Unmanned aerial vehicle pipeline inspection device and method based on wireless radio frequency

Abstract

The invention discloses an unmanned aerial vehicle pipeline inspection device and method based on wireless radio frequency, and relates to the field of unmanned aerial vehicle pipeline inspection. A plurality of current inspection routes are screened from the perspective of the penetrability of wireless radio frequency signals to pipelines; therefore, the penetrating power level of the wireless radio frequency signals corresponding to the plurality of data acquisition points on the screened inspection route to the pipeline is higher, so that the penetrating power of the wireless radio frequency signals sent by the current unmanned aerial vehicle to the pipeline is optimal when the unmanned aerial vehicle performs inspection, and the inspection precision and accuracy of the unmanned aerial vehicle to the pipeline are ensured; an independent variable coefficient, an independent variable index coefficient and an offset parameter of an initial radio frequency pipeline penetrability grade mapping equation are subjected to multiple times of iterative adjustment at the same time by adopting a wild horse optimization algorithm, and along with iteration, the mapping error of the initial radio frequency pipeline penetrability grade mapping equation is smaller and smaller. And finally, the mapping requirement is met.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) pipeline inspection, and more particularly, to a UAV pipeline inspection device and method based on radio frequency (RF). Background Art

[0002] In pipeline interiors or underground structures, RF signals are vulnerable to interference from obstacles such as metal materials and concrete, resulting in a significant attenuation of signal strength. If these effects are not prevented, it may lead to communication interruption or delay between the UAV and the control terminal, and even pose a risk of out-of-control. The insufficient penetration of RF signals during UAV inspection reduces the transmission integrity of key information such as high-definition images and sensor data, leading to a decrease in the recognition rate of subtle defects such as pipeline corrosion and cracks, and potentially missing safety hazards. Summary of the Invention

[0003] In view of the problems in the related art, the present invention provides a UAV pipeline inspection device and method based on RF to overcome the above-mentioned technical problems existing in the prior related art.

[0004] To solve the above technical problems, the present invention is implemented through the following technical solutions:

[0005] The present invention provides a UAV pipeline inspection method based on RF, including the following steps:

[0006] S1. Set several types of characteristic parameters of pipelines that affect the RF penetration ability to obtain a set of RF penetration pipeline influence parameter types;

[0007] S2. Collect the RF penetration pipeline influence parameter data and the RF signal penetration ability level data of the pipeline during the historical UAV inspection by sending RF signals in cooperation with the set of RF penetration pipeline influence parameter types to obtain a historical RF pipeline penetration ability level data set and a historical RF penetration pipeline influence parameter data matrix;

[0008] S3. Use the historical RF pipeline penetration ability level data set and the historical RF penetration pipeline influence parameter data matrix to construct a final RF pipeline penetration ability level mapping equation;

[0009] S4. Uniformly select several data collection points on several pre-set inspection routes to obtain a current inspection route data collection point matrix;

[0010] S5. Input the data of each data acquisition point in the current inspection route data acquisition point matrix into the final radio frequency pipeline penetration ability level mapping equation for mapping to obtain the penetration ability level data of the radio frequency signal at each data acquisition point, and select the inspection route with the largest sum of the penetration ability level data of the radio frequency signal as the current final inspection route.

[0011] Preferably, the S1 includes the following steps:

[0012] S11. Set several types of characteristic parameters of the pipeline that affect the radio frequency penetration ability to obtain a set of radio frequency penetration pipeline influence parameter types; then set several levels for the penetration ability of the radio frequency signal emitted by the unmanned aerial vehicle on the pipeline to obtain a set of pipeline radio frequency signal penetration ability levels;

[0013] By setting the set of radio frequency penetration pipeline influence parameter types, on the one hand, it provides mapping parameter types for constructing the mapping relationship between the relevant characteristic parameters of the pipeline and the traditional radio frequency signal ability; on the other hand, it provides a basis for collecting the relevant data of the pipeline.

[0014] Preferably, the S2 includes the following steps:

[0015] S21. In cooperation with the set of radio frequency penetration pipeline influence parameter types and the set of pipeline radio frequency signal penetration ability levels, collect the radio frequency penetration pipeline influence parameter data and the penetration ability level data of the radio frequency signal on the pipeline during the historical inspection of the pipeline using the unmanned aerial vehicle to send radio frequency signals, to obtain a historical radio frequency pipeline penetration ability level data set and a historical radio frequency penetration pipeline influence parameter data matrix;

[0016] By collecting the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix, it provides data support for constructing the final radio frequency pipeline penetration ability level mapping equation later.

