89 results about "Sequence reconstruction" patented technology

The invention relates to the field of log fault analysis, in particular to a log aggregation fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal heterogeneous log, carrying out sliding time sequence slicing processing, carrying out time sequence association sequence reconstruction, and constructing a time sequence reconstruction log data stream; log event deep semantic analysis is carried out on the time sequence reconstruction log data stream, event semantic topological evolution is carried out, and a multi-dimensional event topological representation matrix is constructed; performing routine event behavior analysis and abnormal fault mode inference based on the multi-dimensional event topology representation matrix, and marking abnormal fault points; and the occurrence timestamp and the abnormal propagation rate of the abnormal fault point are calculated, fault space-time diffusion evolution is carried out, and a dynamic fault propagation path map is constructed. Through efficient and accurate fault traceability analysis, the fault diagnosis efficiency is greatly improved, and the stability and reliability of log data are improved.

A deep learning system for time series prediction comprising a preprocessor that receives time series input sequences, truncates them by removing terminal values, and appends padding values to maintain the original sequence length. An encoder compresses these padded sequences into latent space representations, while a decoder reconstructs predicted sequences matching the original length, specifically trained to reconstruct values matching the removed terminal values in positions corresponding to the padding values. A training system optimizes the encoder and decoder by minimizing differences between original sequences and predicted sequences. The system can process multiple time horizons simultaneously while maintaining statistical properties and providing uncertainty quantification through confidence intervals. This approach enables accurate short-term forecasting while preserving both temporal patterns and statistical relationships in the predicted sequences.

The invention belongs to the technical field of document content extraction, and particularly discloses a document content extraction method and system based on multimodal model collaboration, a terminal and a medium. Comprising the following steps: identifying the type of an input to-be-processed document, and judging the document type; on the basis of the type identification result, calling a multi-modal model to analyze the document content, and outputting space coordinates, visual features and semantic features of document elements; generating a content sequence according with a reading habit through a semantic sequence reconstruction algorithm; paragraph boundary detection, paragraph recombination and semantic association modeling of charts and texts are completed based on the multilayer attention network and the graph neural network; grammar error correction, format optimization and title hierarchy generation are carried out by using a large language model and a hierarchical classification network; and converting the identification result into a structured output file. According to the method, the processing requirements of different types of documents can be considered, and high-precision analysis and efficient output are realized under the scenes of complex layouts, multiple languages and formula tables.

The invention discloses a prefabricated cabin welding seam quality detection method based on self-supervised learning, and the method comprises the following steps: collecting a multi-source welding seam image and process parameters, carrying out the synchronous calibration, and constructing a multi-modal observation matrix; the image is input into a DINOv2 model based on a visual Transform, dense features are obtained, and inter-frame registration and sequence reconstruction are completed; performing difference analysis on adjacent frames, extracting spatial changes and marking potential defects; the dense features and the process parameters of the corresponding time periods are fused, joint features are constructed and classified according to rules, and a preliminary result is output; inputting the defect area and the process parameters into an improved DEER model, extracting an influence path and amplitude, and generating a sensitivity score and a confidence coefficient; final judgment is given in combination with the preliminary result, the sensitivity and the confidence coefficient, and feedback is formed according to judgment and process difference. According to the method, high-precision and explainable detection and process optimization of weld defects are realized, and the method is suitable for online / offline quality control and tracing.

The invention discloses an interactive three-dimensional teaching resource generation system and method based on 3D Gaussian splashing, and belongs to the technical field of three-dimensional reconstruction, and the system comprises a multi-view image obtaining module which is used for extracting a multi-view image sequence related to a teaching theme from a teaching video resource, and determining the category of each structural region in each view image; the enhanced three-dimensional reconstruction module is used for reconstructing the multi-view image sequence into a three-dimensional teaching model; determining the anchor point attribute of each anchor point in the three-dimensional teaching model; the intelligent interaction module is used for acquiring a user instruction; determining a target anchor point according to the user instruction, and obtaining an anchor point attribute corresponding to the target anchor point; and the three-dimensional visualization module is used for performing rendering display on the three-dimensional teaching model and displaying the anchor point attribute corresponding to the target anchor point. The three-dimensional teaching model which has interaction capability and is subjected to structured knowledge labeling can be generated. The technical problem that a current three-dimensional model lacks intelligent interaction capability is solved.

