43 results about "Quantitative classification" patented technology

The invention relates to the technical field of power system automation, in particular to a monitoring and maintenance system and method for a transformer substation. The system comprises a quantitative classification module, a difference analysis module and a grading adjustment module. Equipment in a transformer substation is classified through the quantitative classification module, correction parameters such as aging and load loss are introduced for single-equipment monitoring through the difference analysis module, misjudgment and missed judgment caused by equipment state changes are reduced by combining multi-level threshold values, associated equipment is modeled through a GNN model, neighbor states are aggregated, the equipment health degree is output, and the equipment quality is improved. The method comprises the following steps of: quantifying a fault propagation probability, pre-warning cascading fault risks in advance, and adjusting a threshold value of a single device in a linkage manner while a hierarchical adjustment module takes risk response measures, so that single device monitoring is adaptively matched with an associated device risk state, full-coverage accurate monitoring is realized, and local and global risks are considered.

The invention relates to the technical field of geological engineering safety risk evaluation, in particular to a regional tectonic stress risk quantitative analysis method for tunnel engineering. The method comprises the following steps: collecting multi-source geomechanical parameters, simulating and inverting a tectonic stress field, and verifying the tectonic stress field; constructing a multi-scale geomechanical model, coupling simulation tectonic stress and gravity stress, and generating a stress space basic layer; utilizing Kriging interpolation and inversion decomposition to obtain a stress value and a directional diagram layer; calculating an included angle between the maximum principal stress direction and the tunnel axis, and performing vector decomposition to obtain a vertical stress component; and carrying out standardized normalization on the factors, determining a weight calculation risk value, and finally generating a tectonic stress risk quantitative grading graph and carrying out GIS visual rendering. The tunnel engineering-oriented risk quantitative analysis mechanism is constructed by retaining the directivity and spatial heterogeneity characteristics of the tectonic stress tensor, and the authenticity, resolution and engineering applicability of tectonic stress identification are remarkably improved.

The invention relates to the technical field of speech analysis, in particular to an Alzheimer's disease recognition method and system based on speech features, and the method comprises the following steps: extracting frame-level parameters to construct a sequence, recognizing sparse and fractured sections, generating an abnormal trend, and completing speech feature recognition. According to the method, multiple parameters such as the mean value of amplitude absolute values, the maximum difference value and the minimum difference value are serialized and integrated, a double analysis mechanism for the sparsity and the jump of the voice amplitude fluctuation is formed by combining multi-section continuous ratio comparison and mutation trend positioning, the overlapping degree of trend indexes in adjacent frame sections is calculated, and sites in a trend structure are extracted; a trend structure line of the time sequence is established, directional change and point location density of the trend structure line are extracted, quantitative classification of abnormal trends in the frame sequence is completed, cross-scale feature coupling recognition from voice micro fluctuation to time sequence trends is achieved, the discrimination degree and accuracy of voice features of the Alzheimer's disease are improved, and the recognition accuracy of the Alzheimer's disease is improved. And the stability and the discrimination efficiency of the identification result are obviously improved.

The invention discloses a salt lake carbonate rock multi-parameter quantitative classification and reservoir evaluation method, and relates to the technical field of oil-gas exploration. The method comprises the following steps: selecting a key well in an exploration block, collecting a plurality of salt lake carbonate rock samples, preparing an analysis sample under an anhydrous condition, quantitatively measuring a salinity parameter, a mixing index and a diagenesis strength parameter by utilizing the analysis sample, and determining a salinity code, a mixing code and a diagenesis phase code. And naming the salt lake carbonate rock based on the salinity code, the mixed product code and the lithogenous phase code, directly evaluating the salt lake carbonate rock reservoir according to a naming result, and performing single well comprehensive evaluation on the salt lake carbonate rock reservoir. According to the method, the influence of salinity, mixed accumulation and diagenesis strength on formation and evolution of the salt lake carbonate rocks is fully considered, accurate division of the types of the salt lake carbonate rocks is achieved, the reservoir naming result is directly associated with reservoir evaluation, and technical support is provided for standardized evaluation of the salt lake carbonate reservoir.

