Data online calibration method and device of temperature and humidity laser radar, and storage medium

Abstract

The invention relates to a data on-line calibration method of a temperature and humidity laser radar. The method comprises the following steps: step 1, preparing radar data and calibration basis data in the same time period; 2, data processing; 3, judging whether the signal is influenced by weather and environment, and if so, repeating the step 1 for the data in the next same time period; otherwise, performing the step 4; step 4, calculating inversion parameters, and calculating the radar data and the calibration basis data to obtain new calibration parameters; 5, calculating an inversion profile according to the new calibration parameters and the original calibration parameters, and calculating a relative deviation with the calibration basis data; 6, if the relative deviation of the new parameters is smaller, updating the parameters; otherwise, the original parameters are reserved, and the step 1 is repeated for the data in the next same time period. According to the method, online calibration can be realized, parameters of each calibration result are automatically optimized, and the radar state and different application scenes are adapted, so that a long-term accurate inversion result is obtained.

Description

Technical Field

[0001] The present invention relates to data calibration of a laser radar, and in particular to an online data calibration method, device and storage medium for a temperature and humidity laser radar. Background Art

[0002] Atmospheric lidar has the advantages of high temporal and spatial resolution, high detection sensitivity and strong anti-interference ability, which makes it irreplaceable in the fields of atmospheric monitoring (environmental protection, meteorology, etc.). Atmospheric temperature and humidity profiles are very important model parameters in atmospheric models and are widely used in atmospheric dynamics and climate change.

[0003] In the current temperature and humidity radar system, the temperature profile is generally inverted based on the sensitivity of the high and low quantum number spectra of pure rotational Raman scattering of nitrogen and oxygen to temperature, and the humidity profile is inverted based on the Raman scattering signals of nitrogen and water vapor molecules. Before the deployment of the temperature and humidity lidar or during routine operation and maintenance, it is necessary to calibrate the parameters in combination with the sounding data to obtain accurate temperature and humidity profiles.

[0004] The current calibration methods are mostly manual configuration, which will introduce human operation errors, cumbersome operation, difficult real-time update, low efficiency and poor accuracy. For example, the current common calibration method is to determine a set of calibration parameters before the equipment is deployed and operated. With the change of equipment status or the influence of the external environment, the parameters will become incompatible, resulting in a huge deviation between the inversion results and the actual situation. In addition, a single calibration has a certain degree of randomness and cannot represent the status of the entire maintenance cycle. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method for online calibration of temperature and humidity laser radar data, and the specific technical solution is as follows: A method for online calibration of temperature and humidity laser radar data, comprising the following steps: Step 1: Prepare radar data and calibration data for the same time period. Step 2: Data processing: smoothing and denoising the radar data; Step 3: Determine whether the signal is affected by weather or environment. If so, repeat step 1 for the next data of the same time period; otherwise, proceed to step 4. Step 4: Inversion parameter calculation: calculate the radar data and the calibration data to obtain new calibration parameters; Step 5: Calculate the inversion profile using the new calibration parameters and the original calibration parameters respectively, and calculate the relative deviation from the calibration basis data; Step 6: If the relative deviation of the new parameter is smaller, update the parameter; otherwise, keep the original parameter and repeat step 1 for the next data of the same time period.

[0006] Preferably, the calibration parameters in step four include humidity calibration and temperature calibration. During the humidity calibration, the constant is calibrated by the signal ratio of water vapor to nitrogen, the atmospheric transmittance correction function and the calibration basis data; during the temperature calibration, the calibration parameters are determined by performing quadratic fitting on the signal intensity ratio and the calibration basis data.

[0007] Furthermore, the calibration constant during humidity calibration is: ; C H is the calibration constant; q(z) is the atmospheric transmittance correction function; P H (z) is the signal of water vapor; P N (z) is the signal of nitrogen; w(z) is the calibration basis data.

[0008] Furthermore, during the temperature calibration, the calibration parameters A, B, and C are determined by performing a quadratic fit between the signal intensity ratio H and the calibration basis data T, and the formula is as follows: .

[0009] Preferably, the relative deviation RD calculation formula in step 5 is as follows: ; Among them, x i is the calibrated inversion data, y i Calibration based data.

[0010] A computer-readable storage medium stores a computer program / instruction, which, when executed by a processor, implements the steps of a method for online calibration of temperature and humidity laser radar data.

