574 results about "Downtime" patented technology

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 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 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 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 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 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 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.

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 embodiments of the present application relate to the field of computer technology. Specifically, it relates to a predictive maintenance system and method for distributed energy storage devices based on AI. This method realizes precise monitoring of the operating status of distributed energy storage devices through the comprehensive collection of multi-dimensional operation data and in-depth analysis of the AI prediction model. Specifically, this method can effectively identify potential abnormal characteristics of the device, combine the quantitative evaluation of the maintenance urgency index, generate targeted predictive maintenance instructions, significantly improve the timeliness and accuracy of device maintenance, reduce the risk of unplanned downtime, extend the service life of the device, and at the same time optimize the efficiency of maintenance resource allocation.

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 memory fault management system and method, a server and electronic equipment, and relates to the technical field of computers, the memory fault management system comprises a processing circuit of a hardware memory, a hardware layer and a processor are arranged on the processing circuit, and the processor is further divided into a kernel layer, a user layer and an input and output layer. Through cooperative work of the hardware layer and each software layer, hierarchical detection, classified processing and automatic isolation of memory error data are realized, and server downtime caused by memory fault error data is effectively prevented. And meanwhile, through a linkage mechanism of a kernel mode and a user mode and in combination with visual display, the monitorability and maintainability of memory errors are enhanced, operation and maintenance personnel can quickly position and repair problems conveniently, and the operation and maintenance cost of the system is reduced.

The invention discloses a remote intelligent operation and maintenance system and method for coal mine equipment, and relates to the technical field of fault diagnosis, and the system comprises a local client which is used for obtaining fault sample data of the coal mine equipment and training a local model; the cloud server is used for aggregating the parameters of the local model to obtain a diagnosis model; and the controller is used for acquiring the real-time state data and the real-time operation data in the operation process of the coal mine equipment, and obtaining an early warning result of the fault of the coal mine equipment based on the diagnosis model. According to the method, the diagnosis model is constructed in a federated learning mode, and the diagnosis model is written into the controller of the coal mine equipment, so that fault early warning can be carried out in the operation process of the coal mine equipment, the downtime of the equipment is greatly shortened, and the benefits of coal mine production are improved.

The invention relates to the technical field of electrical equipment testing, in particular to an intelligent detection and classification management method and system for IDC equipment, and the method comprises the following steps: collecting the current, voltage, power consumption and temperature of a server in an IDC cabinet, synchronously recording the CPU occupancy rate and the GPU occupancy rate, storing the I / O load and the network throughput rate, extracting server log entries, and storing the server log entries in an equipment exception log library; and obtaining IDC equipment operation state data. According to the invention, by synchronously monitoring the current, voltage, power consumption and temperature of the IDC equipment and collecting the operation data of the server, the new scheme optimizes the comprehensiveness of the data and the real-time performance of the monitoring, and the monitoring strategy provides finer risk assessment and classification management by dynamically analyzing the state fluctuation of the equipment, so that the reliability of the IDC equipment is improved. Therefore, the accuracy of fault prediction and the efficiency of maintenance are improved, the analysis of abnormal logs and operation data is comprehensively utilized, the prediction capability of potential problems is enhanced, and the system downtime rate and the maintenance expenditure are effectively reduced.

Provided in the embodiments of the present application are downtime fault analysis methods and apparatus, an electronic device, and a storage medium. A downtime fault analysis method comprises: acquiring downtime state information of a downtime server, the downtime state information being used for describing running state information of the downtime server when a downtime fault occurs; performing feature extraction on the downtime state information to obtain a downtime feature of the downtime server; acquiring feature rules and a corresponding relationship between the feature rules and fault root causes; and determining a target feature rule matched with the downtime feature, and, on the basis of the corresponding relationship, determining a target fault root cause corresponding to the target feature rule as a downtime root cause of the downtime server. The embodiments of the present application can improve the analysis efficiency and accuracy for downtime fault analysis.

The invention discloses an active power distribution network fault recovery method and device considering participation of a temperature control load, and the method comprises the steps: analyzing the operation characteristics of a load side air conditioner and heat storage type electric heating in an active power distribution network fault recovery frame, and depicting an operation characteristic curve at different power failure time, the influence of the regulatable capability of the two kinds of loads on the operation state is explored at the same time; starting and stopping time of the two loads is calculated based on the operation characteristic curve, and the power requirement of load aggregation can be solved according to the law of large numbers; representing power requirements of the temperature control load in different time periods by using the state characteristic quantity, and calculating a dynamic load recovery weight based on the power requirements; an active power distribution network fault recovery mathematical model can be established according to the dynamic load weight, an objective function is to maximize recovery of a power-losing load and minimize the number of times of switching actions, and constraint conditions comprise node voltage constraint, branch power flow constraint and active power distribution network safe operation constraint; and on the basis of the fault recovery mathematical model of the active power distribution network, a power-losing load is recovered through a multi-period network reconstruction technology. The device comprises a processor and a memory.