The invention provides an object motion trail prediction method and device, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: reconstructing an obtained multi-view time sequence two-dimensional image sequence of a target object, obtaining an original particle point cloud sequence, and generating a sparse key point cloud sequence after sampling; constructing a particle map sequence based on the spatial position of the key point at each moment in the sequence, and performing spatial semantic completion; performing object-level dynamic space-time aggregation on the complemented key point feature sequence to obtain aggregated key point features, inputting the aggregated key point features into a particle diagram converter, and outputting key point updating features after long-range force propagation modeling through the converter; and decoding and predicting key point displacement at a future moment based on the updated feature, and generating a future movement track of the target object according to the key point displacement. Based on the method, the invention further provides a device for predicting the object motion trail. According to the method, the fine appearance and the stable and accurate motion trail of the object can be predicted at the same time.

The invention discloses an intelligent strip mine slope rock mass monitoring method based on multi-source sensor fusion, and relates to the technical field of mine engineering safety monitoring, and the method comprises the following steps: laying a reference beacon and an asynchronous sampling array in a strip mine slope area, constructing a cross-scale time-frequency observation layer, obtaining ground surface high-frequency vibration data caused by a heavy mine car, and carrying out real-time monitoring on the ground surface high-frequency vibration data; and establishing a vibration fingerprint baseline, and calibrating the inherent frequency neighborhood of the slope rock mass. According to the method, coupling sensing is realized by constructing vibration fingerprints and inherent frequency neighborhoods, coherent decomposition and shadow sequence reconstruction are combined to decouple data, risk boundaries are established and time correction optimization is carried out, weight dynamic adjustment is fused to extract real deformation response, and finally false signals are suppressed by using a phonon structure and inverse phase micro-pulses. And precise monitoring and dynamic regulation and control of the slope state are realized.

The invention provides a UPS (Uninterrupted Power Supply) with a multistage self-protection mechanism, and relates to the technical field of UPS protection, the UPS comprises a power body and a protection system, the power body comprises an abnormal manifold construction module, a protection strategy regulation and control module, a multi-dimensional risk isolation module and a dynamic sequence reconstruction module; the protection system is internally provided with a super-dimension protection network, the super-dimension protection network comprises an operation state super body, a risk characteristic super body and a self-adaptive protection priority algorithm, and an abnormal manifold construction module of the scheme collects a voltage fluctuation period, current peak intensity and a load sudden change rate in real time through a high-precision sensor; the sampling frequency is dynamically optimized in combination with an adaptive threshold adjustment algorithm, a high-dimensional abnormal manifold model is constructed by using a topology abnormal clustering algorithm, a protection manifold matrix is generated, and transient interference and continuous faults are accurately distinguished.

The invention relates to the technical field of platinum smelting, and discloses a platinum smelting impurity prediction and process optimization system based on deep learning. A feature extraction module of the system extracts a multi-scale smelting feature component through a deep convolutional neural network and calculates a complexity index; the key feature determination module identifies key smelting features most related to impurity sources; the optimization parameter generation module is used for generating personalized optimal regulation and control parameters in combination with historical smelting baseline characteristics, smelting state characteristics and complexity indexes; the sequence reconstruction module reconstructs the feature sequence and decomposes the feature sequence into a slow-varying component reflecting the steady-state characteristic of the impurity and a fast-varying component reflecting the dynamic change of the impurity; the impurity prediction module determines impurity types and generates a mapping relation between the impurity types and smelting process parameters; and the process optimization execution module generates a control instruction based on the mapping relation to realize smelting process optimization. According to the system, through cooperation of multiple modules, precise prediction of platinum smelting impurities and dynamic optimization of the process are achieved.

