421 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.

A method for offline Koopman system identification is provided which includes generating snapshots representing a thermal comfort quantification index, reshaping these snapshots into 3D tensors, and splitting them into training, validation, and testing datasets. An encoder is used to reduce state space shape, while a dynamic auxiliary network calculates the A and B matrices representing a reduced order system. The auxiliary output advances the reduced order system into a time step with the control of the boundary condition. A decoder is used to reconstruct the system to its original state for another time step, and a loss function for reconstruction, linearisation and prediction is computed.

The invention provides a fault diagnosis data annotation method and device, electronic equipment and a medium, belongs to the technical field of data annotation, and aims to solve the problem of low efficiency of manual data annotation in related technologies. The method comprises the steps that a work order processing record is acquired from a work order system, and the work order processing record comprises a log identification code used by a fault processing terminal for processing a work order and a fault label of a fault component; downloading a corresponding log packet from a log system according to the log identification code; performing standardization processing on the log data in the log packet to obtain standardized log data; and marking the standardized log data by using the fault label of the fault component to obtain a training sample for training a fault diagnosis model.

The invention relates to the field of intelligent management systems, in particular to an automatic complaint management system and method based on agent workflow, and the system comprises five agents (agents): complaint acceptance, complaint classification, complaint assignment, complaint processing and user feedback. The method comprises the following steps: a complaint acceptance agent preprocesses information, and a complaint classification agent classifies the information in a grading manner; the complaint dispatching agent divides complaint into an automatic processing flow or an offline work order flow according to the classification; replying the processing scheme in the knowledge base of the complaint processing intelligent body checking system; and the user feedback agent performs data updating on a system knowledge base and the like according to the user evaluation. According to the method, automatic management is realized by using the agent workflow, complaints are graded in combination with an emotion word embedding technology, and problems needing to upgrade complaints are manually processed in combination with a work order system; the problem that complaints do not need to be upgraded is automatically processed by the agent workflow, and the processing efficiency and the user experience are improved.

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 discloses an intelligent thermal work order system based on AI algorithm data processing, particularly relates to the field of place name mapping and conflict processing, is used for solving the problems of space conflict and precision in matching of dialect place names and standard place names, and aims at solving the problems of space conflict and precision in matching of dialect place names and standard place names by collecting and analyzing implicit association between place name description in historical work orders and actual work order coordinates. And by combining public GIS (Geographic Information System) data, community text information and a local database, automatically processing and updating geographical name mapping. When facing place names with dialect characteristics or outdated place names, effective matching can be carried out, and the place name mapping dictionary can be continuously updated and optimized online through machine learning and natural language processing technologies; in this way, the mapping mechanism can adapt to urban development, language transition and user habit changes in real time, it is ensured that the work order system can accurately and rapidly recognize and locate the place name, and then the scheduling efficiency and the accuracy of resource allocation are effectively improved.

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 invention relates to the technical field of textile industry, and discloses a textile production equipment recommendation scheduling method, which comprises the following steps: S1, extracting production process information: before scheduling, firstly extracting process information of a product to be produced from an order system; s2, obtaining candidate equipment information: after the production process information is obtained, extracting detailed information of all available equipment from an equipment management system; s3, judging a forced constraint condition; s4, multi-dimensional matching of priority conditions is carried out; s5, calculating the matching degree of the raw materials; s6, matching the opening value of the equipment; s7, scheduling optimization is carried out; and S8, generating a scheduling scheme. Through fine matching of process parameters and equipment parameters, the selected equipment can be ensured to be most suitable for the current production task, the equipment adjustment time and the production preparation time are reduced, so that the production efficiency is improved, the use of the equipment can be reasonably arranged, the downtime caused by frequent switching of the equipment or the process is reduced, and the production efficiency is improved. The continuous production capacity is improved.

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.

The invention relates to the field of inspection of offshore oil and gas production platforms, and discloses a one-key intelligent video automatic inspection method, system, medium and equipment for an offshore unmanned platform, and the method comprises the steps: obtaining the field video data of an inspection route, carrying out the intelligent video analysis according to different inspection contents on the inspection route, judging whether an abnormal image exists or not, and if yes, sending the abnormal image to a server; in an abnormal state, an alarm is given and a work order is sent to an electronic work order system; a platform fire and gas system, a central control system and equipment abnormal alarm signals correspond to nearby on-site video monitoring equipment in a soft docking mode, position information of the alarm signal equipment is read, the corresponding video monitoring equipment is linked, video window popup and alarm are carried out, and a work order is sent to an electronic work ticket system; embedding an IP address of a field video monitoring device in each point position of the platform three-dimensional model, and giving an alarm and sending a work order to an electronic work order system in an abnormal state; and performing on-site abnormity checking according to the abnormal state information, and forming an inspection analysis report according to a checking or maintenance result.

The invention relates to the technical field of large models, in particular to an intelligent order building method and system based on an operation and maintenance industry large model, and the method comprises the following steps: collecting a work order list of a work order system; cleaning, integrating and standardizing the collected work order list to form a data set required by fine tuning of the model; selecting a pre-training model, performing fine tuning training on the pre-training model by using the data set, and outputting a fine-tuned model; executing a task of predicting work order classification by using the fine-tuned model; operation and maintenance groups and operation and maintenance personnel in the region are automatically identified through a model classification result; calling a large model for reasoning and returning a work order title; realizing a system integration process; the method and the system have the beneficial effects that the method and the system for intelligent order establishment based on the operation and maintenance industry large model, provided by the invention, realize automatic order establishment of links such as fault repair and equipment inspection in the operation and maintenance industry by applying big data and artificial intelligence technologies, and greatly improve the working efficiency.

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 provides a decoration progress management method based on virtual reality, and the method comprises the steps: carrying out the immersive roaming of a decoration virtual reality scene through a virtual reality head-mounted display device, obtaining the interaction operation data of a decoration material and a construction scheme of a user, and employing a recommendation algorithm based on user collaborative filtering, recommending an optimized decoration scheme in the decoration virtual reality scene according to the interactive operation data; the optimized decoration scheme is discussed and modified in a virtual space shared by multiple persons through a virtual reality collaboration platform, a distributed data synchronization mechanism is adopted, decoration virtual reality scenes among different users are synchronized, and the synchronization delay of the distributed data synchronization mechanism does not exceed 20 ms; and according to the evaluated decoration scheme, a construction plan in the order system is updated, a decoration task work order is generated, a construction result of each stage is accepted in the decoration virtual reality scene, and an acceptance result is synchronized to the order system.

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 is suitable for the technical field of order processing, and provides an ordering system and method, electronic equipment and a computer readable storage medium, and the system comprises an order generation module, at least one store module in communication connection with the order generation module, and at least one supplier module in communication connection with the order generation module, the store module is used for pushing a commodity list to the order generation module, the supplier module is used for pushing supplier information to the order generation module and receiving an order list sent by the order generation module, and the order generation module is used for generating a replenishment demand list corresponding to the store module according to the commodity list and the supplier information. Order splitting and / or order merging are / is carried out on the replenishment demand lists corresponding to the store modules, order lists of different order types are obtained, and the order types corresponding to the order lists are used for indicating distribution modes of the order lists. According to the invention, the problem of low efficiency and accuracy of order list generation can be solved.