628 results about "Filter algorithm" patented technology

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention relates to the technical field of geological monitoring, and discloses a side slope deformation monitoring method based on spaceborne synthetic aperture radar, which comprises the following steps: S1, data acquisition: acquiring multi-source spaceborne synthetic aperture radar (SAR) data of a side slope area, the multi-source spaceborne SAR data comprising C-band SAR data and L-band SAR data; s2, self-adaptive preprocessing: self-adaptive preprocessing is performed on the multi-source spaceborne SAR data according to a ground surface coverage scene of a slope area, and the ground surface coverage scene comprises a low vegetation area, a medium-high vegetation area, a flying dust interference area and a high and steep terrain area. According to the slope deformation monitoring method based on the spaceborne synthetic aperture radar, a self-adaptive wave band fusion strategy is adopted for different vegetation coverage scenes, a time sequence coherence enhancement algorithm is combined, the vegetation area monitoring precision is remarkably improved, and noise signals are effectively eliminated and interference errors are reduced through flying dust and construction interference dynamic identification and a multi-dimensional filtering algorithm.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention discloses a post-stack seismic data denoising method based on multi-dip-angle structure guided filtering, and belongs to the field of seismic data denoising, and the method comprises the following steps: carrying out the processing of input seismic data through guided filtering, and obtaining a fault likelihood attribute; dividing the seismic data into a fault region and a continuous stratum region based on fault likelihood attributes; in the fault region, performing filtering processing on the seismic data by using an edge-preserving smoothing algorithm to obtain a filtering result of the fault region; in the continuous stratum area, calculating a local inclination angle of the seismic data, and performing filtering processing on the seismic data by adopting a structure guiding filtering algorithm which performs smoothing along the direction of the local inclination angle to obtain a filtering result of the continuous stratum area; and fusing the filtering result of the fault region and the filtering result of the continuous stratum region to obtain final de-noised seismic data. According to the method, efficient noise suppression can be performed on post-stack seismic data, and meanwhile, the protection capability on a complex geological structure is remarkably improved.

The invention relates to the technical field of software-defined wide area network data transmission, and discloses a software-defined wide area network data transmission method and system based on edge computing. According to the method, network state data are collected through distributed probes deployed at edge nodes, and a dynamic transmission path between the nodes is calculated after data cleaning and feature extraction. A collaborative filtering algorithm is utilized to analyze historical and real-time data to generate a transmission optimization strategy, and a fuzzy logic decision maker is combined to generate a dynamic routing strategy. And predicting a future congestion state based on network characteristics and a routing strategy, and adjusting flow scheduling parameters accordingly. And finally realizing visual display and time sequence storage of the network state data, the routing strategy and the scheduling parameters. The method can effectively reduce transmission delay and improve the utilization rate of network resources.

The invention relates to the technical field of monitoring, and discloses a multi-modal video public opinion intelligent monitoring method and system, and the system comprises a multi-modal data collection module, a data processing module, a coefficient generation module, an intelligent analysis module, a dynamic threshold module, and a public opinion visual platform. According to the method, a visual, voice and character cross-modal cooperative computing framework is established, and the one-sidedness problem of traditional single-modal analysis is solved; a recognition-tendency-propagation three-dimensional index system is constructed, and accurate quantitative evaluation of public opinion evolution is achieved; designing a static comprehensive coefficient threshold value and a dynamic growth threshold value of the dual-threshold grading system, and cooperating with an imaginary main body filtering algorithm and KOL polarization value calculation, so that the false alarm rate is remarkably reduced, and meanwhile, rapid early warning response is realized; a three-dimensional propagation map is generated through propagation path concentration analysis, content feature evolution tracking and influence radiation, and a complete public opinion evolution chain and hotspot traceability are provided.

The invention belongs to the technical field of artificial intelligence personalized services, and particularly relates to a reinforcement learning-fused user portrait dynamic construction and personalized recommendation implementation method, which comprises the following steps of: acquiring multi-dimensional user data; using the multi-dimensional user data to construct a multi-dimensional dynamic user portrait through a preset algorithm; based on the multi-dimensional dynamic user portrait, combining a collaborative filtering algorithm and a deep learning algorithm to generate a scene recommendation strategy; and monitoring user feedback and behavior data in real time, and optimizing the scenarized recommendation strategy by using the real-time monitored user feedback and behavior data. According to the method, the dynamic user portrait is constructed, scene recommendation is generated, and the strategy is optimized in real time by relying on reinforcement learning, so that the individuation degree, recommendation accuracy and user satisfaction of the service are remarkably improved, and the limitation of a traditional service mode is effectively broken through.

