Multi-component gas monitoring method and system

Abstract

This application relates to a multi-component gas monitoring method, comprising: S100, a processing unit sending a control command to control a rotating device to switch filters; S200, after the filter switching is completed, the processing unit sending a pre-calibrated calibration matrix to an imaging detector; S300, the imaging detector calibrating the spectral image data collected in the monitoring area according to the calibration matrix; S400, the processing unit sending the calibrated spectral image data to a host computer; S500, the host computer calculating the spectral image data and outputting an infrared video signal to a display device; S600, the display device displaying the infrared video signal; S700, returning to step S100 and continuing the next monitoring cycle. This invention simplifies system operation, avoids the complexity and time delay caused by switching OCC calibration parameters, improves the efficiency and accuracy of multi-gas monitoring, and is particularly suitable for real-time monitoring in dynamic environments, exhibiting higher stability and reliability.

Description

Technical Field

[0001] This application relates to the field of monitoring technology, and in particular to a method and system for monitoring multi-component gases. Background Technology

[0002] With the advancement of industrialization and urbanization, gases are widely used in petrochemicals, natural gas transportation, and other fields. However, gas leaks frequently lead to explosions and poisonings, seriously threatening lives, property, and the environment. Infrared detection technology, as a non-contact detection method, has become an important technology for gas leak monitoring due to its unique advantages. Infrared detectors can identify gases by utilizing the absorption characteristics of gas molecules to infrared radiation of specific wavelengths. Different gases have characteristic absorption spectra; by using a multispectral imaging system composed of filters across multiple wavelengths, different gas components can be accurately identified, achieving efficient gas leak monitoring.

[0003] Multispectral imaging systems typically employ several imaging methods: multi-detector multi-lens imaging, beam splitting imaging, miniature narrowband filter array imaging, graduated filter imaging, and filter wheel imaging. Multispectral monitoring systems using filter wheel imaging can switch between different filters in an extremely short time (the filter switching cycle can reach 5 to 10 milliseconds) by driving the wheel. Through the alternating use of multiple filters, response signals from multiple gases can be acquired simultaneously, allowing the extraction of concentration information for different gases from the spectral image data. Multispectral monitoring systems utilize only a single optical system, offering the advantage of a single optical path. The rotation of the filter wheel in front of the detector changes the filter, acquiring multi-band images. The advantages of wheel-type multispectral cameras include replaceable filters, flexible application, and applicability to various scenarios; they have been applied in industrial inspection, airborne remote sensing, and other fields.

[0004] However, in practical applications, cross-absorption may occur between different gases, meaning that multiple gas molecules may absorb infrared light within the same or similar wavelength range, causing interference during the identification process. Traditional infrared detector technology, combined with an optical structure using switchable filters, typically requires calibrating multiple sets of on-chip non-uniform OCC calibration parameters. Switching the filter simultaneously switches the corresponding on-chip non-uniform OCC calibration parameters to identify different gases. However, existing calibration methods usually require complex experimental calibration and frequent switching of on-chip non-uniform OCC calibration parameters, making it difficult to meet the needs of real-time industrial monitoring. Thus, in multispectral imaging systems, accurately distinguishing different gas types in real time becomes a significant challenge. Therefore, a new calibration method is urgently needed that can efficiently and accurately calibrate and identify multi-component gases. Summary of the Invention

[0005] In view of this, the main objective of the present invention is to provide a method and system for monitoring multi-component gases.

[0006] This invention provides a method for monitoring multi-component gases, comprising:

[0007] The S100 processing unit sends control commands to control the rotating wheel device to switch the filter;

[0008] After the filter switching is completed, the processing unit sends the pre-calibrated calibration matrix to the imaging detector.

[0009] The S300 imaging detector calibrates the spectral image data acquired within the monitoring area according to the calibration matrix.

[0010] The S400 processing unit sends the calibrated spectral image data to the host computer.

[0011] The S500 host computer calculates the spectral image data and outputs infrared video signals to the display device;

[0012] The S600 display device displays the infrared video signal;

[0013] S700 returns to step S100 and continues to execute the next monitoring cycle.

[0014] As one implementation of the first aspect, the method further includes step S000 of calibrating the imaging detector to obtain a calibration matrix.

