332 results about "Downtime" patented technology

The invention discloses a dynamic script compiling and hot updating system based on Grouping and an implementation method of the dynamic script compiling and hot updating system. The system comprises a script source code management module, a dynamic compiling engine, an intelligent cache manager, an exception handling and recovery system and a hot update controller. And the script source code management module receives and preprocesses the Grouping script to generate a unique identifier for subsequent management. And the dynamic compilation engine executes compilation operation according to the identifier to ensure that a compilation result accords with a specification. And the intelligent cache manager efficiently stores and retrieves the compiling result, so that the repeated compiling cost is reduced. And the exception handling and recovery system monitors the execution state in real time, and automatically starts a recovery process in case of exception to guarantee stable operation of the system. The hot update controller realizes seamless update and version switching of scripts, and ensures service continuity. Through cooperative work of the modules, high-reliability, high-performance and zero-shutdown deployment is realized.

The invention discloses an industrial PLC control system, and belongs to the technical field of PLC control systems, and the system comprises a function distribution module which divides the production process of each type of circuit board into a plurality of logic steps, the logic steps are packaged and converted into independent function module modules, and a multi-device cooperation module carries out the scheduling data interaction with each processing device in real time. The edge calculation module is used for acquiring operation parameters, performing multi-device cooperative adaptive control on each processing device in the circuit board production workshop, predicting a fault risk, judging a risk level and performing fault positioning when a fault occurs according to the operation parameters of the processing devices, and generating a fault self-healing strategy to perform fault repair. Meanwhile, personnel and equipment interaction logs are recorded, fault event reasons are traced, the control logic reconstruction time can be shortened, the production change adaptability and the production reliability are improved, the equipment cooperation precision and the stability of the circuit board production process are improved, the fault processing time is shortened, and the production shutdown loss is reduced.

A method for generating an auxiliary troubleshooting strategy for communication faults of a locomotive power battery system. The method comprises: acquiring a communication fault troubleshooting record of a locomotive power battery system; extracting fault causes at various levels for each type of communication faults, and establishing a fault tree; when there is a specific alarm communication fault, determining the time type and duration of the communication fault, and intercepting a fault subtree of the alarm communication fault; qualitatively analyzing the fault subtree to obtain a minimum cut set; quantitatively analyzing the fault subtree to obtain critical importance degrees of bottom events, and ranking same in a descending order; correcting the ranking of the importance degrees of the bottom events, so as to form a bottom event importance degree ranking table; and successively performing troubleshooting on the basis of the bottom event importance degree ranking table. Auxiliary troubleshooting strategies for communication faults are provided accurately, which improves the efficiency and accuracy of troubleshooting communication faults by maintenance staff, and helps to take corresponding maintenance measures, thus improving the utilization rate of new energy locomotives, and reducing downtime costs.

The invention discloses a material batching and conveying process monitoring system based on digital twinning, and belongs to the technical field of industrial automation and intelligent manufacturing. The system comprises a physical entity layer used for storing, proportioning and conveying materials; the data acquisition and sensing layer is used for acquiring multi-source heterogeneous data of materials and equipment in the physical entity layer in real time; the digital twinborn model layer is used for constructing and operating a digital twinborn model synchronously mapped with the physical entity layer; and the application service layer is internally provided with a multi-modal fusion fault diagnosis module, and the multi-modal fusion fault diagnosis module is used for diagnosing material batching and conveying faults. According to the method, the digital twinborn and multi-modal AI algorithms are deeply fused, the early warning and accurate root cause positioning of equipment faults are realized, the diagnosis confidence is improved through evidence fusion, the process decision is optimized in combination with a simulation sand table, the production continuity and quality stability are improved, the non-planned shutdown and maintenance cost is reduced, and the system is endowed with the continuous self-evolution capability.

