Factory inspection method and system based on three-dimensional virtualization

Abstract

The invention discloses a factory inspection method and system based on three-dimensional virtualization, and relates to the field of three-dimensional virtual factory inspection. According to the factory inspection method based on three-dimensional virtualization, three-dimensional position coordinates and point cloud data of monitoring equipment in a factory are obtained through an inspection robot and comprehensively analyzed, an equipment operation safety index is obtained, the equipment safety index is compared with a preset threshold value, and corresponding monitoring measures are taken according to a result. The method comprises the following steps: detecting a first device in a factory area, updating an initial factory area three-dimensional virtual model, identifying a second monitoring device after the first device is detected, obtaining a corresponding movement track, and controlling an inspection robot to detect and analyze the second monitoring device, and the inspection robot obtains multi-angle three-dimensional point cloud data of the monitoring devices in the factory area; according to the method and the system, the equipment state is monitored, and is combined with the equipment operation data for comprehensive analysis to obtain the operation safety of the monitoring equipment, so that accurate equipment state evaluation is provided, and the problems of missing detection and misjudgment are avoided.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional virtual factory inspection, and in particular to a factory inspection method and system based on three-dimensional virtualization. Background Art

[0002] 3D virtual technology has been widely used in many fields, especially in manufacturing, construction, urban management and other fields, and has important application value. 3D virtual technology collects, processes and visualizes 3D spatial data to build a virtual 3D model, which can accurately reflect the geometric shape, spatial layout and related information of the physical space, allowing users to view, analyze and simulate various phenomena and operations in the physical world in real time in a virtual environment. Inspection is an important measure to ensure the normal operation of equipment, systems and facilities. Traditional inspection methods usually rely on manual inspection, which has problems such as large manpower investment, low efficiency and high risk of omissions. With the advancement of industrial automation and informatization, more and more companies are beginning to use intelligent technology to optimize and upgrade inspection work to achieve more efficient and accurate management.

[0003] Prior art, such as a patent application with announcement number: CN108733755B, discloses an intelligent inspection method and system based on three-dimensional information of transmission lines, the steps of which are: obtaining three-dimensional data of the target object, extracting the model, position information and / or accessory component information of the target object of the transmission line; generating a three-dimensional model of the target object and / or accessory components using three-dimensional modeling technology; obtaining the local coordinate information of each target object in three-dimensional space according to the type information of each target object, and calculating the three-dimensional spatial position of the center of each target object; determining the safe inspection operation distance, determining the hovering coordinates in combination with the three-dimensional spatial position of the component to be inspected, formulating the inspection path and / or local flight strategy according to the direction of the transmission line and the hovering coordinates, and generating a flight inspection task.

[0004] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. First, it is difficult for the existing technology to cover all monitoring equipment and their operating conditions. Especially in a complex factory environment, the movement trajectory between equipment is not optimized enough, resulting in some areas not being covered or the inspection efficiency being low, which in turn affects the integrity and accuracy of the inspection task. In addition, the existing technology has a weak response to the real-time dynamic situation of multiple devices and multiple angles in the inspection area, and it is difficult to adjust the inspection strategy according to real-time changes. Finally, the existing technology lacks a dynamic and real-time judgment mechanism for the evaluation of equipment operation safety, thereby failing to promptly detect potential anomalies or equipment anomalies, which in turn leads to delays in inspection efficiency and equipment maintenance response. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a plant inspection method and system based on three-dimensional virtualization, which solves the problem that the prior art is difficult to cover all monitoring equipment and their operating conditions, especially in a complex plant environment, and the movement trajectory between equipment is insufficiently optimized, resulting in some areas not being covered or the inspection efficiency being low, thereby affecting the integrity and accuracy of the inspection task. In addition, the prior art has a weak response to the real-time dynamic conditions of multiple devices and multiple angles in the inspection area, and it is difficult to adjust the inspection strategy according to real-time changes. Finally, the prior art lacks a dynamic and real-time judgment mechanism for the evaluation of equipment operation safety, thereby failing to promptly detect potential anomalies or equipment anomalies, thereby resulting in delayed inspection efficiency and equipment maintenance response.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a factory inspection method based on three-dimensional virtualization, comprising the following steps: obtaining the three-dimensional position coordinates of several monitoring devices in the inspection factory area, and detecting and analyzing the first monitoring device in the inspection factory area through an inspection robot, that is, obtaining three-dimensional point cloud data from several angles, and performing fusion processing to obtain comprehensive three-dimensional point cloud data of the first monitoring device in the inspection factory area; and simultaneously obtaining the equipment operation data of the first monitoring device in the inspection factory area, and performing comprehensive analysis in combination with the comprehensive three-dimensional point cloud data to obtain the equipment operation safety index of the first monitoring device in the inspection factory area; judging and analyzing the equipment operation safety index of the first monitoring device in the inspection factory area with a preset equipment operation safety index threshold, and taking corresponding monitoring and processing measures based on the judgment and analysis results; and comparing the comprehensive three-dimensional point cloud data, equipment operation data, and equipment operation safety index of the first monitoring device in the inspection factory area with the equipment operation safety index stored in the database. The initial three-dimensional virtual model of the factory area is updated; after the detection of the first monitoring device in the inspection plant area is completed, several adjacent monitoring devices within the set range of the first monitoring device in the inspection plant area are identified, and the road condition image data between each adjacent monitoring device within the set range of the first monitoring device in the inspection plant area is obtained, and the road condition analysis is performed respectively to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and an arrangement analysis is performed, and the second monitoring device in the inspection plant area is selected based on the arrangement analysis result; the inspection robot is controlled to move based on the movement trajectory between the first monitoring device in the inspection plant area and the second monitoring device, and after the inspection robot arrives at the second monitoring device in the inspection plant area, the second monitoring device in the inspection plant area is detected and analyzed; after the detection of the second monitoring device in the inspection plant area is completed, the road condition analysis, arrangement analysis, and detection analysis steps are repeated until the remaining monitoring devices in the inspection plant area are inspected.

[0007] A plant inspection system based on three-dimensional virtualization, comprising: a data acquisition module, an equipment safety analysis module, an equipment monitoring and processing module, a data update module, a road condition analysis module, a detection and analysis module, and a duplicate detection module; the data acquisition module is used to acquire the three-dimensional position coordinates of several monitoring devices in the inspection plant area, and detect and analyze the first monitoring device in the inspection plant area through the inspection robot, that is, to acquire three-dimensional point cloud data of several angles, and perform fusion processing to obtain the comprehensive three-dimensional point cloud data of the first monitoring device in the inspection plant area; the equipment safety analysis module is used to synchronously acquire the equipment operation data of the first monitoring device in the inspection plant area, and perform comprehensive analysis in combination with the comprehensive three-dimensional point cloud data to obtain the equipment operation safety index of the first monitoring device in the inspection plant area; the equipment monitoring and processing module is used to judge and analyze the equipment operation safety index of the first monitoring device in the inspection plant area with a preset equipment operation safety index threshold, and take corresponding monitoring and processing measures based on the judgment and analysis results; the data update module is used to obtain the equipment operation safety index of the first monitoring device in the inspection plant area based on the comprehensive three-dimensional point cloud data, equipment operation data of the first monitoring device in the inspection plant area The initial three-dimensional virtual model of the factory area stored in the database is updated based on the data and the equipment operation safety index; the road condition analysis module is used to identify several adjacent monitoring devices within the set range of the first monitoring device in the inspection plant area after the detection of the first monitoring device in the inspection plant area is completed, and obtain the road condition image data between each adjacent monitoring device within the set range of the first monitoring device in the inspection plant area, and respectively perform road condition analysis to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and perform arrangement analysis, and select the second monitoring device in the inspection plant area based on the arrangement analysis result; the detection and analysis module is used to control the movement of the inspection robot based on the movement trajectory between the first monitoring device in the inspection plant area and the second monitoring device, and after the inspection robot arrives at the second monitoring device in the inspection plant area, perform detection and analysis on the second monitoring device in the inspection plant area; the repeated detection module is used to repeat the road condition analysis, arrangement analysis, and detection analysis steps after the detection of the second monitoring device in the inspection plant area is completed until the remaining monitoring devices in the inspection plant area are inspected.

[0008] The present invention has the following beneficial effects:

[0009] (1) This factory inspection method based on three-dimensional virtualization obtains multi-angle three-dimensional point cloud data of monitoring equipment in the factory through inspection robots, and combines it with equipment operation data for comprehensive analysis to obtain the operating safety of the monitoring equipment, thereby providing accurate equipment status assessment, thereby avoiding missed detection and misjudgment problems. In addition, the calculation and threshold judgment of the equipment operation safety index can timely discover potential safety hazards and help maintenance personnel quickly take measures to prevent accidents, thereby improving the accuracy of equipment monitoring and real-time response capabilities, thereby improving the operating safety of factory equipment.

[0010] (2) The factory inspection method based on three-dimensional virtualization analyzes road image data and the movement trajectory arrangement between equipment, so that the inspection robot can select the optimal inspection path, reduce repeated routes, and thus improve the inspection efficiency. It also reduces the ineffective driving and waiting time of the inspection robot in the factory by automatically selecting adjacent equipment and planning the movement trajectory, so that the inspection task can be completed in a shorter time, thereby significantly saving the inspection time and improving the inspection work efficiency.

