Unmanned aerial vehicle automatic intelligent inspection method and inspection system

Abstract

The present application relates to the technical field of unmanned aerial vehicle inspection system, and provides an unmanned aerial vehicle automatic intelligent inspection method and an inspection system, which comprises an unmanned aerial vehicle terminal, a ground management platform, a data transmission terminal and an inspection control terminal; the ground management platform is used for planning an inspection task and a flight trajectory for the unmanned aerial vehicle terminal; the unmanned aerial vehicle terminal is used for performing inspection and collecting sensing data required by the inspection task according to the inspection task and the flight trajectory; the inspection control terminal is used for monitoring the state and environmental changes of the unmanned aerial vehicle terminal in real time, and combining the sensing data to control the flight position of the unmanned aerial vehicle terminal in the process of collecting the sensing data; and the data transmission terminal is used for realizing sensing data transmission between the unmanned aerial vehicle terminal and the ground management platform based on 5G wireless communication technology. The present application has the effect of improving the quality and efficiency of unmanned aerial vehicle inspection data processing.

Description

Technical Field

[0001] This invention relates to the technical field of unmanned aerial vehicle (UAV) inspection systems, and specifically to an automatic intelligent inspection method and system for UAVs. Background Technology

[0002] Hydropower plants are a crucial pillar industry for ensuring the quality of daily electricity use for the public, and their operation directly impacts people's life satisfaction and happiness. Therefore, to improve the quality of power supply equipment inspections, drones should be flexibly used for intelligent inspections. This leverages technological advantages, minimizes labor costs, increases work efficiency, and further optimizes power service quality by reducing workload.

[0003] Currently, the inspection of hydropower plants and other critical infrastructure generally relies on traditional manual or semi-automated methods, resulting in low efficiency, large coverage blind spots, incomplete data collection, and insufficient real-time response capabilities. In recent years, with the rapid development of drone technology, sensor technology, and data communication technology, drone-based intelligent inspection systems have become an important way to solve these problems. While existing technologies include data acquisition and inspection platforms based on multi-sensor fusion, they still lack innovative calculation methods for parameters of unconventional objects in terms of dynamic assessment of environmental parameters and multi-source data fusion judgment, thus limiting the intelligence level and risk prediction accuracy of the inspection system.

[0004] Many drone inspection systems have been developed, which generally include a ground control terminal, an analysis terminal, and a drone terminal. The ground control terminal controls the drone terminal to perform inspections; the drone terminal collects inspection data and transmits it to the analysis terminal; the analysis terminal analyzes the inspection data and synchronizes the analysis results with the inspection data back to the ground control terminal. Because the inspection process and data processing of these drone inspection systems are relatively simple, lacking inspection control and data processing optimization, errors in the inspection data are prone to occur, resulting in reduced quality and efficiency of drone inspection data processing. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the aforementioned drone inspection systems by proposing an automatic intelligent drone inspection method and system.

[0006] The present invention adopts the following technical solution:

[0007] The unmanned aerial vehicle (UAV) automated intelligent inspection system includes a UAV terminal, a ground management platform, a data transmission terminal, and an inspection control terminal. The ground management platform plans inspection tasks and flight paths for the UAV terminal. The UAV terminal performs inspections and collects the required sensor data based on the inspection tasks and flight paths. The inspection control terminal monitors the UAV terminal's status and environmental changes in real time, and controls the UAV terminal's flight position during sensor data collection based on the sensor data. The data transmission terminal uses 5G wireless communication technology to transmit sensor data between the UAV terminal and the ground management platform.

[0008] The data transmission terminal includes a raw data preprocessing module and a data transmission module; the raw data preprocessing module is used to preprocess the sensor data to generate target data; the data transmission module is used to transmit the target data to the ground management platform.

[0009] Optionally, the ground management platform includes an inspection task planning module, a flight trajectory planning module, and a task monitoring module; the inspection task planning module is used to plan inspection tasks for the UAV terminal and determine the preset areas and target equipment to be inspected; the preset areas are flight areas pre-set by the administrator within the locations specified in the inspection task; the flight trajectory planning module is used to plan flight paths and flight altitudes for the UAV terminal; the task monitoring module is used to monitor the flight status of the UAV terminal and the execution of inspection tasks in real time.

[0010] Optionally, the UAV terminal includes a UAV body, a flight control module, a sensor data acquisition module, and a task execution module; the flight control module, sensor data acquisition module, and task execution module are installed on the UAV body; the flight control module is used to adjust the flight position and flight status of the UAV body according to the inspection task, flight path, and flight altitude of the ground management platform; the task execution module is used to receive the inspection task issued by the ground management platform and determine the data type required to be collected by the inspection task; the sensor data acquisition module is used to collect the required sensor data during the inspection process.

[0011] Optionally, the inspection and control terminal includes a real-time status monitoring module, an environmental change sensing module, and a flight position control module; the real-time status monitoring module is used to monitor the flight status of the UAV in real time; the environmental change sensing module is used to acquire changes in environmental data of the UAV during flight; and the flight position control module is used to adjust the position of the UAV in a preset area based on changes in sensor data and environmental data.

[0012] Optionally, the flight position control module includes a feedback control index calculation submodule and a flight position control submodule within a preset area; the feedback control index calculation submodule is used to calculate the feedback control index of the UAV body currently in the preset area based on changes in sensor data and environmental data; the flight position control submodule within the preset area is used to control the flight position of the UAV body within the preset area based on the conditions of the preset area, the flight status of the UAV body, and the feedback control index.

