1323 results about "Feature Dimension" patented technology

The invention relates to the technical field of data fusion, and discloses a multi-source heterogeneous data fusion method and system based on edge computing, and the method comprises the steps: obtaining a heterogeneous data stream, carrying out the data type recognition and data feature extraction, and obtaining an original feature set; according to the original feature set, unifying feature dimensions and adjusting a time reference to obtain time sequence vector data; according to the time sequence vector data, filling the feature value of the missing time point to obtain a multi-modal feature; performing block storage on the multi-modal features, verifying the synchronism of adjacent modals, distributing modal synchronization weight coefficients, and finally generating a fusion feature matrix; according to the fused feature matrix, identifying and filtering redundant feature dimensions, establishing a feature association map, and executing feature merging to obtain a simplified feature matrix; according to the simplified feature matrix, feature importance scores are calculated, sorting weight coefficients are arranged and distributed in a descending order according to the scores, and a multi-modal fusion semantic vector is generated. The method improves the accuracy of data analysis and decision.

The invention discloses an intelligent partial discharge online monitoring and fault diagnosis system based on multi-sensor fusion, and the system comprises a multi-sensor collection module which is used for synchronously collecting data in a partial discharge process; the data preprocessing module is used for preprocessing the partial discharge signal data; the feature fusion module is used for constructing a fusion weighted feature matrix; the feature dimension reduction module is used for constructing a fusion feature matrix after dimension reduction; the partial discharge classification model training module is used for constructing a partial discharge type classification model by adopting a lightweight capsule network; the hyper-parameter search optimization module is used for optimizing the partial discharge type classification model; the classification model deployment module is used for deploying the optimized partial discharge type classification model; and the online reasoning and fault diagnosis module is used for receiving data in real time, generating a fault alarm signal and recording and returning fault event information. According to the invention, a real-time partial discharge on-line monitoring and intelligent fault diagnosis scheme is provided for equipment.

The invention provides a multi-modal document understanding model, a training method, a reasoning method and equipment, global visual features are extracted by using a weight-frozen first visual encoder, the understanding ability of the model to natural scene images is enhanced, a second visual encoder extracts fine-grained features based on high-resolution document images and region-of-interest information, and the understanding ability of the model to natural scene images is enhanced. And the analysis precision of the complex document is improved. And the information interaction module improves the intelligent understanding ability of a specific area in combination with the position of the region of interest input by the user. The feature fusion module splices multi-modal features in a channel dimension, so that visual information from different sources is efficiently integrated. The linear layer converts feature dimensions, so that the visual features are adaptive to the input requirements of the large language model, and the large language model combines visual and text information to generate a text understanding result conforming to semantic logic. According to the model, the capability of analyzing and extracting the fine granularity of the document information is improved by combining the two-way visual encoder with the selection of the region of interest of the user on the document image of the image-text structure.

The invention discloses a farmland abandoned land identification method based on multi-source remote sensing data and time sequence correction. The method comprises the following steps: 1, data acquisition and preprocessing; 2, extracting a key phenological period; 3, constructing a feature library; 4, training and predicting a random forest model; and 5, post-processing and precision evaluation are carried out. The method comprises the following steps: firstly, integrating multi-source remote sensing image data, and strictly preprocessing the multi-source remote sensing image data to ensure the time-space consistency of the data; and accurately extracting key phenological period information by deeply analyzing the minimum vegetative period and the vegetative peak period of the vegetation through NDVI and NDSI time sequences. In the feature construction stage, multiple spectral indexes are fused, principal component analysis and feature importance screening are applied, feature dimensions are optimized, and model performance is improved. A random forest model is adopted for training and prediction, and a high-precision abandoned land spatial distribution diagram is finally generated by combining an oversampling technology and a category weight adjustment technology.

