185 results about "Order management system" patented technology

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The embodiment of the invention provides a multi-platform e-commerce order management method and system based on cloud data analysis, and the method comprises the steps: carrying out the analysis and standardization processing of order data through employing a dynamic format conversion channel, constructing a dynamic priority evaluation matrix in combination with real-time logistics load and historical aging data, and carrying out the analysis and standardization of the order data. A geographic position weight model is constructed based on equipment fingerprint identification and address similarity calculation, an order topology aggregation scheme is generated through a spatial incidence matrix, a distributed decision tree is constructed according to inventory fluctuation prediction and supplier response rate, a picking path is optimized, and finally logistics node data is integrated to construct a visual tracking interface. According to the technical scheme, abnormal order real-time early warning and compensation path planning are achieved through a self-repairing mechanism, logistics state holographic projection is generated, the flexibility and efficiency of the multi-platform e-commerce order management system are improved, and the problems of high complexity, poor expansibility, performance bottleneck and the like existing in a traditional system are effectively 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 information, in particular to an omni-channel order management system based on a block chain. Comprising an order management and digital twinning module, an inventory management and cloud network cooperation module, a payment and intelligent contract module, a logistics and edge computing module, a customer service and after-sales management module, an authority management and security module, a data analysis and prediction module and a compliance and audit module. The order management and digital twinning module creates a digital model for an order through a digital twinning technology, and displays the change of an order state in real time; and the inventory management and cloud network cooperation module synchronizes inventory information of each channel in real time and dynamically allocates resources by using a cloud network fusion technology. Through digital twinning, an enterprise can dynamically monitor each link of an order in real time, when a complex supply chain is processed, problems can be predicted in advance, delay or order loss is avoided, and more efficient and more transparent order fulfillment is realized.

The invention relates to the technical field of logistics order delivery management, in particular to a cross-border e-commerce logistics order management system based on big data, which comprises a data acquisition processing unit, an order clustering delivery unit and a delivery execution feedback unit, the data acquisition and processing unit collects orders, geographic information and real-time traffic data from a cross-border e-commerce platform and performs cleaning preprocessing, repeated orders are removed by using a Hash algorithm, the order clustering and delivery unit clusters the orders by using a density peak clustering algorithm, and the clustering effect is optimized by combining the order emergency degree and the customer loyalty, so that the service quality of the cross-border e-commerce platform is improved. A genetic algorithm is adopted to optimize the delivery sequence, the crossover and mutation probability is dynamically adjusted, a fitness function is calculated by considering the delivery distance, time cost and regional limitation penalty factors, a delivery execution feedback unit sends an optimized route to a delivery vehicle, client feedback evaluation data is collected, a delivery strategy is dynamically adjusted by means of an upper confidence bound algorithm, and the delivery efficiency is improved. And the delivery efficiency and the customer satisfaction are improved.

The invention relates to the technical field of logistics storage management, and discloses a logistics storage warehousing management method based on big data, which comprises the following steps: step 1, acquiring order data in an order management system through Internet of Things equipment, acquiring cargo information by using an RF ID tag and bar code scanning equipment, acquiring storage environment parameters by using a temperature and humidity sensor and a light sensor, and storing the storage environment parameters in a database; a built-in sensor of a forklift and a built-in sensor of an automatic guide vehicle are used for collecting equipment state data, real-time image data of goods are collected through a high-definition camera, and the collected data comprise timestamps, data source identifiers and initial data values. According to the method, modeling is carried out on historical order quantity, warehousing frequency, cargo types and time sequence characteristics by utilizing a long-short-term memory network model, so that time dependence and seasonal change trend of data are accurately captured, and accurate prediction of future warehousing demands is realized; and the effects of improving the prediction accuracy and providing a reliable basis for goods allocation and resource scheduling are achieved.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

The invention provides a cross-border logistics order management method and a cross-border logistics order management system, and the method comprises the steps: carrying out the similarity comparison of package feature data and historical package feature data, and obtaining a similarity comparison result; if the preset similarity threshold is not exceeded, determining that the to-be-sent parcel is a target parcel; inputting the parcel feature data of the target parcel into a pre-trained digital twinborn model to obtain a digital twinborn body; carrying out analog simulation on the digital twinborn body based on the digital twinborn model, and generating a standard logistics track and a standard transportation cost; after the target parcel is delivered, an actual logistics track, actual transportation cost and current logistics environment information are acquired; and when the logistics deviation value exceeds a logistics abnormal threshold value or the cost deviation value exceeds a cost abnormal threshold value, generating alarm information. In the mode, automatic identification of repeated delivery, logistics abnormity and transportation cost abnormity can be realized, so that the intelligent and precise level of cross-border logistics order management is improved.

