17results about How to "Strong nonlinear fitting ability" patented technology

The embodiment of the invention relates to the field of foundation pile reverse design, in particular to a foundation pile reverse design method and equipment based on multi-objective optimization and machine learning. One specific implementation mode of the method comprises the steps that a foundation pile actual measurement data set is collected; updating processing is conducted, and a foundation pile actual measurement updating data set is obtained; dividing the foundation pile actual measurement update data set to obtain a foundation pile actual measurement sample data set and a foundation pile actual measurement test data set; based on the foundation pile actual measurement sample data set and the foundation pile actual measurement test data set, constructing a machine learning model for predicting the bearing capacity of the foundation pile; generating an initial foundation pile design data set; based on a machine learning model for predicting the bearing capacity of the foundation pile, optimizing the initial foundation pile design data set to obtain target foundation pile design data; and sending the target foundation pile design data to a foundation pile construction control terminal to construct a foundation pile. According to the implementation mode, the foundation pile design efficiency and accuracy are improved.

The invention relates to the field of sensors, and discloses a sensor drift correction method and system based on transfer learning, and the method comprises the following steps: S1, collecting sensor data, including three-axis acceleration data, temperature, humidity and air pressure environmental parameters, and S2, calculating the modulus length of a three-axis composite vector. A combined kernel function combining a periodic kernel, a linear kernel and an RBF kernel can simultaneously capture periodic fluctuation of environmental factors, a long-term aging trend of a sensor and short-term random noise, adaptive distribution alignment of a source domain and a target domain is realized through a maximum mean value difference loss function, the model is enabled to be rapidly adapted in the target domain, and meanwhile, the adaptive distribution alignment of the model in the target domain is realized. A slope intercept combined judgment matrix is introduced, accurate distinguishing of zero drift, real motion and tilt events is achieved, the accuracy rate reaches 96% or above, measurement distortion caused by error compensation is effectively avoided, and the system reliability and the data credibility are remarkably improved.

The invention discloses a goaf three-dimensional shear wave velocity field interpolation method based on a micro-motion detection technology. The method comprises the steps of S1, data preparation; s2, network design; s3, data processing; s4, performing model training; s5, verifying the model; s6, optimizing the model; and S7, model application. The artificial intelligence technology based on the three-dimensional convolutional neural network model has the characteristics of strong adaptability and strong non-linear fitting ability, can better process complex terrains and scenes, can automatically learn complex features in an underground velocity field, including spatial distribution of velocity, geological structure and the like, and has a good application prospect. The accuracy and robustness of interpolation can be improved through the strong non-linear fitting capability and adaptability, and the accuracy of velocity field establishment is improved; compared with a traditional interpolation method, the above interpolation method can generally complete training and prediction in a short time, and the calculation efficiency is improved.

The application discloses a kind of based on spot image moment feature telescope primary and secondary mirror calibration method, belong to telescope computer-aided alignment field.The specific steps of the calibration method are as follows, first, randomly give the free degree misadjustment of telescope system secondary mirror, utilize unbiased finite impulse response moment to extract the spot image feature of multiple field of view within reasonable range under the misadjustment state of telescope;Then utilize fully connected neural network to construct the model between the free degree misadjustment of secondary mirror and the multiple field of view spot image feature matrix;In actual use, after the multiple field of view spot image collected by CCD camera is extracted into feature matrix using unbiased finite impulse response, it is input into the trained neural network model to solve the misadjustment of secondary mirror, to guide the alignment of telescope system.This method aims to wavefront sensor as the image quality evaluation benchmark of telescope without, and without multiple iterations, to improve the efficiency of system primary and secondary mirror alignment and the imaging quality of system.

The application belongs to the technical field of fault identification, and discloses an equipment processing device fault identification method and system based on artificial intelligence. The method comprises the following steps: in a cloud data center, using an artificial intelligence algorithm, an equipment processing device fault identification engine is constructed, and a multi-modal data acquisition device is connected through an edge computing gateway; using the multi-modal data acquisition device, real-time monitoring multi-modal data of the equipment processing device is acquired, and is uploaded to the cloud data center through the edge computing gateway; in the cloud data center, according to the real-time monitoring multi-modal data, using the equipment processing device fault identification engine, fault identification is carried out, and real-time fault identification results and real-time fault maintenance strategies are obtained. The application solves the problems of single data dimension, limited model depth and precision, and lack of response mechanism in the prior art.

The application discloses a river cross section shape identification method coupling artificial intelligence and river bed geomorphology, and comprises the following steps: unmanned aerial vehicle complete river channel topography measurement; river cross section type knowledge graph construction; cross section shape characteristic parameter construction; double-encoder framework construction based on artificial intelligence; most similar cross section searching; satellite remote sensing river width and elevation acquisition and water surface elevation correction; unknown cross section geometric shape adjustment. The application couples artificial intelligence and river bed geomorphology, solves the problem that a purely data-driven artificial intelligence model lacks understanding of physical causality, and deduces unreasonable black box results in a physically unreasonable way when the result is generalized to rivers or extreme hydrological events outside the training set; and realizes river cross section parameter acquisition under non-contact measurement conditions. The application breaks through the problem of accurate measurement of river cross section parameters under severe conditions such as flood disaster water damage, major geological disasters, unreachable boundaries and overseas key rivers.

