60 results about "Data driven algorithms" patented technology

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

The invention relates to the technical field of civil engineering safety monitoring, and discloses a multi-sensor fusion scaffold intelligent monitoring and early warning system and method, and the method comprises the steps: deploying a multi-source sensor group at a scaffold key node, collecting data in real time, carrying out the noise reduction fusion processing through an edge calculation module, and constructing a dynamic deformation feature vector; inputting a time sequence prediction model; a self-feedback adjusting unit is triggered to drive an executing mechanism to conduct deformation compensation, and data optimization control parameters are fed back in real time; and based on the compensated deformation state, the remote platform generates a hierarchical maintenance decision and synchronously pushes the hierarchical maintenance decision to the terminal. According to the invention, all-dimensional sensing of deformation, load and environmental parameters of the bridge supporting scaffold is realized through heterogeneous sensor cooperative networking and redundancy check. Local monitoring blind areas can be eliminated, cross validation of a physical model and a data driving algorithm is combined, the reliability of deformation monitoring and the robustness under environment interference are remarkably improved, and more comprehensive data support is provided for construction safety.

The invention discloses a coal drop pipe full-life-cycle operation and maintenance decision-making method. The method comprises the steps of S1, coal drop pipe full-life-cycle data collection; s2, constructing and updating a digital twinborn body of the coal drop pipe: S2.1, constructing a multi-dimensional digital twinborn body comprising a geometric model, a physical model, a behavior model and a rule model, and comprehensively describing the characteristics of each aspect of the coal drop pipe; s2.2, dynamically updating the twinborn body, and dynamically updating the twinborn body through a data driving algorithm by using real-time monitoring data; s3, health state evaluation and reliability prediction, including short-term health diagnosis and long-term reliability prediction; s4, carrying out dynamic operation and maintenance decision making based on risks and cost; and S5, performing decision issuing execution and closed-loop feedback. According to the method, the design data, the real-time monitoring data, the environment data and the operation and maintenance historical data are integrated, the coal drop pipe digital twinborn body is constructed and dynamically updated, it is ensured that the model is consistent with the physical entity state, and the comprehensiveness and timeliness of the data are improved.

The invention discloses an infrared ship scene generation method and system based on collaborative optimization of a physical prior model and a data-driven algorithm, and a storage medium, and belongs to the technical field of infrared image generation and artificial intelligence fusion. Comprising the following steps: preprocessing an acquired real infrared ship image and an original visible light ship image; different types of ship databases are established through semantic segmentation, and a visible light ship foreground image is obtained; synthesizing a new visible light ship image, and converting the new visible light ship image into a pseudo-infrared image; segmenting the new visible light ship images, classifying the new visible light ship images according to materials, covering the segmented image masks into the corresponding preprocessed real infrared ship images, extracting real infrared gray values of ship parts, establishing a linear regression model, and outputting gray values of ships of different materials; and optimizing the pseudo infrared image, correcting the gray scale of the ship area by using the gray value, and finally generating an infrared ship scene. According to the method, the contradiction between physical consistency and generation efficiency is solved, and the problems of high cost and data shortage of infrared ship image acquisition are solved. Enhancement of infrared ship data can be used for sea ship detection and identification scenes.

The invention discloses an intelligent fault diagnosis early warning system for mine electromechanical equipment, which comprises a data sensing module used for collecting multi-mode state data of the mine electromechanical equipment and carrying out anti-interference preprocessing, and an edge intelligent module used for receiving the preprocessed data and executing fault diagnosis and dynamic threshold adjustment. The cloud evolution module is used for receiving a diagnosis result and original data and completing self-evolution through data enhancement and model updating, and the application service module provides graded early warning, residual life prediction and operation and maintenance decisions based on the diagnosis result and evolution information; the method effectively captures early weak fault features through multi-modal data deep fusion in combination with a physical mechanism model and a data-driven algorithm, greatly improves the recognition accuracy of major faults, and improves the recognition accuracy of the major faults through deploying a lightweight CNN-LSTM model at an edge end and focusing on model evolution and data enhancement at a cloud end. And collaboration of edge quick response and cloud continuous optimization is realized.

