55 results about "Sequence diagram" patented technology

The invention discloses a two-dimensional code full-life-cycle tracking management system based on dynamic encryption, relates to the technical field of two-dimensional code tracking management, and is characterized in that a target object life cycle state sequence diagram is constructed, and a two-dimensional code coding index is dynamically generated based on a current state, an equipment environment and a time factor; and dynamic generation and secure binding of two-dimensional code content are realized in combination with a multi-factor asymmetric encryption mode. Meanwhile, through path consistency verification and state chain continuity judgment, validity verification is carried out on a two-dimensional code access behavior, and the validity of the two-dimensional code is judged in combination with an environment deviation detection mechanism; compared with the prior art, the method can effectively prevent the two-dimensional code from being counterfeited, replayed and visited unauthorized, improves the uniqueness, verifiability and safety of the two-dimensional code in the whole life cycle, and is suitable for high-safety product tracking, anti-counterfeiting authentication, multi-stage identity recognition and other scenes.

The invention relates to the technical field of voice visualization, in particular to a visual configuration method and system for a voice social product activity page, and the method comprises the following steps: disassembling voice display content, recognizing page composition and voice attributes, generating a voice fragment set, generating a time sequence chain group according to a trigger type and a sequence, and correspondingly playing a playing instruction and an interaction action. And generating a nested sequence graph and a dependency chain graph, and finally generating a visual configuration scheme. According to the method, structured recognition and unified mapping are completed in voice display content, an ordered trigger chain is constructed in combination with page loading and interaction actions, voice playing and page process synchronization are achieved, interaction coordination is improved through logic correspondence of a playing instruction and an operation action, and time migration and attachment path series playing logic are integrated; the page state change and the voice sequence are fused, redundant content is eliminated, a reusable configuration structure is constructed, the construction efficiency is improved, and the deployment and maintenance cost is reduced.

The invention discloses a monitoring analysis method and system based on big data, and relates to the technical field of monitoring data analysis, and the method comprises the following steps: S1, collecting multi-source behavior perception data in real time, carrying out the data preprocessing, and constructing a fusion data flow structure; s2, constructing a time sequence video analysis model, outputting an atomic behavior tag sequence, extracting key micro-feature indexes, and performing judgment and evaluation on individual behavior offset; s3, on the basis of the atomic behavior tag sequence, constructing a behavior sequence diagram, performing behavior chain matching and evolution evaluation, and taking exception supervision measures; s4, comprehensive risks are evaluated through the multi-source behavior perception data, and graded early warning is carried out; s5, manual auditing and event label feedback are carried out, abnormal behaviors are judged, and whether the behaviors deviate from a normal mode or not is analyzed; the problem that in dense areas such as shopping mall corridors and escalators, some theft preparation behaviors do not have the violent characteristic, are submerged by normal behaviors and cannot be captured is solved.

The invention relates to the technical field of knowledge maps and intelligent decision, and discloses an intelligent decision support method and system based on enterprise knowledge maps, and the intelligent decision support method comprises the steps: collecting original data to obtain a standardized data set; performing knowledge extraction and fusion, and obtaining a domain knowledge graph by adopting an ontology evolution mechanism; symbol rule extraction and vector representation learning are carried out, and a two-way attention fusion mechanism is adopted to obtain an inference result and an explanation link; performing time sequence correlation analysis, and obtaining a decision sequence diagram by adopting a time decay attention mechanism; evaluating the supply chain toughness by adopting a health state prediction method and a network topology vulnerability analysis method to obtain a toughness enhancement scheme; performing multi-objective optimization sorting to obtain a recommended decision scheme; and performing knowledge verification by executing feedback, and obtaining an optimized knowledge graph by adopting an incremental updating method. According to the method, symbol reasoning and vector reasoning can be fused, and accurate and interpretable intelligent decision support is provided.

