70 results about "Timing diagram" patented technology

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The invention provides a training method and system of a network community dynamic graph generation model and a graph generation method, and is applied to the technical field of data mining. The method comprises the following steps: acquiring a historical dynamic time sequence diagram of a target network, and carrying out step-by-step feature extraction on the historical dynamic time sequence diagram by utilizing a feature extraction module according to a time sequence to sequentially obtain a diagram embedding representation, a hidden variable and a hidden state; inputting the graph embedded representation into a community detection module to obtain a community detection result; inputting the hidden state into a community prediction module to obtain a community prediction result of the prediction time step; inputting the hidden variable and the hidden state of the previous time step into a topological structure generation module to obtain a generated network diagram; determining training loss based on the community detection result, the community prediction result of the prediction time step and the generated network diagram; and updating model parameters based on the training loss. According to the method, the problems that application scene requirements sensitive to dynamic changes cannot be met and the quality of generated dynamic graph data is not high in the prior art are solved.

The invention provides a novel traffic information dynamic time sequence diagram topological structure, which relates to the technical field of dynamic time sequence diagrams, and comprises the following steps: configuring a namespace and time granularity system of a traffic information dynamic time sequence diagram, setting a global timestamp rule and a propulsion strategy, defining an information identification bit structure, and setting a visibility and consistency control strategy. And a unified time window scheduling and snapshot life cycle management mechanism is configured. A dynamic extensible time sequence naming system is formed by establishing a unified naming space, time granularity delta t and a global timestamp rule, windowed organization and snapshot chain management of traffic information are achieved, the structure can automatically advance snapshot in a time driving mode and a data driving mode, time sequence consistency and real-time performance are guaranteed, and the time sequence naming efficiency is improved. The problems that node time management is disordered and data cannot be traced in a traditional traffic map model are solved.

The invention discloses a method for constructing vTEE and certification in an AMD SEV environment, provides a refined communication and fusing sequence diagram for the vTEE for the first time, and defines a module interaction sequence under two scenes of normal task execution and anomaly detection. According to the method, a trusted cloud security management platform initiates a request, a protection component coordinates a calculation component to collect an SEV-SNP report and vTEE key data, signature combination and uploading verification are carried out, and finally, the platform compares with a reference value to determine whether the vTEE is trusted or not. According to the method, a double-layer certification mechanism of SEV hardware certification and vTEE equipment measurement is adopted, an SEV-SNP certification report proves that a hardware environment, a VM mirror image and a vTEE operation environment are not tampered, vTEE key data measurement supports complete trust chain verification of equipment firmware, instruction streams and strategy states, and end-to-end certification is achieved. Equipment-level measurement, strategy issuing, abnormal fusing and remote certification are realized through an external protection part, and the system has important commercial landing value and strong system expandability.

The invention discloses an industrial firewall device based on time sequence atlas detection, which relates to the technical field of industrial control network security and comprises the steps of protocol identification, feature extraction, time sequence atlas construction, baseline modeling, online detection and response protection. Extracting time sequence characteristics such as a request-response relationship, a time interval and an event sequence, and converting the time sequence characteristics into a time sequence graph on which nodes correspond to communication events and edges correspond to time sequences; and generating a baseline map based on normal data, comparing the deviation between the current map and the baseline map in real-time communication, and executing a protection action after judging abnormality. The device for realizing the method comprises a data acquisition module, a feature extraction module, an atlas modeling module, a detection analysis module, a protection response module, a man-machine interaction module and a wide-voltage power supply module which are electrically connected in sequence. According to the method and the device, hidden time sequence attacks can be accurately identified, multiple industrial protocols are supported, and the adaptive resource-limited industrial equipment is designed in a lightweight manner.

The invention discloses an intelligent lock recognition method based on a time sequence diagram neural network, and the method comprises the following steps: collecting multi-source event data of an intelligent lock, unifying the format, embedding codes, and generating an event feature vector set; constructing a time-varying multi-relation graph, and executing message passing and feature aggregation to obtain a neighborhood aggregation result; constructing a dual-frequency memory time sequence diagram neural network, and extracting long-term and short-term behavior characteristics in slow-frequency and fast-frequency memory units; inputting the slow frequency memory state and the fast frequency memory state into a gating fusion layer, and combining time and scene information to generate a full-time-domain embedded vector; calculating identity matching, abnormal risk and forgery credibility, and outputting a comprehensive identification result; and executing instant response and security processing according to an identification result, and carrying out network updating. According to the method, the sequence diagram neural network and the double-frequency memory mechanism are utilized, multi-element dynamic recognition of the intelligent lock is achieved, and the method has the advantages of being high in self-learning, high in recognition accuracy and excellent in safety robustness.