The application provides a load action and target behavior recognition method based on a space time sequence image, and the method comprises the following steps: acquiring a time sequence image sequence of a space target to be recognized, and performing target region segmentation on the time sequence image sequence to obtain a segmentation mask; based on the segmentation mask, a plurality of query pixel points are selected, the time sequence image sequence and the query pixel points are input into a dense pixel point tracking model to obtain position coordinates, visibility indication and tracking confidence of the query pixel points in the time sequence image sequence, and a time sequence pixel track set of a load region and a target main body region is formed; according to a monitoring object of interest, inference logic of load action driving or main body motion driving is adopted, the load action driving is used for performing quantitative classification on the load action and the main body motion, and then performing quantitative identification on the target behavior, and the main body motion driving is used for directly performing quantitative identification on the target behavior; and finally, a target behavior category and a corresponding quantitative index are output, and reliable basis is provided for in-orbit state evaluation and abnormal diagnosis.

The present application belongs to the quality detection technical field, disclose a kind of kiwi internal defect nondestructive identification method, device, equipment and storage medium.The method includes: sample kiwi is scanned, and original scanning image is obtained;Defect feature information is obtained according to original scanning image;Defect evaluation dataset is determined according to defect feature information;Classification model is constructed according to defect evaluation dataset;The defect volume ratio of kiwi to be classified is obtained by classification model;According to the defect volume ratio, the defect quantitative classification of the kiwi to be classified is carried out.By the above-mentioned mode, the sample kiwi is scanned based, and the subsequent kiwi needing defect identification classification is automatically analyzed by the way of constructing classification model, the quantitative classification of the defect of kiwi is realized, so as to realize the method of artificial damage visual inspection, reach the purpose of internal defect nondestructive automatic analysis and classification of kiwi, improve the efficiency and effect of kiwi quality detection.

The invention discloses a shaft comprehensive geologic model construction method based on continuous quantitative grading of surrounding rocks. The problems that in shaft construction, surrounding rock strength grade evaluation representativeness is insufficient, permeability coefficient K dimensionless quantification is deficient, and high-permeability section systematic overestimation is caused are solved. The method comprises the following five core steps: 1, acquiring full-depth parameters of a drill hole, taking a rock core through drilling, and measuring lithology and permeability coefficients of surrounding rock; 2, calculating a lithology index Ai; 3, calculating a permeability coefficient by combining hydrological and lithologic parameters of the surrounding rock so as to obtain a hydrological index Bi; 4, coupling Ai and Bi to obtain a comprehensive index Ci; and 5, dividing the Ci into I-V levels, and generating a total depth statistical table containing the depth, the Ci and the levels. On-site application shows that the grade division of the surrounding rock is more accurate, and the time lt is divided in real time; the method is suitable for underground engineering such as a vertical shaft, an inclined shaft and a deep-buried tunnel needing grading of the surrounding rock of-200 m on the earth surface, and the popularization prospect and the economic value are wide.

This invention relates to intelligent transportation technology and aims to provide a data-driven robust site selection method for heterogeneous risk management in drone-assisted accident handling. The method includes: cleaning and preprocessing historical traffic incident data, extracting accident tags and performing quantitative classification based on the U-I-A multidimensional evaluation framework to obtain a structured dataset; decomposing uncertain demand into basic normal demand and multi-source heterogeneous fluctuations, and establishing a structured uncertainty set based on the quantitatively classified structured dataset; modeling the robust site selection planning model as a minimax robust optimization problem, and then equivalently reconstructing it into a solvable single-stage mixed integer linear programming model; using a solver to solve the problem, obtaining the globally optimal hangar site selection scheme and the number of supporting drones, and outputting the results. This site selection method enhances the reliability and robustness of the drone emergency response system; it enables scientific planning of drone site deployment, achieving rapid and stable emergency response.

