112results about How to "Accurate division" patented technology

The invention relates to a display screen backlight brightness adjusting method and system based on a Mini QLED technology. The method comprises the following steps: dividing a display image signal into a brightness signal, a chrominance signal and a saturation signal, and generating image feature data; performing sub-pixel-level boundary detection to obtain a plurality of initial backlight sub-regions, and calculating region brightness distribution data; executing region clustering operation and region merging to obtain a plurality of target backlight sub-regions and calculating backlight partition mapping data; carrying out nonlinear feature transformation, and calculating backlight unit control data of the Mini LED backlight unit; outputting the backlight compensation amount of each target backlight sub-region by adopting a deep Q network; and performing real-time feedback correction, calculating a deviation value between an actual display parameter and a target parameter of each target backlight sub-region, and outputting a backlight optimization control signal. According to the invention, the backlight brightness is accurately adjusted, and the consistency of the display effect is improved.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention relates to the technical field of image processing, in particular to an intelligent detection method and system for shaft forgings. The method comprises the following steps: acquiring gray values, positions and gradient directions of all pixel points in a surface image of the shaft forging; clustering all pixel points in the surface image by using a condensed hierarchical clustering algorithm to obtain a plurality of regions; calculating the suspicious degree of each region; determining a scratch burr area in the surface image of the shaft forging to realize intelligent detection of the shaft forging; in each round of iteration process of the condensed hierarchical clustering algorithm, for two to-be-combined clusters in each round of iteration process, calculating the separation degree of the two clusters; calculating the feature similarity of the two clustering clusters; determining the number of clustering clusters of the current iteration; a plurality of regions of the surface image of the shaft forging are determined. By dynamically adjusting the number of clusters, the problem of wrong merging or premature iteration stopping of a traditional condensed hierarchical clustering algorithm under the fixed number of clusters is avoided.

The invention discloses an intelligent industrial chain construction method and system based on multi-dimensional qualification affirmation, and the method comprises the steps: constructing a multi-dimensional qualification evaluation model based on industrial characteristic data; obtaining the preprocessed multi-dimensional qualification data of the industry chain related enterprises; analyzing the multi-dimensional qualification data by using a multi-dimensional qualification evaluation model to obtain a multi-dimensional quantitative index of the qualification data of the industry chain related enterprises; based on the multi-dimensional quantitative indexes, a machine learning algorithm is adopted to carry out classification to obtain enterprise category distribution, and comprehensive scoring and sorting are carried out on industry chain related enterprises according to set weights to obtain a sorting result; interaction data, transaction intensity data and resource flow data among related enterprises of the industry chain are acquired; and constructing an intelligent industrial chain network diagram according to the data obtained in the steps. The method is helpful for understanding an industrial chain structure, identifying key enterprises and evaluating potential risks.

The invention relates to the technical field of data processing, in particular to a shrimp group health condition assessment method based on deep clustering analysis, and the method comprises the steps: monitoring health parameters; determining a shrimp group dispersion grid; determining a temporary grid; identifying a non-shrimp group abnormal grid; calculating an actual health index; and adjusting the grid step length and evaluating health. According to the method, the health condition of the shrimp group can be accurately monitored and evaluated by combining physiological and behavior data such as real-time density, saturation and activeness, abnormal grids are eliminated step by step, more representative grids are screened out, evaluation deviation caused by environmental fluctuation or measurement errors is avoided, and the evaluation accuracy is improved. The dynamic monitoring of the health condition of the prawn group is realized, and the grid step length is automatically adjusted by comparing the ideal health index with the actual health index, so that the health assessment precision and reliability are improved, and the problem of inaccurate health assessment result caused by unstable environmental parameters is effectively solved.

The invention discloses a transformer substation misoperation risk assessment method and system based on adaptive learning, and relates to the technical field of transformer substations, and the method comprises the steps: collecting and preprocessing multi-dimensional feature data related to the electrical misoperation risk of a transformer substation; performing adaptive feature selection on the preprocessed feature data by using a TabNet algorithm, evaluating the importance of different features through an attention mechanism, and dynamically adjusting the weights of the features; performing deep learning modeling on the selected features by adopting a CatBoost algorithm, processing a nonlinear relationship between category features and numerical features, and establishing a risk level judgment model; and in combination with output results of the TabNet and CatBoost algorithms, dividing risk levels of electrical misoperation of the transformer substation, setting a risk level quantitative standard and a dynamically optimized risk level division criterion, and completing assessment of the electrical misoperation risk of the transformer substation. According to the method, the risk level judgment criterion can be adjusted in real time, and the problem that a traditional static evaluation method cannot adapt to switching operation dynamic changes is effectively solved.

