573 results about "Order management" patented technology

The invention relates to the technical field of order management, and discloses a supply chain-oriented intelligent order management method, which comprises the steps of obtaining corresponding multi-modal data through an order demand flow, a production equipment state, logistics sensor dynamic information and an inventory topological graph; analyzing relevance between orders and equipment based on a space-time diagram convolutional network, and generating a capacity allocation scheme; calculating a logistics path planning scheme, predicting a stock stockout risk and generating a replenishment suggestion; if the high-priority order exists, inserting a productivity plan and adjusting an equipment process chain; if resource conflicts occur, dynamically allocating resources; if the path risk value exceeds the threshold value, standby path switching is triggered; adjusting weighting parameters through an adaptive federation algorithm, generating a global strategy and issuing the global strategy to the client; the client dynamically adjusts local configuration and uploads execution effect data in real time; and if abnormity is detected, triggering global strategy regeneration and updating the model through federated learning increment. According to the invention, efficient management of supply chain orders can be realized.

The invention, which relates to the technical field of supply chain information, discloses a comprehensive supply chain control system comprising a transaction and collaborative service module, an order management and decision support module and a data interaction and integration module. The transaction and cooperation service module is used for processing transaction information of supply chain participants, ensuring information safety and accuracy, managing the full life cycle of a contract and performing real-time monitoring, establishing a real-time communication and cooperation project management channel, and improving cooperation efficiency; according to the invention, the transaction and collaborative service module has a significant effect, encryption processing ensures the security of transaction information, leakage and tampering are prevented, contract management is efficient and flexible, a multi-parameter evaluation optimization template reduces the default risk, a collaborative mechanism improves the collaborative efficiency of all parties, and the response speed of a supply chain is enhanced.

The invention discloses a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

The invention relates to the technical field of intelligent logistics distribution, in particular to an automobile market logistics distribution scheduling system based on AI optimization, which comprises a data acquisition module, an order management module, a traffic flow analysis module and a scheduling decision module. Wherein the data acquisition module is used for acquiring current position data and traffic flow data of a delivery vehicle and order information of a customer in real time; the order management module is used for carrying out priority ranking on the orders; the traffic flow analysis module is used for predicting traffic conditions in a period of time in the future and identifying a road section and a time period which are about to be congested; and the scheduling decision module is used for optimizing the scheduling and distribution paths of the vehicles to generate an optimal scheduling scheme. According to the invention, by combining real-time traffic prediction, order priority ranking and a path optimization algorithm, the logistics distribution efficiency is effectively improved, distribution delay and resource waste are reduced, and timely distribution of high-priority orders is ensured.

The invention belongs to the technical field of logistics management, and discloses a logistics order management optimization method and device, equipment and a storage medium. The method comprises the following steps: collecting multi-source logistics data in real time through a multi-source heterogeneous sensor; performing space-time dimension feature extraction and correlation analysis on the multi-source logistics data based on a space-time diagram neural network to generate a feature tensor; performing multi-objective optimization by adopting an improved hybrid swarm intelligence optimization algorithm in combination with the feature tensor to generate a plurality of initial optimization schemes for logistics order management; screening out a target optimization scheme from the initial optimization schemes by using a virtual logistics network constructed by a digital twinning technology; in the delivery process, the order execution state is monitored in real time through the Internet of Things equipment, and when an abnormal condition is detected, secondary optimization is performed on the target optimization scheme, so that heterogeneous data streams can be synchronously processed, the optimization time consumption is reduced, secondary optimization can be performed on the optimization scheme for an emergent abnormal condition, and the optimization efficiency is improved. And the logistics order management efficiency is improved.

The invention, which relates to the technical field of the warehousing system, discloses an intelligent warehousing system comprising a warehouse management information system and an equipment monitoring and scheduling system. The warehouse management information system comprises a goods management module, a warehouse management module, an order management module, an inventory management module, a warehouse-in and warehouse-out management module, an operation management and implementation module and an abnormity early warning module. According to the intelligent warehousing system, real-time tracking and data collection of articles are achieved through RFID, sensors, bar codes and the like, the data accuracy is improved, the warehouse management system integrates the functions of inventory control, order processing, cargo tracking and the like, automatic management of warehousing operation is achieved, inventory layout is optimized through data analysis and a machine learning algorithm, and the demand trend is predicted; and the decision-making efficiency is effectively improved.

