413 results about "Feature extraction algorithm" patented technology

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention discloses a VLM model intelligent decision-making-based driving method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: extracting key visual features from continuous multi-frame driving scene images based on a preset visual feature extraction algorithm; inputting a user instruction and a time sequence visual Token corresponding to the key visual features into a preset VLM model for multi-modal alignment, and generating a target planning Token; inputting the target planning Token and the time sequence vision Token into a preset trajectory generation model to obtain a predicted trajectory; and converting the predicted trajectory into a control signal, and controlling the mobile device to complete a moving action based on the control signal. The problem of modal difference between a semantic space and an action space is solved.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

The invention relates to the field of breeding industry, and discloses a pig behavior-based pig health condition analysis method and system, and the method comprises the steps: carrying out the comprehensive monitoring of pig behaviors, capturing the gait, feeding mode, excretion behavior and activity range of a pig in real time based on a behavior feature extraction algorithm, and obtaining a behavior state video stream sequence; multi-dimensional time sequence correlation analysis is carried out on the behavior state video stream sequence, and historical behavior data, pig weight changes and physiological parameters are combined; based on the dynamic time sequence feature vector, dynamically identifying a change track of pig behaviors by applying a self-adaptive behavior identification algorithm; performing correlation analysis on the detected abnormal behavior pattern and the potential health risk of the pig, fusing the environmental factors, group behavior data and health history of the pig, and identifying a potential health problem; and based on a risk early warning result, automatically adjusting environmental parameters and feeding management strategies, and providing intervention measure suggestions. The pig health management system has the advantage of improving the efficiency and accuracy of pig health management.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention provides an L1-L4 lumbar vertebra segment-oriented multi-feature fusion diagnosis system and method, and the system comprises an image preprocessing module which is used for receiving a lumbar vertebra CT image sequence of a patient; a centrum anatomy partition module; the multi-dimensional image feature extraction module is used for extracting four types of quantitative features from each sub-region; the clinical multi-modal data coding module is used for independently acquiring and processing three types of clinical data: a multi-modal graph attention fusion network; and the segment-level diagnosis output module outputs diagnosis results of three levels. Through a parallel processing architecture and an optimized feature extraction algorithm, the whole diagnosis process only needs 45 seconds from data input to report generation, time is saved compared with manual film reading, and the consistency of diagnosis results is remarkably improved.

The invention provides an engineering test method and system for a storage chip and a medium, and belongs to the technical field of semiconductor packaging. The method comprises the following steps: acquiring read-write operation data, temperature data and voltage data of a storage chip in real time through a distributed sensor network, performing dimension reduction and segmentation processing on test data by using a multi-dimensional feature extraction algorithm, extracting statistical features and correlation features, inputting a fault prediction model constructed based on a deep learning algorithm, and performing fault prediction. Fault type and probability prediction is realized; and judging whether the chip has a fault by combining a preset threshold value, if the chip has the fault, accurately positioning a fault area by adopting a dynamic probe test technology, determining a fault time point and a trigger condition through time sequence analysis, and finally generating a fault diagnosis report and providing a repair suggestion. According to the invention, the accuracy and efficiency of fault detection of the storage chip can be effectively improved.

Provided are a method for training a transformer fault detection model, a fault diagnosis method, and a related device. The method includes: obtaining an initial voiceprint signal of a transformer and a fault type corresponding to the initial voiceprint signal; preprocessing the initial voiceprint signal to obtain an input signal, and establishing an input signal dataset; performing feature extraction on a first input signal in the training dataset based on a preset feature extraction algorithm to obtain a first voiceprint feature; training an initial detection model based on the first voiceprint feature and a first fault type corresponding to the first input signal to obtain a first training result; determining a loss function based on the first training result and the first fault type; and iteratively adjusting a weight value of the initial detection model until the loss function converges to obtain a fault detection model.

The invention discloses a gastrodia elata quality detection method based on a computer vision technology, relates to the technical field of intelligent detection, and is used for solving the problems of low detection efficiency and poor recognition accuracy in traditional gastrodia elata detection. According to the invention, synchronous acquisition and preprocessing of multi-source data are carried out through an industrial camera, an infrared imager, a 3D scanning device and a spectrometer, a multi-scale feature extraction algorithm is utilized to generate quality feature vectors containing forms, textures, colors and defects, and a cross-modal three-dimensional characteristic spectrum is constructed. The deep learning model is combined to realize automatic quality evaluation and grading; when the reject ratio exceeds a threshold value, main influence factors of unqualified products are analyzed, and an interpretable quality evaluation report is generated; the precise detection of the quality of the gastrodia elata is realized; and the detection efficiency and the identification accuracy are effectively improved.

