15results about How to "Comprehensive recognition" patented technology

The invention belongs to the technical field of oil-gas field development, and discloses a low-salinity formation water identification method, which comprises the following steps of: obtaining data corresponding to a test oil well through drilling data, and comparing step by step according to the obtained data to judge whether water is discharged from a formation; the invention further discloses a low-salinity formation water recognition device and equipment and a storage medium. The method is suitable for identifying the low-salinity formation water, whether water flows out of the formation or not is judged in real time by draining the test oil well, an important basis is provided for identifying the low-salinity formation water, and the defects of a formation water judgment method are overcome.

The invention relates to an intelligent low-altitude economic scene passenger emotion pacifying method and system based on a large model. The method comprises the following steps: constructing a multi-modal emotional state data set of low-altitude travel passengers; wherein the multi-mode emotional state data set comprises passenger face video sequence data, in-cabin voice and audio data, heart rate variability time sequence data, respiratory frequency time sequence data and external situation context data; performing emotional depth feature extraction based on multi-source space-time signals on the multi-modal emotional state data set to obtain multi-source emotional features; emotion recognition is carried out based on the multi-source emotion features; based on the emotion recognition result, constructing a pacifying strategy generation model; and utilizing a pacifying strategy generation model to intelligently pacify the emotion of the passenger, and evaluating and feeding back the pacifying effect in real time. According to the emotion and specific inducements recognized in real time, pacifying measure suggestions which conform to safety specifications, fit scenes and are natural in language can be generated, and timeliness and specialty of passenger cabin service are improved.

The utility model discloses an earth-rock engineering transport vehicle access registration system, which relates to the technical field of engineering control, and comprises a main control device which is respectively in signal connection with an exit registration device, an entrance registration device, a network module and a barrier gate device, and the exit registration device comprises an exit identification device and an exit snapshot device. The entrance registration device comprises an entrance identification device and an entrance snapshot device. According to the utility model, the exit snapshot device and the entrance snapshot device are arranged to photograph and record the appearance, cargo loading and the like when the vehicle enters and exits the parking lot, so that the vehicle information registration is more comprehensive; through signal connection between the master control device and the exit registration device, the entrance registration device and the barrier gate device, automatic identification of vehicle information and automatic control of the barrier gate are realized. When the vehicle arrives at the entrance and exit, the exit identification device and the entrance identification device can rapidly identify vehicle information and transmit the vehicle information to the master control device to control a barrier gate switch, so that the vehicle entrance and exit management efficiency and the authenticity and objectivity of the entrance and exit information are improved.

This invention relates to the field of robot control technology, specifically to an end-to-end closed-loop control method and system for robots based on multimodal perception fusion. It includes: a perception access layer for standardized access to data from multiple sensor types; a fusion processing layer for spatiotemporal alignment and multimodal fusion processing of the standardized access data; a decision control layer for generating execution instructions based on the fusion processing results; and an execution feedback layer for feeding back the execution status to the fusion processing layer to form closed-loop control. This technical solution can solve the problems of standardized access and real-time fusion of multi-source heterogeneous data, and construct a closed-loop control architecture with high fault tolerance and low latency.

This invention provides a vision system and device for detecting defects in optical convex lenses, comprising three sets of vision inspection modules. The first and third vision inspection modules are mirror-symmetrical, respectively detecting defects on the left and right edges of the sample. The first vision inspection module includes a first inspection camera and a first inspection light source; the second vision inspection module includes a second inspection camera and a second inspection light source; and the third vision inspection module includes a third inspection camera and a third inspection light source. The first, second, and third inspection cameras are located at the same height above the sample. The first and third inspection light sources are located diagonally above opposite sides of the sample, with the same vertical distance from the sample. The second inspection light source is located diagonally above the sample, with a greater vertical distance from the sample than the first inspection light source. By combining dual-station collaborative inspection with special reflective film optical path optimization technology, full coverage and high-precision detection of surface defects in optical convex lenses are achieved.

