575 results about "Nonlinear transformation" patented technology

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

The invention discloses a word embedding vector extraction method and system based on a large language model, and belongs to the field of natural language processing, and the word embedding vector extraction method based on the large language model comprises the following steps: S1, embedding a global context attention module, generating a Q / K / V vector through linear transformation, and calculating a global attention weight in a cross-position manner; s2, splicing coding features of adjacent layers, and performing gating fusion to generate a global context dynamic semantic state; s3, multi-head attention joint coding is carried out on the dynamic semantic state of the current layer and the global state; s4, performing average pooling to generate a global semantic vector, and calculating a dynamic attention weight; s5, performing weighted fusion on the dynamic weight and the global vector to generate an enhanced code; s6, outputting a final word vector by a GELU nonlinear transformation full-connection layer; the method has the beneficial effects that the global context semantic capture capability is enhanced, and the expression effect of the word embedding vector is improved.

The invention discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention provides a photovoltaic prediction method based on a data double decomposition and deep learning optimization model, and the method comprises the steps: collecting and preprocessing historical photovoltaic power data and meteorological associated data, carrying out the double decomposition of the preprocessed historical photovoltaic power data, and obtaining photovoltaic power component data; combining the photovoltaic power component data with meteorological associated data to construct a plurality of groups of photovoltaic-meteorological component data sets; an iTransform-KAN photovoltaic power prediction model is constructed, the photovoltaic-meteorological component data set is used to train and test the iTransform-KAN photovoltaic power prediction model, and the trained iTransform-KAN photovoltaic power prediction model is obtained; and determining a final predicted value through a linear superposition strategy based on the photovoltaic power component predicted value. According to the method, precise stripping of multi-scale features of photovoltaic power and adaptive learning of nonlinear transformation are realized, so that the capability of modeling a complex dynamic relationship is improved.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention discloses a PCB defect real-time detection method based on multi-scale feature fusion, and relates to the technical field of PCB defect real-time detection methods, and the method comprises the steps: obtaining a to-be-detected PCB image, carrying out the size normalization and pixel value standardization processing of the image, and obtaining a standardized image meeting the input requirements of a model; inputting the standardized image into a backbone network of a teacher detection model, and extracting a multi-scale primary feature map containing texture information in different directions through a grouping convolution structure; transmitting the multi-scale primary feature map to a neck network of a teacher detection model, and performing weighted fusion on feature maps of different scales by using a learnable weight to generate a multi-scale fusion feature map; and in an up-sampling path of the neck network, generating channel description information after global pooling is performed on the deep fusion feature map, generating a channel attention weight through nonlinear transformation, acting the weight on a primary feature map of a corresponding level, and outputting an enhanced feature map.

The invention discloses a hyperspectral fusion imaging method and device based on deep nonlinear transformation tensor low-rank representation, and the method comprises the following steps: collecting a priori multispectral image while collecting hyperspectral compression measurement in an observation scene, and taking the two data as input at the same time; based on tensor representation and deep learning theories, novel deep nonlinear transformation tensor low-rank regularization for the hyperspectral image is established, and global high-dimensional low-rank correlation of the hyperspectral image in a deep nonlinear transformation domain is fully described; taking the regularization item as a target function, modeling compression imaging and a spectrum degradation process into two data fidelity items, and constructing a fusion imaging model and a loss function; and minimizing a loss function through a learning algorithm to optimize the model, and fusing and reconstructing a complete hyperspectral image. According to the novel method and the novel device provided by the invention, high-precision fusion imaging of the observation scene can be realized without manual intervention and explicit intermediate steps.

The invention provides an infrared small target detection method and system based on depth-guided low-rank sparse decomposition, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining all original infrared images shot by remote sensing equipment, and sequentially stacking the original infrared images according to an obtaining time sequence, and obtaining an infrared original tensor; performing low-rank background and sparse target decomposition processing based on the infrared original tensor to obtain a low-rank sparse tensor decomposition model; a low-rank background tensor containing nonlinear transformation is obtained through processing of a constructed hierarchical nonlinear tensor ring background module; processing through a sparse target module fused with an attention mechanism to obtain a sparse feature tensor of the infrared small target area; and reconstructing a low-rank sparse tensor decomposition model guided by the deep neural network, and carrying out solving processing to obtain a final infrared small target detection result. According to the invention, accurate, robust and rapid detection can be carried out on a small target under a complex background.