The invention relates to the technical field of production management, in particular to an SAP-based material inventory management system, which comprises a material inventory checking module, a material shortage production pre-work module, a material waiting area storage module, a manual material picking assembly module and a material waiting area assembly module, the material inventory checking module comprises a material prediction model and is used for checking whether the material inventory meets the material demand quantity in the work order task or not and forming a material list; if not, additionally generating a material shortage signal and a material shortage list; the material shortage production forecasting module is used for executing forecasting and completing material shortage assembly; the material waiting area storage module is used for pushing the products subjected to material shortage assembly out of the production line to a material waiting area and binding the positions of the products; and the material waiting area assembling module is used for further material assembling of the products subjected to material shortage assembling. Through the management system, the technical problem of how to efficiently and accurately meet production task requirements under the normalized condition of material shortage in the production process is solved.

This invention discloses a nonlinear temperature compensation method for the cold start segment of a digital accelerometer. By testing the cold start segment of the accelerometer, the nonlinear output characteristics of acceleration caused by cold start are isolated. Simultaneously, a time-dependent, near-normally distributed nonlinear temperature compensation model is established. Furthermore, an intelligent optimization algorithm is used to optimize the coefficients in the temperature compensation model, finally obtaining the model coefficients for temperature compensation in the cold start segment of the accelerometer, thereby achieving high-precision cold start of the accelerometer. This invention has been successfully applied to digital quartz flexural accelerometers and is suitable for temperature compensation of various accelerometers with strong nonlinear temperature characteristics in the cold start segment.

The present application relates to the technical field of vehicle configuration recommendation, and discloses a truck configuration intelligent recommendation method, system, device and medium. The method comprises the following steps: obtaining daily operation conditions and corresponding static configuration data of a plurality of sold trucks; extracting the internal relationship between different configuration parameter combinations in the static configuration data of each sold truck and the mapping relationship between the daily operation conditions and truck performance indexes; determining the rationality evaluation score of different configuration parameter combinations according to the internal relationship between the different configuration parameter combinations, and obtaining the matching relationship between the daily operation conditions of each sold truck and the corresponding static configuration data in combination with the mapping relationship between the daily operation conditions and the truck performance indexes; and constructing a configuration condition matching model through the matching relationship between the daily operation conditions and the corresponding static configuration data of all sold trucks, so as to perform truck configuration intelligent recommendation, and the recommendation accuracy and efficiency are both higher.

This invention belongs to the technical field of permeable concrete, and provides a low-carbon permeable concrete and its preparation and intelligent screening method. The low-carbon permeable concrete comprises, by weight, 75-85 parts red mud, 175-200 parts slag, 800-1400 parts coarse aggregate 1, 600-1200 parts coarse aggregate 2, 38-48 parts water, 3-4 parts NaOH, and 85-95 parts water glass. The preparation method is as follows: coarse aggregate 1 and coarse aggregate 2 are mixed to form a dual-graded coarse aggregate; red mud and slag are mixed to form a red mud-slag geopolymer; an alkali activator solution is prepared using water, NaOH, and water glass; the dual-graded coarse aggregate is added to a mixer, followed by the sequential addition of 50% alkali activator solution, red mud-slag geopolymer, and the remaining 50% alkali activator solution, with stirring performed after each addition; the intelligent screening method utilizes normalized product combined with machine learning algorithms to quickly screen out two concrete experimental groups with superior performance.

This invention proposes a method and system for predicting icing thickness on transmission lines, relating to the field of transmission line safety monitoring technology. The method includes: acquiring multi-dimensional meteorological data and corresponding icing thickness data of a target transmission line; preprocessing the multi-dimensional meteorological data; calculating the grey correlation value between the preprocessed meteorological data and the icing thickness data; when the grey correlation value is greater than a preset threshold, the current meteorological data is taken as strongly correlated meteorological data for icing thickness; constructing a lag feature vector between the current time and the previous L historical times based on the strongly correlated meteorological data; inputting the lag feature vector as an input vector into a hybrid kernel extreme learning machine model; optimizing the hyperparameter vector of the hybrid kernel extreme learning machine model using an improved Harris Eagle optimization algorithm; training the hybrid kernel extreme learning machine model using the optimized hyperparameters; and outputting the icing thickness prediction result using the trained model.

