143 results about "Bayesian probability" patented technology

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

The invention discloses a method and a system for rapidly screening peanut endophytic mildew, particularly relates to the technical field of nondestructive testing of agricultural products, and is used for solving the problems of false detection and missing detection caused by low contrast and gradient boundary in an initial mildew stage and an internal diffusion sample in an existing detection method. Based on a hyperspectral imaging technology, a filtering kernel size is dynamically adjusted through conjoint analysis of frequency domain energy distribution and spatial local variance, and detail features of a gradient boundary in an image are enhanced; verifying and screening high-confidence candidate regions by adopting spectrum and spatial feature consistency, analyzing and extracting principal component features representing a mildew diffusion trend in combination with a texture direction, and dynamically splicing the principal component features with a spectrum gradient direction to construct a fusion feature vector; quantizing boundary confidence through a Bayesian probability classification model, combining with a neighborhood similarity constraint region growing algorithm to generate a mildewed mark graph with continuous space and consistent features, and introducing a dynamic threshold correction mechanism to adaptively optimize a segmentation standard; and the detection precision and the anti-interference capability of the fuzzy boundary are improved.

The invention provides an intelligent debris flow early warning method and system based on a physical model and an algorithm, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: S1, carrying out an indoor soil physical mechanics test, building a physical model, and carrying out the parameter calibration of the physical model through combining a stability criterion equation of a rainfall infiltration-matrix suction coupling effect; s2, simulating different conditions, and generating and outputting debris flow occurrence process data under different working conditions by the physical model; s3, taking the generation process data and the real-time monitoring data as input, constructing a two-channel input network, and training a correction model to obtain corrected response data; s4, on the basis of an SHAP interpretability framework, constructing a Bayesian probability early warning model driven by multi-source heterogeneous data; and S5, triggering real-time early warning of the early warning platform according to the debris flow real-time occurrence probability of the monitoring point, and constructing a mapping relation between soil deformation and critical parameters through dynamic simulation of a physical model on a soil mechanical behavior.

The invention relates to the technical field of sewage treatment, in particular to an intelligent municipal sewage resource utilization decision support method and system, and provides a method for realizing real-time monitoring and historical data analysis of a sewage treatment system by constructing a multi-domain knowledge coupled heterogeneous decision model; the method comprises construction of a knowledge graph in the sewage treatment field and coupling with real-time data, and a hierarchical decision scheme is formed. On this basis, a collaborative mechanism of microcosmic, mesoscopic and macroscopic layer decisions is established, and risk awareness decision optimization is executed; dynamically dividing risk levels through a Bayesian probability decision framework, and realizing closed-loop self-evolution of data-model-decision; meanwhile, a resource value quantitative model is constructed, and decision evaluation of multi-target balance is carried out; according to the invention, the problem of'knowledge isolated island 'of a traditional system is effectively solved, the decision accuracy is improved, and collaborative decision-making of expert knowledge and data driving is realized.

A dynamic vision SLAM (Simultaneous Localization and Mapping) method based on an extended Bayesian model belongs to the technical field of autonomous robot navigation and computer vision crossing, and mainly comprises the following steps: performing prior dynamic object recognition on a current frame image by using an improved PWt-YOLO network, and outputting a binary mask and semantic probability distribution; oRB feature extraction is carried out by using a hierarchical quadtree algorithm and combining an adaptive threshold value; establishing an extended Bayesian probability model, fusing semantic prior, optical flow residual and epipolar geometric constraints of two-dimensional Gaussian distribution constructed based on dynamic object boundaries, realizing time sequence transmission of dynamic feature probabilities through a Markov chain, and executing feature point filtering according to joint dynamic probabilities; and robust pose estimation is realized through RANSAC-PnP, a sliding window and the like. According to the method, the problem of SLAM system positioning drift in a dynamic environment can be effectively solved by constructing a multi-modal fusion dynamic feature discrimination system.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention discloses a method for tracing polluted water quality of a water outlet of a park based on Bayesian reasoning, and belongs to the technical field of data processing and management systems.The method comprises the steps that production management data of enterprises in the park is acquired to generate enterprise pollution feature vectors, and the enterprise pollution feature vectors are associated with enterprise geographic coordinates to construct a dynamic pollution fingerprint database; and acquiring water quality parameters of a water outlet monitoring point in real time, inputting the water quality parameters into the Bayesian probability model, calculating to obtain basic pollution probability distribution, calling the dynamic pollution fingerprint database and the pipe network topology coefficient to carry out space-time correction, generating a traceability probability matrix, and determining and outputting a target pollution enterprise list. According to the invention, production management data of an enterprise, real-time water quality monitoring data of a water outlet and pipe network physical constraint conditions are deeply fused, a probabilistic reasoning model is adopted for intelligent decision making, accurate and efficient management and positioning of a pollution source can be realized, compliance evidences are automatically generated, and the method is suitable for large-scale popularization and application. Therefore, the efficiency and the intelligent level of park environment management are improved.