The invention relates to a natural resource asset management and territorial space planning system fusion method and system. The method comprises the following steps: encoding territorial space planning vector data and natural resource asset registration data to generate a space-time binding code; constructing a spatial topological graph and a time sequence diagram based on the codes, respectively extracting planning constraint feature vectors and asset dynamic feature vectors, and fusing the vectors to generate a planning asset coupling scoring matrix; recognizing a conflict unit set based on the planning asset coupling scoring matrix; based on the conflict unit set and a preset business rule base, performing classification processing on each conflict unit in the conflict unit set to obtain a conflict type; and based on the conflict type, generating a service execution instruction set through a preset reinforcement learning decision model. According to the method, through space-time unified coding and feature fusion processing, the accuracy of data association and the scientificity of decision making are improved, so that collaborative governance of territorial space planning and natural resource asset management is realized.

The invention discloses a power distribution network fault positioning method based on a time sequence diagram convolutional network, and the method comprises the following steps: 1), obtaining the time sequence measurement data of a power distribution network node, and carrying out the preprocessing, and obtaining a plurality of local time sequence segments; 2) performing feature extraction on the local time sequence segments by using a multi-layer one-dimensional convolutional network to obtain time sequence features; 3) processing the time sequence features by using a time sequence diagram neural network to obtain node features; and 4) fusing and mapping the features of the nodes by using a full connection layer, and outputting a fault positioning result. According to the method, the local feature mode and the global fault propagation dynamic state can be captured at the same time, the fault positioning accuracy of the power distribution network is greatly improved, and a solid technical support is provided for safe and stable operation of an intelligent power grid system.

The invention designs and implements a performance test environment construction method oriented to cross-chain transaction of block chains. The system supports a user to customize test scripts and execution parameters according to test requirements, tasks are executed and workloads are initiated at the same time through multiple actuators, a load scene of a tested cross-chain application system is created, the whole process of cross-chain transaction from an initiator to a receiver is tracked at the same time, key data are collected, and performance test indexes are calculated. And a visual result report is generated. Comprising the following steps: analyzing system requirements, and describing and analyzing functional requirements and non-functional requirements of a system; carrying out summary design on the system, and respectively designing and describing the system from a logic view angle, a process view angle, a development view angle and a physical view angle; detailed design is carried out on each module of the system, and a scheme is designed for the modules from two different perspectives of a class diagram and a time sequence diagram; and carrying out system testing and summarization on the system.

The invention provides an unsupervised APT detection method based on a time sequence diagram attention network and a deviation network. The unsupervised APT detection method comprises the following steps: acquiring a system audit log of a fixed-width time window by using a sliding time window; entities related to the events and interaction relations between the entities are extracted, and a dynamic traceability graph is constructed in combination with occurrence time of the events; modeling the interaction behavior of each node adjacent to the node in the time dimension by using a time sequence diagram attention network so as to extract a node time dependent feature representation; predicting the type of an edge between adjacent node pairs by using a multi-layer perceptron to obtain a prediction error; evaluating the degree of deviation between the type of the predicted edge and the corresponding reference standard by using a deviation network; and based on the prediction error and the deviation degree, identifying the time window subjected to the APT attack and the corresponding attack chain. The method is remarkably superior to a traditional detection method in the aspects of detection precision and robustness, and an efficient and extensible solution is provided for APT detection.

The program causes the assist device (10) to function as: an acquisition unit (120) that acquires a control program (12) executed by the PLC in order to control the device and time length information (114) relating to the time length of the operation of the device specified in the control program; a generation unit (140) that generates a sequence diagram of operations on the basis of the control program (12) and the time length information (114); and an interface unit (170) that outputs display data for displaying the generated time series diagram.

