Comprehensive control system of intelligent pipeline inspection unmanned aerial vehicle

Abstract

The invention discloses an intelligent pipeline inspection unmanned aerial vehicle comprehensive control system, and particularly relates to the field of unmanned aerial vehicle intelligent pipeline inspection. Comprising an intelligent pipeline inspection data batch division module, an intelligent pipeline inspection data acquisition module, an intelligent pipeline inspection data analysis module, an intelligent pipeline inspection data comprehensive evaluation module and a real-time feedback early warning module. The intelligent pipeline inspection data acquisition module comprises a pipeline state data acquisition unit and an unmanned aerial vehicle state data acquisition unit and is used for acquiring target data in real time and transmitting the acquired data to the intelligent pipeline inspection data analysis module; the intelligent pipeline inspection data analysis module comprises a pipeline state data analysis unit and an unmanned aerial vehicle state data analysis unit and is used for analyzing the data transmitted by the intelligent pipeline inspection data acquisition module; according to the method, various data in the pipeline inspection process are divided into different data batches, so that the comprehensiveness and accuracy of inspection are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) intelligent pipeline inspection, and more specifically, to an integrated control system for an unmanned aerial vehicle (UAV) intelligent pipeline inspection. Background Art

[0002] In modern infrastructure construction, various pipelines are widely distributed. They are like the "lifeline" of the city, ensuring important functions such as energy transmission and communication transmission. The traditional pipeline inspection method relies on a large amount of manpower, which is not only inefficient, but also difficult to fully and timely discover the hidden dangers of the pipeline in complex terrain and harsh environment, posing a challenge to pipeline maintenance and safe operation. With the rapid development of drone technology, intelligent sensing technology and data analysis technology, it has provided strong support for the birth of intelligent pipeline inspection drone integrated control system, bringing a new inspection mode and change.

[0003] The existing intelligent pipeline inspection drone integrated control system is mainly composed of a drone flight platform, a multi-source data acquisition module, a high-speed data transmission link, an intelligent data analysis center, and a remote control and feedback module. The drone flight platform is equipped with a variety of advanced sensors, which can flexibly inspect pipelines in various complex environments according to preset routes. The multi-source data acquisition module can collect relevant data including pipeline temperature, pressure, appearance integrity, surrounding geological conditions, etc. The collected data is transmitted to the intelligent data analysis center in real time through a high-speed data transmission link. The center uses advanced algorithms and models to quickly organize, deeply analyze and intelligently diagnose massive data, and then generate detailed inspection reports and hidden danger warning information. This information is fed back to the operator through the remote control and feedback module so that corresponding measures can be taken in time to ensure the safe and stable operation of the pipeline.

[0004] However, the existing pipeline inspection drone control system still has certain limitations. In terms of data collection, insufficient data collection on complex environmental factors around the pipeline and some potential safety hazards leads to poor data integrity and comprehensiveness. In the data analysis stage, the processing capacity of complex data is limited, which affects the reliability and accuracy of the inspection results and cannot meet the growing needs of pipeline safety management.

[0005] Therefore, there is an urgent need for an intelligent pipeline inspection drone integrated control system to solve the problems of insufficient data collection and inaccurate data analysis in the existing system, so as to achieve more efficient, accurate and intelligent inspection of the pipeline and provide solid guarantee for the safe and stable operation of the pipeline. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent pipeline inspection drone integrated control system, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent pipeline inspection drone integrated control system, comprising: Intelligent pipeline inspection data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Intelligent pipeline inspection data acquisition module: including pipeline status data acquisition unit and drone status data acquisition unit, used to collect target data in real time and transmit the collected data to the intelligent pipeline inspection data analysis module; the pipeline status data acquisition unit is used to collect pipeline structure data, pipeline appearance data, pipeline medium data and pipeline operation data; the drone status data acquisition unit is used to collect drone flight data and data transmission data; Intelligent pipeline inspection data analysis module: including pipeline status data analysis unit and drone status data analysis unit, used to analyze the data transmitted by the intelligent pipeline inspection data acquisition module, and transmit the analysis results to the intelligent pipeline inspection data comprehensive evaluation module; the pipeline status data analysis unit includes pipeline structure data analysis nodes, pipeline appearance data analysis nodes, pipeline medium data analysis nodes and pipeline operation data analysis nodes; the drone status data analysis unit includes drone flight data analysis nodes and data transmission data analysis nodes; Intelligent pipeline inspection data comprehensive evaluation module: including the intelligent pipeline inspection drone comprehensive control data analysis unit, which is used to comprehensively analyze the data transmitted by the intelligent pipeline inspection data analysis module and transmit the analysis results to the real-time feedback warning module; Real-time feedback warning module: used to establish a preset value of the intelligent pipeline inspection comprehensive evaluation index, judge the intelligent pipeline inspection comprehensive evaluation index value according to the preset value of the intelligent pipeline inspection comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0008] Preferably, the pipeline structure data includes the pipeline radial deformation rate Rd, the pipeline axial displacement Ad and the weld undercut depth Wd; the pipeline appearance data includes the surface roughness Sr, the surface crack width Sc and the surface corrosion pit depth Sd; the medium data in the pipe includes the medium density change rate Dd, the medium viscosity change rate Vv and the medium sand content Ss; the pipeline operation data includes the pipeline vibration energy Ve, the medium flow deviation rate Fd and the medium pressure fluctuation amplitude Pf.

[0009] Preferably, the UAV flight data includes the flight attitude change frequency Af, the battery power consumption rate Bd and the positioning accuracy deviation distance Pa; the data transmission data includes the data packet loss rate Lr, the signal transmission delay Ds and the communication bandwidth utilization rate Cu.

[0010] Preferably, the pipeline structure data analysis node is used to establish a pipeline structure data calculation model, and the pipeline structure data transmitted by the intelligent pipeline inspection data acquisition module is imported into the pipeline structure data calculation model to obtain the pipeline structure risk coefficient value. The pipeline structure data calculation model is specifically expressed as: , where α i Represents the pipeline structure risk coefficient value calculated for the i-th time, Rd i represents the radial deformation rate of the pipeline collected for the i-th time, Ad i represents the axial displacement of the pipeline collected for the i-th time, Ad max Indicates the historical maximum value of the pipeline axial displacement, Wd i represents the weld undercut depth collected for the ith time, Wd max Indicates the historical maximum value of weld undercut depth.

