Low-altitude temperature and humidity profile detection system

Abstract

The invention relates to the technical field of meteorological observation and environment monitoring, and discloses a low-altitude temperature and humidity profile detection system, which comprises a sonde module used for collecting temperature, humidity and Beidou satellite navigation chip positioning signals in a low-altitude environment and transmitting the signals to a low-altitude detection signal receiver module; the signal preprocessing module is used for denoising and smoothing the received signal; the profile inversion module is used for inverting a temperature and humidity profile based on the processed signal; and the display and output module is used for displaying the temperature and humidity profile in real time and outputting the temperature, humidity and wind data to external equipment. The system is based on a low-altitude detection signal receiver and a low-altitude sonde carried by a 20 # meteorological balloon, and detection of low-altitude (generally within the height of 3000 meters from the ground) temperature, humidity and wind direction and wind speed vertical change conditions is completed jointly. High-precision and high-efficiency real-time detection and comprehensive data display of low-altitude temperature and humidity wind are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological observation and environmental monitoring, and specifically to a low-altitude temperature and humidity profile detection system. Background Art

[0002] In the fields of meteorological observation and environmental monitoring, low-altitude meteorological detection is a key technology. The temperature profile reflects the thermal structure of the atmosphere at different altitudes and is an important basis for analyzing the formation of inversion layers, the law of pollutant diffusion, and atmospheric convection activities. Especially in urban pollution monitoring, crop growth environment assessment, and aviation meteorological prediction, accurate detection of the low-altitude temperature profile can not only improve the accuracy of meteorological prediction but also provide strong support for environmental governance and agricultural decision-making. The temperature, humidity, and wind profiles reflect the temperature, humidity, and wind structures of the atmosphere at different altitudes and can be used for the analysis of low-altitude meteorological characteristics in artificial weather modification, atmospheric environment evaluation, insect migration tracking, and comparative analysis with ground-based remote sensing vertical observation systems.

[0003] Existing low-altitude temperature and humidity detection technologies usually adopt inversion methods based on sensor data acquisition and mathematical optimization, which can achieve the inversion of temperature and humidity profiles with a certain accuracy. Traditional methods such as Gaussian filters and least squares methods perform well in noise elimination and data fitting and are suitable for scenarios with low noise and stable boundary conditions. At the same time, these methods rely on fixed model parameter settings, are easy to operate, have a low computational complexity, and can meet the basic detection requirements in some static environments.

[0004] However, there are still some deficiencies in the existing technologies; on the one hand, the signal processing method lacks adaptability and cannot dynamically adjust the filtering parameters to cope with changes in environmental noise, resulting in excessive smoothing of signal details when the noise is high, or ineffective noise reduction when the noise is low; on the other hand, the traditional profile inversion model does not fully consider the influence of multi-dimensional environmental parameters such as humidity and wind speed, and the physical rationality of the inversion results is insufficient; in addition, the real-time performance of the optimization algorithm is weak, and the computational efficiency is low in high-resolution scenarios, making it difficult to meet the rapid inversion requirements in complex environments; finally, the data management function is relatively single and cannot support real-time visualization, data export, and long-term storage at the same time, restricting the in-depth analysis and use of data by users. Summary of the Invention

[0005] In view of the deficiencies of the existing technologies, the present invention provides a low-altitude temperature and humidity profile detection system, which solves the problems of lack of adaptability in signal processing, insufficient accuracy in profile inversion, low computational efficiency, and single data management function in the existing technologies.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A low-altitude temperature and humidity profile detection system, comprising: A radiosonde module, which is used to collect temperature, humidity and Beidou satellite navigation chip positioning signals in the low-altitude environment and transmit the signals to the receiver module; A signal preprocessing module, which is used to denoise and smooth the received signals; A profile inversion module, which is used to invert the temperature and humidity profiles based on the processed signals; A display and output module, which is used to display the temperature and humidity profile data in real time and output it to external devices.

