27results about How to "Realize intelligent evaluation" patented technology

The invention provides an intelligent operation and maintenance method and system for a photo-thermal power generation system based on the Internet of Things, and relates to the technical field of information technologies, and the method comprises the steps: obtaining temperature, humidity and vibration environment parameter data collected by a sensor disposed on power generation equipment; and receiving the environmental parameter data, comparing the environmental parameter data with a preset threshold value, and judging whether the environmental parameters exceed a normal range or not. According to the method, environmental parameters such as temperature, humidity and vibration of the equipment are collected in real time by arranging the sensors, when the parameters exceed the preset threshold value, performance parameter collection is triggered, weighted average calculation is carried out in combination with the environmental parameters, and the equipment health index is obtained; according to the method, intelligent assessment and predictive maintenance of the health state of the power generation equipment are realized, the operation reliability and the maintenance efficiency of the equipment are improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

The invention discloses a ship navigation risk assessment and decision-making method and system, a medium and a product. The method comprises the following steps: acquiring ship navigation parameters, and meteorological data and ocean data within a future preset duration; determining a meteorological influence value and an ocean influence value according to the meteorological data and the ocean data; inputting the sailing speed value, the load tonnage value, the meteorological influence value and the ocean influence value into a preset sailing risk assessment function to obtain a sailing risk value in a future preset duration; when the navigation risk value exceeds a preset risk threshold value, searching a reachable port; determining harbor information of reachable harbors according to a preset harbor database; determining an optimal reachable port based on the wind sheltering condition, the berth state and the geographic position; calculating an optimal wind-sheltering path between the ship and the optimal reachable port and the sailing duration and sailing fuel consumption of the optimal wind-sheltering path; and visually displaying the optimal wind sheltering path, the sailing duration and the sailing fuel consumption. By implementing the method, the ship navigation decision making efficiency is improved.

The invention relates to the technical field of concrete detection, and discloses a concrete quality rating method and system based on a multilayer neural convolutional network, and the method comprises the steps: building a standardized performance index data set, training the standardized performance index data set through employing a support vector machine algorithm, and building a concrete material resistance evaluation model; environment parameters of a construction site are collected in real time through a wireless sensor network, and the influence of the current construction environment on concrete is judged; and inputting the construction environment parameters into the concrete material performance evaluation model to obtain an optimal concrete material ratio suggestion, uploading the concrete material performance parameters to a cloud server in the concrete use process, and uploading a concrete quality evaluation report to a concrete quality monitoring platform after concrete construction is completed. According to the invention, intelligent evaluation and optimization of the concrete material performance are realized, and the concrete quality and efficiency are improved.

The invention discloses an industrial control system and a control device, and relates to the technical field of industrial control, a data acquisition module obtains spraying path precision data and spraying thickness consistency data, and a data processing module analyzes and extracts a spraying path precision deviation value and a thickness drift speed abnormal value; the anomaly detection module comprehensively calculates a health score by using a machine learning model, evaluates the health state of the spraying robot and predicts a potential fault; the early warning module performs real-time early warning when fault prediction exceeds a threshold value, and the control module dynamically adjusts control parameters of the spraying robot, corrects the path and thickness and triggers maintenance suggestions through health state change trend prediction, so that the self-diagnosis capability and the control precision of the industrial control system are remarkably improved, potential problems can be found in the early stage, and the safety of the industrial control system is improved. The shutdown maintenance risk and the product rejection rate are reduced, so that the production efficiency and the product quality are improved.

The invention discloses a hydroelectric generating set health state assessment method based on multi-dimensional data, and the method comprises the steps: S1, obtaining the operation data of a hydroelectric generating set, and carrying out the data preprocessing; s2, sensing data extraction is carried out on the preprocessed hydroelectric generating set operation data; s3, performing anomaly detection on the operation sensing data of the hydroelectric generating set; s4, carrying out sensing abnormal frequency statistics on the operation sensing abnormal data of the hydroelectric generating set; s5, thermal efficiency and operation water conservancy efficiency calculation is carried out on the operation sensing data of the hydroelectric generating set, and operation performance index analysis is carried out; s6, performing health state calculation according to the analyzed hydroelectric generating set operation performance index data; and S7, constructing a health assessment model to assess the health state of the hydroelectric generating set, uploading assessment data to the cloud platform, and executing a preset health early warning task. According to the method, the health state is calculated by combining the operation performance index and the sensing abnormal frequency data, so that more comprehensive health assessment is realized.