This invention discloses a method for quantifying and classifying suspected electricity theft based on multi-dimensional weighted fusion. The method includes: collecting multi-source heterogeneous data through a graded suspected electricity theft early warning analysis platform; extracting abnormal gradient features through grid load anomaly gradient identification; constructing personalized behavioral benchmarks based on a user electricity consumption behavior profile perception model; quantifying the degree of electricity consumption deviation using a dynamic baseline electricity theft deviation assessment model; generating a comprehensive quantitative value for suspected electricity theft through multi-dimensional weighted fusion; classifying the suspected electricity theft according to preset standards; and utilizing dedicated hardware acceleration and parameter optimization. Six functional units work together to achieve the entire process of data collection, feature extraction, deviation assessment, and quantitative classification. This method overcomes the limitations of traditional single-dimensional detection, dynamically adapts to grid operating conditions and user characteristics, improves the accuracy of suspected electricity theft identification and the rationality of classification, provides efficient technical support for power regulation, and ensures the safe and stable operation of the power grid.

This invention discloses a method and system for quantitative classification and ranking of exploration zones. The method includes: acquiring source rock data, reservoir data, and caprock data corresponding to each exploration zone; determining the oil source index corresponding to each exploration zone based on the source rock data; determining the reservoir index corresponding to each exploration zone based on the reservoir data; determining the caprock index corresponding to each exploration zone based on the caprock data; determining a preset classification corresponding to each exploration zone based on the oil source index, reservoir index, and caprock index; and ranking each exploration zone based on the ranking information corresponding to each preset classification and the oil source index, reservoir index, and caprock index corresponding to each exploration zone. This invention quantitatively classifies and ranks exploration zones based on source rock data, reservoir data, and caprock data, thereby improving the accuracy of exploration zone classification and ranking results and enhancing the effectiveness of exploration deployment.

The application discloses an electrocardiogram ST segment shape quantitative analysis method, device and equipment and a storage medium. The method collects multi-lead electrocardiogram signals, performs linear regression fitting on corresponding electrocardiogram data when it is detected that the ST segment of each lead in the multi-lead electrocardiogram signals is depressed, obtains an ST segment trend line, and generates a shape quantitative classification result of the ST segment depression shape. The upward sloping ST segment corresponds to a conventional follow-up suggestion, the horizontal ST segment corresponds to a myocardial ischemia monitoring suggestion, and the downward sloping ST segment corresponds to an emergency coronary artery evaluation suggestion. The shape characteristics of the ST segment and the individual physiological characteristics of a patient are comprehensively considered, a risk assessment correction mechanism with the shape characteristics of the ST segment as the dominant factor and the individual physiological characteristics of the patient as the supplementary factor is adopted, a visual report containing a corrected risk assessment result and a corresponding clinical treatment suggestion is generated, and clear and operable risk assessment results and hierarchical diagnosis and treatment suggestions can be provided for clinicians.

The application discloses a method for predicting endocrine disruptors based on a harmful outcome path network, and belongs to the endocrine field, and comprises the following steps: acquiring experimental activity data containing qualitative data and quantitative data according to a pre-constructed harmful outcome path network; acquiring endocrine disruptor list data as a second data set; constructing a target qualitative prediction model according to the qualitative data; predicting target activity data of the second data set by using the target qualitative prediction model to obtain a target activity data set; constructing an endocrine disruptor effect qualitative prediction model by using the target activity data set; constructing a target quantitative prediction model according to the quantitative data; predicting whether a to-be-tested compound has an endocrine disruptor effect by using the endocrine disruptor effect qualitative prediction model; and predicting the corresponding target quantitative activity of the compound with the endocrine disruptor effect by using the target quantitative prediction model to obtain a quantitative harmful outcome path. The application realizes qualitative identification and quantitative classification of endocrine disruptors EDCs.

