43 results about "Chain model" patented technology

The embodiment of the invention discloses a causal chain-based front-end state anomaly detection method and system. The method comprises the following steps: S1, identifying a calling relation among an event monitor, an asynchronous task and a state change logic by analyzing a source code, and constructing an expected causal chain model; s2, dynamically verifying whether the actually occurring causal chain conforms to an expected model or not during the application running period; wherein in the step S1, the analysis of the source code comprises but is not limited to an AST technology; in the step S2, the hijacking mode of the key function comprises but is not limited to a Proxy mechanism, function packaging and a Hook technology. According to the method, the expected causal chain model is constructed, the actual path is dynamically verified during operation, system inconsistency caused by causal chain abnormity is avoided, the AI model is utilized to generate the repair strategy suggestions, a traditional low-efficiency troubleshooting process depending on logs and breakpoint debugging is upgraded to intelligent decision, and the repair efficiency is remarkably improved.

A device may include an interface manager configured to implement a build interface, and to build a visual model in response to user operations on the build interface. A device may include a supply chain model creation component configured to determine a supply chain model in response to the visual model. A device may include a supply chain model execution component configured to operate the supply chain model as a digital twin of at least a portion of a physical supply chain, to interpret at least one physical supply chain execution value, and to provide a notification to a user of the supply chain command platform in response to a comparison of the supply chain model and the at least one physical supply chain execution value.

A method, apparatus, and system for performing a task using a chain of thought model. An initiation prompt for execution by at least one machine learning model is provided, including an objective and instructions to define a plurality of agents for achieving the objective and an arrangement of the plurality of agents. A response to the initiation prompt is received defining the plurality of agents and the arrangement of the plurality of agents. The arrangement comprises a hypergraph in which a plurality of nodes represents the plurality of agents, respectively, and a plurality of edges represents data connections between the plurality of agents. A first agent and a second agent are instantiated based on the response. The first agent is represented by a first node of the plurality of nodes in the hypergraph. The second agent is represented by a second node. An output of the second agent is obtained.

The invention discloses a search intention recognition method and device, equipment and a storage medium, and relates to the technical field of reinforcement learning, and the method comprises the following steps: generating training data based on an original corpus; performing supervision fine tuning processing on the lightweight language model based on the training data to obtain a fine tuning trained lightweight language model; performing strategy optimization on the lightweight language model subjected to fine tuning training through an improved GRPO reinforcement learning algorithm to obtain a lightweight language model subjected to reinforcement training; constructing a double-model hybrid architecture based on a short thinking chain model and a thinking chain-free model in the lightweight language model subjected to enhanced training; and outputting a final sequence of the related documents of the search terms of the user through the double-model hybrid architecture. According to the technical scheme of lightweight language model fine tuning, reinforcement learning optimization and double-model hybrid reasoning, the target search scene recognition accuracy is improved, the real-time response speed is high, and training time consumption and reasoning resource requirements are reduced.

The invention relates to the technical field of encrypted communication, and discloses an intelligent analysis method and system based on multi-source data, and the method comprises the steps: obtaining multi-dimensional metadata of an encrypted communication flow, and generating a feature vector set; calculating a state transition probability of a communication session by using a Markov chain model, dividing the communication session according to a time window and mapping the communication session to a discrete state, and generating a session state evolution sequence; analyzing a session state evolution sequence based on a hierarchical topic model, mapping the state sequence to a behavior topic through potential Dirichlet allocation, mapping the behavior topic to an intention category through a hierarchical Dirichlet process, and outputting a communication intention category and a confidence score; according to the method, the limitation that static feature analysis cannot reflect the complete life cycle of the session is overcome, the problem of semantic gaps caused by limited metadata information dimensions is solved, and the accuracy and robustness of intention recognition are improved.