[0013] When the feedback control index calculation submodule performs the calculation, the following formula is satisfied:

[0014]

[0015] Where G represents the feedback control index of the UAV currently within the preset area; K represents the reduction factor; X i This represents the raw data value of the i-th sensor in the sensor data acquisition module; N represents the total number of sensor types in the sensor data acquisition module; α i Y represents the amplification factor of the i-th sensor; i Z represents the noise impact coefficient of the i-th sensor in the sensor data acquisition module. The higher the noise level in the raw data of the i-th sensor, the larger the corresponding noise impact coefficient. i Max represents the reliability score of the i-th sensor in the sensor data acquisition module. i β represents the preset maximum value of the summation term for the i-th type of sensor, used to standardize the value of the sensor summation term; i γ represents the nonlinear control coefficient of the i-th sensor; i Q represents the adjustment coefficient for the i-th sensor; i represents the sensitivity coefficient of the i-th sensor; w represents the weighting coefficient of the product term;

[0016] When G < g ref At that time, the flight position control submodule within the preset area adjusts the flight position of the UAV within the preset area according to the conditions of the preset area and the flight status of the UAV itself, optimizing the G value until G≥g ref g ref This represents the threshold for judging the flight position of the drone itself when the drone terminal collects sensor data.

[0017] Optionally, the raw data preprocessing module includes a multi-type sensor data preprocessing submodule and a target data generation submodule; the multi-type sensor data preprocessing submodule is used to preprocess the raw data from various sensors; the target data generation submodule is used to package the preprocessed data into target data.

[0018] Optionally, the multi-type sensor data preprocessing submodule includes an image data preprocessing unit, a temperature data preprocessing unit, a vibration data preprocessing unit, and a humidity data preprocessing unit. The image data preprocessing unit performs gradient enhancement and noise reduction processing on the raw data from the image sensor and generates an image enhancement index. The temperature data preprocessing unit performs deviation analysis on the raw data from the temperature sensor and generates a global temperature index. The vibration data preprocessing unit performs amplification and interference suppression processing on the raw data from the vibration sensor and generates a global vibration index. The humidity data preprocessing unit performs data smoothing and filtering on the raw data from the humidity sensor and generates a global humidity index. The target data generation submodule packages the preprocessed sensor data, image enhancement index, temperature global index, vibration global index, and humidity global index into target data.

[0019] An automated intelligent inspection method for drones, applied to the aforementioned automated intelligent inspection system for drones, includes:

[0020] S1, planning inspection tasks and flight trajectories;

[0021] S2, based on the inspection task and flight trajectory, performs inspections and collects the sensor data required by the inspection task.

[0022] S3 monitors the status and environmental changes of the drone terminal in real time, and adjusts the flight position of the drone terminal during the process of collecting sensor data by combining sensor data.

[0023] S4 enables sensor data transmission between the UAV terminal and the ground management platform based on 5G wireless communication technology.

[0024] The beneficial effects achieved by this invention are:

[0025] 1. By setting up UAV terminals, ground management platforms, data transmission terminals, and inspection and control terminals, the system can plan, execute, and monitor inspection tasks in real time. This helps improve the autonomy and data collection efficiency of UAVs during inspections. Furthermore, by using 5G wireless communication technology to achieve efficient data transmission, the system can enable intelligent, remote, and real-time control of UAVs for inspection tasks, thereby improving the quality and efficiency of UAV inspection data processing.

[0026] 2. By setting up the inspection task planning module, flight trajectory planning module, and task monitoring module in the ground management platform, inspection tasks, flight paths, and altitudes can be accurately planned, and the flight status of the UAV can be monitored in real time. This helps to ensure the accurate execution of inspection tasks and the optimization of flight paths, thereby improving the safety and efficiency of UAV task execution, and thus helping to reduce errors and improve the accuracy of inspections.

[0027] 3. By setting up the flight control module, sensor data acquisition module, and mission execution module in the UAV terminal, the UAV can autonomously adjust its flight position and status under the guidance of the inspection mission, and collect the required sensor data in real time. This allows the UAV to flexibly adjust its working status according to mission requirements, thereby ensuring the accuracy and stability of mission execution, which in turn helps to improve the quality of inspection and reduce human intervention.

[0028] 4. By setting up the real-time status monitoring module, environmental change perception module, and flight position control module in the inspection and control terminal, the drone's status and environmental changes can be monitored in real time, and the drone's position can be adjusted according to the data. This is conducive to flexibly adjusting the flight position according to changes in the actual flight environment, thereby enhancing the adaptability and emergency response capability of the inspection, and thus improving the adaptability and flexibility of the drone in complex environments.

[0029] 5. By setting up the multi-type sensor data preprocessing submodule and the target data generation submodule in the raw data preprocessing module, corresponding preprocessing and target data generation can be performed on data from different sensors, thereby improving the quality and consistency of various types of data, reducing inconsistencies between data, and ensuring the accuracy and uniformity of data. This facilitates efficient data integration and further processing, and improves the quality and efficiency of UAV inspection data processing.

[0030] 6. By setting up image data preprocessing unit, temperature data preprocessing unit, vibration data preprocessing unit and humidity data preprocessing unit in the multi-type sensor data preprocessing submodule, specific preprocessing can be performed on the data of each type of sensor, improving the quality of data of each type of sensor, which is conducive to enhancing the accuracy and reliability of data, thereby improving the overall intelligent inspection capability of the system, thus helping to improve the multi-functional application effect of UAV in various complex environments, and improving the quality and efficiency of UAV inspection data processing.

[0031] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0033] Figure 2 This is a schematic diagram of the flight position control module in this invention;

[0034] Figure 3 This is a schematic diagram showing the running effect of a portion of the program code for calculating the G value in this invention;

[0035] Figure 4 This is a schematic diagram illustrating the running effect of supplementary program code for calculating the G value in this invention;

[0036] Figure 5 This is a schematic diagram of the original data preprocessing module in this invention;

[0037] Figure 6 This is a schematic diagram of the method flow of the UAV automatic intelligent inspection method in this invention;

[0038] Figure 7 This is a schematic diagram summarizing the entire system operation process in another embodiment of the present invention;

[0039] Figure 8 This is a schematic diagram illustrating the on-site effect of a drone body inspection in another embodiment of the present invention. Detailed Implementation

[0040] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0041] Example 1: This example provides an automated intelligent inspection system for unmanned aerial vehicles (UAVs). The corresponding practical application scenario is as follows: The UAV in the system performs an inspection task and flies to a sufficiently large location, such as a hydropower plant. The space within the location is pre-divided by the administrator into several preset areas based on the equipment present. These preset areas are large enough to allow the UAV to adjust its position without obstruction. The UAV needs to complete inspection actions within each preset area, collect sensor data, preprocess the sensor data, and transmit it. Combined with... Figure 1 As shown, the UAV automatic intelligent inspection system includes a UAV terminal, a ground management platform, a data transmission terminal, and an inspection control terminal. The ground management platform is used to plan inspection tasks and flight trajectories for the UAV terminal. The UAV terminal is used to perform inspections and collect sensor data required by the inspection tasks and flight trajectories. The inspection control terminal is used to monitor the status and environmental changes of the UAV terminal in real time, and to control the flight position of the UAV terminal during the sensor data collection process based on the sensor data. The data transmission terminal is used to realize sensor data transmission between the UAV terminal and the ground management platform based on 5G wireless communication technology.