The invention relates to a DEMATEL-BWM fusion assembly sequence evaluation method based on a Bayesian probability model. Assembly sequence evaluation is carried out on a complex mechanical object. Collecting data, establishing a direct influence matrix, normalizing the direct influence matrix into a standard influence matrix, performing Bayesian updating, and performing exponential transformation processing on an output posteriori mean value to obtain a preference matrix; determining optimal and worst elements, performing pairwise comparison to obtain optimal and worst vectors, modeling the optimal and worst vectors as polynomial distribution, and modeling the output weight vectors as Dirichlet distribution; processing input of multiple decision groups through a Bayesian hierarchical model, establishing a joint probability model, and performing decomposition through conditional independence and probability chain rules; the prior distribution and observation data are used, posterior distribution is obtained through Bayesian updating, and a confidence level is used to generate a weighted directed graph; and outputting the aggregation weight, and evaluating the assembly sequence. The problem that an assembly sequence evaluation method is low in efficiency is solved, the inconsistency of subjective judgment is reduced, and the method is more fault-tolerant, concise and efficient for deviation.

The invention discloses an external damage hidden danger identification method and system based on AI image and radar dual verification, and relates to the technical field of artificial intelligence and multi-source perception fusion, and the method comprises the following steps: extracting the motion trail features of perception data in a target region, solving a coordinate transformation matrix through an iterative nearest point algorithm, carrying out the time sequence alignment, and carrying out the recognition of the motion trail features of the perception data; obtaining the calibrated sensing data; taking the calibrated image data and the calibrated radar data as input, and outputting a radar detection result and a visual identification result; projecting radar coordinates to an image coordinate system according to a coordinate transformation matrix based on a visual identification result and a radar detection result, outputting a space overlapping degree, and generating a matching result set; carrying out confidence fusion on the matching result set through a Bayesian probability model, and judging an external damage hidden danger level in combination with a radar detection result; according to the invention, through the AI vision and radar dual verification fusion technology, the problems of inconsistent multi-source perception and low external damage hidden danger identification precision are solved.

The invention relates to the technical field of geological disaster early warning, and discloses a geological deformation early warning method and system based on AI vision, and the method comprises the steps: obtaining surface micro-deformation features through an AI vision system, and constructing a geological map structure network; fusing a nonlinear geomechanical equation in a graph neural network to realize physical constraint driven feature extraction; creating a multi-dimensional graph structure model representing different depth layers, and realizing cross-layer information transmission through a physical information guide type graph attention mechanism; deploying a graph neural network of a Bayesian probability framework to carry out geological stress field distribution modeling, and quantizing prediction uncertainty; and finally, generating intelligent early warning decision information with risk grade division. According to the method, the AI vision and the physical constraint graph neural network are combined, accurate inference of the underground stress field is realized, the uncertainty can be quantitatively predicted, and the reliability and timeliness of geological disaster early warning are improved.

The invention belongs to the technical field of medical data processing, and particularly provides a multi-mode heterogeneous medical data dynamic weighting intelligent disease analysis system. The system specifically comprises the following modules: a multi-modal heterogeneous data preprocessing module, a multi-modal data quality dynamic evaluation and quality control module, a cross-modal embedding space conversion network, a medical knowledge graph engine, a large model training architecture, a multi-modal decision fusion module, a disease analysis interpretability enhancement module and a Bayesian probability graph model integration module. And an edge cloud collaborative reasoning module. According to the invention, the accuracy and robustness of multi-modal disease analysis are improved, the adaptability quality control of different modal data is realized through the multi-dimensional dynamic quality evaluation module, and the reliability of basic data is improved in combination with intelligent quality control restoration.

