33results about How to "The method is accurate and efficient" patented technology

The invention provides a light field camera calibration method for three-dimensional topography measurement, and the method comprises the steps: calibrating calibration plates at different positions in a space and corresponding light field original images, and obtaining a corresponding relation between a light field parallax image and three-dimensional space information; shooting a plurality of defocusing soft light pure color calibration plates by using a light field camera to obtain a light field white image; performing calculating according to the white image of the light field camera to obtain a vignetting removing matrix; carrying out iterative calculation to obtain a light field camera micro-lens sub-pixel-level center coordinate matrix; enabling the light field camera to shoot a plurality of dot calibration plates with known three-dimensional space positions and perform vignetting removal processing; and establishing a light field mathematical model from the three-dimensional coordinates to the parallax, and performing fitting calculation according to the light field three-dimensional imaging rule and the dot three-dimensional space information to obtain a dot center coordinate and a parallax value corresponding to dot calibration. According to the invention, a light field parallax image can be efficiently and accurately converted into three-dimensional space informationwithout main lens distortion.

The invention relates to a method for predicting gas well bottom flow pressure, which belongs to the fields of gas well dynamic analysis and working system optimization. Including (1) measuring or collecting the casing pressure, formation depth, relative density of natural gas, reservoir temperature, gas production, natural gas compression factor, and measured gas well bottomhole pressure parameters of a certain block; (2) making a gas drilling well Underflow pressure SVM predictor; (3) Input the measured or collected parameters into the SVM predictor for self-learning to determine the key parameters of the SVM predictor, namely the kernel function parameters , insensitivity coefficient and penalty factor (4) Input the casing pressure of the well to be predicted, the middle depth of the formation, the relative density of natural gas, the reservoir temperature, the gas production rate, and the compressibility factor of natural gas into the SVM predictor to obtain the prediction result of the bottomhole flowing pressure of the gas well to be predicted. The method is accurate and efficient, and it is a new practical method for predicting the bottom hole pressure of gas wells.

The present disclosure generally relates to DNA-based data storage. An exemplary method for storing input data on nucleic acid comprises: converting the input data into a set of nucleotide sequences and synthesizing a set of nucleic acids comprising the set of nucleotide sequences. The converting comprises a data processing step comprising converting the input data into a binary string, and a nucleotide encoding step comprising converting the binary string using a 5-bit transcoding framework to obtain the set of nucleotide sequences.

The invention discloses a shelf commodity layering method based on deep learning, and relates to the technical field of artificial intelligence and machine vision deep learning. The method comprises the following steps: S1, obtaining shelf pictures with different angles, different illumination and different resolutions; S2, training a commodity detection model; S3, training a shelf layer segmentation model; S4, detecting the shelf image to obtain a position box and category information of the commodity; S5, carrying out shelf layer segmentation on the shelf image to obtain a shelf layer area;S6, obtaining a minimum enclosing rectangle of the shelf layer, and then obtaining a shelf line; and S7, obtaining a central point of the commodity position box, and then judging a shelf layer where the commodity position box is located. According to the method, commodities on the shelf can be automatically layered to obtain the number of shelf layers where the commodities are located, so that whether the commodities are displayed at gold positions can be judged. The method is efficient and accurate, and replaces a low-efficiency and error-prone manual mode.

The invention discloses a device and method for while-drilling detection of a formation interface through azimuthal resistivity. The device comprises a non-magnetic drill collar, a circuit unit, a plurality of antennas and antenna tuning units corresponding to the antennas, wherein the non-magnetic drill collar serves as a carrier of the whole device and is provided with a compensation resistivity detection point. The circuit unit is installed inside the non-magnetic drill collar. The antenna tuning units are distributed on the outer circular surface of the non-magnetic drill collar and connected with the circuit unit. The method includes common resistivity measurement and azimuthal resistivity measurement, the distance between the device and the formation interface is calculated through the azimuthal resistivity measured value, previous real-time geological data are provided for well drilling, and the purpose of optimizing the well drilling quality is realized.