The invention relates to the technical field of maintenance work order management, in particular to an intelligent management system for after-sales maintenance work orders, which realizes equipment clustering based on a K-means algorithm, considers different user habits and use environment differences, and improves the fault diagnosis precision. A fault propagation network is constructed in combination with association rule mining and a graph convolutional network, and a fault influence path between hardware is described, so that accurate identification of target fault hardware is realized, alternative hardware is intelligently determined in combination with recent maintenance records, and misjudgment and repeated maintenance are avoided. And the intelligent order dispatching module is used for preferentially dispatching the nearby maintenance personnel with the required spare parts by matching the position information of the maintenance personnel with the real-time spare part inventory, so that the maintenance response efficiency is improved. And the model error correction module continuously monitors the operation state of the equipment after maintenance is completed, automatically adjusts node features and edge weight parameters if a prediction result does not accord with actual maintenance, updates a graph convolution model through an incremental learning mode, and improves the self-adaption and evolution capability of the model.

The invention relates to the technical field of purchase management, in particular to an intelligent purchase management method and system, and the method comprises the steps: data collection, data processing and storage, demand prediction, supplier management, purchase plan generation, order tracking and management, data analysis and reporting and the like. The system is composed of a data acquisition module, a data processing module, a demand prediction module, a supplier management module, a purchase plan generation module, an order management module, a data analysis and report module and a user interface module. According to the invention, automation and intellectualization of the purchasing process can be realized, the purchasing cost is reduced, the supply chain management capability is enhanced, and the overall competitiveness is improved.

The present invention is applicable to the field of work order management, and discloses an intelligent work order management method, system, equipment and medium. The method first classifies work orders according to the differences in work order types to obtain work orders of different categories, and vectorizes the characteristics of each type of work order from two dimensions: work order processing complexity and required resources, to construct a work order feature vector; based on the work order feature vector, a work order processing strategy selection model is established; in the process of work order processing, resource consumption data of each link is collected to construct a feature matrix of work order resource consumption and value contribution; a work order resource allocation recommendation model is established using a collaborative filtering algorithm; after the work order processing is completed, a work order cost accounting model is established based on the actual resource consumption and work order characteristics; in addition, in the entire process of work order processing, data of each link is continuously collected, and each model is updated and optimized in real time to achieve a dynamic balance between processing efficiency and cost sharing.

The invention discloses an intelligent production management order arrangement system, which belongs to the technical field of production management, and comprises an order management module, an intelligent production arrangement module, a resource integration and optimization module, a real-time monitoring and adjusting module and a data analysis and decision support module, through setting an order management module, an intelligent production scheduling module and a resource integration and optimization module, the order management module manages and grades orders during use, and the intelligent production scheduling module produces an optimal production schedule according to the order and equipment information and an algorithm. The resource integration and optimization module intelligently distributes resources by means of detailed modeling and analysis of resources such as equipment information and manpower, maximum utilization of the resources is achieved, the efficiency of production management scheduling is greatly improved, and the requirements of industry production can be met according to different equipment information.

The invention provides a supply chain order placing management method and system based on big data analysis, and relates to the technical field of supply chain management. The method comprises the following steps: respectively carrying out qualification hierarchical auditing on a purchaser and a supplier to obtain a comprehensive auditing result; the purchaser multi-source data and the supplier multi-source data are integrated, and a dynamic demand-supply cooperation plan is generated; according to a purchaser order, based on a compliance audit result, automatically matching a compliance and adaptive supplier list; a multi-level supply chain bidirectional view is constructed, and cross-level commodity rapid positioning and inventory synchronization are realized through cascade screening; supplier performance data, purchaser demand change data and environment data are monitored in real time, and dynamic risks actually occurring in the performance process are identified; and according to the dynamic risk linkage access risk file, triggering graded early warning and dynamically adjusting the operation authority, thereby effectively solving the problems existing in the traditional supply chain order placing management method, and improving the transparency, flexibility and risk management capability of the supply chain.

