33 results about "Visualization algorithms" patented technology

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention discloses a mangrove forest carbon sink dynamic monitoring method and system based on multi-source remote sensing fusion, and the method comprises the steps: employing a deep convolutional neural network to extract spectral features and texture features for a remote sensing data set, and generating a feature set containing the preliminary distribution information of a mangrove forest; distinguishing the mangrove forest from other vegetation through a random forest classifier from the feature set, and judging a vegetation interference area to obtain a mangrove forest distribution map; analyzing the dynamic change of the carbon sink quantity in the carbon sink distribution data by adopting a long-short-term memory network to obtain a carbon sink quantity change sequence; according to the carbon sink quantity change sequence, detecting spatial-temporal heterogeneity of the carbon sink quantity through a sliding window algorithm, determining key change nodes, and generating a trend feature set; according to the prediction result, the distribution range and the carbon sink amount change are integrated through grid overlay analysis, and a comprehensive monitoring layer is generated; and extracting a spatial-temporal change trend of the mangrove forest ecosystem from the comprehensive monitoring layer, and outputting a dynamic monitoring report by using a visualization algorithm.

The invention relates to the technical field of geomechanical data research, in particular to a geomechanical data visualization method and system based on a mixed data model. According to the technical scheme, the method comprises the steps of data collection and arrangement, mixed data model construction, visual algorithm design and application and visual result display and interaction. The data collection and arrangement comprises the step of collecting geomechanical data including geologic structure data, rock mechanics parameter data and ground stress data. According to the method, geologists can be assisted to deeply understand the complex relationship between geologic structure evolution and rock mechanical behaviors, and powerful data visualization support is provided for development of geologic theories; geological risks can be predicted in advance, an engineering design scheme is optimized, and engineering safety and economic benefits are improved; through accurate visualization and deep analysis of geomechanical data, the possibility and the potential influence range of disaster occurrence can be evaluated more accurately, and key technical support is provided for disaster early warning and prevention and control measure formulation.

A system for and a method of diagnosing a mental illness in a patient are disclosed. The method measures signals, such as EEG signals, on a patient and applies a trained machine learning model on these signals. The model classifies the patient as having a mental illness or of being a normal control. The results are communicated to a user. In addition, a sub-type of a mental disorder may be identified by using other machine learning techniques on the features of the signals, such as a neural network, clustering, dimension reduction, and visualization algorithms. One such technique is t-distributed stochastic neighbor embedding (t-SNE).

The invention relates to the field of power distribution network graph optimization, in particular to a power distribution network planning graph visibility optimization method, which comprises a layered architecture module, a technical module, a data integration module, a data processing module, a data evaluation module, a visualization algorithm module and a user interaction module. The layered architecture module is divided into a presentation layer, a control layer, a business logic layer and a data access layer through a modern software system; the presentation layer is responsible for interacting with a user and providing a user interface; the control layer is used for processing user input, the processing speed and the response time of the system can be effectively improved and the maintenance cost can be effectively reduced by setting the modules to be mutually connected for use and improving the system performance and architecture, and the maintenance cost of the system is reduced and the availability of the system is enhanced through modular design and automatic deployment. The stability and reliability of the system are improved, uninterrupted operation of key services is ensured, technical innovation is promoted, and a theoretical basis and practical guidance are provided for technical innovation in related fields.

The invention relates to a community structure discovery-oriented large-scale complex network visualization method. The method comprises the following steps: acquiring a large-scale complex network; extracting topological structure features of the large-scale complex network by using a representation learning technology to obtain a node representation vector set; performing dimension reduction processing on the node representation vector set by using PCA and t-SNE algorithms to obtain an output vector set; recursive clustering is carried out on the output vector set, each level in the hypergraph is divided, a community formed after clustering of each level is constructed as a super node, the relation between nodes in the community is combined as a hyperedge, and a hypergraph structure is constructed; and visualizing the hypergraph structure by using a diversified visualization algorithm. By adopting the method, the visualization effect of the complex social network can be improved.

