11results about How to "Realize intelligent evaluation" patented technology

The invention discloses an integrated tree model-based intelligent evaluation method for the construction quality of a fabricated prefabricated beam bridge, and belongs to the field of bridge engineering quality evaluation, and the method comprises the steps of constructing a three-level quality evaluation index system of beam bridge construction; according to the actual measurement data of the second-level indexes, obtaining standardized characteristic data through mixed index preprocessing; a random forest model, an XGBoost model and a LightGBM model are constructed, and model training and hyper-parameter optimization are carried out; presetting model performance evaluation indexes, and selecting one model with the optimal comprehensive performance from the three candidate models as a construction quality evaluation model; and obtaining a construction quality grade evaluation result according to the construction quality evaluation model and the standardized feature data of the to-be-evaluated T-beam bridge. According to the method, intelligent evaluation of the construction quality of the fabricated T-beam bridge is realized, the limitation of traditional subjective weighting is eliminated, and the objectivity and accuracy of evaluation are remarkably improved by learning a historical data rule and continuously optimizing.

The application discloses a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence, which comprises an original data collection module, an original data optimization module, a time sequence double-end recovery evaluation model establishment module, a recovery evaluation model performance optimization module and a whole-cycle intelligent recovery evaluation module. The application relates to the technical field of medical data processing, in particular to a prostate cancer patient pelvic floor muscle recovery evaluation system based on artificial intelligence. The scheme innovatively proposes a time sequence double-end recovery evaluation model combining an in-hospital baseline end and a home continuous time sequence end, improves the real-time performance of patient pelvic floor muscle recovery evaluation, extracts short-term time sequence recovery features by using a bidirectional long short-term memory network, extracts long-term time sequence recovery features by combining an improved convolution residual network with a parallel activation function, significantly improves the accuracy of the recovery evaluation results, and introduces an improved optimization algorithm combined with a dynamic local search strategy, thereby significantly improving the stability and accuracy of the output results of the model.

The invention relates to the technical field of blasting effect evaluation, and discloses a millisecond blasting effect intelligent evaluation analysis method and system and a medium, and the method comprises the steps: arranging a multi-source sensor to collect vibration, displacement, hydraulic support stress and earth surface deformation data before and after blasting, and constructing a standardized blasting data set; calculating vibration amplitude variation based on stress wave propagation characteristics, and judging the crushing effect; evaluating the rock mass stability and the working face safety by combining the displacement increment and the mine pressure strength; a response parameter set is formed through multi-time-sequence node monitoring, monitoring partitions are divided, prediction and actual indexes are compared, and a verification result is generated; and feeding back a correction evaluation threshold value, and iteratively optimizing blasting effect evaluation. According to the invention, accurate, dynamic and closed-loop intelligent evaluation of the millisecond blasting effect can be realized.

The invention provides an intelligent evaluation method for a phase modulation pressurized water system of a pumped storage power station. The intelligent evaluation method comprises the following steps: S1, acquiring a system operation state characteristic quantity and a performance index characteristic quantity according to monitoring data; s2, performing normalization processing on the performance index characteristic quantity, constructing performance index vectors representing system performance, and forming a performance index vector set; s3, calculating a mean vector of the performance index vector set under the normal state of the system, and taking the mean vector as a system performance index standard vector; s4, calculating the deviation between each performance index vector in the performance index vector set and the performance index standard vector, and taking the deviation as a system performance index early warning index; and S5, integrating the real-time operation state early warning threshold value and the performance index early warning threshold value to carry out intelligent evaluation of the system. According to the method, the performance indexes are calculated and obtained based on the instruction operation control data to supplement the defect of lack of parameter monitoring, and the running state of parameter monitoring and the calculated and obtained performance indexes are comprehensively utilized to carry out comprehensive intelligent evaluation.

This application relates to a method, system, device, and medium for intelligent screening of clothing images. The method includes: acquiring a dataset of clothing images to be processed; performing basic quality filtering to obtain a screening set; performing image enhancement on the screened images to generate a standardized set; extracting the visual feature vector and quality score of each image; identifying the main body region based on the features using a clothing region segmentation model to generate a mask; combining the mask and features to extract semantic labels of design elements to form a structured design point set; matching the design point set with a pre-built clothing trend database to generate a trend matching score; simultaneously calculating a design innovation score based on its historical frequency of occurrence; dynamically weighting the quality score, trend score, and innovation score to generate a comprehensive score; and finally, performing a hierarchical decision based on the comprehensive score and quality score, outputting the images to be added to the database and their corresponding level labels. This application realizes intelligent evaluation and screening of clothing designs.

