733 results about "Downtime" patented technology

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 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 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 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 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 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 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 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.

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.

The invention belongs to the technical field of fault diagnosis, and provides a new energy centralized control system intelligent fault diagnosis and recovery system, which comprises a data acquisition module, a fault diagnosis module, a fault recovery module and a self-learning module, and is characterized in that the data acquisition module, the fault diagnosis module, the fault recovery module and the self-learning module are electrically connected in sequence; equipment data are acquired in real time through the data acquisition module, the fault type and position are accurately judged through the fault diagnosis module, the fault recovery module can automatically select and execute a recovery strategy, normal operation of the system is rapidly recovered, meanwhile, the self-learning module continuously learns and optimizes, the accuracy and reliability of the system are improved, and the service life of the system is prolonged. The operation and maintenance cost is integrally reduced, the downtime is shortened, and the flexibility and adaptability of the system are enhanced.

The invention discloses an air compressor fault monitoring and operation and maintenance management method and system based on the AI technology, and relates to the technical field of air compressors, the key points of the scheme are as follows: based on the operation data of an air compressor, reading pressure and temperature data to carry out data standardization processing, and utilizing a CNN network to carry out feature extraction on the pressure and temperature data; and identifying an abnormal fluctuation mode of sudden increase or sudden decrease, and monitoring the change trend of the abnormal mode to obtain abnormal mode tracking information. The method is used for carrying out continuous monitoring and feature extraction on pressure and temperature data, an abnormal fluctuation mode can be effectively recognized, the development trend of the abnormal fluctuation mode can be predicted, short-term data abnormity can be captured through application of the long-term and short-term memory network, long-term data changes can be analyzed, prediction of potential risks in the future is provided, and the method is suitable for popularization and application. The accuracy and timeliness of fault prediction are improved, so that a maintenance team can carry out intervention before the problem develops to a serious fault, and the downtime caused by the fault is remarkably reduced.

The invention relates to the technical field of intelligent manufacturing, in particular to an electrical manufacturing execution management system based on cloud computing, which comprises a scheduling optimization module, an energy management module, an equipment health and state monitoring module and a production efficiency improvement module. According to the method, accurate scheduling of production resources is realized by capturing electrical production task progress, equipment use conditions, process parameters and energy consumption data, then the operation mode, time and speed of equipment are regulated and controlled, resource allocation is more flexible, the energy consumption data are analyzed by relying on cloud computing, the energy use mode and equipment load are optimized, and the energy utilization efficiency is improved. Effective energy distribution is realized, energy efficiency is improved, equipment faults can be predicted in advance in combination with an equipment health assessment model, a maintenance period is adjusted according to an equipment state, production interruption caused by equipment shutdown is avoided, deep analysis is performed on production scheduling, then a production sequence and resource distribution are adjusted, production efficiency is improved, and production efficiency is improved. And the stability and the flexibility of the production line reach a higher level.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention relates to the technical field of industrial automation online monitoring, in particular to a fault monitoring and self-recovery method and system for an industrial personal computer, and the method comprises the steps: detecting and regulating the external environment temperature of the industrial personal computer to accord with a starting mechanism before power-on, and starting industrial personal computer equipment; hardware information of the industrial personal computer is read, circulation monitoring is carried out, whether hardware faults exist or not is checked, and parameter data of the industrial personal computer are synchronously collected; and through the prediction model, the processed parameter data is evaluated, whether non-hardware abnormity exists is judged, and fault self-recovery and abnormity processing are carried out. According to the invention, real-time monitoring is carried out through the MCU before and after the industrial personal computer is started, so that the real-time performance and accuracy of fault detection are improved; fault recovery is automatically carried out according to a fault analysis result, manual intervention is reduced, and downtime and maintenance cost are reduced; through environment monitoring and control, stable operation of the industrial personal computer in an extremely complex industrial environment is ensured; and through real-time monitoring and fault prediction, production interruption is avoided, and the production efficiency is improved.