[0042] The data transmission terminal includes a raw data preprocessing module and a data transmission module; the raw data preprocessing module is used to preprocess the sensor data to generate target data; the data transmission module is used to transmit the target data to the ground management platform.

[0043] Optionally, the ground management platform includes an inspection task planning module, a flight trajectory planning module, and a task monitoring module; the inspection task planning module is used to plan inspection tasks for the UAV terminal and determine the preset areas and target equipment to be inspected; the preset areas are flight areas pre-set by the administrator within the locations specified in the inspection task; the flight trajectory planning module is used to plan flight paths and flight altitudes for the UAV terminal; the task monitoring module is used to monitor the flight status of the UAV terminal and the execution of inspection tasks in real time.

[0044] Optionally, the UAV terminal includes a UAV body, a flight control module, a sensor data acquisition module, and a task execution module; the flight control module, sensor data acquisition module, and task execution module are installed on the UAV body; the flight control module is used to adjust the flight position and flight status of the UAV body according to the inspection task, flight path, and flight altitude of the ground management platform; the task execution module is used to receive the inspection task issued by the ground management platform and determine the data type required to be collected by the inspection task; the sensor data acquisition module is used to collect the required sensor data during the inspection process.

[0045] Optionally, the inspection and control terminal includes a real-time status monitoring module, an environmental change sensing module, and a flight position control module; the real-time status monitoring module is used to monitor the flight status of the UAV in real time; the environmental change sensing module is used to acquire changes in environmental data of the UAV during flight; and the flight position control module is used to adjust the position of the UAV in a preset area based on changes in sensor data and environmental data.

[0046] Optional, combined Figure 2 As shown, the flight position control module includes a feedback control index calculation submodule and a flight position control submodule within a preset area; the feedback control index calculation submodule is used to calculate the feedback control index of the UAV body currently in the preset area based on changes in sensor data and environmental data; the flight position control submodule within the preset area is used to control the flight position of the UAV body within the preset area based on the conditions of the preset area, the flight status of the UAV body, and the feedback control index.

[0047] When the feedback control index calculation submodule performs the calculation, the following formula is satisfied:

[0048]

[0049] Wherein, G represents the feedback control index of the drone currently within the preset area; K represents the reduction factor, which is set by the administrator based on experience. In this embodiment, the selection rule for this value is as follows: the more types of sensors, the larger the K value, K = 1 * 10. M M = number of sensor types - 1; X i This represents the raw data value of the i-th sensor in the sensor data acquisition module; in this embodiment, one sensor corresponds to one sensor type; N represents the total number of sensor types in the sensor data acquisition module; α i This represents the amplification factor of the i-th sensor. In this embodiment, the selection rule for this value is as follows: the more times the i-th sensor fails, the smaller the amplification factor becomes. i = 1 - number of failures / 10. The maximum number of failures for the sensor is 9. When the sensor fails for the 10th time, it needs to be replaced.

[0050] Y i This represents the noise impact coefficient of the i-th sensor in the sensor data acquisition module. The higher the noise level in the raw data of the i-th sensor, the larger the corresponding noise impact coefficient.

[0051]

[0052] Where, λ i σ represents the noise impact factor of the i-th sensor; ei The standard deviation of the measurement error of the i-th sensor is obtained through experimental verification and calculation: in a laboratory environment, a finite number of repeated measurements are performed on the sensor, such as calibration point detection, to obtain the distribution of the measurement error and calculate the standard deviation; alternatively, it can be obtained from the technical documentation provided by the manufacturer. μ i σ represents the average measurement value of the i-th sensor, i.e., the average value of the historical sensing data acquired by this sensor in the corresponding preset area; envi σ represents the standard deviation of the environmental noise of the i-th sensor, mainly measuring the interference of environmental factors, such as temperature, humidity, vibration, and light, on the sensor's measurement results. Examples include the additional error of a temperature sensor under different ambient temperatures and the fluctuation of a humidity sensor under different humidity environments. It is obtained by testing the sensor's noise level under different environmental conditions. The sensor output is recorded through a finite number of repeated measurements under different environmental conditions, and then its standard deviation is calculated. instiηi represents the standard deviation of the intrinsic noise of the i-th sensor, mainly measuring the inherent noise of the sensor's hardware during operation, such as thermal noise from electronic components and interference caused by loose mechanical structures. It is usually related to the sensor's internal design, chip selection, and circuit stability. It is obtained by testing the stability of the sensor under long-term operation or different operating conditions. This can be achieved by monitoring the sensor's output fluctuations over a long period in a stable environment while maintaining a constant input, and then calculating the standard deviation. η1, η2, and η3 represent different weighting coefficients. The specific methods for determining their values ​​can be, but are not limited to: 1. Setting based on administrator experience: If the administrator knows which of the measurement error, environmental noise, and instrument noise is the primary or dominant factor, then a larger weight can be set accordingly. For example, a sensor that is easily affected by environmental interference can be given a larger weight. 2. Data regression after a finite number of experiments: Retrieve the sensor's historical measurement data under different environments, and based on the actual measurement error, use regression methods, such as least squares, to fit an optimal weighting combination. 3. Iterative optimization: Continuously record the sensor output and true value during actual use, and iteratively correct each weighting coefficient until the performance requirements set by the administrator are met.