[0017] Preferably, the S3 includes the following steps:

[0018] S31. Construct an initial radio frequency pipeline penetration ability level mapping equation in cooperation with the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix;

[0019] S32. Use the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix to adjust the initial radio frequency pipeline penetration ability level mapping equation; after the adjustment is completed, obtain the final radio frequency pipeline penetration ability level mapping equation;

[0020] Based on the least squares principle, by taking various types of characteristic parameters of pipelines that affect the radio frequency penetration ability as independent variables and the penetration ability level data of radio frequency signals as the dependent variable, a mapping from the characteristic parameter data of the pipeline to the penetration ability level data of the radio frequency signal is realized; then, based on the collected historical data, the mapping accuracy of the constructed mapping equation is optimized and adjusted, ensuring the mapping accuracy of the final radio frequency pipeline penetration ability level mapping equation, and further ensuring the accuracy of the selection basis for subsequent selection of the UAV inspection route.

[0021] Preferably, S32 includes the following steps:

[0022] S321. Input each row of data in the historical radio frequency penetration pipeline impact parameter data matrix into the initial radio frequency pipeline penetration ability level mapping equation for mapping to obtain an initial mapping data set of historical radio frequency penetration ability levels;

[0023] S322. Calculate the error between the initial mapping data set of historical radio frequency penetration ability levels and the historical radio frequency pipeline penetration ability level data set to obtain historical radio frequency pipeline penetration ability level mapping error data;

[0024] S323. Set a radio frequency penetration ability mapping error threshold; when the historical radio frequency pipeline penetration ability level mapping error data is greater than or equal to the radio frequency penetration ability mapping error threshold, adjust the initial radio frequency pipeline penetration ability level mapping equation until the historical radio frequency pipeline penetration ability level mapping error data is less than the radio frequency penetration ability mapping error threshold, and obtain the final radio frequency pipeline penetration ability level mapping equation; otherwise, there is no need to adjust the initial radio frequency pipeline penetration ability level mapping equation, and the initial radio frequency pipeline penetration ability level mapping equation is used as the final radio frequency pipeline penetration ability level mapping equation;

[0025] By inputting the collected historical data into the initial radio frequency pipeline penetration ability level mapping equation for mapping and then comparing the mapped data with the actual data, it can be known whether the mapping accuracy rate of the initial radio frequency pipeline penetration ability level mapping equation meets the requirements, and further determine whether it is necessary to adjust the initial radio frequency pipeline penetration ability level mapping equation; among them, by setting a radio frequency penetration ability mapping error threshold, a quantitative determination standard is provided for determining whether the mapping accuracy rate of the initial radio frequency pipeline penetration ability level mapping equation meets the requirements.

[0026] Preferably, the adjustment of the initial radio frequency pipeline penetration ability level mapping equation in S323 includes the following steps:

[0027] S3231. Set the value ranges of the coefficients of each independent variable, the coefficients of the exponents of the independent variables, and the bias parameters in the initial radio frequency pipeline penetration ability level mapping equation to obtain the penetration level mapping independent variable coefficient value range matrix and the penetration level mapping bias parameter value range;

[0028] Construct a radio frequency penetration ability level mapping adjustment wild horse population; set the maximum number of iterations of the radio frequency penetration ability level mapping adjustment wild horse population to and the current number of iterations to which are respectively denoted as the penetration mapping adjustment maximum number of iterations and the penetration mapping adjustment current number of iterations;

[0029] S3232. Generate the initial position matrix of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population according to the penetration level mapping independent variable coefficient value range matrix and the penetration level mapping bias parameter value range to obtain an initial position matrix set;

[0030] S3233. Construct the fitness function of the radio frequency penetration ability level mapping adjustment wild horse population;

[0031] S3234. Start the iteration. Before the iteration, set the penetration mapping adjustment current number of iterations to 1; in the first round of iteration, use the fitness function b′ of the radio frequency penetration ability level mapping adjustment wild horse population to calculate the fitness values of the initial position matrices of each wild horse in the initial position matrix set to obtain a first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the wild horse as the first global best fitness and the first global best position respectively; update the initial position matrices of each wild horse in the initial position matrix set according to the first global best fitness and the first global best position; after the update is completed, increment the penetration mapping adjustment current number of iterations by 1 and enter the next round of iteration;

[0032] In each subsequent round of iteration, use the fitness function of the radio frequency penetration ability level mapping adjustment wild horse population to calculate the fitness values of the position matrices of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population updated in the previous round of iteration to obtain a second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position matrix of the wild horse as the second global best fitness and the second global best position respectively; update the position matrices of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population updated in the previous round of iteration according to the second global best fitness and the second global best position; after the update is completed, increment the penetration mapping adjustment current number of iterations by 1 and enter the next round of iteration;