[0011] (3) The 3D virtual factory inspection method updates the initial 3D virtual model of the factory in real time based on comprehensive 3D point cloud data, equipment operation data and safety index, thereby improving the accuracy and real-time performance of the 3D virtual model of the factory, and providing continuous and accurate support for the digital management of the factory. By continuously updating and optimizing the virtual model, managers can more intuitively understand the operating status of the equipment, the safety situation in the factory and its changing trends, assisting decision-making and planning. At the same time, the data accumulated over a long period of time can provide a more accurate basis for equipment maintenance, fault prediction and optimization management, thereby facilitating the transformation of factory management into digital and intelligent ones.

[0012] (4) The factory inspection system based on three-dimensional virtualization can realize the automation and intelligence of the inspection process by integrating multiple modules. With the support of the data acquisition module, the inspection robot can automatically collect the three-dimensional point cloud data and equipment operation data of the equipment, thereby improving the inspection efficiency. The equipment safety analysis module and the equipment monitoring and processing module can calculate the safety index of the equipment in real time and compare it with the preset threshold value, automatically judge the equipment status, and respond in time, thereby reducing the risk of delayed processing. In addition, the road condition analysis module and the detection analysis module can further reduce the ineffective driving of the inspection robot in the factory area by automatically planning the inspection path and optimizing the movement trajectory between equipment, thereby improving the intelligence level of path selection, and the repeated detection module ensures the continuity and stability of the system, making the entire inspection process more accurate and efficient, thereby enhancing the reliability and intelligent management level of the inspection process.

[0013] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flow chart of a factory inspection method based on three-dimensional virtualization.

[0015] Figure 2 The present invention is a block diagram of a plant inspection system based on three-dimensional virtualization. DETAILED DESCRIPTION

[0016] The embodiment of the present application solves the problem that the prior art is difficult to cover all monitoring equipment and their operating status, especially in a complex factory environment, where the movement trajectory between equipment is insufficiently optimized, resulting in some areas not being covered or the inspection efficiency being low, thereby affecting the integrity and accuracy of the inspection task. In addition, the prior art has a weak response to the real-time dynamic conditions of multiple devices and multiple angles in the inspection area, and it is difficult to adjust the inspection strategy according to real-time changes. Finally, the prior art lacks a dynamic and real-time judgment mechanism for the evaluation of equipment operation safety, thereby failing to promptly detect potential anomalies or equipment anomalies, thereby resulting in delayed inspection efficiency and equipment maintenance response.

[0017] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0018] First, the three-dimensional position coordinates of all monitoring devices in the factory area are obtained by the inspection robot. The inspection robot detects the first monitoring device, obtains three-dimensional point cloud data from multiple angles, and performs fusion processing to obtain the comprehensive three-dimensional point cloud data of the first monitoring device. At the same time, the inspection robot synchronously obtains the equipment operation data of the first monitoring device, and combines it with the comprehensive three-dimensional point cloud data for analysis, so as to obtain the safety index of the first monitoring device. Then, by comparing with the preset safety index threshold, a judgment analysis is performed, and appropriate monitoring and processing measures are taken according to the judgment result. Then, based on the comprehensive three-dimensional point cloud data, equipment operation data and safety index of the first monitoring device, the initial three-dimensional virtual model of the factory area stored in the database is updated, and after the first device is detected, all adjacent monitoring devices within the set range of the first monitoring device are identified and selected, and the road condition image data of the first monitoring device and each adjacent device within the set range are obtained, and the corresponding movement trajectory is analyzed. Finally, the second monitoring device is selected based on the arrangement analysis result. Finally, the movement trajectory between the first monitoring device and the second monitoring device controls the inspection robot to inspect other devices in sequence, and the remaining monitoring devices are subjected to the same detection, analysis and judgment processing until the inspection of all devices is completed.

[0019] See also Figure 1 , an embodiment of the present invention provides a technical solution: a factory inspection method based on three-dimensional virtualization, comprising the following steps: obtaining the three-dimensional position coordinates of several monitoring devices in the inspection factory area, and detecting and analyzing the first monitoring device in the inspection factory area through an inspection robot, that is, obtaining three-dimensional point cloud data from several angles, and performing fusion processing to obtain comprehensive three-dimensional point cloud data of the first monitoring device in the inspection factory area; and synchronously obtaining the equipment operation data of the first monitoring device in the inspection factory area, and performing comprehensive analysis in combination with the comprehensive three-dimensional point cloud data to obtain the equipment operation safety index of the first monitoring device in the inspection factory area; comparing the equipment operation safety index of the first monitoring device in the inspection factory area with a preset equipment operation safety index The threshold is judged and analyzed, and corresponding monitoring and processing measures are taken based on the judgment and analysis results (the specific process is: if the operation safety index of the first monitoring device in the inspection plant area is higher than the preset equipment operation safety index threshold, the first monitoring device will continue to be monitored; if the operation safety index of the first monitoring device in the inspection plant area is lower than or equal to the preset equipment operation safety index threshold, the first monitoring device will be marked as a device to be repaired, and a warning will be issued to relevant personnel, that is, relevant maintenance personnel will be dispatched to repair the device to be repaired); and the initial plant area 3D virtual model stored in the database is updated based on the comprehensive 3D point cloud data, equipment operation data, and equipment operation safety index of the first monitoring device in the inspection plant area New processing (in this implementation example, the coordinate system used in the initial three-dimensional virtual model of the factory area is the global coordinate system, and the initial three-dimensional virtual model of the factory area is a three-dimensional stereogram of each device in the initial state of the factory area (that is, it has just been established and the internal equipment has also been installed)); after the detection of the first monitoring device in the inspection plant area is completed, several adjacent monitoring devices within the set range of the first monitoring device in the inspection plant area are identified, and the road condition image data between each adjacent monitoring device within the set range of the first monitoring device in the inspection plant area (with the first monitoring device itself as the center) is obtained (obtained through the image acquisition device (that is, camera) on the inspection robot), and road condition analysis is performed separately to obtain the first monitoring device in the inspection plant area and The moving trajectory between each adjacent monitoring device within the set range is analyzed and arranged, and the second monitoring device in the inspection plant area is selected based on the arrangement analysis result; the inspection robot is controlled to move based on the moving trajectory between the first monitoring device and the second monitoring device in the inspection plant area, and after the inspection robot arrives at the second monitoring device in the inspection plant area, the second monitoring device in the inspection plant area is tested and analyzed (including fusion processing, comprehensive analysis, judgment analysis, and update processing); after the second monitoring device in the inspection plant area is tested, the road condition analysis, arrangement analysis, and detection analysis steps are repeated until the remaining monitoring devices in the inspection plant area are inspected (that is, all monitoring devices in the initial three-dimensional virtual model of the plant area have been updated).

[0020] In this implementation example, all three-dimensional position coordinates are based on the global coordinate system, that is, the global coordinate system takes the center point of the horizontal plane of the factory as the origin, the east direction of the factory as the X-axis, the south direction of the factory as the Y-axis, and the top direction of the factory building as the Z-axis, that is, perpendicular to the horizontal plane of the factory.

[0021] The three-dimensional point cloud data specifically refers to the three-dimensional coordinates of each measuring point of each component, the comprehensive three-dimensional point cloud data specifically refers to the comprehensive three-dimensional coordinates of each comprehensive measuring point of each component, the equipment operation data specifically refers to the equipment operation temperature value, the equipment operation vibration amplitude value, the equipment operation current value, the equipment operation voltage value, the equipment cumulative operation time value, the equipment operation lubricant viscosity value, the equipment operation lubricant contamination concentration value, the equipment operation lubricant acidity value, the equipment operation noise value, the equipment operation electric field strength value, the equipment operation magnetic field strength value, and the road condition image data specifically refers to the pixel value and two-dimensional coordinates of each pixel point in the road condition image (with the inflection point in the upper left corner of the inspection image as the origin, and the X-axis is the horizontal direction of the inspection image, and the Y-axis is the vertical direction of the inspection image).

[0022] The three-dimensional coordinate system is established with the current position of the acquisition device (such as a laser scanner installed on an inspection robot) as the origin, and the X-axis is the forward direction of the acquisition device (i.e., the orientation angle of the acquisition device), the Y-axis is the forward direction of the acquisition device perpendicular to the factory inspection robot, and the Z-axis is the direction of the acquisition device perpendicular to the ground.

[0023] The comprehensive three-dimensional coordinates are based on the global coordinate system, that is, the global coordinates take the center point of the horizontal plane of the factory as the origin, the east direction of the factory as the X-axis, the south direction of the factory as the Y-axis, and the top of the factory as the Z-axis, that is, perpendicular to the horizontal plane of the factory.

[0024] The operating temperature of the equipment is the average temperature of all components when the equipment is running, and the temperature of all components when the equipment is running can be obtained through temperature sensors. Excessive temperature will cause metal and other materials to expand, causing components to loosen, deform or crack. At the same time, electronic components and circuit boards will suffer insulation damage or short circuit due to high temperature, causing equipment shutdown or damage. Low temperature causes the lubricating oil or liquid of the equipment to become sticky, affecting the activity and transmission efficiency of components, and some materials may become brittle, causing equipment to crack or damage, thereby affecting the safe operation of the equipment.

[0025] The viscosity value of the equipment operating lubricant is the "heaviness" of the lubricant flow when the equipment is running, that is, the ability to resist flow. It can be obtained through the lubricant viscosity sensor. Too low viscosity cannot fully lubricate the equipment parts, resulting in excessive wear; while too high viscosity increases the burden on the system, reduces efficiency, and may cause overheating, thus affecting the safe operation of the equipment.

