30results about How to "Comprehensive forecast" patented technology

The invention provides a stress performance analysis method of a combined cable-stayed bridge and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining target bridge tower information, a target event set matched with the target bridge tower information, a training sample set and a preset cable-stayed bridge cable force prediction model; preprocessing the training sample set to generate a training sample set with target feature data; processing a preset cable-stayed bridge cable force prediction model based on the training sample set with the target feature data to generate a target cable-stayed bridge cable force prediction model; the target event set matched with the target tower information is processed, and tower cable force early warning information of the target tower bridge is generated; the target bridge tower information is processed based on the target cable-stayed bridge force prediction model, and attribute information of a target bridge tower is generated; and processing the attribute information of the target bridge tower and the bridge tower cable force early warning information of the target tower bridge based on the target cable-stayed bridge cable force prediction model, and generating target cable force early warning information of the target bridge tower information.

The invention discloses a short-time traffic flow prediction method and system based on a multi-feature map, and relates to the technical field of traffic flow prediction, and the method comprises the steps: respectively constructing an occupancy rate feature map, an average speed feature map and a traffic flow feature map through three kinds of data, including traffic flow, occupancy rate and average speed data, collected by a road sensor at the same time; node feature information of three feature maps is aggregated by using a GCN, supervised learning training of occupancy features and average speed features is realized by using an improved Transform encoder, and feature interaction fusion of obtained feature vectors of occupancy and average speed is realized in a multi-feature fusion traffic flow prediction model. The occupancy and average speed features are utilized to assist a multi-feature fusion traffic flow prediction model to better capture traffic flow features, and more accurate traffic flow prediction is provided.

The invention relates to the technical field of prediction model construction, in particular to a method for constructing a corrosion rate prediction model of a metal material. The method comprises the following steps: acquiring bridge design data, collecting metal material and electrolyte component data, and constructing a bridge protection primary battery simulation model; electrolyte concentration change information is collected by dynamically analyzing water flow fluid and ion migration, so that the potential difference and the current density of positive and negative electrodes of the primary battery are calculated; evaluating the metal corrosion rate of the protection electrode, and establishing a corresponding prediction model; according to the method, multiple steps including metal activity analysis, ion activity estimation, current density calculation and the like are refined, so that the accuracy and reliability of a prediction result are ensured. According to the invention, the metal corrosion rate prediction model is more accurate through the corrosion rate prediction optimization of the metal material.

The invention provides an equipment abnormality diagnosis method and a related device, and relates to the field of equipment monitoring, and the method comprises the steps: firstly training a target prediction model constructed based on an auto-encoder neural network based on training data (sound features, vibration features and electrical features) corresponding to target equipment; the target training model is used to acquire the working condition data of the to-be-tested equipment in the working process, the abnormity diagnosis is performed on the to-be-tested equipment based on the working condition data, the model training is performed by using the multi-dimensional data, the abnormal state of the to-be-tested equipment can be comprehensively predicted, and the accuracy of the abnormity diagnosis is improved. Moreover, the target prediction model constructed based on the autoencoder neural network is used for predicting the abnormal condition of the to-be-detected equipment, does not need to depend on professional domain knowledge and does not depend on historical fault data, and improves the generalization capability of the scheme.

The invention provides a learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention discloses a shield tunneling attitude prediction method and system based on an improved LSTM. The method comprises the steps that a shield tunneling attitude prediction model is constructed, a training set is adopted to train the model, a test set is adopted to test the model, and the trained shield tunneling attitude prediction model is obtained; wherein the shield tunneling attitude prediction model comprises a time sequence convolutional network, an improved LSTM network and a multi-head attention mechanism layer which are connected in sequence; according to the improved LSTM network, a peep hole link is added on the basis of the LSTM network, the peep hole link connects the state of a previous unit with a forgetting gate and an input gate, the state of the previous unit is added into calculation of the forgetting gate and the input gate, and shield construction data to be predicted are obtained; and inputting to-be-predicted shield construction data into the trained shield tunneling attitude prediction model to obtain a tunneling attitude prediction result of the to-be-predicted shield construction data.