The invention discloses a robot control method and system based on long-term and short-term human motion intentions, and relates to the technical field of robot control. According to the method, based on a time sequence trajectory prediction module of a Transform neural network, multi-step accurate prediction of a short-term trajectory of human motion is realized. And establishing a dynamic probability distribution model of a human long-term moving target by adopting an iterative Bayesian probabilistic reasoning method to identify a human final target point. And through real-time updating of the contact force and the weight, the period changing intention is calculated. Through the synergistic effect of short-term time sequence trajectory prediction, long-term intention probabilistic reasoning and haptic feedback, accurate prediction of a human short-term motion trajectory, robust recognition of a long-term target intention and dynamic response to intention sudden change are realized, and the prediction accuracy of the short-term trajectory is ensured while the prediction accuracy of the long-term target intention is improved. The robustness of long-term target recognition and intention sudden change response is remarkably improved, the defects of an existing human motion intention prediction method are overcome, and the intelligence and efficiency in a man-machine cooperation task are improved.

The invention relates to the technical field of cloud computing and distributed computing scheduling, in particular to a server-free MapReduce job scheduling optimization method. Comprising the following steps: initializing Bayesian genetic algorithm operation parameters; forming an initial population; the current population executes variable neighborhood search to generate a new solution, and the population is updated; constructing a Bayesian probability model; generating a new solution through Bayesian probability sampling and genetic manipulation, and updating the population; updating a global optimal solution; and judging whether the time limit is reached or not, if so, outputting a globally optimal solution and the corresponding maximum completion time, and if not, continuing iteration. The application of the method has the positive effects of minimizing the maximum completion time of the operation and improving the robustness of the algorithm and the scheduling efficiency.

The invention provides a multivariate data-based printing machine top project approval design method, which relates to the technical field of printing equipment, and comprises the following steps of: acquiring printing machine operation data comprising printing quality parameters, equipment working condition parameters and environment parameters; performing feature analysis and parameter extraction through an adaptive fuzzy clustering algorithm, dynamically adjusting a sample classification membership degree, identifying a key parameter combination influencing the printing quality, and constructing a correlation matrix; constructing a dynamic Bayesian probability network model based on a parameter mapping relation, constructing a core network structure through an information entropy value and information gain, and establishing probability association between a printing quality parameter and an equipment working condition parameter; and determining the optimal parameter configuration of the function modules of the printing machine by utilizing a bidirectional constraint dynamic programming algorithm and combining the coupling weight and the state transition equation among the function modules, and generating a top design scheme.

The invention discloses a Lidar point cloud registration optimization method based on geometric feature reliability, and the method comprises the steps: carrying out the preprocessing and initial alignment of a laser radar scanning point cloud, extracting the features of a maximum principal curvature and a minimum principal curvature based on the local curvature of the point cloud, dividing a point region into two types of geometric features of angular points and plane points according to the threshold value of the maximum principal curvature, and carrying out the registration of the angular points and the plane points. On the basis, fitting quality factors including fitting errors, local curvatures and spectral entropies of the linear features and the plane features are calculated respectively, then the three factors are fused into a unified reliability weight based on a Bayesian probability model, and finally the feature reliability weight is introduced into an optimization objective function of iterative nearest point registration to execute weighted ICP registration. And outputting positioning and attitude determination results. According to the method, the reliability of geometric features is quantitatively evaluated, and a weighted optimization framework is constructed, so that the point cloud registration precision and robustness are remarkably improved, and the problem that a traditional ICP algorithm is sensitive to unreliable features in feature degradation or high-dynamic scenes is effectively solved.

The invention relates to the technical field of power system energy storage optimization, and discloses a terminal micro-grid energy storage optimization configuration method and system and a computer readable storage medium. Comprising the steps of generating load demand probability distribution and a new energy output confidence interval through deep learning and Bayesian probability modeling based on historical load and new energy monitoring data, extracting boundary parameters and mapping the boundary parameters into a quantum state initial solution set, and generating a quantum coding instruction set; a quantum algorithm population is initialized, a non-dominated solution set sequence is generated through quantum gate evolution operation, and a Pareto leading edge output optimization configuration scheme is screened; verifying feasibility in combination with local voltage data, and generating an executable control instruction set after fine tuning of capacity-position weight; and driving the energy storage unit to execute charging and discharging operation, collecting operation data, updating model parameters, finally separating probability weights and quantum deviations, adjusting a Bayesian model and resetting quantum gate parameters to generate a calibration instruction set. According to the invention, the energy storage configuration efficiency and precision can be obviously improved.

