609 results about "Bayesian inference" patented technology

The invention provides a sewage system traceability analysis and intelligent monitoring method, system and device based on a graph neural network, and a storage medium, and belongs to the technical field of environment monitoring and artificial intelligence. The invention aims to solve the technical problems of low efficiency, low precision, difficulty in processing multi-source data, poor monitoring network and the like of the existing sewage system pollution tracing method. The method comprises the following steps: constructing a sewage system knowledge graph fusing multi-source heterogeneous data such as water quality and water volume; adopting a multi-scale graph neural network model to learn pollution propagation characteristics based on the knowledge graph; after a pollution event occurs, pollution path backtracking is carried out in combination with physical models such as flow conservation so as to identify a pollution source; bayesian inference is introduced to carry out uncertainty quantification on a traceability result so as to assess the credibility of the traceability result; and finally, dynamically optimizing the layout of the monitoring points based on information gain and other criteria. According to the invention, rapid and accurate positioning of the pollution source can be realized, and the method is suitable for intelligent supervision of an urban sewage system.

The invention relates to the technical field of chip heat management, and discloses a chip heat distribution prediction modeling method and device, and the method comprises the steps: obtaining chip design information, carrying out the three-dimensional grid division through a pre-training transfer learning neural network, and constructing an electricity-heat-force multi-physics field coupling model; the method comprises the following steps: monitoring operation load data in real time, carrying out thermal simulation and generating a temperature distribution characteristic matrix, collecting data through a temperature sensor array, simulating thermal diffusion by using particle filtering and Bayesian inference, compressing and reconstructing the temperature distribution characteristic matrix by using a variational auto-encoder, generating a digital twinborn body, and optimizing a heat dissipation strategy by using deep reinforcement learning. The temperature control strategy is generated, the chip heat management precision is improved, the heat dissipation strategy is optimized, the performance and stability are improved, and the problem of insufficient prediction of temperature distribution in a traditional heat management scheme in the prior art is solved.

The invention provides an intelligent grabbing control method and system based on tactile perception, and the method comprises the steps: obtaining a tactile feedback signal through a sensor array of a humanoid robot, carrying out the frequency domain decomposition of the signal through a Fourier transform method, and carrying out the analysis of the pressure and deformation data of each contact point on the surface of a to-be-grabbed object, calculating to obtain frequency characteristic distribution of the tactile feedback signal; if the initial estimation value of the object rigidity gradient exceeds a threshold value, performing finite element analysis on the object rigidity gradient, and combining a time domain processing result of the tactile feedback signal to obtain object rigidity gradient distribution; carrying out probability modeling on the deformation field by adopting a Bayesian inference method according to the gradient distribution of the rigidity of the object, and carrying out iterative optimization on a modeling result to obtain dynamic update parameters mapped by the deformation field; and regenerating an action sequence of the end effector of the humanoid robot based on the optimized strategy according to a trigger condition of grabbing failure detection, and determining a final grabbing control parameter.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention relates to the technical field of mechanical fault diagnosis, and discloses a gearbox fault diagnosis method and system based on lightweight variational Bayesian learning, and the method comprises the steps: collecting a to-be-diagnosed vibration signal of a gearbox, carrying out the preprocessing of the to-be-diagnosed vibration signal, and obtaining an original vibration signal and the fault feature frequency of the original vibration signal; determining amplitude modulation-frequency modulation sparse combination representation of the original vibration signal according to the fault characteristic frequency of the original vibration signal, and constructing a joint probability model according to classification distribution containing sparse vectors and the amplitude modulation-frequency modulation sparse combination representation of the original vibration signal; performing variational Bayesian inference solution on the joint probability model by using the non-overlapping sub-sequence of the original vibration signal and natural gradient optimization to obtain posterior probability estimation of a sparse coefficient; and determining an activation component according to the posterior probability estimation of the sparse coefficient and the sparse precision parameter, and matching the activation component with a pre-established multi-scale amplitude modulation-frequency modulation sparse dictionary to obtain a fault type and a confidence coefficient thereof.

