439 results about "Classification rule" patented technology

The invention relates to a safe backup and recovery method and system for medical data, and the method comprises the steps: obtaining the multi-dimensional attribute information of the medical data, and carrying out the dynamic classification of the multi-dimensional attribute information based on a preset classification rule, and obtaining an attribute information label; performing differential encryption on the medical data according to the attribute information tag to obtain encrypted medical data, writing the encrypted medical data into a multi-level storage node, and performing multi-level backup construction to obtain a cross-platform backup topology; performing traceability node extraction on the multi-level storage nodes according to the abnormal node data access behavior to obtain traceability node information, and performing data traceability verification on the traceability node information according to the backup topology configuration to obtain a trusted recovery chain; and performing recovery node selection on the multi-level storage nodes according to the trusted recovery chain, and performing multi-node collaborative decryption reconstruction to obtain a recovery verification report. According to the method, the redundancy storage cost can be reduced while the data recovery reliability is guaranteed.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention provides a building energy-saving control system based on centralized control of the Internet of Things, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting personnel activity data, environment parameter data and equipment state data in a building in real time; the data preprocessing module is used for carrying out cleaning, denoising and time sequence alignment processing; the area use mode recognition module is used for analyzing the use conditions of equipment in different areas in the building according to a multi-layer classification rule; the energy-saving control strategy generation module is used for analyzing equipment in the building and generating a region-level energy-saving control instruction set; the execution control module is used for controlling an air conditioning system, a lighting system and an elevator system in the building in real time; the feedback regulation module is used for monitoring real-time feedback data of the equipment operation parameter set and feeding back to the energy-saving control strategy generation module in combination with environmental parameter data change; according to the invention, autonomy and accuracy of building energy-saving control are improved.

The invention discloses an intelligent data query method and device based on a large model and a medium, and relates to the technical field of large model application. The method comprises the steps of receiving a natural language query text input by a user, and determining a target interface group from pre-associated interface groups through a preset business line classification rule and a keyword matching algorithm; inputting the natural language input text into a pre-trained large model to analyze a parameter set required by interface calling; wherein the parameter set comprises at least two of the following items: a time range field, a business main body field and a statistical dimension field; calling an API interface in the target interface group according to the parameter set, and obtaining original business data; performing context supplementation on the original business data based on the business logic rule to generate a standardized data result containing business background description; and reconstructing the standardized data result according to a display format preset by a user, and outputting a combined visual result containing charts, tables and text description.

The invention relates to the technical field of text classification, and provides a text classification method based on a large model and a rule engine, and the method comprises the steps: S1, storing multi-level rule classification labels, and constructing a classification rule template library; s2, receiving text data from various data sources, and preprocessing the text data; s3, performing rule matching on the text data based on the classification rule through a rule engine, and outputting a rule classification result; and S4, when any one of the following conditions is met, large language model classification is triggered: a, a classification rule is not matched; b, matching a classification rule, wherein the rule confidence is smaller than a rule confidence threshold; c, the text data length exceeds the preset text data length; d, matching a specific business scene label; outputting a model classification result; and S5, when the rule engine classification in the S3 and the large language model classification in the S4 are parallel, executing the strategy. The output reliability and the service continuity are guaranteed, and the method is suitable for scenes with high accuracy requirements such as financial compliance examination and the like.

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

The invention relates to the technical field of databases, in particular to an intelligent mapping and classifying method based on heterogeneous data sources, which comprises the following steps: collecting heterogeneous data streams through an API (Application Program Interface) gateway and converting the heterogeneous data streams into structured data packets; using a semantic topology engine to fuse BERT semantic extraction, a graph convolutional network and a dynamic time warping technology to generate a cross-source association graph; constructing a field type clustering center by adopting a meta-learning framework based on the atlas, generating an initial classification rule through mode compatibility measurement, and dynamically updating the rule by means of adversarial training; outputting a DSL configuration script in combination with a template engine and an AST compiling technology; and dynamically adjusting a graph convolution weight and classifier parameters by using a strategy gradient algorithm through a reinforcement learning agent, and establishing a mapping-classification-verification collaborative optimization mechanism. According to the method, cross-source data semantic association accuracy is improved, small sample adaptive classification is realized, and system robustness and efficiency are improved.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and system based on dynamic data monitoring, and the method comprises the following steps: obtaining geological displacement monitoring data, setting a time window range, respectively calculating a local extreme point and a local zero point of the monitoring data in a time window, and calculating the local extreme point and the local zero point of the monitoring data in the time window; obtaining a multi-scale reference value; and based on the multi-scale reference value, calculating a comprehensive statistical magnitude, and generating a data quality evaluation parameter. According to the method, a local extreme point and a zero point are dynamically calculated by setting a time window range, a multi-scale reference value is constructed to carry out comprehensive statistic generation and data evaluation, and collaborative discrimination of piecewise linear regression and threshold comparison are executed to form a critical index; and performing characteristic quantity recursive residual spectrum analysis and matrix decomposition operation by combining the power spectral density distribution and the spectral density matrix to establish an early warning characteristic sequence, and realizing multi-stage early warning indication output based on a grading rule.

