931 results about "Directed acyclic graph" patented technology

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.

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 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.

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 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 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 discloses a mine rescue training and danger dynamic simulation method based on virtual reality, and the method comprises the following steps: S1, modeling a rescue process into a directed graph structure, and outputting a task state diagram bound with a virtual reality scene; s2, collecting an operation behavior, a task state, an environmental parameter and a physiological response, and constructing a training state vector; s3, inputting the state vector into the deep Q network to calculate a jump node evaluation value, and outputting a jump node; s4, modeling the control rule as a rule node, constructing a directed acyclic graph, binding a weight, and outputting a rule graph; s5, performing self-evolution on the rule atlas according to training feedback, and outputting an evolution structure; s6, reasoning in the evolution rule map, fusing the rule recommendation node and the jump node, and outputting a final task node; and S7, executing jump control according to the final node, loading a virtual scene, and pushing environment parameters, task contents and risk information. According to the invention, intelligent path decision and training process adaptive optimization of the mine rescue task are realized.

The invention relates to the technical field of education collaborative management, and discloses a higher vocational education collaborative management system based on a multi-source knowledge graph and an intelligent agent, and the system comprises an ontology graph module which is used for obtaining data from all business systems and generating versioned knowledge graph snapshots; the rule compiling module is used for compiling laws and regulations and standards to form a discrete constraint set; the event influence module is used for determining an influence sub-graph and screening a sub-constraint set; the task decomposition optimization module is used for decomposing tasks and selecting bid-winning execution plans; the course arrangement solving module is used for solving a course arrangement result based on the constraint model; the post matching module is used for comparing the student ability with the post demand to obtain a post qualification set; and the evidence write-back module is used for constructing an evidence directed acyclic graph, generating a fingerprint hash value and forming a new version knowledge graph snapshot. According to the invention, data unified modeling, rule structured expression, task automatic execution and whole-process traceability in higher vocational education management are realized.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention relates to an unmanned aerial vehicle path planning method and system based on GNN and high-order security constraints. The method comprises the steps that a navigation scene where an unmanned aerial vehicle is located is represented as a heterogeneous directed graph, message passing and feature updating are conducted through the GNN, a risk-aware attention mechanism is introduced, and an interpretable decision result is output; a differentiable HoCBF-QP optimization layer is introduced, an original control command output by a strategy network is used as input, quadratic programming with high-order control barrier function constraints is solved online, minimum-amplitude safety correction is carried out on the control command, and an actuator control command meeting safety constraints is output; starting a HoCBF safety shield during operation so as to strictly ensure that all safety constraints are met before execution; a calculation task of the whole control cycle is modeled into a directed acyclic graph form, and parallel execution is carried out on heterogeneous multiple cores by utilizing a real-time scheduling strategy. The problem that unmanned aerial vehicle navigation control is not effective and unified in three aspects of structure, safety and scheduling is solved.

The invention discloses a multi-modal data and dynamic knowledge graph fusion method, relates to the technical field of knowledge graphs, and solves the technical problems that multi-modal data weight evaluation is single in dimension and lacks a data source, time and scene collaborative quantization mechanism. In combination with data source authority and time freshness, it is ensured that data value evaluation better meets service requirements, dimension weight coefficients can be flexibly adjusted according to scenes, different from fixed coefficients in the prior art, requirements of different fields are met, data full-link metadata is stored through a directed acyclic graph, conflict sources can be rapidly traced, and the data value evaluation efficiency is improved. And automatic classification and targeted correction of conflict types are realized based on a predefined rule, and the error rate of conflict correction is reduced.

The invention discloses a processing method and system for presenting double intelligent agents based on business disassembly and visualization. The processing method comprises the following steps: receiving a natural language request of a power system by a service disassembling agent, carrying out semantic analysis on the natural language request through a small language model, and executing semantic retrieval based on a power system knowledge graph to realize intent recognition and task disassembling of knowledge enhancement. Generating a corresponding task instruction chain by using a directed acyclic graph structure; the task instruction chain is sent to an instruction registration center, the instruction registration center dispatches a visual presentation agent, and an interactive instrument panel and a natural language abstract are generated through a large language model. According to the technical scheme, automatic understanding and efficient decomposition of power system services are achieved, the intelligence and automation level of data processing and visualization is improved, the task scheduling and management capacity of the system is enhanced, manual intervention is effectively reduced, and the operation and maintenance efficiency and the data display accuracy are improved.