The invention discloses a geotechnical engineering model optimization training method and system based on a multi-source knowledge base. The method comprises the following steps: constructing a geotechnical engineering multi-source knowledge base; collecting field sensor monitoring data in real time and preprocessing to obtain standardized time sequence monitoring data; performing time sequence feature extraction on the standardized data by adopting a Mamba model to generate a time sequence state feature data set; generating initial computable prior information and parameter constraint conditions of the geotechnical engineering model based on the knowledge base, and establishing an initial particle state set; iteratively updating the particle state by adopting a particle flow filtering algorithm to obtain geotechnical engineering model parameter posterior distribution; carrying out model numerical simulation and calculating a simulation error evaluation index; and judging whether the posterior distribution reaches a predetermined optimization convergence condition according to the error evaluation index, and outputting the geotechnical engineering model after optimization training. According to the method, the accuracy and convergence stability of geotechnical engineering model parameter optimization are effectively improved.

The invention provides a tunnel health monitoring method based on distributed optical fiber sensing, and relates to the technical field of tunnel engineering health monitoring, and the method comprises the steps: carrying out the risk assessment of a tunnel through employing an analytic hierarchy process, setting a differential optical fiber laying scheme according to an assessment result, and laying distributed optical fiber sensors on the inner surface of a tunnel lining; the method comprises the following steps: acquiring original strain data through Brillouin optical time domain analysis, and preprocessing by using a moving average filtering algorithm; building a finite element model based on actual physical data of the tunnel, inverting strain data into full-field node displacement, and obtaining a complete strain tensor of a finite element unit; performing unit-level strain state evaluation, time sequence trend risk evaluation and spatial clustering risk evaluation to obtain a comprehensive risk value; and setting a dynamic evaluation threshold interval, and completing tunnel accurate region health evaluation. Accurate monitoring and risk assessment of the tunnel health state are achieved, and the method can be widely applied to the field of safety monitoring of highway and railway tunnels.

The invention discloses a shield construction muck quantity real-time monitoring system and method based on three-dimensional laser scanning, and relates to the technical field of construction monitoring, the shield construction muck quantity real-time monitoring system comprises a hardware module, a software algorithm module and a system integration module, the software algorithm module comprises a point cloud data processing module, a volume calculation module, a data analysis module and an early warning module, the point cloud data processing module adopts a hierarchical noise filtering algorithm and performs point cloud completion based on a generative adversarial network, and the volume calculation module adopts a slice integration method. Through a hierarchical noise filtering algorithm and a generative adversarial network point cloud complementation technology, noise points generated by interference of dust, diffuse reflection and the like are effectively eliminated, sparse point clouds and large-area holes in a low-reflectivity area are repaired, a complete and high-precision muck pile three-dimensional surface model is reconstructed, a reliable data basis is provided for volume calculation, and the method is suitable for mass production. And the point cloud quality and the model integrity in a complex construction environment are remarkably improved.

The invention provides a parallel orientation control method and system for multiple welding devices, and the method comprises the steps: S1, collecting real-time three-dimensional space coordinate data and device attitude angle information from multiple operation devices through a multi-source sensor, and obtaining an initial positioning information set; s2, performing multi-source data fusion processing on the initial positioning information set by adopting a state estimation filtering algorithm to obtain a fused operation equipment position and attitude estimation result; s5, acquiring the corrected accurate equipment positioning information and the three-dimensional geometric model data of the target workpiece, and generating a task allocation scheme of multi-equipment collaborative operation; and S7, simulating a potential motion interference scene among multiple devices according to the preliminary movement path sequence and the device attitude adjustment specification, and judging whether a path conflict condition exists or not. According to the method, the positioning precision and the operation efficiency of multi-device collaborative operation are remarkably improved, path conflicts are effectively avoided, and the stability and the safety of coordinated control in a complex scene are ensured.

