13 results about "Visualization algorithms" patented technology

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.

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 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 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 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 discloses an insulating cylinder defect detection method and a storage medium. The method comprises the following steps: acquiring multi-angle image data of an insulating cylinder, and respectively preprocessing edge and center features through anisotropic diffusion filtering and a CLAHE algorithm to generate a high-contrast image; enhancing edge detection by adopting an improved Scharr operator, adaptively dividing rectangular sub-blocks based on an edge density function, and ensuring that each sub-block comprises at least one potential defect unit; extracting multi-scale LBP entropy value features by using wavelet transform, and constructing a support vector machine classification model to realize accurate defect classification; and finally marking the position and the size of the defect through a visualization algorithm. According to the method, a dynamic partitioning strategy and multi-scale texture analysis are fused, and the technical problems that a traditional method is high in false detection rate and small defects are missed are solved.

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