147 results about "Resource adaptation" patented technology

The invention relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

The invention relates to the technical field of data storage, can be applied to business scenes such as medical health and financial science and technology, and discloses a multi-Pool storage resource adaptive writing method which comprises the following steps: monitoring available storage space, read-write input / output rate, average response time and data access frequency of each Pool; dynamically adjusting the current write-in weight of the Pool according to the monitoring result; converting the current write-in weights of all Pool into probability values, and constructing a discrete probability distribution table; selecting a target Pool through a pseudo-random number generator according to the probability distribution table; and writing the to-be-stored data into the target Pool. According to the method, the running state of each Pool is continuously monitored, and the writing weight is adjusted in real time, so that the problem of resource allocation rigidity under a fixed weight strategy is avoided; in combination with a discrete probability distribution table constructed based on normalized weight and a pseudo-random selection mechanism, selection of the target Pool has certain load balancing elasticity, and data can be more effectively guided to be written into the Pool with light load and excellent performance.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention provides a dynamic configuration computing resource management method and system based on a power wireless terminal, and the method comprises the steps: obtaining a real-time operation state data set and a power business demand data set of the power wireless terminal, and carrying out the multi-dimensional feature fusion processing of the real-time operation state data set and the power business demand data set; the method comprises the steps of generating a resource adaptation feature set, performing real-time strategy matching processing on the resource adaptation feature set based on a preset resource adaptation rule base, generating a computing resource configuration strategy set, performing adjustment operation on computing resources of the power wireless terminal, and generating a resource allocation verification result and terminal operation efficiency feedback data. And continuously comparing the terminal operation efficiency feedback data with a preset efficiency optimization threshold value, and carrying out iterative updating processing on the resource adaptation rule base according to a comparison result. According to the invention, accurate matching between computing resource allocation and power business demand fluctuation can be realized.

The invention provides a computing power resource management and distribution method and system and a medium. The method comprises the following steps: integrating various computing power resources in a private network, establishing a computing power resource information base, analyzing a computing power resource request, establishing a service demand model, optimizing and matching a computing power resource configuration template, obtaining a computing power adaptation template, and establishing a service demand model; optimal computing power resource adaptation is carried out according to dynamic information of target computing power resources matched with the computing power adaptation template, isolation processing is carried out at the same time, monitored performance index data are processed, a load trend is predicted, a corresponding computing power resource adjustment strategy is obtained, and the computing power resources are optimized and expanded according to big data analysis; therefore, efficient utilization and elastic expansion of the computing power resources of the private network are realized by constructing the dynamic computing power resource pool and the resource scheduling model and monitoring, adjusting and optimizing an expansion mechanism, and the processing efficiency and security of big data services are remarkably improved.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

The invention relates to a lightweight asynchronous message processing method and system for a high-concurrency scene, and the method comprises the steps: receiving a message request of a client, carrying out the preprocessing of the message request, and generating a to-be-processed message; distributing the to-be-processed message to a multi-level priority queue based on a priority scheduling mechanism; dynamically adjusting the number of core threads and the maximum number of threads of the thread pool according to the resource state data, and forming a thread pool resource configuration scheme of the current scheduling period; dynamically calculating the message processing quantity of the current scheduling batch based on the message accumulation quantity of each priority queue, the thread pool resource configuration scheme, the CPU utilization rate in the resource state data and the JVM heap memory utilization rate; and based on the out-of-heap memory buffer area reference, assigning the to-be-processed message object to a corresponding processing thread to execute a service processing logic until the processing operation of the messages in all the scheduling units is completed. The method has the effect of improving the resource adaptability of the asynchronous message processing architecture.

Embodiments of this application provide an adaptive data flow transmission method, an apparatus, and a system, relating to the field of communication technologies. The method is used to perform, when a frame rate of a downlink data flow of a service changes, network resource adaptation promptly based on the frame rate of the data flow, to avoid a waste of network resources or a packet loss of media frame data, so as to improve network resource utilization and user experience.

The invention relates to the technical field of industrial networks, in particular to a time-sensitive service flow resource adaptation method for large-bandwidth multicast flow awareness, which comprises the following steps: S1, a controller collects registered service flow information and real-time network state information; s2, after service flow priorities are divided according to the information, a mapping relation between service flows and multiple queues is established, and a forwarding rule is established according to the mapping relation; s3, establishing an optimization target and a resource constraint according to the mapping relation and the forwarding rule; and S4, designing a Markov decision according to the optimization target and the resource constraint, training the neural network model according to the Markov decision and the double-delay depth deterministic strategy gradient so as to output a scheduling decision, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. According to the invention, a basic queue model support is provided for end-to-end deterministic transmission of the unicast stream and the multicast stream through the mapping relation and the forwarding rule, the utilization rate of network resources and the number of service streams capable of being subjected to incremental scheduling are improved, and the worst end-to-end delay of the large-bandwidth multicast stream is reduced.

