16 results about "Artificial neural network algorithm" patented technology

The invention discloses a multispectral remote sensing-based mountain original red soil nutrient content inversion method and system. Relates to the technical field of soil nutrient monitoring. The method comprises the following steps: step 1, collecting multispectral remote sensing image data of a mountain original red soil area; 2, preprocessing the multispectral remote sensing image data; 3, extracting soil nutrient content data from the preprocessed multispectral remote sensing image; 4, establishing an inversion model of the mountain original red soil nutrient content by adopting an artificial neural network algorithm; and 5, processing the soil nutrient content data by using the inversion model to obtain a soil nutrient content distribution diagram, and evaluating and analyzing the soil nutrient content distribution diagram. According to the inversion model, through combination of unmanned aerial vehicle multispectral remote sensing and an artificial neural network algorithm, the main nutrient content of the mountain original red soil can be efficiently and accurately inverted, a rapid new means is provided for soil nutrient monitoring, and precision agriculture development is assisted.

The application relates to the technical field of marine hydrological monitoring, and discloses a seawater suspended sediment concentration low-altitude observation method, which uses a low-altitude observation platform to carry digital photography equipment, obtains seawater suspended sediment concentration images, collects water samples in the photographed sea area, positions the water samples through a global satellite positioning system, uses a filtering method to measure the suspended sediment content, pre-processes the obtained seawater suspended sediment concentration images, realizes automatic splicing of the images and automatic imaging of orthographic images, trains the processed image data and the measured suspended sediment concentration data based on an optimized artificial neural network algorithm, establishes a seawater suspended sediment concentration inversion model, and evaluates the model precision. The method uses the low-altitude observation platform to obtain high-resolution images, uses the optimized artificial neural network model to invert the suspended sediment concentration, has the advantages of high precision, low cost, flexibility, timeliness and the like, and can effectively make up for the deficiency of satellite remote sensing.

The invention provides a method for predicting nasopharyngeal carcinoma stem cell depletion characteristics by using a blood marker, which comprises the following steps: acquiring cancer tissue samples of a plurality of nasopharyngeal carcinoma patients, and constructing a transcriptome-based nasopharyngeal carcinoma stem cell depletion score for each patient by using single sample gene enrichment analysis ssGSEA; collecting blood marker data of the same batch of patients, including 21 blood routine data, and performing data preprocessing; creating a stem cell depletion scoring model based on the blood marker, and training 21 blood routine data of a nasopharyngeal carcinoma patient by using an artificial neural network ANN algorithm to obtain a trained model; joint visualization is carried out through SHAP feature importance, and the effectiveness of senescence scoring and blood scoring is evaluated by combining correlation analysis and survival analysis; and collecting blood marker data of another batch of patients, performing data preprocessing, inputting the data into the trained model to obtain a score corresponding to each patient, and performing survival analysis to verify the efficiency of the model. According to the invention, effective and low-cost individual aging prediction can be realized.

The invention relates to the technical field of data processing, and particularly discloses an AI-based air quality prediction method and system, and the method comprises the steps: S1, dividing a preset region into independent grids of a preset size, and setting the information of each grid; s2, extracting influence factors of grid air quality, wherein the influence factors comprise influence factors of spatial change and influence factors of time change; s3, constructing a space classifier based on an artificial neural network algorithm, and capturing the influence of space factors on the air quality; constructing a time classifier based on a convolution LSTM algorithm, and learning time periodic features in the time sequence data; and S4, through a collaborative algorithm, according to results of the space classifier and the time classifier, obtaining gridding air quality data with a time sequence. By adopting the technical scheme of the invention, the air quality prediction accuracy of mountainous regions and river valley cities can be effectively improved.

The invention discloses a shield tunnel segment assembly error quantification and segment position prediction method, and relates to the technical field of shield subway tunnel construction. The position deviation of a point B is calculated through coordinate data of a central position A of a segment ring where a laser target is located, the distance of a central point B of a segment ring on the outermost side and the position of the laser target actually measured on site in combination with the horizontal and vertical coordinate orientation of the laser target, and then the measured data is compared with the data of the actual position of the segment. And central position data and error rules of the segment assembly rings under different tunneling stratums are obtained through calculation. Error correction is carried out by combining data comparison of a trial tunneling section, a segment position prediction model is established through an artificial neural network algorithm, and the relative position of a next segment is determined. According to the method, the relative position relation between the duct piece position and the tunnel line type can be judged in the tunneling process, the relative position of the duct piece of the next ring can be predicted, errors in the duct piece splicing process can be remarkably reduced, and the splicing quality and the forming quality of the shield tunnel duct pieces are guaranteed.