[0011] Preferably, the pipeline appearance data analysis node is used to establish a pipeline appearance data calculation model, and the pipeline appearance data transmitted by the intelligent pipeline inspection data acquisition module is imported into the pipeline appearance data calculation model to obtain the pipeline surface damage coefficient value. The pipeline appearance data calculation model is specifically expressed as: , where β i represents the pipeline surface damage coefficient value calculated for the i-th time, Sr i represents the surface roughness of the i-th collection, Sr max Indicates the historical maximum value of surface roughness, Sc i represents the surface crack width collected at the i-th time, Sc max Indicates the historical maximum value of surface crack width, Sd i represents the depth of the surface corrosion pit collected at the i-th time, Sd max Indicates the historical maximum depth of surface corrosion pits.

[0012] Preferably, the in-pipe medium data analysis node is used to establish an in-pipe medium data calculation model, and the in-pipe medium data transmitted by the intelligent pipeline inspection data acquisition module is imported into the in-pipe medium data calculation model to obtain the pipeline medium stability balance coefficient value. The in-pipe medium data calculation model is specifically expressed as: , where γ i Indicates the pipeline medium stability balance coefficient value calculated for the i-th time, Dd i Denotes the density change rate of the medium collected for the i-th time, Dd max Indicates the historical maximum value of the medium density change rate, Vvi Vv represents the viscosity change rate of the medium collected for the i-th time, max Indicates the historical maximum value of the medium viscosity change rate, Ss i represents the sediment content of the medium collected for the i-th time, Ss max Indicates the historical maximum sand content of the medium.

[0013] Preferably, the pipeline operation data analysis node is used to establish a pipeline operation data calculation model, and the pipeline operation data transmitted by the intelligent pipeline inspection data acquisition module is imported into the pipeline operation data calculation model to obtain the pipeline operation health coefficient value. The pipeline operation data calculation model is specifically expressed as: , where ∂ i Represents the pipeline operation health coefficient value calculated for the i-th time, Ve i represents the pipeline vibration energy collected for the i-th time, Ve max Indicates the historical maximum value of pipeline vibration energy, Fd i Indicates the deviation rate of the medium flow in the pipe collected for the i-th time, Fd max Indicates the historical maximum value of the medium flow deviation rate in the pipe, Pf i Pf represents the pressure fluctuation amplitude of the medium in the tube collected for the i-th time. max Indicates the historical maximum value of the pressure fluctuation amplitude of the medium in the pipe.

[0014] Preferably, the UAV flight data analysis node is used to establish a UAV flight data calculation model, import the UAV flight data transmitted by the intelligent pipeline inspection data acquisition module into the UAV flight data calculation model, and obtain the UAV flight reliability coefficient value. The UAV flight data calculation model is specifically expressed as: , where ℓ i Af represents the UAV flight reliability coefficient value calculated for the i-th time, i Indicates the frequency of flight attitude change collected for the i-th time, Af max Indicates the historical maximum frequency of flight attitude changes, Bd i Indicates the battery power consumption rate of the ith collection, Bd max Indicates the historical maximum value of battery power consumption rate, Pa i Indicates the positioning accuracy deviation distance of the i-th acquisition, Pa max Indicates the historical maximum value of the positioning accuracy deviation distance.

[0015] Preferably, the data transmission data analysis node is used to establish a data transmission data calculation model, import the data transmission data transmitted by the intelligent pipeline inspection data acquisition module into the data transmission data calculation model, and obtain the data transmission quality coefficient value. The data transmission data calculation model is specifically expressed as: , where χ iIndicates the data transmission quality coefficient value calculated for the i-th time, Lr i Indicates the packet loss rate of the data collected for the i-th time, Lr max Indicates the historical maximum value of data packet loss rate, Ds i Denotes the signal transmission delay of the i-th acquisition, Ds max Indicates the historical maximum value of signal transmission delay, Cu i represents the communication bandwidth utilization rate of the ith collection, Cu max Indicates the historical maximum value of communication bandwidth utilization.

[0016] Preferably, the intelligent pipeline inspection drone integrated control data analysis unit is used for the intelligent pipeline inspection drone integrated control data calculation model, and the pipeline structure risk coefficient value, pipeline surface damage coefficient value, pipeline medium stability balance coefficient value, pipeline operation health coefficient value, drone flight reliability coefficient value and data transmission quality coefficient value transmitted by the intelligent pipeline inspection data analysis module are imported into the intelligent pipeline inspection drone integrated control data calculation model to obtain the intelligent pipeline inspection comprehensive evaluation index value; the intelligent pipeline inspection drone integrated control data calculation model is specifically expressed as: , where CE represents the calculated comprehensive evaluation index value of intelligent pipeline inspection, α i represents the pipeline structure risk coefficient value calculated for the i-th time, β i represents the pipeline surface damage coefficient value calculated for the i-th time, γ i represents the pipeline medium stability balance coefficient value calculated for the i-th time, ∂ i represents the pipeline operation health coefficient value calculated for the i-th time, ℓ i represents the UAV flight reliability coefficient value calculated for the i-th time, χ i Represents the data transmission quality coefficient value calculated for the i-th time.

[0017] Technical effects and advantages of the present invention: The present invention significantly improves the comprehensiveness and accuracy of the inspection by dividing various types of data in the pipeline inspection process into different data batches. By collecting pipeline structure data, pipeline appearance data, pipeline medium data, pipeline operation data, drone flight data, and data transmission data in multiple dimensions, this multi-faceted data collection mode greatly reduces the limitations of previous inspection methods and builds a richer and more reliable data foundation for subsequent data evaluation; The present invention uses data analysis algorithms to conduct in-depth mining of the collected data, thereby calculating the pipeline structure risk coefficient value, pipeline surface damage coefficient value, pipeline medium stability balance coefficient value, pipeline operation health coefficient value, drone flight reliability coefficient value and data transmission quality coefficient value, accurately locking in areas where problems may exist. By comprehensively evaluating and deeply integrating the data processing results, the reliability and effectiveness of the inspection results are greatly improved; Through real-time monitoring and continuous tracking, the present invention immediately issues an alarm notification once abnormal data is found. Relevant technical personnel and maintenance personnel can timely grasp the real-time status and potential risks of the pipeline, and quickly adjust and optimize the pipeline maintenance strategy or inspection plan, thus providing a strong guarantee for realizing efficient and accurate pipeline inspection and scientific and reasonable operation and maintenance decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0019] 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 only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] As attached Figure 1 The intelligent pipeline inspection UAV integrated control system shown includes a system operation database, a system processor and a user information terminal, as well as an intelligent pipeline inspection data batch division module, an intelligent pipeline inspection data acquisition module, an intelligent pipeline inspection data analysis module, an intelligent pipeline inspection data comprehensive evaluation module, and a real-time feedback warning module.

[0021] The system operation database includes all data texts of an intelligent pipeline inspection drone integrated control system, and collects information texts output by each module in real time. The system processor is used to control the information text instructions output by each module. The user information terminal is an information output device for receiving an intelligent pipeline inspection drone integrated control system.