The invention belongs to the technical field of load prediction in a low-voltage distribution area, and particularly relates to a time sequence prediction method for attention mixed multi-scale decomposition, which comprises the following steps: S1, preprocessing original time sequence data to obtain a standardized sequence X; s2, inputting X into MJDA, and outputting uniform high-dimensional representation U after feature enhancement; s3, inputting U into TCDA, and carrying out cross-dimension dependence modeling and deep nonlinear transformation to obtain a final enhanced feature U1; s4, inputting U1 output by the TCDA into a hybrid expert predictor group; the predictor group is composed of K parallel expert predictors, and a corresponding expert prediction result is obtained; meanwhile, U output by the MJDA is processed through a noise perception gating network, and weight distribution U used for expert predictor fusion is generated; and according to the U, carrying out weighted summation on the output of the K expert predictors to obtain a prediction result. According to the method, high-precision and high-stability load prediction can be realized in a low-voltage distribution area environment with limited resources.

The invention provides a deep steganography image secret information blind extraction method based on self-supervised learning. The deep steganography image secret information blind extraction method comprises a coding stage, a self-supervised learning task generation supervision signal, attention coupling through an attention coupling module and a decoding stage. The coding stage comprises the following steps of: respectively taking two secret-containing images as input, performing Haar transformation method processing, splitting input features generated by the processing of the Haar transformation method of the secret-containing images into two paths through a bidirectional coupling mechanism of a coupling layer, realizing feature decoupling and cross-path interaction by utilizing nonlinear transformation, different paths are enabled to focus different components of secret-containing images respectively, and meanwhile, information lossless in the feature transformation process is ensured through reversible design. According to the method, the self-supervised learning technology is innovatively utilized, the model can deeply mine the characteristics and rules of the secret-containing image, and therefore secret information blind extraction independent of a secret key is achieved.

The invention provides a flash encryption method for a security chip, the security chip and electronic equipment, and belongs to the field of digital security, the security chip comprises two flash encryption modules which are connected in parallel, the first flash encryption module carries out encryption and decryption processing on target data of which the values are all '1', and the second flash encryption module carries out encryption and decryption processing on other target data; each flash encryption module is used for encrypting and decrypting target data by using a Feistel structure; dividing the target data into a preset number of groups, encrypting each group by using different round functions, and performing nonlinear transformation on each round function by using different S boxes; and each flash encryption module scrambles the address based on a preset scrambling formula, and stores the encrypted ciphertext based on a scrambling result. The encryption delay can be reduced, the encryption strength is enhanced, and the software and hardware communication cost is reduced.

The invention belongs to the technical field of polymer additive manufacturing, and relates to a melt electrostatic direct writing preparation method of a polymer fiber scaffold with a continuous gradual change structure, which comprises the following steps of: designing a basic homogeneous pattern according to the transition interface characteristics of a to-be-repaired tissue, and discretizing the basic homogeneous pattern into a dense ordered point array; designing nonlinear transformation functions f and g according to structural change characteristics at a transition interface, mapping points one by one by using the nonlinear transformation functions f and g so as to obtain a point column, performing equidistant sampling on the point column to obtain an expected printing path, and finally performing melt electrostatic direct writing based on the expected printing path so as to obtain the melt electrostatic direct writing. And preparing the polymer fiber scaffold with the continuous gradually-changed structure. According to the method, a curve printing process is adopted to simulate a smooth transition interface between tissues, so that the structure and mechanical bionic degree of the polymer fiber scaffold are improved.

The invention belongs to the technical field of image data processing, and particularly discloses a single low-illumination image enhancement method for simulating multi-exposure image fusion. The method comprises the following steps: carrying out nonlinear transformation on a single low-illumination input image to generate a virtual exposure sequence containing a reference frame and a plurality of non-reference frames; inputting a virtual exposure sequence into a VMEIF-Net network, processing a reference frame and a non-reference frame through a multi-branch feature coding structure, introducing a space attention module to a non-reference frame branch to screen features consistent with the reference frame, iteratively optimizing texture details by adopting a feature feedback unit, and inhibiting artifacts through a global feature analysis unit, so as to obtain a VMEIF-Net network; and generating an enhanced image through feature fusion and decoding operation. According to the invention, through multi-branch feature extraction and feedback connection, high-level feature information is transmitted back to a feature fusion stage, so that the information transmission efficiency is improved; by introducing the global context sensing module, the receptive field range is expanded, generation of artifacts can be inhibited, and rich image detail information is kept.