The invention discloses an intelligent computer performance testing system based on a Shell script, belongs to the technical field of computer performance testing, and aims to solve the technical problem of how to improve the automation degree of computer performance testing, avoid man-machine interaction and further improve the testing efficiency and accuracy. According to the technical scheme, a test package program installation module is used for triggering and automatically installing a software test package program; the file generation module is used for generating an HPL.Dat file when the mpich software is installed; the file updating module is used for updating the HPL.Dat file generated in the process of installing the software test package program; the execution module is used for transmitting data; the test preparation module is used for providing test information for testing the performance of the computer and processing the test information; and the test item forming module is used for constructing test items according to the separated test information so as to obtain computer system performance test content and record test item information.

The invention discloses a load prediction method based on combination of CNN-BiLSTM and XGBoost, and belongs to the field of power load prediction. The method comprises the following steps: acquiring historical load data and historical meteorological data, and preprocessing the historical load data and the historical meteorological data to obtain preprocessed historical load data; decomposing the preprocessed historical load data by adopting a multiplication seasonal adjustment method to obtain a trend sequence, a seasonal sequence and an irregular sequence; a CNN-BiLSTM hybrid model is constructed, and a trend sequence is predicted; constructing an XGBoost model, and predicting a seasonal sequence and an irregular sequence; and multiplying the predicted trend sequence by the predicted seasonal sequence and the irregular sequence, and evaluating a result to obtain predicted load data, thereby realizing load prediction based on combination of CNN-BiLSTM and XGBoost. According to the method, the CNN-BiLSTM hybrid model is used, on the basis of unidirectional flow of the LSTM network from the past to the future, the data flow direction from the past to the past is added, the time sequence feature structure of the data is fully mined, and the precision and generalization ability of load prediction are improved.

The invention discloses an energy demand prediction system based on big data analysis. The system comprises a data acquisition module used for acquiring historical energy data of a selected area in a preset time period; the feature engineering module is used for performing feature selection on the historical energy data to obtain core features; the data expansion module is used for obtaining an initial data set according to the core features and expanding the initial data set to obtain a training data set; the model construction module is used for constructing an energy demand prediction model and performing training based on the training data set; the demand prediction module is used for acquiring real-time energy data and predicting energy demand based on the energy demand prediction model and the real-time energy consumption data. According to the method, the accuracy and efficiency of energy demand prediction are improved by comprehensively utilizing the big data technology and the machine learning algorithm, and the method is of great significance to resource optimization configuration and sustainable development of the energy industry.

The invention discloses a farmland abandoned risk comprehensive assessment and prediction method and device and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a data source of a to-be-detected region, obtaining an abandoned farmland extraction result through data preprocessing, time sequence data synthesis and machine classification learning, further determining abandoned farmland pattern spots of the to-be-detected region, and obtaining abandoned farmland image spots of the to-be-detected region. Determining the abandoned farmland rate according to the abandoned farmland pattern spots, carrying out the assignment of the abandoned farmland risk indexes according to the abandoned farmland rate, carrying out the comprehensive evaluation of the abandoned farmland risk, and determining the abandoned farmland risk grade; determining a regression coefficient based on a cultivated land abandoned rate and a cultivated land abandoned risk index assignment result by using a logistic regression model; determining the contribution rate of the abandoned farmland risk index to the abandoned farmland based on the regression coefficient; and predicting the abandoned farmland risk based on the regression coefficient and the contribution rate. According to the invention, scientific, effective, comprehensive and real-time comprehensive assessment and prediction can be carried out on the abandoned farmland risk.