[0026] The equipment operation lubricant contamination concentration value includes the content value of pollutants (including but not limited to foreign matter, wear particles, and dust) in the lubricant when the equipment is running. It can be obtained through a particle counter. The pollutants in the lubricant will cause the internal wear of the equipment to increase and the lubrication effect to decrease. If the contamination concentration is too high, the lubricant cannot effectively reduce friction, resulting in component damage or jamming, thereby affecting the safe operation of the equipment.

[0027] The acidity value of the equipment operating lubricant is the content of acidic substances (including but not limited to octanoic acid, heptanoic acid, nitric acid, and sulfuric acid) in the lubricant when the equipment is running. It can be obtained through a PH sensor. The acidic substances in the lubricant can cause corrosion, especially for metal parts, resulting in corrosion and increased wear of the parts and loss of lubrication effect. If there are too many acidic substances, the corrosiveness of the lubricant will increase, which will accelerate the aging and abnormality of the equipment. A too low acidity value means that the lubricant lacks necessary additives, resulting in poor lubrication effect, thus affecting the safe operation of the equipment.

[0028] The equipment operation noise value refers to the noise value generated by the internal structure of the equipment during operation (including but not limited to those due to friction, vibration, airflow, and component impact), which can be obtained through acoustic sensors. The larger the noise value, the more it indicates that the equipment has mechanical abnormalities, loose parts, bearing wear or insufficient lubrication, which may affect the safe operation of the equipment.

[0029] Specifically, the specific steps for obtaining the comprehensive three-dimensional point cloud data of the first monitoring equipment in the inspection plant area are as follows: perform coordinate system conversion processing on the three-dimensional coordinates of each measurement point of each component of each angle of the first monitoring equipment in the inspection plant area, and obtain the converted three-dimensional coordinates of each measurement point of each component of each angle of the first monitoring equipment in the inspection plant area (that is, converted to the global coordinate system); identify and analyze the converted three-dimensional coordinates of each measurement point of each component of each angle of the first monitoring equipment in the inspection plant area, and obtain a number of overlapping measurement points and a number of non-overlapping measurement points of each component of the first monitoring equipment in the inspection plant area; mark the converted three-dimensional coordinates of each overlapping measurement point of each component of the first monitoring equipment in the inspection plant area and the converted three-dimensional coordinates of each non-overlapping measurement point as the comprehensive three-dimensional coordinates of each comprehensive measurement point of each component of the first monitoring equipment in the inspection plant area, that is, the comprehensive three-dimensional point cloud data.

[0030] Among them, the specific process of coordinate system conversion is: obtain the X-axis rotation angle, Y-axis rotation angle, Z-axis rotation angle of the acquisition device coordinate system relative to the global coordinate system and the X-axis coordinate value offset, Y-axis coordinate value offset, and Z-axis coordinate value offset of the origin of the acquisition device coordinate system relative to the origin of the global coordinate system, and establish a rotation matrix and a translation vector, and transform and analyze the three-dimensional coordinates of each measurement point of each component of each angle of the first monitoring device in the inspection plant area in combination with the rotation matrix and the translation vector to obtain the converted three-dimensional coordinates of each measurement point of each component of each angle of the first monitoring device in the inspection plant area.

[0031] In this implementation scheme, by converting the coordinates of all measurement points into a global coordinate system, the measurement data of equipment at different angles and positions can be unified into a coordinate system, so that all measurement data can be analyzed and compared in the same reference frame, avoiding the coordinate difference problem between different acquisition angles. Secondly, in the converted three-dimensional point cloud, the overlapping measurement points and non-overlapping measurement points are identified and analyzed to find errors in the measurement data. Finally, by measuring the equipment from multiple angles and performing coordinate conversion, more detailed information can be obtained from different perspectives, reducing the problem of incomplete or biased data due to the limitation of a single perspective, thereby improving the overall measurement accuracy. Moreover, by performing three-dimensional coordinate conversion on each measurement point of each component, a three-dimensional model of the equipment can be accurately constructed, which is helpful for the digital management of the equipment, and further supports subsequent fault diagnosis, maintenance planning and equipment optimization.

[0032] Specifically, the specific steps for obtaining the equipment operation safety index of the first monitoring equipment in the inspection plant area are as follows: obtain the reference three-dimensional coordinates corresponding to each comprehensive measurement point of each component of the first monitoring equipment in the inspection plant area, and perform comprehensive analysis in combination with the comprehensive three-dimensional coordinates to obtain the equipment deviation index of the first monitoring equipment in the inspection plant area; obtain the equipment operation rated power value, equipment operation critical temperature value, equipment operation critical vibration amplitude value, equipment expected operation time value, equipment operation lubricant reference viscosity value, equipment operation lubricant reference acidity value of the first monitoring equipment in the inspection plant area; conduct a comprehensive analysis of the equipment operation rated power value, equipment operation current value, and equipment operation voltage value of the first monitoring equipment in the inspection plant area to obtain the equipment load index of the first monitoring equipment in the inspection plant area; and conduct a comprehensive analysis of the equipment operation critical temperature value, equipment operation critical vibration amplitude value, equipment expected operation time value, equipment operation temperature value, equipment operation vibration amplitude value, equipment cumulative operation time value, and equipment load index of the first monitoring equipment in the inspection plant area to obtain the equipment abnormality index of the first monitoring equipment in the inspection plant area; The equipment operation lubricant viscosity value, equipment operation lubricant reference viscosity value, equipment operation lubricant contamination concentration value, equipment operation lubricant acid value, and equipment operation lubricant reference acid value are standardized (i.e., unit removal); the equipment operation lubricant viscosity value, equipment operation lubricant reference viscosity value, equipment operation lubricant contamination concentration value, equipment operation lubricant acid value, and equipment operation lubricant reference acid value of the first monitoring equipment in the inspection plant area after the standardized processing are comprehensively analyzed to obtain the equipment wear index of the first monitoring equipment in the inspection plant area; the equipment operation critical noise value, equipment operation critical electric field strength value, and equipment operation critical magnetic field strength value of the first monitoring equipment in the inspection plant area are obtained, and combined with the equipment operation noise value, equipment operation electric field strength value, and equipment operation magnetic field strength value of the first monitoring equipment in the inspection plant area, a comprehensive analysis is performed to obtain the equipment reliability index of the first monitoring equipment in the inspection plant area; the equipment offset index, equipment load index, equipment abnormality index, equipment wear index, and equipment reliability index of the first monitoring equipment in the inspection plant area are comprehensively analyzed to obtain the operation safety index of the first monitoring equipment in the inspection plant area.

[0033] Among them, the formulas for calculating the equipment deviation index and operation safety index of the first monitoring equipment in the inspection plant area are as follows: Among them, SbW is the equipment deviation index of the first monitoring equipment in the inspection plant area, (Xg mn ,Yg mn ,Zg mn ) is the comprehensive three-dimensional coordinate of the nth comprehensive measurement point of the mth component of the first monitoring equipment in the inspection plant area, (Xc mn,Yc mn ,Zc mn ) is the reference three-dimensional coordinate corresponding to the nth comprehensive measurement point of the mth component of the first monitoring equipment in the inspection plant area, m=1, 2, 3, ..., m0, m0 is the number of components, n=1, 2, 3, ..., n0, n0 is the number of comprehensive measurement points, SbA is the operation safety index of the first monitoring equipment in the inspection plant area, SbW is the equipment offset index of the first monitoring equipment in the inspection plant area, β1 is the equipment offset index stored in the database, SbG is the equipment anomaly index of the first monitoring equipment in the inspection plant area, β2 is the anomaly coefficient stored in the database, NbS is the equipment wear index of the first monitoring equipment in the inspection plant area, β3 is the wear coefficient stored in the database, WdX is the equipment reliability index of the first monitoring equipment in the inspection plant area, β4 is the anti-interference coefficient stored in the database, β1+β2+β3+β4=1, e is a natural constant, which is taken as 2.71 in this implementation example.

[0034] It needs to be explained that the specific process of β1, β2, β3, and β4 is: obtain the initial equipment offset index, initial equipment abnormality index, initial equipment wear index, and initial equipment reliability index of each monitoring device in the inspection plant under the initial detection (i.e., the first detection when the equipment in the plant is installed and starts running), and perform mean analysis to obtain the mean of the initial equipment offset index, the mean of the initial equipment abnormality index, the mean of the initial equipment wear index, and the mean of the initial equipment reliability index in the inspection plant under the initial detection, and perform sum analysis to obtain the safety sum value, and perform proportion analysis on the mean of the initial equipment offset index, the mean of the initial equipment abnormality index, the mean of the initial equipment wear index, and the mean of the initial equipment reliability index in the inspection plant under the initial detection, respectively, with the safety sum value, and use the proportion analysis results as the corresponding coefficients.

[0035] Furthermore, the initial equipment deviation index of each monitoring device in the inspection plant area under the initial detection is consistent with the equipment deviation index acquisition steps of the first monitoring device in the inspection plant area, and the calculation logic and calculation method are consistent.

[0036] The initial equipment abnormality index of each monitoring device in the inspected plant area under the initial detection is consistent with the equipment abnormality index acquisition steps of the first monitoring device in the inspected plant area, and has the same calculation logic and calculation method.

[0037] The initial equipment wear index of each monitoring device in the inspection plant area under the initial detection is consistent with the equipment wear index acquisition steps of the first monitoring device in the inspection plant area, and the calculation logic and calculation method are consistent.

