24 results about "Analysis Dataset" patented technology

The application discloses a battlefield information processing method based on multi-modal data fusion, comprising the following steps: acquiring multi-modal original data of battlefield information, standardizing different original data to obtain a structured data set; performing time and space alignment on the structured data set to obtain a space-time reference data set; performing feature completion on the space-time reference data set to obtain a complete analysis data set; performing hierarchical encryption on the complete analysis data set to obtain an encrypted transmission data set; performing distributed storage on the encrypted transmission data set to obtain a secure storage data set; performing isolation processing on the secure storage data set to obtain an intermediate result data set; performing real-time detection and protection on the intermediate result data set to obtain a final protection data set; and performing processing detection and credibility judgment on the final protection data set to obtain a final credible information analysis result.

Computer-implemented method for generating a trained combustion property determination machine learning model (24) for determining at least one combustion parameter (18) of a given fuel using analysis data sets which are each characteristic of a composition of a given fuel, comprising the steps - Providing a trainable combustion property determination machine learning model (22) which includes a set of trainable parameters and which, based on an analysis data set acquired for a given fuel as input (2), generates at least one combustion parameter (18) as output (6); - Generating a training dataset comprising a variety of acquired analytical datasets on training fuels, which are used to generate the training dataset, as well as a variety of combustion parameters (18); - Training the combustion property determination machine learning model (22) based on the training data set; - Determine at least one influencing factor (28) by applying a method to explain the trained combustion property determination machine learning model (24).

The embodiment of the application relates to the field of data processing, and provides a financial institution decision analysis method based on data driving and related devices, which comprises the following steps: obtaining endogenous business data set and exogenous correlation data set of a financial market; performing business expansion analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining business expansion analysis data set; performing risk management analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining risk management analysis data set; performing resource allocation analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining resource allocation analysis data set; performing conflict checking processing according to the business expansion analysis data set, the risk management analysis data set and the resource allocation analysis data set, and performing decision suggestion quantization output, obtaining a financial institution decision analysis result, and more accurate financial institution decision analysis can be realized.

This application relates to the field of smartwatch technology, and more particularly to a multi-dimensional child protection method, device, storage medium, computer program product, and smartwatch. The method acquires multi-source physiological and behavioral monitoring data, location information, and scene clue information from the smartwatch, and generates a dataset to be analyzed based on these data. Based on the dataset, the current state of the wearer is determined, and the data acquisition strategy for the multi-source physiological and behavioral monitoring data is adaptively adjusted according to the current state. The dataset is then preprocessed to obtain a processed dataset. A fusion representation is constructed from the processed dataset to obtain fusion features, and the target event type and corresponding handling instructions are determined based on these features. Based on the handling instructions, the smartwatch outputs interactive prompts and sends alarm information associated with the target event type to the monitoring terminal.

This invention relates to the technical field of data optimization processing, and discloses a method, apparatus, equipment, and medium for optimizing medical AI intelligent diagnosis and insurance claim risk hedging strategies. The method includes: acquiring input patient historical medical data and insurance data and integrating the data to form a basic analysis dataset; establishing patient-disease-insurance relationships based on graph neural networks, generating dynamic risk feature vectors, and constructing a multimodal medical data fusion and dynamic diagnosis model based on the dynamic risk feature vectors; based on the multimodal medical data fusion and dynamic diagnosis model, using AI intelligent diagnosis of real-time patient medical data, and mapping the patient disease probability and confidence level output by the AI ​​intelligent diagnosis results into insurance claim risk values. This application can be applied to the development of business systems in fintech, healthcare, and elderly care, improving the accuracy of medical diagnosis and risk assessment through deep coupling and a two-way feedback optimization mechanism between medical data and insurance risk control.

The present invention relates to a user-customized data compliance management method and system for: analyzing dataset license compliance by using an AI model; allowing a plurality of real or virtual agents to collaborate (discuss) and assess the analyzed result; and further refining data so as to satisfy a risk class not smaller than a preset threshold value.

