Pipeline detection and evaluation method and system based on Internet of Things

Abstract

The invention provides a pipeline detection and evaluation method and system based on the Internet of Things. The method comprises the following steps: acquiring detection data of urban pipelines detected by a plurality of pipeline detection devices through the Internet of Things; wherein the plurality of pipeline detection devices at least comprise a pipeline CCTV detection robot, a pipeline periscope and a pipeline sonar detector; evaluating the detection data, and determining the pipeline problem distribution of the urban pipeline; and distributing output pipeline problems. According to the pipeline detection and evaluation method and system based on the Internet of Things, the urban pipeline is detected through cooperation of various pipeline detection devices, the detection efficiency is improved, the detection range is expanded, the accuracy of the detection result is improved, various defects in the pipeline are comprehensively and accurately captured, and complex pipeline problems are effectively recognized; the degree of manual inspection of urban pipelines is reduced, the labor cost is reduced, and the personnel safety risk is avoided to a certain extent.

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 particularly important. As an important part of urban infrastructure, the urban pipeline system undertakes many important functions such as water, electricity, gas, and sewage discharge. The health of the pipeline directly affects the normal operation of the city and the daily life of residents, so regular inspection and evaluation of the pipeline has become very important.

[0003] Traditional pipeline inspection methods mainly rely on manual inspections and simple inspection equipment. These methods often have problems such as low efficiency, limited detection range, and inaccurate results. Manual inspections also have disadvantages such as high labor intensity, long inspection cycles, and personnel safety risks. In addition, traditional inspection equipment is difficult to fully and accurately capture various defects inside the pipeline, cannot effectively identify complex pipeline problems, and is difficult to adapt to the increasingly complex urban pipeline network.

[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 detection and evaluation method based on the Internet of Things, which detects urban pipelines in a coordinated manner through a variety of pipeline detection equipment, thereby improving the detection efficiency, expanding the detection range, and improving the accuracy of the detection results. It can comprehensively and accurately capture various defects inside the pipeline and effectively identify 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] Acquire detection data of urban pipelines detected by multiple pipeline detection devices through the Internet of Things; wherein the multiple pipeline detection devices at least include: 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 evaluation 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 tasks are sent 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 monitors and controls 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 of each first monitoring view in the monitoring view sequence, the supported control object, and the supported control instruction set; wherein the supported control object is 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 perspectives in the monitoring perspective sequence in sequence order;

[0020] Each time the 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 support control object corresponding to the first surveillance view based on which the access is made with 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;

[0021] and / or,

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

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

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

[0025] When the command object corresponding to the newly generated sight position of the user in the sight position-command object comparison table remains unchanged for more than a preset time, the unchanged command object is used as the target command 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 of each first monitoring perspective in the monitoring perspective sequence, supported control objects, and supported control instruction sets 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 of the traversed working progress; wherein the supported monitoring perspective is a continuous and unchanged local perspective when the fixed perspective changes due to the changes in the working postures of the two pipeline CCTV inspection robots;

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

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

[0033] Based on the key elements of the work progress, determine the support control objects and support control instruction sets;

[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 sight line movement history of the user in the most recent monitoring time and the view angle change history of the two pipeline CCTV detection robots in the most recent monitoring time includes:

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

[0037] Classifying the aligned sequences into paired groups to obtain paired groups with at least one of 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 number threshold are taken as the first target category, and the other remaining categories are taken as the second target category;

[0040] Extracting a first same landing point area and a first perspective changing subject from the pairing group of the same first target category, and pairing different area positions in the first same landing point area 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 a target time axis;

[0042] If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective change 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 change subject respectively to obtain a second pairing group;

[0043] Determine a sight 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 time axis belongs to the standard type; N is a positive integer;

[0046] or,

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

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

[0049] An Internet of Things acquisition module, 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 at least include: 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 tasks are sent 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 monitors and controls 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 updating 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 of each first monitoring view in the monitoring view sequence, the supported control object, and the supported control instruction set; wherein the supported control object is 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 perspectives in the monitoring perspective sequence in sequence order;