The invention discloses a distributed sensor abnormal event identification method for intelligent traffic, and particularly relates to the technical field of traffic information perception and identification, and the method comprises the following steps: generating an abnormal information initial confidence value through deploying a sensor node with a confidence value dynamic adjustment function; during fusion processing, independent identification priorities of single node anomalies are reserved; after sequence reconstruction of a unified time reference, judging whether a response condition is met or not in combination with trajectory evolution; if yes, an abnormal response process is triggered, a space-time compensation set is constructed based on historical data of the sensing blind area for verification, and finally an abnormal intervention instruction is output and a traffic scheduling system is linked; according to the method, the sensing sensitivity, the fusion accuracy and the response timeliness of the abnormal information in a complex traffic environment are improved, the problem of an identification blind area that single-point abnormity is covered is avoided, state reconstruction and supplementary verification of the sensing blind area are realized, and the rapid intervention capability of an intelligent traffic system on emergencies is enhanced.

The invention discloses a neural network reconstruction method for a multispectral image, which is used for performing complete band sequence reconstruction on an acquired limited band image, and comprises the following steps: S11, inputting a multi-channel image into a trained neural network; s21, feature extraction and multi-scale coding are carried out; s31, the reconstruction reasoning module outputs all target wave bands; and S41, comparing with a real image and calculating loss, wherein the loss comprises MSE, frequency domain residual error and structural similarity. The invention provides a neural network reconstruction module with wave band prediction capability, which is specially used for recovering a complete wave band image sequence from a few physically acquired key wave band images.

The invention relates to the technical field of medical information management, in particular to a chronic disease patient home nursing remote management system which comprises a volatility analysis module, a symbol recognition module, a correlation analysis module, a task decoupling module and a nursing task generation module. According to the invention, by analyzing the fluctuation amplitude of the continuously collected physical sign data in the specific time window, the short-term sudden change trend which cannot be revealed by single-time manually reported data can be explored, and the data change direction is further symbolized to identify a continuous change mode with clinical significance; and the time overlapping degree of the continuous sudden change time periods of different physical sign data can be calculated, so that the synchronous association reaction among various physical sign indexes is accurately identified, the potential composite physiological risk is revealed, and the dependency decoupling and execution sequence reconstruction of the high-risk task are realized based on the quantitative evaluation of the task emergency degree and the delay degree.

The invention discloses a DNA sequence reconstruction method and system based on multi-scale attention and contrast learning, and relates to the technical field of DNA storage data reconstruction. Comprising the following steps: collecting a plurality of DNA sequence copies, screening out abnormal length sequences, and constructing a standardized clustering data set; performing one-hot coding and filling processing on the DNA sequence; extracting context dependent features and cross-sequence variation features; an Inter-Sequence multi-head attention mechanism is constructed to calculate the similarity between the sequences, and a weighted sequence tensor is generated; a global dependency relationship in the sequence is extracted through an Intra-Sequence multi-head attention mechanism; local offset features caused by insertion and deletion errors are extracted through a multi-size convolutional network; inputting a double-layer long-short-term memory network for sequence-level modeling, and outputting base reconstruction probability distribution; and constructing positive and negative sample pairs, calculating comparison loss, combining cross entropy loss to form a joint loss function, and outputting a high-precision DNA sequence reconstruction result. The method has high accuracy and robustness under the conditions of complex noise and multiple types of errors.

The invention provides a Gaussian neural field dynamic scene reconstruction system based on depth consistency constraint, and relates to the technical field of computer graphics, and the system comprises an estimation module which generates a target frame initial depth map; the calculation module reconstructs the point cloud and obtains a point cloud normal direction and a pixel normal direction; the optimization module is used for iteratively correcting the initial depth based on the two types of normal consistency to obtain an optimized depth map; the alignment module is used for determining a scale parameter through regression by taking the first target frame as a reference, and carrying out scale transformation on the depths of other frames to form a consistent depth sequence; the reconstruction module is used for constructing or training a Gaussian neural field based on the sequence and outputting a three-dimensional representation; in addition, the calculation module can contain multi-dimensional wavelets and sparse reconstruction and is used for multi-scale noise suppression and direction weighted fitting. Reference frame selection is based on frame-level quality, geometry and scale stability indexes; according to the system, the intra-frame geometric credibility and the cross-frame scale consistency are improved, ghosting and tearing are reduced, and the stability and integrity of dynamic scene reconstruction are enhanced.