The invention discloses a space-frequency domain graph learning-based adaptive deep fake face detection method and system, and the method comprises the steps: randomly extracting an image frame from a video, intercepting a face image, adjusting the feature dimension of the face image, and transmitting the face image to a depth adaptive wavelet module and a normalized residual homomorphic composition neural network module; a depth adaptive wavelet module extracts frequency features of the face image; a normalized residual homograph neural network module extracts spatial domain features of the face image; performing weighted fusion on the frequency domain features and the spatial domain features by using a self-adaptive feature fusion module based on gated convolution, realizing class attention guidance by using the gated convolution, dynamically adjusting the channel of a feature map and the weight of a spatial dimension, and finally obtaining fusion features; and performing classification according to the fusion features by using a classifier. According to the method, the extraction capability of forged detail clues is enhanced, and the detection precision and stability of the model are remarkably improved.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The present disclosure relates to the technical field of smart power grids. Disclosed are a method and apparatus for constructing a high-order tensor network of a large-scale power grid, and a device and a medium. The method comprises: acquiring multiple category attribute sets of heterogeneous nodes in a large-scale power grid; performing feature extraction on the multiple category attribute sets, so as to obtain attribute features of the heterogeneous nodes; on the basis of the attribute features of the heterogeneous nodes, using a deep hash mapping model to establish multiple feature sub-spaces; using a breadth learning strategy to align the multiple feature sub-spaces to a unified feature dimension; on the basis of an attribute feature vector of the unified feature dimension, using a distance measurement method to calculate a distance measurement value between the heterogeneous nodes; on the basis of the attribute feature vector of the unified feature dimension and the weight of a heterogeneous node interaction relationship, determining a basis tensor for representing the heterogeneous node interaction relationship; and on the basis of the basis tensor and the weight of the heterogeneous node interaction relationship, using a tensor multiplication operation rule to construct a high-order tensor network of a large-scale power grid.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention relates to the technical field of plant growth-promoting bacterium screening, in particular to a thunberg fritillary bulb growth-promoting bacterium colony screening method and system based on image recognition. The screening method comprises the following steps: establishing a three-dimensional gradient dilution system of a thunberg fritillary bulb rhizosphere soil sample, and collecting a bacterial colony original image under a specific wavelength combination by adopting a multispectral imaging device. According to the method, by establishing a multispectral collaborative imaging mechanism and a dynamic growth modeling system, the technical problems of feature dimension deficiency and space-time correlation fracture in a traditional method are effectively solved. An improved wavelet transform algorithm is adopted to break through the optical interference limitation of an agar matrix, and high-fidelity extraction of bacterial colony intrinsic textures is realized; according to the constructed three-dimensional feature space fusion model, morphological topological parameters, texture entropy distribution and growth differential features are deeply coupled through nonlinear mapping, and the biological correlation of feature representation is remarkably improved.

The invention discloses a voiceprint clustering method guided by a speaker recognition pre-training model based on short voices, and the method comprises the following steps: obtaining an original voice signal, and randomly combining a plurality of enhancement strategies to achieve data enhancement; performing voice segmentation on the voice signal after data enhancement based on a uniform segmentation mode; extracting voiceprint features of the segmented voice based on an attention mechanism of global time-frequency domain context modeling; and obtaining a clustering result based on K-means clustering and spectral clustering, matching the clustering result with a real speaker tag, performing reverse transmission based on angle-dependent AAM-Softmax loss, and outputting a voiceprint clustering result. According to the method, the influence of environmental interference on feature extraction can be overcome, feature dimensions which are more effective for identity identification can be screened, a clustering output effect which is superior to that of a mainstream algorithm can be obtained with a relatively low parameter quantity, and the robustness under a noise interference condition is improved.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention relates to the technical field of intelligent garbage sorting, and discloses a garbage sorting method based on artificial intelligence. According to the method, heterogeneous data fusion is performed on a multi-source garbage feature data set through a semantic alignment module, feature dimensions are unified, redundant items are eliminated, and a standardized feature set is output. And extracting a spatial position identifier and a physical state identifier of the junk entity, and dynamically dividing time slices by adopting a transfer learning model to generate a periodic junk flow data set. The coordinates of the sorting starting point and the sorting ending point are converted into a three-dimensional space grid to construct a migration track topological graph, flow data and physical state identifiers are combined, sorting paths are clustered and analyzed according to material categories, the material flow intensity in a specific period of a target area is quantified, and a dynamic index matrix is generated. According to the method, space-time correlation analysis of multi-modal junk data is realized, and the adaptability of sorting path planning in a complex environment is improved.