The invention discloses an intelligent order flexible processing production scheduling management method, and relates to the technical field of order management. The method comprises the steps of obtaining demand data from a baking order management system and performing priority classification on orders to ensure timely delivery of important orders and improve customer satisfaction. The future consumption of the baking raw materials is predicted through a time sequence prediction algorithm, the inventory cost is reduced, and the production continuity is guaranteed. And configuration of production equipment, workers and raw materials is optimized through mixed integer linear programming, reasonable utilization of resources is achieved, and the production efficiency is improved. The production progress and the variation of raw material consumption are controlled and monitored through the statistical process, abnormal conditions are recognized in time, and stable product quality is ensured. And flexible processing scheduling adjustment is carried out according to the production abnormity index, and the adaptability of the production system is enhanced. In conclusion, according to the method, the intelligent level of production management is effectively improved, the production process is optimized, and powerful support is provided for enterprises in the market with fierce competition.

The invention provides an order change-oriented AIAgent multi-task collaborative decision-making method and system, and the method comprises the steps: obtaining an order change basic data set containing historical order data and real-time change information, and constructing an order change association knowledge graph based on the order change basic data set; calling a multi-task collaborative decision AIAgent to perform multi-task collaborative analysis processing on the order change associated knowledge graph, and generating a collaborative decision result including task priority ranking, a resource allocation scheme and a risk coping strategy; and according to the collaborative decision result, generating an order change coping decision instruction including an execution node time planning and resource scheduling instruction, and sending the order change coping decision instruction to an order management system to trigger a dynamic adjustment operation. According to the invention, the comprehensiveness, coordination and operability of order change coping are effectively improved.

The invention relates to the technical field of order management, in particular to an order management system based on online payment and a payment safety management and control method, and the system comprises a behavior sequence recognition module, a sequence offset comparison module, a state chain construction module, a jump imbalance detection module and a risk level linkage module. According to the invention, through comparison of behavior data of multiple successful transactions, a standardized payment process can be established, abnormal behavior deviation can be effectively identified, the execution sequence of the payment process can be accurately evaluated through time difference analysis, potential risks can be discovered, the traceability of the payment process can be enhanced, and the payment efficiency can be improved. The construction of the state chain and the sorting check ensure that the state circulation accords with logic, reverse, repeated or abnormal jump can be identified in time, the abnormal frequency of behavior offset and state jump is combined, a multi-dimensional risk assessment standard is adopted, a risk order is identified and frozen in real time, the abnormity in the payment process is accurately identified, and the payment security and stability are improved.

The invention discloses a work order generation method, a work order processing method and a work order management system. The work order generation method comprises the following steps: performing text extraction on multi-modal problem data reported by a user to obtain corresponding target text data; analyzing the multi-modal problem data by using the problem classification model to obtain a corresponding problem type, inputting the target text data into a work order template matched with the problem type to obtain a work order to be processed, and determining a generation timestamp of the work order to be processed; analyzing the multi-modal problem data by using an emotional tendency recognition model to obtain a corresponding emotional tendency; and packaging the emotional tendency, the question type, the work order to be processed and the corresponding generation timestamp corresponding to the multi-modal question data into a work order information packet, sending the work order information packet to a server, and receiving a question result fed back by the server. The technical problem that the work order generation accuracy is poor due to the fact that the multi-modal feedback data of the user cannot be fully understood in the related technology is solved.

A cloud-based edge-as-as-service (EaaS) system allows edge gateways to be easily configured and deployed on the cloud for collection, contextualization, and egress of industrial data to downstream applications, including analytic applications, work order management systems, or visualization systems. The EaaS system uses predefined device profiles to automatically discover relevant data items on plant floor devices and present these data items to a user. Model configuration interfaces served by the EaaS system allow the user to map selected data items to predefined models for organizing or contextualizing the selected data, and for egressing the contextualized data to the target applications. These model configurations can be deployed on the cloud platform as edge gateways that use the resulting information models to collect and contextualize relevant items of device data during runtime and to export the modeled data to the target application.