[0022] The intelligent pipeline inspection data batch division module is used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence.

[0023] In this embodiment, it is specifically necessary to explain that: the method of dividing by equal time is to divide the database according to the characteristics of each target data through the background system running the database. According to the real-time requirements of each target data, it is collected once at an equal interval and divided into different collection times.

[0024] The intelligent pipeline inspection data acquisition module includes a pipeline status data acquisition unit and a drone status data acquisition unit, which are used to collect target data in real time and transmit the collected data to the intelligent pipeline inspection data analysis module; the pipeline status data acquisition unit is used to collect pipeline structure data, pipeline appearance data, pipeline medium data and pipeline operation data; the drone status data acquisition unit is used to collect drone flight data and data transmission data.

[0025] In this embodiment, it is specifically necessary to explain that: the pipeline structure data includes the pipeline radial deformation rate Rd, the pipeline axial displacement Ad and the weld undercut depth Wd; the pipeline appearance data includes the surface roughness Sr, the surface crack width Sc and the surface corrosion pit depth Sd; the medium data in the pipe includes the medium density change rate Dd, the medium viscosity change rate Vv and the medium sand content Ss; the pipeline operation data includes the pipeline vibration energy Ve, the medium flow deviation rate Fd in the pipe and the medium pressure fluctuation amplitude Pf in the pipe; the UAV flight data includes the flight attitude change frequency Af, the battery power consumption rate Bd and the positioning accuracy deviation distance Pa; the data transmission data includes the data packet loss rate Lr, the signal transmission delay Ds and the communication bandwidth utilization rate Cu.

[0026] The radial deformation rate of the pipeline is a key indicator to measure the degree of deformation of the pipeline in the radial direction. Under the influence of factors such as soil pressure, ground subsidence, and external impact for a long time, the pipeline may undergo radial deformation. If the deformation rate is too large, it will seriously affect the structural integrity of the pipeline and greatly increase the risk of pipeline rupture and leakage. For the integrated control system of the intelligent pipeline inspection drone, accurately mastering this parameter can timely discover potential pipeline safety hazards and provide an important basis for maintenance decisions. The collection method is: the drone is equipped with a high-precision laser rangefinder. During the inspection process, the drone flies in a circle around the pipeline, and the laser rangefinder emits a laser beam to the surface of the pipeline at very short time intervals. After the laser beam encounters the surface of the pipeline and reflects back, the rangefinder measures the time difference between laser emission and reception, combined with the propagation speed of the laser in the air, and accurately calculates the distance to the pipeline surface at different angles. The distance data obtained by real-time measurement is compared with the original pipeline diameter data, and after complex algorithm processing, the radial deformation rate of the pipeline can be obtained.

[0027] The axial displacement of the pipeline reflects the position change of the pipeline in the axial direction. Crustal movement, nearby large-scale engineering construction, groundwater level changes, etc. may cause axial displacement of the pipeline. Excessive displacement may cause the seal failure of the pipeline connection, causing medium leakage, and even causing serious safety accidents. By monitoring this parameter, the intelligent pipeline inspection drone integrated control system can detect abnormal displacement of the pipeline in time so as to take corresponding reinforcement or repair measures to ensure the safe and stable operation of the pipeline. The collection method is: pre-set high-precision reflective markers at the starting point of the pipeline and multiple key positions along the pipeline. When inspecting, the drone uses its high-definition camera to shoot these markers. With the help of advanced image recognition technology, the drone can accurately identify the position of the marker in the image. Then, through triangulation, according to the relative position relationship between the drone and the marker and the shooting angle of the camera and other information, the axial displacement of each monitoring point relative to the starting point is accurately calculated. To ensure the accuracy of the measurement, the system will analyze and process the data of multiple measurements, remove abnormal values, and finally obtain reliable axial displacement data.

[0028] The weld undercut depth: The weld is a key part of the pipeline connection, and the weld undercut is a common welding defect. Excessive undercut depth will significantly weaken the strength of the weld, reduce the overall bearing capacity of the pipeline, and make the pipeline more prone to rupture when subjected to internal pressure and external loads. The monitoring of the weld undercut depth by the integrated control system of the intelligent pipeline inspection drone can timely detect weld quality problems, evaluate the structural safety of the pipeline, and provide an accurate basis for arranging weld repair work. The collection method is as follows: the drone carries a professional ultrasonic flaw detector. When approaching the pipeline weld, the ultrasonic flaw detector emits high-frequency ultrasonic pulses. These ultrasonic pulses enter the weld at a specific angle. When encountering defects such as undercuts, the ultrasonic waves will be reflected, refracted, and scattered. The flaw detector receives the reflected ultrasonic signal and accurately calculates the depth of the weld undercut through complex algorithm analysis based on the propagation time, amplitude, phase and other characteristics of the signal. In order to comprehensively detect different parts of the weld, the drone needs to move slowly along the weld to ensure that the flaw detector fully covers the weld to obtain accurate undercut depth data.

[0029] The surface roughness of the pipeline has an important influence on the flow characteristics of the medium in the pipe and the corrosion rate of the pipeline. The rougher the surface, the greater the resistance of the medium when flowing in the pipe, which not only increases the energy consumption of transportation, but also may cause the medium to be retained in certain parts, thereby accelerating the corrosion of the pipeline. In addition, the rough surface is more likely to accumulate dirt, moisture and corrosive substances, providing favorable conditions for corrosion reactions. The intelligent pipeline inspection drone integrated control system monitors the surface roughness, which helps to evaluate the operating efficiency and corrosion risk of the pipeline and provide a reference for formulating a reasonable maintenance plan. The collection method is: the drone is equipped with a contact surface roughness measuring instrument. The core component of the measuring instrument is a stylus with a high-precision sensor. When the drone approaches the pipeline surface, the stylus gently contacts the pipeline surface. As the drone moves along the pipeline surface, the stylus moves up and down with the microscopic undulations of the surface. The sensor records the vertical displacement changes of the stylus in real time and transmits these data to the drone's control system. The control system processes these displacement data through a specific algorithm to calculate the roughness value of the pipeline surface. In order to obtain representative surface roughness data, the measuring instrument will measure the pipeline surface at multiple different locations and then take the average value as the final result.

