Pipeline robot path detection early warning method

Abstract

The invention discloses a pipeline robot path detection early warning method, and particularly relates to the technical field of pipeline detection.The method comprises the steps that a model updating abnormal coefficient of a 3D model in a pipeline is obtained, the model updating abnormal coefficient is compared with a preset model updating abnormal coefficient threshold value, an alarm is given out according to a comparison result, and when the alarm is not given out, the pipeline robot path detection early warning is carried out; the method comprises the following steps: acquiring a clearness index of an image in a pipeline, continuously adjusting the angle of a camera until the clearness index of the shot image in the pipeline is greater than a preset clearness index threshold value, and obtaining a pipeline detection result according to the corresponding pipeline image, so that whether the updating state of a 3D model in the pipeline is influenced or not can be judged; according to the invention, the camera angle can be adjusted finally, clear and accurate pipeline images can be shot, the influence on the path planning and detection effect of the robot is reduced, and the final detection result in the pipeline is accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline detection, and in particular to a pipeline robot path detection and early warning method. Background Art

[0002] A pipeline robot path detection and early warning method refers to using data from multiple sensors, such as ultrasound, laser, vision, etc., through data fusion technology, to build a 3D model of the inside of the pipeline, and update the environmental model in real time to adjust the robot's path to avoid dynamic obstacles; this method can optimize the robot's path planning in complex pipelines, ensure obstacle avoidance, reduce energy consumption, and improve the accuracy and efficiency of detection tasks. In addition, through real-time data analysis, the robot can also dynamically adjust the path to adapt to emergencies or changing environmental conditions; In addition, when performing detection, the image inside the pipeline is basically captured by the camera installed on the robot, and the image is analyzed to obtain the detection result inside the pipeline. If the detection effect is not good, the angle of the camera needs to be changed to continue to capture images with better effects. However, if the update status of the 3D model inside the pipeline is affected, it may be impossible to capture clear and accurate pipeline images no matter how the camera angle is adjusted, which will affect the robot's path planning and detection effect, making the final detection result inside the pipeline inaccurate. Summary of the invention

[0003] The purpose of the present invention is to solve the problem and propose a pipeline robot path detection and early warning method.

[0004] The present invention proposes a pipeline robot path detection and early warning method, the method comprising: Acquire data source information when updating the 3D model inside the pipeline, and obtain a data source inconsistency coefficient according to the data source information; Obtaining the time information of the update of the 3D model inside the pipeline, and obtaining the update time delay index according to the time information; The spatial resolution information of the model when the 3D model inside the pipeline is updated, and the resolution accuracy deviation coefficient is obtained according to the spatial resolution information; The model update anomaly coefficient is obtained according to the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient; Compare the model update abnormal coefficient with the preset model update abnormal coefficient threshold, and issue an alarm according to the comparison result; When no alarm is issued, the clarity index of the image inside the pipeline taken by the robot is obtained, and the angle of the camera is continuously adjusted according to the clarity index until the clarity index of the image inside the pipeline taken is greater than the preset clarity index threshold, and the pipeline detection result is obtained according to the corresponding pipeline image.

[0005] Optionally, obtaining a data source inconsistency coefficient according to the data source information includes: For each data involved in updating the internal 3D model of the pipeline, the total number of corresponding data sources is obtained; and the total number of different data corresponding to all data sources is obtained, and the total number of different data is divided by the total number of data sources to obtain the abnormal coefficient of each data source. ; The calculation formula is: , where and They are the total number of different data and the total number of data sources; The data source inconsistency coefficient is obtained based on the data source anomaly coefficient of all data and the corresponding preset weight value. The calculation formula is:

[0006] In the formula, is the data source inconsistency coefficient, It is the sequence number of the data involved in updating the internal 3D model of the pipeline. The preset weight value corresponding to each data involved in updating the 3D model inside the pipeline, =1, 2, 3, 4, ..., , Indicates the total number of data involved in updating the 3D model inside the pipeline. Is a positive integer.

[0007] Optionally, obtaining the update time delay index according to the time information includes: Get the timestamps of the internal 3D model of the pipeline from its establishment to each complete update, and the timestamps of each complete update and the timestamps of the last complete update, get the update time interval sequence, and mark the data in the update time interval sequence as , The sequence number indicating the number of data in the update time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Will update the data in the time interval series Data with a time greater than the preset maximum update time will be re-marked as , Indicates the data in the update time interval sequence The sequence number of data that is greater than the preset maximum update time, =1, 2, 3, 4, ..., , Is a positive integer; calculate the update time delay index , the calculation formula is: .

