72 results about "Quantitative classification" patented technology

The invention discloses a reservoir classification method based on rock electricity parameters, and belongs to the field of hydrocarbon reservoir evaluation. The method is based on rock electricity experiment theoretical model deduction, a theoretical value range and a value trend of rock electricity experiment data of reservoirs under different pore structures are formed, and reservoir pore structure subareas on a rock electricity data F-phi plate are formed; and corresponding electrical logging data are utilized in combination with rock electricity experiment data and mercury injection capillary pressure curve data to determine rock electricity method quantitative classifications, and the method at least includes: Step 1) establishing a rock electricity method classification bases; Step 2) forming a classification plate; Step 3) establishing a quantitative classification evaluation basis; and Step 4) determining classification grades of the reservoirs. According to the reservoir classification method based on rock electricity parameters in the invention, by checking distribution of the rock electricity data on the F-phi plate and discovering that rock samples of reservoirs of different classes fall on distinguishable different subareas on the F-phi plate, continuous evaluation knowledge can be provided, and a verification effect is good.

A wearable device is disclosed. From one or more sensors sensing health information of a user, a wearable device automatically obtains at least one health related measurement. The wearable device computes at least one of a vitality index and a health index based on at least one health related measurement and classifies, based on the vitality and health indices, the health of the user into some predetermined health condition classes. The wearable device then transmits the classified health condition class(es), via network connection, to a health service engine and receives health assistance information that is adaptively determined in accordance with the health condition class(es).

A real time health monitor deployed on a mobile device is disclosed. From one or more sensors sensing health information of a user, the real time health monitor automatically obtains at least one health related measurement. The real time health monitor computes at least one of a vitality index and a health index based on at least one health related measurement and classifies, based on the vitality and health indices, the health of the user into some predetermined health condition classes. The real time health monitor then transmits the classified health condition class(es), via network connection, to a health service engine and receives health assistance information that is adaptively determined in accordance with the health condition class(es).

The invention provides a method of volcanic rock oil deposit effective reservoir quantitative classification. The method comprises the following steps of obtaining reservoir quality parameters of a reference volcanic rock reservoir; utilizing a K-means dividing and clustering method to conduct clustering analysis on the reservoir quality parameters to build a classification standard of the volcanic rock oil deposit effective reservoir; based on the classification standard of the volcanic rock oil deposit effective reservoir, utilizing a linear judgment function method to build a linear judgment function of various effective reservoirs; based on the linear judgment function, determining the type of a target volcanic rock reservoir. According to the technical scheme, technological support can be further provided for oil deposit efficient development and deployment, and favorable reference can also be provided for perfecting the classification evaluation standard and the technological system of the volcanic rock oil deposit effective reservoir.

The invention relates to a classification method for chest CT (Computed Tomography) images based on deep learning. In the method, characteristics of the chest CT images are extracted and analyzed by use of a relatively mature deep learning method at present, and a model structure is trained by use of an existing image data set to implement quantitative classification of multiple indexes affectingdisease judgment. Therefore, a quantified physiological index classification result is provided for a diagnosis doctor, and the work load and the analytical judgment difficulty of an occupational physician are reduced.

The invention discloses a large-scale bridge engineering risk assessment method. In the implementation of the method, a work-risk decomposition structure WBS-RBS is adopted to carry out bidirectionaldecomposition on a construction project according to a construction process and risk types, a risk decomposition matrix RBM is constructed, opinion investigation is carried out through a plurality ofexperts in the industry to know risk characteristics, and an initial risk value r is calculated through quantitative classification of risk probability and loss; when the correction weight omega of the risk source is determined, performing risk relative importance judgment and opinion consistency inspection by adopting the FAHP, and performing weighted correction on the risk value; summarizing thesurvey data of the plurality of experts and calculating to obtain a comprehensive risk value; and determining the risk severity level according to the magnitude of the multi-level comparison comprehensive risk value and a risk evaluation index threshold value, so that the evaluation result conforms to the engineering practice. The method can accurately, objectively and systematically identify andevaluate the risk, and can lay a foundation for the safety and high quality of large-scale bridge construction.