[0007] Preferably, the radiosonde module includes: A temperature sensor, which is used to collect temperature signals at different altitudes; A humidity sensor, which is used to measure the humidity distribution at different altitudes; A Beidou satellite navigation chip, which is used to measure the wind direction and wind speed distribution at different altitudes; A wireless transmission module, which is used to send the collected signals to the receiver module; A power module, which is used to provide power support for the radiosonde module.

[0008] Preferably, the temperature sensor includes: A thermistor, which is used to sense the ambient temperature; A signal conditioning circuit, which is used to convert the temperature signal into a standard electrical signal; A temperature signal calibration unit, which is used to calibrate the temperature signal output by the sensor.

[0009] Preferably, the signal preprocessing module includes: A Gaussian filter, which is used to remove high-frequency noise from the received temperature signals; A dynamic parameter adjustment module, which is used to adjust the window width of the Gaussian filter according to the noise intensity; A data format conversion module, which is used to format the denoised signals into a data format for processing by the profile inversion module.

[0010] Preferably, the dynamic parameter adjustment module includes: A noise intensity detection unit, which is used to calculate the noise energy of the signal; A parameter calculation unit, which is used to calculate the filter window width based on the noise energy; A filter control unit, which is used to adjust the parameters of the Gaussian filter in real time according to the calculation results.

[0011] Preferably, the profile inversion module includes: An optimization objective module, which is used to construct an objective function to describe the fitting error, gradient smoothness and high-frequency noise suppression of the temperature and humidity signals; A dynamic boundary processing module, which is used to set the time-related temperature and humidity gradient boundary conditions; A non - linear solution module for inversing the temperature and humidity profiles based on the objective function and boundary conditions.

[0012] Preferably, the optimization objective module includes: A data fitting unit for calculating the error between the observed signal and the target profile; A gradient smoothing unit for constraining the smoothness of the first - order derivative of the profile; A noise suppression unit for limiting the high - frequency noise components of the profile; A non - linear constraint unit for non - linearly correcting the target profile by combining humidity and wind speed.

[0013] Preferably, the non - linear solution module includes: A discretization unit for discretizing the optimization objective function into a non - linear algebraic equation system; An initial value generation unit for generating an initial solution for profile inversion based on a deep learning model; An iterative solution unit for solving the non - linear equation system using the Newton - iteration method.

[0014] Preferably, the display and output module includes: A real - time display unit for displaying the inversed temperature and humidity profiles in the form of a chart; A data interface unit for exporting the profile data to an external device through a standard interface; A data storage unit for saving historical profile data for long - term analysis.

[0015] Preferably, the real - time display unit includes: A chart generation module for dynamically plotting the temperature and humidity profile curves; A parameter display module for real - time displaying the intensity and signal - to - noise ratio information of the current signal; An interaction control module for adjusting the display mode or exporting the data range through user input.

[0016] The present invention provides a low - altitude temperature and humidity profile detection system, having the following beneficial effects: 1. The present invention combines the innovative technologies of the generalized variational method and deep learning, and can accurately inverse the low - altitude temperature and humidity profiles in complex environments. Different from the problem that traditional methods are limited in accuracy in high - noise environments, this technical solution effectively solves the insufficient description of the atmospheric dynamic characteristics by introducing multi - dimensional non - linear constraints, ensuring the accuracy of the inversion results.

[0017] 2. By using deep learning to generate the initial profile and combining it with the Newton iteration method, the speed and stability of the optimization solution are greatly improved. Different from the existing technologies that rely on global optimization algorithms, this solution enables the inversion process to converge quickly through the efficient prediction of the initial solution, while reducing the waste of computing resources and meeting the real-time requirements.

[0018] 3. By dynamically adjusting the parameters of the Gaussian filter, the signal preprocessing effect is adaptively optimized for different noise intensities. This processing method avoids the problem of poor effects of traditional static parameter filters in a changing environment, and can not only effectively remove noise but also retain the key details of the temperature and humidity signals.