The invention relates to the technical field of logistics information processing, and particularly discloses a high-quality and high-efficiency logistics order management system. The system comprises a registration and login terminal, an intelligent review terminal, an order processing terminal and a block chain evidence storage terminal, and is modularly deployed based on a distributed architecture. The intelligent review terminal introduces an image recognition and rule engine to realize automatic order splitting and suggestion merging; the order processing terminal combines a scoring model and a path optimization algorithm to realize dynamic payment routing and multi-target path planning; the block chain evidence storage terminal generates a non-tampering credible receipt based on hash and zero knowledge verification, and supports cross-chain auditing. The system supports GeoHash fragmentation, Flink parallel computing, high-availability container scheduling and hot and cold data hierarchical storage, and the order processing efficiency, the system elasticity and the data security are remarkably improved. The logistics order full-life-cycle management method is suitable for logistics order full-life-cycle management in a high-concurrency and high-credibility scene.

The invention discloses a customer service work order management system based on network employer service, which relates to the technical field of work order management and comprises a multifunctional service module, a distributed management module, an elastic module, an operation and maintenance monitoring module and a real-time notification module. The multifunctional service module comprises a user service unit, a work order service unit, a communication service unit, a knowledge base service unit and a data analysis unit, and is used for serving a network employer; the distributed management module is used for verifying user validity, determining user legality and creating a work order based on a legal user; and the elastic module is used when the customer service work order service response is overtime. According to the invention, omnibearing real-time monitoring and deep analysis are carried out on the system, discovered problems and work order state change conditions are pushed to related personnel in time, timely transmission of information is ensured, failure retry is carried out based on an exponential backoff algorithm, the reliability of notification is ensured, and notification loss caused by network fluctuation is avoided.

The invention discloses an industrial PLC (programmable logic controller) equipment data acquisition and intelligent monitoring method, which comprises the following steps: connecting multiple types of PLC equipment through a standardized industrial communication protocol to acquire original data, analyzing and converting through a point attribute configuration table, and combining with a dynamic dictionary mapping table to generate readable information. And the system is dynamically associated with a work order management system to realize production monitoring and analysis. A layered modular architecture is adopted, cross-platform deployment is supported, the efficiency, accuracy and intelligent level of data acquisition and monitoring are effectively improved, and the complex scene requirements of industrial production are met.

The invention discloses a main and sub work order collaborative circulation method based on process model locking nodes, and relates to the technical field of work order circulation methods. The method comprises the following steps: firstly, constructing a main-sub work order classification system, and establishing an independent process model containing key nodes and locking nodes for two types of work orders; then associating the main work order with the sub-work orders through a work order management system, and establishing a node mapping relation table; implanting an identifier in a locking node of the sub-work order process model, and when the sub-work order process model is transferred to the node, sending a locking instruction to the main work order by the system to suspend the main work order; the task completion state of the sub-work order locking node is collected in real time, and after verification is passed, the main work order releases limitation and continues to flow; and the process model can be dynamically adjusted, the exception is processed, and many-to-many association is supported. According to the method, the work order circulation efficiency is improved, the abnormal response time is shortened, the association configuration efficiency is improved, the cross-factory collaborative delay is reduced to 50ms, and the state verification accuracy is high; the problems of work order association missing and low collaboration efficiency are effectively solved, and the labor cost and the delay risk are reduced.

Systems and methods to generate and schedule tasks for automated guided carts (AGCs) may include an overhead sensor system, a warehouse management system, an order management system, and a traffic controller. The overhead sensor system may detect trolleys and totes positioned at various trolley locations within a material handling facility. Based on the sensor data and data associated with states of portions of the material handling facility, the order management system may generate logically feasible tasks to transport trolleys and totes within the facility, and may also sequence the tasks in a temporally feasible order. Then, the traffic controller may instruct execution of the sequence of tasks by AGCs to facilitate various material handling processes within the facility.

