226 results about "Confidence assessment" patented technology

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs dynamic monitoring system and method based on digital twinning, and aims to detect data drift in real time, calculate a standard deviation change rate to generate abnormal judgment, analyze model stability and drift rate synchronism to output deviation indexes, screen data abnormal items to form quality evaluation, and realize dynamic monitoring of the intelligent water affairs. And comparing node synchronization difference to trigger compensation correction, and generating a monitoring adjustment result through integrity verification. According to the invention, through comparison of the multi-cycle standard deviation change rate and the environmental parameters, sensor drift classification and confidence evaluation are realized, the reliability of the model is enhanced by constructing a composite attenuation coefficient, abnormal sampling is positioned by combining the data change rate and an outlier map, and data synchronization is guaranteed by adopting a difference matrix and state feedback. And version verification and repair records are introduced to realize closed-loop tracing.

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

The invention relates to the technical field of data processing, in particular to a multi-modal data alignment method and system, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data; the preprocessed multi-modal data are sent to an event anchor point detection module, and multi-modal emergency detection, anchor point marking and preliminary global alignment are carried out; performing time resampling and numerical value standardization on the multi-modal data after coarse alignment; mapping the resampled and standardized multi-modal data to a unified time axis; performing local fine alignment and global adjustment on the multi-modal feature sequence after unified time axis mapping; and performing confidence evaluation and anomaly correction on the fine alignment result, and outputting a final alignment sequence. According to the invention, a high-precision and multi-modal data real-time alignment method which is light in weight and has adaptive correction capability can be realized on end side equipment with limited resources.

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention provides a thin film surface defect detection method and system, and relates to the technical field of defect detection.According to the thin film surface defect detection method and system, an intelligent secondary verification link is constructed by introducing a defect confidence evaluation mechanism based on form and energy distribution, so that the detection performance is fundamentally improved; according to the mechanism, real physical defects with regular forms and concentrated energy and pseudo defects caused by electromagnetic interference, instantaneous film wrinkles and the like can be accurately distinguished, and the problem of high false alarm rate caused by dependence on single signal strength in the prior art is effectively solved while the high detection rate of low-contrast defects is reserved; besides, the judgment model based on physical characteristics has natural robustness for background noise generated in high-speed motion, and an adjustable confidence threshold value endows the system with extremely high practical flexibility, so that the system can adapt to complex and changeable industrial environments and different quality control standards, and the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the intelligent level of the whole detection system are obviously enhanced.

The invention discloses an entity alignment method for a multi-modal crop knowledge graph, and belongs to the technical field of knowledge graphs and agricultural intelligent analysis, and the method comprises the steps: obtaining a data feature item set of an agricultural field, and carrying out the time sequence compensation, and generating a growth time sequence feature parameter; performing feature extraction on the data feature item set, performing fusion to generate a multi-modal fusion feature mapping graph, constructing a dynamic feature matching network based on growth time sequence feature parameters, and performing entity node traversal on a reference knowledge graph to generate a candidate alignment set and a corresponding difference unit set; filtering conflict nodes in the candidate alignment set according to a three-dimensional confidence evaluation model to generate an effective alignment chain; and generating a graph updating instruction based on the difference unit set, and reconstructing a knowledge graph topological structure in combination with the effective alignment chain. According to the method, multi-modal feature bridging, dynamic weight optimization of growth stage perception and an incremental conflict resolution mechanism are adopted, so that cross-domain agricultural entity accurate alignment and real-time adaptive updating of the knowledge graph can be realized.

The invention discloses a multi-mode instruction analysis system for a humanoid robot. Comprising a voice input module, a visual input module, a voiceprint feature extraction module, an object recognition and pose estimation module, a multi-modal alignment network based on a space-time attention mechanism, a scene semantic tree construction module, an instruction node mapping module, a confidence evaluation module and a decision module. According to the system, accurate alignment of voice and visual information is realized through a space-time attention mechanism, environment information is represented in combination with a scene semantic tree structure, and the instruction analysis accuracy is improved. And dynamically evaluating the confidence coefficient by adopting a fuzzy instruction backtracking algorithm, and if the confidence coefficient is lower than a threshold value, starting multi-round dialogue clarification to reduce misoperation. According to the method, multi-modal data are fused, the historical interaction learning ability is optimized, the understanding efficiency and interaction robustness of complex instructions are remarkably improved, the method is suitable for scenes such as family service and logistics storage, and the intelligent level of man-machine cooperation is enhanced.