The invention relates to the technical field of access control, in particular to a method for controlling multi-user concurrent access of a server, which comprises the following steps of: analyzing task types and resource demand characteristics requested by users in real time, and generating a triple comprising a calculation mode identifier, a data topology fingerprint and a time constraint factor; performing space-time coupling matching on the triad and a server node, calculating a space-time weight score, and generating a binding execution unit based on the space-time weight score; quantum fragmentation scheduling is carried out on the bound execution unit, a reverse dependence barrier is inserted between adjacent atomic operation sequences, and execution track data is generated in execution; and adjusting calculation mode matching through an execution track feedback mechanism, and performing calculation mode identifier remapping of an unexecuted task by utilizing the path efficiency of the completed task. According to the method, the computing resource adaptation deviation of the task is effectively reduced, the remote data access overhead is reduced, and the task throughput and the execution stability are improved.

The invention relates to the technical field of emergency rescue intelligent decision, and discloses an emergency rescue management system and method based on rescue timeliness. The method comprises the following steps: constructing a space-time three-dimensional grid by fusing multi-source information, and performing dynamic scene reconstruction by using a multi-level mapping rule to obtain preliminary and deep rescue scenes. And after combined anomaly diagnosis is carried out on the double scenes, a task topological structure is driven to dynamically evolve according to a diagnosis report, and a rescue task sequence is generated. And then cross-dimension resource adaptation is executed, a resource list is formed, key bottleneck nodes are predicted and identified in combination with real-time feedback, and finally a rescue instruction chain is generated. According to the method, deep fusion and structured analysis of field information in a space-time dimension are realized, a closed-loop decision-making mechanism with task and resource dynamic cooperation and adaptive optimization is established, and timeliness and overall toughness of rescue actions in a complex disaster environment are enhanced.

The invention discloses an RDMA asynchronous communication optimization method and system based on a lock-free queue, a medium and a processor, and relates to the technical field of remote communication. The method comprises the following steps: constructing a lock-free annular buffer area at an edge node, registering the lock-free annular buffer area as an RDMA memory area, and configuring a queue and a thread pool; the collection thread concurrently writes data, and the RDMA submission thread asynchronously completes remote transmission based on a buffer area state; and the queue processing thread performs closed-loop linkage transmission and buffer area states through'batch polling-result analysis-state synchronization-exception processing ', and dynamically adjusts the buffer area and queue parameter optimization performance at the same time. The problems that an existing RDMA scheme is poor in thread cooperation, weak in resource adaptation, not timely in state linkage and the like are solved, transmission delay and CPU overhead are greatly reduced, communication stability and the resource utilization rate are improved, and the method is suitable for edge computing and other high-concurrency data interaction scenes.

The invention relates to the technical field of communication networks, in particular to a time slot management method and system for a TDMA (Time Division Multiple Access) communication network. The method comprises the following steps of: setting a service priority according to a preset rule, and performing priority judgment on each accessed service request; executing a time slot resource allocation strategy according to the service priority in combination with the current available time slot resource condition; obtaining business historical data, and carrying out data preprocessing and feature extraction; utilizing a reinforcement learning model to train and establish a mapping relation between the business historical data and the time slot width configuration; and a high-priority guarantee mechanism is combined with an exclusive guarantee mechanism to realize the adaptation of the time slot width and the broadband demand. According to the method, the resource adaptation precision is improved by dynamically adjusting the time slot width. And intelligent optimal configuration of the time slot width is realized by analyzing historical business data characteristics and combining a reinforcement learning algorithm. A time slot management system with determinacy and flexibility is constructed by fusing hard real-time guarantee of static time slots and elastic scheduling of dynamic time slots.