The invention is suitable for the technical field of automobile commutator reinforcing rings, and particularly relates to an automobile commutator reinforcing ring production control method and equipment, and the method comprises the steps: obtaining all to-be-processed order information in a reinforcing ring order management system in real time; order analysis is carried out on the to-be-processed order information to obtain priority processing information; performing pre-production analysis based on the priority processing information to obtain production line model information of the reinforcing ring in the production workshop; performing performance simulation analysis according to the production line model information to obtain performance result information; performing control parameter optimization on the performance result information to obtain production control information; wherein the production control information is used for controlling the commutator reinforcing ring production equipment to produce the reinforcing ring. According to the automobile commutator reinforcing ring production control method provided by the invention, the performance of the automobile commutator reinforcing ring can be predicted, so that the utilization rate and the production speed of automobile commutator reinforcing ring production equipment are increased, and the structural stability and durability are improved.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor status and operational data from industrial devices on the plant floor, as well as mobile industrial robots that traverse the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk.

The invention relates to the technical field of electric power storage, and particularly discloses an electric power storage resource global collaborative management method and system, and the method comprises the steps: constructing an electric power professional warehouse data graph with an Internet of Things physical ID as a core node, the Internet of Things physical ID being a unique physical identifier of an electric power storage resource; associating material data of a safety supervision warehouse, a regulation and control warehouse and a logistics warehouse, and integrating key data entities of a safety tool verification system, a scheduling equipment management system and a work order management system; a unified data format and a data mapping rule are defined, and standardized interaction of cross-system heterogeneous data is achieved. According to the method, standardized real-time synchronization of cross-system data is realized by constructing a power professional warehouse data graph and relying on an internet of things entity ID active synchronization engine; by using a multi-dimensional rule engine, based on a professional warehouse type-material attribute-business scene three-dimensional rule base, accurate management and control are provided for warehouse-in, warehouse-in, warehouse-out and exception handling of materials, and the asset utilization efficiency is effectively improved.

The invention relates to the field of intelligent work order dispatching, in particular to a distributed work order management system based on intelligent dispatching. The invention provides a distributed work order management system based on intelligent assignment, and the system comprises an organizational structure engine module which employs a father-child node tree structure, divides operation and maintenance regions according to the three levels of province, city and district, and carries out the node binding of the responsible person authority level of the region; the intelligent order dispatching module is used for realizing automatic matching of the work order and the operation and maintenance personnel through a preset algorithm; the data analysis module collects work order data in real time to generate a multi-dimensional analysis chart, and predicts the regional fault probability through machine learning; and the early warning feedback module is used for setting an SLA threshold value and automatically upgrading and alarming an overtime work order. The work order system realizes digital management through an automatic and standardized process, greatly reduces the time consumption cost of offline communication, and improves the operation and maintenance efficiency.

The invention provides an unmanned aerial vehicle material distribution energy consumption minimization method in a dynamic environment, and the method comprises the steps: data preparation: carrying out the data collection of an unmanned aerial vehicle, including the data collection of a sensor disposed on the unmanned aerial vehicle and the real-time obtaining of the transportation goods quality data of a distribution goods order management system; model establishment: establishing an unmanned aerial vehicle distribution system model according to an unmanned aerial vehicle cross-regional material distribution scene, establishing a sensing information quantitative model based on data acquired by a sensor, and representing an unmanned aerial vehicle energy consumption condition through an unmanned aerial vehicle energy consumption model; model conversion: based on a system model, converting an unmanned aerial vehicle flight energy consumption minimization problem into a Markov model; model training: adopting a sensing and control integrated unmanned aerial vehicle cross-regional mobile architecture, fusing an observation space of an EKF into a state space of A2C, dynamically adjusting system behaviors, and performing model training; and decision implementation: according to the real-time dynamic environment information, the unmanned aerial vehicle generates a flight route meeting the minimum energy consumption, and material distribution is realized.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

The invention provides an e-commerce integrated order management system, and relates to the technical field of e-commerce, and the system comprises an order processing module which is used for receiving, splitting, merging and updating order states, automatically optimizing order circulation, improving the order processing efficiency, guaranteeing the real-time synchronization of the order states, and reducing the processing delay. In the invention, the data analysis module analyzes user behavior data (such as browsing, purchasing and returning) and market sales data in real time and predicts future commodity demands and sales trends by using the prediction model, and the system can automatically provide a real-time report for an operation team based on the data, so that the user experience is improved. The module helps to adjust the inventory replenishment cycle, sales promotion activities and commodity shelving plans, and has the core advantages that market changes are identified through big data analysis, response measures are automatically generated, the inventory management precision is improved, and the inventory overstock and stockout conditions are reduced.