[0011] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of a method for online calibration of temperature and humidity laser radar data.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The online calibration method for temperature and humidity laser radar data provided by the present invention can realize online calibration. Each calibration result automatically optimizes parameters and adapts to the radar state and different application scenarios, thereby obtaining long-term and accurate inversion results.

[0013] By statistically analyzing the stability of parameters over a period of time, it is also possible to evaluate the operating status of the equipment, thereby optimizing the maintenance cycle and improving the reliability and operating efficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the present invention; Figure 2 It is a comparison chart of the calibration effect of the fitting. DETAILED DESCRIPTION

[0015] The present invention will be further described with reference to the accompanying drawings.

[0016] like Figure 1 and Figure 2 As shown, a method for online calibration of temperature and humidity laser radar data includes the following steps: Step 1: Prepare radar data and calibration data for the same time period. Step 2: Data processing: smoothing and denoising the radar data; Step 3: Determine whether the signal is affected by weather or environment. If so, repeat step 1 for the next data of the same time period; otherwise, proceed to step 4. Step 4: Inversion parameter calculation: calculate the radar data and the calibration data to obtain new calibration parameters; Step 5: Calculate the inversion profile using the new calibration parameters and the original calibration parameters respectively, and calculate the relative deviation from the calibration basis data; Step 6: If the relative deviation of the new parameter is smaller, update the parameter; otherwise, keep the original parameter and repeat step 1 for the next data of the same time period. The calibration parameters for a long time can be exported and their stability can be statistically analyzed to evaluate the stability of the calibration and the operating status of the equipment.

[0017] The calibration parameters in step 4 include humidity calibration and temperature calibration. During the humidity calibration, the constant is calibrated by the signal ratio of water vapor to nitrogen, the atmospheric transmittance correction function and the calibration basis data; during the temperature calibration, the calibration parameters are determined by performing quadratic fitting on the signal intensity ratio and the calibration basis data.

[0018] The steps of humidity calibration are as follows: Radar equation: ; Wherein, the subscripts H and N represent water vapor molecules and nitrogen molecules, respectively. K represents the Raman scattering channel factor; σ(Π) represents the Raman scattering cross-sectional area; N(z) represents the molecular number density at different heights; T0(z), T H (z) and T N (z) represents the atmospheric transmittance of the laser emission wavelength, water vapor Raman scattering wavelength and nitrogen Raman scattering wavelength, respectively, expressed as: ; In fact, α is the extinction coefficient.

[0019] The water vapor mixing ratio is defined as: the mass of water vapor m contained in the same volume at a certain height z H (z) and dry air mass m dry (z), that is, w(z)=m H (z) / m dry (z). Therefore: ; Define C separately H , q(z) is the system calibration constant and the atmospheric transmittance correction function, expressed as: ; but, ; Therefore, the signal ratio of water vapor to nitrogen is P H (z) / P N (z), atmospheric transmittance correction function q(z), and calibration constant C based on the calibration data w(z) H ,Right now .

[0020] The temperature is calibrated as follows: Radar equation: ; P1 and P2 represent the high and low quantum number Raman scattering echo signal intensities respectively, σ(J, T) is the rotational Raman scattering cross section of quantum number J at temperature T; α(z) is the extinction coefficient at z; d is the integral sign; z is the height.

[0021] The signal intensity ratio H(T, z) has a definite relationship expression with temperature: ; The parameters A and B are calibrated according to the above expressions. However, in actual situations, several adjacent Raman scattering lines will be selected. In this case, the above formula will be adjusted to: ; Based on the verification test of the primary and secondary calibration methods, the test results show that the calibration effect of the secondary fitting is better. Figure 2 As shown, therefore, the calibration parameters A, B, and C are determined by performing a quadratic fit between the signal intensity ratio H and the calibration basis data T.

[0022] Calculate the parameters A, B, and C by the least squares method. ; Make Minimum; make ; S takes the derivative of A, B, and C respectively, and sets them equal to 0, and we get: ; After finishing, we can get: ; The above formula is a system of three linear equations. A, B, and C can be obtained by elimination.

[0023] The relative deviation RD calculation formula in step 5 is as follows: ; Among them, x i is the calibrated inversion data, y i Calibration based data.

[0024] Advantages of this embodiment: (1) No manual calibration is required. You only need to read the radar data regularly and calculate the calibration data online. The operation is convenient, no operational errors will be introduced, and the calibration accuracy is improved. (2) Real-time optimization of calibration parameters can ensure the long-term inversion accuracy and multi-scenario applicability of equipment data products.