The invention discloses a composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

The invention discloses an industrial data acquisition and analysis method based on the industrial Internet of Things, and belongs to the field of the industrial Internet of Things. Industrial equipment parameters are acquired by setting edge detection points, an acquisition and evaluation value is calculated according to equipment importance, an operation environment and parameter fluctuation, and an acquisition frequency is matched; then sorting equipment and collection frequency of each node responsible for collection, evaluating a node load value, and distributing a sharing node for the heavy load node; analyzing the collected data, calculating a diagnosis area, judging a fault parameter and performing early warning; for fault equipment, the similarity between the diagnosis curve and the database curve is analyzed through a dynamic time warping algorithm, and the fault type is determined; the system optimizes the data acquisition and analysis process, improves the fault diagnosis precision, reduces the maintenance cost and equipment downtime, is suitable for equipment management and maintenance in an industrial Internet of Things environment, and effectively improves the intelligent level and efficiency of industrial production.

The invention specifically relates to a facility intelligent operation and maintenance decision-making method based on big data and artificial intelligence, and relates to the technical field of industrial facility operation and maintenance and intelligent decision-making, and the method comprises the steps: simulating the multi-dimensional operation state of a corresponding physical facility in a future preset time period after each candidate decision is executed; according to the method, the dynamic optimization of the operation and maintenance decision is realized by relying on the multi-scheme deduction capability of the digital twin and combining the non-dominated sorting genetic algorithm II and the dynamic weight evaluation system; the maintenance cost, the fault risk and the production efficiency are balanced by generating five types of candidate schemes, simulating the fault probability, the cost and the production efficiency within seven days and dynamically adjusting the weight based on the equipment health index, the production load and the like; a closed-loop feedback mechanism corrects model parameters through actual data, incremental learning and reinforcement learning continuously optimize decision experience, fault shutdown loss is reduced, operation and maintenance requirements in different scenes are met, and practicability and adaptability of the method are enhanced.

A system for dynamically routing digital transactions to mitigate failures caused by system downtime and overcapacity is provided. The system includes a processor and a memory, where the processor retrieves real-time performance data of multiple transaction systems upon receiving a transaction request. This data includes time-window-based features, event-based features, and transaction success metrics. A first machine learning model predicts system downtimes by analyzing past failure rates, response latency, and scheduled maintenance. A second machine learning model determines transaction success probabilities by dynamically weighting real-time features. The processor selects the optimal transaction system based on predicted success probabilities, ensuring a higher likelihood of transaction completion. An adaptive feedback loop refines predictions by continuously updating model parameters using an adaptive decay-rate technique. This approach enhances transaction reliability by intelligently routing payments through the most stable and efficient system, significantly reducing transaction failures in digital payment ecosystems.

The invention discloses an electrical complete equipment state monitoring and early warning system based on multi-parameter intelligent sensing, belongs to the technical field of electrical equipment state monitoring, and can clearly point out a specific fault mode (such as contact resistance increasing overheating) and a possible position (such as an A-phase bus connection point). Through multi-parameter time sequence feature fusion and an intelligent diagnosis model, deep mining of a fault source is realized, the operation and maintenance efficiency is greatly improved, and the maintenance work is turned from blind troubleshooting to accurate disposal. Weak precursor signals of early and slow faults can be captured by extracting depth time sequence characteristics (such as temperature rise rate and dominant frequency offset rate) strongly related to a fault evolution mechanism. The AI model is utilized to learn complex modes of these precursor, and early warning can be given out before the equipment performance is obviously degraded, so that predictive maintenance of'nipping in advance 'is realized, and unplanned shutdown and major accidents are effectively avoided.

The invention relates to the technical field of resource scheduling, in particular to an AI-assisted multi-factory collaborative production scheduling system which comprises a path filing module, a task sorting module, a strategy switching module, a node evaluation module and a task mapping module. According to the invention, the device sharing node is identified through the transverse connection of the device and the path node, the resource cross interference degree is effectively mastered, the task priority is accurately adjusted, the coordination of resource allocation and task execution is optimized, and the flexible adjustment capability in the task execution process is improved; node resource state switching and fluctuation changes are dynamically monitored, abnormal node load and processing time features are accurately extracted, a path replacement strategy is finely judged in combination with equipment shutdown time sequence analysis and resource residual feature classification, and the flexibility and adaptability in multi-factory collaborative production scheduling are enhanced. Equipment faults and state fluctuation are pre-judged, and a path strategy is adjusted, so that the risk of task delay caused by production rhythm fluctuation and resource interference is effectively reduced.

