Autonomous Charging and Energy Management Method and System for Pipeline Robots

Abstract

The present invention relates to the technical field of data processing, and discloses a method and system for autonomous charging and energy management of a pipeline robot. The system includes: a battery unit and a central control module. The central control module includes an acquisition module, a charging module, a processing module, and an adjustment module. The acquisition module determines the interface position coordinates of the pipeline robot based on all target voiceprint signals. The charging module determines whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit, and determines the charging node of the battery unit based on the battery attenuation model. The processing module determines the output voltage adjustment factor of the battery subunit based on the pipeline inflection point. The adjustment module adjusts the output voltage based on the output voltage adjustment factor. When the battery subunit leaves the pipeline inflection point, the adjusted output voltage is adjusted again. The present invention improves the charging autonomy and energy management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more particularly, to a method and system for autonomous charging and energy management of a pipeline robot. Background Art

[0002] As an important infrastructure for transporting media such as gas, oil, and water, the real-time safety status of pipelines is directly related to the normal operation of industrial production and social life. For internal detection of pipelines, pipeline robots are usually used.

[0003] However, for traditional wired pipeline robots, due to the limitation of the length of the power supply cable, the detection distance is restricted. Moreover, for traditional cable-free pipeline robots, the weight of the robot itself increases due to the large-capacity battery, thus affecting the mobility. Once the battery runs out of power, it is necessary to disassemble the pipeline robot at the pipeline, which is time-consuming and poses a risk of pipeline leakage. Secondly, the continuous full-load operation of the drive wheels will result in energy waste, and there is a lack of adaptive adjustment of the battery in complex working conditions (such as bend inflection points).

[0004] Therefore, it is necessary to design a method and system for autonomous charging and energy management of a pipeline robot to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for autonomous charging and energy management of a pipeline robot, aiming to solve the problems that once the battery runs out of power, it is necessary to disassemble the pipeline robot at the pipeline, which is time-consuming and poses a risk of pipeline leakage, and there is a lack of adaptive adjustment of the battery in complex working conditions (such as bend inflection points).

[0006] On the one hand, the present invention provides a system for autonomous charging and energy management of a pipeline robot, including:

[0007] A battery unit and a central control module, the central control module is electrically connected to the battery unit, and the central control module includes a collection module, a charging module, a processing module, and an adjustment module;

[0008] The collection module is configured to obtain the initial voiceprint signals of at least three voiceprint sensors evenly deployed on the pipeline robot, preprocess each initial voiceprint signal, determine all target voiceprint signals according to the results of the preprocessing, determine the interface position coordinates of the pipeline robot based on all the target voiceprint signals, and obtain the interface target position coordinates of the wireless power supply device;

[0009] The charging module is configured to determine whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit. When it is determined to charge the pipeline robot, the charging node of the battery unit is determined based on the battery attenuation model;

[0010] The processing module is configured to, when the charging module completes charging based on the charging node or drives based on the remaining power, acquire multiple pipeline images of the pipeline, perform image processing on the multiple pipeline images to obtain the pipeline merged image of the pipeline. The battery unit includes a plurality of battery subunits. The output voltage of each battery subunit is determined based on the pipeline merged image. When there is a pipeline inflection point in the pipeline and there is a battery subunit reaching the pipeline inflection point, the output voltage adjustment factor of the battery subunit is determined based on the pipeline inflection point;

[0011] The adjustment module is configured to adjust the output voltage based on the output voltage adjustment factor, drive the pipeline robot to move forward according to the adjusted output voltage. When the battery subunit leaves the pipeline inflection point, the adjusted output voltage is adjusted again.

[0012] Further, when preprocessing each initial voiceprint signal and determining all target voiceprint signals according to the preprocessing result, it includes:

[0013] The acquisition module removes the signal noise of each initial voiceprint signal based on the first preset algorithm for signals, and performs time-frequency conversion on each initial voiceprint signal after removing the signal noise using the second preset algorithm for signals;

[0014] Each initial voiceprint signal after time-frequency conversion is preprocessed, and the preprocessing includes signal compression and signal normalization.

[0015] Further, when determining the interface position coordinates of the pipeline robot based on all target voiceprint signals and acquiring the interface target position coordinates of the wireless power supply device, it includes:

[0016] The acquisition module establishes a pipeline coordinate system with the center of the pipeline robot as the origin, and determines the interface position coordinates based on triangulation. The interface position coordinates are obtained by the following formula:

[0017] ;

[0018] Wherein, represents the position coordinates of the first voiceprint sensor, represents the position coordinates of the second voiceprint sensor, represents the position coordinates of the third voiceprint sensor, represents the interface position coordinates, represents the distance difference between the interface position and the first voiceprint sensor and the second voiceprint sensor, represents the distance difference between the interface position and the first voiceprint sensor and the third voiceprint sensor;

[0019] Based on the pipeline coordinate system, determine the coordinate of the interface target position as .