The invention discloses a wetland water quality detection method based on clustering processing. The method comprises the steps of data acquisition, data preprocessing, data preprocessing, clustering processing and wetland water quality detection. The invention belongs to the technical field of water quality detection, and particularly relates to a wetland water quality detection method based on clustering processing, according to the scheme, a covariance matrix of wetland water quality data is dynamically updated, the adaptability of wetland detection to environmental changes is improved, and the accuracy of a detection effect is improved through disturbance analysis and calculation of information gain; feature optimization is carried out by considering information loss and regularization, and the convergence of wetland detection is improved; distance measurement based on feature weight is designed by introducing density adjustment, and an inter-cluster similarity function is designed by introducing a normalization factor and comprehensively comparing all point pairs, so that accurate division of monitoring points is realized; the data is filled based on the LSTM neural network, and clustering evaluation is performed based on the Gaussian mixture model, so that the accuracy of a final water quality detection result is ensured.

The invention provides a tight oil reservoir fluid occurrence type identification method and system based on nuclear magnetic resonance, and belongs to the technical field of petroleum geology exploration and development. The method comprises the following steps: on the basis of comparison between a nuclear magnetic resonance T2 relaxation time cumulative distribution curve of a tight oil reservoir sample and a high-pressure mercury injection pore throat diameter cumulative distribution curve, dividing pore throat types, and converting T2 relaxation time into pore throat diameters; judging occurrence fluids in different types of pore throats through a nuclear magnetic resonance spectrum to obtain fluid occurrence types corresponding to different signal domain ranges, and calculating the relative fluid content of each fluid occurrence type; and based on the signal domain range of each fluid occurrence type of the plurality of samples, establishing a nuclear magnetic resonance fluid occurrence type identification chart, and defining an identification standard of each fluid occurrence type. According to the method, the pore size distribution of the T2 relaxation time is combined with the nuclear magnetic resonance spectrum, the pore size range corresponding to the fluid occurrence type is determined, and the relative content of the fluid is calculated through the signal intensity of each signal domain, so that the accuracy is higher.

The invention discloses an unmanned aerial vehicle management and control method and system based on Beidou grid codes, and a computing device. The method comprises the steps of determining a ground area corresponding to an area suitable for flight; acquiring ground elevation information of the ground area, and extending upwards by a predetermined height based on the ground elevation information to form a flight space area of the unmanned aerial vehicle; performing grid division on the flight space area based on the Beidou grid code so as to divide the flight space area into a plurality of space grid units; periodically acquiring flight information of the unmanned aerial vehicle; based on the flight information of each unmanned aerial vehicle, allocating a corresponding low-altitude task to each subordinate unmanned aerial vehicle through the master control unmanned aerial vehicle, and sending a control instruction to each subordinate unmanned aerial vehicle to control each subordinate unmanned aerial vehicle to execute the low-altitude task; and determining each space grid unit corresponding to the space position information of the unmanned aerial vehicle at each time point, and generating a grid flight path of the unmanned aerial vehicle so as to monitor and manage the flight process of the unmanned aerial vehicle based on the grid flight path.

The invention relates to the technical field of concrete crack detection, in particular to a concrete internal crack detection method based on image-impact echoes, which comprises the following steps: acquiring and marking a concrete crack data set, and preprocessing through a data enhancement technology; capturing multi-scale features of the crack by using an RFB network module, and training a target detection model; the trained model is applied to detect target concrete, the crack degree and coordinates are obtained, and a two-dimensional crack graph is drawn to divide crack areas; then, based on a crack area layout monitoring point, recording an echo signal of the concrete by using an impact echo technology, extracting crack characteristics and inputting the crack characteristics into the machine learning model, and obtaining a trained model to analyze an internal crack of the target concrete; and expanding the two-dimensional crack graph into a three-dimensional crack graph to obtain an actual crack degree. According to the method, the detection efficiency is improved, a visual effect is provided, and an effective technical means is provided for concrete structure crack monitoring.

