77 results about "Sequence diagram" patented technology

The invention discloses a space-time carbon emission prediction method, system and device and a medium. The method comprises the steps of obtaining load historical sequence data; carrying out load flow calculation by adopting a first load flow analysis algorithm to obtain a load flow calculation result; based on the load flow calculation result, carbon flow analysis is carried out through power transmission between the nodes, and the carbon emission coefficient of each node is obtained; constructing a time sequence diagram neural network model, and performing preliminary training on the time sequence diagram neural network model; performing hyper-parameter optimization on the preliminarily trained time sequence diagram neural network model by adopting a first hyper-parameter optimization technology to obtain an optimized time sequence diagram neural network model; and inputting load sequence data and unit power output data at the current moment into the optimized sequence diagram neural network model, and carrying out online prediction on a carbon emission coefficient. According to the method, the real-time prediction of the carbon emission coefficient is realized based on the characteristic learning of the historical operation and monitoring data of the power system.

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 discloses an anti-misoperation intelligent control method and system based on centralized control integration, and relates to the technical field of electric power automation, and the method comprises the following steps: obtaining first data of a three-party centralized control system, and constructing a dynamic instruction pool and an operation sequence diagram; based on the dynamic instruction pool and the operation sequence diagram, checking operation consistency through a space-time coupling protocol, and constructing a power flow constraint network in combination with a compliance checking rule and power grid operation data; generating risk early warning by using a mixed integer programming method and an N-1 risk matrix according to the power flow constraint network; and generating an anti-error decision according to the risk early warning, and feeding back the anti-error decision to the three-party centralized control system. The method is used for solving the problems that operation instruction control of a traditional anti-misoperation system lacks dynamics and consistency check is weak.

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 relates to a power system load prediction method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of smart power grids. The method comprises the following steps: obtaining a power load time sequence diagram according to historical power load data of a power system; according to the power load time sequence diagram, respectively determining a periodic fluctuation property and a seasonal fluctuation property of the power load time sequence diagram; the periodic fluctuation property refers to existence of unfixed periodic fluctuation or absence of unfixed periodic fluctuation, and the seasonal fluctuation property refers to existence of fixed seasonal fluctuation or absence of fixed seasonal fluctuation; according to different combinations of the periodic fluctuation property and the seasonal fluctuation property, determining different target prediction models from a plurality of preset prediction models based on a time sequence; and obtaining predicted load data of the power system according to the historical power load data and the target prediction model. The method can improve the prediction accuracy.

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 discloses a dynamic electronic target sequence intention recognition method and system based on a time sequence diagram structure, and the method comprises the steps: obtaining multi-moment target data for a target object which dynamically changes in a real-time scene; performing sequence division on the multi-moment target data by adopting an FP-Growth algorithm to obtain an electronic target sequence set; modeling and coding each electronic target sequence in the electronic target sequence set by adopting a heterogeneous time sequence diagram model to obtain a heterogeneous time sequence diagram sequence; according to the heterogeneous time sequence diagram sequence, time sequence dynamic hidden relation prediction and time sequence dynamic feature prediction are carried out respectively, and a hidden relation and prediction features are obtained; the hidden relation comprises hidden nodes and hidden edges; and according to the hidden relationship and the prediction features, constructing graph data at the next moment, and performing intention recognition through a graph attention convolutional network. The problem that the time-space incidence relation of the target behavior changing along with time is difficult to capture in the prior art is solved, and the prediction and intention recognition precision is improved.

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 an AI-driven construction drawing compliance examination system and method, and relates to the technical field of construction drawing examination. An initialization module establishes a construction element database based on an input construction drawing and a BIM model, automatically generates a construction sequence diagram by using an AI engine, and synchronously generates simulation scene parameters; the map construction module automatically loads matched construction process specifications and process standards and then constructs a specification map, the specification map is converted into a standardized map format, the simulation review module automatically calls the specification map through a simulation engine, component behaviors and space states are compared in real time, and if construction abnormities exist in the simulation process, the simulation review module reviews the component behaviors and the space states. And automatically marking corresponding process nodes to form a violation event set. According to the review system, the normal form transformation of construction drawing review from static rule matching to dynamic process evolution is realized, and the insight ability and the real-time response ability to complex construction logic and space behaviors are remarkably enhanced.