The invention discloses a flood discharge dam crack simulation prediction and stability analysis method and a flood discharge dam crack simulation prediction and stability analysis device, relates to the field of water conservancy projects, and aims to realize comprehensive analysis and stability prediction of crack causes. According to the method, a nonlinear finite element model of the flood discharge dam section downstream facade crack is established, loads such as self weight, temperature, water pressure, sand pressure, uplift pressure and valve thrust are applied to the model, then flood discharge dam construction, water storage and whole-process simulation calculation of operation are conducted, then the crack initiation and expansion process of the flood discharge dam section downstream facade crack is inverted, and the flood discharge dam section downstream facade crack is obtained. And performing corresponding repair processing on the crack, evaluating the state of the processed crack through analogue simulation, and analyzing the expansion stability of the crack. According to the method, various influence factors are more comprehensively considered, more accurate crack cause analysis is provided, and the method has the advantages of being efficient, accurate, high in stability, high in controllability, low in detection cost and the like.

The embodiment of the invention provides an image category prediction method and device, electronic equipment and a storage medium, belongs to the technical field of image processing, and is suitable for the field of medical science and technology. The method comprises the steps that a sample image marked with a reference image category is acquired, image-text conversion is carried out on the sample image, an image description text is obtained, and the reference image category is used for representing the category of the sample image; obtaining a target image, and performing image-text conversion on the target image to obtain a target description text; performing similarity calculation on the image description text and the target description text to obtain a text similarity; performing feature extraction on the target image to obtain image features; performing similarity calculation on the image features and the reference image category to obtain category similarity; and based on the text similarity and the category similarity, performing category prediction on the target image. According to the embodiment of the invention, the accuracy of image category prediction can be improved.

The application discloses a kind of multi-modal rumor detection method and system based on layered attention network, text feature vector is extracted by layered attention model, visual feature vector in news picture is extracted by VGG19 model, and news sample is judged as rumor or non-rumor according to the text feature vector and visual feature vector;Layered attention model includes word encoder and sentence encoder based on self-attention mechanism, the importance between current word and context word and the importance between current sentence and context sentence are measured, word vector representation and sentence vector representation that fuse context information are obtained, so that the features of news document hierarchy can be captured;The importance of the feature vector of each picture different area relative to text is measured by attention mechanism, which can make the model focus more on the picture area with higher importance, improve the detection accuracy of the model, and multi-modal detection is more suitable for multi-modal form news, to get more comprehensive and accurate rumor prediction.

The present invention relates to a calculation-based method for analyzing phosphorylated proteins, comprising: a phosphorylated protein sequence prediction step (20) for predicting the probability of presence of each phosphorylation combination of a target protein using a calculation model, and selecting a phosphorylated protein in which the probability of presence is greater than or equal to a prescribed probability; a phosphorylated protein three-dimensional structure prediction step (30): respectively replacing phosphorylated serine and phosphorylated threonine in the phosphorylated protein sequence obtained by the phosphorylated protein sequence prediction step with aspartic acid and glutamic acid, and predicting the three-dimensional structure of the replaced protein by using a calculation model; and a classification and clustering step (40): classifying and clustering the phosphorylated proteins according to the three-dimensional structure obtained in the phosphorylated protein three-dimensional structure prediction step. According to the method, the three-dimensional structure of the phosphorylated protein can be predicted comprehensively, quickly and at low cost only by means of calculation of the AI model without an experiment, and then the phosphorylated protein capable of being used as a biomarker or a drug action target is screened based on the obtained three-dimensional structure.

The invention belongs to the technical field of gas reservoir exploration, and particularly relates to a method and a system for depicting a water invasion dominant channel space of a fractured-porous gas reservoir. The method comprises the following steps: establishing a dual-medium fine geological model, wherein the dual-medium fine geological model comprises a matrix porosity model and a discrete fracture network model; obtaining a matrix permeability model according to the matrix porosity model, and obtaining a fracture permeability model according to the discrete fracture network model; matrix heterogeneity characterization parameter gridding is carried out based on the matrix permeability model, and fracture heterogeneity characterization parameter gridding is carried out based on the fracture permeability model. According to the method, the near-wellbore zone water invasion channel or the seepage barrier can be accurately judged, the whole gas reservoir water invasion channel or the area to be subjected to water invasion can be predicted, the influence of human factors is reduced, and the method is visual, fine, high in calculation efficiency and high in accuracy for the crack-pore type reservoir. And predicting the geometrical morphology and spatial configuration relation of the water invasion channel with comprehensive evaluation parameters.

