209 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 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 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 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.

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 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.

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.

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.

The invention discloses a software supply chain security management method and system, and relates to the technical field of network security. Identifying open source components, vulnerabilities and license information in the software assets through a software component analysis engine, verifying the availability of the vulnerabilities in an isolation sandbox, and generating a dynamic report; analyzing license compliance based on a legal knowledge graph, generating a risk disposal instruction in combination with a vulnerability verification result, and pushing the risk disposal instruction to a DevOps platform or a work order system for execution; and meanwhile, multi-source intelligence is fused through a federated learning engine, 0day vulnerability risks are predicted, and assets are associated to push hierarchical prompts. According to the method, dynamic risk management and control of the full life cycle of the software supply chain are realized, the problems of detection blind areas, static evaluation, weak compliance and the like in traditional management are solved, the safety and management efficiency of the supply chain are remarkably improved, and the method is suitable for key industries such as finance and electric power.

An appliance management method is designed to make power distribution equipment, provided on a power supply channel, control and / or monitor an appliance to be electrically connected to the power distribution equipment. The appliance management method includes an authentication step and a management step. The authentication step includes allowing a high-order system to perform authentication on the power distribution equipment by making the power distribution equipment access the high-order system via a public network after the power distribution equipment has been energized. The management step includes making the power distribution equipment that has been authenticated in the authentication step control and / or monitor the appliance.

The invention provides an automatic waybill ordering method and system based on logistics docking, and the method comprises the steps: constructing a logistics resource intelligent agent set and a logistics resource market based on logistics resource data, obtaining customer demand data, constructing a customer demand intelligent agent based on the customer demand data, and carrying out the customer demand intelligent agent set and the logistics resource market; and generating a corresponding logistics risk portrait in combination with a preset logistics risk map and a multi-factor risk convolution mechanism, putting the customer demand agent into a logistics resource market for game prediction, obtaining a corresponding game prediction result, generating a corresponding logistics risk assessment report based on the game prediction result, and performing logistics risk assessment. The method comprises the steps of performing logistics resource screening based on a logistics risk assessment report, generating corresponding logistics waybill data, generating a corresponding electronic logistics waybill in combination with a waybill adaptive conversion mechanism, obtaining an actual performance data set of the electronic logistics waybill, and performing feedback optimization on a module corresponding to an automatic waybill ordering system in combination with the actual performance data set. Therefore, the quality of service provided for merchants is improved.

The application discloses a steel bar production line intelligent scheduling system based on artificial intelligence. In the application, the dynamic adaptation problem of multi-specification steel bar mixed production is solved through a virtual-real fusion decision mechanism. In view of the demand of alternate rolling of screw thread steel and bright round steel, the system analyzes order parameters in real time, dynamically optimizes the matching logic of roll gap adjustment sequence and cold bed stepping rhythm, and ensures that the steel bars with different surface characteristics reduce transition waste during process switching. Through equipment health degree prediction and material mechanics simulation, the peak value of the main drive of the rolling mill is automatically reduced and the cooling time is prolonged when high-strength steel bars are rolled, so that the roll peeling or internal cracks of the steel bars caused by stress mutation are avoided. For small batch orders, the system intelligently compresses the shutdown window of die and groove changing, pre-adjusts the process parameters by using the idle gap of the equipment, and significantly reduces the production capacity breakpoint caused by specification switching.

This application provides a method, apparatus, storage medium, and electronic device for reviewing merchant annual inspection work orders, relating to the field of work order review technology. It enables automated review of merchant annual inspection work orders, shortening the review cycle, improving review efficiency, enhancing the standardization and objectivity of the review process, and further improving the accuracy of the review results. The method includes: automatically logging into a merchant annual inspection work order system through a robotic process automation system to obtain a target annual inspection work order for a target merchant and a corresponding image for review of the target merchant. The target annual inspection work order includes the annual inspection items to be reviewed and the registration information of the target merchant. The image for review is processed using a visual large-scale model based on preset information processing rules to obtain structured information corresponding to the annual inspection items to be reviewed. The structured information and registration information are compared according to preset review rules for the annual inspection items to be reviewed to generate the review result for the target merchant.

A consumables ordering system includes an input device, a storage device, and a control device. The input device receives an input of an instruction of a user. The storage device contains ordering prohibition information, indicating whether ordering of consumables used in an electronic device is prohibited. The control device acts as an automatic ordering device. The automatic ordering device keeps from ordering the consumables, when the ordering prohibition information is indicating that the ordering of the consumables is prohibited, and orders the consumables when the ordering prohibition information is indicating that the ordering of the consumables is not prohibited. The automatic ordering device causes the ordering prohibition information, upon receipt of an instruction to prohibit the ordering of the consumables through the input device, to indicate that the ordering of the consumables is prohibited.

The application discloses a second-order system privacy protection average consistency method based on edge disturbance, which comprises the following steps: determining the communication topology graph and Laplace matrix of a multi-agent system, and representing the information interaction relationship between agents by the Laplace matrix; establishing a dynamic model of the multi-agent system; in the initial iteration, each agent generates a disturbance signal for each edge connected to it, and determines the type of attacker; after confirming the attack type, the agent adds a disturbance signal to the transmission of position information and speed information to the neighbor, and the disturbed information is transmitted to the neighbor; after the disturbance signal stage ends, the agent switches to a standard second-order dynamic average consistency algorithm, and updates its own state according to the neighbor feedback information. The application can guarantee privacy protection without sacrificing the convergence accuracy of the consistency algorithm, is suitable for a resource-limited environment, and can effectively resist privacy theft of internal and external attackers.