[0020] Further, when determining whether to charge the pipeline robot according to the interface position coordinate, the interface target position coordinate, and the remaining power of the battery unit, it includes:

[0021] The charging module presets a remaining power threshold and a deviation distance threshold in advance;

[0022] When the remaining power of the battery unit is greater than the remaining power threshold, it is determined not to charge the pipeline robot;

[0023] When the remaining power of the battery unit is less than or equal to the remaining power threshold, determine the deviation distance based on the interface position coordinate and the interface target position coordinate, compare the deviation distance with the deviation distance threshold, and determine whether to charge the pipeline robot according to the comparison result;

[0024] The deviation distance is obtained by the following formula:

[0025] ;

[0026] Wherein, represents the deviation distance, represents the interface target position coordinate, represents the interface position coordinate;

[0027] When the deviation distance is less than or equal to the deviation distance threshold, it is determined to charge the pipeline robot;

[0028] When the deviation distance is greater than the deviation distance threshold, it is determined to adjust the pipeline robot based on the interface target position coordinate, and charge the pipeline robot according to the adjusted interface position coordinate.

[0029] Further, when it is determined to charge the pipeline robot, when determining the charging node of the battery unit based on the battery attenuation model, it includes:

[0030] The charging module obtains historical charging data, constructs a model data set according to the historical charging data, samples the model data set according to a preset ratio to obtain a training set and a test set, uses grid search to find the establishment parameters of the random forest model, and establishes a random forest model;

[0031] Use the training set to fit the random forest model, substitute the test set into the random forest model and evaluate it. When the evaluation value reaches the preset evaluation value threshold, the battery attenuation model is obtained;

[0032] Substitute the remaining power of the battery cell into the battery attenuation model to determine the charging node of the battery cell.

[0033] Further, when obtaining multiple pipeline images of the pipeline and performing image processing on the multiple pipeline images to obtain the pipeline merged image of the pipeline, it includes:

[0034] The processing module performs image processing on the multiple pipeline images, and the image processing includes geometric correction and adjustment of image contrast;

[0035] Extract feature points from the multiple pipeline images after image processing, and determine the association information between the images according to the RANSAC algorithm. The association information includes relative position and rotation relationship. Based on the association information, register the multiple pipeline images after image processing, and merge the registered multiple pipeline images according to the weighted mean to obtain the pipeline merged image.

[0036] Further, when determining the output voltage of each battery subunit based on the pipeline merged image, it includes:

[0037] The processing module parses the pipeline merged image to determine the inner diameter of the pipeline, and determines the output voltage of each battery subunit according to the inner diameter of the pipeline;

[0038] The output voltage is obtained according to the following formula:

[0039] ;

[0040] Where represents the output voltage, and represent weight coefficients, and , represents the inner diameter of the pipeline, represents the friction coefficient between the driving wheel of the pipeline robot and the pipeline.

[0041] Further, when there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, when determining the output voltage adjustment factor of the battery subunit based on the pipeline inflection point, it includes:

[0042] The processing module determines the inflection point image of the pipeline inflection point, analyzes the inflection point image to determine the bending radius and bending angle of the pipeline, and determines the output voltage adjustment factor of the battery subunit according to the bending radius and the bending angle;

[0043] The output voltage adjustment factor is obtained according to the following formula:

[0044] ;

[0045] Wherein, represents the output voltage adjustment factor, represents the bending angle, represents the bending radius.

[0046] Furthermore, when adjusting the output voltage based on the output voltage adjustment factor, driving the pipeline robot to move forward according to the adjusted output voltage, and when the battery subunit leaves the pipeline inflection point and adjusts the adjusted output voltage again, it includes:

[0047] The adjustment module determines the target output voltage according to the output voltage and the output voltage adjustment factor, and the target output voltage is the product value of the output voltage and the output voltage adjustment factor;

[0048] When the battery subunit leaves the pipeline inflection point, the adjustment module restores the target output voltage of the battery subunit to the output voltage.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows: By acquiring the initial voiceprint signal through multiple voiceprint sensors and using preprocessing to determine the target voiceprint signal, the interface position coordinates of the pipeline robot and the interface target position coordinates of the wireless power supply device can be accurately located, thereby improving the accuracy of the charging process. Dynamically judge whether to charge, and combine the battery attenuation model to obtain the charging node, thereby optimizing the charging time and charging efficiency, prolonging the battery life, ensuring that the pipeline robot obtains energy at the same time, enhancing the autonomy and intelligence level. By acquiring the pipeline image and obtaining the pipeline merged image, the environment of the pipeline and the pipeline inflection point can be accurately judged, so as to adjust the output voltage of the battery subunit in real time to meet the energy requirements in different pipeline environments, avoid the risk of being unable to pass due to insufficient energy, and through the secondary adjustment after leaving the pipeline inflection point, further ensure the efficient operation of the pipeline robot, effectively improving the operation stability and energy management efficiency of the robot in a complex pipeline environment.

[0050] On the other hand, the present application also provides a method for autonomous charging and energy management of a pipeline robot, which is applied to the above-mentioned autonomous charging and energy management system of the pipeline robot, and includes:

[0051] Obtain the initial voiceprint signals of at least three voiceprint sensors evenly deployed on the pipeline robot, preprocess each initial voiceprint signal, determine all target voiceprint signals according to the results of the preprocessing, determine the interface position coordinates of the pipeline robot based on all the target voiceprint signals, and obtain the interface target position coordinates of the wireless power supply device;

[0052] Judge whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates and the remaining power of the battery unit. When it is determined to charge the pipeline robot, determine the charging nodes of the battery unit based on the battery attenuation model;

[0053] When charging is completed based on the charging nodes or driving is performed based on the remaining power, obtain multiple pipeline images of the pipeline, perform image processing on the multiple pipeline images to obtain the merged pipeline image of the pipeline, determine the output voltage of each battery subunit based on the merged pipeline image. When there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, determine the output voltage adjustment factor of the battery subunit based on the pipeline inflection point;

[0054] Adjust the output voltage based on the output voltage adjustment factor, drive the pipeline robot to move forward according to the adjusted output voltage. When the battery subunit leaves the pipeline inflection point, adjust the adjusted output voltage again.

