74 results about "Random projection" patented technology

The invention discloses a multi-source equipment health collaborative operation and maintenance method in a complex environment based on edge federation, and relates to the technical field of equipment collaborative operation and maintenance. Vibration acoustic emission current waveforms are mapped to a unified time-frequency grid at an edge gateway, and an encrypted sparse index is generated and uploaded; training a global model by combining a graph regular base network with a gradient direction and a distribution distance, and injecting fault information by using a new working condition protection door; after being issued by a sparse adaptation layer of double-temperature-zone distillation and random projection compression, fine adjustment is carried out on site under few samples through temperature gradual fusion and reversible orthogonal mapping, and only unit gradient direction and health labels are uploaded; the center adopts entropy constraint Bayesian filtering to fuse information to generate a health index, a maintenance schedule and a spare part plan are formed by integer programming according to confidence intensity mapping risk popularity, a result is differentially pushed and audited, and a closed loop of collection, learning, evaluation and decision is realized.

The invention provides a privacy protection advertisement monitoring method, system and device and a medium, and is used for solving the problem of effectively monitoring dynamically generated advertisement contents on the premise of protecting user dialogue context privacy in a generative artificial intelligence application. The method comprises the steps that firstly, an advertisement text and context information are obtained, and a first vector is obtained through semantic embedding processing; performing privacy protection conversion on the vector by adopting a random projection method to obtain a second vector, wherein the conversion can reduce the dimension and scatter semantic distribution; inputting the processed vectors into a pre-trained multi-dimensional analysis model to obtain real-time analysis results of content compliance, content correctness, emotional tendency, creativity quality and the like; and finally summarizing analysis results according to advertisement activities and outputting a report. According to the method, differential privacy noise can be selectively added, and a delivery decision is generated based on a multi-threshold judgment model. According to the invention, the privacy of the user is protected, and the advertisement content is efficiently monitored in real time.

The invention discloses a magnetic method data physical property inversion method and system based on a deep learning terrain disturbance layer, and relates to the technical field of geophysical exploration, and the method comprises the steps: building a magnetic anomaly data set, generating an underground three-dimensional magnetic anomaly model through a mixed hexahedron model and a terrain disturbance function, and obtaining corresponding magnetic anomaly data through forward modeling, each group of magnetic anomaly data corresponds to different terrain parameter combinations; constructing a deep learning inversion network model based on UNet; and training the deep learning inversion network model based on the magnetic anomaly data, taking the trained training set parameters as the weight of the deep learning inversion network, verifying the effect by using the verification set after the training is finished, and predicting the effect according to the test set. According to the method, a traditional same-dimensional random projection inversion method is combined with a deep learning network structure, and dynamic transformation of terrain parameters is realized by using a terrain disturbance layer, so that the network keeps high inversion precision and stability under different terrain fluctuation conditions.

The invention provides a music copyright credibility verification and circulation method, system and device and a medium, and belongs to the technical field of block chains. The method comprises the following steps: uploading an audio to a circulation platform, framing and windowing a smart contract, calculating short-time energy, and generating time domain hash through random projection hash; after windowing, an MFCC coefficient is extracted through Fourier transform, Mel filtering, logarithm compression and DCT, and after dimensionality reduction, frequency domain Hash is generated through locality sensitive Hash. And setting weights according to music types, splicing time domain and frequency domain Hash to obtain composite Hash, forming digital fingerprints by combining with copyright person biological characteristic Hash, and linking and binding songs and creator vouchers by taking copyright numbers as keys. Based on the digital fingerprint, a music copyright NFT token is generated by using an ERC-721 protocol, and a circulation NFT token is generated by using an ERC-1155 protocol. And when the copyright is checked, the target audio is uploaded to generate a digital fingerprint, infringement is judged by comparing the similarity, and a corresponding NFT token is inquired for evidence collection.

The invention relates to the technical field of e-commerce, in particular to a multi-merchant user behavior deep learning analysis method, which comprises the steps of collecting interaction data of each merchant end, calculating a gradient based on a local learning model, packaging slices and uploading the slices to a cloud end; aggregating gradients, constructing a behavior relation graph and generating a causal tensor, and inputting the causal tensor and the graph domain multi-scale features into a time sequence embedding model to obtain time embedding; generating commercial tenant high-dimensional pulse vectors through random projection and binary mapping, and aggregating the commercial tenant high-dimensional pulse vectors into cross-commercial tenant vectors; executing a generative reverse process on the cross-merchant vector according to a noise strategy to obtain a prediction vector; the prediction vector and time are embedded and mapped into an energy matrix, an action vector is sampled through quantum optimization, and the action vector and a cross-merchant vector are input into a reinforcement learning network to output a recommendation decision; and generating an update gradient according to user clicking and payment feedback, and returning the update gradient to the merchant end to form a self-calibration closed loop. According to the method, cross-merchant collaborative recommendation is realized on the premise of not exposing original data, and cold start recall and real-time conversion rate are improved.