[0015] As one implementation of the first aspect, step S000 specifically includes:

[0016] S001 places the imaging detector in a constant temperature chamber to maintain its preset temperature, and the lens is aimed at a blackbody with the same temperature as the environment. The host computer sends a calibration command to the processing unit, and the processing unit enters the calibration mode after receiving the calibration command.

[0017] After the S002 processing unit controls the filter wheel to move to the designated filter position, it acquires the spectral image data of the current scene through the imaging detector;

[0018] The S003 processing unit calculates the mean value of all pixels in the spectral image data and sets this mean value as the calibration target value.

[0019] S004 adjusts the OCC calibration parameters of the imaging detector one by one, so that the pixel value of each corresponding pixel is close to the calibration target value;

[0020] S005 stores the adjusted OCC calibration parameters in the memory of the processing unit, and records them as the OCC calibration parameter group corresponding to the current filter.

[0021] S006 Repeat steps S002 to S005 to obtain the four sets of OCC calibration parameters corresponding to the filter respectively;

[0022] S007 generates a calibration matrix based on four OCC calibration parameter groups.

[0023] As one implementation of the first aspect, the four OCC calibration parameter groups are group A, group B, group C and group D, and each OCC calibration parameter group is an N-row M-column matrix;

[0024] OCC calibration parameters are extracted alternately from the OCC calibration parameter set according to a predetermined rule, specifically including:

[0025] The first row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups A and B, in the following order: occa1, occb2, occa3, occb4, ..., occbM;

[0026] The second row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups C and D, in the following order: occc(M+1), occd(M+2), occc(M+3), occd(M+4), ..., occd(2M);

[0027] The third row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups A and B, in the following order: occa(2M+1), occb(2M+2), occa(2M+3), occb(2M+4), ..., occb(3M);

[0028] Following the above alternation rule, each row of OCC calibration parameters is extracted sequentially from the four sets of OCC calibration parameters until a complete N-row, M-column calibration matrix is ​​generated.

[0029] As one implementation of the first aspect, the OCC calibration parameters of the imaging detector are adjusted one by one based on the successive approximation method, so that the pixel value of each pixel is close to the calibration target value.

[0030] A multi-component gas monitoring system, using the above-described multi-component gas monitoring method, the system includes a lens, a rotating device, a filter, an imaging detector, a host computer, and a display device.

[0031] The lens is used to provide a field of view of the monitored area;

[0032] The rotating device is equipped with filters with different bandpass filter coatings. By driving the rotating device, the filters with different bandpass filter coatings are placed between the imaging detector and the lens.

[0033] The imaging detector is used to acquire radiation signals within the field of view, calibrate the radiation signals according to the pre-calibrated calibration matrix to generate spectral image data, and transmit the spectral image data to the processing unit;

[0034] The processing unit is used to send control commands to control the rotating wheel device to switch the filter, and to send the pre-calibrated calibration matrix to the imaging detector, and to send the calibrated spectral image data to the host computer.

[0035] The host computer is used to calculate the spectral image data and output infrared video signals;

[0036] The display device is used to display the infrared video signal.

[0037] As one implementation of the first aspect, the processing unit is also used to pre-calibrate the imaging detector to obtain a calibration matrix.

[0038] In summary, this invention achieves full coverage of all radiation signals by rotating filters. The radiation signal corresponding to each filter is acquired in real time, ensuring simultaneous monitoring of multiple gases. This invention eliminates the need to adjust OCC parameters each time a filter is switched, consistently using the same optimal OCC calibration matrix for gas identification. This simplifies system operation, avoids the complexity and time delay caused by switching OCC calibration parameters, and improves the efficiency and accuracy of multi-gas monitoring. It is particularly suitable for real-time monitoring in dynamic environments, exhibiting higher stability and reliability. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the process flow of a multi-component gas monitoring method according to the present invention;

[0040] Figure 2 This is a schematic diagram of the calibration mode process for a multi-component gas monitoring method according to the present invention;

[0041] Figure 3 This is a schematic diagram of the OCC calibration parameter set for a multi-component gas monitoring method according to the present invention;

