149results about How to "Efficient mining" patented technology

The invention discloses a user experience design method based on a natural language processing technology, relates to the technical field of natural language processing technologies and user experience design, and adopts the natural language processing technology to intelligently collect and analyze user feedback information. The method is constructed on the basis of advanced natural language understanding and sentiment analysis technologies, and an intelligent feedback management system realizes efficient mining and deep insight of mass text data and covers deep information extraction, sentiment recognition and quantification and automatic report generation; according to the application of the natural language processing technology in the field of user feedback collection and analysis, not only is the crossing from passive receiving and manual processing to active detection and intelligent analysis realized, but also the user experience is improved through seamless voice interaction and accurate path guidance. The user experience and the response speed of the enterprise to the user demand are greatly improved, and the intelligent upgrading of the feedback system is really realized.

The invention discloses a hotspot mining system and method based on Internet finance and economics information and a medium, and relates to the field of Internet finance and economics hotspot mining systems, and the system comprises an information collection module which collects finance and economics information in a multi-source manner, determines a collection period according to source characteristics, and carries out the screening and storage; the text preprocessing module is used for cleaning and analyzing texts, standardizing terms and carrying out classification labeling; the hotspot evaluation index calculation module is used for calculating indexes such as classification weighting, TF-IDF and the like; the hotspot mining and analyzing module is used for setting threshold mining hotspots, analyzing emotion and the like; and the visual display module is used for visually displaying the hot spots and supporting interaction and pushing. According to the invention, financial information is integrated, and hotspots are mined accurately. The information acquisition efficiency is improved, and decision-making investment and research are assisted. Information spreading is promoted, and financial field development is promoted.

The invention discloses a semi-supervised speech imagination intention decoding method based on electroencephalogram collaborative clustering. The method comprises the following steps: step 1, collecting electroencephalogram data of speech imagination; 2, performing feature extraction on the preprocessed electroencephalogram data; 3, performing feature clustering and sample clustering on the original data matrix to obtain a clustering indication matrix of features, a clustering indication matrix of samples and a coefficient matrix; step 4, completing optimization solution of the clustering indication matrix of the features, the clustering indication matrix of the samples and the coefficient matrix under a semi-supervised framework; and 5, displaying a decoding result obtained by the semi-supervised learning model based on the electroencephalogram sample and feature collaborative clustering through a visualization means. The method can fully explore and utilize a bidirectional complex interaction mode of features and samples and label information of speech imagination in speech imagination intention decoding research and system based on electroencephalogram signals.

The invention provides a light guide plate surface defect detection method based on a GCC-YOLOv8 network model. The method comprises the following steps: acquiring an image of a light guide plate to be detected; the image is input into a GCC-YOLOv8 network model for defect detection; outputting surface defect information of the light guide plate based on a detection result; wherein the GCC-YOLOv8 network model is constructed based on a YOLOv8 network model, and comprises a backbone network unit, a neck network unit and a detection head network unit with three parallel detection branches, which are arranged in sequence; two GKA-C2f1 modules are arranged at the tail end of the backbone network unit; the neck network unit comprises an FPN network unit and a PAN network unit; the FPN network unit comprises two up-sampling modules, two C2f2 modules, two Concat modules, an SPPF module and a CPCA module; the PAN network unit comprises two CBS modules, two Concat modules, two C2f2 modules, three CPCA modules, a CFC module and an SFC module. The light guide plate surface defect detection method has good light guide plate surface defect detection performance and robustness, and also has good detection speed.