The invention discloses a distributed audio and video collaborative production system based on a cloud architecture, and relates to the technical field of audio and video processing and cloud computing, the distributed audio and video collaborative production system comprises a cloud service platform, a distributed storage module, an audio and video processing node and a client, the cloud service platform is based on a micro-service architecture, containerization and Kubernetes management are used, tasks are accurately distributed by means of a resource adaptation factor formula, and the cloud service platform is used for storing the tasks. The distributed storage module adopts a Ceph system, guarantees data reliability through a data robustness formula, carries out hierarchical storage according to data value density, distributes audio and video processing nodes in multiple places, is matched with professional hardware, processes audios and videos through a high-speed network connection platform, and has a visual interface at a client. The system has obvious advantages, the cloud platform accurately distributes tasks to improve efficiency, the storage module guarantees data reliability and hierarchical storage to reduce cost, the processing node processes audio and video with high performance, the client side is convenient to operate, the real-time cooperation and intelligent auditing module guarantees collaboration and content compliance, an interface is opened to expand an application scene, and the system competitiveness is enhanced.

The invention provides a space-air-ground three-dimensional communication cross-network cooperative reliable transmission method, device and system. The system comprises a generalized service layer, a resource adaptation layer and a fusion network layer, the generalized service layer receives a service request initiated by a user, analyzes the service request to obtain a service attribute, and sends the service attribute to the resource adaptation layer; the resource adaptation layer performs service resource adaptation mapping by using a reinforcement learning algorithm according to the service attribute and the current network state information, generates a service deployment strategy and issues the service deployment strategy to the fusion network layer; and the fusion network layer schedules a corresponding network component according to the received service deployment strategy, and completes path selection, forwarding scheduling and coding strategy application tasks corresponding to the service request. The system has multi-protocol identification and unified packaging capabilities, and can receive multiple data formats and carry out standardized processing. According to the method, multi-path flexible scheduling, network coding and covert channel methods are integrated, and the limitation of an existing communication system in the aspects of protocol compatibility, transmission efficiency and reliability is broken through.

The invention provides a load balancing algorithm system of a server cluster, which relates to the field of computer server clusters and comprises a priority management module, a resource monitoring module, a load distribution module, a cooperative communication module, a cost evaluation module and a large flow algorithm splitting and sequential execution module. The large-scale process algorithm splitting and sequential execution module comprises a process splitting unit, a sequential arrangement unit and a load balancing execution unit. The method has the beneficial effects that a large-scale process algorithm is split and sequenced, so that the method runs according to a specified sequence under the balance of a plurality of loads, and the complex service processing capability of the system is improved; and meanwhile, algorithms such as service priority, resource adaptation, distributed cooperation and cost benefit optimization are comprehensively applied, the resource utilization rate, the system performance and the cost benefit are improved, the load balancing effect of the server cluster is enhanced, a distributed cooperation mechanism avoids a single-point fault, local load changes can be quickly responded, and the stability and availability of the cluster are improved.

The invention provides a cross-heterogeneous computing power scheduling method based on an AI (artificial intelligence) agent, which utilizes the advantage that the AI agent can dynamically generate or adjust task nodes and dependency relationships thereof, adopts a dynamic recursion splitting method to split tasks, and performs task computing property evaluation on subtasks by corresponding agent members to form a computing power demand scheme. And the computing power scheduling agent transmits the computing power demand to the resource adaptation module, forms a computing power available scheme, performs matching adjustment with a computing power demand scheme, and completes task processing. According to the method, the most suitable computing power type is dynamically judged and selected through the intelligent agent, the task processing cost can be controlled, the cross-node communication delay can be reduced, for the tasks in sequence, the continuity between the tasks is ensured, the waiting time is shortened, and higher-quality and more efficient computing power distribution services are provided for users.

The invention discloses a resource integration platform and a resource optimization configuration method for kindergarten integrated service, and relates to the field. According to the resource optimization configuration method for the kindergarten integrated service, resource service data of each functional area of the kindergarten integrated service to be optimized is obtained and comprehensively analyzed, and a resource pressure bearing index of each functional area is obtained; obtaining infant individual care characteristic data of each functional area, performing data analysis to obtain a comprehensive care demand index of each functional area, and performing comprehensive analysis in combination with the resource pressure bearing index to obtain a care resource regulation index of each functional area; according to the invention, the preset resource optimization configuration measures are taken for each functional area based on the nursing resource regulation and control index, so that the actual nursing load state of the functional area can be accurately reflected, and manpower tension and space waste caused by the fact that the actual nursing demand of the infant is not matched are avoided; and therefore, the nursing matching accuracy and the overall resource adaptation level can be improved.