The invention discloses a normal-pressure shale gas enrichment and high production target optimization method based on three-factor gas control. The normal-pressure shale gas enrichment and high production target optimization method comprises the following steps that (1) on the basis of single well dissection of a normal-pressure shale gas well and analysis of target enrichment and high production laws, the three main control factors affecting normal-pressure shale gas enrichment and high production are determined; (2) by taking the normal-pressure shale gas enrichment and high production main control factors as a main line, parameters highly related with the main control factors are optimized as analysis parameters; (3) different weights are given to the analysis parameters, and a normal-pressure shale gas enrichment and high production target optimization parameter list is established; and (4) an analysis target is subjected to quantitative classification analysis according to a target comprehensive evaluation value, and thus optimization of an exploitable area and well position deployment are guided. The normal-pressure shale gas enrichment and high production target optimization method has the beneficial effects that normal-pressure shale gas exploration of the Wufeng formation and the Long maxi formation in a southeast Chongqing district is guided effectively, and the exploration success rate is increased.

The invention discloses a user data processing system which comprises a user data capture module, a data integration processing module, a quantitative classification statistics module, a residence track analysis module, a store resource database, a store resource updating module, a management server and a guide push initiation module. The user data capturing terminal is respectively connected withthe quantitative classification statistics module and the data integration processing module; wherein the quantitative classification statistics module is connected with the comprehensive quantitative evaluation module through the resident trajectory analysis module, the commodity resource database is connected with the comprehensive quantitative evaluation module, and the management server is respectively connected with the data integration processing module, the comprehensive quantitative evaluation module, the store resource database and the guide push initiation module. According to the invention, the user position information and the consumption record are analyzed to determine the preference and interest of the user, so that the guidance information can be accurately pushed to guidethe user to shop, the satisfaction degree of the user is greatly improved, and the sales of commodities in the shopping mall is promoted.

The invention discloses a health insurance risk evaluation system based on biological information. The health-insurance risk evaluation system comprises a risk evaluation database system, a database search engine and a biological underwriting and examination risk index analysis system, wherein the risk evaluation database system is connected to the biological underwriting and examination risk index analysis system by the database search engine. The risk evaluation database system comprises a historical risk evaluation database, a realistic risk evaluation database, a predictive risk evaluation database, a financial risk grade evaluation database, a biological recognition risk evaluation database, a health insurance underwriting index library and an actuarial rate index library. The biological underwriting and examination risk index analysis system comprises a biological gene detecting subsystem, a human health pre-warning subsystem and a biological underwriting and examination risk control subsystem. The health insurance risk evaluation system disclosed by the invention adopts a multi-dimensional information database set with quantitative classification of information, combines the biological information to evaluate the health insurance, and is beneficial to reducing the evaluation risk and reducing the underwriting cost.

The invention discloses a weighted grey target theory based fault-tolerant motor health status assessment method. The method includes the steps of 1), introducing a grey target theory, setting a grey target under the condition of no standard model and finding out a target center from the grey target; 2), selecting a frequency band energy value of a fault-tolerant motor electromagnetic torque as a status indicator of health assessment; 3), adopting a wavelet packet-grey neural network method to further predict the frequency band energy value to obtain a pattern sequence to be identified prior to grey target transformation and target center degree calculation; 4), based on the target center degree classification principle, subjecting health status of a fault-tolerant motor to quantitative classification; 5), meanwhile considering weight of different frequency bands during assessment of the fault-tolerant motor health status, adopting grey contribution degree to improve the grey target algorithm, judging out the degree of good or bad status a system is in, providing quantified system health status, and laying a foundation for formulating fault tolerant transient control strategies.

Disclosed are a quantitative classification frame system of original cigarettes and a frame technology of the crude tobacco. The quantitative classification frame system comprises a double-line leaf distributing, cutting and releasing machine, a first leaf base conveyor and a first non-leaf base conveyor, wherein the double-line leaf distributing, cutting and releasing machine is used for cutting off the original cigarettes and classifying the same into leaf bases and non-leaf bases, and the first leaf base conveyor and the first non-leaf base conveyor are contrary in conveying direction. By the quantitative classification frame system, both quantitative classification frame and quantitative mixed frame can be performed upon leaf base parts and non-leaf base parts.

