Calibration and inversion method for single-photon laser radar gas detection

Abstract

The invention provides a calibration and inversion method for single-photon laser radar gas detection, and the method comprises the steps: carrying out the dead-zone time correction of time-domain photon counting data, and obtaining a time-domain instrument response function; based on the time-domain instrument response function and the scanning time-frequency characteristic curve, time-frequency mapping processing is adopted to obtain a frequency-domain instrument response function; according to the frequency domain instrument response function, establishing a corresponding mapping relation between the target gas column concentration and the ambient temperature and the relative absorption area as a concentration domain instrument response function; obtaining the current relative absorption area of the target gas through absorbance analysis and integration according to photon counting data actually measured by the single-photon laser radar on the target gas area; according to the environment temperature of the target gas area and the current relative absorption area, the concentration domain instrument response function is adopted to invert the column concentration of the target gas, the calibration efficiency is improved, the application range is widened, and high-precision inversion of the gas column concentration is achieved.

Description

Technical Field

[0001] This invention relates to the field of lidar gas detection technology, and in particular to a calibration and inversion method for single-photon lidar gas detection. Background Technology

[0002] Single-photon detectors possess ultra-high sensitivity to individual photon events, with detection probabilities approaching the quantum limit, providing unprecedented capabilities for measuring weak signals. Single-photon lidar based on multi-pulse code modulation further translates this extreme advantage into "hundred-meter-level" remote sensing capabilities: achieving high signal-to-noise ratio ranging of non-cooperative targets at the hundred-meter level and quantitative monitoring of gas leaks at milliwatt-level average power. This provides an engineerable "non-contact-quantitative" method for monitoring gas leaks in large industrial facilities, long-distance pipelines, and complex terrain. The system utilizes rapid wavelength tuning technology to densely scan the entire gas absorption line within milliseconds, acquiring high-resolution "absorption spectrum-path length" data, thus simultaneously providing the column concentration and absorption path length of the leak source in a single measurement.

[0003] However, multi-pulse modulated lidar faces two major bottlenecks in field applications. First, the laser's wavelength-time response, influenced by high-speed current modulation and temperature control, exhibits significant nonlinearity. This, coupled with the transient thermal effects of the driving circuit, leads to a "stretch-compression" distortion in the scanning waveform, causing absorption peak shift and line broadening, making it impossible to directly match theoretical models. Second, in scenarios with strong gas absorption or close-range high-reflectivity targets, the instantaneous count rate of a single-photon detector can reach 0.1–5 Mcps, far exceeding its dead time of 200–5000 ns, resulting in a "stacking" effect: photon arrival is delayed, the waveform trailing edge is clipped, and the peak value is reduced, thus systematically underestimating the absorption depth. These two distortions, coupled together, significantly increase the error of traditional linear inversion based on the Beer-Lambert law.

[0004] Currently, the engineering community generally adopts the "multi-point calibration" strategy: multiple known concentration points are prepared in a constant-temperature sealed pool, and the absorption waveform is measured one by one at a stable laboratory temperature. Then, an empirical curve of "concentration-absorption area" is established by fitting a polynomial or neural network. However, this method has four major drawbacks: (1) Large workload: a complete calibration requires a high time cost, and recalibration is required every time a batch of lasers or fiber couplers are replaced; (2) Temperature blind zone: the on-site temperature can be within a certain range. The temperature fluctuates between 20°C and 50°C, while experimental calibration is usually performed at room temperature; (3) the concentration coverage is sparse: actual leaks may range from tens of ppm.m to tens of thousands of ppm.m, and the calibration points cannot be exhausted, making it difficult to estimate the interpolation error; (4) it cannot be migrated: different gases (CH4, CO2, H2S, etc.) have huge differences in absorption cross-section, and a set of calibration curves can only correspond to one gas, which seriously limits the versatility of the equipment. Therefore, it is urgent to develop a new calibration and inversion method that can fundamentally overcome the limitations of the above-mentioned multi-point empirical calibration. Summary of the Invention

[0005] This invention provides a calibration and inversion method for gas detection in single-photon lidar, which solves the technical problems of large calibration workload, poor adaptability to field environment and limited concentration coverage in the prior art.