The invention provides a complex task scene-oriented multi-task allocation algorithm preferential method, which comprises the following steps of: on the basis of a task chain and an OODA theory, considering node attributes, heterogeneity and connection edge directivity on the basis of a task chain model, and establishing an effective task network model; according to the effective task network model, establishing a dynamic reconstruction strategy after random failure and deliberate attack failure; initializing an effective task network model, performing failure analysis on unmanned cluster nodes and connecting edges, and realizing task success rates of different task allocation algorithms by utilizing an adjacent matrix and an arrival matrix; according to the task success rate numerical simulation results of different task allocation algorithms, the advantages and disadvantages of the allocation algorithms are evaluated, and preferential analysis of the task allocation algorithms in different task scenes is realized. The task success rate of the cluster in a complex task environment is greatly improved, the task allocation and execution process is simulated and optimized based on the task success rate of the effective task network, the control decision of the cluster is effectively supported, and the task execution efficiency of the cluster is improved.

The invention relates to the technical field of supply chain data management, and discloses a supply chain data management method, system and device and a storage medium, and the method comprises the steps: obtaining a structured parameter set of an enterprise node and all supply chain data of the enterprise node; constructing a system supply chain model, and performing data processing operation on the system supply chain model to obtain a directed graph model of the system supply chain model; performing data storage configuration operation on the enterprise node to obtain storage data of the enterprise node; clustering analysis operation is carried out on the enterprise nodes, and node supply chain models including the enterprise nodes are established in a classified mode; constructing an information propagation path diagram of the supply chain; performing data analysis and mining operation on the supply chain real-time data to obtain useful information corresponding to enterprise nodes; and performing data sharing operation to complete data updating and synchronization of the corresponding enterprise node. According to the method, enterprise supply chain data management can be realized, and the collaboration efficiency of a supply chain system is improved.

The invention provides a global optimization method and device for a refinery enterprise supply chain, and the method comprises the steps: obtaining a supplier set, a sales company set, a refinery plant set, a transportation set, a product set and a spatial position set of a refinery enterprise, and constructing a supply chain model, represented by a network graph, of the refinery enterprise; simplifying the supply chain model represented by the network graph by using a graph sampling algorithm to obtain a sampled supply chain model, constructing an effective lower bound function of a supply chain optimization problem, and solving a first optimal solution according to the effective lower bound function and the sampled supply chain model; reducing the supply chain model represented by the network graph by using a graph reduction algorithm to obtain a reduced supply chain model, constructing an effective upper bound function of a supply chain optimization problem, and solving a second optimal solution according to the effective upper bound function and the reduced supply chain model; and generating a global optimal solution of the supply chain model according to the first optimal solution and the second optimal solution.

The application discloses a kind of based on blockchain's federated learning method, device and blockchain node, it is related to blockchain technology field, to solve the problem of poor model precision that existing based on blockchain's federated learning framework is trained.The method comprises: training initial model using own sample until target sample residual that satisfies preset condition is obtained, different alliance chain members have different attribute characteristics;Collaborative training request is issued to blockchain, so that the participants in the alliance chain that respond to collaborative training request collaboratively train blockchain model, and target sample residual is carried in collaborative training request;Collaboratively train blockchain model using common sample with participant, and submit the model parameter obtained after training to blockchain.In this way, the requester publishes the residual of preliminary training in blockchain, so that each participant collaborates to train blockchain model using its unique data characteristics, and higher precision model training can be achieved.

The application discloses a product analysis method and system based on a life cycle-system level data chain model, realizes transparent recording and tracing of product life cycle data through a decentralized data storage platform of a block chain, applies smart contract technology, accurately cooperates global and local decisions, and breaks the collaboration bottleneck of a traditional manufacturing system. Meanwhile, the problem of small sample data imbalance is solved by using a distributed sharing mechanism of the block chain, and the data credibility and the overall availability of the system are improved. On the basis of the decentralized data storage platform, a product life cycle and system level double digital main line block chain data model is established, an intelligent manufacturing data analysis and knowledge learning model is established in a method domain, the block chain is combined with edge computing, data processing is more efficient and intelligent, the intelligent level of the manufacturing system is further improved, and a comprehensive, efficient and intelligent solution is provided for digital transformation.

