31 results about "Quantitative classification" patented technology

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.

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 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 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.

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.

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 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 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.

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.

This invention discloses a quantitative classification and assessment method for the hazard of fires in lithium battery systems. This invention relates to the field of fire assessment technology, and obtains a multi-dimensional evaluation index set characterizing the degree of fire hazard. The multi-dimensional evaluation index set includes the heat release rate (QHR) characterizing the fire's heat release capacity, the smoke toxicity (TOX) quantifying the toxicity of combustion products, the explosion potential (EXP) reflecting the risk of flammable gas accumulation and sudden release, and the fire spread rate (SPD) characterizing the fire's expansion trend. This invention constructs a multi-dimensional fire hazard assessment index including heat release rate, smoke toxicity, explosion potential, and fire spread rate, and combines this with the analytic hierarchy process (AHP) to determine the relative weights of each hazard-causing factor. This achieves quantitative modeling and comprehensive assessment of the hazard degree of fires in lithium battery systems, more objectively reflecting the true hazard characteristics during the fire development process, and improving the scientific rigor and consistency of fire risk assessment results.

This invention discloses a morphology-based method and system for classifying pores in tight reservoir porous media, comprising: acquiring microstructural images of rock tight reservoir samples using field emission scanning electron microscopy; labeling the SEM images with two types of samples, pores and matrix / background, as training sets, and training a segmentation model for rock samples; segmenting the sample SEM images using the trained segmentation model to generate binary images that identify the background and pores; automatically measuring pore morphology parameters based on the binary images to obtain pore roundness and aspect ratio; classifying pore shapes according to preset quantitative classification thresholds for pore roundness and aspect ratio, and outputting statistical classification results; this invention achieves a systematic and objective characterization of pore cross-sectional shape, transforming the traditional subjective judgment-dependent pore morphology description into a quantifiable and comparable structural index, solving the problem that traditional pore morphology classification relies on subjective judgment and lacks unified quantitative standards.

The present application relates to a kind of thin interbed type low permeability reservoir grading evaluation and dessert prediction method, device, medium and equipment, method first fuses micro pore throat structure and mobile fluid parameter, establishes multiple parameter collaborative quantitative classification chart, realizes the fine classification of single oil layer;Further according to the classification result statistics thickness proportion, macroscopic reservoir combination mode is divided;On this basis, with geological classification as constraint, the quality of reservoir is sensitive to the construction of seismic dessert factor by coordinate rotation, realize the three-dimensional space prediction of high-quality reservoir;Further through well seismic quantitative calibration, the quantitative relationship model between seismic attribute and reservoir quality is established, and the attribute threshold is determined accordingly;Finally, the threshold is used to distinguish the prediction result dessert area, and the development area of high-quality reservoir is accurately defined.The present application realizes the whole chain quantitative prediction from rock physical mechanism to three-dimensional dessert scientific definition, significantly improves the thin interbed low permeability reservoir dessert prediction precision and the operability of development decision.

This invention relates to the field of network security management technology, and in particular to a security defense decision-making system and method applied to computer networks. It performs full-coverage monitoring of all nodes within a target network area, constructs a unified security information dataset, and uses multi-dimensional weighted calculations based on traffic anomalies, access record anomalies, and vulnerability risk levels. Combined with node topology importance, it achieves dual quantitative classification of single-node and regional network risks. Using a digital twin model, it can quickly locate high-risk nodes and weak links, directly pinpointing the risk factors with the highest scores. It also senses attack trends and automatically generates primary and secondary attack paths. Simultaneously, it selects optimal interception points based on a priority list of intersection points and path interception filtering methods, outputs a defense deployment list, and establishes a closed-loop mechanism for interception execution, effect evaluation, and secondary defense. When ineffective, the strategy is automatically iterated, and multiple ineffective attempts trigger manual warnings, improving the efficiency and effectiveness of security protection in the network environment.

The application discloses a dammed lake risk quantitative grading fuzzy evaluation method, comprising the following steps: determining a dammed lake risk evaluation factor set and a dammed lake risk evaluation grade set; calculating the membership degree of each evaluation factor in the evaluation factor set to each evaluation grade in the evaluation grade set, and constructing a membership degree matrix; based on the importance scoring of the pairwise comparison of the evaluation factors in each level, calculating the weight of each evaluation factor in each level, and finally integrating to obtain a final weight vector; and determining the dammed lake risk evaluation grade based on the membership degree matrix and the weight vector. The dammed lake risk quantitative grading fuzzy evaluation method provided by the application solves the problem of inconsistent evaluation factor weight assignment by experts, and has a reasonable evaluation index system and grading, a feasible information acquisition method, a scientific weight vector, and reliable risk grade evaluation results. The method has good applicability and is suitable for risk grade evaluation of all dammed lakes.