The invention relates to an intelligent procurement collaboration method and system, and the method comprises the following steps: S1, building a supplier knowledge graph based on multi-source heterogeneous data fusion through employing a graph embedding algorithm, achieving the associated storage of industrial and commercial information, performance records and quality reports through a Neo4j graph database, calculating the weight of a supplier node through employing a PageRank improved algorithm, and carrying out the calculation of the weight of the supplier node; s2, based on the three-dimensional supplier portrait matrix, using an improved collaborative filtering algorithm to carry out demand matching, analyzing a purchase demand document through an ElasticSearch semantic analysis engine, combining TF-IDF weighted cosine similarity calculation to realize intelligent recommendation, and generating a purchase demand scheme with a weight score. The supplier knowledge graph is constructed through the multi-source heterogeneous data fusion and graph embedding algorithm, the scattered industrial and commercial information, performance records and quality reports are stored in an associated mode, traditional data island limitation is broken through, and effective integration of supplier multi-dimensional features is achieved.

The invention relates to the technical field of information, and particularly discloses an online mall platform intelligent management method and system, and the method comprises the following steps: S1, constructing a data collection system, and obtaining platform operation data, user behavior data and external market data in real time; s2, acquiring historical sales data, analyzing the historical sales data by adopting a Prophet time sequence model, predicting a future sales trend and inventory requirements, and generating a dynamic replenishment suggestion; s3, optimizing inventory management through a dynamic programming algorithm, calculating an optimal inventory threshold value in combination with external market data, and performing automatic replenishment triggering; s4, establishing a user portrait according to the user behavior data, and generating a personalized coupon strategy based on a collaborative filtering algorithm; s5, dynamically matching the work order and the customer service according to a multi-attribute decision algorithm; and S6, storing quality assurance information, and ensuring non-tampering and traceability of construction time, material batches and technician ID data. Therefore, the operation efficiency and the user experience of the shopping mall platform are improved.

The invention relates to the technical field of medical monitoring, and provides a high-precision sleep electrocardio continuous monitoring system and method.The high-precision sleep electrocardio continuous monitoring system and method.A modular closed-loop framework is adopted, and synchronous acquisition of electrocardio, movement and respiration signals is achieved through a multi-modal sensing and high-precision synchronous acquisition module; the signal quality multi-dimensional traceability diagnosis module performs feature extraction and noise classification on the input signal; the dynamic traceability filtering processing module intelligently calls a corresponding filtering algorithm to perform signal purification according to the noise type; and the intelligent output and self-adaptive resource management module outputs high-quality electrocardiosignals and realizes dynamic optimization of system power consumption. By establishing a complete signal quality evaluation system and an intelligent processing mechanism, the whole process optimization of the sleep electrocardiosignals from collection to output is realized, and the accuracy of monitoring data and the cruising ability of the system are remarkably improved.

The invention provides an airborne point cloud building contour point extraction method based on a constraint triangulation network and semantic ring analysis, and belongs to the field of airborne laser radar data processing, and the method comprises the steps: obtaining an airborne laser radar point cloud data set of a to-be-processed region; performing classification processing by using a filtering algorithm to obtain a surface point set and a non-surface point set; building candidate seed points are extracted, and building point clouds are extracted; a Delaunay triangulation network is constructed based on the building point clouds, and constraint is carried out based on the distance between the point clouds; constructing a concurrent triangle set, and extracting contour points and contour connecting edges; and constructing a topological graph, performing traversal search on the topological graph, determining the minimum inclusion rectangle of the closed loop, and judging the contour point type corresponding to the closed loop according to the geometrical shape parameter and the spatial distance parameter of the minimum inclusion rectangle. According to the method, the triangulation network is constructed by using the disordered contour points, and the sequence relationship and semantic information between the points are considered, so that accurate extraction of the semantic contour points is realized.

