A microelectronic gas analysis system based on chips and intelligent manufacturing

Abstract

The present invention discloses a microelectronic gas analysis system based on chip and intelligent manufacturing, which relates to the technical field of chip and artificial intelligence system. The system includes the following components: microelectronic integrated sensor module: a gas sensor chip manufactured based on MEMS technology, which is integrated with a high-sensitivity sensing material layer through a precision manufacturing process, and can quickly respond to and capture target gas molecules, thereby achieving high-precision detection of multiple gases, reducing cross-interference, and ensuring the accuracy of analysis results. The present invention realizes the miniaturization and integration of sensors through the application of microelectronic integrated sensor modules, and combined with highly sensitive sensing materials, can quickly respond and accurately capture target gas molecules in a very short time, greatly improving the sensitivity and response speed of gas detection, so that the system can provide more reliable and accurate analysis results when facing rapidly changing gas environments and occasions requiring immediate response.

Description

Technical Field

[0001] The present invention relates to the technical field of chips and artificial intelligence systems, and specifically to a microelectronic gas analysis system based on chips and intelligent manufacturing. Background Art

[0002] In recent years, with the rapid advancement of science and technology, the combination of microelectronics technology and intelligent manufacturing is profoundly changing the traditional industrial landscape. The development of microelectronics technology, especially the mature application of MEMS (micro-electromechanical systems), has made the miniaturization and integration of sensors possible, bringing revolutionary changes to the field of gas analysis. At the same time, the application of intelligent manufacturing technology has greatly improved the performance consistency and production efficiency of products through precise control and optimization of production processes, making the mass production of microelectronic gas analysis systems a reality. This technological integration not only reduces the size of the equipment and enhances portability, but also significantly improves the overall performance of the detection system, enabling it to adapt to a wider range of gas monitoring needs, expanding from laboratory research to industrial sites, environmental monitoring and even home safety.

[0003] Despite significant progress in gas analysis technology, traditional analytical instruments still have many shortcomings. These devices are often bulky and complex to install and operate, which not only increases deployment costs but also limits their application in space-constrained or extreme environments. In addition, traditional gas detectors have limited ability to identify specific gases, slow response speeds, and are easily affected by cross-interference, which greatly reduces the accuracy of the analysis results. In actual applications, these defects often require professionals to perform frequent calibration and maintenance, which not only increases labor costs, but also affects the real-time and reliability of data, and cannot meet the growing demand for instant and accurate gas analysis in modern society.

[0004] However, the high cost and complexity of traditional gas analysis technology not only hinder its popularization in small and medium-sized enterprises and remote areas, but also to a certain extent inhibit technological innovation and development in the fields of environmental monitoring and industrial safety. The high maintenance costs and long response time make it impossible to respond quickly in emergency situations, increasing the potential risk of environmental pollution and the probability of safety accidents. In addition, the energy efficiency of these devices is generally low, and long-term operation consumes a lot of electricity resources, which runs counter to the global trend of energy conservation and emission reduction. Therefore, the development of a microelectronic gas analysis system is of great significance to promoting sustainable development, improving public safety and promoting environmental protection. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a microelectronic gas analysis system based on chips and intelligent manufacturing. By adopting advanced MEMS technology, the system realizes the miniaturization and integration of sensors, which not only greatly reduces the volume, but also significantly improves the sensitivity and response speed of gas detection. The highly sensitive sensing material layer can quickly capture target gas molecules and can achieve accurate detection even under low concentration conditions. At the same time, it effectively reduces cross interference and ensures the high accuracy and reliability of the analysis results.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a microelectronic gas analysis system based on chip and intelligent manufacturing, characterized in that the system includes the following components:

[0007] Microelectronic integrated sensor module: Gas sensor chip manufactured based on MEMS technology, through precision manufacturing process, integrated with highly sensitive sensing material layer, quickly responds to and captures target gas molecules, realizes high-precision detection of multiple gases, reduces cross-interference, and ensures the accuracy of analysis results;