The invention provides a temperature monitoring method based on a dual-light fusion image. The method comprises the following steps: S11, synchronously acquiring a visible light image and an infrared thermal image inside and outside a switch cabinet in an intelligent station building; s12, performing local adaptive convolution on the visible light image and the infrared thermal image, extracting local shallow layer features, and performing heterogeneous association to obtain heterogeneous association degree; s13, based on an anisotropic diffusion mechanism, decomposing the infrared thermal image interfered by sunlight in the switch cabinet to obtain a detail layer image and a base layer image, and performing enhanced fusion on texture features of the detail layer image and the base layer image through the distribution gradient and heterogeneous association degree of textures in the visible light image to obtain an enhanced infrared thermal image; and S14, inputting the enhanced infrared thermal image into a pre-trained temperature detection model for identification, obtaining a temperature abnormal region, and carrying out early warning on a temperature characteristic value. According to the invention, the heterogeneous correlation enhancement of the dual-light fusion image is realized, so that the reliability of temperature monitoring is improved.

The invention discloses a deep heat storage large-scale fracturing construction scheme design method based on a weak structure, and relates to the technical field of high-temperature geothermal resource exploration and development, and the method comprises the following steps: depicting and characterizing the weak structure of a deep heat storage large-scale fracturing stratum to obtain a comprehensive characterization large model; based on the comprehensive characterization large model, delineation and fracturing performance evaluation are conducted on a weak structural zone of the deep heat storage scale fractured stratum, and a delineated area is obtained; on the basis of the delineated area, the farthest disturbance range and distance of fracturing transformation of the weak structure of the deep heat storage scale fractured stratum are predicted; and determining a well spacing, designing a well location, deploying a well pattern and optimizing a fracturing layer section based on the fracturing property and the predicted farthest disturbance range and distance of thermal storage fracturing transformation, and completing the design of a deep thermal storage large-scale fracturing construction scheme. According to the method, through construction and analysis of the comprehensive characterization large model, the stratum weak structural zone suitable for fracturing can be accurately delineated, the farthest disturbance range and distance of fracturing transformation are predicted, and efficient mining is achieved.

The invention provides a ship trajectory prediction and anomaly detection method, device and equipment based on a deep cross LSTM network, and a storage medium, and the method comprises the steps: S1, preprocessing AIS data, and extracting ship navigation features; step S2, constructing a deep cross LSTM network model based on the attention mechanism and a coder-decoder, which comprises the following steps: in a coder part, converting input multivariate time series data into high-dimensional vector representation, and in combination with the multi-head attention mechanism and an LSTM network, capturing a dependency relationship and periodic change among the time series data, and constructing a deep cross LSTM network model based on the multi-head attention mechanism and the LSTM network; the learning of the network model on the data relationship is enhanced and the calculation efficiency is improved; and S3, training the deep cross LSTM network model, and predicting ship navigation features by using the trained deep cross LSTM network model. The network model is applied to two different prediction tasks, namely a joint prediction task and a historical data prediction task.

The invention relates to an enhanced urban terrestrial heat exploitation system and method based on nanofluid. The system comprises a heat collection pipeline, a heat exchange assembly and a nanofluid adjusting assembly. The nanofluid adjusting assembly is connected into the heat collecting pipeline. The nanofluid adjusting assembly comprises a storage pool, a control unit, a concentration detection sensor, a temperature sensor and a concentration adjusting unit, the concentration detection sensor, the temperature sensor and the concentration adjusting unit are connected with the control unit, the storage pool communicates with the heat collecting pipeline, the concentration detection sensor is used for detecting the concentration of nanofluid in the storage pool, and the temperature sensor is used for detecting the temperature of the nanofluid. And the concentration adjusting unit is used for adjusting the concentration of the nanofluid in the storage tank. Compared with the prior art, the method has the advantages that the fluid concentration in the whole circulation pipeline is adjusted in real time according to the appropriate heat transfer concentration of the hybrid nanofluid at all temperatures, then the heat exchange performance of the nanofluid is effectively guaranteed, and the heat exchange efficiency of the geothermal circulation system is improved.

