Road inspection method and system based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of road inspection, in particular to a road inspection method and system based on an unmanned aerial vehicle. The road inspection method comprises the following steps: guiding an unmanned aerial vehicle to fly along a preset path to cover a specified inspection road area, and adjusting a flight strategy through a set program according to real-time environment data; multiband vibration signals are collected in real time through a high-frequency vibration sensing module on the unmanned aerial vehicle and are used for capturing subtle changes and abnormities on the surface of an inspection road; an analog-to-digital conversion technology is used to convert a multiband vibration signal into a digital signal. According to the road inspection method and system based on the unmanned aerial vehicle provided by the invention, the efficiency and accuracy of road inspection are improved by combining the flexible flight capability of the unmanned aerial vehicle and the data processing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of road inspection, and in particular to a road inspection method and system based on an unmanned aerial vehicle. Background Art

[0002] Road inspection refers to a series of inspection work such as daily cleaning, maintenance, reconstruction, safety testing, management and planning of road systems such as highways and urban roads, with the aim of ensuring traffic safety and road quality. Traditional road inspection methods mainly rely on manual inspection and ground detection equipment, which are not only inefficient but also costly when faced with a wide range of road networks. In addition, manual monitoring is easily limited by factors such as weather and time, resulting in inaccurate and incomplete inspection results, especially when identifying small cracks and subtle changes in roads, traditional methods seem to be unable to cope with the situation. Therefore, the problem raised by the present invention is: how to improve the accuracy and timeliness of road inspections. Summary of the invention

[0003] The present invention provides a road inspection method and system based on an unmanned aerial vehicle to solve the technical problems of insufficient accuracy and timeliness of road inspection in the prior art.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: On the one hand, a road inspection method based on an unmanned aerial vehicle is provided, and the road inspection method comprises the following steps: Guide the drone to fly along a preset path to cover the designated inspection road area, and adjust the flight strategy according to real-time environmental data through set programs; The high-frequency vibration sensor module on the drone collects multi-band vibration signals in real time to capture subtle changes and anomalies on the road surface during inspection. Use analog-to-digital conversion technology to convert multi-band vibration signals into digital signals; The digital signal is input into the data processing module, and the deep learning model is used to perform real-time analysis of vibration characteristics to generate analysis results and key data reflecting subtle changes and anomalies on the inspected road surface; After analyzing the vibration characteristics, the analysis results and key data are transmitted to the remote control station in real time through the data communication module for real-time monitoring of the inspection road and subsequent inspection report generation.

[0005] On the other hand, a road inspection system based on a drone is provided, the system comprising: High-frequency vibration sensing module, used to collect and analyze vibration signals on the road surface in real time to capture potential anomalies; The signal acquisition module is equipped with an analog-to-digital conversion unit for converting analog signals into digital signals, and also has an error correction unit; Data processing module, equipped with a deep learning unit, supports fast vibration data analysis with multi-layer neural networks, combined with real-time adaptive learning algorithms to improve accuracy; Data communication module, supporting multi-band data transmission and encryption protocols, for real-time transmission of analysis results and key data; Remote control station for receiving analysis results and key data; The control decision module is used to dynamically adjust the inspection path based on the analysis results, plan the inspection path and perform inspection task management.

[0006] The beneficial effects of the present invention are: The drone-based road inspection method and system provided by the present invention improves the efficiency and accuracy of road inspection by combining the flexible flight capabilities and data processing technology of drones. The drone flies along a preset path and collects multi-band vibration signals in real time through a high-frequency vibration sensing module, which can effectively capture subtle changes and anomalies on the road surface. In addition, the vibration signal is converted into a digital signal through analog-to-digital conversion, and a deep learning model is used for real-time analysis, which can quickly generate analysis results and key data on road health conditions.

[0007] The drone-based road inspection method and system of the present invention can not only cope with various complex and changeable environmental conditions, but also have stronger adaptability to environmental changes by dynamically adjusting signal analysis parameters. As a result, the inspection system can still maintain high-precision signal analysis capabilities under different climatic conditions and more accurately reflect the actual conditions of the road surface. At the same time, the real-time data transmission and feedback mechanism enables the remote control station to immediately obtain and process the analysis results, improving the timeliness of inspection decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of a road inspection method based on a drone in one embodiment of the present invention; Figure 2 This is a schematic diagram of a road inspection based on a drone in one embodiment of the present invention; Figure 3 A thread diagram of a deep learning model in one embodiment of the present invention; Figure 4 A schematic diagram of a curve showing the influence of temperature, humidity and wind speed on a vibration signal in an embodiment of the present invention; Figure 5 Schematic diagram of normal and abnormal vibration signal waveforms in one embodiment of the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0010] The present invention provides the following preferred embodiments: Embodiment 1 In order to solve the problems of low efficiency and accuracy limitation caused by environmental factors in current road inspections, this embodiment proposes a road inspection method based on drones. Through a series of program control steps, the drone can fully cover the designated inspection road area, and dynamically adjust the flight strategy in combination with real-time environmental data to improve the accuracy and timeliness of the inspection.