The steam generator state real-time monitoring method based on artificial intelligence belongs to the field of artificial intelligence and comprises the following steps: S1, data acquisition and labeling; S2, sample generation is performed by using a quantum generative adversarial network based on random projection embedding to realize data expansion; S3, the expanded data is input into a feature extraction model to perform training of the feature extraction model, and a five-layer fully connected neural network is used for feature extraction; S4, the feature-extracted data is input into a feature dimension reduction model to perform training of the feature dimension reduction model, and a self-encoding neural network algorithm based on local preserving projection is used to realize feature dimension reduction; S5, the dimension-reduced data is input into a classifier to perform training of the classifier model; and S6, steam generator state recognition and monitoring are performed.The steam generator state real-time monitoring method based on artificial intelligence can solve the problems of insufficient sample quantity and lack of data diversity and enhances the robustness of the model when the model has noise or fuzzy classification boundary data.

The invention relates to a distributed constraint optimization algorithm and system based on subgradient rescaling, and belongs to the technical field of information communication. According to the algorithm, for the distributed constraint optimization problem of the time-varying directed network, a time-varying directed graph meeting consistent joint strong connectivity and column randomness is constructed; initializing node variables; in each iteration, nodes transmit and receive variables and update the variables, and the variables and state variables are updated through a Polyak random projection technology; and repeatedly iterating to a stop rule, and outputting an optimal variable. The problem of imbalance of the time-varying directed network and the problem of complex constraint processing are solved, the calculation load is reduced, the problem of secondary optimization is avoided, the optimization efficiency is higher, and the application range is wider.

The invention discloses a sales supply chain management method and system based on Internet data, and relates to the technical field of supply chain optimization, and the method comprises the steps: carrying out the decomposition through discrete wavelet transform, calculating an approximate kernel distance through Gaussian random projection, generating low-dimensional embedding through low-rank decomposition and ADMM iterative optimization, and initializing the prediction demand of Poisson distribution. The method comprises the following steps: quantifying through a Shapley value, initializing a particle swarm, obtaining an individual and global optimal position, carrying out local guide search, outputting an optimal scheme through domain operation for updating, and generating an optimal logistics path and a scheduling scheme through SA disturbance and DE-GA variation. According to the method, the prediction precision is improved by introducing Poisson distribution and Shapley value quantification prediction requirements, an efficient logistics path and a scheduling scheme are generated through multi-target particle swarm optimization and a hybrid variation strategy, and the adaptability and the operation efficiency of a sales supply chain in a dynamic market environment are improved.

The invention relates to a lightweight airborne visual tracking method and device based on random projection and a medium, belongs to the technical field of airborne computer vision and target tracking, and can reduce the calculation and storage cost of airborne platform feature processing, guarantee the target tracking precision and adapt to resource limited scenes. According to the invention, lightweight airborne visual tracking is realized based on locality sensitive hash (LSH) random projection, the method is suitable for resource-limited scenes of airborne platforms such as unmanned aerial vehicles and helicopters, and typical challenges such as target scale dramatic change, complex background interference and high-altitude high-angle shooting visual angle offset under airborne vision can be efficiently processed. The core requirements of an airborne system on low computing power occupation, low storage consumption and high real-time performance of a tracking algorithm are met; the LSH random projection technology is organically combined with an existing lightweight tracking architecture, on the premise that tracking precision is not remarkably lost, the calculation and storage cost of feature processing is further reduced, and the method becomes a constraint of breaking through airborne visual tracking resources-performance.

The invention relates to the technical field of information security, in particular to a federal data privacy protection method based on self-adaptive sparse homomorphic encryption, which comprises the following steps: firstly, carrying out local model training and calculating weight parameter difference at a client, and carrying out parameter block screening through a dynamic sparse proportion to generate a mask vector; only encrypting the reserved parameter block by adopting a CKKS homomorphic encryption algorithm, and generating a low-dimensional weight for contribution degree evaluation through random projection; and the server side calculates a contribution weight based on the cosine similarity of the projection weight and the historical accuracy, and completes weighted aggregation in a ciphertext state. According to the method, through a sparse-encryption-projection triple protection mechanism, the communication overhead is remarkably reduced while the model precision is ensured, the gradient leakage risk is effectively prevented, and the method is suitable for distributed modeling scenes of sensitive data of medical treatment, finance and the like.

