26results about How to "Quick Discrimination" patented technology

The invention provides a carbonate rock dominant productivity reservoir identification method. The carbonate rock dominant productivity reservoir identification method comprises the following steps: S1, acquiring element logging curve data, an electric imaging basic image, a conventional logging curve, core experiment data and gas logging data of a well in a research block; s2, by means of element logging curve data and conventional logging curve data, the mineral content of the region is obtained, and the lithofacies AL of the research region is determined; s3, a reservoir type parameter AE is given; solving an electric imaging porosity spectrum parameter by utilizing the electric imaging two-dimensional curve, and solving a porosity spectrum evaluation parameter AD; s4, analyzing the relationship between the conventional logging curve and the gas logging data, determining the boundary between the dominant gas production reservoir and the low-productivity reservoir, and constructing a productivity evaluation parameter AO; and S5, according to the calculated parameter values, respectively calculating evaluation standards and weights of different parameters, calculating the carbonate reservoir in the research area according to the standards and the weights to obtain a reservoir comprehensive quality index, and according to the reservoir comprehensive quality index, obtaining advantageous capacity reservoir distribution.

The invention discloses individualized variable parameter closed-loop ultrasonic stimulation equipment and method based on deep learning, and belongs to the technical field of transcranial ultrasonic stimulation. The individualized variable parameter closed-loop ultrasonic stimulation equipment comprises an electroencephalogram signal acquisition module, an electroencephalogram signal processing module, an electroencephalogram signal deep learning judgment module and an ultrasonic stimulation module; the electroencephalogram signal acquisition module acquires electroencephalogram signals of an experimental subject within a specified finite time and transmits the electroencephalogram signals to the electroencephalogram signal processing module; the electroencephalogram signal processing module preprocesses and displays the electroencephalogram signals in real time, splits the electroencephalogram signals according to specified time, and stores all electroencephalogram signal segments in a picture format; the electroencephalogram signal deep learning discrimination module discriminates an electroencephalogram abnormal level and appoints effective ultrasonic stimulation parameters; and the ultrasonic stimulation module performs variable parameter ultrasonic stimulation on the experimental subject. According to the method, deep learning training is carried out on large sample electroencephalogram signals, an electroencephalogram signal deep learning model is established, the electroencephalogram abnormal level of any other experimental subject is judged, and variable parameter closed-loop ultrasonic stimulation is carried out.

The invention discloses a rapid detection method of limestone mineral powder, and belongs to the technical field of road engineering material detection. The rapid detection method comprises the following steps: respectively acquiring a diffraction spectrum and a fluorescence spectrum of a mineral powder sample by using XRD and XRF scanning equipment and an image processing technology; determining mineral composition according to characteristic peaks in an actually measured diffraction spectrogram of the mineral powder sample, and calculating relative mass percent as an index for identifying whether the mineral powder sample is limestone mineral powder or not; the method comprises the following steps: carrying out comparative analysis on a characteristic peak in an actually measured fluorescence spectrum of a mineral powder sample and a standard fluorescence spectrum, and taking the relative mass percent of calcium oxide (CaO), magnesium oxide (MgO) and silicon dioxide (SiO2) in the mineral powder sample as an index for judging the quality of the mineral powder sample. According to the method, the property and the quality of the asphalt mixture mineral powder can be quickly and accurately judged, the operation is simple, effective supervision on the lithology of the rock processed by the limestone mineral powder is effectively realized, and a solid foundation can be laid for subsequently providing a limestone mineral powder quality grading method.

The present invention discloses a method for determining the necessity of stratified water intake at a water intake of a water diversion and regulation project. The method comprises the following steps: step 1: collecting data on the parameters of the water diversion and regulation project and the environmental status of the water source area, the water diversion line area and the water receiving area; step 2: predicting the water temperature along the water diversion channel; step 3: predicting the water temperature of rivers and lakes in the water receiving area; and step 4: determining the acceptable degree of water temperature changes of ecologically sensitive targets of rivers and lakes in the water receiving area. The present invention has the advantage of being able to quickly identify the necessity of water temperature stratification.