[0006] On one hand, the present invention provides a calibration and inversion method for gas detection in single-photon lidar, comprising: Acquire time-domain photon counting data of a single-photon lidar under conditions of no target gas absorption and the scanning time-frequency characteristic curve of the laser source in the single-photon lidar; Dead-time correction is performed on the time-domain photon counting data to obtain the time-domain instrument response function; Based on the time-domain instrument response function and the scanning time-frequency characteristic curve, a time-frequency mapping process is used to obtain the frequency-domain instrument response function. Based on the frequency domain instrument response function, a corresponding mapping relationship between the target gas column concentration and the ambient temperature and relative absorption area is established, which serves as the concentration domain instrument response function. Based on the photon count data measured by single-photon lidar on the target gas region, the current relative absorption area of ​​the target gas is obtained through absorbance analysis and integration. Based on the ambient temperature of the target gas region and the current relative absorption area, the column concentration of the target gas is retrieved using the concentration domain instrument response function.

[0007] This invention provides a calibration and inversion method for gas detection in single-photon lidar. It performs dead-time correction on time-domain photon count data to obtain a time-domain instrument response function. Based on the time-domain instrument response function and the scanning time-frequency characteristic curve, a time-frequency mapping is used to obtain a frequency-domain instrument response function. According to the frequency-domain instrument response function, a mapping relationship between the target gas column concentration and ambient temperature and relative absorption area is established, serving as the concentration-domain instrument response function. Based on the photon count data measured by the single-photon lidar on the target gas region, the current relative absorption area of ​​the target gas is obtained through absorbance analysis and integration. Based on the ambient temperature and current relative absorption area of ​​the target gas region, the column concentration of the target gas is inverted using the concentration-domain instrument response function. This method solves the problems of complex calibration procedures, poor environmental adaptability, and insufficient versatility caused by relying on multi-point empirical calibration. It also solves the waveform distortion under high count rate conditions and nonlinear distortion during high-speed tuning of the laser source in pulse-modulated lidar, improving calibration efficiency and adaptability, and achieving high-precision inversion of gas column concentration. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0009] Figure 1 This is a schematic flowchart of the calibration and inversion method for gas detection in single-photon lidar provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structural principle of a single-photon lidar system provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the time-domain instrument response function calibration method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the MZ interferometer structure provided in an embodiment of the present invention; Figure 5 A flowchart illustrating the frequency domain instrument response function calibration method provided for the implementation of this invention; Figure 6 A schematic flowchart of the concentration domain instrument response function calibration method provided for the implementation of the present invention; Figure 7 A schematic flowchart illustrating the integrated inversion method for gas column concentration, environmental background depth, and signal flux provided for the implementation of this invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0011] Figure 1 This is a schematic flowchart of the calibration and inversion method for gas detection in single-photon lidar provided in an embodiment of the present invention.

[0012] See Figure 1 The calibration and inversion method for gas detection in single-photon lidar includes the following steps.

[0013] Step 101: Obtain the time-domain photon count data of the single-photon lidar under the condition of no target gas absorption and the scanning time-frequency characteristic curve of the laser source in the single-photon lidar.

[0014] In this step, measurements are performed under the zero-point condition of the single-photon lidar system and when there is no gas absorption. The single-photon lidar system is used when the laser beam does not pass through the target gas region, or when it is ensured that there is no target gas absorption in the environment. The single-photon lidar system performs time-correlated counting of the received photons, accumulating them to form a time-domain photon accumulation histogram data, i.e., photon count data.

[0015] The signal output from the laser source is fed into a Mach-Zehnder (MZ) interferometer. The MZ interferometer generates interference fringes (time-domain interference waveforms) that vary with time. The frequency changes of the interference fringes directly reflect the frequency-time characteristics of the laser source during rapid tuning. By performing time-frequency transformations on the time-domain interference waveform (such as short-time Fourier transform, wavelet transform, fringe method, etc.), its frequency scale is converted from the radio frequency domain to the optical frequency domain. After appropriate translation and scaling, a scanning time-frequency curve that characterizes the nonlinear characteristics of the light source's frequency scanning is obtained, i.e., the scanning time-frequency characteristic curve.

