96 results about "Schema analysis" patented technology

The invention discloses an unmanned aerial vehicle monitoring and countering integrated system, and relates to the technical field of unmanned aerial vehicle monitoring, and the system comprises a sensing module which is used for carrying out the monitoring of an environment through a plurality of sensors, and obtaining original data; the fusion module is used for processing the original data by adopting a data fusion algorithm to obtain target information; the identification module is used for outputting the type and behavior mode of the unmanned aerial vehicle; the analysis module is used for evaluating the type and behavior mode of the unmanned aerial vehicle by adopting a dynamic threat evaluation method and generating early warning information; the strategy module is used for formulating a dynamic countering strategy; the execution module is used for implementing corresponding countering measures and monitoring the countering effect in real time; and the recording module is used for recording all results in the whole process. Through the technical means of multi-sensor cooperative monitoring, data fusion processing, classification identification, dynamic threat assessment, game decision making, intelligent countering and the like, omnibearing perception, accurate identification, intelligent assessment and efficient disposal of the unmanned aerial vehicle are realized.

The invention relates to the technical field of cost assessment, in particular to a civil construction project cost assessment optimization system, which comprises a feature analysis module, a cost estimation module, a real-time monitoring module, a model training and tuning module, a risk analysis module, a decision support module, a financial simulation module and a reinforcement learning cost adjustment module. According to the invention, a convolutional neural network algorithm and a principal component analysis method in the feature analysis module excavate building information model data and screen key features, and the cost estimation module combines a multi-layer perceptron neural network and a linear regression model to enhance the cost mode analysis capability and calculate the project cost. The digital twinborn technology and the dynamic programming algorithm realize tracking of project progress and real-time monitoring of cost change in the real-time monitoring module, and the genetic algorithm and the machine learning algorithm realize continuous optimization and self-learning of model parameters in the model training and tuning module. And a Monte Carlo simulation method and a sensitivity analysis method are used for identifying and evaluating project risks in the risk analysis module.

The invention belongs to the technical field of transformer data processing, and particularly relates to a power transformer data cleaning method and system. The method mainly comprises the following steps: obtaining original data of the power transformer, carrying out abnormal data detection and correction on the original data based on physical rule filtering, outlier detection and time sequence mode analysis, extracting different types of data features from the corrected data, and carrying out cross-modal feature fusion on the extracted different types of data features; constructing a fault propagation reasoning initial model according to the data features after cross-modal feature fusion, the basic information of the power transformer and historical test data; and dynamically iterating the fault propagation reasoning initial model through a dynamic ontology updating mechanism to form a fault propagation reasoning model so as to evaluate the state of the power transformer. The space-time barrier of multi-source data is broken through, efficient integration and collaborative analysis of the data are realized, a unified data base is provided for transformer state evaluation, and the utilization rate of the multi-source data is improved.

The invention relates to the technical field of ultrasonic equipment quality control and evaluation, and discloses an ultrasonic quality control dynamic evaluation method and system, and the method comprises the steps: collecting real-time quality control data of ultrasonic equipment based on multi-dimensional feature fusion and dynamic mode analysis, and constructing a quality control database after labeling and preprocessing. The method comprises the following steps: extracting multi-dimensional features from a database to construct a feature matrix, slicing by using an adaptive window segmentation algorithm, and eliminating redundant sub-matrixes by using a dynamic time warping algorithm to form a global feature tensor and similarity association graph; the method comprises the following steps: acquiring current data in real time, converting the current data into a target feature sub-matrix, calculating a dynamic matching degree with a global feature tensor, outputting reconstructed quality control features through multi-modal fusion, and evaluating a quality control state by calculating deviation and combining a dynamic threshold rule. The method and the system can accurately evaluate the quality of the ultrasonic equipment in real time, improve the diagnosis accuracy and have a wide application prospect.