The invention provides a high-availability system based on a container platform, a container calling method and electronic equipment, and the method comprises the steps: pre-creating and activating a container copy through a container scheduling module according to the resource state of each container node and a priority constraint condition in the early stage of fault occurrence, and then when a fault event occurs, calling the container copy in advance; and in response to the fault event reminding message, calling an interface of the container scheduling module to create a new service instance in the container copy, carrying out synchronous processing on the container state based on the new service instance, and migrating the flow on the fault container instance to the new container instance. Therefore, in a high-concurrency scene, by selecting the embodiment of the invention, the flow is immediately taken over through the pre-activated standby container copy when a fault occurs, so that zero-shutdown container switching is realized, the high availability of a container platform is remarkably improved, and smooth operation of services is ensured.

The invention discloses a remote intelligent control system and control method for a crane, and relates to the technical field of crane control, the system comprises the following components: a physical entity layer, a digital twinborn layer, a 5G transmission layer, a VR interaction layer and a virtual-real calibration layer; according to the method, an equipment state predictive modeling technology is adopted through a digital twinborn layer, historical operation data of a crane are collected, an equipment state database is constructed, key features related to equipment faults are extracted, and an equipment state prediction model is constructed based on a long-short-term memory network; according to the method, the change trend of equipment state parameters in the future 72 hours can be predicted, when the predicted state parameters approach or exceed a fault threshold value, the system can automatically mark corresponding parts in the digital twin, generate maintenance suggestions and synchronize the maintenance suggestions to a VR interaction layer to prompt an operator, and the maintenance suggestions are displayed in the VR interaction layer. According to the predictive maintenance mechanism, equipment faults can be early warned and processed in advance, the non-planned downtime is shortened, and the operation and maintenance cost is reduced.

The invention relates to the technical field of embedded equipment control, and discloses a multi-event cooperative control method and device for audio equipment, equipment and a storage medium, and the method comprises the steps: constructing a queue structure body of multi-event circulation according to hardware parameters of the audio equipment, and achieving the efficient cooperative processing of events through a classification rule and a time scheduling method. The method has the advantages of effectively managing event queues, reducing high-priority event delay, avoiding equipment downtime and improving user experience and equipment performance.

The invention relates to the technical field of predictive maintenance and health management of industrial equipment, in particular to an intelligent electromechanical integrated monitoring system based on the industrial internet, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module acquires an equipment dynamic operation parameter set, inherent physical attribute parameters and historical maintenance data of target electromechanical equipment; calling preset process topology data, economic loss data and maintenance cost data; the state evaluation module iteratively calculates an accumulated degradation index representing the health condition of the equipment; the importance quantification module calculates a process importance coefficient representing the criticality of the equipment in the production line based on the process topology data; the risk calculation module generates a production risk degree which quantifies the shutdown influence; the decision generation module generates a dynamic maintenance decision instruction; according to the invention, systematic risk cognition from the individual equipment to the overall production line is realized, extensive management of all equipment is avoided, and allocation of maintenance resources is more strategic and targeted.

The invention relates to an express cabinet equipment fault intelligent early warning and remote operation and maintenance method. The method comprises the following steps: obtaining a physical topology connection relationship of a target express cabinet as a domain knowledge constraint; performing time sequence causal discovery based on the constraint to generate an initial health causal graph; according to the deployment scene, migrating parameters from the pre-constructed knowledge base for fine tuning to obtain a scene adaptive health causal graph; collecting data in real time and generating a real-time causal graph based on the same constraint; abnormal recognition and early warning are performed by comparing graph structure differences of the real-time graph and the scene-adaptive health causal graph; after early warning, virtual intervention simulation can be carried out in digital twinning, a remote instruction is issued according to a result, and the model is updated according to feedback. According to the method, the early warning accuracy and the positioning precision of composite and intermittent faults are effectively improved, predictive maintenance and remote closed-loop operation and maintenance are realized, and the operation and maintenance cost and the equipment downtime are remarkably reduced.