[0016] Step 102: Perform dead-time correction processing on the time-domain photon counting data to obtain the time-domain instrument response function.

[0017] In this step, the calibration of the time-domain instrument response function can be found in [reference needed]. Figure 3The time-domain photon accumulation histogram is a statistical histogram formed by the system under zero-point conditions, where a single-photon detector accumulates and counts the echoes of a large number of laser pulses (or the emitted light itself when there is no target) over a long period. The distortion-free photon counting waveform is the waveform obtained by applying dead-time correction and normalization to the data in the time-domain photon accumulation histogram. Noise baseline removal is performed on the distortion-free photon counting waveform to obtain the noise-free photon counting waveform. Pulse segmentation is performed on the noise-free photon counting waveform to obtain the time-domain instrument response function.

[0018] Before performing dead-time correction on the time-domain photon counting data, the following steps are also included: Determine whether the signal detection probability during calibration is lower than a preset probability threshold; If so, the time-domain photon counting data is directly accumulated to obtain the time-domain instrument response function; If not, the time-domain photon counting data is subjected to dead-time correction to obtain the time-domain instrument response function.

[0019] There are generally two methods to obtain the time domain response. The first method is calibration under low signal throughput conditions, where the signal detection probability is less than a preset probability threshold (e.g., 5%). Under this condition, a stable photon counting waveform can be obtained by accumulating the photon counting results over a long period. Since the detection probability within a single pulse is low, the effect of the detector dead time on the counting result can be ignored. Therefore, the obtained waveform can be approximated as the true system response without dead-time distortion, and used to directly characterize the system's intrinsic time-domain instrument response function. The second method is calibration under high signal throughput conditions, where the signal detection probability is greater than 5%. This method can acquire a large amount of photon counting data in a shorter time, thus significantly improving calibration efficiency. However, within this operating range, the detector dead time effect cannot be ignored, and the photon detection probability is suppressed, leading to distortion of the counting waveform, which deviates from the true system response function. Therefore, it is necessary to correct the waveform distortion caused by the dead time to restore the distortion-free time-domain instrument response function.

[0020] Step 103: Based on the time-domain instrument response function and the scanning time-frequency characteristic curve, time-frequency mapping is used to obtain the frequency-domain instrument response function.

[0021] Step 104: Based on the frequency domain instrument response function, establish the corresponding mapping relationship between the target gas column concentration and the ambient temperature and the relative absorption area, and use it as the concentration domain instrument response function.

[0022] Step 105: Based on the photon count data measured by the single-photon lidar on the target gas region, the current relative absorption area of ​​the target gas is obtained through absorbance analysis and integration.

[0023] Step 106: Based on the ambient temperature of the target gas region and the current relative absorption area, the column concentration of the target gas is retrieved using the concentration domain instrument response function.

[0024] In this step, the ambient temperature of the target gas region and the current relative absorption area can be substituted into the concentration domain instrument response function to retrieve the column concentration of the target gas. The time domain instrument response function, frequency domain instrument response function, and concentration domain instrument response function do not need to be calibrated again after one calibration; they can be reused multiple times in subsequent concentration retrieval processes.

[0025] In this embodiment, dead-time correction processing is performed on the time-domain photon counting data to obtain the time-domain instrument response function. Based on the time-domain instrument response function and the scanning time-frequency characteristic curve, time-frequency mapping processing is used to obtain the frequency-domain instrument response function. According to the frequency-domain instrument response function, a corresponding mapping relationship between the target gas column concentration and the ambient temperature and relative absorption area is established as the concentration-domain instrument response function. Based on the photon counting data of the target gas region measured by the single-photon lidar, the current relative absorption area of ​​the target gas is obtained through absorbance analysis and integration. Based on the ambient temperature and the current relative absorption area of ​​the target gas region, the column concentration of the target gas is inverted using the concentration-domain instrument response function. This solves the problems of complex calibration process, poor environmental adaptability and insufficient versatility caused by relying on multi-point empirical calibration. It also solves the waveform distortion under high count rate conditions of pulse-modulated lidar and the nonlinear distortion problem in the high-speed tuning process of the laser source, improving calibration efficiency and adaptability, and realizing high-precision inversion of gas column concentration.

