1632 results about "Data analysis system" patented technology

The invention discloses a multi-agent interactive efficient data analysis system, which comprises a natural language analysis unit, which is used for carrying out preliminary analysis on a natural language query request input by a user based on a natural language analysis model, and predicting a user intention to obtain the user intention; the task intelligent routing unit is used for exchanging information through a communication mechanism between the intelligent agents according to the intention of the user and distributing the data analysis tasks to the corresponding intelligent agents; the agent execution unit is used for executing various operations required by a data analysis task by an agent, including task disassembly and strategy evaluation, information storage and memory calling, external tool use and action execution, and obtaining an analysis result; and the agent report generation unit is used for generating a data analysis report based on the analysis result according to a preset report template and a formatting rule. And automation of a data analysis task is realized through efficient cooperation and a task allocation mechanism among multiple agents.

The invention discloses a multi-parameter in-situ detection data analysis system for a marine ranch, and relates to the technical field of data analysis, the multi-parameter in-situ detection data analysis system comprises a multi-parameter sensor group and a self-adaptive multi-dimensional trigger type sampling control module, the multi-parameter sensor group comprises an environmental parameter sensor and a biological behavior sensor, the environmental parameter sensor is used for measuring real-time environmental parameters of the marine ranch, the biological behavior sensor is used for monitoring biological behavior parameters of the marine ranch in real time, and the self-adaptive multi-dimensional trigger type sampling control module comprises a data preprocessing unit and a dynamic sampling strategy unit. The self-adaptive multi-dimensional trigger type sampling control module is designed, so that sampling modes are dynamically switched according to real-time dual-mode data, environment sudden changes are effectively captured, data redundancy or key change sampling leakage caused by traditional fixed sampling frequency is avoided, monitoring precision is improved, energy optimization is achieved, the service life of equipment is prolonged, and the system is suitable for large-scale popularization and application. And high quality and real-time performance of data are ensured.

A data analytics system uses a grid-based data structure to improve the usability of LLMs in the analysis of large data sets, to synthesize information for use in other generative AI contexts, and to improve a user's ability to interface with an LLM. A grid-based data structure is a data structure or database that stores the results of column prompts applied to sources. The grid-based data structure may store the results in a relational manner. For example, a grid-based data structure may have rows that correspond to sources (e.g., documents, files, or databases) and columns that correspond to prompts. Each cell of the grid-based data structure stores the output of the column prompt applied to a source using an LLM. Thus, each column prompt may be systematically applied to each source to generate information based on the sources in an organized way.

The invention relates to the technical field of data analysis, and discloses a data analysis system for multi-joint exercise rehabilitation monitoring. The system collects three-dimensional motion data, myoelectricity activity signals and spatial position data of multiple joints in real time through a multi-mode sensor array. The system comprises a data acquisition module, a preprocessing module, a space-time analysis module and a result output module. The preprocessing module is used for carrying out noise removal, normalization and feature extraction on the original data; the space-time analysis module constructs a monitoring model based on a graph space-time network, and analyzes space-time characteristics of multi-modal data through dynamic joint relation modeling by adopting an intelligent chip. And the result output module generates a visual report including exercise coordination evaluation, injury risk prediction and rehabilitation progress judgment, and feeds back the visual report to the rehabilitation equipment in real time. Besides, the system also has distributed storage and real-time feedback functions, the accuracy and efficiency of rehabilitation monitoring are improved, and the problems of single data processing and inaccurate spatial-temporal feature capture in the prior art are solved.

