33results about How to "Speed ​​up the extraction process" patented technology

The invention relates to a corn disease intelligent grading evaluation and renovation recommendation method fusing multi-modal information, and relates to a corn diversity disease identification and multi-stage evaluation model based on position coding. The method comprises the following steps: introducing a position attention mechanism, and carrying out multi-layer stacking by taking an inverse movement residual module integrated with position coding as an element movement block; the capabilities of complex image feature extraction and spatial information analysis are enhanced, and a scab region is segmented, so that disease type distinguishing and severity evaluation are realized; constructing a model based on a corn disease entity relationship triple library: performing deep context coding on a corn disease text, realizing classification and prediction of complex relationships among disease entities through a multi-layer structure, and analyzing complex links and influence factors among diseases; and a corn disease treatment method recommendation model combined with an implicit relationship: according to a disease identification and grading result and a disease triple, mining an optimal implicit relationship among different disease entities through complex number embedding and a graph neural network, and giving an accurate disease treatment method recommendation.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.

The invention discloses a real-time face change detection method, system and device based on deep learning and a medium. The method comprises the following steps: acquiring a key frame image sequence in a video stream in real time; processing the key frame image sequence according to a preset deep learning model to obtain a face change probability score of each key frame image; processing the key frame image sequence according to a preset frequency domain analysis algorithm to obtain a frequency domain anomaly score of each key frame image; processing the key frame image sequence according to a preset optical flow analysis algorithm to obtain a motion coherence score of each adjacent key frame image; and according to a preset multi-modal fusion algorithm, processing the face change probability score, the frequency domain anomaly score and the motion coherence score to obtain a face change detection result of the key frame image sequence. According to the method, the efficiency of real-time dynamic face change detection is improved by combining fusion of various processing algorithms.

The invention discloses a small target detection method for early-stage oil leakage of a transformer based on cavity convolution, and belongs to the technical field of computer vision and target detection. A detection model optimizes a backbone network and a head network of an original YOLOv8 model, and adds a feature enhancement module to the backbone network of the original YOLOv8 model; multiple pieces of discriminative semantic information are extracted by using a multi-branch structure, and the calculated amount is not increased and the context information in a larger range can be obtained by introducing hole convolution into branches; a feature fusion module of the head network adopts an improved feature fusion module, and the weights of channel features are adaptively adjusted by introducing learnable weight parameters; a normalized attention mechanism module is added to a head network, and weighted output of a channel is achieved in a residual connection mode. Compared with a traditional transformer early leakage detection means, the method has the advantages that the extraction and expression ability of effective features is enhanced, and high transformer early leakage oil detection accuracy can be obtained.

The invention provides an image processing method and system based on a neural network, a storage medium and electronic equipment, and relates to the technical field of neural networks, and the method comprises the steps: obtaining an initial tensor of a to-be-optimized feature image; in the first path, based on the initial tensor, performing convolution extraction to obtain a first tensor containing a channel dimension attention weight factor; in the second path, after the space dimension is converted into the channel dimension based on the initial tensor, a second tensor containing a space attention weight factor is obtained through convolution extraction; obtaining an attention weight matrix based on the first tensor and the second tensor; and based on the initial tensor and the attention weight matrix, obtaining the tensor of the feature image after attention enhancement. According to the method, the problem of insufficient global information extraction capability of the convolutional neural network is solved, pixel-level channel and spatial dimension attention modeling can be performed on the tensor of the feature image, and the correlation extraction and modeling capability of the convolutional neural network on the image features under a large scale is effectively improved.

The invention belongs to the technical field of bioinformatics, and particularly relates to a single-cell multi-omics data clustering framework based on deep collaborative contrast learning. According to the framework, the association relationship between cells is fully utilized, and meanwhile, the feature interaction of the same cell between different omics data is further enhanced, so that efficient clustering of multiple omics data is realized. In order to make full use of topological information between cells, a graph self-encoder and a feature information enhancement module under different omics are designed, and extraction and information enhancement of cell features are achieved. Meanwhile, a comparative learning technology is introduced to enhance interaction among different omics characteristics of the same cell, and finally single-cell multi-omics clustering is realized through a multi-omics deep collaborative clustering module.