The invention discloses an overseas warehouse operation intelligent wave order management method and system, and the method comprises the steps: constructing a commodity incidence matrix, mining a frequent item set, determining the commodity correlation degree, carrying out the centralized picking of high-correlation commodities, reducing the round-trip times, shortening the walking distance, improving the picking efficiency, planning a picking path in combination with the warehouse layout and inventory information, and achieving the intelligent wave order management of the overseas warehouse operation. The method comprises the following steps: preferentially selecting short-distance commodities in the same area, further reducing the picking time, allocating priorities according to order emergency degrees and customer levels, ensuring that important orders are processed in time, improving customer satisfaction, dynamically adjusting the sequence in the picking process, avoiding invalid walking caused by stockout, enhancing path flexibility and adaptability, and collecting warehouse-in and warehouse-out information in real time. Inventory is updated immediately, data are ensured to be accurate and timely, labor intensity and operation time of picking personnel are reduced, labor cost is reduced, and storage equipment loss and maintenance cost are reduced.

The invention provides an internet right product agent management system, and relates to the technical field of product agent management. The system comprises a product management module, an order management module, an agent management module, a database and a data analysis module. The system processes on-off and stock of products through the product management module, the order management module calculates commission, and the agent management module achieves credit evaluation. The data analysis module predicts requirements through a multi-layer architecture; a data input layer integrates internal and external data; the analysis engine layer adopts feature coding and a time attention mechanism to model space-time correlation, and outputs a demand prediction formula; and the decision output layer generates an agent personalized report through inventory calibration, credit grading and regional weighting strategies, recommends products and early warns risks. The system realizes intelligent acquisition and sales decision making, and improves the operation capability and market influence of the system.

The present application relates to the technical field of order management, and in particular to an order management method and system for a flower selling platform. The method comprises: obtaining pending orders; analyzing pending orders and determining bouquet information; analyzing bouquet information and determining bouquet packaging logic; determining the current amount to be packaged according to pending orders; and determining a combination production plan according to the bouquet packaging logic and the current amount to be packaged. Through the above-mentioned plan, it is possible to directly replace manual judgment of flower orders, which is more scientific and reasonable, and can also avoid errors caused by human judgment and reduce the probability of errors.

The invention relates to the technical field of network operation and maintenance, and relates to an intelligent management and control method and device for an operation and maintenance work order, a computer program product and electronic equipment. The method comprises the steps of performing intention recognition on demand description information in an operation and maintenance work order based on a work order management and control agent, and determining a work order type and a work order task of the operation and maintenance work order according to an intention recognition result; according to the work order type and the work order task, generating an operation and maintenance guidance scheme of the operation and maintenance work order based on the operation and maintenance corpus knowledge base through the work order management and control agent; determining a target operation and maintenance worker based on the work order type, a work order adaptation model and a work order intelligent scheduling strategy through a work order management and control agent, wherein the work order adaptation model is a model representing the adaptation degree of the operation and maintenance worker and the operation and maintenance work order; and automatically distributing the operation and maintenance work order and the operation and maintenance guidance scheme to the target operation and maintenance personnel, and performing automatic order staring management and control on the operation and maintenance work order through the work order management and control agent so as to complete the disposal of the operation and maintenance work order. The management and control processing efficiency and quality of the operation and maintenance work order can be improved.

The invention discloses an intelligent order management method for a cross-border e-commerce platform, and the method comprises the steps: obtaining the order data of cross-border e-commerce platform merchants in a set time period, and extracting good comment orders; extracting a target order according to the signing time, the receiving confirmation time and the evaluation time of the well-commented order; and determining whether the target order is abnormal according to the evaluation text content of the target order, the commodity browsing duration and the commodity browsing frequency, and when the ratio of the number of the abnormal orders to the total number of the orders reaches a second threshold value, marking that the cross-border e-commerce platform merchant is abnormal. Whether the order is abnormal or not is comprehensively evaluated from the aspects of the good reputation number, signing time, good reputation time, evaluation text and the like of the order of the e-commerce platform, then whether the merchant to which the order belongs carries out the click farming behavior or not can be evaluated, meanwhile, the buyer can be evaluated according to the buyer data of the abnormal order, the abnormal order is recognized from multiple aspects, and the user experience is improved. The order abnormity identification accuracy of the e-commerce platform is improved, and the order management range is expanded.

