A pipeline detection and evaluation method and system based on the Internet of Things

Abstract

The present invention provides an Internet of Things-based pipeline inspection and assessment method and system, comprising: obtaining inspection data from multiple pipeline inspection devices inspecting urban pipelines via the Internet of Things; wherein the multiple pipeline inspection devices include at least a pipeline CCTV inspection robot, a pipeline periscope, and a pipeline sonar detector; evaluating the inspection data to determine the distribution of pipeline problems in the urban pipelines; and outputting the pipeline problem distribution. The Internet of Things-based pipeline inspection and assessment method and system of the present invention utilizes multiple pipeline inspection devices to collaboratively inspect urban pipelines, thereby improving inspection efficiency, expanding the inspection range, and increasing the accuracy of inspection results. It comprehensively and accurately captures various defects within pipelines and effectively identifies complex pipeline problems. Furthermore, it reduces the need for manual inspections of urban pipelines, lowers labor costs, and, to a certain extent, avoids personnel safety risks.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a pipeline detection and evaluation method and system based on the Internet of Things. Background Art

[0002] With the continuous advancement of urbanization, the construction and maintenance of urban infrastructure has become increasingly important. Urban pipeline systems, as a crucial component of urban infrastructure, undertake numerous critical functions, including water, electricity, gas, and sewage disposal. The health of pipelines directly impacts the normal operation of cities and the daily lives of residents, making regular inspection and evaluation of pipelines crucial.

[0003] Traditional pipeline inspection methods primarily rely on manual inspections and simple testing equipment. These methods often suffer from low efficiency, limited detection range, and inaccurate results. Manual inspections also present drawbacks such as high labor intensity, long inspection cycles, and personnel safety risks. Furthermore, traditional inspection equipment struggles to comprehensively and accurately detect various internal pipeline defects, effectively identifying complex pipeline issues and struggling to adapt to the increasingly complex urban pipeline networks.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a pipeline inspection and evaluation method based on the Internet of Things. By collaboratively inspecting urban pipelines through multiple pipeline inspection devices, the method improves inspection efficiency, expands the inspection range, and improves the accuracy of inspection results. It comprehensively and accurately captures various defects inside the pipelines and effectively identifies complex pipeline problems. In addition, it reduces the degree of manual inspection of urban pipelines, reduces labor costs, and avoids personnel safety risks to a certain extent.

[0006] An embodiment of the present invention provides a pipeline detection and evaluation method based on the Internet of Things, comprising:

[0007] Acquiring detection data of urban pipelines from multiple pipeline detection devices through the Internet of Things; wherein the multiple pipeline detection devices include at least: a pipeline CCTV detection robot, a pipeline periscope, and a pipeline sonar detector;

[0008] Evaluate the inspection data to determine the distribution of pipeline problems in urban pipelines;

[0009] Output pipeline problem distribution.

[0010] Optional IoT-based pipeline inspection and assessment methods also include:

[0011] Based on the distribution of pipeline problems, joint operation tasks are planned. Among them, the joint operation tasks include: two pipeline CCTV inspection robots jointly continue to inspect urban pipelines;

[0012] The joint operation task is assigned to two pipeline CCTV inspection robots through the Internet of Things;

[0013] When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user can monitor and control the two pipeline CCTV inspection robots through the Internet of Things;

[0014] Obtain new inspection data from two pipeline CCTV inspection robots through the Internet of Things;

[0015] Based on the newly detected data, the output pipeline problem distribution is updated.

[0016] Optionally, the auxiliary user monitors and controls the two pipeline CCTV inspection robots through the Internet of Things, including:

[0017] The real-time progress axis of the two pipeline CCTV inspection robots performing joint operation tasks within the preset progress range in the future is obtained through the Internet of Things;

[0018] Based on the real-time progress axis, plan the monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence; the supported control objects are one or both of the two pipeline CCTV inspection robots;

[0019] Through the Internet of Things, users are connected to two pipeline CCTV inspection robots based on the first monitoring perspective in the monitoring perspective sequence in sequence;

[0020] Each time access is made, the duration of the access is maintained to the duration of the view corresponding to the first surveillance view based on which the access is made, and the user is supported to control the supported control object corresponding to the first surveillance view based on which the access is made using at least one supported control instruction in the supported control instruction set corresponding to the first surveillance view based on which the access is made;

[0021] and / or,

[0022] Based on the work progress axis, plan the second monitoring angle of view for two pipeline CCTV inspection robots;

[0023] Through the Internet of Things, users can monitor two pipeline CCTV inspection robots from a second monitoring perspective and accumulate monitoring time;

[0024] When the monitoring duration exceeds a preset duration threshold, a gaze position-command object comparison table is determined based on the user's gaze movement history during the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots during the recent monitoring duration;

[0025] When the user's newly generated gaze position remains unchanged for more than a preset time period in the corresponding instruction object in the gaze position-instruction object comparison table, the unchanged instruction object is used as the target instruction object;

[0026] Displaying the control instruction set corresponding to the target instruction object to the user;

[0027] When the user selects a target control instruction from the control instruction set, the target instruction object is controlled accordingly based on the target control instruction.

[0028] Optionally, the planning of the monitoring perspective sequence and the perspective duration, supported control objects, and supported control instruction sets of each first monitoring perspective in the monitoring perspective sequence based on the real-time progress axis includes:

[0029] Traverse the progress of multiple jobs on the real-time progress axis in order of progress;

[0030] During each traversal, the supported monitoring perspective is determined based on the robot's working posture changes and fixed perspective during the traversed work progress. The supported monitoring perspective is the local perspective that remains unchanged when the fixed perspective changes due to the changes in the working posture of the two pipeline CCTV inspection robots.

[0031] Determine a first monitoring perspective based on the supported monitoring perspectives and the key element set of the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective are determined;

[0032] Determine the duration of the view based on the estimated execution time of the traversed job progress;

[0033] Determine the support control objects and support control instruction sets based on the key elements of the operation progress;

[0034] After the progress of each operation is traversed, the first monitoring perspective obtained in each traversal is sorted according to the traversal order to obtain a monitoring perspective sequence.

