18results about How to "High precision prediction" patented technology

The invention relates to the technical field of intelligent traffic prediction, in particular to a time-space correlation traffic flow prediction method based on deep learning, which comprises the following steps: S1, collecting traffic flow data and related external environment data in real time, and establishing an initial traffic network topology structure; s2, calculating the dynamic weight of each node; s3, dynamically adjusting the weight of each edge in the traffic network according to the node weight change in the S2 so as to update the topological structure of the traffic network; s4, performing feature extraction on the spatial dependency relationship among the traffic nodes, and extracting time evolution features of the traffic flow data at the same time; s5, constructing an adaptive space-time fusion model; s6, traffic flow prediction is carried out, and adaptive space-time fusion model parameters are optimized; according to the method, through dynamic fusion of the spatial-temporal characteristics and adaptive adjustment of the model parameters, the precision and real-time performance of traffic flow prediction are effectively improved, and the adaptability and generalization ability of the model in a complex dynamic traffic scene are enhanced.

The invention discloses a machine learning virtual power plant load prediction method based on multi-factor fusion, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, including historical load data, meteorological data, calendar information and social activity data, and carrying out the preprocessing of the historical load data and the meteorological data; s2, a multi-head attention mechanism and a cross-modal learning technology are adopted to construct a multi-factor fusion model, and fusion vectors of historical load data, meteorological data, calendar information and social activity data are calculated; s3, constructing a multi-scale load prediction model, and constructing a training set for the multi-scale prediction model; s4, training the multi-scale prediction model by using the training set to obtain a trained multi-scale prediction model; and S5, performing power load prediction according to the trained multi-scale prediction model. The method not only can effectively process complex and changeable influence factors, but also has high-precision prediction and high adaptability.

The invention provides a vegetation prediction model construction method, a vegetation prediction method, a vegetation prediction device and vegetation prediction equipment, and relates to the technical field of data processing. The vegetation prediction model construction method comprises the following steps: taking ecological environment data of a target area as an independent variable, taking vegetation spatial distribution data corresponding to the target area as a dependent variable, and training a first random forest model to obtain a vegetation spatial distribution prediction model; taking the output of the vegetation parameter spatial distribution prediction model and the period-by-period meteorological data as independent variables, taking the period-by-period vegetation feature change data as dependent variables, training a second random forest model, and obtaining a vegetation feature time change prediction model; and splicing the vegetation spatial distribution prediction model and the vegetation feature time change prediction model to obtain a vegetation prediction model. According to the technical scheme, the data utilization efficiency can be improved, the model training time is shortened, and meanwhile the adaptability of the vegetation prediction model and the accuracy of vegetation prediction are improved.

The invention discloses an urban electricity consumption prediction method, system and device fused with time-frequency features and a storage medium, and the method comprises the steps: firstly, carrying out the data preparation and preprocessing, and extracting the features of a plurality of time scales, such as years, quarters, months, weeks and days, from the historical data of multi-dimensional time series data including power consumption, economy, weather and the like; secondly, performing time-frequency analysis on the data with different time scales by using short-time Fourier transform to generate a time-frequency characteristic graph; and then, multi-scale feature fusion is carried out based on a feature pyramid network (FPN), and a fused feature matrix is input into a Transform encoder so as to realize synchronous time-frequency information embedding. And finally, a Transformer decoder decodes and predicts urban daily power consumption at a specific time point in the future according to the time-frequency characteristic matrix. The method realizes multi-time scale power consumption accurate prediction by combining time and frequency information, and is widely applied to intelligent power grid scheduling, energy management and related fields.

The present invention discloses a method for simulating and predicting the spatial pattern of land for coordinated spatial planning and carbon reduction control, and belongs to the technical field of land space planning. The present invention includes collecting data, preprocessing the collected data, and establishing a basic database for optimizing low-carbon land use; combining energy carbon emissions with spatial mapping technology to calculate the annual land use carbon emissions, and using the GM (1,1) model to predict the land use carbon emission coefficient of the target year; based on Markov and linear programming models, combined with natural development trends and land space planning requirements, predict and optimize the quantitative structure of low-carbon land use; using the PLUS model, simulate and predict the land use pattern under different scenarios; evaluate the carbon reduction benefits of the low-carbon land use optimization method of coordinated spatial planning, and propose multi-scale low-carbon decision-making and optimization suggestions. The method of the present invention not only reduces policy risks and problems in actual implementation, but also provides a more accurate and dynamic carbon emission optimization solution.