The invention relates to a carrier CSMA time slot allocation method and device based on dual-mode communication. According to the method, the number and the type of the slave nodes are updated in the current routing period, and then the carrier CSMA time slot is subjected to phase line division in the next routing period, so that the phase line time slot can be more accurately divided, channel conflicts are avoided, on the other hand, dynamic adjustment can be performed based on the number of the corresponding slave nodes in each routing period, and the routing efficiency is improved. Communication resources are utilized to the maximum extent, and the communication efficiency of the system is improved.

The invention discloses a mineral reserve analysis method and system based on multi-parameter clustering, a storage medium and equipment, and belongs to the field of electrical data clustering analysis, and the method comprises the following steps: S1, obtaining the distribution of a plurality of parameters representing the electrical properties of rocks and ores through a geophysical inversion method; s2, extracting different parameters and recording corresponding positions to obtain data sets corresponding to all the parameters; s3, according to the data groups corresponding to all the parameters, carrying out mineral trap pre-division on the to-be-detected area; s4, clustering analysis is carried out in combination with the data sets corresponding to different parameters, iteration revision is carried out on the pre-division result of the mineral reservoir entrainment, and final mineral reservoir entrainment division based on multiple parameters is obtained after iteration is finished; and S5, estimating mineral reserves according to the final mineral trap division. According to the method, the mineral traps are divided by comprehensively utilizing various parameters which are relatively sensitive to electrical parameter distribution of rocks and ores, so that the mineral traps are divided more accurately, and meanwhile, the judgment accuracy and the reserve estimation precision are improved through clustering.

The invention relates to a new energy power distribution network collaborative optimization system based on a distributed regulation and control framework. The system comprises a cluster module, a virtual power plant module, a power distribution network module and an optimization module. The cluster module gathers a plurality of small-capacity dispersedly accessed photovoltaic power supplies to form a plurality of photovoltaic power supply clusters; the virtual power plant modules form a plurality of virtual power plants, each virtual power plant comprises a high-capacity photovoltaic power station, an energy storage device and a load, and the virtual power plants serve as an optimization main body to supply power to the internal load and reversely transmit electric quantity to the power distribution network module so as to generate benefits; and the power distribution network module is used for connecting a plurality of photovoltaic power supply clusters and a plurality of virtual power plants. The invention relates to the technical field of new energy power distribution networks. Dispersed small-capacity photovoltaic power supplies are divided into photovoltaic clusters, large-capacity photovoltaic power stations are combined with energy storage and loads to form a virtual power plant, it is guaranteed that the photovoltaic power supplies connected in different forms fully participate in collaborative optimization, and the adverse effect of photovoltaic power generation intermittency and randomness on a power distribution network is reduced.

The invention discloses a power grid project database processing method and device, equipment and a storage medium. The method comprises the steps that all GIM initial data of a target power grid project and unified management of multi-version and multi-type data are acquired; extracting header information of each piece of GIM initial data, and determining an extension file corresponding to each piece of GIM initial data according to the header information; each piece of GIM initial data is stored in each data table structure; processing each data table structure according to a relational database management system technology so as to extract a unique identifier of each piece of GIM initial data; determining primary key information and foreign key information corresponding to each data table structure; and constructing a target database based on each data table structure, the unique identifier, the primary key information and the foreign key information. According to the processing method of the power grid project database provided by the embodiment of the invention, the efficiency and quality of power grid project data management can be remarkably improved, and a solid technical basis is provided for sustainable development and intelligent upgrading of power grid projects.

The invention relates to the technical field of motor position control, in particular to a direct-current servo motor position control method and system based on operation analysis, and the method comprises the steps: obtaining a target direct-current servo motor, obtaining a target model according to the target direct-current servo motor, obtaining a moving position parameter, and obtaining a plurality of parameter adjustment intervals, obtaining a plurality of parameter combinations based on a plurality of parameter adjustment intervals, obtaining an optimal operation state of a target direct current servo motor, obtaining a moving target site, obtaining a current site of the target direct current servo motor, constructing a moving path based on the current site and the moving target site, obtaining curve characteristics of the moving path, dividing the moving path, and obtaining the target direct current servo motor. The method comprises the steps of obtaining multiple sections of divided paths, simulating the divided paths, confirming energy-optimal parameter combinations, summarizing the energy-optimal parameter combinations, obtaining an energy-optimal parameter combination set, and completing direct current servo motor position control based on operation analysis on the basis of the energy-optimal parameter combination set. According to the invention, efficient energy consumption management and accurate position control can be realized.