The invention discloses a multi-mode teenager idiopathic scoliosis screening method based on a back RGB-D image, and belongs to the technical field of image processing and scoliosis screening, and the method specifically comprises the steps: collecting an RGB-D image pair of a subject in an Adams anteflexion posture, carrying out the manual marking of an RGB color image, constructing and training a back key point detection model, and carrying out the detection of the back key point. And establishing a calculation rule of a maximum trunk rotation angle, a minimum trunk rotation angle and a back contour asymmetry index, and establishing an AIS classification rule based on the RGB-D data set, thereby completing AIS classification of the to-be-detected subject. According to the non-contact radiation-free AIS screening method, comprehensive utilization of back two-dimensional and three-dimensional information is achieved by means of data complementation of the RGB mode and the D mode, the problem of feature deficiency in a single mode is solved, and the method has the advantages of being fast, efficient, robust and objective.

The invention relates to a data transmission method and device, a DTU and a cloud, and the method comprises the steps: obtaining original perception data collected in a preset time period; dividing the original sensing data according to a preset importance degree division rule to obtain high-priority data, medium-priority data and low-priority data; the high-priority data, the middle-priority data and the low-priority data are compressed according to different compression strategies respectively, a high-priority data packet, a middle-priority data packet and a low-priority data packet are obtained, and the compression ratios of the compression strategies corresponding to the high-priority data, the middle-priority data and the low-priority data are sequentially increased; and uploading the low-priority data packet, the medium-priority data packet and the high-priority data packet to the cloud. According to the embodiment of the invention, the method can prevent the perception data with low importance from wasting transmission resources, reserves more transmission resources for the perception data with high importance, and guarantees the timely, accurate, smooth and efficient transmission of the perception data with high importance.

The invention provides a data classification and grading method and device based on an artificial intelligence large model technology, and relates to the field of data security. The method comprises the following steps: constructing a data classification and grading catalog according to a preset classification rule, and storing each row of the classification and grading catalog as a document record in a target vector database; respectively carrying out first retrieval operation, second retrieval operation and third retrieval operation on the data classification and grading catalog by taking the business system name, the data table name and the field name as first query conditions so as to obtain a first matching document, a second matching document and a third matching document in the target vector database; sorting the first matching document, the second matching document and the third matching document according to a preset sorting mode, and obtaining a sorting result; and inputting a sorting result into the large language model, and performing data security early warning through the large language model. According to the method and the device, the security problems that corresponding security measures cannot be taken, sensitive data is leaked and the like when a data governance engineer identifies the metadata according to the data classification and classification catalog and judges the classification and security level of the metadata are solved.

The invention provides a data classification method, device and equipment and a computer storage medium, and the method relates to the technical field of computers, and is used for improving the efficiency and accuracy of data classification and improving the flexibility of data classification. The method comprises the following steps: acquiring data features corresponding to to-be-classified data; obtaining a corresponding query condition based on the data feature, and obtaining at least one target classification rule matched with the to-be-classified data from a preset classification rule set based on the query condition; inputting the to-be-classified data and at least one target classification rule into a trained label generation model, and generating at least one data classification label corresponding to the to-be-classified data based on description information included in the at least one target classification rule and the data features corresponding to the to-be-classified data; wherein each data classification label represents one data type; and based on the at least one data classification tag, determining a target classification result corresponding to the to-be-classified data.

The invention provides a medical record information extraction and analysis method and system based on a deep learning large model, and the method comprises the steps: carrying out the semantic understanding of a medical record text through a deep learning model, and recognizing key clinical features; extracting the key clinical features, and carrying out vectorization processing on the extracted key clinical features; performing sentence segmentation and word segmentation on the case text based on a rule engine and the key clinical features; adjusting the segmented words based on a symptom classification rule; and carrying out word frequency statistics on the adjusted segmented words. According to the method, accurate recognition and structured conversion of key clinical features can be ensured through a semantic understanding technology, knowledge graph construction and machine learning model optimization are supported, and a new view angle and a new tool are provided for exploration and research of diseases.

