Laboratory analytical instrument automatic calibration system based on artificial intelligence

Abstract

The invention discloses a laboratory analytical instrument automatic calibration system based on artificial intelligence, and belongs to the technical field of instrument automatic calibration. Comprising the following modules: an intelligent hardware interaction module for realizing plug-and-play of different instruments and accurate conveying of standard substances; the instrument digital twinning module automatically identifies the characteristics of the instrument and simulates the behaviors of the instrument; the multi-mode AI calibration module is used for optimizing calibration parameters and adjusting the calibration process in real time; the environment intelligent compensation module calculates and generates a compensation coefficient in real time, and eliminates the influence of environmental factors on a calibration result; the instrument health management module is used for evaluating the health state of the instrument and predicting the service life of a core component, and realizing conversion from passive maintenance to predictive maintenance; the calibration knowledge graph module is used for realizing automatic extraction, reasoning and application of calibration knowledge through a graph neural network and continuously optimizing a calibration strategy; and the intelligent scheduling and management module is used for generating an optimal calibration plan, coordinating the work of each module and realizing reasonable distribution and efficient utilization of calibration resources.

Description

Technical Field

[0001] The present application relates to the technical field of automatic instrument calibration, and more specifically, to an automatic calibration system for laboratory analytical instruments based on artificial intelligence. Background Art

[0002] With the continuous advancement of science and technology, the widespread use of laboratory analytical instruments has led to increasingly stringent requirements for instrument accuracy and performance. However, traditional calibration methods often rely on manual operations, which are cumbersome and susceptible to human interference. This leads to low calibration efficiency and difficulty in ensuring accuracy. This is particularly complex in modern laboratories, where the number and variety of instruments are constantly increasing. In addition, changes in the experimental environment, such as temperature and humidity, can significantly affect instrument calibration results, making traditional calibration methods difficult to handle in various complex environmental conditions.

[0003] Currently, some high-end laboratory instruments have begun to incorporate automated calibration technologies, but these systems still have certain limitations and cannot fully adapt to different instrument types, different calibration requirements, and different environmental conditions. Furthermore, traditional health management methods, which rely primarily on regular inspections and maintenance, lack accurate predictions and timely warnings. This results in equipment failures not being repaired before they occur, thus impacting experimental progress and data quality.

[0004] With the rapid development of technologies such as artificial intelligence, digital twins, and the Internet of Things, effectively integrating these emerging technologies into instrument calibration and health management has become a crucial approach to improving the efficiency of laboratory analytical instruments, ensuring calibration accuracy, and achieving precise maintenance. In particular, leveraging intelligent algorithms to automatically optimize the calibration process, compensate for environmental impacts in real time, and integrate digital twin technology to provide virtual simulation and rehearsal have become key to enhancing the automation and intelligence of laboratory analytical instruments.

[0005] In summary, how to use artificial intelligence, digital twins and environmental intelligent compensation technology to achieve automation, precise calibration and health management of laboratory analytical instruments has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the purpose of this application is to provide an artificial intelligence-based automatic calibration system for laboratory analytical instruments, including the following modules:

[0007] The intelligent hardware interaction module is responsible for the physical connection of instruments, the transmission of reference materials, and environmental monitoring, enabling plug-and-play of different instruments and precise delivery of reference materials.

[0008] The instrument digital twin module automatically identifies instrument characteristics and simulates their behavior by building a virtual model of the instrument, allowing calibration processes to be rehearsed in a virtual environment.

[0009] Multimodal AI calibration module uses deep learning models to optimize calibration parameters and adjust the calibration process in real time to ensure calibration accuracy;

[0010] The environmental intelligent compensation module establishes a multi-dimensional correlation network between environmental parameters and instrument responses, calculates and generates compensation coefficients in real time, eliminates the impact of environmental factors on calibration results, and ensures the consistency of calibration results under different environmental conditions;

[0011] The instrument health management module analyzes the dynamic response characteristics of the instrument during calibration to assess the instrument's health status and predict the lifespan of core components, enabling a shift from reactive maintenance to predictive maintenance.

[0012] The calibration knowledge graph module builds a knowledge graph containing multi-dimensional calibration information, realizes the automatic extraction, reasoning and application of calibration knowledge through graph neural networks, and continuously optimizes the calibration strategy;

[0013] The intelligent scheduling and management module generates the optimal calibration plan based on a multi-objective optimization algorithm, coordinates the work of each module, and realizes the rational allocation and efficient utilization of calibration resources.

[0014] Furthermore, the intelligent hardware interaction module includes the following components:

[0015] Universal interface adapter unit, which realizes automatic identification and conversion of multiple instrument protocols and interfaces, and supports plug-and-play of heterogeneous instruments;

[0016] The automatic standard material delivery unit ensures accurate quantitative transmission and switching of calibration standard materials through a precise fluid control system and multi-channel selector, thus ensuring calibration accuracy.

[0017] Environmental parameter monitoring unit collects and provides real-time temperature, humidity, air pressure and electromagnetic field data to ensure that environmental factors during the calibration process are accurately monitored;

[0018] Physical interaction control unit, which executes hardware-level instrument operation instructions, including precise control of start, stop, reset and sample loading actions;

[0019] Power supply and signal stabilization unit, providing stable power supply and signal conditioning to eliminate the influence of external interference on the calibration process;

[0020] Safety monitoring unit, real-time monitoring and ensuring hardware safety during the calibration process, including overload protection, leak detection and emergency shutdown functions.

[0021] Furthermore, the instrument digital twin module includes the following components:

[0022] The instrument model building unit quickly generates a high-precision digital virtual model based on the instrument's parameters and structural data, accurately reproducing the instrument's characteristics;

[0023] The instrument behavior simulation unit simulates the operating status and response characteristics of the instrument in a virtual environment, providing a reliable simulation basis for the calibration process;

[0024] Virtual calibration environment unit, which builds a complete virtual calibration scenario including instruments, reference materials and environmental conditions, and realizes high-precision simulation of the calibration process;

[0025] Model-to-entity synchronization unit ensures real-time synchronization between the virtual model and the physical instrument status, and dynamically adjusts model parameters according to actual operating conditions;

[0026] The instrument abnormality simulation unit simulates various instrument failures and abnormal conditions in a virtual environment and rehearses response strategies during the calibration process.

[0027] Furthermore, the instrument model building unit adopts a multi-level modeling strategy, including:

[0028] Physical model layer, which establishes a basic model based on the physical principles and structural characteristics of the instrument;

[0029] The statistical model layer captures the statistical characteristics and random fluctuations of the instrument through data-driven methods;

[0030] The neural network model layer uses deep learning technology to simulate nonlinear behaviors that are difficult to express through physical equations.

[0031] Furthermore, the multimodal AI calibration module includes the following components:

[0032] A multi-source data fusion unit that integrates multimodal data from instrument sensors, historical calibration records, and reference material properties to construct a unified data representation;

[0033] A calibration parameter deep learning unit uses deep neural networks to model the nonlinear relationship between calibration parameters and instrument response, automatically optimizing calibration parameter combinations;

[0034] A real-time calibration strategy generation unit dynamically generates the optimal calibration strategy based on the deep learning model and current status, including calibration sequence, parameter adjustment amplitude, and stabilization time to adapt to different working conditions;

[0035] Anomaly detection and adaptive adjustment unit, which monitors abnormal patterns in the calibration process in real time, identifies calibration responses that deviate from the expected ones, and automatically adjusts the calibration strategy to deal with abnormal situations;

[0036] The calibration accuracy assessment unit evaluates the accuracy and precision of the calibration results in real time, quantifies the calibration quality through uncertainty analysis, and provides a basis for decision-making on whether to terminate or continue the calibration process.

