1113 results about "Downtime" patented technology

The invention relates to the technical field of operation and maintenance scenes, in particular to a fault diagnosis prediction method and system based on an operation and maintenance scene, and the method comprises the steps: collecting equipment data, and constructing a scene fault diagnosis model; performing nonlinear fusion on the collected operation and maintenance data and historical maintenance records to obtain a real-time data fusion result; constructing an adaptive prediction algorithm based on interaction of a scenarized fault diagnosis model and real-time data fusion; and establishing a multi-level warning system according to a prediction result. According to the method, through multi-dimensional data acquisition, nonlinear data fusion, adaptive algorithm prediction and a multi-level warning system, accurate prediction and effective prevention of equipment faults are realized. Experimental results show that compared with a traditional method and a general machine learning method, the method has remarkable advantages in the aspects of early warning accuracy, false alarm rate, early warning time in advance and the like, meanwhile, the maintenance cost is effectively reduced, the downtime of equipment is shortened, and the method has good application prospects.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention discloses a memory fault repairing method and device, equipment, a medium and a computer program product, and relates to the technical field of computers.The memory fault repairing method includes the steps that memory error information sent by a memory controller is obtained, a fault target memory page can be positioned, and a standby memory page is obtained from a standby memory pool; the memory address mapping table is updated, the physical address of the target memory page with the fault is mapped to the physical address of the standby memory page, the repairing process does not depend on triggering of a system management interrupt mechanism, memory repairing in the system running stage is achieved, the system downtime caused by memory fault repairing is shortened, and therefore the memory repairing efficiency is improved. The problem of high delay in memory fault processing can be solved, and the technical effect of improving the memory fault repairing efficiency is achieved.

The invention discloses an industrial data acquisition and management method based on the Internet of Things, and relates to the technical field of data management, and the method comprises the following steps: carrying out the demand analysis of an industrial environment and a production process, and determining to-be-monitored industrial equipment indexes in the industrial environment and the production process, namely an equipment performance index and a production performance index; according to the industrial data acquisition and management method based on the Internet of Things, equipment performance indexes and production performance indexes are monitored in real time, so that the reliability of equipment is remarkably improved, the accidental downtime is shortened, and through comprehensive analysis of an equipment state evaluation index, a fault analysis evaluation index and a production performance evaluation index, the reliability of the equipment is improved. The method can predict possible faults and performance reduction of the equipment, so as to take preventive maintenance measures in advance, not only can reduce production interruption caused by equipment faults, but also can improve the efficiency and effect of maintenance work by optimizing a maintenance plan.

The invention discloses a multi-sensor data fusion control method for an automatic assembly line, and relates to the technical field of data fusion, and the method comprises the following steps: when multi-source data fusion is carried out, firstly, data information is obtained in real time through a multi-source sensor, the obtained data information is preprocessed, and the accuracy and integrity of data are ensured; by analyzing data quality and fusion performance indexes and system robustness and environmental adaptability indexes in real time, the system can accurately detect the influence of electromagnetic interference and environmental fluctuation on data and dynamically evaluate the state. And an early warning and adjustment strategy is automatically triggered in case of abnormality, so that data fusion continuity is guaranteed, and downtime and human intervention are reduced. And when the abnormity exceeds the self-repairing capability of the system, the system can give an early warning, so that the efficient and safe operation of the production line is ensured, and the capability of dealing with sudden problems is improved.

The disclosure provides a method for upgrading components of a container-based cluster. The method generally include receiving, at the container-based cluster, an indication of one or more pods and one or more nodes in the cluster to upgrade, adding an annotation to each of the one or more nodes having at least one of the one or more pods running thereon, performing a pod upgrade, and performing a node upgrade, wherein performance of the pod upgrade and the node upgrade overlap at least partially in time, and wherein performing the node upgrade comprises: selecting a first node, determining at a first time that the first node includes an annotation, refraining from upgrading the first node at the first time, determining at a second time after the first time that the first node does not include the annotation, and upgrading the first node at the second time.

