1011 results about "Device status" patented technology

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention belongs to the field of artificial intelligence, particularly relates to a medical resource scheduling optimal configuration method for data analysis, and aims to solve the problems of non-uniform resource allocation, response lag, difficulty in data integration decision and the like in existing medical resource scheduling. According to the method, multi-source heterogeneous medical data (such as patient diagnosis and treatment, medical care, equipment states and department parameters) are uniformly collected, preprocessed and deeply analyzed, and real-time resource state characterization is constructed; based on the characterization, the demand is accurately predicted, supply is evaluated, an optimization algorithm is used for dynamic scheduling configuration, and an optimal scheme is generated. Then, scheduling is executed according to the scheme, and iterative optimization is fed back in real time; the medical service efficiency and quality can be remarkably improved, the patient experience is optimized, the operation cost is reduced, emergencies are effectively dealt with, and the resource management toughness of medical institutions is enhanced.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention discloses a campus intelligent terminal equipment management method and system based on multi-source state synchronization, and relates to the technical field of intelligent terminals. The campus intelligent terminal equipment management method and system based on multi-source state synchronization comprises the following steps: S1, a terminal cooperatively collects a context state data set, and normalization processing is carried out; s2, constructing a migration mirror image synchronization state data set, and carrying out migration handover of stage switching and playback; s3, constructing a pulse interference detection model, and performing interference window suppression and online correction; and S4, constructing a dual-time reference transaction consistency verification and migration submission determination closed loop, and carrying out linkage rollback and governance. According to the method, the state consistency and the operation stability in the cross-node migration process of the campus intelligent terminal are effectively improved, and the problem of pulse interference in equipment state migration is solved.

The invention discloses a root cause analysis method based on an IT operation and maintenance system, and the method comprises the steps: obtaining the physical position data and real-time environment data of IT equipment, carrying out the matrix construction through a dual topological relation based on the physical position data and the real-time environment data, and obtaining an IT equipment spatial topological matrix containing environmental impact factors. And obtaining operation state data of the IT equipment, performing spatio-temporal conjoint analysis and dimension reduction based on the operation state data and the IT equipment spatial topology matrix to obtain IT equipment dynamic prediction data, and obtaining an IT equipment state dynamic prediction curve according to the IT equipment dynamic prediction data. If the IT equipment state dynamic prediction curve monitors abnormity, a fault propagation path is generated through a reinforcement learning algorithm, and IT equipment fault root causes are determined in combination with a multivariate fault knowledge graph and a causal graph model. According to the method, the problem of early warning lag of the IT operation and maintenance system can be solved, and the problem that root cause positioning has high dependence on artificial experience is solved.

A system may be configured to receive and process various signals to generate a natural language description of a user's environment, called situational context data. The signals may include sensor data, device status, user activity, user input, and / or inferences made using such data. The situational context data may express a user-centric description of the user's environment; for example: “User is taking a walk in the park on a sunny afternoon” or “activity: driving location: highway”, etc. The system may send the situational context data to various system components that may, for example, process speech, select applications / skills for handling user inputs, and / or that implement those applications / skills. The applications / skills may use the situational context data to provide recommendations, generate responses, and / or perform actions that are more relevant to the user's current environment.

The invention discloses an Internet of Things equipment real-time early warning method and system based on digital twinning, and relates to the technical field of equipment operation management, and the method comprises the steps: collecting multi-modal original data of Internet of Things equipment, constructing a graph structure, and generating a semantic graph vector; obtaining a digital twinborn model state corresponding to the target equipment, forming an extended state vector by the digital twinborn state, the physical equipment state and the semantic map vector, and inputting the extended state vector into an extended Kalman filter for state fusion to obtain a state estimation result; and performing Monte Carlo simulation according to the state estimation result, generating a plurality of abnormal state samples, calculating a residual mean value between the abnormal samples and the current estimation result, correcting a covariance parameter of the extended Kalman filter, and generating an early warning tag. Fine estimation and risk level early warning of the state of the Internet of Things equipment are realized, and the state fusion precision and the abnormal response timeliness are improved.