[0037] Furthermore, the environmental intelligent compensation module includes the following components:

[0038] The correlation network construction unit establishes a multi-dimensional mapping relationship between environmental parameters and instrument responses to form a dynamically adjusted correlation model;

[0039] Compensation coefficient calculation unit, accurately calculates the environmental compensation coefficient to ensure that the calibration results are not affected by environmental changes;

[0040] The historical environmental impact analysis unit mines the impact patterns of environmental changes on instrument calibration in historical data and optimizes the adaptability of compensation strategies;

[0041] Extreme environment adaptation unit, which establishes special compensation models for unconventional environmental conditions and expands the environmental adaptability range of the calibration system;

[0042] The environmental disturbance warning unit monitors and warns of abnormal environmental changes that may exceed the compensation capability in real time, ensuring the stability of the calibration process.

[0043] Furthermore, the instrument health management module includes the following components:

[0044] Dynamic response characteristic analysis unit captures and analyzes the transient response curve of the instrument during calibration, identifies performance deviations and abnormal modes, and provides accurate health status feedback;

[0045] The health status assessment unit quantitatively evaluates the overall health status of the instrument based on a multi-dimensional indicator system and generates an intuitive health report;

[0046] The core component life prediction unit combines historical degradation patterns and current working conditions to predict the remaining service life and performance boundaries of key components;

[0047] The maintenance strategy generation unit formulates personalized preventive maintenance plans based on health assessment and life prediction results, optimizing maintenance timing and resource allocation;

[0048] The performance degradation trend tracking unit continuously records actual operating data and monitors the changing trajectory of instrument performance parameters, identifying signs of accelerated degradation and providing early warnings to prevent sudden failures.

[0049] Furthermore, the health status assessment unit is based on a multi-dimensional health indicator system, including:

[0050] Response time indicator, reflecting the changing trend of instrument response speed;

[0051] Signal stability index, which measures the degree of fluctuation of the instrument output signal;

[0052] Sensitivity index, which evaluates the instrument's ability to detect small changes;

[0053] Linearity index reflects the change in the linear relationship between the input and output of the instrument;

[0054] Noise level index, which measures the noise background of the instrument signal;

[0055] Temperature drift index, which evaluates the sensitivity of the instrument to temperature changes.

[0056] Furthermore, the calibration knowledge graph module includes the following components:

[0057] Knowledge extraction unit, which automatically extracts structured knowledge from calibration history records, equipment manuals, and expert experience to form a standardized calibration knowledge base;

[0058] The knowledge graph construction unit organizes calibration parameters, methods, results and their relationships into a multi-dimensional semantic network to build a complete calibration knowledge representation system;

[0059] The graph neural network reasoning unit uses graph convolutional networks to train and reason on knowledge graphs, explore implicit calibration rules, and optimize calibration strategies.

[0060] Similar case retrieval unit, based on semantic similarity analysis, quickly retrieves historical calibration cases that match the current situation, providing accurate reference for decision-making;

[0061] Knowledge update and evolution unit, dynamically integrating new calibration data and practical experience to continuously optimize the knowledge graph structure;

[0062] The cross-domain knowledge transfer unit uses transfer learning technology to adapt and apply the calibration experience of specific instruments to the calibration process of new or rare instruments;

[0063] The calibration knowledge consulting unit analyzes patterns and associations in the knowledge graph to provide knowledge support and optimization suggestions for the multimodal AI calibration module.

[0064] Furthermore, the intelligent scheduling and management module includes the following components:

[0065] The multi-objective optimization scheduling unit comprehensively considers time efficiency, resource utilization and calibration accuracy requirements, and generates the optimal calibration task scheduling plan based on the intelligent optimization algorithm;

[0066] Resource allocation management unit, which monitors the availability of calibration equipment, reference materials, and human resources in real time, and intelligently adjusts allocation strategies to avoid resource conflicts and improve overall utilization efficiency;

[0067] The task priority assessment unit dynamically calculates the task priority based on the instrument importance, usage frequency and calibration urgency to ensure that critical tasks are executed first;

[0068] The calibration progress monitoring unit tracks the execution status of each calibration task in real time, generates visual progress reports, and provides abnormal warnings and adjustment suggestions to ensure that tasks are completed on time;

[0069] The emergency response processing unit automatically analyzes the impact range and generates adjustment strategies for sudden failures or abnormal situations, dynamically optimizes the calibration plan, and ensures stable system operation.

[0070] Compared with the prior art, this application has the following beneficial effects:

[0071] This application achieves automated and precise calibration of laboratory analytical instruments by integrating multiple intelligent modules. Its innovations include: automatically building a virtual model of the instrument through the instrument digital twin module to simulate and optimize the calibration process; using the multimodal AI calibration module, combined with a deep learning model, to achieve dynamic optimization and accuracy improvement of calibration parameters; eliminating interference from environmental factors through the environmental intelligent compensation module to ensure calibration consistency; the instrument health management module to achieve a transition from passive maintenance to predictive maintenance; and through the calibration knowledge graph module, automatically extracting, inferring, and applying calibration knowledge to continuously optimize the calibration strategy. The combination of these technologies not only improves the efficiency and accuracy of calibration, but also enables real-time monitoring of the instrument's health status and maintenance prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a schematic diagram of the structure of the automatic calibration system for laboratory analytical instruments based on artificial intelligence disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0073] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0074] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0075] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0076] like Figure 1As shown in the figure, the automatic calibration system of laboratory analytical instruments based on artificial intelligence includes the following modules:

[0077] The intelligent hardware interaction module is responsible for the physical connection of instruments, the transmission of reference materials, and environmental monitoring, enabling plug-and-play of different instruments and precise delivery of reference materials.

[0078] The instrument digital twin module automatically identifies instrument characteristics and simulates their behavior by building a virtual model of the instrument, allowing calibration processes to be rehearsed in a virtual environment.

[0079] Multimodal AI calibration module uses deep learning models to optimize calibration parameters and adjust the calibration process in real time to ensure calibration accuracy;

[0080] The environmental intelligent compensation module establishes a multi-dimensional correlation network between environmental parameters and instrument responses, calculates and generates compensation coefficients in real time, eliminates the impact of environmental factors on calibration results, and ensures the consistency of calibration results under different environmental conditions;

[0081] The instrument health management module analyzes the dynamic response characteristics of the instrument during calibration to assess the instrument's health status and predict the lifespan of core components, enabling a shift from reactive maintenance to predictive maintenance.

[0082] The calibration knowledge graph module builds a knowledge graph containing multi-dimensional calibration information, realizes the automatic extraction, reasoning and application of calibration knowledge through graph neural networks, and continuously optimizes the calibration strategy;

[0083] The intelligent scheduling and management module generates the optimal calibration plan based on a multi-objective optimization algorithm, coordinates the work of each module, and realizes the rational allocation and efficient utilization of calibration resources.

[0084] The intelligent hardware interaction module in this embodiment realizes the intelligentization of instrument physical connection, standard substance transmission and environmental monitoring. In terms of instrument physical connection, this module can support various types and brands of analytical instruments through advanced interface technology and protocol adaptation, realizing plug-and-play function. This means that users can connect new instruments to the system without complex configuration and debugging, greatly improving the compatibility and expandability of the system, and reducing the cost and time of laboratory equipment update and upgrade. In terms of standard substance transmission, this module uses high-precision transmission devices and intelligent control systems to ensure that standard substances can be accurately delivered to the designated position of the instrument. Its accurate delivery capability not only improves the accuracy of calibration, but also avoids errors and pollution that may be caused by manual operation, improving the reliability and repeatability of the calibration process. In addition, the environmental monitoring function enables the module to real-time perceive environmental parameters such as temperature, humidity and air pressure in the laboratory, providing data support for subsequent calibration compensation. Through the integration of these functions, the intelligent hardware interaction module provides a solid foundation for the stable operation of the entire calibration system, ensuring the efficiency, accuracy and automation of the calibration process.