The embodiment of the invention provides a role-based authority management method and device, and the method comprises the steps: binding a basic authority to a preset basic role, and determining a basic role template library; constructing a user-defined role, endowing the user-defined role with a user-defined permission, and binding the user-defined role to the basic role template library according to a permission inheritance rule to determine a role permission template library; when a user is endowed with multiple roles and performs permission operation, constructing a directed acyclic graph based on the multiple roles and corresponding permissions, traversing the directed acyclic graph, executing permission union set operation according to a preset role weight value, and determining a corresponding user permission result; the method comprises the steps of collecting user operation behavior data flow in real time, inputting the operation behavior data flow into a pre-trained abnormal operation detection model for data processing, determining a corresponding abnormal probability value, correcting a permission result according to the abnormal probability value, and authorizing a user according to the corrected permission result. The team management efficiency can be improved through role permission optimization.

The invention discloses a cross-system full-link metadata processing method and device.According to the method, a separated metadata storage architecture is adopted, entity attributes are stored in a JSON document form, relationships between entities are stored in an independent relationship table in a quintuple structure, and efficient storage and rapid query are achieved; through a topology driving processing framework, depth-first traversal is carried out based on a hierarchical structure of a directed acyclic graph, and multi-thread concurrent processing is started at a mode level, so that the processing efficiency of large-scale heterogeneous metadata is improved; the entity life cycle event scheduler is used for monitoring entity change events, triggering index updating, relation maintenance and other operations, and real-time response of metadata change is ensured. Meanwhile, in combination with a search index synchronization mechanism, a hierarchical entity association model, a connector abstraction factory and a fingerprint cache optimization mechanism, efficient management, real-time synchronization and deep association analysis of cross-system metadata are realized. According to the method, the storage efficiency, the real-time performance, the concurrent processing capability and the expansibility are remarkably improved.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention discloses a large language model LLM-based hierarchical workflow automatic generation method. The method comprises the following steps: firstly, processing natural language task description input by a user through a pre-trained large language model to obtain a structured task component; then, related workflow modes and optimal practices are retrieved from a pre-constructed domain knowledge base based on the structured task component, and the workflow modes and the optimal practices are fused to form knowledge-enhanced workflow description; based on the knowledge-enhanced workflow description, automatically converting the knowledge-enhanced workflow description into a standardized directed acyclic graph (DAG) through the steps of node extraction, type classification, dependency analysis, structure verification and the like; and finally, performing quality evaluation and iterative optimization on the generated DAG, and finally realizing end-to-end automatic generation from a natural language to an executable workflow. The technical problems that an existing workflow generation method based on template matching seriously depends on a preset template, is insufficient in flexibility and is difficult to adapt to complex and changeable user requirements can be solved.

The invention relates to the field of intelligent manufacturing, in particular to a papermaking process intelligent parameter adjustment method and system oriented to energy consumption optimization, and the method comprises the following steps: S1, obtaining a historical production and energy consumption report, a product quality standard, a productivity plan, an equipment capability curve and safety red line, and an energy price and carbon emission coefficient, and defining a multi-objective optimization function and constraint; s2, acquiring production process data, and preprocessing the data to obtain time sequence sample data; s3, performing feature construction based on the time sequence sample data to obtain a feature set, and constructing a process-energy consumption directed acyclic graph by adopting NOTEARS to obtain a causal atlas; s4, constructing a short-term energy consumption prediction model and a quality prediction model, and performing short-term energy consumption prediction and quality prediction based on the feature set, the causal atlas, the target KPI and the constraint; and S5, performing multi-objective strategy optimization according to the objective function and constraint, short-term energy consumption prediction and quality prediction results. On the premise of ensuring the product quality and the production safety, the energy consumption of a unit product can be effectively reduced.