[0008] Optionally, the spatial resolution information obtains a resolution accuracy deviation coefficient including: Get the latest internal 3D model of the pipeline, and mark each sampling point with the spatial resolution , The sequential number indicating the number of sampling points. =1, 2, 3, 4, ..., , represents the total number of sampling points, and is a positive integer; According to the spatial resolution of each sampling point and the minimum spatial resolution of the preset sampling points The resolution accuracy deviation coefficient is obtained, and the calculation formula is: In the formula, is the resolution accuracy deviation coefficient.

[0009] Optionally, the model update anomaly coefficient obtained according to the data source inconsistency coefficient, the update time delay index and the resolution accuracy deviation coefficient includes:

[0010] In the formula, Update the anomaly coefficients for the model, , and They are the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient. are preset proportional coefficients for the data source inconsistency coefficient, update time delay index, and resolution accuracy deviation coefficient, respectively, and Both are greater than 0.

[0011] Optionally, comparing the model update abnormal coefficient with a preset model update abnormal coefficient threshold, and issuing an alarm according to the comparison result includes: The model update anomaly coefficient is compared with the preset model update anomaly coefficient threshold. If the model update anomaly coefficient is not less than the preset model update anomaly coefficient threshold, an alarm is immediately issued; if the model update anomaly coefficients are all less than the preset model update anomaly coefficient threshold, no alarm is issued.

[0012] Optionally, the Sobel operator is used to calculate the horizontal and vertical gradients of the image in the pipeline. The calculation formula is:

[0013]

[0014] In the formula, is the horizontal gradient of the image, is the vertical gradient of the image, is the original input image; The edge intensity image is obtained according to the horizontal gradient and the vertical gradient. The calculation formula is:

[0015] In the formula, edge intensity image; Use the Laplacian operator to calculate the edge intensity image and get the transformed image ; The calculation formula is:

[0016] In the formula, Represents the second-order derivative of the edge intensity image in the horizontal direction; Represents the second derivative of the edge intensity image in the vertical direction; Calculate the average pixel value of the transformed image. The calculation formula is:

[0017] In the formula, is the average pixel value of the transformed image, is the height of the image, is the width of the image, is the pixel value of the transformed image at position; Calculate the variance of the change image. The calculation formula can be:

[0018] is the variance of the changing image, and the variance of the changing image is used as the clarity index of the image in the pipeline.

[0019] Beneficial effects of the present invention: The present invention proposes a pipeline robot path detection early warning method, which obtains the model update abnormality coefficient by acquiring the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient of the 3D model inside the pipeline, and compares the model update abnormality coefficient with a preset model update abnormality coefficient threshold, and issues an alarm according to the comparison result. When no alarm is issued, the clarity index of the image inside the pipeline taken by the robot is obtained, and the angle of the camera is continuously adjusted according to the clarity index until the clarity index of the image inside the pipeline taken is greater than the preset clarity index threshold, and the pipeline detection result is obtained according to the corresponding pipeline image. In this way, the update status of the 3D model inside the pipeline can be judged, and it is judged whether the update status of the 3D model inside the pipeline is affected, and an alarm is issued to remind the staff to take corresponding measures, so that the camera angle can be adjusted in the end so that a clear and accurate pipeline image can be captured, reducing the impact on the robot's path planning and detection effect, so that the final detection result inside the pipeline is accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the accompanying drawings.

[0021] Figure 1 A flow chart of a pipeline robot path detection and early warning method is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0024] The embodiment of the present invention provides a pipeline robot path detection and early warning method. Figure 1 , Figure 1 A flow chart of a pipeline robot path detection and early warning method provided by an embodiment of the present invention. The method comprises the following steps: Acquire data source information when updating the 3D model inside the pipeline, and obtain a data source inconsistency coefficient according to the data source information; Obtaining the time information of the update of the 3D model inside the pipeline, and obtaining the update time delay index according to the time information; The spatial resolution information of the model when the 3D model inside the pipeline is updated, and the resolution accuracy deviation coefficient is obtained according to the spatial resolution information; The model update anomaly coefficient is obtained according to the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient; Compare the model update abnormal coefficient with the preset model update abnormal coefficient threshold, and issue an alarm according to the comparison result; When no alarm is issued, the clarity index of the image inside the pipeline taken by the robot is obtained, and the angle of the camera is continuously adjusted according to the clarity index until the clarity index of the image inside the pipeline taken is greater than the preset clarity index threshold, and the pipeline detection result is obtained according to the corresponding pipeline image.

