Passive Fourier Transform Infrared Spectroscopy Gas Remote Sensing Control System

Abstract

The present invention discloses a passive Fourier transform infrared spectroscopy gas remote sensing control system, which relates to the technical field of remote sensing control. It includes an environmental perception module, a dynamic interference correction module, a multi-modal fusion module, a decision-making display module, and an execution feedback module. The environmental perception module synchronously obtains three-dimensional terrain data, surface reflection polarization states, and atmospheric turbulence intensity parameters through lidar, multi-spectral polarization cameras, and quantum meteorological sensors, generates a dynamic terrain-meteorological coupling field model, and corrects spectral signals in real time based on the coupling field model. By comprehensively considering terrain reflectivity, slope angle, polarization state, and turbulence intensity, the accuracy and robustness of spectral signal correction are improved. Moreover, a four-dimensional data is real-time converted into a three-dimensional visualization scene by using an AR holographic projection unit, and a three-level early warning mechanism is established to improve the decision-making efficiency and response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of telemetry control, and particularly to a passive Fourier transform infrared spectroscopy gas telemetry control system. Background Art

[0002] In recent years, with the rapid development of artificial intelligence technology, gas telemetry control systems based on passive Fourier transform infrared spectroscopy technology have been widely used in scenarios such as industrial pollution source monitoring, emergency response to sudden leakage incidents, and military poison gas detection, and can achieve non-contact real-time monitoring and three-dimensional imaging analysis of large-scale and multi-component gases.

[0003] However, in the actual application process, signal attenuation and misjudgment are easily caused by terrain factors. Specifically, the reflection of infrared radiation by complex surfaces (such as metal roofs and water surfaces) will introduce interference signals, resulting in spectral superposition distortion, and obstacles such as mountains, buildings, or vegetation will block the infrared radiation transmission path, resulting in the complete loss of monitoring signals. Therefore, the traditional passive Fourier transform infrared spectroscopy gas telemetry control system lacks the ability to adapt to dynamic environments, seriously affecting the application reliability of the passive Fourier transform infrared spectroscopy gas telemetry control system. Summary of the Invention

[0004] In view of the above situation, the present invention improves the accuracy and robustness of spectral signal correction by real-time correcting spectral signals based on a coupled field model and comprehensively considering terrain reflectivity, slope angle, polarization state, and turbulence intensity.

[0005] The technical solution it adopts is to include an environmental perception module, a dynamic interference correction module, a multimodal fusion module, a decision display module, and an execution feedback module. The environmental perception module synchronously obtains three-dimensional terrain data, surface reflection polarization state, and atmospheric turbulence intensity parameters through a lidar, a multispectral polarization camera, and a quantum meteorological sensor, and generates a dynamic terrain-meteorological coupled field model;

[0006] The dynamic interference correction module performs real-time spectral signal correction based on the dynamic terrain-meteorological coupled field model;

[0007] The multimodal fusion module performs abnormal detection of spectral signals, and then constructs a four-dimensional data association model using hypergraph theory;

[0008] The decision display module uses an AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time and establishes a three-level early warning mechanism at the same time;

[0009] The execution feedback module performs multi-device collaborative positioning according to the early warning instruction to real-time locate the leakage source.

[0010] Further, the environmental perception module establishes a dynamic terrain - meteorological coupling field model through the following steps;

[0011] 1 - 1. Use lidar point cloud data to construct a digital elevation model D with a resolution ≤ 0.1 m;

[0012] 1 - 2. Obtain the surface reflection polarization state P1 through a multi - spectral polarization camera and establish a polarization state database;

[0013] 1 - 3. Measure the refractive index fluctuation through a quantum meteorological sensor, fit the data using the Kolmogorov turbulence model, calculate the atmospheric turbulence intensity E, and establish a turbulence interference model;

[0014] 1 - 4. Integrate terrain and meteorological data, simulate the interaction based on the coupling field theory and machine learning algorithm, and establish a dynamic terrain - meteorological coupling field model.