The invention discloses an AI-based rail transit dynamic safety early warning system, which comprises a sensing layer for deploying an intelligent sensor network with dynamically adjustable spacing, integrating a steel rail health monitoring suite to carry out microdefect detection on a steel rail, capturing vibration deformation characteristics under high-speed operation by a dynamic monitoring array, acquiring external environment data by an environment sensing module, and sending the external environment data to an early warning layer; preprocessing the collected data by utilizing an edge computing node; according to the cognitive layer, a line adaptive layer performs adaptive fusion on multi-line features through three-stage training of'basic training-meta training-fine tuning 'in combination with a dynamic weight generation mechanism, meanwhile, a causal cognitive module constructs a three-stage variable causal graph, a hybrid inference engine combines a CLIPS symbol inference engine, a Bayesian probability network and a D-S evidence theory, and the line adaptive layer performs multi-line feature fusion on the basis of the CLIPS symbol inference engine. Logic deduction and uncertainty quantification of fault attribution are realized; according to the decision-making layer, a dynamic threshold generator adjusts an early warning boundary in real time based on an LSTM prediction model, and an emergency plan engine recommends a maintenance strategy in combination with a digital twinborn simulation result.

The invention provides a staged forgetting rate-based student knowledge mastering prediction method and related device, and the method comprises the steps: firstly obtaining education platform log answer data, carrying out the standardization processing of the data, obtaining a standardized answer data set, and determining the stage affiliation result of each student based on the data set through employing a Bayesian probability inference method, meanwhile, a student answering time sequence is constructed, context distance data between questions in the sequence is calculated, then a staged forgetting rate knowledge tracking model is constructed in combination with a student stage attribution result, the answering time sequence and the context distance data, training is completed, and finally the trained model is adopted to predict the knowledge mastering condition of the student. By matching the corresponding forgetting rate parameters for the students in different learning stages, the knowledge forgetting characteristics of the students in different stages can be accurately captured, the prediction precision of the knowledge mastering state of the students is effectively improved, and the prediction result is more fit for the actual learning level of the students.

The invention discloses a dynamic octree mapping method and system based on probability semantic fusion and a storage medium, and mainly solves the problem that a traditional octree map lacks semantic information and cannot support an advanced cognitive task. According to the implementation scheme, the method comprises the following steps: acquiring an RGB image, depth information and pose data through a sensor; extracting pixel-level semantic probability distribution by using a pre-trained semantic segmentation network; geometric information in the perception data is projected to a three-dimensional space based on the pose, and a three-dimensional observation point with semantic information is formed; performing ray projection on octree nodes, respectively updating geometric occupancy states of penetrated non-end-point nodes, and synchronously updating geometric occupancy states and semantic belief distribution of end-point surface nodes by using three-dimensional observation point data; semantic information is continuously optimized through Bayesian probability fusion, and dynamic pruning and map optimization are realized based on semantic entropy. According to the method, the map storage efficiency and the calculation speed can be remarkably improved while refined and real-time updated geometric and semantic information is given. The method can be used for map construction and storage of visual SLAM positioning, and fine three-dimensional object modeling and environment information description.

The invention discloses a multi-layer circuit board quality inspection method and system based on machine learning, and the method comprises the steps: carrying out the time-space registration of collected multi-source data through an adaptive weighted fusion algorithm, and generating a multi-mode quality inspection data set containing a line topological structure and material characteristics; outputting a fused circuit board defect sensitive feature vector set by using a pre-trained nested attention deep learning model based on the multi-modal quality inspection data set; inputting the defect sensitive feature vector set into a twin network architecture, positioning a potential defect area through a dynamic anchor frame generation mechanism, carrying out multi-label classification on defect types in combination with a Bayesian probability model, and synchronously introducing a defect severity evaluation module to quantify the influence degree of defects on circuit performance, and outputting a detection result containing the defect position type and severity. According to the embodiment of the invention, the collaborative judgment of the type, position and severity of the defect can be realized, and the detection precision and generalization capability of the defect of the multilayer circuit board are improved.

The invention discloses a transmission lubricating oil system intelligent fault diagnosis method considering measurement uncertainty, and the method comprises the steps: employing a layered injection strategy for a multi-source fault type of a target lubricating oil system; and then establishing a mapping relation between the system state and the test point response by constructing a fault-test correlation matrix (matrix D). And thirdly, under the imperfect test assumption, aiming at the dynamic uncertainty characteristics of the sensor measurement error, constructing a hybrid quantitative model based on a Bayesian probability framework to carry out testability model optimization on the target lubricating oil system. And finally, based on the optimized testability model, proposing a dynamic fault diagnosis strategy, constructing a multi-dimensional misdiagnosis cost model including false alarm, leak detection, system damage and the like, and realizing optimal distribution of test resources in combination with an information entropy algorithm and a cross-domain optimization technology. Through a real-time data-driven adaptive diagnosis mechanism and a multi-level fault identification framework, the fault detection accuracy under a complex working condition is significantly improved.