[0026] In one embodiment of this specification, dead-time correction processing is performed on time-domain photon counting data to obtain a time-domain instrument response function, including: Dead-time correction is performed on the time-domain photon counting data to obtain the corrected photon counting waveform; The photon counting waveform is normalized to obtain a distortion-free photon counting waveform. The distortion-free photon counting waveform is subjected to noise baseline removal processing to obtain a noise-free photon counting waveform; The time-domain instrument response function is obtained by pulse segmentation of the noise-free photon counting waveform.

[0027] In this embodiment, dead-time correction is performed on the time-domain photon counting data to obtain the corrected photon counting waveform; the photon counting waveform is normalized to obtain a distortion-free photon counting waveform; noise baseline removal is performed on the distortion-free photon counting waveform to obtain a noise-free photon counting waveform; pulse segmentation is performed on the noise-free photon counting waveform to obtain the time-domain instrument response function, which can accurately correct the waveform distortion caused by the dead-time effect of the single-photon detector at high count rates and effectively suppress measurement noise.

[0028] In one embodiment of this specification, based on the time-domain instrument response function and the scan time-frequency characteristic curve, a time-frequency mapping process is used to obtain the frequency-domain instrument response function, including: The time-domain interference waveform of the laser source is obtained using an optical dynamic frequency calibration device; The time-domain interference waveform is converted into a time-frequency domain waveform through time-frequency transformation; By shifting and scaling the time-frequency domain waveform, the scanning time-frequency characteristic curve is obtained; Based on the time axis mapping relationship, the scanning time-frequency characteristic curve is mapped to the time-domain instrument response function to obtain the frequency-domain instrument response function.

[0029] In this embodiment, the optical dynamic frequency calibration device can be a Mach-Zehnder interferometer. The time-domain interference waveform of the laser source is acquired; through time-frequency transformation, the time-domain interference waveform is converted into a time-frequency domain waveform; the time-frequency domain waveform is translated and scaled to obtain the scanning time-frequency characteristic curve; based on the time axis mapping relationship, the scanning time-frequency characteristic curve is mapped to the time-domain instrument response function to obtain the frequency-domain instrument response function. This achieves accurate calibration of the nonlinear distortion during the rapid tuning of the laser source, accurately maps the system response from the time domain to the frequency domain, and establishes a direct correlation between frequency and system response.

[0030] In one embodiment of this specification, a mapping relationship between the target gas column concentration and ambient temperature and relative absorption area is established based on the frequency domain instrument response function, which serves as the concentration domain instrument response function, including: Obtain the absorption spectrum dataset of the target gas; the absorption spectrum dataset contains spectral information under different temperatures and column concentrations. The pulse equivalent transmittance under different conditions is obtained by weighted integration of the absorption spectrum dataset and the frequency domain instrument response function. Convert the pulse equivalent transmittance to the pulse equivalent absorbance and calculate the corresponding relative absorption area; Establish the correspondence between temperature, column concentration, and relative absorption area; By fitting the correspondence, a continuous mapping function is obtained, which serves as the instrument response function in the concentration domain.

[0031] In this embodiment, the calibration process for the concentration domain instrument response function is as follows: Figure 6As shown. The absorption spectrum dataset can be extracted from the HITRAN database. The HITRAN database can calculate gas absorption spectrum data under specific temperature and concentration conditions. The absorption spectrum dataset is weighted and integrated with the frequency domain instrument response function, and cross-correlation is also performed to obtain the pulse equivalent transmittance under different conditions. Based on the Beer-Lambert law, the pulse equivalent transmittance is converted into pulse equivalent absorbance, and the corresponding relative absorption area is calculated. By iteratively traversing the relative absorption areas under different temperature and concentration conditions, a correspondence between temperature, column concentration, and relative absorption area is established. Fitting this correspondence yields a continuous mapping function of concentration with respect to temperature and relative absorption area, which serves as the concentration domain instrument response function. This avoids relying on a large number of experimental calibration samples, significantly improving calibration efficiency, expanding the applicable range of concentration and temperature, and enhancing versatility.