The invention relates to the technical field of geophysical exploration, in particular to a three-dimensional resistivity overwater detection method and system based on cooperation of an unmanned aerial vehicle and an unmanned ship, and the system comprises a main unmanned ship, a plurality of auxiliary unmanned ships, an unmanned aerial vehicle auxiliary platform and a control terminal. The main unmanned ship is used for providing an electric field source; the auxiliary unmanned surface vehicle is used for dragging a floating cable in water along a sailing path, and a measuring module is arranged on the auxiliary unmanned surface vehicle and used for measuring the potential difference between power supply electrodes in the floating cable; the unmanned aerial vehicle auxiliary platform is used for providing air guidance and obstacle avoidance navigation for the main unmanned ship and the auxiliary unmanned ship; and the control terminal is in communication connection with the positioning module and the measurement module and is used for receiving the potential difference information and the electrode positioning information and obtaining a resistivity space distribution image of the target water area by adopting a three-dimensional forward and reverse modeling and visualization algorithm. According to the invention, the operation safety, the measurement efficiency and the space coverage range are obviously improved, and the method is suitable for rapid three-dimensional detection of complex water areas such as rivers and lakes.

The application discloses a fish scale protein polypeptide anti-chronic heart failure efficacy evaluation system, relates to the technical field of biological active polypeptide evaluation and cardiovascular disease function analysis, and comprises a raw material processing module for preparing fish scale protein polypeptides, a CHF model construction module for animal experiments, a function detection module, and a data processing and analysis module. Multidimensional statistics and visual algorithm are adopted to integrate tissue remodeling indexes such as heart function, oxidative stress and inflammatory reaction, so that comprehensive evaluation of the efficacy of fish scale protein polypeptides is realized. The application can realize systematic, quantitative and mechanistic evaluation of the anti-chronic heart failure effect of fish scale protein polypeptides, solve the problems of single evaluation index, poor repeatability of results and lack of mechanism correlation in the prior art, and has the advantages of comprehensive evaluation, reliable results, wide application range and strong generalizability.

The invention relates to a shaft risk visualization analysis method. The method comprises the following steps: a data acquisition step: acquiring attribute data of a preset type of multi-source data source of a shaft; a preprocessing step: preprocessing the attribute data of various preset types; a risk prediction step: inputting a pre-constructed artificial intelligence model based on the pre-processed attribute data of various preset types, and performing risk prediction; and a real-time visualization step: according to a preset panoramic visualization algorithm, generating a corresponding visual component and a display layout relationship thereof according to the association relationship of the pre-processed attribute data of various preset types, and displaying the visual component and the display layout relationship thereof.

The invention belongs to the technical field of data processing and analysis, and particularly relates to an industrial system operation mode description method based on a dynamic clustering algorithm DeepDPM. The method comprises the following steps: firstly, collecting actual operation data of a system; preprocessing the data, and dividing a training set and a verification set according to a fixed proportion; and then establishing an AE model, pre-training the AE model by using the training set, storing the trained AE model and the weight, and verifying the effect of the trained AE model by using the verification set. Inputting the preprocessed data into the trained AE model for feature extraction; then, the extracted feature data are input into a DeepDPM model to be clustered; and finally, carrying out visual display on a clustering result by using a dimension reduction visualization algorithm: t-distributed random neighborhood embedding (t-SNE for short). According to the invention, the efficiency and accuracy of operation mode prediction in an industrial system are improved.