[0055] It can be understood that the above-mentioned method and system for autonomous charging and energy management of a pipeline robot have the same beneficial effects and will not be elaborated here. Brief Description of the Drawings

[0056] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered as a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0057] Figure 1 is a schematic structural diagram of a system for autonomous charging and energy management of a pipeline robot provided by an embodiment of the present invention;

[0058] Figure 2 is a flowchart of a method for autonomous charging and energy management of a pipeline robot provided by an embodiment of the present invention;

[0059] Figure 3 is a schematic structural diagram of a pipeline robot provided by an embodiment of the present invention;

[0060] Figure 4The top view of the pipeline robot provided by the embodiment of the present invention;

[0061] Figure 5 The three-dimensional schematic diagram of the pipeline robot provided by the embodiment of the present invention.

[0062] In the figure, 1 is the robot body; 2 is the voiceprint sensor; 3 is the interface; 4 is the camera; 5 is the driving wheel; 6 is the central control module; 7 is the battery subunit; 10 is the front connecting arm; 11 is the rear connecting arm. Detailed implementation manners

[0063] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0064] In some embodiments of the present application, referring to Figure 1 as shown, a pipeline robot autonomous charging and energy management system includes:

[0065] A battery unit and a central control module, the central control module is electrically connected to the battery unit, and the central control module includes an acquisition module, a charging module, a processing module, and an adjustment module.

[0066] The acquisition module is configured to obtain the initial voiceprint signals of at least three voiceprint sensors evenly deployed on the pipeline robot, preprocess each initial voiceprint signal, determine all target voiceprint signals according to the results of the preprocessing, determine the interface position coordinates of the pipeline robot based on all the target voiceprint signals, and obtain the interface target position coordinates of the wireless power supply device.

[0067] The charging module is configured to determine whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit. When it is determined to charge the pipeline robot, the charging node of the battery unit is determined based on the battery attenuation model.

[0068] The processing module is configured to obtain multiple pipeline images of the pipeline when the charging module completes charging based on the charging node or is driven based on the remaining power, perform image processing on the multiple pipeline images to obtain a merged pipeline image of the pipeline. The battery unit includes a plurality of battery subunits. Determine the output voltage of each battery subunit based on the merged pipeline image. When there is a pipeline inflection point in the pipeline and there is a battery subunit reaching the pipeline inflection point, determine the output voltage adjustment factor of the battery subunit based on the pipeline inflection point.

[0069] The adjustment module is configured to adjust the output voltage based on the output voltage adjustment factor, drive the pipeline robot to move forward according to the adjusted output voltage. When the battery subunit leaves the pipeline inflection point, adjust the adjusted output voltage again.

[0070] Specifically, the acquisition module collects the initial voiceprint signals through at least three voiceprint sensors installed on the pipeline robot. The voiceprint sensors can locate the interface position of the pipeline robot by the signal reflection time. During the process of obtaining the initial voiceprint signals, some environmental noises or internal pipeline noises may interfere with the initial voiceprint signals. By preprocessing each initial voiceprint signal, the interference of noises and other factors can be eliminated, thus ensuring the accuracy of the target voiceprint signals. Then, the interface position coordinates of the pipeline robot are calculated using the target voiceprint signals, and the interface target position coordinates of the wireless power supply device are obtained. By determining the interface position coordinates and the interface target position coordinates of the pipeline robot, precise positioning data can be provided for the subsequent charging process. The charging module determines whether to charge the pipeline robot based on the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit. If the remaining power is insufficient for subsequent driving, it is determined that the pipeline robot needs to be charged, and the charging node of the battery unit is determined based on the battery attenuation model. When the charging is completed or the pipeline robot is driven based on the remaining power, the processing module uses a camera to obtain multiple pipeline images inside the pipeline and performs image processing to obtain the merged pipeline image of the pipeline. Combining the merged pipeline image, the processing module evaluates the spatial structure of the pipeline, thereby calculating the output voltage of each battery subunit. (The remaining power of the battery unit represents the sum of the remaining powers of each battery subunit.) The output voltage of the battery subunit needs to be adjusted according to the geometric structure of the pipeline. Especially when there are pipeline inflection points in the pipeline, the situation of pipeline inflection points is more complex than that of straight pipelines, and the motion load requirement for the pipeline robot is higher. If one of the battery subunits reaches a pipeline inflection point, the processing module determines the output voltage adjustment factor of this battery subunit according to the characteristics of the pipeline inflection point to ensure that this battery subunit stably supplies energy in the area of the pipeline inflection point, thus meeting the motion load of the pipeline robot. The adjustment module precisely adjusts the output voltage of this battery subunit according to the obtained output voltage adjustment factor, ensuring that the pipeline robot can be flexibly adjusted according to the actual situation at the pipeline inflection point, not only ensuring a smooth passage at the pipeline inflection point but also optimizing the energy utilization efficiency. When this battery subunit leaves the pipeline inflection point, the adjustment module will adjust the adjusted output voltage again to ensure the stable movement of the pipeline robot.