The invention relates to the technical field of urban space information processing, in particular to an urban functional area fine classification method based on multi-modal data collaboration. According to the method, POI data, AOI data, land utilization data, remote sensing images and other multi-source heterogeneous data are fused, a multi-dimensional feature system is constructed, and an urban functional area classification model covering a single functional area, a composite functional area, a mixed functional area and a non-functional area is provided. According to the method, firstly, block-level basic units are constructed based on an open street map (OSM), then coordinate transformation and function classification are carried out on POI and AOI data, features such as POI density, AOI area proportion and land utilization structure are extracted respectively, and finally, function type identification is carried out on urban block units according to set classification rules. Compared with the prior art, the method has the advantages that through multi-source information fusion and discrimination logic optimization, the accuracy and applicability of urban functional area recognition are remarkably improved, and the method is suitable for scenes such as urban planning, land utilization evaluation and urban management.

A method and system for controlling a heating, ventilation, and air conditioning system (HVAC) in a building based on adaptive model predictive control (MPC) is disclosed, and belongs to the technical field of intelligent building adaptive control. The method may include: acquiring operation data of the HVAC under different working conditions, and performing preprocessing on the acquired data; calculating a marginal fitness measure (MAD) of each data point for each class based on the preprocessed data, and calculating a fitness measure of each data point for each class by combining MADs of all descriptors to obtain a global fitness measure (GAD); setting a GAD threshold, and classifying current operation states of the system according to classification rules; and adopting adaptive control strategies for different operation states since model parameters of each class are fixed after classification.

The invention relates to the technical field of tea quality evaluation, and particularly discloses a tea quality digital comprehensive evaluation system based on multi-source data fusion, and the system comprises a multi-source data collection module which is used for obtaining multi-dimensional data of tea; the multi-dimensional feature extraction module is used for extracting chemical quality parameters, physical characteristic parameters and process correlation parameters and generating a comprehensive quality embedding vector; the digital evaluation model construction module is used for generating a quality grade division rule and a quality defect traceability path; and the tea quality comprehensive evaluation module is used for generating a multi-dimensional quality evaluation report according to the quality grade division rule and the quality defect traceability path. Through three technical breakthroughs of multi-source heterogeneous data fusion, intelligent algorithm collaborative analysis and dynamic feedback control, the method has remarkable advantages in the aspects of accuracy, efficiency, traceability, industrial application value and the like, and an innovative solution is provided for standardized production and quality control of tea leaves.

The invention relates to the technical field of Internet of Things science and technology, and discloses an intelligent processing method, device and equipment for Internet of Things data and a medium, and the method comprises the steps: obtaining initial data which are transmitted and collected by different types of equipment to a platform unified interface of an Internet of Things platform; sorting and classifying the initial data, and distributing the classified initial data to different first message queues according to classification rules of classification; after the initial data passes through the calculation engine, calculating the initial data to obtain different efficient data, and storing the different efficient data into different second message queues; and sending the efficient data conforming to the preset alarm calculation rule to a preset large model to obtain an intelligent processing instruction, and pushing the intelligent processing instruction to a task arrangement service for arrangement so as to be read and executed by a corresponding external device.

There is provided a system and method of defect examination on a semiconductor specimen. The method comprises: obtaining an inspection dataset informative of a group of defect candidates and attributes thereof resulting from examining the specimen by an inspection tool; classifying, by a classifier, the group of defect candidates into a plurality of defect classes such that each defect candidate is associated with a respective defect class; and ranking, by a decision model, the group of defect candidates into a total order using a sorting rule. Each defect candidate is associated with a distinct ranking in the total order representative of the likelihood of the defect candidate being a defect of interest (DOI). The decision model is previously trained to learn the sorting rule pertaining to the plurality of defect classes associated with the group of defect candidates and a series of attributes in the inspection data.

The invention discloses an AI-based archive digitization full-process management and control method, which comprises the following steps: acquiring archive image data and text data, constructing an original archive data set, and generating an initial directory set; constructing an archive directory template set, executing template matching and structure adaptation, and generating an initial structure recognition result; setting a classification rule of directory deviation and structural contradiction, constructing a deviation rule base, executing rule scanning and node comparison, and generating a deviation contradiction label sequence; constructing an evolution state vector, inputting the evolution state vector into an improved GMN model, and generating a structure repair state vector through a structure guiding unit, a deviation sensing unit, a path fusion unit and a node evolution unit in sequence; and executing result updating and conflict rearrangement, completing standard template alignment and semantic coverage verification, and generating a final structure recognition result. According to the method, the structural integrity, the semantic consistency and the automatic repair capability of directory recognition are improved, and the method has relatively high adaptability and intelligent level.

