1929 results about "Fault tolerance" patented technology

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention provides an industrial robot autonomous collaborative decision-making method and system based on multi-modal sensing and a medium, and belongs to the technical field of industrial robot intelligent control. The method comprises the steps of performing cross-modal space-time alignment to eliminate data space-time differences by collecting visual, tactile and auditory information, realizing multi-modal feature fusion in combination with a dynamic weight adjustment mechanism and an attention calculation model, and generating a joint decision strategy through a deep learning optimization model. The system dynamically allocates sensor weights according to task types, introduces a multi-objective optimization mechanism of energy consumption, precision and safety, and sets a fault-tolerant rule to automatically recover the weights or recalibrate the sensors. According to the method, the problems of rigid data fusion and single optimization dimension in traditional multi-modal decision making are solved, and the precision, the response speed and the environmental adaptability of collaborative operation of the industrial robot are remarkably improved.

A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

The present invention provides a digital twin simulation method and system for an industrial simulation platform, the method comprising: collecting the operation data of the industrial training production line through a sensor array to obtain a real-time mapping data stream; constructing a digital twin model according to the real-time mapping data stream, using a semantic ontology library and a graph neural network to perform correlation analysis on the multimodal data of the physical space and the digital twin model, and obtaining a twin knowledge graph of the physical-digital dual space; according to the twin knowledge graph and a preset expert knowledge base, using a reinforcement learning method to perform collaborative learning on the operation conditions of the physical-digital dual space to obtain an elastic fault-tolerant boundary; according to the twin knowledge graph and the elastic fault-tolerant boundary, the deviation between the real-time action of the operator in the physical space and the standard action in the digital space is graded and judged, and corresponding feedback and warning information are output. The present invention overcomes the limitations of the existing fixed standard action model and provides personalized guidance for operators with different proficiency levels.

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The invention provides a workshop multi-agent deep reinforcement learning scheduling method based on real-time production conditions, and relates to the technical field of production scheduling and industrial automation, and the method comprises the steps: collecting production data in real time through a distributed sensor network, and constructing a multi-dimensional time sequence matrix; establishing a three-dimensional decision space containing time, resource and task dimensions, and adopting an improved multi-agent depth deterministic strategy gradient algorithm; dynamically calculating the real-time production urgency degree of the production batch, and analyzing cross-unit cooperation to generate a cooperation efficiency factor; the two are combined to optimize a scheduling scheme through a course learning strategy; and verifying the scheme by using a virtual twin environment and feeding back and updating model parameters. The method breaks through the limitations of insufficient dynamic adaptability, low efficiency of multi-target coordination and the like of traditional scheduling, realizes real-time response to dynamic production conditions such as equipment faults and order adjustment, balances time efficiency, energy consumption economy and abnormal fault tolerance through a multi-agent coordination and closed-loop verification mechanism, and improves resource coordination efficiency under complex production constraints.

