1331 results about "Directed acyclic graph" patented technology

The invention provides an electric power line health state assessment prediction method and system based on big data, and relates to the technical field of electric power line health assessment, and the method comprises the steps: preprocessing historical operation data, generating standardized data, extracting fault features through employing a space-time heterogeneous graph network, constructing a basic hierarchical knowledge base for automatic marking, and carrying out the prediction of the health state of an electric power line. Finally generating a training sample set; a directed acyclic graph is modeled, a main transmission line is identified, risk sections are divided, a knowledge base is optimized, an inspection scheme is adaptively generated based on an inspection strategy model of reinforcement learning, the health state of a power line is evaluated, and a health state report is generated; according to the invention, the fault prediction accuracy and inspection efficiency of the power line are improved.

The invention discloses an electromechanical construction method and system based on a BIM technology, and relates to the field of electromechanical construction, and the method comprises the steps: collecting a three-dimensional point cloud of a construction site; a construction site buult BIM model is constructed; constructing an electromechanical BIM model of the construction site; obtaining a logic dependency relationship between electromechanical tasks in the electromechanical BIM model by utilizing a judgment matrix method DSM, and extracting attribute parameters of task nodes; constructing a directed acyclic graph (DAG) for task collaborative optimization according to the logic dependency relationship between the electromechanical tasks and the time sequence and resource parameters of the nodes; obtaining task nodes on the critical path according to a directed acyclic graph (DAG) of task collaborative optimization; establishing a multi-objective optimization model according to task nodes on the critical path, and solving through a multi-objective particle swarm optimization algorithm MOPSO by taking the shortest construction period and the highest resource utilization rate as optimization objectives and taking process logic and resource occupation as constraint conditions to obtain a resource optimization configuration set; and the collaboration of electromechanical construction tasks is improved.

The invention relates to the technical field of intelligent operation and maintenance monitoring and self-adaptive rule engines, in particular to a monitoring strategy system and method based on a rule base. The method comprises the following steps: constructing a unified state vector model through multi-source heterogeneous data collection, enhancing state representation in combination with context semantics, and verifying the validity of the state representation; a dynamic rule cutting mechanism is adopted, a graph structure is constructed based on semantic redundancy and conflict relations between rules, and an optimal non-redundant rule subset is screened in combination with a greedy cutting algorithm; generating a strategy graph through rule semantic fusion, aggregating semantics by using a graph neural network, constructing a directed acyclic graph to solve action conflicts, and generating a safe and efficient response sequence; a cross-scene migration mechanism is introduced, and source scene strategy semantics are projected to a target scene through a mapping matrix, so that lightweight migration and self-evolution of a knowledge base are realized. According to the method, the dynamic adaptability of the rule base is improved, the redundancy execution risk is reduced, and multi-scene seamless migration is supported.

The invention provides an industrial robot distributed collaborative debugging method and system, and relates to the technical field of distributed control, and the method comprises the steps: building a directed acyclic graph model of an industrial robot collaborative debugging task, analyzing the directed acyclic graph model through a critical path method, and determining a critical path of the collaborative debugging task; reliability modeling is carried out on each industrial robot node in the collaborative debugging task, and the failure probability of each collaborative debugging sub-task under the current scheduling scheme is calculated in combination with the time redundancy characteristics of each collaborative debugging sub-task; according to the obtained directed acyclic graph model of the industrial robot collaborative debugging task, the key path of the collaborative debugging task and the failure probability of each collaborative debugging sub-task under the current scheduling scheme, defining a state space of a task scheduling scheme, searching the state space through a dynamic planning algorithm, and scheduling the task scheduling scheme according to the searched state space; and obtaining a scheduling scheme with the minimum global failure probability as an optimal fault-tolerant scheduling strategy at the current moment.

The invention relates to the technical field of cloud computing, in particular to an intelligent capacity planning method and system in a hybrid cloud environment. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, heterogeneous resources in a hybrid cloud are modeled by using a declarative description language, a dependency relationship among the resources is identified, and a directed acyclic graph (DAG) is constructed; the resource operation tasks are divided in batches, and the tasks without the dependency relationship are executed in parallel; synchronizing the running state of each resource in real time, and performing state consistency verification and abnormity marking; triggering a retry mechanism when an exception occurs; and after the resource arrangement process is completed, summarizing data in the whole process, and performing performance evaluation and strategy optimization on resource operation. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, the use condition of resources can be monitored and analyzed in real time, the resource demand can be accurately predicted, a reasonable resource scheduling strategy can be generated, manual intervention is reduced, and elastic expansion and optimal configuration of cloud resources are realized.

