330 results about "Complex system" patented technology

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 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 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 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.

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.

The invention relates to a superconducting power knowledge system construction method and system based on a lightweight large model. According to the method, multi-source data is utilized for unified representation, deep semantic information is obtained through a lightweight large model, knowledge extraction is achieved, and a basic knowledge graph is constructed; further forming a knowledge graph which can be dynamically updated through knowledge fusion; and in combination with real-time data, adaptive multi-hop reasoning is executed, and a diagnosis and decision result with a causal chain is generated. According to the method, the semantic understanding and reasoning ability of a lightweight large model is taken as a core, the structural expression advantage of a knowledge graph is combined, the limitation of updating lagging and fixed rule reasoning rigidness of a traditional static knowledge base is broken through, and precise cognition and causal chain inference of a complex system state are achieved. The method has universality and mobility, and is suitable for knowledge modeling and intelligent diagnosis of various electric power scenes and other complex industrial systems. The method is verified by taking a superconducting power system as an example, and the feasibility and effectiveness of the construction path are proved.

The invention discloses a diagnosis scheme generation method and system based on multi-scale hypergraph and constraint optimization, and belongs to the technical field of industrial equipment health management. The method comprises the following steps: receiving fault diagnosis query information expressed by a natural language, and carrying out semantic analysis on the fault diagnosis query information to form semantic guide information; task nodes are extracted from the technical information set, and a multi-scale nested subgraph is constructed according to the relation between the task nodes; based on the multi-scale nested subgraph, dynamically reconstructing a task path, representing task nodes as hypergraph nodes, and constructing a multi-scale hypergraph; performing structure sensing regularization processing on the multi-scale hypergraph to obtain the multi-scale hypergraph; performing cross-scale attention processing on the multi-scale hypergraph to obtain a multi-scale fusion hypergraph; and generating fault diagnosis paths based on the multi-scale fusion hypergraph, sorting the fault diagnosis paths, and outputting a diagnosis scheme. According to the invention, the intelligent level and decision-making efficiency of complex system fault diagnosis can be significantly improved.

The invention relates to a knowledge graph and thinking chain reasoning-based complex system demand identification and management method. The method comprises the following steps of S1, constructing a system demand classification network model; s2, constructing a demand structured extraction model based on a thinking chain; and S3, constructing a general demand knowledge graph and a target graph, and complementing the target graph. Through fusion of the knowledge graph and the thinking chain reasoning technology, the problems that in the prior art, classification model precision is low, extraction model leak detection is high, and graph model universality is poor are solved, and the method is suitable for demand modeling in the fields of subways and high-end equipment.

Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and / or maintenance. A method can include determining, responsive to data received from a real robot having one or more real sensors and operating in a real environment, that the real robot needs assistance to navigate from a current state of the real robot within the real environment, causing simulated data to be obtained from one or more simulated sensors within a simulated environment at least partially modeling the real environment, the one or more simulated sensors including at least one simulated sensor different from the one or more real sensors, and using the simulated data to control operation of the real robot within the real environment in order to navigate the real robot from the current state.

The invention discloses an intelligent operation and maintenance alarm causal relationship analysis method based on BERT. According to the method, the semantic similarity of core switch link interruption and access layer equipment offline is improved by 40% through semantic modeling upgrading and joint coding of equipment types and alarm contents, and compared with an independent coding scheme, the misjudgment rate is remarkably reduced. Through organic combination of deep semantic modeling and dynamic time sequence analysis, the crossing of the intelligent operation and maintenance alarm causal relationship from rule driving to data intelligent driving is realized, and an efficient analysis tool is provided for intelligent operation and maintenance of a complex system. According to the method, accurate causal modeling of complex system alarm data is realized through semantic similarity calculation and multi-dimensional weight dynamic fusion under time sequence constraint, and the method is widely applied to large-scale distributed system fault analysis and root cause positioning scenes in industries such as power systems, communication networks, internet platforms, cloud computing infrastructures and industrial Internet of Things.