[0032] In one embodiment of this specification, based on the photon count data measured by a single-photon lidar on a target gas region, the current relative absorption area of ​​the target gas is obtained through absorbance analysis and integration, including: The measured photon count data is preprocessed to obtain a pulse sequence; Convert the pulse sequence into pulse equivalent transmittance; Convert pulse equivalent transmittance to pulse equivalent absorbance; Based on a preset standard absorbance template, the pulse equivalent absorbance is scaled and translated using least squares to obtain the absorbance curve. Integrate the absorbance curve to obtain the current relative absorption area.

[0033] In this embodiment, the pulse sequence is converted into pulse equivalent transmittance, and the pulse equivalent transmittance is converted into pulse equivalent absorbance. Based on the standard absorbance template, the absorbance is scaled and translated using the least squares method to obtain an optimized absorbance curve. The absorbance curve is integrated to obtain the current relative absorption area. This overcomes the absorbance baseline shift and amplitude distortion caused by laser intensity fluctuations and improves the robustness of relative absorption area extraction.

[0034] Common multi-pulse modulated single-photon lidar system structures include: Figure 2 As shown. A single-photon lidar system generates a sequence of pulses of different wavelengths from a laser. These pulses pass through a modulator and beam splitter before entering the scanning unit. The laser beam is guided through the target gas region and absorbs the light from the gas molecules, subsequently illuminating the background target surface. The echo signal returns along the original optical path, is coupled through the beam splitter, and enters the single-photon detection and acquisition module. Finally, the computer performs waveform reconstruction and gas parameter inversion processing. During gas absorption, the laser propagation attenuation in the gas follows Beer-Lambert's law, which is related to time... tRelated input light intensity With output light intensity The relationship between them can be shown by the following formula (1): (1); in, The column concentration along the target gas path. Let be the frequency of the laser light at time t. For gas relative to laser frequency v Absorption line strength, For temperature T and pressure P The relevant gas absorption coefficient, It is the reflectivity of the background target. The angle between the laser beam and the normal of the target surface Distance between target and lidar D System efficiency Correlation coefficients. Since the laser source outputs a pulse sequence with wavelengths continuously tuned over time t, different wavelengths correspond to different absorption intensities of the gas absorption spectrum. The selective absorption of the laser pulses by gas molecules causes the echo signal to form intensity attenuation characteristics corresponding to the absorption spectrum in the time-domain pulse sequence, i.e., manifested as a "depression" structure in the pulse sequence. By integrating this "depression" region, its equivalent absorption area is obtained as an inversion feature quantity, and a quantitative mapping relationship between it and the gas column concentration can be established, thereby achieving direct inversion of the gas concentration parameter.

[0035] In one embodiment of this specification, dead-time correction processing is performed on time-domain photon counting data, including the following formula (2): (2); in, and These represent the order of bins. Indicates the first The photon detection probability after dead-time correction in each time channel For the first The probability of each bin detection is obtained directly from the calibrated photon counting waveform. For the first The probability of detecting each bin. d The number of bins covered by the detector's dead time. r This represents the number of bins covered in one laser cycle. N The dead time represents the number of complete cycles covered by the dead time, expressed by the formula. It means that, among them This is the floor operator.

[0036] In this embodiment, the pulse waveform that has been flattened and delayed by the detector dead zone effect can be recovered, thereby ensuring the physical authenticity of the time-domain waveform base data on which all subsequent processing steps depend, which helps to achieve high-precision inversion.

[0037] See Figure 4 The Mach-Zehnder (MZ) interferometer is used to calibrate the time-frequency distortion generated during rapid tuning of a light source, achieving precise characterization of the light source's frequency domain characteristics. The laser, after passing through the MZ interferometer, forms interference fringe signals. By analyzing the interference fringes, the instantaneous frequency variation of the light source over time can be inverted, thus completing the frequency domain calibration of the light source's tuning nonlinear characteristics. Coupler 1 splits the input laser into two paths. A time-delay fiber introduces a fixed optical path difference between the two paths. Coupler 2 recombines the light from the long and short arms. A photodetector receives the interference light signal from coupler 2 and converts it into a time-varying voltage signal. An oscilloscope records the voltage signal output by the photodetector, forming a time-domain interference waveform.