The invention discloses a financial market emotional fluctuation early warning system, and relates to the field of security, according to the scheme, modal features of three texts, voices and social public opinions are input into a multi-layer perceptron to calculate modal weights, and canonical correlation analysis alignment is adopted after weighted fusion, so that high-quality fused emotional features of a unified measurement space are realized. A weighted directed investor relation network is constructed through interaction frequency and Pearson correlation based on a fusion vector, a group emotion evolution mode is captured by applying sequence diagram convolution, a high-correlation emotion community is divided through modularity maximization iteration, community abnormal emotions are jointly detected by means of an auto-encoder and a first-class support vector machine, and an early warning signal is triggered. And finally, soft update adjustment is performed on an early warning threshold value by using a depth deterministic strategy gradient algorithm, so that the system has early response capability and low false alarm rate in a complex and changeable transaction environment, and reliable and accurate decision support is provided for market risk management and control.

The invention relates to the technical field of energy management, in particular to a multi-source heterogeneous energy load prediction method and system for a manufacturing enterprise cluster, and the method comprises the steps: collecting original energy data from a multi-source heterogeneous system in the manufacturing enterprise cluster and external environment information related to the energy load change, preprocessing the original energy data and the external environment information to obtain standardized energy time sequence data and final external environment information; based on the standardized energy time sequence data, the final external environment information and a pre-established equipment information model, constructing a triple heterogeneous atlas, and extracting structural semantic features of each node in the triple heterogeneous atlas; based on the structure semantic features, constructing a coupling sequence diagram, modeling the coupling sequence diagram, and identifying key factors; and inputting the structure semantic features and the key factors into a multi-scale prediction model, and outputting hourly load prediction curves of various energy media in a future prediction period.

The invention discloses a power distribution network equipment fault root cause analysis data modeling method, relates to the technical field of power distribution network fault analysis, and aims to solve the technical problems of low fault root cause analysis accuracy and slow response under high-frequency topology dramatic change and edge terminal computing power fluctuation of an existing power distribution network. Comprising the following steps: S1, acquiring equipment operation data and real-time topology information by a heterogeneous edge terminal of a power distribution network, constructing a dynamic lightweight time sequence diagram model with online self-adaptive capability according to an edge terminal computing power high-frequency dynamic topology dramatic change scene, and outputting a fault root cause coarse positioning result; s2, the heterogeneous edge terminal uploads topology increment information and a coarse positioning result in a grading manner based on topology change intensity, wherein the topology increment information is structured data of local topology change of the power distribution network; and S3, the cloud platform updates the global time sequence diagram according to the topology change intensity in a grading manner. The method has the advantages of improving the fault root cause analysis accuracy and the response speed.

The invention provides a trial management system for a control program, which shortens the development period of the control program for uniformly controlling a plurality of control devices in an FA. This trial management system is provided with: a virtual execution unit that causes a simulation program to virtually execute a control program created by a control designer; a step execution unit that executes the control program for each step set in advance to actuate the control device in reality when the execution result of the virtual execution unit satisfies a reference set in advance; a presentation unit that generates a sequence diagram indicating the passage of an operation in each step and presents the sequence diagram to a field operator; a reception unit that receives evaluation of the time series diagram by the field operator; and an update unit that updates the reference on the basis of the evaluation received by the reception unit.

The invention relates to a rolling bearing fault diagnosis method based on multi-sensor fusion and a GAF-CNN. The method comprises the steps of collecting a plurality of one-dimensional time sequence signals of a rolling bearing through a plurality of sensors; converting each one-dimensional time sequence signal into a corresponding two-dimensional time sequence diagram by adopting a Grubrum angle field image coding algorithm; inputting the plurality of generated two-dimensional sequence diagrams into a pre-established neural network model, and extracting a feature vector from each two-dimensional sequence diagram in parallel by the model so as to output a plurality of feature vectors; performing feature fusion on the plurality of output feature vectors to generate a fusion feature; and finally carrying out fault diagnosis on the rolling bearing based on the fusion feature. The invention aims to solve the technical problems of low diagnosis reliability caused by incomplete information of a single sensor and insufficient diagnosis precision caused by insufficient feature extraction when a one-dimensional time sequence signal is directly input into a neural network in the prior art.

