162 results about "Cosine Distance" patented technology

The invention relates to the technical field of big language models, and discloses a big language model training method based on knowledge graph enhancement, comprising the following steps: S1, constructing a multi-source heterogeneous knowledge graph; s2, coding the mixed attention heterogeneity map; s3, bidirectionally mapping a pre-training task; and S4, position specific gating fusion. According to the big language model training method and system based on knowledge graph enhancement, a same proton graph is established for a structured triple and text entity description, nodes are connected across graph edges to form a heterogeneous graph, associated edges are established through entity linking and syntactic analysis, multi-source knowledge is modeled in a unified mode, and the problem of low fusion efficiency is solved; mixed attention coding adopts a layering mechanism, a semantic level calculates weights according to type compatibility, a node level calculates similarity aggregation features through cosine distance and path length, entity vectors are generated through pooling, map structures and semantics are explicitly learned, reasoning accuracy is improved, and the problem of knowledge understanding superficial layer is solved.

The invention discloses an individual electroencephalogram conversion system, method and device fusing vision and electroencephalogram, and particularly relates to the technical field of brain-computer interfaces, and the method comprises the steps: obtaining and preprocessing electroencephalogram data of a subject and a corresponding stimulation image; respectively encoding the electroencephalogram data and the stimulation image of the subject by using an image encoding module and an electroencephalogram superficial layer feature extraction module; image encoder parameters are fixed, and electroencephalogram superficial layer feature extraction module parameters are updated through a multi-modal fusion strategy and self-adaptive contrast learning to obtain a pre-trained electroencephalogram superficial layer feature extraction module; electroencephalogram data between the subject pairs are obtained and preprocessed; initializing electroencephalogram converter model parameters, and loading pre-trained electroencephalogram superficial layer feature extraction module parameters; inputting source subject electroencephalogram data into an electroencephalogram converter to generate target subject electroencephalogram data, calculating time domain cosine distance loss, time domain mean square error loss and frequency spectrum constraint loss with real data, and updating electroencephalogram converter parameters to obtain a final electroencephalogram converter model.

The invention discloses an electric power equipment safety long text knowledge retrieval method based on sparse and dense joint enhancement, which comprises the following steps of: encoding an electric power equipment safety long text and retrieval query by utilizing a pre-training language model for performing supervision and fine tuning on an electric power equipment safety field label data set, and generating a semantic word vector; a weighted sparse vector fusing semantic information and a dense semantic vector enhanced through nonlinear transformation are generated through fine-grained semantic enhancement branches; respectively calculating sparse similarity based on a common item weight product and dense similarity adopting a double-distance weighted fusion strategy combining a cosine distance and an Euclidean distance; and carrying out weighted summation on the two similarity scores through the learnable weight to obtain a final similarity, and realizing accurate sorting retrieval of the long text knowledge. The method gets rid of dependence on global features of sentence vectors, can accurately capture local key information in a long text, and has high retrieval precision and semantic robustness.

The invention provides a machine equipment abnormal sound detection method, a medium, equipment and a product, and relates to the technical field of abnormal sound detection, and the method comprises the steps: carrying out the data enhancement processing of an original audio during the operation of machine equipment, and dividing the original audio into a source domain sample and a target domain sample; the waveform features of the enhanced audio are converted into FFT spectrogram features, amplitude spectrogram features and logarithmic Mel spectrogram features; constructing three networks, extracting three embedded features, and then connecting; based on the splicing features of the source domain samples, obtaining a plurality of clustering centers of each category of samples in the source domain through clustering, and taking the minimum value of cosine distances between the source domain samples and all the clustering centers as the similarity score of the source domain; and based on the splicing features of the target domain samples, taking the minimum value of the cosine distance between the target domain samples and the sample mean value as the similarity score of the target domain, and taking the smaller value of the two similarity scores as an abnormal score. The method can effectively cope with the situation of domain generalization.