The invention relates to the technical field of ultrasonic and molybdenum target detection, in particular to an intelligent breast lump diagnosis system based on ultrasonic-molybdenum target multi-modal image and serum P16 protein detection, and the intelligent breast lump diagnosis system is characterized in that an ultrasonic image, a molybdenum target image and multi-modal data of serum P16 protein detection are fused; and optimal integration of cross-modal features is realized through a dynamic weight strategy. The ultrasonic image provides morphological and hemodynamic information of the lumps; the molybdenum target image reveals the characteristics of calcification density, calcification cluster heterogeneity, structure distortion depth and the like; serum P16 protein detection prompts malignant risks from the molecular level, and especially has important value in early recognition of triple negative breast cancer. A feature association graph is constructed through a graph neural network (GNN), the weight is dynamically adjusted in combination with a Bayesian probability integration formula, and optimal fusion of different modal features is ensured. The limitation of a single mode is made up, and the sensitivity and the specificity of diagnosis are also remarkably improved.

The invention discloses a photovoltaic cleaning robot path planning method fusing string level power generation analysis, and belongs to the technical field of data processing, and the method specifically comprises the steps: dividing historical string power generation data according to the same interval to construct an entropy model, collecting the data in real time, calculating an interval entropy, comparing the interval entropy with the model, and recognizing a suspected low power generation interval; key cleaning group strings are screened in combination with spatial neighborhood information, and a posterior probability is calculated through a Bayesian probability model to determine a cleaning priority; power station layout constraints and robot motion characteristics are combined, and an optimal cleaning path is generated by adopting a path planning algorithm; through multi-dimensional data fusion and an intelligent algorithm, precise identification and path optimization of photovoltaic string cleaning requirements are realized, the cleaning efficiency is effectively improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of concrete detection, in particular to a concrete structure crack damage evaluation system based on acoustic emission sensing, which comprises a signal acquisition module, a characteristic discrete evolution module, a probability inference module, an energy gradient analysis module and a boundary defining module. According to the method, a multi-dimensional evolution set is constructed by extracting the amplitude and energy standard deviation of an acoustic emission signal in a continuous time window, the synchronous change trend of the multi-parameter standard deviation is analyzed by using a Bayesian probability model, and the non-uniform expansion posterior probability is calculated to lock a key signal set of dominant damage expansion. An energy fluctuation coefficient is calculated and a space sequence is generated by combining sensor space coordinates, and a fluctuation coefficient stable interval is identified according to a gradient attenuation rule of energy along with a distance, so that a physical boundary of a crack damage dynamic active region is quantitatively defined, and accurate evaluation of a non-uniform expansion state and an active range of a concrete crack is realized.

The invention discloses an air duct vibration on-line monitoring system based on multiple sensors and a fault prediction method, and belongs to the technical field of air duct state monitoring and fault prediction.The method specifically comprises the steps that air duct vibration acceleration and noise signals are collected in real time through the sensors, and a time-aligned signal sequence is formed through analog-to-digital conversion and preprocessing; extracting a time-frequency domain feature from the vibration signal sequence, extracting a sound pressure level and a harmonic distortion degree from the noise signal sequence, and generating an air duct feature vector through feature fusion; constructing a Bayesian probability model based on the vector, calculating a fault occurrence probability, judging existence and generating a confidence score; calculating the sliding sample entropy of the vibration signal sequence, and marking an entropy value abnormal interval; and a union set of a fault interval diagnosed by the Bayesian probability model and an entropy abnormal interval is obtained, after time sequence trend analysis, early warning information is generated and pushed to an operation and maintenance platform when a joint judgment condition is met, and online updating of a normal vibration entropy model is triggered, so that the fault detection and early warning precision is improved.

