring and a terminal, and the method comprises the steps: obtaining inventory management basic data and external dynamic influence data of a to-be-monitored warehouse; based on an AI visual technology, according to the sample image data set and the label image data set, performing dynamic optimization processing on current inventory data in the inventory management basic data to obtain dynamically optimized inventory data; according to the inventory management basic data and the dynamic optimization inventory data, carrying out inventory structure evaluation to obtain inventory structure evaluation data; performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic influence data to obtain inventory dynamic adjustment data; according to the dynamic optimization inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, an inventory health evaluation report is generated, and the purpose of more accurate and more comprehensive warehouse inventory intelligent management is achieved.

The invention discloses a material digital centralized management and control system based on the Internet of Things, which relates to the technical field of digital management and control and comprises a supply chain management unit, a loss management unit, a cost accounting unit, an inventory management unit, a dynamic adjustment unit and a user management unit. The system can accurately plan the purchase quantity and time, screen high-quality suppliers, guarantee the quality and stability of material supply from the source, deeply analyze the loss data in transportation, storage and processing, formulate a targeted strategy, reduce material loss, reduce waste, dynamically check the cost in real time, perform apportionment analysis according to different dimensions, and accurately control the cost. Through intelligent warehouse-in and warehouse-out management, regular inventory checking and intelligent inventory prediction, it is ensured that inventory data is accurate, inventory is reasonably planned, an inventory strategy is dynamically adjusted, inventory overstock or stockout is reduced, and inventory cost and service level are balanced.

The invention discloses a commodity receiving method and system based on digital twinning, belongs to the technical field of industrial internet and supply chain management, and aims to solve the technical problems of non-transparent information, tedious process, low efficiency, difficult inventory management and lack of effective monitoring in traditional commodity receiving. According to the technical scheme, data collection and twinborn modeling are carried out, specifically, digital and intelligent management of commodity receiving is achieved by building real-time mapping of a physical warehouse and a virtual twinborn body, multi-source data of material information, inventory data and position information of the physical warehouse are collected through Internet of Things equipment, and after cleaning processing is carried out, the real-time mapping of the physical warehouse and the virtual twinborn body is carried out; a three-dimensional modeling technology is adopted to construct a virtual twinborn body, and the consistency of the virtual twinborn body and a physical warehouse is ensured through a dynamic updating mechanism; real-time data processing and synchronization; intelligent receiving is realized; inventory monitoring and prediction; and performing user management and authority control.

The invention provides a logistics supply chain management method and system based on a block chain technology, and relates to the technical field of data processing, and the method comprises the steps that a storage center scans a traceability code and verifies goods information when goods are warehoused, and after verification is passed, the goods are encrypted and stored in a block chain to obtain warehouse-in data; a dynamic inventory database is established according to the goods category and the storage location number through the warehousing data, and an inventory state early warning index is generated. According to the logistics supply chain management method and system based on the block chain technology provided by the invention, dynamic and accurate management of inventory, real-time optimization of logistics paths and credible traceability of full-chain information are realized through distributed storage and non-tampering characteristics of the block chain in combination with an intelligent contract and the Internet of Things technology, the supply chain collaboration efficiency is improved, and the logistics supply chain management efficiency is improved. And the operation risk is reduced.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

The invention discloses a business data quality treatment method supporting system hidden danger identification, and relates to the technical field of computer information processing, and the method comprises the steps: S1, collecting a real-time batch tracking data stream from an enterprise inventory system through a multi-source data interface, and obtaining an initial data set containing inconsistent formats and missing batch numbers; s2, scanning a data stream according to the initial data set by applying a preset automatic detection rule, judging positions and types of abnormal points by utilizing a rule matching mode and a threshold comparison mechanism to obtain an abnormal point set, and activating a real-time stream analyzer to process field complete verification to generate an abnormal log record; according to the business data quality treatment method supporting system hidden danger identification, real-time and accurate anomaly detection and repair are realized, the consistency and reliability of inventory data are improved, and the enterprise inventory management efficiency is remarkably optimized.

The invention relates to the technical field of warehouse management, and discloses an in-warehouse goods checking method, system and device and computer equipment, and the method comprises the steps: obtaining a tray code on a target checking goods location and the information of a material box on a corresponding tray; identifying the material box information by adopting a deep learning identification algorithm, and determining material specifications; based on the material box information, a preset stacking rule corresponding to the material specification and an AI algorithm, the number of the material boxes on the tray is calculated; and binding the material specifications, the number of the material boxes and the tray codes, and comparing with the upper inventory data based on the tray codes to obtain a cargo inventory result. According to the invention, the tray code and the material box information on the target inventory goods allocation can be directly obtained without continuously moving out the goods at the target inventory goods allocation, the difference between the inventory data and the actual inventory data can be found in time, and the problems of low inventory efficiency and increased inventory time caused by continuously moving out the goods at the target inventory goods allocation are solved.

Systems and methods for tracking inventory items in an area of real space are disclosed. The method includes receiving a signal generated in dependence on sensors. The signal indicates a change to a portion of an image of an area of real space. The method includes, in response to receiving the signal, implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. The method includes implementing a trained item classification model to determine a classification of the inventory item. The method includes updating an inventory database with inventory item data determined in dependence on the classification of the inventory item to provide an updated map of the area of real space as a result of the received signal indicating the change to the portion of the image.

