12results about How to "Prediction is simple" patented technology

The invention relates to the technical field of chemical engineering and machine learning, and provides a method for predicting the solubility of carbon dioxide in a deep-eutectic solvent based on a machine learning model, and the method comprises the steps: collecting CO2 solubility data, carrying out the coding and feature descriptor conversion of a deep-eutectic solvent molecular structure through an open-source chemoinformatics software package RDKit, and constructing a data set. Four algorithms of XGBoost, random forest, a deep neural network and a convolutional neural network are adopted to construct a model, and hyper-parameters are optimized through grid search and k-fold cross validation. After the model is trained, the prediction performance is evaluated, and the optimal model is selected for CO2 solubility prediction. The method improves prediction efficiency and accuracy, has strong adaptability and generalization ability, and provides an effective tool for formula optimization of the eutectic solvent and improvement of CO2 capture efficiency.

The invention discloses a method for predicting the grain morphology of a titanium alloy cast ingot. The method comprises the steps that S1, longitudinal cutting structures of the titanium alloy cast ingot under different processes are collected, and an equiaxed crystal zone and a columnar crystal zone are distinguished; s2, establishing a titanium alloy ingot numerical model through software; s3, actual smelting parameters are input into the titanium alloy ingot numerical model, and solidification dynamic information is output; s4, a CET marking program is used for making the longitudinally-cut tissue correspond to the solidification dynamic information in the spatial position; s5, drawing a G-R macroscopic structure map; s6, the critical temperature gradient G * and the critical solidification rate R * are obtained according to the macroscopic structure atlas, and titanium alloy as-cast structure prediction is achieved. According to the prediction method for the grain morphology of the titanium alloy cast ingot, temperature field and flow field information corresponding to the cast ingot under different smelting parameters is obtained through numerical simulation, a certain relation is established between solidification dynamic information in the cells and an actual solidification structure, and the macrostructure morphology of the titanium alloy cast ingot is predicted more rapidly and accurately.

The invention discloses a power distribution network power demand prediction method and device based on big data analysis and a medium, and belongs to the technical field of power demand prediction, and the method comprises the steps: dividing a power distribution network into a plurality of power nodes, collecting the power data of the power nodes, and carrying out the screening to obtain power abnormal nodes; demand information of the power abnormal node is collected, and a power consumption demand event is extracted according to the demand information; acquiring event information of the power consumption demand event, and evaluating according to the event information to obtain a propagation effect condition of the power consumption demand event; acquiring a real-time prediction demand value of a power abnormal node, and determining power change values of other power nodes in combination with a propagation effect condition; and collecting real-time environment data of the power node, predicting according to the real-time environment data to obtain a basic power demand value, and superposing the power change value to obtain a real-time power demand value. The accuracy of power distribution network power demand prediction based on big data analysis is improved.

The invention provides a photovoltaic output prediction method considering haze / dust factors. The method comprises the following steps: firstly, preprocessing data, screening out abnormal data and invalid data, and complementing missing data; the method comprises the following steps: firstly, analyzing haze / sand dust data, then classifying weather data by using a decision tree algorithm, then deeply understanding an internal rule and an operation mechanism by analyzing a haze / sand dust formation mechanism and a photovoltaic power generation principle, determining main characteristics, and then firstly establishing a haze / sand dust influence model 1 and a photovoltaic power generation power prediction model 2 for the haze / sand dust data; the model 1 obtains influence parameters of haze / dust factors on the photovoltaic power generation power, and the model 2 outputs a predicted value of the photovoltaic power generation power. The advantages of mechanism analysis and a data driving model are combined, and the accuracy and reliability of the model are improved.

The present invention provides a method and system for predicting the cracking tendency of magnesium alloy casting, comprising: step 1: preparing magnesium alloy by smelting and casting process; step 2: determining the stretching temperature according to the lowest solidus temperature of the magnesium alloy; step 3: determining the stretching strain rate according to the linear contraction coefficient and cooling rate of the magnesium alloy at the stretching temperature; step 4: stretching the magnesium alloy by the stretching strain rate to determine the maximum strength of the magnesium alloy at the stretching temperature; step 5: predicting the cracking tendency of magnesium alloy casting by the maximum strength. Compared with the prior art, the present invention predicts the cracking tendency of magnesium alloy casting by the maximum strength of the magnesium alloy, and does not need to prepare a complex special evaluation mold for the cracking tendency. The prediction of the cracking tendency of magnesium alloy casting can be achieved by an ordinary high-temperature stretching machine, and the purpose of predicting the cracking tendency of magnesium alloy casting is achieved by simple and economical means.

This invention provides a method and apparatus for predicting obstacle trajectories, an unmanned vehicle, and a storage medium. The obstacle trajectory prediction method includes: determining the target road scene currently occupied by the unmanned vehicle based on location information; if the target obstacle's number is among multiple obstacle numbers in the obstacle prediction queue, then determining the target obstacle's motion constraint boundary conditions in the target road scene based on a preset correspondence between the road scene, the obstacle, and the obstacle's motion constraint boundary conditions; if the target obstacle's motion information satisfies the target motion constraint boundary conditions, then obtaining target motion observation information based on the location information and the target obstacle's motion information; and predicting the target obstacle's trajectory within a specified time based on the target motion observation information and size information, thereby improving the effectiveness of the predicted trajectory and reducing prediction costs.