The invention relates to a high-fidelity cloud rendering cluster scheduling method and system, and the method comprises the steps: receiving a rendering task, generating a multi-level priority queue according to the task complexity, timeliness and resource demand classification, and automatically optimizing a scheduling strategy; monitoring the resource state of the heterogeneous computing node in real time; allocating tasks by using preemptive and round-robin scheduling strategies, and adjusting and coping with resource fluctuation in combination with a dynamic code rate; the tasks are decomposed by adopting a spatial blocking and time framing strategy, and an execution sequence is controlled according to a topological sorting algorithm; abnormal nodes are identified through heartbeat detection, and affected subtasks are migrated through incremental task updating. According to the method, efficient resource allocation, scheduling algorithm and idle key frame scheme can be realized, the GPU directly outputs the video stream, the rendering speed is remarkably improved, resource waste and operation cost are reduced through a dynamic resource allocation mechanism, a fault-tolerant mechanism is provided, the task is ensured to be normally completed under the condition of node fault, and the task efficiency is improved. And distributed rendering and synthesis of large-scale high-resolution images are supported.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a business process visualization early warning and strategy response method, which comprises the following steps: setting a physical interrupt node to collect interrupt operation track data, generating multilevel logic interrupt type structured data based on the operation track data, the method comprises the following steps: acquiring a preset business strategy, mapping the preset business strategy into a visual topological graph, identifying a preset business strategy verification failure type, counting interruption frequency, triggering a grading early warning instruction, and generating a page element weight adjustment parameter and a fault-tolerant threshold set. According to the method, the visual topological graph is constructed and the high-frequency interruption behavior is identified, so that real-time identification and graded early warning of business strategy verification failure are realized, and adaptive adjustment of page elements and strategy thresholds is further driven, so that the user insurance conversion rate is effectively improved and the front-end operation loss risk is reduced.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention provides a distributed multi-mode data cross-trust-domain data sharing method and system, and relates to the technical field of data sharing, and the method comprises the steps: carrying out the type recognition and format standardization processing of heterogeneous data provided by a plurality of data source nodes; dividing the data source node into different trust domains based on the security level, the authorization range and the protection strategy; constructing a cross-trust-domain data mapping model to establish a data synchronization channel; verifying the access authority through the smart contract and recording an access log; a distributed consensus mechanism based on a Byzantine fault-tolerant algorithm is adopted to synchronize data states, and data conflicts are solved through version control. According to the invention, secure sharing and state consistency maintenance of heterogeneous data are realized, and the reliability and fault tolerance of cross-trust-domain data sharing are improved.

The invention discloses a goaf unmanned aerial vehicle inspection system based on monitoring and early warning, and the system comprises an observation data collection module which is used for collecting settlement and fracture main variables to form a time stamp observation set; the data fusion module is used for generating a unified monitoring data set by adopting a fractal small-world network coding consensus algorithm; the initial route planning module is used for constructing a route sequence based on a main variable evolution tensor and a risk map; the flight path execution module is used for collecting a flight path and environment data; the fault-tolerant control module triggers a fractional order sliding mode fault-tolerant and degradation mechanism based on the consistency error; and the closed-loop optimization module is used for dynamically updating the main variable tensor and the path cost function and outputting an optimized track and a structured early warning result. The system has high dynamic responsiveness and multi-source risk adaptability.

The invention relates to the technical field of computing power scheduling, provides a distributed computing power scheduling method and system, breaks through the limitation that a traditional computing power scheduling system depends on a single index or a static strategy by constructing an integrated architecture of data perception, load prediction and intelligent scheduling, and constructs a self-adaptive and closed-loop optimized intelligent scheduling system. The scheduling system obtains multi-source data in real time and fuses the multi-source data into a perception vector to accurately describe the running state of the system, accurate pre-judgment of load changes is achieved through hierarchical load prediction, and resources can be efficiently allocated in different scenes in combination with double scheduling paths and execution feedback optimization; according to the scheduling system, the resource utilization rate of the distributed system is effectively improved, the energy consumption cost is reduced, the task execution delay is reduced, the system stability and the fault-tolerant capability are enhanced, and an innovative computing power scheduling solution is provided for a large-scale distributed computing scene.

The invention discloses a power grid equipment state sensing driving dynamic response method based on the Internet of Things technology, and relates to the technical field of power system automation and informatization, and the method comprises the following steps: S001, collecting original multi-dimensional state signals of a plurality of sensors deployed in a power grid equipment state sensing channel, constructing an electromagnetic disturbance recognition model, and carrying out the recognition of the original multi-dimensional state signals; frequency domain and time domain feature extraction is carried out on the signals, and a feature comparison parameter set used for distinguishing electromagnetic interference and real faults is generated. Frequency domain and time domain features are extracted through an electromagnetic disturbance recognition model, dynamic threshold judgment and response strategy adjustment are achieved in combination with multi-source sensing data, interference and faults can be accurately distinguished, early warning and protection logic can be corrected in real time, protection actions can be accurately triggered, closed-loop control is achieved, the delayed fault tolerance and multi-source verification capacity is achieved, and the method is suitable for large-scale popularization and application. The false operation rate and the false stop risk are effectively reduced, and the intelligence, the safety and the stability of power grid operation are improved.