[0071] It can be understood that by combining a voiceprint sensor and a camera, the pipeline robot can accurately obtain the environmental information of the pipeline, locate the interface position of the pipeline robot and the wireless power supply device, and independently determine whether to charge the pipeline robot, enabling the pipeline robot to complete tasks independently in a complex pipeline environment, reducing manual intervention and improving the operating efficiency of the pipeline robot. According to the remaining power of the battery unit and the interface position coordinates, the charging nodes of the battery unit are dynamically determined. The intelligent charging strategy ensures the charging of the pipeline robot, avoids overcharging and attenuating the service life of the battery unit, and reduces energy waste at the same time. By precisely controlling the output voltage of each battery subunit, the system can achieve efficient energy management, thereby improving the adaptability and operation efficiency of the pipeline robot in a complex environment.

[0072] In some embodiments of the present application, when preprocessing each initial voiceprint signal and determining all target voiceprint signals according to the preprocessing results, it includes: the acquisition module removes the signal noise of each initial voiceprint signal based on the first preset signal algorithm, performs time-frequency conversion on each initial voiceprint signal after removing the signal noise using the second preset signal algorithm, and preprocesses each initial voiceprint signal after time-frequency conversion. The preprocessing includes signal compression and signal normalization.

[0073] Specifically, the first preset signal algorithm includes Wiener filtering or wavelet transform, and one of them can be specifically selected according to the actual environment of the pipeline. Through the first preset signal algorithm, the signal noise of each initial voiceprint signal can be removed, the signal-to-noise ratio of the signal can be improved, and the influence of external factors on the initial voiceprint signal can be reduced. The second preset algorithm includes Fourier transform and Hilbert transform, and one of them can be specifically selected according to the actual environment of the pipeline. Time-frequency conversion can reveal the frequency characteristics of the initial voiceprint signal changing with time, thereby capturing details and instantaneous changes, which helps to extract key information in the signal. Secondly, signal compression helps to reduce the amount of information in the signal while retaining the effective information of the signal. Especially during the long-term operation of the pipeline robot, the compressed signal can be quickly transmitted and processed accordingly. Signal normalization eliminates the processing errors caused by signal amplitude differences, enabling signals collected from different sources to be compared and analyzed under the same processing framework, improving the accuracy and reliability of the target voiceprint signal, and laying a foundation for subsequent determination of the interface position coordinates.

[0074] In some embodiments of the present application, when determining the interface position coordinates of the pipeline robot based on all target voiceprint signals and obtaining the interface target position coordinates of the wireless power supply device, it includes: the acquisition module establishes a pipeline coordinate system with the center of the pipeline robot as the origin, and determines the interface position coordinates based on triangulation. The interface position coordinates are obtained from the following formula:

[0075] ;

[0076] Wherein, represents the position coordinates of the first voiceprint sensor, represents the position coordinates of the second voiceprint sensor, represents the position coordinates of the third voiceprint sensor, represents the interface position coordinates, represents the distance difference between the interface position and the first and second voiceprint sensors, represents the distance difference between the interface position and the first and third voiceprint sensors. Based on the pipeline coordinate system, the interface target position coordinates are determined as .

[0077] Specifically, a pipeline coordinate system is established with the center of the pipeline robot as the origin. By triangulation positioning and combining at least three voiceprint sensors, the accuracy and efficiency of determining the interface position coordinates are improved. Since the wireless power supply device is fixedly placed in the pipeline, when determining the interface position coordinates, the interface position coordinates can also be directly obtained. Traditional positioning methods use a large amount of data such as point clouds for positioning. Although accurate positioning can also be achieved, for a pipeline robot with a large amount of existing motion data, it is necessary to simplify and accurately determine the interface position coordinates. In contrast, triangulation positioning uses the time difference and distance difference between voiceprint sensors, without the need to process a large amount of positioning data, reducing the dependence on human experience while being able to quickly and accurately locate the interface position coordinates, thereby improving the efficiency of autonomous charging management.

[0078] In some embodiments of the present application, when determining whether to charge the pipeline robot based on the interface position coordinates, the interface target position coordinates, and the remaining battery power of the battery unit, it includes: the charging module pre-sets a remaining power threshold and a deviation distance threshold. When the remaining battery power of the battery unit is greater than the remaining power threshold, it is determined not to charge the pipeline robot. When the remaining battery power of the battery unit is less than or equal to the remaining power threshold, the deviation distance is determined based on the interface position coordinates and the interface target position coordinates, and the deviation distance is compared with the deviation distance threshold. Whether to charge the pipeline robot is determined according to the comparison result. The deviation distance is obtained by the following formula:

[0079] ;

[0080] Wherein, represents the deviation distance, represents the interface target position coordinates, Indicates the interface position coordinates. When the deviation distance is less than or equal to the deviation distance threshold, it is determined to charge the pipeline robot. When the deviation distance is greater than the deviation distance threshold, it is determined to adjust the pipeline robot based on the interface target position coordinates, and charge the pipeline robot according to the adjusted interface position coordinates.