The application discloses a kind of soft measurement modeling methods of fermentation process based on time sequence diagram network, belongs to the technical field of soft measurement of fermentation process.It includes the following steps: (1) data acquisition and integration: the penicillin fermentation process under different conditions is obtained, and the data is divided, collected and integrated;(2) data selection: the data is selected, the redundant useless data is removed and the causal diagram between variables is established;(3) modeling training: algorithm model is constructed, and learning training is carried out;(4) model prediction: the trained algorithm model is used for prediction, and the prediction result is given.The application proposes a kind of soft measurement modeling method of fermentation process based on time sequence diagram network, improves the prediction accuracy of key product quality of fermentation process;The method uses graph convolution network and long short-term memory, extracts data in time and space dimensions, increases the generalization of model;The method can accurately measure the key product quality of different fermentation processes.

This application discloses an interface dependency parsing and automated testing method, device, and storage medium, comprising: extracting the minimum path set of the target medical business scenario from the interface call sequence diagram based on the target medical business scenario; using the minimum path set as the test path, parsing the data dependency relationship of each interface in the test path, extracting data flow rules to generate test data; calling the interfaces of the test path according to the test data to generate integrity verification results; comparing the integrity verification results with the expected behavior of the interface call sequence diagram to locate abnormal test nodes; and identifying changes in the interface dependency relationship of abnormal test nodes. This application automatically constructs and dynamically updates the interface call sequence diagram from real call logs through a time sequence pattern mining algorithm, replacing manual dependency analysis. When business logic changes, the test path and data flow rules are re-extracted to ensure that the test plan is updated synchronously with the business, thereby reducing the maintenance cost of test scripts.

The invention discloses a social network link prediction-oriented time sequence diagram network parallel training acceleration method. The method comprises the following steps: firstly, calculating a redundancy score and an old score for each user interaction edge in a training set, and calculating a comprehensive information loss score according to the redundancy score and the old score; secondly, according to a preset core set retention proportion alpha, selecting all user interaction side comprehensive information loss scores and upper alpha quantiles of repetitiveness as threshold values, and discarding user interaction with the comprehensive information loss scores lower than the threshold values, so that a simplified core set is obtained through single-time preprocessing; then carrying out adaptive batch division on the obtained core set, and dynamically determining an acceptable maximum user interaction number in each batch according to a redundancy and old comprehensive information loss score; and finally, training the time sequence diagram neural network based on the divided batches to realize social network link prediction. The method not only improves the training efficiency, but also greatly improves the precision of the model.

The invention discloses a foundation pit adjacent operation tunnel state prediction method based on a space-time decoupling network, and the method comprises the steps: obtaining historical tunnel change data and foundation pit real-time monitoring data, extracting corresponding foundation pit feature data according to the historical tunnel change moment, and carrying out the prediction of the foundation pit adjacent operation tunnel state; and screening out a key foundation pit feature set having a significant causal relationship with the tunnel state change. And constructing a time sequence diagram network set based on the screened key foundation pit characteristics, the historical tunnel change data and the space relationship and the stress conduction relationship between the foundation pit and the tunnel. And establishing a space-time decoupling network model, training by using the time sequence diagram network set, learning complex space-time coupling characteristics between the foundation pit and the tunnel, and obtaining a trained prediction model. And inputting key foundation pit characteristic data of the foundation pit at the current moment, and realizing prediction of deformation, convergence and displacement of the adjacent operation tunnel at a plurality of moments in the future. The dynamic influence of foundation pit construction disturbance on the tunnel can be effectively captured, and the accuracy and real-time performance of tunnel safety state prediction are improved.

The application relates to the technical field of agent dynamics, and particularly discloses a multi-agent dynamics modeling method based on PEACE, which comprises the following steps: constructing time sequence diagram data; extracting invariant observation embedding data; constructing agent model data; based on system parameters, generating prompt context through coding and self-attention mechanism, initializing individual prototype prompts, decoupling and fusing the individual prototype prompts and the invariant observation embedding, combining graph ordinary differential equations to construct an individual agent model, and realizing decoupling of the two types of embedding by minimizing mutual information loss; generating enhanced data; cooperatively optimizing and training; and performing dynamics prediction. In this way, the scheme can improve robustness in a distribution shift scenario.

The application provides a risk transmission prediction method and system based on a time sequence diagram and a large model joint deduction, relates to the technical field of artificial intelligence and knowledge engineering, and comprises the following steps: extracting forward-looking time sequence facts based on hierarchical prompt words and a structured constraint mechanism; extracting conflict events and generating time sequence relationships; updating a historical time sequence knowledge graph based on a time sequence relationship and a confidence weighted update strategy; determining a central entity, initiating a structured query to the updated historical time sequence knowledge graph from the central entity, and generating a context knowledge subgraph; converting the context knowledge subgraph into a natural language description with logical relationships, injecting a risk hypothetical event, constructing a prompt word as a new input of a large model, and outputting a structured JSON object containing a complete reasoning chain; and based on a risk path refining and visualizing algorithm with influence weight attenuation, converting the structured JSON object to obtain risk warning information and a visualized transmission path diagram.

