1203 results about "Order processing" patented technology

The invention relates to the technical field of intelligent operation and maintenance and artificial intelligence crossing of a power system, in particular to an intelligent power work order processing method of an RPA fused multi-modal large model, which analyzes multi-modal work order data such as texts, voices, images and the like through a domain adaptation large language model, and realizes fault key information extraction and conflict resolution in combination with a dynamic knowledge graph; performing work order priority scoring and resource allocation by using space-time constraint reinforcement learning; an analysis result is converted into an automatic execution script through an RPA engine, and a whole-process closed loop of order sending, processing and feedback is achieved; meanwhile, a feedback optimization and conflict resolution cooperation mechanism is constructed, and the knowledge graph and the model precision are continuously iterated. The method improves work order processing efficiency and analysis precision, enhances decision scientificity, and is suitable for an intelligent operation and maintenance scene of a power system.

The invention provides an automatic order dispatching method and system for a hotel, and the method comprises the steps: carrying out the analysis of hotel order contents according to task attributes in hotel orders, extracting key information, including task types, priorities and complexity, constructing task attribute feature vectors, carrying out the clustering of the task attribute feature vectors, and carrying out the clustering of the task attribute feature vectors; dividing the tasks with high similarity into the same category to obtain task sets of different categories; in the hotel order distribution process, the task execution state is monitored in real time, whether the task execution state is abnormal or overtime or not is judged, and if yes, task redistribution is carried out to other available processing personnel in combination with the hotel order task type and the customer service working time; after the hotel order is completed, the task execution data is counted, the average execution time and the completion quality index of each task are calculated, a task execution report is formed, and the hotel order processing flow and the task distribution strategy are continuously improved.

The invention relates to the technical field of industrial production scheduling optimization, and discloses an industrial production process intelligent scheduling and optimization method and system.The industrial production process intelligent scheduling and optimization method comprises the steps that resource competition hotspots are identified by establishing a resource time two-dimensional matrix; according to the method, the resource competition relationship between the emergency order and the existing order can be accurately identified, global re-planning is replaced by local fine tuning, the optimal insertion position is determined by constructing an order interference degree evaluation model, the hotspot intensity is reduced by implementing a resource competition mitigation strategy, and optimal embedding of the emergency order is realized by adopting a progressive order insertion algorithm. The disturbance to an existing production plan is minimized, the emergency order processing efficiency and the system stability are remarkably improved, and the technical problem of intelligent scheduling of emergency orders in industrial production is solved.

The invention relates to an implementation method and device for an EtherCAT master station system based on a non-operating system, and the method comprises the steps: achieving the adaptation of an SOEM protocol stack to a hardware abstraction layer of an EtherCAT master station through the byte order processing based on a bit mask and a staged shift strategy and the EtherCAT frame receiving and transmitting control based on annular buffer area index management; on the basis of non-real-time task priority scheduling processing of a hardware timer, main circulation driven by a single-thread event and overtime monitoring of a blocking communication synchronization mechanism in combination with the timer, adaptation of an SOEM protocol stack to an EtherCAT master station operating system abstraction layer is achieved; through network scanning and communication parameter configuration, a memory management mechanism based on CoE standard mapping and a periodic position mode of a slave station, adaptation of an EtherCAT master station application layer is realized. According to the invention, through optimization and adaptation of the SOEM protocol stack, operation-system-free and microsecond-level communication jitter of the EtherCAT master station system is realized.

The invention provides an e-commerce service intelligent multi-warehouse delivery method and system, and the method comprises the steps: generating a global inventory view according to the user order information of a cross-e-commerce platform and a corresponding logistics performance demand, and carrying out the dynamic adjustment through a preset priority rule, a sorting priority sequence is generated through a dynamically adjusted inventory distribution strategy, spectral feature matching is carried out on mixed goods in all warehouses according to the sorting priority sequence and SKU identification information, goods sorting is completed in combination with target warehouse illumination environment parameters corresponding to inventory geographical distribution, and goods identification information is generated; then generating a cross-warehouse allocation decision by using a distributed inventory sharing model, and finally generating a multi-warehouse collaborative delivery instruction according to the cross-warehouse allocation decision and the cargo identification information; according to the technical scheme provided by the invention, intelligent management of the whole process from order processing to final cargo distribution is realized, the efficiency of inventory management and logistics scheduling is improved, and the accuracy and timeliness of order processing are ensured.