[0025] Based on a pipeline robot path detection and early warning method provided by an embodiment of the present invention, through the above method, the update status of the 3D model inside the pipeline can be judged to determine whether the update status of the 3D model inside the pipeline is affected, and an alarm is issued to remind the staff to take corresponding measures, so that the camera angle can be adjusted to capture clear and accurate pipeline images, reducing the impact on the robot's path planning and detection effect, so that the final detection result inside the pipeline is accurate.

[0026] In one embodiment, obtaining the data source inconsistency coefficient according to the data source information includes: For each data involved in updating the internal 3D model of the pipeline, the total number of corresponding data sources is obtained; and the total number of different data corresponding to all data sources is obtained, and the total number of different data is divided by the total number of data sources to obtain the abnormal coefficient of each data source. ; The calculation formula is: , where and They are the total number of different data and the total number of data sources; The data source inconsistency coefficient is obtained based on the data source anomaly coefficient of all data and the corresponding preset weight value. The calculation formula is:

[0027] In the formula, is the data source inconsistency coefficient, It is the sequence number of the data involved in updating the internal 3D model of the pipeline. The preset weight value corresponding to each data involved in updating the 3D model inside the pipeline, =1, 2, 3, 4, ..., , Indicates the total number of data involved in updating the 3D model inside the pipeline. Is a positive integer.

[0028] It should be noted that the preset weight values ​​are set by professionals based on actual situations and are not specifically limited or elaborated.

[0029] It should be noted that when the 3D model inside the pipeline is updated, the larger the data source inconsistency coefficient is, the more the update status of the 3D model inside the pipeline is affected, which will result in the inability to capture clear and accurate pipeline images no matter how the camera angle is adjusted, thus affecting the robot's path planning and detection effect, and making the final detection result inside the pipeline inaccurate. The reason is that when the 3D model inside the pipeline is updated, the larger the data source inconsistency coefficient is, the greater the inconsistency or difference in the data source used in the model update process. This inconsistency will have a negative impact on the accuracy and consistency of the model, leading to the following problems: Reduced model accuracy: 3D models are built based on multi-source data. If there are inconsistencies in these data sources, such as poor data quality or asynchronous data collection at certain data collection points, these inconsistent data will cause the accuracy of some parts of the model to decrease. Such an inaccurate model cannot truly reflect the actual conditions inside the pipeline.

[0030] Deterioration of visual effects: When the 3D model inside the pipeline is inaccurate, it is difficult to capture accurate and clear images no matter how the camera adjusts the angle. This is because the error of the model itself will affect the alignment and rendering of the image, resulting in deviation or distortion of the visual performance, making it impossible to provide a true scene reproduction.

[0031] Robot path planning is affected: The robot's path planning relies on an accurate 3D model to determine the route. If there is an error in the model, the robot may plan the path based on incorrect information, resulting in the planned path not being consistent with the actual situation of the pipeline. This will cause the robot to encounter obstacles or deviate from the planned route when performing tasks, seriously affecting the detection effect.

[0032] Inaccurate detection results: Due to inaccurate 3D models and incorrect path planning, the robot's detection results will have errors. The accuracy of the detection results is directly related to the reliability and consistency of the data. When the data source inconsistency coefficient is large, it means that there are large differences in the data used in the detection process, causing the final detection results to deviate from the actual situation.

[0033] Therefore, the larger the data source inconsistency coefficient is, the more serious the data inconsistency is during the model update process and the lower the accuracy of the model is, which will lead to a decrease in the reliability of the entire detection process, thereby affecting the final detection effect and the accuracy of the results.