The invention discloses a perovskite solar cell and a preparation method thereof, and a photovoltaic module, the perovskite solar cell comprises a substrate, a hole extraction layer, a perovskite layer, an electron transport layer and a first electrode which are sequentially laminated, the material of the hole extraction layer comprises R-2Ph-xPACz, and the structure of R-2Ph-xPACz is shown as a formula 1: # imgabs0 #, r is any one of CH3CH2O, CH3O, (CH3) 3C or a heterocyclic ring containing lone pair electrons, and x is 2-10. The R-2Ph-xPACz disclosed by the invention is relatively good in hole extraction and transmission effect and excellent in spreading uniformity, and the energy conversion efficiency of the solar cell can be further improved.

The invention relates to an intrusion object detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring to-be-detected image data; performing multi-scale feature extraction on the to-be-detected image data by adopting a mixed attention mechanism through a multi-scale feature extraction layer to obtain a plurality of image data intermediate features of different scales; performing multi-direction expansion convolution processing on each image data intermediate feature through a feature enhancement layer to obtain a plurality of image data enhancement features of different scales; and through a detection layer, feature fusion is carried out on the image data enhancement features to obtain multi-scale fusion features, and intrusion object detection is carried out based on the multi-scale fusion features. By adopting the method, the false detection rate of intrusion detection of small-size intrusion objects can be reduced.

The invention relates to the technical field of big data processing and information analysis, in particular to an information analysis method based on a big data processing framework, which constructs a domain-specific knowledge graph by introducing domain expert knowledge and literature data, and constructs a graph convolutional neural network model based on the knowledge graph. Semantic analysis and relation reasoning are carried out on the unstructured data, an association rule algorithm is utilized, modes and rules among the data are found, a more valuable association analysis result is generated, and the problem that when an existing information analysis technology is used for processing the multi-source heterogeneous unstructured data, the data processing efficiency is high is solved. The problems of difficulty in data fusion, insufficient semantic understanding and insufficient relationship mining depth exist.

The present invention discloses a multi-operation computing device courseware processing method and device, belonging to the technical field of electronic whiteboard computing devices, comprising the following steps: step 1, reading all current canvases at a predetermined time, obtaining each element in the canvas; step 2, identifying and determining the element type of the element, and judging the identified element type; step 3, for each element, based on the priority relationship between the preset element type and the computing device, obtaining the computing device corresponding to the current element; processing the element through the computing device; step 4, writing and saving other attribute contents of the element. The present invention improves the extraction speed and presentation efficiency of elements through dual computing devices, and improves the use efficiency of electronic whiteboard interactive software.

The application relates to the technical field of electronic reconnaissance and signal processing, and discloses a radiation source individual fine identification method and system based on multi-modal feature fusion, which comprises the following steps: performing preprocessing on obtained radiation source original signals to obtain signal pulse units; extracting time domain sequences of the signal pulse units and performing multi-domain transformation to generate a first time-frequency image, a second time-frequency image and a bispectrum image; inputting the first time-frequency image, the second time-frequency image, the bispectrum image and the time domain sequence into a multi-modal fusion neural network; extracting corresponding image depth features and comprehensive sequence features through three image processing branches and a sequence processing branch; performing cross-modal fusion on the three image depth features and the comprehensive sequence features through a fusion module to output a radiation source individual identification result. The application improves the identification accuracy of radiation source individuals in a complex electromagnetic environment.

The present invention provides a continuous gravity detection method, medium and system based on a seafloor crawler, which belongs to the field of geophysical technology, and includes: using a seafloor crawler equipped with an absolute gravimeter and a relative gravimeter to perform grid detection of a test area. First, the relative gravity data of the center point of each grid unit is obtained to establish a relative gravity matrix. Then, the Bayesian probability model and the Markov chain Monte Carlo method are used to determine the abnormal points, boundary points and transition points of the relative gravity data. These feature points are clustered to obtain multiple cluster centers and flying points. Next, the seafloor crawler is controlled to traverse the path of the cluster centers and flying points to obtain absolute gravity data. Finally, in combination with the relative gravity matrix, the Kriging interpolation method is used to calculate the absolute gravity matrix of the entire test area as the gravity detection result. The present invention solves the technical problem that the prior art is difficult to achieve the detection of high-precision seafloor gravity data.