[0030] Surface crack width: Pipeline surface cracks are one of the hidden dangers that seriously threaten pipeline safety. The size of the crack width is directly related to the risk of pipeline rupture. As the crack width increases, the ability of the pipeline to withstand internal pressure and external stress will gradually decrease. The intelligent pipeline inspection drone integrated control system can accurately measure the surface crack width, which can timely evaluate the safety status of the pipeline, predict the development trend of the crack, and provide a key basis for taking emergency repair measures or formulating long-term maintenance plans. Its acquisition method is: using advanced digital image correlation technology. The drone uses a high-resolution industrial camera to shoot the pipeline surface to obtain a clear image. During the shooting process, the camera will shoot the same area multiple times at different times or different positions. These images are compared and analyzed through the digital image correlation algorithm. The algorithm will find the displacement changes of the feature points in the image to calculate the width of the surface crack. In order to improve the measurement accuracy, the system will pre-process the captured images, including image enhancement, denoising and other operations, and will also combine the camera's shooting parameters and the actual size of the pipeline for precise geometric correction to ensure the accuracy of the measurement results.

[0031] Depth of surface corrosion pits: The presence of surface corrosion pits will reduce the effective wall thickness of the pipeline, and reduce the strength and bearing capacity of the pipeline. The greater the depth of the corrosion pits, the higher the risk of leakage and rupture of the pipeline. The intelligent pipeline inspection drone integrated control system monitors the depth of surface corrosion pits, which can accurately assess the degree of corrosion of the pipeline, provide important data support for judging the remaining service life of the pipeline, and help decision makers take anti-corrosion measures or arrange pipeline replacement in time. The collection method is: the drone is equipped with an advanced laser scanning sensor. During the inspection process, the laser scanning sensor emits a laser beam to scan the pipeline surface in all directions. After the laser beam encounters the pipeline surface, it can construct a three-dimensional model of the pipeline surface based on the time and angle information of the reflected light. By analyzing the three-dimensional model, the system can accurately measure the depth of the corrosion pits. In order to ensure the accuracy of the measurement, the laser scanning sensor will scan the pipeline multiple times at different angles, and then fuse these data to generate a high-precision three-dimensional model of the pipeline surface, thereby obtaining accurate corrosion pit depth data.

[0032] The medium density change rate: The change in medium density in the pipe is an important indicator reflecting the medium state and pipeline operation. The change in medium density may indicate a change in medium composition, the mixing of other substances or a leak. For example, in an oil pipeline, if the medium density changes suddenly, it may be due to the mixing of water, impurities or light hydrocarbons, which will not only affect the medium transportation quality, but also the corrosion rate of the pipeline. The intelligent pipeline inspection drone integrated control system monitors the medium density change rate and can timely detect abnormal conditions of the medium, providing a basis for adjusting the transportation process, troubleshooting leakage points or taking corresponding treatment measures. The collection method is as follows: During the inspection process, the drone collects multiple medium samples from the pipe at different times through a professional sampling device. After each sample is collected, it is immediately measured using a high-precision density meter. The density meter uses the Archimedes principle or other advanced measurement techniques to accurately measure the density value of the sample. The drone transmits the density data measured at different times back to the ground control center, and the control center obtains the medium density change rate by calculating the ratio of the difference in density at adjacent times to the time interval. To ensure the accuracy and reliability of the measurement, the sampling device will take samples at different locations in the pipe, and the density meter will be calibrated and maintained regularly.

[0033] The medium viscosity change rate: The medium viscosity affects the flow characteristics and transportation efficiency of the fluid in the pipe. The change in viscosity may mean that the medium has undergone a chemical reaction, temperature change or has been contaminated. For example, in a chemical raw material transportation pipeline, abnormal changes in medium viscosity may cause pipeline blockage, increased pump load and other problems, seriously affecting the normal operation of the pipeline. The intelligent pipeline inspection drone integrated control system monitors the medium viscosity change rate, can promptly detect changes in medium properties, and provide important information for adjusting transportation parameters, cleaning pipelines or taking other maintenance measures. The collection method is: a high-precision rotational viscometer is equipped in the drone's sampling device. When the drone collects the medium sample in the pipe, the rotational viscometer starts working immediately. The rotational viscometer rotates in the medium sample through a rotating rotor to measure the resistance torque of the rotor. According to the relationship between the resistance torque and the medium viscosity, the viscosity value of the medium is calculated by a specific algorithm. The drone collects multiple samples at different times and measures the viscosity, and transmits these data back to the ground control center. The control center obtains the medium viscosity change rate by calculating the ratio of the viscosity change value at adjacent moments to time. To ensure the accuracy of the measurement, the rotational viscometer is calibrated before each use and the measurement environment is strictly controlled.

[0034] The sand content of the medium: solid particles such as sand in the medium will cause serious wear on the inner wall of the pipeline and reduce the service life of the pipeline. Excessive sand content may also cause pipeline blockage and affect the normal transportation of the medium. The intelligent pipeline inspection drone integrated control system monitors the sand content of the medium, which can timely evaluate the wear risk of the pipeline, provide a basis for taking anti-wear measures, installing filters or adjusting the transportation process, thereby extending the service life of the pipeline and ensuring the stability of transportation. The collection method is as follows: after the drone collects the medium sample in the pipe, the sand content is measured by filtering and weighing. First, the sample is filtered through a filter with a specific aperture so that solid particles such as sand are trapped on the filter. Then, the filter with the trapped sand is dried to remove moisture. Finally, a high-precision electronic balance is used to weigh the dried filter and sand, and the weight of the filter is subtracted to obtain the weight of the sand. Then, according to the volume of the sample, the sand content of the medium is calculated by the formula. In order to ensure the representativeness of the measurement results, the sampling device will take multiple samples at different locations and at different times in the pipe, and then conduct statistical analysis on the measurement results to obtain the average sand content.

[0035] The pipeline vibration energy: A pipeline in normal operation will have a certain regular vibration, and its vibration energy is in a relatively stable range. When the pipeline vibration energy suddenly increases, it may be due to abnormal medium flow rate, resonance between the pipeline and surrounding objects, loosening of the pipeline structure, or external impact. Excessive vibration energy may cause fatigue damage to the pipeline and shorten the service life of the pipeline. The intelligent pipeline inspection drone integrated control system monitors the pipeline vibration energy, can timely detect abnormal vibration conditions of the pipeline, evaluate the operating status of the pipeline, and provide a basis for taking corresponding vibration reduction or reinforcement measures. The collection method is: install multiple high-precision acceleration sensors on the drone. When flying close to the surface of the pipeline, the acceleration sensor can obtain the vibration acceleration data of the pipeline in real time. The acceleration sensor transmits these data to the control system of the drone, and the control system obtains the vibration velocity data of the pipeline by integrating the acceleration data. Then, the vibration energy of the pipeline is calculated according to the kinetic energy formula. In order to accurately measure the vibration energy of the pipeline, it is necessary to arrange acceleration sensors at multiple locations to monitor vibrations in different directions, and then conduct a comprehensive analysis of these data to obtain an accurate vibration energy value.