The invention discloses a patient report outcome management method and system, and relates to the technical field of medical information. The method comprises the following steps: firstly, dividing role permissions, and defining an information viewing and operating range; key operation behaviors are collected and coded according to a unified format to generate a role behavior chain; extracting a content degradation factor from the behavior chain, determining a weight through a random forest algorithm, and carrying out weighted calculation on a content degradation index; a term vector is generated by using a biomedical model, and a medical compliance index is calculated through a cosine distance; content optimization indexes are calculated by fusing the two indexes, and fields needing to be optimized are marked by comparing threshold values; when the field content is the content optimization item, optimizing the original field content; and comparing data of the experimental group and the control group through A / B test, calculating to obtain an optimization effect index, and automatically replacing the optimization effect index with optimization content if the optimization effect index reaches the standard to form a final patient report. According to the optimization effect index, whether the optimized field content is replaced with the original field content or not is determined, and a final patient report is obtained.

A method for calculating domain relevance scores for responses generated by LLMs is disclosed. The method includes receiving a response generated by LLM corresponding to user query. The user query is associated with a domain. The method further includes splitting the response into a plurality of response chunks using a splitting technique. The method further includes generating a plurality of response vector embeddings based on the plurality of response chunks using at least one sentence transformer. The method further includes computing a plurality of cosine distances between the plurality of response vector embeddings and a corresponding plurality of training data vector embeddings, wherein the plurality of training data vector embeddings corresponds to domain-specific training data of the LLM. The method further includes calculating a domain relevance score corresponding to the response, based on a sum of the plurality of cosine distances and a number of the plurality of chunks.

The invention relates to the technical field of remote sensing earth observation, in particular to a satellite video multi-target tracking method and system based on space-time state propagation and progressive trajectory association, and the method comprises the steps: obtaining a target detection result and a state transition result of adjacent frames in a video sequence frame image through a target tracking model; the target detection result comprises a target detection frame position representation and a target category representation, the state transition result comprises a coordinate transition result and a foreground probability, calculating a spatial position matching degree by using an intersection-to-union ratio, and calculating an adjacent frame target appearance feature similarity by using a cosine distance; and combining the spatial position matching degree and the appearance feature similarity to calculate a target space-time matching affinity, associating each target tracking frame in each video sequence frame based on the target space-time matching affinity, and adding the tracking frames to the corresponding target trajectory fragments to generate each target tracking trajectory. The core intelligence information can be accurately and efficiently extracted from the satellite video data, and the utilization efficiency of the satellite video data is improved.

The invention provides an unmanned aerial vehicle tea tree disease detection method based on morphological perception, and belongs to the technical field of agricultural information and computer vision. The method comprises the steps of firstly collecting tea garden images and constructing a data set; then, a target detection network embedded with a differentiable morphological sensor module is constructed, and the differentiable morphological sensor module extracts multi-scale shape features by using differentiable morphological operation; an adversarial learning mechanism is introduced in training, and the distinguishing ability of the model on disease and health areas is enhanced through a discriminator; after the training is completed, mining a difficult case sample based on the cosine distance between the morphological characteristics and the disease prototype vector, and carrying out supplementary training; and finally, integrating a plurality of models with optimal performance, and generating a final detection result through weighted reasoning. The method effectively strengthens the perception capability of the model for the subtle morphological characteristics of the diseases, solves the problems of low disease detection precision and insufficient difficult sample learning in a complex tea garden background, and remarkably improves the detection accuracy and robustness.

The invention discloses a high-voltage circuit breaker state monitoring and risk prediction method, and the method comprises the steps: carrying out the dynamic calibration of a health degree model based on an electromechanical-thermoelectric-material coupling differential equation set and unscented Kalman filtering through synchronous collection of mechanical, electrical, environmental and insulation characteristic quantities; and adaptive dynamic threshold optimization is realized by combining kernel density clustering and a deep Q network, identity verification is completed by using operation fingerprint cosine distance comparison, and finally a precise risk early warning instruction is generated. According to the method, the problems of poor multi-parameter synchronism, threshold staticization, identity verification deficiency and the like in traditional monitoring are solved, the fault recognition accuracy and the early warning coverage rate are remarkably improved, and the operation and maintenance cost and the power grid power failure risk are reduced.