The invention discloses a planting industry insurance claim data analysis method and system, and the method comprises the steps: obtaining planting farmland disaster remote sensing image data, carrying out the recognition and classification of farmland disaster types through a deep learning algorithm, and obtaining a farmland disaster type distribution diagram; according to the farmland disaster type distribution diagram, constructing a disaster situation assessment index system, determining the weight of each index, calculating a comprehensive disaster situation index, and carrying out quantitative assessment on the farmland disaster situation to obtain a farmland disaster situation quantitative assessment result; historical disaster data and corresponding claim settlement data are acquired, and a claim settlement prediction model is constructed through a feature combination and parameter optimization method; and combining a farmland disaster situation quantitative evaluation result with the claim settlement prediction model, inputting disaster situation parameters and predicting a claim settlement amount for a newly occurring farmland disaster, and if the predicted claim settlement amount exceeds a preset threshold value, triggering a claim settlement flow. According to the invention, intelligent assessment and claim settlement prediction of farmland disasters are realized, and the claim settlement efficiency and accuracy are improved.

The invention discloses a coal mine fireproof monitoring and analysis method based on unmanned aerial vehicle inspection, which relates to the technical field of coal mine fireproof monitoring and analysis, and comprises the following steps: when an unmanned aerial vehicle arrives at a coal mine goaf area for inspection, acquiring environmental parameter information of the coal mine goaf area in real time, wherein the characteristic information comprises gas distribution characteristic information and airflow dynamic characteristic information. According to the method, the gas distribution characteristic information and the airflow dynamic characteristic information are acquired in real time, and the gas distribution index is generated by utilizing the machine learning model which is learned in advance, so that the goaf gas distribution is intelligently evaluated. According to gas distribution indexes, abnormal distribution is divided into low risk, medium risk and high risk, refined inspection strategies such as frequency increasing, path expanding, dynamic height adjusting and multi-machine collaborative operation are adopted for different risk levels, comprehensive monitoring and quick response are achieved, inspection resource distribution is optimized, and coal mine safety accidents are effectively prevented.

The invention discloses a perioperative patient anesthesia visual monitoring system which comprehensively analyzes basic body indexes, test indexes and operation information of a patient object to be anesthetized and ASA grading information given by a doctor by adopting a deep learning-based data processing technology. The method comprises the following steps: firstly, extracting semantic feature representation of each piece of patient information through vectorization processing, then performing context semantic association reinforcement on the semantic feature field effect distribution of each piece of patient information, and performing cross-modal semantic association fusion on each piece of patient information on the basis to realize deep global understanding of the patient information. Therefore, intelligent assessment and visual display of the anesthesia risk of the patient are realized. In this way, more comprehensive and accurate decision support can be provided for anesthetists, and the efficiency and safety of anesthesia management of patients in the perioperative period are remarkably improved.

The invention belongs to the technical field of building structure monitoring, and particularly relates to an intelligent monitoring system and method for a prestressed cable structure, the system comprises a cable body and an intelligent monitoring module for monitoring the stress change in the cable body, the cable body comprises a steel strand with a center steel wire removed and an optical fiber, and the optical fiber is connected with the intelligent monitoring module. The optical fiber is installed at the position of a center steel wire of the steel strand through the optical fiber protection shell, a buffer layer is arranged between the optical fiber and the inner wall of the optical fiber protection shell, the optical fiber is installed in the optical fiber protection shell through the elastic clamp, and the data collector is connected to a tail fiber of the cable body through the tail fiber leading-out component and used for receiving optical signals transmitted by the optical fiber. The optical fiber embedded in the central position in the cable body instead of a central steel wire of a steel strand is used as a sensing element, tiny stress changes can be more accurately captured, and high-precision monitoring of the stress state of the cable body is achieved.

The invention relates to the field of compressor performance detection, and particularly discloses a hydrogen refueling station hydrogen compressor performance detection method and system, and the method employs an artificial intelligence technology based on deep learning to carry out the continuous monitoring and data analysis of the outlet pressure, inlet pressure and inlet temperature of a hydrogen compressor in a working state. The method comprises the following steps: respectively mining time sequence change modes of various working state parameters, then performing time sequence collaborative analysis on inlet and outlet pressure data to reveal the compression degree of hydrogen, and further combining temperature change characteristics at an inlet of a compressor on the basis to realize intelligent evaluation on an exhaust temperature reference value of the compressor, so as to realize the intelligent evaluation of the exhaust temperature reference value of the compressor. And whether the exhaust temperature performance of the compressor is qualified or not can be judged according to the deviation between the actual exhaust temperature and the reference value. Through the mode, real-time monitoring and accurate evaluation of the performance of the hydrogen compressor can be realized, so that timely maintenance or adjustment is facilitated, and stable operation of the hydrogen compressor and safe supply of hydrogen are ensured.

