12results about How to "Rich input" patented technology

The invention provides a user behavior data processing method and device, electronic equipment and a computer program product, and belongs to the field of advertisement data processing. The method comprises the following steps: acquiring and integrating heterogeneous data from a plurality of data sources; according to performance indexes of entities related to advertisement putting in a predefined evaluation period, the entities are distributed to different entity sets, and the entity sets comprise a main entity set and an aggregation entity set; taking the entity set as an operation granularity, generating a combined feature set based on the integrated heterogeneous data, the combined feature set comprising a first type of features obtained based on behavior data and attribute data of all users in the corresponding entity set in the preset time period, obtaining a second type of features based on the historical time sequence data of the corresponding entity set; and inputting the combined feature set into a trained machine learning model to obtain a target index related to the entity set.

The invention relates to the technical field of oil quality analysis, in particular to an aviation oil property analysis method and system based on spectral oblique projection. According to the method, when the oblique projection operator is constructed, the limitation of a traditional oblique projection operator is broken through through dynamic multi-component cooperation and self-adaptive residual error separation, key information which is in complex association with properties in the aviation oil spectrum is extracted more accurately, the pertinence and robustness of feature extraction are improved, and the accuracy of feature extraction is improved. Higher-quality input variables are provided for an aviation oil property prediction model, and the method is a core technical support for realizing high-precision aviation oil property analysis.

The invention provides a trailer hinge angle real-time estimation method and system based on feature point tracking. The method comprises the steps of obtaining a first image frame of a target trailer at a current moment and a second image frame of the target trailer at a previous moment; determining a first feature point list of the first image frame according to a second feature point list in the second image frame; according to the first feature point list and a preset feature point template, calculating to obtain a visual estimation angle and confidence; a first Kalman filter at the current moment is updated according to the visual estimation angle and the confidence coefficient, an updated second Kalman filter is obtained, and a hinge angle estimation value at the current moment is output based on the second Kalman filter; and within a preset time period after the current time, according to the motion data of the target trailer, outputting the hinged angle estimated value of each time within the preset time period through the second Kalman filter, thereby improving the accuracy and real-time performance of real-time estimation of the hinged angle of the trailer.

The invention discloses a snake wound classification and recognition model construction method based on serum marker electrochemical detection and deep learning, and belongs to the technical field of biosensing and intelligent diagnosis. The method comprises the following steps: firstly, screening based on proteomics to obtain multiple serum protein biomarkers related to snake bite types, constructing a multi-electrode electrochemical sensing platform, and collecting electrochemical signals through cyclic voltammetry (CV) to obtain electrochemical fingerprint characteristics of a serum sample; and after the obtained signal is subjected to normalization and data enhancement processing, the signal is input into a one-dimensional convolutional neural network (1D-CNN) model for feature learning and classification identification. Through combination of multi-index detection of biomarkers and an end-to-end deep learning algorithm, the method realizes rapid and accurate identification of three different snake wound types of agkistrodon acutus, cobra and trimeresurus remeresurus. The invention constructs a high-sensitivity and rapid diagnosis method for snake wounds, and the method has good popularization and clinical transformation potential.

The invention relates to the technical field of sports health monitoring, and particularly discloses a real-time running fatigue monitoring method and system, electronic equipment and a storage medium, and the method comprises the following steps: receiving real-time heart rate data in a running process; extracting a plurality of heart rate variability characteristics from the real-time heart rate data; inputting the plurality of heart rate variability characteristics into a fatigue monitoring model; wherein the fatigue monitoring model is obtained in advance based on indoor running data training, and is obtained by carrying out cross-scene optimization by adopting transfer learning and incremental training strategies and utilizing outdoor running data; the multiple heart rate variability characteristics are processed through the fatigue monitoring model, and a real-time fatigue quantification result is generated; and outputting fatigue state information according to the real-time fatigue quantification result. The technical problems that subjective evaluation is poor in real-time performance, the application of an existing sensing technology is limited, and the dynamic adaptability of a model is insufficient are solved, and the running fatigue state real-time monitoring capacity and the cross-scene adaptability are improved.

This invention discloses a multi-factor driven logical sequence generation method and apparatus, comprising: a feature input module, a multi-factor fusion module, a dynamic constraint module, a logical generation module, a reordering and evaluation module, and a feedback learning module. The method involves receiving text information from multiple sources, performing structured parsing and normalization to obtain a standardized set of text feature vectors; extracting multi-factors and constructing multi-factor constraints; generating a multi-factor driven logical sequence based on the set of multi-factor constraints; performing dynamic information updates and output constraint adjustments; generating candidate logical sequences; and designing multiple-dimensional indicators for re-evaluation. This invention significantly improves the accuracy, adaptability, and optimality of logical text sequences by addressing the problems of insufficient input information processing, lack of dynamic adaptability, and insufficient post-optimization mechanisms in the logical text sequence generation process.