The invention discloses a community work order intelligent management method and device, and relates to the technical field of community service management.The community work order intelligent management method comprises the steps that user work order data containing texts, voices and images are received through a multi-mode interface, and the user work order data are cached to a preprocessing queue; based on a semantic segmentation model, performing multi-dimensional analysis on the user work order data in the preprocessing queue, generating a structured work order comprising an equipment identifier, a fault feature and a geographic position, and associating a problem type label for the structured work order; according to the equipment identifier, the fault feature, the geographic position, the problem type label and an order division decision model, obtaining an assignment instruction of the structured order; and allocating the structured chemical worksheet to a corresponding processing terminal according to the allocation instruction, and updating the processing state of the structured chemical worksheet. According to the invention, the efficiency and response speed of community work order management are greatly improved.

The invention relates to the technical field of enterprise operation management, in particular to a retail enterprise inventory collaborative management system based on intelligent order fulfillment and cross-channel demand prediction, which comprises an order management module, an intelligent order fulfillment module, a demand prediction module, an inventory collaborative management module, a data analysis module and a user management module. Compared with a simple scheme in the prior art that the historical logistics cycle is directly adopted as the predicted arrival time, and the scheme cannot accurately reflect the current logistics progress and the actual situation, the scheme of the invention adopts a more refined prediction formula; the logistics average period T, the current logistics progress P and the coefficients beta and gamma corresponding to the logistics average period T and the current logistics progress P are comprehensively considered, so that the arrival time can be predicted more accurately, more reliable logistics information is provided for customers, and the customer experience and credibility are improved.

The invention relates to a goods transaction management platform based on a goods counting code and an intelligent verification technology, and the platform comprises a goods counting code generation module, a goods tracking module, an intelligent verification module, a transaction integration module, an intelligent contract module, and an analysis module. The block chain technology is combined to record cargo circulation information, so that full-process tracing is realized; the cargo tracking module collects data such as position and environment in real time through Internet of Things equipment, and provides dynamic monitoring and abnormal early warning for environment-sensitive cargos. The intelligent verification module is combined with the Internet of Things technology to ensure the authenticity of goods information; the transaction integration module supports order management, payment confirmation, refund processing and logistics synchronous sharing; the intelligent contract module automatically executes contract terms based on a block chain technology, so that the transaction efficiency and reliability are improved; and the analysis module carries out risk prediction through a time sequence analysis and anomaly scoring method.

The invention discloses an agricultural product wholesale market voice automatic order generation system based on artificial intelligence, and relates to the technical field of order management. Comprising an order template generation unit used for generating an order template; the acquisition unit is used for acquiring voice information; the first extraction unit is used for extracting Mel-frequency cepstral coefficient characteristics and residual Mel-frequency cepstral coefficient characteristics of the voice information; the feature splicing unit is used for splicing the features; the operation unit is used for taking the splicing features as input of a speech recognition model to obtain text information; the second extraction unit is used for extracting product names and product quantities in the text information; the matching unit is used for matching the product names with agricultural product names in an agricultural product database to determine agricultural products; and the adding unit is used for adding the agricultural products and the product quantity to the order template to obtain an agricultural product wholesale order. The system disclosed by the invention can improve the order generation efficiency, improve the user experience and improve the operation efficiency of the market.