The invention relates to the technical field of robots, and discloses a remote upgrading method of a robot, the robot and a system. According to the method, equipment state information of the robot is obtained, under the condition that the equipment state information meets upgrading conditions, an upgrading request is obtained, the upgrading request is sent to a cloud server, an upgrading package sent by the cloud server is obtained, and the robot is remotely upgraded according to the upgrading package. The equipment state information of the robot is obtained, if the equipment state information meets the upgrading condition, it is determined that the robot is remotely upgraded, and therefore the success rate of remote upgrading of the robot can be increased.

The invention discloses an equipment state prediction method and device, equipment and a readable storage medium, and is applied to the technical field of internet equipment management, and the method comprises the steps: determining a feature vector corresponding to each type of equipment data based on a data modal characteristic and business demand coupling principle, and carrying out the alignment of each feature vector, and obtaining an equipment state feature vector; uploading the equipment state feature vector to a server, so that the server updates an equipment state prediction model by utilizing cross-equipment group knowledge on the premise of not sharing equipment data; predicting by utilizing a latest equipment state prediction model based on the equipment state feature vector, and determining an equipment abnormal state; the latest equipment state prediction model is a model for global updating and local incremental updating of the edge node based on the server. According to the method, the information of each mode is deeply associated, global updating and local incremental updating are carried out on the model based on the server, the latest model is used for prediction, and the accuracy of state prediction is remarkably improved.

The invention provides a distributed intelligent multi-machine cooperative operation system and method, a deployment device and equipment, belongs to the technical field of industrial automation and intelligent operation system crossing, and aims to solve the problems of low reliability, rigid static task distribution, poor compatibility of heterogeneous equipment and insufficient fault-tolerant capability caused by a centralized architecture in the prior art. The operating system comprises a communication module, a task scheduling module, a heterogeneous equipment adaptation module, a fault-tolerant and self-healing module and a human-computer interaction module. The operating method comprises the following steps: S1, establishing an equipment communication network; s2, equipment state visualization and task instruction issuing; s3, dynamically allocating tasks and scheduling the tasks; and S4, carrying out equipment fault monitoring and self-healing recovery strategies.

According to the equipment state recognition method and device, the equipment and the storage medium provided by the embodiment of the invention, the multi-modal data in the operation process of the target equipment is acquired, and the multi-modal data comprises the image data, the text data and the temperature data; performing feature extraction and embedded coding on the multi-modal data through a feature extraction model to obtain embedded vectors of all modals, and aligning the embedded vectors of all modals to a unified target semantic embedding space; through a modal attention mechanism, the fusion weight of each modal is adjusted according to the state feature of the target device, and the embedding vectors of each modal are fused according to the fusion weight of each modal to construct a multi-modal fusion vector; and recognizing the state of the target equipment and / or performing abnormal risk early warning through a recognition model according to the multi-modal fusion vector. By introducing a modal attention mechanism, dynamic weighted fusion of image, text and temperature modals is realized, the accuracy and generalization ability of state recognition are remarkably improved, and the accuracy and robustness of the model under complex working conditions are improved.

The invention discloses a central control system of a digital multimedia exhibition hall, and relates to the technical field of central control systems, a time-space correlation analysis module constructs a fault propagation probability graph of exhibition hall equipment and audience behaviors, and a coupling degree analysis module verifies a cross-layer causal relationship by using a directional disturbance injection method based on the fault propagation probability graph; a coupling degree index is fitted by analyzing an incidence matrix of a resource scheduling operation and a fault propagation chain, a dynamic priority decision module fits a fault propagation cost gradient, the coupling degree index and the fault propagation cost gradient are input into a priority function, and a collaborative priority of a short-term suppression action and a long-term eradication action is calculated. And the strategy execution module calls the execution sequences of the two types of actions through the interface of the central control platform, and executes corresponding strategy actions. The central control system is not only high in dynamic adaptive capacity, but also capable of responding to audience demands and equipment state changes in real time, improving exhibition management efficiency and enhancing immersive experience of audiences.