This application discloses an AI-based cross-scene furniture e-commerce interaction system and method. The system includes: an event receiving and recording module, a sequence verification module, a representation vector generation module, a credibility calculation module, an attribution classification module, and a sample update module. The system receives interaction events, establishes room scene records and product version records, and generates version influence vectors; it performs sequence verification on the same user events, constructs a time series diagram, and generates representation vectors; it calculates event credibility by combining scene validity period, product version validity period, time deviation, and associated anomalies; it performs replay verification and attribution classification on low-credibility negative feedback events, and updates training samples, user preference states, and product recommendation states accordingly. This system can improve the accuracy of interaction data processing and recommendation updates.

The application provides an algorithm and electricity collaborative system and method, belonging to the field of algorithm and electricity collaboration, comprising: a data acquisition module that acquires power data, algorithm data and price data, and constructs a power supply time sequence diagram and an algorithm demand time sequence diagram; a prediction module that generates power supply prediction values and algorithm demand prediction values based on the power supply time sequence diagram and the algorithm demand time sequence diagram; an intelligent matching module that generates a matching decision scheme based on the power supply prediction values and the algorithm demand prediction values; a task scheduling module that performs differentiated scheduling based on the matching decision scheme; and a feedback optimization module that is used for evaluating the task scheduling result and feeding back the evaluation result to the prediction module and the intelligent matching module. The application predicts power supply and algorithm demand, generates active scheduling decisions based on the prediction results, and the task scheduling module performs spatiotemporal flexible scheduling of tasks, thereby significantly improving the consumption utilization rate of new energy power and the supply stability of algorithm services.

The invention discloses a total load prediction method and system based on industry division and a medium, and the method comprises the steps: obtaining historical load data and influence factor data divided according to the industry and resident life, and carrying out the preprocessing of the historical load data and influence factor data; constructing a time-varying industrial heterogeneous graph according to the preprocessed data; based on the time-varying industry heterogeneous graph, mining a dynamic evolution rule and key influence factors associated with loads among different industries; inputting the preprocessed data, the dynamic evolution law and the key influence factors into a pre-constructed industrial-level time sequence diagram prediction model, and outputting industrial load prediction results and total load prediction results of each industry under different quantile levels; carrying out consistent alignment on a total load prediction result and a total point prediction value, and carrying out conformal calibration on the prediction value; and according to the calibrated prediction value, based on multi-time-domain modeling, outputting a load prediction result of industry and total load prediction under a specified time dimension. The total load prediction precision and the engineering practicability are remarkably improved.

The invention relates to a micro-service resource prediction and regulation method and system based on time sequence diagram learning and reinforcement learning. The regulation and control method comprises the following steps: data acquisition and preprocessing; constructing a time sequence heterogeneous graph; learning and training the dynamic space-time diagram; thinking chain reasoning prediction; reinforcement learning regulation and control decision making: performing resource regulation and control decision making based on a prediction result; and executing and feeding back a result. The system comprises a micro-service time sequence heterogeneous graph construction module; a dynamic space-time diagram learning module; a thinking chain reasoning enhancement prediction module; a reinforcement learning regulation and control module; and an LLM enhanced decision interpretation module. Compared with the prior art, the method has the following beneficial effects that the prediction precision is remarkably improved, the complex nonlinear dependence between the micro-services is captured through dynamic space-time diagram learning, and compared with a traditional regression model, the prediction error is reduced by more than 40%; regulation and control perspectiveness is enhanced, a reinforcement learning strategy can trigger resource adjustment in advance when a flow rising trend is observed, and the system overload risk is reduced by 60%.