The invention belongs to the technical field of laser radars, and discloses a water surface reflected light suppression method and system in laser radar water level measurement, and the method comprises the steps: employing a multispectral fusion and dynamic polarization modulation technology, precisely filtering the interference of water surface reflected light, matching with a time-frequency domain combined filtering algorithm, and deeply analyzing signal features. An intelligent environment sensing mechanism monitors environment changes in real time, a machine learning model accurately predicts the trend of reflected light, system parameters such as laser transmitting power and receiving end gain are adjusted in advance, measurement errors are reduced, the water level measurement precision of the laser radar is greatly improved, and the water level measurement precision of the laser radar is greatly improved. The system reliability and stability are remarkably enhanced, the scheme is excellent in performance under severe conditions such as strong light by means of the capability of dynamically adapting to complex environment changes, the modular design of the system facilitates integration with an existing laser radar system, expansibility and compatibility are good, and the system can quickly adapt to equipment of different specifications and models.

An unmanned aerial vehicle target detection tracking method based on intelligent image matching comprises the steps that an unmanned aerial vehicle collects images to make a data set, a model file is obtained through twin neural network training, the model file is loaded to detect a video stream, a current frame target position and detection frame information are obtained, whether the frame is a first frame is judged, and if yes, the target is detected; using a target position information result to initialize a kernel filtering algorithm, and constructing a measurement vector; if not, calculating correlation by using a kernel correlation filter, taking the highest response position as a prediction bit, and constructing a measurement vector; calculating a Kalman gain, and inputting the Kalman gain into a Kalman filter to obtain a predicted state estimated value = (); comparing y-axis deviations of the detector and the tracker, and setting a threshold value for judgment; according to the method, target tracking under resource limitation is realized through light-weight twin network detection in combination with kernel filtering and improved Kalman filtering, the problems of high target loss rate and low precision are solved, and the method is suitable for the fields of military reconnaissance, intelligent autonomous attack and the like.

The invention discloses a subway tunnel full-section intelligent inspection robot system. The system comprises a mobile platform; a disease detection module; the data processing unit adopts an embedded industrial mainboard, is internally provided with a filtering algorithm and a ResNet50 deep learning model, processes the image and the point cloud in real time, automatically identifies the type, the position and the size of the disease and generates a three-level report; a main channel of the communication module is a double 5G industrial module, supports real-time uploading of a disease result, automatically switches a local cache when 5G is interrupted and supplementarily transmits the disease result afterwards, and is provided with a Bluetooth unit for on-site short-distance data calling; the power supply module adopts two groups of 12V / 100Ah lithium iron phosphate batteries which are connected in parallel, the endurance is greater than or equal to 4 hours, a brake energy recovery device is integrated, the battery is charged back during downhill or deceleration, the endurance is prolonged by about 15%, and one-hour fast charging is supported; and an auxiliary function module. According to the method, the consistency and reliability of detection data are remarkably improved, and a stable basis is provided for subsequent high-precision disease recognition.

The invention discloses a risk early warning method and device, electronic equipment and a storage medium, and relates to the technical field of risk early warning. According to the risk early warning method and device, personalized learning is carried out based on a medical knowledge base in combination with multi-modal semantic embedding, and a user exclusive causal map is generated to consider individual differences; the model is updated in real time, the health state posterior probability is accurately calculated through a filtering algorithm, and misjudgment is reduced; a target event probability and a high-probability causal path are deduced, a hierarchical intervention instruction and an active disturbance calibration mechanism are matched, a user feedback dynamic optimization map is combined, and medical rationality and personalized adaptation are guaranteed, so that the problems that individual differences are ignored, high false alarms and missing alarms are likely to be generated, and the accuracy and the reliability of medical analysis are poor in existing threshold judgment or statistical correlation analysis can be solved. According to the technical scheme of the invention, the technical problems in the prior art are solved, and the technical effects of reducing the false report and missing report rate of health risks, enhancing the individual suitability, maintaining the medical rationality, and improving the continuous effectiveness of the home health service and the user credibility are achieved.

The invention provides a multi-modal data joint query analysis method and system supporting natural language interaction, and relates to the technical field of data query analysis. Historical operation data, audio data and text data of a user on an intelligent search platform supporting natural language interaction are collected; modeling the historical operation data to obtain a user preference vector, performing voice recognition and text standardization processing on the audio data to obtain standard query data, and integrating the standard query data and the text data into context information; an attention mechanism-based algorithm is used for semantic understanding and intention recognition to obtain an intention feature vector, and the intention feature vector is fused with a user preference vector to obtain a classification result; and finally, based on the result, querying in a preset multi-modal database through a collaborative filtering algorithm to obtain a joint query result, so that personalized accurate query of the multi-modal data under natural language interaction can be realized, and the result fits the intention and long-term preference of the user.