[0033] S3235. When When it reaches, stop the iteration to obtain the final global best position and the final global best fitness; otherwise, continue the iteration until it reaches; take the final global best fitness as the optimized penetration ability level mapping error; when the optimized penetration ability level mapping error is less than the radio frequency penetration ability mapping error threshold, substitute each position component of the final global best position into the initial radio frequency pipeline penetration ability level mapping equation to obtain the final radio frequency pipeline penetration ability level mapping equation; otherwise, return to S2234 to continue the iteration until the optimized penetration ability level mapping error is less than the radio frequency penetration ability mapping error threshold;

[0034] The wild horse optimization algorithm can effectively explore different regions of the solution space. By simulating the behavior of wild horses in the natural environment, the algorithm can continuously discover new solutions during the search process; it can usually find better solutions within fewer iterations, improving the efficiency of the algorithm; based on the above advantages, this solution uses the wild horse optimization algorithm to simultaneously perform multiple iterative adjustments on the independent variable coefficients, independent variable exponent coefficients, and bias parameters of the initial radio frequency pipeline penetration ability level mapping equation, and uses the mapping error of the initial radio frequency pipeline penetration ability level mapping equation as the fitness function; therefore, as the iteration progresses, the mapping error of the initial radio frequency pipeline penetration ability level mapping equation becomes smaller and smaller, and finally meets the mapping requirements.

[0035] Preferably, the S4 includes the following steps:

[0036] S41. Preset a number of inspection routes for the current inspection of the pipeline by the unmanned aerial vehicle to obtain the current inspection route set;

[0037] S42. Uniformly select a number of data collection points on each inspection route in the current inspection route set to obtain the current inspection route data collection point matrix;

[0038] By selecting a number of data collection points on each inspection route, it provides a collection basis for subsequent data collection on each inspection route, and further provides data support for subsequent selection of inspection routes.

[0039] Preferably, the S5 includes the following steps:

[0040] S51. Cooperate with the current inspection route data collection point matrix and the radio frequency penetration pipeline impact parameter type set to collect data on each type of radio frequency penetration pipeline impact parameter of the pipeline at each current inspection route data collection point to obtain the current radio frequency penetration pipeline impact parameter data set matrix;

[0041] S52. Input each current wireless radio frequency penetration pipeline impact parameter data set matrix in the current wireless radio frequency penetration pipeline impact parameter data set matrix into the final wireless radio frequency pipeline penetration ability level mapping equation for mapping to obtain the current wireless radio frequency pipeline penetration level data matrix;

[0042] S53. Select the inspection route corresponding to the row of data with the largest sum of data in the current wireless radio frequency pipeline penetration level data matrix as the current final inspection route;

[0043] By collecting the wireless radio frequency penetration pipeline impact parameter data of each data collection point on each inspection route, it provides a data basis for subsequently obtaining the penetration ability level data of the wireless radio frequency signal at each data collection point for the pipeline, and further provides data support for subsequently selecting the inspection route; Since the stronger the penetration ability of the wireless radio frequency signal for the pipeline, the more in-depth and accurate the detection of the pipeline. Therefore, by selecting the inspection route with the largest sum of the penetration ability level data of the wireless radio frequency signal at multiple data collection points as the final route of the current inspection, the inspection effect during the current inspection is better.

[0044] A drone pipeline inspection system based on wireless radio frequency includes a wireless radio frequency penetration impact parameter type setting module, a historical drone pipeline inspection data collection module, a wireless radio frequency penetration ability level mapping equation construction module, a current inspection route data collection point setting module, and a current inspection route screening module.

[0045] The present invention has the following beneficial effects:

[0046] 1. In the present invention, by screening the current several inspection routes from the perspective of the penetration ability of the wireless radio frequency signal for the pipeline, the higher the penetration ability level of the wireless radio frequency signal corresponding to multiple data collection points on the selected inspection route, thus ensuring that the penetration ability of the wireless radio frequency signal sent by the current drone during inspection reaches the optimal, and ensuring the inspection accuracy and accuracy of the drone for the pipeline.

[0047] 2. In the present invention, by constructing and optimizing the initial wireless radio frequency pipeline penetration ability level mapping equation, the accuracy of the selection basis for subsequently selecting the drone inspection route is ensured.