The invention provides a salinization type quantitative inversion method based on feature weighting and residual error correction, and relates to the field of remote sensing technology and machine learning, and the method comprises the steps: collecting actual measurement concentration data of soil salt ions in a target region, and obtaining a remote sensing image of a corresponding region; extracting feature information data from the remote sensing image; constructing a salinization type quantitative inversion data set through the feature information data and the measured data; evaluating the correlation between each feature and the soil salinity by using a feature selection method to obtain a feature importance weight vector; the first stage is used for preliminary prediction of the soil ion concentration, the second stage is used for introducing a residual learning mechanism, the first stage residual error is fitted to improve the prediction precision, and ion concentration data of the target area are predicted and generated; and according to an inversion result, obtaining a two-dimensional space-time distribution diagram of soil salinization type quantitative classification. The technical scheme of the invention is suitable for high-precision identification and classification management of regional scale soil salinization types.

The invention provides a multi-axial fatigue life prediction model construction method considering a metal material failure form. The method comprises the steps of determining the metal material failure form, determining a critical plane position, introducing a material sensitivity coefficient and constructing a multi-axial fatigue life prediction model. According to the multi-axial fatigue life prediction model construction method, the accuracy of multi-axial fatigue life prediction is effectively improved through quantitative classification of failure forms, introduction of material sensitivity coefficients and optimization of damage parameters, operation is easy, a large amount of complex test data is not needed, and the method is suitable for various metal materials and has good engineering application value.

The invention provides a load action and target behavior identification method based on a space time sequence image, and the method comprises the steps: obtaining a time sequence image sequence of a to-be-identified space target, carrying out the target region segmentation of the time sequence image sequence, and obtaining a segmentation mask; based on the segmentation mask, selecting a plurality of query pixel points, inputting the time sequence image sequence and the query pixel points into a dense pixel point tracking model, obtaining position coordinates, visibility indication and tracking confidence of the query pixel points in the time sequence image sequence, and forming a time sequence pixel track set of the load area and the target main body area; according to a monitored concerned object, reasoning logic of load action drive or subject motion drive is adopted, the load action drive firstly performs quantitative classification on load action and subject motion and then performs quantitative identification on a target behavior, and the subject motion drive directly performs quantitative identification on the target behavior; and finally, outputting a target behavior category and a corresponding quantitative index, thereby providing a reliable basis for on-orbit state evaluation and abnormality diagnosis.

The invention provides a ship gear box corrosion defect quantitative detection method based on a depth feature interaction network, and the method comprises the steps: constructing a three-stage classification system, obtaining a sample set, carrying out the ultrasonic signal wavelet packet decomposition and time-frequency diagram generation, and obtaining a time-frequency information gray-scale map; extracting local time-frequency features of the time-frequency information grey-scale map; fusing the four local time-frequency features to obtain a global feature, and predicting a corrosion level and a corrosion level probability in combination with the four local time-frequency features and the global feature; and performing end-to-end training on the model by using the training set so as to evaluate prediction accuracy and robustness, and predicting a real-time sample set by using the trained model. The method can realize high-efficiency, high-precision and non-destructive automatic gearbox corrosion quantitative classification detection, and is suitable for online evaluation of various complex working conditions of a real ship gearbox.

A method for facilitating qualitative assessment of natural language data via artificial intelligence is disclosed. The method includes receiving, via an application programming interface, an input from a source, the input including the natural language data; determining, by using a model, a confidence score for the input, the confidence score relating to a clarity level of the natural language data; determining, by using the model, whether the confidence score exceeds a predetermined threshold; generating, by using the model, a request for additional information when the confidence score is below the predetermined threshold, the request including a prompt in a natural language format; and transmitting, via the application programming interface, the request back to the source.