The invention relates to the technical field of medical instruments, and discloses an electric adjusting system for pressure in an anaesthetic mask tube. The system comprises a physiological data acquisition module for acquiring multi-source physiological data to generate a synchronous data set; the multi-modal feature modeling module is used for generating a multi-dimensional joint probability feature matrix by using a Bayesian probability graph model; the dynamic pressure decision module is used for constructing a self-adaptive controller output pressure regulation target value based on deep reinforcement learning; and the multi-target cooperative control module generates a steady-state pressure control strategy through a mixed integer programming model and an improved ant colony algorithm. In addition, the system is further provided with a virtual pressure estimation module for pressure leakage diagnosis and redundant valve switching, and an abnormal interruption protection module for guaranteeing the safety of a patient. By means of the system, precise and intelligent adjustment of the pressure in the anaesthetic mask tube is achieved, and the safety and stability of the anesthesia process are improved.

The invention relates to the technical field of bridge monitoring, in particular to an intelligent bridge monitoring system and method based on wireless communication, and the system comprises six modules: a digital twin engine module constructs a three-dimensional virtual model synchronous with a physical bridge; the information acquisition module acquires real-time operation data; the data fusion processing module fuses the real-time data, the cable static asset data and the historical operation and maintenance data and maps the data to a virtual entity; the knowledge graph construction and reasoning module realizes fault root tracing and influence analysis by means of entity association analysis and a Bayesian probability model; the early warning module judges the fault level, pushes a differential cooperation instruction and generates a customized diagnosis report; the simulation deduction module supports parameter modification, simulates a future state through a physical model, and visually outputs risk assessment. According to the invention, bridge frame virtual-real linkage monitoring, accurate fault handling and active risk pre-judgment are realized, and the operation and maintenance intelligence level and the system reliability are improved.

The invention provides a protein mass spectrum data analysis method and system based on protein stability. The method comprises the following steps: determining scale constraint parameters of a sliding window during smoothing of protein mass spectrum data according to local noise variances at data points in the protein mass spectrum data; carrying out loss constraint on the edge of a protein stability characteristic peak in the protein mass spectrum data based on the scale constraint parameter in combination with a preset sliding window to obtain edge retention data of the protein stability characteristic peak; determining a fuzzy membership degree of each data point belonging to a protein stability characteristic peak according to a plurality of pre-identification peaks in the edge retention data and spatial distribution characteristics of each data point in the edge retention data; and performing baseline correction on the edge retention data based on Bayesian probability in combination with all fuzzy membership degrees, and further extracting peptide fragment signal peaks of the protein. According to the technical scheme provided by the invention, the effective peptide fragment signal peak in the protein mass spectrum data can be analyzed in a non-stationary background noise state.

The invention relates to the technical field of edge data transmission, and discloses an intelligent electric meter edge data transmission method and system based on Bayesian dynamic gaming, and the method comprises the steps: mapping the channel state data of an electric meter to a frequency band set according to the center frequency of a channel, and enabling the channel state data to serve as a frequency band resource pool of the electric meter; a Bayesian probability graph model is adopted to deduce power consumption behavior space-time distribution characteristics of the user, and a power consumption activeness probability distribution graph of the user is output; and constructing a non-cooperative game model oriented to frequency band distribution and solving to obtain an optimal frequency band use strategy of each ammeter, performing ammeter working channel distribution, and performing edge data transmission by using the distributed ammeter working channels. According to the method, the electric meter channel state and the user historical power consumption data are collected, the Bayesian graph model is constructed to deduce the user active probability distribution, the user active probability distribution is introduced into the non-cooperative game model, the optimal channel allocation strategy of each electric meter is realized through the Bayesian Nash equilibrium solver, and the edge data transmission efficiency is improved.

The invention relates to a detection signal anti-multipath interference method and system based on multi-modal feature fusion, and belongs to the technical field of radio detection, and the method comprises the following steps: 1, unmanned plane signal fingerprint feature extraction: analyzing physical layer parameters of a signal; step 2, unmanned aerial vehicle signal airspace DOA estimation and error analysis: acquiring signal spatial spectrum information by using an array antenna; 3, physical modeling of a ship environment: constructing a multi-path reflection path prediction model in combination with the ship three-dimensional model; 4, dynamic DOA error correction, wherein DOA estimation deviation caused by multiple paths is suppressed through iterative optimization of signal features and a physical model; 5, multi-modal feature fusion decision making: constructing a feature fusion mechanism based on Bayesian probability, and distinguishing a real signal from a false alarm signal; the method has the beneficial effects that suppression of the multipath effect is realized, and unmanned aerial vehicle signal detection in a dynamic complex scene is facilitated.