2814results about How to "Efficient extraction" patented technology

The invention discloses a textile product defect detection method and system, and the method comprises the steps: collecting a textile surface image, and carrying out the preprocessing; constructing a feature extraction model, and extracting multi-modal features of the textile fabric surface defects; analyzing the extracted features, and classifying defect types; positioning defect positions, and marking defect areas and types; and outputting a defect detection report, and optimizing model parameters according to a detection result. Through multi-modal feature extraction and a feedback optimization mechanism, the classification accuracy and the positioning precision of textile fabric defect detection are remarkably improved, and particularly, the method has outstanding performance in detection of complex and low-contrast defects. According to the invention, the detection efficiency is greatly improved, false detection and missing detection are reduced, and an efficient and intelligent solution is provided for quality control of the textile industry.

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

The invention relates to the field of geological disaster monitoring and early warning, in particular to a slope catastrophe early warning method and system. The method comprises the following steps: establishing a space-time database, storing and managing preprocessed slope data from multiple monitoring sources, and sorting to obtain a slope catastrophe data set; performing feature extraction on the slope catastrophe data set to obtain a key information set reflecting slope catastrophe features; analyzing the key information set to obtain a slope catastrophe value; calculating an early warning threshold value and a real-time displacement prediction value of the side slope based on the side slope data in the space-time database, and fusing the real-time displacement prediction value and the side slope catastrophe value to obtain a fusion value; and evaluating the real-time stable state of each monitored section of the slope according to the obtained fusion value and an early warning threshold value, and judging whether to send out an early warning signal and an alarm signal or not according to an evaluation result. According to the invention, accurate identification of the slope catastrophe value and the potential risk area is realized, and the accuracy and reliability of the early warning system are improved.

The invention relates to the technical field of electric power short-term load prediction, in particular to an industrial park short-term load prediction method and system based on a Diffusion-Transform-AR model. Acquiring non-stationary historical load data and meteorological data of the industrial park, and performing missing value processing on the data; performing correlation analysis on the data by using the maximum information coefficient, selecting meteorological data with relatively high correlation, and reprocessing time data; performing time sequence decomposition on the load data by using a seasonal trend decomposition method to obtain trend term, seasonal term and residual term features; constructing a diffusion Transform prediction model, and taking the residual term, the meteorological data and the time data as input prediction residual; constructing an autoregression prediction model and taking the trend term as an input prediction trend; according to the method, the advantages of various models are combined, time sequence features are deeply mined, the prediction precision and the model generalization ability are effectively improved, and the method is suitable for complex industrial park load prediction scenes.

The invention provides an igniter robot production line control method and system based on deep reinforcement learning, and relates to the technical field of robot production line control, and the method comprises the steps: collecting the real-time state data of a production line station, extracting features through a multi-scale residual neural network and a spatial pyramid pooling module, and carrying out the feature extraction; channel attention and space attention modules are combined to perform feature enhancement, a cross-modal encoder is used to perform time sequence modeling, task decomposition is performed based on hierarchical reinforcement learning, soft constraint trajectory optimization and adaptive impedance control are used to generate a station control instruction, and a multi-target Pareto evaluation and priority dual experience pool is used to perform sample screening. Network parameters are optimized and updated through a near-end strategy, and a production line optimization decision scheme is output, so that the production line control precision can be improved, the system robustness can be enhanced, and the optimization of the production efficiency is realized.

The invention relates to the technical field of machine vision, and discloses a lead screw module surface defect online detection method and device based on machine vision, and the method comprises the steps: carrying out the image collection and preprocessing of the surface of a lead screw module, and obtaining a preprocessed image; decomposing the structure layer and the texture layer to obtain an initial structure layer and an initial texture layer; generating an initial contour image based on the initial structure layer and a full convolution generator network, and performing edge detection and morphological processing to obtain a target contour image; performing texture optimization based on a mask weighted least square method and a filtering technology to obtain a target texture layer; carrying out feature extraction and fusion through an edge perception multi-stage interaction network to obtain a multi-scale feature map; based on the multi-scale feature map, defect detection and positioning are carried out through the full convolutional network, edge optimization is carried out in combination with the target contour image, a target defect detection result is obtained, and the method improves the accuracy of online detection of the surface defects of the lead screw module.