[0038] The calibration procedure for the frequency domain instrument response function is as follows: Figure 5 As shown, the time-domain interference waveform is obtained using an MZ interferometer; the time-domain signal is mapped to the radio frequency domain time-frequency distribution curve through time-frequency conversion. E ( t , F RF ),in t Represents time, F RF This represents the frequency in the radio frequency domain. Based on this, the radio frequency domain time-frequency curve undergoes frequency scaling and mapping extension to convert it to the optical frequency domain. To achieve the reconstruction and calibration of the optical frequency-time relationship, as shown in the following formula (3): (3); in, Optical frequency; Transmittance curves from the HITRAN database; For the instantaneous frequency in the radio frequency domain; This corresponds to the frequency of the lowest energy point in the transmittance curve. To align the laser frequency sweep calibration curve with the absorption lines in the optical frequency domain database, the shift factor is expressed as... The first term represents the frequency point with the lowest energy corresponding to the laser scanning spectrum curve, and the second term represents the frequency point with the lowest transmittance in the transmittance curve of the HITRAN database. This represents the free spectral range of the MZ interferometer. It can be determined by the following formula (4): (4); in, Represents the refractive index of the transmission medium. This represents the optical path difference between the two arms of the MZ interferometer. c Let be the speed at which light travels in a vacuum.

[0039] In one embodiment of this specification, the scanning time-frequency characteristic curve is mapped to the time-domain instrument response function based on the time-axis mapping relationship to obtain the frequency-domain instrument response function, including the following formula (5): (5); in, The laser frequency, For the frequency domain instrument response function, For time-domain instrument response function, The inverse frequency-to-time mapping relationship determined for scanning the time-frequency characteristic curve.

[0040] In this embodiment, the accuracy of the system in the frequency domain is ensured, so that when performing dot multiplication with the theoretical spectrum of the HITRAN database, the spectral line position drift and shape distortion introduced by the frequency modulation nonlinearity of the laser can be effectively compensated.

[0041] In one embodiment of this specification, the absorption spectrum dataset is weighted and integrated with the frequency domain instrument response function to obtain the pulse equivalent transmittance under different conditions, including the following formula (6): (6); in, For the first j Pulse equivalent transmittance of each pulse For the frequency domain instrument response function, This is the theoretical gas transmittance spectral curve. In the frequency domain, the first j The starting frequency point after pulse is divided into pulses. It is the first j The cutoff frequency point after a pulse is divided into pulses; The pulse equivalent transmittance is converted into pulse equivalent absorbance, as shown in the following formula (7): (7); in, Let be the pulse equivalent absorbance of the j-th pulse.

[0042] In this embodiment, a reliable conversion from an ideal, continuous theoretical absorption spectrum to a discretized, systematic pulsed equivalent absorbance is achieved.

[0043] In one embodiment of this specification, the absorbance curve is integrated to obtain the current relative absorption area, including the following formula (8): (8); in, This refers to the relative absorption area. The equivalent absorbance of the first pulse; Indicates the first The equivalent absorbance of each pulse; This indicates the number of laser modulation pulses; it iterates through the set of column concentrations within a set range. and temperature set A relative integral area can be established. A one-to-one correspondence is established between the concentration point X and the temperature T in the database, thus forming a set of integral areas. Then, by performing polynomial surface fitting on the mapping relationship of this set, the mapping function of concentration with respect to temperature and relative integral area is obtained. This function is the concentration-domain instrument response function.

[0044] In this embodiment, the overall morphological information of gas absorption spectral lines can be effectively condensed, and the influence of random noise and local fluctuations can be suppressed, thereby generating stable scalar values ​​suitable for surface fitting and final table lookup inversion, improving the noise resistance and stability of the inversion results.