[0035] Optionally, the determining of the sight line position-command object comparison table based on the user's sight line movement history within the most recent monitoring period and the view angle change history of the two pipeline CCTV inspection robots within the most recent monitoring period includes:

[0036] Align the gaze movement history with the view angle change history to obtain an aligned sequence;

[0037] Classifying the aligned sequences into paired groups to obtain at least one paired group in each of the multiple categories;

[0038] Count the total number of paired groups in each category;

[0039] The categories whose total number exceeds the total threshold are regarded as the first target category, and the other remaining categories are regarded as the second target category;

[0040] Extracting a first common landing point region and a first perspective-changing subject from the pairing group of the same first target category, and pairing different region positions in the first common landing point region with the first perspective-changing subject respectively to obtain a first pairing group;

[0041] Expand the paired groups of the same second target category on the time axis to obtain the target time axis;

[0042] If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective changing subject from the pairing group corresponding to the same second target category, and pair different area positions in the second same landing point area with the second perspective changing subject respectively to obtain a second pairing group;

[0043] Determine a gaze position-command object comparison table based on the first pairing group and the second pairing group;

[0044] The trigger conditions include:

[0045] The type with more than N consecutive perspective change items on the target timeline belongs to the standard type; N is a positive integer;

[0046] or,

[0047] The types of the first and last perspective change items in at least one perspective change cycle on the target time axis are of standard type.

[0048] An embodiment of the present invention provides an Internet of Things-based pipeline detection and evaluation system, comprising:

[0049] An Internet of Things acquisition module, configured to acquire, through the Internet of Things, detection data of urban pipelines from a plurality of pipeline detection devices; wherein the plurality of pipeline detection devices include at least: a pipeline CCTV detection robot, a pipeline periscope, and a pipeline sonar detector;

[0050] The test data evaluation module is used to evaluate the test data and determine the distribution of pipeline problems in urban pipelines;

[0051] Problem output module, used to output pipeline problem distribution.

[0052] Optional, IoT-based pipeline inspection and evaluation system also includes:

[0053] Issue update module for:

[0054] Based on the distribution of pipeline problems, joint operation tasks are planned. Among them, the joint operation tasks include: two pipeline CCTV inspection robots jointly continue to inspect urban pipelines;

[0055] The joint operation task is assigned to two pipeline CCTV inspection robots through the Internet of Things;

[0056] When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user can monitor and control the two pipeline CCTV inspection robots through the Internet of Things;

[0057] Obtain new inspection data from two pipeline CCTV inspection robots through the Internet of Things;

[0058] Based on the newly detected data, the output pipeline problem distribution is updated.

[0059] Optionally, the problem update module assists the user in monitoring and controlling two pipeline CCTV inspection robots through the Internet of Things, including:

[0060] The real-time progress axis of the two pipeline CCTV inspection robots performing joint operation tasks within the preset progress range in the future is obtained through the Internet of Things;

[0061] Based on the real-time progress axis, plan the monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence; the supported control objects are one or both of the two pipeline CCTV inspection robots;

[0062] Through the Internet of Things, users are connected to two pipeline CCTV inspection robots based on the first monitoring perspective in the monitoring perspective sequence in sequence;

[0063] Each time access is made, the duration of the access is maintained to the duration of the view corresponding to the first surveillance view based on which the access is made, and the user is supported to control the supported control object corresponding to the first surveillance view based on which the access is made using at least one supported control instruction in the supported control instruction set corresponding to the first surveillance view based on which the access is made;

[0064] and / or,

[0065] Based on the work progress axis, plan the second monitoring angle of view for two pipeline CCTV inspection robots;

[0066] Through the Internet of Things, users can monitor two pipeline CCTV inspection robots from a second monitoring perspective and accumulate monitoring time;

[0067] When the monitoring duration exceeds a preset duration threshold, a gaze position-command object comparison table is determined based on the user's gaze movement history during the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots during the recent monitoring duration;

[0068] When the user's newly generated gaze position remains unchanged for more than a preset time period in the corresponding instruction object in the gaze position-instruction object comparison table, the unchanged instruction object is used as the target instruction object;

[0069] Displaying the control instruction set corresponding to the target instruction object to the user;

[0070] When the user selects a target control instruction from the control instruction set, the target instruction object is controlled accordingly based on the target control instruction.

[0071] Optionally, the problem updating module plans the monitoring perspective sequence and the perspective duration, supported control objects, and supported control instruction sets of each first monitoring perspective in the monitoring perspective sequence based on the real-time progress axis, including:

[0072] Traverse the progress of multiple jobs on the real-time progress axis in order of progress;

[0073] During each traversal, the supported monitoring perspective is determined based on the robot's working posture changes and fixed perspective during the traversed work progress. The supported monitoring perspective is the local perspective that remains unchanged when the fixed perspective changes due to the changes in the working posture of the two pipeline CCTV inspection robots.

[0074] Determine a first monitoring perspective based on the supported monitoring perspectives and the key element set of the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective are determined;

[0075] Determine the duration of the view based on the estimated execution time of the traversed job progress;

[0076] Determine the support control objects and support control instruction sets based on the key elements of the operation progress;

[0077] After the progress of each operation is traversed, the first monitoring perspective obtained in each traversal is sorted according to the traversal order to obtain a monitoring perspective sequence.