The invention discloses a remote sensing image semantic segmentation method based on a double-branch multi-scale fusion network, and relates to the technical field of remote sensing image semantic segmentation. According to the method, a double-encoder and single-decoder structure, a multi-scale feature fusion channel and spatial path information are adopted; an adaptive efficient parameter fine tuning SAM2 large model knowledge is utilized to accelerate new task learning, global feature and local feature information of the SAM2 large model knowledge is effectively captured through a global local window multi-head self-attention module, a mixed Logit adjustment loss function based on mutual information is adopted to adjust category weight aiming at the problem of sample category imbalance of a traditional remote sensing data set, and the new task learning is accelerated. The remote sensing image semantic segmentation effect is improved; according to the method, pixel classification and ground feature correspondence are realized, the segmentation problems of complex background, unbalanced sample category and large scale change are effectively solved, the accuracy and stability of image segmentation are improved, and rapid and accurate segmentation of the remote sensing image is realized.

The invention provides an unmanned ship autonomous navigation control method and system based on artificial intelligence, and relates to the technical field of control, and the method comprises the steps: collecting the real-time navigation data of an unmanned vehicle through a multi-source sensor, inputting the navigation environment feature data into a pre-trained reinforcement learning model, and enabling the reinforcement learning model to adopt a dual Q network structure, performing iterative optimization on the navigation strategy based on the environment reward function to generate a navigation strategy optimization parameter; constructing a multi-target route optimization model according to the navigation strategy optimization parameters, performing global optimization on a route by adopting an improved ant colony algorithm, and outputting optimal navigation path data; a hierarchical navigation control model is established according to the optimal navigation path data, the hierarchical navigation control model comprises a strategic layer, a tactical layer and an execution layer, a navigation control instruction is output through the hierarchical navigation control model, and autonomous navigation control of the unmanned vehicle is achieved.

The invention provides a semantic element-based knowledge question and answer accuracy improving method, which comprises the following steps of: extracting information from text data, and constructing semantic nodes; word segmentation processing is performed on a question text input by a user, a word sequence is obtained, and semantic element representation of a question is formed; in the knowledge recall stage, a golden investment algorithm is applied to achieve knowledge retrieval; matching the candidate answers with semantic elements of the user question, and scoring and sorting according to keywords; and according to the query intention of the user question and the sorted candidate answers, constructing cue words of the large model, calling the large model to generate a final answer, and presenting the output answer according to a specified format. By integrating semantic analysis, knowledge recall, natural language processing and other technologies, the intellectualization level and response accuracy of the knowledge question-answering system are remarkably improved, and higher-quality service is provided for users.

The invention proposes a GIS equipment partial discharge mode recognition method based on deep learning, and relates to the technical field of GIS partial discharge recognition, and the method comprises the steps: collecting a partial discharge signal of GIS equipment through an ultrahigh frequency sensor, and carrying out the signal conditioning; performing digital sampling and processing on the conditioned partial discharge signal to generate a phase-resolved pulse sequence (PRPS) map, and performing intelligent noise reduction processing on the PRPS map, the PRPS map including three-dimensional information of phase, amplitude and frequency; inputting the PRPS atlas into a preset neural network model, and extracting atlas features; and identifying and classifying the partial discharge type based on the extracted map features, and outputting an identification result. According to the method, automatic acquisition, intelligent processing and accurate identification of the partial discharge signals are realized, and compared with a traditional manual identification method, the identification efficiency and accuracy are greatly improved, and reliable technical support is provided for state monitoring and fault diagnosis of GIS equipment.

The invention discloses a traffic incident detection system based on multi-modal data, and belongs to the technical field of traffic incident detection. The invention aims to solve the problems of poor traffic event detection capability and incapability of effectively associating context information caused by insufficient video data in a training process of an existing traffic event detection method. Comprising the following steps: acquiring high-dimensional visual features of a sample event image and a sample event video by adopting a visual encoder; obtaining a text feature vector of the cue word description text of the sample event by adopting a text encoder; a feature mapping layer is adopted to obtain high-dimensional visual features aligned with the text feature vectors; common multi-modal features are obtained through a multi-modal joint learning module; obtaining a semantic prediction result and an answer type text sequence of the sample event through a large language module; and obtaining a traffic event classification result through a traffic event classification module. The method is used for on-line detection of traffic events.

The invention belongs to the field of artificial intelligence and financial science and technology, and relates to a document segmentation method, which comprises the following steps: carrying out style feature recognition and title text recognition on a to-be-segmented document to obtain a title style result and a title text result, and then carrying out recognition verification on the title style result and the title text result through a multi-modal title recognition large model to obtain a segmented document. And obtaining a final title recognition result, constructing tree hierarchical representation based on the title recognition result to obtain a document chapter structure, and segmenting the to-be-segmented document according to the document chapter structure. The invention further provides a document segmentation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the to-be-segmented document can be stored in a block chain. According to the method and the device, the document is segmented in a chapter form, the semantic integrity of segmentation is ensured, and key information is effectively extracted and structured.