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 provides a self-healing graph scheduling system and method for a multi-agent system. According to the method, in the multi-agent task flow graph, when any node fails or needs to be upgraded, bypass or hot replacement can be automatically completed within the time lower than a preset failure threshold value, and it is ensured that task topology is continuously acyclic, data are not lost, and services are not interrupted. According to the method, in a directed acyclic graph of a multi-agent task process, a main / standby node is configured for each edge, and the health state of the nodes is monitored in real time through dual-channel Gossip heartbeat; when the main node meets the failure condition, the flow is automatically redirected to the backup node at the millisecond level, the node state is recovered by using the XOR-delta snapshot, and the topology acyclic property is verified at the same time, so that the task is ensured to be continuous and traceable. Experimental results show that the average recovery time is reduced to 18 ms, the annual downtime is reduced by more than ten times, and the method is suitable for intelligent finance, industrial internet, automatic driving and other real-time scenes needing parallel agent collaboration and high availability.

The invention relates to the technical field of fault diagnosis, in particular to a fault prediction method and system based on observable data, and the method comprises the following steps: collecting real-time energy efficiency data, including power consumption, temperature and vibration data, of a gear and a bearing of mechanical equipment, calculating the deviation between the real-time energy efficiency data and a set energy efficiency reference, and calculating the energy efficiency of the gear and the bearing; and generating real-time energy efficiency deviation data. According to the method, the energy efficiency data of the key parts are monitored in real time, the abnormal state of the equipment is recognized in real time, the maintenance cost and downtime caused by fault diffusion are remarkably reduced, the deviation mode is analyzed through the random forest model, the abnormity judgment precision is improved, and the reliability of the equipment is improved. According to the method, fault propagation is analyzed by constructing the dependency graph and utilizing a graph theory method, the influence range and key nodes of potential faults are effectively predicted, the pertinence and efficiency of a maintenance strategy are greatly enhanced, and on the whole, the method improves the safety and reliability of a system, and meanwhile efficient operation of equipment is guaranteed.

Systems and methods for real-time monitoring and control of well operations at a well site use machine learning (ML) based analytics at the well site. The systems and methods perform ML-based analytics on data from the well site via an edge device directly at the well site to detect operations that fall outside expected norms and automatically respond to such abnormal operations. The edge device can issue alerts regarding the abnormal operations and take predefined steps to reduce potential damage resulting from such abnormal operations. The edge device can also anticipate failures and a time to failure by performing ML-based analytics on operations data from the well site using normal operations data. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

The present application relates to the technical field of model training, and discloses a data processing system and method, and a medium. According to the present application, a model training task is executed by means of acceleration devices in a host, and training data, an intermediate result, and weight data are stored by means of a memory board in the host, so that the task does not need to be executed by the host, and the data does not need to be stored by the host, thereby reducing the load of the host. The acceleration devices execute the model training task, so that the model training efficiency can be improved. Only the host is needed for task allocation in the whole training process, and a large amount of computing and memory access is unloaded to the acceleration devices and the memory board, so that the downtime risk and the model weight data recovery difficulty in the training process can be reduced.

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 provides a working procedure and working hour splitting method and system, electronic equipment and a storage medium. The working procedure and working hour splitting method comprises the steps of obtaining event data of Internet of Things sewing equipment; based on the event data, acquiring a plurality of relatively long downtime intervals in a specific time period; for each long downtime interval, event data of finishing sewing of the last garment by the employee is screened out, and an event data set is formed; the event data set comprises event data of motor starting, motor stopping and preset thread trimming times; based on all the event data sets, obtaining data of working hours of the splitting process; and based on the data of the working hours of the splitting process, splitting event data of preset thread trimming times to obtain the working hours of the employees for completing a single process. According to the method, refined splitting of single-process working hours can be realized, and the problem of out-of-order process data caused by rework or low proficiency of staff is solved. In addition, only the number of times of thread trimming needed to be split needs to be provided, and the difficulty of scheme application is reduced.

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.