The invention belongs to the technical field of civil aviation operation safety evaluation, and discloses a machine learning-based flight return standby identification method and system. The method comprises the following steps: establishing a flight learning feature sample by comprehensively considering data in the aspects of time, course, track, barometric altitude, radio altitude and the like of flight data; large-batch training and learning are carried out on flight data actually generated in the flight process; based on a machine learning algorithm of a gradient boosting decision tree, whether a flight is a homeward voyage standby flight or not is quickly judged from mass trained and learned flight data, key quantized data of the flight standby flight is extracted, and operation safety evaluation is carried out on the flight. According to the method, automatic auxiliary analysis support is provided for airline operation safety analysis.

The application relates to the technical field of network security, in particular to a self-defined private network asset fingerprint management method and system, which comprises the following steps: based on network access events, analyzing behavior tracks and historical modes, identifying abnormal activity paths, judging abnormal relations between asset permission groups and access actions, screening risk assets and checking permission levels, adjusting fingerprint permissions, analyzing security event aggregation conditions to identify invalid states, and detecting fingerprint behavior deviation rates through operation time sequence analysis. The application identifies path deviation and traffic anomaly by analyzing the multidimensional changes of network access behavior, comprehensively judges operation risk and permission matching conditions by combining the time sequence correlation of permission allocation and access actions, dynamically adjusts fingerprint permission categories, further identifies the abnormal state of the fingerprint in real time through the aggregation analysis of security events and operation time sequences, and realizes behavior consistency verification in the use process of the asset fingerprint.

The invention provides a 20K liquid phase chip for simultaneously and accurately identifying various goose varieties and application, the 20K liquid phase chip covers 22005 SNP molecular markers, and the SNP molecular markers are specifically composed of a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt The SNP molecular marker is composed of 4991 genetic differentiation specific sites, 12 important character associated sites and 17002 whole genome background sites of 22005 goose varieties, and the specific positions of the 22005 SNP molecular marker sites are shown in a table 1 in the specification. According to the method, 16 goose varieties can be accurately distinguished, the identification efficiency is high, the detection process can be completed within 24 hours, 48-96 samples can be detected at a time, and the technical repetition rate is gt; the cost of the chip is far lower than that of a high-density chip, and the chip is suitable for carrying out multi-variety and large-scale germplasm monitoring and identification work in a breed conservation field, a research and development unit and the like, and is high in practicability.

The invention discloses a dynamic game-based immediacy decision-making method, which specifically comprises the following steps of: establishing a combat action dynamic game selection model, and determining a red-blue action set of the combat action selection model; determining a battlefield state transition probability of the battlefield action selection model; obtaining a red-blue confrontation strategy according to the red-blue action set and the state transition probability; constructing a state utility function in the battle action selection model; and determining the optimal strategy of the red and blue parties through the zero-sum game. Compared with the prior art, the method not only can dynamically evaluate the advantages and disadvantages of different tactical selections, but also can preferably select an appropriate action sequence and select an optimal action decision in a complex game environment of friend or foe in combination with intelligence update and battlefield feedback.

The application relates to an equatorial ionospheric irregularity automatic detection method and system, and belongs to the field of artificial intelligence. The method comprises the following steps: downloading GOLD satellite data, pre-processing and labeling, and constructing an original data set; inputting the original data set into a deep learning network model for training to obtain a trained deep learning network model; calculating the performance index of the trained deep learning network model, and outputting an equatorial ionospheric irregularity identification result. The application solves the problem of low accuracy in identifying and detecting equatorial ionospheric irregularities caused by the influence of factors such as slant range, background airglow intensity, instrument characteristics and threshold.

The application provides a vehicle lamp fault detection system and a vehicle, and relates to the technical field of vehicle lamp control. The system comprises a photosensitive module, a thermosensitive module, an electric signal detection module and a vehicle lamp control module. The photosensitive module is used for detecting a light intensity signal of a target vehicle lamp. The thermosensitive module is used for detecting a temperature signal of the target vehicle lamp. The electric signal detection module is used for detecting an electric signal when the target vehicle lamp is working. The vehicle lamp control module performs fault type detection of the target vehicle lamp based on the light intensity signal, the temperature signal and the electric signal. Through multi-dimensional signal acquisition of the photosensitive module, the thermosensitive module and the electric signal detection module, the application can realize accurate diagnosis of the fault type of the vehicle lamp, break through the limitation of traditional binary detection, can distinguish the fault type of the vehicle lamp, and thus significantly improve the vehicle lamp fault detection effect.