This invention provides a rapid depression detection method based on a weighted degree transfer network, comprising: acquiring and preprocessing the EEG signals of a subject to obtain a one-dimensional EEG time series; mapping the one-dimensional EEG time series into a complex network using a weighted horizontal visualization algorithm; extracting degree and intensity sequences; using the deduplicated set of degree values ​​as nodes of the new network; tracing the degree transfer path between adjacent time points; calculating the product of the degree difference and intensity difference between adjacent time points as the edge weights of the degree transfer path, thereby constructing a weighted degree transfer network; normalizing the network to obtain a transition probability matrix; calculating its Shannon entropy and MPR statistical complexity as a joint feature vector; and using a machine learning classifier to identify the depression state. This invention does not require presetting parameters such as embedding dimension, and by fusing the dynamic difference features of degree and intensity, it can quickly and accurately capture abnormal nonlinear dynamic patterns in the EEG signals of depression.

The application provides a risk transmission prediction method and system based on a time sequence diagram and a large model joint deduction, relates to the technical field of artificial intelligence and knowledge engineering, and comprises the following steps: extracting forward-looking time sequence facts based on hierarchical prompt words and a structured constraint mechanism; extracting conflict events and generating time sequence relationships; updating a historical time sequence knowledge graph based on a time sequence relationship and a confidence weighted update strategy; determining a central entity, initiating a structured query to the updated historical time sequence knowledge graph from the central entity, and generating a context knowledge subgraph; converting the context knowledge subgraph into a natural language description with logical relationships, injecting a risk hypothetical event, constructing a prompt word as a new input of a large model, and outputting a structured JSON object containing a complete reasoning chain; and based on a risk path refining and visualizing algorithm with influence weight attenuation, converting the structured JSON object to obtain risk warning information and a visualized transmission path diagram.

The invention discloses a new energy battery anti-static foam box flaw detection method based on artificial intelligence, and relates to the technical field of foam box flaw detection. The method comprises the steps that foam box charge dissipation data is collected and preprocessed, a dimension reduction feature vector is obtained and input into a support vector machine model, an internal structure deviation degree score is output, and a three-dimensional feature map is generated through mapping of a three-dimensional visualization algorithm; collecting surface response data, calculating an optimal threshold through a multi-target threshold optimization method, and recognizing surface abnormal data in combination with the three-dimensional feature map; constructing a fusion feature vector based on the surface abnormal data, inputting a support vector machine model optimized by an improved quantum genetic algorithm, and outputting a defect classification result; and collecting foam box state data, combining a defect classification result, performing weighted summation by adopting an analytic hierarchy process to obtain a risk value, and dividing safety levels according to the risk value to complete the defect detection of the anti-static foam box.

The application discloses a flow visualization monitoring method in a cloud environment, and the method comprises the following steps: obtaining full flow in the cloud environment and time stamps of the flow by mirroring all virtual instance flows in the cloud environment; removing the full flow; classifying the removed full flow according to classification rules; determining the topological relationship of each classified flow by using a visualization algorithm on the five-tuple information and the time stamps of the classified full flow; storing the five-tuple information, the time stamps, the topological relationship of the classified full flow and the corresponding visualization parameters of each classification in a pre-set storage structure; and visualizing the classified full flow according to the topological relationship of each classified flow and the corresponding visualization parameters of each classified flow. The application also discloses a flow visualization monitoring device in a cloud environment. The application can realize the visualization monitoring of the flow in the cloud environment.

The present invention proposes a navigation big data platform monitoring system and method; wherein the system includes: a plurality of sensor devices; an edge server subsystem having a plurality of computing nodes, the plurality of computing nodes being arranged in various areas of the ship, respectively connected with corresponding sensor devices, for processing and storing the monitoring data of the corresponding sensors, the plurality of computing nodes forming an edge computing cluster through information interaction, and pointing to the same heterogeneous database; and a central control subsystem storing a plurality of visualization algorithm modules and providing a plurality of application services, the plurality of application services being used to retrieve corresponding data from the heterogeneous database, and processing data according to the visualization algorithm modules loaded in the current application service; the central control subsystem and the edge server subsystem communicate through a data exchange network formed by a plurality of communication modes. The present invention can realize on-site processing, storage and analysis of data to flexibly and reliably cope with complex and changeable weather.