Embodiments of the present disclosure disclose a hot line-based disaster emergency unmanned aerial vehicle scheduling method, device, equipment and medium. A specific embodiment of the method comprises: in response to obtaining hot line data, processing unstructured text and structured data in the hot line data to obtain a to-be-analyzed data set; analyzing and processing the to-be-analyzed data set to obtain analysis result information; in response to the analysis result information representing a disaster emergency event to be processed by an unmanned aerial vehicle, correlating and matching the to-be-analyzed data set and the obtained external multi-source data, generating a scheduling control instruction set based on the standardized analysis data set, and controlling the unmanned aerial vehicle to perform scheduling operations based on the scheduling control instruction set. This embodiment realizes the overall improvement of disaster event identification, positioning, path planning and unmanned aerial vehicle response by intelligently analyzing hot line data and deeply integrating unmanned aerial vehicle scheduling, and provides key technical support for intelligent emergency management of various disasters.

The application relates to a kind of enterprise diagnosis and operation report joint generation method and system based on multi-agent cooperation, comprising the following steps: obtaining the multidimensional operation data of target enterprise and carrying out time sequence unified processing, generating analysis dataset;Parallelly call several corresponding professional analysis agents;Synchronize the analysis dataset into professional analysis agent, obtain diagnostic score result;The obtained diagnostic score result is fused and conflict is resolved, and comprehensive diagnostic conclusion is generated;According to the comprehensive diagnostic conclusion, match text narrative template, data visualization chart component and conclusion suggestion corpus, and fill and associate through report integration agent, generate enterprise operation diagnosis report;In summary, by obtaining multidimensional operation data, carrying out time sequence unified processing and calling professional analysis agent parallel analysis, fusing diagnostic result to generate comprehensive conclusion and matching template to generate report, the problem of diagnosis and report generation in the prior art can be solved.

This invention belongs to the field of air conditioning operation and maintenance technology, specifically involving an intelligent operation and maintenance system for air conditioning units based on multi-sensor technology. The system collects operating parameters of the air conditioning unit through a data acquisition module and transmits these parameters to a noise reduction module via a data transmission module. The noise reduction module filters, reduces noise, enhances features, and normalizes the data to form a standardized analysis dataset. A fault diagnosis module analyzes the standardized analysis dataset output by the noise reduction module to identify the fault type and severity level of the air conditioning unit, triggering an alarm if a fault is detected. A prediction module predicts future parameters based on historical data and the standardized analysis dataset output by the noise reduction module, triggering a warning when the predicted value exceeds a preset threshold. An energy consumption optimization module generates energy consumption optimization control commands based on the fault diagnosis results and trend prediction results. Finally, the system adjusts the operating parameters of each air conditioning unit according to the energy consumption optimization control commands, reducing costs, increasing efficiency, saving manpower, and improving the safety of air conditioning operation and maintenance.

The application discloses a kind of irrigation area water-energy-food system efficiency measure and spatio-temporal heterogeneity driving analysis method, system and electronic equipment, constructs including input index, desired output index and non-desired output index efficiency evaluation index system;Obtain the input index data, desired output index data and non-desired output index data of each evaluation period in each irrigation area;According to efficiency evaluation index system, establish efficiency measure model, and obtain the corresponding WEFSE of each evaluation period in each irrigation area;On the basis of obtaining WEFSE, construct driving analysis data set, regression analysis is carried out using spatio-temporal geographic weighted regression model, and the spatio-temporal heterogeneity result of driving factor influence is obtained.The present application reflects the characteristics of multi-element non-synchronous control and ecological environment constraint by constructing efficiency evaluation index system, based on efficiency measure model, carbon dioxide emission and grey water emission are included in efficiency measure as non-desired output, and spatio-temporal heterogeneity is described.