The invention provides an SLA demand-oriented computing network multi-element fusion arrangement method, which comprises the following steps of: matching a demand timeliness priority queue with a directed acyclic graph of container arrangement, and searching an arrangement scheme with optimal resource utilization efficiency and most balanced load by applying a dynamic planning method to form an initial arrangement decision; in combination with a preset calculation network element arrangement strategy template, dynamic selection and adjustment are carried out according to actual conditions, including selection of an arrangement strategy with a preferential resource utilization rate in a service peak period and selection of an arrangement strategy with a preferential cost in a service valley period; and recording and analyzing various calculation network elements generated in a container arrangement process, including resource occupation, starting time, a dependency relationship and an arrangement decision, and optimizing a resource prediction and scheduling algorithm according to historical data.

The invention provides a multi-model collaborative knowledge graph construction method, system and device and a storage medium, and the method comprises the steps that a routing engine receives a knowledge graph construction request, matches a preset rule base according to target domain parameters, and generates a node assembly sequence; the scheduling engine constructs a task execution directed acyclic graph according to the task execution directed acyclic graph; the execution engine preprocesses the original document according to the graph to generate a structured document block set with metadata; calling a pre-training model in the small model resource pool to perform entity and relation extraction to form a preliminary entity set and an association relation set; attribute completion and implicit relation reasoning are carried out on the preliminary entity set, and a completion entity attribute set and a newly-added relation set are generated; performing entity alignment on the preliminary entity set and the complemented entity attribute set by the large model to obtain a fused entity set; and performing conflict resolution on the incidence relation set and the newly-added relation set to obtain a fusion relation set. And storing the fusion entity set and the fusion relationship set to a knowledge graph database.

A system and method for facilitating collection of Road User Charges (RUC) using a digital currency within a Distributed Ledger Technology (DLT) network are disclosed. The system generates secure digital wallets for Road Users and Facility Owners, receives trip, position, distance, and cost data from vehicles, sensors, and facility systems, and encodes settlement logic into smart contracts. These contracts automatically execute jurisdiction-specific disbursements across blockchain, directed acyclic graph (DAG), or hashgraph frameworks, ensuring scalability and auditability without reliance on centralized tolling infrastructure. An AI Activity Module processes multi-source telemetry—including vehicle sensors, GNSS / PNT data, roadside infrastructure, and third-party traffic feeds—to detect congestion, forecast conditions, optimize routing, and dynamically adjust segment-level RUC pricing in real time. The integration of AI-driven adaptive pricing with distributed ledger settlement provides a decentralized, infrastructure-agnostic framework for secure, transparent, and verifiable road use fee collection across multiple jurisdictions, distinct from conventional tolling systems.

Systems and techniques for are described herein. Natural language workflow descriptions are received from expert users, the descriptions are processed using language models to identify action sequences, and directed acyclic graphs (DAGs) are generated that represent workflows by mapping actions to automated agents. These DAGs are stored as composite action recipes in a vector database. End users can then submit natural language queries, which the system matches to stored recipes. Selected recipes are executed in a sandboxed environment, with the system sequentially invoking automated agents according to the DAG structure. The systems and techniques described herein provide real-time feedback during execution and support features such as graphical workflow validation, dynamic parameter collection, role-based access control, and dry run simulations. This approach enables efficient creation and reuse of complex workflows based on natural language inputs.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention relates to the technical field of script execution, and provides a multi-script execution method for a virtual controller, and the method comprises the steps: setting a script structure, a graph model structure and a graph node structure; the method comprises the following steps: performing grammar and semantic check on script data to be executed, converting the script data into a script object according to a script structure, converting the script data into a directed acyclic graph according to a graph model structure, and traversing the directed acyclic graph to obtain an execution sequence of graphs in each level of the directed acyclic graph; and scheduling the script object according to an FIFO algorithm, traversing the directed acyclic graph during scheduling to obtain graph nodes in each execution sequence, and performing interpretive execution on the script object according to an ignition rule and the graph nodes in each execution sequence. The method aims at analyzing and executing various scripts, the reliability of the virtual production line is further improved, and the use cost of the platform is reduced.