The invention discloses a golf motion simulation method and system based on digital twinning, and the method comprises the steps: obtaining an original data set containing a timestamp and a space coordinate from the cooperative motion of a plurality of parts and equipment of an athlete body through a sensor network technology in combination with a high-speed image capture device, and obtaining a preliminary multi-source motion information record; aiming at the initial multi-source motion information record, correcting time synchronization deviation and space matching error by adopting a data preprocessing module, and determining a corrected unified data framework by comparing timestamps and coordinate mapping relationships of data sources; according to the corrected unified data framework, a feature extraction algorithm is used for separating and marking the action analysis details and key parameters at the ball hitting moment, and a feature vector set containing the body posture and the equipment state is obtained. According to the invention, a highly vivid virtual environment support is provided for exercise training and match analysis, and the simulation precision and application value of a virtual exercise scene are effectively improved.

The invention discloses a rural historical building protection evaluation system based on big data, and relates to the technical field of building protection. Comprising the following steps: a data processing module normalizes environmental data to generate an environmental degradation factor, extracts a crack depth parameter and a facade weathering parameter from structure detection data through a feature extraction algorithm, and generates a building life attenuation curve through correlation analysis; and the decision module establishes a maintenance optimization model according to the building life attenuation curve and historical maintenance data, generates a risk level through threshold comparison, evaluates expected effects of different maintenance modes on degradation delay, and outputs an optimal maintenance suggestion. According to the method, accurate prediction of the rural historical building degradation process and optimal recommendation of the maintenance strategy are realized, and the scientificity and efficiency of protection work are improved.

The invention discloses an intelligent pressure regulation and control method and system for artery compression, and the method comprises the steps: collecting the arterial blood pressure waveform and vascular elasticity parameters of a puncture part of a patient in real time, and the multi-dimensional pressure data of a pressure sensor; constructing an initial pressure parameter set containing a pressure distribution gradient and a vascular response coefficient through a dynamic feature extraction algorithm; carrying out collaborative analysis on the initial pressure parameter set and blood flow characteristic parameters obtained through blood flow monitoring, and generating a regulation and control matrix containing an optimal compression force range and a time decay function; performing multi-target optimization processing on the regulation and control matrix through a parallel optimization algorithm to generate a real-time pressure control instruction set; based on the deviation value of the real-time pressure execution data fed back by the user interface and the blood flow monitoring data, a grading alarm mechanism is triggered, and a compression strategy is automatically corrected. By utilizing the embodiment of the invention, accurate regulation and control of the artery compression process can be realized, and the hemostatic effect is optimized and the medical quality is improved on the premise of ensuring the safety of a patient.

The invention discloses a traditional Chinese medicine decoction piece finished product quality detection and analysis method and system, and belongs to the field of medicines.The method comprises the steps that S1, a compound component database is built, the database integrates pharmacopoeia standards, clinical composition structures, medicinal material source attributes and pharmacodynamic related parameters, and a standard model is built in a multi-level structuring mode; s2, collecting multi-modal map data of the traditional Chinese medicine decoction piece samples; s3, encoding the collected atlas data by adopting a heterogeneous feature extraction algorithm to generate a fusion structure feature vector; s4, inputting the fusion structure feature vector into a multi-channel evaluation model, and carrying out weight comparison, classification judgment and threshold evaluation; and S5, generating a quality detection report. The method has the beneficial effects that high-precision quality detection combining multi-modal fusion, intelligent discrimination and traceability visualization is realized, and the quality consistency evaluation and risk identification capability of the traditional Chinese medicine decoction pieces is effectively improved.

The invention discloses a motion recognition method and system based on a redox photoelectric memristor, a terminal and a storage medium, and the method comprises the steps: selecting classical motions based on a human body motion data set, extracting time sequence data, and coding the time sequence data into an optical pulse sequence; constructing a reservoir array composed of a plurality of photoelectric memristors, and expanding an optical pulse sequence signal into a high-dimensional state vector; constructing a supervised training model, solving a weight matrix, and constructing a memristive cross array; and outputting an action classification result through simulation domain operation based on the output current multi-path light current signals in combination with the memristor cross array. According to the method, the motion features can be directly fed into a rear-end classification network for action recognition without depending on a complex digital feature extraction algorithm, so that the transmission and processing overhead of redundant data is fundamentally eliminated; a high-efficiency, low-delay and high-robustness hardware solution is provided for real-time and anti-noise motion recognition in scenes such as intelligent monitoring and man-machine interaction.