[0036] The medium flow deviation rate in the pipe reflects the degree of difference between the actual flow and the preset flow. Excessive flow deviation may be due to pipeline leakage, improper adjustment of branch pipeline valves, flow meter failure or unstable medium supply. This will not only affect the balance and efficiency of the entire transportation system, but may also lead to the failure to meet the needs of downstream users. The intelligent pipeline inspection drone integrated control system monitors the medium flow deviation rate in the pipe, and can detect abnormal flow conditions in time, providing a basis for quickly locating problems and adjusting transportation strategies. The collection method is: the drone communicates with the flow meter installed on the pipeline to obtain real-time medium flow data. At the same time, the drone obtains the preset standard flow value of the pipeline from the ground control center. Then, the medium flow deviation rate in the pipe is calculated. In order to ensure the accuracy of the data, the communication between the drone and the flow meter adopts a reliable wireless communication protocol, and the flow meter will be calibrated and maintained regularly. In addition, the system will monitor and analyze the flow data in real time, detect abnormal fluctuations in time and issue an alarm.

[0037] The pressure fluctuation amplitude of the medium in the pipe: The stability of the pressure of the medium in the pipe is one of the key indicators for the normal operation of the pipeline. If the pressure fluctuation amplitude is too large, it may mean that there are problems such as valve failure, pipeline blockage, leakage or unstable medium flow in the pipeline. These problems will not only affect the medium transportation efficiency, but also threaten the structural safety of the pipeline. The intelligent pipeline inspection drone integrated control system monitors the pressure fluctuation amplitude of the medium in the pipe, and can promptly detect abnormal conditions in the operation of the pipeline, providing an important basis for rapid troubleshooting and ensuring the safe operation of the pipeline. The collection method is: the drone carries a high-precision pressure sensor. During the inspection process, the drone is connected to the reserved pressure detection interface on the pipeline and the pressure sensor is connected to the pipeline. The pressure sensor measures the pressure value of the medium in the pipe in real time and transmits the data to the drone control system at very short time intervals. The control system records the maximum and minimum pressure values ​​within a period of time, and the difference between the two is the pressure fluctuation amplitude. In order to ensure the accuracy of the measurement, the pressure sensor will be calibrated regularly, and the data will be filtered during the measurement process to remove noise interference.

[0038] The frequency of flight attitude change: During the inspection process, a stable flight attitude is essential for obtaining accurate pipeline data. If the frequency of flight attitude change is too high, it may indicate that the drone is disturbed by airflow, the flight control system fails, the sensor error or the operator's improper operation. These problems will cause the drone to deviate when taking pipeline images or collecting data, affecting the accuracy and reliability of the data. The intelligent pipeline inspection drone integrated control system monitors the frequency of flight attitude change, which can promptly detect abnormal conditions in the flight of the drone, and provide important support for ensuring the smooth progress of the inspection task. The collection method is: using the drone's built-in inertial measurement unit (IMU). IMU is a device composed of sensors such as accelerometers, gyroscopes and magnetometers, which can measure the acceleration, angular velocity and magnetic field strength of the drone in real time. By analyzing these data, the control system can accurately determine the attitude change of the drone. During the flight, the control system monitors the attitude of the drone at a certain time interval and counts the number of attitude changes per unit time, that is, the frequency of flight attitude changes. In order to improve the accuracy of the measurement, the IMU will be calibrated regularly, and the system will filter and fuse the attitude data to remove noise and errors.

[0039] The battery power consumption rate: Understanding the battery power consumption rate is crucial for planning the inspection route and mission duration of the drone. If the power consumption rate increases abnormally, it may be due to battery aging, excessive drone load, harsh flight environment, or power system failure. This may cause the drone to be unable to complete the task due to insufficient power during the inspection process, or even a dangerous situation. The intelligent pipeline inspection drone integrated control system monitors the battery power consumption rate, which can promptly detect abnormal conditions of the battery and power system, and provide a basis for the reasonable arrangement of inspection tasks, timely replacement of batteries, or troubleshooting of power system failures. The collection method is as follows: the drone's battery management system monitors the battery power, voltage, current and other parameters in real time. During the flight, the battery management system records the battery power data at a certain time interval. The battery power consumption rate is obtained by calculating the ratio of the difference in power at adjacent moments to the time interval. In order to ensure the accuracy of the measurement, the battery management system will model and calibrate the battery's charge and discharge characteristics, and will consider the impact of factors such as battery temperature and aging on power consumption. In addition, the system will also monitor and analyze the battery power consumption rate in real time, and issue an alarm in time when abnormal changes are found.

[0040] The positioning accuracy deviation distance: Accurate positioning is the basis for ensuring that the UAV inspects the pipeline according to the predetermined route and accurately records the location of the pipeline data. If the positioning accuracy deviation is too large, the collected data may not match the actual pipeline location, affecting the subsequent analysis and evaluation of the pipeline condition. For example, when marking the location of pipeline defects, if the positioning accuracy deviation is large, it may cause great trouble to the maintenance personnel and delay the maintenance time. The intelligent pipeline inspection UAV integrated control system monitors the positioning accuracy deviation, can promptly detect the abnormal situation of the positioning system, and provide a basis for improving the positioning accuracy and ensuring the quality of inspection data. The collection method is as follows: UAVs usually use the global positioning system (GPS) or other satellite positioning technologies for positioning. Before the inspection, select multiple reference points with known precise coordinates. The UAV performs positioning tests on these reference points, compares the coordinates measured by the UAV with the actual coordinates, and calculates the positioning error. By measuring the positioning errors of different reference points multiple times and performing statistical analysis on these error data, the average value of the positioning error is calculated, which is the positioning accuracy deviation. In order to improve the positioning accuracy, the UAV can also be combined with other auxiliary positioning technologies, such as inertial navigation, visual positioning, etc., and the positioning system will be calibrated and optimized regularly.

[0041] The data packet loss rate: Data packet loss will lead to incomplete pipeline inspection data and affect the accurate judgment of pipeline status. A high data packet loss rate may be due to interference in the communication link, insufficient signal strength, transmission equipment failure or network congestion. This will not only reduce the quality of the inspection data, but also cause some important pipeline abnormality information to fail to be transmitted to the ground control center in time, delaying the timing of fault handling. The intelligent pipeline inspection drone integrated control system monitors the data packet loss rate and can promptly discover problems in data transmission, providing a basis for optimizing communication links and improving data transmission reliability. The collection method is as follows: When the drone transmits data with the ground control center, the sender numbers each data packet. After receiving the data packet, the receiving end sorts and counts it according to the data packet number. The data packet loss rate is obtained by calculating the ratio of the number of lost data packets to the total number of data packets sent, and then multiplying it by 100%.