A porous media grouting test system and method taking temperature effects into consideration. The system comprises a porous media formation simulation model (1), a grouting system (2), a water level adjustment system, a formation temperature control system and a data analysis system, wherein the porous media formation simulation model (1) is used for simulating a porous media formation; the water level adjustment system and the formation temperature control system are used for simulating the conditions of different water saturation levels and different water temperatures and ground temperatures; and the data analysis system is used for comparing different time-series data collected by sensors such as a temperature sensor and a pressure sensor with data such as temperature data and pressure data that is obtained by means of coupled solving of a heat transfer equation and a momentum equation, so as to verify and analyze a grouting diffusion path. Free simulation of different water saturation levels is realized by means of a water level adjustment system, and simulation of different ground temperatures is realized by means of a formation temperature control system, thereby realizing the research on grouting diffusion under different water levels and different water temperatures and ground temperatures.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention provides an automobile magnesium alloy pre-twin crystal deformation data analysis system, and relates to the technical field of data processing, and the system comprises a twin crystal correction module which is provided with a first target monitoring point and a second target monitoring point in a pre-twin crystal area, the first target monitoring point is located at a crystal boundary intersection, and the second target monitoring point is located at the center of a twin crystal zone; acquiring orientation difference angle and strain energy density difference data of the two monitoring points in real time, calculating a covariance matrix of change characteristics of the two monitoring points, and correcting a proliferation rate parameter and an orientation rotation parameter in the dynamic evolution matrix according to a correlation coefficient of the covariance matrix to obtain a corrected dynamic evolution matrix; and the optimization module is used for adjusting a hot extrusion process parameter group according to the twin crystal density gradient distribution output by the corrected dynamic evolution matrix, the parameter group comprises a mold temperature, an extrusion speed and a strain path, and the twin crystal density is dynamically controlled in a target interval. According to the invention, the complex stress state of the automobile part under the actual working condition can be accurately simulated.

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

Provided in the present application is a data analysis method, which is applied to the field of artificial intelligence (AI) and is executed by a data analysis system. The data analysis system is used for carrying out data analysis by means of multiple rounds of dialogue. The method comprises: receiving input content of a user, performing intention classification on the basis of the input content, and determining a task type indicated by the input content; when the task type indicated by the input content is an instruction type task, determining a sub-task type of the instruction type task by means of instruction keyword matching, and acquiring historical dialogue associated content of the input content; and processing the historical dialogue associated content on the basis of the sub-task type to obtain an adjusted data analysis result. When a data analysis result is an unexpected result, the method can perform directed adjustment on the data analysis result by means of an instruction type task, and the directed adjustment of "what you see is what you get" can be combined with an interaction mode of natural language dialogue, thereby improving data analysis efficiency and user experience.

The invention discloses an intelligent logistics supply chain management data analysis system based on a cloud platform, and relates to the technical field of intelligent logistics, the intelligent logistics supply chain management data analysis system comprises a supply chain management platform, and the supply chain management platform is in communication connection with the following modules: a data acquisition and processing module, which is used for acquiring logistics resource data from each link in a supply chain, the collected logistics resource data are preprocessed; and the digital twinborn simulation module is used for constructing a digital twinborn model of the supply chain and carrying out real-time dynamic simulation on the logistics process. According to the invention, through the resource optimization configuration model of federated learning training and in combination with an optimal scheme of digital twinborn simulation, global scheduling is carried out on logistics resources, and through a knowledge graph, potential conflicts in goods allocation are identified, the warehouse space utilization rate is optimized, and resource allocation at a global perspective is realized, so that the optimization limitation of a single node is broken through; the vehicle utilization rate, the warehouse space utilization rate and the equipment utilization rate are remarkably improved, and the overall operation cost is reduced.

The invention relates to the technical field of data processing, and further relates to a soil environment damage and risk assessment big data analysis system. The system comprises a data acquisition module, a data processing and fusion module, a spatial correlation analysis module and a risk assessment module. The data acquisition module is used for setting a plurality of sampling points in a target soil area and sending the acquired data to the cloud big database; the data processing and fusion module is used for extracting sparse features of the preprocessed data; the spatial correlation analysis module is used for obtaining spatial correlation indexes; the risk assessment module is used for constructing a risk prediction model; and obtaining a current soil environment risk value corresponding to each sampling point. According to the method, the comprehensiveness, the real-time performance and the precision of evaluation are remarkably improved, and meanwhile, the method has the functions of dynamic response, multi-dimensional data integration and automatic early warning.

Systems, computer program products, and methods are described herein for application anomaly detection using advanced computational AI machine learning modeling. In this way, performance metric data is extracted and the variances are derived by comparing the performance metrics of the application workload for the current time period against the same for a previous period. The AI model is trained at regular intervals by using the derived performance metrics data to identify only the candidate workloads which are degrading or underperforming at an early state, while avoiding reporting workloads that are not causing impact to performance stability of applications across an entity database network. The system monitors application workload across a relational database of an entity for degraded application performance and identifies changes in application workload performance and applies the anomaly detection artificial intelligence machine learning model.