The invention relates to an industrial intelligent ERP (Enterprise Resource Planning) production management system and a key technology, belongs to the technical field of industrial informatization software, artificial intelligence voice interaction and equipment predictive maintenance, and aims at solving the problems that a traditional industrial ERP system is insufficient in flexibility, workshop voice interaction is difficult, and equipment maintenance is passive and low in efficiency. The core innovation of the invention lies in that: a module hot plug architecture is adopted, so that a user can switch a full-function ERP system / a lightweight chemical single system / a purchase-sale-stock combined system within 5 seconds; according to an anti-noise voice engine, the instruction recognition rate gt in the 90dB workshop environment is determined; 92%, response delay lt; 200 ms; a closed loop is predictively maintained, an equipment health model is called in real time in work order execution, and shutdown loss is reduced by 40%. The problems that a traditional ERP system cannot be flexibly split, the workshop voice interaction rate is low, and equipment maintenance is passively responded are solved.

The invention relates to the technical field of natural language processing, in particular to an intelligent customer service work order system and method based on LangGraph, and the system comprises a multi-channel access module, a LangGraph core arrangement module, an Al Agent pool, a knowledge base module, a work order management module, and a data analysis and monitoring module. The method has the beneficial effects that the advanced LangGraph technology is introduced, revolutionary improvement is brought to an intelligent customer service work order system, the processing complexity, the automation degree, the user experience, the flexibility and the controllability of the intelligent customer service work order system are all superior to those of the prior art, and remarkable social benefits and economic benefits are achieved.

The invention discloses an electronic resume data application method and a computer readable storage medium, and the method comprises the following steps: connecting at least two data source systems, and respectively extracting electronic resume data of the same nuclear power equipment from the at least two data source systems, the electronic resume data of the nuclear power equipment comprises maintenance work order data and fault notification order data of the nuclear power equipment on a work order system and input / output information of the nuclear power equipment on an IO information system; constructing a first risk prediction model based on the input / output information of the nuclear power equipment and the maintenance work order data, and quantifying the operation risk of the nuclear power equipment; and training a first machine learning model based on the fault notification data and the input / output information of the nuclear power equipment, and predicting the fault of the nuclear power equipment. According to the method, data islands are broken, comprehensive integration of data is realized, and a scientific basis is provided for operation and maintenance management of the nuclear power station through quantified operation risks of the nuclear power equipment and predicted faults of the nuclear power equipment.

To provide a consumable item ordering system and a consumable item ordering program that can reduce the possibility of duplicate ordering of consumable items.SOLUTION: A device management system comprises an automatic ordering unit that automatically orders toner used in an image forming apparatus. When an automatic ordering inhibition flag indicating whether automatic ordering of toner is inhibited indicates that the automatic ordering of toner is inhibited, the automatic ordering unit does not execute the automatic ordering of toner. When receiving an instruction from a user for causing the automatic ordering inhibition flag to indicate that the automatic ordering of toner is inhibited, the automatic ordering unit causes the automatic ordering inhibition flag to indicate that the automatic ordering of toner is inhibited (S264).SELECTED DRAWING: Figure 25

The application discloses a time delay control method and system based on an error compensation type extended state observer, which comprises the following steps: a time delay controller acquires input signals and each output state of a tracking differentiator, and outputs a first control signal; an estimation error model of a displacement is constructed for an extended state observer; based on the first control signal, a total disturbance signal and an estimation error of the extended state observer, a controlled object is converted into an integrator series type form, and a displacement signal is output based on the controlled object; the displacement signal is input into the tracking differentiator to obtain real-time estimation values of each state of a nano-positioning stage; and the real-time estimation values of each state are fed back to the time delay controller. By introducing the extended state observer, the application solves the problem that the stability of a control system is easily affected by the uncertainty of a control input matrix; by introducing the tracking differentiator, the application solves the difficult problem that high-order system states and their derivatives required by time delay control are not easy to obtain.

The present application belongs to the field of testing technology and discloses a method, electronic device, and computer-readable storage medium for testing a work order system. The method includes: upon receiving a test start instruction, each work order client sends a work order creation request to a work order server; and upon receiving a work order operation task sent by the work order server, executing the work order operation task and returning the operation execution result to the work order server; upon receiving a work order creation request sent by any work order client, the work order server creates a work order and sends a work order assignment task to one of the work order clients; and upon receiving an operation execution result returned by any work order client based on the received work order operation task, sending the next work order operation task to one of the work order clients based on the operation execution result, and performing performance monitoring to obtain performance test data. In this way, costs are reduced and test accuracy is improved.

The present application relates to the technical field of cleaning robot scheduling control, and particularly relates to a cleaning robot dynamic task scheduling method and system based on property work orders. The method comprises the following steps: receiving work order data stream pushed by a property work order system, analyzing work order identification, emergency identification, task type, task location and expected completion time, and generating an emergency work order task record; obtaining a current task queue of a cleaning robot and robot state information based on the record, matching a task execution time window, task location and robot state information, and generating an insertion conflict evaluation record containing a candidate insertion position and a queue disturbance evaluation record; performing local re-planning accordingly, generating a local re-planning task sequence containing an original task reservation sequence and a recovery anchor point, and issuing a scheduling instruction, writing back execution state data and updating a current task queue. The present application forms a scheduling closed loop for sudden work order insertion, original task recovery and work order state record.