The embodiment of the invention provides a polypropylene film defect detection method, equipment and a medium. The method comprises the following steps: screening normal samples in a polypropylene film image to construct an image data set; extracting multi-scale features of the polypropylene film image; performing channel attention and space attention calculation on the multi-scale feature to obtain a first feature; performing channel conversion on the first features of different scales to obtain second features of the same channel; performing nonlinear transformation and local spatial feature extraction on the second feature to obtain a third feature; performing defect detection and defect positioning according to the third feature to obtain a detection result; the prior that a large number of defect samples are needed in traditional detection is eliminated, and efficient defect detection can be achieved after learning of a large number of normal samples.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention provides a control method, system and control device based on a communication system, and the method comprises the steps: obtaining a current control state and a communication state of each terminal device, and carrying out the nonlinear transformation and structure splicing, so as to construct a joint state representation; obtaining the scheduling priority of each task, and selecting K tasks with the highest scheduling priority to form a task set; for each task in the task set, using a control trajectory prediction function to calculate a predicted action trajectory of a target task in future T steps; inputting the predicted action trajectory into a pre-constructed auto-encoder, and generating a low-dimensional control intention vector; in response to the control intention vector, decoding the control intention vector by using a decoder, generating a restored predicted motion trajectory, and minimizing a reconstruction error, so that the restored predicted motion trajectory approaches an original predicted motion trajectory as much as possible; and executing a control action corresponding to the restored predicted action track, and performing closed-loop feedback optimization.

The invention discloses a storage-efficient large model reasoning method for many-core supercomputing, and relates to the field of machine learning. The overall architecture of the method is based on video memory optimization of Attention and FFN modules in a Transform model, and high storage efficiency in a reasoning stage is realized through matrix block calculation and dynamic video memory management. The method comprises the following steps: firstly, carrying out parameter partitioning and serial calculation: in an Attention module, vertically cutting a Q and K parameter matrix into a plurality of sub-blocks along a column direction, and keeping the input complete; serially calculating the product of each sub-block and the input to obtain a local Q matrix and a local K matrix, and immediately performing QKT block multiplication to obtain a partial attention score; aggregating calculation results of all the sub-blocks to obtain a complete attention score matrix; afterwards, normalizing the attention score by using Softmax, carrying out serial multiplication on the normalized attention score and a V block subjected to delay calculation, and splicing a result to obtain Attention output; optimizing an FFN module, vertically cutting parameters of a full connection layer into sub-blocks, inputting complete data, sequentially carrying out serial calculation on the complete data and the sub-blocks, and splicing nonlinear transformation results of all the sub-blocks in real time.

The invention discloses a power transformer periodic time sequence prediction method and a multivariable periodic time sequence prediction method based on a hybrid model, and the method comprises the steps: collecting the operation data of a transformer, including oil temperature and load variables, and carrying out the standardization, missing value processing and periodic time feature embedding; constructing a multi-branch hybrid model, fusing a bidirectional state space encoder, a time convolution network, a local window attention mechanism and a periodical enhancement network, and respectively capturing long-term periodical dependence, multi-scale local periodical features, a short-term periodical mode and periodical feature enhancement representation; the multi-branch features are adaptively integrated through a global context weighted fusion mechanism, efficient nonlinear transformation is performed on the fusion features by adopting a multi-order Chebyshev polynomial projection module, and a prediction sequence of oil temperature and load variables is generated.

The invention provides a capacitor voltage transformer error online detection method and system, and the method comprises the steps: collecting a secondary output signal of a three-phase capacitor voltage transformer, and carrying out the data preprocessing; based on the collected data, constructing a deep neural network learning model based on a stacked auto-encoder, and extracting deep features of the data through multilayer nonlinear transformation to obtain reconstructed data; taking the original data and the reconstructed data as input data, respectively carrying out co-integration analysis, and calculating a co-integration matrix and an equalization error; combining the reconstruction data with the equilibrium error, constructing a composite statistical magnitude and calculating a statistical magnitude threshold value; and collecting online measurement data of the capacitor voltage transformer, calculating monitoring statistics and carrying out abnormity diagnosis. The technical problems that disturbance components and error information in measurement data are difficult to distinguish, non-linear and non-stationary components in measurement errors of the capacitor voltage transformer are difficult to analyze, and the online detection accuracy and real-time performance of the measurement errors are low are solved.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention discloses a multi-modal data joint embedding method based on a hierarchical progressive arithmetic interaction network, and the method comprises the following steps: obtaining text data and image data from the same semantic entity, and extracting a text local feature, a text global feature, an image local feature and an image global feature; inputting the text local feature and the image local feature into an atomic layer, and processing based on a Cartesian product to generate a first-order interaction feature; inputting the first-order interaction features into a combination layer, and carrying out nonlinear transformation processing to generate enhanced nonlinear interaction features; and inputting the nonlinear interaction features into the aggregation layer, and generating a multi-modal joint embedding vector in combination with the text global features and the image global features. The method effectively solves the problems of modal isomerism, single interaction level, lack of dynamic adaptability and the like in the prior art.