The invention discloses an intelligent substation process layer physical link fault diagnosis method, and relates to the field of intelligent substations. The method comprises the steps of analyzing an SCD file, and obtaining related information of a logic link of a process layer of a whole station and switch configuration information; Obtaining a path node set of a whole station process layer logic link; Analyzing the chain scission signal, obtaining information related to the chain scission signal, and preliminarily screening out a path node set possibly having problems based on the information; Traversing other non-triggered chain scission signals; based on process layer message information related to the triggered chain scission signal, combining with network message analysis; path nodes possibly having problems are further eliminated based on the starting point port power information, the end point port power information and other means of the logic link related to the triggered broken link signal, and the rest path node sets are ranked according to the probability to obtain a final diagnosis result. On the basis of probability statistics, the fault diagnosis range is furtherreduced, and the positioning accuracy is enhanced.

The invention discloses a method for selecting nervous necrosis virus resistant individuals of epinephelus lanceolatus. The method comprises the following steps: (1) obtaining SNP site information of the epinephelus lanceolatus; (2) analyzing the disease resistance weight value of the SNP marker and selecting the SNP marker; (3) obtaining candidate epinephelus lanceolatus individual SNP site information and selecting individuals with nervous necrosis virus resistance; and (4) detecting the selection accuracy of disease-resistant individuals. According to the method, the SNP marker with the maximum nervous necrosis virus resisting weight is screened from the SNP markers of the epinephelus lanceolatus, so that a method capable of selecting nervous necrosis virus resisting individuals is established.

The invention discloses a fault self-healing method and device, equipment and a storage medium, and the method comprises the steps: extracting a restoration operation record and corresponding alarm information, carrying out the parameterization of the restoration operation record and the alarm information to obtain a plurality of parameters, and carrying out the instantiation of the plurality of parameters to obtain a plurality of variables; and establishing a knowledge model and a self-healing script according to the plurality of variables, obtaining a knowledge question and answer library containing a mapping relationship between the knowledge model and the self-healing script, and selecting the corresponding self-healing script from the knowledge model according to the description of the fault to perform fault processing. According to the method, based on the fact that operation and maintenance personnel perform first-time repair operation on a certain alarm, a repair operation logis templated, then in the automatic self-healing process, template parameters are instantiated, a self-healing script template and an alarm knowledge question and answer library are established, and the first-time repair method is popularized and generalized; therefore, automatic self-healing of the alarm is realized, and the method is simple, efficient and accurate.

The invention discloses a device and method for fast measuring of heat capacity of a thin film material. In particular, the device includes a control device, a clock synchronizer, a flat peak laser device, a fast thermometer and a heat capacity output device; the control device is in signal connection with the clock synchronizer, and the clock synchronizer is in signal connection with the flat peak laser device and the fast thermometer; in a working state, the control device sends a starting signal to the clock synchronizer, the flat peak laser device and the fast thermometer are in coordinated interaction, and the flat peak laser device irradiates lasers having the energy space distribution of flat peaks to the sample surface; at the same time, the fast thermometer captures the surface temperature of a sample at a certain time point at the heating process of the sample, the test data are inputted into a heat capacity output device, and required heat capacity parameters are obtained. The device has simple structure, the method is high in efficiency and accurate, and reliable parameter data can be provided for setting of various thermophysical properties of ultra-thin semiconductorthin films.

The invention discloses an automatic laser shot device, a shot point identification method and an automatic shot point correction method. The automatic laser shot device comprises an automatic shot control system and an automatic shot point identification system. The automatic shot control system comprises a first wireless module, a matrix keyboard, a single chip microcomputer, a serve motor control table, a laser gun and the like. The single chip microcomputer is connected with the matrix keyboard and controlled by the matrix keyboard. The serve motor control table is connected with the matrix keyboard and controlled by the matrix keyboard. The laser gun is located on the serve motor control table. Based on the gray value of a pixel, the shot point identification method adopts a non-neighboring-group identification method. According to the automatic shot point correction method, the serve motor control table is adjusted according to the difference value of the coordinates of a shot point and the coordinates of a target, so that shot deviation is corrected. The automatic laser shot device is low in cost and danger, and high in objectivity and precision. The non-neighboring-group identification method based on the gray value of the pixel is adopted to determine the shot point, so that the method is more precise and more efficient, and the precision the method is up to 0.88%.