The invention relates to the technical field of heterogeneous computing power allocation, in particular to a heterogeneous computing power dynamic allocation method based on big data analysis, which comprises the following steps: analyzing a source, a target, a type and dependency of a data task, normalizing parameters and extracting features, identifying a key task, combining a computing power resource queue and historical scheduling efficiency, optimizing an allocation state, and allocating the heterogeneous computing power according to the allocation state. And screening the computing power resources which run normally, analyzing the dominant frequency temperature and response performance, adjusting the distribution relation, and forming a dynamic distribution sequence. According to the method, standardization, normalized mapping and feature aggregation are carried out on the data task multi-dimensional labels, deep mining and classification of task features can be achieved, dynamic linkage of task requirements and computing power resource states is completed according to big data analysis, relevance between the tasks and heterogeneous computing power resources is finely quantified, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. And in combination with a resource response expression and grading mechanism, a task allocation strategy is automatically adjusted, and the resource adaptation degree and allocation sequence matching during task processing are guaranteed.

The invention relates to the technical field of industrial networks, in particular to a clustering network flow scheduling method based on star flash, which comprises the following steps: S1, collecting service flow information and cluster node state information according to a management G node and a terminal T node; s2, establishing a clustering network flow scheduling model according to the service flow information and the cluster node state information; s3, establishing target optimization and constraint conditions according to the clustering network flow scheduling model; and S4, designing a Markov decision according to target optimization and constraint conditions, training a neural network model according to the Markov decision and a multi-agent near-end strategy, outputting a scheduling decision according to the trained neural network, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. And the service flow is transmitted according to the multi-dimensional resource adaptation result and a multi-queue unicast / multicast flow shaping mechanism. According to the invention, the load balancing capability of the star flash clustering network system can be improved, and the end-to-end transmission delay of the service flow can be reduced at the same time.

The invention provides an intelligent content resource matching method and system for innovation and entrepreneurship tutoring demands, and relates to the technical field of natural language processing, and the method comprises the steps: firstly obtaining a demand interaction data set of a target user in an innovation and entrepreneurship tutoring scene, including a plurality of historical tutoring session records and corresponding user feedback annotation information; through tutoring demand feature extraction processing, a demand semantic association feature and a resource adaptation feature set of a target user are obtained. Then, multi-level strategy matching processing is carried out based on a preset strategy adaptation network, a resource adaptation strategy set of the target user is generated, dynamic strategy adjustment is carried out on the resource adaptation strategy set according to user feedback labeling information, and an optimized resource adaptation strategy set is obtained; and the optimized resource adaptation strategy set is sent to the resource scheduling engine to trigger the tutoring resource matching operation, so that intelligent matching of the innovation and entrepreneurship tutoring resources is realized, and the matching efficiency and accuracy are improved.

The invention relates to the technical field of agricultural robots, in particular to an agricultural robot computing task collaboration method and system, and the method comprises the following steps: S1, environment perception and data collection: collecting environment perception data of a farmland operation region in real time; s2, generating a dynamic task priority queue: generating the dynamic task priority queue; s3, heterogeneous computing resource adaptation model construction: constructing a heterogeneous computing resource adaptation model, and outputting a task-resource matching matrix; s4, generating an initial task allocation scheme: generating the initial task allocation scheme; s5, environment interference monitoring and compensation: adjusting the initial task allocation scheme; and S6, task execution and queue updating: executing the adjusted initial task allocation scheme, and continuously updating the dynamic task priority queue. According to the method, the working efficiency can be remarkably improved while the working precision is ensured, the working time is shortened, the resource allocation is optimized, and the manual intervention is reduced.

The invention discloses an APT (Advanced Persistent Threat) attack defense method based on edge intelligence, which aims to improve the dynamic perception capability, strategy response capability and resource adaptability of an edge network to APT attacks, and comprises the following steps of: firstly, constructing a topological adjacency matrix of an edge node communication network and defining five states and state conversion processes of a life cycle of edge equipment; the method comprises the following steps: firstly, designing an attack defense model based on optimal control and differential game, then introducing a system evolution model based on hidden confrontation, and accurately simulating dynamic interaction of attack and defense in an actual edge network, secondly, designing an attack defense model based on optimal control and differential game, and finally, adopting a Nash strategy reinforcement learning mechanism based on a multi-agent deep Q network, optimizing an edge game strategy, and finally, obtaining an attack defense result. And the attack detection performance is improved. According to the method disclosed by the invention, the modeling precision and defense effectiveness of the system in a complex APT attack scene are remarkably improved, and the method is suitable for key infrastructure network environments with relatively high requirements on safety, timeliness and expandability, such as industrial Internet and Internet of Things.