[0019] 4. The present invention realizes an integrated design of real-time visualization, multi-dimensional data export and historical storage in the display and output module. Compared with the existing single data display scheme, this module enables users to not only intuitively view the inversion results but also conveniently manage the data, providing rich support for subsequent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the system structure diagram of the present invention; Figure 2 is the module architecture diagram of the radiosonde of the present invention; Figure 3 is the module architecture diagram of the signal preprocessing of the present invention; Figure 4 is the module architecture diagram of the profile inversion of the present invention; Figure 5 is the module architecture diagram of the display and output of the present invention; Figure 6 is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to the attached Figure 1 - attached Figure 5 , the embodiments of the present invention provide a low-altitude temperature and humidity profile detection system, including: A radiosonde module, configured to collect temperature, humidity and Beidou satellite navigation chip positioning signals in the low-altitude environment and transmit the signals to the receiver module; The main function of the radiosonde module is to accurately collect low-altitude environmental parameters, including synchronous acquisition of temperature, humidity, and Beidou satellite navigation chip positioning signals, and send the signals to the receiver module through the wireless transmission module for subsequent processing and profile inversion. Closely connected to the signal preprocessing module and the profile inversion module, the radiosonde module ensures the source accuracy and integrity of the system data and is the basis for implementing the technical solution of the present invention.

[0023] In this embodiment, the structural design of the radiosonde module includes a temperature sensor, a humidity sensor, a Beidou satellite navigation chip, a wireless transmission module, and a power supply module. The specific implementation method is as follows: In this embodiment, the temperature sensor uses a high-precision thermistor, and the humidity sensor uses a high-precision humidity-sensitive resistor, which has a short response time and high precision and is suitable for real-time monitoring under complex meteorological conditions.

[0024] Generally, the thermistor and the humidity-sensitive resistor are connected to the signal conditioning circuit. The signal conditioning circuit includes an amplification unit and a filtering unit, which are used to convert the temperature signal collected by the sensor into a standard electrical signal and reduce the influence of high-frequency noise.

[0025] Specifically, the temperature signal represents the sampled temperature value at a certain altitude . The collected temperature signal can be expressed by the following formula: ; Where: : Target temperature signal; : Thermistor noise; : Altitude at which the temperature signal is collected.

[0026] To reduce noise interference, in some embodiments, the signal collected by the temperature sensor can be transmitted to the wireless module after passing through the conditioning circuit to ensure the reliability of the signal.

[0027] In this embodiment, the humidity sensor uses a humidity-sensitive resistor sensor. Such sensors have the characteristics of high sensitivity and strong anti-interference ability and are suitable for humidity collection in the low-altitude environment.

[0028] As an option, the signal collected by the humidity sensor can be expressed as: ; Where: : Humidity at altitude ; : Reference humidity value; : Humidity change component, affected by environmental conditions.

[0029] In this embodiment, the humidity signal is synchronously collected with the temperature signal and the positioning signal of the Beidou satellite navigation chip, and is transmitted to the receiver through the wireless module together.

[0030] In a possible implementation, a 20# meteorological balloon is used to carry a radiosonde. During the ascent, through the positioning of the Beidou satellite navigation chip, the wind direction and wind speed at different altitudes can be measured.

[0031] Through the positioning of the Beidou satellite navigation chip, the magnitude and direction changes of the wind speed are calculated in real time, and a standard output signal is generated.

[0032] In this embodiment, the radiosonde module is equipped with a low-power narrow-band Internet of Things communication module (NB-IoT). This module transmits the temperature, humidity, and Beidou satellite navigation chip positioning signals to the receiver in the form of data packets, and has the characteristics of low transmission delay and strong anti-interference ability.

[0033] In some embodiments, the data transmission format includes a timestamp, signal strength, and parameter values. For example: ; Where: : Acquisition time; : Acquired temperature signal; : Acquired humidity signal; : Acquired Beidou satellite navigation chip positioning signal.

[0034] In this embodiment, the power supply module is powered by a laminated battery and supports long-term continuous operation. In some embodiments, to extend the battery life, the system is designed with a low-power mode. When the sampling frequency decreases, the module will automatically reduce power consumption.

[0035] Specifically, the switching condition of the low-power mode can be judged according to the following formula: ; Where: : Current power consumption; : Reference power consumption; : Sampling frequency.