[0045] The integrated inversion process of gas column concentration, environmental background depth and signal flux is as follows: Figure 7 As shown. First, the waveform preprocessing of the histogram is performed, and the time-correlated photon count data and ambient temperature measurement data are used as input. Through cyclic cross-correlation operation, the measurement pulse is aligned with the time-domain instrument response function IRFtime; then, the pulse is segmented by the threshold method to extract the effective signal part, and the dead time correction is performed on the signal waveform, thereby reducing the amount of correction calculation while ensuring accuracy. The cyclic cross-correlation is shown in the following formula (9): (9); in, This represents the input photon counting measurement waveform. This represents the Fast Fourier Transform operator. Indicates its conjugate transformation. This represents the inverse Fourier transform operator. This is a cyclic cross-correlation function. In one processing branch, the pre-processed data is used as input for range and flux estimation. It undergoes another cyclic cross-correlation calculation, and the peak value of the resulting correlation waveform is extracted to eliminate drift errors caused by the dead time of the single-photon detector. Then, the background environment depth D is obtained through range conversion, and the signal flux is obtained through signal pulse integration. .

[0046] The preprocessed data is processed in another processing branch. The preprocessed segmented pulses are calculated using formula (6) to obtain the pulse equivalent transmittance. Then, the pulse equivalent transmittance is converted into pulse equivalent absorbance using formula (7).

[0047] In one embodiment of this specification, a preset standard absorbance template is selected from the pulse equivalent absorbance calibration process. Based on the preset standard absorbance template, the pulse equivalent absorbance is scaled and translated using least squares to obtain the absorbance curve, including the following formulas (10) and (11): (10); (11); in, For the first j Standard absorbance templates corresponding to each pulse; s This represents the amplitude scaling factor of the standard absorbance template; b The baseline shift factor represents the standard absorbance template; For the first j The absorbance value of each pulse after scaling and translation.

[0048] In this embodiment, during the calibration process, a set of theoretical gas absorption spectrum data under standard temperature and typical concentration conditions is selected from the HITRAN database. Combined with the calibrated frequency domain instrument response function, the corresponding pulse equivalent absorbance curve is calculated as a preset standard absorbance template. The standard absorbance template represents the gas absorption spectral shape under ideal conditions, free from noise and baseline drift. By finding the optimal scaling factor and baseline shift factor to achieve the best match between the measured curve and the theoretical template, non-specific drift in signal amplitude and baseline caused by changes in target reflectivity and background fluctuations can be eliminated. This allows for the extraction of spectral shape changes caused by gas absorption, improving the accuracy and reliability of concentration inversion in complex field environments.

[0049] The device embodiments described above are merely illustrative. 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.

[0050] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as OM / AM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A calibration and inversion method for gas detection in single-photon lidar, characterized in that, include: Acquire time-domain photon counting data of a single-photon lidar under conditions of no target gas absorption and the scanning time-frequency characteristic curve of the laser source in the single-photon lidar; Dead-time correction is performed on the time-domain photon counting data to obtain the time-domain instrument response function; Based on the time-domain instrument response function and the scanning time-frequency characteristic curve, a time-frequency mapping process is used to obtain the frequency-domain instrument response function. Based on the frequency domain instrument response function, a corresponding mapping relationship between the target gas column concentration and the ambient temperature and relative absorption area is established, which serves as the concentration domain instrument response function. Based on the photon count data measured by single-photon lidar on the target gas region, the current relative absorption area of ​​the target gas is obtained through absorbance analysis and integration. Based on the ambient temperature of the target gas region and the current relative absorption area, the column concentration of the target gas is retrieved using the concentration domain instrument response function.

2. The calibration and inversion method for gas detection in single-photon lidar according to claim 1, characterized in that, The dead-time correction process performed on the time-domain photon counting data to obtain the time-domain instrument response function includes: The time-domain photon counting data is subjected to dead-time correction processing to obtain the corrected photon counting waveform; The photon counting waveform is normalized to obtain a distortion-free photon counting waveform. The distortion-free photon counting waveform is subjected to noise baseline removal processing to obtain a noise-free photon counting waveform; The time-domain instrument response function is obtained by pulse segmentation of the noise-free photon counting waveform.

