AR inspection method and device applied to waste incineration station and electronic equipment

Abstract

The invention provides an AR inspection method and device applied to a waste incineration station and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring in-station temperature data for a waste incineration station sent by a temperature acquisition sensor; constructing a thermodynamic diagram according to the in-station temperature data; scanning data, sent by the laser radar, of the waste incineration station are obtained; according to the scanning data, constructing an obstacle distribution map; acquiring environment data of the waste incineration station, wherein the environment data comprises gas concentration data and vibration data; generating a dynamic environment model of the waste incineration station based on the thermodynamic diagram, the obstacle distribution diagram, the gas concentration data and the vibration data; according to the dynamic environment model, an inspection strategy of inspection personnel corresponding to the waste incineration station is planned, and the inspection personnel wear AR glasses; and displaying the dynamic environment model and the inspection strategy to inspection personnel through AR glasses. By implementing the technical scheme provided by the invention, the inspection efficiency of the waste incineration station can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to an AR inspection method, device and electronic equipment applied to a waste incineration station. Background Art

[0002] In industrial scenarios such as waste incineration stations, inspection is an important part of ensuring stable operation of equipment and preventing safety hazards.

[0003] At present, the traditional inspection method mainly relies on manual observation and manual recording. The inspection process is limited by the subjective judgment and visual range of personnel, and it is impossible to fully perceive the real-time changes in the complex environment. Especially in waste incineration stations, the environmental characteristics of high temperature, high humidity and toxic gases pose a higher challenge to manual inspection, because the harsh environmental conditions can easily cause fatigue or omissions of inspectors, further reducing the efficiency of inspection. Therefore, this inefficient traditional inspection method is no longer conducive to the inspection of waste incineration stations.

[0004] Therefore, there is an urgent need for an AR inspection method, device and electronic equipment for use in waste incineration stations. Summary of the invention

[0005] The present application provides an AR inspection method, device and electronic equipment applied to a waste incineration station, which is convenient for improving the inspection efficiency of the waste incineration station.

[0006] In a first aspect of the present application, an AR inspection method applied to a waste incineration station is provided, the method comprising: obtaining in-station temperature data for the waste incineration station sent by a temperature acquisition sensor; constructing a heat map based on the in-station temperature data; obtaining scanning data for the waste incineration station sent by a laser radar; constructing an obstacle distribution map based on the scanning data; obtaining environmental data of the waste incineration station, the environmental data including gas concentration data and vibration data; generating a dynamic environment model of the waste incineration station based on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data; planning an inspection strategy for an inspector corresponding to the waste incineration station based on the dynamic environment model, the inspector wearing AR glasses; and displaying the dynamic environment model and the inspection strategy to the inspector through the AR glasses.

[0007] By adopting the above technical solutions, combined with the data of temperature sensors, lidar, gas sensors and vibration sensors, the environment and equipment status of the waste incineration station can be fully monitored to avoid the limitations of a single data source. The heat map constructed by temperature data can intuitively display the high-temperature areas in the station, helping inspectors to quickly identify high-risk areas around the incinerator. Using lidar scanning, garbage accumulation, equipment failure points or other physical obstacles can be accurately located to avoid unexpected obstructions in the inspection path. Combined with gas sensors and vibration sensors, potential gas leakage risks and equipment abnormalities can be dynamically identified to further improve detection accuracy and coverage. Through comprehensive perception of data, inspection strategies are formulated based on actual conditions, significantly reducing the omissions of problems caused by manual observation and improving the accuracy of inspections. The dynamic environment model generated based on multidimensional data can fully reflect the real-time status of the waste incineration station, including temperature distribution, obstacle location, gas diffusion and equipment operation abnormalities. The dynamic environment model can be updated in real time with the input of new data to ensure the immediacy and accuracy of the inspection strategy. The heat map and obstacle distribution map generated by the model can intuitively mark high-risk areas to assist inspectors in completing their tasks more safely and efficiently. The construction of dynamic models makes up for the inability of traditional inspections to perceive changes in real time, and helps to promptly discover and deal with potential hidden dangers. Based on the dynamic environmental model, the inspection strategy can automatically plan the inspection route according to the real-time risks, avoiding high temperature, gas leakage and physical obstacle areas, thereby reducing the time and energy waste of inspectors. Inspectors can intuitively see the heat map, obstacle distribution map and risk markers through AR glasses, which greatly reduces the difficulty of understanding and judgment. With the help of automated data analysis and intelligent planning, the dependence on the experience of inspectors is greatly reduced, promoting the standardization and intelligence of waste incineration station inspections. Therefore, it is easy to improve the inspection efficiency of waste incineration stations.

[0008] Optionally, constructing a thermal map based on the temperature data in the station specifically includes: obtaining a two-dimensional image of the regional surface temperature sent by a thermal imaging sensor in a preset area in the waste incineration station; if it is determined that the area corresponding to the target area is greater than a preset area threshold, obtaining temperature point data sent by an infrared sensor corresponding to the target area, wherein the target area is any one of a plurality of preset areas; using an inverse distance weighted interpolation algorithm to insert the temperature point data into continuously distributed temperature data; based on the continuously distributed temperature data, combined with the two-dimensional image, removing redundant areas to obtain target temperature data; using a three-dimensional grid modeling algorithm to map the target temperature data to spatial grid units to obtain the thermal map.

