78 results about "Schema analysis" patented technology

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.

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 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 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.

Various aspects of the disclosure relate to a quantum cloud enabled API parsing engine to automatically and intelligently identify API based schemas improves efficiency and dynamically increases accuracy through use of an artificial intelligence-based schema analysis engine. A dynamic request is received from a cloud-based application to fetch data from different sources. The API request is received by a quantum cloud environment including a dual quantum storage system for storing multiple API schemas. The quantum cloud-based application includes an AI-based query schema analysis engine that may process the input request across the various API schemas and then formats the API response before sending back to source cloud-based application. The AI-based query schema analysis engine trains an AI data set to find a match based on a schema variable parameter. The AI-based query schema analysis engine leverages a quantum key pool schema processor generated matched encrypted schema area.

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 multi-source heterogeneous data management method and device, electronic equipment and a storage medium, and the method comprises the steps: dynamically selecting a first processing node through a mode analysis model based on a mode processing performance parameter of a data cleaning node; dynamically selecting a second processing node through a timestamp calibration model based on a time calibration performance parameter of the time sequence calibration node; dynamically selecting a third processing node through a value domain conversion model based on the code conversion performance parameter of the code conversion node; and executing a mode conflict analysis task by using the first processing node, executing a time sequence calibration task by using the second processing node, executing a coding system conversion task by using the third processing node, and generating standardized data. An end-to-end data governance process from mode analysis to time sequence calibration to code conversion is formed, finally, a data set with a unified structure, time alignment and code standardization is generated, and the data governance efficiency and accuracy are remarkably improved.

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 an applet activity configuration exception checking method, system and device and a medium. The method specifically comprises the steps that configuration demand information of current applet activity is determined, evolution optimization is conducted on multi-dimensional rules in a dynamic rule base on the basis of a genetic algorithm and a quantum heuristic algorithm in combination with rule weights, and an optimal rule combination is generated; based on the optimal rule combination, a graph neural network is adopted to establish a relation topology model between configuration parameters for the current applet activity, a configuration parameter set of the current applet activity is determined, and the relation topology model is used for capturing a non-linear dependency relationship of cross-configuration items; and in combination with the configuration change historical data, performing time sequence mode analysis on the configuration parameter set of the current applet activity by adopting an LSTM network to obtain a time sequence logic detection result. According to the method, the applet activity configuration abnormity check is comprehensively, accurately and efficiently realized, the configuration accuracy is improved, the manual intervention cost is reduced, and the stable operation of the activity is ensured.

The invention discloses a novel power distribution network multi-objective optimization planning method and system under a power system. The method comprises the following steps: setting a limiting condition of a sample two-dimensional space radius required by power utilization load mode estimation and a limiting condition of a historical data time length of a reference sample; taking an initial sample two-dimensional space radius and an initial historical data time length as reference values, taking a limiting condition of the sample two-dimensional space radius and a limiting condition of the historical data time length as an exploration boundary, and performing random disturbance in the exploration boundary to generate a plurality of combined parameters; performing electrical load mode analysis on each combination parameter, obtaining dominant mode proportion information and mode boundary clear information of an electrical load mode corresponding to each combination parameter, and screening the combination parameters to determine a plurality of groups of available combination parameters; and determining the applied combination parameters in the available combination parameters to perform optimization planning of the voltage grade of the new charging pile accessed to the power distribution network.

The application discloses a water source conservation influence factor analysis method based on geographical space-time weighted regression, comprising the following steps: collecting influence factor data of a target basin as a first data set; calculating historical water source conservation values of a target region and performing spatial aggregation analysis as a second data set; performing spatial pattern analysis on the first data set and the second data set by using a multi-scale geographical weighted regression method to obtain spatial characteristics of various influence factors; performing space-time change analysis on the first data set and the second data set by using a geographical space-time weighted regression method to obtain space-time characteristics of various influence factors; and obtaining influence characteristics of various influence factors on water source conservation of the target basin according to the spatial characteristics and the space-time characteristics. The application captures change characteristics of the influence factors at different spatial scales, reveals change trends of the factors over time, ensures comprehensiveness of analysis, comprehensively combines data in two dimensions of space and time, and is more accurate in prediction and analysis.

The application discloses a kind of water and electricity plant intelligent operation and maintenance management system and method based on machine learning, belong to water and electricity plant operation and maintenance technical field.The application collects water and electricity plant multidimensional data, carries out data preprocessing, storage enters data warehouse;Unified metadata label is defined, and data blood relationship tracking mechanism is established;Determine the starting point of tracing, based on storage location backtracking, trace data access history, according to data flow chart backtracking, integrated data traceability chain is obtained;Entity recognition and relationship extraction are carried out, and Neo4j is used to build associated graph;The relationship between power grid dispatching data and equipment operation is analyzed, and the associated graph is adjusted;Identify dominant abnormality and implicit abnormality, based on associated graph fusion, obtain water and electricity plant abnormal data;The trend of equipment operation data and sensor monitoring data is predicted, combined with residual analysis positioning deviation value, obtain data fluctuation mode, analyze correlation, obtain the source of water and electricity plant abnormal data.

The invention discloses an intelligent micro-grid coordination control system based on cloud edge coordination, which belongs to the technical field of micro-grid coordination control and comprises a data acquisition module, a behavior pattern analysis module, a coordination control module and a fault isolation and rescheduling module. The data acquisition module is used for collecting micro-grid and large power grid data information in a region, including voltage amplitude, voltage phase and short-circuit current, performing type division on micro-grids, including commercial park micro-grids, residential quarter micro-grids, industrial park micro-grids and campus micro-grids, performing statistics on different micro-grid data, including power generation power and real-time energy storage, and performing real-time energy storage on the micro-grids; establishing a micro-grid database; and the behavior pattern analysis module is based on different micro-grid data in the region in the micro-grid database. According to the intelligent micro-grid coordination control system based on cloud edge coordination, the micro-grid fluctuation rule is analyzed, the grid-connected matching strategy is optimized, the fault processing mechanism is perfected, and the running stability and the power resource utilization efficiency of the micro-grid are improved.