The invention discloses a park weed accurate identification method and system based on an improved YOLOv11 deep convolutional network, and the method comprises the steps: 1, constructing a multi-scene park weed image data set, 2, carrying out the data enhancement and preprocessing, 3, constructing an improved YOLOv11 lightweight network model, 4, designing a self-adaptive loss function, and 5, carrying out the recognition of a multi-scene park weed image data set. Multi-stage model training and optimization are executed; and step 6, weed real-time identification and result output are realized. According to the method, a lightweight attention mechanism and an improved multi-scale feature fusion structure are introduced, the extraction and distinguishing capability of the network on small-scale weed features under a complex background is remarkably enhanced, the false detection and omission ratio is effectively reduced, and the dynamic label distribution strategy and a loss function fusing global context information are adopted, so that the robustness of the network is improved. And the detection robustness and the positioning precision of the model in dense, shielded and form-variable weed scenes are improved.

The invention relates to the technical field of image processing, and discloses a landslide surface crack three-dimensional evolution unmanned aerial vehicle image extraction and quantification method and system, and the method comprises the steps: carrying out the bundle adjustment of an ultrahigh overlapping degree aerial image of a landslide surface, and obtaining an image shooting position, an image attitude parameter and a sparse three-dimensional point cloud; constructing a high-precision digital earth surface model; performing digital mosaic on the aerial image with the ultrahigh overlapping degree to obtain an orthoimage; performing semantic segmentation on the orthoimage to obtain a crack binary segmentation map of the orthoimage so as to identify a main crack and a secondary crack; performing three-dimensional geometric parameter analysis on the main crack and the secondary crack to obtain a crack three-dimensional space attribute parameter; carrying out evolution trajectory tracking on the crack three-dimensional space attribute parameters to obtain evolution data so as to construct a visual map of the landslide surface; according to the invention, the efficiency of extracting and quantifying the three-dimensional evolution unmanned aerial vehicle image of the landslide surface crack can be improved.

The invention provides a video anomaly detection method and system combining cross attention and memory optimization, and the method comprises the steps: inputting RGB data and optical flow data into an appearance encoder branch and a motion encoder branch respectively; gradually extracting appearance features and motion features of different scales by using a convolution module and a maximum pooling layer; then, taking branch output features of the appearance encoder and the motion encoder as input data of a cross attention mechanism, enriching representation of the appearance features by using the motion features, and realizing fusion of the appearance and the motion features; thirdly, inputting the fused features of the appearance and the movement into a memory module to further optimize the features, and improving the extraction and expression ability of normal features; finally, a future frame decoder is utilized, appearance features of different scales are fully utilized, decoding is carried out step by step, generated future frames are obtained, and the video anomaly detection precision is improved.

The invention belongs to the field of motor fault recognition, and particularly discloses a permanent magnet synchronous motor winding short circuit recognition method, which comprises the following steps: S1, constructing a convolutional neural network model, and inputting three-phase current signals and vibration signals as input signals; s2, establishing at least two input channel groups, introducing a space attention module, performing feature extraction on the three-phase current signal and the vibration signal, and splicing the extracted features; and S3, training the convolutional neural network model by adopting the public data set. According to the invention, through a multi-input mechanism, the current and vibration signals of the PMSM are effectively separated and processed, so that the characteristics of the sample data are accurately extracted. On the basis of current and vibration signals, an attention mechanism is introduced into a convolutional neural network, and a multi-input strategy is adopted, so that the signal feature extraction and selection capability of the network is enhanced.

The embodiment of the invention provides a training method and device for a trajectory prediction model of an automatic driving vehicle. The method comprises the following steps: acquiring a historical driving data set of a target vehicle; based on the historical driving data set, constructing a target unlabeled data set, a target labeled data set and a preference optimization data set; based on the target unlabeled data, pre-training the initial visual motion model to obtain a pre-trained model; training the pre-training model based on the target labeled data set to obtain a training model; and based on the preference optimization data set, performing direct preference optimization training on the training model to obtain a trajectory prediction model. The technical problem that the accuracy of automatic driving track prediction is low is solved.