The application discloses a kind of based on big data's wisdom campus safety early warning platform, including following steps: data preprocessing module, for collecting campus multi-source heterogeneous security data and pre-processing, form early warning analysis dataset;Candidate pair generation module, for extracting record link identification and generating candidate record pair set;Candidate pair screening module, for carrying out gate screening based on campus reachable domain constraint;Matching weight formation module, for executing the matching weight formation based on identity-event double-track comparison;Associated record link module, for executing Fellegi-Sunter probability record link algorithm to generate uniform association index;Matching determination result updating module, for executing the three-value determination boundary self-updating of grey zone upgrade backflow;Hierarchical early warning module, for output hierarchical early warning result.The application improves the association merging capability and hierarchical early warning accuracy of wisdom campus multi-source heterogeneous security data.

The application provides a wind power standard loss classification and loss reason analysis model and method, which comprises a data acquisition layer, a data preprocessing layer, and a core function layer; the data acquisition layer is arranged on a wind power generation device and is used for acquiring multi-source heterogeneous data of the wind power generation device; the data preprocessing layer is used for receiving the multi-source heterogeneous data, processing the multi-source heterogeneous data, and outputting a standardized loss analysis data set; the core function layer comprises a wind power standard loss classification module and a loss reason intelligent analysis module; a result output layer is in communication connection with the core function layer and outputs a visual calculation result of the core function layer. Through hierarchical classification design, the problem of non-standard and non-uniform classification in the prior art is solved, the loss data can be summarized, compared and analyzed, a unified standard support is provided for loss evaluation of a wind power plant, and explicit loss and implicit loss are included to ensure comprehensiveness of loss classification.

The application discloses a three-dimensional design model compliance analysis method and system based on a large language model, comprising the following steps: obtaining three-dimensional design model related data and design specification constraint conditions, and building a data interaction channel through a collaborative review platform; calling a semantic geometry collaborative verification algorithm to process data and extract characteristic parameters; generating a rule adaptation weight matrix through a spatial rule learning adaptation model; constructing a mapping relationship between a characteristic vector and a design specification through a CAD point cloud characteristic coding mapping model; performing multidimensional compliance verification based on the foregoing results; and outputting comprehensive analysis data sets. Through multi-unit collaborative linkage, the system integrates algorithms and platform functions, realizes deep collaborative processing of three-dimensional design model geometry and semantic data, improves the accuracy of rule adaptation and characteristic mapping, comprehensively covers the compliance verification dimensions, and meets the high-precision analysis requirements in complex scenarios.

The application relates to the technical field of working mode control, and discloses a working mode reconstruction control method and system of a dual-laser processing system, which is used for solving the problem that cross-mode features coexist when working mode reconstruction is performed, and comprises the following steps: obtaining mode switching feature parameters of a mode analysis data set, analyzing the mode switching feature data to obtain a cross-mode index, and determining whether the cross-mode features coexist in the current mode analysis data set according to the cross-mode index; if it is determined that the cross-mode features coexist in the current mode analysis data set, it is determined that the current mode analysis data set corresponds to a mode switching transition stage, and data with consistent working mode directions is extracted from the mode analysis data set to form an effective mode analysis data set, so that the accuracy of a working mode determination result is effectively improved, and the accuracy of mode reconstruction control and the stability of a processing process are improved.

The invention discloses an artificial intelligence endometrial cancer screening method and system, and relates to the technical field of endometrial cancer screening. The method comprises the following steps: determining risk association indexes needing to be monitored emphatically according to basic health information of a target screening crowd; extracting historical risk index distribution characteristics corresponding to different pathological stages from a historical endometrial cancer screening database, and extracting risk association factors between risk association indexes and endometrial cancer incidence probabilities according to the historical risk index distribution characteristics; processing the current risk index distribution characteristics of the target screening crowd, and extracting a target association factor from the risk association factors; counting the original detection data corresponding to the risk association index to obtain a to-be-analyzed data set, and calculating a first risk value and a second risk value corresponding to the risk association index in the to-be-analyzed data set according to the target association factor, thereby improving the prevention and treatment effect of the endometrial cancer.