The invention relates to a reservoir compressibility evaluation method based on cluster analysis of logging petrophysical facies division. The method includes the steps: acquiring the brittleness index profile of a target block according to the rock mechanics parameter test result of coring data and interval transit time data; acquiring petrophysical response characteristic data of the target block, acquiring reservoir physical property parameter logging data of the target block, and according to the brittleness index profile, the petrophysical response characteristic data and the reservoir physical property parameter logging data of the target block, dividing reservoir petrophysical facies classes based on a cluster analysis method by using a K-nearest neighbor algorithm and a core representative index; and evaluating the compressibility of a target well according to the petrophysical facies classification result. The method solves the technical problem that a single compressibility evaluation result of the prior art is not representative, and the reservoir compressibility quantitative classification result can be obtained based on the cluster analysis method.

The invention relates to a prior knowledge-based multi-modal ethnic identity sense quantitative measurement method and belongs to the intelligent pattern recognition and ethnology research crossing filed. According to the measurement method, ethnic minority emotion pictures are adopted as basic materials; ethnic minority identity sense survey questionnaire questions are converted into the induction materials of electrophysiological signals; after the EEG information of subjects is acquired, the EEG information implying identity senses is classified through a fuzzy neural network learning-basedEEG emotion feature extraction and classification algorithm; questionnaire survey results and EEG classification results are fused, so that ethnic identity sense quantitative measurement is realized;and the objective feeling of the subjects for the ethnic identity sense and national identity sense can be automatically analyzed. With the method of the invention adopted, the subjective influence of questionnaire surveys can be decreased, the objectivity of identity sense classification can be enhanced, and ethnic identity sense quantitative classification is more accurate and more efficient.

The invention belongs to the technical field of traffic engineering, and relates to a bus stop optimization setting method in view of quantitative classification. The method comprises the following steps of 1, setting improvement on traffic capacities of a bus stop and adjacent roads as an optimization target; 2, building a bus stop comprehensive service level assessment model, and determining a quantitative classification standard; 3, building a bus stop optimization measure set; and 4, quantitatively classifying the bus stop comprehensive service levels under different optimization measures,and selecting the optimal optimization measure. According to the method provided by the invention, the optimized bus stop setting method is systematically quantized, the traffic capacity of the bus stop and the traffic capacities of the adjacent roads are integrated to form the bus stop comprehensive service level, the assessment model and the quantitative classification standard are built, the optimization measure set is built by using berth numbers and stop types as basic elements, and the optimal optimization measure of the bus stop is finally acquired through quantitatively classifying the bus stop comprehensive service level under the different optimization measures.

The invention discloses a method for identifying carbonate reservoir physical properties while drilling. The method comprises the following steps: obtaining the nuclear magnetic analysis data and well log analysis data of the carbonate reservoir physical properties; performing correlation analysis on the nuclear magnetic analysis data and the well log analysis data, and creating a relationship between the nuclear magnetic analysis data and the well log analysis data; determining the nuclear magnetic analysis classification standards of a reservoir according to the relationship between the nuclear magnetic analysis data and the well log analysis data as well as the well log classification standards of the carbonate reservoir; identifying the carbonate reservoir while drilling by the nuclear magnetic analysis classification standards of the reservoir. Compared with a reservoir classification mode based on well log interpretation data, the physical property parameters of the horizontal-section reservoir can be quickly obtained by the nuclear magnetic analysis technology, so that the method provided by the invention can realize quantitative classification of the reservoir physical properties while drilling and accurate evaluation of reservoir quality.