The application relates to a transformer on-line fault diagnosis and early warning method based on transformer transformation ratio, which comprises the following steps: collecting primary side and secondary side voltages of the transformer in real time, and calculating corresponding real-time voltage transformation ratio; comparing the real-time voltage transformation ratio with the rated transformation ratio of the transformer to determine whether the transformer is faulty and the fault type; based on the real-time voltage transformation ratio, transformation ratio prediction is carried out to obtain a transformation ratio prediction value; the transformation ratio prediction value is compared with the rated transformation ratio of the transformer to determine whether early warning is needed; if early warning is needed, the transformation ratio prediction value is compared with the rated transformation ratio of the transformer to determine whether the transformer is faulty and the fault type. Compared with the prior art, the application directly uses voltage parameters to diagnose the transformer fault, can comprehensively, accurately and quickly identify the transformer fault, and simultaneously adopts a prediction algorithm, can realize timely fault early warning by transformation ratio prediction of the transformer.

This invention discloses a method for comprehensively identifying PFAS in the environment by combining targeted analysis, suspicious screening, and non-targeted identification. The method includes the following steps: S1, performing full-can / DDA / DIA analysis on environmental samples using liquid chromatography-high-resolution mass spectrometry (LC-HDMS); S2, performing LC-HDMS analysis on several PFAS standards; S3, collecting the names and molecular formulas of several PFAS based on publicly available chemical databases and literature, calculating their precise mass numbers, and establishing a suspicious screening database in conjunction with an open-source MS2 spectral library; S4, establishing a non-targeted identification workflow by combining the mass number characteristics, characteristic MS2 fragmentation, characteristic neutral loss, and homologue characteristics of PFAS; S5, integrating targeted analysis compounds, candidate compounds from suspicious screening, and candidate compounds from non-targeted identification to comprehensively identify PFAS in environmental samples. This invention achieves comprehensive identification of known and unknown per- and polyfluoroalkyl compounds through three complementary analytical techniques.

The invention discloses a method for identifying unknown organic phosphodiesters in an environment based on a diazomethane derivatization technology. The method comprises the following steps: S1, establishing an OPDEs suspicious database; s2, carrying out methyl derivatization on the OPDEs to be detected; s3, carrying out suspicious screening on the OPDEs; s4, determining characteristic fragments of the derivatization OPDEs; and S5, carrying out non-targeted screening on the OPDEs. According to the suspicious-non-targeted comprehensive screening method for the unknown OPDEs in the environmental sample, the limitation that an OPDEs analysis method in the environment can only recognize known types of OPDEs can be solved, and comprehensive recognition of the unknown OPDEs in the environmental sample can be achieved.

The invention discloses a supplier illegal behavior intelligent identification method and system based on a Monte Carlo method, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of an original bidding record data set, and obtaining an analysis bidding record data set; extracting multi-dimensional features by a multi-dimensional analysis feature mechanism, and carrying out supplier relation network feature quantitative processing to obtain an initial feature engineering data set; fusing the initial feature engineering data set into an enhanced multi-dimensional feature engineering data set by using multi-granularity time analysis mechanism time sequence mode analysis; constructing an abnormal behavior portrait by using a Monte Carlo simulation model, constructing a multi-level data view, simulating the multi-level data view, and obtaining a quantitative comprehensive risk score; and performing assessment and identification by using a multi-dimensional risk assessment and intelligent identification terminal to obtain an integrity comprehensive risk score, dividing suppliers based on a preset threshold, and generating an illegal behavior adjustment report. According to the invention, differentiated schemes are provided for suppliers with different risks, and the scientificity and pertinence of management and control are improved.

The invention discloses an identification method of low-temperature compost energy metabolism core protein, and belongs to the technical field of protein screening. In order to solve the problems of single screening dimension, inaccurate key protein identification and unclear mechanism analysis in the prior art, the invention provides a low-temperature compost energy metabolism core protein identification method, which comprises the following steps of: acquiring protein expression data of a low-temperature microorganism sample by utilizing a macro proteomics sequencing technology, screening differential expression proteins based on a set threshold value, and identifying a low-temperature compost energy metabolism core protein. The method comprises the following steps: identifying proteins in a significant enrichment pathway through GO function annotation and KEGG pathway enrichment analysis, carrying out importance ranking on proteins in significant enrichment related pathways by using a random forest model, screening key proteins in combination with the connectivity and network centrality of a protein interaction network, and finally identifying the energy metabolism core proteins in the low-temperature compost. The identification method provided by the invention has the advantages of strong systematicness, high screening accuracy, wide applicability and the like, and can provide key technical support for efficiency improvement and mechanism analysis of low-temperature compost.