The invention provides a mask pattern deviation value determination method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a pattern density distribution diagram of a historical design layout and pattern information of each pattern in the historical design layout, and the pattern information comprises a position relation between the patterns and a pattern type corresponding to each pattern; determining a long-range deviation evaluation model according to the graph density distribution diagram; determining a short-range deviation evaluation model according to the graphic information; and determining a deviation value of the mask pattern to be corrected according to the long-range deviation evaluation model and the short-range deviation evaluation model. According to the embodiment of the invention, the accuracy of determining the mask pattern deviation value can be improved.

[Problem to be solved] To predict the influence of vegetation on features with high accuracy. [Solution] A vegetation management system that manages the influence of vegetation on predetermined features, the vegetation management system comprising: an acquisition unit that acquires remote sensing image data of vegetation; the classification unit is used for classifying trees included in vegetation according to the growth vitality representing the future growth potential on the basis of the remote sensing image data; a growth prediction unit that predicts the growth of the tree on the basis of the classification result obtained by the classification unit; a risk determination unit that determines a risk of contact with the predetermined feature; and a visualization unit that outputs and visualizes the determination result obtained by the risk determination unit.

The invention discloses a method for predicting the residual fatigue life of a titanium alloy component based on frequency mixing nonlinear ultrasound. The method comprises the following steps: firstly, building a detection system; the detection system is controlled to generate low-frequency f1 and high-frequency f2 sinusoidal signals to carry out frequency mixing excitation on the titanium alloy component, and ultrasonic response signals are collected; performing frequency domain analysis on the ultrasonic response signal, extracting a characteristic amplitude and calculating a nonlinear modulation parameter beta m; then, a Nazarov-Sutin equation and an Elber equation are combined, and a theoretical prediction model of the nonlinear modulation parameter beta m changing along with the fatigue life percentage f is constructed; finally, actual measurement parameters are substituted into the model to reversely derive the consumed fatigue cycle number N, and the residual fatigue life Nr is predicted by combining the critical failure total cycle number Nt of the component. According to the method, the quantitative mapping relation between the nonlinear acoustic parameters and fatigue damage accumulation is revealed by establishing the physical model, and accurate quantitative prediction of the residual fatigue life of the titanium alloy component is achieved.

The invention discloses a terrain simulated annealing inversion solving method, device and equipment and a medium, through innovative combination of multi-dimensional initial search and a genetic algorithm optimization mechanism, dual improvement of prediction precision and efficiency is realized, and the terrain simulated annealing inversion solving method has remarkable technical advancement and wide application prospects. The method has the beneficial effects that the prediction performance is remarkably improved, the multi-dimensional initial search and a genetic algorithm optimization mechanism are fused, so that the scheme shows excellent performance in complex and variable prediction tasks, and the prediction precision and stability are greatly improved. And calculation efficiency optimization: a dynamic mutation probability adjustment mechanism effectively reduces unnecessary calculation overhead and accelerates a convergence process, so that the scheme has relatively high calculation efficiency while maintaining high-precision prediction. The embodiment of the invention can efficiently and accurately carry out simulated annealing inversion solution of the terrain, and can be widely applied to the technical field of data processing.

A sensor arrangement determination device (10) determines the arrangement of a sensor for measuring the temperature in a target space (80), the sensor arrangement determination device (10) comprising an acquisition unit (20) and a determination unit (30). The acquisition unit (20) acquires air conditioner information (21) and space information (22). The air conditioner information (21) is information in which an air conditioner (A) that affects the temperature is associated with the influence of the distance from the air conditioner (A) on the temperature. The spatial information (22) is information relating to the arrangement of the air conditioner (A) in the target space (80), and the determination unit (30) determines, on the basis of the air conditioner information (21) and the spatial information (22), an arrangement candidate (31) for the constant sensor (S1) and an arrangement candidate (32) for the training data acquisition sensor (S2) in the target space (80).

The invention discloses an SAA-DoA high-precision direction finding method based on LSTM time sequence prediction, and the method comprises the following steps: sequentially switching a plurality of antenna units through a sequential sampling mode of a switch antenna array, and obtaining the signal sampling data of a target direction; performing phase processing on the signal sampling data, and extracting an instantaneous frequency time sequence representing carrier frequency drift; inputting the instantaneous frequency time sequence into a pre-trained long short-term memory network model, learning a dynamic change rule of carrier frequency drift through the model, and predicting an instantaneous frequency value of an unsampled time slot; based on the predicted instantaneous frequency value, reconstructing a complete parallel sampling data matrix through phase integral operation; based on the reconstructed parallel sampling data matrix, an array signal processing algorithm is adopted to calculate the arrival angle of the signal; according to the method, the precision of DoA estimation under the SAA architecture and the robustness in a real wireless environment are remarkably improved.

