Boundary layer detection system based on lidar

Abstract

The present invention relates to the field of laser radar application technology, and discloses a laser radar-based boundary layer detection system, which includes: data acquisition, laser radar modeling, feature extraction, comprehensive evaluation, and graded warning modules. The data acquisition module sets points in the monitoring area and around the equipment to collect atmospheric parameters and equipment operation data and pre-process them; the modeling module constructs a three-dimensional point cloud model, simulates atmospheric flow and radar detection characteristics, and combines real-time data parameter simulation; the feature extraction module extracts boundary layer height, temperature and humidity gradients, and turbulence intensity through algorithms; the comprehensive evaluation module performs correlation calculations after normalization processing, and compares with baseline values ​​to evaluate stability; the graded warning module calculates the evaluation value in combination with environmental factors when unstable, and generates a three-level warning after secondary comparison. The system realizes high-precision, dynamic monitoring and graded warning of the atmospheric boundary layer, improving detection accuracy and reliability.

Description

Technical Field

[0001] The present invention relates to the field of laser radar application technology, and in particular to a boundary layer detection system based on laser radar. Background Art

[0002] The atmospheric boundary layer (ABL), a key region of interaction between the atmosphere and the underlying surface, has a crucial influence on numerous phenomena, including weather evolution, pollutant dispersion, and climate change. Accurately monitoring the structure and characteristics of the ABL is of irreplaceable importance for numerous fields, including weather forecasting, environmental monitoring, and aerospace.

[0003] Traditional boundary layer detection methods have significant limitations. For example, while sounding rockets offer high accuracy, they are extremely expensive and cannot provide continuous monitoring. Radiosondes are limited in detection range and temporal resolution, making it difficult to capture rapid changes in the boundary layer. Ground-based meteorological stations can only obtain localized single-point data, failing to fully reflect the spatial distribution characteristics of the boundary layer.

[0004] With the development of lidar technology, its application in atmospheric detection is becoming increasingly widespread. Lidar offers advantages such as high temporal and spatial resolution and non-contact detection, enabling real-time, dynamic monitoring of the atmospheric boundary layer. However, existing lidar-based boundary layer detection systems still have some problems. On the one hand, most existing systems only use lidar echo signals for simple inversion of boundary layer height, lacking comprehensive analysis of key parameters such as atmospheric temperature and humidity gradients and turbulence intensity, making it difficult to fully assess boundary layer stability. On the other hand, existing systems have shortcomings in data processing and model building, making it impossible to fully utilize the massive data captured by lidar, resulting in a need to improve the accuracy and reliability of detection results.

[0005] Furthermore, existing systems have limited adaptability to complex atmospheric environments. For example, in the presence of typical boundary layer features such as inversion layers and mixing layers, the detection accuracy of existing systems decreases significantly. Furthermore, under varying meteorological conditions, existing systems require manual parameter adjustments, making automated and intelligent monitoring difficult.

[0006] At the same time, the existing system lacks an effective early warning mechanism. When the boundary layer shows signs of instability, it cannot issue early warning information in time, making it difficult to meet the needs of real-time boundary layer monitoring and early warning in practical applications. Summary of the Invention

[0007] The object of the present invention is to provide a boundary layer detection system based on laser radar to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a boundary layer detection system based on laser radar, the system comprising:

[0009] Data acquisition module, lidar modeling module, feature extraction module, comprehensive evaluation module and graded warning module;

[0010] The data acquisition module is used to set up monitoring points around the monitoring area and the lidar equipment, and deploy monitoring devices to collect atmospheric parameters of the monitoring area and operating status data of the lidar equipment in real time, and pre-process the collected data;

[0011] The LiDAR modeling module is used to construct a three-dimensional point cloud model of the atmospheric boundary layer and simulate the flow characteristics of the atmosphere using point cloud processing technology. At the same time, it uses echo signal analysis technology to simulate the detection characteristics of the LiDAR. Based on the real-time collected atmospheric parameters and equipment operating status data, it simulates the parameters of the monitoring area and LiDAR equipment.

[0012] The feature extraction module is used to construct a boundary layer height extraction algorithm, a temperature and humidity gradient extraction algorithm, and a turbulence intensity extraction algorithm based on the collected atmospheric parameters and equipment operating status data, and transmit the real-time collected atmospheric parameters and equipment operating status data to the constructed extraction algorithm to calculate and obtain the boundary layer height, temperature and humidity gradient, and turbulence intensity;

[0013] The comprehensive evaluation module is used to normalize the obtained boundary layer height, temperature and humidity gradient, and turbulence intensity, perform correlation calculation to obtain a first evaluation value and preset a first benchmark value, and perform preliminary comparative evaluation and analysis on the stability of the boundary layer in the monitoring area;

[0014] The graded warning module is used to further calculate and obtain a second evaluation value in combination with environmental factors when analyzing that the boundary layer in the monitoring area is unstable, and to preset a second benchmark value and perform a secondary comparative evaluation with the second evaluation value to further analyze the performance of the boundary layer in the monitoring area under different atmospheric parameters and lidar detection conditions.

[0015] Preferably, the data acquisition module includes an atmospheric parameter acquisition unit, a boundary layer feature acquisition unit and a data preprocessing unit;

[0016] The atmospheric parameter acquisition unit includes a temperature and humidity acquisition unit and a wind speed and direction acquisition unit, which is used to deploy a meteorological sensor group on the vertical section of the monitoring area to monitor and collect the atmospheric parameters of the monitoring area in real time, and transmit them to the data preprocessing unit through wired transmission. The meteorological sensor group includes a temperature and humidity sensor group and a wind speed and direction sensor group. The atmospheric parameters include temperature and humidity data and wind speed and direction data;

[0017] The temperature and humidity acquisition unit is used to monitor the temperature and humidity data of the area in real time based on the temperature and humidity sensor group. The temperature and humidity sensor group includes a temperature probe, a humidity probe and a data collector, which respectively collect the temperature value, humidity value and sampling frequency of the temperature and humidity data;

[0018] The wind speed and direction acquisition unit is used to collect wind speed and direction data in real time based on a wind speed and direction sensor group, wherein the wind speed and direction sensor group includes a cup anemometer, a wind vane anemometer, and a signal converter. The wind speed and direction data includes wind speed, wind direction, measurement height, and signal strength;

[0019] The boundary layer feature acquisition unit is used to build a communication interface to connect with the control system of the laser radar equipment, read the scanning parameters of the laser radar in the control system in real time, and extract and summarize the scanning angle, scanning frequency and pulse width in the scanning parameters of the laser radar in real time to obtain the operating status data of the laser radar equipment.

[0020] Preferably, the data preprocessing unit is used to filter out noise and outliers from the collected atmospheric parameters and equipment operating status data, and unify the data formats from different sources. At the same time, the collected atmospheric parameters and equipment operating status data are feature screened through time series analysis to obtain the temperature value, humidity value, scanning angle and pulse width at the predicted time.

