AI Multimodal Analysis

Multimodal Data Analysis

Integration of images, spectrographs, tables, text and experimental conditions and establishment of cross-modular analysis processes to assist scientific teams in detecting variable correlations, sample differences and interpretable patterns.

Presentation of multi-mode data analysis
01

Solution Overview

For materials, life sciences, medical images, micro-images, spectral data, test curves, experimental tables and report texts, clean, align, feature extraction and visualization of data from multiple sources. Explanatory drawings of conclusions and delivery reports are developed through statistical analysis, machine learning and multi-mode integration models.

Multisource IntegrationHarmonization of images, spectrographs, text, tables and experimental conditions
Feature extractionStripping, peaks, curves, semantics and statistical indicators
Linked InsightSample grouping, variable relationships and unusual sources detected
Visual outputForm charts, reports and interactive analysis panels
02

Applications

  • Joint analysis of SEM, TEM, AFM, microimage and structural parameters
  • XRD, XPS, FTIR, Raman, UV mass processing
  • Harmonization of experimental conditions, sample numbers, test results and reporting text
  • Finding the statistical relationship between material structure, preparation conditions and performance
  • Visualized conclusions for paper charts, project reports and internal decision-making
Multi-modular access to images, spectrographs, tables and experimental records
Multiple Sources Gathering
Multi-source data access for image spectrograph tables
Data access
Cross-modular characterization alignment analysis
Feature Alignment
Visualization of scientific data insight panel
Visualize Insight
03

Overall process

1. Data collationSample number
File Structure
2. Standardized treatmentFormat Conversion
Quality control
3. Model integrationSpectrum
Relationship Alignment
4. Statistical analysisGroup Comparison
Relevant analysis
5. Chart BuildVisualize
Explanation of results
6. Reporting deliveryCompilation of conclusions
Reuse Template
04

Technical modules

  • Uniform sample index:Match images, spectrographs, tables and text with sample numbers and experimental batches.
  • Image and spectroprocessing:Support division, peak recognition, curve alignment, noise reduction, homogenization and mass statistics.
  • Characteristics integration:Combining shapes, spectropeaks, experimental conditions and performance indicators into modelable characterization tables.
  • Explanatory analysis:Output key features, sample grouping, anomaly and variable association.
05

Project value

Get through the decentralized data.Co-comparison of data from different instruments and formats
Improved analytical efficiencyReduce manual preparation and repetition of graphics
Enhancing the credibility of conclusionsSupporting scientific judgement with statistical and interpretable models
Create a reuse processAdditional subsequent samples could follow the same treatment template
Related
AI-003 Formulation, Process, and Performance Prediction 10-18 working days AI-006 Machine Learning Prediction 7-15 working days AI-007 Experimental Design and Multiobjective Optimization 10-18 working days