The invention is suitable for the technical field of visual inspection, and provides an automobile interior injection molding part defect detection method and system based on visual inspection, and the method comprises the steps: collecting an original image set under a preset light source condition, carrying out the defect feature extraction of the original image set through employing a preset defect feature extraction algorithm, and obtaining a defect feature set, performing similarity matching on the defect feature set and a preset defect template by using a preset defect matching algorithm to obtain a target similarity; when the target similarity meets a preset similarity threshold value, determining the defect type of the preset defect template as the defect type of the defect feature set; performing defect feature extraction processing on the internal defect features of the transparent and / or semitransparent injection molded part by using a transparency analysis algorithm to obtain an optimized defect feature tag set; and training the to-be-trained defect detection model based on the original image set and the optimized defect feature tag set to obtain a target defect detection model, so that accurate identification of the injection molding part defect can be realized.

The invention relates to the technical field of video water flow velocity detection, and discloses a river video velocity measurement method and device based on a multi-feature fusion PSA-ResNet network, and a medium. The method comprises the following steps: constructing an STIA data set; extracting low-level textural features of the space-time image by using a CLBP multi-feature extraction algorithm, performing channel-level fusion on a feature map and an original RGB image to form a six-channel CLBP-STIA feature map, and constructing a CLBP-STIA data set according to the six-channel CLBP-STIA feature map; fusing a pyramid segmentation attention module PSA in a residual block of the residual network ResNet to construct an angle classification model, and training the model by using a CLBP-STIA data set; generating enhanced feature representation of the to-be-detected space-time image by referring to the above mode, then inputting the enhanced feature representation to the trained classification model, and outputting a corresponding texture principal direction angle; and acquiring the actual length of the velocity measurement line in the video image, and calculating the actual flow velocity of the surface flow feature on the velocity measurement line in combination with the texture main direction angle. The texture main direction is accurately estimated, so that the speed measurement precision and efficiency are improved.

The invention relates to the technical field of agricultural machinery automation, in particular to a weeding machine automatic row control translation system and method based on machine intelligence, and the method comprises the steps that a multi-mode sensing module obtains environment data and crop information data; the environment map construction module outputs row reference parameters through a crop row feature extraction algorithm, integrates the environment data and the row reference parameters, solves the three-dimensional pose of the weeding machine through a beam adjustment algorithm, and constructs a high-precision map of the working environment; the decision control module obtains a target row translation track based on a near-end strategy optimization algorithm, and outputs a control instruction; the self-adaptive execution module is used for driving the weeding execution mechanism to automatically perform row-to-row translation weeding along crop rows; and the self-learning module is used for establishing a row translation performance evaluation index according to the full-link operation data, and dynamically updating a target row translation track through a continuous learning algorithm. Therefore, the problems of high seedling injury risk, poor terrain adaptability, low operation efficiency and the like in the prior art are solved.

The invention relates to an intelligent oral disease risk prediction and analysis system, which comprises an oral health data acquisition module, a data preprocessing and standardization module, a multi-modal data fusion module, a risk prediction and suggestion module and a medical interaction interface module, the data preprocessing and standardization module is used for preprocessing original data and performing standardization operation, the multi-modal data fusion module is used for multi-modal data fusion, and the risk prediction and suggestion module is used for oral disease risk prediction and generation of personalized medical intervention suggestions. And the medical interaction interface module is used for checking a prediction report by a user, receiving medical suggestions and providing feedback information. According to the intelligent oral disease risk prediction analysis system, a multi-modal oral data feature extraction algorithm based on a convolutional neural network is proposed to perform feature extraction on data; and an oral disease risk prediction algorithm based on machine learning is provided to predict the oral disease risk.

The invention discloses a multi-dimensional fault diagnosis system based on cooperation of a transformer substation auxiliary control device and a cloud platform, and the system comprises the steps: collecting multi-modal sensor data through the transformer substation auxiliary control device, carrying out the unified format conversion and normalization processing, and extracting the feature parameters of each modal through a feature extraction algorithm; the model construction module is used for training the historical multi-modal sensor data marked with the fault type label through a cloud platform by utilizing a deep learning algorithm to obtain an initial fault diagnosis model, and optimizing the initial fault diagnosis model according to the preprocessed multi-modal sensor data; a fault diagnosis model of collaborative optimization of the cloud platform and the substation auxiliary control device is obtained; and the fault diagnosis module analyzes each modal characteristic parameter through a fault diagnosis model cooperatively optimized by the cloud platform and the substation auxiliary control device, and judges and identifies whether the substation equipment has a fault and the type and severity of the fault. According to the invention, the efficiency and accuracy of transformer substation fault diagnosis are improved.