The invention discloses a large model infectious disease prediction and early warning method and system based on multi-stage learning, and mainly aims at constructing a multi-stage learning model for data, learning basic features, time sequence features and expansion features in the data through different stages, and then predicting and early warning the infectious diseases through a re-defined attention mechanism model. According to the scheme, different information and a self-defined attention fusion mechanism are respectively learned according to learning models which are constructed in sequence and aim at different features and are in different stages, so that the learning efficiency is improved. The results of different stages are fully fused through attention traversal pairwise calculation, different weight factors are given to the results of each stage, and then weighted fusion is performed, so that multi-stage, comprehensive and accurate prediction and early warning of the infectious disease epidemic situation are realized through fusion of multi-source data.

The invention belongs to the technical field of magnetotelluric denoising, and particularly discloses a magnetotelluric denoising method and system based on a hybrid model. The method comprises the following steps: decomposing a magnetotelluric time sequence signal into a noise-containing contour modal signal and a noise-free contour modal signal by adopting a variational mode decomposition method; decoding the noise-containing contour modal signal, inputting the low-dimension vector set into an LSTM model, obtaining the position of the noise signal, and encoding the position; adopting a bidirectional long-short time memory network to cut off the position of the noise signal of the magnetotelluric time sequence signal, and performing bidirectional prediction on the cut time sequence signal to obtain the time sequence signal without noise at the noise position; and based on the noise-free contour modal signal and the noise-free time sequence signal at the noise position, reconstructing a noise-free magnetotelluric time sequence signal by using VMD. According to the invention, the low-loss denoising of the magnetotelluric signal can be realized, and the influence on a real signal is reduced.

According to the method for predicting the drum strength of the vanadium-titanium sinter, the drum strength of the vanadium-titanium sinter is predicted through three components of TiO2, SiO2 and CaO in a vanadium-titanium sinter mixture, and the drum strength of the vanadium-titanium sinter is predicted through the interaction effect of TiO2, SiO2 and CaO added in prediction. And the interaction influence of TiO2 and R and the influence of SiO2 + CaO on the drum strength of the vanadium-titanium sinter are fully considered, so that the prediction is more comprehensive, scientific and accurate.

This application provides a method, apparatus, storage medium, and processor for fault prediction of power modules. The method includes: acquiring historical operating data of the power module under multiple states; analyzing and selecting historical operating data that meets preset conditions as positive sample data; processing the positive sample data and inputting the processed positive sample data into a fault prediction data model to train the fault prediction data model and obtain a trained fault prediction data model; acquiring real-time operating data of the power module to be predicted; inputting the real-time operating data into the trained fault prediction data model to output the fault type of the power module to be predicted through the trained fault prediction data model, thereby realizing state monitoring and fault prediction of internal components of the power module, constructing an accurate fault prediction data model, and timely and accurately predicting potential faults of the power module, with more comprehensive predictions and a greater reduction in losses caused by faults.

The invention provides a system and method for detecting and classifying rail defects by using artificial intelligence, and the system comprises a receiving module which is used for inputting one or more rail images; the preprocessing module is used for preprocessing the rail image; the Transform module is used for carrying out defect detection and false alarm analysis on the preprocessed image by adopting a two-stage Transform model; and the output module is used for outputting and displaying a result containing defect, misinformation and unknown information. The method has the advantages that the method is easy to implement, the false alarm rate of an existing railway track detection system can be reduced, and the defect detection rate is increased. The method is easy to implement, and the risk of accidents or injuries in the defect detection process is reduced. And the service life of the track is prolonged, and the requirement for large-scale maintenance or replacement is reduced to the maximum extent.