[0008] Intelligent processing and adaptive algorithm module: The system has a built-in high-performance, low-power microprocessor, equipped with intelligent algorithms and machine learning models for gas analysis optimization, real-time analysis of sensor data, automatic identification of gas types, adjustment of detection strategies, and adaptive optimization of working parameters according to environmental changes, reducing false alarms and improving analysis accuracy;

[0009] Remote control center module: receives data sent by the low-power remote communication function module, controls the working state of the microelectronic integrated sensor module, and sends the processed data to the display module, and sends the data to the gas analysis display adjustment module through the network interface;

[0010] Low-power remote communication function module: Integrates low-power Bluetooth remote communication technology, enabling the system to wirelessly transmit analysis results to the remote control center in real time, facilitating centralized management and remote control, and transmits gas analysis results and system status information to the remote control center in real time;

[0011] Gas analysis display and adjustment module: receives data sent by the remote control center module, displays gas composition and concentration information, and provides an operation interface to facilitate users to set parameters and query data.

[0012] Furthermore, the microelectronic integrated sensor module comprises the following specific steps:

[0013] (1) Design and preparation of the sensing material layer: Based on the characteristics of the target gas, metal oxides, conductive polymers and nanocomposites are selected as sensing materials, and the sensing layer is accurately prepared on the MEMS chip through chemical synthesis and physical deposition methods;

[0014] (2) Microstructure design and manufacturing: Using MEMS technology, design and manufacture microstructured sensor chips to optimize gas diffusion paths, speed up response speed and selectivity;

[0015] (3) Integrated packaging: The prepared sensing layer is integrated with the microstructure chip to ensure the stability and durability of the sensor chip;

[0016] (4) Performance testing and calibration: Each sensor unit is subjected to rigorous performance testing, including evaluation of key indicators such as sensitivity, selectivity, response time, and recovery time. The frequency and time domain features that reflect the characteristics of the gas are extracted from the sensor signal through the Fourier transform algorithm to prepare data for subsequent identification and analysis.

[0017] Furthermore, the microelectronic integrated sensor module uses the Fourier transform algorithm to utilize the changes in the time domain contained in the signal generated by the sensor to reflect the fluctuation of gas concentration over time, converts the time domain signal into a frequency domain signal, and identifies the reaction characteristics of the specific frequency component in the signal and the absorption of specific gas molecules. The algorithm formula is: , x is the time domain signal sequence, X[k] is the corresponding frequency domain coefficient, N is the sequence length, It is a rotation factor that decomposes the complex time domain signal into a series of frequency components through calculation, and then extracts the characteristic frequencies and frequency bands related to the target gas, providing key information for gas identification.

[0018] Furthermore, the specific steps of the intelligent processing and adaptive algorithm module are:

[0019] (1) Feature data reception and preprocessing: Receive feature raw sensor signals from the microelectronic integrated sensor module, perform signal amplification and denoising, and use digital filtering technology to eliminate environmental noise and system noise;

[0020] (2) Gas identification and concentration estimation: Use machine learning algorithms to classify and regress feature vectors to identify gas types and estimate concentrations;

[0021] (3) Adaptive optimization: Dynamically adjust algorithm parameters and model structure based on historical data and current environmental feedback to adapt to environmental conditions with different temperature and humidity changes;

[0022] (4) Decision-making and control: Based on the results of identification and concentration estimation, the system determines the gas excess alarm and sends instructions to the remote control center module through the remote communication module to adjust the working status of the sensor, including increasing the sampling frequency and starting the cleaning program.

[0023] Furthermore, the intelligent processing and adaptive algorithm module uses a low-pass filtering algorithm to remove high-frequency noise, retain the low-frequency and DC components in the signal, stabilize the sensor signal, extract the slow trend of gas concentration changes, and allow all frequency components below the cutoff frequency to pass, while components above the cutoff frequency are attenuated. For time domain signals, the differential equation is: , y[n] is the filtered signal, x[n] is the input signal, It is a parameter related to the cutoff frequency, which provides a clearer data basis for subsequent gas identification by improving signal quality.