The embodiment of the invention provides an equipment control method and device, electronic equipment and a readable storage medium. The method comprises the steps that equipment state information, user behavior data and environment data are acquired in real time; constructing constraint conditions based on the equipment state information, the user behavior data and the environment data under the condition that a scene generation request is received; the scene generation request is an instruction used for indicating a scene type; the constraint condition is a rule entry used for indicating a local model output range; inputting the constraint condition and the scene generation request into the local model, and controlling the local model to output a scene control strategy which is matched with the scene type and meets the constraint condition; and according to the scene control strategy, each device is controlled to operate, so that the efficiency and safety of generating the scene control strategy are improved.

The invention provides a task priority and equipment state linkage path adjustment method and related equipment, and relates to the technical field of intelligent distribution center systems. Task queue information, production system feedback information and equipment state information are obtained in real time, the dynamic priority score of each task is calculated based on the information, the urgency of a production critical path and the equipment suitability are integrated in the scores, and therefore it is ensured that priority calculation focuses on the core target of avoiding production interruption. Then, the system ranks the tasks according to the dynamic priority scores, and adopts a strategy of'high priority priority planning + path reservation '; the path of the high-priority task is preferentially planned and reserved on the space-time map, and path conflicts among multiple devices are avoided from the source. The low priority tasks are then directed to avoid these reserved areas. Finally, the planned conflict-free path is issued to the robot equipment for execution, and the overall operation efficiency and stability of the intelligent distribution center system are comprehensively improved.

The invention discloses a shared device replacement management method and system based on AI glasses code scanning confirmation. The method comprises the following steps: generating a replacement data packet containing a dynamic verification code request; generating a unique dynamic verification code based on the received returning data packet, and synchronously pushing the unique dynamic verification code to the renter mobile terminal and the returning point micro server; collecting a dynamic verification code displayed on a renter mobile terminal through an interactive interface of the AI glasses; and carrying out matching verification on the dynamic verification code and a corresponding dynamic verification code stored in the micro server of the returning point, automatically finishing returning confirmation operation of the shared equipment at the returning point after the verification is passed, and updating the state of the equipment. According to the embodiment of the invention, rapid confirmation and accurate matching of the shared device replacement can be realized, and the security and efficiency of the replacement process are improved.

The invention discloses a cigarette cut tobacco batch production circulation control system and method based on single-point condition judgment, and the system comprises a work order parameter verification module which is used for receiving a work order issued by a scheduling system, and carrying out the work order information integrity verification, process parameter compliance verification and equipment suitability verification; the regular expression configuration module is used for providing a visual interface and configuring a regular judgment rule set of equipment starting conditions, ending conditions and abnormal ending conditions; and the single-point judgment execution mechanism is embedded into the PLC control layer, acquires equipment parameters in real time, dynamically evaluates regular conditions based on a finite-state machine model, and triggers an equipment start-stop instruction. According to the system, the cigarette cut tobacco batch switching time is remarkably shortened, and the work order circulation efficiency is improved; the manual intervention frequency is further reduced, and the misoperation rate is further reduced; and the problem of production interruption caused by disconnection of the equipment state and the process parameters is solved.

The invention provides a collaborative scheduling method and device based on multiple devices of a port and a medium, and belongs to the field of port scheduling, and the method comprises the steps: determining a total scheduling time window of port operation according to the end time of an early warning time period of extreme weather; dividing the total scheduling time window into a plurality of continuous rolling windows; obtaining the device state of the starting time of the current rolling window, and obtaining a to-be-scheduled task subset corresponding to the current rolling window; based on a rolling strategy and an improved grey wolf optimization algorithm, according to the working state corresponding to the current rolling window and the to-be-scheduled task subset, solving to obtain an optimal local scheduling sequence corresponding to the current rolling window; and after the task is executed according to the optimal local scheduling sequence of the current rolling window, updating the equipment state and the task state, switching to the next rolling window, and obtaining the corresponding optimal local scheduling sequence until the end time of the total scheduling time window is reached. The scheduling capability of the port in extreme weather is effectively improved.