The application discloses a B cell immune repertoire antibody sequence feature analysis and specific screening method and related equipment, which can be applied to the technical field of data processing. According to the application, after the antibody sequencing sequence is subjected to germ line comparison and recognition to obtain corresponding first sequencing Fv sequence and germ line Fv sequence, the first sequencing Fv sequence is subjected to gene integrity filtering and structure integrity filtering, clonotype division and root node sequence reconstruction to obtain a second clonotype collection, and then a first lineage forest is constructed, and according to the second sequencing Fv sequence, the second clonotype collection or the first lineage forest, sequence feature analysis in multiple dimensions is carried out, so that the antibody sequence features can be analyzed from multiple dimensions such as sequence quality, sequence abundance, mutation degree, mutation preference and aggregation degree, the filtered BCR sequence has high affinity, and according to the sequence feature analysis results in multiple dimensions or the first lineage forest, a visualized graph is generated, so that relevant personnel can view the sequence features.

This application provides a Gaussian neural field dynamic scene reconstruction system based on depth consistency constraints, relating to the field of computer graphics technology. The system includes: an estimation module that generates an initial depth map of the target frame; a calculation module that reconstructs the point cloud and obtains the point cloud normal and pixel normal; an optimization module that iteratively corrects the initial depth based on the consistency of the two types of normals to obtain an optimized depth map; an alignment module that, using the first target frame as a reference, determines the scale parameters through regression and performs scale transformation on the depths of the remaining frames to form a consistent depth sequence; a reconstruction module that constructs or trains a Gaussian neural field to output a three-dimensional representation based on this sequence; in addition, the calculation module may include multi-dimensional wavelets and sparse reconstruction for multi-scale noise reduction and direction-weighted fitting of normals; the reference frame selection is based on frame-level quality, geometric, and scale stability indices. This system improves intra-frame geometric reliability and cross-frame scale consistency, reduces ghosting and tearing, and enhances the stability and integrity of dynamic scene reconstruction.

The application relates to a computing power chip-based state detection method and system, a computer device and a medium. The method comprises the following steps: obtaining multi-dimensional hardware feature data of a computing power chip to construct a standardized time sequence data set; learning the standardized time sequence data set based on deep learning and physical constraint conditions of the hardware feature data to construct a health assessment model, and selecting target sample data to generate a health benchmark time sequence; obtaining a to-be-detected data sequence, extracting statistical features, and performing sequence reconstruction on the statistical features and the health benchmark time sequence based on a multi-modal self-encoder to obtain a reconstructed sequence; determining a health state deviation degree of the computing power chip based on the reconstructed sequence and the to-be-detected data sequence, and determining the current health state of the computing power chip. The application can realize system-level aging modeling and detection evaluation of the whole life cycle of the computing power chip, accurately learn the health mode of the computing power chip, and significantly improve the accuracy and precision of the aging state detection.

The application discloses an evaporation dish evaporation amount homogenization sequence reconstruction method based on multi-source data fusion, specifically, daily evaporation dish evaporation amount data of each station in a target area, meteorological data and station metadata are screened, automatic secondary quality control is performed on the screened data, and a high-quality basic data set is obtained; a random forest regression and recursive feature elimination cross-validation method is used for selecting features, and finally a partial least squares regression model is used for splicing and interpolation on the daily evaporation dish evaporation amount data, so that the evaporation dish evaporation amount reconstruction sequence is generated; the evaporation dish evaporation amount reconstruction sequence is subjected to homogenization test, breakpoints are identified and corrected, and the evaporation dish evaporation amount homogenization reconstruction sequence is obtained, so that the problem of discontinuous evaporation amount data sequence caused by non-climatic factors such as mixed use of large and small evaporation dishes is solved, and the limitations of traditional conversion coefficient method and multivariate regression equation, such as large difference between stations, insufficient consideration of meteorological conditions and the like, are overcome.