The invention relates to the field of knowledge graph construction, in particular to a policy knowledge graph construction method and system based on digital human interaction data analysis. The policy knowledge graph construction system based on digital human interaction data analysis comprises a knowledge graph preliminary construction module, a first graph updating module, a second graph updating module and a digital human interaction module. According to the method, the policy source webpage content change is monitored in real time, and the question information in the digital human interaction data is deeply mined, so that the full-life-cycle dynamic maintenance of the policy knowledge graph is realized; on one hand, a webpage policy updating event is accurately captured, and knowledge injection is automatically completed in combination with a semantic unit matching algorithm; and on the other hand, the conflict characteristics questioned by the user in the interaction data are extracted through dialogue semantic analysis, atlas correction is triggered after confidence assessment and multi-stage verification, it is ensured that the end-to-end timeliness from policy release to user perception is controlled within a reasonable period, and the policy response accuracy of the digital human service is remarkably improved.

The invention relates to the technical field of software time synchronization, and discloses a software time synchronization method and system for multi-sensor data fusion, and the method comprises the steps: extracting temperature, load and drift frequency characteristics through principal component analysis based on the working state and historical drift data of a sensor, and constructing a confidence evaluation model to calculate the credibility of a timestamp; identifying an abnormal node group by using k-means and an isolated forest algorithm, analyzing a phase deviation fluctuation and network delay interaction effect, extracting a nonlinear drift feature in combination with a Prophet algorithm, and calculating a phase correlation value by using Hilbert cross-correlation; and dynamically adjusting node clock parameters and generating a calibration timestamp according to the network influence weight and the stability evaluation result. According to the method, the problem of time desynchrony caused by clock drift and network delay factors in a distributed system is effectively solved, and the overall time consistency and reliability of the system are improved.

The invention discloses a false information multi-source association reasoning system based on a knowledge graph, and the system comprises a data collection and standardization module which is used for carrying out the multi-source collection and duplicate removal of a text, and generating a structured input data set; the semantic extraction and normalization module is used for executing alias merging and disambiguation and outputting a semantic extraction result; the entity alignment module is used for cross-platform entity matching and confidence evaluation, entity identification unification and attribute and alias merging; the graph construction and anchor point module is used for constructing a fact graph and a traceability graph; the candidate constraint generation module is used for forming a candidate constraint set based on the multi-source evidence statistics support degree; the space-time constraint and weight reduction module is used for executing path consistency and joint constraint according to Allen and RCC8 to obtain an updated evidence weight result; and the evidence chain and pushing module is used for enumerating and scoring the evidence chain and pushing the evidence chain through an external interface. According to the invention, false information multi-source association reasoning is realized.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention provides an automatic target disease identification method and system based on cloud-edge collaboration, and the method comprises the steps: collecting an original road surface monitoring data set through an edge calculation node disposed in a road monitoring region, generating a multi-dimensional disease feature set in the edge calculation node, uploading the multi-dimensional disease feature set to a cloud analysis platform, and carrying out the recognition of a target disease through the cloud-edge collaboration. And performing feature matching degree calculation on the multi-dimensional disease feature set and a standard disease pattern in a cloud disease feature library through a cloud analysis platform, generating a target disease type identification result and a corresponding confidence coefficient evaluation parameter, and judging a result according to a relationship between the confidence coefficient evaluation parameter and a preset threshold value. And adjusting a feature extraction strategy of the edge computing node, generating an edge node adaptive optimization instruction set, and triggering feature extraction rule updating and disease recognition model parameter iteration operation for a subsequent monitoring period. According to the method, the disease identification accuracy is improved, and the real-time performance of edge calculation and the global optimization capability of cloud analysis are considered at the same time.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention discloses a dynamic data closed-loop system based on real-time positioning confidence evaluation and an optimization method. The dynamic data closed-loop system and the optimization method are suitable for high-precision positioning scenes such as automatic driving and unmanned systems. The system collects data of a GPS, an IMU, a laser radar and a camera through a multi-source sensor, and a confidence evaluation module is adopted to fuse sensor data quality, environment interference indexes and historical track consistency to calculate positioning confidence. Based on confidence score, the system triggers different data acquisition strategies and sensor parameter configurations, and dynamically optimizes resource allocation. The system comprises an edge end fusion optimization module and a cloud model iteration module, constructs a closed loop mechanism, uploads data to a cloud for model updating and returns parameters when the confidence coefficient is abnormal, and improves the adaptability and robustness of the system. Experiments show that the method can effectively reduce the positioning error, optimizes the use of computing resources, and has good real-time performance, expansibility and engineering feasibility.