[0081] Specifically, the intelligent judgment of the remaining power of the battery unit by the charging module and according to the interface position coordinates ensures the autonomous ability of the pipeline robot during the charging process, eliminating the need for manual disassembly of the pipeline robot and avoiding the risk of leakage caused by pipeline damage. At the same time, the intelligent charging process saves the energy and time of manual operation, ensuring the autonomy and reliability of the charging process. When the deviation distance is greater than the deviation distance threshold, the pipeline robot needs to be charged, but there is a deviation between the interface position coordinates and the interface target position coordinates and they cannot be effectively aligned. The pipeline robot is adjusted according to the interface target position coordinates to reduce the deviation distance to meet the charging requirements, improving the intelligence and automation of charging.

[0082] In some embodiments of the present application, when it is determined to charge the pipeline robot, when determining the charging node of the battery unit based on the battery decay model, it includes: the charging module obtains historical charging data, constructs a model data set according to the historical charging data, samples the model data set according to a preset ratio to obtain a training set and a test set, uses grid search to find the establishment parameters of the random forest model, establishes the random forest model, fits the random forest model with the training set, substitutes the test set into the random forest model and evaluates it. When the evaluation value reaches the preset evaluation value threshold, the battery decay model is obtained, and the remaining power of the battery unit is substituted into the battery decay model to determine the charging node of the battery unit.

[0083] Specifically, the historical charging data includes various charging states, charging times, required charging amounts, etc. of the pipeline robot at different times. A model dataset is constructed based on the historical charging data, and the model dataset is sampled according to a preset ratio to obtain a training set and a test set. Usually, 70 - 80% is used as the training set, and the rest is used as the test set. The training set is used to train the random forest model, while the test set is used to evaluate the performance of the trained model. By using grid search to exhaustively search the range of hyperparameter values of the random forest model and find the best combination of hyperparameters, a random forest model is established, which improves the prediction ability of the model. The random forest model includes multiple decision trees, the depth of the tree, the number of leaf nodes, etc., aiming to capture complex relationships in the data. By fitting the random forest model with the training set, the overfitting ability of the random forest model is reduced, and at the same time, patterns and relationships in the data are tried to be learned in the training set to improve its prediction or classification ability. The test set is substituted into the random forest model for evaluation, and the evaluation indicators include accuracy, loss function value, recall rate, etc., which are used to measure the comprehensive performance of the model. When the evaluation value reaches the preset evaluation value threshold, it is considered that the model can stably approach the global optimal solution, and thus the model is determined as the battery attenuation model. The charging node is determined according to the remaining power of the battery cell, and this charging node includes the energy ratio charged to (the battery cell itself has its own attenuation during actual charging, so it cannot be charged to 100%) and the actual charging time, etc., improving the working efficiency of the subsequent pipeline robot.

[0084] In some embodiments of the present application, when obtaining multiple pipeline images of the pipeline and performing image processing on the multiple pipeline images to obtain a merged pipeline image of the pipeline, it includes: the processing module performs image processing on the multiple pipeline images, and the image processing includes geometric correction and adjustment of image contrast. Feature points are extracted from the multiple processed pipeline images, and the association information between the images is determined according to the RANSAC algorithm. The association information includes relative position and rotation relationship. Based on the association information, the multiple processed pipeline images are registered, and the registered multiple pipeline images are merged according to the weighted mean to obtain the merged pipeline image.

[0085] Specifically, when the charging module completes charging according to the charging node or is driven based on the remaining power, the processing module uses a camera to take pictures of the pipeline to determine the environmental information of the pipeline. A single pipeline image may not comprehensively reflect the actual situation of the pipeline. Therefore, multiple pipeline images are obtained to obtain the pipeline environment. By geometric correction and adjusting the image contrast, the details and features of the pipeline images are enhanced, thereby improving the image quality. Feature points are extracted from the multiple processed pipeline images, and the association information between the images is determined according to the RANSAC algorithm. The relative position and rotation relationship between the images can be accurately obtained, and according to affine transformation and perspective transformation, etc., the multiple processed pipeline images are registered to eliminate the translation, rotation, and deformation caused by different shooting angles between the images, thereby achieving precise alignment between the images. The registered multiple pipeline images are merged according to the weighted mean, improving the overall image effect and stitching quality of the merged pipeline images, laying a reliable foundation for subsequent determining the output voltage of each battery subunit and performing energy management.

[0086] In some embodiments of the present application, when determining the output voltage of each battery subunit based on the merged pipeline image, it includes: the processing module parses the merged pipeline image to determine the inner diameter of the pipeline, and determines the output voltage of each battery subunit according to the inner diameter of the pipeline. The output voltage is obtained according to the following formula:

[0087] ;

[0088] Wherein, represents the output voltage, and represent the weight coefficients, and , represents the inner diameter of the pipeline, represents the friction coefficient between the driving wheel of the pipeline robot and the pipeline.