The embodiment of the invention discloses a multi-agent task collaboration method and device and a medium, and the method is characterized in that the method comprises the steps: registering functions corresponding to all agents to a dynamic service directory through an MCP protocol; disassembling the to-be-executed task to obtain a plurality of to-be-executed sub-tasks, matching each to-be-executed sub-task with the capability range of each agent in the dynamic service directory, and establishing a communication channel between each to-be-executed sub-task and the corresponding agent; performing dynamic routing distribution on the to-be-executed sub-tasks through the cooperative bus, and selecting an optimal execution node according to the real-time requirements of the sub-tasks and the node load state of each corresponding agent; according to a subtask dependency relationship defined by a directed acyclic graph, determining an execution mode of each subtask to be executed; and executing each to-be-executed sub-task at the optimal execution node based on the communication channel and the execution mode of each to-be-executed sub-task.

The invention relates to the technical field of multi-agent collaboration, and discloses a multi-agent collaboration system with a task automatic decomposition and hierarchical rechecking mechanism. The system comprises a task planning framework construction module, a dynamic role matching module, a layered review triggering module and a cooperative execution monitoring module. Wherein the task planning framework construction module is used for generating a directed acyclic graph task framework consisting of a plurality of logic dependence subtasks after receiving a top-layer complex task; the dynamic role matching module screens skill matching degrees based on sub-node requirements, and dynamically binds execution agent identifiers and sub-task nodes; the hierarchical review triggering module is used for embedding configurable review nodes in a task framework, selecting a review hierarchy of AI cross validation or manual approval according to a sub-task risk level, and generating a node review strategy set; and the cooperative execution monitoring module generates a task execution track sequence according to the node state flow. According to the system, the efficiency and reliability of multi-agent cooperative processing of complex tasks are improved.

The invention provides a distributed storage method based on source code semantic partitioning, and particularly relates to the technical field of cloud data distributed storage. The method comprises the steps of performing semantic partitioning on a source code, and segmenting the source code into a plurality of semantic blocks according to dimensions such as functional semantics, an abstract syntax tree structure, author information and version information; generating metadata containing information such as grammar type tags, file paths, line number ranges, author identifiers, version identifiers and access popularity for each semantic block; constructing a weighted directed acyclic graph (DAG) based on the semantic chunks and the dependency relationship thereof; superposing a metadata layer in the DAG structure, and recording information such as function call dependency, inter-block reference relationship and version evolution chain; blocks with relatively high access frequency and close semantics are aggregated into super blocks, the traversal depth is reduced, and meanwhile, hot data and cold data are differentiated for hierarchical storage by adopting a cold and hot data management strategy; and evaluating a parent block aggregation degree through a BDS algorithm, determining a block sorting priority, and optimizing super block boundary division. Compared with the prior art, the method has the advantages that the semantic retrieval efficiency, the incremental updating capability and the distributed query performance of the source code storage system are improved.

The invention provides an intelligent question number and index management engine and system based on dynamic reward optimization, relates to the technical field of data learning, and is used for enterprise index analysis, attribution diagnosis and decision support. The system is provided with an index governance layer, index caliber, computational logic, blood relationship, credibility score and version information are managed in a unified mode through a dynamic knowledge graph, unified semantic constraint is carried out on a multi-agent analysis process, and index consistency and traceability are guaranteed. The system also establishes a causal cognition module, based on time sequence data and in combination with expert priori, generates and corrects a business index causal directed acyclic graph, realizes root cause positioning and anti-fact simulation, and answers what change is and what intervention is. The multi-agent collaborative analysis core is responsible for natural language intention analysis, index compliance verification, automatic access, causal inference, narrative generation and chart presentation, calculates a multi-target composite reward value based on user feedback and interaction behaviors, and adaptively adjusts output; and the user corrects and writes back to form closed-loop learning.