[0063] Each time the 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 support control object corresponding to the first surveillance view based on which the access is made with 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;

[0064] and / or,

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

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

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

[0068] When the command object corresponding to the newly generated sight position of the user in the sight position-command object comparison table remains unchanged for more than a preset time, the unchanged command object is used as the target command 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 of each first monitoring perspective in the monitoring perspective sequence, supported control objects, and supported control instruction sets 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 of the traversed working progress; wherein the supported monitoring perspective is a continuous and unchanged local perspective when the fixed perspective changes due to the changes in the working postures of the two pipeline CCTV inspection robots;

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

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

[0076] Based on the key elements of the work progress, determine the support control objects and support control instruction sets;

[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 sight line position-command object comparison table based on the sight line movement history of the user in the most recent monitoring time and the view angle change history of the two pipeline CCTV detection robots in the most recent monitoring time, including:

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

[0080] Classifying the aligned sequences into paired groups to obtain paired groups with at least one of 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 number threshold are taken as the first target category, and the other remaining categories are taken as the second target category;

[0083] Extracting a first same landing point area and a first perspective changing subject from the pairing group of the same first target category, and pairing different area positions in the first same landing point area 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 a target time axis;

[0085] If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective change 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 change subject respectively to obtain a second pairing group;

[0086] Determine a sight 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 time axis belongs to the standard type; N is a positive integer;

[0089] or,

[0090] The types of the first and last viewing angle change items in at least one viewing angle 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 partly become apparent from the description, or 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 in conjunction with 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 As shown, including:

[0098] S1. Acquire detection data of urban pipelines detected by multiple pipeline detection devices through the Internet of Things; wherein the multiple pipeline detection devices at least include: a pipeline CCTV detection 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 images of the inside of pipelines in real time 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 damaged areas that are not easy to see 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 distribution of pipeline problems, that is, the inspection data is evaluated to determine the distribution of pipeline problems in urban pipelines; finally, the distribution of pipeline problems is output.

[0102] This application uses a variety of pipeline detection equipment to collaboratively detect urban pipelines, thereby improving detection efficiency, expanding the detection range, and improving the accuracy of detection results. It can comprehensively and accurately capture various defects inside the pipeline and effectively identify 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 continue 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 two pipeline CCTV detection robots through the Internet of Things;

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

[0109] The distribution of pipeline problems reflects which positions in the urban pipelines require what kind of joint operation of the two pipeline CCTV inspection robots. Therefore, the system first plans joint operation tasks based on the distribution of pipeline problems, such as two pipeline CCTV inspection robots jointly performing pipeline inspection; the CCTV inspection robots use high-precision cameras to collect images inside the pipeline to identify problems such as cracks, corrosion, and water accumulation; when performing tasks, the system uses the Internet of Things technology to send task instructions to the two robots in real time and obtain their status information simultaneously; at this time, users can remotely monitor and control the two robots in real time through the Internet of Things platform, such as viewing camera images, adjusting robot positions, changing working modes, etc.; the Internet of Things connection not only supports real-time data upload, but also provides a control interface when users need it, ensuring that problems in the operation process can be quickly detected and corrected, thereby assisting users to monitor and control the two pipeline CCTV inspection robots through the Internet of Things; at the same time, data analysis is performed on the latest data collected by the robots to update the pipeline problem distribution and provide a basis for future task planning; making pipeline inspection operations more efficient and accurate, and reducing the risks caused by human operational errors or sudden environmental problems.

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

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

[0112] In S611, after accepting the joint operation task, the two pipeline CCTV inspection robots will plan the task progress timeline, on which there are multiple operation progresses set according to the execution sequence when the two pipeline CCTV inspection robots perform the joint operation task; the preset progress range may be within the next 10 minutes; when obtaining the real-time progress axis, a part of the progress axis within the preset progress range in the future is intercepted from the task progress timeline;