The invention discloses a daily load curve clustering method based on a mixed distance improved FCM algorithm, and belongs to the technical field of data processing, and the method comprises the steps: collecting daily load data of a user at a sampling frequency of 5 minutes, carrying out the corresponding data preprocessing, analyzing the fluctuation characteristics of a daily load curve, and carrying out the clustering of the daily load curve; euclidean distance representing load curve power amplitude information and cosine distance representing load curve power fluctuation direction information are constructed respectively, and then an entropy weight method is adopted to construct a hybrid similarity distance (HSD) fusing the Euclidean distance and the cosine distance as a similarity criterion between daily power load curves; replacing the Euclidean distance in the FCM algorithm with the constructed HSD, then calculating a corresponding membership degree and a clustering center, iteratively updating the membership degree and the clustering center by using a target function based on an intra-class error weighted quadratic sum, and completing daily load curve clustering according to a preset maximum iteration number; and finally, determining the optimal clustering number and the corresponding optimal daily power load curve clustering result by using the sum of intra-class errors (SAE), thereby effectively solving the prominent problems of poor effect, low efficiency and the like of the traditional power load curve clustering method.

A network response retrieval enhancement generation method fused with a large language model, an electronic device and a computer readable storage medium belong to the field of text data processing, are used for automatically generating network responses, and are technically characterized in that an inquiry appeal text and a prompt are input into the large language model, and inquiry appeals and types are output by the large language model; inputting similar cases and prompts of the appeal text into the large language model, and outputting a response normal form to the political appeal of the type in the similar cases by the large language model; a prompt is input into the large language model, the response normal form is generalized through the large language model, and a first framework suitable for appeal response is generated; the method for obtaining the similar cases of the appeal text comprises the following steps: calculating an appeal vector after keyword embedding is carried out on the appeal text; determining a target sub-case library according to a cosine distance between the appeal vector and each sub-case library center based on appeal content clustering; and determining similar cases according to cosine distances between the appeal vectors and the appeal vectors of the cases in the target sub-case library.

The invention discloses a spatial data management method and system based on artificial intelligence, and relates to the technical field of spatial data management, and the method comprises the steps: defining the number of wavelet decomposition layers, calculating a self-adaptive threshold value, screening edges of an incidence matrix, generating a sparse incidence matrix, constructing a projection matrix, and projecting a feature matrix to a feature vector space through employing a matrix multiplication method. Generating a comprehensive feature matrix; and using a logistic function to define a nonlinear stream function, forming state vectors, calculating a mean value of the state vectors, fusing the mean value with the node feature matrix to form a dynamic feature matrix, and constructing a graph convolutional network model to predict the fault probability of the nodes. According to the method, fine-grained dynamic features of spatial data are accurately mined through combination of multi-scale wavelet decomposition and adaptive threshold screening, feature energy screening and a cosine distance matrix are introduced, the discrimination ability of feature selection and dimension reduction is improved, and the spatial data are extracted by using the graph convolutional network and combining a sparse embedding mechanism. And the generalization ability of the model in node fault prediction is obviously enhanced.

The invention discloses a referring multi-target tracking method based on multi-modal correlation graph alignment and a multi-level cross fusion model, and relates to a computer vision technology. The method comprises the following steps: sampling and enhancing training data containing a video frame sequence and referred sentences, and extracting visual and text features; performing early fusion through a cross attention module, processing through an encoder, and initializing query by using a semantic injection mechanism; in the decoder, the self-attention and semantic clearness intensifier adjusts feature interaction, and multi-level fusion is carried out on output of each layer; a correlation graph is constructed by adopting cosine distance, triple loss learning is carried out after samples are screened, and a fine-grained corresponding relation between region-level vision and word features is established; and finally, performing branch prediction and optimization. A precise relation is captured through a multi-modal correlation graph alignment model, semantic understanding is enhanced by using a multi-stage cross fusion model, and the adaptive learning ability of the model is improved. Experiments show that multiple indexes are remarkably improved, positioning and tracking are more accurate, and robustness is higher.