The invention belongs to the field of hydroelectric generating set RCM maintenance, and discloses a hydroelectric generating set RCM maintenance strategy determination method and system based on risk priority, and the method comprises the steps: building an FMEA analysis database based on the historical operation data, fault event ledger and equipment state monitoring data of a hydroelectric generating set; performing quantitative evaluation on each fault mode of the FMEA analysis database from occurrence degree O, severity S and undetectability D, and calculating a risk priority number RPN of each fault mode; risk grades are divided according to the risk priority number RPN, a dynamic maintenance strategy is generated based on the risk grades, the dynamic maintenance strategy is output to the operation and maintenance management system, the actual operation condition and fault risks of equipment are fully considered, the reliability and safety of the hydroelectric generating set are effectively improved, the maintenance cost is reduced, and the non-planned downtime is shortened.

This invention relates to a service fault location system and method based on a BMC (Browser Control Center). The system includes: establishing a communication link between the BMC and the server under test (DUT) using a customized IPMI protocol; configuring test parameters and controlling the DUT to power on and start a restart test program; the BMC collecting current hardware-level data; the BMC performing temporary storage and determining whether an anomaly occurred during the current test round; when an anomaly occurs during the current test round, the BMC receives the current customized IPMI message, obtains the anomaly level information, and controls the CPLD (Content Management Logic Controller) to perform a hard restart or not perform a hard restart based on the anomaly level information, and persistently stores the on-site data. No additional test equipment or server hardware / software modifications are required, resulting in lower testing costs and broad applicability; in the event of an abnormal downtime, the BMC can directly operate the CPLD to perform a hard restart, greatly improving testing efficiency and continuity.

Deep neural networks related to patching deployment for autonomous machine applications. In various examples, rapid resolution of a deep neural network (DNN) failure mode can be achieved by deploying a patch neural network (PNN) trained to operate effectively on the failure mode of the DNN. The PNN can operate on the same or additional data as the DNN and can generate new signals that resolve the failure mode of the DNN in addition to those signals generated using the DNN. A fusion mechanism can be employed to determine which output to rely on for a given instance of the DNN / PNN combination. Thus, failure modes of the DNN can be resolved in a timely manner that requires minimal downtime or shutdown time of the DNN, features controlled using the DNN, and / or semi-autonomous or autonomous functions as a whole.

A method for managing a manufacturing network includes obtaining historical alarm data indicating alarm status information for each of a plurality of alarms during a historical time period. At least one of the plurality of alarms is associated with a target machine in the manufacturing network, and at least one other of the plurality of alarms is associated with at least one other machine upstream or downstream of the target machine in the manufacturing network. The method further includes executing a trained machine learning model using the historical alarm data to generate downtime predictions for the target machine during future time intervals. The trained machine learning model takes the historical alarm data during the historical time period as input to generate the downtime predictions. The downtime predictions are provided to facilitate the management of the manufacturing network.

The invention relates to a device (1) for avoiding downtimes of a machine tool (2), comprising a machine tool (2), a safety sensor system (3), at least one fault sensor (4) and a computing unit (5), wherein the computing unit (5) is connected to the machine tool (2), the safety sensor system (3) and the fault sensor (4); wherein the computing unit (5) is configured to control the machine tool (2) in accordance with a production program for producing at least one workpiece part; wherein the device (1) is configured to identify a fault of the machine tool (2) by means of the fault sensor (4); wherein the device (1) is configured to identify a danger to the machine tool (2) by means of the safety sensor system (3); wherein the computing unit (5) is configured to stop the machine tool (2) if a danger to the machine tool (2) is identified; wherein the computing unit (5) is configured to evaluate the fault if a fault of the machine tool (2) is identified and a danger to the machine tool (2) is ruled out, and to control the machine tool (2) in a manner deviating from the production program and to continue production of the at least one workpiece part in accordance with the evaluation of the fault.