[0085] The instrument digital twin module in this embodiment uses advanced modeling technology and sensor data fusion to accurately restore the physical structure and working principle of the instrument, generating a virtual model highly consistent with the actual instrument. In the virtual environment, this module can simulate various calibration scenarios and operating conditions, and pre-act the calibration process to discover potential problems and risks. This pre-act function not only reduces the trial-and-error cost in the actual calibration process, but also improves the calibration efficiency and success rate. By automatically identifying instrument characteristics, the digital twin module can dynamically adjust the parameters of the virtual model according to the model, configuration and usage history of the instrument, so that it always maintains the best match with the actual instrument. This enables calibration strategies to be customized for different instruments, further improving the accuracy of calibration. In addition, the virtual model can also be used as a training and demonstration tool to help operators better understand and master the calibration principles and operation processes of the instrument, and improve the professional level of laboratory personnel.

[0086] The multimodal AI calibration module in this embodiment builds a powerful deep learning framework by integrating multiple data modalities, such as the instrument's response signal, environmental parameters, historical calibration records, etc. This multimodal data fusion method can fully utilize the advantages of various information, enabling the model to more comprehensively understand the complex relationships in the calibration process. By learning and analyzing a large amount of historical data, the deep learning model can automatically discover hidden laws and patterns in the data, thereby realizing intelligent optimization of calibration parameters. During the actual calibration process, the module can monitor the response of the instrument in real time and dynamically adjust the calibration strategy according to the current state. This real-time adjustment capability enables the calibration process to quickly adapt to changes in the instrument state and interference from environmental factors, ensuring high precision and stability of the calibration results. In addition, the multimodal AI calibration module also has the ability of self-learning and self-optimization. As the number of calibrations increases and the data accumulates, it will continuously improve its own performance and further improve the calibration accuracy.

[0087] The intelligent environmental compensation module in this embodiment effectively eliminates the impact of environmental factors on calibration results by establishing a multidimensional correlation network between environmental parameters and instrument responses, calculating and generating compensation coefficients in real time. During the actual calibration process, laboratory environmental conditions such as temperature, humidity, and air pressure can significantly affect instrument performance, leading to deviations in calibration results. This module utilizes advanced data analysis techniques and machine learning algorithms to deeply explore the complex relationship between environmental parameters and instrument responses, constructing a multidimensional correlation network model. Through this model, the module can monitor changes in environmental parameters in real time and quickly calculate corresponding compensation coefficients based on current environmental conditions. These compensation coefficients can be applied in real time during the calibration process to correct the instrument response, thereby ensuring the consistency and accuracy of calibration results under different environmental conditions. In addition, the intelligent environmental compensation module also has adaptive learning capabilities, which can continuously optimize the correlation network model based on new data, making it better adaptable to factors such as environmental changes and instrument aging. This intelligent compensation mechanism not only improves the reliability and stability of calibration, but also reduces the number of recalibrations caused by environmental factors, improving calibration efficiency and reducing calibration costs, providing an important guarantee for the accurate calibration of laboratory analytical instruments.

[0088] The instrument health management module in this embodiment achieves an assessment of the instrument's health status and a prediction of the life of core components by analyzing the dynamic response characteristics during the instrument calibration process. The module uses advanced signal processing technology and machine learning algorithms to perform in-depth analysis of the real-time data of the instrument during the calibration process. By monitoring the dynamic response characteristics of the instrument, the module can promptly detect abnormal changes in instrument performance, such as response delays, decreased sensitivity, etc., thereby accurately assessing the instrument's health status. This real-time monitoring and evaluation function enables laboratory personnel to understand the instrument's operating status in a timely manner and take measures in advance for maintenance and care to avoid instrument failures from having a serious impact on experimental results. In addition, the module can also predict the service life of core components based on the instrument's historical data and current status. By establishing a life prediction model, the module can accurately estimate the remaining service life of core components and provide early warnings to remind laboratory personnel to replace or repair them. This predictive maintenance method not only reduces the risk of sudden instrument failures, but also reduces maintenance costs and improves the efficiency and reliability of instrument use.

[0089] The calibration knowledge graph module in this embodiment realizes the automatic extraction, reasoning and application of calibration knowledge and continuously optimizes the calibration strategy by constructing a knowledge graph containing multi-dimensional calibration information. This module uses graph neural network technology to organize and store various information involved in the calibration process, such as instrument characteristics, calibration parameters, environmental conditions, historical calibration records, etc., in the form of a graph. This graph structure can intuitively display the associations between different information, facilitating knowledge extraction and reasoning. Through the automatic learning and reasoning capabilities of the graph neural network, the module can quickly extract knowledge related to the current calibration task from the knowledge graph and generate the optimal calibration strategy based on this knowledge. This knowledge-driven calibration method not only improves the accuracy and efficiency of calibration, but also continuously optimizes the knowledge graph based on new calibration data and experience, achieving continuous improvement of the calibration strategy. In addition, the calibration knowledge graph module can also serve as a platform for knowledge sharing and inheritance, facilitating experience exchange and learning among laboratory personnel. In this way, the laboratory can accumulate rich calibration experience and knowledge, and improve the overall technical level and innovation ability. In short, the calibration knowledge graph module provides powerful knowledge support for the intelligence and efficiency of the calibration system and is an important tool for achieving accurate calibration and knowledge management.

[0090] The intelligent scheduling and management module in this embodiment generates an optimal calibration plan based on a multi-objective optimization algorithm and coordinates the work of each module to achieve the rational allocation and efficient utilization of calibration resources. This module utilizes an advanced multi-objective optimization algorithm to develop a scientific and reasonable calibration plan by comprehensively considering multiple factors, including the priority of the calibration task, the instrument's usage status, environmental conditions, and the load of each module. This optimization algorithm balances the relationships between different objectives, such as calibration accuracy, calibration efficiency, and resource utilization, ensuring that the overall performance of the system is maximized while meeting calibration requirements. To coordinate the work of each module, the intelligent scheduling and management module monitors the operating status of each module in real time and dynamically adjusts the task allocation and workflow of each module according to the calibration plan. In this way, the module ensures the coordinated operation of each module, avoids resource conflicts and task backlogs, and improves the operating efficiency and stability of the system. Furthermore, the intelligent scheduling and management module also has adaptive adjustment capabilities, allowing it to promptly adjust the calibration plan and resource allocation strategy based on unexpected situations that arise during the calibration process, such as instrument failures and environmental changes, to ensure the smooth completion of the calibration task. This intelligent scheduling and management method not only improves the flexibility and adaptability of the calibration system, but also reduces the complexity of human intervention, providing a strong guarantee for the efficient calibration of laboratory analytical instruments.

[0091] In summary, the automatic calibration system for laboratory analytical instruments based on artificial intelligence realizes the intelligence, automation and efficiency of the calibration process by integrating multiple functional modules. The intelligent hardware interaction module provides a stable and reliable hardware foundation for the system, ensuring the connection of the instrument and the accurate delivery of standard substances; the instrument digital twin module optimizes the calibration strategy through virtual modeling and preview; the multimodal AI calibration module uses deep learning technology to achieve intelligent optimization and real-time adjustment of calibration parameters; the environmental intelligent compensation module eliminates the influence of environmental factors on the calibration results, ensuring the consistency of the calibration results; the instrument health management module realizes predictive maintenance of the instrument through dynamic monitoring and life prediction; the calibration knowledge graph module realizes automatic calibration of the instrument through knowledge

[0092] Furthermore, the intelligent hardware interaction module includes the following components:

[0093] Universal interface adapter unit, which realizes automatic identification and conversion of multiple instrument protocols and interfaces, and supports plug-and-play of heterogeneous instruments;

[0094] The automatic standard material delivery unit ensures accurate quantitative transmission and switching of calibration standard materials through a precise fluid control system and multi-channel selector, thus ensuring calibration accuracy.