[0011] like Figure 1 and Figure 2 As shown, the road inspection method based on drone includes the following steps: S101, guiding the UAV to fly along a preset path to cover a designated inspection road area, and adjusting the flight strategy according to real-time environmental data through a set program.

[0012] S102. The high-frequency vibration sensor module on the drone is used to collect multi-band vibration signals in real time to capture subtle changes and anomalies on the surface of the inspection road.

[0013] S103, using analog-to-digital conversion technology to convert the multi-band vibration signal into a digital signal.

[0014] S104: Input the digital signal into the data processing module, and use the deep learning model to perform real-time analysis of the vibration characteristics to generate analysis results and key data that reflect subtle changes and anomalies on the inspected road surface.

[0015] S105. After analyzing the vibration characteristics, the analysis results and key data are transmitted to the remote control station in real time through the data communication module for real-time monitoring of the inspection road and subsequent inspection report generation.

[0016] Specifically, in this embodiment, the drone is equipped with a high-frequency vibration sensing module, which is used to collect complex signal data including multi-band vibration signals in real time. It should be understood that the multi-band vibration signal is a basic analog vibration signal that captures subtle changes and potential anomalies on the road surface. Common vibration sensor models on the market, such as acceleration sensors and laser Doppler vibrometers, can be used in this embodiment.

[0017] Furthermore, in the process of signal acquisition, this embodiment uses analog-to-digital conversion technology to convert the above analog vibration signal into a digital signal. Because digital signals can be analyzed and processed more complexly. It is understandable that the selection of analog-to-digital converter will directly affect the quality of subsequent analysis data, so a high-precision analog-to-digital conversion chip is selected, such as a model with 16-bit or higher conversion resolution, to improve the accuracy of the measurement data.

[0018] Furthermore, the digitized vibration signal is input into the data processing module. This module uses a deep learning model to perform real-time analysis of the vibration signal to identify and extract feature information reflecting changes in road conditions. The thread diagram of the deep learning model is shown in the figure below. Figure 3 As shown. It is important to understand that the selection and training of deep learning models need to comprehensively consider the complexity of road conditions and the size of existing data. For example, a convolutional neural network (CNN) can be selected to process the graphical features in vibration signals, while transfer learning technology can be used to enhance the model's adaptability to specific roads.

[0019] Furthermore, after the vibration characteristic analysis is completed, the present embodiment transmits the analysis results and key data to the remote control station in real time through the data communication module. The communication technology used here must not only ensure the transmission speed, but also ensure the integrity and security of the data. It is understandable that encryption protocols such as TLS or IPSec are applied to protect the privacy and security of data transmission.

[0020] The benefit of this embodiment is that by combining the flexible navigation capabilities of drones and automated data processing technology, it is possible to optimize road inspection work. Through real-time data analysis and transmission, the remote control station can accurately obtain the health status of the road, quickly generate inspection reports and make maintenance decisions. It should be understood that this process greatly reduces the time and manpower required for manual intervention while ensuring the continuity of inspection operations. Through this embodiment, it is ensured that road inspections can still maintain efficiency and accuracy under complex and changing environmental conditions. The method of this embodiment can be widely used in many fields such as public transportation management and infrastructure maintenance, to realize a technology-driven intelligent management model, and thus improve the quality and efficiency of public facility management.

[0021] Embodiment 2 In order to solve the problem that the accuracy of signal analysis in traditional road inspection methods is affected by environmental factors, this embodiment further refines the environmental data collection and analysis parameter adjustment mechanism of the drone during the vibration signal collection process. In this context, this embodiment realizes the automatic collection of environmental parameters such as temperature, humidity and wind speed through an integrated sensor module, and dynamically adjusts the signal analysis parameters based on these environmental data, thereby improving the analysis accuracy of the vibration signal.

[0022] Furthermore, in this embodiment, the drone is equipped with a multifunctional sensor module, which not only captures the vibration signal of the road surface, but also measures the ambient temperature, humidity and wind speed in real time. These data are uniformly transmitted to the signal processing unit through the environmental data acquisition unit. It should be understood that these three types of environmental data will have different degrees of influence on the characteristics of the vibration signal. For example, temperature changes may cause changes in material properties, and wind speed may affect the stability of the drone and the quality of data acquisition.