[0034] In one embodiment, obtaining an updated time delay index according to the time information includes: Get the timestamps of the internal 3D model of the pipeline from its establishment to each complete update, and the timestamps of each complete update and the timestamps of the last complete update, get the update time interval sequence, and mark the data in the update time interval sequence as , The sequence number indicating the number of data in the update time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Will update the data in the time interval series Data with a time greater than the preset maximum update time will be re-marked as , Indicates the data in the update time interval sequence The sequence number of data that is greater than the preset maximum update time, =1, 2, 3, 4, ..., , Is a positive integer; calculate the update time delay index , the calculation formula is: .

[0035] It should be noted that the preset maximum update time is set by professionals based on actual conditions and is not limited or elaborated on in detail.

[0036] It should be noted that when the 3D model inside the pipeline is updated, the larger the update time delay index is, the more the update status of the 3D model inside the pipeline is affected, which will make it impossible to capture a clear and accurate pipeline image no matter how the camera angle is adjusted, thus affecting the robot's path planning and detection effect, making the final detection result inside the pipeline inaccurate. The reasons are: Model data aging: If the update interval of the 3D model inside the pipeline is too long, the model data will age, and the gap between the reflected internal structure and the actual state will increase. The larger the update delay index, the more serious the deviation between the model and the actual situation.

[0037] Reduced image clarity and accuracy: Update delays can cause old data to dominate the 3D model inside the pipeline. Even if the camera angle is adjusted properly, the captured image may not match the actual situation and cannot reflect the current state of the pipeline. This is because the structural information and shape in the model may have changed, but the update delay prevents these changes from being reflected in the model in a timely manner.

[0038] Path planning and detection effects are affected: The robot relies on accurate 3D models for path planning and environmental perception. If the model information is delayed, the robot may navigate based on incorrect assumptions, resulting in incorrect path selection or inaccurate detection results. Especially when the internal environment of the pipeline is complex and dynamic, delayed model data will directly affect the robot's detection effect and the overall mission success rate.

[0039] Therefore, the larger the update time delay index is, the worse the real-time performance of the model is, which directly affects the monitoring and maintenance effects within the pipeline.

[0040] In one embodiment, the spatial resolution information obtains the resolution accuracy deviation coefficient including: Get the latest internal 3D model of the pipeline, and mark each sampling point with the spatial resolution , The sequential number indicating the number of sampling points. =1, 2, 3, 4, ..., , represents the total number of sampling points, and is a positive integer; According to the spatial resolution of each sampling point and the minimum spatial resolution of the preset sampling points The resolution accuracy deviation coefficient is obtained, and the calculation formula is: In the formula, is the resolution accuracy deviation coefficient.

[0041] It should be noted that the minimum spatial resolution of the preset sampling points is set by professionals according to actual conditions, and will not be limited or elaborated on in detail.

[0042] It should be noted that when the resolution accuracy deviation coefficient of the sampling point in the 3D model inside the pipeline is larger, it means that the update status of the 3D model inside the pipeline is affected, which will make it impossible to capture a clear and accurate pipeline image no matter how the camera angle is adjusted, thereby affecting the robot's path planning and detection effect, making the final detection result inside the pipeline inaccurate. The reasons are: Resolution deviation and model accuracy: When the resolution accuracy deviation coefficient is larger, it means that the spatial resolution of the 3D model inside the pipeline deviates more from the preset minimum resolution. This means that the details of some areas in the model may be insufficient, resulting in an inability to accurately represent the actual structure of the pipeline.

[0043] Image clarity and accuracy: The spatial resolution of the 3D model directly affects the details and clarity of the image. If the model resolution is not high enough, even if the camera angle is adjusted, the captured image may still lack the necessary details and cannot accurately reflect the actual situation of the pipeline.

[0044] Path planning and detection performance: The robot relies on the accuracy of the 3D model when performing path planning and detection. If the resolution of the model is not high enough, the robot may make decisions based on an incomplete or ambiguous model, resulting in incorrect path planning or inaccurate detection results.

[0045] Adaptability to dynamic changes: A high-precision model can better adapt to possible dynamic changes inside the pipeline. A model with large resolution deviation may not be able to provide accurate environmental information when responding to emergencies, affecting the robot's ability to perceive and respond to the environment.

[0046] Therefore, the larger the resolution accuracy deviation coefficient is, the greater the deviation of the model in representing the internal details of the pipeline, which in turn affects the detection effect of the robot and the accuracy of path planning.