The invention relates to a quantitative classification and identification method for oil sand interlayers. The quantitative classification identification method comprises the following steps: firstly, dividing interlayers of a cored well into interlayers type I, interlayers type II and interlayers type III; secondly, on the basis of classifying conditions of the interlayers of the cored well in the first step, sampling all the interlayers of the cored well, and measuring logging curve values of cored well samples corresponding to all the classified interlayers in belonged depth sections; thirdly, respectively drawing frequency distribution histograms of natural gamma logging values and density curve logging values of the interlayers of different types, taking characteristic values corresponding to the natural gamma logging values, and taking characteristic values corresponding to the density curve logging values; fourthly, establishing an XOY coordinate system, wherein X coordinates are natural gamma curve characteristic values, and Y coordinates are density curve characteristic values, and forming a quantitative classification and identification chart for the interlayers of the oil sand; fifthly, judging the types of the interlayers of a non-cored well according to the quantitative classification and identification chart for the interlayers of the oil sand, obtained in the fourth step.

The invention, which belongs to the power grid field, relates to a risk assessment and assistant decision-making method and system for the regional grid operation. The method comprises: S1, acquiringrelated information of a grid as an input condition; S2, searching for a weak point of the grid and providing a possible accident analysis result and a corresponding processing measure and advice based on quantitative classification of a risk evaluation index; and S3, generating a risk assessment report to assist in the analysis of a grid operation risk, carrying out visual displaying, and carrying out a graphical simulation operation. According to the invention, the weak point of the grid is searched quickly by using the related information of the grid mode as the input condition, the possible accident analysis result as well as the corresponding processing measure and advice is provided, and the risk assessment report is generated to help the dispatcher analyze the grid operation risk. Therefore, quasi-synchronous safety assessment of the grid operation mode is realized; the workload of the operation dispatcher is reduced substantially; the risk assessment integrity and accuracy areimproved; and the power supply reliability of the grid is enhanced.

The invention relates to a lung cancer TMN staging prediction method based on deep learning. The method comprises the steps of extracting and analyzing chest CT image features by adopting a current relatively mature deep learning method; finally, adopting an existing image data set for training a model structure to achieve quantitative classification of multiple indexes influencing lung cancer staging judgment. A quantified index classification result is provided for a diagnosis doctor. The workload of an occupational doctor is reduced. The staging diagnosis efficiency of the doctor is improved. The doctor is assisted in making a treatment scheme.

The invention discloses a classification evaluation method for a complex fault block reservoir. The method includes the following steps: (1) performing fine reservoir description on a target complex fault block reservoir and establishing a fine geological model; (2) performing numerical reservoir simulation to modify and improve the fine geological model; (3) utilizing the modified and improved fine geological model to obtain geological classification evaluation index parameters; (4) performing classification evaluation on the target complex fault block reservoir according to quantitative criteria of the geological classification evaluation index parameters. According to the method, main control factors affecting the reservoir development effect are selected as the classification evaluation index, and the quantitative classification evaluation criteria are utilized to comprehensively judge the development difficulty of the complex fault block reservoir, so that a comprehensive classification evaluation result of the reservoir is more reasonable, and the method has more promotion value.

The invention relates to a method for estimating the signal quality of MSK and O-QPSK signals. Firstly, the signal quality of MSK and O-QPSK signals is estimated based on the statistical characteristics of a chip. Secondly, the chip synchronization is established, and the establishment of the chip synchronization is before the synchronization of spread spectrum code phases and the synchronization of frames. Thirdly, with the assistant of a chip symbol synchronization signal, the simple nonlinear operation of a baseband signal and the simplified linear average of an operation result are carried out, and the high resolution estimation of the current signal quality can be rapidly obtained. The signal quality can be accurately estimated in a short time, an estimation value is subjected to delicate quantitative classification, and the accurate use of the estimation value by an upper application is facilitated.

The invention relates to a quantitative classification and recognition plate construction method and system of deep-water gravity current sandstone reservoirs. The quantitative classification and recognition plate construction method is characterized by comprising the following steps: classifying the deep-water gravity current sandstone reservoirs and constructing 9 types of rock phase combinations of the deep-water gravity current sandstone reservoirs; selecting deep-water gravity current sandstone reservoir samples to be subjected to quantitative recognition; acquiring logging curve characteristic values of each type of the rock phase combinations on the deep-water gravity current sandstone reservoir samples; constructing a quantitative classification and recognition plate of the deep-water gravity current sandstone reservoir samples according to the acquired logging curve characteristic values. The quantitative classification and recognition plate construction method and system canbe widely applied to the technical field of oil and gas field development geology.

