A steady-state homo-optical path system for unmanned aerial vehicle (UAV) Libs laser detection

Abstract

The application relates to the technical field of new material detection, in particular to a steady-state same-light-path system for Libs laser detection of an unmanned aerial vehicle, which comprises a detection information acquisition module, a laser detection configuration module, a configuration dynamic optimization module, a battery endurance optimization module, a data processing module, a detection optimization control module, a meteorological adaptation optimization module and a detection result pushing module. The application preliminarily configures the battery endurance, the data processing architecture and the detection wavelength through multiple dimensions such as detection positions and detection types, dynamically adjusts the data processing architecture, optimizes the computing power monitoring process, realizes efficient utilization of resources, avoids interruption of detection caused by insufficient computing power or battery endurance problems, pushes key configuration information and detection results in time through the detection result pushing module, provides support for subsequent decision-making, and improves the intelligent level of the laser detection system.

Description

Technical Field

[0001] This invention relates to the field of new material detection technology, and in particular to a steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles. Background Technology

[0002] Traditional LIBS systems typically require a stable optical path and high-precision laser focusing to ensure that laser energy is effectively applied to the sample and accurate spectral data is obtained. However, in UAV applications, the complexity and instability of the flight environment severely affect the stability of the laser optical path. This leads to reduced reliability of measurement results, or even the inability to obtain valid data. To address these issues, a novel steady-state optical path system is urgently needed to achieve stable LIBS laser detection on UAV platforms.

[0003] Chinese Patent Publication No. CN110954527B discloses a novel automated detection system for floating atmospheric particulate matter, including a computer terminal, a laser remote emission unit, a ground angle adjustment unit, and a UAV detection unit. The laser remote emission unit is connected to the computer terminal via signal transmission. One end of the ground angle adjustment unit is fixedly connected to the laser remote emission unit, and the other end is connected to the computer terminal via signal transmission. The UAV detection unit is also connected to the computer terminal via signal transmission. However, this solution is not suitable for UAVs to perform laser detection on targets far from human presence, and it cannot address the computational limitations of real-time processing and analysis, nor can it solve the problem of meteorological influences caused by the environment. Therefore, it has low efficiency for long-distance unmanned laser detection. Summary of the Invention

[0004] To address this, the present invention provides a steady-state co-optical path system for Libs laser detection by unmanned aerial vehicles (UAVs), which overcomes the limitations of existing technologies that are not applicable to UAVs for laser detection of target materials far from human presence and cannot match the computational power constraints for optimized real-time processing and analysis.

[0005] To achieve the above objectives, the present invention provides a steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles, comprising:

[0006] The detection information acquisition module is used to acquire laser detection information;

[0007] The laser detection configuration module is used to configure the battery life and data processing architecture according to the laser detection information, and to pre-select the detection wavelength.

[0008] The configuration dynamic optimization module is used to dynamically optimize the configuration of the data processing architecture based on laser detection information, and also to monitor the computing power of the drone in real time and perform initial optimization on the real-time monitoring process of the drone's computing power.

[0009] The battery life optimization module is used to perform secondary optimization on the real-time monitoring process of the drone's computing power based on laser detection information;

[0010] The data processing module is used to process the laser detection information to obtain the laser detection results;

[0011] The detection optimization control module is used to analyze the surface characteristics of the target based on the laser detection information, optimize the pre-selected configuration of the detection wavelength, and correct the analysis of the surface characteristics of the target.

[0012] The weather adaptation optimization module is used to optimize the analysis process of the surface characteristics of the target based on the laser detection information;

[0013] The detection result push module is used to push information on battery life configuration, data processing architecture, detection wavelength, and laser detection results.

[0014] Further, the laser detection configuration module calculates the drone's flight mileage L based on the coordinates A(x, y, z) of the target to be detected in the laser detection information and the drone's starting point coordinates A0(x0, y0, z0). It then compares the drone's flight mileage L with preset minimum mileage nodes Lmin and Lmax, determines the level of the monitoring location's positioning mileage based on the comparison results, and configures the battery life accordingly.

[0015] When Lmin≥L, the laser detection configuration module determines that the level of the monitoring location positioning mileage is Level 1, and configures the battery life to configure a Level 1 battery for the drone.

[0016] When Lmin < L ≤ Lmax, the laser detection configuration module determines that the level of the monitoring location positioning mileage is level two, and configures the battery life to be level two battery life for the drone.

[0017] When L > Lmax, the laser detection configuration module determines that the level of the monitoring location positioning mileage is level three and does not configure the battery life.

[0018] Furthermore, the laser detection configuration module inputs the target features from the laser detection information into the detection type analysis model to obtain the target type output by the detection type analysis model. The target type includes solid, liquid, and gas, and the data processing architecture is configured according to the target type, wherein:

[0019] When the target to be detected is a solid, the laser detection configuration module selects UAV local data processing as the data processing architecture configuration;

[0020] When the target to be detected is a liquid, the laser detection configuration module selects UAV distributed data processing as the data processing architecture configuration;

[0021] When the target type to be detected is gas, the laser detection configuration module selects UAV hybrid data processing as the data processing architecture configuration.

[0022] Furthermore, the laser detection configuration module compares the transmittance J of the target object in the laser detection information with the preset maximum transmittance Jmax and preset minimum transmittance Jmin, where Jmin = 20% and Jmax = 80%. Based on the comparison result, it judges the transparency of the material and pre-selects and configures the detection wavelength according to the judgment result, wherein:

[0023] When Jmin≥J, the laser detection configuration module determines that the material is opaque, pre-selects and configures the detection wavelength, and sets the detection wavelength to 1064nm;

[0024] When Jmin<J≤Jmax, the laser detection configuration module determines that the material is semi-transparent, pre-selects and configures the detection wavelength, and sets the detection wavelength to 532nm;

[0025] When J > Jmax, the laser detection configuration module determines that the material is transparent, pre-selects and configures the detection wavelength, and sets the detection wavelength to 266nm.

[0026] Furthermore, the configuration dynamic optimization module compares the real-time UAV CPU utilization Q in the laser detection information with the preset UAV CPU utilization Q0, where 70% ≤ Q0. Based on the comparison result, it judges the real-time computing power of the UAV and dynamically optimizes the configuration of the data processing architecture based on the judgment result, wherein:

[0027] When Q≤Q0, the configuration dynamic optimization module determines that the real-time computing power of the UAV is sufficient and does not perform dynamic optimization on the configuration of the data processing architecture.

[0028] When Q > Q0, the configuration dynamic optimization module determines that the real-time computing power of the UAV is insufficient, and dynamically optimizes the configuration of the data processing architecture. The optimization scheme is to directly adjust the configuration of the data processing architecture to UAV hybrid data processing.

