96results about How to "Improve forecasting efficiency" patented technology

The invention relates to the technical field of machine learning, and provides a drill hole rock mass structure quantitative prediction method and system based on machine learning, and the method comprises the steps: constructing an associated data set with a drill hole television image and / or an actual rock mass structure disclosed by an exploration adit as a benchmark, and taking an actual rock mass structure quantitative value as a benchmark; and calculating an overall deviation correction coefficient of a rock mass integrity coefficient and a rock quality index based on the associated data set, performing deviation correction on original data by using the correction coefficient, and realizing automatic mapping from double-index input to rock mass structure quantitative predicted value output by a machine learning model. The decision coefficient of the prediction model on a test set reaches 0.9 or above, the average absolute error does not exceed 0.3, the prediction time of a single group of data does not exceed 1.5 seconds, and the prediction precision and efficiency are far higher than those of a traditional experience method. The method supports local off-line deployment, adapts to a field network-free environment, constructs a data closed-loop mechanism to realize continuous iteration of the model, and provides a reliable basis for dam foundation safety assessment.

This invention belongs to the field of equipment life prediction technology, and discloses a method and related apparatus for predicting the remaining life of a photovoltaic inverter. The method includes: acquiring junction temperature data of the IGBT modules in the photovoltaic inverter under test; determining the number of junction temperature thermal cycles of the IGBT modules in the photovoltaic inverter under test based on the junction temperature data; calculating the cumulative damage of the IGBT modules in the photovoltaic inverter under test based on the number of junction temperature thermal cycles; and calculating the remaining life prediction result of the photovoltaic inverter under test based on the cumulative damage of the IGBT modules. This invention can more accurately predict the remaining life of the IGBT modules, and thus more accurately predict the overall remaining life of the photovoltaic inverter. The prediction process does not require extensive data collection and training of the prediction model, effectively reducing the difficulty of life prediction and improving prediction efficiency.

This application provides a video image component prediction method and apparatus, and a computer storage medium. The method may include: acquiring a reference value set of a first image component of the current block; determining multiple first image component reference values ​​from the reference value set of the first image component; performing a first filtering process on the sample values ​​of the pixels corresponding to the multiple first image component reference values ​​respectively to obtain multiple filtered first image reference sample values; determining the image component reference value to be predicted corresponding to the multiple filtered first image reference sample values; determining the parameters of a component linear model based on the multiple filtered first image reference sample values ​​and the image component reference value to be predicted; performing a mapping process on the reconstructed value of the first image component of the current block according to the component linear model to obtain a mapping value; and determining the predicted value of the image component to be predicted of the current block based on the mapping value.

The application discloses a multiple wave prediction method and device, a computing device and a storage medium. The method comprises the following steps: acquiring frequency domain data of any shot point at any seismic trace; generating a seismic data matrix according to the frequency domain data, the seismic data matrix being an upper triangular matrix with a main diagonal line of 0, and any non-first column of the seismic data matrix containing frequency domain data of at least one seismic trace of the same shot point; constructing a multiple wave matrix, the multiple wave matrix being an upper triangular matrix with a main diagonal line of 0, and any non-first column of the multiple wave matrix containing a multiple wave model of at least one seismic trace of the same shot point; establishing a functional relationship between the multiple wave matrix and the seismic data matrix; and obtaining model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship. According to the scheme, the prediction efficiency of the multiple wave can be improved under the premise of ensuring the prediction accuracy of the multiple wave.

The invention discloses a fault prediction type industrial flow instrument based on a deep learning algorithm and a diagnostic analysis system, and relates to the field of industrial automation and intelligent monitoring. The system comprises the following components: a data acquisition module, a data processing module, a dynamic adaptive learning module based on reinforcement learning, a fault prediction module and a diagnostic analysis module. Through the dynamic adaptive learning module based on reinforcement learning, unique state definition, action adjustment, reward function design and a reinforcement learning algorithm execution mechanism, fault prediction model parameters can be optimized in real time according to working condition changes, a hybrid neural network architecture of the fault prediction module is combined with an attention mechanism, and fault prediction accuracy is improved. The method further enhances the capability of capturing fault features, all the modules are in close cooperation and collaborative optimization, and compared with a traditional fault prediction system, in a complex and changeable industrial environment, the fault prediction precision is greatly improved, the prediction efficiency is remarkably improved, and potential faults can be found in advance.