This invention discloses a method for sensing the operational status of a distribution network, relating to the field of distribution network monitoring and intelligent operation and maintenance. The method includes: synchronous acquisition of multi-source ubiquitous data to construct a low-cost ubiquitous sensing network; edge-side data preprocessing and lightweight analysis; topology-adaptive multi-source data fusion: constructing a distribution network topology map based on neural networks and mining the spatial and temporal correlations of multi-source data; multi-dimensional quantitative assessment of operational status: constructing a multi-dimensional status assessment system; precise fault location and hierarchical early warning: fusing multi-terminal information to achieve precise fault location; and online model iteration and closed-loop optimization. This distribution network operational status sensing method, by employing a low-cost ubiquitous sensing network combined with dedicated sensors and non-intrusive monitoring, covers the entire distribution network—from source to grid to load to storage—solving the problems of large blind spots and high deployment costs associated with traditional sensing methods. It achieves effective monitoring of end nodes and branch lines, realizing the goal of full-area, blind-spot-free sensing.

The invention relates to the technical field of power distribution network fault line selection, and discloses a fault line selection method, system and equipment based on a bus zero sequence current neutral point ungrounded device and a medium. Fault judgment is carried out according to phase comparison of the zero-sequence current of the two buses associated with each line; and for the edge distribution line, calculating the zero-sequence current of the edge line, respectively comparing the zero-sequence current with the starting current, and carrying out fault judgment by combining the phase and amplitude comparison of the bus zero-sequence current. Based on the bus zero-sequence current formed by converging the line zero-sequence current, the fault line identification method based on phase comparison is provided according to the access mode of the bus zero-sequence current, the method does not depend on zero-sequence current information of all lines in the system and does not need to test alternate cutting lines; whether the line is a fault line or not can be quickly judged only depending on the bus zero-sequence current information related to the line.

The invention discloses a coal mine goaf type rapid discrimination method for mine disasters, and relates to the technical field of geophysical exploration. According to the rapid discrimination method, goaf type classification is carried out on measurement points in a known non-water-containing goaf, a water-containing goaf and a non-goaf range, a mapping relation between different parameters of each measurement point and goaf types is established, and a model training sample is formed. SPSS software is utilized to carry out Bayes modeling training on the established samples, and Bayes discrimination models of different types of goaf areas and three parameters are constructed. And performing Bayes model training by using SPSS software, and when the reestimation accuracy of a training sample exceeds 80%, inputting the optimal parameter combination calculated by the measuring points of the whole region into the Bayes model to realize rapid judgment of the goaf type of the measuring points of the whole region.

The invention discloses a fluid type identification method and system based on a nuclear magnetic resonance logging dual-acquisition mode, and the method comprises the steps: obtaining dual-TW logging data and dual-TE logging number according to a combined observation mode of nuclear magnetic resonance logging; based on a nuclear magnetic resonance relaxation theory, according to nuclear magnetic resonance logging data in a double-TW acquisition mode, obtaining a differential spectrum delta T2 and a differential spectrum porosity delta phi; according to the nuclear magnetic resonance logging data in the double-TE acquisition mode, T2 distribution under the double-echo interval is obtained, and the geometric mean difference value delta T2LM of the movable fluid under the T2 distribution under the double-echo interval is calculated; and according to the differential spectrum porosity delta phi and the geometric mean value difference delta T2LM, constructing a comprehensive fluid indication factor of a reservoir fluid identification standard, and carrying out comprehensive identification on the fluid type of the reservoir. According to the invention, two observation modes of nuclear magnetic resonance double TW and double TE are integrated together, the amount of observed data is large, the amount of information is rich, and the fluid type can be rapidly and visually identified.

The invention belongs to the technical field of electric power fault identification, and particularly relates to an electric power system steel-cored aluminum strand failure analysis method based on image processing, which comprises the following steps: acquiring wire surface data information; acquiring internal data information of the wire; the wire surface data information and the wire internal data information are sent to an analysis terminal; comparing the data with stored data in each characteristic standard database to obtain each characteristic failure early warning threshold value; according to the method provided by the invention, when the lead fails, the failure type of the lead can be quickly judged, the accident reason can be quickly found out, the cost is low, the detection efficiency is high, and a feasible analysis scheme is provided for different environments.