[0024] Furthermore, the intelligent processing and adaptive algorithm module uses a linear regression algorithm for quantitative estimation of gas concentration, and predicts the concentration of unknown gas by establishing a linear relationship model between input characteristics and output gas concentration. The algorithm formula is: , y represents the predicted gas concentration, w is the weight parameter, and b is the bias term.

[0025] Furthermore, the intelligent processing and adaptive algorithm module converts the preprocessed and feature-extracted data into a set of feature vectors, where each vector corresponds to a feature representation of a set of sensor readings. At the same time, each feature vector corresponds to a known gas category label, and a maximum margin hyperplane is found between the two types of samples to achieve optimal classification. For the linearly separable case, the optimization goal is to minimize the total distance from the samples within the interval boundary to the hyperplane while ensuring that all samples are correctly classified. The training data set is ( , ), i=1, 2, 3,...n, is the eigenvector, is the feature vector and the category label, the optimization problem of linear SVM can be expressed as: , for nonlinearly separable data, the original data is mapped to a higher-dimensional feature space, making the originally inseparable problem linearly separable. For new unknown samples x′, classification is performed through decision rules: , through the cross-validation method, it is tuned to adapt to gas detection tasks in different environments. A large number of labeled gas sample data sets are used to train the model to learn the characteristic patterns of different gases. The feature vectors collected in real time are input into the trained model to identify the gas type and estimate the concentration.

[0026] Furthermore, the remote control center module specifically comprises the following steps:

[0027] (1) Data reception: Receive data sent by the low-power remote communication function module through the network interface, including gas composition and concentration information detected by the microelectronic integrated sensor module;

[0028] (2) Data display and storage: The data is sent to the gas analysis display and adjustment module for real-time display and stored in the database of the remote control center for subsequent data analysis and historical record query;

[0029] (3) Remote control: Remotely control the working status of the microelectronic integrated sensor module according to user needs and preset automation processes, including starting and stopping detection and adjusting detection parameters;

[0030] (4) Alarm and notification: When the detected gas composition and concentration exceed the safety threshold, the remote control center module will trigger an alarm and notify relevant personnel via email and text message.

[0031] Furthermore, the low power consumption remote communication function module has the following specific steps:

[0032] (1) Data encapsulation and encoding: The gas detection data and system status information generated by the microelectronic integrated sensor module are collected and compressed and encrypted to reduce the amount of transmitted data and ensure data security;

[0033] (2) Low-power wireless transmission: Use low-power Bluetooth technology to send packaged data to the remote control center wirelessly, and save power to the greatest extent possible through signal modulation and transmission power control;

[0034] (3) Link management and energy-saving strategy: Implement a dynamic link management strategy, enter sleep mode when there is no data transmission demand, activate only at the scheduled time and when a wake-up signal is received, and dynamically adjust the transmission power according to the signal strength;

[0035] (4) Data reception and decoding: Receive and decode wireless signals to restore the original data. The decoding process includes error detection and correction algorithms to ensure the accuracy and integrity of data transmission.

[0036] Furthermore, the gas analysis display adjustment module includes a fault diagnosis and self-repair mechanism, which automatically detects system faults, performs preliminary diagnosis, and takes corresponding measures to try to repair and isolate the faults to ensure stable operation of the system.

[0037] Compared with the existing technology, this microelectronic gas analysis system based on chip and intelligent manufacturing has the following beneficial effects:

[0038] 1. The present invention realizes the miniaturization and integration of sensors through the application of microelectronic integrated sensor modules. Combined with highly sensitive sensing materials, it can quickly respond and accurately capture target gas molecules in a very short time, greatly improving the sensitivity and response speed of gas detection, so that the system can provide more reliable and accurate analysis results when facing rapidly changing gas environments and situations requiring immediate response.

[0039] 2. The adaptive control strategy module and machine learning model of the present invention give the system strong adaptability and intelligence. The system can automatically adjust the thermal management strategy according to different operating conditions, predict future operating trends, and optimize the control parameters accordingly. This adaptive control strategy enables the system to maintain efficient operation in various complex environments. At the same time, the machine learning model can continuously learn and optimize the control strategy based on historical data, making the system more intelligent and adaptable.

