680 results about "Complex system" patented technology

The invention discloses an intelligent analysis and remote control algorithm based on digital twinning. The intelligent analysis and remote control algorithm comprises a digital twinning model building and dynamic updating module, an intelligent analysis algorithm module based on digital twinning, a self-adaptive remote control algorithm module and an algorithm process and implementation module. Through multi-dimensional modeling and data fusion, the precision of the digital twin model is improved, and the system state can be predicted more accurately. In combination with deep learning and a multi-objective optimization algorithm, intelligence and optimization of a control strategy are realized, and the operation efficiency and safety of the system are improved. Based on model predictive control and a distributed architecture, real-time and accurate remote control of a physical system is realized, and the dynamic adaptability of the system is enhanced. The algorithm framework has good generalization ability, can be suitable for different types of complex systems, and supports to adapt to new application scenarios through modular extension.

Disclosed is a digital twin-based deduction and optimization method and system for an intelligent reflecting surface (IRS) communication system, including: collecting data from a scenario, including relevant data of an IRS physical model and real channel data; performing real-time data transmission on the collected data; establishing a digital twin three-dimensional (3D) model in a digital twin space based on the data after the real-time data transmission; establishing an IRS reflection mechanism model before fusing with the digital twin 3D model, the generalized Snell Equation being used to simplify a complex system in the IRS reflection mechanism model; deducing and optimizing the IRS communication system to obtain an optimization strategy; and feeding the optimization strategy back to the real world to realize the deduction and optimization of the IRS communication system.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention is an advanced deep learning system that combines a latent transformer core with a latent dynamics analyzer. This system processes input data into latent space vectors, which are then analyzed in parallel for both prediction and dynamic modeling. The latent transformer generates short-term predictions, while the latent dynamics analyzer derives equations of motion describing the underlying system dynamics. By integrating spectral analysis and change detection, the system can identify significant shifts in behavior, particularly useful for complex systems like financial markets. The invention enables more accurate predictions, interpretable insights, and early detection of regime changes. Its end-to-end training approach ensures all components work harmoniously, balancing predictive accuracy with physical plausibility and interpretability.

The invention discloses a drainage basin maintenance intelligent management system based on the Internet of Things and intelligent decision, and relates to the technical field of computers. Comprising a multi-source perception and edge access module which is used for realizing multi-protocol access, time and session alignment, data quality labeling, breakpoint resume and edge side anomaly preliminary screening of industrial data, low-power-consumption point location data and video and thermal image data. According to the invention, through the multi-source sensing and edge access module, various types of data such as industrial data, low-power-consumption point location data, video and thermal image data can be processed, the technical problems of multi-protocol access, time and session alignment, data quality labeling and breakpoint resume and the like are solved, the integrity and reliability of the data on the edge side are ensured, and the service life of the data is prolonged. The edge side anomaly preliminary screening function significantly improves the timeliness of early fault discovery, reduces the network bandwidth pressure, effectively solves the problems of anomaly cooperative detection and alarm flooding in a complex system, and improves the accuracy and timeliness of fault early warning.

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention relates to the technical field of power system data processing and network security, in particular to a system log data anomaly detection and management system and method. The system comprises an atlas establishment module, an anomaly identification module and a feedback adjustment module. According to the method, the global knowledge graph is constructed through the graph establishment module, the deviation degree is calculated in combination with historical baselines, direct association anomalies are judged, the potential influence degree between nodes is calculated based on a graph neural network propagation mechanism through the anomaly recognition module, and non-direct association anomalies are recognized through feature vector cosine similarity. A multi-dimensional feature matching rule base is combined to determine an anomaly type and execute response measures, a feedback adjustment module collects execution data through buried points, quantifies and evaluates effectiveness, optimizes a scheme through feature backtracking and root cause positioning and relies on digital twinborn simulation and gray scale landing to realize scheme iteration, and a feedback adjustment module performs feedback adjustment. The accuracy of anomaly detection and the dynamic adaptation capability of response measures are improved, and the method is suitable for log management and risk prevention and control of a complex system.