The invention discloses a building construction process risk assessment method and system based on a fault tolerance mechanism, and particularly relates to the field of building construction process risk assessment, and the method comprises the steps: building a measurement point hierarchical index of a building construction process, synchronously arranging time sequence displacement data, and executing difference calculation to obtain a space gradient evolution sequence; mapping a trend change interval set extracted by sliding window processing into a path node set, and constructing a topological connection relation of the path node set to form an initial risk path map; the data state of each path node in the risk path map is subjected to anomaly identification, the access control effect of the abnormal node in the path is analyzed, and whether the path fracture risk is formed or not is judged in combination with the sensitive monitoring area. By constructing a risk map structure based on a trend path and combining a path interruption node identification and alternative path dynamic deduction mechanism, continuous assessment and map self-correction of the risk trend under the condition of measuring point data failure are realized.

The invention relates to the technical field of artificial intelligence, in particular to a cloud edge collaborative production manufacturing management system based on artificial intelligence, which comprises a task scheduling optimization module, a resource scheduling and load balancing module, a production efficiency evaluation module, a dynamic load adjustment module and a fault tracing analysis module. According to the invention, by accurately analyzing the task dependency relationship, optimizing the task scheduling strategy and improving the response sequence and delay control of task execution, the processing bottleneck problem caused by data transmission delay is avoided, the task execution period is flexibly adjusted, the high-frequency tasks in the production process are effectively managed, and the load unevenness caused by fluctuation is reduced; the method can accurately identify and regulate the production bottleneck, improve the adaptive capability of the production process, accurately identify the potential fault source, shorten the fault diagnosis time, reduce the complexity, more rapidly position the problem, improve the stability, efficiency and fault tolerance of the production system, and integrally enhance the self-optimization capability of the production system.

The invention provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention discloses an energy storage method and system for base station management, and the method comprises the steps: carrying out the multi-modal data fusion processing according to the historical business volume data, real-time user movement mode data and meteorological data of each virtual base station in a target region, and obtaining a dynamic load prediction result of each virtual base station in a target time period; based on a dynamic load prediction result, adopting a distributed constraint optimization algorithm to dynamically allocate energy storage resources, and generating an optimal allocation strategy containing a cross-base-station energy scheduling path; and according to the wireless network topology structure characteristics of the target area, constructing an energy scheduling decision model based on a graph theory, optimizing an energy storage scheduling time sequence, and generating a dynamic energy storage scheduling scheme with a fault-tolerant mechanism so as to realize energy storage for base station management. By utilizing the embodiment of the invention, the energy efficiency of the base station can be improved, energy waste is reduced, and efficient configuration and real-time scheduling of energy storage resources are realized.

Embodiments of the present disclosure provide a deep learning model training method and a deep learning model training system. The deep learning model training method comprises: acquiring an initial deep learning model and a sample data set; and performing distributed training on the deep learning model on the basis of the sample data set according to a preset distributed training policy, and during the adjustment parameter calculation for distributed training, storing model parameters of the deep learning model on the basis of target storage parameters, wherein the target storage parameters are determined on the basis of model specification information of the deep learning model and the preset distributed training policy. The target storage parameters are determined on the basis of the model specification information of the deep learning model and the preset distributed training policy, which fully takes into account the iteration patterns of distributed training, and during the adjustment parameter calculation, the model parameters of the deep learning model are stored on the basis of the target storage parameters, so that high efficiency is achieved while enabling the training of the deep learning model to have high fault tolerance.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention discloses an information management method based on a big data processing technology, and the method comprises the steps: S1, data collection and pre-processing optimization: S1.1, multi-source data cleaning and pre-processing: automatically detecting and correcting errors, missing and abnormal values in data through an integrated intelligent data cleaning technology, such as a machine learning algorithm (such as abnormal detection and missing value filling); the data quality is ensured, data standardization and format unification tools are adopted, heterogeneous data from different sources are converted into a unified format, and follow-up processing is facilitated. The problems of data privacy, quality, real-time performance, complexity and compliance in a traditional big data method are solved by enhancing data quality management, improving data security, optimizing storage and processing architecture and introducing modes such as real-time processing and intelligent analysis. Meanwhile, the expandability and the fault-tolerant capability of the system ensure the high efficiency and the stability of the system in a large-scale data environment, and a more intelligent, safer and more efficient information management solution is provided for enterprises.