[0053] Z i This represents the reliability score of the i-th sensor in the sensor data acquisition module:

[0054]

[0055] Where, δ i This represents the reliability impact factor of the i-th sensor; Lifespan i This indicates the lifespan of the i-th sensor, specifically the indicated working years in this embodiment, obtained from the manufacturer's technical documentation; Max-Lifespan i The maximum lifespan of the i-th sensor is indicated by μ, which is obtained from the manufacturer's technical documentation. i σ represents the average measurement value of the i-th sensor, i.e., the average value of the historical sensing data acquired by this sensor in the corresponding preset area; cali The standard deviation of the calibration error of the i-th sensor measures the error fluctuation between the measured value and the standard reference value during the calibration process. Under controlled conditions, it is obtained by comparing the sensor with a known standard reference source. It represents the inherent measurement error of the sensor and is usually related to the hardware precision of the sensor itself and the accuracy of the calibration process. Specifically, it is calculated by taking multiple measurements and calculating the difference between the measured value and the known standard value, then calculating the standard deviation. P represents the number of calibrations, X j,cal X represents the measured value of the standard reference source in the j-th measurement. refIndicates the standard value of the standard reference source; σ envi This represents the standard deviation of environmental noise for the i-th type of sensor. It primarily measures the interference caused by environmental factors such as temperature, humidity, vibration, and light on the sensor's measurement results. Examples include the additional error of a temperature sensor under different ambient temperatures and the fluctuation of a humidity sensor under different humidity environments. It is obtained by testing the sensor's noise level under different environmental conditions. The sensor output is recorded through a finite number of repeated measurements under different environmental conditions, and then its standard deviation is calculated. (Max-Stability) i The maximum stability score for the i-th sensor indicates how long and how wide a range of changes the sensor can remain stable. This score is obtained from the manufacturer's technical documentation or through long-term stability testing. X t,max This represents the sensor's measurement value at time t, where t is typically greater than one year. X t0 This represents the sensor's measured value under the initial steady-state condition; φ1, φ2, and φ3 represent different proportionality coefficients. Specific methods for determining these values ​​include, but are not limited to: 1. Setting based on administrator experience: If the administrator knows which term in the above formula is the primary or dominant term, then a larger proportion is set accordingly; 2. Data regression after a finite number of experiments: Retrieve historical measurement data of the sensor under different environments, and based on the actual measurement error, use regression methods, such as least squares, to fit an optimal weighted combination; 3. Iterative optimization: Continuously record the sensor output and true values ​​during actual use, iteratively correcting each weight coefficient until the performance requirements set by the administrator are met.

[0056] Max i This represents the preset maximum value of the summation term for the i-th sensor, used to standardize the value of the sensor summation term. The specific value is set by the administrator based on experience, and the selection rule is: the larger the historical average value of the summation term for the i-th sensor, the higher the Max value. i The larger it is, the more likely it is to be Max. i The number of digits in the numerical value is consistent with the number of digits in the average value of the historical summation terms of the i-th sensor; β i This represents the nonlinear control coefficient of the i-th sensor, used to describe the degree of signal amplification caused by nonlinear effects during the measurement process. It indicates the nonlinear amplification effect of the sensor under different operating conditions. γ i This represents the adjustment coefficient for the i-th type of sensor, used to describe the suppression effect of environmental conditions on the sensor's output signal during measurement. It reflects the sensor's nonlinear suppression capability under different environmental conditions. Q i This represents the sensitivity coefficient of the i-th sensor. w represents the weighting coefficient of the product term, with three alternative values: 0.2, 0.25, and 0.3. When the number of sensor types is less than 4, 0.3 is selected; when the number of sensor types is greater than or equal to 4, 0.25 is selected; when the number of sensor types is greater than or equal to 4 and less than 7, 0.25 is selected; and when the number of sensor types is greater than or equal to 7, 0.2 is selected.

[0057] When G < g ref At that time, the flight position control submodule within the preset area adjusts the flight position of the UAV within the preset area according to the conditions of the preset area and the flight status of the UAV itself, optimizing the G value until G≥g ref g ref The threshold value for judging the drone's flight position when the drone terminal collects sensor data is set by the administrator based on experience, and g represents the threshold value for each preset area. ref All settings are pre-set by the administrator. The pre-setting method may include, but is not limited to: the administrator holding the data acquisition module and proceeding to the location with the best data quality within the pre-set area to determine the location. The strategy for adjusting the drone's flight position within the same pre-set area may include, but is not limited to, the following directions:

[0058] 1. First, adjust the flight altitude: The sensor's data acquisition effect may vary with changes in flight altitude. Adjusting the drone's altitude and exploring different flight altitudes within the same preset area can improve data quality. It's important to note that the flight altitude adjustment process should be performed within the effective altitude range. This means that all types of sensors should continue to operate normally during the altitude adjustment process, and sensors that were collecting data correctly before the adjustment should continue collecting data throughout the entire process.

[0059] Adjustment method: Adjust the flight altitude, such as increasing or decreasing it, and recalculate the G-value until the standard is reached.

[0060] 2. Adjust the flight direction: Within the preset area, changes in viewing angle, lighting, or wind speed may cause a decrease in data quality. Adjusting the flight direction can change the sensor's acquisition angle, thereby improving data quality. It is important to note that the flight direction adjustment process should be performed within the effective altitude range. That is, during the flight direction adjustment, the acquisition work of various types of sensors should continue to operate normally, and sensors that were acquiring data normally before the flight direction adjustment should continue to acquire data normally throughout the entire process of flight direction adjustment.

[0061] Adjustment method: Change the flight angle or direction of the drone and recalculate the G-value until the standard is met.

[0062] 3. Finally, fine-tune the flight position: Within the preset area, the drone can find the most suitable data collection point by fine-tuning its flight position;

[0063] Adjustment method: Based on the feedback from the sensor data, make small-range lateral or longitudinal movements, recalculate the G value, and continue until the standard is met.

[0064] It is important to note that when calculating the G value, the number of sensor types N is determined by the number of sensors that are working normally; that is, the sensor data values ​​substituted into the calculation are not zero.