[0047] In one embodiment, the model update anomaly coefficient is obtained according to the data source inconsistency coefficient, the update time delay index and the resolution accuracy deviation coefficient, including:

[0048] In the formula, Update the anomaly coefficients for the model, , and They are the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient. are preset proportional coefficients for the data source inconsistency coefficient, update time delay index, and resolution accuracy deviation coefficient, respectively, and Both are greater than 0.

[0049] It should be noted that It is set by professionals according to the actual situation. Generally, The sum of is 1, for example They can be 0.3, 0.4, 0.3, or other numbers, without limitation.

[0050] In one embodiment, comparing the model update abnormal coefficient with a preset model update abnormal coefficient threshold, and issuing an alarm according to the comparison result includes: The model update anomaly coefficient is compared with the preset model update anomaly coefficient threshold. If the model update anomaly coefficient is not less than the preset model update anomaly coefficient threshold, an alarm is immediately issued; if the model update anomaly coefficients are all less than the preset model update anomaly coefficient threshold, no alarm is issued.

[0051] It should be noted that the preset model update abnormal coefficient threshold is set by professionals based on actual conditions and is not limited or elaborated on in detail.

[0052] It should be noted that when an alarm signal is issued, it means that the update status of the 3D model inside the pipeline is affected, which may make it impossible to capture a clear and accurate pipeline image no matter how the camera angle is adjusted. At this time, the staff is reminded that corresponding processing measures need to be taken to ensure that the inside of the pipeline can be detected. At the same time, the camera adjustment should be stopped to save energy. When no alarm signal is issued, it means that the update status of the 3D model inside the current pipeline is not affected, and the pipeline can continue to be detected based on the current 3D model inside the pipeline.

[0053] In one embodiment, the clarity index of the image in the pipeline includes: The Sobel operator is used to calculate the horizontal and vertical gradients of the image in the pipeline. The calculation formula is:

[0054]

[0055] In the formula, is the horizontal gradient of the image, is the vertical gradient of the image, is the original input image; The edge intensity image is obtained according to the horizontal gradient and the vertical gradient. The calculation formula is:

[0056] In the formula, Edge strength image; the edge strength image shows the edge strength at each location in the image Use the Laplacian operator to calculate the edge intensity image and get the transformed image ; The calculation formula is:

[0057] In the formula, Represents the second-order derivative of the edge intensity image in the horizontal direction; Represents the second-order derivative of the edge intensity image in the vertical direction; the transformed image reflects the second-order changes in the edge intensity image and reveals the details and changes of the edge; Calculate the average pixel value of the transformed image. The calculation formula can be:

[0058] In the formula, is the average pixel value of the transformed image, is the height of the image (number of rows), is the width of the image (number of columns), To transform the image at position ( The pixel value of Calculate the variance of the change image. The calculation formula can be:

[0059] is the variance of the changing image, and the variance of the changing image is used as the clarity index of the image in the pipeline.

[0060] It should be noted that the clarity index of the image in the pipeline is compared with the preset clarity index threshold. If the clarity index of the image in the pipeline is greater than the preset clarity index threshold, the corresponding image in the pipeline is analyzed to obtain the detection result in the pipeline; if the clarity index of the image in the pipeline is not greater than the preset clarity index threshold, the angle of the camera is continuously adjusted until the clarity index of the captured image in the pipeline meets the requirement of being greater than the preset clarity index threshold, and the pipeline detection result is obtained based on the corresponding pipeline image.

[0061] It should be noted that the preset clarity index threshold is set by professionals based on actual conditions and is not specifically limited or elaborated.

[0062] It should be noted that, through the above method, the camera angle can be adjusted so that clear and accurate pipeline images can be captured, reducing the impact on the robot's path planning and detection effect, so that the final detection result in the pipeline is accurate.

[0063] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A pipeline robot path detection and early warning method, characterized in that: The following steps are involved: Acquire data source information when updating the 3D model inside the pipeline, and obtain a data source inconsistency coefficient according to the data source information; Obtaining the time information of the update of the 3D model inside the pipeline, and obtaining the update time delay index according to the time information; The spatial resolution information of the model when the 3D model inside the pipeline is updated, and the resolution accuracy deviation coefficient is obtained according to the spatial resolution information; The model update anomaly coefficient is obtained according to the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient; Compare the model update abnormal coefficient with the preset model update abnormal coefficient threshold, and issue an alarm according to the comparison result; When no alarm is issued, the clarity index of the image inside the pipeline taken by the robot is obtained, and the angle of the camera is continuously adjusted according to the clarity index until the clarity index of the image inside the pipeline taken is greater than the preset clarity index threshold, and the pipeline detection result is obtained according to the corresponding pipeline image.