The present application relates to the field of image processing, and specifically to an intestinal necrosis feature recognition system based on image analysis and a storage medium, wherein a computer program in the storage medium performs the following steps: obtaining medical images of a patient and dividing them into an expiration phase subset and an inspiration phase subset; performing spatial registration and motion artifact correction on the expiration phase subset and the inspiration phase subset to obtain multi-modal images; extracting a blood vessel obstruction feature, a perfusion injury feature and a cell necrosis feature in the multi-modal images; constructing a causal hierarchical relationship among the blood vessel obstruction feature, the perfusion injury feature and the cell necrosis feature based on an intestinal necrosis pathological mechanism; and determining a risk level of the patient having intestinal necrosis and a probability corresponding to the risk level based on the causal hierarchical relationship, the blood vessel obstruction feature, the perfusion injury feature and the cell necrosis feature using a random forest algorithm. The present application solves the problem of insufficient accuracy in recognizing intestinal necrosis features in the prior art.

The application relates to an oral squamous cell carcinoma auxiliary chemical immunotherapy response method and device, which comprises the following steps: acquiring image data and baseline clinical factors of a target user, the image data comprising metabolic parameters and image features of a tumor region; performing clinical parameter standardization processing on the metabolic parameters to obtain a standardized metabolic parameter set related to treatment response; performing stability screening on the image features by using an intragroup correlation coefficient to obtain key features related to treatment response; performing regression analysis on the baseline clinical factors to identify independent prognostic factors related to treatment response in the baseline clinical factors; and performing feature fusion on the standardized metabolic parameter set, the key features and the independent prognostic factors to obtain a response rate and a prognosis prediction result of the target user. The method can more accurately and comprehensively predict the response rate and the prognosis of the target user to the auxiliary chemical immunotherapy.

The present invention provides an emotion recognition method and system based on multimodal perception data, which relates to the field of virtual reality technology. The multimodal VR perception data is finely processed through a VR interactive emotion perception model, and the summary and detail emotion expression vectors are effectively extracted and simplified, which significantly reduces the complexity of data processing while retaining key emotion information. By using the pre-learned summary and detail to guide the emotion expression vector, and combining the feature self-focusing strategy, the present invention can accurately output the expression vector that integrates the self-focusing feature, thereby improving the pertinence and accuracy of emotion recognition. In addition, through the feature interactive focusing strategy, the present invention further explores the deep correlation between the self-focusing expression vector and the summary and detail emotion expression vector sets, thereby more comprehensively predicting the user emotion perception results in VR interaction. As a result, the present invention significantly improves the efficiency and accuracy of emotion recognition in multimodal situations.

The invention provides a convective inception prediction method, device and system based on a stationary meteorological satellite, and relates to the field of meteorological monitoring. Cloud target matching and tracking are carried out by using a neighborhood matching technology based on L1 infrared spectrum data of a stationary orbit radiation imager (AGRI) carried by a FY-4B satellite B stationary meteorological satellite; according to the method, a multi-spectral feature critical value of a satellite cloud picture is not preset, but a time sequence and spatial features of a convection inception spectrum are automatically extracted through a deep learning model based on a 3D space-time attention mechanism, so that more comprehensive and accurate convection inception prediction is realized, and an important technical support is provided for severe convection weather prediction and disaster prevention and reduction.

This invention relates to the field of battery testing technology, and more particularly to a multi-channel battery charge-discharge testing system and method. The method includes the following steps: acquiring voltage data of each battery cell in a multi-channel battery; performing voltage gradient stratification processing on the voltage data of each battery cell to divide the battery cell into multiple voltage intervals; detecting the number density of battery cells within each voltage interval to generate a voltage gradient distribution map; collecting the current distribution coefficients at each connection point of the multi-channel battery; performing current distribution difference analysis on the current distribution coefficients to calculate the actual current distribution ratio of each cell at different charge-discharge stages, generating current distribution difference data; and collecting overall temperature change data of the multi-channel battery during the charge-discharge process, as well as local temperature data of each battery cell. This invention, through battery testing technology, enables the analysis and prediction of the charge-discharge performance of multi-channel batteries, thereby improving the accuracy of multi-channel battery charge-discharge performance evaluation.