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.

The present invention provides a method and system for distributed big data real-time stream processing. By generating a topological sequence diagram based on a power network, a heat network, a natural gas network, and real-time operating state parameters, and combining the real-time operating state parameters, a set of dynamic sharding rules for a multi-energy flow coupling network is generated to calculate a dynamic threshold. When any load factor exceeds the dynamic threshold, a dynamic adjustment instruction for the sharding boundary is generated. Based on the dynamic adjustment instruction and multi-energy flow coupling data, a hierarchical collaborative optimization architecture is constructed to determine the target energy interaction constraints and target timing optimization results between shards. Combining the dynamic adjustment instruction, a distributed dynamic sharding configuration instruction for the multi-energy flow coupling network is generated to perform distributed big data real-time stream processing. The technical solution provided by the present invention realizes real-time data processing and resource collaborative optimization of the multi-energy flow coupling network, and provides an efficient solution for scenarios such as smart energy and industrial Internet of Things.

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 present invention is an abstract modeling and verification method for the code implementation of a core trading system architecture, including: A. Modeling and verifying the architecture of the trading system: extracting relevant information from the code implementation and development design documents of the architecture framework, using abstract modeling techniques to construct a behavior model of the existing system, modeling the process communication process of the system using UML sequence diagrams, converting the UML sequence diagrams into a formal model of CSP, and using a PAT validator to perform verification and analysis of security properties; B. Modeling and verifying the cluster leader election module in the trading system: constructing a state machine model for each node in the cluster leader election process, constructing a system dynamic model, and using a PAT validator to perform verification and analysis of the correctness of the leader election process. The advantages of the present invention are: solving the problem of modeling the communication process between processes in the trading system architecture and the state transition of each node in the cluster leader election process, and improving the modeling and security verification and analysis capabilities.

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 present invention discloses a task offloading method, device and medium based on a timing diagram and graph matching theory. Aiming at the problem of offloading tasks that depend on each other in a mobile scenario, a timing diagram model is adopted to represent the dynamically changing set of base stations during the user's movement from the time dimension. The one-to-many mapping relationship between the offloading nodes to be solved and the subtasks in the task offloading problem is abstracted into a graph homomorphism problem, and the task offloading strategy is solved based on the A* algorithm in graph matching theory. The task offloading method, device and medium based on a timing diagram and graph matching theory provided by the present invention, based on the timing diagram model, comprehensively considers the two restrictive factors of the dependence between subtasks and the mobility of the user, models the task offloading problem as a graph homomorphism problem from the graph matching theory, and proposes a reasonable solution for the offloading problem of dependent tasks in a mobile scenario based on the A* algorithm. This solution significantly reduces the actual completion delay of the application.

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.

A method for verifying data and control couplings of software under test comprises obtaining software architecture and design information for the software; extracting expected control coupling information based on an order in which software component calls are in the software architecture; extracting expected data coupling information from one software component to another software component in a sequence diagram; updating the expected control and data coupling information in an expected control and data coupling sequencing table; deriving actual control coupling information from execution trace data obtained from a requirements based test execution; deriving actual data coupling information from data trace information; and updating the actual control and data coupling information in an actual control and data coupling sequencing table. The method then compares the expected and actual control coupling information, and compares the expected and actual data coupling information, to verify whether software components are interacting with each other as expected.

The invention discloses a Qt code generation method for monitoring source simulation software based on a UML class diagram and a time sequence diagram. The method comprises the following steps: constructing the UML class diagram and the time sequence diagram of the monitoring source simulation software; obtaining an XMI file corresponding to the class diagram and the time sequence diagram; constructing a set based on the XMI file; generating class codes of the Qt; generating an object calling relation of the Qt; generating a signal and slot code; and generating a Qt code file. According to the method, the model of the monitoring source simulation software is specifically applied to the UML class diagram and the time sequence diagram, the process of establishing the model of the monitoring source simulation software is simplified, the design is clear, the structure is indirect, and the efficiency from the model to code generation is better.

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.