[0048] 3. In the present invention, the self-variable coefficient, self-variable exponent coefficient, and bias parameter of the initial wireless radio frequency pipeline penetration ability level mapping equation are simultaneously adjusted iteratively multiple times by using the wild horse optimization algorithm, and the mapping error of the initial wireless radio frequency pipeline penetration ability level mapping equation is used as the fitness function; Therefore, as the iteration progresses, the mapping error of the initial wireless radio frequency pipeline penetration ability level mapping equation becomes smaller and smaller, and finally meets the mapping requirements.

[0049] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a schematic flow chart of a method for inspecting pipelines by an unmanned aerial vehicle based on radio frequency according to the present invention;

[0052] Figure 2 It is a schematic module diagram of a system for inspecting pipelines by an unmanned aerial vehicle based on radio frequency according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The technical solutions in the embodiments of the invention will be clearly and completely described below with reference to the drawings in the embodiments of the invention. Obviously, the described embodiments are only a part of the embodiments of the invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without creative efforts fall within the scope of protection of the invention.

[0054] Embodiment 1

[0055] Please refer to Figure 1 , this embodiment is a method for inspecting pipelines by an unmanned aerial vehicle based on radio frequency, including the following steps:

[0056] S1. Set several types of characteristic parameters of pipelines that affect the radio frequency penetration ability to obtain a set of radio frequency penetration pipeline influence parameter types;

[0057] The S1 includes the following steps:

[0058] S11. Set several types of characteristic parameters of pipelines that affect the radio frequency penetration ability to obtain a set of radio frequency penetration pipeline influence parameter types. The set of radio frequency signal penetration pipeline influence parameter types includes material type parameters, thickness parameters, diameter parameters, bending degree parameters, etc. In terms of materials, metal pipelines (such as steel and iron) are good conductors, which will reflect and absorb electromagnetic waves, resulting in greater signal attenuation; while non-metal pipelines (such as PVC and cement) have less obstruction to radio frequency signals. The wall thickness will affect the attenuation degree. The greater the thickness, the more difficult it is for the signal to penetrate. The pipeline diameter may affect the diffraction ability of the signal. A larger diameter may provide more diffraction paths. The bending degree and structural complexity. Pipelines with more bends may cause multiple reflections of the signal, increasing the path loss. Densely arranged pipelines may form multipath interference, affecting signal propagation. Internal media, such as liquids or gases, with different dielectric constants will affect the signal propagation speed, thereby affecting the penetration ability. For example, a pipeline filled with water may be more difficult to penetrate than an empty pipeline. Then, set several levels for the penetration ability of the radio frequency signal emitted by the drone through the pipeline to obtain a set of pipeline radio frequency signal penetration ability levels. The pipeline radio frequency signal penetration ability levels in the set of pipeline radio frequency signal penetration ability levels are expressed using natural numbers. The larger the value, the stronger the penetration ability of the radio frequency signal through the pipeline.

[0059] S2. In cooperation with the set of radio frequency penetration pipeline influence parameter types, collect the radio frequency penetration pipeline influence parameter data and the pipeline radio frequency signal penetration ability level data during the historical inspection of pipelines by sending radio frequency signals using drones to obtain a historical radio frequency pipeline penetration ability level data set and a historical radio frequency penetration pipeline influence parameter data matrix.

[0060] S2 includes the following steps:

[0061] S21. In cooperation with the set of radio frequency penetration pipeline influence parameter types and the set of pipeline radio frequency signal penetration ability levels, collect the radio frequency penetration pipeline influence parameter data and the pipeline radio frequency signal penetration ability level data of the corresponding pipeline during the historical inspection of pipelines by sending radio frequency signals using drones to obtain a historical radio frequency pipeline penetration ability level data set and a historical radio frequency penetration pipeline influence parameter data matrix a1. a 2i represents the pipeline radio frequency signal penetration ability level data of the i-th group collected during the historical inspection of pipelines by sending radio frequency signals using drones, and a1' represents the total number of groups during the historical inspection of pipelines by sending radio frequency signals using drones. a1 is as follows,

[0062]

[0063] where a1ij It represents the characteristic parameter data of the j-th type corresponding to the pipeline during the inspection of the pipeline by sending radio frequency signals using a drone in the i-th group of collected historical data. a'2 represents the total number of types of characteristic parameters of the pipeline that have an impact on the radio frequency penetration ability set.