The application discloses a medical image class incremental learning method and system based on feature principal direction guided projection, and belongs to the technical field of medical image analysis; the method acquires medical image features through a pre-trained feature extractor; random projection is performed on the features to obtain first-view features; a covariance matrix is calculated based on a feature cache area and is decomposed to extract the first K principal directions, construct a guided projection matrix, and obtain second-view features; the double-view features are spliced to obtain final representation; the classifier weight is directly calculated through a ridge regression closed-form solution without backward propagation training; the application adaptively captures the core structure of the feature space through data-driven principal direction projection, retains the detailed information in combination with random projection, effectively improves the feature separability of fine-grained classes, adopts an analytical classifier updating mechanism, realizes stable class incremental learning without storing historical data, improves the model updating efficiency, and is suitable for efficient continuous learning tasks in various medical image scenes.

Embodiments of the present application provide an alarm storm suppression method and device, computer storage medium and product. The alarm storm suppression method comprises: obtaining alarm data corresponding to an alarm storm; repeatedly randomly sampling the alarm data and constructing an isolation tree based on any preset segmentation method for the alarm data obtained in each sampling, wherein the preset segmentation method comprises a first preset segmentation method and a second preset segmentation method, the first preset segmentation method is to perform splitting based on a numerical feature and a preset segmentation threshold, and the second preset segmentation method is to perform splitting based on a random projection corresponding to a semantic feature; adjusting a path length of the alarm data in each isolation tree based on an alarm device topology weight corresponding to each alarm data, determining an anomaly score of the alarm data based on the adjusted path length; and determining alarm data with an anomaly score exceeding a preset anomaly score threshold as core alarm data. According to the method provided by the embodiments of the present application, the efficiency and accuracy of alarm positioning are improved.

The application discloses a magnetic data physical property inversion method and system based on a deep learning terrain disturbance layer, relates to the technical field of geophysical exploration, and comprises the following steps: establishing a magnetic anomaly data set, generating an underground three-dimensional magnetic anomaly model by mixing a hexahedron model and a terrain disturbance function, and obtaining corresponding magnetic anomaly data by forward calculation, wherein each group of magnetic anomaly data corresponds to different terrain parameter combinations; a deep learning inversion network model based on UNet is constructed; the deep learning inversion network model is trained based on the magnetic anomaly data, the trained training set parameters are used as the deep learning inversion network weight, the effect is verified by using a verification set after the training is completed, and the prediction effect is predicted according to a test set. The application combines a traditional same-dimension random projection inversion method and a deep learning network structure, realizes dynamic transformation of terrain parameters by using a terrain disturbance layer, and enables the network to maintain high inversion precision and stability under different terrain undulation conditions.

The invention discloses a distributed sparse model fingerprint method based on dual-key driving, and the method comprises the following steps: inputting an original model, an owner secret key and an identity label into a computer system; generating a seed based on an owner secret key, calculating a global index set in one or more stable target convolution layers pre-selected in the model, and selecting a weight subset as a fingerprint carrier; constructing a random projection matrix based on the identity label; the weight vector and the random projection matrix generate an original model fingerprint; verifying the suspicious model, repeating the steps to obtain a fingerprint to be detected, and calculating the cosine similarity between the original model fingerprint and the fingerprint to be detected; and judging whether an illegal derivative relationship is formed based on a calibrated threshold value, and outputting an ownership judgment result. According to the method, identity and pseudo-random processes are bound through double keys, and distributed sparse sampling and normalized random projection are combined, so that the uniqueness and robustness of fingerprints are improved, various attack scenes can be effectively resisted, and the ownership of the model is accurately judged.