[0029] Furthermore, the configuration dynamic optimization module inputs the UAV's pre-flight actions from the laser detection information into the computing power simulation model, obtains the UAV action tendency pre-consumption computing power Dy output by the computing power simulation model, compares the UAV action tendency pre-consumption computing power Dy with the preset UAV action tendency pre-consumption computing power Dy0, and judges the UAV action tendency computing power consumption based on the comparison result. Based on the judgment result, the dynamic optimization process of the data processing architecture configuration is initially optimized, wherein:

[0030] When Dy≤Dy0, the configuration dynamic optimization module determines that the computing power consumption of the UAV action tendency is low, and does not perform the initial optimization of the dynamic optimization process of the data processing architecture configuration.

[0031] When Dy > Dy0, the configuration dynamic optimization module determines that the computing power consumption of the UAV's directional behavior is high, and performs an initial optimization of the dynamic optimization process of the data processing architecture configuration, using the initial optimization coefficient qy = 0.7 + 0.3 × e -(Dy-Dy0) Let e ​​be the base of the natural logarithm. The preset drone CPU utilization Q0 is initially optimized to obtain the preset drone CPU utilization Q0y1 after the initial optimization. Let Q0y1 = Q0 × qy. Replace the preset drone CPU utilization Q0 with the preset drone CPU utilization Q0y1 after the initial optimization, and then compare the real-time drone CPU utilization Q with the preset drone CPU utilization Q0y1 after the initial optimization.

[0032] Furthermore, the battery life optimization module compares the remaining battery level Md of the drone in the laser detection information with the preset remaining battery level Md0, and judges the remaining battery level of the drone based on the comparison result. Based on the judgment result, it performs a secondary optimization of the dynamic optimization process of the data processing architecture configuration, wherein:

[0033] When Md > Md0, the battery life optimization module determines that the drone's battery has sufficient remaining power and does not perform secondary optimization on the dynamic optimization process of the data processing architecture configuration.

[0034] When Md≤Md0, the battery life optimization module determines that the drone's battery is insufficient and performs a secondary optimization on the dynamic optimization process of the data processing architecture configuration. Based on the secondary optimization coefficient yc=0.85, the preset drone CPU utilization rate Q0y1 after the initial optimization is optimized to obtain the preset drone CPU utilization rate Q0y2 after the secondary optimization. Q0y2=Q0y1×yc is set, and the preset drone CPU utilization rate Q0y1 after the initial optimization is replaced with the preset drone CPU utilization rate Q0y2 after the secondary optimization. The real-time drone CPU utilization rate Q is then compared with the preset drone CPU utilization rate Q0y2 after the secondary optimization.

[0035] Furthermore, the data processing module uses a spectrometer to convert the spectrum of the target to be detected in the laser detection information into an electrical signal to obtain raw spectral data. It then subtracts the background signals of the spectrometer itself and the ambient light from the raw spectrum, performs wavelength correction on the raw spectral data, compares the raw spectral data with the elemental characteristic spectral database, identifies the characteristic peak positions and the element types corresponding to the characteristic peak positions in the raw spectral data, determines the element content based on the intensity of the characteristic peaks, and outputs the element type and element content as the laser detection result.

[0036] Furthermore, the detection optimization control module calculates the laser detection difficulty H based on the surface flatness f and light source absorptivity g of the target surface in the laser detection information, compares the laser detection difficulty H with the preset laser detection difficulty H0, judges the surface characteristics of the target based on the comparison result, and optimizes the detection wavelength based on the judgment result, wherein:

[0037] When H≤H0, the detection optimization control module determines that the surface characteristics of the target are easy to detect and does not optimize the detection wavelength.

[0038] When H > H0, the detection optimization control module determines that the surface characteristics of the target are difficult to detect, and optimizes the detection wavelength. The optimization method is to calculate the appropriate detection wavelength Sc based on the target thickness hd, target material concentration nd, and molar absorptivity γ in the laser detection information, replace the detection wavelength with the appropriate detection wavelength Sc, and then detect the target.

[0039] Furthermore, the detection optimization control module compares the element types Z = [z1,z2,z3,...,zn] in the laser detection results with the preset difficult-to-detect element types T = [T1,T2,T3,...,Tm], where n is the order of the element types and m is the preset difficult-to-detect element type order. Based on the comparison results, the module judges the difficulty of detecting the element types and corrects the judgment process for the surface characteristics of the detection target based on the judgment results.

[0040] When there is no preset difficult-to-detect element type T that matches the element type Z in the laser detection result, the detection optimization control module determines the difficulty level of the detection element type as easy to detect and does not correct the judgment process of the surface characteristics of the detection target.

[0041] When there is a preset difficult-to-detect element type T that is consistent with element type Z in the laser detection result, the detection optimization control module determines that the difficulty of the detected element type is difficult to detect, and corrects the judgment process of the surface characteristics of the detection target. The preset laser detection difficulty H0 is corrected according to the element content Yh to obtain the corrected preset laser detection difficulty H0j. H0j is set to (1-Yh)×H0j, and the preset laser detection difficulty H0 is replaced with the corrected preset laser detection difficulty H0j. The laser detection difficulty H is then compared with the corrected preset laser detection difficulty H0j again.

[0042] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention acquires laser detection information in real time through a detection information acquisition module, enabling subsequent adjustments to the UAV and laser configurations. The system also calculates the UAV's flight mileage through a laser detection configuration module, configuring a suitable battery to ensure sufficient power for flight and detection tasks. Furthermore, it analyzes the characteristics of the target to be detected, obtains the target type, and configures the data processing architecture accordingly. This allows for advance planning of the UAV's data processing methods for the target, avoiding improper data processing that could lead to significant deviations in results. Finally, it assesses the transparency of materials and pre-selects the detection wavelength based on this transparency. The system configures and pre-sets the detection wavelength for the target to avoid situations where the laser detection wavelength cannot fully detect the target, thus improving the intelligence of UAV laser detection. The system also uses a dynamic optimization module to acquire the pre-consumption computing power of UAV movement trends and judges the computing power consumption of UAV movement trends based on this pre-consumption. The system then performs an initial optimization of the data processing architecture configuration based on the computing power consumption of UAV movement trends, reducing the preset value of the UAV CPU utilization. This improves the accuracy of detecting insufficient computing power in real-time UAV computing power, enabling better early warning of insufficient real-time computing power and timely dynamic adjustments to the data processing architecture configuration. To optimize and avoid inaccurate target detection results due to insufficient drone computing power, the system improves the accuracy of target detection and the matching of computing power. This is achieved through real-time monitoring of the drone's remaining battery level and a secondary optimization process that dynamically optimizes the data processing architecture configuration based on this level. This secondary optimization involves reducing the preset drone CPU utilization value after the initial optimization to prevent inaccurate target detection due to insufficient drone battery power, thereby improving the matching of drone computing power. The system also uses a data processing module to convert the raw spectral data into element types and element content, and uses these element types and element content as the basis for laser detection results. The system outputs data in rows so that the detection wavelength can be adjusted subsequently based on the element type and content. The system also uses a detection optimization control module to determine the surface characteristics of the target, optimize the detection wavelength, select the most suitable wavelength for laser detection, and improve the accuracy of Libs laser detection. It also determines the difficulty of detecting different element types and corrects the determination process for the target surface characteristics based on this difficulty. If the detection optimization control module determines that an element type is difficult to detect, it lowers the preset laser detection difficulty value based on the element content, making it easier to determine that the target surface characteristics are difficult to detect, thus allowing for adjustment of the detection wavelength for more difficult-to-detect elements.The system selects the most suitable detection wavelength for laser detection of the target, improving the accuracy of Libs laser detection. It also uses a weather adaptation optimization module to determine the weather adaptation status and optimize the analysis process of the target surface characteristics based on this status. When the weather adaptation status is unsuitable, the difficulty of Libs laser detection increases significantly due to the influence of visibility, rainfall, and wind. In this case, the preset laser detection difficulty value needs to be reduced, making it easier to determine if the target surface characteristics are difficult to detect. This optimizes the detection wavelength to better meet the detection requirements of the target, thereby improving the accuracy of Libs laser detection. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the steady-state co-optical path system used for Libs laser detection of UAVs in this embodiment;