[0015] Further, the dynamic interference correction module realizes spectral signal correction through the following steps;

[0016] 2 - 1. Based on the dynamic terrain - meteorological coupling field model, extract the terrain reflectivity D1, slope angle θi, surface reflection polarization state P1, and atmospheric turbulence intensity E of the target area, where θi is the angle between the normal vector of the terrain surface and the gravity direction;

[0017] 2 - 2. According to the terrain reflectivity D1 and slope angle θi, establish a reflection compensation coefficient table for different terrain categories at the infrared characteristic wavelength λj, and generate a three - dimensional compensation matrix WD, where the matrix element WD(i,j) represents the reflection correction gain value of the i - th terrain category at the wavelength λj;

[0018] 2 - 3. Extract the corresponding polarization correction coefficient K from the database according to the surface reflection polarization state P1, and correct the original spectral signal S to obtain the polarization - corrected signal SP = S×K;

[0019] 2 - 4. Calculate the signal attenuation coefficient Ke = exp(-0.01×E×L), and then compensate the polarization - corrected signal SP to obtain the turbulence - compensated signal SE = SP÷Ke, where L represents the optical path, specifically the actual propagation path length of the light passing through the atmosphere from the measured gas target to the infrared sensor, and exp represents the exponential function;

[0020] 2 - 5. Match the three - dimensional compensation matrix WD with the turbulence - compensated signal SE in the spatio - temporal domain, call the corresponding correction gain WD(i,j) based on the real - time terrain category i and wavelength λj, and compensate the corrected spectral signal S1, S1 = SE × WD(i,j) × cos(θi)+ ε, where ε represents the spectral signal S1 error correction value.

[0021] Furthermore, the multimodal fusion module realizes data fusion and anomaly monitoring through the following steps;

[0022] 4-1. Set the dynamic spectral signal threshold S2, S2 = α×S0×exp(-β×E) + γ×D_avg, where S0 is the preset reference signal intensity, α, β, and γ are adaptive weight coefficients, D_avg represents the average reflectivity of the digital elevation model D in the target area, and E is the atmospheric turbulence intensity;

[0023] 4-2. When S1≥S2, trigger the anomaly feedback mechanism, send a terrain-meteorological data re-sampling instruction to the environmental perception module, and synchronously update the dynamic terrain-meteorological coupling field model, and re-execute the spectral signal correction process of the dynamic interference correction module until S1<S2;

[0024] 4-3. When S1 is less than S2, integrate the compensated and corrected spectral signal S1 data, digital elevation model D, polarization state database, and turbulence interference model, and construct a four-dimensional data association model using hypergraph theory.

[0025] Furthermore, the decision display module uses the AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time, and at the same time establishes a three-level early warning mechanism. When S1≥0.8S2, a yellow early warning is issued, a voice prompt is triggered, and the primary inspection program is started;

[0026] When S1≥1.2S2, an orange early warning is issued, historical data is automatically retrieved for comparison, the spectral data in the same area in the past 72 hours is compared, a current spectrum-historical average spectrum diagram is generated, and a first early warning instruction is sent to the execution feedback module;

[0027] When S1≥1.5S2, a red early warning is issued, the abnormal area is marked in red and flashes, a collaborative positioning request is sent to the neighboring monitoring stations, the real-time video stream is superimposed, and a second early warning instruction is sent to the execution feedback module.

[0028] Furthermore, when the execution feedback module receives the first early warning instruction, it recalculates the optimal scanning path based on the digital elevation model D, and sends a collaborative monitoring request to the neighboring devices, sharing the terrain-meteorological coupling field model of the current device, and synchronizing the scanning parameter settings;

[0029] When the execution feedback module receives the second early warning instruction, it switches to the high-frequency sampling mode, increases the sampling frequency, sends a collaborative monitoring request to the neighboring devices, shares the terrain-meteorological coupling field model of the current device, divides the monitoring area into grids, and each device is responsible for scanning the specified grid to locate the leakage source in real time.

[0030] Further, in step 2-2, different terrain categories include plains, hills, and mountains, and the reflection compensation coefficient table is continuously simulated according to the machine learning algorithm.