The invention discloses a multi-modal attack identification method fusing BMama and difference to guide trans-attention, which comprises the following steps: simulating a false data injection attack, a denial of service attack, an address resolution protocol spoofing attack and a domain name system spoofing attack, collecting physical layer sensor data and network layer flow data, and preprocessing multi-modal data; bMama is constructed to perform dynamic time modeling on multi-modal data, a graph neural network is combined to adversariate a variational auto-encoder, features of a power grid system topology and a communication topology structure are fused, and robustness of potential representation is enhanced through adversarial training; the method comprises the following steps of: guiding feature complementary fusion by using modal difference through a difference guide iteration cross-attention fusion mechanism, improving the capability of distinguishing complex attacks, finally carrying out attack detection and classification on fused modals, and executing end-to-end optimization according to a weighted combination of loss of each part. The method can effectively detect and classify the multi-modal attack in the smart power grid, and enhances the safety and reliability of a complex system.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

The invention discloses an unauthorized vulnerability detection method, device and equipment and a readable storage medium, and is applied to the field of security detection, and the method comprises the steps: carrying out the semantic recognition of real business flow data through a large language model, and determining a to-be-detected interface; performing semantic analysis on the parameters of the to-be-detected interface by using a large language model to determine target parameters; extracting a parameter value with an unauthorized vulnerability risk in the target parameter from the historical real service flow data; generating a test effective load of the to-be-detected interface based on the target parameter and the parameter value by utilizing a large language model and a preset rule base; and performing unauthorized vulnerability detection on the to-be-detected interface by using the test payload, and determining a detection result. According to the method, the natural language understanding capability of a large language model is utilized, the limitation of traditional regularization preprocessing and effective load generation is broken through, and the method is adaptive to diversified scenes of a complex system.

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

Disclosed in the present invention are a wind field correction method and system applied to a single-station remote sensing device. The method comprises the following specific steps: S1, acquiring wind field data by means of a data information acquisition apparatus, and obtaining radial wind speed information of a current acquisition point by means of a data processing module; S2, on the basis of the hypothesis of an uniform wind field, using a classical wind speed reconstruction algorithm for inversion to obtain a three-dimensional wind speed vector of the acquisition point; S3, establishing an error model; and S4, on the basis of an error factor β calculated by means of the error model, performing data correction on the three-dimensional wind speed vector of the acquisition point obtained by means of inversion in S2. Compared with the prior art, the present invention provides a high-precision correction method, which can be operated on low-end hardware, and can also be directly installed and operated on hardware of a measurement device, so that measurement results can be corrected in real time, no additional complexity can be brought to users, and costs are reduced; and the present invention is suitable for solving complex system problems when accurate boundary conditions cannot be obtained.

The invention discloses a metering equipment distribution decision simulation analysis method, and belongs to the technical field of computer data processing and simulation, and the method comprises the steps: fusing multi-source heterogeneous data to construct a space-time knowledge data set; performing causal inference based on the data set, and establishing a causal relationship graph; performing risk propagation deduction and causal link evolution simulation on the map to generate a risk situation set; generating an anti-fact intervention strategy for the risk situation; and performing quantitative analysis to output an optimal distribution strategy, and feeding back and updating the causal relationship graph by using an analysis result. According to the method, the data processing architecture of the dynamic knowledge graph is constructed by adopting causal inference, multi-step propagation inference and link evolution simulation can be carried out on risks in a complex system, and the accuracy and robustness of a data processing system for carrying out prospective decision making in an uncertain environment are remarkably improved.

The invention discloses a construction method of a multi-dimensional time sequence anomaly detection system. The method comprises the following steps: acquiring multi-dimensional time sequence data; preprocessing the obtained multi-dimensional time sequence data; constructing a depth anomaly detection algorithm, constructing an association graph of the multi-dimensional time series data by using cosine similarity and a TopK strategy, learning the association graph through a graph neural network, extracting space and time features of the multi-dimensional time series data, and calculating an anomaly score of each data point; and constructing an anomaly detection system suitable for real-time multi-dimensional time series data based on Apache Flink. Partitioning and efficient detection of multi-dimensional time series data are completed through a real-time stream processing module, and an anomaly positioning map and an anomaly propagation map are generated. The method has the advantages of high efficiency, accuracy, real-time performance and the like on the anomaly detection of the multi-dimensional time series data, can effectively deal with a large amount of multi-dimensional time series data generated in the operation process of a complex system, and timely discovers and locates the anomaly condition so as to support quick response of system managers.

