79 results about "Bayesian probability" patented technology

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 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 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 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 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 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 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 relates to the technical field of lithium ion battery safety monitoring and fault early warning, in particular to a lithium battery thermal runaway dynamic early warning method and system based on feature fusion and Bayesian reasoning, and the method comprises the steps: collecting multi-mode time sequence data of gas concentration, temperature value and voltage value of a lithium battery in real time through a distributed sensor array; performing data cleaning, standardization processing and feature extraction to obtain standardized time sequence feature data; a deep learning time sequence model is utilized, a cross-modal attention mechanism and a bidirectional long-short-term memory network are combined, multi-modal feature fusion and time sequence modeling are achieved, and a gas-temperature-voltage three-dimensional feature sequence is generated; dynamically calculating a thermal runaway risk probability through a Bayesian probabilistic reasoning model; and finally, thermal runaway identification, risk assessment and dynamic early warning are completed through an intelligent early warning algorithm, and information containing early warning levels, time and suggested measures is output. The early warning real-time performance and accuracy are improved, the multi-scene prevention and control requirements are met, and the system is easy to deploy and maintain.

The invention relates to the technical field of rail transit simulation modeling and production and education fusion, in particular to a rail transit simulation configuration model generation method based on AIGC and XR, which comprises the following steps of: preliminarily checking generated model component parameters; scene assembly is carried out according to the actual line layout; dynamically correcting a train operation logic and signal control mechanism; model component and scene adjustment is supported based on modular design. According to the AIGC and XR-based rail transit simulation configuration model generation method, resource blocks with relatively high topic correlation are screened through a Bayesian probability formula according to a topic segmentation and feature enhancement technology of an AIGC platform, feature attribute overlapping interference is eliminated, CNN is introduced to verify model component parameters, and constraint fitting is performed in combination with a loss function; the XR simulation resource extraction and model parameter precision is improved; the virtual scene and the real physical space are aligned by combining the SLAM space positioning and data intercommunication technology, so that XR virtual-real deep fusion is realized, and the immersive interaction experience is improved.

The application discloses a kind of machine room equipment intelligent remote operation and maintenance method and platform, it is related to intelligent operation and maintenance technical field, including, construct bayesian probability model, and combine optical signal characteristics and current harmonic characteristics, predict the failure probability distribution of machine room equipment;According to the failure probability distribution of machine room equipment, convert fault coordinates into equipment logical identifier, and detect the quantum teleportation link of machine room equipment, generate machine room equipment switching instruction according to detection result;According to machine room equipment switching instruction, send Bell state measurement command to fault equipment, and carry out quantum state reconstruction and machine room equipment switching.The application constructs multilayer bayesian probability model based on Gaussian distribution, Granger causality test and variational inference, realizes the nonlinear correlation analysis of optical signal wavelength shift and current harmonic characteristics, can accurately capture the weak signs of potential equipment failure, to significantly improve the sensitivity and reliability of failure prediction.

The invention discloses a few-sample readability evaluation method and system, electronic equipment and a storage medium, and belongs to the technical field of natural language processing. The method comprises the steps of obtaining a to-be-evaluated text, and constructing a global prompt with an independent structure and at least one local prompt for the to-be-evaluated text; combining the text with each prompt, inputting the combined text and prompt into a pre-training language model, and extracting global and local prompt feature representations; respectively calculating similarity distribution between each prompt feature representation and a plurality of preset static category prototypes, wherein the static category prototypes are kept fixed in model training; and performing joint modeling on the similarity distribution from different prompts based on a Bayesian probability fusion mechanism, generating fused posterior probability distribution, and determining the readability level of the text according to the fused posterior probability distribution. According to the method, multi-dimensional language features can be effectively modeled, tag semantic fuzziness is properly processed, and high-precision and high-robustness readability evaluation is kept in a few-sample scene.

The present application relates to a kind of multi-priority differential fault prediction and active prevention method for vehicle-mounted fiber channel network, belong to vehicle-mounted network communication technical field.The present application method includes: receiving the state report frame that each network node periodically reports, obtain the state information of each network node connected link from the state report frame, based on each state information, the network overall health index value corresponding to it, the bayesian probability anomaly value and the bayesian probability anomaly trend value of each network node are calculated;Based on each state information, network overall health index value, bayesian probability anomaly value and bayesian probability anomaly trend value, network fault risk prediction is carried out;Based on the predicted fault risk, the network optimization instruction frame carrying network optimization instruction is issued to optimize network.The present application method realizes the advance accurate prediction and active prevention of vehicle-mounted fiber channel network fault, effectively improves the reliability and security of network operation, improves the safety of vehicle operation.