[0040] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 This is a process operation diagram of a microelectronic gas analysis system based on chips and intelligent manufacturing.

[0043] Figure 2 This is a flow chart of a microelectronic gas analysis system based on chips and intelligent manufacturing. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] A microelectronic gas analysis system based on chip and intelligent manufacturing, the system includes the following components:

[0046] Microelectronic integrated sensor module: Gas sensor chip manufactured based on MEMS technology, through precision manufacturing process, integrated with highly sensitive sensing material layer, quickly responds to and captures target gas molecules, realizes high-precision detection of multiple gases, reduces cross-interference, and ensures the accuracy of analysis results;

[0047] Intelligent processing and adaptive algorithm module: The system has a built-in high-performance, low-power microprocessor, equipped with intelligent algorithms and machine learning models for gas analysis optimization, real-time analysis of sensor data, automatic identification of gas types, adjustment of detection strategies, and adaptive optimization of working parameters according to environmental changes, reducing false alarms and improving analysis accuracy;

[0048] Remote control center module: receives data sent by the low-power remote communication function module, controls the working state of the microelectronic integrated sensor module, and sends the processed data to the display module, and sends the data to the gas analysis display adjustment module through the network interface;

[0049] Low-power remote communication function module: Integrates low-power Bluetooth remote communication technology, enabling the system to wirelessly transmit analysis results to the remote control center in real time, facilitating centralized management and remote control, and transmits gas analysis results and system status information to the remote control center in real time;

[0050] Gas analysis display and adjustment module: Receives data sent by the remote control center module, displays gas composition and concentration information, and provides an operation interface to facilitate users to set parameters and query data. It is also equipped with a fault diagnosis and self-repair mechanism to automatically detect system faults, perform preliminary diagnosis, and take corresponding measures to try to repair and isolate faults to ensure stable operation of the system.

[0051] Embodiment 1

[0052] This embodiment provides a microelectronic gas analysis system based on chips and intelligent manufacturing. By integrating highly sensitive microelectronic sensor chips and advanced intelligent algorithms, the system can complete high-precision detection of multiple gases in a very short time. It not only has a fast response speed, but also can effectively reduce cross interference, ensuring the accuracy and reliability of the analysis results.

[0053] In the specific implementation, the system uses a gas sensor chip manufactured by MEMS technology through a microelectronic integrated sensor module, which integrates a sensing material layer of metal oxides, conductive polymers and nanocomposites. Through chemical synthesis and physical deposition methods, the sensing layer is accurately prepared on the MEMS chip, and the microstructured sensor chip is designed and manufactured to optimize the gas diffusion path, speed up the response speed and selectivity, integrate the sensing layer with the microstructure chip for packaging, ensure the stability and durability of the sensor chip, perform performance testing and calibration on each sensor unit, and use the Fourier transform algorithm to use the signal generated by the sensor to include changes in the time domain, reflecting the fluctuation of gas concentration over time, converting the time domain signal into the frequency domain signal, and identifying the reaction characteristics of the specific frequency components in the signal and the absorption of specific gas molecules. The algorithm formula is: , x is the time domain signal sequence, X[k] is the corresponding frequency domain coefficient, N is the sequence length, It is a rotation factor that decomposes the complex time domain signal into a series of frequency components through calculation, and then extracts the characteristic frequencies and frequency bands related to the target gas, provides key information for gas identification, and extracts the frequency and time domain characteristics of gas characteristics.

[0054] The intelligent processing and adaptive algorithm module receives the original sensor signal sent by the microelectronic integrated sensor module, performs signal amplification and denoising, uses a low-pass filtering algorithm to retain the low-frequency and DC components in the signal, stabilizes the sensor signal, extracts the slow trend of gas concentration changes, and allows all frequency components below the cutoff frequency to pass, while components above the cutoff frequency are attenuated. For time domain signals, the differential equation is: , y[n] is the filtered signal, x[n] is the input signal, It is a parameter related to the cutoff frequency. By improving the signal quality, it provides a clearer data basis for subsequent gas identification. It uses machine learning algorithms to classify and regress feature vectors to identify gas types and estimate concentrations. According to historical data and current environmental feedback, it dynamically adjusts algorithm parameters and model structures to adapt to environmental conditions with different temperature and humidity changes. Based on the results of identification and concentration estimation, it determines the gas excess alarm and sends instructions to the remote control center module through the remote communication module to adjust the working status of the sensor.