The invention discloses a cross-linking agent production control system and method based on the Internet of Things, and relates to the technical field of chemical production control, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module for obtaining spectral data in a reaction kettle and obtaining temperature data and pressure data of different positions on the wall of the reaction kettle; the data processing and control module is used for converting the spectral data into reaction data of a cross-linking agent intermediate product based on a meta-calibration model; on the basis of the temperature data and the pressure data of different positions, state data, including a local overheating area and a pressure abnormal area, of a cross-linking agent intermediate product are identified by adopting an image identification and mode analysis algorithm; and the decision execution module is used for establishing a dynamic model fusing the production characteristics of the cross-linking agent based on the reaction data and the state data, training an ML model by using historical data and real-time IoT data based on the dynamic model, and automatically generating and executing an optimal control strategy under security constraints.

The invention discloses an autism symptom recognition system based on multivariate emotion path analysis, and the system comprises a multi-modal data collection module which is used for collecting the multi-modal data of a participant, and the multi-modal data comprises skin electrical activity data, eye movement fixation data, electroencephalogram activity data and facial expression data; the data feature extraction module is used for extracting multi-modal features from the multi-modal data; the emotion cognition mode analysis module is used for establishing a path relation model of the multi-modal data and the emotion cognition stage variables, establishing a path relation model of the emotion cognition stage variables and the emotion cognition ability, and calculating to obtain a path coefficient representing each modal; and the deep learning module is used for carrying out orthogonal fusion and autism identification on the mapping features of the multiple modes in an inverted order mode according to the path coefficients corresponding to the multiple modes. According to the method, multi-modal data are systematically collected, orthogonal fusion is carried out on multi-modal mapping features in a reverse order mode, and autism identification can be better carried out.

The invention relates to an intelligent agricultural system based on modular collaboration and AI Agent driving. Environmental sensing, video monitoring and equipment operation data are comprehensively acquired through a data acquisition module; the data processing module carries out real-time abnormity diagnosis and mode analysis; the core framework module cooperatively schedules the data storage module, the user management module and the external interface module to realize cross-layer resource and data integration; the business logic module is connected in series with each layer of events to dynamically generate backlogs and drive multi-domain function execution; and the AI service module carries a multi-modal agricultural agent Agent, has the capabilities of natural language understanding, multi-round dialogue memory, agricultural knowledge reasoning, equipment control decision making, abnormity pre-judgment and personalized suggestion generation core AI, coordinates operation of each module, and realizes intelligent jump from passive response to active suggestion. According to the invention, the problems of response lag, operation splitting and data analysis fragmentation of an existing agricultural system are solved, and the real-time performance, the collaboration and the decision accuracy of agricultural management are remarkably improved.

The invention provides a geographic information surveying and mapping data intelligent analysis method and system, and relates to the technical field of surveying and mapping data analysis, and the method comprises the steps: determining a to-be-analyzed region, and obtaining geographic information surveying and mapping data; gridding the to-be-analyzed region to form a plurality of spatial positions, constructing a time sequence, and extracting multi-dimensional trend feature vectors in different time windows based on the time sequence; for each spatial position, screening to obtain a final candidate neighborhood; screening to obtain an effective neighborhood according to the multi-dimensional trend feature vector; performing interpolation processing on the time sequence of the target spatial position based on the effective neighborhood to obtain a complete time sequence of each spatial position; and performing spatio-temporal change mode analysis on the complete time sequence, identifying a spatio-temporal evolution rule and generating a spatio-temporal change analysis report. According to the method, accurate identification and intelligent analysis of the spatial-temporal evolution law of the geographic surveying and mapping data can be realized, and an interpretable analysis result with geographic significance is provided.

The invention is suitable for the technical field of coating detection, and provides a scraper fineness testing method based on machine vision and an intelligent algorithm, and the method comprises the following steps: capturing a particle distribution image through a 4K line scanning camera and a multi-angle LED light source; through Gaussian-median hybrid filtering, adaptive threshold segmentation (calculating a dynamic threshold based on a neighborhood mean value or a Gaussian weighted mean value), sub-pixel edge refinement (bicubic interpolation) and form / color / texture feature extraction, particles, bubbles, scratches and other interference items are distinguished, A-method and B-method dual-mode analysis is supported, and a particle distribution diagram and a form parameter report are generated. According to the method, manual operation errors are eliminated, the recognition precision of particles with the particle size larger than or equal to 2.5 microns is improved, automation and data traceability of the detection process are achieved, and the method is suitable for paint fineness detection within the range of 0-150 microns.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.