The invention provides an intelligent operation and maintenance work order processing method, device and equipment based on multi-modal data fusion and a medium, and the method comprises the steps: collecting alarm information of each business program or business subsystem in an IT operation and maintenance scene, the alarm information comprising a text log, a performance index, configuration information and network topology data; the collected alarm information is preprocessed; extracting key information according to the pre-processed alarm information; based on the extracted key information, generating a root cause hypothesis chain by analyzing an application relationship and a topological dependency relationship of each component in the plurality of service subsystems; according to the extracted key information and the root cause hypothesis chain, combining historical processing records in a knowledge base to generate a work order containing a suggested solution, and pushing the work order to a corresponding processor; and receiving a solution fed back by the processor, and completing work order processing.

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 customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention discloses a logistics order management system and method, and belongs to the technical field of logistics information management. The method comprises the following steps: step 1, generating a unique digital fingerprint for each logistics order; step 2, performing multi-level security encryption and state synchronization management on order information; 3, constructing a digital twinborn model corresponding to the order, collecting and preprocessing key data in real time and visually displaying the key data in the logistics process, and realizing dynamic monitoring of the whole process; step 4, based on an order state consensus mechanism, automatically matching optimal logistics resources and driving an order processing flow, and generating a verifiable order execution proof chain at the same time; and step 5, performing intelligent analysis and adaptive optimization on the full-life-cycle data of the order, and continuously improving the overall efficiency and precision of logistics management.

The invention relates to the technical field of artificial intelligence, in particular to a cloud printing order resource dynamic allocation system based on an artificial intelligence algorithm, which comprises an order analysis and clustering module, a cloud printer performance state sensing module, an order matching and allocation strategy making module and a real-time monitoring and dynamic adjustment module. The order analysis and clustering module extracts order features by using a composite technology, carries out clustering analysis and determines the emergency degree of the order features; the cloud printer performance state sensing module collects information through the Internet of Things and a sensor network, monitors the state of the cloud printer in real time and predicts the fault probability; the order matching and distribution strategy making module optimizes an order distribution strategy according to the order emergency degree and the cloud printer state so as to improve the efficiency; the real-time monitoring and dynamic adjusting module monitors the working efficiency of the cloud printer in real time, compares order requirements and adjusts the order requirements in time, and it is ensured that the printing task is completed smoothly. The method is used for solving the technical problem that the current cloud printing order processing effect is poor.

The invention discloses a commodity order information intelligent processing system, which relates to the technical field of commodity order information intelligent processing and comprises a commodity information acquisition module, an intelligent analysis module and a processing execution module. In order to solve the defects that an existing system is insufficient in response to real-time market fluctuation and demand prediction, cannot dynamically balance inventory and order quantity, is limited in collaborative scheduling capability among multiple warehouses, and is easy to cause order delay or inventory redundancy; according to the invention, commodity order information is acquired through the commodity information acquisition module, the intelligent analysis module analyzes the commodity order information, and then the processing execution module executes processing; according to the invention, the optimal processing capability of each warehouse can be comprehensively evaluated, and the warehouse most suitable for delivery is matched. Through the intelligent process, the operation efficiency of the warehouse can be improved, the inventory pressure can be reduced, the resource allocation can be optimized, the order processing accuracy and the delivery speed can be finally improved, and the overall response capability of the supply chain can be enhanced.

The invention relates to the technical field of intelligent work order scheduling platform design, and discloses an intelligent work order scheduling system, method and device and a medium, and the method comprises the steps: the system achieves the efficient and intelligent scheduling of a device fault work order through integrating a plurality of function modules. Firstly, the work order access module receives equipment fault information of different channels, and it is ensured that the information is comprehensive and timely. Then, the portrait construction module constructs a multi-dimensional ability portrait by using the historical service data of the maintenance personnel, and provides a data basis for intelligent matching; and then, the intelligent distribution module accurately matches and associates the most suitable maintenance personnel with the work order according to the portrait data and the work order parameters. And the order grabbing interaction module generates a to-be-grabbed work order list and an order grabbing interface, so that the scheduling flexibility is enhanced, and the scheduling efficiency is improved. The execution tracking module monitors the work order processing progress in real time, and transparent and controllable scheduling is ensured. And finally, the knowledge base question and answer module provides professional knowledge support for the maintenance personnel and helps the maintenance personnel to quickly solve the work order problem.