The invention relates to the technical field of order management, in particular to an intelligent material order management method and system, and the method comprises the following steps: based on collected order data, analyzing the feature expression of the order data through an auto-encoder, comparing the feature data with newly input order data, and calculating a difference value; and marking the order with the difference value exceeding a threshold value to generate an abnormal order identifier. According to the invention, the self-encoder is used to deeply analyze order data, automatically identify orders having significant differences with normal data, and carry out abnormal marking in real time, so that execution errors are effectively prevented and fraud risks are reduced, and the application of the long and short term memory network further enhances the prediction capability of abnormal order behaviors. Through the analysis of the time sequence data, the potential order execution problem can be tracked and predicted, the constructed dynamic relation graph between the materials and the orders provides a global view for the material flow and inventory management, and the optimization of the material use and the cost control are promoted.

The invention belongs to the technical field of order management, and discloses an order management method based on cloud computing service. The method comprises the steps that the cloud platform receives a passenger position sent by a passenger terminal; acquiring real-time traffic data, defining a screening radius, and generating an order dispatching area for passenger positions; driver positions sent by all driver ends in the order sending area are received; road traffic information is obtained, the road traffic information, the passenger position and all driver positions are fused, the shortest driving time from each driver position to the passenger position is calculated, and a time sorting table is generated; the order is sent to the driver end with the shortest receiving time, and if the driver end does not receive the order, the order is sent to the driver end in sequence according to the time sorting table; according to the invention, cloud computing and big data analysis technologies are fully utilized, intelligent optimization and dynamic adjustment of order distribution are realized, the waiting time of passengers and the unloaded driving rate of drivers can be effectively reduced, the user experience is effectively improved, and the overall operation efficiency is improved.

The invention discloses an efficient logistics storage scheduling system, and relates to the technical field of logistics storage scheduling, and the technical scheme is that the system comprises a storage scheduling management module, the connection end of the storage scheduling management module is provided with a data analysis optimization module, and the storage scheduling management module comprises an automatic scheduling module and a storage safety and stability guarantee module. The beneficial effects of the system are that through the automatic warehousing operation module, the links of manual operation are reduced, rapid and accurate cargo processing is realized, the overall logistics efficiency is significantly improved, the transportation route can be intelligently optimized according to the real-time order quantity and the transportation demand, and the transportation efficiency is improved. According to the method and the system, the waiting time and the empty driving rate are reduced, the logistics efficiency is further improved, the manpower demand is reduced through automatic and intelligent storage scheduling, the labor cost is reduced, and through real-time inventory state monitoring and accurate demand prediction, inventory overstock and excessive replenishment can be reduced, and the inventory cost is reduced.

The invention discloses a dynamic order distribution optimization system based on deep learning, and relates to the technical field of order management, and the system comprises a multi-dimensional dynamic data collection module which is used for obtaining the multi-dimensional dynamic data of each service item in real time; the project emergency feature calculation module generates a project emergency feature value reflecting the resource allocation priority; the resource reachability feature extraction module outputs a resource reachability feature value reflecting the task assignment difficulty level; the composite dispatch risk feature fusion and optimization model module performs dynamic weighted fusion on the two types of feature values to generate a composite dispatch risk feature vector, inputs the composite dispatch risk feature vector into an asynchronous parallel genetic optimization model to perform dispatch path search, and introduces a convergence premature recognition mechanism to prevent the algorithm from falling into local optimum; and the dispatching strategy dynamic adjustment and feedback optimization module continuously optimizes model parameters through feedback of an actual service execution result to realize closed-loop iteration update of a dispatching scheme.

A method, system, and program product includes an integrated artificial intelligent system for generating a work order by including processes configured to execute demand forecasting, tailored pricing, service provider matching, and dynamic job order management. An example leverages data analytics to forecast demand, determine tailored prices, match users with nearby service professionals, teams, and offers via a marketplace network, and manage job orders using a dynamic work order management tool. The system provides service delivery and enhances customer satisfaction. Generating the work order may include performing a task selected from a list that includes at least one of: demand forecasting, tailored pricing, service provider matching, and dynamic job order management. Based on the performed task, the system may match a user with nearby service professionals via a marketplace network to generate a job order and manage the job order.