The invention relates to an equipment full-life-cycle predictive maintenance method and system, equipment and a medium. The method comprises the steps that multi-device multi-source heterogeneous data is acquired and processed to obtain a multi-modal feature matrix and a preliminary device relation graph; equipment causal association is learned by combining causal inference and a graph neural network with a model, and a heterogeneous equipment causal graph is constructed; detecting abnormity and positioning root causes based on the time sequence diagram neural network; and optimizing a maintenance strategy through anti-factual reasoning in the digital twin, and generating an optimal maintenance sequence and report. By adopting the method, the data utilization rate and the association identification precision can be improved, abnormal accurate detection and root cause positioning are realized, a reliable basis is provided for operation and maintenance, the maintenance efficiency is improved, and the fault risk is reduced.

The application relates to the technical field of integrated circuit timing analysis, and discloses an FPGA timing library synthesis method and system. The method comprises the following steps: constructing a timing diagram according to a circuit netlist and an SDF file, and establishing a mapping relationship between nodes in the timing diagram and model ports according to a port mapping file to generate a port mapping set; extracting timing paths between the model ports in the timing diagram by using a path extraction algorithm based on the timing diagram according to the timing diagram, the port mapping set and a timing constraint file; and calculating timing arc delays according to the timing paths, and outputting the timing arc delays in a standard timing library format. The application reduces the degree of manual participation, improves the generation efficiency and data accuracy of the timing library, and facilitates error positioning.

The application provides a collection implicit fraud identification method and system based on a timing graph, including: collecting full-cycle data and preprocessing to form an original data set; constructing a basic graph with a collection target user as the core; mining fine-grained attributes and extracting exclusive fraud features to form a feature-rich conversation timing semantic graph; performing multi-dimensional reasoning, extracting core risk features and quantifying to form a comprehensive risk feature matrix; inputting a risk judgment model to perform implicit fraud judgment and outputting a judgment result. The application constructs a conversation timing semantic basic graph with a collection target user as the core, not only deeply fuses the implicit fraud features and the collection interaction basic features, but also multi-dimensionally and deeply mines and quantitatively represents the implicit fraud features, realizes the graphed and timed unified representation of the collection full-cycle interaction data, and improves the accuracy and comprehensiveness of the collection fraud identification.

The application discloses a blast furnace molten iron silicon content prediction method based on a time sequence diagram convolution network. First, the blast furnace ironmaking data is sequenced through a time window, and the sequenced time segment is input into a double-channel parallel network architecture to extract the coupling relationship between variables and the dynamics within variables. For the spatial channel of the coupling relationship between variables, a graph structure learning module first converts the time segment into an irregular graph structure, and carries out graph convolution operation on the basis of the learned graph structure. For the time sequence channel of the dynamics capturing, a long short-term memory network is used to capture the time sequence information in the data. This architecture realizes the spatiotemporal feature collaborative extraction of the blast furnace ironmaking data, is beneficial to the more accurate modeling of the complex conditions of the blast furnace by the model, improves the representation ability of the model, and thus improves the prediction accuracy of the model.

The invention discloses a teacher interactive learning data processing method and system based on big data. The method comprises the following steps: S1, collecting data such as interaction and learning progress of teachers and students to generate an interaction event time sequence queue; s2, dynamically constructing an interaction time sequence diagram and updating node features in real time; s3, aggregating node features layer by layer based on a graph neural network to obtain a learning interaction state; s4, establishing a dynamic learning ability label, and periodically associating and verifying historical behaviors to screen and optimize a learning target; s5, a diversity evaluation factor and a fitness threshold value are set, and a differential learning path is evolved and generated through a self-adaptive genetic algorithm; s6, monitoring interaction differences in real time, and backtracking and adjusting map node features; and S7, generating and pushing a personalized learning path in real time according to the adjusted node features and evolution states. According to the method, a dynamic interaction closed loop and a self-adaptive evolution mechanism are constructed, and real-time personalized feedback and accurate path optimization are realized.

The application provides a method and device for event prediction based on a timing diagram rule, the method comprising: obtaining timing diagram data, and adding event prediction results of a machine learning model to the timing diagram data to obtain timing diagram extension data; extracting a subgraph for verifying prediction rule reliability from the timing diagram extension data according to a verification ratio; training a rule creator according to the timing diagram extension data and the subgraph to obtain a timing event prediction rule set; and obtaining a prediction event set according to the timing event prediction rule set and the timing diagram data. By embedding the machine learning model for event prediction into a rule-based association relationship system as a predicate, the association rule can not only use the existing machine learning model event prediction result, but also use a logical condition to improve the prediction result of the machine learning model, has stronger expression ability, and does not have to constrain constant interval time or have a common focus constraint on a graph pattern.