1126 results about "Mixture model" patented technology

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention belongs to the technical field of iron stick yam image recognition detection, and discloses an iron stick yam intelligent system and method based on image recognition, and the method comprises the steps: dynamically adjusting the polarization direction to generate a polarization suppression image, and generating a space mapping relation; based on the polarization suppression image, segmenting the main body area of the iron stick yam, filling and optimizing the hole edge, generating a high-precision contour mask, further extracting the texture difference between wrinkles and cracks, generating a direction sensitive characteristic pattern, and evaluating morphological defects; constructing a comprehensive feature vector, training a Gaussian mixture model to generate reference distribution, calculating a quality deviation index, and judging a risk level; constructing a double-branch feature vector, outputting a preliminary comprehensive quality score through a complementary aggregation model, introducing a planting density-curvature physical model, obtaining a final quality score, dynamically adjusting a quality judgment threshold, dividing quality grades, and triggering a response strategy according to confidence grade to form a closed loop; and comprehensiveness and accuracy of Chinese yam quality evaluation are improved.

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention discloses an invading object detection method and device applied to a perimeter security system, and relates to the technical field of invading object detection.According to the invading object detection method and device, a background model is dynamically updated by combining adaptive background modeling with time sequence analysis, background stability is kept in dynamic environments such as illumination variation and leaf shaking, and the invading object detection efficiency is improved. Different types of background changes are distinguished through a Gaussian mixture model, and the coexistence condition of long-term static background and short-time dynamic change can be adapted; in the target detection stage, a front-and-back frame difference method and a background subtraction method are combined, a moving target is extracted by utilizing complementary characteristics of the two methods, in the target classification and behavior analysis stage, spatial morphological characteristics of the target are extracted, the motion trend of the target is analyzed and predicted in combination with a time sequence, and track prediction is performed by adopting Kalman filtering. Even if the target is shielded for a short time or moves slowly, the tracking stability can still be kept.

The invention relates to the technical field of gesture recognition, in particular to an anti-interference optimized gesture recognition method. Comprising the following steps: acquiring a gesture video stream through a camera, constructing a dynamic background model by using a frame difference method and a Gaussian mixture model, eliminating a static background and interference, and extracting a target area image; performing local brightness histogram analysis on the target region image, and optimizing the image quality by adopting a region adaptive compensation algorithm and a multi-scale edge enhancement technology; positioning a gesture area in real time by using a color histogram and a feature matching algorithm, and dynamically updating a gesture track in combination with Kalman filtering; and extracting gesture shapes, tracks and dynamic mode features through deep learning, comparing the features with a standard model library, and outputting gesture categories and corresponding function instructions. According to the method, a multi-level optimization strategy is adopted for a complex background, a dynamic target and a changeable illumination environment, so that the anti-interference capability and the recognition precision of gesture recognition are improved.

The invention discloses a multi-source heterogeneous agricultural data element fusion system and method, and relates to the technical field of data fusion processing, and the method comprises the steps: carrying out the data screening of cleaned data; the screened data is preprocessed by the constructed agricultural field knowledge base, the logic consistency of the data is checked through a rule engine, and a corresponding coping strategy is adopted according to a check result; identifying abnormal points in the multi-source data by adopting a Gaussian mixture model and a constructed logic rule combination, and correcting or complementing the detected abnormal points in combination with a specific scene; key features are selected through information gain, multi-source fusion is carried out on the key features and original multi-source data, an abnormal mode library is established after abnormal data modes are classified, and source categories of the abnormal data are matched from a fault matching rule library according to abnormal features of the abnormal data; and the fault matching rule base is established, so that the source category of the abnormal data can be accurately identified, and a targeted solution is provided for processing the abnormal data.