The invention belongs to the field of advertisement putting, and provides an advertisement putting method and system based on multi-source data analysis, and the method comprises the steps: collecting multi-source original data related to a user; identifying a plurality of cognitive state nodes based on the click behavior data and the transaction path data; constructing a cognitive behavior causal atlas based on the plurality of cognitive state nodes, wherein nodes of the causal atlas represent user cognitive states; predicting an advertisement response probability and a conversion probability of a target user by adopting a Bayesian inference model in combination with a historical behavior sample and a path structure in the causal atlas, and estimating a state transition probability of the user from a current state node to a target state node based on different advertisement intervention contents; and based on the state transition probability and the causal atlas structure, determining an optimal advertisement intervention path of the user from the current cognitive state to the expected conversion state, and constructing a corresponding advertisement putting sequence based on the path.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The invention belongs to the crossing field of environmental engineering and hydrology and hydrodynamics, and particularly relates to a traceability method based on coupling hydrodynamics and a pollutant degradation equation, which comprises the following steps: firstly, acquiring and preprocessing multi-source heterogeneous monitoring data, then selecting a one-dimensional Saint-Venant equation or a two-dimensional shallow water equation according to a water body form to solve a hydrodynamic field, and finally, determining the hydrodynamic field. A multi-component convection-diffusion-reaction equation is coupled to simulate pollutant migration and transformation; an LSTM module is introduced to identify suspected pollution events, pollution source parameters are inverted through a two-channel framework of PDE constraint optimization and Bayesian inference, uncertainty is quantified, model parameters are updated online in combination with data assimilation, and finally the uncertainty is quantified and verified. The method considers traceability precision, efficiency and compliance, supports multiple water bodies and multiple data sources, and is suitable for complex water body pollution traceability.

The invention discloses a cosmic ray muon scattering imaging method, system and device, and relates to the field of ray imaging, and the method comprises the steps: obtaining the direction information of muons in a to-be-imaged region; calculating the most probable three-dimensional scattering track of the muons in the to-be-imaged region based on Bayesian inference and a particle multiple coulomb scattering distribution function according to the direction information of the muons in the to-be-imaged region; and reconstructing a scattering density image in the to-be-imaged region according to the direction information of the muon in the to-be-imaged region and the most probable three-dimensional scattering track. By calculating the most probable three-dimensional scattering track, the scattering points can be effectively reconstructed, the utilization efficiency of muon events is improved, the scattering points are prevented from being separated from the to-be-imaged area, the most probable three-dimensional scattering track better fits the real scattering track, and then the quality of the scattering density image is improved.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention belongs to the technical field related to tunnels, and provides a subsea tunnel cross-fault water inrush catastrophe early warning decision-making method and system in order to solve the problems of inaccurate water inrush recognition, lack of interpretability and the like when an existing subsea tunnel passes through a fault zone high-risk scene. Full-process response control from weighting, prediction to explanation of the input features is realized; driving force analysis is carried out by adopting an interpretable model, so that a transparent and credible risk assessment result is provided; and by combining key driving force in the multi-source heterogeneous data and through numerical simulation of the water inrush catastrophe condition, real-time identification and early warning of the tunnel water inrush risk are comprehensively realized, the identification accuracy of the water inrush risk in the tunnel construction process and the completeness of an early warning mechanism are effectively improved, and the method has important engineering application value.

The invention belongs to the technical field of robot navigation, and particularly relates to a humanoid robot navigation method based on visual semantic segmentation and radar obstacle detection, which comprises the following steps: synchronously acquiring an RGB image and a depth image of a current environment of a humanoid robot by using a visual sensor, and acquiring point cloud data by using a laser radar; performing semantic segmentation on the preprocessed RGB image; radar point cloud obstacle detection; fusing the semantic segmentation map and the laser point cloud map, introducing a Bayesian decision to judge whether the laser point cloud map is passable or not, and then calculating a fusion cost value to obtain a fusion cost map; adopting an RRT * / TEB algorithm to output an optimal path; using a nonlinear optimization solver to generate a foothold sequence accurate to each step; according to the method, a semantic-geometric two-dimensional navigation cost model is constructed; bayesian reasoning is deeply bound with a navigation scene, so that the navigation adaptability of an unstructured environment is improved; the navigation method is suitable for the humanoid robot, and is low in energy consumption, low in navigation deviation and high in safety.

The invention discloses a watershed water resource supply and demand risk prediction and evaluation method based on a multilevel Bayesian causal network, and relates to the technical field of water resource supply and demand risk management.The watershed water resource supply and demand risk prediction and evaluation method comprises the steps that a water resource supply and demand risk diagnosis knowledge graph is constructed according to key variables and interrelations input by a user; constructing a multi-level Bayesian causal network structure; estimating conditional probability distribution among the nodes, and performing parameter learning and structure training on the Bayesian causal network; carrying out risk path identification through a reverse Bayesian reasoning method; outputting a posterior probability of water resource supply and demand risk prediction; based on a preset fuzzy character string matching algorithm, typical risk events and risk features are extracted; and according to the posterior probability and the risk characteristics, comprehensively evaluating the water resource supply and demand risk level. The method can improve the systematicness and scientificity of risk identification, is suitable for multi-link and multi-scale risk assessment and scheme comparison and selection in a complex drainage basin, and has high practical value and popularization prospect.

