533 results about "Domain adaptation" patented technology

The invention discloses a lithium battery health state estimation method based on transfer learning, and the method comprises the steps: firstly carrying out the normalization and time sequence reconstruction of a battery charging voltage-capacity curve, and constructing a unified input sequence; extracting long-time-sequence degradation characteristics based on a Mama network, and completing SOH regression prediction through a two-stage full connection layer; in the cross-domain adaptation stage, in combination with an alignment strategy of dynamic time warping and weighted maximum mean difference, time sequence matching and feature distribution alignment in the degradation stage are realized; meanwhile, on the basis of a sample weighting mechanism of a Wasserstein distance, the effectiveness of migrating source domain knowledge to a target domain is improved; through a two-stage strategy of source domain pre-training and source-target joint training, a relatively low prediction error and a relatively high fitting degree can be kept under the condition that a target domain is not labeled; the method shows good generalization ability and robustness under different battery types and different working conditions, and can provide reference for health management of the electric vehicle.

The invention discloses an energy storage battery health feature extraction and state evaluation method based on transfer learning. The method comprises the following steps: S1, constructing a source domain health feature library and pre-training a model; according to the method, dependence on complete cyclic data is broken through, high precision and robustness are still achieved under the conditions of data sparsity and working condition difference, and the method is suitable for intelligent operation and maintenance and predictive maintenance of an energy storage power station. And meanwhile, common incomplete and partial charge and discharge data fragments under actual working conditions can be directly utilized for feature extraction and state evaluation, dependence on complete charge and discharge cycles is avoided, and the application scene of the data driving method is greatly widened.

The invention discloses an open set domain adaptive image classification method based on a difference prompt learning technology of a pre-training vision-language model. According to the method, high-quality pseudo-open class images are generated, and de-noising text embedding and de-noising visual embedding are obtained by using a differential prompt learning technology, so that class characteristics of a source domain, a target domain and pseudo-open class samples are effectively extracted, and irrelevant noise is inhibited. According to the method, a vision-text comparison loss mechanism, a triple distance comparison loss mechanism and a negative sample penalty mechanism are further designed, a known category and an unknown category are effectively distinguished in a feature space, and the semantic alignment capability of cross-domain similar samples is enhanced. The method can significantly improve the classification accuracy and model robustness in an open set domain adaptation task, has the advantages of simple structure, high calculation efficiency, good generalization performance and the like, and is suitable for image classification, cross-domain transfer learning and other related application scenes.

An industrial big data-driven vertical federated transfer-based anomaly detection method and system. The method comprises: acquiring a source domain data set and a target domain data set from an industrial scenario, the source domain data set being constructed on the basis of industrial data having known anomaly labels, and the target domain data set being constructed on the basis of industrial data without anomaly labels; and on the basis of a preset vertical federated transfer model: performing vertical federated feature extraction: mapping the source domain data set and the target domain data set into a common feature space to obtain potential features; performing domain adaptation: extracting features having domain invariance and discriminability from among the potential features; and performing joint domain alignment: aligning the distance between domains, and mapping the features having domain invariance and discriminability to obtain the anomaly labels.

The invention discloses an information extraction data annotation and training system based on a large language model, and relates to the technical field of information extraction, and the system comprises a data set construction module which is used for constructing a pre-training data set and a fine tuning data set; the model continuous pre-training module is used for carrying out continuous pre-training on a preset general large language model based on the pre-training data set to generate a field adaptive pre-training model; the model fine tuning module is used for performing supervised fine tuning training on the domain adaptive pre-training model through a two-stage course learning strategy based on the fine tuning data set, and generating an information extraction model; the retrieval enhancement generation module is used for performing entity-semantic retrieval on an input text based on a preset knowledge base, outputting context information related to the input text, and outputting structured information of the input text based on the context information and an information extraction model, the problems of insufficient generalization ability, poor field adaptability and disastrous forgetting of a general large language model are solved, and the accuracy and robustness of information extraction are improved.