[0036] In this embodiment, the acquisition and transmission process of the radiosonde module includes the following steps: The temperature, humidity sensors and the Beidou satellite navigation chip synchronously collect signals.

[0037] The conditioning circuit preliminarily processes the signals to reduce noise interference.

[0038] The wireless transmission module packs the data and sends it to the receiver.

[0039] After the receiver receives the data packet, the signal is input into the signal preprocessing module.

[0040] The radiosonde module realizes the high-precision acquisition and transmission of multi-dimensional environmental parameters, providing a reliable data basis for subsequent signal processing and profile inversion. At the same time, dynamic adjustment and multi-mode design improve the adaptability and working efficiency of the module.

[0041] The signal preprocessing module is used to denoise and smooth the received signals. The main function of the signal preprocessing module is to denoise and smooth the original signals transmitted by the radiosonde module, providing high-quality data input for subsequent profile inversion. This module effectively suppresses noise through a Gaussian filter and, combined with a dynamic parameter adjustment mechanism, realizes the intelligence and adaptability of signal processing.

[0042] In this embodiment, the signal preprocessing module consists of a Gaussian filter, a dynamic parameter adjustment module, and a data format conversion module. The following is a detailed description of the specific implementation method: In this embodiment, the Gaussian filter is used to denoise the temperature signals transmitted by the radiosonde module to eliminate high-frequency interference and random noise.

[0043] Generally, the core function of the Gaussian filter is to perform weighted smoothing on the input signal according to the weight function. Its weight function is a Gaussian distribution, and the formula is as follows: ; Where: : The smoothed signal after filtering; : The original signal, representing the temperature signal collected at altitude ; : The altitude of the current calculation point; : The filter window width, which determines the smoothing degree of filtering.

[0044] As an option, the filter window width can be dynamically adjusted according to the noise intensity to achieve the best denoising effect. When the noise intensity is high, increasing improves the signal smoothness; when the noise intensity is low, decreasing retains more signal details.

[0045] Specifically, the Gaussian filter processes the input signal through convolution operations. Its implementation steps include signal segmentation, weight calculation, and weighted summation, which can significantly improve the signal quality.

[0046] In a possible implementation, the dynamic parameter adjustment module is responsible for real-time adjustment of the filter parameters according to the noise characteristics to ensure the robustness and adaptability of signal processing.

[0047] As an implementation, the noise intensity can be calculated by the following formula: ; Where: : The noise intensity, representing the noise energy in the signal; = - : The height range; : The height at the original signal; : The mean value of the signal.

[0048] According to the noise intensity , dynamically adjust the window width of the filter: ; Where: : The basic window width, used to define the smoothness under the condition of no noise; : The window width of the filter after dynamic adjustment; : The noise intensity, as defined above.

[0049] The dynamic parameter adjustment module realizes the adaptability of signal processing by calculating the noise intensity in real time and automatically optimizing the filter parameters.

[0050] In this embodiment, the data format conversion module is used to convert the filtered signal into the input format required by the profile inversion module.

[0051] Specifically, the conversion module samples and discretizes the height and the signal value to generate a standardized data structure. For example, the generated discrete data format can be expressed as: ; Where: , , , : The discretized height points; : The smoothed signal value corresponding to the height .

[0052] In some embodiments, the data format conversion module also supports multi-parameter input processing, such as the joint sampling and formatting of temperature, humidity, and the positioning signal of the Beidou satellite navigation chip, facilitating the multi-dimensional profile inversion of subsequent modules.

[0053] In this embodiment, the working process of the signal preprocessing module includes the following steps: First, the Gaussian filter denoises the original signal transmitted by the radiosonde module to generate a smoothed signal.

[0054] Then, the dynamic parameter adjustment module adjusts the filter parameters in real time according to the noise characteristics to adapt to different environmental conditions.

[0055] Finally, the data format conversion module discretizes and formats the smoothed signal to generate the input data required by the profile inversion module.

[0056] The signal preprocessing module can effectively reduce the interference of noise on the signal while retaining the true details of the signal, providing high-quality data support for the profile inversion module.