[0021] Preferably, the laser radar modeling module includes a space field simulation unit, an echo characteristic analysis unit and a visualization integration unit;

[0022] The spatial field simulation unit includes a three-dimensional modeling unit and a flow characteristics simulation unit;

[0023] The three-dimensional modeling unit extracts terrain data and surface coverage information of the monitoring area from the geographic information system, uses modeling software to establish a three-dimensional point cloud model of the monitoring area, simulates the terrain undulations, surface material and spatial distribution of the area, and adds typical characteristics of the atmospheric boundary layer to the monitoring area, including the inversion layer, mixing layer and residual layer. After the preliminary modeling is completed, the simulation tool is used to define the physical properties of the density, viscosity and thermal conductivity of the atmosphere for the constructed three-dimensional point cloud model, and at the same time sets the launch point, receiving point and scanning range of the laser radar, and performs static simulation, dynamic simulation and continuous simulation to simulate the flow response of the atmospheric boundary layer in the monitoring area;

[0024] The flow characteristics simulation unit is used to input atmospheric motion parameters, including horizontal wind speed, vertical velocity and turbulence intensity, and then perform point cloud processing and analysis after input to simulate the velocity field, temperature field and humidity field of the flow characteristics of the monitoring area under different meteorological conditions;

[0025] The echo characteristic analysis unit is used to establish an echo model of the laser radar, including the emission wavelength, pulse energy and receiving sensitivity, and then apply the echo signal equation to simulate the interaction between the laser and atmospheric particles. The echo signal analysis technology is used to analyze the detection results of the laser radar and evaluate the intensity distribution, time delay and spectral characteristics of the echo;

[0026] The visualization integration unit is used to import the monitoring area model into the lidar echo model for integration to obtain a digital twin model, and then collect the atmospheric parameters of the monitoring area and the operating status data of the lidar equipment in real time, transmit them to point cloud processing analysis and echo signal analysis for dynamic simulation, and import the dynamic simulation results into the digital twin model, update the status of the monitoring area and the lidar equipment in real time, and display the simulation data through a graphical interface to provide user interaction functions.

[0027] Preferably, the feature extraction module includes a boundary layer height extraction unit, a temperature and humidity gradient extraction unit, and a turbulence intensity extraction unit;

[0028] The boundary layer height extraction unit is used to construct a boundary layer height extraction algorithm, calculate the boundary layer height in the vertical direction of the monitoring area according to the pre-processed atmospheric parameters, and extract the vertical distribution of the atmospheric boundary layer;

[0029] The temperature and humidity gradient extraction unit is used to construct a temperature and humidity gradient extraction algorithm, calculate and obtain the temperature and humidity gradient based on the pre-processed atmospheric parameters, and extract the temperature and humidity change characteristics in the horizontal direction of the monitoring area;

[0030] The turbulence intensity extraction unit is used to construct a turbulence intensity extraction algorithm, calculate and obtain turbulence intensity based on the pre-processed equipment operation status data, and extract the strength of atmospheric turbulence in the monitoring area.

[0031] Preferably, the boundary layer height extraction unit is used to calculate and obtain the boundary layer height by analyzing the vertical distribution of the lidar echo signal in combination with the preprocessed temperature and humidity data, and to extract the interface position between the atmospheric boundary layer and the free atmosphere.

[0032] Preferably, the temperature and humidity gradient extraction unit is used to calculate and obtain the temperature and humidity gradient by comparing the temperature and humidity data of adjacent monitoring points in combination with the pre-processed wind speed and direction data, and to extract the temperature and humidity change rate in the horizontal direction of the monitoring area.

[0033] Preferably, the comprehensive evaluation module includes a multi-parameter fusion unit and a preliminary discrimination unit;

[0034] The multi-parameter fusion unit is used to normalize the obtained boundary layer height, temperature and humidity gradient, and turbulence intensity, perform correlation calculation to obtain a first evaluation value, and perform a comprehensive analysis on the stability of the boundary layer in the monitoring area;

[0035] The preliminary judgment unit is used to preset a first reference value based on the observation specifications and historical data of the atmospheric boundary layer, and perform a preliminary comparative evaluation with the obtained first evaluation value to evaluate the stability of the boundary layer in the monitoring area. The specific evaluation scheme is as follows; when the first evaluation value is greater than the first reference value, it indicates that the boundary layer in the monitoring area is stable under the current meteorological conditions and continues to be monitored in real time; when the first evaluation value is less than or equal to the first reference value, it indicates that the boundary layer in the monitoring area is unstable under the current meteorological conditions, and early warning measures and further observation operations need to be taken.

[0036] Preferably, the graded warning module includes a warning indicator calculation unit and a grade determination unit;

[0037] The early warning indicator calculation unit is used to further analyze the stability of the monitoring area under different atmospheric parameters and lidar detection conditions in combination with the obtained first evaluation value, and perform correlation calculation to obtain a second evaluation value;

[0038] The level determination unit is used to preset a second reference value and the obtained second evaluation value, conduct a secondary comparative evaluation, further analyze the stability of the boundary layer of the monitoring area after the influence of multiple environmental factors, and generate a corresponding warning level. The specific evaluation scheme is as follows; when the second evaluation value is greater than the second reference value, it means that the boundary layer of the monitoring area is still stable under the meteorological conditions after comprehensive environmental factors. At this time, a third-level warning information is generated to remind the monitoring personnel to continue observing the monitoring area; when the second evaluation value is equal to the second reference value, it means that the boundary layer of the monitoring area is unstable under the meteorological conditions after comprehensive environmental factors, and there is a potential risk of change. At this time, a second-level warning information is generated to remind relevant personnel to immediately conduct a detailed detection of the monitoring area; when the second evaluation value is less than the second reference value, it means that the boundary layer of the monitoring area is significantly unstable under the meteorological conditions after comprehensive environmental factors. At this time, a first-level warning information is generated to automatically trigger the warning alarm system, and notify relevant departments and personnel to start the emergency observation plan.

[0039] Preferably, the lidar modeling module also includes a model calibration unit; the model calibration unit is used to obtain historical monitoring data and simulation data at corresponding moments, calculate the deviation value between the two, and adjust the physical property parameters of the three-dimensional point cloud model and the detection parameters of the echo model according to the deviation value to realize dynamic calibration of the lidar modeling module.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The system uses a data acquisition module to set up monitoring points and deploy monitoring devices around the monitoring area and the LiDAR equipment. This system collects atmospheric parameters and equipment operating status data in real time and also pre-processes the data, making the acquired data more accurate and reliable, laying a solid foundation for subsequent analysis and modeling. The atmospheric parameter acquisition unit and boundary layer feature acquisition unit in the data acquisition module comprehensively acquire atmospheric parameters such as temperature, humidity, wind speed and direction, as well as LiDAR scanning parameters. The data pre-processing unit further improves data quality by filtering out noise, standardizing the data format, and selecting features.

[0042] The LiDAR modeling module constructs a three-dimensional point cloud model of the atmospheric boundary layer, simulating atmospheric flow characteristics and LiDAR detection features, and performing parameter simulation based on real-time data. The spatial field simulation unit extracts data from the geographic information system to build a three-dimensional model, adding typical boundary layer features and defining physical properties to perform various simulations. The flow characteristics simulation unit simulates velocity, temperature, and humidity fields under different meteorological conditions. The echo characteristics analysis unit creates an echo model to simulate the interaction between lasers and atmospheric particles. The visualization integration unit integrates the monitoring area model and the echo model to obtain a digital twin model and dynamically updates and displays it. These functions enable the system to more realistically and comprehensively simulate the atmospheric boundary layer and LiDAR detection processes, improving its understanding and prediction capabilities of the boundary layer.