The invention discloses a large language model end-cloud collaborative inference system based on low-rank fine tuning, and belongs to the technical field of inference optimization of end-side cloud computing. Establishing an end-cloud collaborative reasoning architecture, and in an offline stage, performing parameter fine tuning on a large language model by a cloud side based on training data of different downstream tasks; in the online stage, user requests are classified through'variational auto-encoder-Gaussian mixture model 'clustering, whether a low-rank adapter matched with a current task exists in an end side cache is judged, and if yes, reasoning is executed on the end side; and otherwise, forwarding the task to the cloud side. After a plurality of user requests are processed by the architecture, historical user requests and cache states are analyzed based on a Mama model, and an end-side low-rank adapter library is dynamically updated. And monitoring end cloud load and reasoning delay in real time, and issuing the new adapter to the end side according to the task repetition rate increment. According to the method, dynamic balance of the system is realized, and high efficiency and adaptability of the system are ensured while calculation and storage overhead are reduced.

The invention discloses a network space security risk intelligent identification method and system. The method comprises the following steps: deploying traffic collection equipment at key network nodes and boundary equipment, and collecting network traffic data and log data; extracting network behavior features and log key fields, and constructing a data feature model; performing vectorization processing based on the data feature model, performing traffic classification by using a deep neural network, and identifying a device type and a device identifier; carrying out noise reduction, redundant information removal and standardization processing on flow data in the data feature model to generate standardized data and storing the standardized data in a vector database; entity extraction is carried out on the standardized data, a knowledge graph is constructed, data distribution is analyzed through a Gaussian mixture model, the probability of abnormal behaviors is calculated in combination with historical data, and attack risks are calculated based on Bayesian decision; and generating an alarm according to the calculated attack risk, tracing the historical record of the attack risk in the knowledge graph, sending a blocking instruction, and identifying the security risk of the cyberspace.

The invention discloses a distributed photovoltaic power prediction method, system and device based on a Gaussian mixture model and a medium, and belongs to the technical field of photovoltaic power prediction.The distributed photovoltaic power prediction method comprises the steps that a time sequence vector is collected, principal component analysis is carried out on the time sequence vector, low-dimensional feature representation is obtained, and a power feature vector of each photovoltaic power station is formed; performing clustering analysis based on a probability model on the power feature vector to generate a plurality of photovoltaic power station clusters; for each cluster, acquiring meteorological input data through a set data source priority rule and a completion mechanism; and constructing a neural network power prediction model based on the accumulated power data in the cluster and the corresponding meteorological features, and outputting a future power generation power prediction value of the photovoltaic power station in the corresponding cluster. According to the invention, N photovoltaic power stations in a region are divided into M clusters through a GMM clustering method, so that the design is simplified; and the power prediction of the whole area is realized.

The invention provides a photovoltaic cleaning robot sensor data fusion and obstacle avoidance method and system, and relates to the technical field of photovoltaic cleaning robots, and the method comprises the steps: obtaining multi-source sensor data, carrying out the probability distribution modeling through employing a Gaussian mixture model, and achieving the data fusion through employing a Kalman filtering algorithm of an adaptive covariance matrix, and constructing a three-dimensional semantic map, marking obstacle information, and dynamically adjusting a motion track in combination with global path planning and a fuzzy logic controller of a local obstacle avoidance layer to realize smooth obstacle avoidance of the photovoltaic cleaning robot. The environment perception capability and the obstacle avoidance efficiency of the photovoltaic cleaning robot are improved, the collision risk is reduced, and the safety and the efficiency of photovoltaic panel cleaning operation are guaranteed.

The invention relates to a user psychological state monitoring method and system based on voice and semantic recognition, and the method comprises the steps: firstly obtaining voice data and corresponding text data of a user, and respectively extracting a voice spectrum feature vector and a text feature vector; then, inputting the two features into a cross-modal joint coding network model, and aligning a speech spectrum with a text embedding space by using a multi-head attention mechanism to obtain a joint feature; thirdly, confidence coefficients of the joint features are extracted through a Bayesian network model, weighted features are obtained, a Gaussian mixture model is used for fitting an emotion fluctuation trend, and an emotion distribution probability is calculated; the confidence coefficient weight is dynamically adjusted according to the emotion distribution probability, new weighted features are obtained, and after time sequence alignment processing, local information is extracted by using a convolutional neural network and the features are fused; and finally, calculating a classification probability through a softmax function, and determining a psychological state classification result of the user.