The invention discloses an electronic ticket ordering and payment application system based on a block chain, relates to the technical field of block chains, and is used for ticket business whole-process digital management, and a user interaction module supports multi-terminal operation and information management; the block chain order management module is responsible for order chaining and state updating; the multi-mode payment module supports double-track payment and encrypted transmission; the comprehensive matching module is linked with the block chain order management module and comprises a meal subsystem, a tourism subsystem, a hotel reservation subsystem and the like, and the passenger ticket verification module realizes multi-mode verification; the data storage module stores key data; and the intelligent contract scheduling module automatically executes the pre-deployed contract. The problems that an existing system is poor in data safety, low in efficiency and insufficient in adaptability are solved, ticket transaction safety and process automation are improved, data synchronization is guaranteed, multiple languages and multiple currencies are supported, user privacy is protected, multiple scenes are adapted, and travel and supervision requirements are met.

The invention belongs to the technical field of industrial production control, and particularly relates to a diamond roller machining workshop production line process control system which comprises an order management unit, a workshop production line supervision unit and an information visualization unit. The workshop production line supervision unit comprises a real-time data acquisition and processing module, a process parameter intelligent optimization module, an automatic cooperative control execution module and an online closed-loop feedback module; according to the invention, the real-time data acquisition and processing module acquires data and generates a standardized data stream, the process parameter intelligent optimization module dynamically generates optimal process parameters, and the automatic cooperative control execution module performs corresponding control operation on a processing workshop production line. The online closed-loop feedback module conducts online detection on the finished diamond roller and feeds back the detection result to the intelligent technological parameter optimization module, efficient, accurate and intelligent diamond roller machining is achieved, intelligent control over the whole diamond roller machining process is facilitated, and the production efficiency and the product quality are remarkably improved.

The invention provides an order management method and system based on a machine learning model, and belongs to the technical field of alarm devices, and the method specifically comprises the steps: determining the distribution data of an extraction time length deviation date of a matched e-commerce platform in a preset time period, and carrying out the extraction time length deviation date of the matched e-commerce platform based on the distribution data and the historical order data of the matched e-commerce platform; determining a matching e-commerce platform with a variable extraction strategy, taking the matching e-commerce platform as a strategy-variable platform, and on the basis of commodity shelf data in the strategy-variable platform, determining the intersection condition of commodities in the strategy-variable platform and other matching e-commerce platforms with different extraction processing strategies; and the extraction processing strategies matched with the e-commerce platform in different time periods are determined in combination with the inventory data of different commodities, so that the operation stability of the background server is improved.

The invention relates to the field of computer science and technology, and discloses an intelligent operation management platform for multi-scene application aggregation, which realizes full-life-cycle management and accurate association of commodity labels in a label management module, realizes full-process management and control and dynamic optimization of coupons in a coupon management module, and realizes full-process management and control and dynamic optimization of the coupons. Full-link fine management of commodities from classification, attributes to information is realized in the commodity management module, standardized processing and risk monitoring of multi-source and multi-state orders are realized in the order management module, and platform management and control of supplier classification, qualification and association logic are realized in the supplier management module. Accurate configuration and multi-terminal adaptation of advertisement resources are realized in the advertisement management module, authority hierarchical management and control are realized in the platform role management module, and cross-platform user information unification and life cycle management are realized in the platform user management module. And finally, the problems of function fragmentation, response lag, poor industry adaptability and weak risk prevention and control of a traditional platform are solved.

The invention discloses a digital power equipment management system which comprises an equipment automatic management module, a network topology visualization module, an intelligent alarm strategy engine, an automatic inspection execution module, a work order closed-loop management module, a multi-dimensional safety management and control module, an asset full-life-cycle data center and an AI fault prediction module. According to the invention, automatic discovery and full-life-cycle intelligent management of equipment can be realized, network topology dynamic visualization and fault rapid positioning capabilities are improved, a false alarm rate and operation and maintenance cost are reduced through triple verification intelligent alarm and adaptive routing inspection, a standardized operation and maintenance system is constructed in combination with a work order closed-loop process and multi-dimensional safety management and control, and the operation and maintenance efficiency is improved. And potential faults are warned in advance by using an AI prediction technology, so that the intelligent level and the operation reliability of power equipment management are comprehensively improved.