[0113] S612, based on the real-time progress axis, planning the monitoring view sequence and the view duration of each first monitoring view in the monitoring view sequence, supported control objects, and supported control instruction sets; 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 of the two pipeline CCTV detection robots. Based on the planning of how the user needs to monitor the two pipeline CCTV detection robots in the future, the monitoring perspective sequence and the perspective duration of each first monitoring perspective in the monitoring perspective sequence, the supported control objects, and the supported control instruction set can be planned; the monitoring perspective sequence includes the first monitoring perspective that the user needs to enter successively to monitor the two pipeline CCTV detection robots, the perspective duration is the time the user needs to maintain the first monitoring perspective to monitor the two pipeline CCTV detection robots, the supported control objects are the objects that can be controlled when the user maintains the first monitoring perspective, and the supported control instruction set includes the control instructions that can be implemented on the supported control objects when the user maintains the first monitoring perspective;

[0115] S613, connecting the user 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 connected to the two pipeline CCTV inspection robots based on the first monitoring perspective in sequence, 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 to the duration of the view corresponding to the first surveillance view based on the access, and the user is supported to control the support control object corresponding to the first surveillance view based on the access with at least one support control instruction in the support control instruction set corresponding to the first surveillance view based on the access; in S614, after the access, the first surveillance view is maintained to the duration of the corresponding view, and the corresponding support control instruction set is used for the user to control the corresponding support control object, so that the user can control the two pipeline CCTV inspection robots;

[0118] and / or,

[0119] S621. Based on the operation progress axis, plan the second monitoring view angle of two pipeline CCTV inspection robots;

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

[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 real-time total duration of the user monitoring through the second monitoring perspective;

[0123] S623, when the monitoring duration exceeds a preset duration threshold, based on the user's sight line movement history within the most recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots within the most recent monitoring duration, determine a sight line position-command object comparison table;

[0124] In S623, the preset duration threshold may be 5 minutes; the sight movement history is the position change history of the point of sight on the display interface of the mobile terminal display screen when the user monitors through the mobile terminal (mobile phone, computer, etc.) through the second monitoring perspective; the perspective change history is the position change history of the two pipeline CCTV detection robots under the second monitoring perspective; when the monitoring duration exceeds the duration threshold, it means that the sight movement history and the perspective change history can reflect where the user's sight position is located and represent which instruction object the user wants to control, and then determine the sight position-instruction object comparison table;

[0125] S624, when the command object corresponding to the newly generated sight line position of the user in the sight line position-command object comparison table remains unchanged for more than a preset time period, the unchanged command object is used as the target command object;

[0126] In S624, the preset duration may be 20 seconds. The newly generated sight line position of the user refers to the sight line position generated after the monitoring duration exceeds the preset duration threshold. The instruction object corresponding to the sight line position may be queried based on the sight line position-instruction object comparison table. When the instruction object remains unchanged for more than the preset duration, it further indicates that the user wants to control the instruction object, and the unchanged instruction object is used as the target instruction 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 realizes real-time monitoring and control of the robots by combining the progress of the joint operation tasks performed by the two pipeline CCTV detection robots through the Internet of Things technology. First, the system obtains the operation progress axis of the two robots within the future preset progress range through the Internet of Things, that is, the specific information of the robot task progress in the future period is intercepted 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 at the same time, to ensure that the robot operation progress and task execution are carried out smoothly according to the predetermined goals, realizes the monitoring and control assistance for the user, and greatly improves the humanization level.

[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 line of sight position-instruction object comparison table based on the user's line of sight position changes and the robot's motion history data, so as to intelligently judge the user's focus and automatically display 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, 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, based on the real-time progress axis, planning the monitoring perspective sequence and the perspective duration of each first monitoring perspective in the monitoring perspective sequence, supporting control objects, and supporting control instruction sets, 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 working posture changes and fixed viewing angles of the two pipeline CCTV inspection robots in the traversed working progress, 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 changes due to the changes in the working postures of the two pipeline CCTV inspection robots;

[0136] In S6122, the operation posture change is the operation posture change information of the two pipeline CCTV inspection robots when they perform the joint operation of the traversed operation progress, such as the direction, angle adjustment or action type of the robot, and the fixed perspective is the first-person perspective and the third-person perspective of the two pipeline CCTV inspection robots when they perform the joint operation of the traversed operation progress; when the pipeline CCTV inspection robot performs the operation posture change, it will affect the change of the supporting monitoring perspective, and the supporting monitoring perspective is a continuous and unchanged local perspective;