[0055] The remote control center module receives the gas composition and concentration information sent by the low-power remote communication function module through the network interface, and stores it in the database for future analysis and historical query. It remotely controls the working status of the sensor according to user needs or preset rules. Once it detects that the gas concentration exceeds the safety threshold, it immediately triggers an alarm and notifies relevant personnel by email or text message.

[0056] The low-power remote communication function module compresses, encodes and encrypts the sensor data, and then uses low-power Bluetooth technology to wirelessly transmit it to the remote control center to ensure data security and low energy consumption, dynamically manage the wireless link, enter sleep or activation state according to demand, and adjust the transmission power according to the signal strength to achieve the best energy saving effect.

[0057] In addition, the gas analysis display and adjustment module receives and displays gas composition and concentration information, and provides a user-friendly interface to facilitate parameter setting and data query. The system has a built-in fault detection and preliminary repair mechanism that automatically detects potential faults and attempts to isolate them to maintain the continuous and stable operation of the system.

[0058] Embodiment 2

[0059] The intelligent semiconductor temperature difference power generation control system in this embodiment places special emphasis on the machine learning model. It dynamically adjusts the algorithm parameters and model structure according to historical data and current environmental feedback, optimizes the working parameters, uses the linear regression algorithm for training, and continuously iterates and optimizes to maximize the efficiency of gas analysis.

[0060] First, the characteristic original sensor signal from the microelectronic integrated sensor module is received, and the signal is amplified and denoised. The digital filtering technology is used to eliminate the environmental noise and system noise. The machine learning algorithm is used to classify and regress the characteristic vector. The linear regression algorithm is used for quantitative estimation of gas concentration. By establishing a linear relationship model between the input characteristics and the output gas concentration, the concentration of the unknown gas is predicted. The algorithm formula is: , y represents the predicted gas concentration, w is the weight parameter, and b is the bias term to identify the gas type and estimate the concentration, build a machine rest model, and dynamically adjust the algorithm parameters and model structure according to historical data and current environmental feedback to adapt to environmental conditions with different temperature and humidity changes. Based on the results of identification and concentration estimation, the system determines the gas over-limit alarm and sends instructions to the remote control center module through the remote communication module to adjust the working status of the sensor, including increasing the sampling frequency and starting the cleaning procedure.

[0061] Subsequently, the preprocessed and feature-extracted data is converted into a set of feature vectors, where each vector corresponds to a feature representation of a set of sensor readings. At the same time, each feature vector corresponds to a known gas category label. A maximum margin hyperplane is found between the two types of samples to achieve optimal classification. For the linearly separable case, the optimization goal is to minimize the total distance from the samples within the interval boundary to the hyperplane while ensuring that all samples are correctly classified. The training data set is ( , ), i=1, 2, 3,...n, is the eigenvector, is the feature vector and the category label, the optimization problem of linear SVM can be expressed as: , for nonlinearly separable data, the original data is mapped to a higher-dimensional feature space, making the originally inseparable problem linearly separable. For new unknown samples x′, classification is performed through decision rules: , through the cross-validation method, it is tuned to adapt to gas detection tasks in different environments. A large number of labeled gas sample data sets are used to train the model to learn the characteristic patterns of different gases. The feature vectors collected in real time are input into the trained model to identify the gas type and estimate the concentration.

[0062] Finally, the data sent by the low-power remote communication function module is received through the network interface, including the gas composition and concentration information detected by the microelectronic integrated sensor module, and the data is sent to the gas analysis display and adjustment module for real-time display. At the same time, it is stored in the database of the remote control center for subsequent data analysis and historical record query. According to user needs and preset automation processes, the working status of the microelectronic integrated sensor module is remotely controlled, including starting and stopping detection, and adjusting detection parameters. When the detected gas composition and concentration exceed the safety threshold, the remote control center module will trigger an alarm and notify relevant personnel by email and text message.