[0038] The initial equipment reliability index of each monitoring device in the inspection plant area under the initial detection is consistent with the equipment reliability index of the first monitoring device in the inspection plant area in terms of the acquisition steps, calculation logic and calculation method.

[0039] The reference three-dimensional coordinates corresponding to each integrated measurement point of each component of the first monitoring equipment in the inspection plant can be obtained through the initial plant three-dimensional virtual model stored in the database.

[0040] The expected operating time value of the equipment is the service life of the equipment during operation, and can be obtained through the technical specifications of the equipment stored in the database.

[0041] The reference viscosity value of the equipment operating lubricant is the standard value of the viscosity of the lubricant during the operation of the equipment, and can be obtained from the instructions of the lubricant supplier stored in the database.

[0042] The reference acid value of the equipment operation lubricant is the standard acid value of the lubricant during the operation of the equipment, and can be obtained from the instructions of the lubricant supplier stored in the database.

[0043] The critical noise value of equipment operation is the maximum noise value that the equipment can accept during operation, and can be obtained through the technical specifications of the equipment stored in the database.

[0044] The critical electric field strength value of the equipment operation is the maximum electric field strength allowed when the equipment is in operation, and can be obtained through the technical specification manual of the equipment stored in the database.

[0045] The critical magnetic field strength value of the equipment operation is the maximum magnetic field strength allowed by the equipment during operation, and can be obtained through the technical specification manual of the equipment stored in the database.

[0046] The formulas for calculating the equipment load index, equipment abnormality index, equipment wear index, and equipment reliability index of the first monitoring equipment in the inspection plant are as follows:

[0047]

[0048] Among them, SbF is the equipment load index of the first monitoring equipment in the inspection plant area, SgL is the equipment operation rated power value of the first monitoring equipment in the inspection plant area, SdL is the equipment operation current value of the first monitoring equipment in the inspection plant area, SdY is the equipment operation voltage value of the first monitoring equipment in the inspection plant area, SbG is the equipment abnormality index of the first monitoring equipment in the inspection plant area, SwL is the equipment operation critical temperature value of the first monitoring equipment in the inspection plant area, SwD is the equipment operation temperature value of the first monitoring equipment in the inspection plant area, α1 is the temperature coefficient stored in the database, SzL is the equipment operation critical vibration amplitude value of the first monitoring equipment in the inspection plant area, SgD is the equipment operation critical vibration amplitude value of the first monitoring equipment in the inspection plant area, The equipment operation vibration amplitude value of the equipment, α2 is the vibration coefficient stored in the database, ScL is the expected equipment operation time value of the first monitoring equipment in the inspection plant area, ScD is the equipment cumulative operation time value of the first monitoring equipment in the inspection plant area, α3 is the operation time coefficient stored in the database, α4 is the load coefficient stored in the database, and α1+α2+α3+α4=1, NbS is the equipment wear index of the first monitoring equipment in the inspection plant area, NdZ′ is the equipment operation lubricant viscosity value of the first monitoring equipment in the inspection plant area after standardized processing, CkN′ is the equipment operation lubricant reference viscosity value of the first monitoring equipment in the inspection plant area after standardized processing, δ1 is the viscosity coefficient stored in the database, W rN′ is the equipment running lubricant contamination concentration value of the first monitoring equipment in the inspection plant after standardized processing, (it needs to be explained here that the equipment running lubricant contamination concentration value is 0 under ideal conditions. At this time, the lubricant does not contain any pollutants and has the best lubrication effect on the equipment, so there is no need to set a reference value), δ2 is the contamination coefficient stored in the database, SxH′ is the equipment running lubricant acid value of the first monitoring equipment in the inspection plant after standardized processing, CkS′ is the equipment running lubricant reference acid value of the first monitoring equipment in the inspection plant after standardized processing, δ3 is the acid coefficient stored in the database, δ1+δ2+δ3=1, WdX is the equipment reliability index of the first monitoring equipment in the inspection plant number, ZsQ is the equipment operation noise value of the first monitoring equipment in the inspection plant area, LzS is the equipment operation critical noise value of the first monitoring equipment in the inspection plant area, φ1 is the noise coefficient stored in the database, DcQ is the equipment operation electric field strength value of the first monitoring equipment in the inspection plant area, LdQ is the equipment operation critical electric field strength value of the first monitoring equipment in the inspection plant area, φ2 is the electric field coefficient stored in the database, CdQ is the equipment operation magnetic field strength value of the first monitoring equipment in the inspection plant area, LcQ is the equipment operation critical magnetic field strength value of the first monitoring equipment in the inspection plant area, φ3 is the magnetic field coefficient stored in the database, φ1+φ2+φ3=1, e is a natural constant, which is taken as 2.71 in this implementation example.

[0049] It needs to be explained that the specific acquisition process of α1, α2, α3, and α4 is: read the critical temperature value of the equipment operation, the critical vibration amplitude value of the equipment operation, the expected operating time value of the equipment, and the equipment load index of the first monitoring equipment in the inspection plant area, and perform standardization processing on the critical temperature value of the equipment operation, the critical vibration amplitude value of the equipment operation, the expected operating time value of the equipment, and the equipment load index of the first monitoring equipment in the inspection plant area after the standardization processing, and perform sum analysis to obtain the abnormal sum value of the first monitoring equipment in the inspection plant area, and perform proportion analysis on the critical temperature value of the equipment operation, the critical vibration amplitude value of the equipment operation, the expected operating time value of the equipment, and the equipment load index of the first monitoring equipment in the inspection plant area, respectively, and the abnormal sum value, and use the proportion analysis results as the corresponding coefficients.

[0050] The specific acquisition process of δ1, δ2, and δ3 is as follows: obtain the initial equipment operation lubricant viscosity value, the initial equipment operation lubricant contamination concentration value, and the initial equipment operation lubricant acidity value of each monitoring equipment in the inspection plant under the initial detection (i.e., the first detection when the equipment in the plant is installed and starts to run), and perform standardization processing; based on the results after standardization processing, perform mean analysis to obtain the average value of the initial equipment operation lubricant viscosity value, the average value of the initial equipment operation lubricant contamination concentration value, and the average value of the initial equipment operation lubricant acidity value in the inspection plant under the initial detection after standardization; and perform sum analysis to obtain the wear and tear value; perform proportion analysis on the average value of the initial equipment operation lubricant viscosity value, the average value of the initial equipment operation lubricant contamination concentration value, and the average value of the initial equipment operation lubricant acidity value in the inspection plant under the initial detection after standardization, respectively, and use the proportion analysis results as the corresponding coefficients.

[0051] The specific acquisition process of φ1, φ2, and φ3 is as follows: read the critical noise value of the equipment operation, the critical electric field strength value of the equipment operation, and the critical magnetic field strength value of the equipment operation of the first monitoring equipment in the inspection plant area, and perform normalization processing to obtain the normalized critical noise value of the equipment operation, the critical electric field strength value of the equipment operation, and the critical magnetic field strength value of the equipment operation of the first monitoring equipment in the inspection plant area, and perform sum analysis to obtain the stable sum value of the first monitoring equipment in the inspection plant area, and perform proportion analysis on the normalized critical noise value of the equipment operation, the critical electric field strength value of the equipment operation, and the critical magnetic field strength value of the equipment operation of the first monitoring equipment in the inspection plant area with the stable sum value, and use the proportion analysis results as the corresponding coefficients.

[0052] The specific implementation example of calculating the equipment load index, equipment abnormality index, equipment wear index, and equipment reliability index of the first monitoring equipment in the inspection plant area is as follows:

[0053] The equipment operating temperature value of the first monitoring device in the inspection plant area is: 65℃.

[0054] The equipment operating vibration amplitude value of the first monitoring device in the inspection plant area is: 1.5mm / s.

[0055] The equipment operating current value of the first monitoring device in the inspection factory area is: 4.5A.

[0056] The equipment operating voltage value of the first monitoring device in the inspection plant is: 380.0V.

[0057] The cumulative operating time of the first monitoring device in the inspected factory area is: 2341.4 hours.

[0058] The viscosity value of the equipment operating lubricant of the first monitoring equipment in the inspection plant is: 80.0cSt.

[0059] The equipment operation lubricating fluid pollution concentration value of the first monitoring equipment in the inspection plant is: 2%.

[0060] The acidity value of the equipment operating lubricating fluid of the first monitoring equipment in the inspection plant is: 6.5.

[0061] The equipment operating noise value of the first monitoring equipment in the inspection factory area is: 65.0dB.

[0062] The equipment operating electric field strength value of the first monitoring device in the inspection plant area is: 50.0V / m.

[0063] The equipment operating magnetic field strength value of the first monitoring device in the inspection plant area is: 60.0A / m.

[0064] The rated power value of the first monitoring device in the inspection plant area is: 5.0kW.

[0065] The critical operating temperature value of the first monitoring device in the inspection plant is: 85.0℃.

[0066] The critical vibration amplitude value of the first monitoring device in the inspection plant is: 5.0mm / s.

[0067] The expected operating time value of the first monitoring device in the inspection plant is: 20000.0 hours.

[0068] The reference viscosity value of the equipment operating lubricant of the first monitoring equipment in the inspection plant is: 100.0cSt.

[0069] The reference acidity value of the equipment operating lubricating fluid of the first monitoring equipment in the inspection plant is: 7.

[0070] The critical noise value of the first monitoring device in the inspection plant is: 100.0dB.