The invention relates to the technical field of mechanical equipment state monitoring, in particular to a method and sensor for detecting temperature and vibration data of dynamic equipment, and the method comprises the steps: collecting a temperature signal and a vibration signal of the dynamic equipment, and carrying out the fusion processing, thereby obtaining a fusion feature set; establishing a feature distribution baseline based on a Gaussian mixture model, calculating a relative entropy of the fusion feature set and the feature distribution baseline, and generating a dynamic threshold sequence; constructing a detection model according to the fusion feature set and the dynamic threshold sequence, and outputting to obtain an abnormal mode label; performing time serialization processing on the abnormal mode label, predicting a fault probability in a future time period according to the abnormal mode label subjected to time serialization, and generating a fault prediction result; and performing priority ranking on the fault types in the fault prediction result, and generating a maintenance report according to a priority ranking result. The reliability and practicability of temperature vibration data detection of the dynamic equipment are comprehensively improved, and an efficient solution is provided for health management of the dynamic equipment.

The invention provides a data correction method and system combined with lattice structure additive manufacturing process characteristics, and the method comprises the steps: firstly employing a Newton iteration method, taking the radius of a pillar as a variable, optimizing the relative density of an iteration target and generating a lattice structure geometric model under the condition that a process constraint condition is satisfied, and extracting actual geometric parameters after forming through CT scanning, the elastic modulus is corrected through the pillar diameter deviation and the defect volume fraction, a compression failure mechanism is combined, a density-related failure criterion is introduced, a nonlinear relation with the relative density is constructed through specific energy absorption data, material plasticity parameters are inversely optimized, and a defect coupling evaluation model is constructed according to the surface powder sticking rate and the pillar diameter deviation. And establishing a Gaussian mixture model based on a stress-strain curve to screen out abnormal data, and finally fusing the parameters to construct a data correction model. The dot matrix structure design precision can be improved, and then a high-quality data basis is provided for performance prediction-structure design two-way feedback.

The invention belongs to the field of data storage, and discloses a frequency access data storage migration method and device for big data, and the method comprises the steps: obtaining a data access log, carrying out the preprocessing of the data access log, and obtaining a time window statistical matrix; carrying out timeliness attenuation weighting calculation on a plurality of data access times in the time window statistical matrix to obtain a weighted access frequency vector; obtaining a multi-dimensional feature matrix based on the weighted access frequency vector; clustering the feature vector of each time window slice in the multi-dimensional feature matrix through a Gaussian mixture model to obtain a clustering tag vector; inputting the clustering label vector into an access frequency prediction model to obtain an access frequency prediction value of the data in a future preset time step; the migration decision matrix is determined based on the access frequency predicted value and the data storage cost parameter, data migration is carried out based on the migration decision matrix, the data migration complexity can be reduced, and the access speed and the storage cost are balanced.

The invention provides a short-term photovoltaic power prediction method and system, computer equipment and a medium, and belongs to the field of photovoltaic power generation output power prediction.The method comprises the steps that short-term photovoltaic power and meteorological working condition data samples are obtained, and a Gaussian mixture model is used for conducting multi-modal clustering processing on the meteorological working condition data samples to obtain membership probability embedded vectors; through a Pearson's correlation coefficient weighting and sliding window mechanism, extracting features from the data sample and the membership probability embedding vector, and constructing a multi-modal time sequence feature tensor; a multi-head attention mechanism in a traditional Transform network is replaced with a class domain fusion self-attention mechanism, and a class domain fusion attention model is formed; inputting a multi-modal time sequence feature tensor to train a class domain fusion attention model to obtain an initial prediction value; and residual error estimation is carried out on the initial prediction value by using a residual error learning error compensation strategy, a short-term photovoltaic power prediction result is output, and the accuracy and robustness of the model are improved.