[0065] The following is an example of the G-value calculation process, used as an example to illustrate and supplement the formula algorithm for the feedback control index of the UAV currently within the preset area:

[0066] Given the condition: g ref =0.025, K=1000, w=0.25, N=4, with a temperature sensor, vibration sensor, image sensor and humidity sensor respectively;

[0067] Temperature sensor: Measured value X1 = 25℃, Maximum preset value of summation Max1 = 10 11 Noise impact factor λ1 = 0.006, reliability impact factor δ1 = 0.1; measurement error standard deviation σ e1 =0.2℃, environmental noise standard deviation σ env1 =0.1℃, instrument noise standard deviation σ inst1 =0.05℃;

[0068] Vibration sensor: Measured value X2 = 0.1 m / s 2 The maximum preset value for the summation term is Max2 = 10; the noise impact factor is λ2 = 0.0115; the reliability impact factor is δ2 = 0.3; and the standard deviation of the measurement error is σ. e2 =0.02m / s 2 Environmental noise standard deviation σ env2 =0.01m / s 2 Instrument noise standard deviation σ inst2 =0.005m / s 2 ;

[0069] Image sensor: Measured value X3 = 0.8, measured value is standardized image data, i.e., standardized image pixels, maximum preset value of summation term Max3 = 1, noise influence factor λ3 = 0.07, reliability influence factor δ3 = 0.2; measurement error standard deviation σ e3 =0.1, Environmental noise standard deviation σ env3 =0.05, Instrument noise standard deviation σ inst3 =0.02;

[0070] Humidity sensor: Measured value x4 = 45%, Maximum preset value for summation item Max4 = 1018 Noise impact factor λ4 = 0.000333, reliability impact factor δ4 = 0.15; measurement error standard deviation σ e4 =0.03%, Environmental noise standard deviation σ env4 =0.01%, Instrument noise standard deviation σ inst4 =0.005%.

[0071] Substituting into the formula, we get:

[0072]

[0073] Z1≈0.9091, Z2≈0.7692, Z3≈0.8333, Z4≈0.8696;

[0074] For the sake of simplicity in calculation and to demonstrate the usage of the calculation formula, the following β i γ i Q i Adjustments have been made; in practical applications, calculations should be performed according to the above formulas and explanations.

[0075] Due to the characteristics of the sine function, if the input value causes the result of the sine function to be negative, the input value needs to be adjusted first. For example, when the temperature sensor's measurement value is 25℃, due to β... i γ i Q i The adjusted value is now fixed. The specific adjustment method is: Adjustment value = 25 * 0.01 = 0.25; in practical applications, it can be used in conjunction with β. i The value of sin(β) is adjusted so that... i X i The result should be a positive number.

[0076] Temperature sensor product term:

[0077] Vibration sensor product term:

[0078] Image sensor product term:

[0079] Humidity sensor product term:

[0080] Total value of the product terms = (1.2475 × 1.0749 × 1.402 × 1.707) 0.25 ≈1.214;

[0081]

[0082] Temperature sensor addition:

[0083] Vibration sensor summation:

[0084] Image sensor summation:

[0085] Humidity sensor addition:

[0086] The total sum of terms = 0.321 + 0.00907 + 0.8215 + 15.9 = 17.0516;

[0087] G <g ref The flight position control submodule within the preset area controls the flight position of the UAV within the preset area according to the above method, optimizing the G value until G≥g ref .

[0088] Combination Figure 3 and Figure 4 As shown, the following is the core program code for implementing the above calculation example. This program code facilitates the quick calculation of the G value and is also easy to modify, thus adapting to various calculation scenarios:

[0089] import numpy as np

[0090] #Known data from the sensor

[0091] #Temperature Sensor

[0092] mu_1 = Temperature sensor #25 measured value (°C)

[0093] lambda_1 = 0.006# Noise Influence Factor of Temperature Sensor

[0094] beta_1 = 0.1 # Temperature sensor reliability impact factor

[0095] max_1 = 10**11# Preset maximum value of the temperature sensor summation term.

[0096] #Vibration Sensor

[0097] mu_2 = 0.1 # Vibration sensor measurement value (m / s^2)

[0098] lambda_2 = 0.0115# Noise Influence Factor of Vibration Sensor

[0099] beta_2 = 0.3# Vibration sensor reliability impact factor

[0100] max_2 = 10# The preset maximum value of the vibration sensor summation term.

[0101] #Image Sensor

[0102] mu_3 = 0.8# Image sensor measurement value (standardized value)

[0103] lambda_3 = 0.070 # Image sensor noise impact factor

[0104] beta_3 = 0.2 # Image sensor reliability impact factor

[0105] max_3 = 1 # Preset maximum value for image sensor summation terms

[0106] #Humidity sensor

[0107] mu_4 = 45# Humidity sensor measurement value (%)

[0108] lambda_4 = 0.000333# Noise Influence Factor of Humidity Sensor

[0109] beta_4 = 0.15# Humidity sensor reliability impact factor

[0110] max_4 = 10**18# The preset maximum value of the humidity sensor's summation term.

[0111] #Sensor measurements

[0112] measurements=[mu_1,mu_2,mu_3,mu_4]

[0113] lambdas=[lambda_1,lambda_2,lambda_3,lambda_4]

[0114] betas=[beta_1,beta_2,beta_3,beta_4]

[0115] max_values=[max_1,max_2,max_3,max_4]

[0116] #Define the formula for calculating the summation term

[0117] def compute_Yi(lambda_i):

[0118] Calculate the noise impact factor Yi.

[0119] return 1 / (1+lambda_i)

[0120] def compute_Zi(beta_i):

[0121] Calculate the reliability impact coefficient Zi.

[0122] return 1 / (1+beta_i)

[0123] def compute_sum_term(X_i,Y_i,Z_i):

[0124] """Calculate the sum for each sensor"""

[0125] return(np.exp(X_i)-np.exp(-X_i)) / (np.arctan(Y_i)+np.sqrt(Z_i+1))defcompute_product_term(W_i,beta_i,gamma_i,Q_i):

[0126] Calculate the product term for each sensor.