The invention provides a software ecological migration method and system based on intelligent analysis. Wherein a compatibility evaluation database is deployed in a target architecture, an instruction set mapping rule is preset, and migration risk data is generated through semantic verification; dynamically allocating resources through a heterogeneous virtualization scheduling engine, calling a hardware acceleration unit translation defect instruction, and generating virtualization equipment simulation parameters based on the migration risk data; deploying protocol conversion middleware, dynamically analyzing and reconstructing a network protocol data packet, and combining instruction execution delay data to adjust analysis granularity; in cross-architecture transmission, instruction conversion defects are injected into control logic, timing sequence errors are corrected through clock offset compensation, and corrected data are transmitted back to update instruction mapping rules. According to the technical scheme provided by the invention, the data leakage risk is reduced, and zero shutdown in the migration process is ensured.

The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance management system and method based on artificial intelligence, and the system comprises an operation baseline modeling module, a resource consumption prediction module, an automatic resource allocation module, a data correlation analysis module, an abnormal data detection module and a fault path analysis module. According to the method, accurate data support is provided for resource management through real-time monitoring of operation data and baseline performance modeling, resource demands are analyzed and predicted in combination with a time sequence, shortage and excess in resource configuration are identified, automatic allocation of resources is achieved, optimal matching between resource supply and demands is ensured, and the optimal resource allocation efficiency is improved through construction of a dependency graph. The interaction between the data stream and the application is analyzed, abnormal data detection and fault path analysis are helped, fault problems are found in real time, reasons are identified, the downtime of the system is shortened, the continuity of services and the integrity of data are ensured, and the automation degree and the intelligent level of operation and maintenance management are improved.

The invention relates to the technical field of automatic control of tool magazines, in particular to a tool magazine operation control method and system, and the method comprises the steps: obtaining a machining operation task, constructing a tool scheduling time sequence according to the machining operation task, and carrying out the operation scheduling of tools of the same type in a tool magazine according to a time sequence through the tool scheduling time sequence; obtaining use information of each type of tools of the same type, and analyzing according to the use information of the tools of the same type to obtain a priority scheduling strategy of the tools of the same type; setting a tool allocation time sequence node for the tool scheduling time sequence based on the machining operation task, wherein the tool allocation time sequence node is a time node when the tool enters and exits from the tool magazine; and traversing the tool magazine according to the tool allocation time sequence node, determining tool apron distribution information of the same-type tools, and selecting the same-type tools for executing the machining operation task according to the priority scheduling strategy and the tool apron distribution information. The invention aims to solve the problems of unbalanced use of tools, overlong shutdown time and poor anti-interference performance of the system in the operation process of the tool magazine.

The invention relates to the technical field of artificial intelligence, and discloses an operation and maintenance automatic fault diagnosis and repair system based on artificial intelligence, and the system comprises the steps: obtaining equipment operation data, and carrying out the denoising and cleaning; calculating the deviation and trend change of the equipment operation data, and dividing the fault equipment into different abnormal levels according to the abnormal fluctuation degree; executing a shortest path algorithm strategy, and calculating path weights from the fault equipment to all possible fault sources; executing a deep learning algorithm strategy, and performing root cause analysis according to historical data and current fault information of the equipment; automatically generating a repair strategy according to the fault type, executing an AI self-learning repair strategy, automatically generating a corresponding repair scheme, and performing secondary repair on the fault equipment in combination with a distributed self-repair technology; operation data are analyzed in real time according to historical fault data of the equipment, and faults of the equipment are predicted and processed in time; the downtime of equipment can be effectively shortened, the operation and maintenance efficiency is improved, and the labor cost is reduced.

The invention provides a method, a device and a system for switching master and standby control centers of a smart home. In the scheme, real-time operation state data of each control center in the smart home and the number of times of downtime in a first preset time period are acquired, and based on the real-time operation state data and the number of times of downtime, a long-term short-term memory network model is used to score each control center to obtain a final score of each control center; sorting all the final scores from large to small, and determining the control centers corresponding to the final scores ranked in the front preset number as candidate control centers; an improved PBFT protocol is adopted to carry out consensus election on the candidate control hub, a target control hub is elected, the main control hub is switched to the target control hub for operation, and the target control hub meets at least one of the following preset conditions: the highest network communication efficiency, the shortest network delay and the lowest power consumption. The problem that the switching efficiency of the main and standby control centers in the smart home system is low is solved.