2. A pipeline robot path detection and early warning method according to claim 1, characterized in that: The data source inconsistency coefficients obtained based on the data source information include: For each data involved in updating the internal 3D model of the pipeline, the total number of corresponding data sources is obtained; and the total number of different data corresponding to all data sources is obtained, and the total number of different data is divided by the total number of data sources to obtain the abnormal coefficient of each data source. ; The calculation formula is: , where and They are the total number of different data and the total number of data sources; The data source inconsistency coefficient is obtained based on the data source anomaly coefficient of all data and the corresponding preset weight value. The calculation formula is: In the formula, is the data source inconsistency coefficient, It is the sequence number of the data involved in updating the internal 3D model of the pipeline. The preset weight value corresponding to each data involved in updating the 3D model inside the pipeline, =1, 2, 3, 4, ..., , Indicates the total number of data involved in updating the 3D model inside the pipeline. Is a positive integer.

3. A pipeline robot path detection and early warning method according to claim 1, characterized in that: The update time delay index obtained according to the time information includes: Get the timestamps of the internal 3D model of the pipeline from its establishment to each complete update, and the timestamps of each complete update and the timestamps of the last complete update, get the update time interval sequence, and mark the data in the update time interval sequence as , The sequence number indicating the number of data in the update time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Will update the data in the time interval series Data with a time greater than the preset maximum update time will be re-marked as , Indicates the data in the update time interval sequence The sequence number of data that is greater than the preset maximum update time, =1, 2, 3, 4, ..., , Is a positive integer; calculate the update time delay index , the calculation formula is: .

4. A pipeline robot path detection and early warning method according to claim 1, characterized in that: The spatial resolution information obtains the resolution accuracy deviation coefficient including: Get the latest internal 3D model of the pipeline, and mark each sampling point with the spatial resolution , The sequential number indicating the number of sampling points. =1, 2, 3, 4, ..., , represents the total number of sampling points, and is a positive integer; According to the spatial resolution of each sampling point and the minimum spatial resolution of the preset sampling points The resolution accuracy deviation coefficient is obtained, and the calculation formula is: In the formula, is the resolution accuracy deviation coefficient.

5. A pipeline robot path detection and early warning method according to claim 1, characterized in that: The model update anomaly coefficients obtained based on the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient include: In the formula, Update the anomaly coefficients for the model, , and They are the data source inconsistency coefficient, update time delay index and resolution accuracy deviation coefficient. are preset proportional coefficients for the data source inconsistency coefficient, update time delay index, and resolution accuracy deviation coefficient, respectively, and Both are greater than 0.

6. A pipeline robot path detection and early warning method according to claim 1, characterized in that: Compare the model update abnormal coefficient with the preset model update abnormal coefficient threshold, and issue an alarm based on the comparison result, including: The model update anomaly coefficient is compared with the preset model update anomaly coefficient threshold. If the model update anomaly coefficient is not less than the preset model update anomaly coefficient threshold, an alarm is immediately issued; if the model update anomaly coefficients are all less than the preset model update anomaly coefficient threshold, no alarm is issued.

7. A pipeline robot path detection and early warning method according to claim 1, characterized in that: The Sobel operator is used to calculate the horizontal and vertical gradients of the image in the pipeline. The calculation formula is: In the formula, is the horizontal gradient of the image, is the vertical gradient of the image, is the original input image; The edge intensity image is obtained according to the horizontal gradient and the vertical gradient. The calculation formula is: In the formula, edge intensity image; Use the Laplacian operator to calculate the edge intensity image and get the transformed image ; The calculation formula is: In the formula, Represents the second-order derivative of the edge intensity image in the horizontal direction; Represents the second derivative of the edge intensity image in the vertical direction; Calculate the average pixel value of the transformed image. The calculation formula is: In the formula, is the average pixel value of the transformed image, is the height of the image, is the width of the image, is the pixel value of the transformed image at position; Calculate the variance of the change image. The calculation formula can be: is the variance of the changing image, and the variance of the changing image is used as the clarity index of the image in the pipeline.

Images