Systems and methods include a computer-implemented cybersecurity data analytics system. An asset inventory is determined that identifies systems in a network. A baseline activity of the systems is determined using the asset inventory. The determining includes monitoring the systems during a time in which cybersecurity is secure. A real-time model of endpoints is generated using the baseline activity. The real-time model of endpoints includes endpoints communicating between systems in the network and reflects the baseline activity of, and communication among, the endpoints during the time in which cybersecurity is secure. The systems are monitored, including detecting, using the real-time model of endpoints, cybersecurity-related anomalies in the network that deviate from the baseline activity. Actionable alerts are generated by the cybersecurity data analytics system by using at least the detected cybersecurity-related anomalies. The actionable alerts are displayed in a user interface and notify of potential and actual process disruptions.

The invention discloses a smart rural comprehensive management method and system, and particularly relates to the technical field of data management. Monitoring areas are divided according to the geographic distribution density of sensors in a farmland, the accuracy weight of sensor data in each area is calculated by using the data deviation characteristics between adjacent sensors and the fluctuation characteristics of crop growth environment data, and a comprehensive accuracy index reflecting the environment condition is generated. Therefore, the problems of data blind areas and data distortion caused by uneven sensor distribution are solved, a monitoring area is divided into a high-accuracy area, a medium-accuracy area and a low-accuracy area based on the accuracy index, the abnormal condition of the medium-accuracy area is predicted, and the data acquisition frequency and the distribution density are dynamically adjusted according to the prediction result. According to the method, the accuracy and reliability of a data analysis system are effectively improved, managers can master the crop growth environment more accurately, farmland management decisions are optimized, data prejudice and misleading decisions are avoided, and the controllability and efficiency of management are improved.

A search assistant engine is described that integrates with a data intake and query system and provides an intuitive user interface to assist a user in searching and evaluating indexed event data. Additionally, the search assistant engine provides logic to intelligently provide data to the user through the user interface such as determining fields of events likely to be of interest based on determining a mutual information score for each field and determining groups of related fields based on determining a mutual information score for each field grouping. Some implementations utilize machine learning techniques in certain analyses such as when clustering events and determining an event templates for each cluster. Additionally, the search assistant engine may import terms or characters from user interaction into predetermined search query templates to generate tailored search query for the user.

The invention provides a cross-border e-commerce big data analysis system and method, and the method comprises the steps: obtaining multi-dimensional original business data of a cross-border e-commerce platform, carrying out the real-time collection through a distributed data collection system, and obtaining a structured business data set; data collected in real time is cleaned, converted and structured, a unified data model is constructed, and integration of heterogeneous data sources is achieved; storing the processed structured data in a time sequence database to provide data support for real-time analysis; performing real-time aggregation and statistical analysis on the newly added data to generate business index and key performance index data; a Lambda framework is adopted, batch processing and stream processing are combined, the historical data and the real-time data are subjected to full-amount analysis, and an analysis result is obtained; based on an analysis result, a data mining algorithm is adopted to identify a service mode and predict a future trend; a multi-dimensional data cube is constructed, multi-dimensional and multi-granularity data exploration and interactive analysis are supported, and management decision making is assisted.

A data processing and analysis system that optimizes the resources to be used for data storage and refresh events. A partitioner module for a data analysis system can receive a first client criteria and a first client dataset that includes tabular data and calculate scores that are used to generate partitioning strategies. The selected partitioning strategy can be implemented to produce aggregated data that can be stored in an intelligent data mart. The partitions can then be accessed by a data visualization platform for intelligent, dynamic responses to user requests for data analyses and generation of visualizations. By providing synchronous partitioning of data (especially big data) and intelligent refresh, the data can move from the back-end to the front-end with minimal user clicks and minimal latency in performance.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

The invention relates to the technical field of digital marketing, in particular to an intelligent marketing data analysis system and method. According to the invention, through deep analysis of the user interaction data, the user behavior offset is accurately identified, so that the marketing strategy can be dynamically adjusted, and the putting accuracy is improved. By means of the change trend of the residence time sequence, the relative change rate is calculated in combination with the access preference, user interest hotspot distribution information is effectively extracted, and the pertinence of content recommendation is improved. Based on access path stability analysis of interest hotspots, a stable mode of user behaviors is extracted, and an advertisement putting interval is optimized, so that user loss caused by frequent putting is reduced. And user groups with fluency decline or obvious jumping behaviors are screened by utilizing change calculation of interaction fluency, so that the advertisement pushing rhythm is reasonably adjusted, and the advertisement experience is improved. The user access data is synthesized to analyze the conversion change of the user behavior after the advertisement is displayed, and the influence of different advertisement intervals on the user behavior is accurately evaluated.