[0127] return 1+(np.sin(beta_j*X_i)) / (np.cosh(gamma_i*Q_i))

[0128] # Calculate Yi and Zi for each sensor Yi = [compute_Yi(lambda_i) for lambda_i in lambdas]

[0129] Zi=[compute_Zi(beta_i)for beta_iin betas]

[0130] #Calculate the summation term

[0131] sum_terms = []

[0132] fori,measurementin enumerate(measurements):

[0133] sum_term=compute_sum_term(measurement,Yi[i],Zi[i])

[0134] sum_terms.append(sum_term)

[0135] #Calculate the standardized summation term

[0136] normalized_sum_terms=[sum_term / max_values[i]fori,sum_terminumerate(sum_terms)]

[0137] #Calculate the total value of the summed terms

[0138] total_sum_term=np.sum(normalized_sum_terms)

[0139] # Calculate the product term (assuming the weighting coefficient is 0.25, gamma_i = 0.8, and Qi = 0.02)

[0140] product_terms = []

[0141] gamma_i = 0.8 #Assuming gamma_i value

[0142] Q_i = 0.02 #Assuming the value of Q_i

[0143] foriin range(4):

[0144] product_term=compute_product_term(measurements[i], betas[i], gamma_i, Q_i)

[0145] product_terms.append(product_term)

[0146] #Assume the weight of the product term for each sensor is 0.25.

[0147] w_i = 0.25

[0148] adjusted_product_term=np.prod(np.array(product_terms)**w_i)

[0149] #Scaling factor

[0150] K = 1000

[0151] #Calculate G value

[0152] G=(1 / K)*total_sum_term*adjusted_product_term

[0153] # Output the final result

[0154] print(f"Final calculated value of G: {G}").

[0155] In summary, the data transmission terminal includes a raw data preprocessing module and a data transmission module. This ensures that the collected sensor data is preprocessed before being transmitted to the ground management platform, guaranteeing data accuracy and improving data transmission efficiency. Real-time status monitoring and adjustment: The inspection and control terminal, combined with a real-time monitoring module and an environmental change perception module, adjusts the UAV's flight position promptly based on changes in environmental data, optimizing data acquisition and reducing inspection errors caused by environmental factors. Feedback control index: Through the settings of the feedback control index calculation submodule and the flight position control module, the flight position can be adjusted based on sensor data and environmental changes. Algorithms optimize the UAV's flight position within a preset area, ensuring the accuracy and stability of the data acquisition process, reducing unnecessary flight path and position adjustments, and improving the quality and efficiency of UAV inspection data processing.

[0156] Optional, combined Figure 5 As shown, the raw data preprocessing module includes a multi-type sensor data preprocessing submodule and a target data generation submodule; the multi-type sensor data preprocessing submodule is used to preprocess the raw data of various sensors; the target data generation submodule is used to package the preprocessed data into target data.

[0157] Optionally, the multi-type sensor data preprocessing submodule includes an image data preprocessing unit, a temperature data preprocessing unit, a vibration data preprocessing unit, and a humidity data preprocessing unit. The image data preprocessing unit performs gradient enhancement and noise reduction processing on the raw data from the image sensor and generates an image enhancement index. The temperature data preprocessing unit performs deviation analysis on the raw data from the temperature sensor and generates a global temperature index. The vibration data preprocessing unit performs amplification and interference suppression processing on the raw data from the vibration sensor and generates a global vibration index. The humidity data preprocessing unit performs data smoothing and filtering on the raw data from the humidity sensor and generates a global humidity index. The target data generation submodule packages the preprocessed sensor data, image enhancement index, temperature global index, vibration global index, and humidity global index into target data.

[0158] The UAV automated intelligent inspection method is applied to the aforementioned UAV automated intelligent inspection system, as shown in the figure. Figure 6 As shown, the UAV automatic intelligent inspection method includes:

[0159] S1, planning inspection tasks and flight trajectories;

[0160] S2, based on the inspection task and flight trajectory, performs inspections and collects the sensor data required by the inspection task.

[0161] S3 monitors the status and environmental changes of the drone terminal in real time, and adjusts the flight position of the drone terminal during the process of collecting sensor data by combining sensor data.

[0162] S4 enables sensor data transmission between the UAV terminal and the ground management platform based on 5G wireless communication technology.

[0163] Example 2: This example includes all the content of Example 1, providing an automatic intelligent inspection system for unmanned aerial vehicles (UAVs). When the UAV's flight position allows G ≥ g... ref The multi-type sensor data preprocessing submodule begins operation. When the image data preprocessing unit operates, it first performs gradient enhancement and noise reduction processing on the raw data acquired by the image sensor, transforming the raw image data into optimized image data. The gradient enhancement and noise reduction methods can be, but are not limited to: inputting an image; calculating the gradient magnitude; performing gradient enhancement based on the magnification factor and gradient magnitude; outputting an image as a noisy image; inputting a noisy image; selecting a mean filter for filtering; and outputting the filtered image as optimized image data. The process of generating image enhancement metrics satisfies the following formula:

[0164]

[0165] Among them, I * Indicates the image enhancement index at the current moment; I * A larger value indicates a more significant enhancement effect; N represents the total number of pixels in the sampled image, which is sampled from the raw data of the image sensor; ΔI i This represents the gradient change value of the i-th pixel in the image, obtained during the gradient enhancement process. G td G represents the gradient magnitude. x G represents the horizontal gradient of the i-th pixel, obtained by convolving the pixel's grayscale value with the Sobel operator. y The vertical gradient of the i-th pixel is obtained by convolving the pixel's grayscale value with the Sobel operator; ∈ represents a minimal constant used to avoid instability during calculations involving division by zero or square roots, and its value ranges from 10. -3 Up to 10 -5 The specific value is set by the administrator based on experience. The empirical selection rule is as follows: when processing images with severe noise, ∈ can be appropriately increased to avoid excessive amplification of noise during gradient calculation; for images with good quality and less noise, ∈ can be set smaller, typically with a value of 10. -3 Used in low-noise images to reduce excessive noise amplification during edge enhancement; typical value is 10. -5 For scenes with relatively clear images and low noise, it helps improve detail accuracy;i This represents the noise characteristic adjustment factor of the i-th pixel in the image. Common values ​​are 0.1 and 0.01. 0.1 is used for images with strong noise, which helps to reduce noise; 0.01 is used for images with less noise, which can improve the detail of the image. In this embodiment, κ i =0.1. The target data includes optimized image data and image enhancement indicators and their calculation formulas, which helps to reduce storage space, improve the efficiency and accuracy of storage and transmission, and facilitate data restoration to obtain various data in the image preprocessing process. The existence of image enhancement indicators also makes it easier for administrators and the system to judge the quality of image data, so as to improve the accuracy and efficiency of acquisition, and thus improve the accuracy and efficiency of the entire data processing process from data acquisition to transmission.