[0043] The feature extraction module employs multiple extraction algorithms to determine boundary layer height, temperature and humidity gradients, and turbulence intensity. The boundary layer height extraction unit combines echo signals with temperature and humidity data to calculate height. The temperature and humidity gradient extraction unit compares data from adjacent monitoring points and calculates gradients based on wind speed and direction data. The turbulence intensity extraction unit calculates intensity based on equipment operating status data. These algorithms enable the system to accurately extract key characteristic parameters of the boundary layer, providing strong support for assessing boundary layer stability.

[0044] The comprehensive assessment module normalizes and correlates the acquired parameters to obtain a first assessment value, which is then compared with a preset baseline value to provide a preliminary assessment of boundary layer stability. The multi-parameter fusion unit comprehensively analyzes the parameters, while the preliminary judgment unit compares the results with preset baseline values ​​based on standards and historical data. This assessment method can quickly and effectively determine the stability of the boundary layer, providing a basis for subsequent warnings.

[0045] When the boundary layer is unstable, the graded warning module calculates a second assessment value based on environmental factors, compares it with a preset baseline value, and generates a warning level. The warning indicator calculation unit further analyzes the stability to obtain the second assessment value, and the level determination unit generates different levels of warning information based on the comparison results. This provides accurate early warning of boundary layer instability, allowing relevant personnel to take appropriate measures in a timely manner, improving the practicality and reliability of the system.

[0046] In addition, the model calibration unit in the lidar modeling module achieves dynamic calibration by obtaining the deviation values ​​of historical monitoring data and simulation data and adjusting the model parameters, thereby ensuring the accuracy and reliability of the model and enabling the system to operate stably for a long time and adapt to different monitoring environments and conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a working principle diagram of the laser radar-based boundary layer detection system of the present invention;

[0048] Figure 2 This is the design diagram of the data acquisition module;

[0049] Figure 3 This is the design diagram of the data preprocessing unit;

[0050] Figure 4 This is the design diagram of the comprehensive assessment module;

[0051] Figure 5 This is the design diagram of the graded early warning module. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] See also Figure 1-Figure 5 The present invention relates to a laser radar-based boundary layer detection system, which includes: a data acquisition module, a laser radar modeling module, a feature extraction module, a comprehensive evaluation module, and a graded warning module. The specific implementation is as follows:

[0054] The data acquisition module sets up monitoring points and deploys monitoring devices around the monitoring area and LiDAR equipment, collecting atmospheric parameters and LiDAR equipment operating status data in real time, and then preprocesses the collected data. The LiDAR modeling module constructs a three-dimensional point cloud model of the atmospheric boundary layer, using point cloud processing technology to simulate atmospheric flow characteristics. It also uses echo signal analysis technology to simulate LiDAR detection characteristics. Based on the real-time collected atmospheric parameters and equipment operating status data, it simulates the parameters of the monitoring area and LiDAR equipment.

[0055] The feature extraction module uses the collected atmospheric parameters and equipment operating status data to construct boundary layer height extraction algorithms, temperature and humidity gradient extraction algorithms, and turbulence intensity extraction algorithms. The real-time collected data is transmitted to the constructed extraction algorithm to calculate the boundary layer height, temperature and humidity gradient, and turbulence intensity.

[0056] The comprehensive evaluation module normalizes the acquired boundary layer height, temperature and humidity gradient, and turbulence intensity, and obtains a first evaluation value through correlation calculation. It then performs a preliminary comparative evaluation with the preset first benchmark value to analyze the stability of the boundary layer in the monitoring area.

[0057] When the graded warning module analyzes that the boundary layer in the monitoring area is unstable, it calculates a second evaluation value based on environmental factors, and performs a secondary comparative evaluation with the preset second benchmark value to further analyze the performance of the boundary layer in the monitoring area under different atmospheric parameters and lidar detection conditions.

[0058] Example 1:

[0059] In this embodiment, the data acquisition module is the data basis of the entire boundary layer detection system. Its specific implementation method is as follows: the module includes three organic components: an atmospheric parameter acquisition unit, a boundary layer feature acquisition unit, and a data preprocessing unit. The units cooperate with each other to achieve comprehensive and accurate collection and preliminary processing of atmospheric parameters and lidar equipment operating status data in the monitoring area.

[0060] The atmospheric parameter acquisition unit plays a crucial role in data collection. It consists of a temperature and humidity acquisition unit and a wind speed and direction acquisition unit. In practical applications, a meteorological sensor group must be strategically deployed across the vertical cross-section of the monitored area. Real-time monitoring and collected atmospheric parameters are then transmitted to the data preprocessing unit via wired transmission. The meteorological sensor group is a key component, comprising a temperature and humidity sensor group and a wind speed and direction sensor group. The collected atmospheric parameters primarily include temperature and humidity data, as well as wind speed and direction data. These data are crucial for subsequent analysis and evaluation.

[0061] The temperature and humidity acquisition unit relies on a temperature and humidity sensor system consisting of a temperature probe, a humidity probe, and a data collector. The temperature probe accurately captures the temperature within the monitoring area, while the humidity probe acquires the corresponding humidity. The data collector not only records these temperature and humidity data but also accurately records the sampling frequency. The coordinated operation of these three components enables real-time monitoring of the temperature and humidity within the monitoring area, providing reliable data support for subsequent analysis of atmospheric temperature and humidity conditions.

[0062] The wind speed and direction acquisition unit also plays a crucial role, collecting real-time wind speed and direction data based on a wind speed and direction sensor assembly. This sensor assembly includes a cup anemometer, a wind vane, and a signal converter. The cup anemometer, with its unique structural design, accurately measures wind speed; the wind vane accurately determines wind direction; and the signal converter converts the signals collected by the cup anemometer and wind vane into a form that is easy to transmit and process. The collected wind speed and direction data includes information on multiple dimensions, including wind speed, wind direction, measurement altitude, and signal strength. This information is crucial for understanding atmospheric flow conditions.

[0063] The boundary layer feature acquisition unit is implemented by building a communication interface that connects to the lidar device's control system. Once connected, it can read the lidar's scanning parameters from the control system in real time, extracting the scanning angle, scanning frequency, and pulse width from these parameters in real time. This extracted information is then aggregated to obtain the lidar device's operating status data. This data is essential for understanding the lidar's operating status and for subsequent analysis in conjunction with atmospheric parameters.

[0064] The data preprocessing unit is the final step in the data acquisition module. It further processes the previously collected atmospheric parameter and equipment operating status data. First, this data must be filtered to remove noise and outliers. This is because the data acquisition process is inevitably subject to interference from various factors, resulting in the presence of noise and outliers. If this data is not processed, it will affect subsequent analysis and evaluation results. Secondly, since atmospheric parameter and equipment operating status data may come from different sensors and devices, their data formats may vary. Therefore, the data formats of different sources need to be unified to facilitate subsequent processing and analysis. Finally, the collected data is feature-filtered through time series analysis to obtain the temperature, humidity, scan angle, and pulse width at the prediction time. This process can extract valuable information from large amounts of data, providing more accurate data support for subsequent model construction and analysis.

