Multi-component gas monitoring method and system

Abstract

The invention relates to a multi-component gas monitoring method. The method comprises the following steps: S100, a processing unit sends a control instruction to control a runner device to switch a filter; s200, after the filter switching is completed, the processing unit sends a calibration matrix obtained through pre-calibration to the imaging detector; s300, the imaging detector calibrates the spectral image data collected in the monitoring area according to the calibration matrix; s400, the processing unit sends the calibrated spectral image data to an upper computer; s500, the upper computer calculates the spectral image data and outputs an infrared video signal to a display device; s600, displaying the infrared video signal by a display device; and S700, returning to the step S100, and continuing to execute the next monitoring cycle. According to the invention, the system operation is simplified, the complexity and time delay caused by switching OCC calibration parameters are avoided, the efficiency and accuracy of multi-gas monitoring are improved, and the method is particularly suitable for real-time monitoring in a dynamic environment and has higher stability and reliability.

Description

Technical Field

[0001] This application relates to the field of monitoring technologies, and particularly to a multi-component gas monitoring method and system. Background Art

[0002] With the advancement of industrialization and urbanization, gases are widely used in fields such as petrochemical industry and natural gas transportation. However, accidents such as explosions and poisonings caused by gas leakage occur frequently, seriously threatening life safety, property, and the environment. As a non-contact detection method, infrared detection technology has become an important technology for gas leakage monitoring due to its unique advantages. Through the absorption characteristics of gas molecules for infrared radiation at specific wavelengths, an infrared detector can identify gases. The absorption spectra of different gases are characteristic. By using a multi-spectral imaging system composed of filter films in multiple bands, different gas components can be accurately identified to achieve efficient gas leakage monitoring.

[0003] There are usually the following imaging methods for a multi-spectral imaging system: multi-detector multi-lens imaging method, beam separation imaging method, micro narrow-band filter film array imaging method, gradient filter film imaging method, filter film turntable imaging method. The multi-spectral monitoring system designed by using the filter film turntable imaging method can drive the turntable to switch different filter films within an extremely short time (the switching period of the filter film can reach 5 milliseconds to 10 milliseconds). By alternately using multiple filter films, the response signals of multiple gases can be collected simultaneously, and then the concentration information of different gases can be extracted from the spectral image data. The multi-spectral monitoring system only uses one optical system, which has the advantage of the same optical path. By rotating the filter film turntable in front of the detector, different filter films are changed to obtain multi-band images. The advantage of the turntable type multi-spectral camera is that the filter film can be replaced, and it is flexible in application and can be applied to different application scenarios. It has been applied to fields such as industrial detection and aerial remote sensing.

[0004] However, in practical applications, there may be a phenomenon of cross-absorption between different gases, that is, multiple gas molecules may absorb infrared light in the same or similar wavelength range, resulting in interference during the identification process. Traditional infrared detector technology combined with an optical structure with a switchable filter film usually requires calibrating multiple sets of on-chip non-uniform OCC calibration parameters. When switching the filter film, the corresponding on-chip non-uniform OCC calibration parameters are switched simultaneously to achieve the identification of different gases. However, the existing calibration methods usually require complex experimental calibration and frequent switching of on-chip non-uniform OCC calibration parameters, which are difficult to meet the requirements of industrial real-time monitoring. Thus, when a multi-spectral imaging system identifies multi-component gases, how to accurately distinguish different gas types in real time becomes a huge challenge. Therefore, there is an urgent need for a new calibration method that can efficiently and accurately complete the calibration and identification of multi-component gases. Summary of the Invention

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

[0006] A multi-component gas monitoring method provided by the present invention includes:

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

[0008] S200 After the filter is switched, the processing unit sends a calibration matrix obtained by pre-calibration to the imaging detector;

[0009] S300 The imaging detector calibrates the spectral image data collected in the monitoring area according to the calibration matrix;

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

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

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

[0013] S700 Return to step S100 to continue the next monitoring cycle.

[0014] As an implementation of the first aspect, before the method, it further includes step S000 to calibrate the imaging detector to obtain a calibration matrix.

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

[0016] S001 Place the imaging detector in a constant temperature incubator to keep its temperature at a preset temperature, align the lens with a black body at the same temperature as the environment, the host computer sends a calibration instruction to the processing unit, and the processing unit enters the calibration mode after receiving the calibration instruction;

[0017] S002 The processing unit controls the filter wheel to a specified filter position and then collects the spectral image data of the current picture through the imaging detector;

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

[0019] S004 Adjust the OCC calibration parameters of the imaging detector one by one to make the pixel value of each corresponding pixel close to the calibration target value;

[0020] S005 Store the adjusted OCC calibration parameters in the memory of the processing unit, denoted as the OCC calibration parameter group corresponding to the current filter;

[0021] Step S006 repeats steps S002 to S005 to respectively obtain four groups of OCC calibration parameter sets corresponding to the filter plates.