The invention relates to the field of nonlinear system modeling, and particularly provides a global linear modeling method and system for a nonlinear system based on Lie derivative and sparse recognition, a terminal and a medium, and the method comprises the steps: obtaining an explicit nonlinear kinetic equation of a target system, calculating the Lie derivative of a state variable or an output variable of the system based on the kinetic equation, constructing a candidate observation function library directly associated with the physical mechanism of the system; performing screening and dimension reduction on the candidate observation function library by adopting a sparse recognition method to obtain a low-dimensional observation function set; and on the basis of the screened observation function and system operation data, a finite-dimensional global linear model of the target system in the dimension raising observation space is obtained through identification by using a data driving algorithm. According to the method, the online solving efficiency and the control real-time performance are improved, and efficient and reliable multi-target collaborative optimization control of systems such as a wind driven generator is realized.

The invention discloses a ring-down light cavity precision pressure control method and device based on enhanced data-driven PID, and the method comprises the steps: 1) building a ring-down light cavity pressure control device, and integrating a pressure sensor, a proportional valve, a digital multimeter, a DC power supply, a vacuum pump and other key parts; 2) acquiring a proportional valve driving voltage and a pressure feedback signal based on LabVIEW, and constructing a discrete time ARX model of the system; (3) on the basis of the model residual error and the real-time error, an enhanced data driving algorithm is adopted to carry out online self-adaptive updating on PID parameters; 4) filtering high-frequency noise by using a second-order Bessel low-pass filter to improve the signal stability; and 5) realizing closed-loop control under the LabVIEW platform, and monitoring and maintaining the operation performance of the system in real time. The method has the advantages of model self-learning, parameter self-tuning, high robustness, high control precision and the like, is particularly suitable for scenes such as high-precision spectral measurement with extremely high requirements on pressure stability, and has remarkable engineering practical value and popularization and application prospects.

The present invention relates to the technical field of computing power resource scheduling, and particularly to a multi-user computing power quota intelligent cut-in scheduling method and system. The present invention proposes the following solutions. First, obtain the to-be-scheduled task information and GPU status information, and construct the current computing power characteristic matrix based on hardware parameters, dynamic operation metrics, and physical topology. Subsequently, use a neural network model that combines a physical model and a data-driven algorithm, perform feature extraction in combination with an attention mechanism, and generate an updated computing power characteristic matrix through multiple rounds of iterative correction. Based on this matrix, dynamically evaluate the GPU resource allocation situation, determine whether to perform cut-in scheduling, and rearrange the resources of the preempted tasks to optimize the overall computing power utilization rate. This application effectively improves the computing power allocation efficiency and fairness in a multi-tenant environment and is applicable to complex computing task scenarios such as high-performance computing, deep learning training, and inference.

The invention discloses an industrial flue gas image quality evaluation method based on digital-analog hybrid driving, and belongs to the field of image quality evaluation. On the basis of a data-driven convolutional neural network, a self-attention mechanism and a model-driven visual saliency detection operator, the industrial flue gas image quality evaluation method based on digital-analog hybrid driving is constructed through three steps of feature extraction, feature dimension reduction and feature pooling based on digital-analog hybrid driving. According to the industrial flue gas image quality evaluation method designed by the invention, the advantages of model driving and data driving are combined, and the quality of the industrial flue gas image can be more accurately evaluated by combining a physical model, namely visual saliency detection, with a data driving algorithm, namely a self-attention mechanism. Excellent accuracy and robustness are shown in complex industrial environments and different types of flue gas emission and monitoring scenes, and a visual quality evaluation effect close to human eyes is obtained.

The invention relates to the technical field of civil engineering materials, in particular to a concrete setting time prediction method based on data fusion, which comprises the following steps: selecting influence factors such as easily measured environmental parameters and concrete parameters related to concrete setting time, and estimating the sensitivity of each factor to the setting time; determining a value range of each influence factor suitable for practical engineering application in combination with expert experience, and quantifying a value scale of each factor in the value range according to sensitivity; optimizing weight parameters of the data fusion model by adopting a method for feeding back optimization according to a prediction result; and predicting the setting time of the concrete through the optimized data fusion model. According to the method, a data-driven algorithm and big data are combined, various easily-measured environmental factors influencing the concrete setting time and the concrete parameter influence factors are organically integrated through the deep neural network, the concrete setting time is rapidly predicted according to the concrete characteristics and the site construction environment, and the engineering construction efficiency is improved.