[0065] During actual deployment, the location of monitoring points requires scientific planning based on the actual conditions of the monitoring area to ensure comprehensive and accurate coverage of the atmospheric conditions and the operating status of the lidar equipment. In practical applications, if the monitoring area is located in a city center, monitoring points can be set up based on the city's functional zoning. For example, 3-5 monitoring points can be set up in commercial areas, residential areas, and industrial areas to comprehensively cover atmospheric parameters in different environments. The monitoring device can utilize high-precision meteorological sensors, such as PT100 temperature probes, HIH-4000 humidity probes, and A100R cup anemometers and W200P wind vanes for wind speed and direction.

[0066] During data preprocessing, a sliding average filter can be used to filter out noise and outliers. For temperature data, for example, set the sliding window size to 5 and take the average of the two preceding and following data points as the filtered value for each data point. When standardizing the data format, convert all collected data into CSV format for ease of subsequent processing. Principal component analysis can be used for feature screening to extract principal components with high variance contributions, such as temperature, humidity, scan angle, and pulse width, as features at the prediction moment. Monitoring equipment must be deployed securely and reliably to ensure stable operation and continuous data collection. While wired transmission is subject to certain limitations of cabling, it offers the advantages of stable transmission and strong anti-interference capabilities, ensuring data accuracy and integrity during transmission.

[0067] Through the coordinated work of the atmospheric parameter acquisition unit, the boundary layer feature acquisition unit, and the data preprocessing unit, the data acquisition module realizes the efficient acquisition and preliminary processing of the atmospheric parameters and operating status data of the monitoring area and the lidar equipment, laying a solid data foundation for the subsequent work of the entire lidar-based boundary layer detection system.

[0068] Example 2:

[0069] In this embodiment, the lidar modeling module is the core part for realizing accurate simulation and dynamic display of the boundary layer detection system. Its specific implementation method is as follows: the module includes a spatial field simulation unit, an echo characteristic analysis unit, a visualization integration unit and a model calibration unit. Through data interaction and functional collaboration, each unit constructs a digital model that can truly reflect the characteristics of the atmospheric boundary layer in the monitoring area.

[0070] The spatial field simulation unit consists of a three-dimensional modeling unit and a flow characteristics simulation unit. The implementation of the three-dimensional modeling unit requires the extraction of terrain data and surface coverage information of the monitoring area from the geographic information system. These data include the altitude, mountain direction, vegetation distribution, building layout, etc. in the area. Using professional modeling software, the above information is converted into a three-dimensional point cloud model, which can intuitively present the terrain undulations, surface material and spatial distribution of the monitoring area. After the preliminary modeling is completed, it is necessary to add typical features of the atmospheric boundary layer to the model, such as the inversion layer, mixing layer and residual layer. The addition of these features needs to be based on historical meteorological data and research results on the atmospheric boundary layer characteristics of the area. After the addition is completed, the simulation tool is used to define the physical properties of the three-dimensional point cloud model, including parameters such as the density, viscosity and thermal conductivity of the atmosphere, and at the same time set the launch point, receiving point and scanning range of the lidar. On this basis, static simulation, dynamic simulation and continuity simulation are carried out. Static simulation is used to show the state of the atmospheric boundary layer at a certain moment, dynamic simulation can present the change process of the atmospheric boundary layer over a period of time, and continuity simulation can reflect the long-term evolution trend of the atmospheric boundary layer under different meteorological conditions. Through these simulations, the simulation of the atmospheric boundary layer flow response in the monitoring area can be achieved.

[0071] The implementation of the flow characteristics simulation unit requires input of atmospheric motion parameters, including horizontal wind speed, vertical velocity, and turbulence intensity. These parameters are primarily derived from atmospheric parameters collected in real time by the data acquisition module and from historical meteorological databases. After entering these parameters, point cloud processing and analysis techniques are used to simulate the velocity, temperature, and humidity fields of the flow characteristics in the monitored area under different meteorological conditions. Velocity field simulations can demonstrate the horizontal and vertical velocity distribution of the atmosphere, while temperature and humidity field simulations can reflect the spatial distribution of temperature and humidity in the atmosphere. These simulation results provide important physical field data support for subsequent feature extraction and comprehensive evaluation.

[0072] The implementation of the echo characteristics analysis unit first requires the establishment of a lidar echo model, which includes key parameters such as emission wavelength, pulse energy, and receiver sensitivity. These parameters must be determined based on the specific technical specifications of the lidar equipment being used. After establishing the echo model, the echo signal equation is applied to simulate the interaction between the laser and atmospheric particles. Particles in the atmosphere include aerosols, water droplets, and dust. The interaction between the laser and these particles generates an echo signal. Using echo signal analysis technology, the lidar detection results are analyzed to evaluate the echo's intensity distribution, time delay, and spectral characteristics. The echo intensity distribution reflects the concentration and distribution of particles in the atmosphere, the time delay can be used to calculate the distance between the particle and the lidar, and the spectral characteristics help identify the type and nature of the particles in the atmosphere.

[0073] The visualization integration unit is implemented by importing the monitoring area model into the lidar echo model for integration, thereby obtaining a digital twin model. This digital twin model is a virtual mapping of the actual monitoring area and the lidar detection process, capable of reflecting the atmospheric conditions in the monitoring area and the lidar detection status in real time. The atmospheric parameters of the monitoring area and the operating status of the lidar equipment, collected in real time by the data acquisition module, are transmitted to the point cloud processing and analysis and echo signal analysis modules for dynamic simulation. The dynamic simulation results are then imported into the digital twin model to achieve real-time updates of the monitoring area and the lidar equipment status. The simulation data is displayed through a graphical interface that uses visualization technology to present complex atmospheric parameters and simulation results to users in the form of intuitive charts, curves, and three-dimensional models. It also provides user interaction functions, allowing users to operate and view the model according to their needs, such as zooming, rotating, and querying parameters at specific locations.

[0074] The implementation method of the model calibration unit is to obtain historical monitoring data and simulation data at the corresponding time. The historical monitoring data here must be verified accurate data, and the simulation data is the output result of the lidar modeling module in the same time period. By calculating the deviation value between the two, the degree of difference between the model simulation results and the actual monitoring data is determined. According to the deviation value, the physical property parameters of the three-dimensional point cloud model and the detection parameters of the echo model are adjusted, such as adjusting the physical property parameters such as atmospheric density, viscosity, thermal conductivity, or modifying the detection parameters such as the emission wavelength, pulse energy, and receiving sensitivity of the lidar, so as to reduce the deviation between the model simulation results and the actual monitoring data, realize the dynamic calibration of the lidar modeling module, and ensure that the accuracy and reliability of the model can continue to improve with the accumulation of data.

[0075] Blender open-source software can be used for modeling, and geographic information system data can be obtained from the National Geographic Information Public Service Platform. When adding typical features of the atmospheric boundary layer, based on historical meteorological data for the urban area, an inversion layer typically appears at night in winter, with an altitude ranging from 200 to 500 meters. The mixed layer is more prominent during the day, reaching an altitude of 1000 to 1500 meters.

[0076] During the simulation, the atmospheric density was set to 1.2 kg / m³ and the viscosity was set to , with a thermal conductivity of 0.026 W / (m·K). During dynamic simulations, adjust parameters based on seasonal and meteorological conditions. For example, during summer daytime winds, set the horizontal wind speed to 5-10 m / s and the vertical speed to 0.1-0.5 m / s. For continuous simulations, set the simulation time to 24 hours with a time step of 1 minute.