[0042] The signal transmission delay directly affects the real-time control of the UAV and the timeliness of the data. When a quick response is required to abnormal pipeline conditions, excessive transmission delay may result in missing the best processing time, affecting the inspection effect and pipeline safety. For example, when a UAV detects a serious leak in a pipeline, if the signal transmission delay is too high, the ground control center cannot receive the information and issue corresponding instructions in time, which may cause the leakage to worsen further. In addition, for scenarios where the flight attitude of the UAV needs to be adjusted in real time to adapt to complex inspection environments, signal transmission delay is also crucial, which is related to whether the UAV can accurately complete the scheduled inspection tasks. The collection method is: a timestamp mechanism is set between the UAV and the ground control center. When the UAV sends data, the current timestamp is attached to the data packet to record the exact time when the data is sent. After receiving the data packet, the ground control center immediately records the receiving time. By calculating the difference between the receiving time and the sending timestamp, the delay time of the signal from the UAV to the ground control center can be obtained. In order to obtain an accurate signal transmission delay value, the system will perform multiple measurements and test under different flight distances and environmental conditions to obtain representative delay data. At the same time, the system will analyze and compile statistics on the measured delay data to eliminate erroneous data caused by abnormal factors such as instantaneous network fluctuations.

[0043] The communication bandwidth utilization reflects the resource usage of the communication link. If the bandwidth utilization is too high, close to or reaching the maximum carrying capacity of the link, it may cause data transmission congestion, data delay, packet loss and other problems, affecting the transmission speed and stability. On the contrary, if the utilization is too low, it means that the communication resources are not fully utilized, resulting in resource waste. The intelligent pipeline inspection drone integrated control system monitors the communication bandwidth utilization, which helps to reasonably plan the data transmission strategy, optimize the communication link configuration, and ensure efficient and stable data transmission under limited bandwidth resources. The collection method is: the communication bandwidth utilization is calculated by monitoring the data transmission rate and the total bandwidth of the communication link between the drone and the ground control center. The communication equipment of the drone and the ground control center will record the data transmission rate in real time, that is, the amount of data transmitted per unit time. At the same time, the total bandwidth of the communication link is a known fixed parameter. The communication bandwidth utilization can be calculated using the formula "(actual data transmission rate ÷ total bandwidth of the communication link) × 100%". The system will continuously monitor these two parameters and update the calculation results of the communication bandwidth utilization in real time. In order to ensure the accuracy of the calculation, the communication equipment will be calibrated regularly to ensure that the transmission rate is measured accurately. In addition, the system will also analyze the changing trend of communication bandwidth utilization. When abnormal fluctuations in utilization are found, corresponding measures will be taken in a timely manner, such as adjusting data transmission priority, optimizing data encoding methods, etc., to ensure the stable operation of the communication link.

[0044] The intelligent pipeline inspection data analysis module includes a pipeline status data analysis unit and a drone status data analysis unit, which are used to analyze the data transmitted by the intelligent pipeline inspection data acquisition module and transmit the analysis results to the intelligent pipeline inspection data comprehensive evaluation module; the pipeline status data analysis unit includes a pipeline structure data analysis node, a pipeline appearance data analysis node, an in-pipe medium data analysis node and a pipeline operation data analysis node; the drone status data analysis unit includes a drone flight data analysis node and a data transmission data analysis node.

[0045] In this embodiment, it is specifically required to be explained that: the pipeline structure data analysis node is used to establish a pipeline structure data calculation model, and the pipeline structure data transmitted by the intelligent pipeline inspection data acquisition module is imported into the pipeline structure data calculation model to obtain the pipeline structure risk coefficient value. The pipeline structure data calculation model is specifically expressed as: , where α i Represents the pipeline structure risk coefficient value calculated for the i-th time, Rd i represents the radial deformation rate of the pipeline collected for the i-th time, Ad i represents the axial displacement of the pipeline collected for the i-th time, Ad max Indicates the historical maximum value of the pipeline axial displacement, Wd irepresents the weld undercut depth collected for the ith time, Wd max Indicates the historical maximum value of weld undercut depth.

[0046] In this embodiment, it is specifically required to be explained that: the pipeline appearance data analysis node is used to establish a pipeline appearance data calculation model, and the pipeline appearance data transmitted by the intelligent pipeline inspection data acquisition module is imported into the pipeline appearance data calculation model to obtain the pipeline surface damage coefficient value. The pipeline appearance data calculation model is specifically expressed as: , where β i represents the pipeline surface damage coefficient value calculated for the i-th time, Sr i represents the surface roughness of the i-th collection, Sr max Indicates the historical maximum value of surface roughness, Sc i represents the surface crack width collected at the i-th time, Sc max Indicates the historical maximum value of surface crack width, Sd i represents the depth of the surface corrosion pit collected at the i-th time, Sd max Indicates the historical maximum depth of surface corrosion pits.

[0047] In this embodiment, it is specifically required to be explained that: the in-pipe medium data analysis node is used to establish an in-pipe medium data calculation model, and the in-pipe medium data transmitted by the intelligent pipeline inspection data acquisition module is imported into the in-pipe medium data calculation model to obtain the pipeline medium stability balance coefficient value. The in-pipe medium data calculation model is specifically expressed as: , where γ i Indicates the pipeline medium stability balance coefficient value calculated for the i-th time, Dd i Denotes the density change rate of the medium collected for the i-th time, Dd max Indicates the historical maximum value of the medium density change rate, Vv i Vv represents the viscosity change rate of the medium collected for the i-th time, max Indicates the historical maximum value of the medium viscosity change rate, Ss i represents the sediment content of the medium collected for the i-th time, Ss max Indicates the historical maximum sand content of the medium.

[0048] In this embodiment, it is specifically required to be explained that: the pipeline operation data analysis node is used to establish a pipeline operation data calculation model, and the pipeline operation data transmitted by the intelligent pipeline inspection data acquisition module is imported into the pipeline operation data calculation model to obtain the pipeline operation health coefficient value. The pipeline operation data calculation model is specifically expressed as: , where ∂ i Represents the pipeline operation health coefficient value calculated for the i-th time, Ve i represents the pipeline vibration energy collected for the i-th time, Ve maxIndicates the historical maximum value of pipeline vibration energy, Fd i Indicates the deviation rate of the medium flow in the pipe collected for the i-th time, Fd max Indicates the historical maximum value of the medium flow deviation rate in the pipe, Pf i Pf represents the pressure fluctuation amplitude of the medium in the tube collected for the i-th time. max Indicates the historical maximum value of the pressure fluctuation amplitude of the medium in the pipe.