The application discloses a kind of based on big language model's evolvable software vulnerability detection method, comprising: by regular pattern matching identification Source sentence, based on call graph traversal and data dependence analysis execution function level inter-process slice, build cross-function code context, input the big language model of parameter efficient fine-tuning, output the vulnerability propagation path from Source to Sink;When new vulnerability type needs to be extended, the parameter variation characteristics of old data are extracted by multi-step fine-tuning, and the representative core set is selected by random projection dimension reduction and hybrid distance hierarchical clustering, and the training is played back by mixing new data to alleviate catastrophic forgetting;In the actual use process of tool, the false alarm and the false alarm confirmed by user are collected as feedback signal, the core set is clustered and layered filtered and refined based on perplexity and error prediction analysis, the harmful old knowledge that leads to false alarm and false alarm is removed, and verified new mode is supplemented at the same time, to realize the closed-loop evolution of self-improvement.

This invention relates to the field of wind and solar power engineering construction and ecological restoration technology, and discloses a method and system for gravel curtain layer restoration based on multimodal data fusion. The method includes the following steps: acquiring multimodal data including natural environment, engineering disturbance, and remote sensing data, and independently encoding them into feature vectors of a unified dimension; constructing a bidirectional cross-modal attention mechanism to fuse natural environment and engineering disturbance features; constructing a sequence by combining the fused features and remote sensing features, and inputting it into a multi-head self-attention network for joint modeling; extracting high-order features through random projection expansion and third-order interactive operations, and inputting the extracted features into a residual feedforward discrimination network to output the damage level; and matching and outputting restoration schemes from a pre-built scheme library based on environmental and engineering constraints. This invention achieves structured fusion of multi-source information and intelligent decision-making, improving the accuracy and engineering adaptability of gravel curtain layer restoration.

The invention discloses a network security situation awareness method based on deep learning, and the method comprises the following steps: carrying out the unified structure processing and time alignment of multi-source data, and generating a standardized high-dimensional sample; constructing a disturbance sample set through disturbance sampling, executing low-dimensional embedding by utilizing multiple groups of orthogonal random projection functions, and generating a disturbance embedding matrix; constructing a stability map based on the disturbance path similarity between the samples; constructing an attack type coding matrix by adopting an error correction output coding model and training a plurality of binary sub-classifiers to realize robust multi-class attack recognition; and finally, outputting an optimal attack type label through the ECOC model, and generating a network security situation map in combination with the map. Aiming at the problems of low data processing efficiency and weak identification capability of a multi-source heterogeneous network, the invention provides a situation modeling path fusing disturbance sampling, random projection and an ECOC model.

The application discloses a kind of based on random projection's physical property parameter inversion method of potential field data, comprising the following steps: S1: measured potential field data is obtained, according to survey area and depth range is profiled in underground space, sensitivity matrix in inversion is calculated based on potential field data forward theory, and then the forward calculation relationship of full space is established;S2: random projection matrix is designed, and sensitivity matrix is projected to multiple low-dimensional subspace, and the subspace forward calculation relationship is established;S3: based on the regularization equation of subspace forward calculation relationship, and the physical property parameter in subspace is solved using conjugate gradient algorithm;S4: the final physical property parameter inversion result is obtained by the weighted average calculation of multiple physical property parameters in subspace.The physical property parameter inversion method of potential field data based on random projection has higher depth resolution and inversion reliability, and improves the practicability of physical property inversion method in actual data processing.

The invention belongs to the technical field of fault diagnosis, and discloses a double-path incremental fault diagnosis method fusing an attention mechanism and a prototype, and the method comprises the steps: designing a double-branch network, and carrying out the division of labor through a heterogeneous network: extracting a time domain feature through a Hu moment-based convolutional neural network, extracting a frequency domain feature through a scale invariant feature conversion-based convolutional neural network, and carrying out the classification of the time domain feature and the frequency domain feature; the time domain branch network captures the global features of the operation trend of the equipment, and the frequency domain branch network extracts the local features of abnormal signals of a specific frequency band to form a time-frequency complementary feature space, thereby achieving the comprehensiveness of feature coverage. Considering that local frequency domain features of a high frequency band are generally more sensitive to early faults and time domain global features can visually reflect the overall operation state, an attention mechanism is introduced to realize adaptive distribution of weights, and the accuracy of feature extraction is further enhanced. Random projection is adopted to increase feature dimensions, the linear separability of features is improved, the problem of category overlapping in a low-dimensional feature space is solved, and category boundaries are expanded.