The invention relates to an intelligent safety equipment state monitoring method, system and equipment and a medium. According to the method, attitude data of a self-locking device toggle clip are collected in real time, the wearing compliance is judged in advance, and an alarm is triggered immediately for a violation state to realize risk interception; for a compliance state, based on the preprocessed time sequence data, extracting a multi-scale feature vector representing a short-term dynamic state and a long-term trend by using a multi-stage time convolutional network, and dynamically fusing a multi-expert prediction result through an expert hybrid neural network to output a future risk probability value; and finally, a graded early warning signal is generated according to a probability value matching risk grade, so that while the real-time performance of basic state monitoring is guaranteed, accurate prediction and active protection of potential risks in a compliance operation scene are realized, and a dual safety guarantee mechanism of'real-time interception + beforehand prevention 'is formed.

The invention relates to the technical field of power two-ticket management, and discloses a two-ticket full life cycle optimization and management method and system based on workflow, and the method comprises the steps: obtaining the basic data of personnel qualification, equipment state and flow rule, and constructing a typical work ticket library and a typical operation ticket library; calling a work ticket template or an operation ticket template to perform information supplement, performing double verification, and then completing graded auditing and electronic signature according to preset nodes to generate an effective work ticket and an executable operation ticket; and monitoring the execution process of the effective work ticket and the executable operation ticket, and archiving execution data to a historical database to realize multi-dimensional two-ticket full-process data query, generation of an analysis report, update of basic data, a work ticket typical ticket library and an operation ticket typical ticket library. The standardized typical ticket library is constructed by integrating basic data, monitoring and archiving traceability of two tickets are achieved, standardized intelligent management of the two tickets is achieved, and the problems that existing data are scattered, compliance is poor, traceability is difficult, and no improved closed loop exists are solved.

A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.

The invention relates to the technical field of computers, in particular to an AGV task allocation method and system based on multi-objective optimization, and the method comprises the steps: obtaining a task demand set and a state information set of AGV equipment in a current operation scene, and the task demand set comprises a plurality of task units; performing feature extraction processing on the task demand set and the state information set to obtain task feature description of each task unit and capability feature description of each AGV device; performing joint optimization matching processing according to the task feature description and the capability feature description to generate a matching association relationship set of the task and the AGV equipment, wherein the matching association relationship set comprises a corresponding relationship between the task unit and the AGV equipment and a task execution sequence relationship; and generating a task allocation scheme based on the matching association relationship set, wherein the task allocation scheme comprises a to-be-executed task list of each AGV device and execution time sequence arrangement information of each task. Therefore, task requirements and equipment states can be accurately grasped, and efficient and matched innovative task distribution processing is realized.

Operating effectively in an increasingly prolific Internet of Things environment, one needs the ability to message, whether device status, actions taken, recommendations for actions or other information to those with a need to know and support responses to actionable messages. Texts and e-mail are limited; they can neither provide other outputs, be targeted in intelligent fashion nor selectively relayed or contain execution instructions for target devices. The present invention is an Extended-Capability Messaging Appliance (ECMA) that communicates with other IoT devices that provides these capabilities and can include AI. ECMA and non-ECMA AI elements can constitute a Hybrid AI Backbone. The Hybrid AI Integrator receives results from multiple A elements and develops one or more conclusions. An ECMA may be configured as a Body-Area Network for individuals or a Business-Area Network for industry and other commercial applications with an AI Agent called BANDIT that processes requests from a user.

The invention relates to the field of edge computing, in particular to a primary and secondary fusion complete ring main unit energy efficiency optimization system based on cloud edge collaboration. The system comprises an edge device deployment and data acquisition module used for collecting operation electric parameters and device states and generating a service calling request; the service calling module is used for screening target control parameters under security constraints based on the energy efficiency optimization service package; the data synchronization module unloads the data to a cloud platform through a message queue protocol; the cloud service arrangement module aggregates the data updating service package and issues the data updating service package to the edge; and the real-time control module converts the parameters into instructions to drive equipment to act. A cloud training and edge reasoning collaborative architecture is adopted, the problems that local control lacks adaptivity, edge resources are limited and full-cloud control communication time delay exists are solved, and real-time energy efficiency optimization is achieved.