The application discloses a kind of edge computing operation and maintenance alarm solutions, it is related to edge computing technical field;Including: step 1: collect all equipment information in edge computing environment, including the running state of equipment, performance index, fault record;Step 2: according to the running state of equipment and performance index, the state model of equipment is constructed;Step 3: according to the state model of equipment, the alarm model of equipment is constructed;Step 4: when equipment occurs alarm, according to the alarm model of equipment, the reason of alarm is determined;Step 5: according to the reason of alarm, corresponding measures are taken to handle;Step 6: the running data of equipment is carried out deep learning and pattern recognition by applying artificial neural network algorithm.The application can automatically identify the complex relationship between equipment state and alarm by applying artificial neural network and reinforcement learning technology, realize the automatic detection and classification of alarm;Greatly reduce the need of manual intervention, improve operation and maintenance efficiency.

This invention discloses an optimal tilt angle design method for a planar rooftop photovoltaic (PV) system. Using historical local load demand data as a reference, it combines a long short-term memory (LSTM) artificial neural network algorithm to predict the local electricity load demand for the next year. Based on this predicted load demand, the optimal tilt angle is optimized. The obtained optimal tilt angle improves the annual electricity load utilization rate and avoids excessive power shortages in certain months. Furthermore, this invention considers the PV device capacity per unit area under actual installation costs and calculates the PV tilt angle that maximizes the PV device capacity per unit area. Finally, game theory is used to optimize the two schemes, ensuring that the final optimal tilt angle improves both the annual electricity load utilization rate and the PV device capacity per unit area.

The application discloses a duckweed carbon sink evaluation method, system and equipment suitable for a rice-duckweed symbiotic system, relates to the technical field of agricultural production and ecological environment, and discloses the following technical scheme: after rice is harvested, the information of duckweed in the rice-duckweed symbiotic system is collected by using spectral scanning and laser point cloud technology, and the volume of the duckweed is determined based on the duckweed information; the duckweed biomass in the rice-duckweed symbiotic system is determined according to the duckweed volume; the duckweed carbon sink is estimated by using an artificial neural network algorithm and the duckweed biomass, or the duckweed carbon sink is estimated by using a duckweed carbon sink estimation formula and the duckweed biomass. The application can flexibly evaluate the duckweed carbon sink according to information such as rice agronomic management, soil and climate, and provides good technical support for the management and application of duckweed in a rice field.

The application discloses a kind of molecular potential energy field analysis methods based on multivariate feature information transmission, it is related to molecular dynamics technical field, including steps: obtaining the element characteristic vector and structure characteristic vector of target molecular formula;By undercomplete self-encoder constitutes coding network, and the multivariate characteristic vector formed by element characteristic vector and structure characteristic vector is handled to obtain atom representation;According to atom representation, obtain interatomic distance and aggregate adjacent atoms, and update adjacent aggregated atom representation by continuous convolution filter;According to the updated atom representation, atom potential is predicted by artificial neural network algorithm, and the total potential energy of system is obtained according to the atom potential of each atom in target molecular formula.The application is by the use of general local environment characteristics, and makes atom representation contain more feature information, guarantees the feature description of different material systems, and then improves the generality of model.

The application provides an air compressor optimization method and system based on artificial intelligence and cloud edge collaboration. The method comprises the following steps: using a preset full connection neural network algorithm to model an air compressor performance model, obtaining a power-yield neural network model and a power-frequency neural network model, and storing them in the cloud; the edge end adjusts the frequency of the air compressor according to the model; using a preset long short-term memory artificial neural network algorithm to construct a demand prediction model and storing it in the cloud; the edge end predicts the current demand of the air compressor unit according to the demand prediction model, obtains the expected demand, calculates the expected total power of the air compressor unit when the air compressor unit reaches the expected demand, obtains the full load flow rate of the air compressor, compares the expected demand with the full load flow rate, selects the best scheduling optimization scheme of the air compressor unit according to the comparison result, and realizes that the air compressor unit is always in the optimal efficiency state under the working condition, and achieves the purpose of energy saving and cost reduction.

The invention relates to the technical field of biological medicine, and discloses a method for constructing a pregnant woman health index evaluation system, which comprises the following steps: collecting original data of a pregnant woman in a perinatal period, cleaning the original data, removing abnormal values, filling missing values, and carrying out standardization processing to obtain preprocessed data; a random forest feature screening technology is adopted, variables with feature importance higher than an average value are selected, a composite perinatal period bad outcome prediction model is established based on an artificial neural network algorithm, and a prediction value output by the prediction model is converted into a pregnant woman health index through linear conversion; according to the method, features screened in the construction process of a composite perinatal period bad outcome prediction model are used as independent variables, a Logistic regression model is used for prediction, model fitting is carried out for different bad outcomes, respective Logistic regression equations are obtained, and a single bad outcome prediction model is constructed; according to the invention, the early warning capability is improved, the clinical management efficiency is enhanced, and the occurrence rate of bad outcomes in the perinatal period is reduced.