The application belongs to the field of smart grid and computer vision, and particularly relates to a power station wiring diagram text robust generalization detection and recognition method based on an improved SwinTextSpotter v2. The method comprises the following steps: step 1: inputting an image into a text detection and recognition network based on multi-modal learning for training and prediction, obtaining a shared feature map through a shared feature extraction backbone network, and further inputting the shared feature map into a text detection module to obtain a text detection result and a text feature map; step 2: inputting the text feature map into a visual feature extraction and prediction module to obtain a feature sequence, and then matching the predicted feature sequence with a canonical representation obtained by a character structure feature extraction and prediction module to obtain a recognition result; and the like. The application robustly improves the detection and recognition accuracy of the model for irregular text and Chinese character text, and improves the generalization performance of the text detection and recognition of various types of wiring diagrams.

This application discloses a data prediction method, apparatus, device, storage medium, and computer program product, relating to the fields of artificial intelligence and data processing technology. Embodiments of this application can be applied to fields such as mapping and transportation. The method includes: acquiring a universal causal graph to characterize causal knowledge prevalent in different regions, whereby the causal knowledge characterizes causal relationships between attributes of different regions; based on the universal causal graph and observation data of a target region, acquiring the full range of regional attributes corresponding to each region in the target region, including unobserved regional attributes; and based on the full range of regional attributes corresponding to each region in the target region, acquiring predicted values ​​for object flows between regions in the target region. This application solves the problem of inaccurate object flow prediction in some regions due to a lack of observation data, thus improving the accuracy of object flow prediction.

The present application belongs to the technical field of highway slope protection, and provides a highway slope flexible protection net safety state prediction method for highway construction, which comprises obtaining state characteristic data of the highway slope, highway compression characteristic data, state data of the flexible protection net and slope deep displacement data; a slope landslide probability analysis model is established according to the state characteristic data and the compression characteristic data, and a slope landslide comprehensive probability is generated; a state index is generated according to the protection net state data; a landslide impact estimation strength is generated according to the deep displacement data; then a damage analysis model is established in combination with the impact estimation strength, the landslide comprehensive probability and the state index, and a flexible protection net comprehensive damage probability is generated; the safety state of the protection net is predicted accordingly; the present application realizes quantitative evaluation of the damage probability of the flexible protection net by fusing the slope landslide risk, the protection net degradation and the landslide impact strength, and improves the accuracy and practicality of the safety state prediction.

The invention provides a land use scale prediction method based on weight calculation, and relates to the technical field of data processing, and the method comprises the steps: carrying out the space resource calling of a target space data set, and obtaining a space data list; performing spatial topology mapping on the spatial data list to obtain a spatial adjacency matrix; performing spatial weighted updating on the data nodes to obtain mutual plastic feature vectors; performing similarity evaluation on the mutual plastic feature vectors to obtain a similarity weight coefficient, and performing weighted correction on the spatial adjacency matrix to obtain a mutual plastic adjacency weight matrix; carrying out topological structure analysis on the intermodeling adjacent weight matrix to obtain spatial structure characteristics; performing feature-structure collaborative weight distribution on the data nodes to obtain an initial weight distribution field; carrying out compatibility weighted correction on the initial weight distribution field to obtain an optimized weight distribution field; carrying out space overall planning on the optimized weight distribution field to obtain a scale scheme; according to the invention, the efficiency of land scale prediction based on weight calculation can be improved.

This disclosure provides a method and apparatus for encoding / decoding image signals. The method for decoding image signals according to the present invention may include the following steps: determining whether a brightness change exists between a current image including a current block and a reference image of the current image; if a brightness change is determined to exist between the current image and the reference image, determining candidate weight prediction parameters for the current block; determining weight prediction parameters for the current block based on index information for specifying any one of the candidate weight prediction parameters; and performing prediction for the current block based on the weight prediction parameters.