The invention discloses a multi-agent combat task cooperation method for a structure entropy guided graph neural network, and the method comprises the steps: S10, each combat agent interacts with an environment according to an action generated by a strategy network, the environment comprises environment information, task parameters and a preset task target, and the strategy of each combat agent is completely executed in a decentralized manner; collecting complete empirical trajectory data; s20, using the collected data for centralized training; performing value evaluation on the global state of each time step by using a value network; s30, calculating strategy loss and value loss by using a multi-agent near-end strategy optimization algorithm in combination with the output of the strategy network and the value estimation of the output of the value network; updating parameters of the strategy network and the value network by using a gradient descent method; and S40, performing loop iteration. The problems that in a traditional method, the battlefield game dynamic structure sensing ability is insufficient, the hierarchical strategy learning and generalization ability is limited, the adaptability of a model in a small sample area is poor, and the migration efficiency is low are solved.

The invention provides a code retrieval method and device for AI programming and related equipment, and the method comprises the steps: constructing a double-layer embedded vector model which comprises a basic semantic coding layer for parameter freezing and a plurality of independent lightweight adaptation layers which coexist; monitoring implicit feedback data generated by interaction of a user in the target code library to extract positive and negative sample pairs, and mining difficult negative samples based on a current model to form a training sample set; when the cumulative number of the samples reaches a preset trigger threshold value, optimizing and updating at least one lightweight adaptation layer parameter by utilizing a preset comparison loss function, and applying elastic weight constraint to key parameters of the lightweight adaptation layer parameter to obtain an updated double-layer embedded vector model; and performing multi-dimensional performance evaluation on the updated model, and when an evaluation result meets a preset publishing condition, deploying the model to a code retrieval service. The problems that in the prior art, cross-domain is difficult, the adaptation cost is high, and catastrophic forgetting is likely to happen are effectively solved, and zero-labeling self-evolution of the model is achieved.

The embodiment of the invention provides a knowledge graph fusion method and system based on a large model, and the method comprises the steps: carrying out the data standardization, entity feature enhancement and relation semantic annotation of heterogeneous knowledge graphs from different sources; through semantic similarity calculation, context reasoning and alignment confidence evaluation, a multi-stage and multi-mode entity alignment mechanism is constructed in combination with a large language model, and cross-source entity matching is performed on entities in heterogeneous knowledge maps of different sources; based on conflict detection, dynamic weight distribution and a conflict resolution strategy, generating a data fusion result, and unifying multi-source data; and generating a high-quality unified knowledge graph through missing relationship prediction, logic consistency verification and an incremental updating mechanism. According to the knowledge graph fusion method and device, full-process automation, precision and dynamics of knowledge graph fusion are realized, the problems of high manual dependence, weak semantic processing capability, poor cross-domain adaptability and the like in the prior art are effectively solved, and the efficiency and quality of knowledge graph fusion are remarkably improved.

The invention discloses a train traction converter IGBT residual life prediction method, and relates to the field of train traction converter IGBT prediction, and the method comprises the steps: obtaining multi-source domain and target domain IGBT degradation data, and carrying out the signal decoupling and resampling preprocessing of the degradation data; based on the pre-processed degradation data, extracting time sequence features of a low-frequency trend by constructing a feature extraction network; based on the time sequence characteristics of the low-frequency trend, performing multi-source confrontation domain adaptation training by constructing a domain discriminator; on the basis of the training result, dynamic weighted online knowledge distillation is carried out by adopting an online multi-teacher architecture, and an IGBT residual life prediction result is output; and performing global optimization on the model hyper-parameters by using a particle swarm optimization algorithm, and verifying the model at a plurality of prediction starting points. According to the method, through signal decomposition, adversarial domain adaptation and dynamic weighted distillation, high-precision and high-reliability RUL prediction under a data scarcity and cross-equipment scene is realized.

The invention provides an online rail fault diagnosis method and system based on knowledge transfer learning, and belongs to the technical field of crossing of intelligent monitoring and artificial intelligence of railway infrastructures. The method comprises the steps that S1, a server trains a model framework through a source domain data set to obtain a teacher diagnosis model; based on the target domain data set, training the teacher diagnosis model by adopting a mixed training strategy to obtain a student diagnosis model; s2, acquiring real-time multi-modal monitoring data from a target domain line by the edge computing equipment, compensating to obtain corrected data, and dynamically selecting the most important feature subset from the corrected data to form a simplified feature set; and S3, inputting the simplified feature set into a student diagnosis model to obtain a student fault diagnosis result and confidence thereof, and introducing a D-S evidence theory to generate a student fault diagnosis report. The method has the advantages that the accuracy, the real-time performance, the cross-domain adaptability and the overall system safety of railway track fault diagnosis are greatly improved.