The invention discloses a double-target cloud workflow scheduling method under an edge heterogeneous cluster, which constructs a double-target optimization model aiming at an edge heterogeneous cluster application scene, and maximizes the service quality while satisfying the energy consumption constraint. A scheduling algorithm based on distributed mixed integer programming is designed on the basis of decomposition of an original problem, energy consumption and service quality are optimized according to processor node characteristics and load conditions, and scheduling of an edge heterogeneous cluster is achieved. The edge heterogeneous computing cluster scheduling can be effectively balanced in the aspects of multi-objective optimization, heterogeneous resource adaptation, real-time performance, dynamic performance and the like, so that an efficient computing solution is provided for the fields.

The invention discloses an enterprise innovation force cloud evaluation and resource adaptation system and method, and relates to the technical field of enterprise innovation management, and the method comprises the following steps: periodically obtaining industry life cycle data, innovation sign data and external data source data; performing weighted average calculation on the industry life cycle data, the innovation sign data and the external data source data to obtain fused innovation sign data; and performing sliding window processing on the fused innovative sign data in the same period, and outputting a trend curve of the fused innovative sign data. According to the scheme, interference caused by industry periodic fluctuation is effectively eliminated through reference curve dynamic alignment and continuous window joint judgment, meanwhile, the abnormal starting time is positioned in combination with the trend curve, an accurate time anchor point is provided for subsequent causal analysis, the continuous abnormal state in the fused innovation sign data can be accurately recognized, and the accuracy and the reliability of the fused innovation sign data are improved. And misjudgment caused by single fluctuation is avoided.

The invention relates to an impeller machining production line resource dynamic scheduling method and system, and relates to the field of scheduling management. Impeller machining logs are subjected to in-service difference weight distribution by using models and service durations of five-axis machining, blade polishing machining, heat treatment machining and dynamic balance debugging equipment on a target production line; and sorting and centralized value evaluation are carried out on the process durations of the processing logs according to the weight distribution to obtain the centralized durations of various processing, and the impeller processing production line resource scheduling is executed on the basis of the centralized durations, so that the problem that the process occupation durations of the impeller processing production line cannot adapt to the actual operation state and dynamic change of production line equipment for resource scheduling is solved. The technical problem that the overall production efficiency and the product quality are affected due to low resource scheduling precision is solved, and the technical effects that resource adaptive scheduling adjustment can be performed in combination with the actual operation state and the dynamic change of the production line equipment, so that the resource scheduling precision and the production efficiency are improved are achieved.

The invention relates to the technical field of intelligent warehouse logistics, and discloses an intelligent warehouse logistics sorting system and method, and the system comprises a multi-dimensional data collection module, a global state perception and simulation model construction module, a demand prediction analysis module, a task optimization and resource configuration module, and an execution control and feedback adjustment module. The system realizes dynamic demand prediction, task optimization division and resource adaptation through a distributed sensing network, a three-dimensional simulation model and a deep learning algorithm, and ensures material storage safety in combination with environment adaptive regulation and control. The warehouse sorting efficiency can be improved, the resource allocation is optimized, the environmental adaptability is enhanced, and the method is suitable for modern intelligent warehouse scenes.

The invention provides a railway information infrastructure security management and control system based on data processing. The system improves comprehensive detection and dynamic defense capabilities for railway network security threats. The method comprises the following steps: firstly, collecting network behavior elements at the edge of a network in real time by deploying a storage, calculation and transmission integrated unit, and generating a behavior chain data set by adopting clustering compression; thirdly, performing time sequence verification of compliance, credibility and logicality on the behavior chain based on a three-white baseline, and outputting a risk level and an attack path; and finally, in combination with a software-defined network controller, implementing multi-level elastic defense strategies such as link blocking, access control and information recording according to risk levels, and dynamically adjusting a detection threshold and an acquisition frequency through a closed-loop optimization mechanism. Through a sensing-evaluation-defense-optimization closed loop structure, the safety protection precision, the response efficiency and the resource adaptation capability of the railway information system are remarkably improved.

The application discloses a computer scheduling method for multitask operation environment state, and particularly relates to the technical field of computer operating system; the application generates a priority scheduling coefficient by quantitatively calculating a resource adaptation coefficient, a urgency coefficient and a constraint reflection coefficient, and then combining a weighting logic, so as to ensure that high-urgency tasks are preferentially executed to reduce postponement, avoid system overload or overheating through resource adaptation and thermal constraint control, and reserve reasonable heat production space for high-priority tasks, and balance of task execution efficiency, resource utilization and hardware safety is realized.