The invention relates to the technical field of artificial intelligence, in particular to a government affair hotline complaint classification and automatic order sending system and method based on a large model, and adopts a four-layer architecture to realize full-process intelligent management of government affair complaints from acceptance to disposal. The four-layer architecture comprises a multi-mode sensing layer, an intelligent decision-making layer, a dynamic single-layer dispatching layer and a closed-loop self-optimization layer; the method has the beneficial effects that an efficient, accurate and intelligent government affair hotline complaint classification and automatic order dispatching system is constructed by fusing a large model technology and an innovative data processing and order dispatching strategy, rapid and accurate classification and automatic order dispatching of complaint work orders are realized, the work order processing flow is remarkably optimized, the processing period is shortened, and the working efficiency is improved. The quality and efficiency of government affair services are improved, and the satisfaction degree of the public to the government affair services is enhanced.

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 discloses a workshop task allocation method and system based on multiple dimensions, and relates to the technical field of production control management. Comprising the steps of obtaining order data and then performing preprocessing; based on the preprocessed order data, an advanced machine learning algorithm is adopted to construct a decision tree model, and the processing complexity of each order is identified; determining a task allocation scheme by using an optimization algorithm according to the identified order processing complexity and the current state of the production line; monitoring the actual operation condition of each production line in real time, starting a dynamic adjustment mechanism when the work load of a certain production line is unbalanced, and re-evaluating and adjusting task distribution; through intelligent task allocation and resource optimization, the production efficiency and customer satisfaction are improved, and meanwhile, dynamic adjustment and intelligent management of the production process are realized.

The invention relates to the technical field of data processing, in particular to an IPC message queue timeout processing method and device, electronic equipment and a medium, when IPC messages of a user layer are received, timestamps, priorities and timeout time of the IPC messages are obtained, the message processing sequence is adjusted according to the timestamps and the priorities, and the messages are processed according to the sequence. And when it is determined that the message is not correctly processed within the timeout time, marking the message as a dead message and moving the message into a dead message queue, determining a generation reason of the dead message in the dead message queue, and processing the reason. According to the method, the timestamps, the priorities and the timeout time of the messages are comprehensively considered, ordered processing and timeout management are carried out on the messages, and the messages which are not processed in the timeout process are converted into dead messages in time and are processed independently, so that process blockage is avoided, the influence of the dead messages on a system is reduced, the performance overhead is reduced, and the reliability and efficiency of IPC communication are improved.

The invention discloses a pre-accepted work order closed-loop management system, which comprises a sensitive first-aid repair work order automatic research and judgment module, a non-sensitive work order handling module, a pre-accepted work order accurate closed-loop grading management and control module, a closed-loop process management module, a comprehensive auditing module and a visualization module, an intelligent grading management and control rule, a responsibility investigation mechanism, a timestamp technology and the like are embedded into the corresponding module to enhance the disposal effect of the pre-accepted work order; according to the invention, by establishing an online pre-acceptance work order closed-loop management system, sensitive emergency pre-acceptance work orders can be found in time, and the core problems of untimely work order reporting, information blocking, large workload, difficult supervision, difficult management and control, difficult statistical analysis, difficult employee assessment and the like in the existing pre-acceptance work order processing process are solved; and workers are assisted to realize whole-process closed-loop management of processing time node management and control of each link of a pre-accepted work order, accurate order making of sensitive problems, time-limited grading, automatic circulation and the like, and the quality and efficiency of power supply service are improved.

System and methods are provided for automated SKU management. Embodiments include a user interface for receiving diverse catalog files, a Catalog Transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a Search Platform. The Catalog Transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a Dynamic SKU Creation module and a Global Pricing Engine for real-time pricing. Embodiments improve data accuracy and SKU management, facilitating integrated order processing and fulfillment.