The invention discloses a flexible DC power distribution network fault positioning scheme applied to a distribution network terminal, and the scheme comprises the steps: calculating the fault break variables of two ends of a monitoring point in a short time window after a fault; calculating a fault signal slope; identifying internal and external faults; obtaining the moment when a fault wave arrives at a monitoring point through empirical mode decomposition; and completing fault distance measurement. Current at two ends of a monitoring point is acquired through a distribution network terminal, current abrupt change in a short time window is calculated, fault abrupt change signals are extracted through a low-pass filter, current abrupt change slope is calculated, after the internal fault is determined, the fault signal is subjected to empirical mode decomposition to calculate the moment when the signal arrives at the monitoring point, and finally fault distance measurement is completed according to the arrival time, so that the problems of high protection configuration requirement, complex network, and large matching difficulty of system control, fault identification and protection of the flexible direct current power distribution network ring topology structure which is widely applied at present are solved.

The invention discloses an accurate and rapid detection method for pesticide residues in fruits. The accurate and rapid detection method comprises the following steps of: (1) carrying out pulp refining on fruits by using a refiner, and adding water and acetonitrile into the pulp for dispersion; (2) adding a magnetic nano-material into the dispersed pulp and carrying out continuous oscillation extraction; (3) separating the magnetic nano-material for extracting and adsorbing the pesticide from the fruit solution by using a magnet; (4) carrying out ultrasonic elution on the pesticide adsorbed by the magnetic nano material by adopting a small amount of organic reagent, and adding anhydrous sodium sulfate into eluent for dehydration; (5) adding an internal standard compound solution; and (6) analyzing and detecting a to-be-detected liquid by using a gas chromatograph-mass spectrometer. The accurate and rapid detection method for pesticide residues in the fruits has the advantages of being short in operation time, low in organic reagent consumption, low in analysis cost, accurate in analysis result and easy to popularize.

The invention relates to a method for identifying the variety of a plant, and particularly relates to a method for quickly and accurately identifying aromatic and non-aromatic plantlets at the early growth period of paddy rice. The method comprises the following steps of a step 1, preparation of a sample: grinding a blade at the heading initial period of the paddy rice by using liquid nitrogen, weighing 20mg to 50mg of the amount of the sample, and putting into a headspace sample bottle for later use; a step 2, making the sample enter an automatic solid-phase micro-extraction stage, wherein the extraction is divided into three stages of 1), a first stage: the preheating is maintained for 2 to 5 minutes at 80 DEG C; 2), a second stage: the step that an extraction head is at an interval of shaking for 20 seconds and stopping for 5 seconds at 80 DEG C and at the rotational speed of 250rpm / min is adopted, the depth at which the extraction head is inserted into a sample feeding bottle is 22 millimeters, and the extraction stage lasts for 10 to 15 minutes; 3), a third stage: an extract is enabled to directly enter to a sample feeding opening of a gas chromatograph-mass spectrometer; the sample feeding time is 1 minute; the sampling feeding temperature is 260 DEG C; a step 3, making the sample enter to the chromatograph-mass spectrometer, and carrying out analysis.

The invention discloses a subway fault association recommendation method based on prior weight and a multilayer TFP algorithm, and relates to the technical field of tractive power supply systems in rail transit. The method comprises the following steps: firstly, analyzing contact network fault data collected on site; coding all fault types according to corresponding coding rules; dividing the fault data into three layers of fault databases; wherein the layers comprise a fault type layer, a fault equipment layer and a fault attribute layer; aiming at hierarchical characteristics for data, formulating corresponding weights according to the on-site conditions of the subway faults, and calculating the weights of all the layers; and generating a corresponding association rule path, mining top-kmost frequent modes on the basis of the generated strong association rule base to achieve the purpose of reducing the number of modes returned by mining, and finally returning k strong association fault rules with the highest support degree to the user.

