985 results about "Direct 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 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 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 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.

The invention discloses a mine geological environment characteristic monitoring and recovery treatment method, and relates to the field of geological environment monitoring, and the method comprises the steps: collecting mine geological data; adopting a pre-trained LSTM neural network model to predict membership information of geological disaster evolution in a period of time in the future; adjusting a state transition probability matrix in the Markov chain model by using the membership information; predicting the evolution state of the geological disaster in a future period of time by using the adjusted state transition probability matrix; the method comprises the following steps: mapping mine geological data into nodes of a Bayesian network, and constructing a directed acyclic graph containing environmental factors; based on a Bayesian network inference algorithm, calculating the occurrence probability of geological disasters under different environmental factor combinations; fusing the predicted evolution state of the geological disaster in a future period of time with the geological disaster occurrence probability deduced by the Bayesian network to obtain a geological disaster prediction evaluation result; in view of low mine geological disaster time sequence evaluation precision in the prior art, the evaluation precision is improved.

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 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 discloses a calculation task allocation method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-executed calculation task, and extracting the features of the to-be-executed task to obtain a feature label; decomposing the to-be-executed calculation task based on the feature labels to obtain sub-tasks; constructing a directed acyclic graph based on the dependency matrix of each sub-task, and performing topological sorting on each sub-task to obtain a priority and an execution sequence; according to the method, a to-be-executed calculation task is decomposed into a plurality of sub-tasks through feature labeling, a directed acyclic graph is constructed according to the dependency relationship among the sub-tasks, and the sub-tasks are distributed according to the reference execution time, the priority and the execution sequence. The priority and the execution sequence of each sub-task are determined by utilizing topological sorting, and the sub-tasks are allocated to each GPU for execution in combination with the reference execution time of each sub-task on different GPUs, so that the calculation efficiency of the calculation task is effectively 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 discloses a service-based calculation task dynamic abstraction method and system, and relates to the technical field of calculation task scheduling. The service-based calculation task dynamic abstraction method comprises the following steps: receiving and analyzing a to-be-executed task, and dividing the to-be-executed task into a plurality of independent service units; constructing a directed acyclic graph representing the dependency relationship between the service units, and executing topological sorting based on the directed acyclic graph to determine an execution sequence; and according to the resource demand of each service unit and the current system equipment state. According to the method, the technical problems that a traditional task scheduling method comprises but is not limited to the following technical problems that resource allocation is rigid, a heterogeneous computing environment cannot be dynamically adapted, and the hardware utilization rate is low; parallel arrangement is low in efficiency, depends on manual definition of an execution sequence, lacks an automatic arrangement capability and is difficult to deal with a complex task process; the real-time performance is insufficient, the task execution process is solidified, and the resource allocation and execution path cannot be dynamically adjusted according to the runtime state.

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 relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

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 a multi-agent task planning method and device for an own environment, and relates to the technical field of multi-agents, and the method comprises the steps: distributing a plurality of subtasks to corresponding execution agents according to an initial directed acyclic graph and initial own environment information corresponding to a target task; then, when any execution agent finishes executing the corresponding subtask, feedback information generated when the execution agents execute the corresponding subtasks is obtained; and then updating the initial directed acyclic graph based on the feedback information to obtain a target directed acyclic graph, and further adjusting execution agents corresponding to the plurality of subtasks in combination with the current body environment information, the updated target directed acyclic graph and the feedback information. Therefore, the change of the task state and the environment in the body environment can be obtained in time, so that the task planning is adjusted, and the purposes of enabling the cooperation among the multiple agents to better accord with the actual situation and improving the task execution efficiency are achieved.

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 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 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 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 discloses a method for deploying an edge dynamic DAG (Directed Acyclic Graph) server-free function on line to realize quick starting, and belongs to the field of edge computing. The method comprises the steps of 1, defining a dynamic DAG and establishing a modeling criterion of function preheating processing, and 2, constructing a dynamic DAG model with preheating preparation and function scheduling, and the model achieves the minimization of the overall expected execution time through preheating and function deployment. And step 3, converting a quadratic programming problem into a convex optimization problem by processing a quadratic constraint condition. And for the converted problem, a search algorithm based on random rounding is adopted to solve an optimal solution. And step 4, designing an online preheating and function scheduling algorithm. The algorithm includes function weight calculation, warm container adjustment, and optimal container selection to handle multiple online arrival requests. According to the method, through an online preheating and dynamic scheduling strategy, the cold start problem is effectively relieved, and the execution efficiency of the server-free function in the edge computing environment is improved.

