529 results about "Data analysis system" patented technology

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 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 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.

The invention discloses an industrial data analysis system and method based on digital twinning and causal inference, and the system comprises a physical sensing layer which is used for collecting multi-source heterogeneous data of an industrial site; the digital twinborn platform layer is used for constructing and operating a virtual twinborn model corresponding to the physical entity; the intelligent analysis engine layer is integrated with a causal analysis module and a federal learning module which are associated; the application and interaction layer is used for visualizing the analysis result and issuing a control instruction; wherein the causal analysis module is used for constructing a causal graph based on the multi-source heterogeneous data and performing causal inference. Through federal learning, on the premise of protecting data privacy of all parties, cross-organization and cross-region collaborative modeling and knowledge sharing are realized.

Techniques for executing privacy-preserving aggregation queries on a database table include receiving a query, obtaining the true output, and generating deterministic pseudorandom noise based on the query and current database state. This noise is added to the true output to create a privacy-protected result. For subsequent queries, this process is repeated, potentially with updated state data, ensuring that privacy protection adapts to changes in the underlying data. The approach maintains consistency for repeated queries on unchanged data while providing fresh noise when data changes. It balances privacy protection with data utility, allowing for various query types while guarding against privacy attacks. The techniques include displaying the noisy output to a user interface. The techniques offer adaptive privacy protection, query flexibility, and efficient resource utilization, making them suitable for dynamic data environments requiring both privacy and analytical capabilities.

The invention discloses an interactive data insight system based on a large language model. The interactive data insight system comprises an intention recognition and classification module, an entity extraction module, an entity and business term alignment module, an SQL generation module and a structured query verification module. The key technical problems that an existing natural language data analysis system is insufficient in semantic understanding, inaccurate in entity alignment, weak in multi-round dialogue support, unstable in SQL generation, lack of safety and the like can be solved, semantic understanding and natural language interaction of complex business data are achieved, the data analysis threshold is greatly reduced, and the data analysis efficiency is improved. And the query efficiency and the result controllability are improved.

The invention discloses a quantitative recognition device for the number and size of bubbles at a rotating impeller of a multiphase pump. The quantitative recognition device comprises a two-phase flow experiment system, the multiphase pump, a high-speed camera, a light source and the like. The preprocessing and data analysis system and the deep learning system are arranged in the computer; the two-phase flow experiment system and the image acquisition system are electrically connected with the computer through lines. The invention further discloses a quantitative recognition method for the number and size of the bubbles at the rotating impeller of the multiphase pump. According to the method, the problems of low blistering recognition precision, low efficiency and large error caused by the existing manual measurement method are solved, and a reference method is provided for exploring the influence of gas distribution at the impeller in the multiphase pump on pump operation and optimizing design of the multiphase pump.

The invention relates to the technical field of electrical digital data processing, and discloses a data analysis system for dynamic monitoring and intelligent prediction of physical parameters and an implementation method, and the method comprises the steps: setting a sensor node comprising a measurement oscillation circuit and a reference oscillation circuit, so as to obtain a differential frequency signal which is mapped by a physical agent and is subjected to temperature compensation; and the central processing unit calculates the drift rate of the differential frequency signal to predict chronic degradation, and triggers the sensor node to switch to a multi-principle electrical detection mode when the rate exceeds a threshold value so as to diagnose the cause of an acute event. The inherent contradiction between the high sensitivity and the wide temperature range operation universality of the existing monitoring technology is avoided, meanwhile, the system is endowed with the capability of performing cause discrimination on the acute sudden risk, and the monitoring precision and the decision effectiveness are remarkably improved.

An intelligent MES data analysis system and method for workshop production, relating to the technical field of MESs. The system comprises a data collection module, a data processing module and an optimization scheduling module. The data collection module is used for acquiring key data from a workshop device and ambient data of a workshop in real time. The data processing module comprises a prediction unit and a quality management unit, wherein the prediction unit is used for predicting a possible device fault on the basis of data of the data collection module, which is beneficial to scheduling maintenance in advance. By evaluating and calculating the remaining useful life of a production device in the workshop, the time when a next fault occurs in the production device in the workshop can be predicted in advance, thereby facilitating subsequent maintenance, and reducing the loss caused by the fault; and according to production requirements and resource conditions, resource allocation on a production line, such as manpower, materials and machinery, can be automatically adjusted, thereby improving the resource utilization rate, reducing waste, and quickly responding to production changes.