The invention belongs to the field of data mining in deep learning, and particularly relates to a time series prediction system based on a causal relationship. The time series prediction method comprises a causal inference and enhancement algorithm suitable for time series data; a causal-frequency domain attention module based on a graph attention mechanism and Fourier transform; the invention discloses a decoder based on a multi-layer perceptron. According to the method, the modeling capability of the existing multivariable time sequence prediction method on the dependency relationship between the sequence variables and the feature extraction capability on the long-time sequence data can be improved, and the error of the prediction result is effectively reduced.

The invention relates to an intelligent office process processing method and system based on a large language model, and belongs to the field of artificial intelligence. Comprising the steps of obtaining enterprise documents; sequentially carrying out document analysis, document cutting and key information extraction on the enterprise document; constructing an enterprise knowledge base based on an ES library according to the extracted content; receiving a user question about the office process; a semantic text block and a keyword text block about the user question are recalled in the enterprise knowledge base, and the semantic text block and the keyword text block are spliced by using a large language model to obtain a user question answer of the user question; taking the user question and the answer of the user question as question-answer pairs, and performing office process work order automatic filling of the question-answer pairs by utilizing a large language model according to a preset work order filling rule; and outputting the filled office process work order. According to the invention, comprehensive intellectualization and automation of the office process are realized, the office efficiency is improved, the error rate is reduced, and the user experience is improved.

The invention provides a drilling engineering drilling speed prediction network training method and an application method, and belongs to the technical field of drilling engineering optimizing.The training method comprises the steps that data screening is conducted on obtained drilling construction data to obtain effective construction data, variational mode decomposition algorithm noise reduction is conducted on the effective construction data to obtain noise reduction data, and the noise reduction data is used for obtaining the drilling engineering drilling speed prediction network; mechanism model prediction is carried out on the noise reduction data to obtain a mechanism prediction drilling speed, and the noise reduction data and the mechanism prediction drilling speed are combined to obtain drilling speed prediction training data; and inputting the drilling speed prediction training data into the initial drilling engineering drilling speed prediction network, and performing iterative optimization on network parameters of the initial drilling engineering drilling speed prediction network based on a particle swarm optimization algorithm to obtain a well drilling engineering drilling speed prediction network which is completely trained. According to the method, data noise reduction is carried out through the variational mode decomposition algorithm, a drilling mechanism model is combined, data features can be effectively mined, the model accuracy is improved, the calculated amount in the training process is reduced through the particle swarm optimization algorithm, and the training efficiency is improved.

The invention discloses an environment sound classification method and system based on metric learning, and the method comprises the following steps: collecting an environment sound audio, and converting the environment sound audio into a Mel spectrogram; performing data enhancement on the Mel spectrogram; an SPA module and an FPL module are constructed, the SPA module or the FPL module is used to process the environment sound audio after data enhancement, a pre-trained convolutional neural network model is used to carry out feature extraction on the processed environment sound audio, and sound features are obtained; inputting the extracted sound features into a representation space; sound features are optimized in combination with comparison, metric learning and cross entropy loss in the representation space, and the optimized sound features are classified; according to the method, the intra-class variation is considered by using metric learning, so that the features of the same kind of samples are closer and the features of different kinds of samples are more separated in the process of carrying out the classification task, and the classification accuracy is effectively improved.

The invention discloses an enterprise knowledge graph inference completion method and inference completion system, and the method comprises the steps: obtaining a to-be-completed knowledge graph of a target enterprise, inputting the to-be-completed knowledge graph into a trained knowledge graph embedding model, and predicting an incomplete triple to achieve the inference completion of the knowledge graph; wherein the knowledge graph embedding model comprises an entity node coding module, a multilayer graph convolutional neural network module, a Softmax pooling layer and a gating neural network based on a self-attention mechanism. According to the method, the knowledge graph embedding model can provide more accurate semantic information for feature interaction, multi-level feature interaction and aggregation of the triple on sub-tensor kernel granularity and global granularity can be realized, and hidden semantics behind entities and relationships of the knowledge graph can be more efficiently mined. Therefore, the knowledge graph completion accuracy, the semantic modeling capability, the interpretability and the like are obviously improved.

