AI-based infrared spectrum mineral rapid identification system
By combining infrared spectral data acquisition, preprocessing, feature extraction, and self-supervised learning, the problems of spectral data dependence and insufficient training in existing technologies are solved, achieving efficient and accurate mineral identification and multi-mode feedback, and improving the robustness and user interactivity of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANSHAN NORMAL UNIV
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-12
AI Technical Summary
Existing AI-based rapid mineral identification systems based on infrared spectroscopy are highly dependent on the quality of spectral data, resulting in inaccurate identification results. Furthermore, they cannot handle unfamiliar mineral types or complex samples when training data is insufficient.
The system employs an infrared spectral data acquisition module, a data preprocessing module, a feature extraction and selection module, and an AI recognition and classification module. It combines self-supervised learning methods and deep learning algorithms, including denoising, smoothing, dimensionality reduction, self-supervised learning, and multi-modal feedback mechanisms, to improve data quality and recognition range.
It improves data quality, expands the system's identification range, enhances its adaptability to unseen minerals and complex samples, and provides multimodal feedback to improve user experience and identification accuracy.
Smart Images
Figure CN122023901A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of rapid identification technology for infrared spectral minerals, specifically to an AI-based rapid identification system for infrared spectral minerals. Background Technology
[0002] This AI-based rapid mineral identification system using infrared spectroscopy acquires spectral data from mineral samples using an infrared spectrometer and preprocesses it, such as through noise reduction and smoothing, to improve data quality. Next, the system extracts key features related to mineral characteristics from the spectral data, typically including peak positions and absorption intensities. Using machine learning or deep learning algorithms, such as support vector machines or convolutional neural networks, the system inputs these features into a trained model and compares it against a known mineral spectral library, thereby achieving automatic mineral classification and identification. Finally, the identification results are displayed to the user through a visual interface or other feedback methods, providing detailed information about the mineral, such as its chemical composition or potential uses.
[0003] Despite its high efficiency and accuracy in mineral identification, this system still has some limitations. The system is highly dependent on the quality of the spectral data; any errors during the acquisition process or environmental interference can lead to inaccurate identification results. AI models often rely on a large number of labeled samples during training; if the training dataset is insufficient, especially for rare minerals, the model's identification ability may be limited, making it unable to handle unseen mineral species or complex samples. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides an AI-based infrared spectroscopy mineral rapid identification system, which solves the problem that the system is highly dependent on the quality of spectral data, leading to inaccurate identification results; and that the system relies on a large number of labeled samples during training, making it unable to handle unseen mineral types or complex samples.
[0005] To achieve the above objectives, the present invention provides the following technical solution: an AI-based infrared spectroscopy mineral rapid identification system, comprising: Infrared spectral data acquisition module, used to acquire infrared spectral data of mineral samples; The data preprocessing module is used to perform noise reduction, smoothing, and normalization on the acquired infrared spectral data. The feature extraction and selection module is used to extract effective features from the preprocessed spectral data; The AI recognition and classification module is used to classify and identify minerals by using deep learning algorithms to extract features. The results output and feedback module is used to output the recognition results and provide feedback.
[0006] Preferably, the infrared spectral data acquisition module uses an infrared spectrometer, and the acquisition range of the infrared spectrometer is 1000 cm⁻¹. -1 Up to 4000cm -1 It also has a high resolution to accommodate a variety of mineral samples.
[0007] Preferably, the data preprocessing module includes a denoising algorithm, a wavelet transform smoothing algorithm, and a standardization processing module to remove environmental noise from the spectral data, smooth fluctuations, and standardize the data range to improve data quality. The feature extraction and selection module uses principal component analysis to reduce the dimensionality of the spectral data and reduce redundant features.
[0008] Preferably, the AI recognition and classification module is trained using unlabeled mineral samples through a self-supervised learning method to expand the system's recognition range and improve its adaptability to new samples. The AI recognition and classification module adopts an algorithm that combines convolutional neural networks and support vector machines, where convolutional neural networks are used to automatically extract high-dimensional features and support vector machines are used for classification and recognition.
[0009] Preferably, the result output and feedback module includes a graphical user interface for displaying mineral identification results and a data storage function for recording historical identification data for subsequent analysis.
[0010] Preferably, the data preprocessing module further improves the system's ability to process unstable data and reduces the need for human intervention by using automated noise detection and filtering methods.
[0011] Preferably, the result output and feedback module includes a multi-mode feedback mechanism that can provide graphical, textual, and voice feedback based on the recognition results, so that users can perform real-time operations and decisions.
[0012] Preferably, the multi-mode feedback mechanism includes: graphical feedback, textual feedback, voice feedback, alarm feedback, and combined graphical and textual feedback.