The invention discloses a social media data visual subsystem based on a parameter insight associated agent. The system comprises a dialogue module which displays interaction between a user and the agent and receives and analyzes a problem; the action analysis path tree module presents an intelligent agent exploration path in a tree structure, and displays an operation node through a node aggregation and hiding strategy of the tree structure; the mining parameter-result association analysis module supports interactive optimization by using a parallel coordinate graph visualization algorithm hyper-parameter and result evaluation index relationship; the insight visualization module displays insight discovery in an image-text form and supports linkage traceability with the action analysis path tree module; and the rear-end calculation module is responsible for data processing and interaction coordination among the modules. According to the method, full-link transparency in the LLM intelligent agent exploration process is achieved, users are supported to intervene in parameter tuning and path selection in real time, automatic analysis and user control are balanced through deep fusion of multi-modal interaction and automatic visual generation, and the interpretability and credibility of social media data insight are improved.

The invention discloses a metal internal damage monitoring method and device based on flexible electronic sensing detection, and relates to the technical field of structural health monitoring, and the method comprises the steps: attaching a flexible electronic sensor array to the surface of a to-be-detected metal, and employing a preset electrical response signal obtaining mode to obtain an electrical response signal of the to-be-detected metal based on the flexible electronic sensor array; obtaining an electrical response signal associated with the internal damage state of the to-be-detected metal structure; collecting the electrical response signal at a preset sampling frequency, and extracting a plurality of electrical characteristic parameters of the to-be-detected metal from the electrical response signal; inputting the plurality of electrical characteristic parameters of the to-be-detected metal into the metal internal damage prediction model to obtain a damage field prediction value of the to-be-detected metal; and generating an internal damage distribution diagram of the to-be-detected metal based on the damage field prediction value of the to-be-detected metal by adopting a preset two-dimensional visualization algorithm. According to the invention, on-line monitoring of internal defects of the metal structure is realized, and the monitoring accuracy is improved.

The present application relates to the technical field of intelligent medical teaching, and provides an internal examination teaching method for real-time acquisition of cervical pressure data, comprising: S1: building an overall structure of an intelligent internal examination teaching aid; S2: using pressure sensor integration and calibration technology to implement real-time acquisition of high-precision pressure data and three-dimensional coordinate data on the vaginal wall and cervical part; S3: using real-time visualization algorithm for internal operation of the vagina to convert the collected spatiotemporal mechanics signals into three-dimensional motion trajectory, force distribution and dynamic rendering image; S4: constructing a multi-dimensional quantitative evaluation index system, and based on the expert standard operation library and the similarity algorithm, objectively quantifying the score and standardizing the judgment of the operation of the student; S5: realizing real-time fitting, trajectory rendering and multi-dimensional information display of the operation data; S6: executing internal examination simulation training, data acquisition, real-time feedback and comprehensive examination. Through the built-in pressure sensor, the position and mechanics change information of the operation method are collected in real time, and the operation of the student is objectively quantified and judged.

This application relates to a multi-domain intelligent interactive system based on bottle QR codes. The system includes: an encoded data acquisition module that collects encoded data; a storage, query, and judgment module that retrieves data stored locally on the communication device and generates corresponding judgment result requests; a data visualization module that receives the encoded data and judgment result requests, mines tourism geographic information datasets using data extraction algorithms, and then transforms this information into visualized content using visualization algorithms; and a user recommendation module that collects social feature parameters input by users, comprehensively analyzes these parameters and the visualized content, and generates a personalized list of recommended users. This system achieves deep integration of bottle QR codes with multiple domain functions, greatly improving the convenience of information acquisition and social interaction, and meeting diverse needs in tourism and social interaction.