The invention discloses a vehicle-road cloud integrated complex time sequence real-time anomaly detection method and system, and relates to the field of Internet of Vehicles, and the method comprises the steps: S1, constructing an initial anomaly detection model; s2, acquiring a training data set; s3, importing the training data set into the initial anomaly detection model, and carrying out training optimization on the training data set to obtain an optimized anomaly detection model; s4, acquiring data to be predicted; s5, performing anomaly detection on to-be-predicted data by using the optimized anomaly detection model to obtain an anomaly detection result; according to the method, time pooling and a channel attention mechanism are introduced for extracting a complex interaction relationship between a feature dimension and a time dimension; a module based on a channel weight adjustment mechanism is added, the calculation complexity of the model is reduced by dynamically adjusting the channel weight, and the expression ability of the model to key features is improved; and the integrated interactive convolution module is used for capturing a dependency relationship in a multi-dimensional space and supporting simultaneous detection of various types of abnormal events.

The invention discloses a PCB appearance defect real-time detection algorithm based on edge calculation, and belongs to the technical field of computer vision and intelligent manufacturing. According to the algorithm, multi-scale feature extraction is performed on a PCB surface image through a multi-modal feature extraction network, feature dimension compression is realized under the constraint of edge computing resources in combination with a lightweight convolutional neural network, and the computing efficiency and the detection precision are balanced. And dynamically adjusting the weight distribution of the local defect features and the global structure features by adopting an adaptive attention algorithm, and enhancing the significance expression of the tiny defects. By fusing a process feature mapping mechanism of a PCB process parameter database and combining a temperature sensitivity coefficient sensed by real-time environment temperature, the suitability of a detection result and a production condition is improved. And a low-delay defect classification decision is realized by using a lightweight classifier.

The invention discloses a body feeling evaluation method based on electroencephalogram-electromyographic signal fusion and dynamic interaction modeling. The body feeling evaluation method comprises the steps that EEG signals and EMG signals in the lower limb movement process of a subject are synchronously collected; carrying out band-pass filtering, artifact removal and wavelet transform processing on the acquired signals, extracting multi-channel time-frequency features, and forming a preprocessing feature matrix; fusing the time-frequency features of the EEG signal and the EMG signal, constructing a multi-modal feature set, and compressing feature dimensions by adopting a sparse coding method; inputting the compressed feature sequence into a neural network model combining a long short-term memory network and an attention mechanism, and carrying out dynamic interaction modeling; and an interaction index sequence is generated based on model output, and an interaction matrix is constructed through a sliding window and Gaussian kernel smoothing processing, so that visualization of brain-muscle interaction strength and dynamic quantification of a proprioceptive function are realized. The invention further provides a system for implementing the method. The method is high in objectivity, high in feature extraction precision and excellent in dynamic modeling capability.