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 application discloses an anti-misoperation intelligent control method and system based on integrated control, relates to the technical field of power automation, and comprises the following steps: acquiring first data of a three-party integrated control system, constructing a dynamic instruction pool and an operation time sequence diagram; based on the dynamic instruction pool and the operation time sequence diagram, checking operation consistency through a space-time coupling protocol, combining compliance verification rules and power grid operation data, and constructing a power flow constraint network; according to the power flow constraint network, using a mixed integer programming method and an N-1 risk matrix, generating a risk early warning; according to the risk early warning, generating an anti-misoperation decision, and feeding back to the three-party integrated control system. The application is used to solve the problems that operation instruction management and control of a traditional anti-misoperation system lacks dynamicity and consistency checking is weak.

The application discloses a kind of parallel error prevention check methods of power grid dispatching instruction objectification data, it is related to power system dispatching automation technical field, including the following steps: according to dispatching log data generation operation time sequence diagram, parse operation sequence, and extract instruction conflict feature of operation time sequence diagram;Based on beam forming technology, the vibration signal of the mechanical vibration signal of substation equipment obtained in advance is focused, and a voiceprint feature is generated;Operation sequence and instruction conflict feature are encoded to obtain operation time sequence data;Parallel check processing operation time sequence data and voiceprint feature, to generate a check result.The application is used to solve the technical problem that single dependence logical rule check cannot effectively identify device actual physical state anomaly, leading to misjudgment and security risk.

According to systems and techniques disclosed herein, a method for generating an interactive display may include receiving a plurality of real-time event data comprising a plurality of real-time event actions associated with a game identifier. The method may further include generating an event sequence based on the plurality of real-time event actions. The method may further include generating the interactive display including at least a graphical representation of the event sequence. The graphical representation of the event sequence may include one or more real-time event elements, and one or more interactive elements. The one or more interactive elements may be configured to cause the interactive display to update the one or more real-time event elements in response to one or more user interactions. The method may further include transmitting, to a user interface, the interactive display.

The invention discloses a transformer substation E-key lock management method and system. The method comprises the steps that an operation order is converted into a structured sequence diagram through operation sequence modeling; dynamic permission activation is realized based on a state machine model; performing five-prevention logic real-time verification in linkage with a substation monitoring system; digital signature and bidirectional authentication are adopted to ensure instruction security; local decision execution is realized through intelligent verification of the lockset; a sequence state is automatically advanced based on an operation progress. The system comprises a management background, an intelligent mobile terminal and an intelligent electronic lock, and a complete closed-loop management system is formed. According to the invention, deep binding of lock operation and substation operation processes is realized, the technical problems of extensive management and disjunction with safety regulations of a traditional lock are solved, and the operation safety, the operation and maintenance efficiency and the system reliability are remarkably improved.

The invention relates to the technical field of vehicle video data analysis, and discloses a vehicle video data analysis method based on traffic liability definition. According to the method, firstly, a video library is built, the video library comprises multiple traffic scene videos, each video has an event sequence diagram, and then the event sequence diagrams of the same scene are combined into a comprehensive sequence diagram. Then, acquiring a liability demand and an analysis demand of an accident, selecting a comprehensive sequence diagram and extracting a basic sequence diagram according to the liability demand and the analysis demand, and marking a to-be-identified part; and then constructing a responsibility judgment model, after analyzing the to-be-identified part to obtain a complete sequence diagram, checking key frame nodes, determining nodes of which the dispute probability is greater than a critical threshold value as dispute nodes, and generating mark reminding. In addition, when a video library is built, videos can be collected and preprocessed, and key frame nodes are extracted through an optical flow method or a convolutional neural network model. The method can improve the efficiency and accuracy of traffic responsibility definition.

The invention discloses a sequence diagram simulation execution visualization method and system based on UML (Unified Modeling Language) specification driving, and relates to the technical field of UML / SysML (Unified Modeling Language / SysML) modeling and simulation verifier.The method comprises the following steps: firstly, converting a SysML sequence diagram model into an Interaction semantic structure conforming to a UML standard; then, a simulation engine performs semantic verification on the structure and compiles the structure into an executable simulation step network with a clear dependency relationship; then, the engine performs event-driven dynamic execution according to the network in the step, and issues an execution state event through an event bus; and finally, subscribing the events by a user interface, and displaying the execution processes of message transmission, lifeline activation and complex control flow in real time in an animation form. According to the method, high conformity and execution efficiency of simulation to the UML standard are ensured through strict semantic conversion and compilation type execution architecture.