[0023] Furthermore, in the vibration signal analysis process, this embodiment introduces an environmental adjustment factor A(t) for automatically adjusting the vibration signal analysis parameters. The calculation formula of the environmental adjustment factor A(t) is:

[0024] Where A(t) represents the environmental adjustment factor, which is used to automatically adjust parameters during vibration signal analysis. env is the ambient temperature, is the temperature reference value, H env is the ambient humidity, W env is the ambient wind speed, and α, β, and γ are adjustment coefficients. It is understandable that these adjustment coefficients have been verified by a large amount of experimental data, and only by selecting appropriate values ​​can the accuracy and response speed of parameter adjustment be guaranteed. For example, the temperature adjustment part uses an exponential model to reflect the nonlinear effect of temperature changes, while humidity and wind speed are processed through logarithmic functions and linear functions to capture their different change patterns, such as Figure 4 shown.

[0025] Furthermore, while processing the vibration signal, this embodiment uses a deep learning algorithm to optimize the analysis process in order to better adapt to the changing measurement environment. During the process, the deep learning model processes the vibration signal data after environmental adjustment. This means that in the signal preprocessing stage, the environmental changes have been preliminarily filtered and the variables have been normalized, so that the model can extract the characteristic information of the road vibration signal more efficiently and accurately. It should be understood that by adaptively adjusting the weights and biases of the analysis model, this embodiment can maintain a high recognition accuracy under different environmental conditions.

[0026] Through this embodiment, the inspection results are not only more accurate, but also reduce the signal analysis errors caused by environmental changes, so that the drone inspection system can collect and process data faster and more reliably. This method is particularly suitable for large and complex road systems at home and abroad that are affected by large temperature and humidity fluctuations. It can effectively reduce the environmental dependence of inspection operations and improve the overall adaptability and resilience of the system. It should be emphasized that with the introduction of the environmental adjustment mechanism, the stability of the technical solution of this embodiment is ensured under a wide range of application conditions, making it suitable for a variety of complex road inspection scenarios.

[0027] Embodiment 3 In order to solve the problems of low data processing efficiency and low accuracy of vibration feature analysis under different road conditions in existing road inspection methods, this embodiment further refines and applies deep learning algorithms, integrates adaptive algorithm optimization mechanisms, and realizes real-time self-learning adjustment of vibration feature analysis parameters. This not only improves the processing capability of road vibration data, but also enhances the system's recognition accuracy of abnormal vibration features.

[0028] Specifically, this embodiment uses a multi-layer neural network to perform fusion processing on the collected digital signals to more comprehensively extract the breadth and depth of vibration features. The architecture design of the multi-layer neural network takes into account the characteristics of complex data and usually includes multiple convolutional layers and fully connected layers. This design enables the network to identify and extract vibration features representing different frequency bands and intensities. It should be understood that the convolutional layer can capture local features in the signal through filtering operations, while the fully connected layer is used to integrate these features for subsequent analysis and decision-making.

[0029] Furthermore, the adaptive algorithm function applied in this embodiment is to gradually optimize the model parameters through step learning so that it can adapt to different road surface materials and environmental conditions. The step learning mechanism provides a method to gradually update the weighting value through historical inspection data, so that the model can continuously improve the ability of vibration data classification and dynamic prediction when responding to different challenges. It can be understood that this dynamic weighting mechanism enables the model to have the characteristics of rapid response, and can adjust the analysis strategy in a short time according to environmental changes or road surface types.

[0030] Furthermore, when analyzing the inspection data of vibration characteristics, the system of this embodiment can identify potential abnormal patterns in the data in real time. Once a vibration characteristic beyond the normal range is detected, an early warning message is immediately generated and fed back to the remote control station through the communication module. This real-time early warning mechanism ensures that the inspection system can respond quickly, timely troubleshoot and locate potential road problems, and facilitate rapid repairs. It should be understood that real-time feedback not only relies on the analysis ability of the algorithm, but also relies on a low-latency and highly reliable data communication link to deliver information to the decision-making end in the shortest time.

[0031] Through this embodiment, the accuracy and efficiency of vibration feature analysis are improved, especially when dealing with complex and changeable road conditions, the method of this embodiment shows strong adaptability. This embodiment shows how to effectively embed advanced artificial intelligence and deep learning technologies into road inspections, making drone inspections an intelligent and efficient tool, and providing a new solution for improving the timeliness and effectiveness of road maintenance. The drone road inspection designed and optimized in this way shows broad prospects for application in various climates and geographical environments, which helps to achieve continuous monitoring and maintenance of road health.

[0032] Embodiment 4 In order to solve the problem of poor vibration signal feature extraction capability in traditional road inspection methods, this embodiment further refines the architecture design of the multi-layer neural network in the data processing module to improve the accuracy and efficiency of vibration feature analysis. Specifically, the multi-layer neural network introduces convolutional layers and pooling layers to improve the feature extraction capability, and dynamically adjusts the network weight parameters through an adaptive algorithm to adapt to different road and environmental conditions.