[0077] In practical applications, data transmission between units requires an efficient and stable protocol to ensure data is not lost or distorted during transmission. Modeling software and simulation tools should be selected based on the characteristics of the monitoring area and the accuracy requirements of the system to ensure effective modeling and simulation. Model calibration is an ongoing process that requires regular monitoring to adapt to changes in atmospheric conditions in the monitoring area and the status of the lidar equipment.

[0078] Through the collaborative work of the spatial field simulation unit, echo characteristic analysis unit, visualization integration unit and model calibration unit, the lidar modeling module realizes three-dimensional modeling, flow characteristic simulation, echo characteristic analysis, dynamic display and model calibration of the atmospheric boundary layer in the monitoring area, providing accurate and reliable model support for the subsequent feature extraction, comprehensive evaluation and graded warning of the boundary layer detection system, enabling the system to more accurately understand the atmospheric boundary layer conditions in the monitoring area.

[0079] Example 3:

[0080] In this embodiment, the feature extraction module is the core link for realizing the accurate calculation of key parameters of the boundary layer. Its specific implementation method is as follows: the module includes a boundary layer height extraction unit, a temperature and humidity gradient extraction unit, and a turbulence intensity extraction unit. Each unit extracts key parameters reflecting the characteristics of the atmospheric boundary layer from the collected raw data through algorithm construction and data processing, providing data support for subsequent comprehensive evaluation and early warning.

[0081] Implementation of the boundary layer height extraction unit first requires the construction of a boundary layer height extraction algorithm based on the physical properties of the atmospheric boundary layer and the principles of lidar detection. In practice, the boundary layer height at different times in the vertical direction of the monitoring area is calculated based on atmospheric parameters processed by the data preprocessing unit, including temperature, humidity, wind speed and direction data. In specific implementation, the vertical distribution characteristics of the lidar echo signal are analyzed and combined with the preprocessed temperature and humidity data to comprehensively determine the location of the interface between the atmospheric boundary layer and the free atmosphere. When the lidar laser beam passes through the atmospheric boundary layer, the intensity, frequency, and other characteristics of the echo signal change due to the different distribution and properties of atmospheric particles in the boundary layer and the free atmosphere. The boundary layer height extraction unit utilizes these changes, combined with atmospheric stability information reflected by the temperature and humidity data, to calculate the boundary layer height through an algorithm, thereby extracting the vertical distribution of the atmospheric boundary layer. For example, if the lidar echo signal exhibits significant intensity attenuation or frequency shift at a certain altitude, and the temperature and humidity data at that altitude also show a corresponding gradient change, that altitude can be determined as the boundary layer altitude.

[0082] The implementation of the temperature and humidity gradient extraction unit requires the construction of a temperature and humidity gradient extraction algorithm, which is based on the temperature and humidity differences between adjacent monitoring points and the characteristics of atmospheric flow. Based on the pre-processed atmospheric parameters, including the temperature and humidity data, wind speed and direction data of each monitoring point, the temperature and humidity gradient in the horizontal direction of the monitoring area is calculated to extract the temperature and humidity change characteristics in the horizontal direction of the monitoring area. In specific implementation, by comparing the temperature and humidity data of adjacent monitoring points, analyzing the temperature and humidity differences at different locations at the same time, and then combining the pre-processed wind speed and direction data, considering the influence of atmospheric flow on the temperature and humidity distribution, the temperature and humidity gradient is obtained through algorithm calculation. Wind speed and direction data can reflect the horizontal movement direction and speed of the atmosphere. When there is horizontal flow in the atmosphere, the horizontal distribution of temperature and humidity will change. Therefore, when calculating the temperature and humidity gradient, wind speed and direction data need to be used as an important correction factor. For example, between adjacent monitoring points A and B, if the temperature at monitoring point A is 25°C and the humidity is 60%, and the temperature at monitoring point B is 22°C and the humidity is 65%, and the wind speed is 5m / s and the wind direction is from A to B, then when calculating the temperature and humidity gradient, it is necessary to consider the impact of wind speed and direction on temperature and humidity transmission, so as to more accurately calculate the horizontal temperature and humidity change rate.

[0083] The implementation of the turbulence intensity extraction unit requires the construction of a turbulence intensity extraction algorithm based on the operating status data of the LiDAR equipment and the physical characteristics of atmospheric turbulence. Based on the preprocessed equipment operating status data, including the LiDAR scanning parameters and echo signal characteristics, the intensity of the atmospheric turbulence in the monitoring area is calculated to extract the intensity of the atmospheric turbulence in the monitoring area. Atmospheric turbulence can cause instability and fluctuations in the LiDAR echo signal. The turbulence intensity extraction unit analyzes these fluctuation characteristics and combines them with the LiDAR scanning parameters such as scanning angle, scanning frequency, and pulse width to calculate the turbulence intensity through an algorithm. For example, when the intensity and frequency of the echo signal fluctuate dramatically during the LiDAR scanning process, and the changes in the scanning parameters are also relatively obvious, it can be determined that the atmospheric turbulence intensity is high at this time.

[0084] The boundary layer height extraction algorithm can be used using the following formula: ,in For the The height corresponding to the sudden change point of the laser radar echo signal intensity, is the number of mutation points. For example, when the laser radar echo signal has intensity mutations at heights of 300 meters, 320 meters, and 350 meters, the boundary layer height rice.

[0085] The temperature and humidity gradient extraction algorithm can use the difference method, and the calculation formula is: , ,in 、 is the temperature value of the adjacent monitoring point, 、 is the humidity value of the adjacent monitoring point, For example, if the distance between adjacent monitoring points A and B is 1000 meters, the temperature at point A is 25°C and the humidity is 60%, and the temperature at point B is 22°C and the humidity is 65%, then the temperature gradient is , humidity gradient .

[0086] The turbulence intensity extraction algorithm can be calculated based on the standard deviation of the lidar echo signal, and the formula is: ,in is the standard deviation of the echo signal velocity, For example, if the standard deviation of the laser radar echo signal velocity in a certain period of time is 0.5m / s and the average velocity is 5m / s, then the turbulence intensity is .

[0087] During feature extraction, data input requires real-time interaction with the data preprocessing unit to ensure that input atmospheric parameters and equipment operating status data are valid data after noise filtering, unified formatting, and feature selection. The algorithm must integrate atmospheric science theory and lidar detection technology, fully considering various influencing factors such as atmospheric stability, topography, and land cover to improve the accuracy and reliability of feature extraction.

[0088] Boundary layer height, temperature and humidity gradient, and turbulence intensity are interrelated parameters that collectively reflect the characteristics of the atmospheric boundary layer. Changes in boundary layer height affect the distribution of temperature and humidity gradients and turbulence intensity, while changes in temperature and humidity gradients and turbulence intensity, in turn, affect the stability of boundary layer height. Therefore, during feature extraction, it is necessary to comprehensively consider the interrelationships between these three parameters to avoid bias in the extraction of a single parameter.

[0089] Furthermore, the implementation of the feature extraction module also needs to consider real-time requirements, ensuring that key parameters can be extracted from the collected real-time data in a timely manner to provide timely data support for subsequent comprehensive assessments and graded warnings. In terms of algorithm design, it is necessary to optimize the computational process and improve computational efficiency to meet the system's real-time requirements.