[0137] S6123, based on the supported monitoring perspectives and the key element set of the traversed operation progress, determine a first monitoring perspective; wherein all key elements in the continuously visible key element set under the first monitoring perspective;

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

[0139] S6124, determining the duration of the view based on the estimated duration of the progress of the traversed job progress;

[0140] In S6124, the operation progress has an estimated execution time of the progress, which 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 of the progress 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 the 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 operation progress based on the 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 operation posture changes of the robot and their fixed viewing angles to determine a supporting monitoring viewing angle that is not affected by the changes. This viewing angle should be able to continuously and effectively observe key operation elements and reflect dynamic changes in the operation to ensure that the viewing 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 point out the objects that need to be controlled and the control instruction set to ensure precise operation and intervention of the robot's operations; finally, all the 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 to 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, based on the user's sight line movement history within the most recent monitoring time and the view angle change history of the two pipeline CCTV inspection robots within the most recent monitoring time, determines the sight line position-command object comparison table, including:

[0148] S6231, performing time-series alignment on the sight movement history and the view angle change history to obtain an alignment sequence;

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

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

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

[0152] S6233, counting the total number of paired groups in each category;

[0153] S6234, taking the categories whose total number exceeds the total number threshold as the first target category, and the other remaining categories as the second target category;

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

[0155] S6235, extracting a first same landing point area and a first perspective changing subject from the pairing group of the same first target category, pairing different area positions in the first same landing point area with the first perspective changing subject respectively, to obtain a first pairing group;

[0156] In S6235, when the total number exceeds the total number threshold, the first same landing point area and the first perspective changing subject in the corresponding first target category are confirmed by the user multiple times: the user's sight is in the first same landing point 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; then different area positions in the first same landing point area are paired with the first perspective changing subject respectively to obtain a first pairing group;

[0157] S6236, expanding 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 pairing groups of the same second target category are expanded on the time axis, and when displayed, they are set at corresponding time positions on the time axis according to the generation time of one sight line movement history data and one viewing angle change history data in each pairing group;

[0159] S6237: If the target time axis meets the trigger condition, extract the second same landing point area and the second perspective change 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 change subject respectively to obtain a second pairing group;

[0160] In S6237, if the target time axis meets the trigger condition, extracting the second same landing point area and the second perspective change subject from the pairing group corresponding to the same second target category can be used to determine the second pairing group, and pairing different area positions in the second same landing point area with the second perspective change subject respectively to obtain the second pairing group;

[0161] S6238, determining a sight position-command object comparison table based on the first pairing group and the second pairing group;

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

[0163] The trigger conditions include:

[0164] The type with more than N consecutive viewpoint change items on the target timeline belongs to the standard type; N is a positive integer; the viewpoint change item refers to the historical data of sight movement in the pairing group of the second target category on the target timeline, and the standard type refers to the type of change action that the user wants to control when the CCTV robot changes its viewpoint, such as the autonomous action made by the CCTV robot according to the on-site environment;

[0165] or,

[0166] The types of the first and last perspective change items in at least one perspective change cycle on the target time axis belong to the standard type. If the types of more than N consecutive perspective change items belong to the standard type or the types of the first and last perspective change items in at least one perspective change cycle belong to the standard type, it means that the second same landing area and the second perspective change subject extracted from the pairing group corresponding to the same second target category can be used to determine the second pairing group; a perspective change cycle contains at least 3 perspective change items, and the time axis distance between two adjacent perspective change items in a perspective change cycle on the target time axis is less than 10 seconds.