[0022] Step S007 generates a calibration matrix based on the four OCC calibration parameter sets.

[0023] As an implementation manner of the first aspect, the four OCC calibration parameter sets are respectively Group A, Group B, Group C, and Group D, and each OCC calibration parameter set is a matrix with N rows and M columns.

[0024] Alternately extract OCC calibration parameters from the OCC calibration parameter sets according to a predetermined rule, which specifically includes:

[0025] Alternately extract the first row of OCC calibration parameters from the OCC calibration parameters of Group A and Group B, and the extraction order is: occa1, occb2, occa3, occb4,..., occbM.

[0026] Alternately extract the second row of OCC calibration parameters from the OCC calibration parameters of Group C and Group D, and the extraction order is: occc(M + 1), occd(M + 2), occc(M + 3), occd(M + 4),..., occd(2M).

[0027] Alternately extract the third row of OCC calibration parameters from the OCC calibration parameters of Group A and Group B, and the extraction order is: occa(2M + 1), occb(2M + 2), occa(2M + 3), occb(2M + 4),..., occb(3M).

[0028] According to the above alternate rule, alternately extract each row of OCC calibration parameters from the four groups of OCC calibration parameters until a complete calibration matrix with N rows and M columns is generated.

[0029] As an implementation manner of the first aspect, based on the successive approximation method, adjust the OCC calibration parameters of the imaging detector one by one to make the pixel value of each pixel close to the calibration target value.

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

[0031] The lens is used to provide the field of view of the monitoring area.

[0032] The rotating wheel device is internally provided with filter plates with different band-pass filter coatings. By driving the rotating wheel device, the filter plates with different band-pass filter coatings are placed between the imaging detector and the lens.

[0033] The imaging detector is used to collect radiation signals within the field of view, calibrate the radiation signals according to a calibration matrix obtained through prior calibration to generate spectral image data, and transmit the spectral image data to the processing unit;

[0034] The processing unit is used to send control instructions to control the rotating wheel device to switch the filter, and to send the calibration matrix obtained through prior calibration to the imaging detector, and transmit the calibrated spectral image data to the host computer;

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

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

[0037] As an implementation manner of the first aspect, the processing unit is further used to calibrate the imaging detector in advance to obtain a calibration matrix.

[0038] In summary, the present invention realizes full coverage of all radiation signals by rotating the filter. The radiation signals corresponding to each filter are collected in real time to ensure the simultaneous monitoring of multiple gases. The present invention does not need to adjust the OCC parameters each time the filter is switched, and always uses the same set of optimal OCC calibration matrices for gas identification, simplifies the system operation, avoids the complexity and time delay brought by switching the OCC calibration parameters, improves the efficiency and accuracy of multi-gas monitoring, is particularly suitable for real-time monitoring in a dynamic environment, and has higher stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flowchart of a multi-component gas monitoring method of the present invention;

[0040] Figure 2 It is a schematic flowchart of a calibration mode of a multi-component gas monitoring method of the present invention;

[0041] Figure 3 It is a schematic diagram of an OCC calibration parameter group of a multi-component gas monitoring method of the present invention;

[0042] Figure 4 It is a schematic diagram of a calibration matrix of a multi-component gas monitoring method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To facilitate the understanding of the present application, the present application will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present application are given in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0044] In the following description, the reference numerals representing steps, such as S100, S200... etc., do not necessarily indicate that the steps will be executed in this order. The order of the front and back steps can be interchanged, or they can be executed simultaneously, if permitted.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0046] Before further elaborating on 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 / roles in the present invention, are described. The nouns and terms involved in the embodiments of the present invention are subject to the following explanations.

[0047] OCC (on-chip calibration): On-chip non-uniform calibration, which is used for on-chip non-uniform calibration when the detector images in this embodiment. The OCC data is in binary form, and each OCC data has 12 significant bits.

[0048] Figure 1 It is a schematic flowchart of a method for monitoring multi-component gases according to the present invention. As Figure 1 shown, a method for monitoring multi-component gases includes:

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

[0050] S200 After the filter is switched, the processing unit sends the calibration matrix obtained by pre-calibration to the imaging detector;

[0051] S300 The imaging detector calibrates the spectral image data collected in the monitoring area according to the calibration matrix;

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

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

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

[0055] S700 Return to step S100 and continue to execute the next monitoring cycle.