The invention discloses an equipment anomaly tracing method and system based on a digital twinborn and graph neural network, and belongs to the technical field of industrial intelligent operation and maintenance and fault diagnosis. The invention provides an innovative solution integrating digital twin high-fidelity simulation and a graph structure deep learning algorithm, aiming at the technical bottlenecks that the generalization performance of an existing data driving method is insufficient under the conditions of fault sample scarcity and category imbalance and the traceability accuracy of unknown and composite faults is poor. The method comprises the following steps: constructing a high-fidelity digital twin integrating multi-dimensional physical attributes and a system topology structure; based on a fault mode, influence and harmfulness analysis method system, constructing a fault mode library comprising a plurality of single fault modes and composite fault modes, and generating an enhanced training data set with accurate labels through an automatic fault injection mechanism; training a graph neural network model with a multi-level attention mechanism by using the data set so as to learn a propagation rule of a fault in a complex system topology; and finally deploying the model to carry out abnormity traceability analysis on real-time industrial Internet of Things monitoring data. According to the method, the fault diagnosis generalization ability and the positioning precision under the sample imbalance condition are remarkably improved.

The invention relates to the technical field of Internet of Things, and discloses a remote diagnosis system and method for a complex system in an Internet of Things environment, and the system comprises a data collection module which is used for collecting the operation state information of the complex system; the data transmission module is used for transmitting the running state information to the cloud processing center; the cloud processing center is used for performing fault analysis according to the received operation state information based on a machine learning algorithm, generating a fault diagnosis result and a corresponding maintenance suggestion and transmitting the fault diagnosis result and the corresponding maintenance suggestion to the user terminal; and the user terminal is used for receiving the fault diagnosis result and the maintenance suggestion, remotely accessing the system state report of the complex system and remotely controlling the complex system. According to the invention, efficient remote monitoring, accurate fault diagnosis and real-time maintenance guidance of the complex system in the Internet of Things environment can be realized, the maintenance cost is reduced while the maintenance efficiency is improved, and thus the maintainability and safety of the complex system are enhanced.

The invention belongs to the technical field of root cause analysis in intelligent operation and maintenance, and discloses a method for mining fault propagation weight parameters, and the method comprises the following specific steps: S1, supervising data root cause probability initialization, S2, supervising data topology perception labeling, S4, unsupervised data space-time slicing, S5, multi-modal root cause reasoning, S6, parameter increment fusion and S7, online adaptive optimization. Precise modeling and dynamic adaptation are achieved through multi-stage collaborative optimization, and the root cause positioning capacity of a complex system is remarkably improved: a supervised and unsupervised data dual-drive strategy is adopted, a root cause probability baseline is constructed by utilizing work order history, and alarm streams are processed in combination with time-space slice standardization to form a structured knowledge base; the method comprises the following steps: quantifying a transition probability in a CMDB dependency relationship through topology perception annotation and a four-dimensional observation matrix, and constructing a feature matrix containing TP / FP counting to support accurate calculation of a probabilistic graph model;

The invention provides a simulation test key factor screening-oriented method and system and a computer program product. The method comprises the following steps: generating a structured configuration file; constructing a supervised learning data set based on the structured configuration file; based on the supervised learning data set, training a machine learning classification model to obtain a current key factor set; and repeating the steps, judging whether the current key factor set is converged or not, and if the current key factor set is converged, stopping simulation and outputting the current key factor set of the last round as a final key factor. According to the technical scheme of the invention, a high-dimensional, nonlinear and high-coupling complex system can be automatically processed; key factors are objectively recognized through machine learning, a small-batch iteration verification mechanism is adopted, and the screening accuracy and efficiency are improved while the number of times of simulation is remarkably reduced.

Methods and systems for generating, publishing, processing, and consuming linked data events and streams being shared by complex systems in order to construct a semantic web of the data and thereby form a consolidated coherent view of the data are provided. A method includes: receiving a set of metadata that relates to a set of events; generating, based on the metadata, an ordered node hierarchy map and a modern data event schema; receiving a set of tabular data; generating, based on the tabular data and the ordered node hierarchy map, the set of events; streaming the set of events over a streaming pipeline; processing the streamed set of events to generate a linked data media format; and outputting the linked data media format.