[0064] S3. Construct the final radio frequency pipeline penetration ability level mapping equation by using the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline impact parameter data matrix;

[0065] The S3 includes the following steps:

[0066] S31. Construct the initial radio frequency pipeline penetration ability level mapping equation in cooperation with the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline impact parameter data matrix; as follows,

[0067]

[0068] In the formula: is the dependent variable of the initial radio frequency pipeline penetration ability level mapping equation, representing the radio frequency signal penetration ability level data of the pipeline; is the i-th independent variable of the initial radio frequency pipeline penetration ability level mapping equation, representing the characteristic parameter data of the j-th type corresponding to the pipeline during the inspection of the pipeline; are respectively the corresponding independent variable coefficient and independent variable exponential coefficient; α is the bias parameter of the initial radio frequency pipeline penetration ability level mapping equation;

[0069] S32. Adjust the initial radio frequency pipeline penetration ability level mapping equation by using the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline impact parameter data matrix; after the adjustment is completed, the final radio frequency pipeline penetration ability level mapping equation is obtained;

[0070] The S32 includes the following steps:

[0071] S321. Input each row of data in the historical radio frequency penetration pipeline impact parameter data matrix into the initial radio frequency pipeline penetration ability level mapping equation for mapping to obtain the historical radio frequency penetration ability level initial mapping data set represents the data obtained by inputting the i-th row of data in the historical radio frequency penetration pipeline impact parameter data matrix into the initial radio frequency pipeline penetration ability level mapping equation for mapping;

[0072] S322. Calculate the error between the initial mapping dataset of the historical radio frequency penetration ability level and the historical radio frequency pipeline penetration ability level dataset to obtain the historical radio frequency pipeline penetration ability level mapping error data b. The calculation formula is as follows:

[0073]

[0074] S323. Set the radio frequency penetration ability mapping error threshold. When the historical radio frequency pipeline penetration ability level mapping error data is greater than or equal to the radio frequency penetration ability mapping error threshold, adjust the initial radio frequency pipeline penetration ability level mapping equation until the historical radio frequency pipeline penetration ability level mapping error data is less than the radio frequency penetration ability mapping error threshold, and then obtain the final radio frequency pipeline penetration ability level mapping equation; otherwise, there is no need to adjust the initial radio frequency pipeline penetration ability level mapping equation, and the initial radio frequency pipeline penetration ability level mapping equation is used as the final radio frequency pipeline penetration ability level mapping equation.

[0075] The adjustment of the initial radio frequency pipeline penetration ability level mapping equation in S323 includes the following steps:

[0076] S3231. Set the value range of each independent variable coefficient, independent variable exponent coefficient, and bias parameter in the initial radio frequency pipeline penetration ability level mapping equation to obtain the penetration level mapping independent variable coefficient value range matrix and the penetration level mapping bias parameter value range respectively represent the lower limit and upper limit of the value of the bias parameter in the initial radio frequency pipeline penetration ability level mapping equation. As follows:

[0077]

[0078] Among them, respectively represent the lower limit and upper limit of the value of the i-th independent variable coefficient in the initial radio frequency pipeline penetration ability level mapping equation. respectively represent the lower limit and upper limit of the value of the i-th independent variable exponent coefficient in the initial radio frequency pipeline penetration ability level mapping equation.

[0079] Construct a radio frequency penetration ability level mapping adjustment wild horse population. Set the maximum number of iterations of the radio frequency penetration ability level mapping adjustment wild horse population to be and the current number of iterations to be They are respectively denoted as the maximum number of iterations for penetration mapping adjustment and the current number of iterations for penetration mapping adjustment; the search space dimension of the wild horse population for radio frequency penetration ability level mapping adjustment is 2·a′2 + 1;

[0080] S3232. Generate the initial position matrix of each wild horse in the wild horse population for radio frequency penetration ability level mapping adjustment according to the penetration level mapping independent variable coefficient value range matrix and the penetration level mapping bias parameter value range, and obtain the initial position matrix set c = {c1,..., c j ..., c c′}, where c j represents the initial position matrix of the j-th wild horse in the wild horse population for radio frequency penetration ability level mapping adjustment, and c′ represents the size of the wild horse population for radio frequency penetration ability level mapping adjustment; c j is as follows,

[0081]

[0082] Among them, c j1i and c j2i and c j3 respectively represent the position components of c j on the i-th independent variable coefficient, the i-th independent variable exponent coefficient, and the bias parameter dimension of the initial radio frequency pipeline penetration ability level mapping equation; the calculation formulas are as follows respectively,

[0083]

[0084] In the formula: rand j1i and rand j2i and rand j3 respectively represent random numbers between 0 and 1 generated for c j1i and c j2i and c j3 ;

[0085] S3233. Construct the fitness function b′ of the wild horse population for radio frequency penetration ability level mapping adjustment; as follows,

[0086]