[0056] By pre-calibrating and setting a calibration matrix as the optimal parameter, the present invention can adapt to different filter plates and simultaneously meet the parameter requirements for multi-component gas detection. In the prior art, every time the filter plate is switched in a rotating wheel type gas monitoring device, the imaging detector needs to perform an OCC calibration, resulting in a high time delay and operation complexity during the switching process of the device, which affects the monitoring efficiency and accuracy. However, by presetting a general calibration matrix as the optimal parameter, the present invention does not require recalibration when the filter plate is switched, significantly improving the working efficiency. Especially in an environment where the filter plate needs to be switched frequently, it has higher operation convenience and response speed.

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

[0058] Specifically, Figure 2 It is a schematic flow chart of the calibration mode of a multi-component gas monitoring method of the present invention, specifically including:

[0059] S001 Place the imaging detector in a constant temperature incubator to keep its temperature at a preset temperature, align the lens with a black body at the same temperature as the environment temperature, and the upper computer sends a calibration instruction to the processing unit. After receiving the calibration instruction, the processing unit enters the calibration mode;

[0060] Specifically, place the imaging detector in a constant temperature incubator to ensure that its temperature remains at a preset value (for example, 25 °C). Maintain a constant working environment temperature through the temperature control system to effectively reduce the influence of temperature changes on the imaging quality. Align the lens of the imaging detector with a black body at the same temperature as the environment temperature as the radiation source to ensure the stability of the input signal. In this environment, the system sends a calibration instruction to the processing unit through the upper computer, and after receiving the instruction, the processing unit enters the calibration mode and is ready to perform the next OCC parameter calibration.

[0061] This step ensures that the imaging detector works in a stable temperature control environment, minimizing the error caused by environmental temperature changes and providing a reliable basis for subsequent spectral data acquisition and calibration.

[0062] S002 After the processing unit controls the filter wheel to the specified filter position, it collects the spectral image data of the current picture through the imaging detector;

[0063] In the calibration mode, the processing unit controls the rotating wheel device to move the filter wheel to the specified filter position to ensure that the current filter plate is consistent with the spectral bandwidth requirements, avoiding inaccurate data caused by incorrect filter selection. At this filter position, the imaging detector collects the spectral image data of the current picture through the lens to ensure that an accurate spectral signal is obtained.

[0064] This step ensures that the spectral data collected each time can represent the radiation characteristics of the specified band by precisely controlling the selection and switching of the filter, thereby improving the accuracy and reliability of the spectral data.

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

[0066] In the collected 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 the reference standard for each pixel in the subsequent calibration process to ensure that all pixels are close to the ideal value after calibration.

[0067] By calculating and setting the calibration target value, the present invention can eliminate the pixel value inconsistency caused by equipment deviation or external factors in the image, ensure that the calibrated data reaches higher precision, and avoid the system performance being limited by the initial equipment error.

[0068] S004 adjusts the OCC calibration parameters of the imaging detector one by one to make the pixel value of each corresponding pixel 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 parameter according to the target value until the pixel value of this pixel 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 and improves the overall accuracy of the imaging detector.

[0070] Specifically, the OCC calibration parameters are adjusted based on the successive approximation method, and the specific 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, and the parts that need to be calibrated in segments include the upper four bits and the middle four bits.

[0073] (2) Preliminary assumption and start of calibration

[0074] According to the characteristics of the OCC calibration parameters, it is known that the larger the OCC calibration parameter, the smaller the pixel value. Therefore, first send an instruction with OCC as h800 and collect 30 frames of image data.

[0075] (3) Pixel value calculation and comparison

[0076] In the collected image data, select a pixel point at a certain x, y position and calculate the average value of the pixel value at this position. Then, compare this average value with the calibration target value. Assume that the calibration target value is 32768.

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

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

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

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

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

[0082] Assume that the calibration result is the value 7.

[0083] (5) Middle four bits calibration

[0084] Continue to perform the calibration value calibration of the middle four bits, send the instruction with OCC as h780, and collect 30 frames of image data.

[0085] Repeat the above similar steps for successive approximation, and finally obtain the calibration value of the middle four bits.

[0086] Through precise adjustment of the OCC calibration parameters of each pixel, the present invention can eliminate the response inconsistency of the imaging detector in different bands, ensure the uniformity of the spectral data of the imaging detector, and improve the accuracy of subsequent gas monitoring and analysis.

