167 results about "Bayesian algorithm" patented technology

The invention relates to the technical field of state detection and fault diagnosis, in particular to an intelligent early warning and fault diagnosis system for a thermal power plant, which comprises a multi-source data acquisition module for acquiring data in real time; the edge computing node is used for performing noise filtering and abnormal value correction on the acquired data; the digital twin modeling unit is used for constructing a dynamic simulation model of the equipment based on a physical model and historical data; the hybrid analysis engine is used for positioning early abnormal detection and fault sources; the visual early warning interface is used for dynamically displaying the health state and the fault probability of the equipment and generating a graded alarm signal; according to the invention, the multi-source data acquisition module acquires equipment multi-dimensional signals in real time, after edge computing node filtering and denoising, a digital twin modeling unit constructs a precise simulation model, a hybrid analysis engine fuses LSTM and a Bayesian algorithm, fault features are deeply mined, data weights are optimized, and the fault detection accuracy is improved. According to the system, the accuracy and timeliness of diagnosis are remarkably improved.

The invention provides a network information operation and maintenance system based on AI multi-modality, and belongs to the technical field of network information operation and maintenance. Heterogeneous operation and maintenance data such as network flow, equipment state, audio alarm and thermal imaging are acquired through an acquisition unit, and various features are extracted in parallel by using a multi-modality feature extraction module to form a unified multi-dimensional feature vector; a hierarchical anomaly detection architecture is established, lightweight and deep anomaly detection models are deployed on an edge side and a cloud end respectively, different modal features are intelligently fused through an attention mechanism by adopting a multi-modal feature fusion model, and a detection threshold is optimized in real time according to a network state by using an adaptive threshold dynamic adjustment Bayesian algorithm. And a multi-modal fusion decision engine is constructed to perform weighted fusion on the anomaly detection result and dynamically adjust the modal weight, so that the technical problem of insufficient accuracy of multi-modal operation and maintenance data fusion processing is solved.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention relates to the technical field of material nondestructive testing, in particular to a CFRP wing skin damage positioning system and method based on multi-modal signal processing and Bayesian optimization DSCN, and the method comprises the steps: carrying out permutation entropy analysis and Higuchi fractal dimension analysis on a signal data set received by a sensor; performing time-frequency feature extraction on the sensor receiving signal data set deviating from the reference; performing damage feature screening on the signal data in the effective time period; training the DSCN structure to obtain an initial CFRP wing skin damage positioning prediction model; performing hyper-parameter optimization on the initial CFRP wing skin damage positioning prediction model according to a Bayesian algorithm; and performing damage positioning prediction on a to-be-detected sample according to the final CFRP wing skin damage positioning prediction model to obtain a corresponding CFRP wing skin damage positioning result. According to the method, effective damage features can be accurately and efficiently extracted from complex signals, and the accuracy and efficiency of CFRP wing skin damage positioning are remarkably improved.

The invention discloses a terminal meter reliability dynamic evaluation method, system and device based on multi-source data features and a medium, and relates to the technical field of terminal meter fault prediction.The method comprises the specific steps that multi-source data of a metering terminal are obtained, and the multi-source data are integrated to form a structured feature matrix; and introducing a mixed feature screening mechanism to carry out mixed feature screening to obtain a key feature set of the terminal meter. And carrying out weight distribution by adopting a Bayesian algorithm to obtain a comprehensive weight. And constructing a comprehensive reliability scoring model, and outputting a comprehensive reliability score of the terminal meter. And dynamically setting a risk threshold according to the environmental parameters, and outputting a comprehensive risk level of the terminal meter. According to the method, terminal meter reliability dynamic evaluation under multi-source data fusion is realized, the accuracy of reliability evaluation is improved through dynamic feature screening and an adaptive weight adjustment mechanism, and fault risks and operation and maintenance weak links under different scenes are effectively identified.