[0089] In some embodiments of the present application, when there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, when determining the output voltage adjustment factor of the battery subunit based on the pipeline inflection point, it includes: the processing module determines the inflection point image of the pipeline inflection point, and parses the inflection point image to determine the bending radius and bending angle of the pipeline, and determines the output voltage adjustment factor of the battery subunit according to the bending radius and bending angle. The output voltage adjustment factor is obtained according to the following formula:

[0090] ;

[0091] Wherein, represents the output voltage adjustment factor, represents the bending angle, represents the bending radius.

[0092] In some embodiments of the present application, when adjusting the output voltage based on the output voltage adjustment factor, driving the pipeline robot forward according to the adjusted output voltage, and when the battery subunit leaves the pipeline inflection point and adjusts the adjusted output voltage again, it includes: the adjustment module determines the target output voltage according to the output voltage and the output voltage adjustment factor, and the target output voltage is the product value of the output voltage and the output voltage adjustment factor. When the battery subunit leaves the pipeline inflection point, the adjustment module restores the target output voltage of the battery subunit to the output voltage.

[0093] Specifically, the output voltage is determined by the inner diameter of the pipeline and the friction coefficient between the driving wheels of the pipeline robot and the pipeline, so that each battery subunit can intelligently adapt to pipelines of different sizes, reducing unnecessary power consumption and thus improving the battery life. For example: is selected as 0.3, is selected as 0.7, is determined to be 10 cm through image analysis, and the friction coefficient between the driving wheels of the pipeline robot and the pipeline is 0.6 (here the material of the pipeline is set as iron and the driving wheels are set as rubber material), and the obtained output voltage is , equals 244V. The output voltage of each battery subunit is dynamically determined according to the pipeline environment, avoiding energy waste caused by simple output. And when any one of the battery subunits reaches the pipeline inflection point, an inflection point image is acquired (using the camera and image processing as well, which will not be repeated here), and the bending radius and bending angle are determined. The output voltage adjustment factor of the battery subunit is determined according to the bending radius and bending angle. For example: the bending angle is , the bending radius is 25 cm, and the output voltage adjustment factor The value is 1.18. The target output voltage is determined by the output voltage and the output voltage adjustment factor, which can ensure that each battery subunit makes corresponding adjustments at the pipe inflection point, so as to smoothly pass through the area at the inflection point, prevent slipping or inability to pass due to energy mismatch at the inflection point of the bend, adaptively adjust the output voltage, reduce the problems of excessive energy supply or insufficient energy supply, effectively cope with the bending angle and bending radius of the pipeline, and enable the pipeline robot to operate stably in various scenarios. By making adjustments for each battery subunit when it reaches a specific bend inflection point, the energy consumption of blind driving is reduced, thus ensuring the running stability of the pipeline robot. Moreover, when the battery subunit that arrives leaves the pipe inflection point, the adjustment module restores the target output voltage of the battery subunit to the output voltage, avoiding excessive energy consumption, improving the efficiency of energy management and the utilization rate of the battery subunit, and further ensuring the level of autonomy and intelligence of the pipeline robot.

[0094] In summary, the beneficial effects of the present invention are as follows: By obtaining the initial voiceprint signals through multiple voiceprint sensors and using preprocessing to determine the target voiceprint signals, the interface position coordinates of the pipeline robot and the interface target position coordinates of the wireless power supply device can be accurately located, thus improving the accuracy of the charging process. Dynamically judge whether to charge, and combine the battery attenuation model to obtain the charging nodes, thereby optimizing the charging time and charging efficiency, prolonging the battery life, while ensuring that the pipeline robot obtains energy, improving the autonomy and intelligence level. By obtaining the pipeline image and obtaining the combined pipeline image, the pipeline environment and pipeline inflection points can be accurately judged, so as to adjust the output voltage of the battery subunit in real time to meet the energy requirements in different pipeline environments, avoid the risk of being unable to pass due to insufficient energy, and through the secondary adjustment after leaving the pipe inflection point, further ensure the efficient operation of the pipeline robot, effectively improving the running stability and energy management efficiency of the robot in a complex pipeline environment.

[0095] In another preferred manner based on the above embodiments, refer to Figure 2 As shown, this embodiment provides a method for autonomous charging and energy management of a pipeline robot, which is applied to the above-mentioned pipeline robot autonomous charging and energy management system, and includes:

[0096] S100: Obtain the initial voiceprint signals of at least three voiceprint sensors evenly deployed on the pipeline robot, perform preprocessing on each initial voiceprint signal, determine all target voiceprint signals according to the results of the preprocessing, determine the interface position coordinates of the pipeline robot based on all the target voiceprint signals, and obtain the interface target position coordinates of the wireless power supply device.

[0097] S200: Determine whether to charge the pipeline robot based on the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit. When it is determined to charge the pipeline robot, determine the charging nodes of the battery unit based on the battery attenuation model.

[0098] S300: When charging is completed based on the charging nodes or driving based on the remaining power, obtain multiple pipeline images of the pipeline, perform image processing on the multiple pipeline images to obtain a merged pipeline image of the pipeline, determine the output voltage of each battery subunit based on the merged pipeline image. When there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, determine the output voltage adjustment factor of the battery subunit based on the pipeline inflection point.

[0099] S400: Adjust the output voltage based on the output voltage adjustment factor, drive the pipeline robot forward according to the adjusted output voltage. When the battery subunit leaves the pipeline inflection point, adjust the adjusted output voltage again.