The invention discloses a historical state architecture construction method and system for a digital power distribution network operating system, and belongs to the technical field of smart power grids. The method specifically comprises the following steps: collecting equipment change operation data of a service site through a homologous maintenance application, preprocessing the data, and storing the data in a unified model to obtain a total historical net rack data set; a third-party data driving algorithm requests historical net rack data set operation at a specified historical moment in the unified model, an operation result is merged with a total historical net rack data set and then merged with a geographic element data set, a historical net rack geographic map is obtained, the historical net rack geographic map is cut into historical state vector tiles, and the historical state vector tiles are transmitted to business applications of all network provinces for digital display. And the historical state architecture construction of the digital power distribution network operation system is realized. According to different change operation types, data maintenance of different processing is carried out on the existing net rack data, and it is guaranteed that equipment changes newly occurring in a service site can be synchronized to the running state net rack data and the historical state net rack data in the system in real time.

This invention proposes a data-driven back-end parameter identification method for underwater wireless power transfer systems, belonging to the fields of wireless power transfer technology and power electronics technology. By employing a data-driven method based on the XGBoost data-driven algorithm, a nonlinear complex mapping model between voltage parameters and coupling parameters is established. At the back end, the multivariate parameters to be identified in the coupling mechanism are inverted based on the Buck input voltage. This method only requires DC voltage acquisition to achieve online identification of coupling parameters, eliminating the need for bilateral communication modules or high-frequency sampling modules, thus reducing hardware costs. It enables rapid identification of multivariate coupling parameters in underwater magnetically coupled wireless power supply systems, exhibiting better adaptability to marine environments. This method solves the problems of existing underwater wireless power transfer systems relying on complex analytical models and requiring bilateral communication and acquisition for parameter identification, while also addressing the difficulty of rapid online identification of coupling parameters in complex underwater environments such as seawater.

The invention discloses a digital intelligent PHM platform for a driving mechanism of coal unloading and storage mechanical equipment of a coal wharf, which comprises five core parts, namely a data acquisition layer, a data transmission layer, a data storage and processing layer, a digital modeling layer and a visualization and decision support layer, and is characterized in that the data acquisition layer comprises a multi-modal sensor arranged at a key part of the equipment; the data storage and processing layer comprises a framework combining edge computing and cloud computing, performs cleaning, noise reduction and feature extraction on original data, and constructs a driving mechanism operation state portrait through a multi-source information fusion technology; the digital modeling layer comprises fusion based on physical modeling and a data driving algorithm, and equipment digital twin bodies capable of being updated in real time are formed; the visualization and decision support layer provides a visual interface and operation and maintenance strategy recommendation; according to the invention, by realizing unified data acquisition and centralized management, the problems of data dispersion and incompatibility in a traditional monitoring system are solved, and a data foundation is laid for full-life-cycle management of equipment.

The invention provides an equipment attitude identification-based wireless charging system anti-offset control method, which comprises the following steps of: firstly, acquiring electrical information of a primary side system under different roll angles and butt joint distances of equipment, specifically voltage and current at two ends of a shunt capacitor and a phase difference between the voltage and the current; the method comprises the following steps: establishing an equipment docking attitude parameter identification model by taking voltage and current at two ends of a shunt capacitor and a phase difference between the voltage and the current as input information and taking a roll angle and a docking distance of equipment as output information, and identifying a docking attitude of the equipment before starting stable charging to ensure that the equipment enters a charging process in an optimal attitude; in the stable charging process, the posture of the equipment is continuously monitored, and the problems of unstable charging, efficiency reduction and the like caused by equipment deviation due to factors such as ocean current impact and the like are solved in real time.

This invention discloses a model for estimating the temperature of coolant in the main pipeline of a nuclear reactor, considering measurement errors. Belonging to the field of nuclear power plant temperature measurement technology, the model includes the following steps: acquiring actual temperature measurement data from the power plant and establishing a dataset corresponding to the measured data and agreed-upon true values; analyzing the actual temperature measurement process and classifying measurement errors; considering different error distribution characteristics, combining a data-driven algorithm with a stochastic model to establish a temperature estimation model; designing a solution method for estimating prior information and calculating model coefficients in the temperature estimation model; optimizing and solving the temperature estimation model using the created dataset, and verifying its accuracy and reliability. This invention solves the problems of difficulty in detailed study of existing measurement errors and difficulty in solving for the true distribution of coolant temperature in nuclear pipelines.