The invention relates to the technical field of model-driven system engineering modeling, in particular to an MBSE-oriented multi-agent collaborative automatic modeling method and system. The method comprises the following steps: acquiring a natural language modeling demand of a user, retrieving in a shared knowledge environment to obtain a knowledge sub-graph related to the demand, constructing a modeling context, and calling a generation agent to generate a current SysML v2 model; calling a verification agent to perform grammar verification and semantic verification based on the domain knowledge graph on the model, and outputting a verification result; when a verification result represents that grammar does not pass or semantic inconsistency exists or an engineering constraint rule is violated, calling a repair agent to execute deterministic repair and iterative verification according to the domain knowledge graph until a preset condition is met, and outputting a final SysML v2 model; notch marking or pocket bottom repairing can be carried out under the condition that the notch is covered. According to the method, the correctness, interpretability and convergence efficiency of complex system modeling are improved.

The invention discloses a fault tree Boolean function equivalent mapping method based on an untrained neural network, and relates to the field of fault tree analysis. In order to solve the problems that in the prior art, a Boolean function mapping structure is not beneficial to parallel expansion, the calculation efficiency is limited, and the Boolean function mapping structure is difficult to efficiently realize on high-parallel platforms such as a GPU, the invention provides a method for generating topological structure data by analyzing a fault tree model; the basic events, the intermediate events and the top events are mapped into neurons of an input layer, a hidden layer and an output layer respectively, a feedforward network with fixed weight and bias is constructed, and a logic activation function is defined in nodes to realize Boolean logic propagation. The input layer receives a basic event state vector, outputs a top event result through forward propagation, and can realize large-scale Boolean function mapping on a parallel platform through batch input matrixes. The method is suitable for reliability analysis, minimum cut set simplification, top event probability calculation, parallelization fault tree solving and the like of a large-scale complex system.

The invention discloses a complex system fault root cause analysis method and system based on a knowledge graph. The root cause analysis method comprises the steps of collecting fault data; the method comprises the following steps: retrieving similar document vectors and knowledge graph data, and fusing heterogeneous knowledge; integrating the generated heterogeneous fusion knowledge and fault data provided by a user into a reasoning text required by the LLM, and generating a report by the LLM through the generated reasoning text; scoring the report, and if the score is lower than a threshold value, triggering to regenerate the report; if the related engineer solves the corresponding fault according to the analysis report, forward feedback is carried out, and the analysis report and fault data are updated to the knowledge system; and if the scheme cannot solve the fault and the fault is solved manually, related engineers input the manually solved scheme into the scheme and update the knowledge system. The efficiency of the whole process is improved.

The invention relates to the technical field of complex system operation and maintenance and industrial intelligence, in particular to a complex system health management and fault early warning system based on digital twinning. In the prior art, the health monitoring problem of a single device or a specific scene is mainly solved, challenges of numerous components, tight coupling, complex fault propagation path and the like in a complex system are difficult to deal with, and particularly, the capability of quantitatively evaluating the overall health state of the system and accurately positioning the root of fault is lacked. By constructing a system architecture of six modules, the problem that the health state of a complex system is difficult to quantitatively evaluate is emphatically solved. According to the system, system virtual mapping is constructed by using a digital twinning technology, a multi-dimensional evaluation system including an instantaneous health degree, a trend health degree and a risk health degree is established by fusing multi-source heterogeneous data, a comprehensive health state score is generated by using an entropy weight-TOPSIS algorithm, and accurate quantification of the health state of a complex system is realized.

A computing system configured to process a plurality of intermediate outputs from machine learning models to generate final outputs may be maintained. A combined confidence metric that reflects a probability that the final outputs are accurate may be determined based on the intermediate outputs. Outputs associated with combined confidence metrics that are below the threshold may be caused to be discarded.

The invention relates to a model prediction control method and device based on disturbance prediction, equipment and a medium, and relates to the technical field of system control, and the method comprises the steps: obtaining the disturbance data and actual state quantities (height, speed and attitude angle) of an aircraft at the current moment in real time, determining a disturbance prediction sequence based on the current and historical disturbance data, and obtaining a disturbance prediction result; and constructing a discrete system state prediction model, performing rolling optimization solving by taking minimization of a preset cost function as a target to obtain an optimal control sequence in a future prediction time domain, and outputting a first control quantity in the optimal control sequence as an actual control instruction at the current moment to an execution mechanism. According to the invention, automatic control of a multivariable complex system is realized, high precision and high real-time performance of control are ensured, and the ability of the system to deal with extreme conditions is improved.