The invention relates to the technical field of archive management, and particularly discloses an archive classification management system based on artificial intelligence, which comprises the following modules: a multi-modal data acquisition module used for integrating texts, images, audios and metadata to generate fusion feature vectors; the dynamic classification engine is used for dynamically optimizing classification rules based on a Transform model and a reinforcement learning framework; the block chain evidence storage module is used for recording a classification operation hash value by using an intelligent contract; the intelligent life cycle management module is used for automatically triggering archiving and destroying processes according to the classification labels; and the self-adaptive value evaluation module is used for dynamically generating a value report through a time sequence neural network. The method has the beneficial effects that 1, the classification efficiency and accuracy are improved; 2, file values are fully excavated; 3, the system adaptability is enhanced; and 4, file safety is guaranteed.

The invention relates to the technical field of researching or analyzing materials by testing chemical or physical properties of materials, in particular to an intelligent analysis method for corrosivity of sulfide molecules in transformer oil. The influence of the oil environment on the adsorption energy is simulated and corrected in combination with molecular dynamics, a microscopic parameter database is constructed, and a corrosion risk grade classification rule and a generation rate prediction model are established. Oil data are collected in real time and input into the model to output a dynamic risk level, and a circulation rate adjusting or corrosion inhibitor adding instruction is triggered. Model parameters are regularly corrected by combining a closed-loop feedback mechanism, and a microscopic database is expanded, so that transfer learning prediction of unknown sulfide corrosion behaviors is realized. According to the method, the limitation of an existing single experiment condition is broken through, the relevance between a microcosmic molecular mechanism and a macroscopic corrosion behavior is dynamically analyzed, and the prediction precision under the multi-factor coupling effect is improved.

The invention provides a refractory material quality detection method and system based on machine learning, and the method comprises the steps: collecting a multi-angle surface image set of a refractory material sample, covering images under different illumination conditions, so as to comprehensively capture material surface features; a pre-trained image feature analysis model is called to extract surface texture features, pore distribution features and microscopic crack features, and an image feature set is formed; performing joint classification processing on the image feature set based on a preset defect classification rule base, and identifying and quantifying defect types and distribution parameters thereof; generating a quality evaluation parameter according to the defect type set and the distribution parameter, comparing the quality evaluation parameter with a preset quality standard threshold value, and outputting a quality detection result; and a detection result is fed back to a production line control system, and sorting operation and process parameter adjustment are automatically triggered to realize intelligent monitoring and optimization of the production process, so that the accuracy and efficiency of quality detection of the refractory material are improved, and the improvement of the production quality is promoted.

The invention provides a screen mixed content classification method and system oriented to efficient compression, and relates to the technical field of screen mixed content classification, and the method comprises the steps: obtaining a screen mixed content image; performing block processing on the screen mixed content image to obtain a plurality of target blocks; determining a first category classification result of each target block in a feature-based classification mode; constructing an improved convolutional neural network model; determining a second category classification result of each target block through an improved convolutional neural network model; and based on the first category classification result and the second category classification result, determining a final category of each target block through a preset classification rule. According to the invention, by fusing the classification results of the first category and the second category, collaborative optimization of the traditional features and the depth features is realized, so that the image content recognition precision in a complex scene is remarkably improved.

The invention is suitable for the technical field of privacy data processing, and provides a privacy grading and sharing control method for tourist travel itineraries, and the method comprises the steps: obtaining travel itinerary data of tourists; dividing the travel data into at least three basic privacy levels based on a preset region sensitivity classification rule, a user identity classification rule and an equipment type classification rule; in the basic privacy level, performing fine-grained sub-level division on the data according to interval division of travel duration, classification of activity risk levels and a historical sharing frequency threshold; based on the basic privacy level and the fine-grained sub-level, geographic position fuzzification and user identity anonymization processing are carried out on the travel data of the high-sensitivity level, and the access range of a shared object is limited through permission grading; and the sensitivity of the fine-grained sub-level is adjusted according to the real-time sharing frequency, and the sharing permission is updated, so that the accurate balance of the travel itinerary data between privacy protection and sharing effectiveness can be realized.