[0031] The α, β, and γ are adaptive weight coefficients. α adjusts the influence of the reference signal intensity, β quantifies the atmospheric turbulence attenuation, and γ correlates with the terrain reflectivity. Through real-time environmental data feedback, the weight coefficients are automatically optimized by comparing with the environmental data comparison table in the database.

[0032] Due to the adoption of the above technical solutions, the present invention has the following advantages compared with the prior art;

[0033] 1. Integrate lidar, multi-spectral polarization camera, and quantum meteorological sensor to synchronously obtain three-dimensional terrain, surface reflection polarization state, and atmospheric turbulence intensity parameters, construct a dynamic terrain-meteorological coupling field model, improve environmental perception accuracy, and based on the coupling field model, correct the spectral signal in real time. Considering terrain reflectivity, slope angle, polarization state, and turbulence intensity comprehensively, improve the accuracy and robustness of spectral signal correction, and use the AR holographic projection unit to convert four-dimensional data into a three-dimensional visualization scene in real time, establish a three-level early warning mechanism, and improve decision-making efficiency and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of the passive Fourier transform infrared spectroscopy gas remote sensing control system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Regarding the foregoing and other technical contents, features, and effects of the present invention, they will be clearly presented in the following detailed description of the embodiments in conjunction with the attached Figure 1 drawings. The structural contents mentioned in the following embodiments are all referenced to the drawings of the specification.

[0036] Embodiment 1, on the basis of the prior art, includes an environmental perception module, a dynamic interference correction module, a multi-modal fusion module, a decision display module, and an execution feedback module. The environmental perception module synchronously obtains three-dimensional terrain data, surface reflection polarization state, and atmospheric turbulence intensity parameters through a lidar, a multi-spectral polarization camera, and a quantum meteorological sensor, and generates a dynamic terrain-meteorological coupling field model;

[0037] The dynamic interference correction module performs spectral signal correction in real time based on the dynamic terrain-meteorological coupling field model;

[0038] The multi-modal fusion module performs abnormal detection on the spectral signal, and then constructs a four-dimensional data association model using hypergraph theory;

[0039] The decision display module uses the AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time, and at the same time establishes a three-level early warning mechanism;

[0040] The execution feedback module performs collaborative positioning of multiple devices according to the warning instruction to locate the leakage source in real time.

[0041] The environmental perception module establishes a dynamic terrain-meteorological coupling field model through the following steps;

[0042] 1-1. Use lidar point cloud data to construct a digital elevation model D with a resolution ≤ 0.1 m;

[0043] 1-2. Obtain the surface reflection polarization state P1 through a multi-spectral polarization camera and establish a polarization state database;

[0044] 1-3. Measure the refractive index fluctuation through a quantum meteorological sensor, fit the data using the Kolmogorov turbulence model, calculate the atmospheric turbulence intensity E, and establish a turbulence interference model;

[0045] 1-4. Integrate the terrain and meteorological data, simulate the interaction based on the coupling field theory using a machine learning algorithm, and establish a dynamic terrain-meteorological coupling field model.

[0046] The dynamic interference correction module realizes spectral signal correction through the following steps;

[0047] 2-1. Based on the dynamic terrain-meteorological coupling field model, extract the terrain reflectivity D1, slope angle θi, surface reflection polarization state P1, and atmospheric turbulence intensity E of the target area, where θi is the angle between the normal vector of the terrain surface and the gravity direction;

[0048] 2-2. According to the terrain reflectivity D1 and slope angle θi, establish a reflection compensation coefficient table for different terrain categories at the infrared characteristic wavelength λj, and generate a three-dimensional compensation matrix WD, where the matrix element WD(i,j) represents the reflection correction gain value of the i-th type of terrain at the wavelength λj;

[0049] 2-3. Extract the corresponding polarization correction coefficient K from the database according to the surface reflection polarization state P1, and correct the original spectral signal S to obtain the polarization correction signal SP = S × K;