The application provides a kind of dry reactor sound signal visual detection method, medium and system, belong to dry reactor sound signal detection technical field, include: first, the sound signal of dry reactor is collected, and pretreatment is carried out to eliminate environmental noise.Then the time-frequency analysis is carried out to the sound signal after pretreatment, and the time-frequency spectrum is obtained.Next, adopt the way of bayesian probability inference and adaptive threshold increase, and highlight the small change in time-frequency spectrum, and obtain the increased time-frequency spectrum.Subsequently, energy distribution, peak frequency and harmonic structure are extracted from the increased time-frequency spectrum, and combined into a multi-dimensional feature vector.Apply dimension reduction algorithm, map high-dimensional feature vector to two-dimensional or three-dimensional space, and obtain the second feature vector.Finally, use unsupervised learning algorithm to carry out cluster analysis on the second feature vector, and assign color or label according to the clustering result for different categories, generate visual classification image output.

The invention relates to the technical field of dust pollution treatment, in particular to a port dry bulk cargo storage yard electronic greenhouse closing system based on multi-dimensional full-time management and control. According to the system, a time reference is established by identifying an operation event, multi-source sensor data is mapped to a unified time axis, and space-time registration is realized by combining Bayesian probability inference; constructing a physical information neural network based on the registration data to predict a continuous dust concentration field; a virtual closed boundary is generated according to the concentration threshold value, and the closing effectiveness is evaluated; calibrating physical parameters by using a CFD model in combination with a parameter inversion method; and in the rolling prediction time domain, constructing an optimization model weighted by the spraying cost, the environmental protection penalty and the control cost, solving an optimal spraying control strategy, and feeding back an execution effect to the model to form closed-loop optimization. By means of the system, the raised dust of the storage yard can be precisely treated, and it can be guaranteed that the raised dust is sealed in the electronic greenhouse in an open-air scene.

This invention discloses a spacecraft pose estimation and uncertainty modeling method based on intrinsic space, belonging to the field of spacecraft pose estimation technology. The method involves directly constructing a hierarchical Bayesian probability model on the rotating manifold SO(3) and translation space, using Fisher and Gaussian distributions respectively, and introducing conjugate priors to achieve the fundamental decomposition and quantification of accidental and cognitive uncertainties. An end-to-end multi-task neural network is used to jointly learn the pose probability model parameters, key points, and segmentation information, and an iterative optimization module is employed to improve estimation accuracy. During the training phase, marginal negative log-likelihood and evidence regularization are jointly optimized; during the inference phase, the probability distribution of pose prediction is obtained through analytical marginalization, and the two types of uncertainty are distinguished. This invention improves pose estimation accuracy while outputting well-calibrated uncertainties, providing a reliable basis for the autonomous and safe operation of spacecraft in orbit.

This invention belongs to the field of port logistics technology, specifically a cargo handling and identification system for port cargo handling equipment. It includes a video acquisition module that acquires continuous video streams from key port operation locations; an image processing module that performs target detection and character recognition to extract visual features and container number information; a temporal state inference module that uses a Bayesian probability model to infer the temporal state of multiple consecutive frames of identification results, eliminating single-frame random errors; and a logistics topology map construction module that establishes a topological relationship model of the port logistics network to characterize the spatial transfer logic of containers between operational stages. The introduction of the temporal state inference module eliminates errors in single-frame image processing and allows for joint inference of multiple consecutive frames of identification results. By utilizing the identification information of historical frames to correct the identification deviation of the current frame, the accuracy of identification can be improved.

The invention discloses an electronic design automation multi-agent collaboration system and method based on a large language model. A collaboration architecture of a task analysis agent, a divergent thinking agent and a decision agent is adopted, wherein the task analysis agent analyzes natural language task description through an improved weighted cosine similarity algorithm and extracts key constraints; the divergent thinking agent generates a plurality of cross-platform compatible EDA script schemes based on few-sample chained thinking prompts and layered random sampling; and the decision-making agent dynamically selects an optimal solution from the candidate scripts and injects a fault-tolerant instruction by fusing a Bayesian probability model and a Laplace approximation uncertainty calibration mechanism. According to the method, the planning reliability of a complex EDA task is remarkably enhanced, seamless adaptation of a heterogeneous EDA tool chain is achieved in a breakthrough mode, meanwhile, the cascade failure risk is fundamentally blocked through a multi-agent error isolation mechanism, and efficient and robust full-process automation support is provided for integrated circuit design.

This invention discloses an epidural anesthesia assistance method and system based on pressure curve characteristics, including a data acquisition and preprocessing module, an anatomical layer segmentation module, a multidimensional feature extraction module, a template matching and initial classification module, a Bayesian probabilistic inference and fusion decision-making module, a risk assessment and feedback output module, and an online adaptive update module. By utilizing real-time pressure monitoring, negative pressure recognition, and biometric frequency extraction technologies, it assists in determining the position of the epidural puncture needle, significantly reducing the rate of epidural puncture mispuncture and improving clinical safety. Through a Bayesian online learning update mechanism, the model parameters can be continuously iterated based on clinically labeled data, adapting to different operator habits and sensor drift, achieving long-term stable operation and individualized calibration. This invention is easy to integrate and has low hardware costs, facilitating large-scale clinical deployment.