The invention relates to the field of coal mine geological disaster monitoring, in particular to a coal mine surface slope remote sensing data fusion monitoring method and system, and the method comprises the steps: collecting multi-source data through a satellite-unmanned aerial vehicle-ground three-stage network, and carrying out the cleaning, correction and standardization preprocessing; utilizing an improved LSTM model which introduces an attention mechanism and a geological parameter correction item to predict slope displacement and risk levels; distributing monitoring resources through a PSO (Particle Swarm Optimization) algorithm, dynamically adjusting an equipment state through a fuzzy PID (Proportion Integration Differentiation) algorithm, and fusing multi-source data through a Bayesian algorithm to generate a high-dimensional matrix; and finally, storing data based on the alliance chain and performing early warning based on a dynamic threshold value. The system correspondingly comprises six core modules. The problems of narrow coverage, poor timeliness and low data utilization rate of traditional monitoring are solved, millimeter-level precision monitoring is realized, the early warning response is less than or equal to 5 minutes, the cost is reduced by more than 40%, and the method is suitable for safety and ecological monitoring of various coal mine slopes.

The invention belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

The invention discloses a telescope parameter efficient optimization method and system based on a Bayesian algorithm and a fluctuation optical TTL coupling noise calculation model, and aims to solve the problem that a fluctuation optical high-precision calculation module is used for improving TTL coupling noise calculation precision in an existing telescope parameter optimization method. And due to frequent calling of a high-time-consumption module, the optimization time is sharply increased, and the efficient design requirement of a space gravitational wave detection task is difficult to meet. According to the method, on the basis of the principle of a Bayesian optimization algorithm, potential optimal to-be-evaluated telescope parameters are searched through an agent model, a collection function and the optimization function of Zermarkes software; and calculating TTL coupling noise of the telescope to be evaluated by combining a light field propagation calculation method based on a wave optics theory with a light field-phase resolving method, carrying out iterative optimization on telescope parameters, and outputting optimal TTL coupling noise and optimal telescope optical parameters.

The invention relates to a landslide susceptibility evaluation method considering InSAR and a multi-stage optimization random forest model. According to the method, non-landslide points are selected through multi-factor control and spatial stratified sampling, feature factors are selected by using a random forest classifier in combination with a Pearson's correlation coefficient matrix and a VIF method, a random forest model is optimized based on a Bayesian algorithm, finally, the performance of the model is evaluated through multiple indexes, and susceptibility indexes are divided into five grades. Compared with the prior art, the method not only takes the earth surface deformation rate identified by InSAR as a characteristic factor, but also performs quantitative analysis on the characteristic factor and the susceptibility zoning result, verifies the consistency of the two factors, can effectively improve the precision of a landslide susceptibility evaluation model, provides important reference for landslide disaster prevention and reduction research, and has a wide application prospect. The method is especially suitable for areas with frequent geological disasters such as southwest regions in China.

The invention discloses a reasoning and interaction system based on multiple dimensions. According to the inference system based on multiple dimensions, the complete process from multi-source data acquisition to inference result output is realized. The acquisition module is responsible for collecting out-hospital examination information and in-hospital examination information of a patient. And the pruning module predicts a targeted set by utilizing the interactively collected patient information and a preset diagnosis knowledge base, dynamically prunes the full-amount question-examination thinking sub-trees according to the targeted set, and optimizes the pruned question-examination thinking sub-trees. And the feature extraction module extracts key features from the interactively collected out-of-hospital and in-hospital question examination information based on the optimized question examination thinking sub-tree. And finally, the inference module inputs the key features into a pre-trained diagnosis model, and the model is combined with a Bayesian algorithm, a deep learning algorithm and a clinical decision consensus to output a final inference result. The problem that dynamic adaptation and efficient and accurate reasoning of multi-dimensional data cannot be achieved on a task set with specific requirements in the prior art is effectively solved.

The application provides a weak fault feature extraction method for an autonomous underwater vehicle propeller, and belongs to the technical field of underwater vehicle fault diagnosis, and comprises two parts: fault feature enhancement and feature fusion. First, the application optimizes parameters by judging the Gaussianity of all modes of multi-source state signals and control signals through negative entropy, completes noise reduction, and extracts and enhances fault features based on a modified Bayesian algorithm. Then, the feature signals are divided into multiple time intervals, the faults occurring in each interval are taken as focal elements, all signals except the longitudinal velocity are subjected to first feature fusion, the first fusion result is subjected to second fusion with the feature signal of the longitudinal velocity, the fault features are further enhanced, and the monotonicity between the fault features and the fault degree is presented. The application can provide a basis for subsequent fault detection and identification, and is particularly suitable for state monitoring of autonomous underwater vehicle propellers.