This invention discloses a real-time emotion monitoring method and system based on a smart bracelet, relating to the field of bracelet monitoring technology. It collects behavioral perception data generated during the user's wearing of the smart bracelet, constructs a behavioral state feature set Mov representing the user's physical activity state, and generates a motion determination result Sta based on this. This clearly distinguishes whether the user is in a moving state before the emotion analysis process is initiated. Based on the motion determination result Sta, an emotion triggering result Trg is generated, ensuring that the emotion analysis process is only triggered when the user is not in a moving state. Physiological perception data is collected only within the time period indicated by the emotion triggering result Trg, allowing the execution of the emotion analysis process, and an emotion analysis dataset Emo is generated. This ensures that the data in the emotion analysis dataset Emo originates from physiological state intervals that better meet the preconditions of emotion analysis, improving the effectiveness and reliability of the emotion analysis.

This application relates to a method and system for intelligent early warning of server status based on a harmonic correlation model. The method includes: synchronously collecting power harmonic data and server status data from a data center to generate a timestamped raw multi-source dataset; forming a standardized analysis dataset after timestamp alignment and preprocessing; calling a correlation analysis model, using machine learning to drive the correlation analysis, calculating the correlation weight between power harmonics and server status data, and generating a harmonic impact baseline curve; real-time monitoring of server status data, and when exceeding a preset early warning threshold, calling the baseline curve to determine the cause and generating an anomaly report; generating tiered early warnings based on the anomaly report and pushing them to designated terminals; and optimizing the correlation analysis model parameters based on the tiered early warning records and maintenance data. This application achieves accurate determination of anomaly causes and tiered early warnings through dynamic correlation analysis of harmonics and server status, improving the accuracy and intelligence level of server status early warnings.

This invention discloses a method and system for automatically generating financial and tax digital resources by integrating machine learning and artificial intelligence, belonging to the field of digital resource generation technology. It includes: setting up a multi-source heterogeneous data input layer to receive multi-source heterogeneous data and obtain input data items, whereby the multi-source heterogeneous data includes text data, image data, time-series data, and structured data; and a feature extraction module based on the input data items to extract the analysis results of text data, image data, time-series data, and structured data respectively, thereby obtaining an analysis dataset. This invention, through the use of an OCR-RCNN hybrid model, can not only accurately recognize handwritten invoice information such as amount and date, but also automatically perform image correction, such as tilting and blurring, significantly improving the recognition accuracy and robustness of the original image data, and overcoming the limitations of applying OCR technology alone.

The invention relates to a method for operating a driver assistance system (1) for a motor vehicle, wherein environmental data describing the environment of the motor vehicle are acquired at least temporarily by means of an environment detection device (6) of the motor vehicle and obstacle data describing obstacles present in the environment of the motor vehicle are determined by means of a pilot device (4) of a driver assistance device (2) using the environment data within the framework of an obstacle detection (5) and, on the basis of the obstacle data and on the basis of pilot parameter data, autonomous driving operation of the motor vehicle is carried out at least temporarily.It is provided that data determined by means of an analysis unit (7) separate from the pilot unit (4) from the environment detection unit (6) and / or the pilot unit (4) are stored as analysis data in an analysis data set and, based on the analysis data and on evaluation parameter data, an identifier is selected from several identifiers and assigned to the analysis data set, the analysis data set together with the identifier being transmitted to a vehicle-external computing unit (3) of the driver assistance system (1), and the computing unit (3) determines the evaluation parameter data and / or the pilot parameter data from the analysis data set and makes them available to the driver assistance unit (2).