The invention provides a branch diameter real-time measurement method and system based on a depth camera, and relates to the technical field of data processing, and the method comprises the steps: extracting a skeleton center line based on a final contour, dynamically laying measurement points along a skeleton line, and calculating a unit normal vector, scanning the contour boundary along the normal direction to determine the intersection point coordinates of the normal and the contour; calculating three-dimensional space coordinates according to the intersection point coordinate set and the corresponding depth value to obtain a section diameter measurement value; and on the basis of the section diameter measurement value, real-time smoothing processing is carried out through an exponential weighted moving average filtering algorithm for dynamically adjusting parameters, instantaneous errors are eliminated in combination with an anomaly detection mechanism, and a smoothed diameter data stream is output. The accuracy of contour extraction and the practicability of the measurement method are improved.

The invention relates to the field of data processing, and provides a multi-dimensional feature driven B2B2C collaborative recommendation method and system. The method comprises the steps of performing multi-dimensional collection on B-end merchant features, C-end user features and commodity features through a heterogeneous data source interface to obtain standardized multi-dimensional features; performing dynamic weight learning on the standardized multi-dimensional feature data set through a multi-head self-attention mechanism to obtain a fusion feature vector; performing three-layer cooperative matrix construction on the fusion feature vector based on tensor decomposition to obtain a multi-dimensional factor matrix; performing causal relationship modeling on the multi-dimensional factor matrix through a causal graph structure-based collaborative filtering algorithm to obtain a deep collaborative network model; and performing real-time recommendation of to-be-recommended items through the deep collaborative network model to obtain a personalized B2B2C recommendation list. According to the method, the complex mode in the business scene can be captured, and the accuracy of the personalized recommendation result is improved.

The invention discloses an element stripping method and device based on multispectral data, equipment and a medium, and relates to the field of remote sensing image processing, and the method comprises the steps: collecting a multispectral image of a target region; a self-adaptive window filtering algorithm is adopted to pre-process the multispectral image to obtain a multispectral enhanced image, and the self-adaptive window filtering algorithm calculates a window adjustment coefficient based on noise density, gray variance and gradient magnitude; spectral features and texture features are extracted from the multispectral enhanced image; fusing the spectral features and the texture features, and constructing fusion features of each pixel in the multispectral enhanced image; determining a ground feature category of each pixel based on the fusion features to strip a plurality of vectorized layers containing different elements; according to the method, the three indexes of the noise density, the gray variance and the gradient magnitude can be fused, the window adjustment coefficient is obtained, dynamic self-adaptive change of the filtering window size is achieved, and higher-precision data support is provided for subsequent element extraction.

The invention discloses a multi-source navigation signal switching and fusion method and a navigation system thereof, and the method comprises the steps: carrying out the quality evaluation of signals from different navigation source GNSS, and determining a main navigation source based on a quality evaluation result; when the quality score of the main navigation source is lower than a preset threshold value or the GNSS observation information meets a preset switching condition, switching between the main navigation source and the standby navigation source is carried out, and smoothing processing is carried out on the new navigation solution and the old navigation solution in the switching process of the main navigation source and the standby navigation source; and performing multi-source data fusion and state estimation on the data of the navigation source GNSS, the inertial measurement unit, the geomagnetic sensor and the air pressure sensor through a filtering algorithm, and outputting a navigation result. According to the navigation system, a plurality of multi-constellation GNSS receiving chips, an inertial measurement unit, a geomagnetic sensor, an air pressure sensor and a micro-control unit are vertically interconnected in a single package through a wafer-level heterogeneous integration process. According to the invention, the positioning precision and robustness in a complex environment can be improved.