[0013] This invention provides an AI-based rapid identification system for infrared spectroscopy minerals. It offers the following advantages: This AI-based infrared spectroscopy mineral rapid identification system acquires high-quality mineral spectral data through an infrared spectral data acquisition module. The data preprocessing module performs denoising, smoothing, and standardization to reduce noise interference, improve data quality, and avoid over-reliance on high-quality spectral data. Principal component analysis reduces the dimensionality of the spectral data, removes redundant features, and optimizes the subsequent feature extraction process. The AI recognition and classification module, trained using self-supervised learning without a large amount of labeled data, effectively expands the system's recognition range, maintaining high accuracy even when dealing with unfamiliar minerals or complex samples. The combination of convolutional neural networks and support vector machines enables the system to automatically extract high-dimensional features and perform accurate classification, improving the robustness and universality of mineral identification.
[0014] The results output and feedback module significantly enhances system interactivity and user experience through a multi-modal feedback mechanism. The system provides various feedback methods, including graphics, text, voice, and alarms, to offer different forms of recognition results based on user needs, helping users quickly understand and process mineral information. Graphical feedback visualizes the spectral characteristics of minerals and the recognition results; text feedback provides detailed mineral information; voice feedback facilitates operation in scenarios where a user interface is unavailable; and the alarm mechanism promptly alerts users when special or dangerous minerals are detected. This combination of text and graphics further enhances the presentation of information, allowing users to obtain mineral recognition results in real time through multiple methods, thus enabling more accurate decision-making. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] Example 1 like Figure 1 As shown, this embodiment of the invention provides an AI-based rapid identification system for infrared spectral minerals, comprising: The infrared spectral data acquisition module is used to collect infrared spectral data from mineral samples. This module utilizes an infrared spectrometer with a collection range of 1000 cm⁻¹. -1 Up to 4000cm -1 It also has a high resolution to accommodate a variety of mineral samples.
[0018] The data preprocessing module is used to denoise, smooth, and normalize the acquired infrared spectral data. This module includes denoising algorithms, wavelet transform smoothing algorithms, and a standardization module to remove environmental noise from the spectral data, smooth fluctuations, and standardize the data range to improve data quality. The feature extraction and selection module uses principal component analysis to reduce the dimensionality of the spectral data and reduce redundant features. Furthermore, the data preprocessing module employs automated noise detection and filtering methods to further improve the system's ability to handle unstable data and reduce the need for human intervention.
[0019] The feature extraction and selection module is used to extract effective features from the preprocessed spectral data.
[0020] The AI recognition and classification module utilizes deep learning algorithms to classify and identify minerals based on extracted features. This module employs a self-supervised learning method, training with unlabeled mineral samples to expand the system's recognition range and improve its adaptability to new samples. The module combines convolutional neural networks (CNNs) and support vector machines (SVMs), where CNNs automatically extract high-dimensional features, and SVMs perform classification and recognition.
[0021] The results output and feedback module is used to output the identification results and provide feedback. This module includes a graphical user interface for displaying the mineral identification results and data storage capabilities to record historical identification data for subsequent analysis. The module also includes a multi-modal feedback mechanism that provides graphical, textual, and audio feedback based on the identification results, enabling real-time operation and decision-making by the user. The multi-modal feedback mechanism includes: graphical feedback, textual feedback, audio feedback, alarm feedback, and combined graphical and textual feedback.
[0022] Experimental Examples Experimental equipment and environment 1. Infrared spectral data acquisition module: using a 1000cm... -1 Up to 4000cm -1 The infrared spectrometer with a collection range has a resolution of 0.5 cm⁻¹. -1 It is adaptable to the collection needs of various mineral samples. The mineral samples include four common types of minerals: quartz, feldspar, mica, and barite.
[0023] 2. Data preprocessing module: Wavelet transform is used for noise reduction, Gaussian filter is used for smoothing, and minimum-maximum normalization is used to normalize the spectral data to eliminate the impact of environmental interference on data quality.
[0024] 3. Feature Extraction and Selection Module: Principal component analysis is used to reduce the dimensionality of the spectral data, select the most discriminative features, and remove redundant information. Automated noise detection and filtering methods further improve the system's ability to handle unstable data.
[0025] 4. AI Recognition and Classification Module: This module employs a deep learning algorithm combining convolutional neural networks (CNNs) and support vector machines (SVMs). The CNNs automatically extract high-dimensional features from the spectral data, while the SVMs perform classification and recognition. Simultaneously, a self-supervised learning method is used to train the system on unlabeled mineral samples, expanding its recognition range and adapting to new samples.
[0026] 5. Results Output and Feedback Module: Displays mineral identification results using a graphical user interface, and provides real-time feedback to users through a multi-mode feedback mechanism.
[0027] Experimental steps 1. Sample collection and data preprocessing: Four mineral samples were randomly selected from the mineral sample library, such as quartz, feldspar, mica and barite, with 10 samples prepared for each mineral to ensure the diversity and representativeness of the samples.