[0078] Optionally, the question updating module determines a gaze position-command object comparison table based on the user's gaze movement history during the most recent monitoring period and the view angle change history of the two pipeline CCTV inspection robots during the most recent monitoring period, including:

[0079] Align the gaze movement history with the view angle change history to obtain an aligned sequence;

[0080] Classifying the aligned sequences into paired groups to obtain at least one paired group in each of the multiple categories;

[0081] Count the total number of paired groups in each category;

[0082] The categories whose total number exceeds the total threshold are regarded as the first target category, and the other remaining categories are regarded as the second target category;

[0083] Extracting a first common landing point region and a first perspective-changing subject from the pairing group of the same first target category, and pairing different region positions in the first common landing point region with the first perspective-changing subject respectively to obtain a first pairing group;

[0084] Expand the paired groups of the same second target category on the time axis to obtain the target time axis;

[0085] If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective changing subject from the pairing group corresponding to the same second target category, and pair different area positions in the second same landing point area with the second perspective changing subject respectively to obtain a second pairing group;

[0086] Determine a gaze position-command object comparison table based on the first pairing group and the second pairing group;

[0087] The trigger conditions include:

[0088] The type with more than N consecutive perspective change items on the target timeline belongs to the standard type; N is a positive integer;

[0089] or,

[0090] The types of the first and last perspective change items in at least one perspective change cycle on the target time axis are of standard type.

[0091] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0092] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0094] Figure 1Schematic diagram of a pipeline detection and evaluation method based on the Internet of Things in an embodiment of the present invention;

[0095] Figure 2 Schematic diagram of a pipeline detection and evaluation system based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0097] The embodiment of the present invention provides a pipeline detection and evaluation method based on the Internet of Things, such as Figure 1 Shown, including:

[0098] S1. Acquire, through the Internet of Things, inspection data of urban pipelines from multiple pipeline inspection devices; wherein the multiple pipeline inspection devices include at least: a pipeline CCTV inspection robot, a pipeline periscope, and a pipeline sonar detector;

[0099] S2. Evaluate the test data to determine the distribution of pipeline problems in urban pipelines;

[0100] S3. Output pipeline problem distribution.

[0101] A variety of pipeline inspection equipment is deployed in urban pipelines. Specifically, CCTV inspection robots use cameras to capture real-time images of the interior of pipelines to detect whether there are cracks, sediments, blockages, corrosion, and other problems in the pipelines. Periscopes are used to collect images and videos of liquid flow and pipe wall conditions in pipelines, and can identify difficult-to-see damaged areas in pipelines. Sonar detectors use sound waves to detect structural defects in pipelines, such as cracks in cement pipes and corrosion in metal pipes. Pipeline periscopes and pipeline sonar detectors can also use robots as carriers when working; each pipeline inspection equipment is connected to the system through the Internet of Things, and it generates inspection data when inspecting urban pipelines, which is obtained by the system through the Internet of Things; the inspection data reflects the problems in different locations of urban pipelines, that is, the pipeline problem distribution, that is, the inspection data is evaluated to determine the pipeline problem distribution of urban pipelines; finally, the pipeline problem distribution is output.

[0102] This application uses a variety of pipeline inspection equipment to collaboratively inspect urban pipelines, thereby improving inspection efficiency, expanding the inspection scope, and improving the accuracy of inspection results. It comprehensively and accurately captures various defects inside the pipelines and effectively identifies complex pipeline problems. In addition, it reduces the degree of manual inspections of urban pipelines, reduces labor costs, and avoids personnel safety risks to a certain extent.

[0103] In one embodiment, the pipeline detection and evaluation method based on the Internet of Things further includes:

[0104] S4. Based on the distribution of pipeline problems, planning joint operation tasks; wherein the joint operation tasks include: two pipeline CCTV inspection robots jointly continuing to inspect urban pipelines;

[0105] S5. Send the joint operation task to the two pipeline CCTV inspection robots through the Internet of Things;

[0106] S6. When the two pipeline CCTV inspection robots perform a joint operation task, the auxiliary user monitors and controls the two pipeline CCTV inspection robots through the Internet of Things;

[0107] S7. Obtain the newly detected data of the two pipeline CCTV inspection robots through the Internet of Things;

[0108] S8. Based on the newly detected data, the output pipeline problem distribution is updated.

[0109] The pipeline problem distribution reflects which locations in urban pipelines require the two CCTV inspection robots to perform the required joint operations. Therefore, the system first plans joint operation tasks based on the pipeline problem distribution, such as two CCTV inspection robots jointly performing pipeline inspections. The CCTV inspection robots use high-precision cameras to capture internal pipeline images to identify cracks, corrosion, water accumulation, and other problems. During the execution of the task, the system uses IoT technology to send task instructions to the two robots in real time and simultaneously obtain their status information. Users can then remotely monitor and control the two robots through the IoT platform, such as viewing camera images, adjusting robot positions, and changing operating modes. The IoT connection not only supports real-time data upload but also provides a control interface when needed, ensuring that problems during the operation can be quickly detected and corrected. This helps users monitor and control the two CCTV inspection robots through the IoT. Simultaneously, data analysis of the latest data collected by the robots updates the pipeline problem distribution and provides a basis for future task planning. This makes pipeline inspection operations more efficient and accurate, and reduces the risks associated with human error or unexpected environmental issues.

[0110] In one embodiment, in S6, assisting the user in monitoring and controlling the two pipeline CCTV inspection robots through the Internet of Things includes:

[0111] S611. Obtaining, through the Internet of Things, a real-time progress axis of the two pipeline CCTV inspection robots performing a joint operation task within a preset future progress range;

[0112] In S611, after accepting the joint task, the two pipeline CCTV inspection robots will plan a task progress timeline. The timeline includes multiple task progresses set according to the execution sequence of the two pipeline CCTV inspection robots when performing the joint task. The preset progress range can be within the next 10 minutes. When obtaining the real-time progress axis, the portion of the progress axis within the preset future progress range is intercepted from the task progress timeline.

[0113] S612. Based on the real-time progress axis, plan a monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence; wherein the supported control objects are one or both of the two pipeline CCTV inspection robots;

[0114] In S612, the real-time progress axis reflects the future joint operation status of the two pipeline CCTV inspection robots. Based on this, a monitoring perspective sequence and the viewing angle duration, supported control objects, and supported control instruction sets of each first monitoring perspective in the monitoring perspective sequence can be planned. The monitoring perspective sequence includes first monitoring perspectives that the user needs to enter in sequence to monitor the two pipeline CCTV inspection robots. The viewing angle duration is the length of time the user needs to maintain the first monitoring perspective to monitor the two pipeline CCTV inspection robots. The supported control objects are objects that can be controlled by the user while maintaining the first monitoring perspective. The supported control instruction set contains control instructions that can be implemented on the supported control objects while the user maintains the first monitoring perspective.

