294 results about "Order system" patented technology

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

The invention discloses a full-process packaging management and control system and method based on a digital twinborn model, relates to the technical field of packaging management, and forms a full-process closed-loop management and control system from external demand perception to internal packaging process optimization through digital twinborn model and multi-source data fusion. By integrating data such as an order system, market feedback, a production plan and an equipment state, the problem of data islands in traditional packaging production is solved, and real-time fusion of external requirements and internal data is realized; a multi-source data weighting and cleaning fusion mechanism is adopted, and a packaging scheme is ensured to be closer to an actual production environment through digital twinborn simulation and evaluation; through a multi-target optimization algorithm, conflicts and coupling effects among a plurality of targets are accurately captured, and the decision accuracy is improved. Through rapid deployment and a closed-loop feedback mechanism, the production line is monitored and adjusted in real time, the production efficiency and quality are improved, the production cost is reduced, the efficiency and the product quality are improved, and meanwhile the environmental influence is reduced.

The invention provides a root cause positioning method which can be applied to the technical field of artificial intelligence. The root cause positioning method comprises the following steps: acquiring data in a configuration management database, a network topology tool, a monitoring system and a work order system to form a multi-source heterogeneous data set; performing knowledge extraction on the multi-source heterogeneous data set, extracting equipment attributes, network topological relations, fault event entities and timestamps, and storing the equipment attributes, the network topological relations, the fault event entities and the timestamps as structured knowledge; mapping real-time index data in the structured knowledge into dynamic attributes of an entity, and constructing a dynamic knowledge graph; based on a graph neural network and in combination with time sequence features, learning a time sequence dependency relationship and a propagation path between fault events in the dynamic knowledge graph; and outputting a root cause entity, a confidence score and a fault propagation path of the fault event through a causal inference algorithm in combination with the multi-dimensional evidence. The invention further provides a root cause positioning device and equipment, a storage medium and a program product.

The invention discloses a video monitoring equipment fault real-time detection, root cause diagnosis and closed loop processing method and system. According to the method, by collecting multi-dimensional operation data of equipment, a dynamic weighted health degree model is constructed to realize early-stage accurate discovery of a fault; performing intelligent alarm grading by combining time sequence prediction and health degree change; realizing automatic root cause diagnosis by utilizing topological correlation analysis, log semantic analysis and case similarity matching; and closed loop processing and model self-optimization are realized through a work order system. According to the method, the problems of lagging fault discovery, difficulty in positioning, slow repair and disjunction in assessment in the prior art are effectively solved, and the operation and maintenance efficiency and the service quality are remarkably improved. The abstract drawing is Figure 1.

The invention discloses a multi-dimensional space-time evidence chain analysis platform and method for sales data abnormal behavior recognition, and relates to the technical field of sales data processing. The analysis platform and method comprises the following steps: S1, collecting sales behavior data generated in the ordering process of a customer and the operation process of an employee through a sales order system, and preprocessing the sales behavior data; s2, constructing an employee continuous operation chain, extracting structured behavior characteristics, and quantifying the employee sales behavior deviation degree; s3, extracting a key field reconstruction operation chain of the employees with abnormal behaviors in the data source, and analyzing the overall risk intensity of behavior combination in the reconstruction operation chain; and S4, further evaluating consistency fluctuation of node behaviors in the abnormal operation chain, and generating a structured fund abnormality report and pushing the structured fund abnormality report to a financial platform interface. The problem that fund abnormity caused by the fact that salesmen make false sales through faking orders is difficult to recognize in a traditional detection method is solved.

The invention discloses a work order auditing management method and system based on a large language model and a medium. The method comprises the steps of receiving input and correspondingly matching a work order auditing rule; based on the matched work order auditing rule, analyzing and processing the input through a large language model NLP, and converting the input into a natural language rule; carrying out synonym and antonym conversion on input white words, and carrying out equal rule conversion on converted words based on a corpus word library; performing rule text and code conversion on the converted natural language rule; and carding the processed rules into SQL statements which can be identified by the system according to the fields and the judger rule base. Updating the rule knowledge base; and writing the processed SQL statements which can be identified by the program into a rule knowledge base. According to the method, non-technical personnel can adjust and modify the work order information verification rule by using a natural language by using a large language model technology, so that the intelligent level of a work order system is enhanced, the development cost of the system is reduced, the auditing efficiency of a work order process system is improved, and the method has relatively good practicability.

The embodiment of the invention relates to the field of large models, and provides a method and a device for searching order information based on a private domain large model and a program product, applied to an order system based on the private domain large model, the method comprises the following steps: receiving a first input of a target user to a query icon, and displaying a session interface realized based on the private domain large model; receiving a second input, indicating order query information, of the target user on the session interface, and determining a first order query intention of the target user according to target key features of the order query information; determining a target query object according to the first order query intention; and according to the first order query intention and the target query object, obtaining first order information matched with the first order query intention by traversing an order system, and displaying the first order information on a session interface. According to the scheme, the condition screening mode is converted into semantic query order related information through a session mode, the order query efficiency and accuracy are improved, and the corresponding order operation is executed according to the queried order information.