The invention relates to the technical field of non-road mechanical vehicle monitoring, in particular to a non-road mechanical vehicle fuel consumption real-time estimation method and system. According to the method, multi-source data are collected and preprocessed, and the current working state of a vehicle is recognized in real time based on classification rules; dynamically setting Kalman filtering parameters according to the identified working state of the vehicle; predicting a predicted fuel state vector and an error covariance matrix at the next sampling moment based on Kalman filtering; meanwhile, the refueling event is detected in parallel, and if the refueling event is detected, the refueling event is recorded, and the fuel oil state and the error covariance matrix are reset; and carrying out fusion processing on the actual observation information and the predicted fuel state vector based on Kalman filtering to obtain optimal fuel state estimation. According to the method, the multi-dimensional observation model is constructed, and the fuel consumption estimation precision is greatly improved through multi-source information complementation; and a dynamic mapping relation between working condition identification and filter parameters is established, so that the filtering algorithm keeps excellent performance under different working conditions.

The invention relates to the technical field of image processing, in particular to a rapid collision detection method based on a three-dimensional digital virtual scene, and the method comprises the steps: collecting the static point cloud information of a to-be-detected region and the laser reflection intensity of the point cloud; determining a unit normal vector and curvature corresponding to each point cloud; determining a characteristic value of each point cloud; based on the characteristic values among the point clouds and the numerical distribution of the curvature, obtaining growth conditions of a region growth algorithm, and segmenting all the point clouds based on the region growth algorithm to obtain an edge structure region, a noise region and a building region; after the point clouds in the noise area are removed, the complexity of each edge structure area is obtained, an abnormal point discrimination threshold value in a filtering algorithm is adjusted, and all the point clouds are filtered; and constructing a BIM model based on the filtered point cloud data to carry out collision experiment simulation. The invention aims to improve the processing quality and precision of the point cloud so as to improve the accuracy of collision detection.

The invention relates to the technical field of data processing, in particular to a large model reasoning optimization method based on context incremental updating, which comprises the following steps: processing a multi-modal data stream through timestamp alignment and a filtering algorithm, extracting features by adopting a shared encoder and a private encoder, and realizing feature decoupling through a depth information bottleneck principle. A dynamic emotion map is constructed by using Gaussian process regression and a random process algorithm, and self-adaptive updating control is realized by combining meta-learning and Bayesian optimization. Incremental state management is realized by adopting a neural Turing machine, reasoning consistency is guaranteed through a generative adversarial network, model parameters are optimized in combination with a digital twin system and reinforcement learning, and a mental health service response is finally generated through a conditional generation model and hierarchical reinforcement learning. According to the method, the problem of asynchronism of multi-modal emotion feature dynamic evolution and context increment updating is effectively solved, accumulated drift of emotion state tracking is eliminated, and the continuity of reasoning logic is guaranteed.

The invention discloses an interactive multi-model aircraft filtering algorithm and system driven by reinforcement learning, and the algorithm comprises the steps: firstly constructing a multi-model state prediction set composed of a Singer model, a current statistical model and a Jerk model, and presetting a state transition probability matrix according to the state evolution correlation between the models, so as to define the interaction relation between the models; in the filtering stage, state estimation preliminary fusion of the three maneuvering models is achieved according to a traditional interactive multi-model algorithm, the fusion proportion of each model is adjusted in real time in combination with a weight correction vector output by a reinforcement learning strategy function, and strategy iteration optimization is conducted through a reward function containing an error improvement item, a matching item and a balance item. The method can significantly improve the filtering precision and fusion stability of the aircraft maneuvering target.

The invention relates to the field of intelligent water affairs, and discloses a precise flow control system for intelligent water affairs, which comprises the steps of collecting local water flow change data in real time, extracting flow velocity disturbance characteristics and pipe network transient pressure response, and judging whether unexpected disturbance trigger exists or not; constructing a local disturbance label library based on disturbance trigger source features and a historical operation model, and identifying disturbance sources and flow direction tendentiousness by using fuzzy discrimination and variable weight filtering algorithms; according to the pipe section hydraulic characteristics corresponding to the mismatching behavior, the flow of the target pipe section is corrected through a high-resolution electric proportional valve set and a two-way feedback control unit; constructing a cross-section response chain through the intelligent edge control node, and generating a multivariable adjustment gradient according to the multi-period callback simulation data; flow state data and peripheral environment variables before and after the current round of regulation and control intervention are collected, and system model parameters are dynamically corrected through the integrated evolutionary prediction network. The stability of the intelligent water affair system is improved.