The application discloses a photovoltaic cleaning robot path planning method fusing group string level power generation analysis, and belongs to the technical field of data processing, and specifically comprises the following steps: constructing an entropy value model by dividing historical group string power generation data according to the same interval, collecting data in real time to calculate interval entropy values and comparing the interval entropy values with the model to identify suspected low power generation intervals; combining spatial neighborhood information to filter key cleaning group strings, calculating posterior probability through a Bayesian probability model to determine cleaning priorities; combining power station layout constraints and robot motion characteristics to generate an optimal cleaning path by using a path planning algorithm; and through multi-dimensional data fusion and intelligent algorithms, the application realizes accurate identification of photovoltaic group string cleaning requirements and path optimization, effectively improves cleaning efficiency and reduces operation and maintenance costs.

The invention discloses a multi-port-based tag anti-serial-reading identification method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: by taking minimization of a signal radiation overlapping region between antennas as a target, adjusting real-time transmitting power of a plurality of antenna ports; after the antenna ports transmit detection signals at the real-time transmitting power, backscattering signals returned by the read tag are obtained through the antenna ports; according to the feature data of the backscattering signal, determining a key feature for judging the affiliation of the read tag; based on the key features, calculating the posterior probability of each antenna port to which the tag belongs through a Bayesian probability framework; and determining the antenna port with the maximum median value of the posterior probabilities as the attribution port corresponding to the tag. The problem of label serial reading can be relieved under the condition that the label reading rate is guaranteed.

The invention provides an optimization decision-making method and device for a carbon transformation risk index model based on multi-source data, and relates to the technical field of carbon transformation risk optimization decision-making, and the method comprises the steps: collecting greenhouse gas emission, financial operation, technical development and market rule data of an enterprise, and constructing a time sequence of multi-dimensional risk assessment sub-indexes; then through a dynamic weight determination module, in combination with index distinction degree evaluation under multiple time resolutions and Bayesian probability fusion of expert priori knowledge, a final index weight capable of being adaptively adjusted along with time is generated, weighted synthesis is carried out based on the weight and the standardized index sequence, and a comprehensive carbon transformation risk index value is obtained; and finally, generating a decision report containing a weight evolution process and index contribution analysis, and providing a scientific basis and decision support for an enterprise to formulate a carbon transformation strategy.

The application discloses a method for normalizing and comparing horizontal gene transfer (HGT) network, comprising the following steps: S1, using Bayesian probability model to normalize HGT according to sequencing amount; S2, in the HGT network, each genome is represented as a vertex, and if HGT occurs between two genomes, there is an edge between the two vertices; S3, using different classification levels to annotate genome sequence, and obtaining HGT network of different classification levels; S4, analyzing HGT network through several topological properties of graph density, transitivity, homophily and algebraic connectivity. The graph density, transitivity, homophily and algebraic connectivity are used to describe the network, and the HGT network is compared among samples in different groups, so that a new analysis direction is provided for the correlation between metagenome and human phenotype, and the metagenome can be more systematically modeled and analyzed by calculating various topological properties to analyze the HGT network.

The application relates to the technical field of automatic driving and computer vision, in particular to a dynamic environment monocular multi-object SLAM method based on instance segmentation and three-dimensional reconstruction, which comprises the following steps: acquiring an image frame sequence collected in the driving process of an automatic driving vehicle; performing feature extraction on each frame image; using an instance segmentation network to obtain dynamic objects and static objects in the image on the key frame image; marking and removing the influence of dynamic feature points; obtaining the current frame dynamic feature points by using Bayesian probability propagation, and removing the dynamic feature points; obtaining the mask, the boundary box and the sparse 3D point cloud of the extracted multiple static objects, and obtaining the initial pose of the multiple static objects by using PCA; after multi-frame observation and sufficient data, the appearance implicit code and the optimized pose of the multiple static objects are obtained by using a three-dimensional reconstruction network, and three-dimensional reconstruction is performed on the multiple static objects. The application can reduce the absolute trajectory error of a traditional SLAM method in a dynamic environment, improve the system pose estimation accuracy and robustness, and improve the semantic degree of a map.

The application discloses a self-adaptive heat management control system based on a knowledge graph, relates to the technical field of intelligent cooking control, and comprises a graph construction module, which creates a structured knowledge graph containing global main cooking system material thermal property nodes, typical cuisine process parameter nodes and various heating stove performance specification nodes, and the nodes are connected through attribute relationship edges; a knowledge extraction module extracts associated nodes and edges in response to a user recipe selection instruction; a temperature analysis module analyzes recommended core processing temperature, maximum safety threshold and texture change temperature range; a data acquisition module acquires real-time temperature and power sequence; and a reasoning control module constructs a Bayesian probability reasoning network based on data and analysis parameters, calculates posterior probability and outputs double-loop control decisions. Through multi-dimensional knowledge association and probabilistic reasoning, the system realizes self-adaptive precise heat management in the cooking process, improves safety and achieves consistency of material texture.