The invention provides a blade root load-based wind power mixed tower load estimation method, which comprises the following steps of: firstly, building a wind turbine generator multi-body dynamics simulation model, simulating dynamic responses under different operation conditions, and generating a simulation data set; then constructing a load estimation model of a fusion mechanism and a data-driven algorithm, and training and verifying the load estimation model by using the simulation data set; and acquiring real-time operation state parameters and blade root loads of the wind turbine generator in an actual wind turbine generator, inputting real-time data into the trained load estimation model, and outputting load estimation values of all sections of the mixed tower in real time as a basis for structural health assessment and control optimization. Finally, a blade root load sensor data validity monitoring and fault-tolerant switching mechanism is introduced, and continuity and reliability of the load estimation process are guaranteed. The method is high in engineering adaptability, easy and convenient to deploy and suitable for various practical application scenes such as structural health monitoring, fatigue life evaluation and intelligent control strategy optimization of the wind turbine generator.

The invention provides a garment simulation method and system based on simulated garments and data driving and application. The method comprises the steps that the simulated garments and garment model grids of the simulated garments are acquired; preprocessing is carried out on the basis of a garment process related to the simulation garment, and animation data of the simulation garment in different postures are generated; smoothing the animation data to generate a garment skeleton; inputting the current human body posture data into a pre-trained prediction network model to predict transformation information of each skeleton on the garment skeleton; and adjusting the clothing skeleton based on the transformation information, and further driving the clothing model grid through the adjusted clothing skeleton. According to the scheme, high-real-time, high-quality and low-resource-consumption garment simulation can be realized, computing resources can be effectively utilized, and the complexity of calculation is reduced, so that a high-quality garment effect is realized on wider equipment. And a data-driven algorithm and a machine learning model are adopted to solve the clothing effect in real time, so that the clothing can adapt to the action of the user more quickly.

The invention discloses a wind turbine generator tower drum monitoring system with a self-healing function. The system comprises a sensing layer, an analysis layer, a decision-making layer and a self-healing layer. The sensing layer collects multi-source response data of the wind turbine generator tower drum in real time, and full-system holographic sensing of the wind turbine generator tower drum is achieved; the analysis layer combines a multi-source data fusion algorithm and a data driving algorithm to carry out real-time state monitoring and future state prediction on the wind turbine generator tower drum prestressed cable; a decision-making layer makes a next action strategy of the fan according to analysis results of a multi-source data fusion algorithm (present) and a data driving algorithm (future) and a linkage decision of the main control system; and the self-healing layer actively triggers the repair system to execute targeted repair operation in real time, so that self-adaptive recovery of the prestressed cable is realized, and stable operation of the wind turbine generator is ensured. According to the invention, a monitoring and self-healing integrated intelligent solution can be provided for operation and maintenance of the tower drum of the wind turbine generator.

This invention discloses a method for predicting the three-dimensional spatiotemporal distribution of algae in aquatic bodies based on a physical information neural network (PINN), belonging to the field of algal bloom prediction and control technology. Addressing the problems of poor generalization of purely data-driven models, difficulty in fitting purely mechanistic models, and insufficient vertical fitting ability in existing algal distribution prediction methods, this invention integrates the advantages of physical mechanisms and data-driven algorithms. It embeds the advection-diffusion-response (ADR) equation of algal growth into the loss function of a physical information neural network (PINN), constructing a hybrid prediction model that combines data fitting accuracy with physical constraints. This achieves accurate prediction of the three-dimensional spatiotemporal distribution of algae and inversely obtains physically meaningful ecological parameters. This invention improves the accuracy, generalization, and physical interpretability of algal distribution prediction and can be effectively applied to the monitoring, early warning, and aquatic ecological environment management of harmful algal blooms.

The invention provides an AI monitoring method for analyzing the corrosion risk of a drilling pipeline based on time sequence data. The AI monitoring method comprises the following steps: S1, acquiring multi-source time sequence data of the drilling pipeline of an offshore drilling platform; s2, preprocessing and standardizing the original data; s3, time series data feature engineering and feature screening; s4, marking corrosion risk labels and dividing a data set; s5, training an AI corrosion risk prediction model; s6, verifying and optimizing the performance of the AI model; s7, performing real-time corrosion risk reasoning; s8, carrying out corrosion risk early warning and data feedback; and S9, historical data redisk and model iterative optimization are carried out. Aiming at the corrosion risk of the drilling pipeline of the offshore drilling platform, a full-process AI monitoring system of data driving, algorithm fusion and closed-loop iteration is constructed, and aims to realize real-time accurate identification, graded early warning and continuous optimization of the corrosion risk, guarantee safe operation and maintenance of the platform and reduce the operation and maintenance cost.