The invention discloses an automatic data analysis system and method based on artificial intelligence, and relates to the technical field of data management, and the method comprises the following steps: S1, data collection and preparation; s2, data annotation and classification; s3, feature selection and extraction; s4, data standardization and normalization are carried out; s5, model selection; s6, training and optimizing the model; s7, performing model evaluation; s8, model interpretation and visualization are carried out; s9, generating an automatic report; and the system is constructed based on the steps. According to the automatic data analysis system and method based on artificial intelligence, by setting a classification and labeling model, a training optimization mechanism and a dynamic model evaluation method, the efficiency and precision of automatic data analysis are remarkably improved, and the quality of data preprocessing is improved; meanwhile, through self-adaptive hyper-parameter adjustment and optimization, the efficiency of the training process and the model performance are optimized; the dynamic evaluation mechanism ensures that the model is more accurate in performance under different data characteristics.

The invention provides a medical examination data analysis system and method based on artificial intelligence. The method comprises the steps of obtaining a time sequence data matrix of multiple examination indexes of a target patient at different time nodes through medical examination data of the target patient; labeling pathological labels of various examination indexes in the medical examination data, and determining medical semantic association features among different examination indexes of the target patient through the pathological labels; according to the medical semantic association features and the time sequence data matrix, determining a time sequence association relationship of different inspection indexes of the target patient on a pathological level, and generating a fusion feature vector of the health state of the target patient according to the time sequence association relationship; and an abnormal evolution feature of the current health state of the target patient is obtained by combining an abnormal detection model with the fusion feature vector, and then an auxiliary analysis result of the pathological risk of the target patient is output to medical personnel. By adopting the scheme of the invention, the dynamic change trend analysis of the disease course risk state of the patient can be realized based on the medical semantic perception ability.

The invention discloses an intelligent patient monitoring and predicting clinical data analysis system, which belongs to the field of medical analysis and comprises a real-time monitoring module, a data acquisition module and a knowledge base building module. According to the method, the nursing quality of the patient can be improved, medical resources can be effectively saved, the processing capacity of complex medical data is enhanced, the scientificity and rationality of clinical decisions are improved, a personalized and timely health management scheme is provided for the patient, a hospital can be more prospective and initiative in disease management, and the disease management efficiency is improved. Therefore, the overall medical service level is improved, uncertainty and patient individualized demand differences existing in clinical data can be effectively handled, and scheme suggestions with higher diversity and flexibility are provided for medical staff during decision making, so that the whole system has real-time performance, and long-term effect optimization across time dimensions can be realized; the treatment effect and experience of the patient can be improved, and a scientific basis is provided for reasonable distribution of medical resources.

The invention relates to the technical field of state monitoring, in particular to an intelligent cervical vertebra state data monitoring system which comprises a data monitoring prediction module, a cervical vertebra risk analysis module, a cervical vertebra intervention making module and an intervention effect evaluation module. According to the invention, through real-time data analysis and continuous monitoring of the posture and pressure change of the cervical vertebra, high-sensitivity tracking of the health condition of the cervical vertebra is realized, the system adopts dynamic probability estimation, the health state can be updated in real time, the accuracy of abnormal recognition is improved, and the accuracy of abnormal recognition is improved by integrating data analysis of the posture and the pressure. The system can identify potential health risks in an initial stage, early warning of risk factors enables medical intervention to be carried out more timely and effectively, the demand and cost of long-term treatment are reduced, a targeted intervention plan is adjusted according to real-time data, it is ensured that each intervention measure accurately matches the current demand of a patient, and the patient experience is improved. The intervention process is optimized, and the cervical vertebra rehabilitation speed and effect are improved.

The invention relates to the technical field of automatic operation and maintenance, in particular to a visual operation and maintenance data analysis system which comprises a node data collection module, an index sorting and screening module, a multi-parameter linkage recognition module, a resource node screening module and an event track tracing module. And analyzing operation data of each node, and grouping node types according to time labels. According to the method, the operation data of different nodes are subjected to hierarchical grouping and archiving, dynamic linkage merging of various operation and maintenance parameters is realized in combination with time labels, operation and maintenance concerned objects are automatically screened under a multi-index change trend, and accurate screening of abnormal nodes and resource redundancy nodes is completed according to a comprehensive feature track. The operation and maintenance data is promoted to be converted from single-point monitoring to full-link linkage analysis, and the intuition and the globality of operation and maintenance event response are improved in the visual archiving and tracing process, so that systematic closed-loop monitoring of node states, resource allocation and operation and maintenance risks is promoted.