[0167] The embodiment of the present invention constructs a sight position-command object comparison table based on the user's sight movement history and the view angle change history of two pipeline CCTV detection robots within a certain time range; firstly, through the time sequence alignment operation, the user's sight movement history is matched with the change history under the robot's view angle to ensure time alignment and form a pairing group; each pairing group consists of a sight position and a corresponding view angle change, and these pairing groups are classified into different categories; in the classification process, pairing groups of the same category have similar sight points and view angle change characteristics, thereby revealing the user's focus on specific areas and robot view angle changes; then, by counting the number of pairing groups in each category, the frequently appearing category (i.e., the first target category) is screened out, and the pairing relationship between the sight position and the view angle 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 groups along 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 cycle 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 cycle 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's instruction object; it can improve the accuracy and intelligence of the robot's operation and optimize the efficiency of the user's interaction with the robot.

[0169] The embodiment of the present invention provides a pipeline detection and evaluation system based on the Internet of Things, such as Figure 2 As 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 at least include: 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] The 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 tasks are sent 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 monitors and controls 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 the user 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 of each first monitoring view in the monitoring view sequence, the supported control object, and the supported control instruction set; wherein the supported control object is 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 perspectives in the monitoring perspective sequence in sequence order;

[0184] Each time the 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 support control object corresponding to the first surveillance view based on which the access is made with 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;

[0185] and / or,

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

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

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

[0189] When the command object corresponding to the newly generated sight position of the user in the sight position-command object comparison table remains unchanged for more than a preset time, the unchanged command object is used as the target command 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 updating module plans the monitoring perspective sequence and the perspective duration of each first monitoring perspective in the monitoring perspective sequence, the supported control objects, and the supported control instruction set 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 of the traversed working progress; wherein the supported monitoring perspective is a continuous and unchanged local perspective when the fixed perspective changes due to the changes in the working postures of the two pipeline CCTV inspection robots;

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

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

[0197] Based on the key elements of the work progress, determine the support control objects and support control instruction sets;

[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 sight position-command object comparison table based on the user's sight movement history within the most recent monitoring time and the view angle change history of the two pipeline CCTV detection robots within the most recent monitoring time, including:

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

[0201] Classifying the aligned sequences into paired groups to obtain paired groups with at least one of 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 number threshold are taken as the first target category, and the other remaining categories are taken as the second target category;

[0204] Extracting a first same landing point area and a first perspective changing subject from the pairing group of the same first target category, and pairing different area positions in the first same landing point area 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 a target time axis;

[0206] If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective change 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 change subject respectively to obtain a second pairing group;

[0207] Determine a sight 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 time axis belongs to the standard type; N is a positive integer;

[0210] or,

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

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

Claims

1. A pipeline detection and evaluation method based on the Internet of Things, characterized in that: include: Acquire detection data of urban pipelines from multiple pipeline detection devices through the Internet of Things; wherein the multiple pipeline detection devices at least include: 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.

2. The pipeline detection and evaluation method based on the Internet of Things according to claim 1, characterized in that: Also includes: 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; The joint operation tasks are sent to two pipeline CCTV inspection robots through the Internet of Things; When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user monitors and controls 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; Based on the newly detected data, the output pipeline problem distribution is updated.