The invention relates to the field of hoisting equipment, in particular to a crane monitoring system and a crane monitoring method, and the operation efficiency, the safety and the reliability of the system are remarkably improved through real-time monitoring analysis, trend analysis and fault analysis. Real-time monitoring and analysis can respond to abnormal conditions in time, operation parameters are optimized, and the equipment utilization rate is improved. Trend analysis supports predictive maintenance and resource optimization, and downtime is reduced. Fault reasons are accurately diagnosed through fault analysis, detailed reports and prevention measures are provided, and safety is guaranteed. Data quality and analysis accuracy are improved through data preprocessing, and long-term stable operation of the system is ensured through historical data analysis and trend prediction. And the comprehensive report and data visualization assist in management decision making, and the operation cost is reduced. On the whole, the data analysis module brings comprehensive optimization and remarkable benefit improvement to the crane monitoring system.

The invention discloses an immersed server operation control method and system, and relates to the technical field of immersed cooling, and the method comprises the steps: judging whether a liquid level is higher than a preset starting liquid level, and activating a cooling liquid circulation system to confirm liquid flow; the liquid inlet speed of the cooling liquid is optimized according to feedback of the flow velocity sensor, and the stable state of the liquid level in the server cabinet is monitored; whether cooling liquid leaks or not is judged based on liquid level acceleration and flow velocity deviation, when the liquid level is insufficient, an alarm is triggered, and the liquid level and circulation time are automatically adjusted. According to the method, the automation level and the operation safety of the system are improved, sufficient supply of the cooling liquid under the high-load condition of the server is ensured, the downtime caused by leakage of the cooling liquid is shortened, the long-term operation cost of the system is reduced through timely maintenance notification, and the service life of the system is prolonged by automatically adjusting the liquid level and the circulation time. And quick recovery of the system when leakage occurs is ensured, so that the reliability and the fault recovery capability of the system are improved.

The invention discloses an online monitoring system and method based on the use state of monitoring equipment, and particularly relates to the technical field of equipment monitoring. According to the method, whether the recovery performance of the RAID level is abnormal or not is judged by writing and reading data in an RAID array, the performance of a storage server is evaluated and divided according to the cache hit rate fluctuation condition of a read-write request in a cache in a storage system and the capacity of a storage device for processing an uncompleted I / O request, and a data writing strategy is optimized; when the system runs under a high load, video data are distributed to a plurality of storage nodes by configuring the storage system to realize load balancing and dynamically store the number of the nodes, so that the performance and the reliability of the storage system are remarkably improved, the load of single storage equipment is reduced, the service life of the equipment is prolonged, and the write-in delay and the data loss are reduced; the stable operation of the system under high load is ensured, and the maintenance cost and the downtime are reduced.

The invention provides an equipment operation and maintenance intelligent decision support system based on large model reasoning, and aims to improve the accuracy of equipment fault prediction and the intelligent level of operation and maintenance decision. The system obtains equipment operation data in real time through a data acquisition module, performs reasoning analysis on equipment health conditions by using a large-scale pre-trained deep learning model, and predicts the residual service life of equipment and various fault types. The system combines multi-task learning and a multi-objective optimization algorithm, intelligently optimizes operation and maintenance decisions, generates personalized maintenance suggestions, helps operation and maintenance personnel to effectively reduce the downtime of equipment, reduces the maintenance cost, and prolongs the service life of the equipment. The system can be widely applied to the fields of manufacturing industry, energy, traffic and the like, and the accuracy and efficiency of equipment management are remarkably improved.