The present invention relates to the technical field of geomechanics data research, and in particular to a geomechanics data visualization method and system based on a hybrid data model. Its technical solutions include data collection and collation, hybrid data model construction, visualization algorithm design and application, and visualization result display and interaction. Data collection and collation: Collect geomechanics data including geological structure data, rock mechanics parameter data, and in-situ stress data. The present invention can help geologists more deeply understand the complex relationship between geological structure evolution and rock mechanics behavior, and provide strong data visualization support for the development of geological theories; it can predict geological risks in advance, optimize engineering design schemes, and improve engineering safety and economic benefits; through the accurate visualization and in-depth analysis of geomechanics data, it can more accurately evaluate the possibility of disasters occurring and the potential impact range, providing key technical support for disaster warning and prevention measure formulation.

The invention discloses a program running visualization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the algorithm structure analysis of an original program code based on a large language model, generating an algorithm structure analysis result of the original program code, and carrying out the algorithm structure analysis of the original program code based on the algorithm structure analysis result; determining at least one visual burying point in the original program code; determining a target program code matched with the original program code in a pre-established visual algorithm library based on the algorithm structure analysis result; based on an algorithm structure analysis result, determining a target visual interface from the candidate visual interfaces; and generating a target log code corresponding to the target visual interface by adopting a large language model based on the deployment mode of the log code in the target program code and the data processing operation corresponding to the visual burying point, inserting the target log code into the visual burying point, and carrying out visual monitoring on the operation of the target program. Through the technical scheme, the code visualization efficiency and accuracy are improved.

The invention discloses a deep learning network visualization method for remote sensing image road extraction, and relates to the field of artificial intelligence deep learning visualization. A remote sensing image road extraction model pre-trained based on a PyTorch deep learning framework can be used for single-sample area-level interpretation result analysis and node-by-node reverse contribution degree propagation to realize contribution degree interpretation of a road segmentation result, and finally, the contribution degree of each node to an extraction result is displayed through a visualization algorithm. A method is provided for optimization analysis of a road extraction network by utilizing a final visual contribution degree thermodynamic diagram, and attribution explanation is carried out on a road extraction process.

The invention relates to the technical field of image matching degree visualization, and provides a three-dimensional model modeling and two-dimensional image matching degree visualization method, which comprises the following steps of: S1, calculating a space mapping angular point of a slice image which is generated by adopting cone beam projection scanning and is used for splicing a full-length image in a double-plane coordinate system; s2, calculating the coordinates of the bone key points in the biplane coordinate system on the basis of the coordinates of the bone key points projected on the normal position and the lateral position of the mark points on the film image, so as to complete the initial position registration of the three-dimensional model in the biplane coordinate system; s3, retaining a film image related to the interested three-dimensional model in the current visualization, and realizing 3D visual display of the three-dimensional model and the film image; and S4, performing interaction of front and side view angle change, currently displayed front and side film images and three-dimensional model pose adjustment based on the 3D visual window. Therefore, a visualization algorithm of the matching degree of the 3D model and the two-dimensional image based on the full-length image of the physical small cone beam projection is realized.

The invention relates to a method for detecting and repairing bubbles between glue pouring layers, which comprises the following steps: laying an electrode net on a concrete base surface, the electrode net being composed of a plurality of metal strips or metal wires which are staggered transversely and longitudinally; conductive adhesive is applied to the concrete base surface and the electrode net, the adhesive layer covers the electrode net, the thickness of the adhesive layer is larger than the height of the electrode net, and the ends of the metal strips or the metal wires can stretch out of the adhesive layer; covering a steel plate above the adhesive layer, and pressurizing and fixing to cure the adhesive layer; a resistance measuring device is used for connecting and measuring the resistance value between every two metal strips or between every two metal strips; processing and analyzing the resistance value data by using a scalar field visualization algorithm to obtain a resistance value distribution cloud picture of the glue layer; positions with resistance values greater than a threshold value in the cloud picture are bubbles; after the bubbles are positioned through the cloud picture, the steel plate above the bubbles is drilled, then the conductive adhesive is injected into the bubbles, the bubbles are filled, and meanwhile the corresponding drilled holes in the steel plate are also filled.