The present invention discloses a data-driven power grid emergency repair risk assessment method and decision-making system, which relates to the field of power grid operation and maintenance technology. The method includes: constructing a power grid emergency repair database through data-driven collection, analyzing and training the power grid emergency repair database based on a power grid emergency repair risk assessment index system, and generating a power grid emergency repair risk assessment network; using digital twin technology to model the target emergency repair power grid area and build a digital twin model of the power grid emergency repair area; extracting risk factors from the digital twin model of the power grid emergency repair area based on the power grid emergency repair risk assessment index system to obtain power grid emergency repair risk factor information; and conducting risk assessment and risk emergency repair decision-making on the power grid emergency repair risk factor information based on the power grid emergency repair risk assessment network. The technical effect of realizing multi-dimensional indicator intelligent assessment of power grid emergency repair risk, improving the comprehensiveness and accuracy of risk assessment, and thus ensuring the efficiency of emergency repair decision-making is achieved.

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 discloses an operation risk early warning method and device for power distribution network equipment and electronic equipment, and relates to the technical field of electric power systems or other related fields, the method comprises the steps that real-time operation data of target power distribution network equipment are collected, the real-time operation data comprise operation parameters of N equipment parts in the target power distribution network equipment, and the N equipment parts in the target power distribution network equipment are connected with the real-time operation data; n is a positive integer; determining the equipment risk level of the target power distribution network equipment through a preset risk assessment model and the operation parameters of all the equipment parts; and determining a risk early warning strategy and risk early warning information based on the equipment risk level, and sending the risk early warning information to the risk prevention and control client based on the risk early warning strategy. The technical problem that safe operation of the power distribution network cannot be guaranteed due to inaccurate operation risk early warning of the power distribution network equipment in related technologies is solved.

The invention discloses a geological layer parameter analysis method and system. The method comprises the following steps: acquiring a geological parameter set of a target geological layer area; inputting the geological parameter set into a pre-trained geological state evaluation model used for identifying the geological layer state, and outputting the geological state of the target geological layer area; calculating the geological stability of the target geological layer area according to the geological parameter set; and determining a geological analysis result of the target geological layer area based on the geological state and the geological stability. According to the embodiment of the invention, the method can overcome the defects of a conventional geological analysis method, achieves the intelligent evaluation of the state of a geological layer, improves the efficiency and accuracy of geological analysis, monitors the change of the geological state in real time, provides scientific and reliable decision support for related engineering, and guarantees the safety and sustainable development of geological engineering.

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 a multi-source fusion new energy power generation control method, which comprises the following steps: acquiring historical fault data and a preset fault evaluation rule, and evaluating the influence range and severity of a current fault to obtain a fault evaluation result; according to the fault evaluation result and a pre-established restart switching rule, obtaining the starting characteristics and the mutual dependency relationship of the energy subsystems, and generating a restart switching scheme; according to the generated restart switching scheme, transient response of the restart process is simulated and predicted, and if a prediction result exceeds a preset threshold value, the restart switching scheme is optimized and adjusted; the optimized restart switching scheme is issued to a control unit and executed, and operation parameters of all the energy subsystems are obtained and analyzed in real time; when it is detected that parameter abnormity exists in the restart process, early warning is triggered, a restart adjustment scheme is obtained and issued again to be executed, and the restart process is dynamically adjusted through an intelligent execution mechanism till all the energy subsystems are stably started and recovered to the normal operation state.

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 present invention relates to the technical field of coal mine data analysis, and in particular to a coal mine information integrated monitoring system and method, comprising a data acquisition module, a data processing module, a support information analysis module, a push slide information analysis module and an intelligent control module; wherein the support information analysis module determines the support pressure average value and the support pressure range at each moment, compares the support pressure range at the same moment with the support pressure reference value in each direction and determines the support pressure type in the corresponding direction, determines the current support pressure average value state according to the current support pressure average value and the historical pressure preset value, and determines whether there is a collapse tendency of the roof in combination with the support pressure type and the current support pressure average value state; the push slide information analysis module determines the support state of a single hydraulic push slide to determine whether to adjust the position of the single hydraulic push slide; the intelligent control module determines the coal mining adjustment strategy according to the collapse tendency and the support state of the hydraulic push slide.

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 invention discloses a security protection effectiveness evaluation method based on a reward model and a chatGLM model, and the method comprises the steps: firstly capturing general evaluation rules, security protection features, security failure points and failure reasons as training data, forming a mapping pair of evaluation data and rule scoring through rough manual intervention, training the reward model, obtaining a scoring model, and carrying out the scoring of the scoring model; meanwhile, the mapping pairs are converted into question and answer form data to conduct fine adjustment on the chatGLM large model, after training is completed, the deep learning technology is adopted in the inference stage to extract input evaluation rules and semantic interaction features of protection evaluation results, then the large model subjected to fine adjustment is input for content expression optimization, and the evaluation rules and the protection evaluation results are obtained. And inputting the optimized evaluation rule and the protection evaluation result into a trained scoring model for scoring to obtain a safety protection effectiveness evaluation value, thereby realizing automatic and intelligent evaluation of a safety protection system, improving evaluation efficiency and accuracy, and meeting the demand of large-scale data processing.

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.