This invention provides a method for predicting the health trend of hydropower units based on the TCN-BiLSTM-Attention model, relating to the field of hydropower generator sets. The method involves: S1: acquiring the original vibration signal of the hydropower generator set and denoising the original vibration signal using a TTAO-VMD-EWT joint denoising model; S2: obtaining multiple operating state parameters of the hydropower generator set and calculating the Pearson correlation coefficient, Spearman correlation coefficient, maximum information coefficient, and distance correlation coefficient between these parameters and the vibration signal; S3: calculating the fusion evaluation index of each operating state parameter and the vibration signal, selecting key features, and constructing a multi-dimensional feature vector together with the reconstructed vibration signal; S4: constructing a TCN-BiLSTM-Attention prediction model to predict and output the predicted trend of the hydropower generator set's vibration signal. This method improves prediction accuracy, increases prediction efficiency, and enhances the accuracy, stability, and timeliness of hydropower generator set vibration trend prediction.

The disclosure provides a kind of integrated energy system multivariate load forecasting method and system considering polyphase coupling, it belongs to garden integrated energy system load forecasting technical field, the scheme includes: obtaining historical load forecasting related data in garden integrated energy system, and carries out corresponding pretreatment;Based on the historical load forecasting related data, the nonlinear relationship between multivariate load and each influencing factor corresponding to the load is mined using the weighted gene co-expression network analysis method, and the influencing factors strongly related to different loads are determined;The characteristics corresponding to the load history data of different loads and their strongly related influencing factors are simultaneously input into the pre-trained load forecasting model, and the load forecasting results corresponding to different loads are obtained;Wherein, the load forecasting model uses MTL framework, uses BiLSTM as the shared layer of MTL, and the prediction tasks under different loads share information through the shared layer.

The application discloses a material performance evolution trend prediction method and device and a storage medium, relates to the technical field of material science, and mainly aims at improving the determination efficiency and accuracy of the material performance evolution trend. The method comprises the following steps: in response to a material performance evolution trend prediction signal of a target material in an aging process, obtaining aging environment information of the target material and historical material performance of a previous aging time point corresponding to a current aging time point; taking the current aging time point and a plurality of future aging time points as to-be-predicted time points, inputting the historical material performance and the aging environment information into a preset performance prediction model to gradually perform material performance recursive prediction of each to-be-predicted time point, and sequentially obtaining material performance of each to-be-predicted time point; and determining the material performance evolution trend of the target material in the aging process based on the historical material performance and the material performance of each to-be-predicted time point.

The application provides a method for accurately predicting pollutant conversion in a water treatment process, and belongs to the technical field of pollutant conversion. The method adopts a degradation path prediction model provided with a multi-receptive field attention framework to perform prediction analysis on pollutant treatment information, so as to obtain pollutant degradation path information. The prediction process is high in efficiency and high in prediction result accuracy, and can guarantee the treatment efficiency of the water treatment process and the effluent water quality.

The application discloses an engineering structure digital twin construction and updating method and device suitable for sparse monitoring data, equipment and a storage medium, relates to the technical field of digital twin, and comprises the following steps: arranging a plurality of measuring points in the linear elastic region of an engineering structure, and obtaining internal measuring point information; a first mapping relationship between the internal measuring point information and the combined effect of the force of the non-linear elastic region on the linear elastic region is established, a combined time series data set is obtained based on the first mapping relationship and sparse environmental parameters; based on the combined time series data set, an associated model of environmental load and structure response is obtained; based on the far-field environmental parameters and the associated model, the implicit environmental parameters are processed through black-boxing, the estimation of the environmental load and the prediction of the structure response are realized, the dependence on explicit modeling of the local environmental parameters which are difficult to measure is reduced, the influence of the local environmental parameters is hidden in the associated model through the black-boxing processing, and the updating and prediction efficiency of the digital twin model under the condition of sparse monitoring data is improved.