The invention discloses an aero-engine group health evaluation method based on multi-working-condition dynamic clustering, and belongs to the field of aero-engine health state evaluation. The method comprises the following steps: firstly, carrying out clustering analysis on set parameters in engine operation data, and carrying out merging processing on small-scale abnormal clusters to obtain a working condition category division result; secondly, constructing a health baseline data set, carrying out standardized preprocessing on sample data in a working condition category division result, and carrying out nonlinear dimensionality reduction to obtain a low-dimensional feature data set; thirdly, performing clustering analysis on the low-dimensional feature data set by adopting a Gaussian mixture model, and calculating an average mahalanobis distance between a sample of each clustering category and a health reference center to obtain multi-level health levels corresponding to different clustering categories; and finally, through fusing the membership soft probability and the sample individual mahalanobis distance, constructing a continuous health score and obtaining a health grade determination interval. According to the method, health state characteristics under different working conditions can be effectively identified, individual difference modeling and group transverse comparison evaluation are supported, and the accuracy is improved.

The invention provides an under-mine video stream dynamic denoising method based on multi-modal fusion, which comprises the following steps: constructing a time sequence synchronous fusion mechanism of visible light, infrared and laser radar data, and realizing time-space alignment of multi-source heterogeneous data; a dynamic noise model is established by introducing a fractional calculus optical flow field concept and combining a Gaussian mixture model, so that a dynamic noise region is accurately identified; an improved self-adaptive wavelet threshold function is constructed, a function threshold parameter can be linked with a dust concentration sensor in real time, and the de-noising intensity is dynamically adjusted according to the actual dust concentration; designing a dual-path feature enhancement neural network to effectively separate and enhance structural features and texture features in the video image; a cascaded detection decision system is created, a lightweight network is used as a primary detector, a high-confidence detection result is directly output, and a low-confidence detection result is input into a Transform correction module for secondary reasoning. According to the invention, dynamic denoising, feature enhancement and target intelligent monitoring of the video stream under the mine can be realized.

The invention relates to the technical field of optical fiber sensing and fault diagnosis, in particular to an optical fiber gyroscope fault monitoring method and system. The method comprises the following steps: extracting four-dimensional characteristics of phase difference, light intensity fluctuation, polarization state drift and temperature drift from an output signal of an optical fiber gyroscope, and constructing a high-dimensional characteristic matrix after unifying a time reference; establishing a dynamic prediction model based on historical data to generate a residual matrix, and orthogonally separating the residual into an internal structure abnormal signal and an environment disturbance signal through covariance characteristic decomposition; mapping the structure residual error sequence into a topological graph, calculating an evolution index in real time, and capturing a fault gradient trend in combination with sliding window gradient analysis; a multi-dimensional vector is constructed by fusing topological features, an adaptive classifier of an online Gaussian mixture model is adopted to identify a fault mode and quantify a health index, and meanwhile, a health measurement result is fed back to a prediction model and topological analysis parameters to realize closed-loop optimization. According to the invention, full-process adaptive monitoring from anomaly detection to health measurement is realized.

The invention discloses a method and a system for analyzing a pollution source of a rainwater pipe network. The method comprises the following steps of: 1, obtaining a first key fluorescence characteristic parameter combination according to basic physicochemical indexes by adopting a fluorescence characteristic parameter prediction model; 2, three-dimensional fluorescence characteristic parameters of the pollution source are obtained, and a pollution source three-dimensional fluorescence characteristic data set is obtained; screening by adopting a multivariate statistical method to obtain a second key fluorescence characteristic parameter combination; 3, determining a fluorescence characteristic parameter combination according to the first key fluorescence characteristic parameter combination and the second key fluorescence characteristic parameter combination; 4, inputting the fluorescence characteristic parameter combination into the Bayesian mixture model to obtain the contribution rate of the pollution source to the receptor water quality, and completing the analysis of the pollution source of the rainwater pipe network; according to the method, the DOM fluorescent fingerprint information is predicted by adopting basic data; and based on the three-dimensional fluorescence characteristic parameters and in combination with a Bayesian mixture model, precise quantitative analysis of the pipe network pollution source is realized.