The invention belongs to the technical field of bioinformatics, and particularly relates to a single-cell multi-omics data clustering framework based on deep collaborative contrast learning. According to the framework, the association relationship between cells is fully utilized, and meanwhile, the feature interaction of the same cell between different omics data is further enhanced, so that efficient clustering of multiple omics data is realized. In order to make full use of topological information between cells, a graph self-encoder and a feature information enhancement module under different omics are designed, and extraction and information enhancement of cell features are achieved. Meanwhile, a comparative learning technology is introduced to enhance interaction among different omics characteristics of the same cell, and finally single-cell multi-omics clustering is realized through a multi-omics deep collaborative clustering module.

The invention provides a multi-scale local contrast attention infrared small target detection method, and the method comprises the steps: inputting an initial infrared image input feature into a preset backbone network for feature coding, and inputting at least one obtained down-sampling infrared image feature and an integrated infrared image feature into a feature enhancement module, the feature enhancement module comprises a plurality of attention modules which are respectively used for processing each down-sampling infrared image feature and processing integrated infrared image features, and the attention modules are used for performing multi-scale channel attention feature and multi-scale space attention feature fusion on input features to obtain attention features; inputting the attention features of different scales output by each attention module into a feature fusion module to obtain fused infrared image features; and inputting the fused infrared image features into a prediction module to obtain a result image of infrared small target segmentation in the infrared image. According to the method, the infrared small target can be effectively segmented.

The invention provides a global distribution knowledge guided cabbage leaf ball section type region division method and device and a medium, and belongs to the technical field of agricultural informatization. The method comprises the following steps: collecting spectral information of regions with different color types of a cabbage leaf ball profile, and performing feature analysis; calculating hyperspectral characteristic indexes of different types of regions of the cabbage leaf ball section based on the original wave band reflectivity; the method comprises the following steps: acquiring a hyperspectral image of a cabbage leaf ball section by using a hyperspectral imager, and obtaining initial spectral features of different types of regions of the cabbage leaf ball section by using a hyperspectral feature index calculation method; constructing a Chinese cabbage leaf ball section type region division model; inputting the initial spectral features into a Chinese cabbage leaf ball section type region division model to train the model; and collecting a to-be-detected cabbage leaf ball section hyperspectral image, and inputting the image into the model for region division. According to the invention, rapid and accurate division and identification of different types of areas of the cabbage leaf ball profile can be realized, and the division precision and the working efficiency are greatly improved.

The invention relates to the technical field of reclaimed water treatment, and discloses an intelligent reclaimed water treatment method which comprises the following steps: acquiring real-time water quality data and historical water quality data of to-be-treated reclaimed water; establishing a water quality scoring model according to the historical water quality data, inputting the real-time water quality data into the water quality scoring model, and outputting multiple influence factor scores of the water quality; generating a comprehensive score of the to-be-treated reclaimed water through scores of various influence factors of the water quality; establishing a reclaimed water treatment model library, selecting a corresponding reclaimed water treatment model in the reclaimed water treatment model library through the comprehensive score of the to-be-treated reclaimed water, and generating a reclaimed water treatment strategy; and executing the reclaimed water treatment strategy, detecting the reclaimed water treatment process, and updating the reclaimed water treatment model library according to a detection result. Through real-time monitoring and automatic adjustment of processing parameters, the automation level of the processing process is improved; therefore, manual intervention is reduced, water quality changes can be quickly responded, and the stability of the treatment effect is kept.

The invention provides a well-seismic combined intelligent stratigraphic contrast method. The well-seismic combined intelligent stratigraphic contrast method comprises the following steps: step 1, making a logging curve sample set; 2, encoding and decoding the original data, and constructing a deep learning framework; step 3, performing different proportion data set experiments and error analysis; and 4, optimizing and correcting an intelligent stratigraphic division result to obtain final horizon division, and realizing well-to-seismic joint intelligent stratigraphic comparison. According to the well-seismic combined intelligent stratigraphic comparison method, the result is analyzed and optimized by utilizing a well-seismic combined method, so that accurate division of geological stratification can be realized, the operation efficiency can be improved, and the research period can be shortened.