The invention relates to a quantitative classification calculation method for quantitatively describing the demands of different positions in an urban park-and-ride facility attraction demand range and belongs to the urban traffic planning field. The method comprises the following steps that: a virtual analysis network of a park-and-ride facility attraction demand range area is constructed according to demand point samples and geographic coordinate positions which are obtained through investigation; the distribution density of sampled demand points is calculated through using a kernel function in probability theory; and the total demand density of each position in the facility attraction demand range is calculated and classified through using an adaptive kernel density analysis theory and a numerical value superposition method. The quantitative classification calculation method can be utilized to quantitatively analyze demand intensity distribution of each point at different directions and different distances in the park-and-ride facility attraction demand range, and can classify the demands in the facility attraction demand range through the total demand density, and has a favorable role of reference in the determination and analysis on distribution characteristics of the park-and-ride facility attraction demand range, and provides a new method for park-and-ride facility demand forecast.

The invention relates to a device for detecting pesticide residue and a detection method. The device comprises a pesticide residue fast detection card, a power supply, a device main body, a temperature control assembly, a camera and a controller; the temperature control assembly and the camera are respectively used for heating at constant temperature and shooting according to the control of the controller. The detection method comprises the following steps: (1) respectively obtaining the reference image of extracting solution of the matter to be detected and the standard image of the extracting solution of the matter without pesticide residue by the device; (2) respectively calculating the tone, saturability and brightness corresponding to each pixel point in the reference image characteristic region and the standard image characteristic region; (3) having quantitative classification process on the corresponding tone, saturability and brightness to obtain the reference quantization series weight vector and the standard quantization series weight vector, calculating the degree of association between the reference quantization series weight vector and the standard quantization series weight vector; (4) obtaining the average degree of association, the average degree of association is more close to 1 and the pesticide residue is less. The operation process is simplified, and the pesticide residue is detected exactly and quickly.

The invention discloses a method for carrying out quantitative classification and qualitative analysis on a flocculating constituent. The flocculated floccules are divided into the following four types according to the spatial form: individual particles, linear floccules, planar floccules and body floccules, and all possible spatial forms are included in the four types of floccules. The apparent viscosity of a solid-liquid system is superposition of contribution of different shapes of floccules to the viscosity after flocculating, and the mathematic model is: Eta= Eta0+Eta1(x)+Eta2(x, DL)+Eta3(x, DA)+Eta4(x, DV). By adopting the method, the problem of qualitative analysis of different types of floccules, which cannot be solved all the time is solved, and a new path is provided for evaluation and control of the flocculating effect.

The invention discloses a tongue colour sensing and quantitative classification method combining an equal interval scale with a classificatory scale in the traditional Chinese medicine, which belongs to the field of objectifying researches on the inspection diagnosis of the traditional Chinese medicine. According to the tongue colour sensing and quantitative classification method disclosed by the invention, a visual psychophysical method is adopted to solve the problem of quantitative classification for tongue colours in the dimensionality of the description rule of the traditional Chinese medicine. According to the tongue colour sensing and quantitative classification method disclosed by the invention, a part of representative tongue colour blocks are taken out from the colour gamut range of the tongue colours and used as sensing simulation quantity values; the description rule of tongue colour scales in the tongue diagnosis of the traditional Chinese medicine is used as the guidance basis of psychological perception evaluation for performing psychophysical experiments, establishing the equal interval scale and classificatory scale of psychological perception, and determining the psychological perception quantities of the tongue colours and the boundaries of the various scales; the psychological perception quantities meeting the tongue colour description rule of the traditional Chinese medicine and corresponding to the tongue colour physical quantities are obtained by creating a mathematic relationship model between the tongue colour physical quantities and the psychological perception quantities, and the scales of the psychological perception quantities are determined. The tongue colour sensing and quantitative classification method disclosed by the invention has the characteristic of realizing tongue colour one-dimensional sensing and quantification meeting the tongue diagnosis description custom of the traditional Chinese medicine, and determining classification boundaries, thus achieving the purpose of really combining the tongue colour quantitative description with the tongue colour qualitative description of the traditional Chinese medicine.