[0071] The critical electric field strength value of the first monitoring device in the inspection plant is: 100.0V / m.

[0072] The critical magnetic field strength value of the first monitoring device in the inspection plant is: 200.0A / m.

[0073] The viscosity value of the equipment operating lubricant of the first monitoring equipment in the inspection plant after standardization is: 0.45.

[0074] The reference viscosity value of the equipment operating lubricating fluid of the first monitoring equipment in the inspection plant after standardization is: 0.52.

[0075] The equipment operation lubricating fluid contamination concentration value of the first monitoring equipment in the inspection plant after standardized processing is: 0.36.

[0076] The acidity value of the equipment operating lubricating fluid of the first monitoring equipment in the inspection plant after standardized processing is: 0.54.

[0077] The reference acid value of the equipment operating lubricating fluid of the first monitoring equipment in the inspection plant after standardized processing is: 0.61.

[0078] The temperature coefficient stored in the database is: 0.33.

[0079] The vibration coefficient stored in the database is: 0.15.

[0080] The runtime factor stored in the database is: 0.21.

[0081] The load factor stored in the database is: 0.31.

[0082] The viscosity coefficient stored in the database is: 0.42.

[0083] The contamination coefficient stored in the database is: 0.23.

[0084] The acidity coefficient stored in the database is: 0.35.

[0085] The noise factor stored in the database is: 0.31.

[0086] The electric field coefficient stored in the database is: 0.35.

[0087] The magnetic field coefficient stored in the database is: 0.34.

[0088] Substitute the above data into the calculation formula of the equipment load index, equipment abnormality index, equipment wear index, and equipment reliability index of the first monitoring equipment in the inspection plant area.

[0089] Then the equipment load index of the first monitoring device in the inspection plant area = ln(1+(5000.0 / (4.5*380.0+1)))≈1.37.

[0090] The equipment abnormality index of the first monitoring device in the inspection plant area = 0.33*|(85.0-65.0) / 85.0|+0.15*|(5.0-1.5) / 5.0|+0.21*|ln(20000.0 / (|2341.4-20000.0|+1))| 1 / 2 +0.31*1.37 1 / 2 ≈0.57.

[0091] The equipment wear index of the first monitoring equipment in the inspection plant = 2.71 (0.42 *|0.45-0.52|+0.23*0.36+0.35*|0.54-0.61|) ≈1.12.

[0092] The equipment reliability index of the first monitoring device in the inspection plant area = 0.31*|1-(65 / 100)|+0.35*|1-(50.0 / 100.0)|+0.34*|1-(60 / 200.0)|≈0.52.

[0093] In this implementation scheme, the operating status of the equipment is fully monitored and analyzed through multiple indicators (such as equipment offset, load, anomaly, wear, reliability, etc.), so as to fully reveal the potential risks in the operation of the equipment, and then accurately predict the safety problems or failures that may occur in the equipment. Through data of different dimensions (such as temperature, vibration, lubricating fluid, noise, electric field, magnetic field, etc.), it is possible to capture a variety of abnormal conditions in the operation of the equipment, thereby avoiding potential risks that may be ignored by a single indicator. Secondly, by monitoring indicators such as the equipment offset index and load index, it is possible to promptly discover whether the equipment has position offset or load exceeds the standard, which helps to promptly discover these problems and carry out maintenance and adjustments in advance, avoiding production stagnation or safety accidents caused by sudden failures. In addition, by analyzing indicators such as the viscosity, acidity, and contamination concentration of the lubricating fluid, the degree of wear of the equipment is evaluated. At the same time, through standardized analysis of the lubricating fluid, the equipment's Wear conditions can be monitored, so that maintenance measures can be taken in advance to avoid premature damage of the equipment. Then, by monitoring key parameters such as noise, electric field strength and magnetic field strength of the equipment, it can be understood whether the equipment is affected by external interference or failure during operation, so as to judge the reliability of the equipment and its adaptability to changes in the external environment, and then evaluate whether the equipment can operate continuously and stably. Finally, by comprehensively analyzing the offset index, load index, abnormality index, wear index and reliability index of the equipment, a complete operation safety index is obtained, which provides a comprehensive evaluation standard for the overall safety of the equipment, so as to help decision makers fully understand the current safety status of the equipment, and make corresponding adjustments or early warnings based on the indicator, and can combine real-time monitoring data to continuously update the safety index of the equipment. By continuously tracking the operating status of the equipment, managers can dynamically adjust the equipment maintenance plan and perform preventive maintenance to avoid sudden failures.

[0094] Specifically, the specific steps for updating the initial three-dimensional virtual model of the factory area stored in the database based on the comprehensive three-dimensional point cloud data, equipment operation data, and equipment operation safety index of the first monitoring equipment in the inspected factory area are as follows: the comprehensive three-dimensional coordinates of each comprehensive measurement point of each component of the first monitoring equipment in the inspected factory area are replaced with the comprehensive three-dimensional coordinates of each comprehensive measurement point of each component of the monitoring equipment corresponding to the initial three-dimensional virtual model of the factory area stored in the database (because the coordinate system used by the first monitoring equipment and the initial three-dimensional virtual model of the factory are consistent, both are global coordinate systems, so the comprehensive three-dimensional coordinates can be directly replaced), and the three-dimensional coordinates of the replaced first monitoring equipment in the initial three-dimensional virtual model of the factory area stored in the database are obtained. Stereoscopic diagram; based on the equipment operating temperature value, equipment operating vibration amplitude value, equipment operating current value, equipment operating voltage value, equipment cumulative operating time value, equipment operating lubricant viscosity value, equipment operating lubricant pollution concentration value, equipment operating lubricant acid value, equipment operating noise value, equipment operating electric field strength value, equipment operating magnetic field strength value, and operating safety index of the first monitoring equipment in the inspected factory area, establish a data interaction panel for the first monitoring equipment after the initial factory area three-dimensional virtual model stored in the database is updated (that is, the above data is used as the label of the data interaction panel); and interactively link and update the data interaction panel of the first monitoring equipment after the initial factory area three-dimensional virtual model stored in the database with the three-dimensional stereoscopic diagram of the replaced first monitoring equipment.

[0095] Among them, the specific process of interactive link update is: interactively link the labels in the data interaction panel of the updated first monitoring device with the three-dimensional stereogram of the replaced first monitoring device in sequence, and display the labels in the data interaction panel of the updated first monitoring device directly above the three-dimensional stereogram of the replaced first monitoring device.

[0096] In this implementation scheme, by replacing the three-dimensional coordinate data of the monitoring equipment in real time, it can be ensured that the equipment position and shape in the virtual model are consistent with the actual situation of the factory area, thereby enhancing the authenticity and practicality of the model, and quickly discovering abnormalities or potential faults. Secondly, multiple operating data of the equipment (such as temperature, vibration, current, voltage, etc.) are visualized through an interactive panel, making complex operating status data intuitive and easy to understand, and the label of the data interactive panel is displayed directly above the three-dimensional model, so that the operating data of the equipment is directly associated with its physical location, thereby facilitating inspection personnel to quickly locate problem equipment and corresponding abnormal indicators. Finally, through the integration of multiple data (operating temperature, vibration amplitude, current, voltage, etc.) with the operation safety index, the operating status of the equipment can be fully evaluated, and potential problems can be discovered in a timely manner. The global coordinate system is used to ensure the correspondence and consistency between the three-dimensional model and the actual equipment, reducing the complexity of data processing.

[0097] Specifically, the specific steps for obtaining the moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are as follows: based on the edge detection algorithm, the pixel value of each pixel point in the road condition image between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is subjected to edge recognition analysis to obtain a number of edge obstacle pixel points in a number of obstacle areas between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; and the edge contour connection processing is performed on each edge obstacle pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range to obtain a number of obstacle pixel points in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; read each obstacle pixel point between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range. The two-dimensional coordinates of each obstacle pixel point in the area are obtained, and coordinate transformation processing is performed to obtain the three-dimensional position coordinates of each obstacle pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; and the three-dimensional position coordinates of the central pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are marked as the three-dimensional position coordinates of each obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; and the three-dimensional position coordinates of the first monitoring device in the inspection plant area and the three-dimensional position coordinates of each obstacle between each adjacent monitoring device within the set range and the preset early warning range (i.e., a circular range with the position of the obstacle as the dot and a radius of 0.2 meters) are comprehensively analyzed to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range.

[0098] Among them, the edge detection algorithm is the Canny edge detection algorithm, which calculates the gradient value of each pixel in the image to identify the area with the most drastic grayscale change in the image, that is, the edge. The Sobel operator is used to calculate the horizontal and vertical gradients of the image, and the gradient direction (that is, the direction of the edge) is calculated to identify the obvious edge area in the road condition image.

[0099] The specific process is: grayscale processing is performed on the pixel value of each pixel point in the road condition image between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, the grayscale pixel value of each pixel point in the road condition image between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is obtained, and the horizontal gradient, vertical gradient and gradient direction are calculated to identify the edge area in the road condition image.

[0100] The non-maximum suppression algorithm is used to remove unclear edges, that is, the pixel points with the largest gradient value are retained, and other pixels are removed to refine the edges.

[0101] Two thresholds are used: a low threshold and a high threshold. Gradient values ​​greater than the high threshold are considered "strong edges", while those below the low threshold are considered "non-edges". Edges between the two are considered "weak edges", which will be retained only when they are connected to strong edges.

[0102] Through edge connection, weak edges are connected to strong edges to obtain a continuous edge contour.