This invention relates to the field of petroleum and natural gas engineering technology, and particularly to a method, equipment, and medium for classifying fluid accumulation in gas wells. The method includes: acquiring oil pressure data, casing pressure data, and daily gas production data of the gas well; calculating the oil-casing pressure difference based on the oil pressure data and casing pressure data; preprocessing the daily gas production data and all calculated oil-casing pressure difference data, and constructing a dataset; adaptively adjusting the original profile coefficient of each data in the dataset based on the profile coefficient of the critical fluid-carrying flow rate sample to determine the optimal number of clusters K; performing cluster analysis on the oil-casing pressure difference data in the dataset based on the optimal number of clusters to obtain a preliminary quantitative classification standard for wellbore fluid accumulation levels; and performing secondary clustering based on the daily gas production within the preliminary quantitative classification standard for wellbore fluid accumulation levels to obtain a refined quantitative classification standard for the degree of wellbore fluid accumulation, thus completing the quantitative analysis of the degree of fluid accumulation in the gas wellbore and improving the accuracy and efficiency of fluid accumulation level classification.

The invention discloses a driving style online identification and quantitative classification method and system. The driving style online identification and quantitative classification method comprises the steps of collecting a three-dimensional motion signal of a vehicle; respectively extracting a longitudinal style characteristic, a transverse style characteristic and a vertical style characteristic from the three-dimensional motion signal, and calculating to obtain a frequency exceeding a comfort threshold, the filtering capacity of a driver to road excitation, steering softness and steering return preference; driving scenes of the vehicle are obtained, and corresponding weights are given to different driving scenes; and generating a driving style portrait based on the frequency exceeding the comfort threshold value, the filtering capability of the driver on the road excitation, the steering softness, the steering return preference and the corresponding weight of the driving scene, and matching a corresponding chassis mode and a driving strategy. According to the method, longitudinal, transverse and vertical three-dimensional motion features are fused at the same time, a traditional one-sided identification mode depending on urgent acceleration and urgent braking is broken through, the driving smoothness and the robustness degree can be distinguished, evaluation is more complete, and the evaluation is closer to the real driving quality.

The invention discloses a suitability evaluation method for development and utilization of a geothermal energy water taking type project. The method comprises the steps that firstly, according to different geothermal resource heat storage types, a dominant marking method and a superposition method are combined for differentiated zoning; then, an evaluation index system is constructed from four dimensions of geology, technology, policy and regulation and economic market, an interaction relationship among indexes is quantified by using a DEMATEL method, and key influence factors such as the thickness and the porosity of the aquifer are objectively identified; and finally, determining a key index weight by adopting AHP, and calculating a comprehensive score of the water taking suitability of each subarea through index quantitative grading and linear weighted summation, thereby realizing scientific and accurate regional suitability grade division. According to the method, the problems of high subjectivity and index homogenization of a traditional method are effectively solved, systematic and visual evaluation on the development suitability of the geothermal energy water taking type project is realized, and a reliable basis is provided for sustainable development and planning management of geothermal resources.

This invention designs a method for screening traits in the quantitative classification of ornamental plant germplasm, mainly consisting of the following steps: trait selection and coding, intra-germplasm consistency test, inter-germplasm discrimination analysis, principal component analysis, and Pearson correlation analysis. The scientific validity and effectiveness of the trait screening technology system proposed in this invention are verified by including species in the population sample and evaluating the kinship clustering probability distribution between species, species and varieties, and varieties, respectively, thereby indirectly reflecting the effectiveness of the trait screening technology system proposed in this invention.

A method for assessing the icing status of wind turbine blades based on dynamic weighted Mahalanobis distance and Gaussian mixture model clustering includes the following steps: acquiring a wind turbine blade icing fault diagnosis dataset; calculating the dynamic weighted Mahalanobis distance between iced samples and normal samples using normal sample data as a benchmark; mapping the dynamic weighted Mahalanobis distance to a health index of the wind turbine blade's operating status; and using the health index and Gaussian mixture model clustering to quantitatively analyze the icing status of the wind turbine blades, thereby deriving a quantitative classification of the severity of the icing status samples. This method utilizes weighted dynamic Mahalanobis distance to map high-dimensional features to a one-dimensional health index, and then achieves objective classification through unsupervised clustering. It not only provides a new, quantifiable, and interpretable status assessment tool for wind turbine blade icing faults but also offers a new paradigm for predictive health management of complex equipment, possessing significant theoretical and engineering application value.