The invention relates to the technical field of natural language processing, in particular to a model prefix parameter and hyper-parameter joint optimization method and system. Comprising the following steps: S1, acquiring a high-dimensional prefix parameter and hyper-parameter joint optimization instruction; s2, generating a random projection matrix, and mapping the high-dimensional prefix parameters into low-dimensional prefix parameters through the projection matrix; s3, searching an optimal low-dimensional prefix parameter in a low-dimensional space through a covariance matrix adaptive evolution strategy, reconstructing the low-dimensional prefix parameter into a dynamic high-dimensional MLP parameter, generating a key-value prefix required by each layer of the model, and embedding and injecting the key-value prefix into the large model; s4, training hyper-parameters are dynamically adjusted based on a particle swarm algorithm and a cosine annealing strategy, and the hyper-parameters are transited step by step in the training process; s5, training the model based on the optimal prefix parameter and the hyper-parameter, and outputting the trained model; and S6, applying the trained model to the medical question and answer task. According to the method, the precision and stability of prefix tuning in the medical question and answer task can be improved.

The present invention discloses a small sample classification method and device based on a random projection metric space, relating to the field of natural language processing technology. The method comprises: obtaining multiple tasks to be classified; inputting the multiple tasks into a classification model based on the random projection metric space; and obtaining classification results for the multiple tasks based on the multiple tasks and the classification model based on the random projection metric space. The present invention constructs a random metric space adapted to specific tasks, constructs a metric space based on task features by fine-tuning the position of sample vectors in the metric space, and specifically uses random vectors to learn the metric space for each task, thereby resolving the problem that the general practice of using a universal metric space is not well suited for all tasks and lacks applicability.

The invention discloses a remote abnormity monitoring electric energy meter with an edge diagnosis function and a monitoring method. The method comprises the following steps: acquiring preprocessed voltage and current synchronous sampling data; generating a current electric energy parameter feature vector through a sliding window and a multi-time scale strategy; outputting a random projection forest leaf node set; calling an extreme learning machine local classifier bound with each leaf node one by one, and performing local reasoning by using the current electric energy parameter feature vector to obtain a corresponding local discrimination score set; and outputting an abnormal power fusion confidence coefficient, and comparing the abnormal power fusion confidence coefficient with an adaptive threshold to generate an abnormal power identification result. According to the invention, the expression and discrimination capability of the inner side of the electric energy meter on diversified abnormal power states is greatly enhanced.

The invention discloses an adaptive pulse parallel method and system for efficient training of a pulse neural network. The method comprises the following steps: initializing a shared weight and a fixed random projection matrix; fragmenting the data and locally accumulating the gradient; calculating a gradient consistency direction after synchronization; generating an adaptive aggregation weight based on the cosine similarity and a Softmax function; weighting the aggregation gradient and synchronously updating the global weight; and dynamically unloading the operator to the CPU or the GPU according to the historical performance during forward propagation. The invention aims to solve the problems of insufficient parallelization capability, high communication overhead and low heterogeneous resource utilization rate in neural network training in the prior art, and realizes efficient parallel training of the pulse neural network without back propagation by combining the pure feed-forward characteristic of the DRTP algorithm, the adaptive gradient aggregation strategy and the adaptive element-by-element unloading mechanism.

Systems and methods for generating single-node representations in graphs composed of linked nodes. The present technology enables dynamic generation of individual node embeddings in sub-linear time (less than O(n), where n is the number of nodes in the graph G), using only the PPR vectors of the individual nodes and a random projection to reduce the dimensionality of the PPR vectors of that node. In one example, the present technology includes a computer-implemented method comprising: obtaining, from a database, a graph having a plurality of nodes; generating, for a given node of the plurality of nodes, a personal PageRank vector; and producing, for the given node, an embedding vector by randomly projecting the personal PageRank vector, wherein the embedding vector has a lower dimensionality than the personal PageRank vector.

This invention relates to the field of navigation technology and proposes a visual inertial sensor time synchronization method and system based on motion consistency. The method calculates the camera angular velocity from acquired camera images; extracts the IMU average angular velocity information from acquired IMU data; constructs a direction-weighted matrix based on the relative amplitude of the camera angular velocity; performs dimensionality reduction on the camera angular velocity and IMU average angular velocity using principal component analysis; then, employs fast outlier removal and interpolation optimization based on random projection; matches the camera frame and IMU sensor angular velocity data through correlation; and performs interpolation optimization to obtain the time deviation for time alignment. This method improves the accuracy, stability, and real-time performance of time synchronization by preprocessing and extracting angular velocity data, using data dimensionality reduction and random projection to accelerate the time alignment process, and dynamically correcting the time deviation.