This utility model relates to the technical field of beverage extraction equipment, specifically disclosing a pecan beverage extraction device with homogenization assistance. The device includes a vessel body, which is the main equipment for pecan beverage extraction. The upper cover of the vessel body is penetrated by the output end of a drive motor, which is connected to a stirring rod. The bottom end of the stirring rod is rotatably connected to the top end of a support frame, and the bottom end of the support frame is aligned with the bottom opening of the vessel body. In this pecan beverage extraction device with homogenization assistance, the drive motor rotates the stirring rod. The spiral-shaped stirring blades on the outer wall of the stirring rod, while rotating, cause the material to move in a circular motion. The spiral shape generates axial thrust, promoting the liquid to circulate up and down, enhancing the stirring effect. This allows the extract to fully contact the pecan raw material, effectively increasing the extraction rate of water-soluble active substances, improving the overall extraction effect, reducing raw material waste, strengthening the stirring action, and accelerating the extraction process.

This invention discloses an automatic classification model and method for diabetic retinopathy. The automatic classification model includes convolutional input, feature extraction, and classification output. The convolutional input consists of 7×7 convolutions and max pooling, responsible for resizing the input image to extract shallow features. Feature extraction includes four alternately connected dense blocks and three transition layers, mainly responsible for extracting feature information of retinal lesions. The classification output consists of global average pooling and fully connected layers, responsible for outputting the classification result of diabetic retinopathy. This invention designs an attention mechanism (SC) based on retinal lesion features to increase the network's focus on lesion features and fully extract retinal lesion features. Using the lightweight network DenseNet121 as the backbone, the dense layers are improved by connecting the SC module after 3×3 convolutions, increasing the classifier's ability to express high-level semantic features and improving classification accuracy. This invention can achieve automatic classification of diabetic retinopathy levels.

This application relates to a method, system, and storage medium for extracting structured data from literature. The method includes: acquiring information about the literature to be processed and information about the domain of interest; generating intent recognition routing results by calling a corresponding domain intent recognition model based on the information about the domain of interest; constructing prompt words based on the information about the domain of interest and the intent recognition routing results, and extracting first target literature information from the information about the literature to be processed; constructing a corresponding domain similarity function based on the intent recognition routing results, and generating second target literature information by removing similar literature from the first target literature information; and outputting experimental information, experimental knowledge graphs, and source relationships in a structured form based on the second target literature information. This application determines the intent recognition routing results through domain intent recognition, adaptively selects the extraction strategy and domain similarity function, and combines multimodal parsing and block processing to achieve high-completeness extraction, cross-domain adaptive fusion, and traceable structured output of experimental information from cross-domain literature.

The invention relates to the technical field of computer vision and agricultural automation, and particularly discloses a method for grading the quality of ziziphus mauritiana based on an improved YOLO model so as to solve the problems that a traditional grading method is low in efficiency, poor in precision and difficult to deal with complex backgrounds and small damage. According to the method, a BCW-YOLO model is provided by constructing a multi-angle Maoyesian jujube image data set and performing data enhancement, a bidirectional feature pyramid network (BiFPN) is integrated on the basis of YOLOv8 to improve the feature fusion capability, a context conversion attention mechanism (COT) is introduced to enhance detail capture, and a WIoUv3 loss function is adopted to optimize the positioning performance. According to the scheme, the model is remarkably superior to a traditional model in the aspects of accuracy, recall rate, mAP and other indexes, the model is particularly excellent in performance in complex background and small damage detection, an effective scheme is provided for achieving automatic and accurate grading of the ziziphus mauritiana, and the method has wide application prospects.

The utility model discloses a kind of stem cell exosome's extraction device with repairing skin and anti-aging, it is related to stem cell exosome extraction technical field.The utility model includes dilution tank, dilution tank top is fixed with quantitative feeding assembly, flush type tank cleaning assembly is arranged in the rear side of dilution tank, filter tank is arranged in the side of dilution tank, a part of flush type tank cleaning assembly is respectively located in dilution tank inside and filter tank inside, and fixed with discharge pipe and blowdown pipe in the side of filter tank, discharge pipe is located above blowdown pipe.The utility model can quantitatively feed exosome and diluent by quantitative feeding assembly, and it is convenient to reasonably configure reagent, and exosome can be diluted to suitable concentration, reduce the interaction between exosome, avoid aggregation to form precipitate, flush simultaneously to dilution tank and filter tank by flush type tank cleaning assembly, accelerate entire exosome extraction process.