The invention provides a steel cutting and production scheduling method and device based on dynamic risk assessment, and relates to the field of steel processing pre-production management. Order, equipment and inventory data are collected in real time, and a multi-dimensional numerical matrix is constructed to achieve data structured expression; adopting a Monte Carlo method to generate a candidate production scheduling sequence, combining leftover material loss and delay risk dynamic evaluation, and fusing simulated annealing and an adaptive threshold mechanism to carry out iterative optimization; and through probability similarity screening and regression fitting, an optimal production scheduling scheme is determined in an error allowable range. The method effectively balances the production efficiency and the resource utilization rate, reduces the cutting loss and the delivery risk, and is suitable for the intelligent production scheduling decision of a complex manufacturing scene.

An intelligent warehouse management method based on the Internet of Things and artificial intelligence comprises the steps that 1) intelligent inventory management is carried out, and each commodity which is put in storage and put out of storage is automatically identified and tracked by utilizing a cargo container label and an image identification technology; the artificial intelligence technology is combined with big data analysis, future inventory demands are predicted, the inventory level is automatically adjusted, and the inventory cost and the stockout risk are reduced; 2) an automatic operation process: monitoring the equipment state in real time through the Internet of Things technology, ensuring the efficient operation of the equipment, and reducing the downtime; 3) performing real-time data analysis and decision support, and collecting various data generated in the storage process, including inventory data, order data and equipment operation data; deep learning and analysis are performed on storage data by using an artificial intelligence technology, intelligent decisions such as inventory prediction and path planning are realized, and storage management and logistics distribution processes are optimized; and 4) cross-system collaboration and information interconnection of a plurality of systems such as data sharing, storage, transportation, sales and the like are carried out to form end-to-end supply chain collaboration.

The invention relates to the technical field of sales management, and discloses a mineral water sales inventory management method based on deep learning, and the method comprises the steps: 1, collecting a data source affecting the sales volume of mineral water, carrying out the format standardization, time alignment and feature extraction of the data source, and carrying out the feature extraction of the data source; generating a group of multi-dimensional input feature vectors for describing external sales driving factors; and 2, inputting the multi-dimensional input feature vector and historical sales volume data into a deep learning model, training to obtain a mineral water future sales volume predicted value, and carrying out inventory state analysis in combination with current inventory data, shelf life parameters and replenishment cycle parameters. According to the method, the technical scheme of feature fusion of fusing external sales driving factors and multi-source structured behavior data and unified input tensor construction is adopted, and the technical effects of dynamic modeling and real-time response of external factors such as weather, holidays and festivals, regional activities and sales promotion states in sales prediction are achieved.

The invention provides an unmanned forklift path planning method, system and device and a readable storage medium. The method comprises the steps that a storage reference base map and a road network topological structure are constructed; establishing mapping association between the nodes and the storage locations and storing the mapping association; real-time inventory data is maintained through a distributed cluster, and the storage location is updated synchronously; marking obstacle nodes based on the inventory information and the mapping relation; planning a path by adopting a path search algorithm and avoiding obstacles; and when the preset path section is completed during driving, the inventory updating obstacle is obtained again, the path is regenerated and compared, and an updating instruction is issued when a difference exists. According to the method and the device, the technical problem that the driving path is easy to fail due to the change of the obstacle node because the traditional unmanned forklift path planning cannot respond to the dynamic change of the warehouse inventory in real time is solved.

The invention discloses a supply chain intelligent replenishment method and system based on data analysis, and relates to the technical field of intelligent replenishment, during the operation of the system, supply chain related data is collected, sales data, inventory data and supplier data are obtained as a supply chain data set, and the supply chain data set is transmitted to a data preprocessing module; the collected supply chain data set is preprocessed to form a first data set, a second data set and a third data set, a supply chain performance index Gyjx is obtained after calculation, historical sales data and market trend analysis are utilized to predict product demands in a period of time in the future, and a supply chain performance index Gyjx is obtained. The supply chain performance index Gyjx is compared with a preset standard threshold value M and a preset standard threshold value N, a replenishment strategy is obtained, an inventory strategy and replenishment parameters are dynamically adjusted according to demand prediction and inventory conditions, operation conditions and inventory changes of all links of the supply chain are monitored in real time, abnormal conditions are found, and early warning is carried out in advance.

The invention relates to the technical field of data verification, and discloses an inventory data accuracy verification system and method applied to financial cloud secondary development. The method comprises the following steps: firstly, performing data missing filling based on data correlation to obtain a processed inventory data set and a processed ex-warehouse data set; secondly, obtaining an inventory suspected data set through data comparison, and performing preliminary verification on inventory inaccurate data in the inventory suspected data set by using an improved clustering algorithm; optimizing the initial clustering center of the improved clustering detection algorithm by using a multi-strategy fusion emerald optimization algorithm to form a new clustering cluster; and finally, inaccurate data final verification is carried out according to the new clustering cluster, inaccurate inventory data is output, and accuracy verification is completed. By processing and analyzing the inventory data, the purpose of checking the accuracy of the inventory data is achieved, and the method is objective and accurate.