The invention discloses a three-dimensional directivity target segmentation method under weak supervision setting, and relates to the technical field of three-dimensional directivity target segmentation. The method comprises the following steps: an instance extraction process: obtaining a point cloud scene and text description, extracting 3D instances in the scene, and representing the 3D instances as instance masks and category score prediction; in the multi-expert mining process, information is extracted from semantic clues of three dimensions of complete context, attribute and category through a plurality of expert models, and semantic distribution of targets is calculated respectively; and a multi-expert aggregation process: according to characteristics of a current sample, automatically endowing input distribution from a more accurate expert model with a larger weight, and eliminating interference information while highlighting a target instance. According to the three-dimensional directivity target segmentation method under weak supervision setting provided by the invention, only the three-dimensional point cloud data and the corresponding directivity description are provided, and mask annotation is not provided, so that time and energy do not need to be consumed for mask annotation.

The invention belongs to the technical field of data processing, and particularly relates to a suggestion decision-making method and device based on a front-end application system user portrait model, and the method comprises the steps: collecting the user behavior data of each user according to a pre-constructed user access behavior monitoring index system; constructing a user portrait model based on the user behavior data; acquiring a user portrait report by using the user portrait model; based on the user portrait report, utilizing a pre-established analysis and prediction model to obtain a predicted suggested decision scheme; the analysis and prediction model is constructed by using a historical user portrait report and a historical suggestion decision scheme. According to the technical scheme provided by the invention, the user demand can be efficiently and quickly mined, and the investigation work of the user is greatly reduced; and core users can be quickly distinguished in a labor-saving manner, which are key marketing groups and which are key construction directions are determined, and the problem of long demand confirmation time caused by multiple internal levels of an enterprise is relieved.

The invention provides a transformer fault detection method and device based on KFWAdaBoost, equipment and a medium, and relates to the technical field of transformers, the method combines a feature generation technology and an ensemble learning strategy, uses a KPP algorithm to generate clustering features, and analyzes the relationship between samples and cluster centers to encode the distribution mode of the samples. The model generates a weighted soft label for each sample in the classification process, and utilizes the soft labels to express fuzziness and similarity between categories, so that the information expression ability of a data set is effectively improved. And sample weights are adjusted through multi-round iteration, learning of samples difficult to classify is enhanced, and a final classification result is output in combination with a voting mechanism. Besides, the generated new data set is not limited to a KFWAdaBoost model, but can be widely applied to training of various classifiers, so that the utilization efficiency of data and the accuracy of diagnosis are improved.

The invention discloses a self-adaptive optimization method for energy storage frequency modulation of a super capacitor and a related product, and belongs to the technical field of wind power generation. The invention provides a self-adaptive optimization method for energy storage frequency modulation of a super capacitor, and the method comprises the steps: collecting wind speed data, building an original wind speed sequence, dividing the original wind speed sequence into a prediction set and a verification set, and adjusting and correcting the original data of the wind speed sequence based on an actual value and a predicted value in the wind speed sequence of the verification set; a difference error and a proportion error are calculated, correction parameters are calculated according to the difference error and the proportion error, self-adaptive adjustment of a prediction model is achieved, inherent rules and characteristics of original data of a wind speed sequence are more effectively mined and utilized through iterative optimization of a prediction result, then the accuracy and reliability of wind speed prediction are improved, and the precision of wind speed prediction is ensured. A data basis with higher precision is provided for a super-capacity energy storage system, and energy waste caused by wind energy fluctuation is reduced.