[0095] Environmental parameter monitoring unit collects and provides real-time temperature, humidity, air pressure and electromagnetic field data to ensure that environmental factors during the calibration process are accurately monitored;

[0096] Physical interaction control unit, which executes hardware-level instrument operation instructions, including precise control of start, stop, reset and sample loading actions;

[0097] Power supply and signal stabilization unit, providing stable power supply and signal conditioning to eliminate the influence of external interference on the calibration process;

[0098] Safety monitoring unit, real-time monitoring and ensuring hardware safety during the calibration process, including overload protection, leak detection and emergency shutdown functions.

[0099] In summary, the intelligent hardware interaction module integrates multiple functional components to achieve comprehensive hardware support and intelligent management for laboratory analytical instruments. The universal interface adapter unit automatically recognizes and converts multiple instrument protocols and interfaces, enabling plug-and-play integration of heterogeneous instruments and significantly improving system compatibility and flexibility. The automatic standard material delivery unit utilizes a precision fluid control system and a multi-channel selector to ensure accurate quantitative transfer and switching of calibration standards, thereby guaranteeing high calibration accuracy and repeatability. The environmental parameter monitoring unit collects real-time data such as temperature, humidity, air pressure, and electromagnetic fields, providing precise environmental monitoring during the calibration process and ensuring the reliability of calibration results. The physical interaction control unit executes hardware-level instrument operation commands, including precise control of start / stop, reset, and sample loading, ensuring the automation and stability of the calibration process. The power and signal stabilization unit provides stable power supply and signal conditioning, effectively eliminating the impact of external interference on the calibration process and further improving calibration accuracy. The safety monitoring unit monitors hardware safety during the calibration process in real time and provides overload protection, leak detection, and emergency shutdown functions to ensure a safe and reliable calibration process.

[0100] Furthermore, the instrument digital twin module includes the following components:

[0101] The instrument model building unit quickly generates a high-precision digital virtual model based on the instrument's parameters and structural data, accurately reproducing the instrument's characteristics;

[0102] The instrument behavior simulation unit simulates the operating status and response characteristics of the instrument in a virtual environment, providing a reliable simulation basis for the calibration process;

[0103] Virtual calibration environment unit, which builds a complete virtual calibration scenario including instruments, reference materials and environmental conditions, and realizes high-precision simulation of the calibration process;

[0104] Model-to-entity synchronization unit ensures real-time synchronization between the virtual model and the physical instrument status, and dynamically adjusts model parameters according to actual operating conditions;

[0105] The instrument abnormality simulation unit simulates various instrument failures and abnormal conditions in a virtual environment and rehearses response strategies during the calibration process.

[0106] In summary, the instrument digital twin module, through the synergistic effect of multiple components, enables comprehensive virtual modeling and simulation of analytical instruments, providing powerful digital support for the calibration process. The instrument model construction unit rapidly generates a highly accurate digital virtual model based on the instrument's parameter and structural data, accurately replicating the instrument's physical characteristics and functionality. The instrument behavior simulation unit simulates the instrument's operating state and response characteristics in a virtual environment, providing a reliable simulation foundation for the calibration process and helping to preemptively verify the effectiveness of calibration strategies. The virtual calibration environment unit further constructs a complete virtual calibration scenario encompassing the instrument, reference materials, and environmental conditions, achieving high-precision simulation of the calibration process and enabling rehearsal and optimization of the calibration process in a virtual environment. The model-to-physical synchronization unit ensures real-time synchronization between the virtual model and the physical instrument state, dynamically adjusting model parameters based on actual operating conditions to ensure that the virtual model remains highly consistent with the physical instrument. The instrument anomaly simulation unit simulates various instrument failures and anomalies in a virtual environment, rehearsing calibration response strategies, helping to identify potential issues and optimize the calibration process.

[0107] Furthermore, the instrument model building unit adopts a multi-level modeling strategy, including:

[0108] Physical model layer, which establishes a basic model based on the physical principles and structural characteristics of the instrument;

[0109] The statistical model layer captures the statistical characteristics and random fluctuations of the instrument through data-driven methods;

[0110] The neural network model layer uses deep learning technology to simulate nonlinear behaviors that are difficult to express through physical equations.

[0111] In summary, the instrument model construction unit achieves comprehensive and high-precision virtual modeling of the characteristics of the analytical instrument by adopting a multi-level modeling strategy. The physical model layer is based on the physical principles and structural characteristics of the instrument, and constructs an accurate basic model, providing a solid physical foundation for the virtual model, ensuring that it can accurately reproduce the basic operating mechanism of the instrument. The statistical model layer captures the statistical characteristics and random fluctuations of the instrument in actual operation through data-driven methods, further enriching the model's description of the instrument's behavior, allowing it to reflect the dynamic changes of the instrument under different working conditions. The neural network model layer uses deep learning technology to simulate complex nonlinear behaviors that are difficult to express through traditional physical equations, making up for the shortcomings of the physical model and statistical model, and enhancing the model's adaptability to complex characteristics of the instrument. The comprehensive application of this multi-level modeling strategy enables the instrument model not only to accurately restore the physical nature of the instrument, but also to effectively capture its randomness and complexity in actual use, providing a highly reliable and flexible virtual platform for subsequent calibration simulation, abnormal pre-play, and strategy optimization, significantly improving the intelligent level and calibration accuracy of the calibration system.

[0112] Further, the multi-modal AI calibration module includes the following components:

[0113] A multi-source data fusion unit integrates multi-modal data from instrument sensors, historical calibration records, and standard substance characteristics to construct a unified data representation;

[0114] A calibration parameter deep learning unit uses deep neural networks to model the nonlinear relationship between calibration parameters and instrument responses, automatically optimizing the combination of calibration parameters;

[0115] A real-time calibration strategy generation unit dynamically generates the optimal calibration strategy based on the deep learning model and the current state, including calibration order, parameter adjustment amplitude, and stabilization time, to adapt to different working conditions;

[0116] An abnormality detection and adaptive adjustment unit monitors abnormal patterns in the calibration process in real time, identifies calibration responses that deviate from expectations, and automatically adjusts the calibration strategy to address abnormal situations;

[0117] A calibration precision evaluation unit evaluates the accuracy and precision of the calibration results in real time, quantifies the calibration quality through uncertainty analysis, and provides the basis for decision-making for the termination or continuation of the calibration process.

[0118] In summary, the multi-modal AI calibration module achieves an efficient, intelligent, and adaptive calibration process by integrating multiple functional components. The multi-source data fusion unit integrates multi-modal data from instrument sensors, historical calibration records, and standard material characteristics to construct a unified data representation, providing a comprehensive and high-quality information base for calibration. The calibration parameter deep learning unit uses deep neural networks to model the complex nonlinear relationship between calibration parameters and instrument responses, automatically optimizing the calibration parameter combination, significantly improving calibration accuracy and efficiency. The real-time calibration strategy generation unit dynamically generates the optimal calibration strategy based on the deep learning model and the current state, including calibration order, parameter adjustment amplitude, and stabilization time, which can flexibly adapt to different working conditions, enhancing the flexibility and adaptability of calibration. The anomaly detection and adaptive adjustment unit monitors abnormal patterns in the calibration process in real time, identifies deviations from expected calibration responses, and automatically adjusts the calibration strategy to address abnormal situations, ensuring the stability and reliability of the calibration process. The calibration accuracy evaluation unit evaluates the accuracy and precision of the calibration results in real time, quantifies the calibration quality through uncertainty analysis, and provides the basis for decision-making on whether to terminate or continue the calibration process, further ensuring the high quality of the calibration results. The synergistic effect of these components enables the multi-modal AI calibration module to achieve an intelligent, high-precision, and adaptive calibration process, significantly improving calibration efficiency and quality, and reducing calibration costs and risks.