[0115] S613. Connecting users to two pipeline CCTV inspection robots based on the first monitoring perspectives in the monitoring perspective sequence in sequence in sequence through the Internet of Things.

[0116] In S613, the user is sequentially connected to the two pipeline CCTV inspection robots based on the first monitoring perspective, so that the user can monitor the two pipeline CCTV inspection robots;

[0117] S614. Each time the access is made, the access duration is maintained until the view duration corresponding to the first surveillance view based on which the access is made, and the user is allowed to control the support control object corresponding to the first surveillance view based on which the access is made using at least one support control instruction in the support control instruction set corresponding to the first surveillance view based on which the access is made. In S614, after the access is made, the first surveillance view is maintained until the corresponding view duration, and the corresponding support control instruction set is used to allow the user to control the corresponding support control object, thereby enabling the user to control the two pipeline CCTV inspection robots.

[0118] and / or,

[0119] S621. Based on the operation progress axis, plan a second monitoring perspective for two pipeline CCTV inspection robots;

[0120] In S621, the time axis reflects the future joint operation status of the two pipeline CCTV inspection robots. These joint operation statuses can be continuously monitored through the second monitoring perspective. The first monitoring perspective is a perspective that dynamically changes based on these joint operation statuses.

[0121] S622. Using the Internet of Things, the user monitors the two pipeline CCTV inspection robots from a second monitoring perspective, and accumulates the monitoring time.

[0122] In S622, similarly, the user monitors through the second monitoring perspective, and the monitoring duration is the total real-time duration of the user monitoring through the second monitoring perspective;

[0123] S623. When the monitoring duration exceeds a preset duration threshold, a gaze position-command object comparison table is determined based on the user's gaze movement history during the most recent monitoring duration and the viewing angle change history of the two pipeline CCTV inspection robots during the most recent monitoring duration.

[0124] In S623, the preset duration threshold may be 5 minutes; the sight movement history is a history of changes in the position of the user's sight on the display interface of the mobile terminal's display screen when the user monitors the device through the second monitoring perspective via a mobile terminal (cell phone, computer, etc.); the perspective change history is a history of changes in the positions of the two pipeline CCTV inspection robots at various times under the second monitoring perspective; when the monitoring duration exceeds the duration threshold, it indicates that the sight movement history and the perspective change history can reflect the user's sight position and, therefore, the command object the user intends to control, and a sight position-command object comparison table is determined;

[0125] S624: When the newly generated gaze position of the user remains unchanged for more than a preset time period in the corresponding instruction object in the gaze position-instruction object comparison table, the unchanged instruction object is used as the target instruction object;

[0126] In S624, the preset duration may be 20 seconds. The newly generated gaze position of the user refers to the gaze position generated after the monitoring duration exceeds the preset duration threshold. The command object corresponding to the gaze position may be queried based on the gaze position-command object comparison table. If the command object remains unchanged for more than the preset duration, it further indicates that the user wants to control the command object, and the unchanged command object is used as the target command object.

[0127] S625. Displaying the control instruction set corresponding to the target instruction object to the user;

[0128] In S625, the target instruction object has a control instruction set, and the user can select a control instruction through the control instruction set;

[0129] S626: When the user selects a target control instruction from the control instruction set, the target instruction object is controlled accordingly based on the target control instruction.

[0130] In S626 , when the user selects the target control instruction, the target instruction object is controlled accordingly based on the target control instruction.

[0131] The embodiment of the present invention uses Internet of Things technology in combination with two pipeline CCTVs to detect the progress of the joint operation tasks performed by the robots, thereby realizing real-time monitoring and control of the robots. First, the system obtains the operation progress axis of the two robots within a preset future progress range through the Internet of Things, that is, it captures the specific information of the robot task progress within a period of time in the future through the time axis. On this basis, the system plans the monitoring perspective sequence according to the real-time progress axis, determines the specific order of monitoring, the duration of each perspective, the supported control objects, and the implementable control instruction set. The user accesses these perspectives in turn through the Internet of Things platform for monitoring, and can control the robot through the corresponding instruction set to ensure that the robot's operation progress and task execution proceed smoothly according to the predetermined goals, thus realizing monitoring and control assistance for the user and greatly improving the level of humanization.

[0132] By combining the real-time progress axis with the monitoring view sequence, efficient monitoring and intelligent scheduling of the robot's dynamic tasks are achieved; especially when the user's monitoring time exceeds the preset threshold, the system can generate a gaze position-instruction object comparison table based on the user's gaze position changes and the robot's motion history data, thereby intelligently judging the user's focus and automatically displaying the relevant control instruction set to the user; when the user maintains attention on a target instruction object for a certain period of time, the system further prioritizes it as a target for control, greatly improving the user's operating efficiency and the system's response intelligence; it not only ensures the smooth completion of the task, but also provides a highly automated operating experience, avoiding problems such as misoperation and inefficiency in manual intervention.

[0133] In one embodiment, the step S612 of planning the monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence based on the real-time progress axis includes:

[0134] S6121. Traverse the multiple job progress on the real-time progress axis in order of progress;

[0135] S6122. During each traversal, based on the changes in the working postures of the two pipeline CCTV inspection robots and the fixed viewing angles during the traversal, a supported monitoring viewing angle is determined; wherein the supported monitoring viewing angle is a local viewing angle that remains unchanged when the fixed viewing angle is affected by the changes in the working postures of the two pipeline CCTV inspection robots.

[0136] In S6122, the working posture change refers to the working posture change information of each of the two pipeline CCTV inspection robots when they are jointly working on the traversed working progress, such as the robot's direction, angle adjustment, or action type. The fixed perspective refers to the first-person perspective and third-person perspective of each of the two pipeline CCTV inspection robots when they are jointly working on the traversed working progress. When the pipeline CCTV inspection robots change their working posture, this will affect the change of the supported monitoring perspective, which is a continuous and unchanging local perspective.

