862 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 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.

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 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 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 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.

The invention relates to the technical field of data analysis, in particular to a territorial space planning data analysis system for ecological restoration, which comprises an interference parameter acquisition module, an ecological disturbance calculation module, a restoration area identification module, a priority level judgment module and a site selection stability screening module. According to the method, the accuracy and efficiency of ecological restoration are improved by capturing various disturbance factors of an ecological sensitive area and dynamically evaluating ecological degradation, and the signal of ecological degradation can be recognized earlier by comprehensively monitoring the change trend of earth surface coverage and the dynamic state of a water body and quickly responding to the subtle change of an ecological environment; ecological service function indexes are adopted for inter-year comparison, a long-term view angle about the health state of an ecological system is provided, prediction accuracy is improved, ecological restoration work can be implemented in a more targeted mode, and the lasting effect of the ecological restoration work is ensured through continuous monitoring of land utilization changes.

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.

The invention provides a digital twinning-based water flow dynamic analysis method and a big data analysis system, and the method comprises the steps: obtaining a historical water flow data set of a target water area, collecting a real-time water flow monitoring data set, and constructing a digital twinning water flow model corresponding to the target water area; performing multi-modal feature fusion processing on the historical water flow data set and the real-time water flow monitoring data set to generate a dynamic water flow feature set, loading the dynamic water flow feature set into a digital twin water flow model based on a spatial distribution topological structure of the digital twin water flow model to generate a water flow dynamic change rule set, and performing dynamic water flow change on the dynamic water flow change rule set. And generating a water flow optimization strategy set according to the water flow dynamic change rule set, and feeding back the water flow optimization strategy set to the water flow regulation and control system to execute dynamic regulation and control operation. By adopting the technical scheme of the invention, the space adaptability and the dynamic response capability of the water flow optimization strategy decision can be obviously improved.

The invention relates to the technical field of intelligent charging, in particular to an edge intelligent calculation enabling type peak-valley energy storage charging system which comprises a power grid frequency judgment module, an energy storage dispatching output module, a load trend recognition module, a multi-station matching sorting module and a load dynamic distribution module. According to the invention, through the continuous data analysis of the power grid frequency, the energy storage unit power and residual energy, and the load current, the system can accurately identify the linkage relationship of various parameters during the frequency abnormal response period, refine the condition judgment of peak-valley energy storage triggering and scheduling, optimize the energy storage output decision path, and improve the reliability of the system. Through dynamic screening of load trends and changing stations, the real-time performance and flexibility of energy allocation among multiple stations are enhanced, the adaptive capacity of the system to complex supply and demand changes is further improved in station matching and priority ranking, energy storage resources and loads are dynamically allocated, the electric energy utilization rate and the cooperative regulation and control level of the energy storage system are effectively improved, and the energy utilization rate and the cooperative regulation and control level of the energy storage system are improved. And intelligent upgrading of energy management is promoted.

The invention discloses a seismic geological disaster monitoring and early warning device, and the device comprises an intelligent sensing system which carries out the real-time collection of geological parameters, facility states and environmental factors through a multi-mode sensor array, and comprises the steps: capturing the strain of a pipeline through a distributed optical fiber sensor, monitoring the vibration through an MEMS accelerometer, and providing deformation data through an InSAR satellite; the analysis system constructs a complex network modeling engine, fuses the historical transition probability matrix and the real-time deformation rate parameter by using a directed weighted network model, and combines a two-channel causal inference engine which comprises a physical causal channel and a data causal channel; the physical causal channel is embedded into a coulomb fracture criterion to calculate a fault stress accumulation rate, the Darcy law simulates pore pressure propagation, and the data causal channel generates an anti-fact sample through a CaualGAN and extracts a real causal chain in combination with a time causal convolutional network; and the execution and feedback system is used for triggering graded early warning based on an analysis result of the analysis system.

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.

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 data processing, in particular to a learning behavior data analysis system and method based on an agent, and the method comprises the steps: determining knowledge points in learning content, generating a visual knowledge point graph, testing the mastering degree of a learner for the knowledge points, generating a visual knowledge point mastering graph, and analyzing the knowledge points. The method comprises the steps of generating an optimal learning task which is most adaptive to the state of a learner based on the mastery degree of the learner and a learning behavior library, collecting real-time learning feature data of the learner during task execution, sensing the emotion and attention state of the learner in real time, and generating a personalized learning path in combination with a knowledge point graph, learning behavior analysis and the state of the learner. Learning tasks are dynamically adjusted according to real-time data, and learning strategies are optimized according to feedback of learners. Through dynamic perception and adjustment capability, the problem that a learning behavior analysis system in the prior art lacks real-time perception and dynamic adjustment of the state of a learner can be effectively solved.

The invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to an atmospheric pollution channel transmission data analysis system which comprises an acquisition and analysis end and a visual display end which are in communication connection. The acquisition and analysis end comprises a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; according to the invention, a full-chain analysis framework from multi-source data acquisition and processing, pollution channel dynamic identification to source analysis and prediction and early warning is constructed, the identification precision and timeliness of a pollution transmission path are significantly improved, and a key supervision park in a monitoring area is determined through pollution management and control decision analysis. The key supervision parks are continuously monitored, and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged, so that the targeted industrial park management is realized, and the pollution of each industrial park to the atmospheric environment is reduced.

The invention discloses a network security data analysis system and method based on artificial intelligence, and relates to the technical field of network security, and the method comprises the steps: constructing a structured triple, mapping the structured triple into graph nodes and edges, and storing the graph nodes and edges in a graph database; extracting graph data from the graph database, and generating a node feature matrix, an adjacent matrix and an edge feature matrix; utilizing a graph attention mechanism to train a node representation vector, and constructing a semantic propagation matrix; obtaining a predicted attack path model; generating a candidate attack path set; constructing a credible scoring model, training and optimizing, and screening high-credibility paths with scores exceeding a threshold value; constructing a multi-layer perceptron model to obtain an attack source prediction model; according to the method, real-time atlas data is obtained, the nodes exceeding the threshold value are marked as risk nodes, the risk nodes are uploaded to a protection system to trigger alarm and check, and the automation and real-time performance of attack source recognition are improved.

The invention discloses a big data analysis system and method for leaking stoppage and repayment of transport vehicles. The big data analysis system comprises a traffic behavior monitoring module which collects vehicle tracks and load records in a road network traffic data flow, analyzes a traffic time distribution rule, calculates a path deviation index, compares the difference between actual load and declaration data, and generates an abnormal traffic index; and the fee evasion risk prediction module is used for positioning passing nodes of high-risk vehicles based on the abnormal passing indexes, analyzing a matching relationship between node characteristics and historical fee evasion events, evaluating a road network risk level and generating a fee evasion probability prediction result. According to the big data analysis system and method for leaking stoppage and repayment of the transport vehicle, a quantum key distribution and lightweight encryption fusion mechanism is constructed, full-link dynamic encryption of data transmission is realized, man-in-the-middle attack and data tampering are effectively resisted, the security of core indexes in links of acquisition, transmission and analysis is guaranteed, and a trusted data basis is provided for intelligent inspection.

The invention discloses a photovoltaic module mismatch diagnosis system and method based on dual-mode thermal detection and AI dynamic optimization, and belongs to the technical field of fault detection of a photovoltaic power generation system.The photovoltaic module mismatch diagnosis system comprises a thermocouple sensor, an infrared thermal imaging module, a wireless data acquisition and transmission module, an AI intelligent data analysis system and a remote operation and maintenance management platform, and the inverter optimization control unit is respectively connected with the remote operation and maintenance management platform and the wireless data acquisition and transmission module. According to the invention, infrared thermal imaging large-range scanning and a surface ultra-thin flexible thermocouple sensor are combined, the temperature of key parts of the assembly is monitored in real time, and mismatch faults are accurately screened; adopting temperature gradient analysis, K-means clustering and a time sequence based on transfer learning to predict and recognize faults such as shadows, microcracks and poor welding, and performing remote early warning; mPPT parameters are dynamically adjusted based on a particle swarm algorithm, and power loss is reduced; the method improves the stability of a photovoltaic system, reduces the inspection cost, and is suitable for a large-scale power station and a distributed photovoltaic system.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent pig raising and selling platform data analysis system integrated with a 4G camera, and aims to solve the problems of data semantic segmentation and space-time mismatching of an existing heterogeneous sensing system. The system comprises an intelligent sensing node deployed in a breeding place and a cloud data analysis platform. The intelligent sensing node integrates an individual identification unit, a multi-modal visual acquisition unit and an edge calculation unit, and realizes data source fusion and information extraction; the data analysis platform constructs individual digital twins, and performs multi-dimensional intelligent analysis through growth prediction, health anomaly detection, market transaction decision and other models. According to the scheme, accurate prediction and optimization decision making of the live pig individual full life cycle data are realized, and powerful support is provided for fine breeding management, health early warning and intelligent transaction.