The invention provides a coal application development system, which comprises a data integration module used for acquiring multi-source heterogeneous data and generating a data model used for providing a standardized protocol and a standardized interface; the event processing and driving module is used for providing an event chain model, defining an event chain, dynamically generating an event chain instance and executing the event chain; the user interaction module is used for providing an interaction device for a user, inputting request data to the event processing and driving module through the interaction device, and receiving output data of the event processing and driving module; and the visualization module is used for displaying the execution data and the execution result of the event chain. The coal application development system provides a standardized protocol and a standardized interface and is suitable for different coal systems, so that cascade connection or resource sharing between different coal systems in the later period is achieved, meanwhile, the coal application development system adopts an event chain driving architecture, high-concurrency processing can be achieved, and the development efficiency is improved. And the method has good expandability and flexibility.

The invention provides a task execution method and device, equipment, a storage medium and a program product, and the method comprises the steps that a thinking chain model automatically generates instruction information according to task description information and current state information of at least one mode; the instruction execution model obtains instruction information and state information, executes a corresponding instruction and outputs execution information; wherein the state information is updated after the instruction is executed; the matching model determines whether the execution result is matched with the instruction information at least according to the instruction information and the execution information; if the task description information is matched with the task description information, and the updated state information does not conform to the task completion state corresponding to the task description information, the updated state information is used as current state information to be input into the thinking chain model, and the thinking chain model continues to automatically generate next piece of instruction information according to the task description information and the state information; and the state information accords with the task completion state corresponding to the task description information.

The invention provides a logistics industry green innovation performance spatio-temporal heterogeneity analysis method, which comprises the following steps of: 1, constructing a multi-dimensional index system, and calculating a logistics industry green innovation performance comprehensive index through a combined weighting method and a TOPSIS method; 2, coupling the weight matrixes of the geography, economy and logistics networks, and constructing a spatial weight matrix; step 3, analyzing a space driving mechanism of logistics industry green innovation performance by using an improved space Durbin model (SDM), and decomposing space effects into four classes of instant / accumulation and direct / indirect; and 4, constructing a space Markov chain model by taking a space lag factor as a condition, and realizing dynamic prediction and club convergence detection of a logistics industry green innovation performance state. According to the method, thoughts and schemes are provided for solving the problem that static evaluation and dynamic prediction are separated in traditional research, an evaluation-mechanism-prediction framework is formed, and accurate support is provided for formulating regional logistics policies.

The invention discloses a progressive chain interactive document generation method, and belongs to the technical field of thinking chain models. The method mainly comprises the following steps of: 1, analyzing blocks from a given document template; 2, analyzing a core task item, a supplementary description item and an example; 3, requiring the user to provide further related information; 4, taking the question and answer process of the previous step as background data to participate in question and answer of the current step; 5, splicing in the step 2, the step 3 and the step 4 to form a cue word, and generating a thinking process and a reply by a large model; step 6, performing post-processing on a return result of the large model so as to meet a consistency requirement and a structure requirement; 7, modifying a result generated by the model by a user, and confirming that the generated result is available; and step 8, writing the confirmed result in each step into a corresponding position in the template, and outputting a document. According to the progressive chain interactive document generation method, the effect of outputting the thinking process is achieved.

The invention relates to a method for optimizing dimension chain models of sub-parts of a product assembly. The method comprises the steps that the dimension chain models of all the sub-parts are constructed based on three-dimensional geometric data, assembly process parameters and measurement dimension data of the sub-parts; determining simulation size data of the size chain model of the product assembly obtained by assembling the sub-parts based on the size chain model of the sub-parts; measuring actual size data of a product assembly obtained by assembling the sub-parts; and correcting model parameters of the dimension chain model of the sub-part based on first deviation data between the simulated dimension data and the actual dimension data of the product assembly. According to the method and the device, the difference between the simulation size data and the actual size data of the product assembly is reduced in an accelerated manner in a pilot production stage or a product development and debugging stage, the actual size data of the product assembly can meet the dimensional tolerance of the size chain model after less times of tests, the dimensional accuracy of the product assembly is effectively improved, and the product assembly is more accurate. And the problem of size out-of-tolerance in the sub-part assembling process is reduced.