[0090] Through the collaborative work of the boundary layer height extraction unit, the temperature and humidity gradient extraction unit, and the turbulence intensity extraction unit, the feature extraction module achieves accurate extraction of key parameters of the atmospheric boundary layer, providing an important basis for the comprehensive evaluation and graded warning of the entire boundary layer detection system, enabling the system to more accurately understand the status and change trend of the atmospheric boundary layer in the monitoring area.

[0091] Example 4:

[0092] In this embodiment, the comprehensive evaluation module is the core part for systematic analysis and preliminary stability judgment of the key parameters obtained by the boundary layer detection system. Its specific implementation method is as follows: This module includes a multi-parameter fusion unit and a preliminary judgment unit. Through the normalization and correlation calculation of parameters such as boundary layer height, temperature and humidity gradient, and turbulence intensity, it realizes a comprehensive evaluation and preliminary judgment of the stability of the boundary layer in the monitoring area.

[0093] The implementation of the multi-parameter fusion unit first requires normalizing the three parameters obtained by the feature extraction module: boundary layer height, temperature and humidity gradient, and turbulence intensity. The normalization process uses the minimum-maximum normalization method, and the formula is: ,in is the original data, and are the minimum and maximum values ​​of the data respectively. For example, if the minimum value of the boundary layer height data is 200 meters and the maximum value is 500 meters, and a certain measurement value is 300 meters, then the normalized value is The association calculation uses the weighted summation formula: ,in is the first evaluation value, 、 and are the normalized boundary layer height, temperature and humidity gradient, and turbulence intensity, respectively. 、 and They are the corresponding weights, which can be set based on experience and historical data. , , . Since the dimensions and numerical ranges of these three types of parameters are different, direct correlation calculations will affect the accuracy of the evaluation results. Therefore, they need to be converted into numerical values ​​of unified dimensions through normalization. Normalization can use common standardization methods, such as minimum-maximum standardization or Z-score standardization. The specific method should be selected based on the distribution characteristics of the parameters and the evaluation requirements of the system. During the normalization process, it is necessary to ensure that the original characteristics of the data are not destroyed, while ensuring the comparability between different parameters.

[0094] After normalization, the multi-parameter fusion unit performs a correlation calculation on these three parameters to obtain a first assessment value. This correlation calculation method is based on the physical characteristics of the atmospheric boundary layer and the interrelationships between the various parameters. The boundary layer height reflects the vertical extent of the atmospheric boundary layer, the temperature and humidity gradient reflects the rate of change of temperature and humidity in the horizontal direction, and the turbulence intensity characterizes the intensity of atmospheric turbulence. These three parameters jointly influence the stability of the boundary layer. During the correlation calculation, different weights can be assigned to each parameter based on its influence on boundary layer stability. The calculation is performed using a weighted summation or other appropriate mathematical model to ultimately obtain a first assessment value that comprehensively reflects boundary layer stability. For example, if the boundary layer height is high, the temperature and humidity gradient is small, and the turbulence intensity is low, it indicates that the boundary layer is relatively stable, and the first assessment value may be large. Conversely, if the boundary layer height is low, the temperature and humidity gradient is large, and the turbulence intensity is high, it indicates that the boundary layer is less stable, and the first assessment value may be small.

[0095] The implementation of the preliminary judgment unit requires a pre-defined first baseline value based on atmospheric boundary layer observation specifications and historical data. These specifications include criteria for determining boundary layer stability under different meteorological conditions, while the historical data is derived from long-term observations and records of the monitoring area. Through analysis of these observation specifications and historical data, a reasonable first baseline value is determined, which serves as the threshold for determining boundary layer stability.

[0096] A preliminary comparative evaluation of the first evaluation value obtained by the multi-parameter fusion unit and the preset first benchmark value is performed to assess the stability of the boundary layer in the monitoring area. The specific evaluation scheme is as follows: When the first evaluation value is greater than the first benchmark value, it indicates that under the current meteorological conditions, the various parameters of the boundary layer in the monitoring area are comprehensively stable. At this time, the system can continue to monitor the monitoring area in real time to continuously monitor changes in the boundary layer. When the first evaluation value is less than or equal to the first benchmark value, it indicates that the various parameters of the boundary layer in the monitoring area are comprehensively unstable. Under the current meteorological conditions, there are unstable factors. Early warning measures and further observation operations are needed to timely understand the specific situation of the boundary layer and provide a basis for subsequent graded early warnings.

[0097] In practical applications, the normalization processing and correlation calculation of the multi-parameter fusion unit require real-time data interaction with the feature extraction module to ensure that the acquired boundary layer height, temperature and humidity gradient, and turbulence intensity are the latest real-time data. The preset first reference value is not static and needs to be regularly adjusted and updated based on factors such as seasonal changes and climate anomalies to ensure the accuracy and effectiveness of the preliminary discrimination unit. For example, the characteristics of the atmospheric boundary layer may change significantly in different seasons, and the boundary layer stability in summer and winter is different. Therefore, the first reference value needs to be adjusted accordingly based on seasonal characteristics.

[0098] Furthermore, the implementation of the comprehensive assessment module also requires consideration of data reliability and accuracy. Before normalization and correlation calculations, the input boundary layer height, temperature and humidity gradients, and turbulence intensity must be validated to eliminate the impact of abnormal data on the assessment results. Furthermore, during the initial comparative assessment process, a sound assessment logic and judgment mechanism must be established to avoid misjudgments due to fluctuations in a single parameter.

[0099] Example 5:

[0100] In this embodiment, the graded warning module is the core part of the boundary layer detection system to achieve risk graded response. Its specific implementation method is as follows: the module includes a warning indicator calculation unit and a level determination unit. Through secondary evaluation and multi-dimensional analysis of the boundary layer stability, it generates warning information of different levels to achieve accurate warning and graded disposal of the unstable state of the boundary layer in the monitoring area.

[0101] The early warning indicator calculation unit, based on the preliminary assessment results from the comprehensive assessment module, further integrates the obtained first assessment value to conduct an in-depth analysis of the stability of the monitored area under different atmospheric parameters and lidar detection conditions. Atmospheric parameters include temperature and humidity data, wind speed and direction data, and boundary layer height. Lidar detection status data includes scanning angle, scanning frequency, pulse width, and echo signal characteristics. This unit establishes a multi-parameter correlation analysis model, using the first assessment value as a basic reference. It also incorporates the temporal and spatial variations of atmospheric parameters and the real-time fluctuations of lidar detection data. Through multidimensional data fusion, correlation calculations are performed to obtain a second assessment value. To establish the multi-parameter correlation analysis model, a multivariate linear regression model is used. The first assessment value, temperature and humidity gradients, wind speed, and echo signal strength are used as independent variables, and the second assessment value is used as the dependent variable. The model is trained using historical data. For example, monitoring data from the past year is collected and divided into training and test sets in a 7:3 ratio. The model is trained and evaluated using the Python Scikit-learn library.

[0102] The second benchmark value can be determined based on statistics of atmospheric boundary layer disasters in the city area over the past five years. For example, when a severe turbulence disaster occurs, the corresponding multi-parameter comprehensive assessment value is used as a reference. After statistical analysis and expert evaluation, the second benchmark value is determined to be 0.6.