The invention discloses a dam interior and exterior deformation monitoring method and system based on deep fusion of finite element and GNSS (Global Navigation Satellite System) monitoring. According to the method, surface displacement observation data are obtained through a GNSS receiver, a finite element model is constructed, and the mechanical response of the finite element model under hydrostatic pressure, temperature and seismic load is simulated. According to the first layer, parallel primary fusion is conducted on GNSS data and finite element output through Kalman filtering, particle filtering, a neural network and a Bayesian algorithm; in the second layer, weights are dynamically distributed on the basis of mean square errors of all algorithm results in a sliding window and reference data, and a high-precision displacement estimation value is generated through weighted integration. The system evaluates data quality and model confidence in real time, dynamically optimizes weight distribution, performs early warning based on multistage thresholds of displacement, speed and acceleration, and finally studies and judges structural risks through time sequence decomposition and abnormal mode recognition. According to the invention, the overall precision and reliability of dam deformation monitoring are significantly improved.

The invention discloses an electric power field operation safety monitoring method and system based on AI driving. The method comprises the following steps: synchronously acquiring voice, operation video and equipment state identification information, and de-noising to generate a multi-modal sequence; using a multi-modal cross reconstruction model to reconstruct other modals for any modal, and identifying suspicious fragments according to reconstruction errors; multi-layer consistency matching is executed on the suspicious fragments, and the consistency matching degree and mismatch attribution are calculated through time soft alignment, semantic embedding unification and semantic graph reasoning; dispersing the voice, action and equipment state elements into an event sequence, and matching the event sequence with a preset process causal graph to position an abnormal event; and finally, abnormal nodes and induced nodes are analyzed in combination with a Bayesian algorithm, and whole-process safety monitoring is realized. According to the method, password inconsistency, action out-of-order, object mismatching and equipment response abnormity in electric power operation can be intelligently identified and traced, and the real-time performance and accuracy of operation safety are improved.

The invention discloses a gas cylinder state on-line monitoring method and system based on a Bayesian network, and relates to the technical field of on-line monitoring, and the method comprises the following steps: determining a to-be-detected gas cylinder, deploying a plurality of sensors on the to-be-detected gas cylinder, and setting a radio frequency identification tag; multiple types of real-time data of the gas cylinder to be detected are obtained based on multiple sensors, and timestamp alignment and normalization processing are carried out; constructing a topological network based on the characteristics of the multiple types of real-time data, and determining topological network nodes; constructing a gas cylinder state monitoring model by using topological network nodes based on a variational Bayesian algorithm; inputting the various indexes of the gas cylinder to be detected into the gas cylinder state monitoring model to identify the risk of the gas cylinder to be detected; and acquiring a risk identification result of the to-be-detected gas cylinder, writing the risk identification result into the radio frequency identification tag, and when the risk identification result is that the to-be-detected gas cylinder is abnormal, pushing the risk identification result and a tag code of the radio frequency identification tag to a maintenance terminal.

The invention provides a speed limit information fusion method and device, and the method comprises the steps: obtaining map speed limit information and map speed limit confidence based on a map navigation system; acquiring image data in front of a road, and recognizing speed limit information in the image data through a speed limit recognition model to obtain camera speed limit information and camera speed limit confidence; taking the map speed limit confidence coefficient and the camera speed limit confidence coefficient as prior probabilities, and obtaining a posterior probability through a Bayesian algorithm based on the prior probabilities and the conditional probabilities; and the final speed limit information is determined based on the posterior probability, the map speed limit information and the camera speed limit information, so that the accuracy and reliability of the speed limit information are improved.