The invention discloses a building waterproof layer damage trend prediction method, which is characterized in that a plurality of groups of sensor clusters are dispersedly arranged on a building waterproof layer, whether the building waterproof layer is damaged or not is reflected by monitoring physical property change data of the building waterproof layer through the sensor clusters, and the physical property change data comprise strain, humidity, temperature, optical wavelength and audio frequency; monitoring physical property change data of the building waterproof layer in real time by each group of sensor clusters in a wireless transmission mode, and transmitting the data to analysis equipment; on an analysis device, layout position data of a sensor cluster on a building waterproof layer, acquired physical performance change data and a time sequence are used as input data of an artificial neural network algorithm; and generating damage probability and damage trend data of the building waterproof layer at each layout node position through an artificial neural network algorithm to predict the damage trend of the building waterproof layer. According to the invention, the problem of monitoring hysteresis due to the damage of the waterproof layer of the video monitoring building can be avoided.

The application relates to a shield tunnel segment assembling error quantification and segment position prediction method, and relates to the technical field of shield subway tunnel construction. The position deviation of a point B is calculated by combining the horizontal and vertical coordinate positions of the position deviation of the point B with the coordinate data of a center position A of a segment ring at a position of a laser target and the distance of the center position B of the outermost segment ring and the actually measured position of the laser target. Then, the measured data and the actual position data of the segment are compared, and the center position data of a segment assembling ring in different tunneling strata and error rules are calculated. The error is corrected by comparing the data of a trial tunneling section, a segment position prediction model is established by using an artificial neural network algorithm, and the relative position of a next segment ring is determined. The application can judge the relative position relationship between the segment position and the tunnel line type during tunneling, and can predict the relative position of the next segment ring. The application can significantly reduce the error in the segment assembling process, and can guarantee the assembling quality and forming quality of the shield tunnel segment.

The application provides an on-chip training method of an in-memory computing memory artificial neural network, and belongs to the field of artificial neural network algorithm optimization.The application follows the Manhattan rule idea, introduces a probability-based ternary update rule, converts high-precision weight update in an ideal classical error back propagation algorithm BP algorithm into ternary weight update, only applies at most one programming pulse to one device in each training batch, reduces the operation times, the training method converges fast and is stable, the recognition accuracy is high after training, the original BP algorithm is slightly changed, and the performance exceeds the Manhattan and threshold-Manhattan rules from the algorithm perspective;The application can efficiently realize on-chip stochastic gradient descent SGD and mini-batch gradient descent MBGD, does not need to store high-precision weight update values, reduces the additional hardware overhead, and optimizes the design of the inference circuit.

The application provides a method and device for evaluating slow obstacle invasion risk, and a storage medium, and the method comprises the following steps: obtaining obstacle data and influence parameters, wherein the influence parameters comprise a vibration factor of a train, a device maintenance cycle and a wind speed; predicting an obstacle position by using a long short-term memory (LSTM) artificial neural network algorithm based on the obstacle data and the influence parameters; and taking the predicted obstacle position as a reference point, evaluating the risk probability of the obstacle based on a pre-set corresponding relationship between a limit range and a risk probability. The method provided by the application predicts the obstacle position based on the obstacle data and the influence parameters by using the LSTM algorithm, takes the predicted obstacle position as the reference point, evaluates the risk probability of the obstacle based on the pre-set corresponding relationship between the limit range and the risk probability, realizes the prediction of the obstacle, plays a role in early prevention, and is beneficial to early planning of operation.

The application relates to a multi-energy virtual power plant optimization scheduling method and system, belongs to the technical field of power optimization scheduling, and solves the problems of incapability of a single prediction model to meet the demand of multi-energy virtual power plant optimization scheduling, a large data set used in prediction calculation, and high operation cost; the application adopts a random forest algorithm to screen influencing factor indexes of wind and light electric set output, uses an artificial neural network algorithm to predict the wind and light electric set output, further makes up for the deficiency of a single algorithm through a hybrid prediction algorithm, and improves the prediction accuracy; a Monte Carlo method is used for simulation analysis for load prediction of electric vehicles; an optimization scheduling model of the multi-energy virtual power plant is established by taking the minimum virtual power plant operation cost as an objective function, and the problem that only a single distributed energy can be predicted and optimized in the prior art is solved.