The invention discloses a marine clastic rock reservoir modeling method based on geological cause information, which relates to the technical field of geological modeling, and comprises the steps of collecting and preprocessing modeling data, obtaining preprocessed data, performing geological background research on a target area, performing profile analysis and interface identification on the preprocessed data, and obtaining a marine clastic rock reservoir model. Extracting and converting inclination angle information from the preprocessed data, identifying and quantifying interlayer features, generating a probability distribution constraint body, performing sequential indication modeling, and verifying, optimizing and applying the model; according to the method, multi-source data is integrated to deeply research the sea entering and sea exiting rule, an interface is accurately identified, a sedimentary facies belt is divided, a foundation is laid for reservoir analysis, dip angle information is innovatively utilized, the dip angle information is converted into a probability value and fused into sequential indication modeling, interlayer distribution is accurately simulated in combination with reservoir characteristics and a variation function, and the reservoir analysis result is obtained. And the characterization capability of the model on a complex reservoir structure is obviously improved.

The invention provides a model training method and a related device. The method comprises the steps of obtaining a training sample set; inputting the training sample set into the initial model, and determining a classification prediction result, a first image quality score and a second image quality score corresponding to each medical image; determining a total loss function based on the classification label result, the classification prediction result, the first image quality score and the second image quality score; and adjusting parameters of the initial model based on the total loss function until the training is completed to obtain an image evaluation model. The first image quality score is used as a quality label, for each medical image, the quality label can dynamically learn a quality evaluation standard from a training sample set, the quality label does not depend on traditional manual labeling, the first image quality score is calculated through the relative classifiability of the medical images, and the quality evaluation accuracy of the medical images is improved. The accuracy of the quality label can be greatly improved by considering the similarity, and the image evaluation model can accurately and quickly perform classification and quality evaluation on the medical image.

The invention provides a multi-group behavior recognition method based on space-time interaction reasoning, and mainly relates to the problem of how to effectively mine space-time interaction so as to carry out individual behavior, group clustering and multi-group behavior recognition. According to the method, a space-time interaction relationship reasoning strategy is put forward to pay attention to interaction relationships between individuals and between groups, meanwhile, features such as appearances, position distances and space-time trajectories are considered, and the accuracy of behavior recognition is improved. Firstly, the interaction relation between individuals is explored by analyzing features of nearest neighbors of the individuals and position distance features. Secondly, dividing the individuals into a plurality of small groups in combination with behavior similarity and spatio-temporal trajectory characteristics; and finally, a group interaction reasoning module is constructed, interaction among different small groups and individuals is mined, and the individuals having small influence on group behaviors are excluded according to importance. According to the method, the space-time interaction characteristics of the individuals and the groups are fully considered, and the problem that space-time interaction characteristics are not fully mined in behavior recognition is solved.

The invention provides a risk grading method of a power grid system and a risk management and control system of the power grid system, and belongs to the technical field of risk grading management and control of the power grid system, and the method comprises the following steps: planning a power grid system line, and dividing the power grid system into a plurality of risk lines; respectively inputting the plurality of risk lines into a risk management and control system, and respectively setting basic risk parameters for the plurality of risk lines; basic risk parameters of adjacent risk lines are coupled based on the basic risk parameters of the main risk line, and correction risk parameters of all the risk lines are generated in a risk management and control system; based on the correction risk parameters of the risk lines, the historical maintenance parameters of the corresponding risk lines are coupled, and the multiple risk lines are divided into high-risk lines, medium-risk lines and low-risk lines according to the comprehensive risk parameters. The risk grading method for the power grid system provided by the invention is higher in accuracy, smaller in error and beneficial to risk management and control of the whole power grid system.