The application relates to the technical field of machine learning, and particularly discloses a risk prediction-based decision generation method and device, equipment and a storage medium. The application combines health risk parameters and market risk parameters to perform health risk prediction, so that the risk prediction is more comprehensive and accurate. The application also predicts medical expenses and adjusts predicted income according to the health risk prediction result, so that the predicted expenses and the predicted income are more accurate. Furthermore, the application obtains a target investment decision according to the predicted expenses, the predicted income and the predicted health risk, thereby improving the accuracy of the target investment decision. When the method is applied to medical and pension businesses and financial and insurance businesses, a reasonable and reliable target investment decision can be obtained, and the health risk hedging capability is improved.

The application discloses a kind of heterogeneous double mechanical arm end pose mapping method, system, device and storage medium, method includes the following steps: S1: obtaining the angular velocity of each joint of main mechanical arm, position and end attitude information, the spatial position and attitude of main mechanical arm end are calculated;S2: the motion trajectory data in the teaching process of main mechanical arm is collected, long short-term memory network model is established, the pose of future time step is predicted, and the motion trajectory of slave mechanical arm is planned;S3: data information is transferred, and communication delay problem is solved;S4: the pose information of main mechanical arm end is solved, joint angle is adjusted, and error is adjusted in combination with proportional-integral-derivative control;S5: position and velocity signal are filtered, and mechanical arm dynamics model is established, adaptive control law is designed, and trajectory smoothing algorithm is used;S6: virtual obstacle map is established, and safe working space is limited.The application realizes the comprehensive, accurate mapping and prediction of the end pose of heterogeneous double mechanical arm.

The present application provides an ecosystem monitoring method and system based on a large model, the method comprising: analyzing ecological data based on a macro-ecological model, multiple single-species distribution models, and a joint species distribution model, and outputting prediction content; fusing the output content of the three types of models with the linkage of environmental data and geographic location, and processing the fusion results using sequential Bayesian filtering to obtain an estimate of the target state of species in the ecological protection area; aligning the estimated value of the target state of species and other multi-source monitoring data according to timestamps, and converting them into input data sets, inputting the target time series and key data features included in the input data set into the target large model according to a preset prompt template, and using the target large model to predict the ecological status in the ecological protection area to monitor the ecological protection area. The present application can make full use of multi-dimensional data and multiple categories of models to predict the ecological status of species and ensure the reliability and accuracy of the prediction.

The present invention discloses an e-commerce system based on big data, relates to the technical field of e-commerce, and is implemented by a data collection module, a data sorting module, a prediction analysis module, and a monitoring optimization module. The present invention uses big data to perform in-depth mining and prediction analysis on the complex e-commerce sales data, and extracts factors related to sales volume to build an accurate sales volume prediction system, and connects and combines sales volume prediction with inventory management, so as to automatically adjust the inventory level according to the prediction results, ensure that the inventory matches the market demand to reduce the inventory cost, and improve the inventory turnover rate and enhance the operational efficiency of the enterprise, and can build a comprehensive profit prediction system and comprehensively consider market fluctuations, cost changes, sales strategies and other factors, so that the prediction of this system is accurate and can provide excellent forward-looking management for e-commerce enterprises.

The invention discloses a baijiu fermentation quality prediction and evaluation method and system, and the method comprises the steps: collecting environmental parameters of a fermented grain fermentation process, and obtaining evaluation parameters of the fermentation quality; constructing a fermentation quality evaluation model, inputting the environmental parameters and the evaluation parameters as sample data into the fermentation quality evaluation model, and training a network model to obtain a trained evaluation model; inputting to-be-measured environmental parameters in the fermented grain fermentation process into the trained evaluation model, and outputting predicted evaluation parameters; and evaluating the fermentation quality by using the predicted evaluation parameters to obtain an evaluation result. According to the method, human interference can be remarkably reduced, evaluation parameters are quickly obtained by using the network model, and reliable and effective evaluation of the fermentation quality is realized.