The invention discloses a bridge safety early warning method and system, and the method comprises the steps: collecting bridge structure response and environment parameter data, and carrying out the preprocessing of the data, and obtaining a distributed multi-source sensing information matrix; constructing a distributed neuron cell automaton network, interacting state information through unit local communication, operating a lightweight feature extraction algorithm, and generating a multi-dimensional feature vector; carrying out distributed modeling by utilizing a pre-trained distributed LSTM-Transform hybrid model, and carrying out parallel calculation on a local prediction result by each unit to obtain a global health state evaluation result; a local gradient change anomaly detection mechanism is established, data anomaly is identified by comparing prediction results of adjacent units, and a potential risk area is positioned by combining a related algorithm; and constructing a hierarchical early warning threshold system, activating a corresponding early warning response by means of local decision logic, executing an alarm operation, generating recovery guidance information and monitoring uploaded data. According to the invention, two types of network models are combined, so that the system reliability and early warning accuracy can be remarkably improved.

The invention discloses an aircraft scheduling optimization method, device and system, and a storage medium. The method comprises the following steps: S1, generating training data for simulating real route scheduling; s2, processing the flight plan according to the training data, and generating all feasible flight strings and the cost of each flight string; and S3, according to the generated flight string and the corresponding cost, aircraft scheduling optimization is carried out through a feature extraction algorithm, a TRS-GCN model and a Gurobi solver. By adopting the technical scheme of the invention, the route scheduling efficiency is improved.

An apparatus an method for generating three-dimensional representations of serial sections, wherein the method includes creating a plurality of slide images using a slide scanner, the slide scanner comprising an image sensor and a stage, receiving, using at least a processor, the plurality of slide images from the slide scanner, extracting, using the at least a processor, a plurality of features from the plurality of serial slide section images using a feature extraction algorithm, registering, using the at least a processor, the plurality of serial slide section images as a function of the plurality of feature, generating, using the at least a processor, a three-dimensional (3D) stack view, wherein the 3D stack view comprises the plurality of serial slide section images, and displaying the 3D stack view through a display device.

The invention relates to an intelligent wind power blade defect identification method based on phased array detection, which aims at the anisotropic characteristic of a wind power blade glass fiber reinforced composite material, and can flexibly adjust the sound beam angle, effectively penetrate through the material and inhibit the scattering of an ultrasonic signal through a low-frequency linear array probe with characteristic parameters and an electronic focusing technology. Defects in different directions such as impurities perpendicular to layers, cracks in the fiber direction, wrinkles and the like can be accurately detected, meanwhile, by combining wavelet packet transformation noise reduction and a feature extraction algorithm, the distinguishing capacity of defect echoes and noise in complex materials is remarkably improved, the method is adaptive to complex structures and material characteristics of wind power blades, the application range of the detection technology is expanded, and the detection efficiency is improved. An ultrasonic phased array detection technology and an intelligent algorithm (a K-means clustering algorithm and a YOLOv8 target detection model) are deeply fused, the limitation that traditional detection depends on manual analysis is broken through, and a full-process automatic detection system of data acquisition, feature extraction and intelligent recognition is constructed.

The present invention relates to the field of nondestructive testing technology and provides a composite material damage imaging method based on the spatiotemporal cross-correlation of ultrasonic guided wave wavefields. The method comprises the following steps: S1, acquiring a wavefield signal of the plate being tested; S2, performing narrowband signal extraction on the wavefield signal; S3, filtering the wavefield signal after narrowband signal extraction; S4, shifting the wavefield signal to obtain wavefield signal cross-correlation features; and S5, using a convex hull algorithm to obtain damage imaging results. This method innovatively extracts the difference coefficient between wavefield signals as a basis for damage location, providing a new approach to feature extraction algorithms based on the time-frequency domain. This method enables automated identification of composite material delamination damage and is sensitive to both surface and internal damage.

The invention relates to the field of lightning arrester parameter measurement, in particular to a lightning arrester parameter measurement system and method.The lightning arrester parameter measurement system comprises an induction unit, a detection unit, a data processing unit and a control and positioning unit and is used for generating and focusing laser on the surface of a lightning arrester, collecting an electrical signal and an optical signal of plasma and sending the electrical signal and the optical signal to a processor; the feature quantity is obtained through a feature extraction algorithm, and core parameters of the inversion lightning arrester are calculated through mechanical learning. Plasma is generated through laser focusing, reduction of errors caused by direct electrical connection and contact resistance with a lightning arrester is avoided, the problems of potential safety hazards and interference of traditional contact type measurement are solved, laser focus global scanning, real-time dynamic monitoring and efficient measurement are achieved through the three-dimensional electric translation platform, and the measurement precision is improved. All key areas on the surface of the lightning arrester are covered, compared with single parameter inversion in the prior art, multiple parameters and microscopic parameters are considered, and the prediction precision of key core parameters such as insulation resistance based on the random forest model is further improved.