The invention discloses an operation and maintenance knowledge intelligent question and answer interaction method, device and equipment based on an LLM large model. According to the method, questions of operation and maintenance personnel are received through a natural language interaction interface, semantics are analyzed through a pre-trained LLM, candidate answers are generated, the accuracy of the answers is verified in combination with a dynamic operation and maintenance knowledge graph (including equipment model and fault code mapping, a historical case library and a real-time updating knowledge base), and finally a verification result is returned. The device comprises an input module, a processing module, a verification module and an output module, the processing module integrates domain knowledge fine tuning and real-time data integration functions, and the verification module supports multi-source data fusion and confidence evaluation. The platform adopts a multi-tenant architecture, supports visual diagnosis, automatic equipment operation and knowledge use report generation, and ensures that data cannot be tampered through a block chain technology.

The invention discloses a personnel trajectory low-delay accurate positioning method based on safety production monitoring, and relates to the field of industrial safety monitoring, and the method comprises the steps: obtaining signal data based on UWB anchor points, obtaining distance data through a laser range finder, and obtaining image data through a thermal imaging camera; establishing a three-dimensional geographic coordinate system based on the geographic space of the production workshop; extracting scene features of a production workshop, and constructing a virtual workshop; through timestamp alignment, consistent signal data, consistent distance data and consistent image data are obtained; calculating multi-dimensional positioning data through confidence evaluation; calculating scene feature weights; the multi-dimensional positioning data and the scene feature weight are imported into a virtual workshop, the virtual workshop performs multi-channel splicing on the multi-dimensional positioning data and the scene feature weight, and a positioning model is constructed; and obtaining positioning data. The method has the advantages that through multi-source heterogeneous data fusion and scene feature constraint, the positioning robustness in a complex industrial environment is improved.

The invention discloses an environmental data processing method and system based on ocean engineering, and relates to the technical field of data processing, and the method comprises the steps: receiving an original observation data flow through a multi-source data preprocessing module, and carrying out the dynamic noise filtering and abnormal value adaptive detection; fusing the multi-source heterogeneous data through a multi-scale data fusion module, and embedding the fused multi-source heterogeneous data into a marine kinetic equation as a soft constraint; non-linear evolution features are extracted from the fusion data through a feature extraction and state representation module, and a high-dimensional environment state vector is constructed; real-time prediction of model parameters is executed through online learning and an inference engine; and performing uncertainty propagation calculation on the processing flow through a confidence evaluation module and generating a final environment state report. According to the method, the adaptive capacity of data preprocessing can be remarkably improved, the physical consistency of multi-source data fusion is improved, the nonlinear evolution law of ocean phenomena is accurately captured, and continuous online optimization and edge side low-delay response of model parameters are achieved.

The invention discloses a feedback type retrieval enhancement generation method for refining and enhancing retrieval, which can give the confidence degree of answers while answering questions, and further process the answers lacking confidence. According to the method, the reliability of answers generated based on retrieval content is measured by introducing a confidence coefficient evaluation mechanism based on a retrieval strategy of first prediction and then matching, when the confidence coefficient is lower than a preset threshold value, it is indicated that a current retrieval result is insufficient to support accurate answering, at the moment, refined and enhanced retrieval is activated, refined questions are generated for original questions, and the accuracy of the questions is improved. Therefore, the understanding and retrieval effects on questions are enhanced, and the answering accuracy is improved.

The invention discloses a hotel room type matching method and device, and the method comprises the steps: obtaining the original information of a newly-added hotel and room type, carrying out the preprocessing of the original information, and generating a standardized hotel and room type feature data set; constructing a multi-level feature vector based on the standardized feature data set; screening and generating a candidate matching set in combination with an inverted index and a locality sensitive hashing algorithm; a matching score matrix is calculated through fusion of the rule matching model, the machine learning model and the deep learning model; generating a final matching conclusion through Bayesian uncertainty estimation and threshold dynamic adjustment; and optimizing the matching model based on the matching conclusion and the business feedback data. Through multi-dimensional feature extraction, multi-model fusion matching and confidence evaluation, the problem of accuracy of hotel room type cross-supplier matching is solved, and the matching efficiency and robustness are improved.