The invention relates to a gearbox fault diagnosis model construction method based on metric guide graph comparative learning, and belongs to the field of gearbox fault diagnosis model construction. The method comprises the following four core stages: firstly, carrying out frequency domain conversion and normalization on vibration signals of the gearbox to generate a node characteristic matrix; secondly, a cosine distance and an Euclidean distance are fused to construct a mixed distance matrix, and a fault diagnosis graph is generated based on a K-nearest neighbor algorithm; then unsupervised graph comparison pre-training is realized through graph data enhancement and a dynamic graph attention network (DGAT); and finally, weak supervision fine tuning is carried out by using a small number of marked samples to complete construction of the gearbox fault diagnosis model. According to the method, the construction of the high-precision gearbox fault diagnosis model can be realized in a scene with extremely few marked samples (1-10 samples per class), and the method is suitable for planetary gearbox health monitoring in the fields of wind turbines, helicopters, hybrid electric vehicles and the like.

The invention discloses an intelligent substation virtual terminal automatic connection method and system based on an SCHO-GO algorithm, and the method comprises the steps: obtaining a comprehensive distance according to an editing distance and a cosine distance represented by a virtual connection vector, and constructing a virtual terminal matching model according to the comprehensive distance; optimizing a distance weight vector in the virtual terminal matching model according to a preset SCHO-GO algorithm to obtain an optimal target distance weight vector, and updating the virtual terminal matching model according to the target distance weight vector to obtain a target virtual terminal matching model; and inputting IED data to be matched into the target virtual terminal matching model, outputting by the target virtual terminal matching model to obtain a comprehensive similarity between the input virtual terminal and the output virtual terminal, and performing matching connection on the input virtual terminal and the output virtual terminal by adopting a preset matching rule according to the comprehensive similarity. The efficiency and the accuracy of automatic connection of the virtual terminal are remarkably improved, and the method is suitable for secondary design of the intelligent substation in a complex equipment naming scene.

The invention relates to the technical field of construction management, in particular to an urban and rural construction data analysis optimization method and system, and the method comprises the following steps: obtaining remote sensing images, analyzing the change characteristics of land parcels, calculating the difference sorting of block land parcels, constructing a functional path chain, analyzing the coupling behaviors of the land parcels, and recognizing abrupt change and coupling double-height regions. And generating an urban and rural construction data analysis result. According to the method, the green vegetation, the building density and the road linear feature of the land parcel are quantified through the remote sensing image data, the frequency and the duration of the mutation feature are recorded, the mutation vector sequence is formed, the sensitivity capture of the dynamic change of the land parcel can be realized in the initial stage, and the excessive dependence on the original data is avoided. And in combination with vector cosine distance comparison between plots in the block, a difference intensity sequence is established, accurate sequencing of spatial disturbance conditions in the region is realized, and the scientificity of plot screening is enhanced.

The application provides a ViT luggage re-identification method based on local data enhancement, aiming to improve the identification accuracy in automatic luggage sorting. Traditional RFID technology is high in cost and susceptible to interference, so the introduction of computer vision technology has become a new trend. The method comprises the following steps: first, a dataset containing 1500 different identity luggage is constructed, each luggage has 10 photos of different angles, and LabelMe and YOLOv5 are used for labeling and detection; second, multiple cameras are arranged on the luggage check-in and sorting pipeline to collect luggage images; then, based on the ViT model, the local data enhancement technology and the position excitation module are applied to improve the attention of the model to the structural features of the luggage and the generalization ability; then, the luggage features are calculated through the multi-layer visual conversion module, and the cosine distance is used for identity comparison; finally, the identity loss, the triplet loss and the contrast loss are used to optimize the model to improve the identification accuracy.