[0087] S005 Store the adjusted OCC calibration parameters in the memory of the processing unit, denoted as the OCC calibration parameter group corresponding to the current filter;

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

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

[0090] Repeat steps S002 to S005 to successively obtain four groups of OCC calibration parameters corresponding to the filters. Each time, by adjusting the filter, ensure independent calibration of the spectral image data of each band, and obtain OCC calibration parameter groups applicable to four different bandwidths.

[0091] S007 Generate a calibration matrix according to the four OCC calibration parameter groups.

[0092] Specifically, the four OCC calibration parameter groups of the present invention are Group A, Group B, Group C, and Group D respectively, and each OCC calibration parameter group is a matrix of N rows and M columns;

[0093] Extract OCC calibration parameters alternately from the four OCC calibration parameter groups according to a predetermined rule, specifically including:

[0094] Extract the first row of OCC calibration parameters alternately from the OCC calibration parameters of Group A and Group B, and the extraction order is: occa1, occb2, occa3, occb4,..., occbM;

[0095] Extract the second row of OCC calibration parameters alternately from the OCC calibration parameters of Group C and Group D, and the extraction order is: occc(M + 1), occd(M + 2), occc(M + 3), occd(M + 4),..., occd(2M);

[0096] Extract the third row of OCC calibration parameters alternately from the OCC calibration parameters of Group A and Group B, and the extraction order is: occa(2M + 1), occb(2M + 2), occa(2M + 3), occb(2M + 4),..., occb(3M);

[0097] According to the above alternating rule, extract each row of OCC calibration parameters from the four OCC calibration parameter groups in turn until a complete calibration matrix of N rows and M columns is generated.

[0098] The following takes an actual example to illustrate the OCC calibration method based on a 4-point square matrix arrangement adopted by the present invention. For example, Figure 3 The figure shows a schematic diagram of the four OCC calibration parameter groups of a multi-component gas monitoring method of the present invention, namely OCCA group, OCCB group, OCCC group, and OCDD 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-taking rule of the present invention, take values from the four OCC calibration parameter groups respectively and arrange them in a matrix of 640×512 according to the rule to form evenly distributed detection points. The specific arrangement method is: take values in the form of "abab" from the OCC calibration parameters of OCCA group and OCCB group and arrange them in one row, that is, the first row is:

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

[0101] Take values in the form of "cdcd" from the OCC calibration parameters of OCCC group and OCDD group and arrange them in the next row, that is, the second row is:

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

[0103] Take alternating values in this way until a complete 640×512 matrix is formed, as Figure 4 shown is the generated OCC calibration matrix.

[0104] As Figure 4 shown is the schematic diagram of the calibration matrix of a multi-component gas monitoring method of the present invention. As Figure 4 shown, each small calibration matrix is composed of four different groups of OCC calibration parameters (such as occa1, occab1, occc641, occd642). Each point corresponds to an OCC calibration parameter, ensuring that each small matrix contains four different OCC calibration parameters of A, B, C, and D. In practical applications, it is equivalent to dividing the picture into 160×128 large pixels, and each large pixel can independently respond to different components of four gases.

[0105] Through the above arrangement method, the present invention can effectively distribute different groups of OCC calibration parameters evenly throughout the picture, realizing uniform response to four gases. Through this arrangement method of the 4-point square matrix, it can be ensured that each large pixel point can uniformly respond to the components of four different gases, evenly distribute different groups of OCC calibration parameters throughout the monitoring area, enabling each part of the area to be accurately calibrated, avoiding errors caused by uneven calibration in local areas, and improving the comprehensiveness and accuracy of gas monitoring.

[0106] Through the four obtained OCC calibration parameter groups, the processing unit inputs them into the calibration matrix generation algorithm, and finally generates a calibration matrix containing four different OCC calibration parameters. This calibration matrix will be used to calibrate the spectral image data collected by the imaging detector in real time to ensure that all data can accurately reflect the gas components in the monitoring area.

[0107] The generation of the calibration matrix ensures the accuracy of the imaging detector in multiple bands, avoiding distortion of spectral image data caused by band differences. This calibration matrix will provide precise parameter support for the real-time gas monitoring of the entire system, thereby improving the accuracy and efficiency of the system for gas component analysis.