A transaction ordering system and method for sequencing transactions processed by a decentralized network in which nodes communicate with a peer-to-peer protocol to process transactions for the exchange of value in the form of main chain assets. Transaction sequencing is accomplished by an auxiliary decentralized network including multiple oracle nodes communication through a peer-to-peer protocol, wherein the auxiliary decentralized network does not store value in the form of mainchain assets and wherein the auxiliary decentralized network is configured to; receive a set of transactions to be executed by the decentralized network; create, based on a predetermined policy, ordering information indicating a sequence in which transactions in the set of transactions are to be processed; and transmit the ordering information to a mempool of decentralized network for execution of the transactions, by the decentralized network, in the sequence.

The invention discloses a method, a system, equipment and a medium for realizing service timing delay processing based on Rocket MQ, belongs to the technical field of information processing, and solves the defects of poor flexibility, resource waste, insufficient scheduling flexibility and incapability of meeting any time precision requirement when a traditional method is used for processing a timing delay message. The defects limit the application of the message queue in some specific service scenes and influence the overall performance and reliability of the system. The method comprises the steps of collecting service data set by a user, extracting delay information in the service data, creating a message producer and generating message content according to the delay information, and creating a delay consumption theme and a corresponding message theme consumer in Rocket MQ message middleware; and according to the delay information in the service data, sending the service data to a corresponding queue of the delayed consumption theme, and carrying out sequential consumption by a message theme consumer according to the delay information. The method is suitable for application scenes such as order system management.

The invention relates to a localized intelligent adaptation system and method based on multi-agent interaction, the system is realized based on a large language model and multi-agent cooperation, and the system comprises an offline knowledge extraction module, a diagnosis prompt generation module, a multi-agent cooperation diagnosis module and a man-machine cooperation module. Wherein the offline knowledge extraction module is used for extracting knowledge blocks and dynamically registering an API (Application Program Interface) of an operation and maintenance tool into the system; the diagnosis prompt generation module is used for screening out most relevant knowledge blocks and operation and maintenance tools; the multi-agent cooperative diagnosis module is used for cooperatively generating a diagnosis report among the agents based on asynchronous communication and a cross review mechanism; and the man-machine cooperation module realizes cooperation of manual auditing and automatic repairing through a natural language dialogue interface and a work order system. The invention aims to improve the intelligent level of fault diagnosis and repair through the following innovation breakthrough, and better meet the requirements of localization adaptation.

The invention discloses a robot safe near-optimal motion planning method based on hybrid reinforcement learning, and belongs to the technical field of robot motion. The method comprises the following steps: initializing an initial state of a robot, a multilayer neural network weight and a high-order control barrier function HOCBF parameter; generating a target navigator through forward propagation of the multi-layer neural network; screening a safety controller from a safety control set defined by the HOCBF; fusing the target navigator and the safety controller to generate a final control strategy; calculating a Bellman error; and updating the initial state of the robot and the weight of the multi-layer neural network based on the final control strategy and the Bellman error. According to the invention, a neural network approximation target navigator and an HOCBF safety controller are deeply fused, so that a hybrid control system with a layered guarantee framework is constructed. The problem of safety guarantee failure caused by Lie derivative disappearance of a traditional CBF in a high-order system is solved, and meanwhile, the technical bottleneck that an existing HOCBF scheme is limited to local optimization is broken through.

The invention relates to a reconciliation and fund management method and device, a reconciliation server and a reconciliation system. The method comprises the steps that business data is acquired from a data source system, the data source system comprises a financial system, an order system, a payment system and a third-party payment system, and the business data comprises order data and bill data; cleaning, converting and normalizing the order data and the bill data; automatically identifying abnormal conditions in the processed order data and bill data based on a preset rule and a difference identification algorithm, generating a difference report and an account checking result report, and automatically marking the abnormal conditions; and according to the difference report and the reconciliation result report, generating and outputting a reconciliation result. According to the method and the system, millions of data volumes can be efficiently processed, differential orders can be accurately identified and processed, a standardized account checking result is output, and a powerful guarantee is provided for fund safety and business operation.