[0100] Specifically, through at least three voiceprint sensors evenly deployed on the pipeline robot, the initial voiceprint signals in the pipeline can be obtained in real time, and these signals are preprocessed to improve the signal quality, obtaining target voiceprint signals. Based on the target voiceprint signals, the interface position coordinates of the pipeline robot are determined, improving the positioning accuracy and ensuring the stability of the charging process. By judging the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit, it is decided whether to charge the pipeline robot. If it is determined that charging is required, the charging nodes will be determined according to the battery attenuation model. Make an intelligent decision based on the energy state of the pipeline robot, thereby ensuring the usage efficiency of the battery unit, ensuring the reliability of the charging process, reducing unnecessary charging waste, thus extending the life of the battery unit, improving the efficiency of energy management. When charging is completed or the remaining power is sufficient, by obtaining multiple pipeline images of the pipeline and performing image processing, the output voltage of each battery subunit is dynamically determined, improving the stability of energy management. When there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, the output voltage adjustment factor of the battery subunit is dynamically determined based on the pipeline inflection point, thereby ensuring that the pipeline robot can perform corresponding movements at the bend inflection point, avoiding the problem of mismatched output voltage of the battery subunit, and improving the adaptability of the pipeline robot and the reliability of energy management. When the battery subunit leaves the pipeline inflection point, the adjusted output voltage is adjusted again to ensure the continuous movement and stable energy supply of the pipeline robot. It can not only ensure the stable operation of the pipeline robot in a complex environment, but also optimize the usage efficiency of the battery subunit, reduce energy waste, and thus improve the autonomous ability and the intelligent level of management.

[0101] Refer to Figures 3 - 5As shown in the figure, the pipeline robot includes a robot body 1. The robot body 1 includes a front connecting arm 10 and a rear connecting arm 11. The tail of the front connecting arm 10 is connected to the head of the rear connecting arm 11. Voiceprint sensors 2 are fixedly connected to the tops of both the front connecting arm 10 and the rear connecting arm 11. A central control module 6 is fixedly connected to the top of the front connecting arm 10. An interface 3 is provided at the top of the front connecting arm 10. A camera 4 is installed at the head of the front connecting arm 10. An interface 3 is provided at the top of the rear connecting arm 11. Three driving wheels 5 are installed on the front connecting arm 10, and three driving wheels 5 are installed on the rear connecting arm 11. Each driving wheel 5 contains a battery sub-unit 7.

[0102] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocksFigure 1 Steps of functions specified in one or more boxes.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. An autonomous charging and energy management system for a pipeline robot, characterized in that, Including: A battery unit and a central control module, the central control module being electrically connected to the battery unit, the central control module including an acquisition module, a charging module, a processing module, and an adjustment module; The acquisition module is configured to obtain initial voiceprint signals of at least three voiceprint sensors evenly deployed on the pipeline robot, preprocess each initial voiceprint signal, determine all target voiceprint signals according to the results of the preprocessing, determine the interface position coordinates of the pipeline robot based on all the target voiceprint signals, and obtain the interface target position coordinates of the wireless power supply device; The charging module is configured to determine whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit. When it is determined to charge the pipeline robot, determine the charging node of the battery unit based on the battery attenuation model; The processing module is configured to, when the charging module completes charging based on the charging node or drives based on the remaining power, obtain multiple pipeline images of the pipeline, perform image processing on the multiple pipeline images to obtain a merged pipeline image of the pipeline. The battery unit includes a plurality of battery subunits. Determine the output voltage of each battery subunit based on the merged pipeline image. When there is a pipeline inflection point in the pipeline and there is a battery subunit reaching the pipeline inflection point, determine the output voltage adjustment factor of the battery subunit based on the pipeline inflection point; The adjustment module is configured to adjust the output voltage based on the output voltage adjustment factor, drive the pipeline robot to move forward according to the adjusted output voltage, and when the battery subunit leaves the pipeline inflection point, adjust the adjusted output voltage again; When determining the output voltage of each battery subunit based on the merged pipeline image of the pipeline, it includes: The processing module analyzes the merged pipeline image of the pipeline, determines the inner diameter of the pipeline, and determines the output voltage of each battery subunit according to the inner diameter of the pipeline; The output voltage is obtained according to the following formula: ； Among them, represents the output voltage, and represents the weight coefficient, and , represents the inner diameter of the pipeline, represents the friction coefficient between the driving wheel of the pipeline robot and the pipeline; The output voltage adjustment factor is obtained according to the following formula: ； Among them, represents the output voltage adjustment factor, represents the bending angle, represents the bending radius.

2. The pipeline robot autonomous charging and energy management system according to claim 1, characterized in that, When preprocessing each initial voiceprint signal and determining all target voiceprint signals according to the results of the preprocessing, it includes: The acquisition module removes the signal noise of each initial voiceprint signal based on a first preset signal algorithm, and performs time-frequency conversion on each initial voiceprint signal after removing the signal noise using a second preset signal algorithm; Perform preprocessing on each initial voiceprint signal after time-frequency conversion, and the preprocessing includes signal compression and signal normalization.