[0033] Furthermore, the introduction of the convolution layer is to be able to locally perceive and capture the features of the input vibration signal, and to capture the spectral characteristics of the vibration signal by applying different convolution kernels to filter the signal. It should be understood that the convolution layer can identify the curve changes and peak features in the vibration signal in terms of extracting local details, which helps to analyze the subtle vibration changes on the road surface, such as Figure 5 The subsequent pooling layer uses downsampling to reduce the data dimension while retaining key information to reduce computational complexity and enhance anti-interference capabilities.

[0034] Furthermore, the adaptive algorithm used adjusts the weight parameters of the multi-layer neural network through dynamic expressions, aiming to further improve the adaptability and learning efficiency of the network under different working conditions. The expression of the adaptive algorithm is:

[0035] Among them, v j (t) represents the vibration amplitude of each characteristic point, ω j and ϕ j are frequency and phase angle respectively, σ is the adjustment coefficient, and x(t) is the displacement caused by vibration. It can be understood that the formula P(t) combines the amplitude, frequency and phase angle of the vibration feature point, and adjusts the network's response ability to complex vibration signals by weighted summation combined with high-order vibration displacement derivatives. It can be understood that the design of the adjustment coefficient σ takes into account the impact of signal noise and vibration amplitude on displacement inference, thereby ensuring stability in a changing environment.

[0036] In this embodiment, the real-time analysis of vibration signals uses the dynamic adjustment mechanism of the network architecture, which enables the algorithm to quickly adapt and update in new environments and uncalibrated road conditions. The neural network can adjust internal parameters through step iterations during operation and make corrections based on real-time input feedback. It should be emphasized that this adaptability not only improves the processing capability of vibration signals of different types of road materials, but also enhances the system's capture speed and response accuracy to sudden abnormal situations.

[0037] Through this example, the system demonstrates the accuracy and efficiency of feature extraction in processing vibration data in complex and highly dynamic environments. The network structure and adaptive algorithm mechanism enable drone inspections to respond to diverse road environments more intelligently and efficiently, providing solid technical support for real-time monitoring and maintenance of roads.

[0038] Embodiment 5 In order to solve the problem of lack of systematic feedback mechanism in traditional drone inspection tasks, this embodiment further optimizes the feedback steps after the inspection is completed to form effective flight strategy optimization suggestions. By introducing the feedback mechanism, the drone records the flight parameters throughout the inspection task, thereby providing data support and optimization direction for subsequent tasks.

[0039] Specifically, during each inspection, the sensors and computing modules inside the drone continuously monitor and record a series of key flight parameters, including speed, flight path, energy consumption, and signal acquisition quality. These parameters not only affect the success of the current mission, but also serve as the basis for building historical mission data. It should be understood that historical mission data provides continuous samples for comprehensive analysis of flight efficiency and signal acquisition quality, and among all historical mission data, the record of specific samples Ri(t) lays an accurate data foundation for subsequent strategy optimization.

[0040] Furthermore, for the recorded historical mission data, the system performs aggregate analysis on specific samples, and the adopted strategy adjustment coefficient ε is used to construct a comprehensive data evaluation formula. By calculating the ratio of the sum of sample data to the quantity, the fluctuation and consistency of these parameters between missions are further calculated and analyzed. It can be understood that this aggregation process helps identify key data features related to flight strategies and evaluate group data performance.

[0041] Furthermore, compare the current task parameter Ej(t) with the average value of the historical task parameters , the system introduces a variation control factor ω to identify and quantify the deviation between actual operations and historical average levels. The generated data deviation information provides the input basis for the strategy adjustment model F(t), and the calculation formula of the strategy adjustment model F(t) is:

[0042] It is understandable that the strategy adjustment model F(t) in this embodiment comprehensively considers the historical aggregation results of flight parameters and mission deviations, and generates optimization suggestions to improve future flight plans. Through the strategy adjustment model F(t), the system outputs dynamic adjustment suggestions covering path adjustment, speed change and signal acquisition frequency. Among them, the strategy model integrates multi-dimensional flight data and deviation information through a combined expression to provide specific and feasible adjustment directions for the drone. It is understandable that the generation of optimization suggestions depends on accurate data analysis and model calculation to ensure that the drone can perform more efficient and stable flights in the next inspection mission.

[0043] Through this embodiment, the feedback process after the inspection task is completed is systematically designed, which can effectively improve the flight strategy and performance of the drone. The accumulation and analysis of historical data not only provides a comprehensive evaluation for the current task, but also provides a basis for the planning of future tasks, ensuring that the drone inspection operation is highly adaptable and reliable in different environments and task backgrounds.

[0044] Embodiment 6 In order to solve the problem that the traditional road inspection report does not accurately record the road health status and cannot effectively analyze the specific situation of subtle changes and abnormal locations, this embodiment further optimizes the generation algorithm of the inspection report to provide a more detailed road health classification record. The classification record can not only reflect the overall status of the road, but also accurately locate the specific location of the abnormality and its severity.