The invention discloses a scheduling method for crushing station multi-machine cooperative control based on edge calculation, and belongs to the technical field of mining mechanical equipment automation control. All devices in the crushing station serve as edge nodes; and the edge node communicates with other edge nodes to share the result and perform multi-machine cooperative control to pre-judge the running state and fault of the equipment. According to the method, efficient operation of the crushing station multi-machine equipment under different working conditions can be kept, self-adaption and cooperative work can be achieved, continuous improvement of dynamic scheduling and control strategies can be achieved, and global optimization of equipment starting and stopping and speed optimization is ensured. Meanwhile, historical data and real-time data are utilized to the maximum extent through reinforcement learning, continuous improvement of equipment fault prediction and state maintenance is achieved, and non-planned downtime is shortened. The innovation and practicability of the multi-machine cooperative control and scheduling method of the mine crushing station remarkably improve the production efficiency and economic benefits of the system.

The invention discloses a fault prediction method and device based on the real-time running state of an elevator and a medium, and relates to the technical field of data processing.The method comprises the steps that historical running data of the elevator are obtained, a fault feature database is established according to historical fault features corresponding to the historical running data, and real-time running data of the elevator are obtained; a real-time running track corresponding to the elevator is determined; determining at least one to-be-selected fault feature corresponding to the real-time operation data based on a fault feature database, and determining a target fault feature with a potential risk from the to-be-selected fault features in combination with the real-time operation trajectory; and the target fault features are input into the trained fault prediction model, and the fault type corresponding to the elevator is output in combination with the predetermined elevator operation features, so that the accuracy and the efficiency of elevator fault prediction are improved, and the downtime and the maintenance cost caused by faults are reduced.

The invention discloses a power plant equipment state maintenance decision-making method and system based on big data analysis, and relates to the technical field of equipment state prediction, and the method comprises the steps: collecting the operation data of power plant equipment in real time; extracting operation equipment characteristics, performing equipment state analysis, and predicting faults; and providing maintenance decision support based on equipment fault prediction. According to the power plant equipment state maintenance decision-making method based on big data analysis, feature fusion is carried out on the extracted vibration features and pressure features, the health scores of the equipment are calculated, whether the equipment in the low health state will break down in the future time period or not is determined, redundant maintenance is avoided, and the maintenance efficiency is improved. The maintenance efficiency is remarkably improved, the cost is reduced, specific maintenance measures in different states are provided according to the equipment state predicted value, the unnecessary maintenance and downtime is reduced, and the equipment management efficiency is improved. According to the invention, better effects are achieved in the aspects of reducing the cost and improving the management efficiency.

The invention discloses a resource collaborative awareness-based Storm flow calculation dynamic scheduling method, and provides the following technical scheme aiming at the problem that the performance of a Storm default scheduling algorithm is reduced in node abnormity, resource fluctuation and rescheduling scenes: firstly, dynamically sensing node busy, downtime and data flow fluctuation events through a real-time monitoring module; triggering a rescheduling process; secondly, constructing a resource collaborative optimization model based on historical task instance resource requirements, node performance indexes and communication overhead, and generating a task instance allocation scheme by taking minimization of inter-node communication cost as a target and combining CPU / memory dynamic threshold constraints; further, a greedy algorithm is adopted to sort high-relevance task instances, the high-relevance task instances are preferentially distributed to nodes with the optimal historical performance, and it is ensured that the node resource utilization rate does not exceed a dynamic threshold value; meanwhile, node load balancing parameters are corrected in real time through time window smoothing processing, and the remaining resource state is updated; and finally, outputting an optimized topology division result, so that the system delay after rescheduling is remarkably reduced, and the throughput is improved. According to the method, rapid recovery and stable operation of the heterogeneous cluster are realized through resource collaborative modeling, historical data driven dynamic scheduling and load balancing optimization.

The invention discloses a method for troubleshooting and processing faults of an industrial personal computer in field operation and maintenance and electronic equipment, relates to the field of industrial personal computer equipment, and aims to improve the efficiency and accuracy of fault processing. According to the method, the running state of the industrial personal computer is monitored in real time, fault data are collected, unstructured documents and historical fault data are converted into structured information through the natural language processing technology, and a knowledge graph is established. Through a vectorization technology and an enhanced search algorithm, the system can quickly retrieve cases similar to current fault symptoms from a knowledge base, and fault processing suggestions described by natural languages are generated through LLM. In addition, the invention further comprises a computer program executed by the central processing unit, data of the industrial personal computer are read through the communication module, and an automatic fault diagnosis process is realized. According to the method and the equipment, the dependence on artificial experience is effectively reduced, the reliability of the industrial personal computer is improved, and the downtime is reduced.