[0108] The present invention also discloses a multi-component gas monitoring system that uses the above multi-component gas monitoring method. The system includes a lens, a rotating wheel device, a filter, an imaging detector, a host computer, and a display device:

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

[0110] A filter is provided inside the rotating wheel device with different band-pass filter coatings. By driving the rotating wheel device, the filter with different band-pass filter coatings is placed between the imaging detector and the lens;

[0111] The imaging detector is used to collect the radiation signals in the field of view, calibrate the radiation signals according to the calibration matrix obtained in advance to generate spectral image data, and transmit the spectral image data to the processing unit;

[0112] The processing unit is used to send control instructions to control the rotating wheel device to switch the filter, and to send the calibration matrix obtained in advance to the imaging detector, and transmit the calibrated spectral image data to the host computer;

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

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

[0115] As an implementation manner of the first aspect, the processing unit is further used to calibrate the imaging detector in advance to obtain a calibration matrix.

[0116] This system and the foregoing multi-component gas monitoring method are based on the same technical concept. The specific working principle and working process will not be elaborated here. Refer to the foregoing method section.

[0117] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0118] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0119] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

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

[0121] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0122] If the above functions are implemented in the form of software function 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 the present application, in essence, or the part that contributes to the prior art or part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0123] Note that the above is only the preferred embodiment of the present application and the applied technical principle. Those skilled in the art will understand that the present application is not limited to the specific embodiments described here. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, more other equivalent embodiments can be included, all of which belong to the protection scope of the present application.

Claims

1. A multi-component gas monitoring method, characterized in that: The method comprises: The S100 processing unit sends a control instruction to control the wheel device to switch the filter; S200: When the filter switching is completed, the processing unit sends the calibration matrix obtained by pre-calibration to the imaging detector; The S300 imaging detector calibrates the spectral image data collected in the monitoring area according to the calibration matrix; The S400 processing unit sends the calibrated spectral image data to the host computer; The host computer S500 calculates the spectral image data and outputs an infrared video signal to a display device; S600: a display device displays the infrared video signal; S700 returns to step S100 to continue executing the next monitoring cycle.

2. The multi-component gas monitoring method according to claim 1, characterized in that: Before the step S100, the step S000 is also included to calibrate the imaging detector to obtain the calibration matrix.

3. The multi-component gas monitoring method according to claim 2, characterized in that: The step S000 specifically includes: S001 places the imaging detector in a constant temperature incubator to keep its temperature at a preset temperature, and aims the lens at a black body with the same temperature as the ambient temperature. The host computer sends a calibration command to the processing unit, and the processing unit enters the calibration mode after receiving the calibration command; S002 The processing unit controls the filter wheel to the specified filter position and then collects the spectral image data of the current picture through the imaging detector; S003: the processing unit calculates the mean value of all pixels of the spectral image data and sets the mean value as the calibration target value; S004: the 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: storing the adjusted OCC calibration parameters in the memory of the processing unit as an OCC calibration parameter group corresponding to the current filter; S006 repeats step S002 to step S005 to respectively complete the acquisition of four groups of OCC calibration parameter groups corresponding to the filters; S007 generates a calibration matrix according to the four OCC calibration parameter groups.

4. The multi-component gas monitoring method according to claim 3, 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 a matrix with N rows and M columns; Alternately extracting OCC calibration parameters from each of the OCC calibration parameter groups according to a predetermined rule specifically includes: Alternately extract the first row of OCC calibration parameters from the OCC calibration parameters of group A and group B, and the extraction order is: occa1, occb2, occa3, occb4, ..., occbM; The second row of OCC calibration parameters are alternately extracted from the OCC calibration parameters of group C and group D, and the extraction order is: occc(M+1), occd(M+2), occc(M+3), occd(M+4), ..., occd(2M); The third row of OCC calibration parameters are alternately extracted from the OCC calibration parameters of group A and group B, and the extraction order is: occa(2M+1), occb(2M+2), occa(2M+3), occb(2M+4), ..., occb(3M); According to the above alternating rule, each row of OCC calibration parameters is extracted from the four groups of OCC calibration parameters in turn until a complete calibration matrix of N rows and M columns is generated.

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

6. A multi-component gas monitoring system, used to implement the multi-component gas monitoring method according to any one of claims 1 to 5, characterized in that: The system includes a lens, a rotating wheel 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 monitoring area; Filters with different band-pass filter coatings are arranged inside the wheel device, and the filters with different band-pass filter coatings are placed between the imaging detector and the lens by driving the wheel device; The imaging detector is used to collect radiation signals within the field of view, calibrate the radiation signals according to a calibration matrix obtained in advance to generate spectral image data, and transmit the spectral image data to the processing unit; The processing unit is used to send control instructions to control the wheel device to switch the filter, and to send a 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 an infrared video signal; The display device is used for displaying the infrared video signal.

7. The multi-component gas monitoring system according to claim 6, characterized in that: The processing unit is also used to pre-calibrate the imaging detector to obtain a calibration matrix.

Images