[0045] Furthermore, this embodiment uses an optimization algorithm C(n) to generate the inspection report content. The calculation formula of the optimization algorithm C(n) is:

[0046] Where n represents different road sections, k is an index variable, indicating the kth anomaly, and N is the total number of anomalies. By introducing the decay function , accurately evaluate the kth abnormal position, D k The data related to the abnormal location is represented by θ, which is the sensitivity parameter controlling the attenuation function. It can be understood as an appropriate adjustment of the scope and degree of abnormal impact to ensure the applicability of the analysis results. This design helps screen out key issues that affect road health, especially during long-term inspections, and can help decision makers quickly locate and deal with potential problems.

[0047] Furthermore, during the inspection process, the algorithm also considers the time variation of road vibration data by using the derivative To reflect the dynamic trend of multi-band vibration signals. It should be understood that the addition of this trend control factor μ provides a dynamic analysis mechanism for the algorithm, which can continuously track changes in road conditions, thereby providing more accurate analysis results for inspection reports. This not only improves the accuracy of anomaly detection, but also enhances the ability to warn of potential road problems.

[0048] Furthermore, in the execution of the entire algorithm, the optimization algorithm C(n) for the inspection report consists of two main parts: impact assessment of abnormal locations and analysis of vibration signal changes. The abnormality assessment part focuses on significant road defects through the attenuation function, while the signal change analysis provides the time series trend of the road surface state. It can be understood that this two-in-one strategy enables the inspection report to not only record statically, but also contain dynamic change information, providing comprehensive and in-depth support for maintenance decisions.

[0049] The benefit of this embodiment is that the inspection report can reflect the health status of the road in a quantitative way by optimizing the algorithm C(n). Minor changes and abnormal conditions can be systematically recorded and evaluated, which helps to discover and deal with road problems at an early stage and reduce the risk of major repairs caused by neglecting details. Furthermore, the promotion and application of this method will greatly improve the technical content of road inspections, ensure that it will stand out in a wider range of applications, and provide strong technical support for urban infrastructure maintenance. It not only saves maintenance costs economically, but also improves road safety and traffic capacity in terms of social benefits.

[0050] Embodiment 7 In order to solve the problem of insufficient efficiency in processing multi-band vibration signals in traditional road inspections, this embodiment further refines the data processing and analysis methods and adopts a solution based on a distributed computing architecture, which effectively improves the data quality and analysis accuracy of vibration signals, thereby improving the ability to judge the health status of roads.

[0051] Specifically, this embodiment introduces a wavelet transform method to perform denoising on multi-band vibration signals. Wavelet transform provides an analysis tool with high resolution in both time and frequency domains, which can effectively separate noise and useful information in the signal, thereby improving data quality. It should be understood that the multi-scale characteristics of wavelet transform make it excellent in capturing signal characteristics, can adapt to different signal frequency changes, and more accurately reflect the subtle vibration characteristics of the road surface.

[0052] Furthermore, after the vibration data is denoised, a convolutional neural network (CNN) is used for automatic recognition and classification. Taking advantage of the convolutional neural network's ability to process images and signals, the vibration pattern can be efficiently identified and matched with the health pattern in the established database. It can be understood that the use of multiple convolutional layers and pooling layers of the convolutional neural network (CNN) has a strong ability to extract deep features, providing higher accuracy and reliability for the determination of road health status.

[0053] Furthermore, the results of processing and analysis are continuously updated through distributed storage and iterative learning architecture. Distributed storage makes large-scale data processing feasible while ensuring the security and reliability of data. It should be further emphasized that iterative learning can adapt to changes in road conditions and the addition of new data by periodically updating and correcting machine learning models, thereby improving the efficiency and accuracy of patrol processing.

[0054] Through this embodiment, the efficiency and accuracy of road inspection are improved. The distributed computing architecture not only accelerates the speed of data processing, but also provides technical support for wide-area inspection. At the same time, combined with wavelet transform denoising and convolutional neural network (CNN) automatic classification technology, it can capture abnormal information in vibration signals more carefully, providing more valuable data support for road maintenance decisions.

[0055] Embodiment 8 In order to solve the problem of lack of comprehensive evaluation and prediction capabilities of vibration signal characteristics during drone inspections, this embodiment further integrates a decision support system based on Bayesian reasoning, aiming to effectively evaluate the comprehensive impact of multi-band vibration signals and identify potential road safety issues in advance. The system provides a credibility score for road health status through probabilistic analysis, thereby providing a more scientific basis for related maintenance decisions.