The invention relates to a circuit board on-line defect detection method and system, and the method comprises the steps: carrying out the synchronous collection and structural integration of multi-source technological parameters such as production line environment temperature and humidity, equipment operation states, material batches and the like, and defect detection images, and achieving the construction of large-sample original data in a production process; through standardization and de-noising preprocessing, multi-modal features are fused, and a distribution mapping model of process and defect features is established by using algorithms such as mutual information analysis and principal component analysis. Based on a feature distribution model and real-time data, a dynamic anomaly detection threshold is adaptively generated, Bayesian inference and evidence reasoning are combined, multi-level confidence levels and risk response suggestions are output, and self-learning evolution of the model and the threshold is realized through a closed-loop feedback mechanism. According to the scheme, the accuracy of anomaly detection, the response timeliness and the risk disposal intelligent level are improved, the method adapts to complex working conditions and batch changes, and the closed-loop optimization and safety control capability of the production process is remarkably enhanced.

The invention relates to the technical field of task scheduling, and provides a metropolitan area network privacy protection computing migration task scheduling optimization method, the resource state of a computing node is processed in real time through Bayesian inference to determine a dynamic trust value in real time, and compared with static trust evaluation in the prior art, real-time risk changes of the computing node can be captured in real time, and the real-time trust value of the computing node is determined in real time. The privacy leakage risk is obviously reduced; the privacy risk estimation value, the task completion time delay and the task execution energy consumption are jointly used as optimization objectives of a multi-objective optimization function, and constraint conditions of the multi-objective optimization function are defined to ensure that the generated optimization scheduling scheme not only meets the privacy security requirement, but also can meet the time delay and energy consumption requirements; according to the method, actual behavior data of the computing nodes are continuously monitored, privacy compliance auditing is carried out, privacy compliance expression is used as new evidence to be fed back to Bayesian inference, dynamic trust values of the nodes are corrected, and long-term reliability and flexibility of task scheduling are remarkably improved.

The invention provides a construction method of a brain rehabilitation cognitive training effect prediction model. The method improves the accuracy and individuation level of cognitive training effect prediction. Firstly, state data of a subject is obtained and preprocessed; thirdly, obtaining unified features by adopting an extraction layer, carrying out combined modeling of a spatial topological relation and dynamic time sequence evolution by adopting a fusion layer based on the unified features and generating spatio-temporal features, and adjusting model parameters by adopting an adaptation layer according to baseline features of a subject to ensure personalized prediction; and finally, in a model operation process, triggering online updating through concept drift detection, and generating a confidence interval and an interpretability score of a prediction result based on Bayesian reasoning. According to the method, through state data processing, personalized adaptation, dynamic adaptation and Bayesian reasoning, the precision, stability and interpretability of brain rehabilitation cognitive training effect prediction are remarkably improved, and powerful support is provided for personalized treatment and clinical decision making.

The invention discloses a sparse Bayesian direction of arrival estimation method based on subspace compression and dictionary optimization, and belongs to the field of array signal processing. According to the method, the data dimension is reduced through the subspace compression technology, and the noise immunity is improved; signal power and noise power are automatically estimated in combination with a sparse Bayesian model, and dependence on information source number information is avoided; the calculation efficiency and the numerical stability are improved through a support set adaptive pruning strategy; and finally, a dictionary fine tuning mechanism is introduced, direction continuous optimization is realized on the basis of an original discrete grid, an off-grid error is eliminated, and direction-of-arrival estimation with sub-resolution precision is realized. The method is a novel method combining subspace compression, sparse Bayesian inference, adaptive pruning and angle optimization, can give consideration to estimation precision, calculation efficiency and application robustness, and is especially suitable for direction estimation under the complex actual conditions of low signal-to-noise ratio, few snapshots, unknown signal source number and the like.

The invention provides a state detection and health diagnosis method and system of an SOFC system, and relates to the technical field of solid oxide battery state monitoring, and the method comprises the steps: obtaining the operation parameters of an SOFC, and optimizing the data consistency and anti-noise capability based on the fusion algorithm of dynamic Bayesian reasoning and the conditional probability distribution of the operation state of the SOFC; frequency domain features are extracted through fast Fourier transform, dimension reduction is carried out in combination with improved principal component analysis, key variables are reserved, and feature expression is optimized for SOFC high-temperature vibration features; predicting SOH (state of health) and RUL (residual life) of the SOFC based on a random forest, a generative adversarial network and an online updating mechanism; and outputting a diagnosis result and generating a dynamic maintenance scheduling plan based on the RUL prediction, and optimizing the system life by minimizing the downtime and the cost. Through integration of multi-source data fusion, frequency domain feature extraction, AI diagnosis and data enhancement and predictive maintenance, limitations in precision, efficiency and service life optimization in the prior art are overcome.