The disclosed technology relates to methods, accent conversion systems, and non-transitory computer readable media for real-time accent conversion. In some examples, a set of phonetic embedding vectors is obtained for phonetic content representing a source accent and obtained from input audio data. A trained machine learning model is applied to the set of phonetic embedding vectors to generate a set of transformed phonetic embedding vectors corresponding to phonetic characteristics of speech data in a target accent. An alignment is determined by maximizing a cosine distance between the set of phonetic embedding vectors and the set of transformed phonetic embedding vectors. The speech data is then aligned to the phonetic content based on the determined alignment to generate output audio data representing the target accent. The disclosed technology transforms phonetic characteristics of a source accent to match the target accent more closely for efficient and seamless accent conversion in real-time applications.

The invention provides a self-supervised sound anomaly recognition method and device based on multivariate feature enhancement, and the method comprises the steps: carrying out the preprocessing of original data, carrying out the coding of the attribute information of machine equipment through employing a Mixup technology, generating a classification tag, constructing an auxiliary classification task, and carrying out the recognition of the classification tag; the method comprises the following steps: respectively extracting a logarithmic Mel spectrogram, a speech spectrogram and a spectrogram of audio data as the input of a multivariate feature extraction network, aggregating the output of the multivariate feature extraction network to serve as the feature of a single audio sample, and enhancing the discrimination and robustness of the feature by using a multivariate feature enhancement method based on cross fusion, so as to improve the robustness of the audio sample. Finally, the features of the feature space are clustered, and a clustering center is generated; in the test stage, to-be-tested data is input into the feature extraction network to extract features, and an exception score is calculated by comparing cosine distances between the features, so that exception is judged; therefore, the sound anomaly detection effect is improved.

The application discloses an intelligent substation virtual terminal automatic connection method and system based on an SCHO-GO algorithm, and the method comprises the following steps: obtaining a comprehensive distance according to an edit distance and a cosine distance of a virtual connection vector representation, and constructing a virtual terminal matching model according to the comprehensive distance; optimizing a distance weight vector in the virtual terminal matching model according to a preset SCHO-GO algorithm, obtaining an optimal target distance weight vector, updating the virtual terminal matching model according to the target distance weight vector, and obtaining a target virtual terminal matching model; inputting IED data to be matched into the target virtual terminal matching model, obtaining a comprehensive similarity of an input virtual terminal and an output virtual terminal by the target virtual terminal matching model, and matching and connecting the input virtual terminal and the output virtual terminal according to the comprehensive similarity and a preset matching rule. The efficiency and accuracy of the virtual terminal automatic connection are significantly improved, and the method is suitable for intelligent substation secondary design in a complex equipment naming scene.

The invention provides a rare vitreoretinopathy severity assessment system based on a large model. The system comprises a visual recalibration module, a semantic position sensing module, a visual-semantic fusion module and a cosine contrast regularization cluster. Inputting the image data into a visual recalibration module to extract visual features, generating a segmentation mask and a feature map, extracting semantic features by a semantic position sensing module, and generating a saliency map; the vision-semantic fusion module fuses the feature map and the saliency map to generate a feature vector; and calculating a cosine distance by cosine contrast regularization clustering, and evaluating the severity by clustering. According to the method, diagnosis can be carried out without training data and training prompts, and the dependence of a traditional method on a large amount of labeled data is broken through; through cooperation of multiple modules, fine-grained features in the image can be captured, the accuracy of severity evaluation is remarkably improved, the interpretability and credibility of clinical application of the system are enhanced, and the system can also be expanded and applied to other complex medical image analysis fields.