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 order processing method and system based on a message queue, and belongs to the technical field of computers, and the method comprises the steps: receiving order data from a business platform, carrying out the preprocessing of the order data, reading related fields from a preset business rule library, and carrying out the data marking of an order; intelligently distributing orders by using a rule engine, and pushing integral messages of the distributed orders to corresponding consumption queues through message queues; after the order adding and inventory reserving operation is successfully submitted, submitting a subsequent state flow operation of the order as an asynchronous task to a state flow queue for processing; key indexes are collected through the real-time monitoring module, monitoring data are fed back to the dynamic rule module, and self-adaptive optimization of an order distribution strategy is achieved. According to the method, the row lock contention of the database can be remarkably reduced, the concurrent processing capability of the system is improved, the system stability and the data consistency are enhanced, and the resource utilization and the system expansibility are optimized.

System and methods are provided for automated SKU management. Embodiments include a user interface for receiving diverse catalog files, a Catalog Transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a Search Platform. The Catalog Transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a Dynamic SKU Creation module and a Global Pricing Engine for real-time pricing. Embodiments improve data accuracy and SKU management, facilitating integrated order processing and fulfillment.

The invention provides a work order generation method and device, an intelligent work order filling system and a work order processing system.The work order generation method comprises the steps that a reference work order with the highest relevancy with equipment basic fault information input by a user is obtained from an enterprise asset management system in a keyword matching search mode; if a reference work order with relatively high relevancy with the equipment basic fault information input by the user cannot be obtained from the enterprise asset management system, determining a historical work order with the highest similarity in a vector database as the reference work order according to vector data corresponding to the equipment basic fault information input by the user; and according to at least one of the confirmation information and the modification information of the user on the reference work order, generating a current work order, the current work order comprising the equipment parameters and the processing method information. According to the method, traditional keyword matching search and large language vector retrieval technologies are ingeniously combined, so that the problems of long time consumption and unstable quality existing in manual work order filling in the prior art are solved.

The invention relates to a warehouse inventory management and optimization method based on artificial intelligence, and the method comprises the steps: constructing a novel mathematical model through comprehensive consideration of the storage position of a commodity, tray partition, order priority, system state and other factors, solving through employing a deep reinforcement learning DQN network, achieving the intelligent decision of a commodity pickup position, and generating an optimal picking path; according to the method provided by the invention, the order processing time can be effectively shortened, aiming at the requirements of small-batch and multi-category orders, the storage efficiency is improved, the movement distance of a stacker and the number of goods taking times are reduced, the operation cost is reduced, and flexible scheduling can be performed according to the priority to adapt to the real-time state change of the system; compared with a traditional method, after the optimization strategy is adopted, the number of times of taking out the trays is remarkably reduced, and the method has the remarkable beneficial effects, can be widely applied to the field of intelligent warehousing, improves the automation, informatization and intelligentization levels of warehousing management and meets the requirements of the modern logistics industry for an efficient warehousing system.

PURPOSE: To provide a market ordering system that provides excellent usability for people concerned in a market for enabling enhanced efficiency of overall distribution.SOLUTION: A market ordering system according to the present invention provides product information from a seller to a purchaser via each respective terminal connected to a server, and payment processing proceeds based on order processing of the purchaser. After the seller and the purchaser complete user registration in the ordering system via each respective terminal, the seller and the purchaser are registered as trading partners with each other via each respective terminal and the server, and after being registered as the trading partners, the registered trading partner and product information intended for the trading partner are displayed together on an order screen of the purchaser's terminal, and payment is made according to ordering operation on the purchaser's terminal for the product information from the seller registered as the trading partner.SELECTED DRAWING: Figure 1

The invention relates to the technical field of power equipment supply chain management, and discloses a power equipment supply chain collaborative management system based on an alliance chain, comprising an alliance chain network layer: a distributed network composed of a manufacturer node, a supplier node, a logistics node, a detection node and a grid company node, each node maintaining account book consistency through a consensus algorithm; the intelligent contract execution layer comprises an order processing contract, a logistics tracking contract, a quality acceptance contract and a settlement management contract, and supports automatic execution of a business process; and the data management platform is provided with a distributed storage unit, an encryption transmission unit and an authority control unit and is used for realizing encryption storage and hierarchical access of key data. Full-process automation of order matching, production plan generation and logistics tracking is realized through an intelligent contract execution layer, an AI prediction module can pre-judge a demand peak value in advance based on historical data, a node processing priority is optimized in combination with a dynamic consensus algorithm, and manufacturer order response time is compressed.