[0042] Figure 4 This is a schematic diagram of the calibration matrix for a multi-component gas monitoring method according to the present invention. Detailed Implementation

[0043] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate preferred embodiments of the application. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0044] In the following description, the labels of the steps, such as S100, S200, etc., do not necessarily mean that the steps will be executed in this way. The order of the steps can be interchanged or executed simultaneously if permitted.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of "belonging" in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0046] Before providing a further detailed description of the specific embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention, as well as their corresponding uses, functions, etc. in the present invention, will be explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0047] OCC (on-chip calibration): On-chip non-uniform calibration. In this embodiment, it is used to perform on-chip non-uniform calibration when imaging the detector. OCC data is in binary form, and each OCC data has 12 significant bits.

[0048] Figure 1 This is a schematic diagram of the process flow of a multi-component gas monitoring method according to the present invention, as shown below. Figure 1 As shown, a method for monitoring multi-component gases includes:

[0049] The S100 processing unit sends control commands to control the rotating wheel device to switch the filter;

[0050] After the filter switching is completed, the processing unit sends the pre-calibrated calibration matrix to the imaging detector.

[0051] The S300 imaging detector calibrates the spectral image data acquired within the monitoring area according to the calibration matrix.

[0052] The S400 processing unit sends the calibrated spectral image data to the host computer.

[0053] The S500 host computer calculates the spectral image data and outputs infrared video signals to the display device;

[0054] The S600 display device displays the infrared video signal;

[0055] S700 returns to step S100 and continues to execute the next monitoring cycle.

[0056] This invention, by pre-calibrating and setting a calibration matrix as optimal parameters, can adapt to different filters and simultaneously meet the parameter requirements of multi-component gas detection. In existing technologies, rotary gas monitoring devices require an OCC calibration of the imaging detector every time a filter is switched, resulting in high time delays and operational complexity during the switching process, impacting monitoring efficiency and accuracy. This invention, by pre-setting a universal calibration matrix as optimal parameters, eliminates the need for recalibration during filter switching, significantly improving work efficiency, especially in environments requiring frequent filter switching, offering greater operational convenience and response speed.

[0057] As one implementation of the first aspect, the method further includes step S000 of calibrating the imaging detector to obtain a calibration matrix.

[0058] Specifically, Figure 2 This is a schematic diagram of the calibration mode flow of a multi-component gas monitoring method according to the present invention, which specifically includes:

[0059] S001 places the imaging detector in a constant temperature chamber to maintain its preset temperature, and the lens is aimed at a blackbody with the same temperature as the environment. The host computer sends a calibration command to the processing unit, and the processing unit enters the calibration mode after receiving the calibration command.

[0060] Specifically, the imaging detector is placed in a constant-temperature chamber to ensure its temperature remains at a preset value (e.g., 25°C). A constant operating temperature is maintained through a temperature control system, effectively reducing the impact of temperature variations on image quality. The imaging detector lens is aligned with a blackbody at the same temperature as the ambient environment, serving as a radiation source to ensure the stability of the input signal. Under these conditions, the system sends calibration commands to the processing unit via a host computer. Upon receiving the commands, the processing unit enters calibration mode, preparing for the next step of OCC parameter calibration.

[0061] This step ensures that the imaging detector operates in a stable temperature-controlled environment, minimizing errors caused by changes in ambient temperature and providing a reliable foundation for subsequent spectral data acquisition and calibration.

[0062] After the S002 processing unit controls the filter wheel to move to the designated filter position, it acquires the spectral image data of the current scene through the imaging detector;

[0063] In calibration mode, the processing unit controls the rotating wheel to move the filter wheel to the designated filter position, ensuring that the current filter matches the required spectral bandwidth and avoiding inaccurate data due to incorrect filter selection. At this filter position, the imaging detector acquires spectral image data of the current scene through the lens, ensuring accurate spectral signal acquisition.

[0064] This step, through precise control of the selection and switching of filters, ensures that each acquired spectral data represents the radiation characteristics of the specified band, thereby improving the accuracy and reliability of the spectral data.

[0065] The S003 processing unit calculates the mean value of all pixels in the spectral image data and sets this mean value as the calibration target value.