The invention belongs to the technical field of artificial intelligence and computer vision, relates to an environmental sanitation video garbage detection method based on uncertainty guide hierarchical retrieval, and aims to solve the problem that an existing environmental sanitation video garbage detection method is high in labeling cost and weak in generalization ability or lacks historical context support, so that complex scenes are missed and missed. The method comprises the following steps: firstly, acquiring an environmental sanitation video frame sequence and dividing the sequence into fragments, extracting visual and garbage category language features through a pre-training encoder, and calculating a negative cosine distance to obtain category probability distribution; secondly, estimating the uncertainty of frames and fragments by using normalized Renyi entropy, retrieving high-confidence reference fragments from short-time, scene and global levels of a clean and junk double-memory library when the uncertainty is high, and directly inputting the high-confidence reference fragments into a pre-trained VLM model when the uncertainty is low; and finally, obtaining a final result through evidence perception time sequence attention fusion, and dynamically updating the memory bank. According to the scheme, additional training is not needed, the detection robustness and accuracy in complex scenes such as low illumination and shielding are effectively improved, and reliable technical support is provided for intelligent environmental sanitation operation.

The invention discloses a wind power industry task matching method and system which are applied to the technical field of intelligent employment, and the method comprises the steps: carrying out the splitting and marking of a task exclusive demand, calculating the multi-dimensional similarity based on a deployed task matching model, combining a cosine distance, a Manhattan distance and a Jaccard distance, and dynamically adjusting the weight, and outputting the optimal technical talents corresponding to each subtask, matching the confidence, replacing talent sorting and subtask parallel execution suggestions, and realizing accurate matching of task requirements and the technical talents. Compared with the prior art, specific wind power scene attributes are given to sub-task splitting for the problem of matching of intelligent employment and task talents of a wind power enterprise, a method for accurately matching the sub-tasks and technical talents is established, intelligent employment is modeled as the problem of matching of a demand pool and a talent pool, simple system design is achieved, and the intelligent employment and task talent matching efficiency is improved. System expansion and function updating are facilitated, and flexible application of the intelligent employment method in wind power enterprises is greatly promoted.

The application discloses a kind of based on composite distance's weighted rough membership clustering RGB-D image segmentation method.For extracting the image feature of RGB-D image, the image feature includes color feature, point cloud feature and normal feature;Initial class center is selected based on density method;Based on adaptive weighting and composite distance measurement method, clustering algorithm is optimized, and weighted rough membership clustering algorithm is obtained;Adaptive weighting includes by local feature weighting method to each feature dimension adaptive weighting;Composite distance measurement method includes by the method of fusing Euclidean distance and cosine distance to calculate the similarity between sample and clustering center;Based on weighted rough membership clustering algorithm, RGB-D image is clustered and region merging is carried out, and segmentation image is generated.The application can better adapt to the characteristics of different types of features by introducing feature weighting and improving similarity measurement method, and ultimately realize higher quality segmentation effect.

The invention belongs to a pattern recognition technology, and particularly relates to a WiFi open set gesture recognition method based on symbiotic metric learning and uncertainty perception, which comprises the following steps: constructing a feature memory library according to original feature vectors of known gesture image samples, acquiring K neighbor view sample sets closest to the cosine distance of the original feature vector of the to-be-identified image from the feature memory library, determining candidate prediction tags of a to-be-detected sample by adopting a majority voting mechanism, and calculating a neighbor distance score value of the to-be-detected sample; and if the neighbor distance score value is greater than a set threshold value, taking the candidate tag as the tag of the to-be-detected sample, otherwise, judging that the to-be-detected sample is an unknown gesture. Aiming at the problems that Wi-Fi signal environment noise interference is strong and feature distribution is loose in an open set scene, the generalization ability of the model in a cross-domain scene is effectively improved through a double-view consistency constraint and symbiotic fusion mechanism, and the rejection rate of unknown gestures and the recognition precision of known gestures are remarkably improved.