The invention discloses a work order distribution method and device and electronic equipment. The method comprises the following steps: obtaining a to-be-processed service work order of an operator, and obtaining service work order data corresponding to the to-be-processed service work order, the service work order data at least comprising attribute information of the to-be-processed service work order; sorting the business work orders to be processed according to the business work order data to obtain a first sorting result; and distributing the to-be-processed business work order to the target object according to the first sorting result. According to the invention, the technical problems of low efficiency and time and labor consumption due to the fact that most service work order processing systems of operators depend on manual work order selection in the prior art are solved.

According to the order production control method and device, the electronic equipment and the storage medium provided by the invention, the order information of the order, the process plan information of the order and the working center information of the working center are acquired, and the working center is used for producing products of the order; decomposing the order into a plurality of processes according to the process plan information, and determining the time required by each process of each order; calculating the earliest starting time of each process of each order according to the order information and the required time; and generating a production scheduling scheme based on the earliest start time and the work center information, and controlling the work center to work based on the production scheduling scheme to produce the order, so that the automation degree of order processing and production scheduling can be improved, and human errors and delay are reduced.

The invention discloses an electric power customer service work order abstract generation method based on voice dialogue, and relates to the technical field of voice recognition, and the method comprises the steps: receiving voice dialogue content; key entities related to electric power are extracted; performing deep understanding on the voice text based on the semantic analysis model; based on the extracted key information, automatically generating an abstract of the electric power customer service work order; submitting the automatically generated work order abstract for manual auditing; the invention discloses an electric power customer service work order abstract generation system based on voice conversation. The system comprises a voice receiving module, a voice text processing module, a keyword extraction module, a semantic analysis module, a work order generation module, a work order auditing module and a work order publishing module. Through advanced voice recognition, natural language processing, semantic analysis and emotion recognition technologies, automation of the work order generation process can be achieved, the work order processing quality and efficiency can be optimized, and therefore remarkable benefits and improvement are brought to electric customer service work.

The invention relates to a logistics order processing efficiency analysis method based on cloud computing, and the method comprises the steps: constructing a multi-dimensional and multi-node behavior log and environment variable synchronous collection system, and obtaining standardized structured data through timestamp calibration, abnormal value elimination, semantic normalization and desensitization processing; and based on node features, sequence features and environment embedding, generating a multi-node multi-scene feature vector, and inputting the multi-node multi-scene feature vector to a multi-order causal nested graph neural network for dynamic causal modeling among nodes, events and constraints. Through combination of anti-fact inference and multi-source evidence fusion, hidden bottleneck automatic identification, attribution report output and optimization suggestion generation are realized; through expert and user feedback mechanisms and working condition and rule change adaptive network structure adjustment, long-term accurate optimization and dynamic closed-loop updating of the bottleneck diagnosis model are realized. According to the invention, the intelligence and adaptive level of order flow bottleneck identification can be improved, and a high-quality data basis and intelligent decision support are provided for flow optimization in a complex logistics scene.

The invention provides an integral shopping mall order rolling processing optimization method and system based on big data, and the method comprises the steps: employing a dynamic priority adjustment mechanism for a conflict order list, and adjusting the order processing priority based on the user behavior track data, including historical exchange preference, and obtaining a resource distribution scheme; integrating the order sequence through a queue reordering mechanism, reordering based on the resource allocation scheme to obtain a final processing sequence, preferentially executing the high-priority request, and maintaining the queue stability at the same time; if residual conflicts exist in the final processing sequence, supplementary resources are extracted from a preset standby inventory through a standby resource set scheduling mechanism, a complete execution plan is obtained, and the plan covers all order processing paths; and on the basis of the complete execution plan, monitoring the order processing progress, and obtaining a stable operation order flow through progress tracking and anomaly detection, real-time state feedback and iterative adjustment.