An image information generation device generates image information indicating the shape of a molded object in which layers comprising a plurality of beads are stacked. The image information generation apparatus includes: a coordinate information acquisition unit that approximates a design shape for each layer of a model using a model that simulates a shape of a bead, and acquires coordinate information of a plurality of points of the approximated model; a contour extraction unit that finds a contour indicating the surface shape of the specified layer on the basis of the coordinate information; and an image information conversion unit that uses the contour to convert the uneven shape of the surface of the specified layer into image information.

The present application is suitable for the technical field of building energy consumption analysis, and provides a building energy consumption dynamic response analysis method and system under meteorological evolution, which comprises the following steps: integrating a multi-source meteorological data set and constructing a building energy consumption reference model; generating sample combinations of uncertain parameters by using a sampling method, running the building energy consumption reference model, obtaining energy consumption output results under different climate scenarios, and statistically analyzing the energy consumption output results; based on the energy consumption output results, using a global sensitivity analysis method to analyze the contribution of each uncertain parameter to the energy consumption, and identifying key driving parameters; based on the sample set generated in the uncertainty analysis step, using a machine learning algorithm to train a building energy consumption prediction model, and outputting building energy consumption prediction results for target meteorological conditions. The present application solves the problem that the traditional method is inaccurate in evaluating under non-stationary climate, and can provide quantitative and robust decision support for climate-adaptive building design and energy-saving strategy formulation.

The invention relates to the technical field of automobile control systems, and discloses a wheel steering angle calibration model training and prediction method, system, device and medium, and the method comprises the steps: obtaining the sample data of vehicle turning, the sample data comprising a steering wheel angle, a vehicle speed interval and a wheel steering angle; in response to a to-be-trained model, obtaining prediction data of the sample data; model parameters of the to-be-trained model are updated to reduce loss of the prediction data and the sample data, a calibration model is obtained, and the calibration model is used for predicting the wheel steering angle in response to the steering wheel angle and the vehicle speed interval. The method has the advantages that the problems of tedious operation and measurement errors in a traditional method are avoided, the internal parameters of the model are automatically adjusted according to different application scenes, and the output precision is continuously improved along with data accumulation.

The invention relates to the field of electric digital data processing, in particular to a terminal identification active and passive scheduling method and system based on a reinforcement learning algorithm, and the method comprises the following steps: passively monitoring network flow, obtaining basic terminal information, and generating a terminal list; actively scanning the terminals in the terminal list, and outputting related data; based on the related data, optimizing the scanning execution strategy through a reinforcement learning algorithm, and outputting the optimized scanning execution strategy; and adjusting the scanning frequency and the scanning intensity of active scanning, and performing active scanning on the terminals in the terminal list. According to the method, the advantages of active scanning and passive monitoring are utilized, and through continuous optimization of reinforcement learning, self-adaptive adjustment of the load of the industrial control terminal and the load of the network where the terminal is located is achieved. The scanning mode combining active scanning and passive monitoring not only can enhance the real-time performance of information acquisition, but also can improve the accuracy and comprehensiveness of terminal identification through mutual evidence and supplement of the acquired information.

The application provides an intelligent machining control method and system based on a physical information neural network, and belongs to the field of precision machining technology, comprising the following steps: constructing a physical kernel by coupling the physical laws of machining process force, heat and vibration, combining the physical kernel with a constructed gated residual compensation network, and forming a hybrid intelligent model; establishing a digital twin with the model as a core prediction engine, and synchronously updating the digital twin by using multi-source state data; in the digital twin, using the hybrid intelligent model to perform rolling time domain forward-looking simulation based on the current synchronization state, predicting the machining results of the force, heat and vibration coupling in the future period, generating process parameter adjustment decision instructions based on the results, and delivering the instructions to a physical machining entity execution mechanism; and performing online incremental learning on the gated residual compensation network based on the machining data after the execution of the instructions, so as to realize dynamic self-adaptive optimization. The scheme realizes high-precision forward-looking prediction of the machining process and real-time active closed-loop control based on the prediction.

The invention provides a bone age prediction method based on multi-modal feature fusion, a storage medium and electronic equipment. The method comprises the following steps: predicting a plurality of key anatomical points in a wrist X-ray image based on a pre-trained key point detection network, and segmenting a plurality of regions of interest; extracting global feature vectors of the regions of interest based on a pre-trained backbone network, extracting local depth feature vectors of a plurality of regions of interest based on two pre-trained lightweight networks sharing weights, and splicing and fusing the local depth feature vectors and gender feature vectors, obtaining significant feature vectors of the local features; calculating an attention weight based on the splicing of the global feature vector and the local feature apparent feature vector, and obtaining a fusion feature vector based on the attention weight; and inputting the fusion feature vector into a preset regression device, and outputting a bone age prediction value. According to the method, high-precision prediction can be achieved, high interpretability is achieved, and bone age prediction adapting to individualized differences is achieved.