This application provides a method, apparatus, and system for detecting printing defects. The method includes: establishing an autoencoder model, which includes an encoder and a decoder; iteratively training the autoencoder model using multiple character images until the loss function value of the current iteration is less than the loss function value of the previous iteration, and determining the encoder of the current iteration as a first-order optimized encoder; performing comparative learning on the first-order optimized encoder to obtain a second-order optimized encoder, such that the feature vector output by the second-order optimized encoder contains information about printing defects in the character images; inputting the image to be tested and a template image into the second-order optimized encoder respectively to obtain a first feature vector and a second feature vector; determining that the image to be tested has printing defects if the cosine distance between the first feature vector and the second feature vector is less than a second predetermined threshold, thus solving the problem of time-consuming and labor-intensive printing defect detection methods in the prior art.

The application discloses a speaker verification method based on a SASFV aggregation model, and relates to the field of speech recognition.The method extracts a log Mel spectrogram through short-time Fourier transform and Mel filtering, generates frame-level features by using an ERes2Net network, introduces a SASFV aggregation model to generate fixed-length speaker-level features in combination with a Fisher Vector variable, a self-attention mechanism and a statistical method, and finally determines the identity of a speaker by using a cosine distance.The application solves the problem that the prior art cannot effectively represent and aggregate features in a short speech task, and significantly improves the accuracy, robustness and performance of a speaker recognition system.

The application provides an event causal relationship mining method and system based on atlas constraint and time sequence optimal transmission, which comprises the following steps: cutting a burst event description text into text blocks; performing atomic event extraction on each text block, and formalizing each atomic event into a binary feature group; mapping any two atomic events and into event nodes and in a preset knowledge atlas, calculating the shortest path topological distance of the event nodes and in the atlas, and constructing a teacher model probability distribution according to the shortest path topological distance; calculating the cosine distance between the vectors and as a basic semantic transmission cost, and performing time sequence constraint on the basic semantic transmission cost to construct a time sequence transmission cost matrix, searching for an optimal transmission matrix that minimizes the total transmission cost; and obtaining an event causal relationship that meets the time sequence optimal transmission under the constraint of a field knowledge atlas by minimizing a joint loss function of the teacher probability distribution and the optimal transmission matrix.

The present application belongs to the technical field of tobacco quality evaluation, and discloses a tobacco quality similarity evaluation method and system based on near-infrared spectroscopy, which comprises the following steps: scanning tobacco samples by using a near-infrared spectrometer to obtain spectral data, reducing the dimension of the spectral data by using principal component analysis to obtain the first D principal components and their scores whose cumulative variance contribution rate is greater than 0.99, calculating the cosine distance and Euclidean distance between samples based on the principal component scores corresponding to the target sample set, converting the two-dimensional vector composed of the above two parameters into a one-dimensional scalar by introducing a kernel function and L2 regularization, and finally obtaining the similarity value, so as to realize the digital evaluation of the quality similarity between target samples. The present application provides a new technical idea for tobacco raw material substitution, leaf group formula auxiliary design, and cigarette product quality stability evaluation.

The invention relates to the field of artificial intelligence and edge computing, and particularly discloses a real-time target detection tracking and prediction system based on edge equipment, which comprises an input acquisition module, a reasoning processing module, a tracking prediction module, an asynchronous pipeline scheduling module, a video rendering and plug flow module and a data interface service module. According to the method, FP16 semi-precision optimization and layer fusion are carried out on a YOLOv5 detection model and a ReID feature network through a TensorRT framework, so that the calculation delay is remarkably reduced; meanwhile, a dynamic frame skipping strategy and a multi-thread asynchronous assembly line are designed, load-aware resource scheduling is achieved, and high-frame-rate stable processing on edge equipment is guaranteed. In the aspect of tracking prediction, a DeepSort algorithm is improved by the system, cosine distance matching of IoU and appearance features is fused, track prediction and index moving average smoothing are performed by using a Kalman filter, and ID consistency and track prediction stability in a complex scene are effectively improved.