The application provides a power transmission tower reinforcing process parameter optimization method and system, and relates to the technical field of power transmission tower reinforcement. The method comprises the following steps: obtaining image data of a power transmission tower to-be-sprayed base material; obtaining heterogeneity characteristic quantitative information of the to-be-sprayed base material by processing the image data; dividing the surface of the to-be-sprayed base material into multiple heterogeneity regions according to the heterogeneity characteristic quantitative information; determining the spraying reinforcement parameters of each heterogeneity region according to the heterogeneity characteristic quantitative information corresponding to the heterogeneity region; and controlling the spraying equipment to spray and reinforce each heterogeneity region according to the spraying reinforcement parameters. The method of the application aims to solve the problems of poor coating performance and the inability to realize real-time adaptive optimization of the base material caused by the uneven surface of the base material in the prior art, and improves the overall quality and reliability of the reinforcing coating.

The invention discloses a lake water quality parameter inversion method based on a remote sensing image, a medium, equipment and a product, and relates to the field of water quality parameter inversion, and the method comprises the steps: obtaining a Sentinel-2 remote sensing image and water quality sampling data, re-sampling a wave band in the remote sensing image to a uniform resolution, and carrying out the time-space matching of the wave band and the water quality sampling data, selecting a dual-band combination, a three-band combination and a four-band combination related to the water quality sampling data from the matched resampling bands, and constructing a training set; a deep learning inversion model is constructed, the deep learning inversion model comprises three feature extraction sub-networks, an attention mechanism and a fusion main network, wave band combinations are input into the three feature extraction sub-networks respectively, obtained three feature vectors are subjected to weighted fusion through the attention mechanism and then input into the fusion main network, and predicted water quality parameter concentration is output; and training the model by using the training set, and performing lake water quality parameter inversion by using the trained model. According to the invention, the inversion precision of water quality parameters is improved.

The invention discloses a business personnel seat number identification method and device, electronic equipment and a program product, relates to the field of artificial intelligence, and is applied to the field of financial science and technology, and the method comprises the steps: carrying out the preprocessing of collection information of a transaction liquidation request when the transaction liquidation request is detected, and obtaining a preprocessed text; extracting a text semantic feature, a character space feature and a business logic feature from the preprocessed text, and performing feature fusion to obtain a fused feature vector; and inputting the fusion feature vector into a target recognition model, and outputting a business personnel seat number recognized from the collection information, the target recognition model being obtained by training a preset recognition model by using historical collection information, and the preset recognition model at least comprising: a bidirectional long short-term memory network, a converter encoder, and a conditional random field model. Through the method and the device, the problem that the seat number of the business personnel in the collection information is difficult to identify based on a traditional keyword matching method in related technologies is solved.

The specification discloses a spatio-temporal knowledge graph construction method, system and device for ecological risk prediction, which accesses spatio-temporal data through a platform data interface, structurally processes the spatio-temporal entity model data containing geometric attributes, time attributes and state attributes, constructs a hybrid spatio-temporal index combining spatial index and time index, performs spatial range retrieval on an entity model library according to a given target entity, determines a candidate entity set, combines the state attributes, calculates the correlation strength between entities through a preset quantitative calculation model, generates an enhanced relationship edge containing a weight value and a quantitative indicator, maps the spatio-temporal entity and the enhanced relationship edge to a graph database, and obtains a spatio-temporal knowledge graph; receives a query request containing spatio-temporal constraint conditions and attribute filtering conditions, performs graph traversal and screening based on the weight value and the quantitative indicator of the enhanced relationship edge, and outputs a query result; and updates the spatio-temporal knowledge graph according to a preset data cycle.

The embodiment of the invention discloses a vehicle deceleration control method, a medium, equipment and a product. The method comprises the steps that a front vehicle image sequence for a front vehicle and the real-time deceleration of a target vehicle are acquired; the front vehicle is in front of the target vehicle; performing image analysis on the front vehicle image sequence to obtain a front vehicle lamp state; determining a front vehicle driving intention according to the front vehicle lamp state; determining a target deceleration and a deceleration change rate according to the driving intention of the front vehicle and / or the real-time deceleration; and generating a control instruction for controlling the target vehicle to decelerate based on the target deceleration and the deceleration change rate. According to the embodiment of the invention, the vehicle lamp state of the front vehicle can be accurately identified, the driving intention of the front vehicle is judged based on the vehicle lamp state, the differential deceleration and the change rate are formulated accordingly, smooth and timely automatic deceleration is realized, the braking distance is remarkably shortened, and the rear-end collision risk is effectively reduced and the driving safety is comprehensively enhanced while the comfort is improved.

The application provides a robot dynamic error compensation method based on a capsule neural network, adopts Newton-Euler method to establish a robot dynamics model, and performs linearization and dynamics parameter identification; uses joint motion data as input to obtain predicted torque for model training by using the linearized dynamics model after the dynamics parameter identification, and calculates error between the predicted torque and actual torque; regards normalized parameters as vectors in a vector space, calculates inner products of the vectors to form a Gram matrix and converts the Gram matrix into a two-dimensional image; learns error between the predicted torque and the actual torque by using a capsule neural network, and obtains a trained capsule neural network; obtains real-time prediction error by using the trained capsule neural network, and compensates real-time predicted torque obtained by the linearized dynamics model after the parameter identification by using the real-time prediction error. The application realizes high-precision and adaptive dynamic error compensation.