The invention provides a PPG big data driven biological feature recognition method, which comprises the following steps: acquiring PPG signal data of trainees, and performing high-pass filtering and low-pass filtering preprocessing on the PPG signal data to obtain preprocessed PPG signals; framing is carried out on the preprocessed PPG signal, a Mel frequency cepstral coefficient feature and a Gammatone frequency cepstral coefficient feature are extracted respectively, the Mel frequency cepstral coefficient feature and the Gammatone frequency cepstral coefficient feature are fused, then principal component analysis dimension reduction is carried out, and a fusion feature is obtained; a universal background model UBM of a Gaussian mixture model is constructed based on the fusion features, zero-order, first-order and second-order Baum-Welch statistics are calculated, a global difference space matrix is estimated from the Baum-Welch statistics, and i-vector identity authentication vectors are extracted; and inputting the i-vector identity authentication vector into a long short-term memory network for training and classification to obtain an identity recognition result of the trainees. According to the invention, the influence of motion artifacts can be eliminated, the overall variability of PPG signals is captured, and an identity authentication scheme is provided for wearable equipment.

The invention discloses a tunnel crack identification method and system based on laser radar and unmanned aerial vehicle photographing, an electronic device and a storage medium, and the method comprises the steps: obtaining space geometric information, photographing the surface image information of the interior of a tunnel, and constructing a three-dimensional texture point cloud model; carrying out feature extraction on geometric morphology features and surface texture features of the three-dimensional texture point cloud model to obtain a preliminary fracture feature set; classifying and preliminarily screening tunnel internal texture features based on the preliminary fracture feature set to obtain a preliminarily screened fracture region set; expanding the fissure regions based on the primarily screened fissure regions to generate an expanded fissure region set; analyzing the point cloud density in the expanded fracture region set through a Gaussian mixture model, judging the depth and width features of the fracture, and obtaining a fine screening fracture feature set; and for the fine screening fracture feature set, adopting a probability optimization algorithm based on a Markov random field to carry out joint probability distribution modeling on geometric and textural features of the fractures so as to obtain a fracture identification result.

The embodiment of the invention discloses a flight path data analysis method based on a depth auto-encoder and a generative adversarial network, relates to the technical field of air traffic management, and adopts a cubic spline interpolation method to enable each flight path to have the same length; a deep auto-encoder (DAE) and a generative adversarial network (GAN) are used as a basic framework to establish a flight trajectory data analysis framework, and two tasks of trajectory anomaly detection and flow pattern recognition are synchronously executed; in the pre-training stage, on the basis of DAE, a discriminator is integrated to establish an abnormal trajectory detection task, and an abnormal score function is formulated in combination with reconstruction loss and discriminator loss; in the fine tuning stage, a Gaussian mixture model (GMM) is adopted for low-dimensional representation of the trajectory in a potential space output by an encoder to establish a flow pattern recognition task, and in order to enhance clustering, a loss function is constructed in combination with clustering distribution enhancement. The method is suitable for civil aviation air traffic management.

The invention relates to an arc fault detection method and system based on a dynamic fuzzy threshold and a storage medium, and the method comprises the steps: collecting the current circuit data of to-be-detected electrical equipment, and carrying out the feature extraction of the current circuit data, and obtaining a current feature parameter; the method comprises the following steps: training historical circuit data by taking a decision tree algorithm as a model architecture, and in the training process, performing parameter updating through a double-sliding window mechanism and optimizing a fuzzy threshold band range through an information gain maximization principle to obtain a dynamic fuzzy threshold decision model; and performing arc fault analysis on the current characteristic parameters through the dynamic fuzzy threshold decision model to obtain a fault detection result, and outputting early warning information when the fault detection result is that an arc fault occurs. According to the method, the dynamic fuzzy threshold value band is constructed through the Gaussian mixture model, and the self-adaptive adjustment of the threshold value is realized in combination with a double-sliding-window online updating mechanism. According to the technical scheme, the accuracy of arc faults can be remarkably improved, and particularly the false alarm rate is reduced.

The invention provides an intelligent control method for sewage biological nitrogen removal and denitrification carbon source addition, which combines an ASM activated sludge mechanism model based on a sewage biological treatment mechanism and an AI artificial intelligence model based on data driving to construct an ASM-AI mixed model, can accurately simulate a nitrogen removal and denitrification reaction process in a sewage treatment process, and has the advantages of simple operation, high accuracy and high practicability. On the basis, the optimal carbon source feeding amount is obtained by setting a target function, carbon source feeding in the denitrification process is intelligently controlled, accurate control over carbon source feeding is achieved through real-time data monitoring, ASM-AI mixed model prediction and continuous training of an ASM-AI mixed model, the nitrogen removal efficiency of denitrification is improved, and the nitrogen removal efficiency of denitrification is improved. The carbon source adding cost is saved, and the stability, high efficiency and economical efficiency of biological sewage treatment denitrification are ensured.