The invention discloses an intelligent group area coverage control-oriented multilayer game reinforcement learning method, which comprises the following steps of: mapping each agent in an intelligent group into a game player, and constructing an income evaluation mechanism in combination with a task target of the agent to form a group game model; constructing a perception model and a communication model based on physical capability constraints of the intelligent agent, designing evaluation indexes of coverage efficiency, operation safety, energy consumption efficiency and communication collaboration, and integrating the evaluation indexes into a vector revenue function of the intelligent agent; a TD3 model is adopted as a game player role of an intelligent agent, and association between reinforcement learning and group game is established; and constructing a multi-layer semi-distributed game reinforcement learning framework, carrying out environment interaction, and solving an intelligent group area coverage control strategy meeting Pareto-Nash equilibrium. According to the method, the balance of individual and collective benefits is realized, the calculation efficiency is improved through a semi-distributed iterative algorithm, and the method is suitable for solving requirements of a large-scale complex system.

The invention belongs to the technical field of magnetic levitation rulers, and relates to a magnetic levitation ruler horizontal motion control method, which comprises the following steps: establishing a mathematical model of a horizontal system; according to the constructed mathematical model, estimating the position of the rotor iron core through induced electromotive force derivation to obtain the horizontal position and the deflection angle value of the rotor iron core; rotor iron core horizontal motion control based on SYSTD is carried out; and setting the parameters of the horizontal control system of the magnetic suspension ruler system. A mathematical model is established based on a magnetic circuit and a dynamics principle, the problems of nonlinearity and strong coupling of a magnetic levitation ruler horizontal displacement system are solved by applying an improved active-disturbance-rejection control algorithm, and a system structure is simplified by utilizing a position-sensorless control technology. According to the method, unknown dynamic and external disturbance is taken as total disturbance, and real-time estimation and compensation are carried out through an extended state observer; the ADRC not only can adjust errors, but also can actively process uncertain factors of the system and improve the control efficiency, does not depend on an accurate mathematical model, and is high in adaptability and robustness in a complex system.

The invention discloses a complex system data visualization modeling method based on GIS and topological graph fusion, and relates to the technical field of data visualization modeling, and the method comprises data fusion modeling, space-time anomaly reasoning, state propagation modeling and multi-dimensional visualization enhancement. The method comprises the following steps: performing unified access, cleaning and standardization processing on operation data, topological relation data, geographic space data, function configuration data and environment data of a complex system, and constructing a unified data model fusing geographic attributes and topological attributes; realizing abnormal root cause reasoning and spatial constraint propagation modeling of a system state by using a convolutional time sequence modeling method combined with a graph neural network; a dynamic evolution rule of a node state is described through a time gating mechanism, and interactive display is realized; according to the method, global visualization of the operation state of a complex system, dynamic backtracking of an abnormal propagation path and hierarchical analysis of key nodes can be realized, and the intelligent monitoring, situation awareness and decision support capabilities of the system are improved.

The invention discloses a cascade fault diagnosis method based on a root cause intensity power set belief rule base. According to the technical scheme, the method comprises the steps of 1, constructing a power set belief rule base; the conclusion hypothesis unit can clearly describe a single-point fault state, a composite fault state and a causal chain fault state. And 2, introducing a root cause strength parameter: introducing the root cause strength parameter into the conclusion hypothesis unit constructed in the step 1 for quantifying the causal influence capability of the fault state as the root cause. And step 3, reasoning fusion: calculating the matching degree and the activation degree of each rule, and applying an evidence theory-based reasoning fusion algorithm to an output result to obtain power set confidence distribution of each fault hypothesis. And step 4, fault diagnosis and fault root cause positioning are carried out, and a main fault root cause is output, and the main fault root cause is the root cause with the maximum root cause strength. The method is mainly used for realizing accurate diagnosis of cascade faults in a complex system by popularizing a traditional BRB model to power set reasoning and introducing a root cause strength concept.