[0050] 2-4. Calculate the signal attenuation coefficient Ke = exp(-0.01 × E × L) according to the atmospheric turbulence intensity E, and then compensate the polarization correction signal SP to obtain the turbulence compensation signal SE = SP ÷ Ke, where L represents the optical path, specifically the actual propagation path length of the light passing through the atmosphere from the measured gas target to the infrared sensor, and exp represents the exponential function;

[0051] From 2-5, perform spatio-temporal domain matching on the three-dimensional compensation matrix WD and the turbulence compensation signal SE, and call the corresponding correction gain WD(i,j) based on the real-time terrain category i and wavelength λj to compensate the corrected spectral signal S1, where S1 = SE × WD(i,j) × cos(θi) + ε, and ε represents the spectral signal S1 error correction value.

[0052] The multi-modal fusion module realizes data fusion and anomaly monitoring through the following steps;

[0053] From 4-1, set the dynamic spectral signal threshold S2, where S2 = α×S0×exp(-β×E) + γ×D_avg, where S0 is the preset reference signal intensity, α, β, and γ are adaptive weight coefficients, D_avg represents the average reflectivity of the digital elevation model D in the target area, and E is the atmospheric turbulence intensity;

[0054] From 4-2, when S1≥S2, trigger the anomaly feedback mechanism, send a terrain-meteorological data re-sampling instruction to the environmental perception module, and synchronously update the dynamic terrain-meteorological coupling field model, and re-execute the spectral signal correction process of the dynamic interference correction module until S1<S2;

[0055] From 4-3, when S1 is less than S2, integrate the data of the compensated and corrected spectral signal S1, the digital elevation model D, the polarization state database, and the turbulence interference model, and use the hypergraph theory to construct a four-dimensional data association model.

[0056] The decision display module uses the AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time, and at the same time establishes a three-level early warning mechanism. When S1≥0.8S2, a yellow warning is issued, triggering a voice prompt and starting the primary inspection program;

[0057] When S1≥1.2S2, an orange warning is issued, automatically retrieve historical data for comparison, compare the spectral data in the same area in the past 72 hours, generate the current spectral-historical average spectrogram, and send the first warning instruction to the execution feedback module;

[0058] When S1≥1.5S2, a red warning is issued, the abnormal area is marked in red and flashes, send a collaborative positioning request to the adjacent monitoring station, overlay the real-time video stream, and at the same time send the second warning instruction to the execution feedback module.

[0059] When the execution feedback module receives the first warning instruction, based on the digital elevation model D, recalculate the optimal scanning path, and send a collaborative monitoring request to the adjacent devices, share the terrain-meteorological coupling field model of the current device, and synchronize the scanning parameter settings;

[0060] When the execution feedback module receives the second warning instruction, it switches to the high-frequency sampling mode, increases the sampling frequency, sends a collaborative monitoring request to neighboring devices, shares the terrain-meteorological coupling field model of the current device, divides the monitoring area into grids, and each device is responsible for scanning the specified grid to locate the leakage source in real time.

[0061] In step 2-2, different terrain categories include plains, hills, and mountains, and the reflection compensation coefficient table is continuously simulated according to the machine learning algorithm.

[0062] The α, β, and γ are adaptive weight coefficients. α adjusts the influence of the reference signal intensity, β quantifies the atmospheric turbulence attenuation, and γ correlates with the terrain reflectivity. The weight coefficients are automatically optimized by comparing the real-time environmental data feedback with the environmental data comparison table in the database.

[0063] In step 4-3, the specific steps of constructing a four-dimensional data association model using the hypergraph theory are as follows;

[0064] a. Establish a hypergraph model that includes four types of data nodes, namely spectral signal intensity values, terrain reflectivity, polarization state distribution, and turbulence intensity sequence;

[0065] b. In a specific wavelength range, forcefully associate the spectral, terrain, and polarization nodes to enhance the coupling of multi-source data;

[0066] c. When the turbulence fluctuation exceeds the threshold, extend and associate the turbulence nodes in the previous 10 minutes of this moment to capture the spatio-temporal influence of turbulence;

[0067] d. Extract multi-modal features through the hypergraph convolutional network, generate a spatio-temporal association matrix, quantitatively describe the coupling strength between the spectral band and the terrain block, realize four-dimensional data fusion, and generate a four-dimensional data association model.