The invention discloses a method and a system for automatically intercepting a high-frequency signal in a constant-speed operation section of mechanical equipment. The method comprises the following steps: acquiring signal data of the mechanical equipment; determining the size and the step length of a first sliding window processing window; performing sliding window processing on the original signal, calculating the variance of each window signal, and performing data compression to obtain a first signal; visually judging whether signals in different operation states in the first signals are in the same order of magnitude or not; determining the window size and step length of second-time window sliding processing; performing sliding window processing on the compressed first signal, calculating the variance of each window signal, and performing data compression to obtain a second signal; determining a threshold magnitude order of data distinguishing of the operation section and the non-operation section; finding out a starting point and an ending point of the operation section, and restoring to original signals to obtain operation section signals; calculating a central point of an operation section, calculating constant-speed operation time according to an actual working condition, and restoring to an original signal; and error analysis is carried out through a starting point and end point signal interception mode and a midpoint signal interception mode.

The invention discloses a website logistics display method and device, equipment and a storage medium, and the method comprises the steps: obtaining a plurality of target websites and a target service range of each target website; determining a target service boundary coordinate corresponding to each target service range; reverse geocoding is carried out on each target service boundary coordinate, target area information of each target website is determined, and the target area information comprises a target area name and a target area code; a target interface is constructed, and the target interface takes the target area code as a key and takes target website group information corresponding to the target area code as a value; and in response to a trigger operation on a target area on the preset map display interface, calling the target interface to query area information of the target area in the target database so as to enable the area information to be displayed in a popped target pop-up box. According to the scheme, a multi-dimensional data view angle can be provided for a user, and the visualization degree of information is improved.

The invention relates to the technical field of medical image processing, and particularly discloses a correction method and device after liver segmentation, electronic equipment and a storage medium, and the method comprises the steps: obtaining a liver segmentation result; removing external noise of the liver segmentation result; temporarily combining the eight sections of liver regions into five sections of liver regions according to the types of liver lobes; performing interleaf correction processing on the five liver regions according to the connected domain contact relationship corresponding to each type of liver region; recovering the five sections of liver regions after the interleaf correction treatment into eight sections of liver regions; three single-leaf two-section areas of the recovered eight-section liver area are extracted, and intra-leaf correction processing is carried out on the three single-leaf two-section areas; according to the method, the segmentation result is optimized step by step in a temporary merging and step-by-step correction mode, and finally more accurate eight-section liver region division is obtained, so that the accuracy of liver region segmentation is improved, and a more reliable basis is provided for subsequent medical analysis and diagnosis.

The invention provides an expressway business form extraction method and system based on a neural network, and the method comprises the steps: obtaining to-be-processed sub-business forms, carrying out the data cleaning, and generating rule data; performing compliance verification on the rule data based on the agreed rule, and if the verification is qualified, storing the rule data as a standard business form according to the category; traversing each standard service form in sequence, identifying coincident entries, taking the coincident entries as association keys, and extracting a high-speed service form set belonging to the same user based on the association keys; and inputting the high-speed business form set into a pre-trained statement extraction model to generate a passing behavior statement. According to the invention, by integrating the expressway business forms, standardized processing and efficient management of data are realized. According to the method, the data compliance is ensured, the business forms of the same user are identified and associated, the traffic behavior statements are generated by using the statement extraction model, and convenient and accurate information support is provided for expressway business management.

The present application relates to the technical field of image state recognition, in particular to a kind of based on pattern recognition's picking and replacing hook AI accurate identification and capture system and method, in the present application, the outline change of hook assembly in image sequence frame, boundary difference and gray dynamic are carried out node construction, and combined with edge displacement accumulation analysis, while through graph neural network, cosine value between nodes and coordinate difference are compared jointly, construct path jump sequence, to enhance the response sensitivity of state mutation, under the condition of complex background interference or local occlusion, still can stably extract the key path of morphological evolution, effectively improve the anti-interference and fault tolerance of spatial path recognition, further statistical modeling is carried out on state rate mutation point by hidden Markov model, by matching standard state mode, paragraph merging and invalid segment rejection operation are carried out to abnormal point section, construct state label sequence, realize the accurate division of high confidence, multi-section continuous state.

The embodiment of the invention relates to the technical field of information, in particular to a data security management and control method and system based on a large language model. The method comprises the following steps: reading target data, and extracting attributes and content keywords of the target data by using a pre-accessed large language model; obtaining a data type of the target data according to the attribute and the content keyword; according to the attribute and content keyword and the attribute and content keyword of historical data, obtaining an association relationship between the target data and the historical data; and obtaining the security level of the target data according to the attribute, the content keyword, the data type, the association relationship and the security level of the corresponding historical data.