Systems and methods in the field of software development and workflows for networked applications. System would allow rotation of test, staging, and production servers by reassigning their respective addressing, with minimal or no modification to the servers. It would reduce or eliminate uncertainty, additional testing, delays, and downtime as a result of server rotation and maintenance.

This invention discloses an intelligent fault diagnosis device and method with BIT (Block Interruption Testing) capability. The method includes: connecting one or more devices under test (DUTs) to the intelligent fault diagnosis device, wherein the DUTs are field replaceable units (LRUs); installing multiple sensors inside and at the external interfaces of each LRU, and transmitting the collected data to a central processing unit; analyzing the collected data using an adaptive algorithm to output a fault risk score A for the current system state; adjusting the parameters of a fault prediction model λ(t) based on the fault risk score A; and obtaining an overall fault diagnosis index F based on the fault risk score A and the fault prediction model λ(t). D This invention generates fault diagnosis reports. It improves fault diagnosis accuracy, enhances fault prediction capabilities, adapts to complex operating conditions, reduces maintenance costs and downtime, provides real-time monitoring and feedback mechanisms, offers scalability and compatibility, and supports data-driven intelligent decision support.

Near-zero downtime maintenance of containerized applications can be achieved via a modified rolling update strategy for a container orchestration platform. During deployment of a first set of containers based on a first deployment that specifies an image of a first software version and a grace period for preserving existing connections to the first set of containers, a second deployment object is received that specifies an image of a second software version and a minimum execution time for a second set of containers after which new connections to the second set of containers are allowed. After deployment of the second set of containers, new connections to the second set of containers are not enabled until the respective containers have executed for the minimum execution time. Existing connections to each container of the first set of containers are preserved until the earlier of their completion or expiration of the grace period.

The application provides a port machine equipment intelligent management method and system based on the Internet of Things, and relates to the technical field of intelligent management of port machinery. The method comprises the following steps: collecting multi-dimensional operation monitoring data through a port machine intelligent edge controller; performing protocol normalization and cleaning processing on the multi-dimensional operation monitoring data and converting the multi-dimensional operation monitoring data into standard Internet of Things data; transmitting and storing the standard Internet of Things data to a unified intelligent port machine Internet of Things platform; calling a big data analysis tool and an AI model to analyze the equipment state, count the load and predict potential faults of the standard Internet of Things data, and generating an equipment health report; and issuing a remote debugging instruction to the port machine intelligent edge controller by the unified intelligent port machine Internet of Things platform to remotely control and manage the port machine equipment, so that the overall intelligent management of the port machine equipment is realized, potential equipment faults are identified in a timely manner, the predictive maintenance capability of the equipment is improved, the data island phenomenon is solved, the equipment operation efficiency is improved, and the fault downtime is reduced.

The application provides a method and device for transmitting information, the method comprising: a first device configuring a detection parameter, the detection parameter comprising a first threshold value, the first threshold value being used for detecting whether a message is lost; and the first device starting quality of service (QoS) resource scheduling when the first device does not receive the message in M periods and M is greater than or equal to the first threshold value. The scheme detects the messages exchanged in a communication system by configuring the detection parameter, and performs resource scheduling in time when the message loss reaches a certain condition, thereby guaranteeing the reliable transmission of the messages, avoiding machine downtime caused by packet loss, and thus guaranteeing production efficiency.