[0068] When the present invention is specifically used, on the basis of the existing technology, the environmental perception module synchronously obtains three-dimensional terrain data, the polarization state of surface reflection, and atmospheric turbulence intensity parameters through a lidar, a multi-spectral polarization camera, and a quantum meteorological sensor, and generates a dynamic terrain-meteorological coupling field model;

[0069] The dynamic interference correction module performs real-time spectral signal correction based on the dynamic terrain-meteorological coupling field model;

[0070] The multi-modal fusion module performs abnormal detection on the spectral signal, and then constructs a four-dimensional data association model using the hypergraph theory;

[0071] The decision display module uses the AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time, and at the same time establishes a three-level warning mechanism;

[0072] The execution feedback module performs multi-device collaborative positioning according to the early warning instruction to real-time locate the leakage source. With the above technologies, by real-time correcting the spectral signal based on the coupled field model and comprehensively considering the terrain reflectivity, slope angle, polarization state, and turbulence intensity, the accuracy and robustness of spectral signal correction are improved.

[0073] The above is a further detailed description of the present invention in combination with specific embodiments, and it cannot be determined that the specific implementation of the present invention is only limited thereto; for those skilled in the art of the present invention and related technical fields, based on the technical solution idea of the present invention, the expansions, operation methods, and data replacements should all fall within the protection scope of the present invention.

Claims

1. Passive Fourier transform infrared spectroscopic gas remote sensing control system, characterized in that, It includes an environmental perception module, a dynamic interference correction module, a multi-modal fusion module, a decision display module, and an execution feedback module. The environmental perception module synchronously acquires three-dimensional terrain data, the polarization state of surface reflection, and atmospheric turbulence intensity parameters through a lidar, a multi-spectral polarization camera, and a quantum meteorological sensor, and generates a dynamic terrain-meteorological coupling field model. The dynamic interference correction module performs spectral signal correction in real time based on the dynamic terrain-meteorological coupling field model. The multi-modal fusion module detects spectral signal anomalies and then constructs a four-dimensional data association model using hypergraph theory. The decision display module uses an AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time and simultaneously establishes a three-level early warning mechanism. The execution feedback module performs multi-device collaborative positioning according to the early warning instruction to locate the leakage source in real time.

2. The passive Fourier transform infrared spectroscopic gas remote sensing control system according to claim 1, wherein The environmental perception module establishes a dynamic terrain-meteorological coupling field model through the following steps: 1-1. Use lidar point cloud data to construct a digital elevation model D with a resolution ≤ 0.1 m. 1-2. Obtain the polarization state of surface reflection P1 through a multi-spectral polarization camera and establish a polarization state database. 1-3. Measure the refractive index fluctuation through a quantum meteorological sensor, fit the data using the Kolmogorov turbulence model, calculate the atmospheric turbulence intensity E, and establish a turbulence interference model. 1-4. Integrate terrain and meteorological data, simulate the interaction based on the coupling field theory using a machine learning algorithm, and establish a dynamic terrain-meteorological coupling field model.