The invention discloses a pure traditional Chinese medicine for treating acute and chronic liver diseases and a preparation method thereof, and belongs to the technical field of liver disease treatment. The pure traditional Chinese medicine preparation comprises the following components in parts by weight: 1-10 parts of vinegar tortoise plastron, 1-5 parts of American ginseng, 1-6 parts of Sichuan yellow, 1-3 parts of donkey-hide gelatin, 1-4 parts of cordyceps sinensis, 1-5 parts of lucid ganoderma, 1-4 parts of prinsepia utilis royle, 1-3 parts of olean, 1-4 parts of cedar parasitism, 1-3 parts of parasitic loranthus, 1-5 parts of grifola, 1-4 parts of couch grass root, 0.5-3 parts of saffron crocus, 1-4 parts of angelica sinensis, 1-4 parts of polygonum cuspidatum, 1-5 parts of penthorum chinense pursh, 0.5-3 parts of calculus bovis factitius and 1-4 parts of radix paeoniae alba. The traditional Chinese medicine composition is prepared from 1-3 parts of oregano, 1-4 parts of fructus lycii, 1-3 parts of schisandra chinensis, 1-4 parts of fructus gardeniae, 1-4 parts of artemisia capillaris, 1-3 parts of dandelion, 1-3 parts of safflower, 1-4 parts of abrus herb, 1-3 parts of selfheal, 1-3 parts of verbena, 1-3 parts of fennel, 1-3 parts of tangerine seed and 1-4 parts of liquorice. According to the invention, through accurate component proportion, the optimal combination of the medicinal components is ensured, the curative effect and safety of the medicine are improved, the preparation process of the traditional Chinese medicine is simplified, and the extraction and purification degree of the medicinal components is improved.

The utility model provides natural product extraction equipment and a discharging device, the natural product extraction equipment comprises an extraction kettle, a discharging device and a flash tower, the extraction kettle is connected with the discharging device through an extraction kettle discharging pipeline, and the discharging device is connected with the flash tower through a filtering conveying pipeline; the discharging device comprises a transversely arranged blade type screw rod, the top of the blade type screw rod is connected with an extraction kettle discharging pipeline, one end of the blade type screw rod is provided with a discharging motor, the other end of the blade type screw rod is provided with a residue discharging pipe, the lower end of the residue discharging pipe is connected with a waste storage tank, and the bottom of the blade type screw rod is provided with a filter cartridge; the outlet of the filter cartridge is connected with a discharge tank, the bottom of the discharge tank is connected with a screw pump, and the outlet of the screw pump is connected with a filter conveying pipeline. The natural product extraction equipment can solve the problems of pollution and blockage caused by overflow and leakage of a solvent when effective components of a natural product are extracted and separated, and the extraction and separation efficiency is improved.

The utility model belongs to the technical field of electronic atomized liquid processing, and particularly relates to a healthy electronic atomized liquid processing device based on an alcohol extraction method, which comprises an extraction tank, the outer surface of the bottom of the extraction tank is fixedly connected with a stirring mechanism, one side of the top end of the extraction tank is provided with a feed port, and the center of the bottom end of the extraction tank is fixedly sleeved with a discharge pipe. The extraction tank comprises a stirring mechanism for stirring raw materials, a spraying mechanism for spraying a solvent and a filtering mechanism for removing impurities; one side of the discharging pipe is fixedly sleeved with an inclined cylinder, and the inclined cylinder is obliquely arranged; in the using process, through the arrangement of the filtering mechanism, solid impurities in a mixed solution can be accurately intercepted, an extracting solution and the impurities can be effectively separated, the purity of the extracting solution is ensured, the impurities are discharged through the impurity outlet pipe, frequent manual intervention is not needed, manpower is saved, the impurities can be continuously and efficiently treated, and continuous and stable operation of the device is guaranteed.