The invention relates to the technical field of industrial distributed control system architecture, and discloses a dual-redundancy design method for a virtual controller VDPU, which comprises the following steps of: deployment of a four-network-port redundant network architecture: dividing four physical network ports into two groups of independent channels, and simultaneously configuring a network link automatic switching mechanism, the working states of the four network ports are monitored in real time, then double isolation of a physical layer and a protocol layer is achieved, and an independent heartbeat transmission channel is established; main / standby heartbeat monitoring and switching control: the main / standby controller periodically interacts state information packets through a special heartbeat channel, and establishes a three-level fault determination mechanism at the same time to perform seamless main / standby switching; a multi-mode data synchronization mechanism is implemented, namely initialization synchronization, increment synchronization during operation and exception recovery synchronization are started, separation of heartbeat signals and data synchronization can be realized through two groups of independent channels, and the fault-tolerant capability and the switching efficiency of the system are improved, so that the operation stability of the system is guaranteed.

The invention relates to the field of air conditioning unit control, in particular to an industrial park elevator system optimization control system and a method thereof.The hardware part comprises a sensor unit, an executing mechanism, a field control unit, communication equipment and a server unit; the software part comprises a real-time operating system, a sensor data acquisition and fusion preprocessing module, a digital twinborn simulation platform, a model prediction control module and a cloud deep reinforcement learning module; the method comprises the steps of S1, data acquisition and primary processing, S2, data transmission and caching, S3, digital twin modeling and system simulation, S4, optimization control strategy generation and issuing, S5, online scheduling and deep reinforcement learning optimization, S6, safety monitoring and exception handling, and S7, closed-loop feedback and continuous optimization. Efficient integration of elevator modules is achieved through digital twinning and model prediction control cooperation; intelligent data processing is realized through deep reinforcement learning and adaptive data fusion; and safe and energy-saving operation is realized through fault tolerance and safety monitoring.

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention relates to the technical field of control adjustment, in particular to an intelligent multi-head welding synchronous control system which comprises a data processing module, a modular cooperative control module, a load sensing module, a data transmission module, a self-adaptive pose execution module and a fault-tolerant module. The data processing module extracts three-dimensional geometrical characteristics of a workpiece through laser scanning, PID control parameters are migrated across working conditions in combination with a dynamic time warping algorithm, the inverse kinematics process is optimized, and the self-adaptive pose execution module drives phase synchronization of multiple welding heads based on a global reference trajectory and predicts and compensates thermal deformation errors through a thermal expansion coefficient. And the fault-tolerant module adopts a wavelet packet decomposition identification fault mode, and updates a historical parameter library in combination with an elastic weight algorithm. According to the method, three-dimensional space pose error accumulation caused by mechanical load fluctuation and signal asynchronization is inhibited through cooperation of multiple modules, and the welding process uniformity and the system dynamic stability are improved.