The invention discloses an alarm analysis method, device and equipment, and the method comprises the steps: determining a target DAG matched with a target alarm analysis scene from a preset directed acyclic graph DAG; the DAG comprises a plurality of rule judgment nodes; the rule judgment nodes comprise at least one natural language rule judgment node, and each natural language rule judgment node is used for calling a large language model based on a preset prompt text to perform binary classification judgment on the input alarm data; inputting the alarm data into the target DAG, and generating a node hit track of the alarm data; the node hit track is used for recording a rule judgment node triggered by the alarm data and a corresponding judgment result; and obtaining an alarm analysis result based on the node hit trajectory. According to the invention, the automatic, interpretable and traceable research and judgment process of the alarm can be realized, and the accuracy, interpretability and maintenance convenience of alarm analysis are improved.

The invention provides a DAG arrangement and execution method and system based on operator dynamic management, and the method comprises the steps: constructing an initial directed acyclic graph DAG, dynamically loading operator plug-ins registered in the initial DAG, obtaining context information during operation, and storing the context information to a context container; dynamically selecting a next execution path based on the real-time data in the context container, generating an executable operator queue according to the updated DAG structure, and monitoring the execution state of the operator node in real time; and dynamically modifying the DAG structure according to a trigger condition in the execution process, and outputting a final result after the DAG is executed. According to the DAG arrangement and execution method and system based on operator dynamic management, flexible configuration of the task process, dynamic scheduling of the operator level and intelligent decision making during operation are achieved, and therefore the universality, adaptability and execution efficiency of the system are remarkably improved.

The invention discloses a cloud edge collaborative task scheduling method based on a directed acyclic graph (DAG) and a near-end policy optimization (PPO) algorithm, and belongs to the technical field of deep reinforcement learning and mobile edge computing. The method comprises the following steps of: initializing a cloud edge environment containing mobile equipment, an edge server and a cloud server, and constructing a task dependency graph containing task calculation amount, dependency relationship and data transmission amount by using a DAG (Directed Acyclic Graph); establishing a function by taking minimization of the total response time of the application program as a target, and converting a scheduling problem into a Markov decision process; and inputting the current state to the trained intelligent agent based on the PPO algorithm, obtaining final action probability distribution through task dependence screening and legal action mask filtering, and sampling and executing scheduling. According to the method, the intelligent agent adopts a PPO algorithm and is trained based on DAG, the adaptive capacity to cloud edge collaborative scene resources and network changes is achieved, task accumulation delay is remarkably reduced, the resource utilization rate is remarkably increased, and the method has wide applicability.

The invention provides a multi-modal neural causal inference micro-service fault positioning method and system, and the method comprises the steps: accessing observability data in a service operation process, and representing the tracking information of each request as a directed acyclic graph of a multi-modal feature; performing multi-modal feature coding and graph self-coding anomaly detection on the calling graph, and identifying an abnormal node through a reconstruction error; based on service topology prior, learning a sparse causal relationship graph between services by adopting a multi-scale neural causal inference method; calculating a node root cause score according to the causal relationship graph and the abnormal score, and executing causal path search to generate a fault propagation path; and marking the potential root cause according to the path weight of the propagation graph and the node popularity, and outputting a visual diagnosis result. According to the method, the system operation state is comprehensively described by fusing three kinds of micro-service system multi-modal data of logs, indexes and Trace in the micro-service system, and the structure-perceived causal diagram is constructed, so that accurate and explainable root cause positioning is realized.

The invention discloses a visual interaction artificial intelligence algorithm automatic arrangement and deployment method, and belongs to the technical field of artificial intelligence development and deployment. The method comprises the steps that after a training data set is uploaded, a system automatically analyzes a format and generates a visual analysis report; importing an algorithm operation environment and codes, and configuring parameters and a required data set through a visual interface; after a user inputs a training task, a system dynamically schedules an algorithm and a data node automatically arranges a process and generates a directed acyclic graph; the intelligent resource scheduler allocates computing resources and monitors a training state; and after the training is completed, deploying a pipeline publishing model API through servitization. According to the method, the training process of the algorithm can be automatically arranged, the training process of the algorithm is greatly shortened, and the problems that in the prior art, an automatic arrangement function is lacked, automatic parameter mapping is not supported, the resource scheduling strategy is single, and the multi-mechanism joint training requirement cannot be met are solved.