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 relates to a rendering method and device, a storage medium, a controller and a program product. The method comprises the following steps: constructing a directed acyclic graph of to-be-rendered components based on dependency information between the to-be-rendered components in an application program; and determining a rendering sequence of the to-be-rendered components based on the directed acyclic graph, and rendering the to-be-rendered components according to the rendering sequence. According to the application, the directed acyclic graph of the to-be-rendered components can be constructed through the dependency information between the to-be-rendered components; according to the embodiment of the invention, the rendering sequence of the to-be-rendered components can be determined through the directed acyclic graph, and the to-be-rendered components can be effectively rendered according to the rendering sequence determined by the directed acyclic graph because any two to-be-rendered components in the directed acyclic graph do not have a cyclic dependency relationship; in the embodiment of the invention, each to-be-rendered component is sequentially processed according to the rendering sequence, so that each to-be-rendered component can be correctly rendered, the rendering efficiency and the rendering accuracy are improved, and an expected visual effect is realized.

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 self-adaptive dynamic task unloading method and system for an edge computing power network, and the method comprises the steps: carrying out the modeling of a computing task of a user, obtaining a directed acyclic graph of the computing task, and generating a task execution sequence according to the topological sorting of the tasks in the directed acyclic graph; features of tasks in the task execution sequence are coded into high-dimensional vectors, sequence information is embedded through position coding, and a task matrix is obtained; the task matrix is input into a trained Transform model, and a task related information sequence is generated; inputting the task related information sequence into the trained dual Q network model to obtain an optimal unloading decision sequence; carrying out local calculation or edge server unloading on the calculation task based on the optimal unloading decision sequence; according to the method, efficient, low-delay and low-energy-consumption task scheduling optimization can be realized, and the task unloading efficiency is 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 server-free workflow dynamic resource configuration system based on resource decoupling, and relates to the field of cloud computing. Comprising a workflow sampling scheduling module, a configuration decoupling search module and a dynamic configuration module. The dynamic configuration module shunts the workload in combination with the input characteristic data, the scheduling workflow sampling scheduling module obtains the decoupled optimal resource configuration, and a mapping relation between the input characteristic and the decoupled resource configuration is established by training a random forest model; the workflow sampling scheduling module analyzes a workflow structure through input, generates a weighted directed acyclic graph and identifies a key path, preferentially searches for optimal configuration for the key path, and then iteratively optimizes a sub-path on the premise of not violating the consistency of the key path; the configuration decoupling search module allocates decoupled CPU / memory resources based on a critical path priority policy. According to the method, the cost is minimized while the service level target is met through an automatic searching method for decoupling the memory and the CPU.

The invention relates to an APP intelligent marketing service method based on intelligent routing and multi-agent cooperation. The method comprises the following steps: receiving input information, wherein the input information comprises user active inquiry information or trigger event information generated based on user behavior monitoring; semantic analysis and intention recognition are carried out on the input information, a task planning directed acyclic graph is generated based on a recognition result, and the task planning directed acyclic graph comprises a plurality of subtask nodes and dependency relationships among the nodes; based on the task planning directed acyclic graph, the state of each functional agent and the historical performance index, executing dynamic routing so as to dispatch the plurality of sub-tasks to the corresponding functional agents; and in the execution process of the plurality of subtasks, performing dependency scheduling and state consistency management on an external tool call chain which is initiated by the functional agent and comprises a plurality of steps, and updating the shared memory associated with the user based on an execution result. By adopting the method, self-adaptive planning, robust execution and continuous optimization of marketing tasks can be realized.

The invention relates to a container fault tracing method and device based on cross-community knowledge fusion and a medium. The method comprises the steps that a container fault traceability model is constructed, wherein the container fault traceability model comprises a data processing module, a knowledge extraction module, a knowledge fusion engine module and a fault traceability module, and key entity extraction is conducted on processed data in the knowledge extraction module through the NLP technology to construct a unified knowledge representation model; in a knowledge fusion engine module, fault-related entities in the unified knowledge representation model and the causal relationship between the entities are aligned through a defined cross-community entity alignment function to construct an initial cross-community knowledge graph, and then dynamic graph updating is performed on the initial cross-community knowledge graph by using a time sequence graph convolutional network. And a causal directed acyclic graph is generated in a fault tracing module according to the final cross-community knowledge graph, and container fault tracing is performed on the causal directed acyclic graph by using a root cause positioning algorithm. By adopting the method, the timeliness of fault diagnosis can be improved.