The invention discloses a hydrological data management method and system based on machine learning. The method comprises the following steps: S1, generating a hydrological monitoring data set with a unified temporal-spatial resolution; s2, constructing a fuzzy entropy matrix of the hydrological monitoring data; s3, generating a hydrological monitoring data causal model expressed in a directed acyclic graph form; s4, identifying the abnormal hydrological monitoring data in real time by dynamically adjusting the abnormal detection threshold value; s5, generating an abnormal traceability candidate path, and forming an abnormal hydrological monitoring data causal chain; and S6, performing fuzzy entropy optimization processing on the abnormal traceability candidate path, screening out a key abnormal causal path according to the fuzzy entropy value of the hydrological variable in each causal chain and the influence weight of the hydrological variable in the causal chain, and further positioning the root cause of the abnormal hydrological monitoring data. The invention provides a more efficient and intelligent technical scheme for water resource management, disaster prevention early warning and environment monitoring.

The invention relates to the technical field of Internet of Things control, and discloses an intelligent Internet of Things equipment linkage control method and system, and the method comprises the steps: collecting Internet of Things terminal data, converting the Internet of Things terminal data into a standardized space-time matrix, and carrying out the feature decoupling processing, and obtaining a high-dimensional feature vector; querying equipment information to calculate the energy transfer efficiency and the communication delay sensitivity of the equipment by using the high-dimensional feature vector so as to construct a dynamic knowledge graph of the Internet of Things equipment; carrying out fault signal injection on the dynamic knowledge graph, analyzing an abnormal propagation path of the equipment and the anti-interference capability of the equipment, and carrying out risk assessment on the Internet of Things equipment to obtain an equipment risk degree; an initial linkage strategy of the Internet of Things equipment is constructed, feasible strategies in the initial linkage strategy are identified, and a feasible strategy subset is obtained; and performing directed acyclic graph decomposition on the feasible strategy subset, performing dynamic resource allocation on decomposition task nodes, and executing a target task. According to the invention, the equipment linkage control collaboration of the intelligent Internet of Things can be improved.

The invention discloses an excess fusion computing power scheduling system based on a directed acyclic graph and a task processing method. The excess fusion computing power scheduling system comprises a task splitting module, an execution graph generation module and a quantum-classical computing power resource module. The task splitting module is configured to split an input calculation task and determine a plurality of task operators, and the task operators comprise quantum task operators and classic task operators. The execution graph generation module is configured to determine an execution graph based on the directed acyclic graph according to a dependency relationship among the plurality of task operators. And the quantum-classical computing power resource module is configured to determine the task computing power resource allocated to each task operator according to the execution graph so as to process the computing task. Therefore, through the over-fusion computing power scheduling system based on the directed acyclic graph, effective management and scheduling of quantum computing resources and classical computing resources can be realized, the processing time of the computing task is shortened, and the processing efficiency of the computing task and the system performance are improved.

A cloud deployment automation system consisting of: a deployment automation device housed in a rack-mountable enclosure, the device comprising: a multi-core orchestration processor configured to execute deployment logic as compiled execution graphs; a storage module operatively coupled to the orchestration processor, the storage storing a set of deployment templates, real-time execution states, telemetry logs, and policy configurations; a secure credential management processing unit embedded in the device, configured to generate, store, and rotate cloud access tokens, API keys, and user-specific credentials, and to provide encrypted access to those credentials during deployment execution; an in-memory workflow execution engine executed by the orchestration processor, configured to analyze a user-defined deployment configuration that includes a declarative specification of infrastructure resources and compile that configuration into a directed acyclic graph (DAG) that represents the resource deployment order, dependency mapping, and rollback relationships, a cloud provider interface subsystem communicatively connected to multiple heterogeneous cloud platforms via appropriate API adapters, the subsystem enabling the orchestration processor to send provisioning requests and receive status events from the platforms; a customizable workflow compiler unit configured to convert graphical workflow definitions or domain-specific language (DSL) scripts into execution sequences that can be used by the workflow execution engine, where the workflow compiler unit supports conditional branching, asynchronous operations, and runtime variable resolution; and A policy enforcement control unit integrated into the deployment automation device, with the policy engine configured to apply organization-specific compliance rules, tagging conventions, security group configurations, and runtime resource limits to all deployment actions in a context-aware manner prior to execution.