The invention discloses a metal material surface microcrack identification and in-situ quantitative statistical distribution characterization method, which comprises the steps of metal material surface preparation, determination of gray threshold values of microcracks, inclusions and other feature structures, setting of energy disperse spectroscopy analysis parameters, scanning electron microscope full-automatic acquisition of feature structure images in a large-size range, and in-situ quantitative statistical distribution characterization of microcracks in a large-size range. Feature extraction and energy spectrum analysis, screening and identification of microcracks, and in-situ quantitative statistical distribution characterization of the number, the area fraction, the length and the like. According to the characteristic that grinding materials and polishing material components can be embedded into microcracks on the metal surface in the metallographic sample preparation process, data mining and screening are carried out by extracting a back scattering electron image of a metallographic surface characteristic object and combining an energy spectrum analysis result and morphological quantitative parameters; according to the method, the inclusion is analyzed, meanwhile, rapid positioning, recognition and quantitative statistics of the surface microcracks are achieved, the method is accurate and efficient, and meanwhile the detection resolution of the microcracks is greatly improved and can be as low as the micron level.

The invention belongs to the technical field of pharmaceutical preparations, and in particular relates to Xianglian granules, Xianglian tablets, their preparation method and application. The invention provides a Xianglian granule, which is characterized in that the weight content of Yuhuanglian containing berberine hydrochloride C20H17NO4·HCl should not be less than 6.0%. The invention also provides the preparation method and application of the Xianglian granule. For example, the active ingredient content of the Xianglian tablets prepared by using the Xianglian granules provided by the invention is significantly increased. In every 0.3g of Xianglian Tablets, the amount of Yuhuanglian (calculated as berberine hydrochloride C20H17NO4 HCl) is more than 18.0mg, which is higher than the existing standard, which is conducive to further improving and improving the therapeutic effect, and avoiding repeated repeated use of patients. Inconvenience from taking medication. The preparation method of the xianglian granule of the invention is simple and convenient, and can be made into dry granules at one time, which reduces the heating temperature of medicines, shortens the heating time, improves the content of active ingredients, and increases the product yield.

The invention provides an HPLC method for measuring three kinds of active ingredients in snow gallbladder, the active ingredients are snow gallbladder, snow gallbladder and oleanolic acid, comprising the following steps: a, taking the snow gallbladder to be tested Powder, extract with ethanol, filter, prepare need testing solution; b, get gentianin, gentalin, oleanolic acid reference substance, mix, add ethanol to dissolve, prepare reference substance solution; c, respectively The test solution and the reference solution are injected into a high-performance liquid chromatograph for detection; d, the contents of the three components in the snow gall are calculated according to the test results. The method of the present invention can simultaneously determine the three components of gentamidin, gentamidin and oleanolic acid, the method is efficient, accurate and has good reproducibility, and provides a basis for the systematic evaluation and quality control of gentamicin, and can more comprehensively Guarantee the quality of snow gall medicinal materials.

The invention relates to a method for detecting puff-by-puff stability of a blueberry-flavored heated cigarette by using a nuclear magnetic resonance hydrogen spectrum, which comprises the following steps of: smoking by using a rotary puff-by-puff smoking machine by taking the blueberry-flavored heated cigarette as an object, collecting aerosol of each puff of heated cigarette, extracting and concentrating by using a solvent, and detecting the puff-by-puff stability of the blueberry-flavored heated cigarette by using a deuterated reagent solvent. Nuclear magnetic hydrogen spectrums of corresponding samples are obtained through the nuclear magnetic resonance technology, and the similarity of spectrograms of all the samples is compared through information such as chemical shift, peak height and peak area on nuclear magnetic resonance hydrogen spectrum fingerprints, so that the puff-by-puff stability of the blueberry-flavored heated cigarettes is measured. The method is accurate and efficient, and is suitable for puff-by-puff stability analysis and detection of the blueberry-flavored heated cigarettes.