The application discloses a kind of based on hybrid fourier neural architecture's gulf hydrodynamic prediction method, belong to hydrodynamic prediction technical field, the method includes data conversion, data preprocessing, topological relationship construction, feature extraction and fusion, model construction and training and prediction etc. steps, by modified trim2nc data packet conversion data format, static and dynamic feature dataset are obtained after preprocessing;Grid topological relationship is constructed using Delaunay triangulation algorithm;Space and time characteristics are extracted and fused respectively by graph neural network, time convolution network;Combining with the ability of Fourier neural operator network with processing irregular geometric boundary is calculated in frequency domain, coupled physical knowledge defines loss function training model, finally realizes gulf hydrodynamic fast and accurate prediction;The application solves the problems of high cost and low efficiency of traditional hydrodynamic model calculation, and considers the prediction accuracy and efficiency, which can provide reliable basis for gulf water environment management and planning.

The embodiment of the invention discloses a conference information display method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current display information in a multi-person conference scene; obtaining first handwriting information corresponding to the current wearing user, second handwriting information corresponding to the current display information, display video information corresponding to the current display information and conference audio information associated with the current display information; identifying the first handwriting information, and determining a display information processing instruction corresponding to the first handwriting information; based on the display information processing instruction, controlling a device worn by the current wearing user to perform marking processing on the current display information; inputting the second handwriting information, the display video information and the conference audio information into a writing prediction model to obtain a writing prediction result; and displaying the writing prediction result in the current display information. According to the technical scheme, the handwriting recognition and prediction efficiency and accuracy can be improved in a complex multi-person conference scene.

This invention discloses a method, system, and electronic device for constructing an air ticket sales volume prediction model, relating to the field of aviation information technology. The method includes: acquiring historical sales information for each flight during a historical sales period; using the historical ticket price, the sales stage identifier, and the historical reference ticket price as input data, and the historical ticket sales volume as a label; using the cumulative parameter of ticket demand over time, the price elasticity coefficient, and the average ticket demand as model parameters to train an initial air ticket sales volume prediction model; and iteratively optimizing the model parameters using a basic particle swarm optimization algorithm until the number of iterations is not less than a preset number, thereby generating a target air ticket sales volume prediction model. This invention can improve work efficiency and reduce labor costs.

The invention provides an indoor temperature determination method fusing physical constraints. The indoor temperature determination method can be applied to the technical field of temperature control and machine learning. The method comprises the steps that heating system driving data at historical moments are processed through a temperature prediction model, the indoor evaluation temperature at a target moment is obtained, and the temperature prediction model is obtained based on a building heat loss value training neural network model; the building thermal loss value is obtained according to a difference degree between a sample evaluation temperature change rate obtained based on a sample indoor evaluation temperature and a sample simulation temperature change rate obtained based on an analogue simulation algorithm, and the simulation temperature change rate represents the interior of a simulation sample building; a temperature instantaneous change result meeting energy conservation is generated at the historical moment due to the instantaneous difference value between the obtained heat and the heat loss, and the building heat loss value is used for physically constraining the indoor evaluation temperature of the temperature prediction model to meet the instantaneous heat balance of energy conservation.

This invention, entitled "An Autonomous Driving Interaction Scene Prediction Method," belongs to the field of autonomous driving technology. The technical problem this invention aims to solve is that existing autonomous driving interaction prediction methods suffer from problems such as separation of prediction and planning, lack of interaction game modeling, inconsistency between trajectory and decision-making, heavy rule dependence, and significant information transmission loss, failing to achieve optimal overall decision-making for the interaction scene. The key technical solution is as follows: Through three-level hierarchical processing at the scene, decision, and trajectory levels, input data such as the predicted vehicle, the host vehicle, and the environment are obtained to complete interaction type classification and obstacle screening; the dominant party is determined based on cost calculation, and then marginal probability prediction is performed for the dominant party and conditional probability prediction for the dominated party based on the dominant relationship. Through alternating iterations, a consistent predicted trajectory of social vehicles and planned speed sequence of the host vehicle are output. The scene and decision results can be used as intermediate supervision items.