The invention discloses a pore pressure real-time prediction method based on a stacked integrated model. The method comprises the following steps: collecting well logging data of a drilled well in a research block and a cable formation test result; using a Bayesian algorithm to invert a global optimal Eaton index; calculating the pore pressure equivalent density by combining a dc index method and an Eaton method, and establishing a data set; according to the data distribution characteristics of the training set and the test set and the characteristic importance sequence, optimization of input parameter combinations is carried out; constructing a machine learning model based on a stacking integration framework; optimizing the base learner and the meta learner, and optimizing the optimized model hyper-parameters by using a Bayesian optimization algorithm; and testing the prediction performance and generalization ability of the optimized model according to the test set, and evaluating a model prediction result. According to the invention, through a stacking integration strategy, advantage integration is carried out on a plurality of models, and the model hyper-parameters are optimized by using the Bayesian algorithm, so that accurate and real-time prediction of the pore pressure profile can be realized.

The invention discloses an intelligent inquiry method and device and electronic equipment, and relates to the technical field of intelligent inquiry and the technical field of data processing.The intelligent inquiry method comprises the steps that symptom information provided by a patient in the current round of inquiry is obtained, and the symptom information serves as latest symptom information; generating a current candidate disease set based on the latest symptom information; for each candidate disease in the current candidate disease set, calculating the posterior probability of the candidate disease after the current round of inquiry based on the latest symptom information by adopting a Bayesian algorithm; based on the posterior probability of each candidate disease in the current candidate disease set, the uncertainty entropy of the current candidate disease set is calculated, and the uncertainty entropy represents the uncertainty of the disease diagnosis result of the current candidate disease set; and determining whether to end the intelligent inquiry or not based on a size relationship between the uncertainty entropy and a preset entropy threshold value. By adopting the scheme, the inquiry efficiency and the inquiry integrity in intelligent inquiry are effectively balanced.

The invention provides a wind power ultra-short-term power prediction method suitable for a microclimate environment, and the method comprises the steps: partitioning a wind power plant, optimizing a parameter group of a bidirectional long-short-term memory network through employing a Bayesian algorithm, deeply mining the deep features of historical measured wind speed data and historical measured power data of each region through employing the bidirectional long-short-term memory network, and carrying out the prediction of the wind power ultra-short-term power. The historical actually-measured wind speed data are fused by using anemometer tower data and meteorological station actually-measured data, so that the accuracy of the actually-measured wind speed is improved; obtaining theoretical prediction power based on historical numerical weather forecast data by using a deep neural network model; wherein the historical numerical weather forecast data is firstly fused by using multi-source numerical weather forecast data, then the fused numerical weather forecast generates high-resolution numerical weather forecast of wind speed, temperature, wind direction, humidity and air pressure of the wind power plant through a statistical downscaling method, and the numerical weather forecast of wind speed, temperature, wind direction, humidity and air pressure of the wind power plant is obtained through iterative optimization. And determining a first weight and a second weight corresponding to each time scale at the current moment in the prediction result in real time, further determining a final power prediction result of one partition by combining the two prediction powers, and then adding to obtain a power prediction result of the whole wind power plant. In this way, the ultra-short-term power prediction precision of each time scale in a microclimate environment, especially the ultra-short-term power prediction precision of a large time scale, can be obviously improved.

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and / or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and / or a generalized linear model. Machine learning models also incorporate ongoing feedback from the users who can either accept or reject suggested matches and hence the models undergo an evolution process and constantly update from user patterns.

The invention discloses an intelligent regulation and control method and system for anchoring force of a freight cableway in a complex stratum, and relates to the technical field of cableway engineering, and the method comprises the following steps: carrying out the temperature decoupling of a wavelength offset through a self-calibration temperature decoupling method, and obtaining an actual strain value and a strain value confidence coefficient; the wavelength offset is extracted from the obtained anchor rod multi-source data set; based on the actual strain value and the multi-source data set of the anchor rod, axial force distribution serves as a solving target, and axial force data of the anchor rod are obtained through a physical constraint inversion method; axial force data is predicted in real time through a Bayesian algorithm, and a compensation regulation and control instruction is generated according to the evolution trend of the predicted axial force data. The method is used for solving the problems that the axial force prediction result is not accurate due to temperature interference and the anchoring force regulation and control means are lagged.