[0137] S6123. Determine a first monitoring perspective based on the supported monitoring perspectives and the traversed key element set of the job progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective are determined;

[0138] In S6123, the operation progress has a key element set, which contains multiple content elements that users need to pay attention to when two pipeline CCTV inspection robots perform joint operations on the operation progress they traverse, such as key steps of the task, specific goals of the robots' current operation, possible changes in the operating environment, etc.

[0139] S6124. Determine the duration of the view based on the estimated duration of the progress of the traversed job;

[0140] In S6124, the operation progress has an estimated execution time. The estimated execution time is the time required for the two pipeline CCTV inspection robots to perform the joint operation of the traversed operation progress. Therefore, the estimated execution time can be used as the view duration.

[0141] S6125. Based on the key element set of the operation progress, determine the support control object and support control instruction set;

[0142] In S6125, the content elements in the key element set will reflect which CCTV detection robot is related to, and if it is related, it will be used as a supported control object, and it will also reflect how the CCTV detection robot can be controlled, and then determine the supported control instruction set;

[0143] S6126. After the progress of each operation is traversed, the first monitoring perspective obtained during each traversal is sorted according to the order of traversal to obtain a monitoring perspective sequence.

[0144] The embodiment of the present invention plans and monitors the progress of the operation based on a real-time progress axis to ensure efficient execution of the operation and precise control of the task. First, the system traverses each operation stage in the order of the operation progress and combines the two pipeline CCTVs to detect the robot's operating posture changes and their fixed viewing angles to determine a supporting monitoring angle that is not affected by changes. This angle should be able to continuously and effectively observe key operation elements and reflect dynamic changes in the operation, ensuring that the angle remains locally stable during the operation. The key element set in the operation progress includes information such as the robot's current operation goals, environmental changes, and key steps of the task. These elements help determine the focus of monitoring and the direction of control.

[0145] Based on the traversed job progress and its estimated execution time, the system further calculates the duration of each monitoring perspective to ensure the integrity and accuracy of the monitoring; the key element set of each job progress will also indicate the objects that need to be controlled and the control instruction set to ensure precise operation and intervention of the robot's operation; finally, all monitoring perspectives determined during the traversal process are sorted in chronological order to generate a complete monitoring perspective sequence for real-time monitoring and adjustment of the job progress.

[0146] Through precise job progress monitoring and perspective planning, changes in robot operations can be reflected in real time, ensuring that the visual monitoring of each operation link is not disturbed, thereby improving the safety and execution efficiency of the operation; through dynamic monitoring perspective adjustment based on the real-time progress axis, the system can maintain continuous attention on key operation elements in a changing operation environment, avoid information loss or omission, and thus improve the controllability and predictability of the operation task; in addition, combined with the intelligent matching of operation progress and robot control instructions, the system can intervene or adjust the robot's operation actions in a timely manner when necessary, ensuring the smooth progress of the operation process and minimizing potential risks; this intelligent and automated operation monitoring and control method is especially important for complex operation environments, and can greatly improve work efficiency and task success rate.

[0147] In one embodiment, the step S623 of determining a gaze position-command object comparison table based on the user's gaze movement history during the most recent monitoring period and the viewing angle change history of the two pipeline CCTV inspection robots during the most recent monitoring period includes:

[0148] S6231, performing temporal alignment on the gaze movement history and the view angle change history to obtain an aligned sequence;

[0149] In S6231, when performing time series alignment, the gaze movement history and the view angle change history are arranged into two data sequences according to time series, and then the two data sequences are aligned;

[0150] S6232, classifying the aligned sequences into paired groups to obtain at least one paired group in each of the multiple categories;

[0151] In S6232, the two aligned data in the two data sequences form a pairing group, which includes a gaze movement history data and a viewing angle change history data. The gaze movement positions in the gaze movement history data in the same category of pairing group belong to the same landing area, such as the area showing the silt condition of the pipeline on the display terminal. The viewing angle change history data in the same category of pairing group are the same.

[0152] S6233. Count the total number of paired groups in each category;

[0153] S6234: The categories whose total number exceeds the total number threshold are regarded as the first target category, and the remaining categories are regarded as the second target category;

[0154] In S6234, the total number threshold may be 6;

[0155] S6235: Extract a first common landing point region and a first perspective-changing subject from the pairing group of the same first target category, and pair different regions in the first common landing point region with the first perspective-changing subject to obtain a first pairing group.

[0156] In S6235, when the total number exceeds the total number threshold, the first same landing area and the first perspective-changing subject in the corresponding first target category have been confirmed multiple times by the user: the user's gaze is in the first same landing area, and the user wants to control the first perspective-changing subject, which can be one or both of the two pipeline CCTV inspection robots; different areas in the first same landing area are paired with the first perspective-changing subject to obtain a first pairing group;

[0157] S6236: Expand the pairing groups of the same second target category on the time axis to obtain a target time axis;

[0158] In S6236, when the total number does not exceed the total number threshold, the paired groups of the same second target category are expanded on the time axis and displayed. When displayed, the paired groups are placed at corresponding time positions on the time axis according to the generation time of the gaze movement history data and the view angle change history data of each paired group.

[0159] S6237: If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective changing subject from the pairing group corresponding to the same second target category, and pair different area positions in the second same landing point area with the second perspective changing subject to obtain a second pairing group;

[0160] In step S6237, if the target timeline meets the trigger condition, the second same landing point area and the second perspective changing subject are extracted from the pairing group corresponding to the same second target category to determine a second pairing group, and different area positions in the second same landing point area are paired with the second perspective changing subject to obtain a second pairing group.