The invention belongs to the field of environment monitoring and computer vision, and particularly relates to a water body color recognition regression method and system based on space-time causality and manifold learning, and the method mainly comprises the steps: carrying out the detection of a current target water body video sequence, extracting a water body region, carrying out the high-dimensional feature dimension reduction of the water body region, and obtaining a water body color recognition result; and performing feature extraction on the water body region through a space-time causal feature learning model, fusing the flow shape learning features and the space-time causal features to obtain fused features, and outputting a finally predicted water body color value. According to the method, end-to-end assembly line design of preprocessing-segmentation-feature modeling-regression is adopted, manual intervention is not needed from video input to color prediction, and through cascade cooperation of five core modules (video preprocessing, water body segmentation, manifold learning, time sequence causal modeling and color recognition), the real-time performance of the system is improved. Full-link automation from environmental interference suppression, feature extraction to result output is realized, information loss of intermediate links is avoided, and recognition efficiency and robustness are improved.

The invention discloses an underground engineering lining disease detection system based on point cloud, and belongs to the technical field of underground engineering detection. In order to solve the technical problems that an existing underground engineering lining disease detection method is low in detection precision, low in automation degree and the like, underground engineering point cloud data to be detected and corresponding position information are collected, and an improved PointNet + + model is adopted for disease recognition. According to the method, original three-dimensional coordinates of a point cloud are expanded into seven-dimensional point cloud data containing coordinates, normal vectors and reflection intensity, the normal vector standard deviation of points in a neighborhood of each candidate point is calculated to serve as local geometric complexity, a local geometric complexity index is fused into sampling distance measurement, then a multi-scale local neighborhood is constructed by combining sphere query, and therefore the multi-scale local neighborhood is obtained. And extracting geometric features, texture features and deformation features by using a PCA feature dimension reduction technology, carrying out feature fusion based on a normal vector weighting mechanism, finally obtaining disease type classification based on a network model, and calculating the size and position of the disease.

The invention discloses a few-sample classification method research based on feature cache optimization, and the method comprises the steps: (1), employing an improved ResNet-based SwAV model as a backbone, and enabling the SwAV model to be used for generating auxiliary features; (2) taking the image data and the text description as original input, and generating text input with rich downstream language semantics by utilizing GPT-3 to serve as text prompt of a CLIP model; (3) through a feature selection method based on feature similarity and difference, the problems of feature redundancy and inaccurate selection in the feature selection process are solved, feature dimensions with high selection value are identified, and normalization and enhancement zooming processing are performed on the features; and (4) utilizing visual contrast knowledge of SwAV, introducing a learnable cache model, and adaptively mixing prediction results from CLIP and SwAV. According to the method, under the CLIP framework without extra training, the accuracy of few-sample image classification is effectively improved through feature cache optimization and a self-adaptive hybrid strategy.

The application discloses a feature editing method for large model content security, which compares and analyzes the sparse coding features of a chat assistant constructed based on a large language model under positive user input and negative user input, extracts the internal response differences of the model to different semantic directions, and the mechanism can automatically and accurately identify the key feature dimensions highly related to the semantic direction of the target attribute. The model activation is mapped to a sparse feature space by using a sparse autoencoder, and each dimension of the feature has independent and interpretable semantic meaning. By injecting a feature guide vector in the space, the interference of the control process on the text grammar, fluency and information density is significantly reduced. The sparse representation mechanism is introduced to structure the intermediate activation features in the reasoning process of the large language model and to intervene in a targeted manner, so that the reply of the chat assistant to the user input conforms to the preset safety specification, and the safety and controllability of the chat assistant in the interaction with the user are improved.

The invention provides an automatic online detection method and system for the surface smoothness of a precise injection mold, and relates to the field of precise manufacturing, the method comprises the following steps: S1, synchronously collecting visible light and near-infrared reflection images of the surface of the mold through multispectral coaxial imaging; s2, calculating a dynamic texture parameter based on a multi-directional gradient field weighted variance, and generating a composite reflection parameter in combination with wavelet domain fusion; s3, fusing the features by adopting a dual-path neural network, and comparing the standard library through a modified Stereer ratio model to output the degree of finish; and S4, adjusting the period in real time according to the detected dynamic standard deviation and linking closed-loop control. According to the method, the restriction of single-band characterization is broken through through multispectral data fusion, and the processing speed is increased by utilizing gradient field parallel computing hardware; in combination with a self-adaptive threshold value and a process parameter dynamic correction mechanism, the problems of poor adaptability of a fixed period, high hardware delay and single feature dimension of a traditional method are solved, and the online detection precision and the process control efficiency are remarkably optimized.