The invention belongs to the technical field of data processing, and provides a data labeling method and system based on cue word driving, a joint guide vector is generated by fusing field features of a labeling task and operation behavior vectors of labeling personnel, labeling errors caused by guide deviation are greatly reduced, and labeling efficiency and preliminary labeling quality are improved; the initial labeling result is analyzed, the domain distribution difference between the labeling defect type and the associated cue word is recognized, and clear targeting is provided for follow-up knowledge graph parameter optimization; a reward function is constructed by taking a labeling defect type and cue word field distribution difference as a state space and combining labeling accuracy and field adaptability, parameters of a cue word knowledge graph are iteratively corrected through reinforcement learning, and high-quality cue words can be continuously output; after the cue word knowledge graph iteration is stable, the fusion coefficient of the joint guide vector is updated based on quality evaluation data feedback, and it is ensured that the joint guide vector and the optimized knowledge graph are cooperatively matched.

The invention discloses a Text2SQL (Structured Query Language) system and method based on cue words. The system comprises a natural language input module for receiving and preprocessing a natural language query statement; the metadata management module is used for managing database metadata information; the cue word generation module is used for generating cue words containing database modes, user problems and SQL requirements on the basis of the query statements and the metadata; the large language model module is used for generating a preliminary SQL based on the cue word; the SQL grammar verification module is used for verifying grammar and feeding back errors; the manual auditing module is used for auditing and editing the SQL statements passing the verification; and the SQL execution module is used for executing the SQL passing the audit and returning a result. According to the method, the accuracy is improved by combining cue word guidance and a large model, the field adaptability is enhanced by relying on metadata, the data security is ensured by introducing manual auditing, the accuracy and security of SQL generation in a complex query scene are remarkably improved, and the development and maintenance cost of the system is reduced.

The invention discloses a robust radio frequency fingerprint identification method based on Barlow Twins domain adaptation. Aiming at the problems of training and deployment environment distribution offset and cross-domain identification performance reduction caused by wireless channel multipath and time-varying characteristics in the prior art, the invention provides a domain adaptation framework combined with feature decoupling. The method comprises the following steps: firstly, converting a received time domain signal into a short-time Fourier transform spectrogram to reserve a time-frequency structure; then, a double-end convolutional encoder is constructed, bottom layer features are extracted through a shared backbone network, and device fingerprints and channel features are obtained in an identity branch and a channel branch respectively; in order to realize thorough decoupling of the two types of features, Barlow Twins-based independence regularization is introduced, and the identity features and the channel features are statistically orthogonal by minimizing a cross-correlation matrix of the identity features and the channel features, so that purer fingerprint features are obtained, channel interference is effectively eliminated, and generalization and recognition precision of the model under an unknown channel are remarkably improved.

The invention discloses a mine gas monitoring and early warning method and system based on a large model, and the method comprises the steps: collecting the multi-modal data under a mine in real time, carrying out the cleaning, denoising, alignment and standardization processing of the multi-modal data, and obtaining the standard multi-modal data; establishing a mine safety knowledge graph, performing SFT supervised fine tuning and field adaptability pre-training by using the mine safety knowledge graph on the basis of the general large language model, and constructing a large model for mine field knowledge enhancement; standard multi-modal data are input into the large model, a multi-modal encoder is used for unified understanding and encoding to extract key information, and panoramic situation description is generated; and based on panoramic situation description, performing causal analysis and risk deduction by using a large model, and generating a mine safety early warning report in combination with a mine safety knowledge graph. The mine gas concentration can be accurately predicted, and the emergency disposal efficiency and effect are improved.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention relates to the technical field of image data processing, in particular to an automatic driving model based on a mixed low-rank expert and a multi-domain adaptive fine tuning method, and the model comprises a general model base in a frozen state and a multi-domain adaptive component in a trainable state; the universal model base comprises an image encoder, a measurement information encoder and a trajectory planner; the multi-domain adaptation component comprises a mixed low-rank expert group and a domain sensing router; the mixed low-rank expert group is used for calculating a difference correction signal according to the input characteristics; and the domain sensing router is used for receiving the shared image features extracted by the image encoder, outputting probability vectors and dynamically activating corresponding experts in the mixed low-rank expert group according to feature distribution of input data. According to the method, plug-and-play type efficient adaptation of a single universal model to multiple downstream domains is achieved with extremely low calculation and storage cost, and the pain point of a traditional multi-domain adaptation method is perfectly solved.