The invention provides a response system for loophole closed-loop management based on active detection and loophole verification, which is characterized in that an asset detector collects asset information which can be detected in flow, and a loophole detector performs targeted loophole detection according to the asset information of the asset detector; the asset and vulnerability information is synchronized with a vulnerability analyzer, the vulnerability analyzer converges vulnerability information and compares and verifies the vulnerability information with a vulnerability library, and vulnerabilities needing to be re-scanned are fed back to a vulnerability detector and an asset detector through a work order system; and finally, the vulnerability analyzer feeds back a result to the generator to form a vulnerability report, and a major industry vulnerability library is locally formed. According to the method, vulnerability verification is carried out on the actively detected vulnerability result, and attention to vulnerability changes is continuously kept, so that closed-loop management of vulnerability security is ensured.

The invention provides an intelligent hospital catering platform and method fusing dining features and health management, and an architecture diagram of the platform, and the platform comprises a patient ordering system which is used for recognizing the dining features of a patient according to the illness state, the dining habits and the nutritional requirements of the patient, and recommending a first personalized menu to the patient based on the dining features of the patient; the employee ordering payment system is used for identifying employee dining features according to the health data, the dietary habits and the nutritional requirements of the employees and recommending a second personalized menu to the employees based on the employee dining features; and the recommendation system is used for recommending the menu to the corresponding user side. By combining an existing payment ordering system on the market and through AI upgrading, the system is more humanized, and scientific diet is achieved. The AI technology is integrated into the patient ordering system and the staff ordering payment system of the hospital, so that the dining experience can be remarkably improved, the nutrition management is optimized, and the patient and the staff are helped to establish healthy dietary habits.

To eliminate necessity to input personal information such as an address and a name and reduce troublesome work of input even in a case of ordering an item on a site used for the first time.SOLUTION: An ordering system includes: a personal information storage unit 40 that stores personal information registered in one site while associating the personal information with a personal identifier, and provides the personal information to another site; an order I / F device 10 that transmits order form information of an item including the at least personal identifier and an item identifier; an order processing device 20 that receives the order form information of the item, acquires the personal information associated with the personal identifier included in the order form information, generates order specification information based on the acquired personal information and the order form information of the item, and transmits the order specification information; and an order specification reception device that receives the order specification information and causes an output device to output the order specification information in order to prepare to deliver the item.SELECTED DRAWING: Figure 1

An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and / or setting a reminder notification.

The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and / or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and / or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.

A system or method for verifying delivery of items in an order using user and delivery client devices. The system receives, from a user device, an order containing at least one item requiring delivery verification. The system determines that a delivery device is within a geofence around the delivery location and sends instructions to display a user interface for initiating verification. Upon interaction, the system instructs the user device to display a signature interface. If the user cannot provide a signature, the system sends instructions to the delivery device to present a fallback verification interface, including at least one of: a document scanning element, image capture element, or text fields for manual entry of identification information. Verification data collected through this interface is received from the delivery device and stored in a verification database, certifying delivery of the order.

The invention discloses a fabric dyeing equipment running machine parameter optimization recommendation method and device, equipment and a medium, and the method comprises the steps: obtaining the cloth type information of a plurality of production batches of an order system and the production information of a corresponding batch in a production execution system, carrying out the coding of the cloth type information and the production information, and obtaining cloth type feature data and process feature data, associating the seed distribution characteristic data with the process characteristic data to form process data items, calculating the seed distribution information of a target production batch by adopting a similarity algorithm when the target production batch information is detected, screening out preliminary process data items in a similarity threshold range, and storing the preliminary process data items in a database; and calculating the process characteristic data of the preliminary process data items by adopting a GBDT gradient decision tree to obtain recommended optimal line machine parameter data, so that the line machine parameter data of the dyeing equipment can be quickly output through system recommendation, and meanwhile, the batch quality problem caused by personal errors is reduced.

The invention discloses a medical equipment remote fault diagnosis, maintenance and guidance system based on a cloud platform. According to the invention, the deep reconstruction of the medical equipment maintenance ecology is realized by constructing the digital service network covering the whole process. The AR cooperation platform of the maintenance engineer end and the real-time data intercommunication of the hospital end break through the space-time limitation of technical support in a traditional maintenance mode, so that remote experts can directly participate in field fault diagnosis and cooperate with historical maintenance archives and fault tree analysis provided by the intelligent work order system, and the first repair rate is greatly improved. An intelligent order sending algorithm of the management background is combined with an order grabbing mechanism of the mobile terminal, so that the geographical distribution efficiency of engineer resources is optimized, precise docking is realized through skill label matching, the industrial pain point of scarcity of special equipment maintenance resources is solved, and the service life of the mobile terminal is prolonged. Modular design and data interconnection characteristics bring systematic improvement to medical equipment operation and maintenance management, and fine control of equipment management cost and continuous improvement of service quality are realized.