[0161] S6238: Determine a gaze position-command object comparison table based on the first pairing group and the second pairing group;

[0162] In S6238, when determining the gaze position-command object comparison table, all first pairing groups and second pairing groups are integrated;

[0163] The trigger conditions include:

[0164] The presence of more than N consecutive viewpoint change items on the target timeline is a standard type, where N is a positive integer. A viewpoint change item refers to the historical gaze movement data in the paired group of the second target category on the target timeline. A standard type refers to the type of change in the CCTV robot's viewpoint that the user's gaze notices, indicating that the user wants to control the robot, such as the CCTV robot's autonomous action based on the on-site environment.

[0165] or,

[0166] The first and last perspective change items within at least one perspective change cycle on the target timeline are of the standard type. If more than N consecutive perspective change items are of the standard type, or if the first and last perspective change items within at least one perspective change cycle are of the standard type, then the second identical landing area and the second perspective change subject extracted from the pairing groups corresponding to the same second target category can be used to determine the second pairing group; a perspective change cycle contains at least three perspective change items, and the timeline distance between any two adjacent perspective change items within a perspective change cycle on the target timeline is less than 10 seconds.

[0167] The embodiment of the present invention constructs a gaze position-command object comparison table based on the user's gaze movement history and the viewpoint change history of two pipeline CCTV detection robots within a certain time range. First, through a temporal alignment operation, the user's gaze movement history is matched with the change history of the robot's viewpoint to ensure temporal alignment and form pairing groups. Each pairing group consists of a gaze position and a corresponding viewpoint change, and these pairing groups are classified into different categories. During the classification process, pairing groups in the same category have similar gaze points and viewpoint change characteristics, thereby revealing the user's focus on specific areas and the robot's viewpoint changes. Then, by counting the number of pairing groups in each category, the frequently occurring category (i.e., the first target category) is screened out, and the pairing relationship between the gaze position and the viewpoint change subject is further analyzed to form a more accurate command object comparison table.

[0168] When processing those categories that do not appear frequently (i.e., the second target category), the system will expand the pairing group according to the timeline to generate a target timeline; when the target timeline meets specific trigger conditions (such as the perspective change item appears more than a certain number of times in a row or the first and last perspective change items in the perspective period meet the standard type), the system will extract new line of sight positions and perspective change subject pairs from these pairing groups to further improve the instruction object comparison table; specifically, the trigger conditions include the perspective change item appearing more than N times in a row on the timeline, or the first and last perspective change items in the perspective change period meet a specific standard type; through the setting of these trigger conditions, the system can dynamically adapt to the user's control needs, and then form a precise mapping relationship between the line of sight position and the robot instruction object; it can improve the accuracy and intelligence of the robot's operation and optimize the efficiency of the user-robot interaction.

[0169] The embodiment of the present invention provides a pipeline detection and evaluation system based on the Internet of Things, such as Figure 2 Shown, including:

[0170] The Internet of Things acquisition module 1 is used to acquire detection data of urban pipelines detected by multiple pipeline detection devices through the Internet of Things; wherein the multiple pipeline detection devices include at least: a pipeline CCTV detection robot, a pipeline periscope, and a pipeline sonar detector;

[0171] The test data evaluation module 2 is used to evaluate the test data and determine the distribution of pipeline problems in urban pipelines;

[0172] Problem output module 3 is used to output pipeline problem distribution.

[0173] The IoT-based pipeline inspection and evaluation system also includes:

[0174] Issue update module for:

[0175] Based on the distribution of pipeline problems, joint operation tasks are planned. Among them, the joint operation tasks include: two pipeline CCTV inspection robots jointly continue to inspect urban pipelines;

[0176] The joint operation task is assigned to two pipeline CCTV inspection robots through the Internet of Things;

[0177] When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user can monitor and control the two pipeline CCTV inspection robots through the Internet of Things;

[0178] Obtain new inspection data from two pipeline CCTV inspection robots through the Internet of Things;

[0179] Based on the newly detected data, the output pipeline problem distribution is updated.

[0180] The problem update module assists users in monitoring and controlling two pipeline CCTV inspection robots through the Internet of Things, including:

[0181] The real-time progress axis of the two pipeline CCTV inspection robots performing joint operation tasks within the preset progress range in the future is obtained through the Internet of Things;

[0182] Based on the real-time progress axis, plan the monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence; the supported control objects are one or both of the two pipeline CCTV inspection robots;

[0183] Through the Internet of Things, users are connected to two pipeline CCTV inspection robots based on the first monitoring perspective in the monitoring perspective sequence in sequence;

[0184] Each time access is made, the duration of the access is maintained to the duration of the view corresponding to the first surveillance view based on which the access is made, and the user is supported to control the supported control object corresponding to the first surveillance view based on which the access is made using at least one supported control instruction in the supported control instruction set corresponding to the first surveillance view based on which the access is made;

[0185] and / or,

[0186] Based on the work progress axis, plan the second monitoring angle of view for two pipeline CCTV inspection robots;

[0187] Through the Internet of Things, users can monitor two pipeline CCTV inspection robots from a second monitoring perspective and accumulate monitoring time;

[0188] When the monitoring duration exceeds a preset duration threshold, a gaze position-command object comparison table is determined based on the user's gaze movement history during the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots during the recent monitoring duration;

[0189] When the user's newly generated gaze position remains unchanged for more than a preset time period in the corresponding instruction object in the gaze position-instruction object comparison table, the unchanged instruction object is used as the target instruction object;

[0190] Displaying the control instruction set corresponding to the target instruction object to the user;

[0191] When the user selects a target control instruction from the control instruction set, the target instruction object is controlled accordingly based on the target control instruction.

[0192] The problem update module plans the monitoring perspective sequence and the perspective duration, supported control objects, and supported control instruction sets of each first monitoring perspective in the monitoring perspective sequence based on the real-time progress axis, including:

[0193] Traverse the progress of multiple jobs on the real-time progress axis in order of progress;

[0194] During each traversal, the supported monitoring perspective is determined based on the robot's working posture changes and fixed perspective during the traversed work progress. The supported monitoring perspective is the local perspective that remains unchanged when the fixed perspective changes due to the changes in the working posture of the two pipeline CCTV inspection robots.