The invention belongs to the technical field of ancient book digital processing, and discloses a training knowledge-fused ancient book word sense disambiguation deep learning method, which comprises the following steps of: constructing a training knowledge graph; preprocessing ancient book image texts; constructing a deep semantic disambiguation model fusing training knowledge; word sense disambiguation reasoning and result output; and system integration and intelligent application interface design. The high-precision word sense disambiguation method oriented to ancient books and texts is constructed by fusing training knowledge and a deep learning technology, so that the recognition capability of complex semantic phenomena such as polysemy words, ancient and modern heterosemy and common and false characters is remarkably improved, the interpretability and field adaptability of the model are enhanced, the dependence on manual training is reduced, and the training efficiency is improved. The method realizes efficient understanding and intelligent processing of the semantics of the ancient books under the condition of low resources, and has good popularization and application prospects and culture inheritance value.

The invention provides a meta-universe virtual-reality interaction method based on causal invariance, belongs to the field of meta-universe virtual-reality interaction technologies, and is used for solving the problems of poor cross-domain adaptation, insufficient long-tail scene coverage and low dynamic robustness in related technologies. According to the method, through sensing layer causal kernel quality evaluation and data enhancement, reasoning layer causal invariance learning and cross-domain parameter migration, decision layer causal attention intention alignment, execution layer causal reinforcement learning control and iteration layer double-threshold knowledge updating, full-link parameter collaborative circulation is realized in combination with a causal data interface; and finally, the accuracy and the stability of virtual-real interaction of the element universe are improved, and efficient adaptation of cross-domain, long-tail and dynamic scenes is realized.

The embodiment of the invention provides a cross-domain generalization model training method, a blood vessel segmentation method, computing equipment, a computer readable storage medium and a computer program product. The cross-domain generalization model training method comprises the steps that source domain image data, label data corresponding to the source domain image data and target domain image data are acquired; respectively inputting the source domain image data and the target domain image data into a blood vessel segmentation model to obtain a source domain segmentation result and a target domain segmentation result; inputting the source domain segmentation result and the target domain segmentation result into a discriminator to generate a discrimination result; constructing a first loss function and a second loss function; and by taking minimization of the first loss function and simultaneous maximization and minimization of the second loss function as optimization objectives, iteratively adjusting network parameters of the blood vessel segmentation model and network parameters of the discriminator to obtain a trained generalized blood vessel segmentation model. According to the technical scheme provided by the embodiment of the invention, the blood vessel automatic segmentation precision and the cross-domain adaptive capacity are improved.

The invention relates to a vertical field large model training system and method based on an incremental ratio and a progressive corpus set. The method comprises the steps that original data of different sources are preprocessed, a corpus set is generated, and the corpus set comprises general corpora and target domain corpora; sampling the corpus set based on an incremental matching algorithm to generate a plurality of incremental training corpus sets; performing first-stage training on a universal base large model through the plurality of incremental training corpus sets, and generating a vertical field base large model through a multi-dimensional loss monitoring mechanism; expanding the corpus set through multi-field corpora distributed in different lengths to generate a progressive corpus set; and performing second-stage training on the vertical domain base large model through the progressive corpus set in combination with an annealing optimization strategy to generate a long context vertical domain large model. According to the method, stable fusion of multi-source knowledge can be realized, and the field adaptability, the long text understanding ability and the training stability of a large language model are remarkably improved.

The embodiment of the invention provides a model optimization method, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial quantization bit width and an initial pruning rate of each convolutional layer in a BEV perception model; on the basis of the initialized quantization bit width of each convolution layer, quantizing the weight of the convolution layer, and according to the initial pruning rate of each convolution layer and the importance score vectors of the multiple output channels, obtaining pruning masks of the output channels; based on the loss function, the quantized weight and the pruning mask training model, obtaining an initial deployment model; and carrying out online quantitative sensitivity evaluation training on the initial deployment model, and when an evaluation condition is met, taking the initial deployment model as a final deployment model. Therefore, the problems that the domain adaptation and generalization ability is limited, the detection precision is reduced, the domain adaptation and generalization ability is difficult to be efficiently utilized by NPU of edge chips such as horizon lines, model evolution in the training process cannot be responded, and error accumulation is serious are solved, and structured sparsity, dynamic adaptation and end-to-end collaborative optimization are achieved.