The application provides a kind of multi-task allocation algorithm optimization method for complex task scene, comprising: based on task chain and OODA theory, considering node attribute, heterogeneity and edge directionality on the basis of task chain model, establish effective task net model;According to the effective task net model, a dynamic reconstruction strategy after random failure and deliberate attack failure is established;Initialize the effective task net model, analyze the failure of unmanned cluster nodes and edges, use adjacency matrix and arrival matrix to realize the task success rate of different task allocation algorithms;Through the numerical simulation results of the task success rate of different task allocation algorithms, the advantages and disadvantages of the allocation algorithm are evaluated, and the optimization analysis of the task allocation algorithm under different task scenes is realized.The application greatly improves the task success rate of the cluster in the complex task environment, the task success rate based on the effective task net is simulated and optimized for the task allocation and execution process, effectively supports the control decision of the cluster, and improves the task execution efficiency of the cluster.

The present invention discloses a sprocket design method, including: selecting a chain model and determining the number of sprocket teeth; determining a chain link according to the number of sprocket teeth and the chain model, the chain link being a closed ring composed of multiple flat chains and multiple vertical chains arranged alternately, and the number of flat chains and vertical chains being the same as the number of sprocket teeth; determining the overall contour of the sprocket according to the selected chain; determining the external tooth profile of the sprocket according to the chain link; and determining the internal tooth profile according to the chain link and the external tooth profile of the sprocket. The sprocket design method of the present invention adopts a reverse design idea, breaks out of the empirical formula for sprocket design, can effectively reduce the design error, improve the design accuracy of the sprocket, so that the chain and the sprocket are nearly perfectly matched, and greatly reduces the quality problems such as chain mismatch, tooth jamming, tooth skipping or severe wear.

The invention provides an outer wall component tolerance distribution optimization method based on multi-tolerance collaboration. The method comprises the following steps: establishing a wallboard two-dimensional analysis system; establishing a dimension chain formula of the wallboard two-dimensional analysis system; an optimization function with the purpose of minimizing the sum of the comprehensive cost coefficients is constructed through correlation coefficients between the tolerances; determining constraint conditions corresponding to the tolerance in the horizontal direction and the vertical direction and constraint conditions of the dimension chain model; an optimal tolerance allocation scheme meeting constraint conditions is obtained through iteration in combination with an improved genetic algorithm containing an elitist strategy; the optimal outer wall component manufacturing tolerance is obtained from the optimal tolerance distribution scheme; according to the relation between the optimal outer wall component manufacturing tolerance and the wallboard two-dimensional analysis system, the sizes of a longitudinal seam, a transverse seam and a diagonal line are obtained; according to the method, multi-tolerance collaboration can be considered, high-accuracy parameters are obtained, and the local optimization problem in the optimization process is avoided by combining the use of an elite strategy.

The invention relates to the technical field of financial risk control and big data, in particular to an enterprise credit dynamic evaluation method and system based on big data, and the method comprises the steps: obtaining commercial operation data, constructing a dynamic transaction graph, and generating a transaction health propagation index; analyzing the real-time operation data flow to obtain a digital asset pledge score; identifying an abnormal transaction group through a cross-chain model; and finally, integrating the indexes to output a dynamic credit evaluation result. According to the method, comprehensive quantitative evaluation is carried out from three dimensions of external ecological risks, internal operation vitality and an associated fraud network, and the defects that a traditional evaluation method is static and isolated, and gang fraud is difficult to identify are overcome.

The invention discloses a cross-chain data sending method and receiving method, electronic equipment and a storage medium. The cross-chain data sending method comprises the following steps: performing block coding on first floating point data to be transmitted to obtain first block floating point data; performing structured packaging on the first block of floating point data to obtain a second block of floating point data; and performing cross-chain transmission on the second block of floating point data to send the second block of floating point data to the second block chain. In a cross-chain scene, data is compressed through block coding, the on-chain storage overhead and the network transmission load of the block chain are reduced, and the data transmission speed and the cross-chain communication efficiency are improved; through structured packaging, the problem of inconsistent analysis of traditional floating point data in a multi-chain environment is avoided, and the exchange efficiency and consistency of the floating point data between multi-chain systems are improved. The method is suitable for cross-chain floating point calculation or data synchronization scenes, in particular to traceability chains, cross-chain model calculation, credible floating point data verification and other scenes with low data precision requirements but large data volume.