[0066] In the acquired spectral image data, the processing unit calculates the mean value of all pixels and uses this mean value as the calibration target value. This calibration target value serves as a reference standard for each pixel in the subsequent calibration process, ensuring that all pixels are close to the ideal value after calibration.

[0067] This invention, by calculating and setting calibration target values, can eliminate pixel value inconsistencies in images caused by device deviations or external factors, ensuring that the calibrated data achieves higher accuracy and avoiding system performance limitations due to initial device errors.

[0068] S004 adjusts the OCC calibration parameters of the imaging detector one by one, so that the pixel value of each corresponding pixel is close to the calibration target value;

[0069] Next, the processing unit adjusts the OCC calibration parameters of the imaging detector one by one. Specifically, for each pixel, the processing unit adjusts its corresponding OCC calibration parameters according to the target value until the pixel value is close to the calibration target value. This process ensures that the response of each pixel to the radiation signal is within the standard range, improving the overall accuracy of the imaging detector.

[0070] Specifically, the OCC calibration parameters are adjusted based on the successive approximation method. The detailed process is as follows:

[0071] (1) Overview of OCC calibration parameters

[0072] The detector OCC calibration parameters consist of 12 bits, of which the lower four bits are invalid. The part that needs to be calibrated in segments includes the higher four bits and the middle four bits.

[0073] (2) Preliminary assumptions and calibration begins

[0074] Based on the characteristics of the OCC calibration parameter, it is known that the larger the OCC calibration parameter, the smaller the pixel value. Therefore, the command with OCC set to h800 is sent first to acquire 30 frames of image data.

[0075] (3) Pixel value calculation and comparison

[0076] In the acquired image data, a pixel at a specific x, y coordinate is selected, and the average pixel value at that position is calculated. Then, this average value is compared with the calibration target value, which is assumed to be 32768.

[0077] If the average value is greater than 32768, send command h900;

[0078] If the average value is less than 32768, send command h700.

[0079] Adjust according to the rule of +1 or -1, gradually approaching the critical value.

[0080] (4) Determine the critical value and calibrate the high 4 bits

[0081] After finding two critical values, determine the absolute difference between the pixel value obtained by the corresponding OCC calibration parameter and 32768, and select the OCC calibration parameter that is closer to the target value 32768 as the calibration result of the high four bits.

[0082] Assume the calibration result is a value of 7.

[0083] (5) Middle four-digit calibration

[0084] Continue calibrating the middle four bits, send the command with OCC h780, and acquire 30 frames of image data.

[0085] Repeat the above steps to approximate the values ​​one by one until the middle four digits of the calibration value are obtained.

[0086] This invention eliminates inconsistencies in the response of the imaging detector across different bands by precisely adjusting the OCC calibration parameters of each pixel, ensuring the uniformity of the spectral data of the imaging detector and improving the accuracy of subsequent gas monitoring and analysis.

[0087] S005 stores the adjusted OCC calibration parameters in the memory of the processing unit, and records them as the OCC calibration parameter group corresponding to the current filter.

[0088] Once the OCC calibration parameters for each pixel are adjusted, the processing unit stores the parameters in memory and records them as the OCC calibration parameter set corresponding to the current filter. Each filter will have an independent set of OCC calibration parameters.

[0089] S006 Repeat steps S002 to S005 to obtain the four sets of OCC calibration parameters corresponding to the filter respectively;

[0090] Repeat steps S002 to S005 to sequentially acquire the four OCC calibration parameter sets corresponding to the filters. Each time, by adjusting the filters, ensure that the spectral image data for each band is calibrated independently to obtain OCC calibration parameter sets suitable for four different bandwidths.

[0091] S007 generates a calibration matrix based on four OCC calibration parameter groups.

[0092] Specifically, the four OCC calibration parameter groups of this invention are group A, group B, group C and group D, and each OCC calibration parameter group is an N-row M-column matrix;

[0093] OCC calibration parameters are extracted alternately from four OCC calibration parameter groups according to predetermined rules, specifically including:

[0094] The first row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups A and B, in the following order: occa1, occb2, occa3, occb4, ..., occbM;

[0095] The second row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups C and D, in the following order: occc(M+1), occd(M+2), occc(M+3), occd(M+4), ..., occd(2M);

[0096] The third row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups A and B, in the following order: occa(2M+1), occb(2M+2), occa(2M+3), occb(2M+4), ..., occb(3M);

[0097] Following the above alternation rule, each row of OCC calibration parameters is extracted sequentially from the four OCC calibration parameter groups until a complete N-row, M-column calibration matrix is ​​generated.