The invention discloses a low-permeability reservoir geological sweet spot identification method based on grey correlation and analytic hierarchy process. The method comprises the following steps: S1, collecting geological parameter data of a low-permeability reservoir; s2, analyzing the geological parameter data obtained in the step S1 by applying a grey correlation algorithm, determining the correlation degree of each geological parameter, and sorting the geological parameters according to the correlation degree of the geological parameters; s3, analyzing and determining a judgment matrix by using an analytic hierarchy process, and calculating the weight of each index; s4, counting the weight obtained in the step S3, establishing a geological dessert comprehensive evaluation model, and calculating a geological sweetness indication factor through the geological dessert comprehensive evaluation model; and S5, according to the geological sweetness indication factor obtained in the step S4, determining reservoir geological sweet spot classification in combination with a geological sweet spot division table, and the like. Through the grey correlation algorithm and the analytic hierarchy process, geological sweet spot identification of the low-permeability reservoir is realized, and a basis is provided for subsequent remaining oil potential tapping scheme evaluation and comprehensive scheme adjustment.

The invention discloses a tree radial growth data acquisition method and system, and belongs to the technical field of intelligent monitoring. The method comprises the following steps: acquiring first radial growth data through a measuring instrument, and then collecting historical remote sensing and environmental monitoring data and corresponding radial growth data to construct a growth data set; then, a BP neural network model fused with a MapReduce algorithm is adopted, the model is trained through parallelization preprocessing and operation of a Map stage and a Reduce stage, and an optimization analysis model is obtained; inputting remote sensing and environment monitoring data acquired in real time into the model to obtain second radial growth data; and finally, calculating according to a given growth anomaly degree formula and correcting the radial growth data of the tree according to a result. The comprehensive utilization of the multi-source data enables the data to be more comprehensive and accurate, the advanced data analysis model can accurately mine the data relationship, and the calculation of the growth abnormality and the data correction mechanism can guarantee the reliability of the radial growth data of the tree.

The invention discloses an intelligent marketing system and method based on a large model, and the method comprises the steps: automatically replying a newest message in a preset time through a self-adaptive chat large model, obtaining the emergency degree of a current customer session according to the recorded message number and message keywords, and obtaining the current customer session according to the emergency degree; the session with the highest emergency degree is screened out to send emergency early warning to the staff; a client database of the current emergency client is connected according to the emergency early warning, and the intention degree of the current emergency client is obtained by using an intention evaluation large model; matching corresponding activities in a marketing activity database according to the intention degree of the current emergency customer, and directionally recommending the corresponding activities to the current emergency customer; and according to the customer levels and the car insurance expiration time in the customer database, acquiring customer IDs of a plurality of customers to be subjected to insurance renewal, and sending an insurance renewal message. The invention relates to the technical field of intelligent insurance marketing, and solves the technical problems that in an existing marketing method, intentional customers are difficult to screen, and stock customer management is incomplete.

The invention relates to a shale oil-water storage capacity quantitative evaluation method and system, and belongs to the technical field of shale testing. The evaluation method comprises the following steps: obtaining volume conversion parameters based on the dry weight of a shale sample, the weight of a saturated water shale sample, the weight of a saturated oil shale sample, a saturated water shale sample T2 spectrum curve and a saturated oil shale sample T2 spectrum curve; based on the volume conversion parameters, respectively carrying out isovolumetric water conversion on the T2 spectrum curve of the saturated oil shale sample and the T2 spectrum curve of the oil-water coexistence simulation; and completing shale oil-water reservoir capacity evaluation based on an isovolumetric water conversion result. According to the method, the volume conversion parameter K conversion method is adopted, the absorption and storage capacity characterization of the interior of the shale pores to the oil-water medium is regressed, the method is not affected by external characterization conditions and human factors, and the evaluation result is more scientific and objective.