To provide a method, device, and a program, capable of accurately predicting stress-strain characteristics of a rubber material in a shorter time and at lower cost, a recording medium, and a design method.SOLUTION: A method for predicting stress-strain characteristics of a rubber material based on an 8-chain model by computer simulation comprises steps of: calculating parameters on the basis of a correlation expression of a molecular chain number n and a segment number N being the parameters of the 8-chain model and at least one of the crosslink density and component formulation amount, created using at least one of the previously-experimentally obtained crosslink density and component formulation amount for a plurality of rubber material samples having different formulations, and previously-experimentally obtained stress-strain characteristics of the rubber material samples, and at least one of the crosslink density and component formulation amount of the rubber material to be analyzed; and predicting the stress-strain characteristics of the rubber material to be analyzed on the basis of the calculated value of the parameters and the 8-chain model.SELECTED DRAWING: Figure 4

Examples related to kinematic joint and chain physical tools for augmented reality implementations are provided. One aspect includes a computing device for kinematic joint simulation, the computing device comprising a display and a processor coupled to a storage system storing instructions that, when executed by the processor, cause the processor to: present a chain simulation interface comprising a plurality of graphical control elements, each graphical control element is configured to adjust at least one physical parameter, receive a selection from a user using a plurality of graphical control elements, update a chain model based on the received selection, and display and animation the updated chain model on a display using a physical engine during kinematic motion simulation, wherein the chain model comprises a plurality of rigid mesh models linked together by spring elements arranged in a tree configuration, thereby forming an assembly skeleton.

The application discloses a Gastrodia elata full-process traceability method and system based on digital twinning and blockchains, and particularly relates to the field of agricultural product quality traceability data processing technology, and is used for solving the problem that the existing traceability system uses a static quality snapshot as the whole-process quality basis, thereby causing the traceability conclusion to deviate from the real quality of the physical object; the method is achieved by the following steps: acquiring initial quality data and environmental parameter sequences after a processing node, simulating active ingredient accumulation and attenuation along the environmental parameter sequences by a digital twinning model to generate a dynamic quality trajectory, separating an irreversible damage component when the environmental parameter sequences abnormally fluctuate, correcting the attenuation factor, and uploading the model state parameters to a blockchain for storage and evidence, when traceability is queried, the dynamic quality trajectory is advanced to the current time point and is consistent with the on-chain model state parameters, physical feasibility is verified based on the ratio of the entropy trap crossing accumulation and the theoretical minimum demand, and according to the verification result, an untrustworthy identifier or a quality change traceability certificate is classified and output.

The invention provides a network security risk detection method and system based on a Markov chain model, and relates to the technical field of network security detection, and the method comprises the steps: obtaining network traffic data; extracting network flow characteristics and load characteristics of the network flow data; calculating an abnormal factor of the network flow data through a statistical method; judging whether the network flow data is abnormal or not according to the abnormal factors; if yes, entering the next step; otherwise, continuing to detect; segmenting the network flow data into a plurality of sliding windows; dividing the network traffic data in the sliding window into a plurality of state intervals; establishing a Markov chain model in the sliding window through a Pearson correlation analysis method; calculating a state transition probability between the state intervals and constructing a state transition probability matrix; calculating the support probability of the network flow data in the sliding window under the Markov chain model; and performing network security risk detection according to the support probability of the network traffic data under the Markov chain model.

The present invention discloses a full-stage trustworthy guarantee method for the federated learning training of AIGC models. Based on blockchain technology and the Nostr protocol, the present invention designs a multi-party data verification mechanism based on the Nostr protocol to ensure the trustworthiness of the data preparation stage, designs a verification strategy combining on-chain and off-chain to ensure the trustworthiness of adapter updates, designs an on-chain election, off-chain model merging, and an on-chain verification strategy to ensure the trustworthiness of model merging, aiming to ensure the trustworthiness of the entire process of federated learning training of AIGC models represented by large language models.