[0044] Figure 2 This is a schematic diagram of the structure of the UAV Libs laser detection device in this embodiment. Detailed Implementation

[0045] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0046] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] Please see Figure 1 The diagram shown is a structural schematic of the steady-state co-optical path system for Libs laser detection of UAVs in this embodiment. The system includes:

[0048] The detection information acquisition module is used to acquire laser detection information;

[0049] A laser detection configuration module is used to configure the battery life and data processing architecture according to the laser detection information, and to pre-select the detection wavelength. The laser detection configuration module is connected to the detection information acquisition module.

[0050] A dynamic optimization module is configured to dynamically optimize the configuration of the data processing architecture based on laser detection information. It also monitors the computing power of the UAV in real time and performs initial optimization on the real-time monitoring process of the UAV computing power. The dynamic optimization module is connected to the laser detection configuration module.

[0051] The battery life optimization module is used to perform secondary optimization on the real-time monitoring process of the drone's computing power based on laser detection information. The battery life optimization module is connected to the configuration dynamic optimization module.

[0052] The data processing module is used to process the laser detection information to obtain the laser detection result. The data processing module is connected to the battery life optimization module.

[0053] The detection optimization control module is used to analyze the surface characteristics of the target based on the laser detection information, optimize the pre-selected configuration of the detection wavelength, and correct the analysis of the surface characteristics of the target. The detection optimization control module is connected to the data processing module.

[0054] The weather adaptation optimization module is used to optimize the analysis process of the surface characteristics of the detection target based on the laser detection information. The weather adaptation optimization module is connected to the detection optimization control module.

[0055] The detection result push module is used to push information on battery life configuration, data processing architecture, detection wavelength and laser detection results. The detection result push module is connected to the weather adaptation optimization module.

[0056] Specifically, the system is applied to a UAV Libs laser detection device. By configuring and optimizing the UAV Libs laser detection device, it achieves optimized control of the steady-state optical path for UAV Libs laser detection, thereby improving the efficiency, accuracy, and adaptability of laser detection. The system uses a detection information acquisition module to collect laser detection information in real time for subsequent adjustments to the UAV and laser configurations. The system also uses a laser detection configuration module to calculate the UAV's flight mileage and configure a suitable battery for the UAV, ensuring sufficient power for flight and detection tasks. Furthermore, it analyzes the characteristics of the target to be detected, obtains the target type, and logs the data based on the target type. The system is configured with a processing architecture that allows for pre-planning of the UAV's data processing methods for targets, preventing improper data processing that could lead to significant deviations in results. It assesses the transparency of materials and pre-selects and configures detection wavelengths based on this transparency. Pre-setting the detection wavelength for the target prevents situations where the laser detection wavelength cannot fully detect the target, thus improving the intelligence of UAV laser detection. The system also uses a dynamic optimization module to acquire the pre-consumption of computing power based on the UAV's motion tendency, assesses the computing power consumption related to the UAV's motion tendency, and adjusts the data processing architecture configuration accordingly. The initial optimization process involves reducing the preset value of the drone's CPU utilization to improve the accuracy of detecting insufficient computing power in real-time. This provides better early warning of insufficient computing power and allows for timely dynamic optimization of the data processing architecture configuration. This prevents inaccurate calculations of detection results due to insufficient drone computing power, thus improving the accuracy of target detection and the matching of detection computing power. A second optimization process is performed by monitoring the drone's remaining battery level in real time and adjusting the data processing architecture configuration based on this level. This second optimization further reduces the preset drone CPU utilization value after the initial optimization. To avoid inaccurate target detection due to insufficient drone battery power and improve the matching of drone computing power, the system also uses a data processing module to convert raw spectral data into element types and content, outputting these as laser detection results. This allows for subsequent adjustment of the detection wavelength based on the element types and content. Furthermore, the system uses a detection optimization control module to assess the surface characteristics of the target, optimize the detection wavelength, select the most suitable wavelength for laser detection, improve the accuracy of Libs laser detection, and determine the difficulty of detecting element types, correcting the target surface characteristic assessment process accordingly.When the detection optimization control module determines that the difficulty of detecting a particular element is "difficult," it lowers the preset laser detection difficulty value based on the element content. This makes it easier to determine that the surface characteristics of the target are difficult to detect, allowing for adjustment of the detection wavelength for harder-to-detect elements. The most suitable detection wavelength is then selected for laser detection of the target, improving the accuracy of Libs laser detection. The system also uses a weather adaptation optimization module to assess the weather adaptation status and optimize the analysis process of the target surface characteristics based on this status. When the weather adaptation status is "unsuitable," the difficulty of Libs laser detection increases significantly due to the influence of visibility, rainfall, and wind. In this case, the preset laser detection difficulty value needs to be lowered to make it easier to determine that the target surface characteristics are difficult to detect, thereby optimizing the detection wavelength to better meet the detection requirements of the target and improving the accuracy of Libs laser detection.

[0057] Specifically, the laser detection information includes the coordinates of the target to be detected, the coordinates of the drone's starting point, the transmittance of the target to be detected, the features of the target to be detected, the real-time CPU utilization of the drone, the drone's pre-flight maneuvers, the remaining battery power of the drone, the surface flatness of the target to be detected, the light source absorptivity of the target surface, the thickness of the target to be detected, the concentration of the substance on the target to be detected, visibility, rainfall, and wind force. The detection information acquisition module acquires the coordinates of the target to be detected and the coordinates of the drone's starting point by inputting coordinate information from the user to the control terminal. The detection information acquisition module also acquires the features of the target to be detected through a camera and the transmittance of the target to be detected through a spectrometer. The system acquires real-time UAV CPU utilization through a CPU monitoring system. The detection information acquisition module acquires the UAV's pre-flight actions by receiving flight commands input from the user on the control terminal. The detection information acquisition module also acquires the UAV's remaining battery level through a power monitoring system. Furthermore, the detection information acquisition module acquires the surface flatness of the target object through a camera, the light source absorbance of the target object surface through a spectrometer, the thickness of the target object through a camera measuring instrument, the concentration of the target object substance through a concentration measuring instrument, and visibility, rainfall, and wind speed through a meteorological instrument.