The invention relates to the technical field of sequence recommendation, and provides a multimodal sequence recommendation method based on Poincare map convolution. The method comprises the steps of obtaining user article historical interaction data of a target user, constructing a user article interaction sequence, and extracting image features and text features of articles; inputting the user article interaction sequence, the image features and the text features into a preset Poincare map convolution multi-modal sequence recommendation model to obtain an article recommendation sequence of a target user; wherein the Poincare map convolution multi-modal sequence recommendation model comprises a multi-modal sequence diagram construction module, a Poincare map volume accumulation module and a Poincare interest prediction module. The method provided by the invention can describe the time behavior and the multi-modal content of the user, does not need to depend on space switching operation, can maintain geometric attributes from representation learning to final prediction, and can improve the multi-modal sequence recommendation effect.

Embodiments of the present application disclose a test case construction method and related device, the method comprising: obtaining a sequence diagram; generating an interaction directed graph corresponding to the sequence diagram; a node in the interaction directed graph corresponding to message or fragment condition information in the sequence diagram, an edge in the interaction directed graph corresponding to an order relationship between messages in the sequence diagram, or an order relationship between fragments, or an order relationship between messages and fragments; for each edge in the interaction directed graph, determining a traversal path of the edge in the interaction directed graph; the traversal path comprising a first sub-path from a starting point of the interaction directed graph to the edge, and a second sub-path from the edge to an ending point of the interaction directed graph; for each determined traversal path, determining a case key element according to respective contents of respective nodes on the traversal path; and constructing a test case corresponding to the to-be-tested software based on the case key element. The method can automatically construct a test case, improve the construction efficiency of the test case, and is less likely to miss a test scenario.

The present application relates to the technical field of vehicle video data analysis, and discloses a vehicle video data analysis method based on traffic responsibility definition. The method first builds a video library containing various traffic scene videos, and each video has an event sequence diagram. Then, the same scene event sequence diagrams are merged into a comprehensive sequence diagram. Next, the responsibility requirements and analysis requirements of the accident are obtained, and the comprehensive sequence diagram is selected and the basic sequence diagram is extracted according to the requirements, and the part to be identified is labeled. Then, a responsibility judgment model is constructed, and after the complete sequence diagram is obtained by analyzing the part to be identified, the key frame nodes are checked, and the nodes with a dispute probability greater than a critical threshold are defined as dispute nodes and a label reminder is generated. In addition, when the video library is built, the video is collected and preprocessed, and the key frame nodes are extracted by the optical flow method or the convolutional neural network model. The method can improve the efficiency and accuracy of traffic responsibility definition.

The invention relates to the field of fault diagnosis, in particular to a port ship loader dynamic monitoring and multi-source information fusion fault diagnosis method, which comprises the following steps of embedding physical constraints into input high-frequency original data through a deep diagnosis module, generating theoretical signals, and determining a fault problem in a mode of constructing a loss function. In consideration of possible causal connection from the past to the present, the causal reasoning module constructs a full-connection time sequence diagram, deletes false association through conditional independence test, obtains a directed causal diagram, and traces a fault propagation path. A fault component identified by the deep diagnosis module is mapped to a fault propagation path, and a finally determined fault problem is judged based on a consistency verification method. According to the method, physical constraints are embedded in the diagnosis process, excessive dependence of a traditional data-driven fault diagnosis method on prior data is overcome in combination with causal reasoning, and fault diagnosis participates in combination with a physical mechanism.

The application discloses an AI-driven construction drawing compliance review system and method, relates to the technical field of construction drawing review, and initializes a construction element database based on input construction drawings and a BIM model, automatically generates a construction sequence diagram by using an AI engine, and synchronously generates simulation scene parameters; a graph construction module automatically loads matched construction process specifications and process standards to construct a specification graph, converts the specification graph into a standardized graph format, and automatically calls the specification graph by using a simulation engine to compare a component behavior and a space state in real time; if there is a construction anomaly in the simulation process, a corresponding process node is automatically marked to form a violation event set. The review system realizes the paradigm change of construction drawing review from "static rule matching" to "dynamic process evolution", and significantly enhances the ability to understand complex construction logic and space behavior and the real-time response capability.