The invention relates to the technical field of cloud computing and intelligent office, in particular to an intelligent printing task allocation and scheduling optimization method based on cloud collaboration, which comprises the following steps of: constructing a multi-dimensional task feature set fusing task attributes, equipment states and network environments; generating an optimal task-equipment mapping relation by introducing a matching degree function and an improved genetic algorithm; when the equipment load fluctuates, a scheduling instruction is dynamically generated based on the equipment topological graph; performing attribute encryption and homomorphic encryption processing on the scheduling instruction to form a secure transmission unit; and through a cloud and edge collaborative verification mechanism, task execution security and consistency are ensured. The method has the advantages of high-adaptability scheduling, load-aware dynamic correction, privacy enhancement execution control and the like, and the text and print resource utilization efficiency and the system intelligence level are improved.

The invention provides a CNN-based asset lease management method and related equipment. The method comprises the following steps: acquiring multi-modal data of target equipment; performing feature extraction on the multi-modal data to obtain equipment state features of the target equipment; determining an equipment state score of the target equipment based on the equipment state characteristics and the equipment operation parameters of the target equipment; determining an asset value and a risk level of the target equipment based on the equipment state score and financial parameters associated with the target equipment; and adjusting a leasing strategy of the target equipment based on the asset value and the risk level.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an information processing method and device based on behavior analysis, equipment and a medium. Based on the behavior data set, generating a contact success probability value, content priority features and response intensity grades, obtaining environment state data, correcting the contact success probability value, generating a sending time window, selecting element combinations in a template library according to the content priority features to generate to-be-sent information, and sending the to-be-sent information to the server; and determining a push frequency threshold according to the response intensity grading, and sending the to-be-sent information in the sending time window according to the push frequency threshold. According to the method, the user contact success rate and the content response efficiency are effectively improved by fusing the multi-dimensional data of the user operation, the equipment state and the environmental factors and dynamically generating a combination strategy of three elements including the content, the opportunity and the frequency, and the mistaken contact interference and the resource waste are reduced.

The invention discloses a method and device for improving printer task management efficiency and a storage medium, and relates to the technical field of printer task management.The method comprises the steps that firstly, printing task parameters and printer real-time data are obtained, and task priorities are calculated through a task priority evaluation formula; dynamically allocating hardware and software resources according to the priority, the real-time data and a rule containing a time slice allocation formula; the states of tasks and equipment are monitored in real time, and an exception handling formula and a conflict coordination formula are used for scheduling when exceptions or conflicts occur; and finally, remaining time is calculated through a printing progress formula and a time prediction algorithm and is fed back to an interactive interface, equipment comprises a data acquisition module, a priority evaluation module and the like, a data storage and model optimization module is further arranged to optimize formula parameters, and the scheme improves task management efficiency and prediction accuracy and optimizes user experience.

The invention discloses a transformer substation one-key sequential control synchronous debugging method based on an autonomous controllable CMS protocol, and the method comprises the steps: configuring a CMS client which comprises a PER coding and decoding module based on an ASN.1 standard, and building communication with spacer layer equipment; analyzing the audited sequence control ticket, verifying the compliance through a rule engine, and if the compliance is not passed, marking an error and giving a suggestion; the edge computing host generates a synchronous debugging instruction in combination with the real-time remote signaling / telemetering data, and issues the synchronous debugging instruction through a LoRaWAN protocol; deploying a sensor to collect the state of target equipment, and giving an alarm in real time when abnormity occurs; and if the debugging accuracy is lower than a preset threshold value, analyzing the deviation and iteratively optimizing the model. The method is compatible with CMS and MMS protocols, and different substations can be adapted without the support of manufacturers; the problems of traditional debugging power failure, flow interference and the like are solved, multi-device synchronous debugging and fault self-repairing are achieved, and the intelligent operation and maintenance requirements of a transformer substation are met.