The invention discloses an early warning threshold setting method based on a moving interval relevance trend. The stable and sensitive relevance relationship between the monitoring data is utilized; arelevance trend line and a structural response normal value envelope line are obtained by adopting relevance movement interval probability statistical characteristics, and then a red early warning limit value is obtained according to a certain guarantee rate on the basis of the normal value envelope line based on a reliability theory, so that a 'two-line three-area ' relevance early warning mechanism is established, and relevance early warning of the system is realized. Compared with a fixed and single time-history early-warning threshold of structural response data, relevance trend early-warning belongs to a comprehensive early-warning system, and massive monitoring data of the bridge health monitoring system can be fully utilized and the internal change rule of the monitoring data can be mastered. The relevance trend early warning threshold value changes along with the change of relevance characteristics, and the dynamic threshold value has higher precision and sensitivity, so thatthe system can give an early warning more scientifically in real time. The method has been realized in a programmed manner, is simple and quick to operate, and has wide engineering application value.

The invention discloses a download management system and method based on big data, and belongs to the technical field of big data download management. The system includes a material library, a distance capture module, an intelligent selection module, a visual evaluation module, a download management module, and an output feedback module; the output end of the material library is connected to the input end of the distance capture module, the intelligent selection module, and the visual evaluation module connected; the output end of the distance grabbing module is connected with the input end of the intelligent selection module; the output end of the intelligent selection module is connected with the input end of the visual evaluation module; the output end of the visual evaluation module is connected with the download The input end of the management module is connected; the output end of the download management module is connected with the output end of the output feedback module. The invention also provides a download management method based on big data, which realizes the automation of color matching of design files and solves the problem of redundant downloads in the download process.

The invention discloses a distributed energy storage user mining method and device, computer equipment and a storage medium. The method comprises the following steps: collecting a data set influencing energy storage construction and subjecting to importance grading; performing quantization processing on the data set; constructing an energy storage evaluation model based on the quantized data set; and evaluating the potential level of energy storage construction of the user based on the energy storage evaluation model. According to the invention, common data sets influencing energy storage construction are provided on the basis of consideration and analysis of different collected and investigated data of users, grading is carried out according to importance, each data set is subjected to quantitative processing, and the potential level of energy storage construction of the users is obtained through analysis and evaluation of the data. The invention is accurate and efficient in evaluation result, has relatively high universality and practicability, and is of great significance in mining the potential of energy storage customers and guiding the energy storage installation scale.

The invention provides a prediction method for aerodynamic characteristics of actual gaseous environment of Mars atmosphere. The method is characterized by calculating a flow field under hypersonic velocity and zero-incidence of a Mars probe by using a chemical non-equilibrium model, extracting temperature at the position of normal shock waves and the mass percents of gas components from the flow field, and obtaining the equivalent specific heat ratio gamma eff value of mixed gas through thermodynamic relations and weighted averaging of the mass percents; inputting the equivalent specific heat ratio gamma eff value into a perfect gas model as a known parameter and simulating the aerodynamic characteristics of the Mars probe in actual gaseous environment of Mars by using a CFD numerical simulation method. The accuracy and reliability of the method are provided by typical example of the American Phoenix Mars lander. The method can rapidly predict the aerodynamic force performance under real gas effect of Mars atmosphere with accuracy and efficiency.

The invention relates to a method for identifying the variety of a plant, and particularly relates to a method for quickly and accurately identifying aromatic and non-aromatic plantlets at the early growth period of paddy rice. The method comprises the following steps of a step 1, preparation of a sample: grinding a blade at the heading initial period of the paddy rice by using liquid nitrogen, weighing 20mg to 50mg of the amount of the sample, and putting into a headspace sample bottle for later use; a step 2, making the sample enter an automatic solid-phase micro-extraction stage, wherein the extraction is divided into three stages of 1), a first stage: the preheating is maintained for 2 to 5 minutes at 80 DEG C; 2), a second stage: the step that an extraction head is at an interval of shaking for 20 seconds and stopping for 5 seconds at 80 DEG C and at the rotational speed of 250rpm / min is adopted, the depth at which the extraction head is inserted into a sample feeding bottle is 22 millimeters, and the extraction stage lasts for 10 to 15 minutes; 3), a third stage: an extract is enabled to directly enter to a sample feeding opening of a gas chromatograph-mass spectrometer; the sample feeding time is 1 minute; the sampling feeding temperature is 260 DEG C; a step 3, making the sample enter to the chromatograph-mass spectrometer, and carrying out analysis.