The invention discloses a cluster job planning method and system based on a dual time window mechanism. The method comprises the steps that jobs are divided into a multi-level structure with an execution sequence, and a directed acyclic graph is constructed to form a task topological structure; constructing a dual time window mechanism; designing a multi-objective optimization function; modeling cluster job scheduling into a multi-agent Markov decision process, setting a hierarchical action space, and designing a reward function reflecting scheduling balance; the method comprises the following steps: constructing a hybrid neural network architecture, extracting a time sequence correlation feature of a task, encoding a heterogeneous feature between the task and a server by using a heterogeneous graph neural network, and generating an intelligent matching score between the task and the server; a multi-agent near-end strategy optimization algorithm is applied, and agent collaborative optimization is achieved through a collaborative mechanism updated by a centralized value function and a distributed strategy. According to the method, fine modeling and state dynamic perception of complex operation topology are realized, and scientificity and controllability of scheduling decision are improved.

The invention discloses a method and equipment for detecting the compliance of an access protocol of a power internet of things terminal based on multi-agent dynamic evolution. According to the method, a directed acyclic graph plan logic, a cue word template and an agent registry are coded into a multi-dimensional constraint space, a rigid rule framework of task decomposition, agent behaviors and data streams is constructed, and on the basis, the task decomposition, the agent behaviors and the data streams are analyzed through a verification module and a node-level error back propagation and path-level weight aggregation feedback mechanism of a positioning agent. Closed-loop cooperative tuning of static template parameters and dynamic execution logic is achieved, the manual intervention requirement is remarkably reduced while the rigidity of protocol detection specifications is ensured, and a self-consistent closed loop of detection task decomposition and agent set management and control is formed. According to the method, the power grid terminal access control efficiency and compliance guarantee strength can be remarkably improved, and a core technical support is provided for large-scale deployment of the power internet of things.

The invention discloses a battery carbon emission influence factor analysis method and device and a storage medium, and belongs to the technical field of data processing. Comprising the following steps: acquiring life cycle data of a battery at the current moment from a data source, constructing a knowledge fusion atlas database of the battery based on the life cycle data through an atlas construction model, generating a carbon emission prediction model according to the knowledge fusion atlas database, and predicting the carbon emission through a feature contribution degree analysis algorithm. Calculating a prediction contribution degree of each factor variable in the carbon emission prediction model, determining a target factor variable according to the prediction contribution degree, constructing a directed acyclic graph of the target factor variable based on a preset electrochemical constraint condition, and calculating conditional probability distribution of the electrochemical constraint condition in the directed acyclic graph, and forming a coupling relation model of the target factor variables, and calculating the sensitivity coefficient of the factor variables in the coupling relation model to the carbon emission. According to the method, the accuracy of the analysis result is improved by quantifying the sensitivity coefficient of each carbon emission factor under the coupling relationship.

The invention discloses a bus carbon efficiency key causal chain identification method, electronic equipment and a medium, and the method comprises the steps: constructing a line-level bus carbon efficiency variable set which comprises a control variable, a covariant and unit passenger mileage carbon emission; the control variable is a bus operation strategy; the covariable is a bus carbon efficiency influence factor; assuming that the line-level bus carbon efficiency variable set obeys a directed acyclic graph structure formed by linear equations, and solving a causal effect matrix; establishing a bus carbon efficiency causal directed graph by taking a line-level bus carbon efficiency variable as a node and a causal effect as an edge weight; and performing post-nonlinear causal relationship test and conditional independence causal test on the bus carbon efficiency causal digraph to obtain an optimized bus carbon efficiency causal digraph, thereby obtaining a bus carbon efficiency key causal link.