The embodiment of the invention provides a task execution method and device based on heterogeneous computing resources, equipment and a medium, and relates to the technical field of analog simulation, and the method comprises the following steps: analyzing a multi-level simulation model, segmenting the multi-level simulation model into simulation tasks which can be independently executed, extracting heterogeneous computing resources required by each simulation task, and executing the simulation tasks according to the heterogeneous computing resources; taking the heterogeneous computing resources of the simulation tasks and the dependency relationship of the simulation tasks as task attributes of the simulation tasks; constructing a directed acyclic graph according to task attributes of the simulation tasks, setting simulation task priorities for the simulation tasks in the directed acyclic graph, and calling heterogeneous computing resources to execute the simulation tasks according to the directed acyclic graph; and dynamically adjusting the priority of the subsequent simulation tasks, and executing the remaining simulation tasks based on the adjusted priority of the simulation tasks until all the simulation tasks in the multi-level simulation model are processed. According to the scheme, the execution efficiency of the simulation task is improved through cooperative work of heterogeneous computing resources.

The invention discloses a personalized information accurate pushing system and method based on artificial intelligence, and relates to the technical field of personalized recommendation, and the method comprises the steps: fusing user multi-platform behavior data and external space-time environment information, and generating a situation label with confidence through employing an improved space-time density clustering algorithm; constructing a causal directed acyclic graph by adopting a causal forest algorithm, quantitatively analyzing a heterogeneity causal effect, inverting a potential intention of the user, and outputting standardized intention inversion probability distribution; in combination with a historical intention and a behavior sequence, training a Transform intention state transition model constrained by causality of a causality directed acyclic graph, and performing multi-step probability deduction to generate an intention evolution path; information is retrieved from the dynamic knowledge graph according to the prediction path, a pushing copywriting matched with the situation is generated through the NLP technology, the optimal pushing opportunity is calculated in combination with the position track of the user, and accurate reaching of personalized information is achieved.

The invention relates to the technical field of textile informatization and block chain encryption traceability, in particular to a textile quality block chain encryption traceability method, which comprises the following steps of: implanting quantum dot-nucleic acid mixed fingerprints into yarns in a spinning stage and generating distributed identities; in the production link, process data are collected in a trusted execution environment, and are packaged into an encrypted packet by using homomorphic encryption, zero-knowledge proof and threshold signature; and after a batch threshold value is reached, calculating a batch commitment and a fragmentation commitment, fragmenting through erasure coding, writing the commitment and the proof into the directed acyclic graph according to a fixed field, and confirming through a Byzantine fault-tolerant consensus. Consumer off-line fingerprint comparison and homomorphic verification can realize authenticity identification, and after networking, batch certification is verified and credits are pledged. And when the time lock expires, the historical quality data is decrypted by multiple subjects in a combined manner, and maintenance suggestion hash is generated and written into subsequent transactions, closed-loop tracing from production to maintenance is realized, and anti-counterfeiting, privacy protection and quality improvement are realized at the same time.

The invention relates to the technical field of data management, and discloses an iterative updating method for a data consanguinity atlas, which comprises the following steps of: capturing data increment and metadata change; constructing a directed acyclic graph of the data blood relationship; performing layered modeling on the data blood relationship graph; performing iterative updating on the blood relationship map based on a layered modeling result; processing and storage of incremental data are optimized through distributed calculation; real-time blood relationship map updating is realized by using a stream processing technology; a machine learning model is used for predicting blood relationship path change, abnormal nodes are detected and repaired, and the step of capturing data increment and metadata change comprises the substeps that data snapshots are obtained, and the data snapshots comprise new snapshots and old snapshots of data. Through a stream processing technology, a machine learning model and an automatic repair mechanism, efficient real-time updating of the data blood relationship map, intelligent path prediction and automatic repair of abnormal nodes are realized, and the system stability and the updating efficiency are improved.