The invention discloses a wind turbine generator equipment fault diagnosis method and system based on a large model. The method comprises the following steps: initializing a feature set according to normal data; selecting a plurality of candidate features according to the mixed score of each candidate feature, and storing the candidate features in a feature set; performing sample division on normal data by using a time sequence segmentation algorithm and constructing multi-dimensional spatial-temporal feature representation; constructing a fault diagnosis model: loading a large language model as an infrastructure, and injecting the multi-dimensional spatio-temporal feature representation into an embedded input layer of the large language model; an adaptive pooling layer is accessed after the output of the large language model, and a double-layer MLP classifier is constructed for realizing the identification and classification of different fault types; the first layer of the double-layer MLP classifier compresses an input feature to half of an original feature dimension and applies GELU activation, and the second layer of the double-layer MLP classifier is mapped to a corresponding fault category space; constructing a knowledge mechanism library, providing prior knowledge of the wind turbine generator for the model, designing a loss function driven by the knowledge of the wind turbine generator, and carrying out model training.

The invention relates to the technical field of machine learning, and discloses a user recharging prediction method and device, equipment and a storage medium, and the method comprises the steps: associating behavior data of multiple platforms of a user through an equipment fingerprint algorithm, obtaining a user multi-dimensional feature set, determining a clustering number based on an elbow rule, and obtaining a user recharging prediction result; and performing clustering analysis on the user multi-dimensional feature set according to the clustering number, generating a user value grouping label, and inputting the user value grouping label into a random forest model to obtain recharging prediction results of different user groups. According to the method, multi-platform user behavior information is comprehensively integrated through an equipment fingerprint algorithm, data islands are broken, user value grouping labels are generated through elbow rule clustering, then the user value grouping labels are input into a random forest model to predict a recharging result, user basic features are considered, value grouping information is integrated, feature dimensions are enriched, and the recharging efficiency is improved. And users with different values can be described more accurately, so that the accuracy of recharging prediction of user groups with different values is improved.

The invention discloses a quantum fuzzy neural network adaptive to high-dimensional input and a classification method, and relates to the field of quantum calculation and fuzzy neural networks and the field of computer vision. The network input layer receives high-dimensional data, amplitude coding, forward and reverse enhanced chain entanglement layer, parameterized quantum transformation and fuzzy set mapping are carried out through a quantum fuzzy feature extraction module, and dynamic dimension fuzzy features are output; high-dimensional neural features are extracted through a DNN feature extraction module to adapt to quantum fuzzy feature dimensions; dynamically distributing the weights of the quantum fuzzy features and the classic neural features through an adaptive feature fusion module; and carrying out Softmax classification on the fusion features through a classifier, and outputting a category probability. According to the method, the high-dimensional data coding efficiency can be effectively improved, the complex fuzzy logic relation learning capability of the quantum part and the quantum state correlation stability are enhanced, the uncertainty of the data is represented, and accurate classification of high-dimensional uncertainty images is realized while noise interference is reduced.

The invention discloses a multi-modal operation and maintenance data method and system based on a large model, and the method comprises the steps: carrying out the preprocessing of obtained multi-modal operation and maintenance data, so as to obtain data feature sequences with the same feature dimension corresponding to different-modal operation and maintenance data; fusing the data feature sequences with the same feature dimension corresponding to the operation and maintenance data in different modes; processing the fusion vector, inputting the processed fusion vector into the fine-tuned large model, and outputting a semantic reasoning result; analyzing the semantic reasoning result into data in a structured form; and secondary verification is carried out on the abnormal event in the data in the structured form to determine final fault information, and the fault information is expressed in the structured form. A multi-source data fusion strategy is combined with large model reasoning, so that storage, retrieval and analysis of massive heterogeneous operation and maintenance data are both real-time and accurate. Depending on the reasoning ability of a large model, the system can timely give a problem analysis result, the fault processing speed is accelerated, and the service quality is ensured.