The present disclosure relates to an intelligent internet of things (IoT) monitoring system, and in particular to techniques (e.g., systems, methods, computer program products storing code or instructions executable by one or more processors) for the implementation of an IoT solution to manage urinary incontinence. Some aspects are directed to the concept of a management platform that allows for end users such as health care providers, caretakers, or medical personnel to manage and monitor one or more subjects through one or more client devices using a network of sensors and IoT devices. Other aspects are directed the concept of a data analysis system configured to train and deploy one or more prediction models for analysis and tracking metrics of health or wellbeing for the one or more subjects.

Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

The invention relates to the technical field of medical diagnosis systems, and discloses an AI physique diagnosis software analysis system which comprises an MCU module used for sending commands to execution modules and receiving and processing data signals of the execution modules to achieve diagnosis operation of the analysis system; the data acquisition module is used for collecting facial images and pulse information physiological indexes of the patient through an acquisition medium; the image processing module is used for analyzing facial features of the patient, the facial features comprise complexion, tongue coating and tongue quality, and corresponding evaluation data are generated through the obtained facial feature information. According to the AI physique diagnosis software analysis system, the accuracy and efficiency of traditional Chinese medicine physique identification are remarkably improved, the subjectivity and professional threshold are reduced, remarkable social and economic benefits are generated, and the system can more accurately evaluate the physique and display experimental data by using a deep learning algorithm and multi-modal data analysis.

The invention relates to the technical field of sports events, in particular to a sports event data analysis system and method.The system comprises a real-time data analysis module, a technical action evaluation module, a dynamic competition prediction module and a decision support module, based on a real-time competition data stream, the positions, scores and instant statistical information of players are received, time sequence analysis is executed, and the real-time competition data stream is analyzed; key data changes are recognized, scoring opportunities and defense position points are extracted, and a data pattern analysis result is obtained. According to the invention, through integration of real-time and historical data, accurate capture and deep analysis of key information in a sports event are realized, through instant receiving and processing of player positions, scores and statistical information, a data mode is identified by using time sequence analysis, dynamic understanding of the event is enhanced, more progress data about the event is allowed to be obtained, and the progress of the event is improved. And through comparison of the time convolution network and the action database, the accuracy of technical action evaluation is improved.

The invention relates to the technical field of real-time data analysis, in particular to a real-time data analysis system based on big data, which comprises an event capture module, a trend identification module, a priority processing module and a data isolation module. According to the method, the data stream is monitored in real time, the timestamps, the change amplitude and the frequency of the data points are extracted, the timeliness and the accuracy of data processing are enhanced, real-time change analysis is carried out on the data, instant understanding of the data stream is enhanced through key change marks, the response speed of market changes is increased, and the market competitiveness is improved. By analyzing the detailed background and development trend of key events, the prediction accuracy and the operation foresight are improved, resources are dynamically reconfigured according to the priority of the events, the key events are ensured to be processed preferentially, the resource utilization efficiency and the operation response speed are greatly improved, data isolation guarantees the concentration and safety of high-priority event processing, and the method is suitable for popularization and application. And the data processing efficiency is obviously improved.

The invention relates to the field of e-commerce recommendation, and discloses a user behavior data analysis system and method based on a time series, and the method comprises the steps: continuously capturing user operation events, extracting a short-term interest fluctuation factor, a category offset index and an interaction strength parameter, and forming dynamic weight mapping for constructing a transient preference map; implicit association detection of a cross-category event chain is executed on the transient preference map, and a multi-dimensional influence gradient vector reflecting the potential interest evolution trend is generated; tracking an offset path of a key interest node through a hierarchical interest-driven algorithm by utilizing a multi-dimensional influence gradient vector, and identifying candidate behavior fragments causing recommendation delay; performing priority label reconstruction on the candidate behavior fragments to form a local behavior network with a weight gradient; based on a local behavior network, key nodes with unbalanced interest distribution in a specific time window are detected, and an evolution path for real-time adjustment of a recommendation engine is generated. The method has the advantage of improving the relevance and instantaneity of the recommended content.