The invention relates to a financial statement field grading method based on prompt learning. The method comprises the following steps: preprocessing original data of a financial statement field text; converting each sample in the standard data set into a plurality of inputs; inputting the plurality of inputs into a pre-training language model, and predicting answers filled in the mask positions of each template function; and inputting the answers into an answer mapper, mapping the answers into corresponding output tag words, and synthesizing the output tag words of each template function to obtain a final financial field grading result. The invention further discloses a financial statement field grading system based on prompt learning. According to the method, the dependence on a large amount of artificial feature engineering is reduced, the prediction process of financial text classification is simplified, and the classification efficiency and accuracy are improved; intelligent grading of financial statement fields is achieved, the classification standard can be dynamically adjusted according to actual requirements, the text understanding and classification capacity is enhanced, and the classification accuracy and consistency are greatly improved.

The invention discloses a marker and a system for predicting the effect of tumor immunotherapy, and relates to the field of tumor immunotherapy, the marker is expressed in various immune cells, participates in activation of the immune cells, is an important effector protein of a human innate immune system, namely LCP2, and can be detected in a tissue sample from a tumor patient. The clinical effect of tumor immunotherapy is predicted by detecting the infiltration level of killer immune cells in tumors.

A battery cell manufacturing apparatus according to an embodiment of the present invention may include: a vacuum chamber in which a battery cell semi-finished product is disposed, the battery cell semi-finished product including a receiving portion for accommodating an electrolyte and an electrode assembly, and an air bag portion connected to the receiving portion and positioned above the receiving portion; a punching unit for forming a degassing hole in the air bag portion; a sealing tool for forming a temporary seal portion spanning between the degassing hole and the receiving portion, and the sealing tool forming a sealing area including the temporary seal portion while moving upward in contact with the air bag portion; a cutting unit for forming an opening by cutting off a portion of the upper end of the air bag portion after forming the temporary seal portion; and an injection unit for injecting gas into the opening.

The invention discloses a method and device for predicting a reservoir thickness error, a storage medium and electronic equipment, and relates to an earthquake prediction technology, and the method comprises the following steps: generating a theoretical model earthquake data volume, and carrying out the relation deduction through a model; constructing an actual data volume, intersecting the actual data volume with the theoretical model seismic data volume, and performing analysis and correction processing to obtain a data source I for predicting the reservoir thickness; a second data source for predicting the reservoir thickness is obtained; and generating a predicted reservoir thickness map. Compared with the prior art, the method has the advantages that (1) a simple and practical technology for predicting the reservoir by using the seismic attributes is provided; (2) the workload is greatly reduced, and the requirement on computer equipment is lower; (3) the requirements on technical and knowledge backgrounds of personnel are low; and (4) the problem that modeling is needed for inversion prediction of the reservoir is solved, particularly, modeling of a complex geological structure area is difficult, reservoir prediction of the complex geological structure area becomes easy and fast, and the precision of seismic amplitude attribute prediction of the reservoir is accurately improved.

The present application relates to a kind of prediction method and device of recovery efficiency of fractured shale reservoir, belong to the technical field of oil and gas exploration and development, the method includes the following steps: respectively constructing the mathematical model of fluid imbibition position in single organic nanometer hole, single inorganic nanometer hole and single microfracture;Scale upgrading is carried out to single nanometer hole and single microfracture using fractal theory, respectively obtain the total number of pore of nanometer hole and the total number of microfracture of core of fractured shale reservoir;Respectively calculate the imbibition oil production in all nanometer holes and the imbibition oil production in all microfractures;The recovery efficiency in the process of spontaneous imbibition and forced imbibition of fractured shale reservoir is calculated respectively.The prediction method and device of recovery efficiency of fractured shale reservoir disclosed in the present application can simply, quickly and efficiently predict the imbibition oil displacement rate, production and recovery efficiency in the process of shale reservoir fracturing and well killing, and have great significance for realizing effective development of shale reservoir.

The present invention discloses a shale TOC seismic quantitative prediction method based on a sensitive parameter body. For a target layer whose TOC content is to be measured, a well logging curve with a high correlation with the TOC content is analyzed and found as a sensitive parameter; sample data is constructed using the sensitive parameter; the sample data is used as input, and the TOC content at the depth corresponding to the sample data is used as output, and an RBF neural network prediction model is obtained by training; the sensitive parameter and post-stack three-dimensional seismic data are then inverted to obtain a sensitive parameter body, and a prediction sample is constructed using the sensitive parameter body, and the prediction sample is input into the prediction model to calculate the corresponding TOC value, thereby realizing the prediction of the TOC content of the target layer. The present invention overcomes the limitation of the prior art that only a few elastic parameters and seismic attributes of pre-stack inversion can be used for seismic prediction of TOC content, as well as the disadvantage of not being widely used. More parameters with a high correlation with the TOC content can be used to realize high-precision prediction of the shale TOC content, and the scope of application is wider and the prediction is easier to realize.