The invention provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

The invention provides a resource intelligent bus and task parallel hydrogen energy data scheduling optimization method and system, relates to the technical field of energy, and aims to solve the problems of resource heterogeneity, lack of real-time performance and dynamic adjustment, insufficient energy efficiency optimization, poor adaptability to abnormal working conditions and the like in the prior art. According to the method, a resource intelligent bus architecture is constructed, and the resource intelligent bus architecture comprises a resource virtualization encapsulation layer, a resource state sensing layer and a global prediction and scheduling layer; in the resource state sensing layer, performing multi-granularity task decomposition on hydrogen energy business requirements, and establishing an inter-task dependency relationship model by adopting a directed acyclic graph; in the global prediction and scheduling layer, a multi-objective optimization model is constructed based on a dependency relationship between resource states and tasks, and resources and tasks are dynamically matched according to an optimal solution of the model; and dynamically scheduling the resources according to a matching result of the resources and the tasks. The problems in the prior art are solved, and the hydrogen energy data scheduling optimization performance is improved.

The invention relates to the technical field of aircraft fault diagnosis, in particular to a vertical take-off and landing aircraft diagnosis generalization enhancement method based on causal weight. Firstly, a directed acyclic graph between flight features and fault types is established through an information flow causal discovery method, the causal relationship between the flight features and the fault types can be clearly presented, and a basis is provided for subsequent analysis. Then, mutual information between nodes is calculated and converted into causal strength for causal enhancement, and expression of the causal relationship in the data is enhanced; in the convolutional neural network, causal weights are used for dynamic weighting to extract local features, causal features of a directed acyclic graph are combined, and feature information of different types is fully utilized. According to the method, features are fused through a graph convolutional network to obtain high-order features, adversarial training is completed through a full-connection network, the constructed fault prediction model integrates various technical advantages, the generalization ability of the model is effectively improved, the model can better adapt to different working conditions and data changes, and aircraft faults are accurately diagnosed.

The invention discloses an efficient task scheduling method based on distributed collaboration, and belongs to the technical field of distributed computing. Aiming at the problems of non-uniform resource allocation, insufficient task type difference adaptation, lack of multi-task dependence global optimization and the like existing in the existing scheduling strategy, the invention provides the following technical scheme: firstly, classifying tasks based on calculation characteristics and establishing a multi-dimensional resource index; secondly, performing dynamic weight evaluation on the heterogeneous resources by adopting an optimal worst weighting method (BWM), and constructing a task-resource matching matrix; a task dependency relationship is modeled through a directed acyclic graph (DAG), and a global priority sequence is generated in combination with a list scheduling algorithm; and finally, single-task optimal node matching is realized by adopting an elimination selection method, and cooperative scheduling is performed on multiple tasks by applying an arithmetic optimization algorithm (AOA). According to the method, accurate resource matching of a calculation-intensive task and a data-intensive task is realized through three technical dimensions of task feature perception, resource dynamic adaptation and dependency relationship collaborative optimization.

The invention discloses an intelligent traffic data storage method, system and device based on the Internet of Things and a medium, and relates to the technical field of electric digital data processing, and the method comprises the steps that an edge node packages received data and inserts the data into a local directed acyclic graph account book; dividing the verified data into Level-0 fragments according to a fixed duration window, and performing variable-length fragmentation based on a Rabin fingerprint algorithm; calculating Hash values of the variable-length fragments, and performing duplicate removal through a Bloom filter and a Hash index table in sequence; generating a local erasure block and a global erasure block for the deduplicated fragments, and storing complete copies of the key fragments on a plurality of storage nodes; and periodically checking a local erasure block and a complete copy, and triggering self-healing reconstruction when detecting that the fragment is lost. According to the method, the technical problems of high delay of distributed write-in consistency, high storage redundancy, complex cross-node time sequence correction, slow data recovery, poor system expansibility and the like are solved.

The invention discloses a file write-in monitoring method and system. The method comprises the steps of obtaining original file write-in event data; according to the file type and the system resource state, a differential sampling strategy is adopted to generate weighted sampling data; detecting anomaly through a local anomaly scoring method and outputting a real-time anomaly detection result; constructing a directed acyclic graph among all levels of the system, and analyzing an abnormal propagation path; identifying a writing behavior mode by adopting a matrix decomposition method; and dynamically optimizing monitoring parameters in combination with a system resource state. According to the invention, intelligent monitoring and abnormity diagnosis of the file writing behavior are realized, the monitoring overhead is reduced, and the accuracy and the real-time performance of abnormity detection are improved.