The invention discloses a traffic flow prediction method based on a complex quantum time sequence chart neural network, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, constructing a time sequence traffic map and extracting an adjacent matrix, a Laplacian matrix and a time sequence characteristic matrix; then, constructing a quantum propagation evolution matrix based on a Laplacian matrix, and mapping real number features to a complex number field by using a full-connection network; performing feature propagation through a quantum evolution operator, and extracting spatial features by adopting a complex graph convolution operation; a deep network is constructed by stacking a plurality of complex quantum graph convolutional layers, and finally, a prediction result is generated through a full connection layer after an absolute value of an output complex feature is obtained. According to the method, the spatial-temporal evolution rule of the traffic network is effectively and uniformly modeled through complex field calculation and a quantum evolution mechanism, the prediction precision and the calculation efficiency are remarkably improved, and reliable technical support is provided for an intelligent traffic system.

Embodiments of the present application disclose a sequence diagram-based test scenario generation method, device, equipment, medium and product. The method comprises: acquiring a design sequence diagram and a preset sequence diagram standard. The design sequence diagram comprises interactive elements for describing message passing and control flow between objects. The sequence diagram standard is used for describing rules and requirements for drawing a sequence diagram. Element relationship extraction is performed on the design sequence diagram according to the sequence diagram standard, to obtain mutual relationships between interactive elements in the design sequence diagram. An interactive directed graph for describing interactive element flow is constructed according to the mutual relationships. Each operation path described by the design sequence diagram is extracted from the interactive directed graph, and a target test scenario corresponding to the design sequence diagram is generated according to each operation path. The technical solution of the embodiments of the present application realizes standardization and automation of test scenario generation, guarantees coverage and accuracy of the test scenario, and reduces dependence on experience of test personnel.

The application discloses a two-dimensional code full life cycle tracking management system based on dynamic encryption, relates to the technical field of two-dimensional code tracking management, and achieves dynamic generation and security binding of two-dimensional code content by constructing a target object life cycle state sequence diagram, dynamically generating a two-dimensional code coding index based on a current state, a device environment and time factors, and combining a multi-factor asymmetric encryption mode; meanwhile, legality verification of two-dimensional code access behavior is carried out through path consistency verification and state chain continuity judgment, and two-dimensional code effectiveness is judged in combination with an environment deviation detection mechanism; compared with the prior art, the application can effectively prevent two-dimensional code forgery, replay and unauthorized access, improve the uniqueness, verifiability and security of two-dimensional codes in the full life cycle, and is suitable for high-security product tracking, anti-fake authentication and multi-stage identity recognition scenes.

The invention provides an emergency operation program risk assessment method considering time limitation. The method comprises the following steps: step 1, constructing an event sequence diagram-fault tree ESD-FT model; 2, constructing three general sub-models, and respectively describing dynamic changes of available time EoAT, response time EoRT and human error probability EoHE under time limitation; step 3, constructing an event sequence diagram-fault tree-dynamic Bayesian network ESD-FT-DBN comprehensive model; and 4, carrying out Monte Carlo simulation. The method overcomes the defects of an existing static risk assessment method in the aspect of processing time dynamic factors, the time dependence relation of all steps in the emergency operation process is brought into the risk analysis category, the assessment process can reflect the change of available time, response time and human error probability along with the accident process, and the risk assessment accuracy is improved by combining a Monte Carlo simulation method. And comprehensive evaluation of the probability of success, failure or timeout of the emergency operation under the emergency condition is realized.