The invention provides a neural network quantitative classification method and system. The neural network quantitative classification method and system are different from the prior art in which only the weight of the neural network model is classified and quantized. According to the method and the system, statistical processing, classification processing and weight quantification processing are carried out on the input information of each layer in the neural network model, so that proper weight quantification processing can be carried out on the output information of each layer in a targeted manner; therefore, the precision of the output information of each layer is improved to the maximum extent under the same quantization bit number. According to the method and the system, the output information of each layer of the neural network model is sorted; in this way, a proper calculation mode can be selected according to the respective output channel characteristics of the output information of each layer, so that the neural network model can obtain the optimal calculation efficiency and reduce the power consumption required by hardware operation during hardware operation.

The invention provides a rapid evaluation method for anchoring quality grade of an anchor rod. The method comprises the steps as follows: acquiring reflected wave data with an acoustic wave reflectionmethod, performing normalization and smoothening on a signal to determine a new zero, obtaining peak values and correcting the x direction, removing a low peak value in the forefront, and performingcalculation according to conditions and a threshold value to realize quantitative classification of anchoring quality of the anchor rod. The method is conducted in the time domain in the whole judgment process, only uses simple operation such as interpolation, peak value judgment and slope calculation, has the characteristics of small calculation quantity, high calculation efficiency and the like,meets the requirement of procedures, can obtain accurate and reliable judgment results and has better application effects.

The invention relates to a method for determining quantitative classification of altered pyroclastic glutenite reservoirs, which comprises the following steps: 1. Selecting a plurality of wall core orrock core samples in pyroclastic glutenite reservoirs, carrying out research analysis on the plurality of wall core or rock core samples, determining the clay content of the wall core or rock core samples, and distinguishing the value of the clay content; 2, determining the alteration argillization degree of the pyroclastic glutenite reservoir in the wall core or rock core sample, and classifyingthe alteration argillization degree of the pyroclastic glutenite reservoir; 3, determining the effectiveness classification of the pyroclastic glutenite reservoir; performing reservoir classificationthrough the alteration index and the reservoir quality factor, and when PI is larger than or equal to 0.05 and SH is smaller than or equal to 0.00, the reservoir is a first-class reservoir; when PI is smaller than -0.035 and SH is larger than 0.020, the reservoir is a three-class reservoir; and the reservoir between the first-class reservoir and the third-class reservoir is the second-class reservoir. According to the invention, the argillization alteration degree of the pyroclastic glutenite reservoir can be quantitatively described; moreover, a reservoir classification standard is established, and a foundation is laid for subsequent glutenite reservoir well logging evaluation.

The invention provides a quantitative classification evaluation method and device for a low-permeability reservoir. The method comprises the following steps: acquiring a capillary pressure curve of a rock core sample; according to the capillary pressure curve of the rock core sample, determining fractal characteristics and fractal dimensions of the micro-pore structure; establishing a cross plot of the fractal dimension of the micro-pore structure and the pore throat characteristic parameters, and determining the correlation between the fractal dimension and the pore throat characteristic parameters; and analyzing the correlation between the fractal dimension and the lithology, physical property parameters and electrical characteristics by using the classification lower limits of different types of reservoirs in a dynamic and static combination manner, and establishing a low-permeability reservoir classification evaluation standard in combination with the correlation between the fractal dimension and pore throat characteristic parameters. According to the method, the complexity of the pore structure of the low-permeability reservoir is quantitatively represented by using fractal dimensions, quantitative classification evaluation of the low-permeability reservoir is realized by combining the correlation between the fractal dimensions and lithology, physical property parameters and electrical characteristics, and a multi-parameter macro-micro combined reservoir classification evaluation standard is established for the low-permeability sandstone reservoir to guide effective development of the low-permeability reservoir.