The invention provides an air conditioner abnormal sound positioning method, an air conditioner and a storage medium. The method comprises the steps that abnormal sound signals collected by a microphone array are obtained; performing noise reduction and enhancement processing on the abnormal sound signal collected by each microphone in the microphone array to obtain a pure abnormal sound signal corresponding to each microphone; performing abnormal sound source positioning operation according to the pure abnormal sound signal; wherein the step of carrying out noise reduction and enhancement processing on the abnormal sound signals collected by each microphone in the microphone array to obtain the pure abnormal sound signals corresponding to each microphone comprises the following steps of: carrying out framing processing on time domain signals of the abnormal sound signals, and converting the time domain signals into Hankel matrixes; performing iterative calculation on the Hankel matrix by using a preset bilateral random projection theory method to obtain a low-rank matrix, and obtaining an estimated pure abnormal sound signal matrix; and performing time domain conversion on the pure abnormal sound signal matrix to obtain a pure abnormal sound signal. According to the invention, the abnormal sound detection precision under complex background noise can be improved.

The application discloses a fast calculation method and device for scattered electromagnetic field based on random matrix approximation, and belongs to the field of fast modeling of scattered field. The method comprises the following steps: precalculating an incident field vector and a reciprocal incident field vector; generating a Gaussian random matrix matched with the column number of a to-be-determined scattered response matrix; performing linear superposition on the incident field vector to obtain a synthetic incident field vector, and constructing a random projection matrix according to the scattered field vector of an equivalent current vector at a receiving point; performing linear superposition on the reciprocal incident field vector to obtain a reciprocal synthetic incident field vector, and constructing a projection coefficient matrix according to the response of a reciprocal equivalent current vector at a transmitting source; and calculating a complete scattered response matrix based on the product of an orthogonal basis matrix and the projection coefficient matrix. In the application, a large-scale geophysical electromagnetic complex scene can be adapted, higher calculation precision and stable convergence are achieved, and the reconstruction of the complete scattered response matrix can be completed at a lower relative error level.

The invention provides a reinforcement learning data selection method and device based on off-line strategy influence estimation. According to the method, firstly, offline approximation of a target strategy gradient is realized through importance sampling and KL divergence constraint; carrying out gradient dimensionality reduction by applying sparse random projection to optimize storage calculation; calculating gradient inner product similarity based on the current strategy check point and the verification set to generate an influence score; and finally, iteratively selecting a high-influence data subset according to the score, and optimizing strategy parameters under a course learning framework. According to the method, the data utilization efficiency is remarkably improved, the calculation cost is reduced, and the high-dimensional gradient processing performance is effectively optimized.

The invention discloses a CUDA (Compute Unified Device Architecture) acceleration-based magnetic resonance image rapid reconstruction method and a magnetic resonance imaging system. The method comprises the following steps: acquiring under-sampling k-space data and full-sampling self-calibration signal data; constructing an overdetermined linear equation set; a sparse random projection matrix is designed, and equation set dimension reduction is completed; using CUDA (Compute Unified Device Architecture) to accelerate to solve the linear equation set after dimension reduction, and obtaining a GRAPPA linear combination coefficient; based on the combination coefficient and the under-sampled k-space data, performing parallel synthesis on missing k-space data through CUDA (Compute Unified Device Architecture); and performing two-dimensional Fourier transform and post-processing on the complete k-space data, and outputting a quickly reconstructed 2D magnetic resonance image. According to the method, the dimension reduction advantage of random projection and the parallel computing capability of CUDA are fused, the technical bottlenecks that in traditional 2D GRAPPA reconstruction, the calculation complexity in the calibration stage is high, and the CPU serial processing speed is low are effectively solved, extra hardware transformation or channel compression dependence is avoided, high-quality 2D magnetic resonance images can be stably output, and the clinical rapid diagnosis requirement is accurately met.

The application discloses a data search processing method, device and equipment, and the method comprises the following steps: obtaining a first abstract resilient dataset of a parallel computing framework algorithm according to a dataset of a file system; performing conversion processing on the first abstract resilient dataset to obtain a second abstract resilient dataset; forming a third abstract resilient dataset according to a random projection seed of a random projection tree and the second abstract resilient dataset; performing calculation processing on the third abstract resilient dataset to obtain a fourth abstract resilient dataset; when judging that a leaf node of the random projection tree reaches a condition according to the fourth abstract resilient dataset, obtaining an optimized random projection tree; and searching for target data according to the optimized random projection tree. Through the above method, the application realizes fast retrieval of target data in a high-dimensional space.