The invention discloses a material-environment coupled self-adaptive riveting parameter optimization method and system. The method comprises the following steps: acquiring force data, speed data, position data, environment data and material data in a riveting process; when the force data is greater than a preset force threshold value, adjusting the parameters of the riveting equipment to obtain stable force data; performing fluctuation mode analysis on the stability strength data to obtain an equipment state report; combining the material data with a preset strength prediction model to obtain initial prediction strength; adjusting the initial prediction force according to the comprehensive correction coefficient to obtain the optimal prediction force; and judging whether the equipment parameters need to be adjusted or not according to the predicted optimal force and stable force data to obtain a judgment result, and adjusting the riveting equipment according to the judgment result and the equipment state report. According to the method, the riveting operation stability and the equipment maintenance efficiency can be improved by predicting and adjusting the riveting force in real time and combining long-term data analysis and identification equipment abrasion and material change.

The invention relates to the field of data processing, in particular to an automatic risk identification method and system for an electric energy meter based on big data of the electric energy meter, and the method comprises the steps: collecting operation data of multiple dimensions of the electric energy meter, segmenting the operation data according to a time window with a fixed time length, and obtaining a data block containing an operation data time sequence of each dimension, performing feature extraction on the current data block and constructing a feature vector; inputting the feature vector into a trained isolated forest model, and calculating an original abnormal score; and correcting the original abnormal score to obtain a corrected abnormal score, constructing an abnormal threshold, and judging that the current data block of the electric energy meter has an abnormal risk when the corrected abnormal score is greater than the abnormal threshold. According to the method, the anomaly detection capability of the isolated forest is deeply fused with physical law and time sequence mode analysis, so that the corrected anomaly score not only contains static feature anomaly information, but also integrates dynamic evolution mode information, and the accuracy and reliability of risk identification of the electric energy meter are comprehensively improved.

This invention discloses a product carbon footprint data management system and method based on the Internet of Things (IoT), relating to the field of environmental protection technology. The method includes: collecting raw data from each stage of a product's entire lifecycle to generate carbon genome metadata; calculating the stage-specific carbon emissions of the product at each stage of its lifecycle based on the carbon genome metadata; constructing a carbon genome sequence of the product based on the carbon genome metadata and the corresponding stage-specific carbon emissions, and performing time-series pattern analysis based on the carbon genome sequence; summarizing the stage-specific carbon emissions of each stage of the lifecycle to calculate the product's total lifecycle carbon footprint; generating a standardized carbon footprint index based on the product's total lifecycle carbon footprint and a dynamic benchmark value; calculating the product's carbon footprint for future periods based on historical carbon genome sequence data; and generating carbon reduction operation parameters based on the product's future carbon footprint prediction results. By accurately collecting and correcting data through IoT sensors, combined with carbon footprint prediction, scientific carbon emission management is provided.

The present invention proposes an analysis method for typical enterprise electricity consumption patterns based on multi-dimensional Isolate-Detect change point detection. First, data cleaning is performed on the collected industrial electricity consumption time series data; then, seasonal decomposition is carried out on the data after data cleaning to eliminate the periodic patterns of the data and extract the residual terms of the data; multi-dimensional change point detection is performed on the residual terms obtained after decomposition based on multi-dimensional Isolate-Detect, and only a single change point is detected in each divided time interval, making the change point detection in the multi-dimensional electricity consumption curve more accurate and the calculation efficiency higher; then, a possible set of change points is obtained by combining the mean test statistic and the maximum test statistic; the multi-sequence fusion is used to screen the possible set of change points for the detected set of change points to determine the final change point positions of the electricity consumption data; finally, based on the obtained change point positions, the standardized time series data is divided to extract the typical electricity consumption behaviors of the electricity consumption.