The invention relates to the technical field of power equipment fault positioning, in particular to a power equipment fault positioning method based on a graph neural network, which comprises the following steps of: establishing a Chain-GNN model to be matched with hardware deployment to pre-process data during normal operation of a circuit, and determining normal operation baseline data; step 2, setting an abnormal score threshold and a bidirectional traveling wave fault rule base, and oppositely and synchronously injecting bidirectional traveling waves into a preliminarily alarmed line, and step 4, after a fault point is confirmed, ensuring normal operation of other equipment; after the sublines are erected, first-aid repair is carried out, and after first-aid repair is completed, all the sublines are disconnected, and original circuit connection is recovered; comparing the normal operation baseline data in the step 1 to determine whether the line recovers normal operation, if so, entering a step 6, and if not, returning to the step 3 to re-determine the fault point; and the Chain-GNN model can automatically archive the whole process data of the fault processing at this time.

The invention discloses a method for determining 13 metabolic regulation core molecules under a same chromatographic system and the construction of a detection discrimination model thereof; 13 metabolic regulation core molecules are methionine, homocysteine, cysteine, vitamin B2, 5-methyltetrahydrofolate, folic acid, pyridoxal phosphate, 4-pyridoxic acid, methylmalonic acid, betaine, choline, S-adenosylmethionine, S-adenosylhomocysteine. Low concentration acetic acid is used as a mobile phase additive in the detection system of the present invention, so that choline with very high polarity can be well retained on the chromatographic column, the response is also greatly increased, and the S-adenosylmethionine matrix effect is greatly reduced by WCX enrichment, and trace S-adenosylmethionine in serum can be accurately determined, further combined with specific chromatographic column selection, etc., it is achieved that 13 compounds are accurately determined under the same chromatographic system, and a discrimination model is established simultaneously, and a rapid interpretation of the clinical determination results of the 13 compounds is achieved.

The invention discloses a layered water taking necessity judgment method for a water taking port of a water diversion project. The method comprises the following steps: step 1, collecting water diversion engineering parameters and environmental status data of a water source area, a water delivery line area and a water receiving area; 2, water temperature along the way of the water delivery channel is predicted; 3, predicting the water temperature of the water supplementing rivers and lakes in the water receiving area; and 4, judging the acceptable degree of the water temperature change of the ecological sensitive target of the water supplementing river and lake in the water receiving area. The method has the advantage that the water temperature layering necessity can be quickly identified.

The invention discloses a flexible monitoring sensor for actively sounding fish and a signal analysis method, and relates to the technical field of aquaculture, and the flexible monitoring sensor mainly comprises a flexible substrate, an interdigital electrode arranged on the substrate, and a piezoresistive sensitive layer covering the electrode. The sensitive layer is made of a PDMS and graphene composite material, and the surface of the sensitive layer is provided with a regularly arranged micro-pyramid structure array. The sensor is attached to the body surface of a fish sounding part, the micro-pyramid structure responds to body surface sound vibration in a preset frequency range to generate deformation, and the sound vibration is converted into a resistance change signal between the interdigital electrodes by utilizing the piezoresistive effect of the composite material. Through the design of the PDMS-graphene composite material and the surface micro-pyramid structure, high-fidelity and direct capture of body surface vibration signals generated by initiative sounding of individual fishes is realized, and the problems of group sound mixing and weak signal relevance in a traditional method are effectively avoided.

The application discloses a frequency stability quantitative discrimination method and system suitable for different scenes and belongs to the technical field of power systems. The method comprises the following steps: obtaining model parameters of a target frequency response physical model of a target power system; identifying the model parameters based on a discrimination model to obtain a frequency response curve of the target frequency response physical model for the target power system; and quantifying the frequency stability of the target power system by discriminating the frequency response curve. The application can identify the model parameters of the frequency response physical model to quickly discriminate the discrimination frequency response curve of the power system, thereby quantifying the frequency stability of the power system.

The invention discloses a crude oil production layer judgment method based on a neural network learning model, and belongs to the technical field of oil reservoir exploration, and the method comprises the following steps: S1, obtaining crude oil samples of different oil production layer sections in a research block; s2, setting gas chromatographic analysis conditions; s3, acquiring a chromatographic curve graph; s4, performing homogenization processing on the chromatographic curve graph to obtain a label data set; s5, establishing an image learning network model, and inputting the training set into the image learning network model for training; s6, adjusting the trained image learning network model through the verification set; and S7, extracting a crude oil sample of an unknown output interval, carrying out gas chromatographic analysis test, carrying out homogenization treatment on a test result, and finally inputting the image learning network model to judge the crude oil source of the unknown output interval. According to the method, on the basis of the neural network, by performing feature matching on the chromatogram of the crude oil sample of the production well of the unknown oil production layer section, the source of the crude oil of the unknown production layer can be quickly identified with high precision.