[0103] During specific implementation, the mutual influence mechanism between atmospheric parameters needs to be considered. For example, a sudden increase in the temperature and humidity gradient may be accompanied by a change in wind speed, thereby exacerbating the turbulence intensity. At this time, even if the boundary layer height has not changed significantly, it may indicate a decrease in boundary layer stability. At the same time, abnormal fluctuations in the lidar detection state, such as a sudden decrease in the echo signal intensity or an abnormal change in the pulse width, may reflect a sudden change in the particle distribution in the atmosphere, which also needs to be used as an important factor in evaluating boundary layer stability. The early warning indicator calculation unit quantifies these factors through an algorithm and couples them with the first evaluation value to obtain a second evaluation value that can comprehensively reflect the influence of multiple environmental factors.

[0104] The implementation of the level determination unit first requires a predefined second baseline value, which is based on historical warning data, atmospheric boundary layer disaster cases, and industry standards. Through statistical analysis of a large amount of historical data, the critical threshold at which the boundary layer transitions from a stable to an unstable state under the combined influence of multiple environmental factors is determined, and this threshold is used as the second baseline value.

[0105] The second evaluation value obtained by the early warning indicator calculation unit is compared with the preset second benchmark value for a second time to further analyze the stability of the boundary layer of the monitoring area under the influence of multiple environmental factors and generate a corresponding early warning level. The specific evaluation plan is as follows:

[0106] When the second assessment value is greater than the second baseline value, it indicates that the boundary layer in the monitored area remains stable under meteorological conditions that take into account environmental factors. Although the initial assessment may show some signs of instability, after comprehensive consideration of multiple factors, the overall stability of the boundary layer remains within a controllable range. At this time, the system generates a Level 3 warning message, which is conveyed to monitoring personnel in the form of a prompt notification, prompting them to continue routine observations of the monitored area and maintain close attention to the boundary layer state to promptly detect possible changes.

[0107] When the second assessment value equals the second baseline value, it indicates that the boundary layer in the monitored area is in an unstable critical state under meteorological conditions that take into account environmental factors, and there is a potential risk of change. In this case, the stability of the boundary layer may change in a short period of time, and more proactive observation measures are required. At this time, the system generates a second-level warning message, which prompts relevant personnel to immediately conduct a detailed survey of the monitored area, including increasing the monitoring frequency, activating backup monitoring equipment, and conducting targeted lidar scans, in order to fully and deeply understand the current state of the boundary layer and provide more detailed data support for subsequent decision-making.

[0108] When the second assessment value is less than the second benchmark value, it indicates that the boundary layer in the monitoring area is significantly unstable under the meteorological conditions after comprehensive environmental factors, and there is a high risk of disasters, such as strong turbulence and accumulation of atmospheric pollution. At this time, the system generates a first-level warning information, which will automatically trigger the early warning alarm system and quickly notify relevant departments and personnel through various means such as sound and light alarms, SMS notifications, and system pop-ups. At the same time, the system will automatically activate the emergency observation plan, including mobilizing mobile monitoring equipment, linking other monitoring systems, and starting the data expedited processing process, to ensure that relevant personnel can obtain information and take emergency measures in the first time, minimizing the adverse effects that may be caused by boundary layer instability.

[0109] In practical applications, the multi-parameter correlation analysis model used in the early warning indicator calculation unit needs to be regularly optimized based on new monitoring data and early warning feedback to improve the accuracy of the second assessment value. For example, if a certain combination of atmospheric parameters has been shown to be highly correlated with boundary layer instability in multiple early warnings, the weight of that parameter in the model can be adjusted accordingly. The second baseline value also needs to be dynamically adjusted based on climate change and environmental changes in the monitored area (such as new buildings and changes in land use types) to ensure the scientific nature of the early warning level determination.

[0110] Furthermore, the tiered warning module must maintain real-time data exchange with the comprehensive assessment module and data acquisition module to ensure that the secondary assessment value is calculated based on the latest monitoring data and assessment results. The early warning information transmission mechanism must be highly reliable to ensure that warning information at different levels reaches the appropriate personnel and systems accurately and promptly, avoiding warning failures caused by delays or errors in information transmission.

[0111] Through the collaborative work of the warning indicator calculation unit and the level determination unit, the graded warning module realizes the refined assessment and graded response to the unstable state of the boundary layer, enabling the boundary layer detection system to take corresponding measures according to different risk levels, improving the system's warning capabilities and efficiency in responding to emergencies, and providing strong technical support for atmospheric environment monitoring and disaster prevention.

[0112] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0113] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A boundary layer detection system based on laser radar, characterized by: It includes data acquisition module, lidar modeling module, feature extraction module, comprehensive evaluation module and graded warning module; The data acquisition module is used to set up monitoring points around the monitoring area and the lidar equipment, and deploy monitoring devices to collect atmospheric parameters of the monitoring area and operating status data of the lidar equipment in real time, and pre-process the collected data; The LiDAR modeling module is used to construct a three-dimensional point cloud model of the atmospheric boundary layer and simulate the flow characteristics of the atmosphere using point cloud processing technology. At the same time, it uses echo signal analysis technology to simulate the detection characteristics of the LiDAR. Based on the real-time collected atmospheric parameters and equipment operating status data, it simulates the parameters of the monitoring area and LiDAR equipment. The feature extraction module is used to construct a boundary layer height extraction algorithm, a temperature and humidity gradient extraction algorithm, and a turbulence intensity extraction algorithm based on the collected atmospheric parameters and equipment operating status data, and transmit the real-time collected atmospheric parameters and equipment operating status data to the constructed extraction algorithm to calculate and obtain the boundary layer height, temperature and humidity gradient, and turbulence intensity; The comprehensive evaluation module is used to normalize the obtained boundary layer height, temperature and humidity gradient, and turbulence intensity, perform correlation calculation to obtain a first evaluation value and preset a first benchmark value, and perform preliminary comparative evaluation and analysis on the stability of the boundary layer in the monitoring area; The graded warning module is used to further calculate and obtain a second evaluation value in combination with environmental factors when analyzing that the boundary layer in the monitoring area is unstable, and to preset a second benchmark value and perform a secondary comparative evaluation with the second evaluation value to further analyze the performance of the boundary layer in the monitoring area under different atmospheric parameters and lidar detection conditions.

2. The laser radar-based boundary layer detection system according to claim 1, characterized in that: The data acquisition module includes an atmospheric parameter acquisition unit, a boundary layer feature acquisition unit and a data preprocessing unit; The atmospheric parameter acquisition unit includes a temperature and humidity acquisition unit and a wind speed and direction acquisition unit, which is used to deploy a meteorological sensor group on the vertical section of the monitoring area to monitor and collect the atmospheric parameters of the monitoring area in real time, and transmit them to the data preprocessing unit through wired transmission. The meteorological sensor group includes a temperature and humidity sensor group and a wind speed and direction sensor group. The atmospheric parameters include temperature and humidity data and wind speed and direction data; The temperature and humidity acquisition unit is used to monitor the temperature and humidity data of the area in real time based on the temperature and humidity sensor group. The temperature and humidity sensor group includes a temperature probe, a humidity probe and a data collector, which respectively collect the temperature value, humidity value and sampling frequency of the temperature and humidity data; The wind speed and direction acquisition unit is used to collect wind speed and direction data in real time based on a wind speed and direction sensor group, wherein the wind speed and direction sensor group includes a cup anemometer, a wind vane anemometer, and a signal converter. The wind speed and direction data includes wind speed, wind direction, measurement height, and signal strength; The boundary layer feature acquisition unit is used to build a communication interface to connect with the control system of the laser radar equipment, read the scanning parameters of the laser radar in the control system in real time, and extract and summarize the scanning angle, scanning frequency and pulse width in the scanning parameters of the laser radar in real time to obtain the operating status data of the laser radar equipment.