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

The invention relates to a Mama-based seismic image super-resolution reconstruction method, which comprises the step of constructing a local feature extraction module, a global feature extraction module and a high-quality seismic section reconstruction module so as to solve the problem of insufficient resolution caused by high-frequency attenuation and sparse acquisition. According to the invention, a Mama model is introduced, and a novel super-resolution reconstruction network is constructed. The local feature extraction module extracts shallow feature information through a convolutional layer; the global feature extraction module obtains global features through the multi-stage residual state space block; and the high-quality seismic image reconstruction module realizes reconstruction of a high-resolution seismic image through pixel recombination and convolution operation. In addition, a Charbonnier loss function is adopted, the gradient disappearance problem is effectively avoided, and the loss function of traditional Mamba is improved. According to the method, the seismic image resolution can be improved, and meanwhile, the calculation complexity and the time cost are remarkably reduced.

The invention discloses a variable working condition migration bearing fault diagnosis method based on a ViTCapsNet network in a noise environment, and the method comprises the steps: firstly obtaining vibration signal data of a rolling bearing in different fault states under different loads, and adding Gaussian white noises of different degrees; performing continuous wavelet transform on the data added with the noise to obtain a two-dimensional wavelet image, and dividing the two-dimensional wavelet image into a training set and a test set; a ViT Encoder model is established, position information capable of being trained is added by using an Embedded module, position features are embedded into the model, and global information in the features is extracted through a multi-head self-attention mechanism in the Encoder module; constructing a capsule network which comprises a primary capsule layer and a digital capsule layer, further extracting spatial information in the features, and performing accurate fault diagnosis; and finally, performing visual interpretation on the features processed by the Encoder module by using Grad-CAM, and completing efficient migration fault diagnosis on a test data set.

The invention relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

The invention relates to an unmanned aerial vehicle visual navigation method and device based on deep learning, and the method comprises the steps: obtaining a visual image collected by an unmanned aerial vehicle visual system, carrying out the feature extraction of the visual image, and obtaining image feature representation; performing type analysis on the image feature representation to obtain scene data, depth information and continuous image frames; performing scene identification on the scene data to obtain an identification result of the obstacle and the passable area; performing optical flow estimation on the continuous image frames and the depth information through a preset optical flow algorithm to obtain a pose estimation result; obtaining a task demand of the unmanned aerial vehicle, and obtaining a navigation control strategy with environmental adaptability; and optimizing the self-adaptive mixed learning model according to the feedback information in combination with a preset attention mechanism and a reward shaping condition to obtain an optimized self-adaptive mixed learning model. The adaptive capacity of the unmanned aerial vehicle in a complex and dynamic environment can be improved.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention discloses an electric energy meter verification fault intelligent judgment system, and relates to the technical field of electric energy meter fault judgment, and the system comprises the following components: a high-precision monitoring and collection unit which collects various parameters of an electric energy meter in real time through a high-precision metering chip and a sensor, so as to more comprehensively understand the operation environment of the electric energy meter, and improve the detection accuracy of the electric energy meter; according to the method, the fault mode recognition model is constructed through the logistic regression model, the relation between the features and the fault modes can be accurately mapped, the cross entropy loss function and the optimization algorithm are adopted, the accuracy of model prediction is effectively improved, and the accuracy of model prediction is improved. According to the method, model hyper-parameters are adjusted and optimized through the random search technology, the performance of the model is further improved, the stability and reliability of the model in practical application are ensured through inspection of a verification set and a test set, powerful support is provided for rapid and accurate judgment of electric energy meter faults, and the efficiency and accuracy of electric energy meter verification are improved.

The invention discloses a dynamic and static target continuous positioning and tracking method and system based on millimeter waves. The method comprises the steps that dynamic point cloud data and static point cloud data reflected by a target are acquired through a millimeter wave radar sensor, the dynamic point cloud data reflect target motion state information, and the static point cloud data reflect vital sign information of the target in a static state; fusing the dynamic point cloud data and the static point cloud data to obtain fused point cloud data, and clustering the fused point cloud data by using a density-based clustering algorithm by taking a reflection point for distinguishing a target from an environment as a target to obtain a clustering result; and on the basis of the clustering result, executing continuous positioning and tracking on the plurality of targets to obtain a tracking result. The method can effectively extract the dynamic and static information of the target, achieves the continuous positioning and precise tracking of multiple targets, and is higher in practicality and application value.