[0087] In the formula, It means that after substituting a set of independent variable coefficients, independent variable exponent coefficients, and bias parameters updated in each iteration process into the initial radio frequency pipeline penetration ability level mapping equation, each row of data in the historical radio frequency penetration pipeline impact parameter data matrix described in S221 is respectively input into the initial radio frequency pipeline penetration ability level mapping equation for mapping to obtain the error between the data set and the historical radio frequency pipeline penetration ability level data set; β is a positive number representing the protection parameter;

[0088] S3234. Start the iteration. Before the iteration, set the current iteration number of the penetration mapping adjustment to 1. In the first round of the iteration process, use the fitness function b′ of the wild horse population for the radio frequency penetration ability level mapping adjustment to calculate the fitness values of the initial position matrices of each wild horse in the initial position matrix set, obtaining the first fitness value set. Take the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the wild horse as the first global best fitness and the first global best position respectively. Update the initial position matrices of each wild horse in the initial position matrix set according to the first global best fitness and the first global best position. After the update is completed, increment the current iteration number of the penetration mapping adjustment by 1 and enter the next round of iteration;

[0089] In each subsequent round of the iteration process, use the fitness function b′ of the wild horse population for the radio frequency penetration ability level mapping adjustment to calculate the fitness values of the position matrices of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population updated in the previous round of the iteration process, obtaining the second fitness value set. Take the maximum fitness value in the second fitness value set and the corresponding position matrix of the wild horse as the second global best fitness and the second global best position respectively. Update the position matrices of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population updated in the previous round of the iteration process according to the second global best fitness and the second global best position. After the update is completed, increment the current iteration number of the penetration mapping adjustment by 1 and enter the next round of iteration;

[0090] S3235. When stop the iteration to obtain the final global best position and the final global best fitness; otherwise, continue the iteration until Take the final global best fitness as the optimized penetration ability level mapping error. When the optimized penetration ability level mapping error is less than the radio frequency penetration ability mapping error threshold, substitute each position component of the final global best position into the initial radio frequency pipeline penetration ability level mapping equation to obtain the final radio frequency pipeline penetration ability level mapping equation; otherwise, return to S2234 to continue the iteration until the optimized penetration ability level mapping error is less than the radio frequency penetration ability mapping error threshold;

[0091] S4. Uniformly select a number of data collection points on a number of pre - set inspection routes to obtain the current inspection route data collection point matrix;

[0092] The S4 includes the following steps:

[0093] S41. For the current inspection of the pipeline by the unmanned aerial vehicle, pre - set a number of inspection routes to obtain the current inspection route set;

[0094] S42. Uniformly select a number of data collection points on each inspection route in the current inspection route set to obtain the current inspection route data collection point matrix;

[0095] S5. Map each data collection point matrix data at each data collection point in the current inspection route data collection point matrix into the final radio frequency pipeline penetration ability level mapping equation to obtain the penetration ability level data of the radio frequency signal at each data collection point, and select the inspection route with the largest sum of the penetration ability level data of the radio frequency signal as the current final inspection route;

[0096] The S5 includes the following steps:

[0097] S51. In cooperation with the current inspection route data collection point matrix and the radio frequency penetration pipeline influence parameter type set, collect the radio frequency penetration pipeline influence parameter data of each type of pipeline at each current inspection route data collection point to obtain the current radio frequency penetration pipeline influence parameter data set matrix as follows,

[0098]

[0099] where, represents the radio frequency penetration pipeline influence parameter data set collected from the j - th data collection point on the i - th inspection route, d represents the total number of data collection points set on each inspection route, and d′ represents the total number of inspection routes set; represents the k - th type of radio frequency penetration pipeline influence parameter data in;

[0100] S52. Input each current radio frequency penetration pipeline influence parameter data set matrix in the current radio frequency penetration pipeline influence parameter data set matrix into the final radio frequency pipeline penetration ability level mapping equation for mapping to obtain the current radio frequency pipeline penetration level data matrix as follows,

[0101]

[0102] Among them, represents the radio frequency signal penetration ability level data obtained by mapping the input into the final radio frequency pipeline penetration ability level mapping equation;

[0103] S53. Select the inspection route corresponding to the row of data with the largest sum of data in the current radio frequency pipeline penetration level data matrix as the current final inspection route.