The invention relates to the technical field of mobile application development, and discloses a mobile application framework rapid construction method and system, and the method comprises the steps: analyzing a state persistence code of a to-be-upgraded basic component, and generating a state mode difference report; recognizing an upper-layer component set based on the component dependency graph and extracting a state access mode; analyzing the state persistence code and the data processing logic of the upper-layer component, and generating a state dependence propagation graph; state change propagation analysis is executed, and an upper layer component state field needing synchronous migration is marked; matching the migration strategy to generate a state migration script and arranging an execution sequence according to an inverse topology sequence; and executing the state transition script to complete component hot replacement and state data loading. According to the method, the problem of cascade state inconsistency caused by non-synchronous migration of an upper-layer component state copy when a basic component state structure is changed is solved.

The present application relates to the technical field of medical image processing, in particular to an ANA cell image multi-mode analysis and prediction method, system, device and medium, through a target detection network, interval cells in an original image are detected, a dynamic sampling mechanism is triggered to cut and sample a sliding window of continuous multiple frames of effective images, and a sample image block set obtained by sampling is input into a constructed multi-label classification network, a CBAM attention module and a category decoupling attention module are embedded in a backbone network of the multi-label classification network, enhanced features are extracted, and the probability that a detection image belongs to one or more categories of cell nucleus types is output. The present application reduces background interference through a dynamic sampling mechanism, enhances small sample category recognition ability through category decoupling attention, optimizes multi-label output through category correlation punishment, and improves the accuracy and clinical practicability of ANA cell image multi-mode analysis.

In embodiments, a method includes dynamically detecting a current revision to a software service running within a container and identifying whether the current revision specifies a significant change or not. In response to the identification, the method further includes selecting a time window in which to analyze the service's usage patterns (the “analyzed time window”) and determining whether to include data regarding the service's usage patterns prior to the current revision in the analyzed time window or not. The method further includes recommending a right-sizing implementation for the service based on the usage patterns in the analyzed time window. In embodiments, until data for the entire analyzed time window has been acquired, the method only implements right-sizing recommendations that increase resources available to the service. Once such data has been acquired, the method implements right-sizing recommendations that both increase and decrease such resources.

The invention discloses a power distribution network architecture evolution planning method, system, equipment and medium, and the method comprises the steps: obtaining power distribution network basic data and social power demand driving factors, and obtaining a power distribution network basic data set through data preprocessing; obtaining a typical development scene set through uncertainty modeling and multi-scene generation and reduction; in combination with system operation constraints, site selection and sizing of the multi-port flexible interconnection device are optimized, and a preliminary site selection and sizing scheme is obtained; a multi-stage investment decision model is constructed and solved, and a preliminary staged construction path is obtained; calculating a power supply reliability index through a fault mode analysis method, and evaluating economy in combination with a cost model to obtain a quantitative evaluation result; and performing global sensitivity analysis through a Morse algorithm, and adjusting an optimization strategy to obtain a final power distribution network architecture evolution planning scheme. The flexibility and the reliability of the power distribution network are remarkably improved.

The invention relates to an intravenous anesthesia instantaneous dose regulation and control method and device based on operation stage recognition, and belongs to the technical field of medical big data and intelligent control. A deep learning model is used for carrying out high-dimensional feature fusion and time sequence mode analysis, current and future surgical operation stages are dynamically and accurately identified and predicted, an ideal target drug concentration is dynamically set by querying a built-in knowledge base generated by big data mining, and a model prediction controller is driven to calculate an optimal instant infusion rate, so that the optimal instant infusion rate is obtained. Advanced and accurate drug regulation and control are achieved, a closed-loop feedback mechanism is introduced, parameters are adaptively adjusted on line according to actual physiological reactions, and control individualization and safety are ensured. According to the invention, the crossing from passive feedback to active predictive control is realized, the stability and safety of anesthesia depth are obviously improved, and the method has great clinical application value.