The invention discloses a memory fault processing method and device, electronic equipment and a readable storage medium, and relates to the technical field of computers.The method comprises the steps that when a memory line fault exists in a memory of a target system, an isolation operation is triggered for a target physical address, the target physical address at least comprises the physical address of the fault line with the memory line fault in the memory, the technical problem of system downtime caused by the fact that the memory line fault cannot be rapidly processed in the related technology is solved, the isolation process is started for the target physical address in time when the memory line fault is detected, and the system downtime is reduced. Performance reduction and data error accumulation caused by the fact that the fault line continues to participate in data reading and writing are avoided, and then the technical effect of reducing the risk of system downtime is achieved.

The invention discloses a task scheduling method which comprises the following steps: receiving a task, scheduling the task or a task fragment of the task to a first execution node in a distributed cluster, and when the first execution node executes the task or the task fragment, scheduling the task or the task fragment of the task to the distributed cluster; and receiving a fragment adjustment request sent by the first execution node according to the load of the task or the task fragment on the first execution node, and according to the fragment adjustment request, adding the task fragment of the task on a second execution node in the distributed cluster, or reducing the task fragment of the task in the first execution node. According to the method, self-adaptive adjustment of the task or the task fragment in the running state can be realized, and resource use is adjusted by dynamically adjusting the task fragment of the task, so that the overall cluster resource achieves load balance, and the risk of task failure or node downtime caused by the fact that an execution node is completely occupied by a single task or the task fragment is avoided.

The invention relates to the technical field of machine tool operation and maintenance, in particular to an intelligent gantry machining center fault monitoring method and system, and the method comprises the following steps: collecting the operation efficiency, fault frequency and maintenance record of a machine tool through real-time monitoring data, carrying out the weight distribution of the operation efficiency, fault frequency and maintenance record, and generating weight health data. According to the invention, by analyzing equipment data, including operation efficiency, fault frequency and maintenance records, potential abnormity and fault trends can be found in time. By allocating the weights to the key operation indexes, the generated weighted health data provides quantitative evaluation for the health state of the equipment, so that a maintenance team can perform maintenance in a targeted manner instead of depending on a conventional predetermined maintenance plan. Besides, the health score of the equipment is updated in real time through trend analysis, so that instant fault diagnosis becomes possible, the non-planned downtime of the equipment is reduced, and the overall efficiency of a production line is improved.

The invention discloses a PDU load distribution system based on reinforcement learning, relates to the technical field of data center resource management, and realizes efficient scheduling of PDU load, server migration and start-stop through combination of a multi-dimensional execution cost model and a digital twin environment. Energy consumption and performance can be balanced in different load scenes by using offline pre-training and hierarchical control, and continuous optimization is performed through online learning under concept drift detection; in addition, after the safety cost and the fault-tolerant overhead are overlaid, key services and sensitive data can be protected preferentially, safety risks and downtime losses are effectively reduced, and finally multi-dimensional collaborative efficient, safe and extensible data center management is achieved. Meanwhile, resource impact and performance jitter caused by large-scale operation are further avoided through batch migration and staged starting and stopping, flexible response can be achieved under multi-dimensional risks, and it is ensured that scheduling robustness and sustainability are kept under heterogeneous loads.

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.

The invention discloses a building electromechanical system fault intelligent detection method and system, and relates to the technical field of building electromechanical system maintenance, and the method comprises the following steps: system data collection, health value evaluation, fault prediction, priority maintenance, and evaluation of the importance of electromechanical equipment needing maintenance through a priority scoring algorithm. And obtaining a maintenance priority score S, and notifying maintenance personnel to perform maintenance and data storage on the electromechanical equipment in sequence according to the maintenance priority score S. The method has the advantages that the equipment health value is quantized through an electromechanical system health evaluation algorithm, the equipment fault risk is predicted in combination with a fault probability prediction algorithm, and whether the equipment needs to be maintained or not is judged in advance; the system is prevented from being damaged by sudden faults, the downtime and the maintenance cost are reduced, the maintenance priority of the equipment is evaluated through a priority scoring algorithm, maintenance resources are reasonably distributed, the maintenance efficiency is improved, excessive maintenance is reduced, and stable operation of the electromechanical system is ensured.