Techniques are disclosed for performing an image sensor profiling process and accompanying architecture that facilitates performing an image quality measurement over a range of different parameters such as light levels, exposure values, contrast targets, confidence intervals, color ratios, temperature, etc. An image sensor profile may then be generated from these measurements includes an image sensor performance profile dataset. This dataset provides contrast detection probability (CDP) measurement data over a full range of operating parameters of the image sensor. The image sensor performance profile dataset may quantify the image quality and performance for a full range of operation. The image sensor performance profile dataset may be implemented to generate an image sensor model, which may be deployed in a vehicle and used to modify the functions thereof during operation.

A device and method are described for a universal analytical data mart and data structure for same. The analytical data mart (ADM) associated data structure is designed to allow data from disparate sources to be integrated, enabling streamlined business intelligence, reporting and ad hoc analysis. Conceptually, the ADM enables analytics and business intelligence from multiple frames of reference including people, such as parties and actors including individuals and organizations, places, such as addresses with geographic information at various levels of view, objects, such as insured properties, automobiles and machinery, and events, milestones which happen at points in time and provide analytical / business value.

This invention relates to the field of data processing technology, and more particularly to an intelligent monitoring and purification system for water circulation in mines. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it performs the following steps: Based on the monitoring time and distribution of the monitoring data in the acquired dataset to be analyzed, it obtains the range of neighborhood parameter constraints; it constructs a waveform graph of the dataset to be analyzed and divides the waveform into segments based on change points to extract the slope, obtaining the initial neighborhood parameter adjustment coefficient; it combines the difference in Pearson correlation coefficients between the current monitoring point and each upstream monitoring point's waveform segments with the maximum and minimum neighborhood parameter values ​​to obtain adaptive neighborhood parameters; it inputs the dataset to be analyzed and the adaptive neighborhood parameters into a local outlier factor algorithm to obtain an anomaly index; by analyzing the impact of changes in system operating status on the monitoring results, the accuracy of water circulation quality monitoring and purification regulation is improved.

This invention provides a method and system for evaluating microendoscopic operations, comprising: preprocessing and extracting features from a microendoscopic operation data stream to generate a raw motion primitive sequence and a device event sequence; obtaining a comprehensive objective performance score within an operation analysis window based on preset microendoscopic operation meta-indicators; simultaneously generating a conditional Pareto front for each anatomical site by combining a dynamic high-efficiency operation library; acquiring environmental state data; analyzing the operation to be evaluated using a preset strategy performance evaluation mechanism to obtain strategy performance evaluation results; performing causal analysis on the operation to be evaluated based on a Bayesian structural equation model and counterfactual attribution using the dynamic high-efficiency operation library to generate a causal analysis dataset; and performing a comprehensive operation evaluation based on the comprehensive objective performance score, the conditional Pareto front, the strategy performance evaluation results, and the causal analysis dataset to generate a comprehensive operation evaluation result, thereby improving the accuracy of microendoscopic operation evaluation.

This invention provides a method and system for monitoring the stability of soil and rock masses based on high-density electrical resistivity methods (EDS), comprising: acquiring apparent resistivity data of the target soil and rock mass monitoring area based on a high-density electrode array; using a parallelized three-dimensional resistivity inversion algorithm to invert the apparent resistivity data into a true three-dimensional resistivity model of the soil and rock mass, and storing the inversion results in time sequence to construct a four-dimensional resistivity database; spatiotemporally aligning and fusing the four-dimensional resistivity database with other monitoring data of the target soil and rock mass monitoring area to obtain an analysis dataset; wherein, the other monitoring data include rainfall, groundwater level, and surface displacement; training a neural network model based on the analysis dataset to construct a soil and rock mass stability monitoring model; and performing real-time monitoring of the soil and rock mass to be monitored based on the soil and rock mass stability monitoring model to obtain monitoring results. This invention elevates the traditional decentralized, delayed, and superficial monitoring mode into an integrated, real-time, and in-depth intelligent early warning system.