The present application discloses a self-distillation and self-learning medical image segmentation method, device and storage medium, the method comprising: using a MOD framework to copy based on a target network model to obtain a teacher model, and using the target network model as a student model; obtaining an image to be processed and performing mask processing on the image to be processed to obtain a mask image; inputting the image to be processed into the teacher model to obtain a first output result output by the teacher model; inputting the mask image into an encoder of a student model to obtain a second output result output by the student model; using a MOD framework to perform distillation processing on the first output result and the second output result and to perform reconstruction processing on the second output result; determining a distillation loss value and a reconstruction loss value of the MOD framework; adjusting parameters of the student model according to the distillation loss value and the reconstruction loss value to obtain a medical image analysis model, and segmenting the tumor image to be segmented based on the medical image analysis model. The present application can improve the segmentation accuracy of the medical image analysis model.

The invention discloses a batch statistics and analysis method, device and equipment for emission data of a hybrid electric vehicle and a storage medium, and relates to the technical field of vehicle emission, and the method comprises the steps: obtaining vehicle model information, an emission type, a test site, a test report file and a data statistics template inputted by a user; determining a target test template according to the vehicle type information, the test site and the emission type; based on the target test template, reading the test report file to obtain test data; and obtaining a data statistics file based on the data statistics template and the test data. A preset test template is matched through a test site input by a user and an emission type, and automatic analysis of reports in different formats is achieved; and the analyzed test data is automatically written into a statistical file according to a standardized format in combination with a data statistical template selected by a user, so that the efficiency and accuracy of data extraction and summarization are remarkably improved.

The application discloses a kind of phthalocyanine copper / inverted pyramid perovskite heterojunction photodetector and preparation method, comprising the following steps: 1) etching out the silicon piece with pyramid surface microstructure, as the base of growing perovskite single crystal;2) preparation perovskite solution, utilize silicon piece and glass piece as the frame of growth area restriction, utilize seed growth method, space limited method and inverse temperature crystallization method combined growth method preparation surface pyramid perovskite single crystal;3) utilize thermal evaporation in perovskite single crystal surface evaporation phthalocyanine copper film formation heterojunction, then utilize conductive carbon paste as upper and lower electrode preparation photodetector.The photodetector prepared by the application is stable at normal temperature and pressure, and has better photoelectric response relative to the control group device in a high humidity environment, and the device has good long-time humidity stability, which has guiding significance for the future high-performance photodetection field in high humidity environment such as sea, ship and island.

The invention relates to the technical field of intelligent traffic video analysis and law enforcement assistance, and particularly provides a traffic violation detection and linkage reporting method and system based on YOLOv13. The method comprises the following steps: accessing a video stream and preprocessing; a SegNextAttention attention and SDI (Serial Digital Interface) feature interaction module is introduced to improve the YOLOv13 network; inputting the image sequence into the improved YOLOv13, and outputting a target bounding box, a violation category and confidence; obtaining stability judgment through target association and time sequence verification, triggering violation events according to a rule base, and performing duplicate removal; and generating an evidence packet containing a panorama and a target cutting picture, binding geographic coordinates for uploading, performing hierarchical alarm on a linkage peripheral, and monitoring the state of the equipment for remote configuration at the same time. According to the method, the problems of false detection, missing detection, category jitter and insufficient engineering closed loop in a complex scene are solved, and the traffic violation discovery and disposal efficiency is improved.

This invention relates to the field of optical fiber geometric reconstruction technology, specifically to a one-dimensional reconstruction analysis method for optical fiber ring stress, comprising: reading and processing finite metadata of the optical fiber ring to obtain Cartesian coordinate system node information; converting the Cartesian coordinate system node information to the cylindrical coordinate system; determining the forward and reverse optical fiber paths to which each node belongs; calibrating the length of each node based on the optical fiber winding process parameters and the optical fiber path to obtain the one-dimensional unfolded length of each node; interpolating based on the one-dimensional unfolded length to obtain a discrete point stress value sequence; conducting an actual optical fiber ring experiment based on the finite metadata to obtain a polarization coupling curve; comparing the polarization coupling curve with the discrete point stress value sequence to obtain the one-dimensional reconstruction accuracy result; this invention can improve the accuracy of optical fiber ring stress analysis results along the optical path.