The invention discloses a work order input method and device based on a large model, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the data cleaning and preset standardization processing of an initial problem description, so as to determine a target problem description corresponding to the initial problem description; analyzing the target problem description by using the target large model to obtain an analysis result, and extracting key problem information in the target problem description based on the analysis result; matching the key problem information with a preset work order category system to determine a target work order type corresponding to the target problem description; filling the key problem information into a target work order template corresponding to the target work order type to obtain initial work order information corresponding to the target problem description; obtaining feedback information corresponding to the initial work order information, adjusting the initial work order information based on the feedback information to obtain target work order information, and inputting the target work order information into a work order system. The work order processing efficiency can be improved, and the manual operation cost can be reduced.

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

In implementations of systems for visual reordering of partial vector objects, a computing device implements an order system to receive input data describing a region specified relative to a group of vector objects that includes a portion of a first vector object and a portion of second vector object. A visual order as between the portion of the first vector object and the portion of the second vector object within the region is determined. The order system computes a modified visual order as between the portion of the first vector object and the portion of the second vector object within the region based on the visual order. The order system generates the group of vector objects for display in a user interface using a render surface and a sentinel value to render pixels within the region in the modified visual order.

The invention discloses a firewall automatic management system and method based on a work order system, and relates to the technical field of network security, and the method comprises the steps: S1, data collection, preprocessing and model construction, S2, work order submission and risk prediction, S3, work order adjustment decision, S4, work order approval and execution, and S5, execution result feedback and monitoring auditing. According to the invention, the mechanical learning and risk prediction module is utilized to carry out comprehensive analysis and learning on a large amount of historical work order data, network flow data, security threat intelligence and firewall operation state data; the module can predict various risks possibly caused by firewall configuration changes related to the work order in a future period of time, and prevents security holes of the firewall due to the fact that the configuration is not adjusted in time.

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 discloses a singular perturbation order reduction method matched with a network construction type wind power plant. The method comprises the following steps: firstly, establishing a small signal model matched with each module of a network construction type wind power plant grid-connected system; constructing a full-order small signal model, solving a broadband oscillation mode, and calculating damping ratios of all modes to obtain an extremely weak damping mode; dividing fast and slow state variables based on the modal participation degree and Hankel singular value weight, and establishing a criterion to supplement the slow state variables based on a singular perturbation method to form a slow state variable set; and determining the order after order reduction, and establishing an order reduction model of the matching network construction type wind power plant grid-connected system in a singular perturbation form. According to the method, the problem that a matched network construction type wind power plant order reduction system of an existing order reduction scheme cannot replace a full-order system to efficiently analyze broadband oscillation can be solved, the order number of the matched network construction type wind power plant can be reduced, and the method has efficient and powerful broadband oscillation analysis capacity while the dynamic characteristics are reserved.

Some embodiments include receiving a plurality of items available for purchase; aggregating the plurality of items into a menu; populating a first interface configured for ordering; receiving an order; requesting delivery quotes to deliver the order to the customer; receiving the delivery quotes to deliver the order to the customer; populating a second interface with information defining the order, requesting confirmation of the order and selection of a delivery network to deliver the order to the customer; populating the second interface with at least one of cost and time from the delivery quotes; receiving a confirmation of the order and a selection of a delivery network to delivery network; notifying the customer of the confirmation and an estimate of a time for delivery of the order; facilitating delivery of the order to the customer using the delivery network; and processing payment for the order and payment for delivery.

The invention discloses a Google Chrome plug-in-based customer service work order system automatic filling method, equipment and a storage device, the Google Chrome plug-in-based customer service work order system automatic filling equipment comprises a Google Chrome plug-in, a data service configuration platform and a back-end data service, and the method comprises the following steps: configuring at least one API (Application Program Interface) on the data service configuration platform, and defining input and output parameters for the API; establishing a mapping relationship between the input and output parameters and a customer service work order system page input box ID; a corresponding API is selected through the Google Chrome plug-in, a query condition is filled in, a query request for work order data is initiated, and a corresponding query result is obtained from a back-end data service; and automatically filling the query result into a page input box of the customer service work order system according to the mapping relationship. Manual operation time can be greatly shortened through an automatic tool, manual input errors are avoided through automatic filling, adaptability is high, and safety guarantee is achieved.

The invention relates to the field of intelligent work orders, in particular to a repair work order generation method and device, electronic equipment and a storage medium, which can shorten the repair difficulty and time of a user, improve the work order distribution accuracy and shorten the response time of the work order. The method comprises the following steps: determining description information of a fault of fault equipment through a large model according to user input content; an initial work order is generated through a work order agent according to the position of a user and description information of a fault of fault equipment, the matching degree of each maintenance personnel in a work order system and the initial work order is determined, and the initial work order is determined according to the position of the user and the positions of the maintenance personnel in the work order system. Determining the distance between the user and each maintainer in the work order system, and determining a first maintainer according to the matching degree and the distance; and generating a target repair work order based on the personal information of the user, the location of the user, the information of the first maintenance personnel and the fault information through the large model.