The invention discloses a power battery fault detection method based on a bidirectional Mama architecture, and the method comprises the steps: firstly collecting the voltage, current, temperature and internal resistance of a power battery as original data, and carrying out the preprocessing of the original data, and generating a data set; secondly, the bidirectional Mama feature extraction network and the weighted reconstruction network are connected in series to construct a bidirectional Mama network model, bidirectional representation modeling of the time dimension and the feature dimension is carried out on the power battery data in the data set, and reconstruction output is carried out through the weighted reconstruction network. And finally, taking the time sequence data of the power battery under the normal working condition as the input of the bidirectional Mamba network for training. And according to a reconstruction error between the output data and the input data of the weighted reconstruction network, a fault threshold is set, and fault detection is realized. The key information in the time sequence data of the power battery is accurately extracted, potential faults in the power battery are efficiently and comprehensively detected, and the safety of the power battery is improved.

The invention provides a steel surface defect detection method and device, and belongs to the technical field of steel defect detection.The steel surface defect detection method comprises the steps that a defect detection network based on a YOLO architecture is constructed, and the defect detection network comprises a lightweight feature extraction network, a self-adaptive multi-scale feature fusion network and a head network; the lightweight feature extraction network comprises four layers of architectures, and each layer of architecture comprises a first convolutional layer and a space expansion feature module; the feature space expansion module comprises a second convolutional layer and an element multiplication operator module; the adaptive multi-scale feature fusion network comprises an adaptive fusion module and a feature space expansion module; training the constructed defect detection network based on the historical steel defect image, and performing channel pruning on the trained defect detection network by using LAMP (Loop-Mediated Isothermal Amplification); and acquiring a to-be-identified steel image, and inputting the to-be-identified steel image into the pruned defect detection network for defect detection. According to the method, the defect detection precision of the network can be improved while the network complexity is reduced.

The invention relates to a dam deformation prediction method with embedded feature selection, and belongs to the field of dam safety monitoring and artificial intelligence, and the method comprises the following steps: obtaining the original monitoring data of an environment quantity and a dam deformation quantity; performing preprocessing of data cleaning, data filling and data normalization on the original data; evaluating the importance of environmental features based on a random forest algorithm, and selecting an optimal feature set in combination with a recursive feature method; constructing a dam deformation prediction model based on a convolutional neural network, a long and short term memory neural network and an attention mechanism deep learning algorithm; and the optimal feature is used as the input of CNN-LSTM-Attention to predict the deformation of the dam. Through the method in the application, the environmental characteristics having significant influence on dam deformation can be effectively determined, the complex mapping relation between the deformation and the environmental characteristics is fully excavated, and accurate dam deformation prediction is realized.

The invention relates to multi-mode signal processing, and provides a signal processing method of a computer system. The method comprises the following steps: acquiring different modal signal data by using a plurality of sensors and performing timestamp synchronization; a depth time sequence alignment network and a multi-layer dynamic time warping algorithm are utilized to ensure signal time consistency; multi-modal signal features are extracted and fused through a multi-stream depth feature extraction network and a cross-modal attention mechanism; the system adopts a dynamic convolution kernel generation and meta-learning technology to carry out adaptive signal processing; noise suppression and signal optimization are carried out by using a multi-mode noise reduction auto-encoder and a generative adversarial network, and parameters are optimized and processed through closed-loop feedback control and reinforcement learning strategies. According to the method, synchronization, fusion, adaptive processing, noise suppression and real-time feedback control of multi-mode signals are realized, and the method has the advantages of high precision, high robustness and real-time performance.

The invention relates to the technical field of side slope monitoring and early warning, in particular to a mine site side slope remote online real-time monitoring and early warning system which comprises a multi-source data monitoring unit, a data processing and analyzing unit, a side slope instability judging unit, an instability grading early warning unit and an early warning verification pushing unit. The method comprises the following steps: collecting monitoring data of a plurality of data sources to obtain time sequence monitoring data of each data source, carrying out noise reduction on the time sequence monitoring data, then decomposing the time sequence monitoring data, calculating a time-frequency feature vector through an energy density function, and optimizing the time-frequency feature vector through feature screening; calculating a slope stability evaluation index by optimizing the time-frequency feature vector, constructing an instability risk index to judge a slope instability risk level, constructing an early warning index, and combining the early warning index with the slope instability risk level to establish a graded early warning mechanism to obtain a slope instability early warning level; and verifying the slope instability early warning grade, and performing early warning according to the slope instability early warning grade passing the verification. According to the invention, the false alarm rate of slope monitoring and early warning can be reduced.