[0057] The profile inversion module is used to invert the temperature profile based on the processed signal; The main function of the profile inversion module is to invert the low-altitude temperature and humidity profiles by combining mathematical optimization and physical constraints. This module combines the generalized variational method and deep learning technology to reconstruct the temperature and humidity profiles in a complex dynamic environment with high precision, ensuring the physical rationality and computational efficiency of the inversion results. The smoothed signal provided by the signal preprocessing module is formatted and then input into this module to complete the inversion and optimization of the profile.

[0058] In this embodiment, the profile inversion module includes an optimization objective module, a dynamic boundary processing module, a nonlinear solution module, and a deep learning assistance module. The following details the specific technical content: In this embodiment, the optimization objective module is used to construct a generalized variational objective function to achieve profile optimization by balancing the signal fitting error, gradient smoothness, high-frequency noise suppression, and nonlinear constraints.

[0059] Generally, the optimization objective function has the following expression: ; Where: : The optimization objective functional; : The preprocessed smoothed temperature signal; : The target temperature profile; : The signal fitting weight, used to control the difference between the inversion result and the smoothed signal; : The gradient smooth weight, used to suppress the first-order fluctuations of the temperature profile; : The curvature smooth weight, used to suppress high-frequency noise; : The nonlinear constraint term, expressing the influence of humidity and wind speed on the temperature profile; : The nonlinear constraint weight, controlling the strength of the humidity and wind speed constraints; : The gradient of the temperature profile, reflecting the rate of change of temperature with height; : The curvature of the temperature profile, which reflects the second derivative of the temperature change.

[0060] Nonlinear constraint term It is expressed as follows: ; Where: : The humidity distribution at altitude ; : The wind speed distribution at altitude ; : The nonlinear constraint functional, which represents the influence of humidity and wind speed on the temperature profile.

[0061] By introducing the nonlinear constraint, the optimization objective function can more accurately describe the atmospheric physical characteristics and enhance the physical rationality of the inversion result.

[0062] In one possible implementation, the dynamic boundary processing module is used to set the time-dependent gradient boundary conditions to constrain the boundary behavior of the inversion result.

[0063] As an option, the boundary conditions are expressed as follows: ; Where: : The gradient of the temperature profile at the altitude boundaries and ; , : The time-dependent boundary value, which is obtained by fitting the real-time meteorological data.

[0064] Generally, the dynamic boundary value is based on the temperature gradient measured by external sensors and is achieved through polynomial fitting.

[0065] In this embodiment, the deep learning assistance module is used to generate the initial profile of the optimization algorithm to improve the efficiency of the inversion calculation.

[0066] Specifically, the neural network model receives the input data , , , and outputs the initial profile . The neural network structure includes: Input layer: containing the feature vector of the sensor data; Hidden layer: multiple layers of nonlinear activation units, which are used to extract the complex features of the profile; Output layer: generating the initial profile value.

[0067] As an implementation, the initial profile can be expressed by the following formula: ; Where: : The initial temperature profile value generated by deep learning; : A neural network model for predicting the temperature profile (dimensionless); : The filtered temperature signal (unit: °C); : The humidity signal (unit: %); : The wind speed signal (unit: m / s).

[0068] In this embodiment, the non-linear solving module discretizes the optimization objective function into a system of non-linear algebraic equations by the finite element method.

[0069] As a possible implementation, the temperature profile is expressed as a linear combination of interpolation basis functions: ; where: : The height at which the temperature profile value; : The total number of interpolation nodes, representing the number of discrete points of the profile; : The temperature value of the interpolation node; : The interpolation basis function, used to describe the profile shape in segments.

[0070] The discretized system of non-linear algebraic equations is as follows: ; where: : The system matrix; : The constraint matrix; : The load vector; : The non-linear constraint vector, discretized from humidity and wind speed ; : The discrete vector of temperature profile values ; : The physical constraint strength coefficient, controlling the influence of non-linear constraints on the optimization result.