The embodiment of the application relates to the battery technical field, and discloses a battery life prediction method and device, electronic equipment and a storage medium. The battery life prediction method comprises the following steps: extracting a capacity diving feature of a battery according to a capacity loss curve of the battery, and extracting a capacity increment feature of the battery according to an IC curve of the battery; wherein the capacity loss curve comprises a component of linear loss of battery capacity and a component of nonlinear loss of battery capacity, and the capacity diving feature is extracted from the component of nonlinear loss of battery capacity; the capacity increment feature and the capacity diving feature are input into a pre-trained battery life prediction model to obtain a life prediction result of the battery, and the battery life prediction model is trained by using at least one capacity diving feature and capacity increment feature of the battery to obtain an LSTM model. The battery life prediction method can realize accurate prediction of the life of a lithium ion battery under edge working conditions, so that fine management of the battery is realized.

The invention relates to an e-commerce user re-purchase prediction method and system based on big data, and the method comprises the steps: 1, obtaining the original behavior data of a user, and carrying out the preprocessing of the original behavior data, and obtaining the behavior data of the user; 2, constructing user behavior characteristics, and obtaining corresponding user behavior characteristic data according to the behavior data of the user; 3, inputting the user behavior characteristic data into a user re-purchase prediction model for user re-purchase prediction to obtain a user re-purchase prediction result; wherein the user re-purchase prediction model is obtained by fusing an XGBoost prediction model, a Light GBM prediction model and a CatBoost prediction model which are trained on the basis of a voting method. Compared with previous logistic regression models and the like, the e-commerce user re-purchase prediction method based on the big data has the advantages that the prediction efficiency is greatly improved, the prediction precision is higher, related merchants can make corresponding marketing schemes, and accurate pushing to the users is realized.

The present application relates to the technical field of mineral exploration, and particularly relates to a rare metal concealed ore body prediction method, comprising collecting multi-modal geoscience data; determining a weak mineralization anomaly area based on a comparison result of a sampling point singularity index and a preset singularity threshold; determining a favorable space range of mineralization in the weak mineralization anomaly area based on a cross gradient function; adjusting the preset singularity threshold under the condition that the favorable space range of mineralization is unqualified based on a residual spectrum similarity; adjusting a weight coefficient of the cross gradient function when an element combination anomaly is unqualified; constructing a generative adversarial network model and expanding a training data set; constructing a three-dimensional convolutional neural network discriminant model, optimizing the three-dimensional convolutional neural network discriminant model based on a mineralization suspected area, and outputting a three-dimensional space occurrence probability distribution of a concealed ore body. The present application solves the problems of insufficient utilization of multi-source geoscience information and difficulty in meeting the precise positioning requirements of deep concealed ore bodies in the spatial structure of geophysical and geochemical exploration.

The application provides a shear wave velocity prediction method, device and equipment, the method comprises the following steps: for each known well, determining a plurality of target logging parameters from all logging parameters according to the first data set of the known well, the influence degree of the target logging parameter on the shear wave velocity of the known well is greater than that of other logging parameters; according to the second data set of the known well, a decision tree model of the known well is constructed; input the to-be-predicted data set of the to-be-predicted well into the decision tree model of the known well, and obtain the initial shear wave velocity output by the decision tree model, the to-be-predicted data set comprises the parameter value of each target logging parameter of the to-be-predicted well at each preset depth; according to the initial shear wave velocity output by the known well in each decision tree model, the target shear wave velocity of the to-be-predicted well is generated. The method of the application effectively improves the accuracy and prediction efficiency of the shear wave velocity prediction.

The invention is suitable for the technical field of structural seismic engineering, and provides a construction method, device and equipment of a wading underground structure seismic response prediction model, and the method comprises the steps: firstly, generating a seismic response database based on a simulation result of a structure-soil-fluid coupling finite element model; then, based on a plurality of seismic oscillation sample feature vectors extracted from the plurality of seismic oscillation sample data, performing clustering analysis on the plurality of seismic oscillation sample data to obtain a training data set; and finally, taking the target seismic oscillation sample data and the water depth parameter in the training sample data as input data, taking the seismic response data of a plurality of monitoring points in the training sample data as labels of the input data, training the initial seismic response prediction model, and obtaining a wading underground structure seismic response prediction model. Through the method, the prediction precision and prediction efficiency of the multi-monitoring-point earthquake response of the wading underground structure are improved.