The invention discloses an ArkTS distributed deadlock intelligent detection and repair method based on deep learning, and the method comprises the steps: building a multi-dimensional lock entity through building the mapping of a distributed API and lock operation and an implicit synchronization mechanism of a component life cycle and a decorator; generating a component state transfer directed graph, a distributed resource access time sequence graph and a task scheduling dependency graph, merging into a global lock dependency graph, and detecting a static deadlock path in combination with linear time sequence logic security attributes; implanting a hook function to obtain a lock state when the application runs, detecting dynamic deadlocks such as single equipment, cross-equipment and state variables through a resource allocation diagram, and deploying a deep learning deadlock prediction model to realize early warning during running; through reverse tracing of violation operation, root causes are analyzed and positioned in combination with deadlock types, distributed deadlocks, task scheduling deadlocks and state variable deadlocks are repaired by adopting strategies such as standardizing lock application sequences and dynamically adjusting task priorities, and the reliability and development efficiency of swan application are remarkably improved.

The present application relates to the field of industrial data analysis, and more particularly to an industrial big data graph computing algorithm, the present application constructs a runtime sequence diagram for industrial equipment by obtaining equipment application data corresponding to a plurality of tasks in a predetermined period of an industrial plant, identifies a heavy operation time domain segment to extract a running state feature corresponding to the heavy operation time domain segment, evaluates a time sequence compactness representation value of the industrial equipment in combination with the running state feature and an average physical distance between the industrial equipment to divide the running state of the industrial plant, collects equipment working features of the heavy operation time domain segment according to the running state, and adaptively evaluates and analyzes the operation scheduling of the industrial plant. The present application improves the accuracy of the running state evaluation of the industrial equipment. At the same time, the present application can adapt to complex scene requirements, dynamically adapt the operation scheduling, realize dynamic balance of the operation efficiency and resource efficiency under different load scenes, and improve the continuity and economy of industrial production.

The invention discloses a visual language-based intelligent agent trajectory navigation system and method. The navigation system comprises a history trajectory processing module, a trajectory sequence diagram model, a dynamic trajectory interaction model, a first trajectory prediction module, a second trajectory prediction module and an optimized trajectory prediction module. The dynamic interaction model introduces an edge weight changing according to a distance relation between intelligent agents to update adjacent nodes in the graph to obtain an intelligent agent dynamic interaction relation; the first trajectory prediction module is used for training the graph convolutional network based on the target body dynamic interaction relationship to obtain an intelligent body dynamic interaction feature sequence; the second prediction module is used for training the intelligent agent dynamic interaction feature sequence based on an attention mechanism to obtain an intelligent agent motion trend track sequence; and the optimized trajectory prediction module is used for calculating and outputting an optimal agent motion trajectory for the agent motion trend trajectory sequence based on a diversity perception loss function. According to the invention, a guarantee is provided for assisting safe navigation of the agent in a complex environment.

The invention discloses a parallel anti-error checking method for objectified data of a power grid dispatching instruction, which relates to the technical field of dispatching automation of a power system and comprises the following steps of: generating an operation sequence diagram according to dispatching log data, analyzing an operation sequence and extracting instruction conflict characteristics of the operation sequence diagram; carrying out focusing processing on a vibration signal of a pre-acquired transformer substation equipment mechanical vibration signal based on a beam forming technology, and generating a voiceprint feature; encoding the operation sequence and the instruction conflict feature to obtain operation time sequence data; and performing parallel verification processing on the operation time sequence data and the voiceprint features to generate a verification result. The method is used for solving the technical problems of misjudgment and potential safety hazards caused by the fact that the actual physical state abnormity of the equipment cannot be effectively identified by singly depending on logic rule verification.

This invention provides an automatic UML sequence diagram generation method compatible with multilingual code analysis. By capturing the characteristics of C++ and Java code, it identifies the code types of each part of the mixed source code and analyzes different code types. For code marked as C++, regular expressions are used to match keywords, class information, and inheritance information of the C++ source code to be analyzed, and these are converted into parameter names. For code marked as Java, syntax and lexical analysis are used to extract class information and method call information of the Java source code to be analyzed. Corresponding Plant UML code is generated and drawn as a comprehensive UML sequence diagram. This invention's automatic UML sequence diagram generation method compatible with multilingual code analysis can intuitively reflect the functional and logical composition of mixed C++ and Java code, and intuitively reflect the interaction relationship between them. It avoids the need to view tedious source code to obtain program information and functional relationships, improving code readability and maintainability.