[0104] Embodiment 2

[0105] Please refer to Figure 2 , this embodiment discloses a drone pipeline inspection system based on radio frequency. The system can implement the method of the above embodiment, including a radio frequency penetration influence parameter type setting module, a historical drone pipeline inspection data acquisition module, a radio frequency penetration ability level mapping equation construction module, a current inspection route data acquisition point setting module, and a current inspection route screening module;

[0106] The radio frequency penetration influence parameter type setting module sets several types of characteristic parameters of the pipeline that have an impact on the radio frequency penetration ability, and obtains a radio frequency penetration pipeline influence parameter type set;

[0107] The historical drone pipeline inspection data acquisition module cooperates with the radio frequency penetration pipeline influence parameter type set to collect the radio frequency penetration pipeline influence parameter data and the radio frequency signal penetration ability level data during the historical inspection of the pipeline by using the drone to send radio frequency signals, and obtains a historical radio frequency pipeline penetration ability level data set and a historical radio frequency penetration pipeline influence parameter data matrix;

[0108] The radio frequency penetration ability level mapping equation construction module constructs a final radio frequency pipeline penetration ability level mapping equation by using the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix;

[0109] The current inspection route data acquisition point setting module evenly selects several data acquisition points on several pre-set inspection routes to obtain a current inspection route data acquisition point matrix;

[0110] The current inspection route screening module maps the data of each current inspection route data acquisition point matrix at each data acquisition point into the final radio frequency pipeline penetration ability level mapping equation to obtain the penetration ability level data of the radio frequency signal at each data acquisition point, and selects the inspection route with the largest sum of the penetration ability level data of the radio frequency signal as the current final inspection route.

[0111] Embodiment III

[0112] A drone pipeline inspection device based on radio frequency stores a program, which when executed by a processor is used to implement the above-mentioned drone pipeline inspection method based on radio frequency.

[0113] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0114] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the invention, so that those skilled in the relevant technical fields can understand and utilize the invention well.

Claims

1. A method for inspecting pipelines by an unmanned aerial vehicle based on radio frequency, characterized in that, It includes the following steps: S1. Set several types of characteristic parameters of the pipeline that affect the radio frequency penetration ability to obtain a set of radio frequency penetration pipeline influence parameter types; S2. Cooperate with the set of radio frequency penetration pipeline influence parameter types to collect the radio frequency penetration pipeline influence parameter data and the penetration ability level data of the radio frequency signal to the pipeline during the historical inspection of the pipeline by using a drone to send radio frequency signals, to obtain a historical radio frequency pipeline penetration ability level data set and a historical radio frequency penetration pipeline influence parameter data matrix; S3. Use the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix to construct a final radio frequency pipeline penetration ability level mapping equation; S4. Uniformly select several data collection points on several preset inspection routes to obtain a current inspection route data collection point matrix; S5. Map the data of each current inspection route data collection point matrix at each data collection point in the current inspection route data collection point matrix into the final radio frequency pipeline penetration ability level mapping equation to obtain the penetration ability level data of the radio frequency signal at each data collection point, and select the inspection route with the largest sum of the penetration ability level data of the radio frequency signal as the current final inspection route.

2. The method for inspecting an unmanned aerial vehicle pipeline based on radio frequency according to claim 1, wherein The S1 includes the following steps: S11. Set several types of characteristic parameters of the pipeline that affect the radio frequency penetration ability to obtain a set of radio frequency penetration pipeline influence parameter types; and then set several levels for the penetration ability of the radio frequency signal emitted by the drone to the pipeline to obtain a set of pipeline radio frequency signal penetration ability levels.

3. The method for inspecting an unmanned aerial vehicle pipeline based on radio frequency according to claim 2, characterized in that, The S2 includes the following steps: S21. Cooperate with the set of radio frequency penetration pipeline influence parameter types and the set of pipeline radio frequency signal penetration ability levels to collect the radio frequency penetration pipeline influence parameter data and the penetration ability level data of the radio frequency signal to the pipeline corresponding to the pipeline during the historical inspection of the pipeline by using a drone to send radio frequency signals, to obtain a historical radio frequency pipeline penetration ability level data set and a historical radio frequency penetration pipeline influence parameter data matrix.

4. The method for inspecting an unmanned aerial vehicle pipeline based on radio frequency according to claim 3, wherein The S3 includes the following steps: S31. Cooperate with the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix to construct an initial radio frequency pipeline penetration ability level mapping equation; S32. Use the historical radio frequency pipeline penetration ability level data set and the historical radio frequency penetration pipeline influence parameter data matrix to adjust the initial radio frequency pipeline penetration ability level mapping equation; after the adjustment is completed, obtain the final radio frequency pipeline penetration ability level mapping equation.