The invention discloses a multivariate data anomaly analysis method based on extended C-Means. The multivariate data anomaly analysis method comprises the steps that (1) anomaly analysis of multivariate time series data is achieved through an extended fuzzy C-Means algorithm and a particle swarm optimization algorithm; (2) putting forward an extended version of an Euclidean distance function and applying the extended version to a C-Means algorithm to calculate the similarity of each variable in multivariate time sequence evaluation; (3) adopting a particle swarm optimization algorithm as a fitness function to calculate an optimal square Euclidean distance; (4) providing a sequence reconstruction technology to quantify an anomaly score of an anomaly level of a data normal structure; and (5) when shape abnormity is detected, eliminating the influence of time migration on similarity evaluation by adopting an autocorrelation coefficient of a time sequence. The multivariate time series data anomaly detection method improves the accuracy of multivariate time series data anomaly detection, and is of great significance to construction of an intelligent damage data platform system.

The present application relates to a kind of persistence endocellulase ancestor and its application.The present application uses the persistence endocellulase of Bacillus subtilis as mother, uses ancestor sequence reconstruction technology, deduces the evolutionary relationship based on systematics analysis, deduces the amino acid sequence of ancestor enzyme from extinct organism using computer calculation.The persistence endocellulase ancestor includes ASR95, ASR106, ASR107, ASR108, ASR109, ASR110, ASR145.Compared with wild-type enzyme, the ancestor enzyme has high catalytic efficiency, good thermal stability and other excellent properties, so that the potential direction of producing reducing sugar by degrading filter paper is realized.

The application discloses a robust multi-sequence reconstruction method based on maximum posterior probability in DNA storage, and comprises the following steps: decoding each sequence in a cluster by using an improved BCJR decoder; converting each sequence in the cluster into corresponding time sequence representation; determining the weight of each sequence; and deducing a MAP algorithm formula of robust multi-sequence decoding, and integrating the sequence weight into joint posterior probability calculation to inhibit the influence of outlying sequences. By proposing a base IDS channel model, the BCJR decoding algorithm is improved, so that the defects that the existing statistical inference sequence reconstruction method is only suitable for binary error correction code are overcome. Therefore, the application can process the quaternary error correction of the base form in the DNA sequence.

The present application belongs to the field of computer vision and artificial intelligence, and particularly relates to a four-dimensional dynamic scene representation and reconstruction method, system and device based on structural equivalence prior, aiming at solving the problems of poor spatio-temporal consistency, large calculation cost and low long sequence reconstruction accuracy in dynamic scene reconstruction. The present application comprises: decomposing the input four-dimensional voxel sequence into multiple two-dimensional feature planes containing spatial projection and spatio-temporal coupling; introducing structural equivalence prior to fuse the plane features, generating unified and continuous spatio-temporal latent space representation by establishing the internal correlation between spatial geometric gradient and time evolution; finally, using a decoding module containing residual connection to decode the latent space representation, and recovering the high-fidelity four-dimensional dynamic scene layer by layer. The present application effectively improves the consistency and accuracy of dynamic scene reconstruction by establishing an explicit spatio-temporal coupling relationship, while taking into account the calculation efficiency, and has broad application prospects in the fields of autonomous driving, augmented reality, etc.