[0103] After the Canny algorithm, the result is a binary image, in which the white area represents the edge and the black area represents the non-edge. In the factory road condition image, the outline of the obstacle will be marked by a high-intensity edge.

[0104] Use the contour tracking algorithm to connect the pixel points on the edge of the obstacle to form a complete obstacle contour, that is, the obstacle area.

[0105] And the specific process of coordinate transformation is:

[0106] Obtain the internal parameter data of the shooting device of the road condition image between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range (the internal parameter data are focal length, two-dimensional coordinates of the principal point (the principal point is a point on the camera imaging plane, which represents the position of the intersection of the imaging optical axis and the imaging plane, and the horizontal center of the image (i.e., half of the width of the image) is the two-dimensional horizontal coordinate value of the principal point, and the vertical center of the image (i.e., half of the height of the image) is the two-dimensional vertical coordinate value of the principal point)) and the depth information value of each obstacle pixel point in each obstacle area;

[0107] An internal parameter matrix is ​​established based on the internal parameter data of the shooting device of the road condition image between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and the two-dimensional coordinates and depth information values ​​of each obstacle pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are analyzed to obtain the three-dimensional coordinates of each obstacle pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range (that is, the shooting device coordinate system is used as a reference, and the three-dimensional coordinates of the shooting device are used as the origin, and the XYZ axes are respectively the horizontal direction of the shooting device, the vertical direction of the shooting device, and the depth direction of the shooting device, that is, the lens direction);

[0108] And perform global coordinate transformation, and the steps of global coordinate transformation are consistent with coordinate system transformation.

[0109] The specific process of the comprehensive analysis is as follows: interpolation analysis is performed on the three-dimensional position coordinates of the first monitoring device in the inspection plant area and the three-dimensional position coordinates of each adjacent monitoring device within the set range to obtain the three-dimensional position coordinates of several moving points between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; and the three-dimensional position coordinates of each moving point between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range and the three-dimensional position coordinates of the adjacent obstacles are analyzed to obtain the distance value between each moving point and the adjacent obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; and the distance value between each moving point and the adjacent obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is compared and analyzed with the preset early warning range respectively to obtain several actual moving points between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; the three-dimensional position coordinates of the first monitoring device in the inspection plant area and the three-dimensional position coordinates of each actual moving point between each adjacent monitoring device within the set range and the three-dimensional position coordinates between each adjacent monitoring device are sequentially connected and processed to generate a moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range.

[0110] The specific process of comparison and analysis is as follows: if the distance value between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range and the adjacent obstacle is higher than the preset warning range (that is, not within the warning range), the moving point is marked as the actual moving point; if the distance value between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range and the adjacent obstacle is lower than or equal to the preset warning range (that is, within the warning range), the moving point is eliminated.

[0111] The formula for calculating the distance between each moving point and the adjacent obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is as follows: Among them, JzL hg is the distance between the gth moving point and the adjacent obstacle between the first monitoring device in the inspection plant area and the hth adjacent monitoring device within the set range, (Xj hg ,Yj hg ) is the three-dimensional position coordinate of the g-th moving point between the first monitoring device in the inspection plant and the h-th adjacent monitoring device within the set range (in this implementation example, the equipment and obstacles in the plant are in the same horizontal plane, so the Z-axis coordinate value in the three-dimensional position coordinates of the moving point and the obstacle is defaulted to 0 during the calculation process), (Xz hg ,Yz hg) is the three-dimensional position coordinate of the adjacent obstacle of the g-th moving point between the first monitoring device in the inspected factory area and the h-th adjacent monitoring device within the set range, h = 1, 2, 3, ..., h0, h0 is the number of adjacent monitoring devices, g = 1, 2, 3, ..., g0, g0 is the number of moving points.

[0112] In this implementation, through the Canny edge detection algorithm and contour tracking, the obstacle area in the road condition image can be accurately identified and extracted, and by refining and connecting these edges, the complete outline of the obstacle can be obtained, thereby effectively identifying the physical obstacles in the factory area, avoiding collision or deviation of the equipment during movement, and by using the internal reference data of the shooting equipment and the depth information of the obstacle area, the two-dimensional image data can be converted into a real three-dimensional spatial position, so as to accurately determine the real position of the obstacle, and provide reliable spatial data for subsequent path planning and equipment control. Secondly, by comprehensively analyzing the position of the obstacle and the preset warning range, the movement trajectory of the monitoring equipment can be updated in real time, so that the equipment can avoid Obstacle-free, thereby ensuring the safety and efficiency of the path during the inspection process, and through interpolation analysis of the three-dimensional position coordinates between the monitoring devices, the precise positions of several moving points between the devices can be generated, thereby ensuring that the generated trajectory is smoother and more coherent, avoiding path errors caused by gaps in sensors or data collection, and by calculating the distance between the inspection equipment and adjacent obstacles, it can accurately assess whether the current path will conflict with obstacles, thereby ensuring that the equipment avoids entering dangerous areas during driving. Finally, by integrating edge detection, depth information and three-dimensional coordinate conversion, the inspection work of the factory area can be made more intelligent and automated, thereby reducing the workload of manual inspections and improving the accuracy and timeliness of inspections.

[0113] Specifically, the specific steps for selecting the second monitoring device in the inspection plant area based on the comparison analysis results are as follows: comprehensively analyze the moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and obtain the moving distance of the moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; and arrange the moving distance of the moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range in ascending order (that is, arrange them from small to large according to the moving distance), and mark the adjacent monitoring device connected to the moving trajectory between the first monitoring device in the inspection plant area in the first sequence and the adjacent monitoring device within the set range (that is, the inspection device whose moving trajectory is connected to the other end of the first monitoring device) as the second monitoring device.

[0114] The specific process of obtaining the moving distance of the moving track between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is as follows:

[0115] The first monitoring device in the moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is marked as the first actual moving point of the corresponding moving trajectory, and the adjacent monitoring devices are marked as the last actual moving point of the corresponding moving trajectory. A comprehensive analysis is performed based on the three-dimensional position coordinates of each actual moving point in each moving trajectory to obtain the moving distance of the moving trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range.

[0116] The formula for calculating the moving distance of the moving track between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range is as follows: Among them, YzL h is the moving distance of the moving track between the first monitoring device in the inspection plant area and the hth adjacent monitoring device within the set range, (Xj h(g+1) ,Yj h(g+1) ) is the three-dimensional position coordinate of the g+1th moving point of the moving trajectory between the first monitoring device in the inspection plant and the hth adjacent monitoring device within the set range (in this implementation example, the devices in the plant are in the same horizontal plane, so the Z-axis coordinate value in the three-dimensional position coordinate of the moving point and the obstacle is defaulted to 0 during the calculation process), (Xj hg ,Yj hg ) are the three-dimensional position coordinates of the g-th moving point in the moving trajectory between the first monitoring device in the inspection plant area and the h-th adjacent monitoring device within the set range, h = 1, 2, 3, ..., h0, h0 is the number of adjacent monitoring devices, and g = 1, 2, 3, ..., g0, g0 is the number of moving points.

[0117] In this implementation scheme, by arranging the moving trajectory distances of all adjacent monitoring devices within the set range of the first monitoring device in ascending order, the monitoring device closest to the first monitoring device can be quickly identified, thereby reducing the search range and improving the selection efficiency, thereby ensuring that the selected second monitoring device is more in line with the inspection requirements in actual operation, and by calculating and comparing the actual moving distance of each moving trajectory, it can be ensured that the inspection device performs inspections along the shortest path, which helps to reduce the energy consumption and inspection time of the equipment and improve the inspection efficiency. Secondly, by determining the distance ranking between the second monitoring device and the first monitoring device, the order of the inspection path can be accurately controlled to avoid unnecessary path duplication or backtracking, thereby reducing time waste. Finally, selecting the monitoring device with the shortest distance as the second monitoring device can avoid excessive movement or detours between devices, reduce unnecessary waiting and calculation time, thereby improving overall efficiency, and enable each device to occupy an independent and non-overlapping path area during the inspection process, thereby improving equipment utilization.

[0118] See also Figure 2 The embodiment of the present invention provides a technical solution: a plant inspection system based on three-dimensional virtualization, including a data acquisition module, an equipment safety analysis module, an equipment monitoring and processing module, a data update module, a road condition analysis module, a detection and analysis module, and a repeated detection module; the data acquisition module is used to acquire the three-dimensional position coordinates of several monitoring devices in the inspection plant area, and detect and analyze the first monitoring device in the inspection plant area through the inspection robot, that is, to acquire three-dimensional point cloud data of several angles, and perform fusion processing to obtain the comprehensive three-dimensional point cloud data of the first monitoring device in the inspection plant area; the equipment safety analysis module is used to synchronously acquire the equipment operation data of the first monitoring device in the inspection plant area, and perform comprehensive analysis in combination with the comprehensive three-dimensional point cloud data to obtain the equipment operation safety index of the first monitoring device in the inspection plant area; the equipment monitoring and processing module is used to judge and analyze the equipment operation safety index of the first monitoring device in the inspection plant area with a preset equipment operation safety index threshold, and take corresponding monitoring and processing measures based on the judgment and analysis results; the data update module is used to obtain the equipment operation safety index of the first monitoring device in the inspection plant area based on the comprehensive three-dimensional point cloud data of the first monitoring device in the inspection plant area The initial three-dimensional virtual model of the factory area stored in the database is updated and processed based on the data, equipment operation data, and equipment operation safety index; a road condition analysis module is used to identify several adjacent monitoring devices within the set range of the first monitoring device in the inspection plant area after the first monitoring device in the inspection plant area is detected, and obtain the road condition image data between each adjacent monitoring device within the set range of the first monitoring device in the inspection plant area, and respectively perform road condition analysis to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and perform arrangement analysis, and select the second monitoring device in the inspection plant area based on the arrangement analysis result; a detection and analysis module is used to control the movement of the inspection robot based on the movement trajectory between the first monitoring device in the inspection plant area and the second monitoring device, and after the inspection robot arrives at the second monitoring device in the inspection plant area, perform detection and analysis on the second monitoring device in the inspection plant area; a repeated detection module is used to repeat the road condition analysis, arrangement analysis, and detection analysis steps after the second monitoring device in the inspection plant area is detected, until the remaining monitoring devices in the inspection plant area are inspected.