3. The passive Fourier transform infrared spectroscopy gas remote sensing control system according to claim 2, wherein The dynamic interference correction module realizes spectral signal correction through the following steps: 2-1. Based on the dynamic terrain-meteorological coupling field model, extract the terrain reflectivity D1, slope angle θi, the polarization state of surface reflection P1, and atmospheric turbulence intensity E of the target area, where θi is the angle between the normal vector of the terrain surface and the gravity direction. 2-2. According to the terrain reflectivity D1 and slope angle θi, establish a reflection compensation coefficient table for different terrain categories at the infrared characteristic wavelength λj, and generate a three-dimensional compensation matrix WD, where the matrix element WD(i,j) represents the reflection correction gain value of the i-th terrain category at the wavelength λj. 2-3. Extract the corresponding polarization correction coefficient K from the database according to the polarization state of surface reflection P1, and correct the original spectral signal S to obtain the polarization correction signal SP = S × K. 2-4. Calculate the signal attenuation coefficient Ke = exp(-0.01 × E × L) according to the atmospheric turbulence intensity E, and then compensate the polarization correction signal SP to obtain the turbulence compensation signal SE = SP ÷ Ke, where L represents the optical path, specifically the actual propagation path length of the light passing through the atmosphere between the measured gas target and the infrared sensor, and exp represents the exponential function. 2-5. Match the three-dimensional compensation matrix WD with the turbulence compensation signal SE in the space-time domain, call the corresponding correction gain WD(i,j) based on the real-time terrain category i and wavelength λj, and compensate the corrected spectral signal S1, S1 = SE × WD(i,j) × cos(θi) + ε, where ε represents the spectral signal S1 error correction value.

4. The passive Fourier transform infrared spectroscopy gas remote sensing control system according to claim 3, characterized in that The multi-modal fusion module realizes data fusion and anomaly monitoring through the following steps; 4-1. Set the dynamic spectral signal threshold S2, S2 = α×S0×exp(-β×E) + γ×D_avg, where S0 is the preset reference signal intensity, α, β, and γ are adaptive weight coefficients, D_avg represents the average reflectivity of the digital elevation model D in the target area, and E is the atmospheric turbulence intensity; 4-2. When S1≥S2, trigger the anomaly feedback mechanism, send a terrain-meteorological data re-sampling instruction to the environmental perception module, and synchronously update the dynamic terrain-meteorological coupling field model, and re-execute the spectral signal correction process of the dynamic interference correction module until S1 < S2; 4-3. When S1 is less than S2, integrate the compensated and corrected spectral signal S1 data, digital elevation model D, polarization state database, and turbulence interference model, and use the hypergraph theory to construct a four-dimensional data association model.

5. The passive Fourier transform infrared spectroscopy gas remote sensing control system according to claim 4, characterized in that, The decision display module uses the AR holographic projection unit to convert the four-dimensional data association model into a three-dimensional visualization scene in real time, and at the same time establishes a three-level early warning mechanism. When S1≥0.8S2, a yellow early warning is issued, triggering a voice prompt and starting the primary inspection program; When S1≥1.2S2, an orange early warning is issued, automatically retrieve historical data for comparison, compare the spectral data in the same area in the past 72 hours, generate the current spectrum-historical average spectrum diagram, and send the first early warning instruction to the execution feedback module; When S1≥1.5S2, a red early warning is issued, the abnormal area is marked in red and flashing, send a collaborative positioning request to the neighboring monitoring station, overlay the real-time video stream, and at the same time send the second early warning instruction to the execution feedback module.

6. The passive Fourier transform infrared spectroscopic gas remote sensing control system according to claim 5, characterized in that, When the execution feedback module receives the first early warning instruction, based on the digital elevation model D, recalculate the optimal scanning path, and send a collaborative monitoring request to the neighboring devices, share the terrain-meteorological coupling field model of the current device, and synchronize the scanning parameter settings; When the execution feedback module receives the second early warning instruction, switch to the high-frequency sampling mode, increase the sampling frequency, send a collaborative monitoring request to the neighboring devices, share the terrain-meteorological coupling field model of the current device, divide the monitoring area into grids, and each device is responsible for scanning the designated grid to locate the leakage source in real time.

7. The passive Fourier transform infrared spectroscopic gas remote sensing control system according to claim 3, characterized in that, In step 2-2, different terrain categories include plains, hills, and mountains, and the reflection compensation coefficient table is continuously simulated according to the machine learning algorithm.

8. The passive Fourier transform infrared spectroscopy gas remote sensing control system according to claim 4, wherein, The α, β, and γ are adaptive weight coefficients. α adjusts the influence of the reference signal intensity, β quantifies the atmospheric turbulence attenuation, and γ correlates with the terrain reflectivity. Through real-time environmental data feedback, the weight coefficients are automatically optimized by comparing the environmental data comparison table in the database.

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