This invention introduces a sophisticated system for deploying projects on cloud platforms, combining Unikernels, Cyber Security Mesh Architecture (CSMA), MQTT protocol with SHA256 encryption, an Unikernel Orchestration Rules Engine (UORE), generative AI, and an innovative caching mechanism. Unikernels offer a secure, isolated environment for applications, reducing overhead and boosting performance. CSMA provides extensive security through analytics, identity management, and policy enforcement. The MQTT protocol, secured with SHA256, ensures the integrity and confidentiality of communications. UORE automates deployment, integrating a TLS terminator and data management for streamlined operation. Generative AI proactively resolves deployment challenges, particularly for complex applications, while the caching mechanism enhances performance and efficiency by minimizing latency. This integrated approach automates and secures the deployment process, enabling scalable, efficient, and real-time project creation and deployment in the cloud, thereby addressing the key challenges of cloud application hosting.

The invention discloses an intelligent operation and maintenance and fault handling method based on an AI large model, and relates to the technical field of artificial intelligence and information, and the intelligent operation and maintenance method comprises the following steps: multi-source heterogeneous data fusion, cross-level fault analysis, handling strategy generation and verification, and resource optimization scheduling. The method has the advantages that multi-source heterogeneous data of the equipment layer, the software layer and the service layer are synchronously collected through the sensing layer, the space-time aligned feature matrix is constructed, the limitation that traditional operation and maintenance only depend on single-level data is solved, the time dependence and level incidence relation of the data can be mined at the same time in combination with a multi-mode AI large model of the analysis layer, and the data processing efficiency is improved. The fault root cause probability distribution and the influence hierarchy are accurately output, misjudgment or missed judgment caused by single hierarchy data deviation is avoided, and the accuracy of complex system fault positioning is remarkably improved.

The invention discloses a complex system comprehensive multi-view consistency detection method based on a large model, and relates to the technical field of information system architecture design. The method comprises the steps that N view models to be detected are determined, and a rule base is constructed and formed; forming a triple set; performing rule retrieval on the triple set to obtain a corresponding rule subset; and constructing a complete prompt statement according to the prompt template and the rule subset, and reasoning the prompt statement by the large language model to obtain a consistency detection result. Based on the MBSE method, the ability of understanding, reasoning and applying knowledge in the field of system architecture and system engineering is remarkably improved, the automation degree, accuracy and robustness of multi-view consistency detection are improved, and the correctness, completeness and consistency of complex information system architecture design are guaranteed.

The invention provides a structured analysis and semantic template normalization method for an unmanned vehicle operation log. For the problems of unstructured unmanned vehicle log data, changeable formats, complex semantics and the like, logs are converted into semantic vectors by adopting a multi-language sentence vector coding model, and semantic grouping is performed by using a small-batch K-means clustering algorithm after dimension reduction is performed by adopting an incremental principal component analysis method; extracting a variable field from a clustering result by using a regular expression and generating a standardized template; and intelligent template combination is realized by calculating cosine similarity and editing distance between the templates. The method has the following three technical characteristics: 1) the template consistency is improved by adopting a strategy of clustering first and then normalizing; 2) introducing double similarity constraints to reduce template redundancy; and 3) retaining variable sequence and semantic information to support subsequent analysis. The method is suitable for log data analysis and intelligent operation and maintenance of various complex systems.

The invention provides a multi-level authority management method and system based on unified login authentication and a medium. According to the invention, identity verification of the user in a plurality of systems is realized through unified login authentication, the token containing user identity information, a permission set and dynamic attributes is generated, and the subsystem is supported to analyze and verify the token. According to the method, dynamic attributes such as time, equipment credibility and geographic position are combined, the permission set of the user is dynamically adjusted, and the permission of the user is evaluated in real time through the preset authorization decision rule. The method supports dynamic inheritance and fine control of multi-level permissions, is suitable for a permission management scene of a complex system, and has high safety, flexibility and expandability.

The invention belongs to the field of system reliability design analysis, and particularly relates to a common cause failure parameter-based fault analysis method, which comprises the following steps: acquiring a fault tree model of an analysis object, and fusing the fault tree model with a common cause failure parameter model; constructing a fault tree model with a common cause failure parameter model; performing common cause failure analysis by using the common cause failure parameter model; expanding the constructed fault tree model with the common cause failure parameter model into an explicit expression common cause failure fault tree model by adopting a universal parameter model expansion method; solving the extended explicit expression common cause failure fault tree model to obtain a top event occurrence probability; according to the method, the implicit common cause failure parameter model is expanded into explicit expression, the automation of the reliability analysis process of the complex system considering the common cause failure influence is realized, the modeling difficulty is reduced, and the reliability analysis working efficiency is improved.