The application relates to the field of nonlinear system modeling, and specifically provides a nonlinear system global linearization modeling method, system, terminal and medium based on Lie derivatives and sparse identification, the method comprising the following steps: obtaining an explicit nonlinear dynamic equation of a target system; based on the dynamic equation, constructing a candidate observation function library directly associated with the physical mechanism of the system by calculating Lie derivatives of system state variables or output variables; screening and reducing the dimension of the candidate observation function library by using a sparse identification method to obtain a low-dimensional observation function set; and based on the screened observation function and system operation data, identifying a limited-dimensional global linear model of the target system in the dimensioned observation space by using a data-driven algorithm. The application improves online solving efficiency and control real-time performance, and realizes efficient and reliable multi-target collaborative optimization control of systems such as wind driven generators.

This invention provides an AI monitoring method for drilling pipeline corrosion risk based on time-series data analysis, comprising the following steps: S1: Multi-source time-series data acquisition for offshore drilling platform drilling pipelines; S2: Raw data preprocessing and standardization; S3: Time-series data feature engineering and feature selection; S4: Corrosion risk labeling and dataset partitioning; S5: AI corrosion risk prediction model training; S6: AI model performance verification and optimization; S7: Real-time corrosion risk inference; S8: Corrosion risk early warning and data feedback; S9: Historical data review and model iterative optimization. This invention addresses the corrosion risk of offshore drilling platform drilling pipelines by constructing a full-process AI monitoring system of "data-driven - algorithm fusion - closed-loop iteration," aiming to achieve real-time and accurate identification, hierarchical early warning, and continuous optimization of corrosion risks, ensuring safe platform operation and maintenance while reducing maintenance costs.

The invention belongs to the technical field of crossing of energy storage systems, data processing and data driving algorithms, and discloses an energy storage system single battery consistency detection method based on density peak clustering and evidence classification, a medium and a program product. Analyzing the association relationship between the historical data of the energy storage system and the related influence factors, and constructing a historical sample library; establishing an energy storage system feature extraction model based on a clustering algorithm, and dividing each battery monomer of the energy storage system into a plurality of categories; and carrying out decreasing mapping on the distance between the real-time operation data and the class center to obtain similarity, completing evidence distribution, calculating and identifying framework evidence, and judging and positioning inconsistent monomers. According to the invention, the accuracy and reliability of consistency detection of the energy storage system can be improved, inconsistent battery monomers in the energy storage system can be accurately positioned, and the reliability and safety of operation of the energy storage system can be improved.

The invention provides a gas inspection path optimization method and system based on a GPS, and relates to the technical field of gas inspection path optimization. The method comprises the steps of collecting core information; constructing a constraint inspection road network; performing routing inspection path planning to obtain an initial routing inspection path set; dynamically adjusting the inspection path to obtain an optimized inspection path set; and screening an optimal inspection scheme through a vine growth optimization algorithm. According to the method, through core logic of data driving, algorithm optimization and dynamic adaptation, gas inspection is converted from experience driving to scientific quantification, the inspection safety and efficiency are improved, meanwhile, resource consumption is reduced, and a technical scheme which can be landed and popularized is provided for safe inspection of urban gas pipelines.

The invention discloses a financial data causal relationship mining method and system for intelligent auditing. The method comprises the steps of obtaining and preprocessing structured financial data and unstructured text data; fusing domain knowledge and a data-driven algorithm to construct a financial causal graph; using a large language model to identify and quantify management layer intervention variables; constructing a structured causal model and executing anti-fact deduction to obtain a financial index prediction value under a non-intervention scene; and comparing the actual observed value with the predicted value, quantifying the manipulation effect and generating a risk clue. The invention further discloses a corresponding system which comprises a data acquisition and preprocessing function module, a financial causal diagram construction function module, a management layer intervention variable recognition and quantification function module, an anti-fact deduction function module, a manipulation effect mining function module and the like. According to the method, analysis from abnormality discovery to causal attribution can be realized, and the net influence of management layer decisions on financial results is quantified, so that the recognition capability and auditing efficiency of complex profit manipulation behaviors are improved.