[0063] In addition, the gas analysis display adjustment module includes a fault diagnosis and self-repair mechanism that automatically detects system faults, performs preliminary diagnosis, and takes appropriate measures to try to repair and isolate the fault to ensure stable system operation.

[0064] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and range of equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A microelectronic gas analysis system based on chip and intelligent manufacturing, characterized in that: The system consists of the following components: Microelectronic integrated sensor module: A gas sensor chip manufactured based on MEMS technology, through precision manufacturing process, integrated with a highly sensitive sensing material layer, quickly responds to and captures target gas molecules, achieves high-precision detection of multiple gases, reduces cross-interference, and ensures the accuracy of analysis results. The specific steps of the microelectronic integrated sensor module are: (1) Design and preparation of the sensing material layer: Based on the characteristics of the target gas, metal oxides, conductive polymers and nanocomposites are selected as sensing materials, and the sensing layer is accurately prepared on the MEMS chip through chemical synthesis and physical deposition methods; (2) Microstructure design and manufacturing: Using MEMS technology, we design and manufacture microstructured sensor chips to optimize gas diffusion paths, speed up response speed and selectivity; (3) Integrated packaging: The prepared sensing layer and the microstructure chip are integrated and packaged to ensure the stability and durability of the sensor chip; (4) Performance testing and calibration: Each sensor unit is subjected to rigorous performance testing, including key indicator evaluations of sensitivity, selectivity, response time, and recovery time. The Fourier transform algorithm is used to extract frequency and time domain features reflecting gas characteristics from the sensor signal to prepare data for subsequent identification and analysis. The Fourier transform algorithm uses the fact that the signal generated by the sensor contains changes in the time domain, reflecting the fluctuation of gas concentration over time, converts the time domain signal into the frequency domain signal, and identifies the reaction characteristics of specific frequency components in the signal and specific gas molecules absorbed. The algorithm formula is as follows: , k = 0, 1, ..., N-1, x[n] is the time domain signal sequence, X[k] is the corresponding frequency domain coefficient, N is the sequence length, It is a rotation factor, which decomposes the complex time domain signal into a series of frequency components through calculation, and then extracts the characteristic frequencies and frequency bands related to the target gas, providing key information for gas identification; Intelligent processing and adaptive algorithm module: The system has a built-in high-performance, low-power microprocessor, equipped with intelligent algorithms and machine learning models for gas analysis optimization, real-time analysis of sensor data, automatic identification of gas types, adjustment of detection strategies, and adaptive optimization of working parameters according to environmental changes, reducing false alarms and improving analysis accuracy. The specific steps of the intelligent processing and adaptive algorithm module are as follows: (1) Characteristic data reception and preprocessing: Receive characteristic raw sensor signals from the microelectronic integrated sensor module, perform signal amplification and denoising, and use digital filtering technology to eliminate environmental noise and system noise. That is, use a low-pass filtering algorithm to remove high-frequency noise, retain the low-frequency and DC components in the signal, stabilize the sensor signal, extract the slow trend of gas concentration changes, and allow all frequency components below the cutoff frequency to pass, while components above the cutoff frequency are attenuated. For time domain signals, the differential equation is: y[n] = αx[n] + (1-α)y[n-1], where y[n] is the filtered signal, x[n] is the time domain signal sequence, and α is the parameter of the cutoff frequency. By improving the signal quality, a clearer data basis is provided for subsequent gas identification; (2) Gas identification and concentration estimation: A machine learning algorithm is used to classify and regress feature vectors to identify gas types and estimate concentrations. When the machine learning algorithm classifies feature vectors, it converts the preprocessed and feature-extracted data into a set of feature vectors, where each vector corresponds to a feature representation of a set of sensor readings. At the same time, each feature vector corresponds to a known gas category label. A maximum margin hyperplane is found between the two types of samples to achieve optimal classification. For the linearly separable case, the optimization goal is to minimize the total distance from the samples within the interval boundary to the hyperplane while ensuring that all samples are correctly classified. The training data set is (x i ,y i ), i=1, 2, 3,...n, x i is the eigenvector, y i is the feature vector category label, and the optimization problem of linear SVM is expressed as: For nonlinearly separable data, the original data is mapped to a higher-dimensional feature space, making the originally inseparable problem linearly separable. For new unknown samples x′, classification is performed using the decision rule: f(x′) = sign(w T x′+b), wherein the cross-validation method is used for tuning to adapt to gas detection tasks in different environments, a large number of labeled gas sample data sets are used to train the model to learn the characteristic patterns of different gases, and the feature vectors collected in real time are input into the trained model to identify the gas type and estimate the concentration. When the machine learning algorithm is used to perform regression analysis on the feature vector, the gas concentration is quantitatively estimated by a linear regression algorithm, and the concentration of the unknown gas is predicted by establishing a linear relationship model between the input feature and the output gas concentration. The algorithm formula is: y=wx+b, y represents the predicted gas concentration, w is a weight vector parameter, and b is a bias term; (3) Adaptive optimization: Dynamically adjust algorithm parameters and model structure based on historical data and current environmental feedback to adapt to environmental conditions with different temperature and humidity changes; (4) Decision-making and control: Based on the results of identification and concentration estimation, the system determines the gas exceeding the limit alarm and sends instructions to the remote control center module through the remote communication module to adjust the working state of the sensor, including increasing the sampling frequency and starting the cleaning program; Remote control center module: receives data sent by the low-power remote communication function module, controls the working state of the microelectronic integrated sensor module, and sends the processed data to the display module, and sends the data to the gas analysis display adjustment module through the network interface; Low-power remote communication function module: Integrates low-power Bluetooth remote communication technology, enabling the system to wirelessly transmit analysis results to the remote control center in real time, facilitating centralized management and remote control, and transmits gas analysis results and system status information to the remote control center in real time; Gas analysis display and adjustment module: receives data sent by the remote control center module, displays gas composition and concentration information, and provides an operation interface to facilitate users to set parameters and query data.