The invention discloses an AI-based gene data analysis system. The system comprises a plurality of omics data matrixes; local association pattern mining is performed on the multi-omics data matrix through a 1D-CNN one-dimensional convolutional neural network, a topological structure of a gene network is identified through continuous coherence analysis, dynamic weights are allocated to sequence features and a topological feature matrix by using a dynamic attention mechanism, and weighted multi-scale feature vectors are output; establishing a multi-modal fusion model based on a Transform architecture to fuse the multi-scale feature vectors, performing fine adjustment on the adaptive disease data set by using the general genome feature of a pre-training model, and outputting a fused feature vector; and inputting the fusion feature vector into an MLP multilayer perceptron for disease risk prediction, generating a disease risk prediction index in combination with an SHAP algorithm, and generating an auxiliary decision scheme according to the prediction index. And the accuracy and generalization ability of disease risk classification are effectively improved.

The invention relates to the technical field of electronic control system testing and diagnosis, and discloses a high-precision control module detection and data analysis system and method, and the system comprises a data collection and excitation execution module which is used for carrying out data interaction with a tested control module and applying an excitation signal; the dynamic divergence calculation module is used for calculating the dynamic divergence representing the internal dynamic relation deviation degree of the tested control module according to the real-time state data of the tested control module and by comparing with a preset reference state space tensor; and the predicted deviation degree calculation module is used for calculating the predicted deviation degree between the actual state and the predicted state of the tested control module according to a preset state observer model. According to the method, a closed-loop self-adaptive test framework is adopted, active exploration is carried out by taking maximization of dynamic divergence as a target, deep faults which are difficult to expose in a conventional test and are caused by dynamic coupling failure can be efficiently found, and the depth and coverage rate of fault detection are greatly improved.

The invention discloses a lighting equipment data analysis system and method based on artificial intelligence, and relates to the technical field of data processing and analys.The method comprises the steps that an initial physical structure of a lamp is constructed, and optical parameters of lamp beads are defined; setting the position and size of a detection surface, establishing a light intensity space distribution model, and defining a light intensity uniformity evaluation function; based on Matlab programming, a detection surface is processed through gridding, and light intensity contribution of a single lamp bead to each grid is calculated and superposed; the calculation complexity is reduced by adopting a grouping and row-dividing strategy, an optimal arrangement scheme is solved through an exhaustion method, and uniformity indexes before and after optimization are compared; establishing a light source model by using Traepro, and setting light tracing parameters; and comparing the light intensity and illumination distribution nephograms before and after optimization, quantifying the uniformity improvement effect, and verifying the effectiveness of the algorithm. The method can effectively improve the situation that effective application is difficult due to the fact that the calculation amount is too large when a total exhaustion method is adopted in the prior art.

A high-temperature and high-pressure core displacement test system includes a CT scanner configured to perform CT scanning, a gripper system configured to grip a core, a machine tool configured to move the gripper system to the CT scanner for CT scanning, a gripper load control system configured to heat and input a fluid into or / and receive a fluid output from the gripper system, and an acquisition and data analysis system configured to acquire a temperature and a pressure within a core gripping cavity, and detection data of the CT scanner and strain rosettes for analysis. According to the high-temperature and high-pressure core displacement test system and method, the strength of the gripper system in a high-temperature and high-pressure environment is improved, the projection performance of CT rays is ensured, and the optical fiber positioning precision and the core deformation measurement precision in a CT rapid scanning state are improved.

The invention relates to the technical field of industrial process control and data analysis, in particular to a polyacrylamide production process monitoring data analysis system based on reinforcement learning, which comprises a data acquisition step: acquiring process data at a first frequency, and receiving quality data at a second frequency; a strategy generation step: outputting an action by using a neural network, and calculating an instant agency reward by using an agency model; an asynchronous experience playback step: constructing a nonlinear storage structure, and mapping lagging quality data back to historical process data fragments to complete a causal closed loop; a model updating step: calculating a delay correction reward, and updating network parameters based on the mixed reward; according to the invention, decoupling of real-time decision and delay calibration is realized through a dual-time scale architecture, and the response capability and high-precision control of a test vacuum period are ensured.