The invention relates to the technical field of image detection, in particular to a multi-modal data video inspection concentration degree analysis and early warning method and system, and the method comprises the steps: collecting face video data, and extracting eye micro-motion features, eyebrow morphological features and forehead myoelectricity visual features in face features; collecting heart rate variability data and galvanic skin response data in the physiological signals; personalized standardization processing is carried out, and Z-score feature vectors relative to the deviation degree of the user's base line level value are obtained through conversion; on the basis of the Z-score feature vector, constructing a time sequence feature vector, inputting the time sequence feature vector into a multi-modal time sequence fusion network for time sequence collaborative mode analysis, and outputting a current concentration degree probability value and a concentration degree prediction value at a future specified time point; and triggering a grading early warning mechanism based on the current concentration degree probability value and the concentration degree predicted value. According to the invention, information complementation is utilized in multi-modal data fusion, so that the robustness and accuracy of state recognition are improved.

An example method includes identifying, using a first NLP agent, a database of an enterprise containing desired data, selecting, using a second NLP agent, a schema analysis tool that is believed to be suited to a type of the database, establishing, using a third NLP agent, a connection to the database, extracting, using a fourth NLP agent, schema information from the database, comparing, using a fifth NLP agent and the schema analysis tool and based on the schema information, a structure of a schema of the database to a structure of a schema of a similar database, determining, based on the comparing, a change to make to the schema of the database, sending, using a sixth NLP agent, an instruction to a network element of the enterprise to make the change to the schema of the database, and validating, using a seventh NLP agent, the change to the schema.

The present application relates to the technical field of image detection, in particular to a multi-modal data video inspection concentration analysis and early warning method and system, the method comprising collecting facial video data, extracting eye micro-motion features, eyebrow shape features and forehead muscle television features in facial features; collecting heart rate variability data and skin electricity reaction data in physiological signals; performing individualized standardization processing, converting into Z-score feature vectors of deviation degree relative to user's own baseline level value; based on the Z-score feature vectors, constructing time sequence feature vectors and inputting into a multi-modal time sequence fusion network for time sequence collaborative mode analysis, outputting current concentration probability value and concentration prediction value at a specified time point in the future; triggering a hierarchical early warning mechanism based on the current concentration probability value and the concentration prediction value. The present application utilizes information complementation in multi-modal data fusion, thereby improving the robustness and accuracy of state recognition.

The invention discloses an intelligent security processing method and system for banking business data, and relates to the field of security processing methods, and the system comprises a data acquisition module, a feature analysis module, an imbalance mode analysis module, a first adjustment module, a second adjustment module and a parameter optimization module. The method comprises the following steps: obtaining original differential privacy budget distribution parameters of a target privacy computing environment, carrying out analysis and feature analysis on historical query utility evaluation data and real-time privacy attack monitoring data, and analyzing whether a security utility imbalance mode exists or not; generating a first budget adjustment coefficient for the query sensitivity characteristic value curve and the utility attenuation characteristic value curve by adopting a dynamic correlation analysis method, generating a second budget adjustment coefficient based on real-time privacy attack monitoring data by adopting a multi-layer perception risk assessment model, and performing optimization adjustment on original differential privacy budget distribution parameters to obtain the privacy attack monitoring data. The optimized differential privacy budget distribution parameters are obtained, and the security control requirements of banking businesses are met.

The application provides a meta-learning enhanced burst order multi-constraint distribution emergency scheduling method, comprising: acquiring the time distribution mode and intensity change trend of continuous burst order flow through historical order data collection, and determining the regularity difference capture result; according to the regularity difference capture result, using meta-learning model training to obtain evolution trend judgment parameters of subsequent demand prediction; if the evolution trend judgment parameters exceed the preset threshold, starting resource allocation optimization to obtain an acceleration processing means allocation scheme under a dynamic coordination mechanism; for the preliminary scheduling sequence, using genetic algorithm iteration to determine the minimum path of the chain delay situation, and obtaining the optimization output of order mode analysis; through the final execution plan, using particle swarm optimization algorithm fusion to determine the compensation measures of resource allocation lag, and obtaining the complete sequence of overall distribution emergency scheduling.

The system according to this embodiment aims to analyze customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an update unit. The collection unit collects customer communication usage data and behavioral patterns. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes optimal pricing plans and promotions based on the analysis results obtained by the analysis unit. The provision unit provides the content proposed by the proposal unit to the user. The update unit periodically re-analyzes the data based on the content provided by the provision unit and updates the proposals.