The invention discloses a voice processing method of an early education robot and the early education robot, and relates to the technical field of voice processing, and the method comprises the steps: receiving a voice instruction inputted by a user for recognition, and when the voice instruction is recognized as a teaching instruction, displaying the teaching content in the teaching instruction to the user; after the teaching content is displayed, receiving feedback voice of a user and collecting a feedback image of the user; the feedback voice and the feedback image are subjected to content consistency verification, after the consistency verification is passed, whether the feedback voice and the feedback image conform to the teaching content is judged, an early education processing result is obtained and displayed to the user, and the early education processing result comprises feedback correctness or feedback error. The technical problems that in the prior art, an early education robot is large in voice recognition computing power consumption and poor in early education effect are solved.

The invention relates to the technical field of pavement health monitoring, in particular to a double-layer continuous reinforcement pavement slab health state vibration sensing method and device based on a vibration sensing performance state monitoring and diagnosis system composed of an impact energy signal monitoring system and a mechanical vibration signal monitoring system. Two application paths of continuous multi-plate short-time rapid monitoring and diagnosis and important plate long-time stable monitoring and diagnosis are constructed, so that the manpower and time cost is reduced, and the bearing capacity of the continuous multi-plate overall composite structure is efficiently, accurately and rapidly judged; and the limitation of time and place is broken through, and long-term stable monitoring and diagnosis of the health state of the important plate are realized.

The invention relates to the field of mineral exploration and evaluation, in particular to a method for calculating mineral formation temperature based on chlorite spectrum. The invention discloses a method for calculating mineral formation temperature based on chlorite spectrum. The method comprises the following steps: S1, recording related information of each chlorite sample; s2, the Fe-OH wavelength value of each chlorite sample is obtained; s3, respectively calculating the mineralization temperature and the content of main elements of the chlorite sample; and S4, according to the mineralization temperature and the main element content information obtained in the S3, determining the mineralization environment and classification of the corresponding chlorite sample. According to the method disclosed by the invention, the conventional calculation of the chlorite mineral temperature is converted from the traditional calculation of obtaining data results of major elements based on laboratory electronic probe analysis and test into the calculation of obtaining characteristic wavelength parameters based on field on-site short-wave infrared spectrum test; and convenience is provided for rapid discrimination of a mineral formation environment and an alteration zone where the mineral formation environment is located, and the working efficiency of prospecting is greatly improved.

The invention discloses an endogenous marker for bisphenol substance exposure and a method for evaluating exposure toxicity effect, which are characterized in that trace and abundance serum metabolites are systematically represented by coupling non-targeted and targeted metabonomics means, so that the coverage range, the confirmation degree and the sensitivity of metabolic marker recognition are improved; the screening of bisphenol substance exposure metabolism markers with strong specificity and high sensitivity is facilitated. According to the method, the high specificity and sensitivity of the histidine / kynurenine ratio in evaluating the exposure toxicity effect of the bisphenol substances are discovered, and a classification model capable of qualitatively predicting the exposure of the bisphenol substances is constructed based on a support vector machine learning algorithm; and a reliable model tool is provided for realizing rapid discrimination of the exposure toxicity effect of the bisphenol substances.

The invention discloses a hydrogen production mode identification method and system based on a tree classifier, and belongs to the technical field of hydrogen analysis, the hydrogen production mode identification method based on the tree classifier comprises the following steps: S1, extracting the original component content of a hydrogen sample; s2, constructing engineering features based on the original component content, wherein the engineering features comprise a component proportion, a composition entropy, logarithm transformation, a component interaction item and a principal component; and S3, hydrogen production mode identification: taking the engineering characteristics as input, synthesizing minority class oversampling for relieving class imbalance, and performing hydrogen production mode judgment through a tree classifier. By adopting the technical scheme, the hydrogen production mode can be quickly analyzed by utilizing the tree classifier based on the original component content and engineering characteristics of the hydrogen sample.