[0098] The following example illustrates the OCC calibration method based on a 4-point square matrix arrangement used in this invention. Figure 3 The diagram shows four OCC calibration parameter groups for a multi-component gas monitoring method according to the present invention: OCCA group, OCCB group, OCCC group and OCCD group, N=512, M=640, and each OCC calibration parameter group is a matrix of 512 rows and 640 columns.

[0099] According to the value selection rules of this invention, values ​​are taken from the four OCC calibration parameter groups and arranged in a regular pattern into a 640×512 matrix to form uniformly distributed detection points. Specifically, the arrangement is as follows: values ​​are taken from the OCC calibration parameters of the OCCA and OCCB groups in the form of "abab" and arranged in one row, i.e., the first row is:

[0100] occa1, occb2, occa3, occb4,..., occb640;

[0101] Take the values ​​in "cdcd" format from the OCC calibration parameters of the OCCC and OCCD groups, and arrange them in the next row, i.e., the second row:

[0102] Occc641, occd642, occc643, occd644,..., occd1280;

[0103] This process continues, alternating values ​​until a complete 640×512 matrix is ​​formed, as follows: Figure 4 The generated OCC calibration matrix is ​​shown below.

[0104] like Figure 4 The diagram shown is a calibration matrix schematic of a multi-component gas monitoring method according to the present invention. Figure 4 As shown, each small calibration matrix consists of four different groups of OCC calibration parameters (e.g., occa1, occab1, occc641, occd642), with each point corresponding to one OCC calibration parameter, ensuring that each small matrix contains four different OCC calibration parameters: A, B, C, and D. In practical applications, this is equivalent to dividing the image into 160×128 large pixels, each of which can produce an independent response to the different components of the four gases.

[0105] This invention, through the above arrangement, effectively distributes different groups of OCC calibration parameters evenly across the entire image, achieving a uniform response to the four gases. This four-point square matrix arrangement ensures that each large pixel can produce a uniform response to the components of the four different gases, evenly distributing the different groups of OCC calibration parameters across the entire monitoring area. This allows for precise calibration in every region, avoiding errors caused by uneven calibration in localized areas and improving the comprehensiveness and accuracy of gas monitoring.

[0106] The processing unit inputs the four sets of OCC calibration parameters into the calibration matrix generation algorithm, ultimately generating a calibration matrix containing the four different OCC calibration parameters. This calibration matrix will be used to calibrate the spectral image data acquired by the imaging detector in real time, ensuring that all data accurately reflects the gas composition within the monitoring area.

[0107] The generation of the calibration matrix ensures the accuracy of the imaging detector across multiple spectral bands, avoiding distortion of spectral image data caused by band differences. This calibration matrix provides precise parameter support for real-time gas monitoring throughout the system, thereby improving the accuracy and efficiency of the system's gas composition analysis.

[0108] This invention also discloses a multi-component gas monitoring system using the above-described multi-component gas monitoring method. The system includes a lens, a rotating device, a filter, an imaging detector, a host computer, and a display device.

[0109] The lens is used to provide a field of view of the monitored area;

[0110] The rotating device is equipped with filters with different bandpass filter coatings. By driving the rotating device, the filters with different bandpass filter coatings are placed between the imaging detector and the lens.

[0111] The imaging detector is used to acquire radiation signals within the field of view, calibrate the radiation signals according to the pre-calibrated calibration matrix to generate spectral image data, and transmit the spectral image data to the processing unit;

[0112] The processing unit is used to send control commands to control the rotating wheel device to switch the filter, and to send the pre-calibrated calibration matrix to the imaging detector, and to send the calibrated spectral image data to the host computer.

[0113] The host computer is used to calculate the spectral image data and output infrared video signals;

[0114] The display device is used to display the infrared video signal.