The invention relates to the technical field of medical resource management, in particular to a medical resource management method and system. The method comprises the following steps: a system obtains equipment end operation data, patient monitoring data and equipment operation parameter data, and generates equipment end operation feature data, patient monitoring feature data and equipment operation feature data through feature extraction; multivariable anomaly detection is carried out based on the equipment end operation feature data and the patient monitoring feature data, and equipment end operation anomaly is identified; and performing equipment response consistency detection based on the equipment end operation feature data and the equipment operation feature data, and detecting equipment parameter execution abnormality. And abnormal attribution is enhanced through a knowledge graph, a historical case and an equipment operation mode are combined, potential reasons of anomalies are analyzed, and medical resource abnormal data are generated. And the system sends abnormal information to the medical resource cloud platform, so that intelligent early warning is realized, the equipment utilization rate is improved, and reasonable allocation of medical resources is guaranteed.

The invention discloses a power grid network attack countering method and system based on flexible gray level self-adaption, and the method comprises the steps: obtaining the multi-level gray point information of a power grid, carrying out the abnormal flow analysis, carrying out the shadow asset recognition processing based on an analysis result, and obtaining the shadow asset recognition data, obtaining known attack data of the power grid, carrying out attack mode analysis, constructing an attack link graph of a known attack mode to obtain an attack link analysis model, recognizing an abnormal access behavior corresponding to shadow asset recognition data through the attack link analysis model, carrying out target asset threat analysis to obtain asset threat analysis data, and sending the asset threat analysis data to the power grid. And attacker positioning is carried out according to the asset threat data, a corresponding attack countering strategy is called in combination with the known attack means similarity to carry out attack source blocking processing on the positioned attacker, and power grid network attack countering data is obtained. The power grid attack is accurately traced and attack countering is carried out from the source, so that data support of attack countering is provided for operation safety of the power grid.

We provide the system. [Solution] Data collection means, An analysis means that analyzes the information obtained by the aforementioned data collection means and predicts the trends of financial assets, A display means for visualizing the predictive information obtained by the analysis means, A means for analyzing consumption patterns using electronic transaction history, A means for presenting an asset management method based on the analysis results of the aforementioned consumption patterns, A system that includes this.

The invention relates to a cryptographic chip, in particular to a low-power-consumption cryptographic chip capable of flexibly switching multiple working modes and a working method of the low-power-consumption cryptographic chip. A slave data write-in module is used for receiving data sent by a DMA (Direct Memory Access) module and storing the data in an embedded FIFO (First In First Out) for a mode analysis module to read; the mode analysis module is used for generating a clock enable signal corresponding to the selected working mode through mode analysis, reading data from the slave data write-in module and sending the data to an internal module corresponding to the selected working mode in the mode accelerator top layer module; the mode accelerator top layer module is used for performing encryption / decryption / hash processing on the data sent by the mode analysis module by utilizing the internal module corresponding to the selected working mode, and sending the processed data to the output data cache FIFO module; according to the technical scheme provided by the invention, the defects of single working mode and relatively high power consumption in the prior art can be effectively overcome.

The application relates to the field of petroleum technology, in particular to a cyclone coupling demulsification separation system based on a high-voltage pulse electric field, which comprises a demulsification separation unit, a data acquisition unit, a pretreatment unit and a data analysis unit. The demulsification separation unit is used for demulsification dewatering of target emulsified oil wastewater through cyclone and spiral electric field. The data acquisition unit is used for collecting demand information. The pretreatment unit is used for initial water content state detection of the target emulsified oil wastewater, and a pretreatment mode is selected according to the initial water content state. The data analysis unit is used for determining an analysis adjustment mode according to a dewatering state, and the analysis adjustment mode is used for detecting a reference value of impurity content or adjusting dewatering parameters. In the adjustment of the dewatering parameters, a first pressure drop and a second pressure drop are detected to determine a pressure drop state, and the data analysis unit determines a pulse voltage adjustment mode according to the pressure drop state. The working parameters are adjusted according to the quality state of actual emulsified oil wastewater and the dewatering effect, so that the precision of demulsification working parameters is improved, and the actual working demand is met.