[0058] Specifically, the laser detection configuration module calculates the UAV's flight distance L based on the coordinates A(x, y, z) of the target to be detected in the laser detection information and the coordinates A0(x0, y0, z0) of the UAV's starting point, and sets... The drone's flight range L is compared with the preset minimum range node Lmin and the preset maximum range node Lmax. The range is defined as 10km ≤ Lmin ≤ 15km and 25km ≤ Lmax ≤ 30km. Based on the comparison results, the positioning range level of the monitored location is determined, and the battery life is configured accordingly.

[0059] When Lmin≥L, the laser detection configuration module determines that the level of the monitoring location positioning mileage is Level 1, and configures the battery life to configure a Level 1 battery for the drone.

[0060] When Lmin < L ≤ Lmax, the laser detection configuration module determines that the level of the monitoring location positioning mileage is level two, and configures the battery life to be level two battery life for the drone.

[0061] When L > Lmax, the laser detection configuration module determines that the level of the monitoring location positioning mileage is level three and does not configure the battery life.

[0062] Specifically, if L > Lmax, the laser detection configuration module determines that the coordinates of the target exceed the maximum flight distance of the UAV and changes the coordinates of the UAV's starting point to obtain the changed UAV starting point coordinates A0g(x0g, y0g, z0g). Based on the changed UAV starting point coordinates A0g(x0g, y0g, z0g) and the coordinates A(x, y, z) of the target to be detected, the UAV flight mileage is recalculated to obtain the changed UAV flight mileage L1. The UAV flight mileage L is replaced with the changed UAV flight mileage L1, and the changed UAV flight mileage L1 is compared with the preset maximum mileage node Lmax until L1 ≤ Lmax.

[0063] Specifically, the coordinates of the target to be detected refer to the coordinates of the location of the target substance; the coordinates of the UAV's departure point refer to the coordinates of the starting point of the UAV's takeoff; the preset minimum mileage node refers to the minimum preset value used to determine the level of the monitoring location mileage; the preset maximum mileage node refers to the maximum preset value used to determine the level of the monitoring location mileage; and the level of the monitoring location mileage refers to the rating of the flight mileage after comparing the UAV's flight mileage with the preset minimum and maximum mileage nodes. The levels of the monitoring location mileage include Level 1, Level 2, and Level 3. The first-level battery refers to the battery used to meet the flight requirements of a drone with a positioning range of level one at the monitoring location. The second-level battery refers to the battery used to meet the flight requirements of a drone with a positioning range of level two at the monitoring location. The modified drone starting point coordinates refer to the coordinates that are closer to the coordinates of the target to be detected by moving the drone's starting point closer to the coordinates of the target to be detected. This embodiment does not limit the specific method of modifying the drone's starting point coordinates. Those skilled in the art can set it according to actual needs. For example, the drone's starting point coordinates and the coordinates of the target to be detected can be connected by a straight line on the map, and the drone's starting point can be adjusted on the straight line to gradually move closer to the coordinates of the target to be detected.

[0064] Specifically, the laser detection configuration module calculates the drone's flight mileage and configures a suitable battery for the drone, ensuring that the drone has enough power to meet flight and detection tasks.

[0065] Specifically, the laser detection configuration module inputs the target features from the laser detection information into the detection type analysis model, obtains the target type output by the detection type analysis model, and the target type includes solid, liquid, and gas. The module then configures the data processing architecture according to the target type, wherein:

[0066] When the target to be detected is a solid, the laser detection configuration module selects UAV local data processing as the data processing architecture configuration;

[0067] When the target to be detected is a liquid, the laser detection configuration module selects UAV distributed data processing as the data processing architecture configuration;

[0068] When the target type to be detected is gas, the laser detection configuration module selects UAV hybrid data processing as the data processing architecture configuration.

[0069] Specifically, the target features to be detected refer to the physical properties of the target, which are attributes directly related to the target's material state, structural characteristics, and environmental conditions. The detection type analysis model refers to a neural network learning model that takes the target features as input and the target type as output. This embodiment does not limit the construction method of the detection type analysis model; those skilled in the art can set it according to actual needs. For example, 70% of the historical material analysis dataset can be divided into a material analysis training set to train the detection type analysis model, 15% of the historical material analysis dataset can be divided into a material analysis validation set to validate the performance of the trained detection type analysis model, and another 15% of the historical material analysis dataset can be divided into a material analysis test set to validate the performance of the detected type analysis model. The type analysis model undergoes accuracy testing, and the above training, verification, and testing steps are repeated until the test accuracy of the detection type analysis model reaches 92%. The historical material analysis dataset refers to the learning dataset used to construct the detection type analysis model. The historical material analysis dataset includes historical target features and historical target types. The UAV local data processing refers to a data processing method in which the UAV directly completes data collection, analysis, storage, and decision-making on the onboard equipment during flight. The UAV distributed data processing refers to a data processing method that achieves a fully distributed data collection, transmission, and analysis process through collaborative operation of multiple UAVs, edge computing, and cloud linkage. The UAV hybrid data processing refers to a data processing method in which UAV local data processing and UAV distributed data processing are performed simultaneously.

[0070] Specifically, the laser detection configuration module obtains the type of the target by analyzing its features and configures the data processing architecture according to the target type. This allows for advance planning of the UAV's data processing method for the target, avoiding improper data processing that could lead to large deviations in the data processing results.

[0071] Specifically, the laser detection configuration module compares the transmittance J of the target object in the laser detection information with the preset maximum transmittance Jmax and preset minimum transmittance Jmin, where Jmin = 20% and Jmax = 80%. Based on the comparison result, it judges the transparency of the material and pre-selects and configures the detection wavelength according to the judgment result, wherein:

[0072] When Jmin≥J, the laser detection configuration module determines that the material is opaque, pre-selects and configures the detection wavelength, and sets the detection wavelength to 1064nm;

[0073] When Jmin<J≤Jmax, the laser detection configuration module determines that the material is semi-transparent, pre-selects and configures the detection wavelength, and sets the detection wavelength to 532nm;

[0074] When J > Jmax, the laser detection configuration module determines that the material is transparent, pre-selects and configures the detection wavelength, and sets the detection wavelength to 266nm.

[0075] Specifically, the transmittance of the target to be detected refers to the ratio of the intensity of transmitted light to the intensity of incident light after light passes through the target to be detected. The preset maximum transmittance refers to the maximum preset value used to judge the transparency of the material. The preset minimum transmittance refers to the minimum preset value used to judge the transparency of the material. The transparency of the material refers to the classification of the transparency of the target to be detected based on the transmittance of the target to be detected. The transparency of the material includes opaque, semi-transparent, and transparent.

[0076] Specifically, the laser detection configuration module determines the transparency of a material based on the transmittance of the target and pre-selects and configures the detection wavelength according to the transparency of the material. The detection wavelength of the target is pre-set to avoid the situation where the laser detection wavelength cannot fully detect the target, thereby improving the intelligence of UAV laser detection.