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 relates to the technical field of gateway equipment management, and discloses an intelligent gateway equipment remote management and control method based on a swan-mong system, which realizes tight connection and data sharing between gateway equipment and the system by using a distributed architecture of the swan-mong system. And real-time working state data of the gateway and the sensor connected with the gateway are collected through the unified equipment management platform. In combination with a machine learning algorithm, a fault prediction model is trained, and the model can predict the potential fault risk of the gateway equipment, including the type, grade and time node of the fault. And deploying the trained model at an edge computing node of the swan gap system, analyzing and processing data in real time, predicting equipment faults, and automatically generating a control instruction according to a preset strategy. Instructions are sent to gateway equipment in real time, and remote management and control are achieved. According to the invention, the stability and reliability of the gateway equipment are improved, real-time equipment monitoring and management are realized, the risk of data loss or error is reduced, and the maintenance cost and downtime are reduced.

The invention discloses a programmable logic controller programming system with an internet of things function, which relates to the technical field of industrial automation control and comprises a main control module based on a heterogeneous multi-core processor architecture and used for executing user program logic and coordinating operation of a communication module, a task distribution module, a dynamic loading module and a safety verification unit; according to the programmable logic controller (PLC) programming system of the internet of things, the real-time performance and the resource efficiency of an industrial control system are remarkably improved through heterogeneous multi-core collaboration, dynamic resource scheduling and multi-protocol compatibility technologies; on the basis of differential division of labor of the ARM and the FPGA, master core global scheduling and slave core high-real-time control are combined, and millisecond-level response of key tasks is guaranteed; compared with a traditional PLC, the system breaks through the limits of protocol closure, resource rigid distribution, shutdown dependence and the like, provides a flexible, stable and safe intelligent control core for the industrial Internet of Things, and promotes industrial automation to evolve towards high efficiency, networking and high availability.

The invention belongs to the technical field of industrial equipment fault diagnosis, and discloses an intelligent fault diagnosis system for an Internet of Things high-speed continuous rolling cold pilger mill, and the system comprises a real-time sensing module which is used for collecting the operation state data of the cold pilger mill in real time in the working process of the cold pilger mill; the cold pilger mill operation state data comprises cold pilger mill state data, operation condition data, electrical parameter data and working environment data; the self-adaptive feature extraction module is used for performing feature extraction on the cold pilger mill state data and the electrical parameter data through self-adaptive variational mode decomposition; the data enhancement module is used for automatically removing noise in the operation condition data and the working environment data and filling missing values based on a self-adaptive preprocessing technology of time sequence analysis; and the utilization efficiency of maintenance resources is improved, and unnecessary downtime and production loss are reduced.

The invention discloses an IT equipment maintenance task dynamic scheduling method fused with fault prediction, and relates to the technical field of maintenance task scheduling. The IT equipment maintenance task dynamic scheduling method fused with fault prediction comprises the following steps: constructing an equipment state spatial-temporal characteristic matrix and a historical fault frequency matrix of IT equipment, and determining an occurrence frequency matrix of each fault type of the IT equipment in combination with a working condition correction factor stored in a database; determining a fault maintenance task amount based on the number of IT devices and the occurrence frequency matrix of each fault type; according to the system and the method, the expected completion state of the fault maintenance task is determined based on the current working state of the resident maintenance personnel, and the working mode and personnel of the resident maintenance personnel are adjusted based on the expected completion state of the fault maintenance task, so that the fault quantity can be intelligently evaluated, and the manual workload distribution can be optimized to improve the fault repair efficiency. Downtime is reduced, and user experience is improved.