The invention relates to the technical field of cable monitoring, in particular to a graphene cable monitoring system based on deep learning, which is used for extracting time-frequency domain characteristics by acquiring current, voltage, electromagnetic wave signals and temperature distribution data and utilizing Fourier transform and wavelet transform to improve the data identification degree. Gaussian filtering noise reduction is carried out on temperature data, and an abnormal hot spot area is identified through image segmentation, so that the local overheating detection capability is improved. And the edge calculation module fuses multi-source data, eliminates acquisition delay by utilizing feature alignment, and improves data synchronism and fusion quality. Through a multi-head self-attention mechanism, time sequence characteristics of historical monitoring data are extracted, and change modes of current, voltage, electromagnetic wave and temperature distribution are learned. And calculating an attention weight matrix to extract correlation between time steps, and forming a time sequence feature matrix. The characteristic matrix is subjected to nonlinear transformation through a feedforward neural network, cable state parameters of a future time step are predicted, the cable state is evaluated in advance, and the fault risk is reduced.

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

The invention discloses a cloud mobile phone equipment fingerprint disguising method and related equipment, and relates to the technical field of cloud mobile phone security, and the method comprises the steps: obtaining hardware entropy source data which comprises current timestamp data, a hardware unique identifier hash value and an environment noise sampling value; non-linear transformation is carried out on the hardware entropy source data based on a preset chaotic mapping algorithm, a virtual parameter set is generated, and the virtual parameter set meets a preset equipment parameter rule; covering the virtual parameter set to a multi-level system interface of the real equipment fingerprint through kernel-level injection operation; monitoring the use state of the virtual parameter set based on a preset life cycle model, and determining a parameter updating trigger condition; and when a parameter updating triggering condition is met, the nonlinear transformation is executed again to generate an updated virtual parameter set, and the updated virtual parameter set covers the multi-level system interface.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

The invention discloses a high-fidelity three-dimensional Gaussian sputtering (3DGS) lightweight method for resource-constrained equipment. The method aims at solving the problems of high storage and computing resource consumption caused by the fact that a large number of parameters are stored in an existing 3DGS technology, and geometric distortion possibly occurring when details of a scene center are processed is overcome. The core of the method lies in a multi-stage progressive optimization framework, and the framework cooperatively applies four key technologies of Gaussian cutting and opacity regularization, dynamic spherical harmonic function adjustment, entropy constraint vector quantization and coordinate space shrinkage. Wherein in Gaussian clipping, redundant gauss are eliminated by quantifying the contribution degree of a Gaussian unit; the dynamic spherical harmonic function adjustment adaptively adjusts the order of color coding according to the scene complexity; the entropy constraint vector quantization is used for compressing a plurality of Gaussian attributes so as to realize more compact representation; and the coordinate space shrinkage is realized through nonlinear transformation, so that the rendering precision of details of the center of the scene is remarkably improved. According to the method, while the rendering precision and quality are kept, remarkable storage compression is realized, and the method is particularly suitable for deployment of resource-limited platforms such as mobile equipment.

The invention provides a multi-merchant hardware equipment supply chain demand intelligent matching method, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining a demand order and supply capability data through a supply chain platform, and enabling the supply capability data to obtain production line test data in real time through an Internet of Things interface; performing preliminary screening based on equipment types and necessary authentication standards; calculating the comprehensive reliability of the optical fiber sensing system by adopting a comprehensive evaluation model fused with nonlinear transformation; calculating the multi-dimensional demand integrating degree of the supplier and the demander through multi-dimensional difference analysis and normalization processing; intelligent sorting is carried out based on weighted scores of configurable weight factors; implementing global productivity monitoring and conflict resolution optimization, and outputting an optimal matching pair; and continuously iteratively optimizing the evaluation model parameters according to the performance feedback data by adopting an incremental learning mechanism. According to the method, accurate matching and dynamic optimization under multi-target constraints are realized, and the supply chain resource configuration efficiency and the system autonomy capability are effectively improved.