The invention discloses a meeting window call system based on a voice sensor, and relates to the technical field of communication. Comprising a full-space voice acquisition module, a dynamic confidence evaluation module, a phase coupling enhancement module, a multi-dimensional speech mode recognition module, a dynamic gain adjustment and tone quality enhancement module and a full-link voice integrity verification module, an array type voice sensing unit is deployed based on audio reflection characteristics, a multi-band full-space acquisition matrix is constructed, voice signals of two parties meeting are acquired, and a basic audio data stream with a high signal-to-noise ratio is output. According to the invention, through array sensing acquisition, dynamic confidence evaluation, phase coupling and harmonic enhancement, multi-dimensional speech recognition and dynamic gain adjustment, accurate recognition and fidelity enhancement of low-volume speech are realized, and through combination of full-link integrity verification and mistaken killing backtracking, weak signals are ensured not to be missed and distorted, so that the accuracy of speech recognition is improved. And the privacy, the security and the information integrity of the meeting call are improved.

The present invention discloses an intelligent control method for sewage treatment equipment at dry bulk cargo terminals, relating to the technical field of sewage treatment equipment control, and is used to address the problem of poor control of sewage treatment equipment at terminals. The method comprises following steps: installing multiple types of sensors at key locations on dry bulk cargo terminals, utilizing edge computing nodes for real-time data acquisition and preprocessing, combining historical and temporal features with machine learning models to classify sewage types, achieving efficient dynamic adjustment of sewage treatment equipment operation parameters, then using weighted voting and confidence assessment to integrate multiple classification results to ensure optimal treatment results, and analyzing the actual sewage treatment situation to optimize equipment control continuously, thereby preventing failures, extending equipment life, and improving sewage treatment effectiveness.

The invention relates to a complex terrain surveying and mapping method, device and equipment and a medium. According to the complex terrain surveying and mapping method, device and equipment and the medium, laser beams with alternating polarization states are synchronously emitted through a dual-wavelength laser radar, an original point cloud with a space-time mark is captured, and earth surface points and noise points are analyzed based on time difference and polarization degradation characteristics; constructing a noise cluster function according to the noise points to identify vegetation artifacts; performing earth surface confidence evaluation on the earth surface points, filtering noise by using an elevation layering strategy, and positioning a fracture area; and finally, based on the denoised cloud layer, the fracture area and the earth surface point confidence coefficient, reconstructing a terrain curved surface, and generating a continuous terrain model. Therefore, precise reconstruction of the terrain sudden change area under vegetation coverage is realized, and the dual technical effects of resisting complex scattering interference and keeping the integrity of the terrain structure are achieved.

The embodiment of the invention provides a knowledge graph fusion method and system based on a large model, and the method comprises the steps: carrying out the data standardization, entity feature enhancement and relation semantic annotation of heterogeneous knowledge graphs from different sources; through semantic similarity calculation, context reasoning and alignment confidence evaluation, a multi-stage and multi-mode entity alignment mechanism is constructed in combination with a large language model, and cross-source entity matching is performed on entities in heterogeneous knowledge maps of different sources; based on conflict detection, dynamic weight distribution and a conflict resolution strategy, generating a data fusion result, and unifying multi-source data; and generating a high-quality unified knowledge graph through missing relationship prediction, logic consistency verification and an incremental updating mechanism. According to the knowledge graph fusion method and device, full-process automation, precision and dynamics of knowledge graph fusion are realized, the problems of high manual dependence, weak semantic processing capability, poor cross-domain adaptability and the like in the prior art are effectively solved, and the efficiency and quality of knowledge graph fusion are remarkably improved.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

The invention relates to the technical field of transformers, and particularly discloses a transformer fault diagnosis method and system.The transformer fault diagnosis method comprises the steps that firstly, concentration data of gas dissolved in transformer oil is collected, the potential fault type of a transformer is preliminarily determined based on a three-ratio method, and a diagnosis result confidence evaluation mechanism is introduced; a confidence score for the potential fault type is calculated. And when the confidence coefficient is lower than a preset threshold value, further combining the potential fault type and the confidence coefficient thereof with basic attribute data of the transformer to form fault diagnosis priori data. And then, deep conjoint analysis is performed on the concentration data of the gas dissolved in the oil and the fault diagnosis prior data by using the trained fault diagnosis model, so that more comprehensive fault features can be captured, and an accurate transformer fault diagnosis result can be obtained. According to the method, a deep learning algorithm is introduced during low-confidence diagnosis of a traditional three-ratio method, so that the accuracy and reliability of transformer fault diagnosis can be remarkably improved.