The invention discloses a Raman spectrum characteristic spectrum peak-based rice aging detection method, and relates to the field of rice aging detection, and the method comprises the following steps: constructing a rice sample set; acquiring original Raman spectrum data in a preset wave number range; performing zero-phase smooth filtering and continuous wavelet transform to obtain optimized Raman spectrum data; extracting characteristic spectrum peaks of a plurality of preset wave numbers and a three-dimensional characteristic vector of each characteristic spectrum peak; generating an aging degree grading rule set and an anti-aging characteristic judgment threshold value; processing a to-be-detected sample through S2-S4 to obtain a three-dimensional feature vector, comparing the three-dimensional feature vector with the aging degree grading rule set and the anti-aging characteristic judgment threshold value, and outputting a corresponding aging detection result. According to the method, a dual data set is constructed, a three-dimensional characteristic spectrum peak vector is extracted, and noise suppression and a self-adaptive clustering algorithm are combined, so that quantitative classification of the rice aging degree and synchronous identification of anti-aging varieties are realized, and the detection precision and efficiency are remarkably improved.

The invention discloses an oil reservoir interval water injection effect evaluation method and system based on PCA-GMM-XGBoost, and relates to the technical field of oilfield development, and the method comprises the steps: collecting and preprocessing multi-source development data of a target oil reservoir interval, forming an initial feature set, and extracting a key index system through principal component analysis; taking the key index system as input, initializing a Gaussian mixture model corresponding to the three-stage water injection effect, iteratively optimizing model parameters by using an expectation maximization algorithm, and completing quantitative classification of the water injection effect according to the posterior probability of each evaluation unit; taking the key index system as input, taking classification labels as targets, constructing and training an XGBoost multi-classification model containing regularization items, predicting and adjusting the direction of a new sample, and outputting a final decision after multi-factor rechecking is carried out on a fuzzy sample. According to the invention, by introducing advanced data analysis and machine learning technologies, accurate quantitative evaluation of the water injection effect and intelligent and scientific decision of the water injection adjustment strategy are realized.

The invention relates to the field of deep coal mining disaster prevention and control, and relates to a deep coal seam outburst risk quantitative characterization method based on a multi-field coupling effect, and the method comprises the steps: S1, building a coding heat-fluid-solid-chemical four-field coupling control equation set; s2, constructing a chemical damage evolution model; s3, based on the field variable distribution obtained by solving the control equation set, establishing an energy abrupt change instability criterion taking the second-order variation of the total potential energy of the system as a core, and determining a critical energy threshold value of an outburst danger; s4, constructing and training a physical model to drive a neural network so as to realize rapid prediction of field variables; and S5, using the trained physical model to drive a neural network to carry out rapid prediction, and based on comparison of a prediction result and the critical energy threshold, realizing quantitative grading and engineering early warning of the deep coal seam outburst risk. The method provides scientific, reliable and practical technical support for accurate prevention and control of outburst disasters in deep coal mining.

The present application relates to the technical field of geological engineering safety risk assessment, and particularly relates to a regional tectonic stress risk quantitative analysis method for tunnel engineering. The method comprises the following steps: collecting multi-source geomechanics parameters, simulating and inverting tectonic stress field and checking; building a multi-scale geomechanics model, coupling simulation of tectonic stress and gravity stress, and generating stress space basic layers; obtaining stress value and direction layers by using Kriging interpolation and inversion decomposition; calculating the angle between the maximum principal stress direction and the tunnel axis, and vector decomposition to obtain the vertical stress component; normalizing the factor, determining the weight to calculate the risk value, and finally generating a tectonic stress risk quantitative classification map and performing GIS visual rendering. The present application retains the directionality and spatial heterogeneity characteristics of the tectonic stress tensor, and builds a risk quantitative analysis mechanism for tunnel engineering, significantly improving the authenticity, resolution and engineering applicability of tectonic stress identification.