The invention relates to an unmanned system cluster distributed task cooperative scheduling method and system. The system comprises a ground station general control scheduling module, an unmanned aerial vehicle cluster and a distributed monitoring and scheduling agent module. The method comprises the following steps: calculating the static priority of each task in a DAG task graph; tasks with high priorities are scheduled firstly; sequentially selecting a node with the minimum power consumption perception completion time EA-EFT from all the unmanned aerial vehicles, and allocating a task to the node; each monitoring scheduling node monitors the task execution progress in real time; and after the node fails, task allocation is stopped, the tasks are rescheduled according to the priorities of the tasks, the optimal allocation nodes of the tasks are recalculated based on an EA-EFT model, and a recommendation mechanism is triggered to take over the tasks of the failed nodes after each scheduling fails. The problems that an unmanned aerial vehicle cluster scheduling scheme is poor in adaptability, low in efficiency and incomplete in fault-tolerant mechanism are solved, and the scheduling efficiency, the system robustness and the energy efficiency of an unmanned aerial vehicle cluster in a complex task scene are improved.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention belongs to the technical field of power system simulation application, and particularly relates to a power system simulation optimization method based on data driving. Based on a data-driven dynamic adaptive space-time segmentation and cooperation model, a fast-varying electromagnetic transient process is mapped to a quantum processor, a slow-varying electromechanical transient process is reserved in a classical computer, a power grid node voltage is coded into quantum bit amplitude, a power grid is dynamically equivalent to quantum Hamiltonian, and a quantum Hamiltonian is obtained. And decoupling the quantum domain of the fast-changing electromagnetic transient state and the classical domain of the slow-changing electromechanical dynamic state to realize self-adaptive balance simulation precision and efficiency. Quantum calculation sparse codes and dynamic error correction are constructed, non-uniform code distance distribution is achieved, cross-scale data transmission errors are reduced, high-frequency harmonic distortion is avoided, high fault-tolerant simulation is achieved under limited quantum resources, code distances are dynamically adjusted through reinforcement learning of a DDPG strategy, noise fluctuation and power grid working condition changes are responded in real time, and resources and fault-tolerant balance are optimized.

The invention discloses an integrated machine room operation and maintenance management system, which relates to the field of information technology management and comprises a plurality of modules such as a distributed data acquisition module, a heterogeneous equipment adaptation module and a standardized communication interface module. The system realizes module cooperation through a two-way synchronous lock mechanism and an intelligent load sensing mechanism; the distributed acquisition module realizes millisecond-level high-precision data synchronization by using an ultra wide band and millimeter wave combined sensing technology; the heterogeneous equipment module eliminates communication compatibility obstacles; the standardized communication module is fused with an optical fiber and a dynamic arbitration mechanism to realize microsecond-level low-delay communication; the resource virtualization module supports unified abstraction and low-delay scheduling of physical resources; the expansion module can complete new node integration within three seconds; the fault-tolerant module can early warn faults ten minutes in advance and realize quick recovery; the intelligent decision-making module optimizes an operation and maintenance strategy based on edge reinforcement learning; and the interaction module supports multi-terminal state reproduction and programming control. The method has the beneficial effects that automation, intelligence and reliability levels of machine room operation and maintenance are improved.

The invention relates to the field of power equipment data processing, in particular to a multi-target dynamic optimization unit load intelligent distribution method, which adapts a micro-service cluster through a containerization protocol, adopts an OPC UA protocol and an MQTT protocol to respectively collect thermal power and new energy data at different frequencies, dynamically analyzes an IEC 61850 power grid instruction, converts and stores the IEC 61850 power grid instruction into a Kafka message queue, and sends the IEC 61850 power grid instruction to a network server. The problem of communication protocol heterogeneity is solved. Based on an IEEE 1588 protocol, sub-millisecond clock synchronization of edge nodes is realized, a cubic spline interpolation algorithm is adopted to align multi-frequency data, an interpolation window is dynamically adjusted in combination with a genetic algorithm, and a spatial-temporal characteristic matrix is generated and stored in a Redis database. And performing multi-objective optimization solution through an NSGA-II algorithm, and dynamically adjusting the weight of an objective function to adapt to the frequency modulation requirement of the power grid. A closed-loop feedback mechanism corrects model parameters through Kalman filtering, actual data are written back to a feature matrix, the robustness of the system is improved in combination with a hierarchical fault-tolerant strategy, the problem of space-time mismatch of data is effectively solved, and the economical efficiency and safety of load distribution are improved.