This invention discloses an underwater acoustic anti-interference transmission method and system based on sparse time-frequency feature mapping. The method includes: at the transmitting end, adaptively generating a fractional-order linear frequency-modulated waveform with a specific time-frequency shear slope based on the Doppler state of the underwater acoustic channel, achieving physical-layer focusing of transmitted energy; at the receiving end, constructing a hybrid observation model containing wide-block sparse channel components and narrow-block sparse burst noise components, and using a dual-channel variational Bayesian algorithm to jointly iteratively infer the posterior probability distribution of environmental burst noise in the fractional-order domain; finally, recovering the original signal through soft-threshold interference cancellation and fractional-order channel equalization. This invention effectively solves the communication failure problem caused by high-dynamic Doppler diffusion and marine biological impulse noise interference in underwater acoustics by actively mapping the waveform and using heterogeneous sparse joint inference at the receiving end, significantly improving transmission reliability in harsh underwater acoustic environments.

The invention discloses a system and method for checking and optimizing questions and answers of a port yard design scheme, and the system comprises a question and answer analysis unit which is used for obtaining design task information, and analyzing the design task information through a natural language processing model to obtain a structured design task; the multi-objective evaluation unit is used for setting a function loss value based on the structured design task and constructing a multi-objective balance loss function according to the function loss value; the iterative optimization unit is used for performing iterative optimization calculation on the multi-target balance loss function through an improved Bayesian algorithm and outputting a preliminary design scheme; and the feedback adjustment unit is used for acquiring user feedback information, analyzing the user feedback information through a natural language model to obtain a feedback quantification result, and updating the multi-target balance loss function according to the feedback quantification result. According to the system and the method provided by the invention, the design scheme for port engineering can be accurately output by processing and quantifying expert feedback suggestions.

The present invention provides a Bo-Transformer-LSTM-based vehicle steering system hydraulic internal leakage fault diagnosis method and system, relating to the field of fault detection technology. The method comprises steps S1: obtaining fault data of a multi-axle special vehicle hydraulic steering system leaking at a preset fault level; S2: normalizing the fault data and dividing the normalized fault data into a training set and a test set according to a preset ratio; and S3: segmenting the input fault data and inputting it into a Bo-Transformer-LSTM model. The hyperparameters of the Bo-Transformer-LSTM model are optimized using a Bayesian algorithm to obtain the optimal parameter combination, and the trained Bo-Transformer-LSTM model is saved. This method can solve the problem of difficulty in accurately locating faults due to the large number of components and strong nonlinearity in hydraulic systems.

The application belongs to the field of wireless communication and perception, and particularly relates to an environment map construction method for an arbitrary continuous irregular rough diffuse reflection scene. The application comprises: modeling a receiving end receiving signal with a continuous rough diffuse reflection probability model; using a two-dimensional off-network sparse Bayesian algorithm to extract channel parameter information from the receiving signal; perceiving the state of scattering points by using parameter information extracted from different observations; fusing the perceived scattering point information of multiple users and multiple base stations, performing data cleaning and clustering, and realizing perception of an arbitrary shape unknown environment including irregular scattering surfaces; and constructing a high-precision physical map by using a rotating polynomial fitting method. The application can greatly reduce the cost of environment map construction and prevent privacy leakage; can be applied to any unknown shape physical scattering scene, and can effectively extract and fuse a higher order of magnitude of environmental parameter state observations.

The invention discloses a carbon capture steam extraction and energy storage collaborative spinning reserve optimization scheduling method and system, and particularly relates to the technical field of comprehensive energy management, and the method comprises the following steps: building a carbon capture power plant flexible operation model, and coupling power adjustment and reserve capacity; steam extraction adjustment is executed to form a combined dispatching structure; the net output characteristic of the system is realized by introducing electric energy and heat energy storage; constructing a multi-time-scale low-carbon scheduling model; generating a multi-target solution set by using a multi-target Bayesian collaborative convergence optimization algorithm; screening an optimal solution based on an approximate ideal solution sorting method and outputting a scheduling instruction set; according to the invention, power regulation and spinning reserve of the carbon capture equipment are coupled, so that the system regulation capability is enhanced; a combined dispatching structure is formed by combining steam extraction regulation, deep peak regulation of a thermal power generating unit is achieved, a multi-time-scale low-carbon optimal dispatching model is constructed based on system net output, a multi-target Bayesian algorithm and an approximate ideal solution sorting method are adopted, low-carbon, economic and standby requirements are considered, and new energy consumption and system stability are improved.