[0025] (3) The equipment status can be judged by the parameter changes, which facilitates equipment maintenance and optimizes the maintenance cycle.

[0026] The present invention provides a method for online calibration of temperature and humidity laser radar data. After the sounding data is acquired and imported at the radar end, the calibration parameters of the current temperature and humidity can be automatically calibrated in combination with the historical calibration coefficients, thereby achieving efficient and accurate online calibration. In the calibration process of existing temperature and humidity radar data products, there are many problems such as complex manual operation, low calibration efficiency, and inaccurate calibration parameters. This online calibration method not only improves the calibration efficiency and accuracy, but also reduces human errors. It is suitable for automatic online calibration of parameters of various temperature and humidity radars. In addition, through long-term automated calibration and data feedback, data support can be provided for the long-term state stability and reliability of the equipment, and the frequency of equipment operation and maintenance can be optimized.

[0027] A computer-readable storage medium stores a computer program / instruction, which, when executed by a processor, implements the steps of a method for online calibration of temperature and humidity laser radar data.

[0028] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of a method for online calibration of temperature and humidity laser radar data.

[0029] The memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus connection. The memory stores computer-executable instructions for implementing the data access control method, including at least one software function module that can be stored in the memory in the form of software or firmware. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory.

[0030] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory is used to store programs, and the processor executes the programs after receiving the execution instruction.

[0031] The processor may be an integrated circuit chip having the ability to process signals. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. The methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0032] The embodiments of the present invention are described with reference to flowcharts of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate means for implementing the functions specified in the flowchart and / or.

[0033] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in the flowchart.

[0034] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operation steps are executed on the computer or other programmable terminal device to produce computer-implemented processing, whereby the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in the flowchart.

[0035] The technical principle of the present invention is described above in conjunction with specific embodiments. These descriptions are only for explaining the principle of the present invention and cannot be interpreted as limiting the protection scope of the present invention in any way. Based on the explanations herein, those skilled in the art can associate other specific implementations of the present invention without paying creative labor, and these methods will fall within the protection scope of the claims of the present invention.

Claims

1. A method for online calibration of temperature and humidity laser radar data, characterized in that: The following steps are involved: Step 1: Prepare radar data and calibration data for the same time period. Step 2: Data processing: smoothing and denoising the radar data; Step 3: Determine whether the signal is affected by weather or environment. If so, repeat step 1 for the next data of the same time period; otherwise, proceed to step 4. Step 4: Inversion parameter calculation: calculate the radar data and the calibration data to obtain new calibration parameters; Step 5: Calculate the inversion profile using the new calibration parameters and the original calibration parameters respectively, and calculate the relative deviation from the calibration basis data; Step 6: If the relative deviation of the new parameter is smaller, update the parameter; otherwise, keep the original parameter and repeat step 1 for the next data of the same time period.

2. The method for online calibration of temperature and humidity laser radar data according to claim 1, characterized in that: The calibration parameters in step 4 include humidity calibration and temperature calibration. During the humidity calibration, the constant is calibrated by the signal ratio of water vapor to nitrogen, the atmospheric transmittance correction function and the calibration basis data; during the temperature calibration, the calibration parameters are determined by performing quadratic fitting on the signal intensity ratio and the calibration basis data.

3. The method for online calibration of temperature and humidity laser radar data according to claim 2, characterized in that: The calibration constant during humidity calibration is: ； C H is the calibration constant; q(z) is the atmospheric transmittance correction function; P H (z) is the signal of water vapor; P N (z) is the signal of nitrogen; w(z) is the calibration basis data.

4. The method for online calibration of temperature and humidity laser radar data according to claim 2, characterized in that: During the temperature calibration, the calibration parameters A, B, and C are determined by performing a quadratic fit between the signal intensity ratio H and the calibration basis data T. The formula is as follows: 。 5. The method for online calibration of temperature and humidity laser radar data according to claim 1, characterized in that: The relative deviation RD calculation formula in step 5 is as follows: ； Among them, x i is the calibrated inversion data, y i Calibration based data.

6. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by the processor, the steps of the method for online calibration of temperature and humidity laser radar data described in claim 1 are implemented.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps of the method for online calibration of temperature and humidity laser radar data described in claim 1 are implemented.

Images