5. A method for inspecting pipelines by an unmanned aerial vehicle based on radio frequency according to claim 4, characterized in that, The S32 includes the following steps: S321. Input each row of data in the historical radio frequency penetration pipeline influence parameter data matrix into the initial radio frequency pipeline penetration ability level mapping equation for mapping to obtain a historical radio frequency penetration ability level initial mapping data set; S322. Calculate the error between the initial mapping data set of the historical radio frequency penetration ability level and the historical radio frequency pipeline penetration ability level data set to obtain the historical radio frequency pipeline penetration ability level mapping error data; S323. Set a radio frequency penetration ability mapping error threshold; when the historical radio frequency pipeline penetration ability level mapping error data is greater than or equal to the radio frequency penetration ability mapping error threshold, adjust the initial radio frequency pipeline penetration ability level mapping equation until the historical radio frequency pipeline penetration ability level mapping error data is less than the radio frequency penetration ability mapping error threshold, and then obtain the final radio frequency pipeline penetration ability level mapping equation; otherwise, there is no need to adjust the initial radio frequency pipeline penetration ability level mapping equation, and the initial radio frequency pipeline penetration ability level mapping equation is used as the final radio frequency pipeline penetration ability level mapping equation.

6. The method for inspecting a pipeline by an unmanned aerial vehicle based on radio frequency according to claim 5, wherein The adjustment of the initial radio frequency pipeline penetration ability level mapping equation in S323 includes the following steps: S3231. Set the value ranges of the coefficients of each independent variable, the exponent coefficients of the independent variables, and the bias parameters in the initial radio frequency pipeline penetration ability level mapping equation to obtain the penetration level mapping independent variable coefficient value range matrix and the penetration level mapping bias parameter value range; construct a radio frequency penetration ability level mapping adjustment wild horse population; set the maximum number of iterations of the radio frequency penetration ability level mapping adjustment wild horse population to be and the current number of iterations to be which are respectively denoted as the penetration mapping adjustment maximum number of iterations and the penetration mapping adjustment current number of iterations; S3232. Generate the initial position matrix of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population according to the penetration level mapping independent variable coefficient value range matrix and the penetration level mapping bias parameter value range to obtain the initial position matrix set; S3233. Construct the fitness function of the radio frequency penetration ability level mapping adjustment wild horse population; S3234. Start the iteration. Before the iteration, set the current iteration number of the penetration mapping adjustment to 1; in each round of iteration, use the fitness function of the radio frequency penetration ability level mapping adjustment wild horse population to calculate the fitness value of the position matrix of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population updated in the previous round of iteration and update the position matrix of each wild horse in the radio frequency penetration ability level mapping adjustment wild horse population updated in the previous round of iteration; after the update is completed, increment the current iteration number of the penetration mapping adjustment by 1 and enter the next round of iteration; S3235. When , stop the iteration to obtain the final global best position and the final global best fitness; otherwise, continue the iteration until ; Take the final global best fitness as the optimized penetration ability level mapping error; when the optimized penetration ability level mapping error is less than the radio frequency penetration ability mapping error threshold, substitute each position component of the final global best position into the initial radio frequency pipeline penetration ability level mapping equation to obtain the final radio frequency pipeline penetration ability level mapping equation; otherwise, return to S2234 to continue the iteration until the optimized penetration ability level mapping error is less than the radio frequency penetration ability mapping error threshold.

7. A method for inspecting pipelines by an unmanned aerial vehicle based on radio frequency according to claim 6, characterized in that The S4 includes the following steps: S41. Preset a number of inspection routes for the current inspection of the pipeline by the unmanned aerial vehicle to obtain the current inspection route set; S42. Uniformly select a number of data collection points on each inspection route in the current inspection route set to obtain the current inspection route data collection point matrix.

8. The method for inspecting a pipeline by an unmanned aerial vehicle based on radio frequency according to claim 7, wherein, The S5 includes the following steps: S51. In cooperation with the current inspection route data collection point matrix and the radio frequency penetration pipeline influence parameter type set, collect the radio frequency penetration pipeline influence parameter data of each type of pipeline at each current inspection route data collection point to obtain the current radio frequency penetration pipeline influence parameter data set matrix; S52. Input each current radio frequency penetration pipeline influence parameter data set matrix in the current radio frequency penetration pipeline influence parameter data set matrix into the final radio frequency pipeline penetration ability level mapping equation for mapping to obtain the current radio frequency pipeline penetration level data matrix; S53. Select the inspection route corresponding to the row of data with the largest sum of data in the current wireless radio frequency pipeline penetration level data matrix as the current final inspection route.

9. A system for implementing a wireless radio frequency-based unmanned aerial vehicle pipeline inspection method according to any one of claims 1-8.

10. A wireless radio frequency-based unmanned aerial vehicle pipeline inspection device, on which a program is stored, and when the program is executed by a processor, it is used to implement the above-mentioned wireless radio frequency-based unmanned aerial vehicle pipeline inspection method.

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