[0056] Specifically, during the vibration signal processing, the decision support system based on Bayesian reasoning deeply analyzes the probability distribution of multiple vibration features. Bayesian reasoning provides a mathematical framework for dealing with uncertainty and variation, which can update the system's understanding of road conditions based on known data. It is understandable that this system not only focuses on the isolated analysis of a single signal feature, but also pays more attention to the combined impact of multiple features. This comprehensive evaluation method ensures the reliability of the credibility score.

[0057] Furthermore, this embodiment implements dynamic data mining technology to extract and learn potential correlation patterns between different vibration characteristics from a large number of historical inspection tasks, and compare the correlation patterns with the vibration characteristics of the current multi-band vibration signal to identify possible abnormal trends. It is understandable that historical data provides a rich sample basis for the system, and by analyzing historical samples, the system can continuously improve its cognitive ability. It should be understood that these correlation patterns not only help the system understand the normal road vibration state, but also provide a reference for discovering anomalies. When the newly collected data does not match the historical pattern, the system can quickly identify possible abnormal trends, providing strong support for dynamic monitoring of road safety.

[0058] Furthermore, when the system detects that the trend change exceeds the preset threshold, the early warning module will be activated. The design of this module takes into account the need for timely response of the system. By generating analysis reports, potential road safety hazards are conveyed to relevant managers in an intuitive form, which improves the timeliness of problem discovery and reduces the risk of potential accidents.

[0059] Through this embodiment, the vibration signal analysis capability of the inspection road has been further optimized. The decision support system based on Bayesian reasoning not only improves the cognition of complex signal patterns, but also strengthens the prediction and early warning capabilities of the system. By integrating dynamic data mining technology, this embodiment realizes closed-loop management of data from collection to decision-making, ensuring that the data processing efficiency and quality of road inspections are greatly improved. This forward-looking design and application not only provides a new direction for the development of drone inspection technology, but also creates more possibilities for the safe management of smart transportation infrastructure.

[0060] Embodiment 9 In order to solve the efficiency and accuracy problems of traditional road inspection systems in vibration signal collection and analysis, this embodiment further designs and integrates a road inspection system based on drones to enhance real-time monitoring and accurate processing of vibration signals. The system consists of multiple modules, which work closely together to achieve comprehensive monitoring and intelligent analysis of road conditions.

[0061] Specifically, the system of this embodiment integrates a high-frequency vibration sensing module to collect and analyze vibration signals on the road surface in real time. The high-frequency vibration sensing module can capture potential vibration anomalies and provide a high-quality data source for subsequent signal processing. It should be understood that precise sensor technology is the foundation of the entire inspection system, and its high-frequency response capability ensures that the details of the vibration signal are captured without omission, thereby improving the reliability and effectiveness of the data.

[0062] Furthermore, after receiving the analog vibration signal, the signal acquisition module converts it into a digital signal using the analog-to-digital conversion unit. In addition, in order to compensate for potential errors in the signal conversion process, the signal acquisition module is also equipped with an error correction unit. It can be understood that the accuracy of the analog-to-digital conversion affects the accuracy of the digital signal analysis, and the existence of the error correction unit further improves the reliability of the signal conversion and ensures the accuracy of the subsequent data processing.

[0063] Furthermore, the data processing module is responsible for in-depth analysis of the collected vibration data. Through its built-in deep learning unit, the module supports fast vibration data analysis of multi-layer neural networks. Combined with real-time adaptive learning algorithms, the system can update and optimize the analysis model in real time, thereby improving the accuracy of the analysis. Under this intelligent processing architecture, data processing is not only more efficient, but also can adapt to dynamically changing road vibration characteristics.

[0064] Furthermore, the data communication module of the entire system is responsible for the transmission of analysis results and key data. It supports multi-band data transmission and encryption protocols to ensure the security and confidentiality of data during long-distance transmission. It can be understood that reliable data communication is the guarantee for the efficient operation of the drone inspection system, and the effective data encryption mechanism prevents possible external interference and data leakage, thereby maintaining the integrity and security of the data.

[0065] On the other hand, the remote control station is responsible for receiving the analysis results and key data, and managing the inspection tasks through the control decision module. This module can dynamically adjust the inspection path, optimize the inspection plan, and manage the inspection tasks based on the real-time analysis results. It should be emphasized that the intelligent decision-making mechanism makes the inspection work more flexible and accurate, which can not only adjust the path in time and reduce invalid inspection routes, but also improve the overall efficiency of the system.

[0066] Through this embodiment, the drone-based road inspection system not only improves the real-time and accuracy of inspections, but also provides an intelligent and efficient road monitoring solution through the coordination of various modules. This design not only enables the inspection system to maintain stable performance output in the face of complex and changing road environments, but also provides important data support for the long-term maintenance of roads, ensuring the safety and smoothness of road traffic.