The invention relates to a zero-carbon / negative-carbon energy supply system based on carbon flow and energy flow coordinated regulation and a design method, and belongs to the technical field of carbon comprehensive energy power generation. An upper capacity design layer of the system adopts a genetic algorithm to obtain an equipment capacity design scheme of each unit, and outputs the equipment capacity design scheme to a lower operation strategy optimization module; the lower-layer operation design optimization layer obtains an optimal operation strategy of the equipment capacity design scheme by adopting a sequential solution algorithm guided by knowledge rules, takes the leveled kilowatt-hour cost under the optimal operation strategy as the operation cost and feeds back the leveled kilowatt-hour cost to the upper-layer capacity design layer, and the upper-layer capacity design layer evaluates the leveled kilowatt-hour cost based on the fed-back operation cost. According to the system, combined optimization of capacity configuration and an operation strategy is realized, the solving efficiency is greatly improved, and feasibility is provided for solving of a large-scale complex system.

The invention provides a signal processing method and a signal processing device. The signal processing method comprises the following steps: receiving and analyzing debugging data from at least one external computer through a unified data interface, and outputting a debugging instruction or capturing a network data signal to corresponding target equipment through a corresponding channel according to an analysis result; receiving heterogeneous signals from a plurality of different types of target devices, and performing preliminary conversion on at least part of the heterogeneous signals; adding channel identification information into the signal processed in the above step and performing protocol conversion to form an aggregated data stream; and outputting the aggregated data stream through the unified data interface. According to the method, multiple external target devices can be debugged at the same time, the debugging result can be output to the external computer through a unified data interface, the problems that in a traditional debugging scheme, tool switching is tedious, and the cooperation efficiency is low are solved, and the debugging efficiency and convenience of a complex system are remarkably improved.

The invention relates to the technical field of data migration and synchronization, in particular to a heterogeneous database real-time incremental synchronization method and system for cloud migration, which can capture a change log stream of a local source database in real time by arranging a plug-in, analyze and reconstruct the change log stream into a uniform event change model, and improve the data migration and synchronization efficiency. According to the method, data processing capabilities such as field mapping and type conversion are completed, functions such as table structure synchronization, writing into a cloud target database and processing and fault-tolerant mechanism aiming at abnormal conditions are realized, and automatic scheduling is realized through a DSServer scheduler, so that efficient, stable and reliable synchronization of cloud migration on a cross-platform database is realized, and the cloud migration efficiency is improved. Low-delay and high-consistency data synchronization from the source database to the target database is achieved, smooth transition of a service system in the migration process is ensured, and the method is particularly suitable for micro-service architecture environments of large complex systems such as a power grid.

A system is used to perform systems modeling related to at least agriculture. The system can include a memory unit that stores executable instructions wherein the instructions can be used to perform systems modeling. The system can obtain, store, and use historical data related to agricultural scenarios. The system can further generate and / or display a human machine interface wherein a user can enter input to tailor the input data based on the preferences and / or assumptions of the user. The system is further configured to perform a simulation based on the user-defined input data. The system can then provide and / or display the results of the simulation as output data The output data can include predicted and / or projected outcomes based on the input data wherein said outcomes can include agricultural information, market information, environmental information, and / or farm management information. The disclosure allows for accurate modeling of complex systems.

The invention discloses a self-adaptive anti-saturation output feedback control method for a complex system based on gain adjustment, belongs to the field of self-adaptive control, and is suitable for the complex system containing nonlinear and unknown time-varying vectors and bounded external interference. The method comprises the following steps: firstly, acquiring a real-time output signal of a controlled object, and then estimating an unmeasurable internal state of the controlled object by a state observer; an initial control signal is generated based on a dynamic model, an actuator saturation limit and the like, and after optimization of a dynamic gain updating law, whether the signal is close to saturation is judged through monitoring conditions; and when approaching, triggering a planning moment, adjusting the signal amplitude to a safety range by a planning function, generating a self-adaptive anti-saturation output feedback control signal, outputting the final output feedback control signal to an actuator, and circularly executing the steps. The method ensures that a controlled object outputs a stable tracking reference signal, the control signal is always in a saturation limit, and stable operation of the system is realized without a sesame phenomenon.