2. According to claim 1, a microelectronic gas analysis system based on chip and intelligent manufacturing is characterized in that: The specific steps of the remote control center module are as follows: (1) Data reception: receiving data sent by the low-power remote communication function module through the network interface, including gas composition and concentration information detected by the microelectronic integrated sensor module; (2) Data display and storage: The data is sent to the gas analysis display and adjustment module for real-time display and stored in the database of the remote control center for subsequent data analysis and historical record query; (3) Remote control: Remotely control the working status of the microelectronic integrated sensor module according to user needs and preset automation processes, including starting and stopping detection and adjusting detection parameters; (4) Alarm and Notification: When the detected gas composition and concentration exceed the safety threshold, the remote control center module will trigger an alarm and notify relevant personnel via email and text message.

3. According to claim 1, a microelectronic gas analysis system based on chip and intelligent manufacturing is characterized in that: The specific steps of the low-power remote communication function module are as follows: (1) Data encapsulation and encoding: The gas detection data and system status information generated by the microelectronic integrated sensor module are collected and compressed and encrypted to reduce the amount of transmitted data and ensure data security; (2) Low-power wireless transmission: Use low-power Bluetooth technology to send packaged data to the remote control center wirelessly, and use signal modulation and transmission power control to save power to the greatest extent; (3) Link management and energy-saving strategy: Implement dynamic link management strategy, enter sleep mode when there is no data transmission demand, activate only at the scheduled time and when a wake-up signal is received, and dynamically adjust the transmission power according to the signal strength; (4) Data reception and decoding: Receive and decode wireless signals to restore the original data. The decoding process includes error detection and correction algorithms to ensure the accuracy and integrity of data transmission.

4. A microelectronic gas analysis system based on chip and intelligent manufacturing according to claim 1, characterized in that: The gas analysis display adjustment module includes a fault diagnosis and self-repair mechanism, which automatically detects system faults, performs preliminary diagnosis, and takes corresponding measures to try to repair and isolate the faults to ensure stable operation of the system.

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