[0049] In this embodiment, it is specifically required to be explained that: the UAV flight data analysis node is used to establish a UAV flight data calculation model, and the UAV flight data transmitted by the intelligent pipeline inspection data acquisition module is imported into the UAV flight data calculation model to obtain the UAV flight reliability coefficient value. The UAV flight data calculation model is specifically expressed as: , where ℓ i Af represents the UAV flight reliability coefficient value calculated for the i-th time, i Indicates the frequency of flight attitude change collected for the i-th time, Af max Indicates the historical maximum frequency of flight attitude changes, Bd i Indicates the battery power consumption rate of the ith collection, Bd max Indicates the historical maximum value of battery power consumption rate, Pa i Indicates the positioning accuracy deviation distance of the i-th acquisition, Pa max Indicates the historical maximum value of the positioning accuracy deviation distance.

[0050] In this embodiment, it is specifically required to be explained that: the data transmission data analysis node is used to establish a data transmission data calculation model, and the data transmission data transmitted by the intelligent pipeline inspection data acquisition module is imported into the data transmission data calculation model to obtain the data transmission quality coefficient value. The data transmission data calculation model is specifically expressed as: , where χ i Indicates the data transmission quality coefficient value calculated for the i-th time, Lr i Indicates the packet loss rate of the data collected for the i-th time, Lr max Indicates the historical maximum value of data packet loss rate, Ds i Denotes the signal transmission delay of the i-th acquisition, Ds max Indicates the historical maximum value of signal transmission delay, Cu i represents the communication bandwidth utilization rate of the ith collection, Cu max Indicates the historical maximum value of communication bandwidth utilization.

[0051] The intelligent pipeline inspection data comprehensive evaluation module includes an intelligent pipeline inspection drone comprehensive control data analysis unit, which is used to comprehensively analyze the data transmitted by the intelligent pipeline inspection data analysis module and transmit the analysis results to the real-time feedback warning module.

[0052] In this embodiment, it is specifically required to be explained that: the intelligent pipeline inspection drone integrated control data analysis unit is used for the intelligent pipeline inspection drone integrated control data calculation model, and the pipeline structure risk coefficient value, pipeline surface damage coefficient value, pipeline medium stability balance coefficient value, pipeline operation health coefficient value, drone flight reliability coefficient value and data transmission quality coefficient value transmitted by the intelligent pipeline inspection data analysis module are imported into the intelligent pipeline inspection drone integrated control data calculation model to obtain the intelligent pipeline inspection comprehensive evaluation index value; the intelligent pipeline inspection drone integrated control data calculation model is specifically expressed as: , where CE represents the calculated comprehensive evaluation index value of intelligent pipeline inspection, α i represents the pipeline structure risk coefficient value calculated for the i-th time, β i represents the pipeline surface damage coefficient value calculated for the i-th time, γ i represents the pipeline medium stability balance coefficient value calculated for the i-th time, ∂ i represents the pipeline operation health coefficient value calculated for the i-th time, ℓ i represents the UAV flight reliability coefficient value calculated for the i-th time, χ i Represents the data transmission quality coefficient value calculated for the i-th time.

[0053] The real-time feedback warning module is used to establish a preset value of the intelligent pipeline inspection comprehensive evaluation index, judge the intelligent pipeline inspection comprehensive evaluation index value according to the preset value of the intelligent pipeline inspection comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0054] In this embodiment, it should be specifically explained that: the preset value of the intelligent pipeline inspection comprehensive evaluation index is recorded as CE def , when CE def <=CE, a normal signal is issued, which means that the preset value of the intelligent pipeline inspection comprehensive evaluation index is less than or equal to the intelligent pipeline inspection comprehensive evaluation index value, indicating that the comprehensive inspection is in good condition; when CE def >CE, an early warning signal is issued to the relevant technical management personnel. This signal indicates that the preset value of the intelligent pipeline inspection comprehensive evaluation index is greater than the intelligent pipeline inspection comprehensive evaluation index value, indicating that the comprehensive inspection status is poor and the relevant technical management personnel need to make adjustments.

[0055] The present invention significantly improves the comprehensiveness and accuracy of the inspection by dividing various types of data in the pipeline inspection process into different data batches. Through multi-dimensional collection of pipeline structure data, pipeline appearance data, pipeline medium data, pipeline operation data, drone flight data and data transmission data, this multi-faceted data collection mode greatly reduces the limitations of previous inspection methods and builds a richer and more reliable data foundation for subsequent data evaluation. The collected data is deeply mined using a data analysis algorithm to calculate the pipeline structure risk coefficient value, pipeline surface damage coefficient value, pipeline medium stability balance coefficient value, pipeline operation health coefficient value, drone flight reliability coefficient value and data transmission quality coefficient value, accurately locking the area where there may be problems. By comprehensively evaluating and deeply integrating the data processing results, the reliability and effectiveness of the inspection results are greatly improved. Through real-time monitoring and continuous tracking, once abnormal data is found, an alarm notification is immediately issued, and relevant technicians and maintenance personnel can timely grasp the real-time status and potential risks of the pipeline, and quickly adjust and optimize the pipeline maintenance strategy or inspection plan, providing a strong guarantee for achieving efficient and accurate pipeline inspection and scientific and reasonable operation and maintenance decisions.

[0056] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent pipeline inspection drone integrated control system, characterized in that: include: Intelligent pipeline inspection data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Intelligent pipeline inspection data acquisition module: including pipeline status data acquisition unit and drone status data acquisition unit, used to collect target data in real time and transmit the collected data to the intelligent pipeline inspection data analysis module; The pipeline status data acquisition unit is used to collect pipeline structure data, pipeline appearance data, pipeline medium data and pipeline operation data; the drone status data acquisition unit is used to collect drone flight data and data transmission data; Intelligent pipeline inspection data analysis module: including pipeline status data analysis unit and drone status data analysis unit, used to analyze the data transmitted by the intelligent pipeline inspection data acquisition module, and transmit the analysis results to the intelligent pipeline inspection data comprehensive evaluation module; the pipeline status data analysis unit includes pipeline structure data analysis nodes, pipeline appearance data analysis nodes, pipeline medium data analysis nodes and pipeline operation data analysis nodes; the drone status data analysis unit includes drone flight data analysis nodes and data transmission data analysis nodes; Intelligent pipeline inspection data comprehensive evaluation module: including the intelligent pipeline inspection drone comprehensive control data analysis unit, which is used to comprehensively analyze the data transmitted by the intelligent pipeline inspection data analysis module and transmit the analysis results to the real-time feedback warning module; Real-time feedback warning module: used to establish a preset value of the intelligent pipeline inspection comprehensive evaluation index, judge the intelligent pipeline inspection comprehensive evaluation index value according to the preset value of the intelligent pipeline inspection comprehensive evaluation index, and send a corresponding signal according to the judgment result.

2. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The pipeline structure data includes the pipeline radial deformation rate Rd, the pipeline axial displacement Ad and the weld undercut depth Wd; the pipeline appearance data includes the surface roughness Sr, the surface crack width Sc and the surface corrosion pit depth Sd; the medium data in the pipe includes the medium density change rate Dd, the medium viscosity change rate Vv and the medium sand content Ss; the pipeline operation data includes the pipeline vibration energy Ve, the medium flow deviation rate Fd and the medium pressure fluctuation amplitude Pf.

3. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The UAV flight data includes the flight attitude change frequency Af, the battery power consumption rate Bd and the positioning accuracy deviation distance Pa; the data transmission data includes the data packet loss rate Lr, the signal transmission delay Ds and the communication bandwidth utilization rate Cu.

4. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The pipeline structure data analysis node is used to establish a pipeline structure data calculation model, import the pipeline structure data transmitted by the intelligent pipeline inspection data acquisition module into the pipeline structure data calculation model, and obtain the pipeline structure risk coefficient value. The pipeline structure data calculation model is specifically expressed as: , where α i Represents the pipeline structure risk coefficient value calculated for the i-th time, Rd i represents the radial deformation rate of the pipeline collected for the i-th time, Ad i represents the axial displacement of the pipeline collected for the i-th time, Ad max Indicates the historical maximum value of the pipeline axial displacement, Wd i represents the weld undercut depth collected for the ith time, Wd max Indicates the historical maximum value of weld undercut depth.

5. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The pipeline appearance data analysis node is used to establish a pipeline appearance data calculation model, import the pipeline appearance data transmitted by the intelligent pipeline inspection data acquisition module into the pipeline appearance data calculation model, and obtain the pipeline surface damage coefficient value. The pipeline appearance data calculation model is specifically expressed as: , where β i represents the pipeline surface damage coefficient value calculated for the i-th time, Sr i represents the surface roughness of the i-th collection, Sr max Indicates the historical maximum value of surface roughness, Sc i represents the surface crack width collected at the i-th time, Sc max Indicates the historical maximum value of surface crack width, Sd i represents the depth of the surface corrosion pit collected at the i-th time, Sd max Indicates the historical maximum depth of surface corrosion pits.

6. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The in-pipe medium data analysis node is used to establish an in-pipe medium data calculation model, import the in-pipe medium data transmitted by the intelligent pipeline inspection data acquisition module into the in-pipe medium data calculation model, and obtain the pipeline medium stability balance coefficient value. The in-pipe medium data calculation model is specifically expressed as: , where γ i Indicates the pipeline medium stability balance coefficient value calculated for the i-th time, Dd i Denotes the density change rate of the medium collected for the i-th time, Dd max Indicates the historical maximum value of the medium density change rate, Vv i Vv represents the viscosity change rate of the medium collected for the i-th time, max Indicates the historical maximum value of the medium viscosity change rate, Ss i represents the sediment content of the medium collected for the i-th time, Ss max Indicates the historical maximum sand content of the medium.

7. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The pipeline operation data analysis node is used to establish a pipeline operation data calculation model, import the pipeline operation data transmitted by the intelligent pipeline inspection data acquisition module into the pipeline operation data calculation model, and obtain the pipeline operation health coefficient value. The pipeline operation data calculation model is specifically expressed as: , where ∂ i Ve represents the pipeline operation health coefficient value calculated for the i-th time, i represents the pipeline vibration energy collected for the i-th time, Ve max Indicates the historical maximum value of pipeline vibration energy, Fd i Indicates the deviation rate of the medium flow in the pipe collected for the i-th time, Fd max Indicates the historical maximum value of the medium flow deviation rate in the pipe, Pf i Pf represents the pressure fluctuation amplitude of the medium in the tube collected for the i-th time. max Indicates the historical maximum value of the pressure fluctuation amplitude of the medium in the pipe.

8. According to claim 1, the intelligent pipeline inspection drone integrated control system is characterized by: The UAV flight data analysis node is used to establish a UAV flight data calculation model, import the UAV flight data transmitted by the intelligent pipeline inspection data acquisition module into the UAV flight data calculation model, and obtain the UAV flight reliability coefficient value. The UAV flight data calculation model is specifically expressed as: , where ℓ i Af represents the UAV flight reliability coefficient value calculated for the i-th time, i Indicates the frequency of flight attitude change collected for the i-th time, Af max Indicates the historical maximum frequency of flight attitude changes, Bd i Indicates the battery power consumption rate of the ith collection, Bd max Indicates the historical maximum value of battery power consumption rate, Pa i Indicates the positioning accuracy deviation distance of the i-th acquisition, Pa max Indicates the historical maximum value of positioning accuracy deviation distance.

9. The intelligent pipeline inspection drone integrated control system according to claim 1 is characterized by: The data transmission data analysis node is used to establish a data transmission data calculation model, import the data transmission data transmitted by the intelligent pipeline inspection data acquisition module into the data transmission data calculation model, and obtain the data transmission quality coefficient value. The data transmission data calculation model is specifically expressed as: , where χ i Indicates the data transmission quality coefficient value calculated for the i-th time, Lr i Indicates the packet loss rate of the data collected for the i-th time, Lr max Indicates the historical maximum value of data packet loss rate, Ds i Denotes the signal transmission delay of the i-th acquisition, Ds max Indicates the historical maximum value of signal transmission delay, Cu i represents the communication bandwidth utilization rate of the ith collection, Cu max Indicates the historical maximum value of communication bandwidth utilization.

10. The intelligent pipeline inspection drone integrated control system according to claim 1 is characterized by: The intelligent pipeline inspection drone integrated control data analysis unit is used for the intelligent pipeline inspection drone integrated control data calculation model, and the pipeline structure risk coefficient value, pipeline surface damage coefficient value, pipeline medium stability balance coefficient value, pipeline operation health coefficient value, drone flight reliability coefficient value and data transmission quality coefficient value transmitted by the intelligent pipeline inspection data analysis module are imported into the intelligent pipeline inspection drone integrated control data calculation model to obtain the intelligent pipeline inspection comprehensive evaluation index value; the intelligent pipeline inspection drone integrated control data calculation model is specifically expressed as: , where CE represents the calculated comprehensive evaluation index value of intelligent pipeline inspection, α i represents the pipeline structure risk coefficient value calculated for the i-th time, β i represents the pipeline surface damage coefficient value calculated for the i-th time, γ i represents the pipeline medium stability balance coefficient value calculated for the i-th time, ∂ i represents the pipeline operation health coefficient value calculated for the i-th time, ℓ i represents the UAV flight reliability coefficient value calculated for the i-th time, χ i Represents the data transmission quality coefficient value calculated for the i-th time.

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