[0115] As one implementation of the first aspect, the processing unit is also used to pre-calibrate the imaging detector to obtain a calibration matrix.

[0116] This system is based on the same technical concept as the aforementioned multi-component gas monitoring method. The specific working principle and workflow will not be repeated here. Please refer to the aforementioned methods section.

[0117] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0118] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0122] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, all of which fall within the scope of protection of this application.

Claims

1. A method for monitoring multi-component gases, characterized in that, The method includes: The S100 processing unit sends control commands to control the rotating wheel device to switch the filter; After the filter switching is completed, the processing unit sends the pre-calibrated calibration matrix to the imaging detector. The S300 imaging detector calibrates the spectral image data acquired within the monitoring area according to the calibration matrix. The S400 processing unit sends the calibrated spectral image data to the host computer. The S500 host computer calculates the spectral image data and outputs infrared video signals to the display device; The S600 display device displays the infrared video signal; S700 returns to step S100 and continues to execute the next monitoring cycle; Before step S100, step S000 is included to calibrate the imaging detector and obtain the calibration matrix. Step S000 specifically includes: S001 places the imaging detector in a constant temperature chamber to maintain its preset temperature, and the lens is aimed at a blackbody with the same temperature as the environment. The host computer sends a calibration command to the processing unit, and the processing unit enters the calibration mode after receiving the calibration command. After the S002 processing unit controls the filter wheel to move to the designated filter position, it acquires the spectral image data of the current scene through the imaging detector; The S003 processing unit calculates the mean value of all pixels in the spectral image data and sets this mean value as the calibration target value. The S004 processing unit adjusts the OCC calibration parameters of the imaging detector one by one, so that the pixel value of each pixel is close to the calibration target value. S005 stores the adjusted OCC calibration parameters in the memory of the processing unit, and records them as the OCC calibration parameter group corresponding to the current filter. S006 Repeat steps S002 to S005 to obtain the four sets of OCC calibration parameters corresponding to the filter respectively; S007 generates a calibration matrix based on four OCC calibration parameter groups.

2. The multi-component gas monitoring method according to claim 1, characterized in that, The four OCC calibration parameter groups are group A, group B, group C and group D, and each OCC calibration parameter group is an N-row M-column matrix; OCC calibration parameters are extracted alternately from each of the OCC calibration parameter groups according to a predetermined rule, specifically including: The first row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups A and B, in the following order: occa1, occb2, occa3, occb4, ..., occbM; The second row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups C and D, in the following order: occc(M+1), occd(M+2), occc(M+3), occd(M+4), ..., occd(2M); The third row of OCC calibration parameters is extracted alternately from the OCC calibration parameters of groups A and B, in the following order: occa(2M+1), occb(2M+2), occa(2M+3), occb(2M+4), ..., occb(3M); Following the above alternation rule, each row of OCC calibration parameters is extracted sequentially from the four sets of OCC calibration parameters until a complete N-row, M-column calibration matrix is ​​generated.

3. The multi-component gas monitoring method according to claim 1, characterized in that, The OCC calibration parameters of the imaging detector are adjusted one by one using the successive approximation method, so that the pixel value of each pixel is close to the calibration target value.

4. A multi-component gas monitoring system for implementing the multi-component gas monitoring method according to any one of claims 1-3, characterized in that, The system includes a lens, a rotating device, a filter, an imaging detector, a host computer, and a display device. The lens is used to provide a field of view of the monitored area; The rotating device is equipped with filters with different bandpass filter coatings. By driving the rotating device, the filters with different bandpass filter coatings are placed between the imaging detector and the lens. The imaging detector is used to acquire radiation signals within the field of view, calibrate the radiation signals according to the pre-calibrated calibration matrix to generate spectral image data, and transmit the spectral image data to the processing unit; The processing unit is used to send control commands to control the rotating wheel device to switch the filter, and to send the pre-calibrated calibration matrix to the imaging detector, and to send the calibrated spectral image data to the host computer. The host computer is used to calculate the spectral image data and output infrared video signals; The display device is used to display the infrared video signal.

5. The multi-component gas monitoring system according to claim 4, characterized in that, The processing unit is also used to pre-calibrate the imaging detector to obtain a calibration matrix.

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