The invention discloses a method for predicting the remaining service life of a control moment gyroscope based on parameter fusion, and relates to the technical field of health management of spacecraft attitude control systems. According to the method, optimal fusion of measurement parameters and implicit parameters is realized through adversarial learning feature extraction and a channel attention mechanism, and the method is particularly suitable for high-precision residual life prediction of the spacecraft control moment gyroscope under variable working conditions. The method comprises four main steps of data preparation and multi-source input, adversarial learning feature extraction, SE-Attention feature fusion and three-stage training optimization. In the data preparation step, original time sequences of voltage and current of a rotor motor are collected through a built-in electrical sensor of a CMG, and key physical parameters of a full-film lubrication friction coefficient, lubricating oil viscosity, a bearing clearance and contact stiffness are inverted based on an electromechanical coupling model; in the adversarial learning step, a game framework of a feature extractor and a working condition discriminator is constructed, and working condition-independent robust feature extraction is realized through a gradient inversion layer; in the SE-Attention feature fusion step, importance weights are adaptively distributed to different feature channels through an extrusion-excitation mechanism; and the three-stage training optimization step adopts a preheating-confrontation-fine tuning strategy to ensure the network convergence. The method solves the problem of domain adaptation, has the technical advantages of strong robustness, high precision, strong generalization, interpretability and self-adaptation, and can realize high-precision RUL prediction under variable working conditions.

The invention discloses a distraction driving detection model based on domain self-adaption, and aims to solve the problem that the performance of an existing distraction driving detection model is reduced due to the fact that distribution differences exist between a source domain and a target domain in the aspects of a driving environment, a shooting view angle, an illumination condition, driver characteristics and the like. According to the model, joint distribution alignment of feature and category prediction is realized through a label guiding domain adversarial network, and the adaptive capacity of the model in a cross-domain scene is remarkably improved. In order to further enhance the discrimination capability and generalization of the model, optimization is carried out from two levels of feature extraction and training strategy: a local feature enhancement module aims to improve the feature representation capability of key local behaviors; and according to the category structured CutMix strategy, the generalization ability of the model is enhanced by constructing a mixed sample. Experimental results show that the detection accuracy and robustness of the model under the cross-domain condition are effectively improved, a reliable technical scheme is provided for driver state monitoring, and the method has important application value for road traffic safety guarantee.

The invention provides a blast furnace abnormal working condition early warning method based on class condition manifold alignment. The blast furnace abnormal working condition early warning method comprises the following steps: (1) representing a learning model based on a time sequence neighborhood comparison submerged space; (2) establishing a class condition manifold alignment model based on domain adaptation; and (3) a prototype dynamic updating mechanism based on confidence degree adjustment. Therefore, accurate early warning of the real abnormal working condition of the blast furnace is realized, the flexibility and the accuracy of early warning of the abnormal working condition are improved, meanwhile, combined characterization of stability and dynamic characteristics of the blast furnace in the operation process is realized, and the problem of knowledge migration caused by data distribution drift and working condition characteristic difference between different blast furnaces is solved.

The invention relates to an FFT-based unsupervised domain adaptive hyperspectral image classification method and system. The method comprises the steps of obtaining a remote sensing hyperspectral image, performing preprocessing, cross-domain alignment operation and edge filling operation, and performing alignment operation by using a local neighborhood; fourier transform is carried out on the source domain image and the target domain image; phase information of the source domain image is reserved, amplitude information is replaced with amplitude information of the target domain image, and the injection intensity of the amplitude of the target domain image is controlled; reconstructing the source domain image fused with the target domain amplitude information through inverse Fourier transform to obtain a data enhanced image; a ResNet model is used to extract joint features about a local space and a spectrum; performing feature extraction by adopting a channel attention mechanism and a space attention mechanism based on Fourier transform to obtain adaptive re-calibration features; unsupervised domain adaptive classification is realized by adopting multiple task heads and conditional adversarial training; and performing classification by using the trained classification model. According to the invention, the accuracy of image classification is improved.