The invention discloses an electrocardio inverse problem solving method based on a neural network, and the method comprises the following steps: obtaining the three-dimensional geometric information of a heart source model, and constructing an electrocardio data set containing a body surface potential and a transmembrane potential; dividing a training set and a test set, and preprocessing data; constructing a neural network model by using a bidirectional long short-term memory network (BiLSTM), and setting and optimizing network parameters; training the constructed neural network model by using a training set; performing simulation prediction on the trained model through a test set to realize reverse reconstruction of the heart potential; and evaluating and analyzing the result, and visualizing the result. According to the method, through the deep learning ability of the neural network model, the problem of discomfort in the electrocardio inverse problem is effectively solved, higher reconstruction precision can be provided, and important technical support is provided for accurate diagnosis and treatment of heart diseases.

The present invention discloses a method, medium and program product for predicting long-distance extreme precipitation of typhoon in inland areas based on deep learning and integration of IDII index, constructs a whole-layer dry intrusion index, comprehensively considers the overall effect of dry cold air at different heights; obtains historical TRP precipitation events, typhoons and reanalysis meteorological data, performs preprocessing and integrates IDII index, and constructs a training data set; uses deep learning technology to construct a prediction model including a feature extraction layer, an IDII feature fusion layer and a TRP prediction output layer; uses TRP sample data sets to train, verify and test the model; uses the trained model to predict future TRP events, and outputs prediction results such as TRP precipitation, occurrence time, location and duration. The present invention integrates the dry intrusion physical quantity IDII and deep learning technology, improves the prediction accuracy of TRP events, and provides technical support for the forecast of inland extreme precipitation and disaster prevention and mitigation.

The application discloses a target tracking method based on an improved CamShift algorithm, and relates to the field of visual target tracking. The method comprises the following steps: acquiring image data, and pre-processing the image data; using a target detection algorithm to detect the target of the pre-processed image data, so as to obtain a tracking target; tracing the historical track of the tracking target, using a Lagrange interpolation method to generate a track prediction window, and using a Kalman filtering algorithm to correct the track prediction window, so as to obtain a corrected track prediction window; improving a continuous adaptive mean shift algorithm through a search window dynamic adjustment strategy, and using the improved continuous adaptive mean shift algorithm to track the tracking target based on the corrected track prediction window, so as to obtain a tracking result. The one-dimensional interpolation polynomial is constructed through the Lagrange interpolation method, and the tracking performance of the continuous adaptive mean shift algorithm on high-speed or nonlinear targets is effectively improved.

Embodiments of the present application disclose a short-term traffic flow prediction method based on a space-time graph neural network. The method comprises: acquiring a corresponding traffic road network in a preset area, and constructing a network graph structure corresponding to the traffic road network; acquiring historical traffic flow data corresponding to the preset area, and taking the historical traffic flow data as node features of the network graph structure to obtain a road network graph with time sequence features; inputting the road network graph with time sequence features into a neural network comprising multiple space-time modules for feature extraction to obtain space-time joint features corresponding to the preset area; and performing full connection layer mapping on the space-time joint features to determine a traffic flow prediction value of each node based on a mapping result. The present application can accurately determine the traffic flow prediction value of each node in the preset area, which helps to take measures in advance to cope with traffic congestion, optimize traffic resource allocation, and improve the operation efficiency and safety of the entire traffic system.

The invention discloses a reservoir attribute inversion method, a reservoir attribute inversion device, reservoir attribute inversion equipment and a storage medium in the field of geophysical exploration, which are used for carrying out regional reservoir inversion prediction according to detected microseismic data and logging data and input drilling data and geological data, and comprises the following steps of: 1, acquiring the microseismic data and the logging data; using an anti-interference detector to detect micro-seismic data and detector tilt acceleration data in the area; step 2, data processing; and step 3, intelligent inversion. According to the method, the anti-interference performance is high, the physical interference resistance of the detector is improved through the anti-interference detector, the interfered seismic wave channels are screened, the subsequent data precision is improved, reservoir inversion is carried out on the collected data through the neural network model for deep learning, and reservoir inversion operation with higher efficiency and higher precision is carried out.

The invention relates to the technical field of renewable energy utilization management, in particular to a wind power generation power prediction and self-adaptive energy storage regulation and control method. According to the technical scheme, the wind power generation power prediction and self-adaptive energy storage regulation and control method comprises a wind power generation power prediction method; compared with a traditional wind power generation power prediction and self-adaptive energy storage regulation and control method, the method mainly depends on historical wind power data and meteorological prediction data for prediction in the aspects of data acquisition and processing, the data preprocessing technology is relatively simple, meanwhile, prediction is generally carried out based on a statistical model or a physical model during model prediction, and the prediction efficiency is greatly improved. Model parameters are often fixed and lack of adaptivity, according to the wind power generation power prediction and adaptive energy storage regulation and control method, more types of data sources are introduced, a finer data preprocessing technology is used, and meanwhile, a machine learning algorithm is introduced, so that the prediction performance of the model is improved; model parameters can be adaptively adjusted according to actual data so as to adapt to the dynamic change characteristics of the wind power plant.