[0166] When the temperature data preprocessing unit operates, it first performs deviation analysis on the raw data collected by the temperature sensor. The deviation analysis method can be, but is not limited to: inputting a temperature dataset; calculating the mean and standard deviation of the temperature dataset; calculating the deviation of each data point from the mean: deviation = absolute value of the difference between the temperature data and the mean / standard deviation; setting a deviation threshold and comparing it with the deviation level; identifying temperature data with a deviation level greater than the deviation threshold as abnormal data and filtering them out. The following formula is satisfied when generating the global temperature index:

[0167]

[0168] Among them, T * This represents the global temperature index at the current moment; the smaller the global temperature index, the higher the temperature data quality; T i This represents the i-th temperature data point in the temperature dataset collected by the temperature sensor up to the current moment; T ref The standard reference working temperature value of the inspection object is represented; N represents the total number of temperature data in the temperature dataset; the target data includes the filtered temperature dataset and global temperature indicators and their calculation formulas, which helps to reduce storage space and improve the efficiency and accuracy of storage and transmission. The existence of global temperature indicators also makes it easier for administrators and the system to judge the quality of temperature data, so as to improve the accuracy and efficiency of data collection, and thus improve the accuracy and efficiency of the entire data processing process from data collection to transmission.

[0169] When the vibration data preprocessing unit operates, it first amplifies and suppresses interference on the raw data collected by the vibration sensor. The methods for amplification and interference suppression include, but are not limited to: inputting the vibration dataset; selecting an amplification factor for amplitude amplification; outputting the amplified vibration data as input for interference suppression; selecting a low-pass filter for filtering; and outputting the interference-suppressed vibration data. The process of generating global vibration indices satisfies the following formula:

[0170]

[0171] Among them, V * V represents the global vibration index at the current moment. * A larger value indicates a stronger amplitude and fluctuation of the vibration signal, usually signifying more intense vibration and potentially indicating significant mechanical stress, equipment failure, or abnormal conditions within the system; ψ i This indicates the magnification factor used when amplifying the amplitude; in this embodiment, 1.5 is selected. i This represents the amplitude of the i-th vibration data point in the vibration dataset. The target data includes the vibration dataset after amplification and interference suppression processing, as well as global vibration indices and their calculation formulas. This helps reduce storage space and improve the efficiency and accuracy of storage and transmission. The existence of global vibration indices also facilitates administrators and the system in judging the quality of vibration data, thereby improving the accuracy and efficiency of data acquisition, and ultimately improving the accuracy and efficiency of the entire data processing flow from data acquisition to transmission.

[0172] When the humidity data preprocessing unit operates, it first performs data smoothing and filtering on the raw data collected by the humidity sensor. The data smoothing and filtering method can be, but is not limited to: inputting a humidity dataset; performing a sliding window averaging on the humidity data, with a window size of 3, i.e., in the humidity dataset, each data point becomes the average of the current data point and the two data points before and after it; and outputting the smoothed humidity dataset. The following formula is satisfied when generating the global humidity index:

[0173]

[0174] Among them, H * This represents the global humidity index at the current moment; H * A larger value generally indicates a greater difference between the humidity data and the reference value, and stronger data fluctuations, which may reflect a larger error or sensor instability; N represents the total number of data points in the humidity dataset; H i This represents the humidity data of the i-th data point in the humidity dataset up to the current moment; H ref The standard reference working humidity value represents the inspection object; the target data includes a smoothed humidity dataset and a global humidity index and its calculation formula, which helps to reduce storage space and improve the efficiency and accuracy of storage and transmission. The existence of the global humidity index also makes it easier for administrators and the system to judge the quality of humidity data, so as to improve the accuracy and efficiency of data collection, and thus improve the accuracy and efficiency of the entire data processing process from data collection to transmission.

[0175] It should be noted that when using the above indicators for judgment, the following methods may be used, but are not limited to: 1. Threshold comparison method: preset a threshold and compare it with the indicator to complete the judgment; 2. Experience observation method: the administrator observes and makes a judgment based on experience; 3. Historical average comparison method: compare the current indicator value with the historical average of the indicator and complete the judgment based on the specific degree of deviation.

[0176] The system's working process in this embodiment can be summarized as follows: [Combined with...] Figure 7 and Figure 8 As shown, the drone is first controlled to fly to one of the preset areas within the site. The drone performs an inspection task, and each sensor starts to work normally. At this time, the G value is continuously calculated to adjust the flight position of the drone within the preset area. When the G value meets the conditions, the next step of data acquisition and data processing begins. Each unit in the multi-type sensor data preprocessing submodule starts to work, and the corresponding target data is obtained after data processing, thereby completing accurate, efficient, and energy-saving data transmission.

[0177] In summary, image data preprocessing involves optimizing the raw data from the image sensor through gradient enhancement and noise reduction, reducing the impact of noise on image quality and ensuring the accuracy of image enhancement. Image enhancement metrics help the system judge the quality of image data and further improve the accuracy of image data acquisition. Temperature data deviation analysis utilizes a deviation analysis algorithm to accurately detect and filter out abnormal values ​​in temperature data, improving the reliability of temperature data and ensuring that the system can accurately acquire effective temperature data during environmental monitoring, reducing the impact of errors and interference. Vibration data amplification and interference suppression improves the readability and accuracy of vibration signals by amplifying and suppressing interference from vibration sensor data, helping the system accurately identify the health status and potential faults of mechanical equipment and provide early warnings. Humidity data smoothing and filtering smooths fluctuations after humidity data is processed by sliding window averaging, reducing environmental noise interference and improving the accuracy and stability of humidity data. This ensures that stable and reliable environmental data can be acquired in real time during inspections, improving the quality and efficiency of UAV inspection data processing.