This invention discloses a quantitative classification modeling method for eggplant fruit color based on CIELAB, comprising the following steps: Step S1, establishing a baseline dataset containing known phenotypic values ​​for purple-red and purple-black fruit; Step S2, calculating color difference derived parameters based on the baseline dataset, and selecting key parameters from the derived parameters that show significant differences between purple-red and purple-black fruit colors; Step S3, assigning weights to the statistical discriminative power of the key parameters in the baseline dataset to construct a weighted comprehensive scoring function; Step S4, calculating the comprehensive score distribution of the obtained baseline dataset to establish fruit color classification rules; Step S5, after confirming the accuracy of the fruit color classification rules, applying the fruit color classification rules to predict the fruit color classification of unknown samples. This invention can eliminate the subjectivity and variability of manual visual inspection, overcome the weakness of single-parameter discriminative power, and achieve objective, accurate, and high-throughput quantitative classification of purple-red and purple-black eggplant fruit colors.

This invention provides a multiphysics simulation method and system for pump-bridge perforation operations, relating to the field of data processing technology. The method includes: Step 1, establishing a three-dimensional wellbore trajectory model including the geometric parameters of the casing deformation section, a mechanical model of the tubing string / tool ​​string, and a hydrodynamic model based on the target well's logging and completion data; Step 2, setting the initial pumping displacement, cable lowering speed, wellhead pressure, wellbore temperature distribution, and tubing string-wellwall friction contact boundary conditions according to the operation design parameters; Step 3, performing a global multiphysics coupled transient simulation calculation based on the initial multiphysics model and boundary conditions to obtain the global transient simulation results including the tubing string's motion trajectory. This invention improves the accuracy of multiphysics simulation in pump-bridge perforation operations, optimizes operation parameters, provides guidance for construction, and enables quantitative classification and early warning of construction risks, thereby reducing operational risks and ensuring safe and efficient operation.

This application discloses a method and system for intelligent interpretation and attitude stability analysis of launch vehicle flight data, relating to the field of launch vehicle flight data processing and attitude analysis technology. The method includes: standardizing and preprocessing multi-source data during launch vehicle flight to obtain a standardized flight dataset; obtaining fused attitude feature data based on the standardized flight dataset and constructing a dynamic adaptive interpretation threshold to complete the intelligent interpretation of the fused attitude feature data; calculating the attitude stability margin value by using the fused attitude feature data, the dynamic adaptive interpretation threshold, and a dynamic correction coefficient for the real-time attitude change rate, thereby achieving quantitative classification and judgment of the launch vehicle's flight attitude stability. This application can improve the accuracy of attitude analysis and the timeliness of early warning, enabling refined proactive control of attitude and effectively ensuring the safe and controllable flight attitude of launch vehicles.

This invention relates to a method for intelligent grading and quantitative non-destructive testing of deer antler slices. It utilizes machine learning and other methods to train an artificial intelligence recognition model, enabling rapid and high-throughput quality grading of deer antler. Simultaneously, it extracts the spectral characteristics of deer antler slices at each grade using bioinformatics and chemometrics analysis, establishing a correlation between the characteristics, physicochemical properties, and spectral features of deer antler slices. This provides a scientific basis for quantitatively predicting the quality of deer antler slices using infrared spectroscopy, thereby achieving non-destructive intelligent grading and quantitative classification of deer antler slices. This invention overcomes the problem of traditional deer antler slice quality grading relying on subjective experience through multi-data fusion, enabling rapid and accurate evaluation of deer antler slice quality.

The invention discloses a MyBatis framework-oriented SQL (Structured Query Language) automatic analysis and optimization method and system, and the method comprises the steps: calling a GenericTokenParser, an XNodeParser and a SqlSourceBuilder tool class officially provided by Apache MyBatis, completing the grammar analysis of a dynamic SQL statement, the recognition and replacement of a parameter placeholder, generating a BoundSql object, and obtaining complete execution plan information; inputting the static SQL text and the execution plan into a configurable artificial intelligence large model interface, and deeply analyzing the performance bottleneck by the large model; and automatically diagnosing the problem severity through a rule engine based on a quantitative index of an execution plan field, dividing the problem severity into three levels of emergency optimization, important optimization and suggested optimization, and generating a structured diagnosis report containing problem description, reason analysis and an executable optimization scheme. According to the method, the MyBatis framework characteristics and the AI large model capacity are deeply fused, the problems that dynamic SQL analysis is inaccurate, diagnosis lacks quantitative grading, an optimized closed loop is incomplete, and rules are statically solidified are solved, and the efficiency and the intelligent level of database performance management are remarkably improved.

The invention discloses an artificial universal intelligent security assessment method and system based on an AHP and a genetic algorithm. According to the scheme, firstly, a data acquisition module extracts data features related to security from an AGI model; then the index hierarchy construction module establishes a safety index system based on an analytic hierarchy process; the weight calculation module calculates an initial weight and performs consistency verification; if the consistency ratio (CR) does not meet the threshold value, the genetic algorithm module executes optimization and correction; the optimized weight is input into a grey clustering module to calculate a clustering coefficient and a comprehensive safety score; and finally, the result output module generates a safety assessment report and a risk level. According to the scheme of the invention, a hierarchical structure system of AGI security risks is established through AHP, and consistency correction and global optimization are carried out by using a genetic algorithm, so that evaluation weight calculation is more objective and stable; and meanwhile, a grey clustering model and a whitening weight function are combined to realize quantitative classification of different risk types, so that a comprehensive safety score with dynamic adjustability and interpretability can be output.