15 results about "Chain model" patented technology

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 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 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 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.

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.

The invention relates to the field of computer simulation and polymer physics, particularly discloses a simulation method for joint coupling motion of a flexible boundary and an internal active chain based on an annular polymer model, and aims to solve the problem that complex dynamic coupling and nonlinear deformation behaviors between the active chain and the flexible boundary are difficult to really simulate in the prior art. The method comprises the following steps: constructing an annular polymer model formed by connecting inactive particles through chemical bonds so as to characterize a flexible boundary; initializing an active chain model based on boundary geometric constraint, and dynamically aligning driving force and chain segment orientation; adjusting the chain parameters according to the conformation features to generate conformation evolution data; kinetic parameters are extracted to establish a diffusion gradient and volume fraction feedback mechanism, and a bidirectional coupling model is formed by combining the acting force of a local density and velocity field calculation chain on the boundary. According to the technical scheme, non-linear phenomena such as asymmetric collapse, oscillation and bud growth of the boundary can be reproduced in a high-fidelity mode, and the physical authenticity and predictive ability of simulation are remarkably improved.

The application discloses a health improvement method and system based on user behavior patterns, and relates to the technical fields of health management and intelligent device data processing.The method comprises the following steps: S1, continuously collecting multi-dimensional behavior data of a user from at least two different types of intelligent devices, wherein the multi-dimensional behavior data comprises physical activity data, sleep structure data and nutrition intake data, and performing noise removal, normalization and timestamp alignment operations on the multi-dimensional behavior data to generate a standardized behavior data sequence with a minute as the minimum time granularity.The application collects multi-dimensional behavior data from at least two types of intelligent devices, generates a high-quality data sequence through sliding window smoothing, outlier rejection and standardization processing, mines behavior time sequence correlation through dynamic time warping, analyzes causal relationships through improved Granger testing, and constructs a personalized behavior chain model, thereby solving the problem that the prior art can only statistically analyze data in isolation.

The invention discloses a deep learning-based wine appearance authenticity identification method, and particularly relates to the technical field of wine anti-counterfeiting. The method comprises the following steps: collecting multi-angle images of wine products, extracting local disturbance response and global diffusion features, and constructing a pseudo feature response chain model; performing chain-level matching on the chain model and a preset genuine product standard chain library, calculating a similarity score, and judging the authenticity of the product according to a set threshold value; if it is judged that the product is a counterfeit product, reversely reasoning a disguised source area based on chain interruption nodes, and outputting a visual traceability graph; according to the method, a structure disturbance propagation chain modeling mechanism is introduced into wine appearance identification for the first time, and the identification precision and the positioning capability of highly-simulated wine can be remarkably improved.

The application discloses a kind of multi-party complex protocol chain mode analysis and modeling method based on model driving, the method includes the following steps: step S1, collection, summary and induction protocol in the chain case of upper chain mode;Step S2, upper chain mode extraction;Step S3, build the upper chain model of multi-party service protocol in line with model driving architecture;Step S4, the realization of visual tool.The method can analyze and extract upper chain elements to the blockchain deployment requirement of complex protocol text when system runs, then model the protocol according to upper chain elements, the model meets the standard of model driving architecture, finally realizes the automatic conversion between models without semantic loss, so that the developer of smart contract can better understand business requirements and reduce communication barriers.

The application discloses a kind of dynamic link access control method and device applied to power system distribution network, the method includes: according to the trusted root data of the platform trusted root and the component data of all platform components, with platform trusted root as trust main line starting point, in combination with the direction of trusted chain control and the expansion rule of trusted chain, obtain the first level trusted chain model, the first level trusted chain model is used to execute trusted verification operation to each platform component;According to component data, determine the corresponding trusted level of each platform component, and according to the trusted level of each platform component, the first level trusted chain model is combined to determine the data control flow, obtain the second level trusted chain model, as the target trusted chain model of data trusted verification operation and data transmission research and judge operation to access data.It can be seen that the application can dynamically adjust the access control according to the real-time permission state of the access terminal, and improve the control security of network access control.

The application discloses an auditable chain model digital watermarking method, comprising the following steps: an owner inputs a salt value and an owner identifier into a computer system; a model training period is divided into training fragments; a model weight of a previous training fragment is used to calculate an identifier of a current training fragment through a hash function; the identifier is used as a seed of a generator to generate watermark information of the current training fragment; a total loss function containing a task loss and a watermark loss is constructed; watermark loss is realized by constraining a target embedded parameter symbol or a value to approximate a binary watermark bit stream; watermark positioning is realized through statistical analysis; a private noise is introduced before a training fragment is saved; a model saved after noise injection is saved as a detection point file, which is used as initial weight of a next fragment; training is repeated and iterated to output a model hash chain; double verification is performed based on the model hash chain according to a verification request, and a verification result is sent to a verifier. The method improves the robustness of the watermark to various attacks and is suitable for model distribution and copyright evidence scenarios.