The invention relates to an infrared and visible light image fusion method based on cross focusing linear attention, which belongs to the technical field of computer vision, and comprises the following steps of: respectively inputting infrared and visible light images into a four-stage encoder for processing, the extracted features are subjected to feature correction through an adaptive feature correction module, and the corrected double-branch features are subjected to feature fusion through a cross focusing linear attention fusion module to obtain multi-scale features of four stages; and inputting the multi-scale features into a decoder, carrying out frequency perception feature aggregation, recovering the feature size through up-sampling to obtain a fused image, and training an image fusion network in combination with a designed joint loss function. Finally, in combination with a feature correction module, a cross focusing linear attention fusion module and a frequency perception feature aggregation module, a fusion image containing rich semantic features can be obtained, and an important promotion effect on downstream tasks is achieved.

The invention discloses a network equipment fault analysis method and device based on multi-dimensional characteristics, and relates to the technical field of network equipment fault analysis, and the method comprises the steps: obtaining the time sequence characteristic data of each network equipment in an Internet of Things cluster during the operation of a current time period, and forming a multi-dimensional network fault identification data set according to the time window dimension, the network equipment dimension and the feature dimension, carrying out encoding processing on the network fault identification data set by adopting a pre-trained encoder to obtain an encoding vector, inputting the encoding vector into a pre-constructed fault prediction decoder, and carrying out fault prediction on the network fault identification data set according to the pre-constructed fault prediction decoder. And outputting the fault probability of each network device in the preset time period. The network equipment fault diagnosis method and device solve the technical problems that when network equipment fault diagnosis is carried out in the related technology, due to the fact that network faults have space-time variability and diversity, single-dimension analysis cannot accurately capture the full view of the faults, and fault diagnosis is delayed and misdiagnosed.

The invention relates to the technical field of low-altitude equipment, in particular to a low-altitude resource intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting the real-time state and network load data of a low-altitude flight equipment group, and constructing a dynamic operation data set; generating an operation mode feature set through multi-dimensional airspace situation awareness and analysis, and performing sparse clustering division based on the feature set to form a network resource demand priority mapping table; traversing the mapping table to dynamically calculate the resource demand, determining a multi-dimensional weight coefficient, and performing high-dimensional feature dimension reduction and optimization through a mixed integer nonlinear programming solver to obtain a resource demand feature vector; constructing a resource scheduling strategy optimization model by adopting a deep reinforcement learning algorithm based on the vector; inputting real-time data into the model to execute a resource scheduling decision, and outputting a dynamic allocation strategy; simulation deduction and compliance verification are carried out on the strategy in the digital twin simulation platform, and cooperative intelligent scheduling of communication, calculation and spectrum resources is achieved.

The invention discloses an intelligent scheduling method for unmanned workshop production, and the method comprises the steps: collecting the feature data of task orders, equipment loads and the like in real time, carrying out the normalization and feature dimension reduction processing, and constructing a standardized task state vector; constructing a task-resource-state causal graph based on a Bayesian network, and identifying and quantifying key factors influencing the scheduling performance; in combination with causal reasoning and anti-factual reasoning, multi-strategy generation and screening under new tasks and abnormal working conditions are realized, and online fine adjustment is performed on selected strategies through a reinforcement learning model; feedback data are collected after scheduling is executed, the causal model is dynamically corrected, continuous self-evolution and generalization ability improvement of the strategy are achieved, and the production flexibility, the resource utilization rate and the scheduling robustness of an automatic workshop can be effectively improved.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

The invention relates to the technical field of big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.