The invention relates to a multi-dimensional aging test data analysis system and method, and aims to realize real-time acquisition and intelligent analysis of multiple performance indexes in the aging process of electronic products. The system comprises an information acquisition module, a test scheduling module, a data processing module, an analysis module, an abnormity judgment module, a report module and a communication module. Cooperative work among the modules is realized based on task driving and data circulation, accurate association of test data is realized through bar code binding, and unified analysis is performed in combination with multi-source information such as USB read-write performance, network transmission state and display resolution. The system realizes multi-task concurrent acquisition through a resource-aware scheduling strategy, and the analysis module identifies performance abnormity and outputs a structured report by combining preprocessing, feature extraction and time sequence prediction. According to the method, the test automation level and the anomaly recognition accuracy are improved.

The invention discloses a tower crane standard knot nut looseness health monitoring system, which relates to the technical field of engineering machinery health monitoring and comprises a nut looseness monitoring sensor, a data acquisition module, a wireless communication module, a remote monitoring module and an alarm module. According to the tower crane standard knot nut loosening health monitoring system, the loosening state of the tower crane standard knot nut can be monitored in real time, the data acquisition module can rapidly process and convert the signals output by the nut loosening monitoring sensor to obtain real-time data of the nut, safety accidents caused by nut loosening are effectively prevented, and the safety of the tower crane standard knot nut loosening health monitoring system is improved. The safety of tower crane operation is improved, the wireless communication module adopted by the system can transmit monitoring data to remote monitoring equipment in real time, so that a worker can remotely monitor the loosening condition of a nut of the tower crane, the working efficiency is greatly improved, the labor cost is reduced, and historical data and real-time data are analyzed through the data analysis system.

Examples of file analytics systems are described that may obtain metadata data from a virtualized file server using a snapshot of the file virtualized file. The analytics system may mount a snapshot to retrieve metadata used in determining an overall size, structure, owner, permissions, and / or storage locations of parts of a file system managed by the virtualized file server. The analytics tool may communicate directly with each of file server virtual machines of the virtualized file server during the metadata collection process to retrieve the respective portions of the metadata. During the metadata collection process, the analytics system may add a checkpoint or marker (e.g., index) after every completed metadata transaction to indicate where it left off accessing the metadata snapshots. The checkpoint may allow the analytics system to return to the checkpoint to resume the process should the process be interrupted for some reason.

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 belongs to the technical field of mold machining, and discloses a mold sensor monitoring and data analysis system and method based on the Internet of Things, and the method comprises the steps that a data collection module collects mold data and injection molding part data; the data acquisition module acquires temperature, pressure and time data of the mold; the image acquisition module acquires plastic part and mold images; the image preprocessing module preprocesses the image; the feature extraction module performs feature extraction on the image; the image analysis module identifies abnormal conditions of the mold and the plastic part; the injection molding simulation module simulates an injection molding process; and the data fusion analysis module comprehensively judges the states of the mold and the plastic part. Mold cracks and plastic part deformation abnormity can be recognized, and batch waste products are avoided; the injection molding simulation module predicts a melt flow path and optimizes the sprue design and the cooling system layout; the influence of thermal expansion on size precision is simulated, and mold testing times are reduced; and the data fusion analysis module combines the image and the sensor data to improve the state judgment reliability.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention relates to the technical field of remote sensing data analysis, in particular to a land resource management data analysis system. According to the method, the spectral reflectivity change under the earth surface multi-direction illumination is obtained and the reflection gradient response information of the spectral reflectivity change is extracted, so that the spatial difference of the earth surface reflection characteristics at different angles can be more comprehensively presented; according to the method, surface land type division is more accurate in gray continuity and spatial structure expression, so that potential fission boundaries are identified and reasonable section classification is carried out through quantitative comparison and spatial aggregation analysis of structure texture differences in boundary identification between different regions, and the identification accuracy of the boundary identification between different regions is improved. The stability and variability of the interior and boundary structures of the land areas are effectively described, and on this basis, the area boundary is reconstructed and combined with texture and reflection clustering grouping, so that the land type state and the distribution boundary of each area can be finely judged.

The invention relates to the technical field of data analysis, in particular to a GIS-based land surveying and mapping data analysis system. According to the method, by introducing the multi-layer soil profile data and combining the parameters such as the soil organic matter content, the porosity and the water permeability, layered evaluation is conducted on the soil quality of different depths, soil stability analysis is finer, and the judgment precision of the tillage suitability is improved. In the aspect of farmland infringement and occupation monitoring, vegetation coverage information is extracted by using remote sensing data, and a vegetation reduction area can be accurately identified in combination with an NDVI index and vegetation density change, so that natural degradation and artificial infringement and occupation can be distinguished, and the farmland protection capability is enhanced. In the terrain analysis process, the terrain farming feasibility index is calculated through gradient and elevation changes, and more scientific quantitative evaluation of farming feasibility is achieved. The hydrological analysis evaluates the soil erosion risk through the water flow impact force, dynamically monitors the stability of the land parcel, and improves the response speed of cultivated land protection and water and soil conservation.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.