638 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 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 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 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 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 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 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.

Applications create log entries comprising data regarding operations performed by the applications. The log entries are provided to an audit-log service to allow auditing of the log entries. An audit-log sidecar for each application is used to send log entries to the audit-log service. The audit-log service may experience downtime. If the audit-log service is unavailable, the log entries are sent to one or more other audit-log sidecars for storage. When the audit-log service again becomes available, all audit-log sidecars send their stored log entries to the audit-log service. In this way, the audit-log service is enabled to determine if there is a discrepancy between log entries reported by an application and log entries reported for the application by other audit-log sidecars. As a result, an attack on an application will not go undetected, even if the attack occurs while the audit-log service is unavailable.

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.