A method including generating a queue of a plurality of workloads for execution in a data processing environment, where the plurality of workloads are organized in the queue based on respective priority values, and where a vulnerability scan is queued with a lower priority value and segmenting the vulnerability scan using at least one selected from a group consisting of: parallel segmentation and iterative segmentation. The parallel segmentation is configured to implement respective segments of the vulnerability scan at predetermined times to reduce downtime or performance impact of the data processing environment and the iterative segmentation is configured to repeatedly segment the vulnerability scan until respective segments of the vulnerability scan can be implemented in the data processing environment without any downtime or significant performance impact. The method further includes implementing the segmented vulnerability scan in the data processing environment.

The invention discloses an operation method and system of a digital twin factory. According to the system, the equipment state monitoring sub-module monitors and displays the equipment parameters in real time, the equipment state monitoring sub-module provides a visual equipment operation state for an operator, potential faults can be found in time, and therefore production interruption caused by the equipment faults is reduced. And secondly, the early warning function of the module can give an alarm before a fault occurs, so that a time window is provided for preventive maintenance, and the influence of fault expansion is avoided. In addition, the real-time monitoring function of the equipment state monitoring sub-module is beneficial for improving the production efficiency because the equipment state monitoring sub-module can ensure that the equipment runs in the optimal state, and the non-planned downtime is reduced. And meanwhile, the data recording and analyzing functions of the module are beneficial to improving the reliability of the equipment, and potential problems of the equipment can be identified by analyzing historical data, so that measures are taken for preventive maintenance, the service life of the equipment is prolonged, and stable operation of a factory is guaranteed.

The invention discloses a system and a method for analyzing the running state of an electromagnetic valve, and relates to the technical field of electromagnetic valve control, the method comprises the following steps: carrying out joint modeling by using a semi-closed feature set Fhc to obtain an incomplete closing probability Puh, generating a dynamic threshold Thp through a self-adaptive threshold, and setting an event level judgment tag Jeh when the incomplete closing probability Puh exceeds the dynamic threshold Thp, so as to obtain the running state of the electromagnetic valve. Compared with an existing scheme that binary judgment is carried out only according to whether the action is completed or not, early and stable recognition is achieved for high-frequency and hidden abnormities such as semi-closed abnormities under common working conditions, and then energy consumption rising and safety risks caused by leakage are blocked in advance; meanwhile, the self-adaptive correction of the dynamic threshold Thp to the environment temperature Tmp and the power supply stability Vst effectively reduces false alarm and missing alarm, so that the judgment is kept consistent under multiple working conditions; the chain type output can directly support operation and maintenance evidence obtaining and trend evaluation, the predictive maintenance efficiency is improved, and unplanned shutdown is reduced.

The invention provides a method and system for offline maintenance of a control panel based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: an AI self-inspection module reads real-time state data of a sensor and an execution part, generates a fault diagnosis result based on an offline fault maintenance information base, generates a fault maintenance scheme, and sends the fault maintenance scheme to a server; and after determining that the user has the maintenance tool, generating a fault inspection instruction, guiding the user to complete inspection, comparing inspection data with a standard, and generating a maintenance guidance scheme. And guiding the user to complete device replacement and equipment assembly, rechecking the operation state of the equipment, and confirming that fault repair is completed. According to the invention, under the condition that the network is unstable or is completely offline, the control panel can also quickly respond to the fault by virtue of the built-in AI module, so that preliminary self-diagnosis and repair are realized. Once the fault information flow is detected, the AI module can immediately analyze the fault type and automatically deploy the corresponding maintenance strategy according to the data in the offline library, so that the downtime is reduced to the greatest extent.

An embedded control device can concurrently execute a number of control apps for controlling a technical system. The embedded device has a number of units, including: a broker unit configured to provide a message bus enabling serialized publish / subscribe topic-based communication between any of the control apps, the units, and the technical system. An updater unit is configured to: in response to a new instance of the broker unit being executed on the embedded control device in addition to the old instance of the broker unit, publish an update topic on the message bus; and cause a handover of control from the old instance to the new instance by transmitting a switch command in the update topic on the message bus. There is also described a method for updating a broker unit of an embedded control device and a computer program product. The broker unit can be updated without downtime.