[0119] Further, the multi-source data fusion unit adopts a hierarchical fusion architecture, including:

[0120] The data cleaning and preprocessing subunit standardizes and filters noise from raw data from different sources;

[0121] The feature extraction subunit extracts key feature vectors from multi-modal data;

[0122] The time and space alignment subunit solves the problem of time and space asynchronization between different data sources;

[0123] The information fusion subunit uses Bayesian networks or deep neural networks to integrate multi-source information into a unified high-dimensional feature representation.

[0124] In summary, the multi-source data fusion unit, through a hierarchical fusion architecture, achieves efficient integration and optimized processing of complex data from diverse sources. The data cleaning and preprocessing subunit standardizes and filters noise from raw data from different sources, ensuring input data quality and consistency and laying the foundation for subsequent processing. The feature extraction subunit extracts key feature vectors from multimodal data, effectively screening out information critical to the calibration process, reducing data redundancy and improving data availability. The spatiotemporal alignment subunit addresses temporal and spatial asynchrony between different data sources. Through precise temporal alignment and spatial correction, it ensures spatiotemporal consistency during fusion of multi-source data, further improving data accuracy. The information fusion subunit utilizes Bayesian networks or deep neural network methods to integrate preprocessed and aligned multi-source information into a unified high-dimensional feature representation, leveraging the strengths of different data modalities and enhancing data representation and the ability to model complex relationships. This hierarchical fusion architecture not only improves data processing efficiency and accuracy but also provides high-quality data support for subsequent calibration parameter optimization and strategy generation, significantly enhancing the performance and reliability of the multimodal AI calibration module.

[0125] Furthermore, a deep neural network is used to model the nonlinear relationship between the calibration parameters and the instrument response, and the calibration parameter combination is automatically optimized, including the following steps:

[0126] Divide the collected calibration parameters and corresponding instrument response data into training sets and test sets;

[0127] Construct a deep neural network, determine the input layer, hidden layer, and output layer structure, and select activation and loss functions to accurately characterize the nonlinear relationship between calibration parameters and instrument response;

[0128] Using the training set data, by minimizing the loss function Gradient descent or Adam optimizer is used to update the parameter θ until the model converges, where L(θ) represents the loss function, which is used to measure the gap between the instrument response predicted by the neural network and the actual response; M represents the total number of samples in the training data set; r i represents the actual instrument response data in the i-th sample; f θ (p i ) represents the neural network f θ For the input calibration parameter p i Predicted instrument response;

[0129] Evaluate the trained model on the test set, calculate the mean square error indicator, and verify Ability to accurately predict instrument responses and ensure good generalization of the model, among which, Indicates that the optimal parameter θ is obtained after training* The neural network model, that is, the optimal nonlinear mapping function;

[0130] Fixed trained model parameters θ * , by solving Automatically optimize the calibration parameter combination to make the instrument response close to the expected value, where p * It represents the best calibration parameter combination obtained by optimization during the calibration phase, which makes the instrument response closest to the expected value; r target represents the desired or target instrument response; Indicates the use of the optimal neural network model The predicted instrument response for any given calibration parameter p.

[0131] In summary, a deep neural network was used to model the nonlinear relationship between calibration parameters and instrument response, enabling automatic optimization of calibration parameter combinations and significantly improving calibration accuracy and efficiency. First, the collected calibration parameter and instrument response data were divided into training and test sets to provide a data foundation for model training and validation. Next, a deep neural network was constructed, with the input, hidden, and output layer structures clearly defined. Appropriate activation and loss functions were selected to accurately characterize the complex nonlinear relationship between the calibration parameters and instrument response. During training, the network parameters were updated using gradient descent or the Adam optimizer by minimizing the loss function using the training set data until the model converged. The loss function measures the difference between the neural network's predicted instrument response and the true response, ensuring that the model accurately learns the mapping between calibration parameters and instrument response. Subsequently, model performance was evaluated on the test set, calculating metrics such as mean squared error to verify the model's ability to predict instrument response and ensure good generalization. Finally, the trained model parameters were fixed, and an optimization solution was used to obtain the optimal calibration parameter combination, bringing the instrument response close to the expected value. This achieves automatic optimization of the calibration parameters and improves calibration accuracy and reliability. This process fully utilizes the powerful fitting capabilities of deep learning, effectively solving complex nonlinear calibration problems that are difficult to handle with traditional methods, and providing intelligent technical support for high-precision instrument calibration.

[0132] Furthermore, the environmental intelligent compensation module includes the following components:

[0133] The correlation network construction unit establishes a multi-dimensional mapping relationship between environmental parameters and instrument responses to form a dynamically adjusted correlation model;

[0134] Compensation coefficient calculation unit, accurately calculates the environmental compensation coefficient to ensure that the calibration results are not affected by environmental changes;

[0135] The historical environmental impact analysis unit mines the impact patterns of environmental changes on instrument calibration in historical data and optimizes the adaptability of compensation strategies;

[0136] Extreme environment adaptation unit, which establishes special compensation models for unconventional environmental conditions and expands the environmental adaptability range of the calibration system;

[0137] The environmental disturbance warning unit monitors and warns of abnormal environmental changes that may exceed the compensation capability in real time, ensuring the stability of the calibration process.

[0138] In summary, the intelligent environmental compensation module, through the synergistic effect of multiple components, achieves comprehensive compensation and intelligent management of environmental factors during the calibration process, significantly improving the stability and consistency of calibration results. The association network construction unit establishes a multidimensional mapping relationship between environmental parameters and instrument responses, forming a dynamically adjusted association model that provides a basic framework for compensation. The compensation coefficient calculation unit accurately calculates environmental compensation coefficients, ensuring that calibration results are unaffected by environmental changes, thereby improving calibration accuracy. The historical environmental impact analysis unit optimizes the adaptability of the compensation strategy by mining the impact patterns of environmental changes on instrument calibration in historical data, further enhancing compensation accuracy. The extreme environment adaptation unit establishes a special compensation model for unconventional environmental conditions, expanding the environmental adaptability range of the calibration system and enabling it to maintain calibration accuracy under a wider range of environmental conditions. The environmental disturbance warning unit monitors and warns of abnormal environmental changes that may exceed the compensation capacity in real time, taking preemptive measures to ensure the stability of the calibration process.

[0139] Furthermore, the calculation process of the environmental compensation coefficient includes the following steps:

[0140] S1, the ideal response R of the instrument is measured under standard environmental conditions ideal (E) As a calibration target, ensure that all data have reference value;

[0141] S2, using the formula Calculate the initial compensation coefficient to directly offset the effect of environmental changes on the instrument response, where C comp is the initial environmental compensation coefficient; R(E) is the actual response value of the instrument under environmental parameter E;

[0142] S3, based on historical data, uses the objective function Perform minimization to further optimize the compensation coefficient, J(C comp ) is the calibration error objective function, which measures the deviation between the instrument response and the ideal response after compensation under different environments; R ideal (E r ) is the ideal response value that the instrument should achieve during the rth measurement under standard environmental conditions; R(E r) is the response value measured by the instrument under actual environmental conditions during the rth measurement; m is the number of historical data samples, which is used to calculate the error objective function during the calibration process;

[0143] S4, under the condition of changing environment, through formula Adjust the compensation coefficient in real time to ensure the calibration effect is continuously accurate, where C comp (t+1) is the environmental compensation coefficient after updating at time t+1; C comp (t) is the current environmental compensation coefficient at time t; α is the learning rate, which is used to control the adjustment step size of the dynamic update of the compensation coefficient; R(E(t)) is the response value of the instrument under the actual environment E(t) at time t; R ideal (E(t)) is the response value of the instrument under ideal environmental conditions at time t;

[0144] S5, continuously monitor the calibration results, compare the calibrated response with the ideal response, and if the error exceeds the preset threshold, return to step S3 to optimize the compensation coefficient, and iterate until a stable and accurate environmental compensation effect is achieved.