The invention discloses a multi-thin ore body multi-mining-area mining structure and a continuous mining method. The multi-thin ore body multi-mining-area mining structure comprises a slope ramp, a segmented haulage roadway, an air return raise, filling slurry, an unmined ore body, a mining area, a filling retaining wall, a connecting channel and a staged haulage roadway. According to occurrence conditions of the ore body, the ore body is divided into a plurality of subsections in a stage, then the subsections are divided into a plurality of layers, the multiple ore bodies are divided into a plurality of mining areas in the layers in the trend of the ore body, stoping work of all the mining areas is propelled at the same time, and the working procedures of rock drilling, explosive charging, ore removal, filling and the like are sequentially carried out in the mining areas. On the basis of the collaborative mining principle, multi-section parallel operation is implemented in the stages, continuous stoping is conducted on a plurality of mining areas in all sections, the thin ore body group multi-section multi-mining-area collaborative continuous mining technology is achieved, the stoping period is greatly shortened, the mine production capacity and the stoping efficiency can be effectively improved, and safe and orderly connection during collaborative mining of the multiple mining areas is ensured.

The invention provides a knowledge graph embedding model and an application method thereof.The embedding model can effectively obtain features between entities and features between relationships by processing a head entity embedding vector, a relationship embedding vector and a tail entity embedding vector, and then shared features containing implicit information are obtained through splicing; rich and comprehensive global shared output features are obtained from the shared features through a shared extraction module; the shared feature is cascaded with a mapping feature obtained by splicing a head entity embedded vector and a relation embedded vector, and then the cascaded shared feature is processed by a relation sensing module to obtain local information related to the relation, namely a relation output feature; the scoring module calculates scores according to the shared output features and the relation output features, and calculates loss through the scores to update the embedding model, so that the embedding model can comprehensively utilize global information and local information, features between entities and relations are effectively mined, and the embedding efficiency is improved. Therefore, the prediction of the links of the corresponding knowledge graph is realized, and the prediction accuracy of the links of the knowledge graph is greatly improved.

The invention relates to the technical field of information mining analysis, in particular to an information mining analysis method in an industrial scene, which comprises the following steps of: acquiring each data source, determining a data state according to a data source correlation degree and a mining difficulty coefficient, and determining a strategy analysis mode according to the data state; when the sub-data is extracted and analyzed, a processing mode is determined according to a dangerous sub-data distribution coefficient and a dangerous turbulence value to obtain a sub-data set, and the processing mode is a first extraction mode determined according to a dangerous end radiation coefficient and an in-end sub-data correlation coefficient, or a second extraction mode determined according to a dangerous gathering area category; when the associated source group is selected and analyzed, a clustering mode is determined according to the feature conversion coefficient and the number of the data sources, the data sources in the associated source group are selected according to the data flow anomaly index, and sub-data corresponding to the selected data sources serve as a sub-data set; the data mining efficiency in the industrial production process can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a knowledge graph completion method and device for relieving sparsity, and the method comprises the steps: constructing an auxiliary triple; performing instruction fine tuning on the large language model by adopting the data of the original knowledge graph to obtain a discriminator; filtering the auxiliary triple by adopting a discriminator, and fusing the filtered triple with the original knowledge graph to obtain an enhanced knowledge graph; performing message aggregation on the enhanced knowledge graph to obtain a graph structure representation fused with neighbor information; and a dynamic suppression strategy is adopted to correct loss in a graph structure representation result fused with neighbor information, the performance of the model on a knowledge graph completion task is improved, and an adjusted knowledge graph is obtained. According to the knowledge graph completion method and device for relieving sparsity, entity information similar to a rare entity is effectively mined through construction of the similarity matrix and the auxiliary triad, so that the answering ability of a system to a rare entity problem is improved.

The invention relates to the technical field of lumbar vertebra segmentation, and particularly discloses a small sample lumbar vertebra segmentation method based on space prior information and prototype iteration updating, comprising the following steps: obtaining a lumbar vertebra MR image sample set comprising a plurality of sample lumbar vertebra MR images; constructing a small sample lumbar vertebra labeling model, and training the small sample lumbar vertebra labeling model by using the lumbar vertebra MR image sample set to obtain a trained small sample lumbar vertebra labeling model; and inputting a to-be-labeled lumbar vertebra MR image into the trained small sample lumbar vertebra labeling model for labeling so as to obtain a lumbar vertebra labeling prediction result of the to-be-labeled lumbar vertebra MR image. According to the small sample lumbar vertebra segmentation method based on space prior information and prototype iteration updating, rapid, accurate and high-throughput lumbar vertebra labeling can be realized.