The invention relates to the technical field of image processing, and discloses a pulmonary embolism focus segmentation method and system based on image recognition, and a storage medium. The method comprises the following steps: extracting a multi-level blood vessel topological structure of a CTPA image through blood vessel diameter gradient analysis; modeling blood vessel density distribution by using a Weibull mixed model to obtain embolism characteristic parameters; the pixel embolism probability is estimated through variational Bayesian reasoning, and a focus distribution diagram is generated; performing multi-scale feature fusion on the lesion probability graph to obtain a segmentation boundary; and obtaining a final embolism focus segmentation result based on the vascular connectivity constraint optimization boundary. The problems that blood vessel level differentiation processing cannot be achieved, and accurate probability modeling and anatomical constraint verification are lacked are solved. The accuracy of pulmonary embolism focus segmentation is improved.

The mold injection control method comprises the steps that three-dimensional point clouds of a current injection molding part and an adjacent previous injection molding part are obtained, the global difference degree between the current injection molding part and the adjacent previous injection molding part is calculated through overall registration, if the global difference degree exceeds a threshold value, a difference mask is generated and covers a real-time image of a cavity, a difference image is formed, and the difference image is subjected to injection molding. And performing hybrid clustering on the difference image and a known defect type database to obtain candidate defect types and membership degrees, calculating posterior probability contribution degrees of the sensing nodes to the candidate defect types by taking the membership degrees as input and combining a Bayesian reasoning algorithm, if the contribution degree of any sensing node exceeds a threshold value, outputting a defect root cause report, and if the contribution degree of any sensing node exceeds the threshold value, outputting a defect root cause report. And a machine table or a mold execution mechanism is driven to carry out correction. According to the invention, the problems of low detection efficiency, large subjective deviation and lagging root cause judgment due to the fact that automobile part defect detection and root cause analysis depend on manual visual detection or single-dimensional automatic detection are solved.

The invention relates to a reclaimed water reuse regulation and control method and system based on artificial intelligence and digital twinning, and the method comprises the steps: collecting initial data in a water treatment system, and carrying out the data preprocessing of the initial data to generate preprocessed data; constructing a digital twinning virtual model, and updating model parameters of the digital twinning virtual model based on the preprocessed data through a Bayesian reasoning algorithm to obtain a target digital twinning virtual model; outputting a water quality prediction result in a future preset time period through a pre-constructed water quality prediction model in the decision-making layer, and performing a rehearsal control strategy in the target digital twin virtual model based on the water quality prediction result to generate an optimization control instruction; performing data anomaly detection and fault diagnosis processing based on the preprocessed data through an anomaly detection model and an isolated forest algorithm to generate a fault diagnosis report; and issuing the optimization control instruction to an execution mechanism. The system response speed is improved, the control strategy perspectiveness is enhanced, and the equipment operation and maintenance intelligent level is improved.

The invention discloses an accident analysis and intelligent intervention method and system based on an intelligent networked automobile, and particularly relates to the technical field of accident analysis, which comprises the following steps: when the intelligent networked automobile approaches an accident-prone place, extracting important features from historical traffic data in the accident-prone place of the intelligent networked automobile by using a t-SNE dimension reduction method; acquiring real-time traffic data of the intelligent networked automobile, and determining similar information of the traffic data of the intelligent networked automobile; bayesian inference is carried out based on historical traffic data in an accident-prone place of the intelligent networked automobile and real-time traffic data of the intelligent networked automobile, and probability information of the traffic data of the intelligent networked automobile is determined by analyzing statistical data and constructing a machine learning mode; through comprehensive analysis of the similar information and the probability information of the traffic data, the risk that the intelligent networked automobile enters the accident-prone place is evaluated, the road traffic safety can be improved, and the accident occurrence rate is reduced.

The invention discloses an intelligent education robot question answering system based on voice recognition and a knowledge graph, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps: performing feature extraction on a user voice signal to obtain a text sequence and a multi-modal context feature; recognizing subject domain judgment and question answering intentions based on supervised classification and keyword rule fusion, and outputting a subject domain prior probability and a question answering target vector; determining polysemy words in the text sequence by using a context window, generating a semantic item sequence and semantic item confidence, constructing a subject domain sub-graph based on a subject domain prior probability, and obtaining a candidate reasoning path and a path scoring vector; determining a target teaching concept and an optimal reasoning path by combining Bayesian inference and consistency verification; generating a personalized question answering result aiming at the question asked by the user through fact retrieval and knowledge derivation in combination with the question answering target vector; accurate and efficient intelligent teaching question answering can be realized, and question answering accuracy and intelligent interaction capability of the education robot are effectively improved.