[0077] Specifically, the configuration dynamic optimization module compares the real-time UAV CPU utilization Q in the laser detection information with the preset UAV CPU utilization Q0, where 70% ≤ Q0. Based on the comparison result, it judges the real-time computing power of the UAV and dynamically optimizes the configuration of the data processing architecture according to the judgment result.

[0078] When Q≤Q0, the configuration dynamic optimization module determines that the real-time computing power of the UAV is sufficient and does not perform dynamic optimization on the configuration of the data processing architecture.

[0079] When Q > Q0, the configuration dynamic optimization module determines that the real-time computing power of the UAV is insufficient, and dynamically optimizes the configuration of the data processing architecture. The optimization scheme is to directly adjust the configuration of the data processing architecture to UAV hybrid data processing.

[0080] Specifically, the real-time drone CPU utilization rate refers to the proportion of computing resources occupied by the drone's built-in processor in processing tasks per unit time, as monitored in real time. The preset drone CPU utilization rate refers to a preset value used to judge the drone's real-time computing power. The drone's real-time computing power status refers to the computing power status obtained by judging the drone's real-time computing power based on the real-time drone CPU utilization rate. The drone's real-time computing power status includes situations where the drone's real-time computing power is sufficient and situations where the drone's real-time computing power is insufficient.

[0081] Specifically, the configuration dynamic optimization module monitors the real-time CPU utilization of the UAV to determine the real-time computing power of the UAV, and dynamically optimizes the configuration of the data processing architecture based on the real-time computing power of the UAV. This avoids situations where insufficient computing power of the UAV leads to inaccurate calculation of the detection results of the target to be detected, thereby improving the detection accuracy of the target to be detected.

[0082] Specifically, the configuration dynamic optimization module inputs the UAV's pre-flight actions from the laser detection information into the computing power simulation model, obtains the UAV action tendency pre-consumption computing power Dy output by the computing power simulation model, and compares the UAV action tendency pre-consumption computing power Dy with the preset UAV action tendency pre-consumption computing power Dy0. Dy0 ≥ 2 × 10 9 The FLOPs / s is used to determine the computational power consumption for UAV motion directional behavior based on the comparison results. Based on these results, the dynamic optimization process of the data processing architecture is initially optimized, including:

[0083] When Dy≤Dy0, the configuration dynamic optimization module determines that the computing power consumption of the UAV action tendency is low, and does not perform the initial optimization of the dynamic optimization process of the data processing architecture configuration.

[0084] When Dy > Dy0, the configuration dynamic optimization module determines that the computing power consumption of the UAV's directional behavior is high, and performs an initial optimization of the dynamic optimization process of the data processing architecture configuration, using the initial optimization coefficient qy = 0.7 + 0.3 × e -(Dy-Dy0) Let e ​​be the base of the natural logarithm. The preset drone CPU utilization Q0 is initially optimized to obtain the preset drone CPU utilization Q0y1 after the initial optimization. Let Q0y1 = Q0 × qy. Replace the preset drone CPU utilization Q0 with the preset drone CPU utilization Q0y1 after the initial optimization, and then compare the real-time drone CPU utilization Q with the preset drone CPU utilization Q0y1 after the initial optimization.

[0085] Specifically, the pre-flight maneuvers of the UAV refer to the flight maneuvers that the UAV will perform. These pre-flight maneuvers include hovering, constant-speed cruise, high-speed turning, dynamic obstacle avoidance, and autonomous landing. The computing power simulation model is a machine learning model that takes the pre-flight maneuvers as input and outputs the pre-consumption computing power of the UAV's maneuver tendencies. The pre-consumption computing power of the UAV's maneuver tendencies refers to the computing power required to execute the pre-flight maneuvers. The pre-set pre-consumption computing power of the UAV's maneuver tendencies refers to a preset value used to judge the computing power consumption of the UAV's maneuver tendencies. The computing power consumption of the UAV's maneuver tendencies refers to the computing power consumption of the pre-flight maneuvers, which includes situations where the computing power consumption is low and high. This embodiment... The specific construction process of the computing power simulation model is not limited. Those skilled in the art can set it according to actual needs, as long as it meets the requirement of outputting the pre-consumption computing power of UAV action directional. For example, 75% of the historical UAV action computing power dataset can be divided into a computing power training set to train the computing power simulation model, 15% of the historical UAV action computing power dataset can be divided into a computing power verification set to verify the trained computing power simulation model, and 10% of the historical UAV action computing power dataset can be divided into a computing power test set to test the verified computing power simulation model, until the test accuracy of the computing power simulation model reaches 92%. The historical UAV action computing power dataset refers to the dataset used to construct the computing power simulation model. The historical UAV action computing power dataset includes historical UAV pre-flight actions and the historical UAV action directional pre-consumption computing power corresponding to the historical UAV pre-flight actions.

[0086] Specifically, the configuration dynamic optimization module obtains the pre-consumption computing power of the UAV's movement trends, judges the computing power consumption of the UAV's movement trends based on the pre-consumption computing power, and performs an initial optimization of the dynamic optimization process of the data processing architecture configuration based on the computing power consumption of the UAV's movement trends. This reduces the preset value of the preset UAV CPU utilization Q0, thereby improving the accuracy of monitoring when judging the UAV's real-time computing power as insufficient. It can better warn of insufficient real-time computing power of the UAV, and dynamically optimize the configuration of the data processing architecture in a timely manner, avoiding the situation where insufficient UAV computing power leads to inaccurate calculation of the detection results of the target to be detected, and improving the detection accuracy of the target to be detected.

[0087] Specifically, the battery endurance optimization module compares the remaining battery level Md of the drone in the laser detection information with the preset remaining battery level Md0, where Md0 ≥ 45%. Based on the comparison result, it judges the remaining battery level of the drone and performs secondary optimization on the dynamic optimization process of the data processing architecture configuration based on the judgment result.

[0088] When Md > Md0, the battery life optimization module determines that the drone's battery has sufficient remaining power and does not perform secondary optimization on the dynamic optimization process of the data processing architecture configuration.

[0089] When Md≤Md0, the battery life optimization module determines that the drone's battery is insufficient and performs a secondary optimization on the dynamic optimization process of the data processing architecture configuration. Based on the secondary optimization coefficient yc=0.85, the preset drone CPU utilization rate Q0y1 after the initial optimization is optimized to obtain the preset drone CPU utilization rate Q0y2 after the secondary optimization. Q0y2=Q0y1×yc is set, and the preset drone CPU utilization rate Q0y1 after the initial optimization is replaced with the preset drone CPU utilization rate Q0y2 after the secondary optimization. The real-time drone CPU utilization rate Q is then compared with the preset drone CPU utilization rate Q0y2 after the secondary optimization.

[0090] Specifically, the remaining battery level of the drone refers to the current remaining power of the drone battery. The preset battery level refers to a preset value used to determine the remaining battery level of the drone. The remaining battery level of the drone refers to whether the remaining power of the drone battery is sufficient. The remaining battery level of the drone includes situations where the remaining battery level is sufficient and situations where the remaining battery level is insufficient.