3. The calibration and inversion method for gas detection in single-photon lidar according to claim 1, characterized in that, The process of obtaining the frequency domain instrument response function based on the time-domain instrument response function and the scanning time-frequency characteristic curve, using time-frequency mapping, includes: The time-domain interference waveform of the laser source is obtained using an optical dynamic frequency calibration device; The time-domain interference waveform is converted into a time-frequency domain waveform through time-frequency transformation; The time-frequency domain waveform is translated and scaled to obtain the scanning time-frequency characteristic curve; Based on the time axis mapping relationship, the scanning time-frequency characteristic curve is mapped to the time-domain instrument response function to obtain the frequency-domain instrument response function.

4. The calibration and inversion method for gas detection in single-photon lidar according to claim 1, characterized in that, The step of establishing a mapping relationship between the target gas column concentration and ambient temperature and relative absorption area based on the frequency domain instrument response function, as the concentration domain instrument response function, includes: Obtain an absorption spectrum dataset of the target gas; wherein the absorption spectrum dataset contains spectral information under different temperatures and column concentrations. The absorption spectrum dataset is weighted and integrated with the frequency domain instrument response function to obtain the pulse equivalent transmittance under different conditions; The pulse equivalent transmittance is converted into pulse equivalent absorbance, and the corresponding relative absorption area is calculated. Establish the correspondence between temperature, column concentration, and relative absorption area; The correspondence is fitted to obtain a continuous mapping function, which serves as the concentration domain instrument response function.

5. The calibration and inversion method for gas detection in single-photon lidar according to claim 4, characterized in that, The process of obtaining the current relative absorption area of ​​the target gas based on the photon count data measured by single-photon lidar on the target gas region, through absorbance analysis and integration, includes: The measured photon count data is preprocessed to obtain a pulse sequence; Convert the pulse sequence into pulse equivalent transmittance; The pulse equivalent transmittance is converted into pulse equivalent absorbance; Based on a preset standard absorbance template, the pulse equivalent absorbance is scaled and translated using least squares to obtain the absorbance curve. Integrating the absorbance curve yields the current relative absorption area.

6. The calibration and inversion method for gas detection in single-photon lidar according to claim 3, characterized in that, Based on the time-axis mapping relationship, the scanning time-frequency characteristic curve is mapped to the time-domain instrument response function to obtain the frequency-domain instrument response function, including: ； in, The laser frequency, For the frequency domain instrument response function, For time-domain instrument response function, The inverse frequency-to-time mapping relationship determined for scanning the time-frequency characteristic curve.

7. The calibration and inversion method for gas detection in single-photon lidar according to claim 5, characterized in that, The absorption spectrum dataset is weighted and integrated with the frequency domain instrument response function to obtain the pulse equivalent transmittance under different conditions, including: ； in, For the first j Pulse equivalent transmittance of each pulse For the frequency domain instrument response function, This is the theoretical gas transmittance spectrum curve. In the frequency domain, the first j The starting frequency point after pulse is divided into pulses. It is the first j The cutoff frequency point after a pulse is divided into pulses; Converting the pulse equivalent transmittance to pulse equivalent absorbance includes: ； in, For the first j The pulse equivalent absorbance of each pulse.

8. The calibration and inversion method for gas detection in single-photon lidar according to claim 7, characterized in that, Integrating the absorbance curve yields the current relative absorption area, including: ； in, This refers to the relative absorption area; Indicates the number of modulation pulses in the laser; The equivalent absorbance of the first pulse; Indicates the first The equivalent absorbance of each pulse.

9. The calibration and inversion method for gas detection in single-photon lidar according to claim 8, characterized in that, The preset standard absorbance template is selected during the pulse equivalent absorbance calibration process. Based on the preset standard absorbance template, the pulse equivalent absorbance is scaled and translated using least squares to obtain the absorbance curve, including: ； ； in, For the first j Standard absorbance templates corresponding to each pulse; s This represents the amplitude scaling factor of the standard absorbance template; b The baseline shift factor represents the standard absorbance template; For the first j The absorbance value of each pulse after scaling and translation.

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