3. The pipeline robot autonomous charging and energy management system according to claim 2, wherein, When determining the interface position coordinates of the pipeline robot based on all the target voiceprint signals and obtaining the interface target position coordinates of the wireless power supply device, it includes: The acquisition module establishes a pipeline coordinate system with the center of the pipeline robot as the origin, determines the interface position coordinates based on triangulation, and the interface position coordinates are obtained according to the following formula: ； Among them, represents the position coordinates of the first voiceprint sensor, represents the position coordinates of the second voiceprint sensor, represents the position coordinates of the third voiceprint sensor, represents the interface position coordinates, represents the distance difference between the interface position and the first and second voiceprint sensors, represents the distance difference between the interface position and the first and third voiceprint sensors; Determine the coordinate of the target position of the interface based on the pipeline coordinate system as .

4. The pipeline robot autonomous charging and energy management system according to claim 3, characterized in that, When determining whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates, and the remaining power of the battery unit, it includes: The charging module preset a remaining power threshold and a deviation distance threshold in advance; When the remaining power of the battery unit is greater than the remaining power threshold, it is determined that the pipeline robot is not charged; When the remaining power of the battery unit is less than or equal to the remaining power threshold, the deviation distance is determined based on the interface position coordinates and the interface target position coordinates, and the deviation distance is compared with the deviation distance threshold, and it is judged whether to charge the pipeline robot according to the comparison result; The deviation distance is obtained by the following formula: ； Among them, represents the deviation distance, represents the interface target position coordinates, represents the interface position coordinates; When the deviation distance is less than or equal to the deviation distance threshold, it is determined that the pipeline robot is charged; When the deviation distance is greater than the deviation distance threshold, it is determined that the pipeline robot is adjusted based on the interface target position coordinates, and the pipeline robot is charged according to the adjusted interface position coordinates.

5. The pipeline robot autonomous charging and energy management system according to claim 4, characterized in that When it is determined to charge the pipeline robot, when determining the charging node of the battery unit based on the battery attenuation model, it includes: The charging module obtains historical charging data, constructs a model data set according to the historical charging data, samples the model data set according to a preset ratio to obtain a training set and a test set, uses grid search to find the establishment parameters of the random forest model, and establishes a random forest model; Use the training set to fit the random forest model, substitute the test set into the random forest model and evaluate it. When the evaluation value reaches the preset evaluation value threshold, the battery attenuation model is obtained; Substitute the remaining power of the battery unit into the battery attenuation model to determine the charging node of the battery unit.

6. The pipeline robot autonomous charging and energy management system according to claim 5, characterized in that, When obtaining multiple pipeline images of the pipeline and performing image processing on the multiple pipeline images to obtain the pipeline merged image of the pipeline, it includes: The processing module performs image processing on multiple pipeline images, and the image processing includes geometric correction and adjustment of image contrast; Feature points are extracted from the multiple pipeline images after image processing, and the association information between the images is determined according to the RANSAC algorithm. The association information includes relative position and rotation relationship. Based on the association information, the multiple pipeline images after image processing are registered, and the registered multiple pipeline images are merged according to the weighted mean to obtain the pipeline merged image.

7. The pipeline robot autonomous charging and energy management system according to claim 6, characterized in that When there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, when determining the output voltage adjustment factor of the battery subunit based on the pipeline inflection point, it includes: The processing module determines the inflection point image of the pipeline inflection point, analyzes the inflection point image, determines the bending radius and bending angle of the pipeline, and determines the output voltage adjustment factor of the battery subunit according to the bending radius and the bending angle.

8. The pipeline robot autonomous charging and energy management system according to claim 7, characterized in that When adjusting the output voltage based on the output voltage adjustment factor, driving the pipeline robot to move forward according to the adjusted output voltage, when the battery subunit leaves the pipeline inflection point, and adjusting the adjusted output voltage again, it includes: The adjustment module determines a target output voltage according to the output voltage and the output voltage adjustment factor, and the target output voltage is the product value of the output voltage and the output voltage adjustment factor; When the battery subunit leaves the pipeline inflection point, the adjustment module restores the target output voltage of the battery subunit to the output voltage.

9. A method for autonomous charging and energy management of a pipeline robot, which is applied to the pipeline robot autonomous charging and energy management system according to any one of claims 1-8, characterized in that, Comprising: Obtain the initial voiceprint signals of at least three voiceprint sensors evenly deployed on the pipeline robot, preprocess each initial voiceprint signal, determine all target voiceprint signals according to the results of the preprocessing, determine the interface position coordinates of the pipeline robot based on all the target voiceprint signals, and obtain the interface target position coordinates of the wireless power supply device; Judge whether to charge the pipeline robot according to the interface position coordinates, the interface target position coordinates and the remaining power of the battery unit. When it is determined to charge the pipeline robot, determine the charging node of the battery unit based on the battery decay model; When charging is completed based on the charging node or driving is performed based on the remaining power, obtain multiple pipeline images of the pipeline, perform image processing on the multiple pipeline images to obtain a pipeline merged image of the pipeline, determine the output voltage of each battery subunit based on the pipeline merged image. When there is a pipeline inflection point in the pipeline and a battery subunit reaches the pipeline inflection point, determine the output voltage adjustment factor of the battery subunit based on the pipeline inflection point; Adjust the output voltage based on the output voltage adjustment factor, drive the pipeline robot to move forward according to the adjusted output voltage, and when the battery subunit leaves the pipeline inflection point, adjust the adjusted output voltage again.

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