[0067] Embodiment 10 In order to solve the problem of insufficient efficiency and adaptability of traditional path planning during drone road inspection, this embodiment further optimizes the control decision module and adds learning and decision optimization functions based on historical mission data. This improvement aims to provide more intelligent path planning and inspection strategies by analyzing historical inspection data to improve the overall operation effect of the system.

[0068] Through the built-in machine learning model, the control decision module conducts a comprehensive analysis of historical task data. It is important to understand that these historical data not only include past inspection paths and test results, but also consider the execution efficiency and priority information derived from these data. By summarizing and integrating this historical information, the system can effectively identify the optimal inspection strategy under different conditions. At the same time, in the process of analyzing this data, the control decision module can continuously learn and optimize its decision-making ability, and provide data-driven decision support for subsequent tasks.

[0069] Furthermore, the present embodiment introduces a calculation formula for the optimization strategy, namely:

[0070] In the formula, R represents the total number of sections that the drone needs to inspect in the path, r represents the index number of the current section, r=1,2,…,R, t is the time variable, representing the time point or time period of the current analysis, and u r (t) represents the priority or utility value of the rth road segment at time t. It should be understood that this formula logically combines the priority of the inspection path and the deviation of the actual execution path to achieve more accurate path planning. Here, λ and σ represent the weight coefficient in path optimization and the weight factor of path deviation, respectively, which can be adjusted according to actual needs to weigh the importance of different inspection indicators. The ΔD in the formula measures the deviation between the ideal path and the actual path. By minimizing this difference, the system can improve the accuracy and execution efficiency of the inspection.

[0071] Understandably, this decision-making optimization process does not operate in isolation. When developing optimization strategies for future inspection tasks, the control decision module will combine current and real-time data and dynamically adjust strategy parameters. In this way, when the drone performs an inspection task, it can intelligently select the best section and path, accurately identify potential problem areas, and thus optimize the allocation and use of inspection resources.

[0072] Through this embodiment, the decision-making optimization function brought by machine learning is introduced, making the drone inspection system more efficient and adaptable when dealing with complex and changing road environments. This not only improves the effect of a single inspection task, but also accumulates knowledge in long-term operation, allowing the system to improve itself and gradually improve its overall performance. Through the above optimization strategy, the system no longer relies on a single path planning solution, but is dynamically adjusted and optimized in a data-driven manner to ensure optimal performance in various inspection scenarios.

[0073] The above embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A road inspection method based on drone, characterized in that: The method comprises the following steps: Instruct the UAV to fly along a preset path to cover a designated inspection road area, and adjust the flight strategy according to real-time environmental data through a set program; The high-frequency vibration sensor module on the drone collects multi-band vibration signals in real time to capture subtle changes and anomalies on the road surface during inspection. Using analog-to-digital conversion technology, converting the multi-band vibration signal into a digital signal; The digital signal is input into a data processing module, and a deep learning model is used to perform real-time analysis of vibration characteristics to generate analysis results and key data reflecting subtle changes and anomalies on the surface of the inspected road; After analyzing the vibration characteristics, the analysis results and the key data are transmitted to the remote control station in real time through the data communication module for real-time monitoring of the inspection road and subsequent generation of inspection reports.

2. The road inspection method based on drone according to claim 1, characterized in that: In the process of real-time acquisition of multi-band vibration signals by the high-frequency vibration sensor module on the drone, the drone automatically collects environmental data including temperature, humidity and wind speed, and automatically adjusts the parameters of signal analysis based on the changes in the environmental data, so as to enhance the analysis accuracy of vibration signals in different environmental data and reflect the environmental changes of the inspection road surface in real time. The parameters of the automatic adjustment of signal analysis are realized by the following calculation formula: , where A(t) represents the environmental adjustment factor, which is used to automatically adjust parameters during vibration signal analysis, and T env is the ambient temperature, is the temperature reference value, H env is the ambient humidity, W env is the ambient wind speed, α, β and γ are the adjustment coefficients.

3. The road inspection method based on drone as claimed in claim 1, characterized in that: The data processing module applies a deep learning algorithm and integrates an adaptive algorithm optimization mechanism to adjust the parameters of vibration feature analysis through a real-time self-learning mechanism, specifically including: The digital signal is fused and processed by using a multi-layer neural network to improve the breadth and depth of the vibration feature extraction; The adaptive algorithm is optimized to adapt to different road materials and environmental conditions, and uses step learning to continuously update the weighted values ​​to improve the accurate classification and dynamic prediction capabilities of vibration data; The inspection data of the vibration characteristics are identified, and once a potential abnormality is detected, an early warning message is generated and fed back to the remote control station in real time.