The invention discloses a cloud computing bare metal hardware market distribution and management optimization system and method. The system comprises a resource access module, a distribution channel management module, an intelligent matching module, a transaction settlement module, a data analysis and optimization module, a safety and operation and maintenance management module and the like. Standardized aggregation of heterogeneous resources is realized through a resource access system, and the problem of fragmented management is solved; the distribution channel management system dynamically adjusts distribution rules and improves the channel cooperation efficiency; the intelligent matching system is combined with user behavior analysis to realize accurate supply and demand connection; the transaction settlement system integrates payment channels and marketing tools and simplifies fund circulation; the data analysis system precipitates full-link data to support decision optimization; the safety and operation and maintenance system guarantees safe and stable operation. According to the method, through the steps of resource access standardization, channel rule dynamic and the like, efficient, intelligent and standardized management of the whole distribution process is realized, and the market distribution efficiency, accuracy and safety are improved.

The invention relates to the technical field of boiler operation management, in particular to a boiler soot blowing steam consumption multi-dimensional data analysis system which comprises a dynamic database construction module, a steam consumption calculation model construction module, an evaluation module and an optimization strategy generation module. The dynamic database construction module is used for cooperatively acquiring the ash scale thickness value of the heating surface partition of the boiler through an infrared thermal imager and a sound wave thickness measuring device, and realizing structured time sequence data fusion in combination with steam parameters and working condition parameters; the steam consumption calculation model building module is used for building a multi-dimensional consumption data set based on space, time and energy efficiency dimensions; the evaluation module outputs a unit ash scale removal steam consumption value, an invalid steam injection proportion value and a regional energy efficiency deviation coefficient value; the optimization strategy generation module generates a soot blowing pressure gradient adjustment strategy and a steam saturation temperature compensation strategy through index correlation analysis, fine evaluation and control optimization of soot blowing energy efficiency can be achieved, and the boiler operation efficiency and the steam utilization rate are improved.

The invention relates to the technical field of medical data analysis, in particular to a urinary system tumor big data analysis system which comprises a target kernel generation module, a kernel matrix calculation module, a parameter optimization module and a risk division module. According to the method, a target kernel matrix reflecting clinical prognosis differences is constructed and serves as an optimization reference, a radiomics and genomics feature kernel matrix is generated through hardware acceleration parallel computing, kernel function width parameters are dynamically iteratively updated based on an alignment degree numerical value so as to ensure that multi-modal feature distribution is highly matched with a prognosis label, and the accuracy of the multi-modal feature distribution is improved. A multi-dimensional feature space containing rich pathological information is constructed by combining a weighted fusion mechanism after centralization processing, so that a support vector machine is trained to determine a high-robustness decision boundary, and precise division of tumor risk levels is realized while high-dimensional data calculation delay is greatly reduced; and the reliability and timeliness of auxiliary diagnosis and treatment results in a complex pathological environment are effectively improved.

The present disclosure relates to a method. A main transaction may be received. The main transaction comprises a set of change records of the at least one source table. Each change record represents at least one data record of a source table of the at least one source table. A least one subset of change records of the set of change records may be determined. Each subset of change records is determined such that it represents a distinct source partition of the at least one source table. A sub-transaction may be determined per subset of change records of the at least one subset of change records. The main transaction may be executed by at least concurrently executing the set of sub-transactions, wherein the execution of each sub-transaction of the sub-transactions comprises concurrently adapting a data store and adapting a metadata store in accordance with the sub-transaction.

A data analytics system including an append-only first data store accessible to multiple clients and a second data store is disclosed. The data analytics system can be configurable to, in response to receiving first instructions from a first target system of a first client, the first target system separate from the data analytics system, create a first pipeline between the append-only first data store and the second data store. The first pipeline can be configured according to the first instructions to generate a client-specific data object and store the client-specific data object in the second data store. The data analytics system can be configurable to tear down the first pipeline upon completion of storing the client-specific data object in the second data store.

