800 results about "Direct acyclic graph" patented technology

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 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 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 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 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 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 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 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 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 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 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 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 provides a coffee beverage robot three-mechanical-arm operation sectionalized time-sharing control method and device, and relates to the technical field of cooperative control. The method comprises the steps that mechanical arm task time sequence analysis is carried out according to a task directed acyclic graph to obtain a three-mechanical-arm function activation time sequence; performing operation task sectioning processing to output a plurality of collaborative operation sectors; task trajectory conflict compensation is conducted according to the multiple sets of mechanical arm activation functions in the multiple collaborative operation sections, multiple sets of collaborative control parameters are output to conduct mechanical arm collaborative control over the coffee beverage robot, and coffee order making is executed. The technical problems that in the prior art, due to the fact that a robot is loose in mechanical arm collaborative scheduling logic, coffee making efficiency is low, resource preemption conflicts occur frequently in the making process, the mechanical arm collision risk is increased sharply, and the coffee order process is delayed are solved. The technical effects that efficient parallel operation of multiple mechanical arms, resource conflict avoidance and accurate synchronization of actions are achieved, and the coffee making efficiency and order response real-time performance are remarkably improved are achieved.

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.

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention discloses a multi-dimensional data asynchronous calculation method and system based on a dynamic dependency graph, and relates to the field of data processing. The method comprises the following steps: constructing a metadata, logic and instance three-layer separation storage model; analyzing the reference relationship to construct a directed acyclic graph, and generating a calculation priority; monitoring data change, and executing asynchronous serialization calculation through a message queue based on priority; the associated document is automatically updated based on anchor mapping. According to the method, the calculation deadlock and the performance bottleneck of large-scale data in the Web environment are solved, logic decoupling and dynamic expansion are realized, the final consistency of the data is guaranteed, and the high-concurrency throughput and the stability are remarkably improved.

The invention relates to a resource allocation and process planning optimization method under complex constraint conditions, which comprises the following steps of: representing various constraint conditions of a process planning process by utilizing a double-layer directed acyclic graph, and generating and constructing an initial feasible solution set meeting all the constraint conditions; initializing a state space, wherein the state space comprises state variables of a process number, an average process length, reward value fluctuation and fitness; noise is collected and coupled to a Q network, action selection is carried out, and low-level heuristic algorithm options are output; generating a new taboo sequence by using the selected low-level heuristic algorithm, and optimizing the current feasible solution; switching a low-level heuristic algorithm according to the improvement amplitude and the execution duration; and outputting an optimal processing feature sequence and process division combination after multiple times of training. Compared with the prior art, the method has the advantages that multiple process decisions can be optimized at the same time, sub-optimal results caused by separate optimization processes are avoided, processing time and resource consumption are remarkably reduced, and production efficiency and scheme adaptability are improved.

The invention relates to a query optimization method and device based on agent planning, electronic equipment and a medium. The method comprises the following steps: pre-retrieving a target tree structure index based on a user query request to obtain a pre-retrieval result set; generating a retrieval-reasoning double-stage directed acyclic graph task chain comprising a task type, a retrieval / reasoning target and a dependency relationship; executing each task according to a topological sequence, and obtaining a target text fragment through query rewriting and expansion; and performing multi-hop logical reasoning in combination with the task chain and the target text fragment, and outputting a final answer. Therefore, dynamic task planning of environment perception is carried out by constructing a retrieval-reasoning dual-stage directed acyclic graph task chain and combining a knowledge base tree structure index; the problems that when an existing retrieval enhancement generation system processes a multi-hop question and answer task, logic association understanding ability among retrieval fragments is insufficient, semantic matching precision is low, and a reasoning path is prone to deviation are solved, and the retrieval recall rate and the answer accuracy under a multi-hop question and answer scene are improved.

The invention relates to the technical field of distributed data processing systems, and discloses a data processing system supporting full localization deployment and a compliance guarantee method thereof.The data processing system supporting full localization deployment comprises an audit event collection module used for collecting and storing processing event hash; the event organization module is used for realizing logic time sequence management of events through topological sorting and causal relationship marking; the hash chain construction module is used for constructing a local-to-global double-layer hash chain by utilizing the constructed directed acyclic graph and event hash; the increment synchronization module is used for executing increment synchronization based on Hash tree difference identification; the conflict solving module is used for detecting and solving event conflicts in the distributed environment by applying a version vector technology aiming at data differences found in the incremental synchronization process; according to the method, the lightweight linear hash chain is used for replacing a traditional binary tree structure, high-efficiency and low-cost audit chain maintenance is realized, and the data integrity is guaranteed in an unstable network environment.

The invention relates to the technical field of block chain technology and artificial intelligence collaboration, and discloses a verifiable multi-agent collaboration method based on smart contract driving, comprising the following steps: task planning generates a directed acyclic graph and a zero-knowledge proof circuit template, submits the directed acyclic graph and the zero-knowledge proof circuit template to a planning contract, and deploys an exclusive zero-knowledge proof contract; the AI agent collaboratively executes the subtasks, and zero-knowledge proof is generated and submitted through a Veckrii auction tool; zero-knowledge proof contract chain verification is carried out, and after verification is passed, a signature is received and multi-layer gating verification is executed; and the governance contract carries out pledge management and cost distribution on the agents, the providers and the experts. According to the method, zero-knowledge proof and a trusted execution environment are combined, and an under-chain execution behavior is converted into an encryption certificate capable of being verified on a chain, so that the problem that the execution process cannot be verified due to the lack of distributed trusted evidence storage in a traditional centralized framework is effectively solved; and after each subtask is completed, double signature and multi-layer gating verification are forcibly executed.