[0195] Determine a first monitoring perspective based on the supported monitoring perspectives and the key element set of the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective are determined;

[0196] Determine the duration of the view based on the estimated execution time of the traversed job progress;

[0197] Determine the support control objects and support control instruction sets based on the key elements of the operation progress;

[0198] After the progress of each operation is traversed, the first monitoring perspective obtained in each traversal is sorted according to the traversal order to obtain a monitoring perspective sequence.

[0199] The question updating module determines a gaze position-command object comparison table based on the user's gaze movement history during the most recent monitoring period and the view angle change history of the two pipeline CCTV inspection robots during the most recent monitoring period, including:

[0200] Align the gaze movement history with the view angle change history to obtain an aligned sequence;

[0201] Classifying the aligned sequences into paired groups to obtain at least one paired group in each of the multiple categories;

[0202] Count the total number of paired groups in each category;

[0203] The categories whose total number exceeds the total threshold are regarded as the first target category, and the other remaining categories are regarded as the second target category;

[0204] Extracting a first common landing point region and a first perspective-changing subject from the pairing group of the same first target category, and pairing different region positions in the first common landing point region with the first perspective-changing subject respectively to obtain a first pairing group;

[0205] Expand the paired groups of the same second target category on the time axis to obtain the target time axis;

[0206] If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective changing subject from the pairing group corresponding to the same second target category, and pair different area positions in the second same landing point area with the second perspective changing subject respectively to obtain a second pairing group;

[0207] Determine a gaze position-command object comparison table based on the first pairing group and the second pairing group;

[0208] The trigger conditions include:

[0209] The type with more than N consecutive perspective change items on the target timeline belongs to the standard type; N is a positive integer;

[0210] or,

[0211] The types of the first and last perspective change items in at least one perspective change cycle on the target time axis are of standard type.

[0212] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A pipeline detection and evaluation method based on the Internet of Things, characterized in that: include: Acquiring detection data of urban pipelines from multiple pipeline detection devices through the Internet of Things; wherein the multiple pipeline detection devices include at least: a pipeline CCTV detection robot, a pipeline periscope, and a pipeline sonar detector; Evaluate the inspection data to determine the distribution of pipeline problems in urban pipelines; Output pipeline problem distribution; Based on the distribution of pipeline problems, joint operation tasks are planned and distributed to two pipeline CCTV inspection robots through the Internet of Things. When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user can monitor and control the two pipeline CCTV inspection robots through the Internet of Things; Obtain new inspection data from two pipeline CCTV inspection robots through the Internet of Things; Update the output pipeline problem distribution based on the newly detected data; The auxiliary user monitors and controls the two pipeline CCTV inspection robots through the Internet of Things, including: The real-time progress axis of the two pipeline CCTV inspection robots performing joint operation tasks within the preset progress range in the future is obtained through the Internet of Things; Based on the real-time progress axis, plan the monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence; the supported control objects are one or both of the two pipeline CCTV inspection robots; Through the Internet of Things, users are connected to two pipeline CCTV inspection robots based on the first monitoring perspective in the monitoring perspective sequence in sequence; Each time access is made, the duration of access is maintained to the duration of the perspective corresponding to the first monitoring perspective based on which the access is based, and the user is supported to control the support control object corresponding to the first monitoring perspective based on which the access is based with at least one support control instruction in the support control instruction set corresponding to the first monitoring perspective based on which the access is based.

2. The pipeline detection and evaluation method based on the Internet of Things according to claim 1, characterized in that: The joint operation tasks include: two pipeline CCTV inspection robots jointly continue to carry out inspection operations on urban pipelines.

3. The pipeline detection and evaluation method based on the Internet of Things according to claim 1, characterized in that: The auxiliary user monitors and controls the two pipeline CCTV inspection robots through the Internet of Things, and further includes: Based on the work progress axis, plan the second monitoring angle of view for two pipeline CCTV inspection robots; Through the Internet of Things, users can monitor two pipeline CCTV inspection robots from a second monitoring perspective and accumulate monitoring time; When the monitoring duration exceeds a preset duration threshold, a gaze position-command object comparison table is determined based on the user's gaze movement history during the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots during the recent monitoring duration; When the user's newly generated gaze position remains unchanged for more than a preset time period in the corresponding instruction object in the gaze position-instruction object comparison table, the unchanged instruction object is used as the target instruction object; Displaying the control instruction set corresponding to the target instruction object to the user; When the user selects a target control instruction from the control instruction set, the target instruction object is controlled accordingly based on the target control instruction.

4. The pipeline detection and evaluation method based on the Internet of Things according to claim 3, characterized in that: The planning of the monitoring perspective sequence and the perspective duration, supported control objects, and supported control instruction sets of each first monitoring perspective in the monitoring perspective sequence based on the real-time progress axis includes: Traverse the progress of multiple jobs on the real-time progress axis in order of progress; During each traversal, the supported monitoring perspective is determined based on the robot's working posture changes and fixed perspective during the traversed work progress. The supported monitoring perspective is the local perspective that remains unchanged when the fixed perspective changes due to the changes in the working posture of the two pipeline CCTV inspection robots. Determine a first monitoring perspective based on the supported monitoring perspectives and the key element set of the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective are determined; Determine the duration of the view based on the estimated execution time of the traversed job progress; Determine the support control objects and support control instruction sets based on the key elements of the operation progress; After the progress of each operation is traversed, the first monitoring perspective obtained in each traversal is sorted according to the traversal order to obtain a monitoring perspective sequence.