The application relates to the technical field of blasting effect evaluation, and discloses a millisecond blasting effect intelligent evaluation and analysis method, a system and a medium, the method comprises the following steps: arranging a plurality of source sensors to collect vibration, displacement, hydraulic support stress and surface deformation data before and after blasting, and constructing a standardized blasting data set; calculating a vibration amplitude variation based on stress wave propagation characteristics, and determining a crushing effect; combining displacement increment and mine pressure intensity to evaluate rock mass stability and working face safety; forming a response parameter set through multi-time sequence node monitoring, dividing a monitoring partition, comparing predicted and actual indexes, and generating a verification result; feeding back a correction evaluation threshold, and iteratively optimizing blasting effect evaluation. The application can realize accurate, dynamic and closed-loop intelligent evaluation of the millisecond blasting effect.

The invention discloses a spinal cord injury post-neurological function comprehensive evaluation system based on big data. The system comprises a data acquisition module, a spinal cord injury multi-modal feature depth selection module, a pre-injury neurological function baseline construction module and a double-reference spinal cord injury neurological function comprehensive evaluation module. The invention relates to the technical field of medical data processing, in particular to a spinal cord post-injury neurological function comprehensive evaluation system based on big data, which innovatively combines a pre-injury neurological function baseline construction module and a double-reference spinal cord injury neurological function comprehensive evaluation module to improve the comprehensiveness and accuracy of evaluation results; a collaborative correction feature screening algorithm fusing ternary mutual information is provided, and the model evaluation precision and stability are improved; the clustering algorithm is innovatively improved by fusing a high-dimensional vector included angle variance quantification method and a core point secondary evaluation strategy based on a reverse neighborhood number evaluation mechanism, the accuracy of a clustering result is improved, and the accuracy of a neural function evaluation result after spinal cord injury is improved.

The application provides a scientific and technological literature innovation evaluation method based on a large model Agent technology, comprising: hierarchical classification of each citation source in a citation dataset in disciplines and research branches, determination of the discipline field code and specific research branch code thereof according to a discipline classification system, and obtaining citation distribution data containing complete classification labels; statistics of the number and proportion of citations of each research branch in the citation distribution data, and calculation of the crossing degree of the literature among different research branches; the large model Agent comprehensively evaluates the innovation value of the literature based on the crossing degree among the branches and the total number of citations, adjusts the weight of different literature according to the crossing degree among the branches, and generates an innovation value score dataset; the large model Agent cross- validates the candidate literature list, confirms the effectiveness of the cross-disciplinary breakthrough characteristics in combination with the information of the proportion of the number of citations of each branch, completes the final labeling, and identifies the breakthrough innovative literature.

The invention discloses an unmanned aerial vehicle automatic analysis obstacle avoidance method, particularly relates to the technical field of unmanned aerial vehicle autonomous flight control, and is used for solving the technical problem that when an existing unmanned aerial vehicle depends on a historical map for a long time to carry out autonomous flight, an obstacle avoidance safety boundary is not reliable due to scale drift of a visual map. The method comprises the following steps: judging task dependency, aligning historical and real-time maps, analyzing a re-projection error structure to quantify scale difference, further calculating a mapping deviation of a historical task trajectory, and comprehensively evaluating an influence degree on a current obstacle avoidance planning safety boundary. And finally, spatial scale compensation is carried out on an environment map or a planned path in a self-adaptive manner according to the influence degree, so that the obstacle avoidance decision reliability and safety of the unmanned aerial vehicle in long-term autonomous operation are improved.

The invention relates to the technical field of data processing and signal analysis, and discloses a steam turbine generator unit vibration-power cross-domain feature fusion analysis method and system, and the method comprises the steps: obtaining vibration signal time sequence data and power signal time sequence data of multiple operation cycles of a steam turbine generator unit, carrying out the preprocessing of the two types of data, and obtaining a power signal time sequence data; obtaining standardized time sequence data which can be used for feature extraction; vibration signal features and power signal features are extracted from the standardized time sequence data, and cross-domain feature vectors of all operation cycles are formed through fusion; inputting the cross-domain feature vector of each operation cycle into a fusion analysis model for processing, and outputting a comprehensive evaluation index of each operation cycle; and on the basis of the multiple operation cycle comprehensive evaluation indexes, operation abnormity identification and early warning judgment of the steam turbine generator unit are executed, and accurate monitoring of the unit operation state is achieved. According to the invention, the technical problem of insufficient evaluation precision caused by a single signal analysis method in the prior art is solved.

The invention belongs to the technical field of electrical system testing, and provides a special vehicle power take-off power generation system load testing method which comprises the following steps: S1, connecting a programmable resistance load box and a high-speed data acquisition system with the output end of a power take-off power generation system; s2, the resistive load box is controlled to load the power take-off power generation system in the starting process according to a preset S-shaped function multi-stage loading strategy, wherein the strategy at least comprises a no-load stage, an S-shaped function-based gradient loading stage and a full-load steady-state stage; and S3, in the loading process, a high-speed data acquisition system is utilized to synchronously acquire an instantaneous value sequence of output voltage and current of the power take-off power generation system. According to the technical scheme, deep detection and intelligent evaluation of the performance of the whole starting, loading and stabilizing process of the power take-off power generation system can be achieved.