The present application belongs to the technical field of soil compaction degree prediction, and particularly relates to a soil compaction quality prediction method, medium and terminal based on image recognition, comprising the following steps: S10, collecting data of soil compaction degree and soil surface compaction pictures of sand under different conditions; S20, preprocessing the collected pictures, including picture normalization, grayscale and binarization processing, and constructing a data set; S30, constructing an improved CNN neural network model based on the preprocessed data set; S40, importing the data set into the improved CNN neural network model for pre-training to obtain a pre-training model; S50, importing the collected pictures into the pre-training model, and performing freezing and fine-tuning on the pre-training model to obtain a sand compaction quality prediction model; and S60, importing the preprocessed surface compaction pictures of the sand to be detected into the prediction model to output a compaction degree prediction value. The present application has simple process and convenient operation, the obtained prediction model has strong robustness and generalization ability, and the prediction efficiency and accuracy of sand compaction quality are high.

A video decoding method according to the present invention comprises the steps of: determining whether to apply a combined prediction mode to a current block; when the combined prediction mode is applied to the current block, obtaining a first prediction block and a second prediction block of the current block; and obtaining a third prediction block of the current block based on a weighted sum operation of the first prediction block and the second prediction block.

This invention relates to the field of hydrodynamic prediction technology, specifically to an intelligent prediction method and system for hydrodynamic effects based on a multi-factor, full-level conditional embedding network. First, key influencing factor parameters of hydrodynamic effects are obtained, and standardized conditional input vectors are constructed and preprocessed. Then, an MHCE-Net is built, comprising conditional input, encoding, and mapping modules, as well as encoders, bottleneck layers, decoders, and output modules. The standardized vectors are mapped to conditional feature layers through feature enhancement and dimension matching, and the corresponding layers of the full-level embedding network are fused with convolutional feature maps. Finally, the model is trained by extracting and reconstructing the fused features, and the actual spatial distribution field of physical quantities is obtained through inverse normalization. This invention enhances feature representation capabilities through pre-encoding enhancement of multi-factor conditional vectors, enabling rapid and accurate prediction of spatial fields related to hydrodynamic effects without complex numerical simulations, significantly reducing computational costs and improving prediction efficiency and accuracy. It is suitable for hydrodynamic effect prediction needs in groundwater environments, deformation fields of water-related engineering structures, and eco-hydrological scenarios.

A Transformer-based method and device for predicting the seismic performance of metal dampers, relating to the field of electronic digital data processing technology, is disclosed to improve the accuracy of predicting the seismic performance of metal dampers while reducing the amount of labeled data. In this method, the prediction device constructs a dual-encoder Transformer neural network model. Dynamic loading displacement time-series data and static geometric parameter data are independently learned by the first and second encoders, respectively. Dynamic time-series features are used as keys, static geometric features as values, and the decoder's own state as queries, thereby enabling an explicit understanding and modeling of the intrinsic relationship between displacement history and the inherent physical properties of the metal damper. Simultaneously, a physical constraint loss function is used to optimize the model during training. This physical regularization reduces the dependence on a large amount of labeled data and ensures that the prediction results strictly conform to physical laws even under unexperienced conditions.

The invention discloses a vehicle fault warning method and device, equipment and a medium. The invention belongs to the technical field of Internet of Vehicles. The method comprises the following steps: acquiring component monitoring data obtained by coding a component unit according to a preset coding rule; analyzing the component monitoring data to obtain monitoring information; when it is identified that the monitoring information comprises fault code information, determining fault information of the component unit according to the fault code information; and sending the fault information to a trip computer so as to display the fault information through the trip computer. By adopting the technical scheme, the purposes of monitoring the running condition of the vehicle in real time and automatically identifying the vehicle fault can be achieved, and the efficiency of analyzing the vehicle fault and the timeliness of fault warning are improved.