3. The pipeline detection and evaluation method based on the Internet of Things as claimed in claim 2, characterized in that: The auxiliary user monitors and controls 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 of each first monitoring view in the monitoring view sequence, the supported control object, and the supported control instruction set; wherein the supported control object is 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 perspectives in the monitoring perspective sequence in sequence order; Each time the 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 with 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; and / or, Based on the work progress axis, plan the second monitoring view of two pipeline CCTV inspection robots; Through the Internet of Things, users can monitor the two pipeline CCTV inspection robots from a second monitoring perspective and accumulate the monitoring time; When the monitoring duration exceeds the preset duration threshold, a sight line position-command object comparison table is determined based on the user's sight line movement history within the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots within the recent monitoring duration; When the instruction object corresponding to the new sight position of the user in the sight position-instruction object comparison table remains unchanged for more than a preset time, 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 as claimed in claim 3 is characterized in that: The method of planning the monitoring view sequence and the view duration of each first monitoring view in the monitoring view sequence based on the real-time progress axis, supporting control objects, and supporting control instruction sets 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 of the traversed working progress; wherein the supported monitoring perspective is a continuous and unchanged local perspective when the fixed perspective changes due to the changes in the working postures of the two pipeline CCTV inspection robots; Determine a first monitoring perspective based on the key element set supporting the monitoring perspective and the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective; Determine the duration of the view based on the estimated execution time of the traversed job progress; Based on the key elements of the work progress, determine the support control objects and support control instruction sets; 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 as claimed in claim 3 is characterized in that: The method of determining the sight position-command object comparison table based on the sight movement history of the user in the most recent monitoring time and the view angle change history of the two pipeline CCTV detection robots in the most recent monitoring time includes: Align the gaze movement history with the view angle change history in time sequence to obtain an aligned sequence; Classifying the aligned sequences into paired groups to obtain paired groups with at least one of each of the multiple categories; Count the total number of paired groups in each category; The categories whose total number exceeds the total number threshold are taken as the first target category, and the other remaining categories are taken as the second target category; Extracting a first same landing point area and a first perspective changing subject from the pairing group of the same first target category, and pairing different area positions in the first same landing point area 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 a target time axis; If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective change 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 change subject respectively to obtain a second pairing group; Determine a sight 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 time axis belongs to the standard type; N is a positive integer; or, The types of the first and last viewing angle change items in at least one viewing angle 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, 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 at least include: 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.

7. The pipeline detection and evaluation system based on the Internet of Things according to claim 6, characterized in that: Also includes: Issue update module for: 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; The joint operation tasks are sent to two pipeline CCTV inspection robots through the Internet of Things; When two pipeline CCTV inspection robots perform joint operation tasks, the auxiliary user monitors and controls 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; Based on the newly detected data, the output pipeline problem distribution is updated.

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 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 of each first monitoring view in the monitoring view sequence, the supported control object, and the supported control instruction set; wherein the supported control object is 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 perspectives in the monitoring perspective sequence in sequence order; Each time the 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 with 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; and / or, Based on the work progress axis, plan the second monitoring view of two pipeline CCTV inspection robots; Through the Internet of Things, users can monitor the two pipeline CCTV inspection robots from a second monitoring perspective and accumulate the monitoring time; When the monitoring duration exceeds the preset duration threshold, a sight line position-command object comparison table is determined based on the user's sight line movement history within the recent monitoring duration and the view angle change history of the two pipeline CCTV inspection robots within the recent monitoring duration; When the instruction object corresponding to the new sight position of the user in the sight position-instruction object comparison table remains unchanged for more than a preset time, 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 updating module plans the monitoring perspective sequence and the perspective duration of each first monitoring perspective in the monitoring perspective sequence, the supported control objects, and the supported control instruction set 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 of the traversed working progress; wherein the supported monitoring perspective is a continuous and unchanged local perspective when the fixed perspective changes due to the changes in the working postures of the two pipeline CCTV inspection robots; Determine a first monitoring perspective based on the key element set supporting the monitoring perspective and the traversed operation progress; wherein all key elements in the continuously visible key element set under the first monitoring perspective; Determine the duration of the view based on the estimated execution time of the traversed job progress; Based on the key elements of the work progress, determine the support control objects and support control instruction sets; 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 sight position-command object comparison table based on the user's sight movement history within the most recent monitoring time and the view angle change history of the two pipeline CCTV detection robots within the most recent monitoring time, including: Align the gaze movement history with the view angle change history in time sequence to obtain an aligned sequence; Classifying the aligned sequences into paired groups to obtain paired groups with at least one of each of the multiple categories; Count the total number of paired groups in each category; The categories whose total number exceeds the total number threshold are taken as the first target category, and the other remaining categories are taken as the second target category; Extracting a first same landing point area and a first perspective changing subject from the pairing group of the same first target category, and pairing different area positions in the first same landing point area 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 a target time axis; If the target timeline meets the trigger condition, extract the second same landing point area and the second perspective change 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 change subject respectively to obtain a second pairing group; Determine a sight 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 time axis belongs to the standard type; N is a positive integer; or, The types of the first and last viewing angle change items in at least one viewing angle change cycle on the target time axis are of standard type.

Images