The invention discloses a medical data processing method and system combining big data and smart medical treatment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a historical anatomical record based on the big data, and carrying out the analysis to obtain an anatomical data set; calling a preset data processing mechanism for processing and analyzing to establish an anatomical medical database; calling a predetermined association analysis strategy to perform association analysis to obtain an association list; performing multi-dimensional feature collection on a target user of smart medical treatment to obtain target feature data; collaboratively forming target input data; and activating the intelligent prediction model for analysis to obtain a target output result and performing early warning intervention processing. The technical problems of low medical data processing efficiency, insufficient association between anatomical knowledge and patient individual features and limited intelligent early warning ability in the prior art are solved, and the technical effects of efficiently mining anatomical data values, accurately associating patient multi-dimensional features and improving intelligent medical prediction and intervention ability are achieved.

The invention provides a rapid pollutant detection method based on spectral feature analysis, and relates to the technical field of environmental pollutant spectral detection, and the method comprises the following steps: obtaining an original absorption spectrum of a sample, and preprocessing and reconstructing an intrinsic spectral signal; extracting three types of features of morphology, frequency domain energy distribution and phase disturbance, and splicing to obtain a multi-dimensional joint feature tensor; pollutant classification and confidence judgment are completed through an improved CNN, and concentration is predicted through a linear regression model synchronously; in combination with a Mahalanobis distance verification result, deep residual network secondary feature mining is triggered for a low-confidence sample, a final type is determined according to a fusion result, and the concentration is corrected; and generating a detection report and dynamically updating the spectral feature intrinsic library. The method is adaptive to a portable ultraviolet-visible spectrophotometer, the single sample detection time consumption is less than 10min, the classification accuracy is greater than or equal to 90%, the unknown pollutant judgment accuracy is greater than or equal to 89%, and the method is suitable for on-site rapid detection.

The present application relates to a kind of methanol rectification quality prediction method and system based on graph attention network, belong to industrial intelligent control field.The method is through DCS acquisition process parameter and pre-processing, set sliding window to generate sample, construct dynamic graph structure including three kinds of nodes of direct measurement, indirect inference and equipment unit, adopt Granger causality test, mutual information etc.Edge relationship is established, and edge weight is self-learned in combination with attention mechanism;Using spatiotemporal graph attention network model, spatial interaction is captured through graph attention layer, and time processing module such as GRU is fused, the deep extraction of spatiotemporal characteristics is realized, finally output methanol quality prediction value, as soft measuring instrument replaces traditional assay, and APC system constructs intelligent closed loop, while guaranteeing quality, reduce energy consumption, improve yield, provide core technical support for the intelligent optimization of methanol rectification process.

The present application provides a kind of coal mine with the integrated equipment of digging anchor, it includes body, supporting device and adjustment component;Supporting device includes support frame and guard plate component, support frame is connected with body and can be rotated around the straight line of the width direction of body relative to body, support frame includes the first frame body and second frame body connected, second frame body can be rotated around the straight line of the length direction of body relative to first frame body, guard plate component is arranged in second frame body;Adjustment component includes first driver, the output end of first driver is drivingly connected with second frame body, first driver is used to drive second frame body to rotate relative to first frame body, so that guard plate component adapts to the inclination angle of roadway roof.The present application can realize the efficient integration of tunneling and supporting by the cooperative work of body, supporting device and adjustment component, so that the equipment can flexibly adapt to various complex conditions of coal mine roadway, provide a safer and more efficient solution for coal mining.