3. The laser radar-based boundary layer detection system according to claim 2, characterized in that: The data preprocessing unit is used to filter out noise and outliers from the collected atmospheric parameters and equipment operating status data, unify the data formats from different sources, and perform feature screening on the collected atmospheric parameters and equipment operating status data through time series analysis to obtain the temperature value, humidity value, scanning angle and pulse width at the predicted time.

4. The laser radar-based boundary layer detection system according to claim 1, characterized in that: The laser radar modeling module includes a spatial field simulation unit, an echo characteristic analysis unit and a visualization integration unit; The spatial field simulation unit includes a three-dimensional modeling unit and a flow characteristics simulation unit; The three-dimensional modeling unit extracts terrain data and surface coverage information of the monitoring area from the geographic information system, uses modeling software to establish a three-dimensional point cloud model of the monitoring area, simulates the terrain undulations, surface material and spatial distribution of the area, and adds typical characteristics of the atmospheric boundary layer to the monitoring area, including the inversion layer, mixing layer and residual layer. After the preliminary modeling is completed, the simulation tool is used to define the physical properties of the density, viscosity and thermal conductivity of the atmosphere for the constructed three-dimensional point cloud model, and at the same time sets the launch point, receiving point and scanning range of the laser radar, and performs static simulation, dynamic simulation and continuous simulation to simulate the flow response of the atmospheric boundary layer in the monitoring area; The flow characteristics simulation unit is used to input atmospheric motion parameters, including horizontal wind speed, vertical velocity and turbulence intensity, and then perform point cloud processing and analysis after input to simulate the velocity field, temperature field and humidity field of the flow characteristics of the monitoring area under different meteorological conditions; The echo characteristic analysis unit is used to establish an echo model of the laser radar, including the emission wavelength, pulse energy and receiving sensitivity, and then apply the echo signal equation to simulate the interaction between the laser and atmospheric particles. The echo signal analysis technology is used to analyze the detection results of the laser radar and evaluate the intensity distribution, time delay and spectral characteristics of the echo; The visualization integration unit is used to import the monitoring area model into the lidar echo model for integration to obtain a digital twin model, and then collect the atmospheric parameters of the monitoring area and the operating status data of the lidar equipment in real time, transmit them to point cloud processing analysis and echo signal analysis for dynamic simulation, and import the dynamic simulation results into the digital twin model, update the status of the monitoring area and the lidar equipment in real time, and display the simulation data through a graphical interface to provide user interaction functions.

5. The laser radar-based boundary layer detection system according to claim 1, characterized in that: The feature extraction module includes a boundary layer height extraction unit, a temperature and humidity gradient extraction unit, and a turbulence intensity extraction unit; The boundary layer height extraction unit is used to construct a boundary layer height extraction algorithm, calculate the boundary layer height in the vertical direction of the monitoring area according to the pre-processed atmospheric parameters, and extract the vertical distribution of the atmospheric boundary layer; The temperature and humidity gradient extraction unit is used to construct a temperature and humidity gradient extraction algorithm, calculate and obtain the temperature and humidity gradient based on the pre-processed atmospheric parameters, and extract the temperature and humidity change characteristics in the horizontal direction of the monitoring area; The turbulence intensity extraction unit is used to construct a turbulence intensity extraction algorithm, calculate and obtain turbulence intensity based on the pre-processed equipment operation status data, and extract the strength of atmospheric turbulence in the monitoring area.

6. The laser radar-based boundary layer detection system according to claim 5, characterized in that: The boundary layer height extraction unit is used to calculate and obtain the boundary layer height by analyzing the vertical distribution of the laser radar echo signal in combination with the pre-processed temperature and humidity data, and to extract the interface position between the atmospheric boundary layer and the free atmosphere.

7. The laser radar-based boundary layer detection system according to claim 5, characterized in that: The temperature and humidity gradient extraction unit is used to calculate and obtain the temperature and humidity gradient by comparing the temperature and humidity data of adjacent monitoring points in combination with the pre-processed wind speed and direction data, and to extract the temperature and humidity change rate in the horizontal direction of the monitoring area.

8. The laser radar-based boundary layer detection system according to claim 1, characterized in that: The comprehensive evaluation module includes a multi-parameter fusion unit and a preliminary discrimination unit; The multi-parameter fusion unit is used to normalize the obtained boundary layer height, temperature and humidity gradient, and turbulence intensity, perform correlation calculation to obtain a first evaluation value, and perform a comprehensive analysis on the stability of the boundary layer in the monitoring area; The preliminary judgment unit is used to preset a first reference value based on the observation specifications and historical data of the atmospheric boundary layer, and perform a preliminary comparative evaluation with the obtained first evaluation value to evaluate the stability of the boundary layer in the monitoring area. The specific evaluation scheme is as follows; when the first evaluation value is greater than the first reference value, it indicates that the boundary layer in the monitoring area is stable under the current meteorological conditions and continues to be monitored in real time; when the first evaluation value is less than or equal to the first reference value, it indicates that the boundary layer in the monitoring area is unstable under the current meteorological conditions, and early warning measures and further observation operations need to be taken.

9. The laser radar-based boundary layer detection system according to claim 1, characterized in that: The hierarchical warning module includes a warning indicator calculation unit and a level determination unit; The early warning indicator calculation unit is used to further analyze the stability of the monitoring area under different atmospheric parameters and lidar detection conditions in combination with the obtained first evaluation value, and perform correlation calculation to obtain a second evaluation value; The level determination unit is used to preset a second reference value and the obtained second evaluation value, conduct a secondary comparative evaluation, further analyze the stability of the boundary layer of the monitoring area after the influence of multiple environmental factors, and generate a corresponding warning level. The specific evaluation scheme is as follows; when the second evaluation value is greater than the second reference value, it means that the boundary layer of the monitoring area is still stable under the meteorological conditions after comprehensive environmental factors. At this time, a third-level warning information is generated to remind the monitoring personnel to continue observing the monitoring area; when the second evaluation value is equal to the second reference value, it means that the boundary layer of the monitoring area is unstable under the meteorological conditions after comprehensive environmental factors, and there is a potential risk of change. At this time, a second-level warning information is generated to remind relevant personnel to immediately conduct a detailed detection of the monitoring area; when the second evaluation value is less than the second reference value, it means that the boundary layer of the monitoring area is significantly unstable under the meteorological conditions after comprehensive environmental factors. At this time, a first-level warning information is generated to automatically trigger the warning alarm system, and notify relevant departments and personnel to start the emergency observation plan.

10. The laser radar-based boundary layer detection system according to claim 4, characterized in that: The lidar modeling module also includes a model calibration unit; the model calibration unit is used to obtain historical monitoring data and simulation data at corresponding moments, calculate the deviation value between the two, and adjust the physical property parameters of the three-dimensional point cloud model and the detection parameters of the echo model according to the deviation value to realize dynamic calibration of the lidar modeling module.

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