[0071] The system of equations is solved by the Newton-iteration method, and its update formula is: ; where: : The temperature profile at the th iteration; : The Jacobian matrix; : The vector of temperature profile values obtained at the th iteration; : The residual vector of the non-linear system of equations, representing the unmet constraint conditions in the current iteration.

[0072] In this embodiment, the operation process of the profile inversion module includes the following steps: First, construct an objective function through the optimization target module, combining the input signal and the smoothing weight; Secondly, the dynamic boundary processing module generates time-related boundary conditions; Then, the deep learning assistance module generates an initial profile, which is used as the initial value for the optimization solution; Finally, the non-linear solution module performs iterative solution on the discrete equation set and outputs the final profile result.

[0073] Through the above design, the profile inversion module of the present invention can efficiently process complex environment data, achieve accurate reconstruction of the low-altitude temperature profile, and provide important support for the monitoring and evaluation of the atmospheric environment. The technical content is fully disclosed.

[0074] The display and output module is used to display the temperature profile data in real time and output it to external devices; The display and output module is used to visually present the temperature profile data generated by the profile inversion module, and at the same time supports data export and storage. Through seamless docking with the profile inversion module, this module can display the temperature profile curve and related parameter information in real time, provide a friendly data interaction function for users, and meet the needs of data storage and long-term trend analysis.

[0075] In this embodiment, the display and output module includes a real-time display unit, a data export interface, and a data storage module. The following details its specific implementation method.

[0076] In this embodiment, the real-time display unit is used to dynamically present the temperature profile data in a visual manner. Specifically, the temperature data and related parameters output by the profile inversion module

[0077] are input into the display unit and a real-time chart is generated. Generally, the display of the temperature profile consists of a two-dimensional curve formed by height and temperature value, and the drawing formula of the curve is: ; Where: , , , : discrete height points; , , , : temperature values at the corresponding heights; : the temperature profile curve generated in real time, representing the two-dimensional relationship between height and temperature.

[0078] As an option, the display unit can dynamically adjust the display range of the chart. For example, the user can select the height interval to be displayed according to the application scenario. In this case, the definition of the curve is restricted to: ; In addition, the real-time display unit supports superimposing and displaying other parameter curves, such as humidity and wind speed to achieve multi-parameter comparison.

[0079] In one possible implementation, the data export interface is used to export the temperature profile data to an external device in a standardized format, supporting further analysis and processing.

[0080] As an implementation, the exported data is formatted into a structured file, such as a CSV or JSON file. The general definition of the format is as follows: ; Where: , , , : height point; : temperature profile value; : humidity distribution; : wind speed distribution.

[0081] Generally, the data export interface includes USB and RS-485 communication interfaces, which can be compatible with a variety of external devices, such as meteorological data processing terminals and experimental analysis systems. As an option, the system also supports uploading data to the cloud through a network interface for centralized storage and remote access.

[0082] In this embodiment, the data storage module is used to long-term store the historical temperature and humidity profile data and wind direction and speed data, and supports trend analysis and retrospective query.

[0083] Specifically, the storage module stores the result of each profile inversion as time series data, and the format is as follows: ; Where: , , , : timestamp, indicating the time of each inversion; : the profile data corresponding to the timestamp, and the format is the same as the exported data.

[0084] As a possible implementation, the storage module is equipped with a local storage unit and a cloud storage interface. When the data volume is large, the historical data can be automatically uploaded to the cloud for distributed storage.

[0085] In this embodiment, the working process of the display and output module includes the following steps: First, the temperature profile data output by the profile inversion module is transmitted to the real-time display unit, and a temperature curve chart is generated.

[0086] Then, the user can adjust the display range or select to overlay other parameters through the interaction interface for visual analysis.

[0087] Next, the user can export the profile data to an external device in a standardized format through the data export interface.

[0088] Finally, the data storage module stores all the observed data for a long time and records it in a time series for subsequent query and analysis.

[0089] The display and output module not only realizes the real-time visualization of the low-altitude temperature profile, but also supports multi-parameter comparison, data export, and long-term storage functions, providing rich interaction means and data management capabilities for system users.