[0145] In summary, the calculation process of the environmental compensation coefficient achieves accurate compensation and dynamic optimization of the impact of environmental changes through a series of systematic steps. First, the ideal response of the instrument is measured under standard environmental conditions to provide a reference target for calibration. Next, the initial compensation coefficient is calculated using a formula to directly offset the direct impact of environmental changes on the instrument response. Subsequently, based on historical data, the objective function is used for minimization to further optimize the compensation coefficient. By minimizing the calibration error objective function, the deviation between the instrument response and the ideal response under different environments is minimized. Under conditions of dynamic environmental changes, the compensation coefficient is dynamically adjusted by updating the formula in real time to ensure the continued accuracy of the calibration effect. Throughout the process, by continuously monitoring the calibration results and comparing them with the ideal response, if the error exceeds the preset threshold, the optimization step is returned to perform iterative adjustments until a stable and accurate compensation effect is achieved.

[0146] Furthermore, the instrument health management module includes the following components:

[0147] Dynamic response characteristic analysis unit captures and analyzes the transient response curve of the instrument during calibration, identifies performance deviations and abnormal modes, and provides accurate health status feedback;

[0148] The health status assessment unit quantitatively evaluates the overall health status of the instrument based on a multi-dimensional indicator system and generates an intuitive health report;

[0149] Core Component Life Prediction Unit, combined with historical degradation rules and current working status, predicts the remaining useful life and performance boundaries of key components;

[0150] Maintenance Strategy Generation Unit, based on health assessment and life prediction results, formulates individualized preventive maintenance plans, optimizes maintenance timing and resource allocation;

[0151] Performance Degradation Trend Tracking Unit, continuously records actual running data and monitors the change trajectory of instrument performance parameters, identifies signs of accelerated degradation and provides early warning to prevent sudden failures.

[0152] In summary, the instrument health management module, through the integration of multiple functional components, realizes comprehensive monitoring, evaluation and management of the health status of analytical instruments, significantly improving the operational reliability and maintenance efficiency of the instruments. The dynamic response characteristic analysis unit can capture and analyze the transient response curve of the instrument during calibration, accurately identify performance deviations and abnormal patterns, and provide real-time feedback on the health status of the instrument. The health status evaluation unit quantitatively evaluates the overall health status of the instrument based on a multi-dimensional index system and generates intuitive health reports to help users quickly understand the instrument's running status. The core component life prediction unit, combined with historical degradation rules and current working status, predicts the remaining useful life and performance boundaries of key components, providing a scientific basis for maintenance decisions. The maintenance strategy generation unit, based on health assessment and life prediction results, formulates individualized preventive maintenance plans, optimizes maintenance timing and resource allocation, and reduces the risk of sudden failures. The performance degradation trend tracking unit continuously records actual running data and monitors the change trajectory of instrument performance parameters, identifies signs of accelerated degradation and provides early warning, further ensuring the stable operation of the instrument. The synergistic effect of these components realizes the transition from passive maintenance to predictive maintenance, prolongs the service life of the instrument, reduces maintenance costs, and ensures high reliability and efficiency of the instrument in long-term operation.

[0153] Further, the health status evaluation unit is based on a multi-dimensional health index system for evaluation, including:

[0154] Response time index, reflecting the change trend of instrument response speed;

[0155] Signal stability index, measuring the fluctuation degree of instrument output signal;

[0156] Sensitivity index, evaluating the instrument's ability to detect small changes;

[0157] Linearity index, reflecting the change of linear relationship between instrument input and output;

[0158] Noise level index, measuring the noise background of instrument signal;

[0159] Temperature drift index, evaluating the instrument's sensitivity to temperature changes.

[0160] In summary, the health status assessment unit constructs a multi-dimensional health indicator system to comprehensively and accurately assess the overall health of the instrument. This system includes response time, which reflects the changing trend of the instrument's response speed; signal stability, which measures the fluctuation of the instrument's output signal; sensitivity, which evaluates the instrument's ability to detect small changes; linearity, which reflects the linear relationship between the instrument's input and output; noise level, which measures the noise background of the instrument's signal; and temperature drift, which assesses the instrument's sensitivity to temperature changes. These indicators characterize the instrument's performance from different dimensions and comprehensively reflect the instrument's status changes during actual operation. By quantifying and analyzing these indicators, the health status assessment unit generates intuitive health reports, providing a scientific basis for instrument maintenance decisions. This helps users promptly identify potential problems and implement preventive maintenance measures, thereby extending the instrument's lifespan and improving its reliability and stability.

[0161] Furthermore, the core component life prediction unit includes:

[0162] The failure mode library construction subunit collects and classifies the typical failure modes and characteristics of the instrument's core components;

[0163] The degradation trajectory modeling subunit builds a mathematical model of component performance degradation based on historical data;

[0164] The life prediction algorithm subunit combines physical models and data-driven methods to predict the remaining useful life of components;

[0165] The usage condition evaluation subunit analyzes the impact of actual usage conditions on component life and dynamically adjusts the prediction model;

[0166] The prediction result verification subunit verifies the prediction results through the actual status of replaced parts and continuously optimizes the prediction model.

[0167] In summary, the core component life prediction unit achieves accurate prediction and dynamic management of the remaining service life of the instrument's core components through the synergistic effect of multiple sub-units. The failure mode library construction sub-unit collects and classifies the typical failure modes and characteristics of core components, providing a rich empirical basis for prediction. The degradation trajectory modeling sub-unit constructs a mathematical model of component performance degradation based on historical data, which can accurately describe the change pattern of component performance over time. The life prediction algorithm sub-unit combines physical models and data-driven methods, comprehensively considers the physical properties of components and actual operating data, predicts the remaining service life of components, and improves the accuracy and reliability of prediction. The usage condition evaluation sub-unit analyzes the impact of actual usage conditions on component life, and dynamically adjusts the prediction model so that it can adapt to different operating environments. The prediction result verification sub-unit verifies the prediction results through the actual status of replaced components, and continuously optimizes the prediction model to ensure the long-term effectiveness of the model.

[0168] Furthermore, the calibration knowledge graph module includes the following components:

[0169] Knowledge extraction unit, which automatically extracts structured knowledge from calibration history records, equipment manuals, and expert experience to form a standardized calibration knowledge base;

[0170] The knowledge graph construction unit organizes calibration parameters, methods, results and their relationships into a multi-dimensional semantic network to build a complete calibration knowledge representation system;

[0171] The graph neural network reasoning unit uses graph convolutional networks to train and reason on knowledge graphs, explore implicit calibration rules, and optimize calibration strategies.

[0172] Similar case retrieval unit, based on semantic similarity analysis, quickly retrieves historical calibration cases that match the current situation, providing accurate reference for decision-making;

[0173] Knowledge update and evolution unit, dynamically integrating new calibration data and practical experience to continuously optimize the knowledge graph structure;

[0174] The cross-domain knowledge transfer unit uses transfer learning technology to adapt and apply the calibration experience of specific instruments to the calibration process of new or rare instruments;

[0175] The calibration knowledge consulting unit analyzes patterns and associations in the knowledge graph to provide knowledge support and optimization suggestions for the multimodal AI calibration module.