The invention belongs to the technical field of computer software, is applied to the field of medical health and the field of financial science and technology, and discloses a data processing method, device and equipment and a storage medium. Responding to the first instruction, calling a preset model to analyze a first detection area in the target image, and generating a first document and a first confidence coefficient; responding to the second instruction, calling a preset model to analyze a second detection area in the target image, and generating a second document and a second confidence coefficient; responding to the third instruction, calling a preset model to analyze a third detection area in the target image, and generating a third document and a third confidence coefficient; summarizing the target information list; and calculating a comprehensive confidence coefficient. The invention provides a data processing method and device, equipment and a storage medium, and solves the problems of error accumulation, poor multi-modal data relevance and lack of result confidence evaluation caused by module cascade processing in the prior art.

The invention discloses a rice seedling leaf age intelligent identification system based on a depth camera, and belongs to the technical field of agricultural intelligent equipment and machine vision. Comprising a monitoring point gridding path planning module, a mechanical arm cooperative positioning data acquisition module, an RGB-D data preprocessing module, a dynamic foreground seedling accurate extraction module, a leaf age recognition confidence evaluation module, a leaf age data visualization presentation module and an integrated control strategy feedback module. RGB color features and connected domain analysis are combined to optimize a mask, a front single seedling is accurately segmented, background interference is eliminated, the confidence coefficient is dynamically calibrated through a deep learning model and image quality features, texture definition and edge continuity features are combined to generate calibrated confidence coefficient, confidence coefficient evaluation is directly associated with an agricultural decision, and the accuracy of the agricultural decision is improved. The leaf age result is generated only based on reliable data, the personnel rechecking demand is reduced, and the decision-making efficiency is improved.

The invention belongs to the technical field of flight simulation training equipment, particularly relates to an intelligent temperature control system for a flight simulator cabin, and solves the problem that the requirements of flight simulator temperature control on multi-dimensional physiological perception fusion, multi-target collaborative optimization and rapid personalized adaptation and active safety protection of new pilots cannot be met in the prior art. According to the method, a multi-modal sensing layer processes multi-channel skin physiological data, physical environment data and pilot electrocardiosignals, a human body heat balance equation for blood flow correction is constructed, and physiological feature vectors including predicted core body temperature, predicted average votes and predicted dissatisfaction percentage are generated; the decision-making layer generates a temperature control instruction and a confidence evaluation value through a meta-learning model, a reward function is in a four-dimensional dynamic weighting form, and the weight is adjusted through an IF-THEN rule according to the state; and the dynamic execution layer adopts a PID closed loop to adjust the partition temperature and triggers response in a grading manner. Accurate and personalized temperature control is achieved, and the training quality and safety are guaranteed.

The invention provides a cloud sensing shared information fusion method for a vehicle and road cloud cooperative system. The method comprises the steps that the vehicle and road cloud cooperative system comprises a cloud, a vehicle end and a road end; the cloud end obtains environment sensing information from other terminals through network communication; a space-time two-dimensional calibration system is constructed, and the space-time two-dimensional calibration system is adopted to calibrate the environmental perception information; dividing a unified global ID for each target by adopting a global target feature matching method, and performing redundancy removal processing on the calibrated environmental perception information by adopting a confidence evaluation comprehensive redundancy removal mechanism; performing pre-estimation compensation on the environment perception information after redundancy removal by adopting a model based on state estimation and kinematics; transmitting the compensated information to a corresponding terminal by adopting a sensing information routing sharing strategy based on the global ID to complete information sharing; according to the method, a resampling optimization strategy and a collaborative prediction correction algorithm are adopted, compared with a traditional linear prediction compensation method, situation information real-time performance and prediction accuracy are remarkably improved, and timeliness and reliability of follow-up decisions are effectively guaranteed.