4. The road inspection method based on an unmanned aerial vehicle as claimed in claim 3, characterized in that: The data processing module uses a multi-layer neural network architecture to perform vibration feature analysis. The multi-layer neural network architecture enhances feature extraction capability by introducing convolution layers and pooling layers, and adjusts weight parameters of the multi-layer neural network architecture through an adaptive algorithm. The expression of the adaptive algorithm is: , where v j (t) represents the vibration amplitude of each characteristic point, ω j and ϕ j are frequency and phase angle respectively, σ is the adjustment coefficient, and x(t) is the displacement caused by vibration.

5. The road inspection method based on drone as claimed in claim 1, characterized in that: The method further comprises: adopting a feedback mechanism to summarize the operating parameters of the UAV after the inspection task is completed, so as to form flight strategy optimization suggestions, wherein the step of adopting the feedback mechanism comprises: During each inspection mission, the drone continuously records flight parameters through its own sensors and computing modules. The flight parameters constitute historical mission data, marked as R i (t), i is a specific sample of the historical task data; The specific samples are summed within the sample size interval of the historical mission data and summed with the sample size of the historical mission data, a strategy adjustment coefficient ε is introduced, and the collected historical mission data is aggregated and analyzed for calculating the flight parameters; For the current task parameter E j (t) and the previous task parameter E j (t) The average value in the historical task data For comparison, a variation control factor ω is introduced to identify and analyze task deviations; The results of the aggregation analysis and the deviation are input into the strategy adjustment model F(t). Through the output results of the strategy adjustment model F(t), optimization suggestions are generated for the inspection flight of the UAV to improve the future flight plan of the UAV. The optimization suggestions include path adjustment, speed change and dynamic adjustment of signal acquisition frequency. The calculation formula of the strategy adjustment model F(t) is: 。 6. The road inspection method based on drone as claimed in claim 1, characterized in that: The inspection report includes a record of the health status classification of the inspection road, which is used to analyze the specific location and severity of the subtle changes and anomalies. The inspection report is generated by the following optimization algorithm: , where C(n) is the optimized inspection report content, n represents different road sections, k is the index variable, representing the kth anomaly, and N is the total number of anomalies. is the attenuation function of the kth abnormal position, θ is the sensitivity parameter controlling the attenuation function, μ is the change trend control factor, is the derivative of the multi-band vibration signal over time.

7. The road inspection method based on drone as claimed in claim 1, characterized in that: The data processing and analysis of the multi-band vibration signal is implemented through a distributed computing architecture, and the steps include: Using a wavelet transform method to perform denoising on the received multi-band vibration signal to improve data quality; Using a convolutional neural network to automatically identify and classify the vibration patterns of the multi-band vibration signal to improve the accuracy of determining the health status of the patrol road; The processed and analyzed results are continuously updated through distributed storage and iterative learning architecture to improve the processing efficiency of large-scale inspections.

8. The road inspection method based on drone as claimed in claim 1, characterized in that: The data processing module is integrated with a decision support system based on Bayesian reasoning, which is used to evaluate the comprehensive impact of the vibration characteristics of the multiple multi-band vibration signals and predict potential problems. The decision support system generates a credibility score for the road health assessment by performing a probability analysis on the multiple vibration characteristics; The decision support system uses dynamic data mining technology to learn and quantify the correlation patterns between different vibration characteristics from historical inspection tasks, and compares the correlation patterns with the vibration characteristics of the current multi-band vibration signal to identify possible abnormal trends; If there is a trend change that exceeds a preset threshold, the decision support system activates the early warning module and generates an analysis report to deal with potential road safety hazards.

9. A road inspection system based on drones, characterized in that: The system comprises: High-frequency vibration sensing module, used to collect and analyze vibration signals on the road surface in real time to capture potential anomalies; The signal acquisition module is equipped with an analog-to-digital conversion unit for converting analog signals into digital signals, and also has an error correction unit; Data processing module, equipped with a deep learning unit, supports fast vibration data analysis with multi-layer neural networks, combined with real-time adaptive learning algorithms to improve accuracy; Data communication module, supporting multi-band data transmission and encryption protocols, for real-time transmission of analysis results and key data; A remote control station, used for receiving the analysis result and the key data; The control decision module is used to dynamically adjust the inspection path based on the analysis results, plan the inspection path and perform inspection task management.

10. The drone-based road inspection system according to claim 9, characterized in that: The control decision module also has learning and decision optimization functions based on historical task data. Through the built-in machine learning model, it analyzes and integrates the historical inspection results corresponding to the historical task data, and formulates optimization strategies for future inspection tasks based on the historical inspection results. The calculation formula of the optimization strategy is: , where R represents the total number of sections that the drone needs to inspect in the path, r represents the index number of the current section, r=1,2,…,R, t is the time variable, representing the time point or time period of the current analysis, and u r (t) represents the priority or utility value of the rth road segment at time t, λ represents the weight coefficient in path optimization, σ represents the weight factor for controlling path change or deviation, and ΔD represents the deviation between the ideal path and the actual path.

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