The invention provides an agricultural product quality intelligent data analysis system, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting agricultural subject record information, patrol inspection time sequence records, quantitative or fast detection spectrum detection data, and block chain stored commitment standard certification data; structural data fusion is executed to generate a cross-department supervision feature matrix; multi-channel parallel processing is executed based on the feature matrix; blockchain certificate storage continuity verification is implemented at a certificate monitoring node, and a data integrity compensation protocol is activated when a chain structure fault is detected; and capturing the agricultural chemical application frequency and dose threshold vector in real time at the input monitoring node. According to the invention, agricultural product quality safety supervision is realized by constructing an intelligent data board.

The invention provides a recessive gastroesophageal reflux event data analysis system and method based on dynamic changes of abdominal pressure. The system and method are suitable for noninvasive detection of atypical reflux clinically. The system comprises an abdominal pressure dynamic detection unit, a throat vibration detection unit, a multi-mode signal processing module, a reflux event judgment module and a data output module. According to the system, by analyzing time sequence linkage between abdominal pressure mutation and throat vibration, a stomach content countercurrent event is recognized, silent type countercurrent characteristic sound is captured with the assistance of a bone conduction sound wave channel, and multi-path redundancy recognition is achieved. The method is suitable for postoperative patients, children, old people and other people who cannot complain, has the technical advantages of continuous wearing, behavior correlation analysis and clinical verifiability, and significantly improves the recognition rate and diagnosis accuracy of recessive gastroesophageal reflux events.

The invention relates to the technical field of resource exploration, and discloses an intelligent resource exploration data analysis system which comprises a data acquisition and preprocessing module which is used for acquiring multi-source exploration data, preprocessing the multi-source exploration data and encrypting uplink storage; the data real-time analysis module is used for performing real-time complex operation on the preprocessed exploration data and outputting an analysis result; and the multi-source data deep fusion module is used for mining potential semantic association among the multi-source data and realizing data deep fusion. By constructing a full-link collaborative architecture of data acquisition and preprocessing, real-time analysis, deep fusion, model optimization and application feedback, and combining a real-time analysis module accelerated by quantum computing and an encrypted data transmission link, the processing efficiency and real-time response capability of resource exploration data are remarkably improved.

The invention relates to the technical field of data analysis, in particular to a newborn health data analysis system which comprises a physical sign data construction module, a trend recognition module, a weight distribution module, a nonlinear modeling module and a prediction curve output module. According to the method, the growth cycle of the newborn is subdivided into continuous stages with different physiological meanings, the change rates of the sign data between the adjacent stages are deeply calculated and compared, the dynamic trends such as acceleration, stability or slowing down of body weight and height changes can be accurately recognized, and then quantitative weights are given to the different growth trends; according to the method, the positive growth situation is more evaluated in subsequent analysis, the growth and slowdown situation is correspondingly adjusted, a nonlinear prediction model is constructed on the basis, the non-uniform-speed natural law of growth and development of the newborn can be fit, the staged trend can be dynamically fused into long-term change track prediction, and the prediction accuracy of the growth and development of the newborn is improved. And the health conditions of the newborn in different developmental window periods are disclosed.

The invention relates to a battery thermal runaway monitoring system and a battery thermal runaway monitoring method. The battery thermal runaway monitoring system comprises an information acquisition device and a data analysis system, the information acquisition device is positioned in the battery pack, is correspondingly connected with at least one battery and is used for acquiring battery information data of the at least one battery; the battery information data comprises characteristic gas concentration data released due to a battery fault, temperature data of a single battery cell, stress data in the single battery cell and working voltage data of the battery; and the data analysis system is connected with the information acquisition device and is used for receiving the battery information data transmitted by the information acquisition device and generating a battery control instruction according to the battery information data. According to the battery module, the integration level can be improved, in-situ monitoring of the single batteries can be achieved from multiple dimensions, the accuracy and timeliness of battery monitoring are improved, and thermal runaway is effectively prevented from being diffused in the battery module.