The invention discloses an inventory real-time monitoring and abnormity repairing system based on multi-node redundancy check. According to the system, fragmentation and multi-replica redundancy guarantee of inventory data are achieved through a multi-node redundancy storage module, Hash fingerprints are generated through a real-time check monitoring module, and abnormity is found through quick comparison; the fault reason and the influence range are analyzed through the anomaly detection and positioning module, copy reconstruction and transaction rollback are completed through the automatic repair and rollback module, and the repair progress and the push alarm are displayed in real time through the visual monitoring and alarm module. According to the inventory real-time monitoring and abnormity repairing system based on the multi-node redundancy check, consistency maintenance and automatic repairing of inventory data are achieved, and continuous and stable operation of businesses is guaranteed. The system is characterized by comprising a multi-node redundancy storage module, a real-time verification monitoring module, an anomaly detection and positioning module, an automatic repair and rollback module and a visual monitoring and alarm module.

The invention discloses a wearing and building recommendation method, electronic equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the disclosed wearing and building recommendation method comprises the following steps: determining alternative shops from a preset shop database according to geographic position data; according to the commodity inventory data of the alternative shops, establishing an alternative wearing and building library; and through a preset wearing and building recommendation large model, determining recommended wearing and building from the alternative wearing and building library according to the user feature data and / or the wearing and building information data. According to the method and the device, the offline clothing consumption process can be optimized, so that the shopping experience of the user during offline clothing purchase is improved.

The invention relates to an inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, and discloses an inventory data synchronization and consistency verification system which realizes continuous recording of inventory operation during a network disconnection period through network state detection and local cache, performs batch transmission after network recovery through a synchronous uploading and verification mechanism, and ensures data integrity. According to the inventory data synchronization and consistency verification system supporting network disconnection fault tolerance, data conflicts are automatically solved through a conflict detection and strategy processing module, a difference report is generated through consistency verification, and secondary synchronization is executed, so that safe synchronization and final consistency guarantee of inventory data are achieved, and it is ensured that the service process is continuous and stable. The system is characterized by comprising a network state detection module, a local cache module, a transaction log management module, a synchronous uploading module, a conflict detection module, a conflict resolution strategy module, a consistency verification module and an abnormity alarm and manual intervention interface module.

The invention discloses a digital store management method and system, and the method comprises the steps: obtaining multi-source data in real time according to customer behavior data, sales data, inventory data and environment data of a store, and generating a time-synchronized multi-source data set; inputting the multi-source data set into a behavior analysis model, extracting space-time mode characteristics of customer behaviors, and generating a customer behavior analysis report; generating an intelligent replenishment scheme according to the customer behavior analysis report and the inventory data; and inputting the customer behavior analysis report and the intelligent replenishment scheme into a visual platform based on augmented reality, generating a visual chart of store operation, and generating a final digital store management scheme through an interactive operation interface and a multi-dimensional screening function. According to the embodiment of the invention, an efficient, intelligent and flexible store management solution can be provided, and the whole-process intelligentization of data acquisition, behavior analysis, inventory optimization and visual decision is realized.

The invention discloses a medical consumable intelligent warehouse based on the Internet of Things, and relates to the technical field of medical consumable management. An intelligent management system is adopted, the intelligent management system comprises a biological feature recognition unit, an access control unit, a material registration unit, a checking unit and an inventory statistics unit, and dynamic data of type, batch and quantity information of medical consumables in a warehouse are monitored in real time through biological feature recognition, material registration, checking and uploading to a cloud server; the system can accurately track the inventory and warehouse-in and warehouse-out conditions of operators and medical consumables, improves the management efficiency, guarantees the consistency of the input information and actual information of the medical consumables through layer-by-layer control, can improve the accuracy of inventory data, and improves the management efficiency. Therefore, the problems of supply chain chaos, product stockout or excessive stock and the like caused by wrong stock information can be avoided, intelligent management of warehouse management is realized, and on the premise of ensuring supply, expired scrapping of medical consumables is reduced as much as possible, and the medical cost is reduced to the maximum extent.

The invention discloses an engineering material intelligent allocation system and method, and the system comprises a multi-source data collection module which is used for collecting engineering material stock data, transportation equipment state data, construction site environment data and construction progress data in real time through Internet of Things equipment; the intelligent analysis module is used for multi-dimensional data fusion analysis and intelligent algorithm driving to carry out dynamic optimization management of the whole process of the engineering materials; and the dynamic scheduling decision module is used for integrating a multi-objective optimization algorithm and a real-time feedback mechanism, and outputting a scheduling instruction set including a transportation scheme, a storage scheme and an emergency scheme, and through implementation of the system, dynamic optimization management and credible traceability of the whole process of engineering materials are realized. The system deeply integrates the Internet of Things, digital twinning, block chain and artificial intelligence technologies, significantly improves the material allocation efficiency, the cost control capability and the emergency response level, guarantees the data security and the multi-party cooperation transparency, and has certain use value and popularization value.