The invention discloses a control method and system for sodium hydrosulfite synthesis and electronic equipment, and the control method comprises the following steps: a production starting instruction is obtained, and the production starting instruction comprises control configuration of a pretreatment stage, a feeding stage, an absorption liquid stage, a heat preservation stage, a large charging stage, a small charging stage, a cold charging stage and a discharging stage. By acquiring the production starting instruction and controlling the operation of each stage according to the preset configuration, the automation of the production process is realized, human errors are reduced, and the strict requirement on highly concentrated spirit of operators is lowered. Operating parameters of each stage can be accurately controlled according to a preset program, and product quality fluctuation caused by unstable operation in a traditional process is avoided. The operation parameters and the feeding sequence of each stage are automatically executed according to the preset configuration, the whole process is provided with safety alarm and termination and abnormity handling programs, the reaction process is continuous, smooth and safe, shutdown waiting or human errors are avoided, and the overall production efficiency and safety are improved.

Embodiments of the present application relate to a multi-component upgrade batch validation method and a computing device. The method comprises: obtaining configuration information of a plurality of components to be upgraded in a target server, the configuration information comprising dependency relationships between the components to be upgraded, upgrade priorities, and preset validation modes; obtaining current scenario state information of the target server; constructing an initial validation sequence based on the dependency relationships and the upgrade priorities; determining scenario constraint conditions according to the current scenario state information, and adjusting and merging the preset validation modes of the components to be upgraded in the initial validation sequence according to the scenario constraint conditions to generate a batch validation plan; wherein the adjustment and merging are optimized to minimize the number of power switches and the number of system restarts as an optimization target to determine actual execution modes and execution stages of the components to be upgraded; and performing validation operations on the corresponding components to be upgraded in sequence according to the batch validation plan. This can shorten downtime and increase upgrade success rate.

The invention discloses a switching method and system of a redundant server, and solves the problems of detection delay and data inconsistency in traditional switching through cooperative work of hardware layer redundancy (redundant components, multi-path architecture and power supply protection), intelligent fault detection (multi-dimensional monitoring and second-level heartbeat) and dynamic load balancing and data consistency guarantee (synchronous replication and seamless switching technology). Experiments show that the scheme shortens the annual downtime of the system, improves the availability, is suitable for key business scenes such as financial transactions and cloud computing, and ensures the service continuity and the data reliability.

This invention discloses a production data-driven intelligent scheduling method and system for mechanical equipment, relating to the technical field of equipment scheduling. The method includes: constructing a set of equipment operating characteristics; comprehensively evaluating the current load rate, processing capacity, and failure risk of each piece of mechanical equipment; constructing a scheduling objective function using equipment survivability scores as scheduling decision weight parameters; performing task allocation and processing path optimization calculations to generate an intelligent scheduling scheme for the mechanical equipment; and monitoring the scheduling execution process in real time to adaptively adjust the production scheduling scheme. This invention solves the technical problems in existing technologies where static scheduling cannot adapt to real-time changes in equipment status and lacks a dynamic risk feedback mechanism, leading to equipment overload, unplanned downtime, and cascading production delays during scheduling scheme execution. It achieves the technical effects of improving the production scheduling system's adaptability to dynamic equipment operating conditions, reducing the risk of unplanned downtime, and optimizing equipment utilization and production delivery reliability.

The application discloses a kind of STK account material self-checking and abnormal self-healing method based on equipment state comparison, belongs to industrial automation control and intelligent warehousing technical field, and the specific steps of the self-healing method are as follows: I: the real-time state data of each device is collected and preprocessed, and according to each state data after processing, the health degree score and state entropy value of each device are calculated;The present application can identify the sub-health and unstable state of equipment in advance, which not only ensures the production safety in case of serious failure, but also avoids unnecessary downtime caused by slight abnormalities, significantly reduces the abnormal interruption in the task execution process, and improves the accuracy and traceability of material management;At the same time, the present application can find potential problems in advance, avoid task out-of-order execution, dependency conflict and deadlock problem, effectively prevent the preemption of concurrent tasks on process resources, and significantly optimize Crane walking path, reduce the overall energy consumption of warehouse, and greatly shorten the abnormal processing cycle.