A digital operation management platform for an electrolytic aluminum enterprise relates to the technical field of production management and comprises an intelligent warehousing system, an order management system, an aluminum processing intelligent manufacturing system, an aluminum smelting intelligent manufacturing system and a supply chain management system. The intelligent warehousing system is in communication connection with the order management system, the aluminum processing intelligent manufacturing system and the supply chain management system, and the intelligent warehousing system is in conveying connection with the aluminum processing intelligent manufacturing system, the aluminum smelting intelligent manufacturing system and the supply chain management system. The aluminum processing intelligent manufacturing system is in communication connection with the order management system and the aluminum smelting intelligent manufacturing system. The platform can improve the operation management efficiency of electrolytic aluminum enterprises.

The invention discloses a logistics early warning management and automatic processing system and method based on multiple platforms and multiple shops, and relates to the technical field of e-commerce logistics management, and the system comprises a data collection and processing module which is used for carrying out the butt joint of order management systems of multiple e-commerce platforms through a standardized interface, obtaining the order logistics data of each shop in real time, and sending the order logistics data to a server; the original logistics data are cleaned, analyzed and standardized, and are aggregated and stored to the unified data storage module according to the dimensions of shops, platforms and logistics providers. According to the logistics early warning management and automatic processing system and method provided by the invention, comprehensive integration and unified display of logistics information are realized through integration of multi-platform order data, the tedious problem of cross-platform information viewing of merchants is solved, the operation requirements of different merchants are flexibly adapted through self-defined early warning rules and an intelligent grading mechanism, and the logistics early warning management and automatic processing system and method are high in practicability. The accuracy of abnormity identification is improved, logistics business work orders are automatically connected, and the abnormity processing flow is greatly simplified.

The invention discloses an intelligent work order management system and method based on cue word optimization, and relates to the technical field of customer service and work order management. According to the system, by integrating natural language processing, image processing and machine learning technologies, user information is obtained, a large model is integrated, replies meeting specific scenes and requirements are generated by utilizing a designed cue word guiding model, and structural order data such as consultation, handling and complaint are automatically created according to dialogue contexts. The AI abstract shorthand realizes intelligent analysis, classification, distribution and tracking of work order contents so as to improve work order processing efficiency and accuracy. Meanwhile, the system has the functions of intelligent work order classification, automatic quality inspection and the like, and the customer service operation efficiency is comprehensively improved.

The invention belongs to the technical field of high-frequency transactions, and particularly relates to an automatic matching system for high-frequency transactions, which comprises a data acquisition and processing module, a transaction strategy engine, an order management system, a matching engine, a risk control system, a network and communication module, a log and monitoring system and an algorithm optimization and performance optimization module, and the data acquisition and processing module is responsible for receiving market information data, transaction information and other related data in real time and processing and analyzing the data in real time so as to make a quick decision. Through the functions of intelligent data processing, strategy generation, efficient order management, precise matching, comprehensive risk control, low-delay communication, system monitoring and optimization and the like, the transaction efficiency, accuracy and stability can be remarkably improved, and powerful technical support and guarantee are provided for high-frequency transactions.

The invention discloses an order management system applied to book and periodical publishing and printing, and relates to the technical field of resource optimization scheduling. Comprising a feature parameter extraction module used for receiving and analyzing order data and extracting feature parameters; the priority calculation and secondary evaluation triggering module is used for calculating order priorities based on the characteristic parameters, and triggering a secondary evaluation mechanism when detecting that the difference value of the priorities of adjacent orders is smaller than a set threshold value and the resource competition flag bit is activated; the allocation decision function construction module is used for calculating the dynamic capacity and constructing an allocation decision function in combination with the priority; the load variance calculation and order migration module is used for calculating equipment load variance and executing order migration operation when the load variance exceeds a preset threshold value; and the production abnormity monitoring and adjusting strategy module is used for calculating the damage degree of the event and selecting a corresponding adjusting strategy according to the damage degree of the event. Through intelligent scheduling, abnormal event response and ink consumption monitoring, equipment resource utilization is optimized, and production continuity is guaranteed.