[0091] Specifically, the battery life optimization module performs secondary optimization by monitoring the remaining battery level of the drone in real time and dynamically optimizing the configuration of the data processing architecture based on the remaining battery level. By reducing the value of the preset drone CPU utilization Q0y1 after the initial optimization, the module further optimizes the dynamic optimization process of the data processing architecture configuration to avoid inaccurate detection of the target due to insufficient drone power, thereby improving the detection accuracy of the target.

[0092] Specifically, the data processing module uses a spectrometer to convert the spectrum of the target to be detected in the laser detection information into an electrical signal to obtain raw spectral data. It then subtracts the background signals of the spectrometer itself and the ambient light from the raw spectrum, performs wavelength correction on the raw spectral data, compares the raw spectral data with an elemental characteristic spectral database, identifies the position of the characteristic peaks in the raw spectral data and the element types corresponding to the characteristic peaks, determines the element content based on the intensity of the characteristic peaks, and outputs the element type and element content as the laser detection result.

[0093] Specifically, the spectrum of the target to be detected refers to the light signal released during the cooling process of plasma formed when laser light is emitted onto the target. The original spectral data refers to the spectral data obtained by converting the spectrum of the target to be detected into an electrical signal. This embodiment does not limit the model of the spectrometer; those skilled in the art can set it according to actual needs, such as limiting the spectrometer model to a Qred portable near-infrared spectrometer. This embodiment does not limit the specific implementation method for wavelength correction of the original spectral data; those skilled in the art can set it according to actual needs, such as using a baseline correction method to correct the wavelength of the original spectral data. The elemental characteristic spectral database refers to a database of characteristic spectral information generated by various chemical elements under specific physical conditions. The characteristic peak position refers to the key physical parameter position corresponding to the strong signal peak in the original spectral data. The intensity of the characteristic peak refers to the difference between the peak signal value and the baseline. This embodiment does not limit the specific implementation method for determining the element content based on the intensity of the characteristic peak; those skilled in the art can set it according to actual needs, such as using a calibration curve to convert the measured intensity of the characteristic peak into element concentration, thereby determining the element content.

[0094] Specifically, the detection optimization control module calculates the laser detection difficulty H based on the surface flatness f and light source absorptivity g of the target surface in the laser detection information, setting H = α × f + β × g, α = 0.2, β = 0.8. It then compares the laser detection difficulty H with a preset laser detection difficulty H0, where H0 ≥ 0.75. Based on the comparison result, it judges the surface characteristics of the target and optimizes the detection wavelength accordingly.

[0095] When H≤H0, the detection optimization control module determines that the surface characteristics of the target are easy to detect and does not optimize the detection wavelength.

[0096] When H > H0, the detection optimization control module determines that the surface characteristics of the target are difficult to detect, and optimizes the detection wavelength. The optimization method is to calculate the appropriate detection wavelength Sc based on the target thickness hd, target material concentration nd, and molar absorptivity γ in the laser detection information, set Sc = hd × nd × γ, replace the detection wavelength with the appropriate detection wavelength Sc, and then detect the target.

[0097] Specifically, the surface flatness of the target to be detected refers to the smoothness of the target surface, with 1 as the standard and 0≤f≤1; the light source absorptivity of the target surface refers to the ability of the target surface material to absorb laser light, with 1 as the standard and 0≤g≤1; the preset laser detection difficulty refers to a preset value used to judge the surface characteristics of the target; the surface characteristics of the target refers to the judgment of the difficulty of detecting the target surface based on the laser detection difficulty; the surface characteristics of the target include easy-to-detect and difficult-to-detect surface characteristics; the thickness of the target to be detected refers to the average overall thickness of the target; and the concentration of the target substance refers to the concentration of the substance to be detected. The target substance is the content of the target substance in the solid sample that can absorb the laser wavelength. The molar absorptivity is a core parameter in spectrophotometry that describes the ability of the target substance to absorb light of a specific wavelength. It reflects the absorption efficiency of solute molecules at a specific wavelength. The molar absorptivity is obtained by comparing the element types in the laser detection results with an expert absorptivity database. This embodiment does not limit the comparison process between the element types in the laser detection results and the expert absorptivity database. Those skilled in the art can set it according to actual needs. For example, the absorptivity of the element types in the expert absorptivity database that are the same as the element types in the laser detection results can be used as the molar absorptivity.

[0098] Specifically, the detection optimization control module determines the surface characteristics of the target and optimizes the detection wavelength, selecting the most suitable detection wavelength for laser detection of the target, thereby improving the accuracy of Libs laser detection.

[0099] Specifically, the detection optimization control module compares the element types Z = [z1,z2,z3,...,zn] in the laser detection results with the preset difficult-to-detect element types T = [T1,T2,T3,...,Tm], where n is the order of the element types and m is the preset difficult-to-detect element type order. Based on the comparison results, the module judges the difficulty of detecting the element types and corrects the judgment process for the surface characteristics of the detection target based on the judgment results.

[0100] When there is no preset difficult-to-detect element type T that matches the element type Z in the laser detection result, the detection optimization control module determines the difficulty level of the detection element type as easy to detect and does not correct the judgment process of the surface characteristics of the detection target.

[0101] When there is a preset difficult-to-detect element type T that is consistent with element type Z in the laser detection result, the detection optimization control module determines that the difficulty of the detected element type is difficult to detect, and corrects the judgment process of the surface characteristics of the detection target. The preset laser detection difficulty H0 is corrected according to the element content Yh to obtain the corrected preset laser detection difficulty H0j. H0j is set to (1-Yh)×H0j, and the preset laser detection difficulty H0 is replaced with the corrected preset laser detection difficulty H0j. The laser detection difficulty H is then compared with the corrected preset laser detection difficulty H0j again.

[0102] Specifically, the preset difficult-to-detect element types refer to element types that are more difficult to detect than ordinary element types. This embodiment does not limit the specific element types of the preset difficult-to-detect element types. Those skilled in the art can set them according to the actual situation. For example, hydrogen, lithium, beryllium, and boron can be set as preset difficult-to-detect element types. The difficulty level of the element type refers to the difficulty level of detecting the element type. The difficulty level of the element type includes the element type being easy to detect and the element type being difficult to detect.

[0103] Specifically, the detection optimization control module judges the difficulty of detecting different types of elements and corrects the judgment process of the surface characteristics of the target based on the difficulty of detecting different types of elements. When the detection optimization control module determines that the difficulty of detecting different types of elements is difficult to detect, it reduces the preset laser detection difficulty H0 value according to the element content, making it easier for the laser detection difficulty H to determine that the surface characteristics of the target are difficult to detect. This allows for adjustment of the detection wavelength for more difficult-to-detect elements, selecting the most suitable detection wavelength for laser detection of the target, and improving the accuracy of Libs laser detection.