The invention relates to the technical field of data management, in particular to an intelligent data asset classification and archiving management system, which comprises a fuzzy semantic feature dynamic deconstruction module for decomposing and reconstructing semantic fuzzy key features in data by utilizing self-adaptive hierarchical deconstruction; the rule generation module is used for inputting hierarchical features and constructing a classification rule tree based on a semantic hierarchical feature map, and rules comprise semantic sensitive rules and classification generation rules; and the fuzzy classification multi-task decision module is used for mapping the classification rules to a plurality of classification paths, distributing initial weights, calculating the classification credibility of each classification path and outputting an optimal path and a classification result. According to the method, the classification efficiency is improved, path missing or misjudgment possibly existing in a traditional method is avoided, meanwhile, a fuzzy conflict analysis module is canceled, the system process is simplified, and the real-time performance is improved.

The embodiment of the invention provides an RTL parallel test method and system for an RISC-V DSP chip and a medium, and the method comprises the steps: obtaining a plurality of test items, classifying the plurality of test items based on a classification rule, and obtaining different types of test items; constructing a plurality of test channels based on an RTL test framework, and obtaining test parameter information of each test channel; matching different types of test items with the test parameters of each test channel to obtain a matching degree, and judging whether the matching degree is greater than or equal to a set matching degree threshold; if yes, the corresponding test channel is matched, and if not, unmatched test items are shielded through the test channel; testing the plurality of test items in parallel according to the matched test channels; the test items are classified by analyzing the categories of the test items, and different test items select different test channels, so that parallel testing of different categories of test items is realized, and the test efficiency is improved.

The invention discloses an idle land recognition method, device and equipment and a storage medium, and belongs to the technical field of machine learning, and the method comprises the steps: obtaining a first land feature set based on the land text data of a plurality of pieces of land; screening the first land feature set to obtain a second land feature set; semi-supervised learning cooperative training is carried out on a first classifier and a second classifier based on the second land feature set, the first classifier and the second classifier are used for idle land identification, the first classifier is realized based on a rotating forest algorithm, and the second classifier is realized based on an extreme gradient lifting algorithm; wherein in the semi-supervised learning cooperative training process, the prediction labels are screened based on a preset idle land classification rule. The method can realize accurate and efficient idle land identification.

The substation inspection management system and the inspection data classification method comprise the following steps: accessing the inspection management system through a single user login mechanism, scanning a two-dimensional code of substation equipment to obtain equipment identification information, loading historical operation data and classification rules of the equipment from a pseudo database, and generating an inspection task identifier; acquiring a state image, an operation parameter and an environment feature of the equipment according to the task identifier, binding the task identifier and the equipment identifier, and adding a timestamp to generate a multi-modal calibration data set; extracting a time migration feature, a frequency domain anomaly feature and a load change feature based on historical data, and constructing a feature set; classifying the calibration data set according to a classification rule, and optimizing the classification rule to generate a preliminary classification result; carrying out consistency comparison on the preliminary classification result and historical data, and if the result is inconsistent, correcting the calibration data and carrying out re-classification; and finally, a classification result is stored in a pseudo database, a temporary cache is cleaned after a task is completed, abnormal data is reserved, and a classification rule is optimized in combination with user feedback.

The invention relates to an automatic driving risk identification model training method and system, and a risk assessment method and system, and belongs to the technical field of vehicle automatic driving. The automatic driving risk identification model training method comprises the steps of performing time synchronization processing on multi-source sensing data to form a driving data set, and performing category labeling on the driving data set according to a set classification rule; the categories comprise traffic infrastructures, traffic auxiliary facilities, traffic participants and traffic environments; performing physical state information labeling on the driving data set subjected to category labeling to obtain a preprocessed data set; generating character risk cues describing different driving scenes based on the preprocessed data set; risk assessment is carried out on different driving scenes, risk states of the driving scenes are marked based on risk assessment results, and a set number of risk targets are marked for the driving scenes which are confirmed to be risk scenes; and forming a sample set by using the character risk prompt words, the risk states and the risk targets corresponding to different driving scenes.

Described embodiments provide systems and methods for mapping high-priority WiFi packets to low latency data pipe. An access point can receive the one or more low latency classification rules from a device monitoring one or more low latency classification rules at a cable modem termination system (CMTS). The access point can be in communication with the CMTS and provide wireless access to one or more devices in a local network. The access point can receive packets corresponding to the one or more low latency classification rules from the CMTS. The access point can communicate, based at least on the one or more low latency classification rules, the packets to a queue used for transmission of low latency traffic to the one or more devices in the local network.