[0090] Please refer to the appendix Figure 6 , the present invention also provides a working process of a low-altitude temperature and humidity profile detection system, including the following steps: S1. The 20# meteorological balloon carries the low-altitude radiosonde and ascends into the air; S2. Detect the signal; S3. Low-altitude detection signal receiver; S4. The computer calculates, processes, and displays; S5. Data saving and storage.

[0091] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-altitude temperature and humidity profile detection system, characterized in that: include: The sonde module is used to collect temperature, humidity and Beidou satellite navigation chip positioning signals in the low-altitude environment and transmit the signals to the receiver module; A signal preprocessing module, used for denoising and smoothing the received signal; Profile inversion module, used to invert temperature and humidity profiles based on processed signals; The display and output module is used to display the temperature and humidity profile data in real time and output it to external devices.

2. A low-altitude temperature and humidity profile detection system according to claim 1, characterized in that: The sonde module includes: Temperature sensor, used to collect temperature signals at different heights; Humidity sensor, used to measure humidity distribution at different heights; Beidou satellite navigation chip, used to measure wind direction and speed distribution at different altitudes; A wireless transmission module, used to send the collected signals to the receiver module; The power module is used to provide power support for the radiosonde module.

3. A low-altitude temperature and humidity profile detection system according to claim 2, characterized in that: The temperature sensor comprises: Thermistor, used to sense ambient temperature; A signal conditioning circuit for converting a temperature signal into a standard electrical signal; The temperature signal calibration unit is used to calibrate the temperature signal output by the sensor.

4. A low-altitude temperature and humidity profile detection system according to claim 1, characterized in that: The signal preprocessing module comprises: Gaussian filter, used to remove high-frequency noise from the received temperature and humidity signals; A dynamic parameter adjustment module, used to adjust the window width of the Gaussian filter according to the noise intensity; The data format conversion module is used to format the denoised signal into a data format for processing by the profile inversion module.

5. A low-altitude temperature and humidity profile detection system according to claim 4, characterized in that: The dynamic parameter adjustment module comprises: A noise intensity detection unit, used to calculate the noise energy of the signal; A parameter calculation unit, used for calculating a filter window width based on noise energy; The filter control unit is used to adjust the parameters of the Gaussian filter in real time according to the calculation results.

6. A low-altitude temperature and humidity profile detection system according to claim 1, characterized in that: The profile inversion module comprises: The optimization target module is used to construct the target function to describe the fitting error, gradient smoothness and high-frequency noise suppression of the temperature and humidity signals; Dynamic boundary processing module, used to set time-dependent temperature and humidity gradient boundary conditions; Nonlinear solution module, used to invert temperature and humidity profiles based on objective function and boundary conditions.

7. A low-altitude temperature and humidity profile detection system according to claim 6, characterized in that: The optimization target module includes: A data fitting unit is used to calculate the error between the observed signal and the target profile; Gradient smoothing unit, used to constrain the smoothness of the first-order derivative of the profile; A noise suppression unit for limiting the high-frequency noise components of the profile; The nonlinear constraint unit is used to make nonlinear corrections to the target profile by combining humidity and wind speed.

8. A low-altitude temperature and humidity profile detection system according to claim 6, characterized in that: The nonlinear solution module includes: A discretization unit is used to discretize the optimization objective function into a set of nonlinear algebraic equations; An initial value generation unit, used to generate an initial solution for profile inversion based on a deep learning model; The iterative solution unit is used to solve the nonlinear equations using the Newton-iterative method.

9. The low-altitude temperature and humidity profile detection system according to claim 1, characterized in that: The display and output module comprises: A real-time display unit, used to display the inverted temperature and humidity profiles in the form of graphs; Data interface unit, used to export profile data to external devices through standard interfaces; Data storage unit, used to save historical profile data for long-term analysis.

10. A low-altitude temperature and humidity profile detection system according to claim 9, characterized in that: The real-time display unit comprises: Chart generation module, used to dynamically draw temperature and humidity profile curves; Parameter display module, used to display the current signal strength and signal-to-noise ratio information in real time; Interactive control module, used to adjust the display mode or export data range through user input.

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