The invention discloses an infrared and visible light image fusion method and system based on high and low frequency separation enhancement, and relates to the technical field of image processing. The method comprises the following steps: acquiring an infrared image and a visible light image to be fused; performing image fusion on the to-be-fused infrared image and the to-be-fused visible light image by using the image fusion network to obtain a fused image; wherein the image fusion step comprises the steps of extracting shallow layer features of an infrared image and a visible light image, processing the shallow layer features by using a feature separation algorithm to obtain high-frequency features and low-frequency features, performing feature enhancement on the high-frequency features by using a feature enhancement algorithm, and fusing the enhanced high-frequency features and low-frequency features to obtain a fused image. According to the method, the high and low frequency features are subjected to feature separation, the high frequency features are subjected to feature enhancement by using a self-attention mechanism, and then the features are subjected to cross fusion, so that important information such as texture details is highlighted, and the image fusion quality is improved.

The invention belongs to the technical field of medical image processing, and particularly relates to a double-encoder semi-supervised heart MRI double-ventricle segmentation method based on improved SAM. A teacher model and a student model which are used both comprise double encoders, prompt encoders and mask decoders, each double encoder comprises a ViT branch and a CNN-Mama branch which are parallel, parameter fine tuning of the ViT branch is realized through a position adapter and a feature adapter, the CNN-Mama branch mainly comprises a convolution fusion Mama block and a second-order dimension attention block, and the CNN-Mama branch is used for realizing fine tuning of parameters of the CNN-Mama block. The convolution fusion Mama block introduces an enhanced convolution block on the basis of a VSS block, and the second-order dimension attention block uses a covariance matrix to mine second-order statistical information of data; carrying out weighted summation on the output features of the ViT branch and the CNN-Mama branch to obtain the output features of the double encoders; the output features of the double-encoder and the prompt encoder are input into a mask decoder to be decoded, and a segmentation result is obtained; and performing joint training on the teacher model and the student model based on interpolation consistency regularization semi-supervision, and using the trained student model as a segmentation model for double-ventricle segmentation of heart MRI. The segmentation model has strong feature extraction capability, can effectively extract the edge information of the ventricles and the boundary information of the double-ventricle connection region, and improves the segmentation precision.

The invention relates to the technical field of acetylene cracking furnace production monitoring, and particularly discloses a CO laser detection system based on a thermal radiation background, which is used for solving the problem that an existing monitoring system is difficult to accurately distinguish CO and water vapor signals because thermal radiation and water vapor generated by high temperature in an existing ethylene cracking furnace interfere infrared absorption measurement of CO. Comprising a quantum cascade laser, a pyroelectric detector, a data processing unit and a signal analysis module, a reference light intensity adjusting module utilizes temperature, humidity and pressure data obtained by an environment parameter sensing module in real time to adjust the CO absorption coefficient through an environment correction factor, the reference light intensity is dynamically calculated and corrected according to the reference light intensity, and the signal analysis module analyzes the signal. The data processing unit is used for dynamic correction of CO concentration monitoring; a CO signal is effectively extracted under a thermal radiation background through a quantum cascade laser and a light beam modulation technology, environment correction and real-time monitoring are combined, CO concentration abnormity is accurately recognized, early warning is given out, and the monitoring sensitivity and reliability are improved.

The invention provides an overhead transmission line defect detection method based on multi-scale feature fusion, and the method comprises the steps: carrying out the correction and registration of a lead image and a laser point cloud image, carrying out the data enhancement, and constructing a detection data set; the method comprises the following steps: respectively inputting two images into a target detection network for training, adding an attention mechanism in a feature extraction network Backbone so as to carry out correlation calculation on a target, and meanwhile, adopting a multi-scale feature fusion detection method in a backbone part of a YOLOV8 operation network, so that the improvement of precision and accuracy is realized on the premise of ensuring the size of a model, and the accuracy of the model is improved. And the requirement of actual overhead transmission conductor defect detection is met.

The invention belongs to the technical field of allele typing, and discloses a gene haplotype typing method and device based on sequencing data and a medium, and the method comprises the following steps: obtaining to-be-detected gene sequencing data from a to-be-detected sample; splitting the sequencing data into genes according to a reference sequence, and performing variation information detection and variation information filtering; after sequence splitting is carried out on the basis of the sequence obtained after variation information filtering, the haplotype sequence obtained after sequence splitting is corrected, and a to-be-detected gene haplotype typing result of the to-be-detected sample is obtained. According to the method, complex calculation is simplified by adopting a mode of firstly splitting sequences into genes, the method does not completely depend on sequence comparison results, and similar typing is finally evaluated by adopting a mode of combining comparison with backtracking variation information scoring, so that typing difference information can be effectively refined, and interference of sequencing data noise on the results is reduced; and a haplotype result is verified for multiple times by adopting a primary typing and correction mode, so that the result accuracy is higher.