An integrated method for supporting complex system design and optimization selects first graphical-textual model frameworks in a problem domain; implements functional requirement verification in a validation domain and guides a physical architecture model; selects second graphical-textual model frameworks in a solution domain to establish a physical architecture model; generates an integrated simulation model oriented towards functional and non-functional requirements in the validation domain; selects third graphical-textual model frameworks in an optimization domain to establish a multidisciplinary optimization model, and establishes binding with preset subsystem parameters in the physical architecture model of the solution domain and output values of variables related to an objective function; automatically feeds an optimal optimization scheme obtained through iterative solving based on intelligent optimization algorithms to a system design model for automatic verification. The integrated method breaks down barriers between models of various stages, improves the development efficiency, and avoids the difficulty of seamless integration.

The application discloses a kind of general complex system governance architecture based on core anchoring+black box credible traceability, it is related to the bottom layer security and credible management of complex system in all industries.The architecture includes core anchoring layer, black box credible traceability layer, distributed quantization coordination layer, realizes behavior bottom line strong management and control through front rigid constraint, realizes traceable and auditable through tamper-proof record throughout the process, realizes large-scale global coordination and automatic deviation correction through distributed quantization.The application can be reused across industries and all scenarios, solves the global common pain points of system out of control, untraceable, difficult to supervise and difficult to coordinate from the root, and is suitable for artificial intelligence, finance, medical care, transportation, energy, justice, agriculture and all complex systems, with high universality, security and social application value.

The invention belongs to the field of complex engineering system design and kinetic analysis, and particularly discloses an analysis method for verifying complex system empirical tolerance rationality and application thereof, and the method specifically comprises the following steps: (1) establishing a parameterized model; (2) constructing a multi-physics field dynamics simulation model; (3) importing boundary conditions of operation conditions; (4) operation quality and performance index extraction; (5) data-driven correlation analysis; and (6) verifying and optimizing the reasonability of the empirical tolerance. The method has universality and expandability, can be widely applied to empirical tolerance verification and design optimization of complex systems in the fields of rail transit, aerospace, power transmission, precision manufacturing and the like, and provides a unified theory and calculation framework for tolerance rationality evaluation.

In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, a machine learning model is used to process time series data. In some embodiments, the machine learning model is trained in an unsupervised manner on a large amount of prior time series data, allowing a highly accurate model to be created from new data. In some embodiments, the training of the machine learning model alternates between a fit optimization and a prune optimization to allow a large amount of training data to be processed that includes unlabeled anomaly records. Because a machine learning model is used, anomalies within complex systems can be detected, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When an anomaly is detected, a command can be sent to the monitored system (e.g., autonomous vehicle) to address the anomaly.

The invention discloses a self-adaptive event trigger tracking control method for a complex system under sensor faults, which belongs to the technical field of event trigger control, and comprises the following steps: firstly, acquiring output signals of a controlled object by using a sensor, and establishing a system dynamics and fault mathematical model by combining nonlinear characteristics, external interference and sensor gain change and measurement errors; based on a model, a nonlinear constraint characteristic is determined, a dynamic gain and state observer is constructed by applying a nonlinear boundary conclusion under a sensor fault, and an unmeasurable state of the system is estimated in real time. And estimating a state by combining the dynamic gain and a state observer, determining a triggering moment by applying integrated event triggering judgment logic, generating a feedback self-adaptive event triggering control signal and outputting the feedback self-adaptive event triggering control signal to an actuator. According to the method, system nonlinearity, sensor faults and external interference can be compensated, it is ensured that accurate tracking reference signals are output, it is ensured that closed-loop signals are bounded and free of the sesame phenomenon, and communication and computing resources are effectively saved.

The application provides an evaluation method for cloud platform quality, comprising the following steps: determining the coverage and pass rate of each quality sub-domain; according to the coverage and pass rate, using a preset coloring rule to determine the coloring condition of each quality sub-domain in the quality map corresponding to the cloud product, wherein the coloring condition is used to represent the quality condition of the quality sub-domain; and using the quality condition to determine the quality score of the cloud platform. The application can comprehensively evaluate each quality domain of the distributed complex system of the cloud platform, supplement the existing quality model according to the particularity of the cloud platform, increase the API quality, the high availability and the monitoring alarm, and make the evaluation result of the quality model more accurate.