The invention relates to the technical field of elevator safety monitoring, in particular to a fault early warning method based on an intelligent elevator. The fault early warning method comprises the steps that multi-dimensional operation parameters of a target elevator are obtained; multi-modal fusion is conducted on the vibration signal component, the noise signal component, the equipment static attribute data and the working condition environment data, and a health degree evaluation matrix of elevator operation is generated through a space-time attention encoder; according to the health degree evaluation matrix, a preset historical fault knowledge base is combined, the dynamic fault probability is calculated through a Bayesian inference model, and the early warning grade is generated. A historical fault knowledge base and a Bayesian inference model are further combined, a self-adaptive maintenance strategy triggered according to an early warning level from static threshold value alarm to dynamic fault probability prediction is realized, on-demand accurate maintenance is realized, and the maintenance efficiency and the resource utilization rate are improved.

The invention discloses a power transmission tower structure safety evaluation method based on computer vision, and relates to the field of power transmission tower structure safety evaluation, and the method comprises the following steps: obtaining a structure static curvature parameter; the dynamic curvature modal area difference square ratio of the rod piece structure is calculated; combining the structure static curvature parameter and the dynamic curvature modal area difference ratio corresponding to each basic evaluation unit into a dynamic and static fusion feature data set; calculating the structure damage posterior probability of each basic evaluation unit through Bayesian reasoning; and according to the structure damage posterior probability distribution results of all the basic evaluation units, carrying out comprehensive safety grade evaluation on the power transmission tower structure. The non-contact monitoring method based on computer vision is adopted, the deformation and displacement information of the nodes is obtained through the image processing technology, the equipment arrangement and maintenance work is greatly simplified, and the overall monitoring cost is reduced.

The invention relates to the technical field of financial risk assessment, in particular to a financial risk assessment method and system fusing multi-dimensional indexes and combined weights, and the method comprises the steps: constructing a financial cooperation risk index system containing multi-dimensional heterogeneous data; executing a combined weight calculation process based on Bayesian inference logic; performing weighted aggregation on the standardized index data and the posterior combination weight, and calculating a comprehensive financial risk index of the target assessment object; and based on the comprehensive financial risk index, in combination with a preset risk threshold model, generating a systematic risk assessment result and a structural risk distribution map for a specific financial cooperation scene. According to the method, complex risk factors are converted into visual quantitative indexes, and macroscopic decisions in transnational financial cooperation are directly supported.

The invention discloses a virtual simulation and security evaluation method and system based on a network security target range, and the method comprises the steps: constructing a network asset knowledge graph for describing a virtualized target range environment through information collection; secondly, based on an attack strategy grammar rule base, adopting a Monte Carlo Tree Search (MCTS) algorithm to carry out intelligent attack simulation on the knowledge graph so as to discover a nonlinear and multi-stage attack path; thirdly, constructing a Bayesian attack graph (BAG) based on the knowledge graph, and performing probabilistic and systematic quantitative evaluation on the security risk of each asset node in the network through Bayesian reasoning; and finally, integrating the attack path and the quantitative risk value, and generating a comprehensive assessment report containing a visual path, a risk sequence and a reinforcement suggestion. According to the target range simulation method and device, the problems that existing target range simulation is insufficient in confrontation authenticity, one-sided in safety evaluation and lack of predictability are solved by combining the strategy simulation of the MCTS and the global quantitative analysis of the BAG.

The invention discloses a factory carbon emission monitoring method and system based on an electricity-carbon calculation model, relates to the technical field of carbon emission monitoring and energy management, and is used for solving the problem that the real-time performance and accuracy of carbon emission monitoring are reduced due to the fact that carbon emission results are mostly presented as coarse-grained indexes. An initial carbon flow distribution matrix is constructed by collecting carbon concentration and electric energy consumption data of distributed energy access points in a plant, a carbon flow model is generated based on a digital twinborn body, a carbon flow weight is dynamically corrected by combining a Bayesian inference algorithm, the total carbon emission amount of the plant is calculated, and a monitoring result is output. According to the method, the spatial precision and the real-time correction capability of carbon emission tracking can be improved, early warning of the carbon emission trend of the key node is realized, and data support and decision basis are provided for a low-carbon operation strategy of a factory.