5. The pipeline detection and evaluation method based on the Internet of Things according to claim 3, characterized in that: The determination of the sight position-command object comparison table based on the user's sight movement history within the most recent monitoring period and the view angle change history of the two pipeline CCTV inspection robots within the most recent monitoring period includes: Align the gaze movement history with the view angle change history to obtain an aligned sequence; Classifying the aligned sequences into paired groups to obtain at least one paired group in each of the multiple categories; Count the total number of paired groups in each category; The categories whose total number exceeds the total threshold are regarded as the first target category, and the other remaining categories are regarded as the second target category; Extracting a first common landing point region and a first perspective-changing subject from the pairing group of the same first target category, and pairing different region positions in the first common landing point region with the first perspective-changing subject respectively to obtain a first pairing group; Expand the paired groups of the same second target category on the time axis to obtain the target time axis; If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective changing subject from the pairing group corresponding to the same second target category, and pair different area positions in the second same landing point area with the second perspective changing subject respectively to obtain a second pairing group; Determine a gaze position-command object comparison table based on the first pairing group and the second pairing group; The trigger conditions include: The type with more than N consecutive perspective change items on the target timeline belongs to the standard type; N is a positive integer; or, The types of the first and last perspective change items in at least one perspective change cycle on the target time axis are of standard type.

6. A pipeline detection and evaluation system based on the Internet of Things, characterized in that: include: An Internet of Things acquisition module, configured to acquire, through the Internet of Things, detection data of urban pipelines from a plurality of pipeline detection devices; wherein the plurality of pipeline detection devices include at least: a pipeline CCTV detection robot, a pipeline periscope, and a pipeline sonar detector; The test data evaluation module is used to evaluate the test data and determine the distribution of pipeline problems in urban pipelines; Problem output module, used to output pipeline problem distribution; Issue update module for: Plan joint operation tasks based on pipeline problem distribution; The joint operation task is assigned to two pipeline CCTV inspection robots through the Internet of Things; When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user can monitor and control the two pipeline CCTV inspection robots through the Internet of Things; Obtain new inspection data from two pipeline CCTV inspection robots through the Internet of Things; Update the output pipeline problem distribution based on the newly detected data; The problem update module assists users in monitoring and controlling two pipeline CCTV inspection robots through the Internet of Things, including: The real-time progress axis of the two pipeline CCTV inspection robots performing joint operation tasks within the preset progress range in the future is obtained through the Internet of Things; Based on the real-time progress axis, plan the monitoring view sequence and the view duration, supported control objects, and supported control instruction sets of each first monitoring view in the monitoring view sequence; the supported control objects are one or both of the two pipeline CCTV inspection robots; Through the Internet of Things, users are connected to two pipeline CCTV inspection robots based on the first monitoring perspective in the monitoring perspective sequence in sequence; Each time access is made, the duration of access is maintained to the duration of the perspective corresponding to the first monitoring perspective based on which the access is based, and the user is supported to control the support control object corresponding to the first monitoring perspective based on which the access is based with at least one support control instruction in the support control instruction set corresponding to the first monitoring perspective based on which the access is based.

7. The pipeline detection and evaluation system based on the Internet of Things according to claim 6, characterized in that: The joint operation tasks include: two pipeline CCTV inspection robots jointly continue to carry out inspection operations on urban pipelines.

8. The pipeline detection and evaluation system based on the Internet of Things according to claim 7, characterized in that: The problem update module assists the user in monitoring and controlling the two pipeline CCTV inspection robots through the Internet of Things, and also includes: Based on the work progress axis, plan the second monitoring angle of view for two pipeline CCTV inspection robots; Through the Internet of Things, users can monitor two pipeline CCTV inspection robots from a second monitoring perspective and accumulate monitoring time; When the monitoring duration exceeds a preset duration threshold, a gaze position-command object comparison table is determined based on the user's gaze movement history during the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots during the recent monitoring duration; When the user's newly generated gaze position remains unchanged for more than a preset time period in the corresponding instruction object in the gaze position-instruction object comparison table, the unchanged instruction object is used as the target instruction object; Displaying the control instruction set corresponding to the target instruction object to the user; When the user selects a target control instruction from the control instruction set, the target instruction object is controlled accordingly based on the target control instruction.

9. The pipeline detection and evaluation system based on the Internet of Things according to claim 8, characterized in that: The problem update module plans the monitoring perspective sequence and the perspective duration, supported control objects, and supported control instruction sets of each first monitoring perspective in the monitoring perspective sequence based on the real-time progress axis, including: Traverse the progress of multiple jobs on the real-time progress axis in order of progress; During each traversal, the supported monitoring perspective is determined based on the robot's working posture changes and fixed perspective during the traversed work progress. The supported monitoring perspective is the local perspective that remains unchanged when the fixed perspective changes due to the changes in the working posture of the two pipeline CCTV inspection robots. Determine a first monitoring perspective based on the supported monitoring perspectives and the key element set of the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective are determined; Determine the duration of the view based on the estimated execution time of the traversed job progress; Determine the support control objects and support control instruction sets based on the key elements of the operation progress; After the progress of each operation is traversed, the first monitoring perspective obtained in each traversal is sorted according to the traversal order to obtain a monitoring perspective sequence.

10. The pipeline detection and evaluation system based on the Internet of Things according to claim 8, characterized in that: The question updating module determines a gaze position-command object comparison table based on the user's gaze movement history during the most recent monitoring period and the view angle change history of the two pipeline CCTV inspection robots during the most recent monitoring period, including: Align the gaze movement history with the view angle change history to obtain an aligned sequence; Classifying the aligned sequences into paired groups to obtain at least one paired group in each of the multiple categories; Count the total number of paired groups in each category; The categories whose total number exceeds the total threshold are regarded as the first target category, and the other remaining categories are regarded as the second target category; Extracting a first common landing point region and a first perspective-changing subject from the pairing group of the same first target category, and pairing different region positions in the first common landing point region with the first perspective-changing subject respectively to obtain a first pairing group; Expand the paired groups of the same second target category on the time axis to obtain the target time axis; If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective changing subject from the pairing group corresponding to the same second target category, and pair different area positions in the second same landing point area with the second perspective changing subject respectively to obtain a second pairing group; Determine a gaze position-command object comparison table based on the first pairing group and the second pairing group; The trigger conditions include: The type with more than N consecutive perspective change items on the target timeline belongs to the standard type; N is a positive integer; or, The types of the first and last perspective change items in at least one perspective change cycle on the target time axis are of standard type.

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