[0119] By integrating multiple modules, the inspection process can be automated and intelligent. With the support of the data acquisition module, the inspection robot can automatically collect the three-dimensional point cloud data and equipment operation data of the equipment, thereby improving the inspection efficiency. The equipment safety analysis module and the equipment monitoring and processing module can calculate the safety index of the equipment in real time, and compare it with the preset threshold, automatically judge the equipment status, and respond in time, thereby reducing the risk of delayed processing. In addition, the road condition analysis module and the detection analysis module can further reduce the ineffective driving of the inspection robot in the factory area through automatic planning of the inspection path and optimization of the movement trajectory between equipment, and improve the intelligence level of path selection. The repeated detection module ensures the continuity and stability of the system, making the entire inspection process more accurate and efficient, thereby enhancing the reliability and intelligent management level of the inspection process.

[0120] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0121] 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 factory inspection method based on three-dimensional virtualization, characterized in that: The following steps are involved: Obtain the three-dimensional position coordinates of several monitoring devices in the inspection plant area, and use the inspection robot to detect and analyze the first monitoring device in the inspection plant area, that is, obtain three-dimensional point cloud data from several angles, and perform fusion processing to obtain the comprehensive three-dimensional point cloud data of the first monitoring device in the inspection plant area; The equipment operation data of the first monitoring equipment in the inspection plant area is simultaneously obtained, and a comprehensive analysis is performed in combination with the comprehensive three-dimensional point cloud data to obtain the equipment operation safety index of the first monitoring equipment in the inspection plant area; The equipment operation safety index of the first monitoring equipment in the inspection plant area is compared with the preset equipment operation safety index threshold, and corresponding monitoring and processing measures are taken based on the judgment and analysis results; The initial plant area 3D virtual model stored in the database is updated based on the comprehensive 3D point cloud data, equipment operation data, and equipment operation safety index of the first monitoring device in the inspected plant area; After the detection of the first monitoring device in the inspection plant area is completed, a number of adjacent monitoring devices within the set range of the first monitoring device in the inspection plant area are identified, and the road condition image data between each adjacent monitoring device within the set range of the first monitoring device in the inspection plant area is obtained, and the road condition analysis is performed respectively to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and the permutation analysis is performed, and the second monitoring device in the inspection plant area is selected based on the permutation analysis result; The inspection robot is controlled to move based on the moving trajectory between the first monitoring device and the second monitoring device in the inspection plant area, and after the inspection robot reaches the second monitoring device in the inspection plant area, the second monitoring device in the inspection plant area is detected and analyzed; After the inspection of the second monitoring device in the inspection plant area is completed, the road condition analysis, arrangement analysis, and detection analysis steps are repeated until the remaining monitoring devices in the inspection plant area are inspected.

2. The three-dimensional virtual factory inspection method according to claim 1 is characterized in that: The three-dimensional point cloud data specifically includes the three-dimensional coordinates of each measuring point of each component, the comprehensive three-dimensional point cloud data specifically includes the comprehensive three-dimensional coordinates of each comprehensive measuring point of each component, the equipment operation data specifically includes the equipment operation temperature value, the equipment operation vibration amplitude value, the equipment operation current value, the equipment operation voltage value, the equipment cumulative operation time value, the equipment operation lubricant viscosity value, the equipment operation lubricant contamination concentration value, the equipment operation lubricant acidity value, the equipment operation noise value, the equipment operation electric field strength value, and the equipment operation magnetic field strength value; the road condition image data specifically includes the pixel value and two-dimensional coordinates of each pixel point in the road condition image.

3. The three-dimensional virtual factory inspection method according to claim 2 is characterized in that: The specific steps to obtain the comprehensive 3D point cloud data of the first monitoring device in the inspection plant are as follows: Performing coordinate system conversion processing on the three-dimensional coordinates of each measuring point of each component of each angle of the first monitoring device in the inspection plant area to obtain the converted three-dimensional coordinates of each measuring point of each component of each angle of the first monitoring device in the inspection plant area; Respectively identify and analyze the converted three-dimensional coordinates of each measuring point of each component of each angle of the first monitoring equipment in the inspection plant area, and obtain a number of overlapping measuring points and a number of non-overlapping measuring points of each component of the first monitoring equipment in the inspection plant area; The converted three-dimensional coordinates of each overlapping measurement point of each component of the first monitoring equipment in the inspection plant area and the converted three-dimensional coordinates of each non-overlapping measurement point are marked as the comprehensive three-dimensional coordinates of each comprehensive measurement point of each component of the first monitoring equipment in the inspection plant area, that is, comprehensive three-dimensional point cloud data.

4. The three-dimensional virtual factory inspection method according to claim 3 is characterized in that: The specific steps to obtain the equipment operation safety index of the first monitoring equipment in the inspected plant area are as follows: Obtain the reference three-dimensional coordinates corresponding to each comprehensive measurement point of each component of the first monitoring equipment in the inspection plant area, and perform comprehensive analysis in combination with the comprehensive three-dimensional coordinates to obtain the equipment deviation index of the first monitoring equipment in the inspection plant area; Obtain the equipment operating rated power value, equipment operating critical temperature value, equipment operating critical vibration amplitude value, equipment expected operating time value, equipment operating lubricant reference viscosity value, and equipment operating lubricant reference acidity value of the first monitoring equipment in the inspection plant area; Comprehensively analyze the equipment operating rated power value, equipment operating current value, and equipment operating voltage value of the first monitoring equipment in the inspection plant area to obtain the equipment load index of the first monitoring equipment in the inspection plant area; A comprehensive analysis is performed on the critical temperature value of the equipment operation, the critical vibration amplitude value of the equipment operation, the expected operation time value of the equipment, the operation temperature value of the equipment, the operation vibration amplitude value of the equipment, the accumulated operation time value of the equipment, and the equipment load index of the first monitoring equipment in the inspection plant area to obtain the equipment abnormality index of the first monitoring equipment in the inspection plant area; Standardize the equipment running lubricant viscosity value, equipment running lubricant reference viscosity value, equipment running lubricant contamination concentration value, equipment running lubricant acid value, and equipment running lubricant reference acid value of the first monitoring equipment in the inspection plant area; Comprehensively analyze the equipment operating lubricant viscosity value, equipment operating lubricant reference viscosity value, equipment operating lubricant contamination concentration value, equipment operating lubricant acid value, and equipment operating lubricant reference acid value of the first monitoring equipment in the inspection plant area after standardization to obtain the equipment wear index of the first monitoring equipment in the inspection plant area; Obtain the critical noise value, the critical electric field strength value, and the critical magnetic field strength value of the first monitoring device in the inspection plant area, and perform a comprehensive analysis based on the equipment operation noise value, the equipment operation electric field strength value, and the equipment operation magnetic field strength value of the first monitoring device in the inspection plant area to obtain the equipment reliability index of the first monitoring device in the inspection plant area; Comprehensively analyze the equipment deviation index, equipment load index, equipment abnormality index, equipment wear index, and equipment reliability index of the first monitoring equipment in the inspection plant area to obtain the operation safety index of the first monitoring equipment in the inspection plant area; Among them, the formulas for calculating the equipment deviation index and operation safety index of the first monitoring equipment in the inspection plant area are as follows: Among them, SbW is the equipment deviation index of the first monitoring equipment in the inspection plant area, (Xg mn ,Yg mn ,Zg mn ) is the comprehensive three-dimensional coordinate of the nth comprehensive measurement point of the mth component of the first monitoring equipment in the inspection plant area, (Xc mn ,Yc mn ,Zc mn ) is the reference three-dimensional coordinate corresponding to the nth comprehensive measurement point of the mth component of the first monitoring equipment in the inspection plant area, m=1, 2, 3, …, m0, m0 is the number of components, n=1, 2, 3, …, n0, n0 is the number of comprehensive measurement points, SbA is the operation safety index of the first monitoring equipment in the inspection plant area, SbW is the equipment offset index of the first monitoring equipment in the inspection plant area, β1 is the equipment offset index stored in the database, SbG is the equipment anomaly index of the first monitoring equipment in the inspection plant area, β2 is the anomaly coefficient stored in the database, NbS is the equipment wear index of the first monitoring equipment in the inspection plant area, β3 is the wear coefficient stored in the database, WdX is the equipment reliability index of the first monitoring equipment in the inspection plant area, β4 is the anti-interference coefficient stored in the database, β1+β2+β3+β4=1, and e is a natural constant.