[0176] In summary, the calibration knowledge graph module integrates multiple functional components to build an intelligent and dynamic calibration knowledge management and application platform, significantly improving the knowledge-based nature and decision-making efficiency of the calibration process. The knowledge extraction unit automatically extracts structured knowledge from calibration history, equipment manuals, and expert experience to form a standardized calibration knowledge base, providing foundational data for subsequent knowledge applications. The knowledge graph construction unit organizes calibration parameters, methods, results, and their interrelationships into a multidimensional semantic network, constructing a complete calibration knowledge representation system and enabling knowledge visualization and association. The graph neural network inference unit uses a graph convolutional network to train and reason on the knowledge graph, discovering implicit calibration rules and optimizing calibration strategies, thereby enhancing the intelligent level of calibration. The similar case retrieval unit, based on semantic similarity analysis, rapidly retrieves historical calibration cases that match the current context, providing accurate reference for decision-making and enhancing the scientific and referenceable nature of calibration decisions. The knowledge update and evolution unit dynamically integrates new calibration data and practical experience, continuously optimizing the knowledge graph structure and ensuring the timeliness and accuracy of knowledge. The cross-domain knowledge transfer unit leverages transfer learning technology to adapt and apply calibration experience from specific instruments to the calibration process of new or rare instruments, expanding the scope of knowledge application. The calibration knowledge consulting unit analyzes patterns and associations within the knowledge graph to provide knowledge support and optimization suggestions for the multimodal AI calibration module, enabling in-depth application of knowledge.

[0177] Furthermore, the knowledge graph construction unit includes:

[0178] The ontology model design subunit defines the concepts, attributes and relationships in the calibration field and builds a standardized knowledge representation framework;

[0179] The entity relationship extraction subunit identifies and extracts entities and relationships from unstructured text and converts them into structured knowledge;

[0180] The Knowledge Integration and Conflict Resolution sub-unit integrates knowledge from different sources and resolves conflicts and contradictions;

[0181] The knowledge graph storage and indexing subunit uses an efficient graph database to store knowledge graphs, supporting fast retrieval and complex queries;

[0182] The knowledge quality assessment subunit evaluates the completeness, accuracy, and timeliness of knowledge, and guides the continuous optimization of the knowledge graph.

[0183] In summary, the Knowledge Graph Construction Unit, through the collaborative work of multiple subunits, achieves efficient organization and management of calibration domain knowledge, providing a solid foundation for the construction of the calibration knowledge graph. The Ontology Model Design Subunit defines concepts, attributes, and relationships in the calibration domain, constructing a standardized knowledge representation framework and providing a unified standard for the standardized expression of knowledge. The Entity Relationship Extraction Subunit identifies and extracts entities and relationships from unstructured text, transforming them into structured knowledge, broadening the knowledge base and enriching its content. The Knowledge Integration and Conflict Resolution Subunit integrates knowledge from diverse sources, resolving conflicts and contradictions to ensure consistency and reliability. The Knowledge Graph Storage and Indexing Subunit utilizes an efficient graph database to store the knowledge graph, supporting fast retrieval and complex queries, improving knowledge access efficiency and application flexibility. The Knowledge Quality Assessment Subunit assesses the completeness, accuracy, and timeliness of knowledge, providing guidance for the continuous optimization of the knowledge graph and ensuring its high-quality development.

[0184] Furthermore, the intelligent scheduling and management module includes the following components:

[0185] The multi-objective optimization scheduling unit comprehensively considers time efficiency, resource utilization and calibration accuracy requirements, and generates the optimal calibration task scheduling plan based on the intelligent optimization algorithm;

[0186] Resource allocation management unit, which monitors the availability of calibration equipment, reference materials, and human resources in real time, and intelligently adjusts allocation strategies to avoid resource conflicts and improve overall utilization efficiency;

[0187] The task priority assessment unit dynamically calculates the task priority based on the instrument importance, usage frequency and calibration urgency to ensure that critical tasks are executed first;

[0188] The calibration progress monitoring unit tracks the execution status of each calibration task in real time, generates visual progress reports, and provides abnormal warnings and adjustment suggestions to ensure that tasks are completed on time;

[0189] The emergency response processing unit automatically analyzes the impact range and generates adjustment strategies for sudden failures or abnormal situations, dynamically optimizes the calibration plan, and ensures stable system operation.

[0190] In summary, the intelligent scheduling and management module achieves efficient scheduling of calibration tasks and intelligent resource management by integrating multiple functional components. The multi-objective optimization scheduling unit comprehensively considers time efficiency, resource utilization, and calibration accuracy requirements, and generates the optimal calibration task scheduling plan based on an intelligent optimization algorithm to ensure the efficiency and accuracy of the calibration process. The resource allocation management unit monitors the availability of calibration equipment, reference materials, and human resources in real time, and intelligently adjusts allocation strategies to avoid resource conflicts and improve overall utilization efficiency. The task priority assessment unit dynamically calculates task priorities based on instrument importance, frequency of use, and calibration urgency, ensuring that critical tasks are executed first and optimizing the execution order of calibration tasks. The calibration progress monitoring unit tracks the execution status of each calibration task in real time, generates visual progress reports, and provides anomaly warnings and adjustment suggestions to ensure timely completion of tasks. The emergency response processing unit automatically analyzes the impact range and generates adjustment strategies for sudden failures or abnormal situations, dynamically optimizing the calibration plan to ensure stable system operation.

[0191] Furthermore, the multi-objective optimization scheduling unit includes:

[0192] The objective function construction subunit converts the calibration time, resource consumption and calibration accuracy targets into mathematical expressions;

[0193] The constraint identification subunit identifies and formally describes resource availability and time limit constraints;

[0194] Optimization algorithm selection subunit, automatically selects the appropriate optimization algorithm according to the problem scale and characteristics;

[0195] The Pareto optimal solution generation subunit generates a set of Pareto optimal solutions in multi-objective optimization problems for decision-making reference;

[0196] The scheme evaluation and recommendation subunit selects the most appropriate scheduling scheme from the Pareto front based on the current system status and management preferences.

[0197] In summary, the multi-objective optimization scheduling unit, through the synergistic effect of its multiple sub-units, achieves efficient generation and optimal selection of calibration task scheduling schemes. The objective function construction sub-unit converts objectives such as calibration time, resource consumption, and calibration accuracy into mathematical expressions, providing a quantitative basis for the optimization process. The constraint identification sub-unit identifies and formally describes constraints such as resource availability and time limits to ensure the feasibility of the scheduling scheme. The optimization algorithm selection sub-unit automatically selects the appropriate optimization algorithm based on the scale and characteristics of the problem, improving optimization efficiency and adaptability. The Pareto optimal solution generation sub-unit generates a set of Pareto optimal solutions in the multi-objective optimization problem, providing a variety of reference schemes for decision-making. The scheme evaluation and recommendation sub-unit selects the most appropriate scheduling scheme from the Pareto frontier based on the current system status and management preferences, ensuring that the scheduling results not only meet the multi-objective requirements of the calibration task but also adapt to the actual operating environment.

[0198] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An artificial intelligence-based automatic calibration system for laboratory analytical instruments, characterized in that: Includes the following modules: The intelligent hardware interaction module is responsible for the physical connection of instruments, the transmission of reference materials, and environmental monitoring, enabling plug-and-play of different instruments and precise delivery of reference materials. The instrument digital twin module automatically identifies instrument characteristics and simulates their behavior by building a virtual model of the instrument, allowing calibration processes to be rehearsed in a virtual environment. Multimodal AI calibration module uses deep learning models to optimize calibration parameters and adjust the calibration process in real time to ensure calibration accuracy; The environmental intelligent compensation module establishes a multi-dimensional correlation network between environmental parameters and instrument responses, calculates and generates compensation coefficients in real time, eliminates the impact of environmental factors on calibration results, and ensures the consistency of calibration results under different environmental conditions; The instrument health management module analyzes the dynamic response characteristics of the instrument during calibration to assess the instrument's health status and predict the lifespan of core components, enabling a shift from reactive maintenance to predictive maintenance. The calibration knowledge graph module builds a knowledge graph containing multi-dimensional calibration information, realizes the automatic extraction, reasoning and application of calibration knowledge through graph neural networks, and continuously optimizes the calibration strategy; The intelligent scheduling and management module generates the optimal calibration plan based on a multi-objective optimization algorithm, coordinates the work of each module, and realizes the rational allocation and efficient utilization of calibration resources.

2. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The intelligent hardware interaction module includes the following components: Universal interface adapter unit, which realizes automatic identification and conversion of multiple instrument protocols and interfaces, and supports plug-and-play of heterogeneous instruments; The automatic standard material delivery unit ensures accurate quantitative transmission and switching of calibration standard materials through a precise fluid control system and multi-channel selector, thus ensuring calibration accuracy. Environmental parameter monitoring unit collects and provides real-time temperature, humidity, air pressure and electromagnetic field data to ensure that environmental factors during the calibration process are accurately monitored; Physical interaction control unit, which executes hardware-level instrument operation instructions, including precise control of start, stop, reset and sample loading actions; Power supply and signal stabilization unit, providing stable power supply and signal conditioning to eliminate the influence of external interference on the calibration process; Safety monitoring unit, real-time monitoring and ensuring hardware safety during the calibration process, including overload protection, leak detection and emergency shutdown functions.

3. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The instrument digital twin module includes the following components: The instrument model building unit quickly generates a high-precision digital virtual model based on the instrument's parameters and structural data, accurately reproducing the instrument's characteristics; The instrument behavior simulation unit simulates the operating status and response characteristics of the instrument in a virtual environment, providing a reliable simulation basis for the calibration process; Virtual calibration environment unit, which builds a complete virtual calibration scenario including instruments, reference materials and environmental conditions, and realizes high-precision simulation of the calibration process; Model-to-entity synchronization unit ensures real-time synchronization between the virtual model and the physical instrument status, and dynamically adjusts model parameters according to actual operating conditions; The instrument abnormality simulation unit simulates various instrument failures and abnormal conditions in a virtual environment and rehearses response strategies during the calibration process.

4. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 3, characterized in that: The instrument model building unit adopts a multi-level modeling strategy, including: Physical model layer, which establishes a basic model based on the physical principles and structural characteristics of the instrument; The statistical model layer captures the statistical characteristics and random fluctuations of the instrument through data-driven methods; The neural network model layer uses deep learning technology to simulate nonlinear behaviors that are difficult to express through physical equations.

5. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The multimodal AI calibration module includes the following components: A multi-source data fusion unit that integrates multimodal data from instrument sensors, historical calibration records, and reference material properties to construct a unified data representation; A calibration parameter deep learning unit uses deep neural networks to model the nonlinear relationship between calibration parameters and instrument response, automatically optimizing calibration parameter combinations; A real-time calibration strategy generation unit dynamically generates the optimal calibration strategy based on the deep learning model and current status, including calibration sequence, parameter adjustment amplitude, and stabilization time to adapt to different working conditions; Anomaly detection and adaptive adjustment unit, which monitors abnormal patterns in the calibration process in real time, identifies calibration responses that deviate from the expected ones, and automatically adjusts the calibration strategy to deal with abnormal situations; The calibration accuracy assessment unit evaluates the accuracy and precision of the calibration results in real time, quantifies the calibration quality through uncertainty analysis, and provides a basis for decision-making on whether to terminate or continue the calibration process.

6. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The environmental intelligent compensation module includes the following components: The correlation network construction unit establishes a multi-dimensional mapping relationship between environmental parameters and instrument responses to form a dynamically adjusted correlation model; Compensation coefficient calculation unit, accurately calculates the environmental compensation coefficient to ensure that the calibration results are not affected by environmental changes; The historical environmental impact analysis unit mines the impact patterns of environmental changes on instrument calibration in historical data and optimizes the adaptability of compensation strategies; Extreme environment adaptation unit, which establishes special compensation models for unconventional environmental conditions and expands the environmental adaptability range of the calibration system; The environmental disturbance warning unit monitors and warns of abnormal environmental changes that may exceed the compensation capability in real time, ensuring the stability of the calibration process.

7. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The instrument health management module includes the following components: Dynamic response characteristic analysis unit captures and analyzes the transient response curve of the instrument during calibration, identifies performance deviations and abnormal modes, and provides accurate health status feedback; The health status assessment unit quantitatively evaluates the overall health status of the instrument based on a multi-dimensional indicator system and generates an intuitive health report; The core component life prediction unit combines historical degradation patterns and current working conditions to predict the remaining service life and performance boundaries of key components; The maintenance strategy generation unit formulates personalized preventive maintenance plans based on health assessment and life prediction results, optimizing maintenance timing and resource allocation; The performance degradation trend tracking unit continuously records actual operating data and monitors the changing trajectory of instrument performance parameters, identifying signs of accelerated degradation and providing early warnings to prevent sudden failures.

8. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 7, characterized in that: The health status assessment unit is based on a multi-dimensional health indicator system, including: Response time indicator, reflecting the changing trend of instrument response speed; Signal stability index, which measures the degree of fluctuation of the instrument output signal; Sensitivity index, which evaluates the instrument's ability to detect small changes; Linearity index reflects the change in the linear relationship between the input and output of the instrument; Noise level index, which measures the noise background of the instrument signal; Temperature drift index, which evaluates the sensitivity of the instrument to temperature changes.

9. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The calibration knowledge graph module includes the following components: Knowledge extraction unit, which automatically extracts structured knowledge from calibration history records, equipment manuals, and expert experience to form a standardized calibration knowledge base; The knowledge graph construction unit organizes calibration parameters, methods, results and their relationships into a multi-dimensional semantic network to build a complete calibration knowledge representation system; The graph neural network reasoning unit uses graph convolutional networks to train and reason on knowledge graphs, explore implicit calibration rules, and optimize calibration strategies. Similar case retrieval unit, based on semantic similarity analysis, quickly retrieves historical calibration cases that match the current situation, providing accurate reference for decision-making; Knowledge update and evolution unit, dynamically integrating new calibration data and practical experience to continuously optimize the knowledge graph structure; The cross-domain knowledge transfer unit uses transfer learning technology to adapt and apply the calibration experience of specific instruments to the calibration process of new or rare instruments; The calibration knowledge consulting unit analyzes patterns and associations in the knowledge graph to provide knowledge support and optimization suggestions for the multimodal AI calibration module.

10. The automatic calibration system for laboratory analytical instruments based on artificial intelligence according to claim 1, characterized in that: The intelligent scheduling and management module includes the following components: The multi-objective optimization scheduling unit comprehensively considers time efficiency, resource utilization and calibration accuracy requirements, and generates the optimal calibration task scheduling plan based on the intelligent optimization algorithm; Resource allocation management unit, which monitors the availability of calibration equipment, reference materials, and human resources in real time, and intelligently adjusts allocation strategies to avoid resource conflicts and improve overall utilization efficiency; The task priority assessment unit dynamically calculates the task priority based on the instrument importance, usage frequency and calibration urgency to ensure that critical tasks are executed first; The calibration progress monitoring unit tracks the execution status of each calibration task in real time, generates visual progress reports, and provides abnormal warnings and adjustment suggestions to ensure that tasks are completed on time; The emergency response processing unit automatically analyzes the impact range and generates adjustment strategies for sudden failures or abnormal situations, dynamically optimizes the calibration plan, and ensures stable system operation.

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