The invention discloses a crack quantitative classification method based on deep learning and a multi-view strategy, and belongs to the technical field of composite material damage detection and quantitative analysis. The method comprises the following steps: 1, adding a convolution block attention module to improve a U-Net model; 2, further improving the model by adopting a balanced cross entropy loss function; 3, outputting and slicing the CT data along YZ, XY and XZ planes; 4, respectively selecting a plurality of representative slices to make three groups of training sets; step 5, respectively training the three improved U-Net models; 6, inferring complete crack information along the YZ plane, the XY plane and the XZ plane; and 7, projecting crack classification results with high accuracy in the YZ plane and the XZ plane to the XY plane, and merging the classification results with the cracks with high accuracy in the corresponding results of the XY plane slice by slice to obtain the optimal classification result of the accuracy of each crack type. According to the method, the accuracy of crack detection can be effectively improved, and the method can be used for quantitatively analyzing the crack evolution behavior of the 3DWC under the out-of-plane shear load.

This application discloses a method and system for real-time location and adaptive repair triggering of leaks in metal roofs. The method includes: S1: real-time acquisition of status data through a multimodal sensing network deployed on the metal roof; S2: identification of potential leakage areas; S3: precise location of leakage points; S4: dynamic assessment and classification of leakage risk; S5: generation of adaptive repair decisions based on a probabilistic principal component analysis model according to the risk level, and determination of repair strategy parameters; S6: control of a repair execution device to accurately deliver repair materials according to the repair strategy parameters, and real-time monitoring of the repair effect. This application constructs a dynamic comprehensive risk value calculation model by integrating real-time leakage rate, impact range estimation, and environmental factor correction, realizing a leap from qualitative judgment to quantitative classification of leakage risk, providing a scientific basis for differentiated and precise repair decisions, and avoiding waste or inadequacy of repair resources.

This invention discloses a live streaming content recognition system and method based on virtual positioning and multimodal reasoning, relating to the field of internet content analysis technology. By analyzing the difference between virtual and actual positioning, and combining it with bullet screen and tipping behavior patterns, this invention forms a cross-modal association evidence chain. It comprehensively calculates a live streaming anomaly index by considering the proportion of fraudulent activity, the proportion of abnormal bullet screens, and the proportion of tipping spam. Each indicator corresponds to an independent weight and allowable threshold, achieving a quantitative classification of the degree of anomaly. This more accurately reflects the risk level of the live streaming room and solves the problem in existing technologies that rely solely on single-modal data for analysis, making it impossible to comprehensively and accurately identify abnormal behavior during the live streaming process and prone to misjudgment.

The application discloses a kind of salt lake carbonate rock multi-parameter quantitative classification and reservoir evaluation method, it is related to oil and gas exploration technical field.The application selects key well in exploration block, collects multiple salt lake carbonate rock samples and prepares analysis sample under waterless condition, quantitatively determines salinity parameter, mixing index and diagenetic intensity parameter using analysis sample respectively, determines salinity code, mixing code and diagenetic facies code, based on salinity code, mixing code, diagenetic facies code is named to salt lake carbonate rock, and according to the naming result, directly evaluate salt lake carbonate rock reservoir, and carry out single well comprehensive evaluation to salt lake carbonate rock reservoir.The application fully considers the influence of salinity, mixing, diagenetic intensity on the formation and evolution of salt lake carbonate rock, realizes the accurate division of salt lake carbonate rock type, and directly associates the reservoir naming result with reservoir evaluation, provides technical support for the standardization evaluation of salt lake carbonate rock reservoir.