The present application relates to a method, device, equipment and medium for verifying the commissioning status of power equipment, and relates to the technical fields of power engineering auditing and artificial intelligence. It includes: parsing the submitted review materials using a multimodal document parsing model; inputting the dispatching operation power time series into a variational autoencoder model for sequence reconstruction calculation to obtain the reconstruction error and generate a time series anomaly feature component; generating a trajectory space anomaly feature component based on the spatio-temporal data of project personnel clock-in and the benchmark coordinates of the equipment ledger, and at the same time performing error level analysis on the on-site commissioning image data to generate an image anti-counterfeiting anomaly feature component; submitting the above multi-source heterogeneous data to a multimodal large model for cross-comparison and logical reasoning, breaking the limitation of only relying on the submitted review materials by a single party, identifying forgery behaviors from the source. With the equipment identifier as the only primary key, in the face of false commissioning scenarios with strong concealment and across multiple links, a multi-dimensional cross-verification closed loop is formed to identify deep造假行为 is misspelled. It should be "deep造假行为 is misspelled. It should be "deep forgery behaviors" here. So the corrected translation is as follows: The present application relates to a method, device, equipment and medium for verifying the commissioning status of power equipment, and relates to the technical fields of power engineering auditing and artificial intelligence. It includes: parsing the submitted review materials using a multimodal document parsing model; inputting the dispatching operation power time series into a variational autoencoder model for sequence reconstruction calculation to obtain the reconstruction error and generate a time series anomaly feature component; generating a trajectory space anomaly feature component based on the spatio-temporal data of project personnel clock-in and the benchmark coordinates of the equipment ledger, and at the same time performing error level analysis on the on-site commissioning image data to generate an image anti-counterfeiting anomaly feature component; submitting the above multi-source heterogeneous data to a multimodal large model for cross-comparison and logical reasoning, breaking the limitation of only relying on the submitted review materials by a single party, identifying forgery behaviors from the source. With the equipment identifier as the only primary key, in the face of false commissioning scenarios with strong concealment and across multiple links, a multi-dimensional cross-verification closed loop is formed to identify deep forgery behaviors.

The application discloses a method and device for constructing an extended image sequence, and relates to the technical field of image sequence reconstruction, and mainly aims to improve the low authenticity of the simulated object extended image sequence constructed at present, so that the difference between the simulated object and the real projected object is large, and the authenticity of the forward projection is poor. The method comprises the following steps: obtaining tomography data of a target projected object; performing tomography image reconstruction processing on the tomography data to generate a tomography reconstruction image sequence of the target projected object, the tomography reconstruction image sequence of the target projected object being used for representing the maximum image sequence reconstructed under the current reconstruction condition; and constructing an extended image sequence of a simulated projected object matched with the target projected object based on the tomography reconstruction image sequence of the target projected object, the extended image sequence being used for representing the extended image sequence of the simulated projected object which is not reconstructed and needs to be virtually constructed under the current reconstruction condition.

The present application relates to the technical field of multi-agent enterprise autonomy management, in particular to a human-computer dual-sovereignty enterprise autonomy management system and method based on four AI centers; the system comprises a planning center, a decision-making center, an execution center and an evolution center; the planning center constructs a hierarchical constraint matrix group and performs reversible constraint reduction processing, generates a folding governance core and a constraint source mapping chain; the decision-making center generates an item execution sequence, a resource allocation table and an artificial confirmation node table; the execution center forms an execution state record set; the evolution center calculates a constraint bearing deviation vector and performs inverse reduction deviation write-back processing, outputs a constraint correction vector, an authorized boundary correction table and a governance sequence reconstruction instruction. The present application is used for realizing constraint reduction, execution control and deviation write-back correction in the enterprise autonomy management process.

The invention discloses a method and a system for realizing DNA storage by aiming at multi-rule rotation coding of Chinese texts, and relates to the technical field of DNA storage. Encoding the Chinese text by using a five-stroke font input method; mapping is carried out according to the positive and negative code tables, and an interval balance and dynamic detection strategy is introduced in the mapping process to control GC content distribution in intervals and the probability of occurrence of homopolymers between the intervals. Scattering and recombining the sequence by using block coding, and compressing by using RLE coding, wherein the RLE coding generates a sequence file and a run-length file; performing GC content constraint on the sequence file by using cross coding; and br compression is adopted for the run-length file to further improve the compression ratio. And generating a DNA sequence through rotary coding. According to the method, five-stroke coding, positive and negative code table mapping and sequence reconstruction strategies are introduced, so that the information storage density is higher, the local GC content of the DNA sequence is more stable, the homopolymer length is smaller, the unexpected motif proportion is lower, and the data has higher reliability and safety in the storage process.