[0178] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. An automated intelligent inspection system for unmanned aerial vehicles (UAVs), characterized in that: It includes a drone terminal, a ground management platform, a data transmission terminal, and an inspection and control terminal; the ground management platform is used to plan inspection tasks and flight trajectories for the drone terminal; the drone terminal is used to perform inspections and collect sensor data required by the inspection tasks according to the inspection tasks and flight trajectories. The inspection and control terminal is used to monitor the status and environmental changes of the UAV terminal in real time, and to control the flight position of the UAV terminal during the process of collecting sensor data in combination with sensor data; the data transmission terminal is used to realize sensor data transmission between the UAV terminal and the ground management platform based on 5G wireless communication technology. The data transmission terminal includes a raw data preprocessing module and a data transmission module; the raw data preprocessing module is used to preprocess the sensor data to generate target data; The data transmission module is used to transmit the target data to the ground management platform; The inspection and control terminal includes a real-time status monitoring module, an environmental change sensing module, and a flight position control module. The real-time status monitoring module is used to monitor the flight status of the UAV in real time. The environmental change sensing module is used to acquire changes in environmental data of the UAV during flight. The flight position control module is used to adjust the position of the UAV in a preset area based on changes in sensor data and environmental data. The flight position control module includes a feedback control index calculation submodule and a flight position control submodule within a preset area. The feedback control index calculation submodule is used to calculate the feedback control index of the UAV body currently located within the preset area based on changes in sensor data and environmental data. The flight position control submodule within the preset area is used to control the flight position of the UAV body within the preset area based on the conditions of the preset area, the flight status of the UAV body, and the feedback control index.

2. The UAV automatic intelligent inspection system as described in claim 1, characterized in that, When the feedback control index calculation submodule performs the calculation, the following formula is satisfied: Where G represents the feedback control index of the UAV currently within the preset area; K represents the reduction factor; X i α represents the raw data value of the i-th sensor in the sensor data acquisition module; N represents the total number of sensor types in the sensor data acquisition module; i Y represents the amplification factor of the i-th sensor; i Z represents the noise impact coefficient of the i-th sensor in the sensor data acquisition module. The higher the noise level in the raw data of the i-th sensor, the larger the corresponding noise impact coefficient. i Max represents the reliability score of the i-th sensor in the sensor data acquisition module. i β represents the preset maximum value of the summation term for the i-th type of sensor, used to standardize the value of the sensor summation term; i γ represents the nonlinear control coefficient of the i-th sensor; i Q represents the adjustment coefficient for the i-th sensor; i represents the sensitivity coefficient of the i-th sensor; w represents the weighting coefficient of the product term; When G < g ref At that time, the flight position control submodule within the preset area adjusts the flight position of the UAV within the preset area according to the conditions of the preset area and the flight status of the UAV itself, optimizing the G value until G≥g ref g ref This represents the threshold for judging the flight position of the drone itself when the drone terminal collects sensor data.

3. The UAV automatic intelligent inspection system as described in claim 2, characterized in that, The ground management platform includes an inspection task planning module, a flight trajectory planning module, and a task monitoring module. The inspection task planning module is used to plan inspection tasks for the UAV terminal and determine the preset areas and target equipment to be inspected. The preset areas are flight areas pre-set by the administrator within the locations specified in the inspection task. The flight trajectory planning module is used to plan flight paths and flight altitudes for the UAV terminal. The task monitoring module is used to monitor the flight status of the UAV terminal and the execution of inspection tasks in real time.

4. The unmanned aerial vehicle (UAV) automatic intelligent inspection system as described in claim 3, characterized in that, The UAV terminal includes a UAV body, a flight control module, a sensor data acquisition module, and a task execution module; the flight control module, sensor data acquisition module, and task execution module are installed on the UAV body; the flight control module is used to adjust the flight position and flight status of the UAV body according to the inspection task, flight path, and flight altitude of the ground management platform; the task execution module is used to receive the inspection task issued by the ground management platform and determine the data type required to be collected by the inspection task. The sensor data acquisition module is used to collect the required sensor data during the inspection process.

5. The unmanned aerial vehicle (UAV) automatic intelligent inspection system as described in claim 4, characterized in that, The raw data preprocessing module includes a multi-type sensor data preprocessing submodule and a target data generation submodule; the multi-type sensor data preprocessing submodule is used to preprocess the raw data from various sensors; the target data generation submodule is used to package the preprocessed data into target data.

6. The unmanned aerial vehicle (UAV) automatic intelligent inspection system as described in claim 5, characterized in that, The multi-type sensor data preprocessing submodule includes an image data preprocessing unit, a temperature data preprocessing unit, a vibration data preprocessing unit, and a humidity data preprocessing unit. The image data preprocessing unit performs gradient enhancement and noise reduction processing on the raw data from the image sensor, generating an image enhancement index. The temperature data preprocessing unit analyzes the deviation of the raw data from the temperature sensor, generating a global temperature index. The vibration data preprocessing unit amplifies and suppresses interference on the raw data from the vibration sensor, generating a global vibration index. The humidity data preprocessing unit smooths and filters the raw data from the humidity sensor, generating a global humidity index. The target data generation submodule packages the preprocessed sensor data, image enhancement index, temperature global index, vibration global index, and humidity global index into target data.

7. An automatic intelligent inspection method for unmanned aerial vehicles (UAVs), applied to the automatic intelligent inspection system for UAVs as described in claim 6, characterized in that, The unmanned aerial vehicle (UAV) automatic intelligent inspection method includes: S1, planning inspection tasks and flight trajectories; S2, based on the inspection task and flight trajectory, performs inspections and collects the sensor data required by the inspection task. S3 monitors the status and environmental changes of the drone terminal in real time, and adjusts the flight position of the drone terminal during the process of collecting sensor data by combining sensor data. S4 enables sensor data transmission between the UAV terminal and the ground management platform based on 5G wireless communication technology.

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