The invention discloses a method and a system for classifying and identifying defective parts of clock parts based on a visual technology, and the method comprises the steps: obtaining the image data of the clock parts, introducing a self-adaptive illumination correction algorithm, eliminating the reflection interference through a histogram equalization and enhancement technology, and achieving the classification and recognition of defective parts of the clock parts. Establishing a dual-path convolutional neural network to respectively extract local texture features and global geometric features in the processed image data, and performing weighted fusion of the features through an SEBlock attention mechanism to obtain fused feature data; establishing a lightweight classifier based on a neural network, inputting the fused feature data into the lightweight classifier for classification, and outputting a preliminary classification result; and generating a defect sample by using a GANs generative adversarial network according to the preliminary classification result, calculating a posterior probability through a Bayesian algorithm, and outputting a target classification result. And the accuracy and integrity of classification identification are improved.

This application discloses a method, system, device, and medium for generating game parameters, relating to the technical field of game design. The method includes: performing correlation mining on the multidimensional behavioral data to obtain an emotional state feature sequence; constructing a personalized emotional response model for the target player based on the emotional state feature sequence, and generating a target emotional state feature trajectory for the target player based on an emotional change template and the personalized emotional response model; acquiring historical game interaction data and constructing an emotional state prediction model based on the historical game interaction data; calling the emotional state prediction model and searching in the game plot parameter space using a Bayesian algorithm to generate target game plot parameters, such that the emotional state feature prediction sequence corresponding to the target game plot parameters matches the target emotional state feature trajectory. This application has the effect of enhancing the personalized immersive experience of players in the game plot.

The invention relates to the technical field of power systems and automation thereof, and particularly discloses an energy scheduling and decentralized transaction management system based on a battery energy storage system. The method comprises the following steps: collecting battery multi-mode health data through a sensor, and calculating and updating the health state of each battery module by using a Bayesian algorithm; system-level virtual charge state equalization is realized based on a chemical neutral virtual battery abstraction layer; predicting energy surplus or deficit of each node through a causal state space model; for deficit nodes, calculating an optimal energy supply path by a multi-hop energy balance controller; and in combination with a battery health state, an operation condition and the like, a transaction price is generated through a personalized energy token pricing engine, and finally, automatic settlement is performed through a block chain smart contract. According to the invention, the health and chemical characteristics of the heterogeneous battery can be deeply perceived, the asset life and the system safety are improved, and the economy and reliability of the distributed energy storage network are enhanced.

The application discloses an energetic material processing parameter optimization method and system based on a force-heat coupling model, and relates to the technical field of intelligent simulation optimization based on specific calculation models. In view of the problems that 3D printing and mechanical processing data are disconnected, and common force-heat coupling models are not suitable for energetic materials in the prior art, the method of the application first collects 3D printing operation and initial state data of the grain, then constructs a force-heat two-way coupling intelligent calculation model special for energetic materials, adopts a Bayesian algorithm to multi-objective optimize processing parameters based on the model, and carries out model-driven closed-loop dynamic processing control. The method of the application realizes full data connection and adaptation to material characteristics, takes into account safety, precision and efficiency, and improves the processing stability and consistency of the energetic material.

The application discloses a kind of terminal meter reliability dynamic evaluation method, system, equipment and medium based on multi-source data characteristics, relating to terminal meter fault prediction technical field, its specific steps are: obtaining the multi-source data of metering terminal, integrates multi-source data and forms structured feature matrix.Introduce mixed feature screening mechanism to carry out mixed feature screening, obtain the key feature set of terminal meter.Bayesian algorithm is used for weight distribution, and the comprehensive weight is obtained.A comprehensive reliability score model is constructed, and the comprehensive reliability score of the terminal meter is output.According to the dynamic setting of the risk threshold value according to the environmental parameters, the comprehensive risk level of the terminal meter is output.The application realizes the terminal meter reliability dynamic evaluation under the multi-source data fusion, improves the accuracy of reliability evaluation through dynamic feature screening and adaptive weight adjustment mechanism, effectively identifies the fault risk and operation weak link in different scenarios.