The geological iterative matching technology based on machine learning assistance is characterized in that a convolutional neural network is used for capturing feature data of a geological image for extraction. Data cleaning standardization is carried out on the extracted data, so that the quality and consistency of the data are ensured, and the accuracy of a subsequent machine learning model is improved. A vector machine (SVM) learning algorithm is utilized to establish an association between the geological data and the model to match the geological model and the actual data. Iterative optimization processing is carried out by using a gradient descent method, and the accuracy and performance of the model are improved step by step. Finally, a neural network visualization algorithm is utilized, the geological data and the result of the model are presented in a visual and understandable mode, and the combination of the geological iteration matching technology based on the machine learning auxiliary algorithm technology helps experts in the geological field to better understand the geological data, recognize the geological mode and improve the geological performance. And optimizing the geologic model to match the actual data.

The invention relates to a multi-element intelligent cross-domain interaction system based on a bottle body two-dimensional code. The system comprises a coded data acquisition module for acquiring coded data; and the storage query judgment module retrieves the local storage data of the communication equipment so as to generate a corresponding judgment result request. And the data visualization module receives the coded data and the judgment result request, mines a tourism geographic information data set through a data extraction algorithm, and converts the information into visual display content by applying a visualization algorithm. And the user recommendation module collects social feature parameters input by the user, comprehensively analyzes the parameters and the visual display content, and generates a personalized recommended user list. According to the invention, deep fusion of the bottle body two-dimensional code and multi-field functions is realized, the convenience of information acquisition and social interaction is greatly improved, and diversified requirements of the bottle body two-dimensional code in tourism and social contact are met.

The invention belongs to the technical field of shale oil and gas exploration and development, and provides an auxiliary analysis method and system for shale oil and gas resource development in order to more accurately provide data reference for evaluating the development potential of a shale oil and gas well. Comprising feature data and yield data of different wells in the shale area; and carrying out data mining on the historical data to obtain the sensitivity of the characteristic parameters which have different degrees of influence on the oil and gas yield. The auxiliary analysis system comprises a preprocessing module, a data management module, a data mining module, a Monte Carlo module, a visualization algorithm module and an account management module. Through the sensitivity of the characteristic parameters mined by the method, researchers can judge which characteristic parameters should focus on according to the sensitivity of the characteristic parameters, and the situation that various characteristic parameters are treated blindly without difference is avoided.

This application provides a multi-source software supply chain intelligent analysis method and system. Among them, it parses the software component identification information submitted by the user, extracts attribute features, locates the node paths between components, and generates a weighted dependency chain list containing direct and indirect dependency chains; uses a Bayesian network model combined with a predefined risk index system to calculate the risk score of each dependency chain, and identifies potential supply chain threat points through an anomaly detection algorithm to generate a risk report; this report covers the overall risk score and threat points of key links or components; based on this report, uses a multi-dimensional visualization algorithm to draw a comprehensive risk map to display groups of components with similar risk patterns; fuses genetic algorithms and simulated annealing algorithms to explore high-risk areas, proposes adjustment suggestions, and generates risk mitigation strategies to reduce the overall risk score. The technical solution provided by the embodiments of this application improves the security and integrity of the multi-source software supply chain.

The invention relates to the technical field of process optimization and experience inheritance, in particular to a process optimization and experience inheritance method and system, and the method comprises the following steps: A, importing a design drawing and a data file; b, identifying and analyzing a design drawing and a data file; c, according to the extracted key information, a preset process rule, a layout rule and a visualization algorithm, generating a process bubble graph; d, performing empirical data association on the empirical data and a process bubble graph; e, performing encryption processing on the associated empirical data and generating an operation guidance two-dimensional code; f, obtaining process parameters and operation guidance required by production by scanning the operation guidance two-dimensional code; according to the process optimization and experience inheritance method, the process information and the association relationship are visually presented through the bubble graph, and the key process experience data are encrypted and stored by using the two-dimensional code, so that accurate storage and convenient transmission of the experience technology are ensured.