The application discloses an intelligent abnormality capturing implementation method of a digital oscilloscope. A to-be-measured signal is input into the digital oscilloscope, the to-be-measured signal is collected by the digital oscilloscope to obtain a sampling signal containing multiple complete periods, then adaptive processing is performed on the sampling signal in amplitude and time, so that the sampling signal meets the requirements of subsequent processing; then multi-dimensional feature extraction is performed on the preprocessed signal, classification of the signal is realized based on multi-dimensional feature information, and then a suitable abnormality detection model is selected according to the classification result of the signal to perform abnormality detection.

The utility model discloses a kind of high-efficiency conveying detection systems. Including conveying unit, direction adjusting unit, sole pattern detection unit and control unit. Conveying unit adopts double-row chain type conveyor belt structure, two rows of conveyor belt are arranged in parallel, direction adjusting unit is arranged in the middle of conveying unit, including visual identification module and the rotary adjusting mechanism of being set on the top of conveyor belt being installed in the both sides of conveyor belt. Sole pattern detection unit is located in conveying unit middle rear section, including bottom visual detection component and defect identification system. Control unit adopts programmable logic controller. The utility model has the beneficial effect that: the utility model has automated direction adjustment, through visual identification module and rotary adjusting mechanism, realize the automatic detection and adjustment of slippers conveying direction, greatly improve production efficiency. The utility model detects quality accurately, can carry out comprehensive, accurate detection to sole pattern, ensure that product quality meets standard.

The application discloses a kind of neuron network hospital data correlation analysis systems, belong to disease prediction technical field.The application is by acquisition module, for obtaining the historical diagnosis and treatment data of the electronic medical record system record of hospital, health correlation state data and the illness data of target patient, the historical diagnosis and treatment data are divided according to disease category, obtain the disease diagnosis and treatment data related to each disease;Construction module is used to generate preset disease correlation model according to health correlation state data, the preset disease correlation model includes: each disease corresponding neuron node and the first connection between neuron node;Optimization module is used to adjust preset disease correlation model according to the disease diagnosis and treatment data, obtain target correlation model;Correlation module is used to determine the associated disease of target patient according to the target correlation model and the illness data, realizes the technical effect of effectively and comprehensively identifying illness.

This invention discloses a method and system for identifying non-full-phase protection in the main wiring of a three-half circuit breaker. The method includes a branch non-full-phase protection identification method based on the phase position of the side and interrupted circuit breakers; branch non-full-phase protection based on the side and interrupted circuit breakers, current sequence components, and phase current-current-free characteristics; a three-phase inconsistency protection identification method for the side circuit breaker; and a three-phase inconsistency protection identification method for the interrupted circuit breaker. The system includes an information acquisition module, an information calculation module, a debouncing processing module, and a signal identification module. This invention perfectly adapts to the complex structure of the main wiring of a three-half circuit breaker; it provides accurate identification, significantly improving the accuracy of non-full-phase operation identification and effectively reducing misjudgments; it has high reliability, with an innovative time-delay blocking mechanism that reliably avoids protection maloperation caused by three-phase inconsistency contact sticking, effectively ensuring the stable operation of the protection system.

This invention relates to a method for locating defects in forgings, aiming to improve the efficiency and accuracy of defect identification. The method first involves grinding the surface of the forging to ensure its smoothness and flatness. Next, an ultrasonic flaw detector with low-frequency and high-frequency emission capabilities is used to emit ultrasonic waves and acquire signals. Wavelet transform technology is used to process and analyze the acquired signals, extracting amplitude, phase, and frequency features to form a feature set. In the defect feature identification stage, feature vectors are constructed and input into a trained support vector machine (SVM) model, outputting the probability of various defects. The defect type is determined based on a set threshold; if the probability exceeds the threshold, the defect is considered to exist. Finally, samples are cut and subjected to tensile tests based on the detection results to confirm the defect characteristics. This invention combines wavelet transform and machine learning techniques, not only achieving precise location of forging defects but also improving detection efficiency, demonstrating promising application prospects and economic value.