The invention discloses a method for identifying insect pests of tobacco leaves. The method comprises the following steps: S1, acquiring near infrared spectrum data and visible light image data of the tobacco leaves; s2, pre-processing parameters are dynamically adjusted according to the data quality indexes, smoothing processing and derivative calculation are carried out on the near infrared spectrum data, and spectrum feature vectors are obtained; s3, processing the visible light image data through a feature extraction algorithm to generate an image feature vector; s4, splicing the preprocessed spectral feature vector and the preprocessed image feature vector to form a fused feature vector; s5, a convolutional neural network model is used to process the fusion feature vector, and the convolutional neural network model comprises an attention mechanism so as to identify the insect pest type; and S6, outputting a pest recognition result. The method can solve the problems that in the prior art, the morphological characteristics of the tobacco diseases cannot be comprehensively captured due to data singleness, near infrared spectrum data are easily interfered by environmental factors, and the characteristics are lost after preprocessing, so that the accuracy of identifying the insect pests of the tobacco is improved.

The invention discloses an energy digital management method and system, and the method comprises the steps: receiving a multi-source heterogeneous energy data collection signal, and generating an initial energy characteristic spectrum based on a space-time correlation feature extraction algorithm; performing graph neural network coding on the initial energy feature graph, constructing an energy knowledge graph vector set, and decomposing a high-dimensional vector in the knowledge graph vector set into a plurality of subspace vectors; performing real-time scheduling decision on the subspace vectors by using an energy flow optimization model driven by reinforcement learning to generate an energy distribution strategy set; a digital twinborn verification algorithm is applied to the energy distribution strategy set, physical-digital space consistency verification is carried out, and a trusted energy scheduling scheme is generated; and performing block chain evidence storage and intelligent contract execution on the credible energy scheduling scheme to generate a non-tampering energy transaction record. By utilizing the embodiment of the invention, the scheduling precision and the response speed of the energy system can be improved, and full-link credible management from decision-making to execution is realized.

The invention discloses an AI simulation method and system for facial cosmetic plastic surgery, and the method comprises the steps: for a specific region with high feature complexity, generating a feature subset containing fine feature distribution through high-performance node distribution processing, combination with an adaptive feature extraction algorithm, and dynamic adjustment of extraction frequency and segmentation granularity; on the basis, a three-dimensional reconstruction algorithm is adopted to dynamically adjust the splicing weight, particularly, the splicing precision is improved and a high-precision three-dimensional model is generated for an area with relatively thin tissue thickness, and finally, a vivid effect simulation diagram is obtained through rendering parameter adjustment driven by a user demand and preferentially rendering a height modification area. According to the method, through adaptive parameter adjustment and fine processing, the modeling precision and rendering efficiency of the complex biological characteristic data are remarkably improved, and the method is suitable for medical images, biological recognition and other scenes.

The invention discloses a depression state prediction method and system based on voice multi-scale time domain perception, and the method comprises the steps: collecting voice signals of a participant reading a unified standardized text, and carrying out the preprocessing of the voice signals, and generating a corresponding Mel spectrogram; extracting time context features by using a spectrum-time domain feature extraction algorithm to obtain joint feature representation; carrying out multi-scale division on the combined feature along the time dimension, and fusing the features of each scale into a global feature; and obtaining depression state discrimination results of the participants based on the global features. By combining the spectrum-time domain feature extraction algorithm and the frame-level time attention, the time domain locality features of depression speech such as inter-sentence pause, hesitant pause, inter-word transition and formant blurring can be explicitly analyzed and accurately positioned on the Mel spectrum, so that the recognition accuracy and the result stability are remarkably improved.

The invention provides an ovarian cancer accurate diagnosis and personalized treatment method based on data integration, and the method comprises the steps: employing a feature extraction algorithm to generate a multi-dimensional feature vector set containing a gene expression level, an image texture parameter, a symptom score and a pathological classification according to a structured data set; performing dimension reduction processing on the multi-dimensional feature vector set through a feature fusion module to obtain a low-dimensional feature representation vector; performing grouping and risk prediction on the patient data by adopting a classification algorithm according to the low-dimensional feature representation vector, and generating a patient subgroup classification and disease risk layering result; according to the patient subgroup classification and the disease risk layering result, a scoring model is adopted to generate a diagnosis scoring result and a personalized treatment recommendation scheme, and a diagnosis report is output.