5. The three-dimensional virtual factory inspection method according to claim 4 is characterized in that: The formulas for calculating the equipment load index, equipment abnormality index, equipment wear index, and equipment reliability index of the first monitoring equipment in the inspection plant are as follows: Among them, SbF is the equipment load index of the first monitoring equipment in the inspection plant area, SgL is the equipment operation rated power value of the first monitoring equipment in the inspection plant area, SdL is the equipment operation current value of the first monitoring equipment in the inspection plant area, SdY is the equipment operation voltage value of the first monitoring equipment in the inspection plant area, SbG is the equipment abnormality index of the first monitoring equipment in the inspection plant area, SwL is the equipment operation critical temperature value of the first monitoring equipment in the inspection plant area, SwD is the equipment operation temperature value of the first monitoring equipment in the inspection plant area, α1 is the temperature coefficient stored in the database, SzL is the equipment operation critical vibration value of the first monitoring equipment in the inspection plant area The vibration amplitude value, SgD is the equipment operation vibration amplitude value of the first monitoring equipment in the inspection plant area, α2 is the vibration coefficient stored in the database, ScL is the expected equipment operation time value of the first monitoring equipment in the inspection plant area, ScD is the equipment cumulative operation time value of the first monitoring equipment in the inspection plant area, α3 is the operation time coefficient stored in the database, α4 is the load coefficient stored in the database, and α1+α2+α3+α4=1, NbS is the equipment wear index of the first monitoring equipment in the inspection plant area, NdZ′ is the equipment operation lubricant viscosity value of the first monitoring equipment in the inspection plant area after standardized processing, and CkN′ is the equipment wear index of the first monitoring equipment in the inspection plant area after standardized processing. The reference viscosity value of the equipment running lubricant of the first monitoring equipment in the factory area, δ1 is the viscosity coefficient stored in the database, WrN′ is the pollution concentration value of the equipment running lubricant of the first monitoring equipment in the inspection factory area after standardization, δ2 is the pollution coefficient stored in the database, SxH′ is the acid value of the equipment running lubricant of the first monitoring equipment in the inspection factory area after standardization, CkS′ is the reference acid value of the equipment running lubricant of the first monitoring equipment in the inspection factory area after standardization, δ3 is the acid coefficient stored in the database, δ1+δ2+δ3=1, WdX is the equipment reliability index of the first monitoring equipment in the inspection factory area, and ZsQ is the inspection reliability index of the first monitoring equipment in the inspection factory area. The equipment operation noise value of the first monitoring equipment in the inspection plant area, LzS is the critical noise value of the equipment operation of the first monitoring equipment in the inspection plant area, φ1 is the noise coefficient stored in the database, DcQ is the electric field strength value of the equipment operation of the first monitoring equipment in the inspection plant area, LdQ is the critical electric field strength value of the equipment operation of the first monitoring equipment in the inspection plant area, φ2 is the electric field coefficient stored in the database, CdQ is the magnetic field strength value of the equipment operation of the first monitoring equipment in the inspection plant area, LcQ is the critical magnetic field strength value of the equipment operation of the first monitoring equipment in the inspection plant area, φ3 is the magnetic field coefficient stored in the database, φ1+φ2+φ3=1, and e is a natural constant.

6. The three-dimensional virtual factory inspection method according to claim 2 is characterized in that: The specific steps for updating the initial plant area 3D virtual model stored in the database based on the comprehensive 3D point cloud data, equipment operation data, and equipment operation safety index of the first monitoring device in the inspection plant area are as follows: Replace the comprehensive three-dimensional coordinates of each comprehensive measurement point of each component of the first monitoring equipment in the inspection plant with the comprehensive three-dimensional coordinates of each comprehensive measurement point of each component of the monitoring equipment corresponding to the initial plant three-dimensional virtual model stored in the database, and obtain a three-dimensional stereogram of the replaced first monitoring equipment in the initial plant three-dimensional virtual model stored in the database; Based on the equipment operating temperature value, equipment operating vibration amplitude value, equipment operating current value, equipment operating voltage value, equipment cumulative operating time value, equipment operating lubricant viscosity value, equipment operating lubricant pollution concentration value, equipment operating lubricant acid value, equipment operating noise value, equipment operating electric field strength value, equipment operating magnetic field strength value, and operating safety index of the first monitoring equipment in the inspected plant area, a data interaction panel for the first monitoring equipment after the initial plant area three-dimensional virtual model stored in the database is established; The data interaction panel of the first monitoring device after the initial plant area three-dimensional virtual model stored in the database is updated and the three-dimensional stereogram of the replaced first monitoring device is interactively linked and updated.

7. The three-dimensional virtual factory inspection method according to claim 2 is characterized in that: The specific steps to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are as follows: Based on the edge detection algorithm, edge recognition analysis is performed on the pixel value of each pixel point in the road condition image between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and a plurality of edge obstacle pixel points of a plurality of obstacle areas between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are obtained; And perform edge contour connection processing on each edge obstacle pixel point of each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and obtain a number of obstacle pixel points in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; Read the two-dimensional coordinates of each obstacle pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and perform coordinate transformation processing to obtain the three-dimensional position coordinates of each obstacle pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; The three-dimensional position coordinates of the central pixel point in each obstacle area between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are marked as the three-dimensional position coordinates of each obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; A comprehensive analysis is performed on the three-dimensional position coordinates of the first monitoring device in the inspected factory area, the three-dimensional position coordinates of each obstacle between each adjacent monitoring device within the set range, and the preset warning range to obtain the movement trajectory between the first monitoring device in the inspected factory area and each adjacent monitoring device within the set range.

8. The three-dimensional virtual factory inspection method according to claim 7 is characterized in that: The specific process of comprehensive analysis is as follows: Perform interpolation analysis on the three-dimensional position coordinates of the first monitoring device in the inspection plant area and the three-dimensional position coordinates of each adjacent monitoring device within the set range to obtain the three-dimensional position coordinates of several moving points between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; The three-dimensional position coordinates of each moving point between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range and the three-dimensional position coordinates of the adjacent obstacles are analyzed to obtain the distance value between each moving point and the adjacent obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range; The distance values ​​between each moving point and the adjacent obstacle between the first monitoring device in the inspection plant area and each adjacent monitoring device in the set range are compared and analyzed with the preset warning range to obtain a number of actual moving points between the first monitoring device in the inspection plant area and each adjacent monitoring device in the set range; The three-dimensional position coordinates of the first monitoring device in the inspection plant area, the three-dimensional position coordinates of each actual moving point between each adjacent monitoring device within the set range, and the three-dimensional position coordinates between each adjacent monitoring device are connected and processed in sequence to generate a movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range.

9. The factory inspection method based on three-dimensional virtualization according to claim 1 is characterized in that: The specific steps for selecting the second monitoring device in the inspection plant based on the comparison analysis results are as follows: Comprehensively analyze the movement track between the first monitoring device in the inspection plant area and each adjacent monitoring device in the set range to obtain the movement distance of the movement track between the first monitoring device in the inspection plant area and each adjacent monitoring device in the set range; The moving distances of the moving tracks between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range are arranged in ascending order, and the adjacent monitoring device connected to the moving track between the first monitoring device in the inspection plant area in the first sequence and the adjacent monitoring device within the set range is marked as the second monitoring device.

10. A plant inspection system based on three-dimensional virtualization, using the plant inspection method based on three-dimensional virtualization according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, equipment safety analysis module, equipment monitoring and processing module, data update module, road condition analysis module, detection and analysis module, and duplicate detection module; The data acquisition module is used to obtain the three-dimensional position coordinates of several monitoring devices in the inspection plant area, and to detect and analyze the first monitoring device in the inspection plant area through the inspection robot, that is, to obtain three-dimensional point cloud data from several angles, and to perform fusion processing to obtain comprehensive three-dimensional point cloud data of the first monitoring device in the inspection plant area; The equipment safety analysis module is used to synchronously obtain the equipment operation data of the first monitoring equipment in the inspection plant area, and perform comprehensive analysis in combination with the comprehensive three-dimensional point cloud data to obtain the equipment operation safety index of the first monitoring equipment in the inspection plant area; The equipment monitoring and processing module is used to judge and analyze the equipment operation safety index of the first monitoring equipment in the inspection plant area and the preset equipment operation safety index threshold, and take corresponding monitoring and processing measures based on the judgment and analysis results; The data updating module is used to update the initial plant area three-dimensional virtual model stored in the database based on the comprehensive three-dimensional point cloud data of the first monitoring device in the inspected plant area, the equipment operation data, and the equipment operation safety index; The road condition analysis module is used to identify several adjacent monitoring devices within the set range of the first monitoring device in the inspection plant area after the first monitoring device in the inspection plant area has finished detecting, and obtain the road condition image data between each adjacent monitoring device within the set range of the first monitoring device in the inspection plant area, and perform road condition analysis respectively to obtain the movement trajectory between the first monitoring device in the inspection plant area and each adjacent monitoring device within the set range, and perform permutation analysis, and select the second monitoring device in the inspection plant area based on the permutation analysis result; The detection and analysis module is used to control the movement of the inspection robot based on the movement trajectory between the first monitoring device and the second monitoring device in the inspection plant area, and after the inspection robot reaches the second monitoring device in the inspection plant area, perform detection and analysis on the second monitoring device in the inspection plant area; The repeated detection module is used to repeat the road condition analysis, arrangement analysis, and detection analysis steps after the detection of the second monitoring device in the inspection plant area is completed, until the remaining monitoring devices in the inspection plant area are inspected.

Images