The invention relates to the field of electrocardiosignal processing, and particularly discloses an electrocardiosignal classification method, device and equipment based on multi-mode electrocardiosignal characteristics and a medium thereof.The method comprises the steps that firstly, an original electrocardiosignal is preprocessed, including denoising, resampling and heartbeat segmentation; then three types of complementary features are extracted in parallel: wavelet energy features are extracted through discrete wavelet transform, deep abstract features are extracted through a neural network comprising a multi-scale residual block, a Transform encoder and a windowed global-local attention module, a knowledge graph is constructed based on clinical prior knowledge, and clinical concept features are extracted through a graph convolutional network; adaptive weighted fusion is carried out on the three types of features through a multi-modal feature fusion module; and finally, classification is performed based on the fusion features, and an unsupervised domain adaptation strategy combining antagonism domain alignment and class condition alignment is adopted in the training process. The method effectively solves the problems that a traditional method is single in feature and insufficient in cross-domain generalization ability.

The invention discloses a target detection method and device based on domain self-adaption, equipment and a medium. The target detection method based on domain adaptation is realized through a target detection model, the target detection model comprises a dynamic domain adaptation module, and the dynamic domain adaptation module comprises an image level domain classifier with a first dynamic confrontation gradient inversion layer and an object level domain classifier with a second dynamic confrontation gradient inversion layer. In the back propagation of the training process of the target detection model, the gradient inversion intensity of the first dynamic confrontation gradient inversion layer is dynamically adjusted according to the image level domain classification loss, and the gradient inversion intensity of the second dynamic confrontation gradient inversion layer in the back propagation is dynamically adjusted according to the object level domain classification loss; according to the method, the target detection precision under the severe weather condition can be improved, the target missing detection rate and the target false detection rate under the severe weather condition are reduced, and then the safety and the reliability of the automatic driving system are improved.

Approaches presented herein provide for unsupervised domain transfer learning. In particular, three neural networks can be trained together using at least labeled data from a first domain and unlabeled data from a second domain. Features of the data are extracted using a feature extraction network. A first classifier network uses these features to classify the data, while a second classifier network uses these features to determine the relevant domain. A combined loss function is used to optimize the networks, with a goal of the feature extraction network extracting features that the first classifier network is able to use to accurately classify the data, but prevent the second classifier from determining the domain for the image. Such optimization enables object classification to be performed with high accuracy for either domain, even though there may have been little to no labeled training data for the second domain.

The invention provides a cross-well-area drilling parameter migration method and system based on migration learning, and the method comprises the steps: firstly obtaining a drilling parameter historical data set, a scene data set and an operation effect data set of a source well area, and an initial drilling scene data set and an operation demand data set of a target well area; constructing a cross-well-area drilling parameter transfer learning model comprising a source domain feature extraction layer, a domain adaptation layer, a knowledge transfer layer and a target domain parameter generation layer, inputting source well area data into the source domain feature extraction layer to extract feature vectors, and inputting the feature vectors and target well area scene data into the domain adaptation layer to adjust a parameter mapping matrix; inputting the vector after domain adaptation and source well area operation effect data into a knowledge migration layer to generate a target domain preliminary parameter feature vector, inputting the target domain preliminary parameter feature vector into a target domain parameter generation layer in combination with target well area operation demand data to generate an adaptive parameter set, and finally transmitting the parameter set to a target well area drilling control system to execute drilling operation. And the accuracy and efficiency of cross-well-area drilling parameter migration are improved.

The invention relates to the technical field of micro-grid load prediction, and discloses a multi-factor efficient fusion micro-grid load short-term prediction model optimization method. According to the method, micro-grid load historical data and multi-source factor data including information such as meteorology and electricity price are collected, and one-hot coding and normalization processing are carried out. Load data is decomposed into high-frequency and low-frequency components by using a variational mode decomposition technology, and prediction is carried out by respectively adopting a long-short-term memory network integrated with an attention mechanism and a linear regression model. And by calculating the difference between the source domain data and the target domain data in the model hidden layer feature space, the domain adaptive capacity of the model is enhanced, and accurate short-term prediction of the microgrid load is realized.