[0028] Infrared spectral data were collected from these mineral samples using an infrared spectrometer, ensuring that the collected data covered 1000 cm⁻¹. -1 Up to 4000cm -1 The wavelength range.
[0029] The collected spectral data is input into the data preprocessing module for denoising, wavelet transform smoothing, and standardization to improve data quality and eliminate the influence of environmental noise.
[0030] 2. Feature extraction and dimensionality reduction: The preprocessed spectral data is input into the feature extraction and selection module, and principal component analysis is used to reduce the dimensionality of the data, reduce redundant features, and extract the most discriminative features.
[0031] 3. AI Recognition and Classification: The extracted features are input into the AI recognition and classification module, where convolutional neural networks are used to automatically extract the features, and support vector machines are used for classification and recognition.
[0032] During training, a self-supervised learning method is adopted, and the training set is expanded using unlabeled mineral samples to improve the system's recognition range and adaptability.
[0033] 4. Results Output and Feedback: The results of mineral identification are displayed through a graphical user interface, showing the types of minerals identified and their classification confidence levels.
[0034] Meanwhile, the system provides voice feedback to users, announcing the mineral identification results for real-time operation and decision-making. Users can also obtain detailed information about minerals, such as mineral name, composition, and uses, through text feedback.
[0035] If a potentially hazardous mineral is identified, the system will promptly alert the user via an alarm.
[0036] Experimental results 1. Recognition Accuracy: When tested on 800 mineral samples (100 samples of each mineral), the system achieved a mineral recognition accuracy of over 95%. The system can accurately distinguish common minerals such as quartz, feldspar, mica, and barite, and can also process complex mineral spectral data to a certain extent.
[0037] 2. Self-supervised learning performance: Through self-supervised learning, the system demonstrated good recognition ability on unseen mineral samples. Especially when faced with unlabeled mineral data, the system was able to make reasonable predictions by drawing on information from other minerals.
[0038] 3. Effectiveness of the Feedback Mechanism: The multi-modal feedback mechanism effectively enhances the user experience. Graphical feedback provides users with intuitive mineral identification results, textual feedback helps users understand detailed mineral information, voice feedback improves the system's usability, and alarm feedback effectively alerts users when abnormal minerals are encountered.
[0039] in conclusion This invention presents an AI-based rapid mineral identification system based on infrared spectroscopy. Through data preprocessing, feature extraction, deep learning algorithms, and a multi-mode feedback mechanism, the system demonstrates superior performance in mineral identification. Experimental results show that the system effectively improves identification accuracy and exhibits good adaptability to unfamiliar mineral samples. It also provides comprehensive and timely feedback, meeting the needs of practical applications.
[0040] Table 1: Experimental Data for Rapid Mineral Identification
[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. An AI-based rapid identification system for infrared spectroscopy minerals, characterized in that, include: Infrared spectral data acquisition module, used to acquire infrared spectral data of mineral samples; The data preprocessing module is used to perform noise reduction, smoothing, and normalization on the acquired infrared spectral data. The feature extraction and selection module is used to extract effective features from the preprocessed spectral data; The AI recognition and classification module is used to classify and identify minerals by using deep learning algorithms to extract features. The results output and feedback module is used to output the recognition results and provide feedback.
2. The AI-based infrared spectroscopy mineral rapid identification system according to claim 1, characterized in that: The infrared spectral data acquisition module uses an infrared spectrometer, and the acquisition range of the infrared spectrometer is 1000 cm⁻¹. -1 Up to 4000cm -1 .
3. The AI-based infrared spectroscopy mineral rapid identification system according to claim 1, characterized in that: The data preprocessing module includes a denoising algorithm, a wavelet transform smoothing algorithm, and a standardization processing module to remove environmental noise from the spectral data, smooth fluctuations, and standardize the data range. The feature extraction and selection module uses principal component analysis to reduce the dimensionality of the spectral data.
4. The AI-based infrared spectroscopy mineral rapid identification system according to claim 1, characterized in that: The AI recognition and classification module is trained using unlabeled mineral samples through a self-supervised learning method, and the AI recognition and classification module adopts an algorithm that combines convolutional neural networks and support vector machines.
5. The AI-based infrared spectroscopy mineral rapid identification system according to claim 1, characterized in that: The result output and feedback module includes a graphical user interface for displaying mineral identification results.
6. The AI-based infrared spectroscopy mineral rapid identification system according to claim 1, characterized in that: The data preprocessing module uses automated noise detection and filtering methods.
7. The AI-based infrared spectroscopy mineral rapid identification system according to claim 1, characterized in that: The result output and feedback module includes a multi-mode feedback mechanism that can provide graphical, textual, and voice feedback based on the recognition results.
8. The AI-based infrared spectroscopy mineral rapid identification system according to claim 7, characterized in that: The multi-mode feedback mechanism includes: graphical feedback, textual feedback, voice feedback, alarm feedback, and combined graphical and textual feedback.