The application discloses a kind of based on multi-parameter dual-channel time series reconstruction energy storage power station operating state early warning method and system.The method of the application includes: obtaining the multi-parameter operating time series data of energy storage power station, extracting key early warning index as input feature from it, constructing original sequence channel and first-order difference sequence channel in sliding time window, constructing dual-channel time series reconstruction early warning model, and training model and calculating reconstruction anomaly score by the weighted sum of original sequence reconstruction error, difference sequence reconstruction error and time series recursive prediction error;Again, the final early warning main detection score is obtained by smoothing the reconstruction anomaly score, the early warning threshold is determined, and normal, first-level early warning, second-level early warning, third-level early warning and fourth-level early warning are output.The application realizes the stability of effective identification and hierarchical early warning of operating state anomaly by constructing original sequence channel and first-order difference sequence channel, and jointly modeling the multi-parameter operating state of energy storage power station.

The application discloses a kind of multi-modal high-voltage switch perception method and related device, comprising: obtaining the detection data of high-voltage switch state detection sensor and image data, combining detection data and image data and judging the state of high-voltage switch by introducing switch state weight matrix;The binary time sequence and environmental factor weight matrix are obtained by binary encoding switch weight matrix, and the binary time sequence is decomposed into several stationary time subsequences by wavelet decomposition technique;The prediction network model is trained by stationary time subsequence, the binary time sequence is input into the trained prediction network model, and the predicted time subsequence is obtained;Sequence reconstruction is carried out to predicted time subsequence, and predicted time reconstruction sequence is obtained, predicted time reconstruction sequence is introduced into environmental factor weight matrix, and switch state prediction sequence is obtained, so as to solve the problem that the prior art has poor high-voltage switch state detection and prediction accuracy.

This invention belongs to the technical field of superconducting motor quench detection, and more specifically, relates to a superconducting motor quench detection method based on terminal voltage differential and unsupervised autoencoder. The method includes: acquiring the terminal voltage signal of a superconducting magnet; suppressing induced voltage interference through moving average differential processing; and obtaining a preprocessed signal after filtering; inputting this signal into a pre-trained unsupervised autoencoder model, which consists of an encoder and a decoder. The encoder extracts deep features through a convolutional neural network, a bidirectional long short-term memory network, and an attention mechanism; the decoder performs sequence reconstruction; and by calculating the reconstruction error and comparing it with an adaptive threshold, the quench state is detected and located. This invention solves the problem that the quench judgment threshold often relies on empirical settings and is difficult to adapt to the complex and ever-changing dynamic operating conditions of the motor.

The invention discloses difunctional glutathione synthetase based on ancestor sequence reconstruction and application of the difunctional glutathione synthetase, and belongs to the technical field of enzyme engineering and bioengineering. A difunctional glutathione synthetase Anc427 with an amino acid sequence shown as SEQ ID NO: 1 is obtained through an ancestral sequence reconstruction technology, and compared with St-GshF, the obtained difunctional glutathione synthetase Anc427 has the advantages that the melting temperature is increased to 56.2 DEG C from 45.4 DEG C through a stability test and an amplification reaction; at the temperature of 40 DEG C, the half-life period is prolonged from 173.29 min to 3465.74 min; the half-life period at 50 DEG C is 266.6 min, and the thermal stability is remarkably improved. Meanwhile, the activity of the bifunctional glutathione synthetase Anc427 is improved by 17% compared with that of St-GshF, and the conversion rate under the substrate concentration of 100 mM is improved by 13.6%. The industrial application value is extremely high.