The invention relates to the technical field of 5G-R network detection and monitoring, discloses a 5G-R network situation awareness method based on distributed monitoring and multi-source information fusion, and aims at solving the problems that a 5G-R network is large in scale, high in dynamic performance and complex in data isomerism. Multi-source data of a core network, a wireless network, special equipment, interface monitoring, detection equipment, a GIS and the like are collected in real time; according to the method, technologies such as Pearson's correlation coefficients, FP-Growth, Bayesian analysis, a time sequence point process, a Hookes theory, a Gaussian mixture model, a decision tree, S-ARIMA, Boxplot, N-sigma, iForest, regression analysis, a neural network and KL divergence are combined to realize multi-source data fusion, intelligent analysis and visual perception, including network alarm, application quality, operation and maintenance and resource management. According to the method, the comprehensiveness, the real-time performance, the fault diagnosis accuracy and the operation and maintenance efficiency of the 5G-R network are improved, and the requirements of low delay and high reliability of a railway scene are met.

The invention discloses a 3D printing partition filling path planning method based on stress distribution. The 3D printing partition filling path planning method is suitable for FDM (fused deposition modeling) printing. The method comprises the following steps: firstly, performing finite element mechanical simulation on a to-be-printed model to obtain node coordinates and stress distribution data; then, an improved Gaussian mixture model (GMM) algorithm is adopted to carry out stress partitioning on the model, and an area boundary is optimized in combination with image processing and a Snake algorithm. Differentiated path planning of a high-stress area, a conventional area and a non-filling area is realized through an adaptive slicing and partition filling strategy, and a G-code file with multiple adjustable parameters is automatically generated. According to the method, the structural strength and the material utilization rate of a printed piece are effectively improved, and the method is suitable for high-performance 3D printing manufacturing of a complex structure.

The invention provides a ship video concentration and intelligent retrieval method and system based on edge nodes, and belongs to the technical field of edge computing, and the method comprises the steps: S1, collecting ship video data; s2, an edge node generates a concentrated video and metadata through an improved Gaussian mixture model, an improved DeepSORT algorithm and a dynamic concentration proportion adjustment algorithm; s3, synchronously concentrating the index information of the videos and the metadata by each edge node through a publishing-subscribing mode; s4, training a lightweight intelligent retrieval model through a knowledge distillation algorithm; s5, calling the lightweight intelligent retrieval model to match the corresponding concentrated video clip, and returning a retrieval result; and S6, collecting feedback information of the user on the retrieval result, and performing incremental optimization on the lightweight intelligent retrieval model through an elastic weight consolidation algorithm. According to the invention, efficient monitoring management is realized through edge localization processing, cross-node cooperation and closed-loop optimization architecture, and the actual requirements of ship safety monitoring and operation management are met.

The invention discloses a ship target detection and tracking method and system based on video image processing, and the method comprises the steps: 1), collecting a channel panoramic video stream through a high-definition camera, and extracting a continuous image sequence; 2) constructing a dynamic channel background model by using a Gaussian mixture model (GMM), and updating the background in real time to eliminate interference of water surface fluctuation and illumination variation; (3) enhancing the data obtained in the step (1) by adopting a Mosaic enhancement method, and establishing a data set; 4) optimizing a Yov7 model, and introducing a lightweight CBAM attention mechanism to carry out model training; (5) the trained weight model is experimented on the verification set, and target detection of the ship is achieved; and 6) adopting a kernel correlation filtering algorithm to realize real-time tracking of the moving ship. According to the method, the technology in the field of computer vision is utilized, the real-time video image number of the large-flow channel is effectively utilized, multi-target recognition and real-time tracking are achieved, and the method has high accuracy and robustness.