The invention relates to a power battery system, in particular to a method for estimating available charge and discharge power of the power battery system. The invention discloses a method for estimating available charge and discharge power of a power battery system. The method comprises the following steps of: testing the maximum static limit charge current and the maximum static limit discharge current of a power battery under various charge states and temperatures; calculating the current average electromotive force of the battery power system; establishing a relation between a dynamic limiting current and a static limiting current based on a first-order RC equivalent circuit model; estimating the dynamic limiting current of the single battery on line in real time; comparing the dynamic limiting current of the single power battery with the maximum current allowed by the system to obtain the available current of the system, and calculating the available current and power of the system. According to the method for estimating the available charging and discharging power of the power battery system, physical modeling and a data driving algorithm are fused, and high-precision and real-time response to battery aging and complex working conditions can be achieved while the calculation efficiency is guaranteed.

This invention belongs to the field of transmission line structural safety assessment and intelligent analysis technology, and discloses a rapid assessment method for the load-bearing safety of power fittings in complex icing and wind environments. This method constructs a refined finite element analysis model including towers, transmission lines, and power fittings, introducing wind loads, icing loads, and de-icing impact loads to analyze the stress distribution and deformation characteristics of power fittings under complex icing and wind environments, identifying weak points in the fittings. It uses the Latin hypercube sampling method to design complex environmental calculation case samples, conducts large-scale finite element simulation analysis, and constructs data samples of the load-bearing characteristics of power fittings in complex icing and wind environments. Based on this, it uses data-driven algorithms such as deep neural networks, support vector machines, or random forests to establish a load-bearing safety assessment model, achieving rapid assessment of the load-bearing safety of power fittings. This invention balances assessment accuracy and efficiency, and is suitable for rapid analysis and engineering applications of the load-bearing safety of power fittings under complex icing and wind environments.

This invention discloses a mechanism-data hybrid-driven method for modeling and controlling wastewater biochemical treatment processes, comprising the following steps: establishing a mechanism model for the target pollutant; screening key kinetic parameters with high sensitivity and high volatility; using experimental datasets to invert the key kinetic parameter dataset based on the mechanism model; constructing a prediction model for the key kinetic parameters using a data-driven algorithm; coupling the prediction model and the mechanism model into a hybrid prediction model, and inputting real-time influent and operating conditions to dynamically predict the concentrations of the target pollutant and its products; based on the prediction results, solving a multi-objective optimization problem with target pollutant removal and product resource recovery as optimization objectives, and obtaining and executing the optimal control parameters. This invention reduces prediction fluctuation bias and improves prediction robustness by constraining the main mechanism framework, and improves prediction accuracy by dynamically fitting highly sensitive and highly volatile parameters using a data-driven algorithm, thereby achieving synergistic optimization of pollutant removal and resource recovery.

The application relates to the technical field of unmanned aerial vehicle scheduling and intelligent optimization, and discloses a multi-unmanned aerial vehicle cooperative task allocation method and device for complex task scenarios, which has the technical scheme as follows: global multi-source data acquisition and space-time heterogeneous hypergraph representation, complex task causal emergence decoupling and counterfactual deduction optimization, hierarchical federated heterogeneous matching degree accurate representation, distributed external generalization causal element reinforcement learning dynamic allocation decision, distributed conflict resolution and digital twin closed loop iterative optimization; by adopting frontier technologies such as space-time heterogeneous hypergraph representation, causal emergence reasoning, hierarchical federated learning, causal element reinforcement learning and digital twin closed loop optimization, a causal driving full-link algorithm architecture is constructed to break through the inherent limitations of traditional correlation-based data-driven algorithms, so that efficient cooperative operation of a large-scale heterogeneous unmanned aerial vehicle cluster in a strong coupling task and an extreme dynamic environment can be realized.

The present invention discloses a lunar nuclear reactor power supply twin operation and control method and system, relating to the technical field of digital twins for space nuclear reactor power supplies. The present invention receives lunar nuclear reactor power supply sensor measurement data as input or correction conditions for simulation calculations; calculates the operating status of the lunar nuclear reactor power supply based on the received sensor measurement data, and implements fault warnings through a data-driven algorithm. If a fault is found, a neural network control algorithm is used to automatically generate fault control instructions, which are sent to a pre-built simulated reactor power supply for verification; depending on the verification status of the simulated reactor power supply, the control instructions are sent to the lunar nuclear reactor power supply via an uplink communication link, or control instruction optimization is performed on the ground; based on digital twin technology, the present invention implements lunar nuclear reactor power supply status monitoring, fault warning, control support and other operation and maintenance functions on the ground, thereby achieving full life cycle safety assurance for the space nuclear reactor power supply.