The invention discloses a data analysis method and system driven by a large model based on mcp, and relates to the technical field of big data analysis. Comprising the following steps: step 1, creating a data analysis system driven by a large model based on mcp; step 2, performing full life cycle management of service metadata and automatic generation of multi-language codes through a unified service abstraction module; 3, a protocol conversion and verification rule is built in through a protocol adaptation layer module and used for heterogeneous data source access and transmission; 4, flow control, fusing degradation and real-time monitoring functions are integrated through a service governance engine module, and multi-model routing and dynamic resource scheduling are carried out in combination with an intelligent analysis engine module; according to the method, the problems of the existing data analysis system in the aspects of multi-source heterogeneous data processing, real-time guarantee, model generalization ability, collaboration and the like are solved.

The invention relates to the technical field of electronic measurement, and particularly discloses a multi-channel synchronous shunt calibration and data analysis system. The system comprises a signal acquisition front end, a delay measurement and compensation module, a synchronous control module, a data analysis and processing module and a system calibration interface, channel delay deviation is measured through a high-precision time-to-digital conversion circuit, and hardware compensation is realized in combination with a programmable delay line array. And a channel delay model and a recursive estimation algorithm are utilized to dynamically track delay drift caused by environment change, so that dynamic recovery and long-term maintenance of nanosecond-level synchronization precision are realized, and the environmental adaptability and synchronization stability of the system are remarkably improved.

The invention relates to the technical field of gene detection, and discloses a mycobacterium tuberculosis drug resistance gene detection data analysis system which comprises a data import module, a signal correction module, a feature extraction module, a multi-stage interpretation module, a result fusion module and a report generation module. The system performs baseline drift correction, abnormal point detection and nonlinear normalization on a fluorescence intensity value output by a detector, extracts multi-dimensional features such as peak intensity, a mutation ratio, a consistency coefficient and an in-batch difference index, and constructs a dynamic threshold function to realize wild type and mutation type layered judgment. A drug resistance risk conclusion is generated through drug resistance characteristic matrix mapping, a standardized detection report is output, and intelligent analysis of a detection result and automatic report generation are achieved. Through multi-stage signal correction, multi-dimensional feature extraction and dynamic threshold interpretation, intelligent analysis and standardized report output of a mycobacterium tuberculosis drug resistance gene detection result are realized, and data judgment accuracy and clinical application efficiency are improved.

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 discloses a geological exploration data analysis system and method based on multi-source data fusion, and relates to the technical field of data analys.The geological exploration data analysis method comprises the steps that a multi-source sensor is used for collecting general characteristic data in geology, and real-time characteristic values are calculated to construct an environment characteristic matrix; calculating the similarity between the real-time environment characteristic matrix and each type of geological data in the knowledge base, and selecting and judging to obtain a real-time survey geological type; respectively calculating data fusion weights of the real-time survey geological types by using the quality score and the relevancy; performing weighted fusion on the real-time data flow during geological survey by using the calculated data fusion weight; according to different types of geological historical survey data, abnormal thresholds of different types of geology are calculated and standardized, and an abnormal threshold database is generated; and extracting an abnormal threshold of the type of the real-time surveyed geology from the abnormal threshold database, judging the fused data by using the abnormal threshold, obtaining whether the real-time surveyed geology is abnormal or not, and performing early warning.

The invention discloses an atmospheric microparticulate monitoring device and method based on multi-mode sensing, and relates to the technical field of ecological environment monitoring. The device comprises a data acquisition system used for carrying out real-time sample acquisition and signal conversion on conventional pollution factors and meteorological parameters in air; the data analysis system is used for carrying out concentration analysis and early warning on the signals converted by the data acquisition system; the error evaluation system is used for performing error evaluation on the key indexes of the data acquisition system according to the acquired samples; and the data transmission system is used for carrying out cross-regional transmission on the sample acquired by the data acquisition system and the analysis result according to a sensitivity quantity algorithm based on an anonymous model. Real-time monitoring of atmospheric micro-particles with high accuracy and strong applicability is realized based on atmospheric monitoring standards independent of a meteorological monitoring framework, and reasonable adjustment of device parameters is realized by cooperation of the atomic fluorescence spectrometer and the enrichment sampling and pretreatment unit.