[0104] Specifically, the weather adaptation optimization module calculates the weather suitability index Kq based on the visibility A, rainfall B, and wind force C from the laser detection information, setting Kq = 0.3 × A + 0.4 × B + 0.3 × C. It then compares the weather suitability index Kq with a preset weather suitability index Kq0, where Kq0 ≥ 0.75. Based on the comparison result, it judges the weather adaptability status and optimizes the analysis process of the surface characteristics of the detected target based on the judgment result.

[0105] When Kq≤Kq0, the meteorological adaptation optimization module determines the meteorological adaptation state as adaptive and does not optimize the analysis process of the surface characteristics of the detected target.

[0106] When Kq > Kq0, the meteorological adaptation optimization module determines the meteorological adaptation state as unsuitable and optimizes the analysis process of the surface characteristics of the detected target using the meteorological optimization coefficient qxy = 1.3 - 0.3 × e-(Kq-Kq0) Let e ​​be the base of the natural logarithm. The preset laser detection difficulty H0 is optimized to obtain the optimized preset laser detection difficulty H0y. The preset laser detection difficulty H0 is replaced with the optimized preset laser detection difficulty H0y, and the laser detection difficulty H is compared with the optimized preset laser detection difficulty H0y again.

[0107] Specifically, the visibility refers to the visibility of the surrounding environment at the current time, with 1 as the standard and 0≤A≤1; the rainfall refers to the rainfall of the surrounding environment at the current time, with 1 as the standard and 0≤B≤1; the wind force refers to the wind force value of the surrounding environment at the current time, with 1 as the standard and 0≤C≤1; the preset weather suitability index refers to the preset value for judging the meteorological suitability state; the meteorological suitability state refers to the judgment of whether the Libs detection operation is suitable based on the weather suitability index; the meteorological suitability state includes a meteorological suitability state of "suitable" and a meteorological suitability state of "unsuitable".

[0108] Specifically, the weather adaptation optimization module judges the weather adaptation status and optimizes the analysis process of the surface characteristics of the target based on the weather adaptation status. When the weather adaptation status is unsuitable, the detection difficulty of Libs laser will be greatly increased due to the influence of visibility, rainfall and wind. At this time, it is necessary to reduce the preset laser detection difficulty H0 value so that the surface characteristics of the target are more easily judged as difficult to detect. This optimizes the detection wavelength so that the detection wavelength is more in line with the detection requirements of the target, thereby improving the accuracy of Libs laser detection.

[0109] Specifically, the detection result push module sends the battery life configuration, data processing architecture, and detection wavelength to the control terminal, which then controls the UAV's Libs laser detection device. The detection result push module also sends the laser detection results to the control terminal, which then sends them to the user.

[0110] Specifically, the control terminal refers to a terminal device in the system used to send instructions, monitor status, and manage equipment operation. This embodiment does not limit the specific implementation of sending battery life configuration, data processing architecture, and detection wavelength to the control terminal. Those skilled in the art can set these according to actual needs. For example, the battery life configuration, data processing architecture, and detection wavelength can be sent to the control terminal via wireless communication. This embodiment does not limit the specific implementation of sending laser detection results to the control terminal. Those skilled in the art can set these according to actual needs. For example, the laser detection results can be sent to the control terminal via wireless communication.

[0111] Please see Figure 2As shown, this is a schematic diagram of the structure of the UAV Libs laser detection device in this embodiment. The device includes:

[0112] The main body of the drone 1 is connected to the first drone wing 2, the second drone wing 3, the third drone wing 4, the fourth drone wing 5, the spectrometer 6, and the Libs laser detection device 7, respectively, and is used to load the various components of the drone.

[0113] The first drone wing 2 is connected to the drone body 1 and the first propeller 201 respectively, and is used to carry the first propeller 201;

[0114] The second drone wing 3 is connected to the drone body 1 and the second propeller 301 respectively, and is used to carry the second propeller 301.

[0115] The third drone wing 4 is connected to the drone body 1 and the third propeller 401 respectively, and is used to carry the third propeller 401.

[0116] The fourth UAV wing 5 is connected to the UAV body 1 and the fourth propeller 501 respectively, and is used to carry the fourth propeller 501.

[0117] The spectrometer 6 is connected to the main body of the UAV 1 and is used to acquire the light source absorbance of the surface of the target to be detected, and also to convert the spectrum of the target to be detected into an electrical signal.

[0118] The Libs laser detection device 7 is connected to the main body of the UAV 1 and is used to emit lasers towards the target to be detected;

[0119] The steady-state co-optical path system (not shown in the figure) used for Libs laser detection of UAVs is located inside the UAV body and is used to control the UAV.

[0120] Specifically, the device is used in uninhabited environments to perform laser detection on target substances in uninhabited areas. With the vertical take-off and landing capability and adaptability to complex terrain of UAVs, the device can penetrate dangerous areas that are difficult for humans to reach. The rapid scanning of the target avoids sample damage and equipment contamination through non-contact detection, adapts to extreme environments, and analyzes the elemental types and composition of the target in real time using spectral data, providing immediate support for resource exploration, pollution source tracing, etc., while reducing labor costs and safety risks.

[0121] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles, characterized in that, include: The detection information acquisition module is used to acquire laser detection information; The laser detection configuration module is used to configure the battery life and data processing architecture according to the laser detection information, and to pre-select the detection wavelength. The configuration dynamic optimization module is used to dynamically optimize the configuration of the data processing architecture based on laser detection information, and also to monitor the computing power of the drone in real time and perform initial optimization on the real-time monitoring process of the drone's computing power. The battery life optimization module is used to perform secondary optimization on the real-time monitoring process of the drone's computing power based on laser detection information; The data processing module is used to process the laser detection information to obtain the laser detection results; The detection optimization control module is used to analyze the surface characteristics of the target based on the laser detection information, optimize the pre-selected configuration of the detection wavelength, and correct the analysis of the surface characteristics of the target. The weather adaptation optimization module is used to optimize the analysis process of the surface characteristics of the target based on the laser detection information; The detection result push module is used to push information on battery life configuration, data processing architecture, detection wavelength, and laser detection results.

2. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 1, characterized in that, The laser detection configuration module calculates the drone's flight mileage L based on the coordinates A(x, y, z) of the target to be detected in the laser detection information and the drone's starting point coordinates A0(x0, y0, z0). It then compares the drone's flight mileage L with preset minimum mileage nodes Lmin and Lmax, determines the level of the monitoring location's positioning mileage based on the comparison results, and configures the battery life accordingly. When Lmin≥L, the laser detection configuration module determines that the level of the monitoring location positioning mileage is Level 1, and configures the battery life to configure a Level 1 battery for the drone. When Lmin < L ≤ Lmax, the laser detection configuration module determines that the level of the monitoring location positioning mileage is level two, and configures the battery life to be level two battery life for the drone. When L > Lmax, the laser detection configuration module determines that the level of the monitoring location positioning mileage is level three and does not configure the battery life.

3. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 2, characterized in that, The laser detection configuration module inputs the target features from the laser detection information into the detection type analysis model, obtains the target type output by the detection type analysis model, and the target type includes solid, liquid, and gas. The module then configures the data processing architecture according to the target type, wherein: When the target to be detected is a solid, the laser detection configuration module selects UAV local data processing as the data processing architecture configuration; When the target to be detected is a liquid, the laser detection configuration module selects UAV distributed data processing as the data processing architecture configuration; When the target type to be detected is gas, the laser detection configuration module selects UAV hybrid data processing as the data processing architecture configuration.

4. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 3, characterized in that, The laser detection configuration module compares the transmittance J of the target object in the laser detection information with the preset maximum transmittance Jmax and preset minimum transmittance Jmin, where Jmin = 20% and Jmax = 80%. Based on the comparison result, it judges the transparency of the material and pre-selects and configures the detection wavelength according to the judgment result. When Jmin≥J, the laser detection configuration module determines that the material is opaque, pre-selects and configures the detection wavelength, and sets the detection wavelength to 1064nm; When Jmin<J≤Jmax, the laser detection configuration module determines that the material is semi-transparent, pre-selects and configures the detection wavelength, and sets the detection wavelength to 532nm; When J > Jmax, the laser detection configuration module determines that the material is transparent, pre-selects and configures the detection wavelength, and sets the detection wavelength to 266nm.

5. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 4, characterized in that, The configuration dynamic optimization module compares the real-time UAV CPU utilization Q in the laser detection information with the preset UAV CPU utilization Q0, where 70% ≤ Q0. Based on the comparison result, it judges the real-time computing power of the UAV and dynamically optimizes the configuration of the data processing architecture according to the judgment result. When Q≤Q0, the configuration dynamic optimization module determines that the real-time computing power of the UAV is sufficient and does not perform dynamic optimization on the configuration of the data processing architecture. When Q > Q0, the configuration dynamic optimization module determines that the real-time computing power of the UAV is insufficient, and dynamically optimizes the configuration of the data processing architecture. The optimization scheme is to directly adjust the configuration of the data processing architecture to UAV hybrid data processing.

6. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 5, characterized in that, The configuration dynamic optimization module inputs the UAV's pre-flight actions from the laser detection information into the computing power simulation model, obtains the UAV action tendency pre-consumption computing power Dy output by the computing power simulation model, compares the UAV action tendency pre-consumption computing power Dy with the preset UAV action tendency pre-consumption computing power Dy0, and judges the UAV action tendency computing power consumption based on the comparison result. Based on the judgment result, the dynamic optimization process of the data processing architecture configuration is initially optimized, wherein: When Dy≤Dy0, the configuration dynamic optimization module determines that the computing power consumption of the UAV action tendency is low, and does not perform the initial optimization of the dynamic optimization process of the data processing architecture configuration. When Dy > Dy0, the configuration dynamic optimization module determines that the computing power consumption of the UAV's directional behavior is high, and performs an initial optimization of the dynamic optimization process of the data processing architecture configuration, using the initial optimization coefficient qy = 0.7 + 0.3 × e -(Dy-Dy0) Let e ​​be the base of the natural logarithm. The preset drone CPU utilization Q0 is initially optimized to obtain the preset drone CPU utilization Q0y1 after the initial optimization. Let Q0y1 = Q0 × qy. Replace the preset drone CPU utilization Q0 with the preset drone CPU utilization Q0y1 after the initial optimization, and then compare the real-time drone CPU utilization Q with the preset drone CPU utilization Q0y1 after the initial optimization.

7. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 6, characterized in that, The battery endurance optimization module compares the remaining battery level Md of the drone in the laser detection information with the preset remaining battery level Md0, and judges the remaining battery level of the drone based on the comparison result. Based on the judgment result, it performs a secondary optimization of the dynamic optimization process of the data processing architecture configuration, wherein: When Md > Md0, the battery life optimization module determines that the drone's battery has sufficient remaining power and does not perform secondary optimization on the dynamic optimization process of the data processing architecture configuration. When Md≤Md0, the battery life optimization module determines that the drone's battery is insufficient and performs a secondary optimization on the dynamic optimization process of the data processing architecture configuration. Based on the secondary optimization coefficient yc=0.85, the preset drone CPU utilization rate Q0y1 after the initial optimization is optimized to obtain the preset drone CPU utilization rate Q0y2 after the secondary optimization. Q0y2=Q0y1×yc is set, and the preset drone CPU utilization rate Q0y1 after the initial optimization is replaced with the preset drone CPU utilization rate Q0y2 after the secondary optimization. The real-time drone CPU utilization rate Q is then compared with the preset drone CPU utilization rate Q0y2 after the secondary optimization.

8. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 7, characterized in that, The data processing module uses a spectrometer to convert the spectrum of the target to be detected in the laser detection information into an electrical signal to obtain raw spectral data. It then subtracts the background signals of the spectrometer itself and the ambient light from the raw spectrum, performs wavelength correction on the raw spectral data, compares the raw spectral data with the elemental characteristic spectral database, identifies the characteristic peak positions and the element types corresponding to the characteristic peak positions in the raw spectral data, determines the element content based on the intensity of the characteristic peaks, and outputs the element type and element content as the laser detection result.

9. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 8, characterized in that, The detection optimization control module calculates the laser detection difficulty H based on the surface flatness f and light source absorptivity g of the target surface in the laser detection information. It compares the laser detection difficulty H with a preset laser detection difficulty H0, judges the surface characteristics of the target based on the comparison result, and optimizes the detection wavelength based on the judgment result. Specifically: When H≤H0, the detection optimization control module determines that the surface characteristics of the target are easy to detect and does not optimize the detection wavelength. When H > H0, the detection optimization control module determines that the surface characteristics of the target are difficult to detect, and optimizes the detection wavelength. The optimization method is to calculate the appropriate detection wavelength Sc based on the target thickness hd, target material concentration nd, and molar absorptivity γ in the laser detection information, replace the detection wavelength with the appropriate detection wavelength Sc, and then detect the target.

10. The steady-state co-optical path system for Libs laser detection of unmanned aerial vehicles according to claim 9, characterized in that, The detection optimization control module compares the element types Z = [z1, z2, z3, ..., zn] in the laser detection results with the preset difficult-to-detect element types T = [T1, T2, T3, ..., Tm], where n is the order of the element types and m is the preset difficult-to-detect element type order. Based on the comparison results, the module judges the difficulty of detecting the element types and corrects the judgment process for the surface characteristics of the detection target based on the judgment results. When there is no preset difficult-to-detect element type T that matches the element type Z in the laser detection result, the detection optimization control module determines the difficulty level of the detection element type as easy to detect and does not correct the judgment process of the surface characteristics of the detection target. When there is a preset difficult-to-detect element type T that is consistent with element type Z in the laser detection result, the detection optimization control module determines that the difficulty of the detected element type is difficult to detect, and corrects the judgment process of the surface characteristics of the detection target. The preset laser detection difficulty H0 is corrected according to the element content Yh to obtain the corrected preset laser detection difficulty H0j. H0j is set to (1-Yh)×H0j, and the preset laser detection difficulty H0 is replaced with the corrected preset laser detection difficulty H0j. The laser detection difficulty H is then compared with the corrected preset laser detection difficulty H0j again.

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