The invention provides a network security isolation method based on a 5G network slicing technology, and belongs to the technical field of 5G network communication, and the method comprises the following steps: constructing a physical network model and service chain model set, and defining an optimization objective function as minimum resource overhead; applying a physical isolation constraint to enable the VNF to monopolize a physical node; constructing an end-to-end time delay model, and carrying out time delay constraint; constructing a service chain deployment optimization problem on the basis of minimizing weighted resource overhead and simultaneously satisfying physical isolation and end-to-end time delay constraints; and converting the optimization problem into a weighted hypergraph matching problem, and iteratively generating an approximate optimal solution by adopting a local search algorithm. The method has the advantages that by constructing a comprehensive constraint model of time delay, joint optimization is performed on each time delay component in a deployment stage; by applying the isolation constraint, the VNF and the virtual link of the high-security service chain are forced to exclusively occupy the physical node and the link resource, and the problem that the existing logic isolation technology cannot meet the high-security service requirement is solved.

The application relates to the technical field of encrypted communication and discloses a method and system for intelligent analysis based on multi-source data, wherein the method for intelligent analysis based on multi-source data comprises the following steps: obtaining multi-dimensional metadata of an encrypted communication flow to generate a characteristic vector set; using a Markov chain model to calculate state transition probability of a communication session, dividing the communication session according to a time window and mapping the communication session to discrete states to generate a session state evolution sequence; analyzing the session state evolution sequence based on a hierarchical topic model, mapping the state sequence to a behavior topic through a latent Dirichlet allocation, mapping the behavior topic to an intention category through a hierarchical Dirichlet process, and outputting a communication intention category and a confidence score; the application overcomes the limitation that static feature analysis cannot reflect the complete life cycle of a session, solves the semantic gap problem caused by limited metadata information dimensions, and improves the accuracy and robustness of intention recognition.

The present application relates to the field of material analysis, in particular to a kind of quantitative analysis method of bridging chain mechanics contribution in polymer nanocomposite material.The quantitative analysis method provided by the present application realizes the quantitative analysis of bridging chain mechanics contribution by artificially constructing the model of bridging chain structure, in combination with the accurate tracking of known particle number and local stress decomposition technology, establishes the quantitative relationship between "bridging chain structure-mechanics contribution", provides analysis tool and theoretical basis for rational design of high-performance nanocomposite material.

The application discloses a kind of execution chain segmentation type construction methods for heterogeneous computing platform, it is related to heterogeneous computing and real-time task management technical field.The present application takes the end-to-end execution flow of complete complex application as object, by identifying the basic execution unit in original application execution flow, the execution resource type and effective interaction boundary of each basic execution unit are combined, and the execution chain model composed of multiple execution segments is constructed, and the execution chain model obtained can be directly served for subsequent scheduling analysis, execution control, timing analysis and cross-platform deployment.The present application can accurately describe the real execution process of complex intelligent application on heterogeneous platform, significantly improve the expression ability of execution chain model to end-to-end application flow, improve the integrity and accuracy of heterogeneous execution process modeling, with strong engineering practicability and industrial application prospect.

The invention discloses an auditable chain model digital watermarking method. The method comprises the following steps: an owner inputs a salt value and an owner identifier to a computer system; dividing the model training period into training fragments; calculating the identifier of the current training fragment through a hash function according to the model weight of the previous training fragment; generating current training fragment watermark information by taking the identifier as a seed of a generator; constructing a total loss function containing task loss and watermark loss; watermark loss is realized by constraining a target embedding parameter symbol or a numerical approximation binary watermark bit stream; a watermark is positioned through statistical analysis, and privacy noise is introduced before training fragment storage; storing the model as a detection point file after noise injection as an initial weight of a next fragment; repeating iteration to complete training, and outputting a model hash chain; and according to the verification request, performing dual verification based on the model hash chain, and sending a verification result to a verifier. The method improves the robustness of the watermark to various attacks, and is suitable for model distribution and copyright evidence obtaining scenes.