[0009] By adopting the above technical solution, a two-dimensional image is obtained through a thermal imaging sensor, and an infrared sensor is called for refined collection under specific conditions. This method can not only fully cover the area, but also provide high-precision temperature data for key areas, ensuring that the details of the thermal map are rich and accurate. The interpolation algorithm converts discrete temperature point data into a continuous temperature distribution, effectively filling the gaps between the sampled data and ensuring the spatial consistency and reliability of the thermal data. Combined with the two-dimensional image, irrelevant areas are removed to reduce the ineffective burden of data processing. This mechanism realizes the dynamic allocation of resources, which not only reduces the system computing cost, but also increases the attention to key areas and optimizes the overall efficiency. By fusing the two-dimensional image with the temperature point data, the visual expression ability of the thermal map is improved, making the generated thermal map more intuitive and clear. Based on the target temperature data, a three-dimensional spatial grid is constructed, and the temperature data is mapped to the spatial unit, so that the thermal distribution has a three-dimensional sense and can intuitively display the spatial changes of the temperature. This processing method can not only show the distribution of temperature, but also reflect the changes in temperature with spatial dimensions such as height and depth, which significantly improves the analysis ability of the thermal map in complex scenes.

[0010] Optionally, constructing an obstacle distribution map based on the scanning data specifically includes: determining point cloud data corresponding to static objects and dynamic objects in the waste incineration station based on the scanning data; using an ICP algorithm to splice multiple point cloud data to obtain a point cloud map; performing height-based point cloud segmentation on the point cloud map to obtain a feature point cloud between the ground and the obstacle; generating a bounding box for the obstacle based on the feature point cloud, and determining the obstacle distribution map.

[0011] By adopting the above technical solution, the point cloud data of static objects and dynamic objects are extracted respectively through scanning data, which can clearly distinguish fixed obstacles from objects that may change, facilitating subsequent processing and identification. The ICP algorithm can efficiently and accurately stitch multiple point cloud data into a complete map, reduce the error caused by changes in scanning angle or position, and ensure the high accuracy and integrity of the point cloud map. Based on the feature point cloud obtained by point cloud segmentation, an accurate bounding box can be generated for each obstacle. This process not only improves the accuracy of obstacle identification, but also provides a clear reference for subsequent path planning and safety assessment. The spliced ​​point cloud map combined with the bounding box of the obstacle can not only show the spatial layout of the station, but also intuitively display the specific location and shape of the obstacle. It provides data support for subsequent inspection path planning, equipment maintenance and risk assessment. The obstacle distribution map can not only identify fixed obstacles in the station, but also be updated in real time when dynamic objects appear, ensuring that the inspection personnel can obtain accurate obstacle information at any time, thereby avoiding accidental collisions or delayed responses. The obstacle distribution map can also provide patrol personnel with the best patrol route planning plan to ensure that high-risk obstacle areas or areas with hidden dangers are avoided, thereby improving patrol efficiency and safety.

[0012] Optionally, the dynamic environment model of the waste incineration station is generated based on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data, specifically including: performing data processing on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data to obtain data to be fused, the data processing including denoising, filtering, time alignment and normalization; according to the data to be fused, superimposing the obstacle detection data with the high temperature area of ​​the thermal imaging to generate a spatial feature map; according to the data to be fused, combining the gas diffusion model and wind direction information, predicting the diffusion path and source position of the harmful gas; according to the data to be fused, combining the vibration abnormality position and the temperature gradient, marking the high-risk equipment position; based on the spatial feature map, the diffusion path, the source position and the high-risk equipment position, constructing the dynamic environment model, the dynamic environment model is used to represent the real-time environmental status and risk distribution of the waste incineration station.

[0013] By adopting the above technical solutions, the environmental status in the waste incineration station can be fully and accurately expressed by processing and fusing the thermal map, obstacle distribution map, gas concentration data and vibration data. This data fusion provides a multi-dimensional perspective, so that the dynamic environment model can more realistically reflect the actual situation on site, rather than just the results of a single sensor. The obstacle detection data is superimposed on the high-temperature area of ​​the thermal imaging to generate a spatial feature map. This method can intuitively display the temperature distribution and spatial position of obstacles in different areas, providing detailed spatial information for subsequent decision-making and actions. Combining gas concentration data, gas diffusion model and wind direction information, the diffusion path and source location of harmful gases can be predicted. This helps to identify potential sources of harmful gas leakage in a timely manner and predict the possible range of its spread, so as to take more targeted preventive measures. By combining vibration data and temperature gradients, the location of equipment that may be at high risk can be marked. This analysis method that combines equipment vibration with temperature changes can effectively identify areas that may pose risks due to equipment failure or abnormal operation, and provide inspection personnel with equipment or areas to focus on. The quality of input data is ensured through steps such as denoising, filtering, time alignment and normalization. These data processing methods can eliminate noise and inconsistencies in sensor data, making subsequent analysis and fusion more reliable and accurate.

[0014] Optionally, the inspection strategy for the inspection personnel corresponding to the waste incineration station is planned based on the dynamic environment model, specifically including: obtaining the personnel status data of the inspection personnel, the personnel status data including workload, current location and skill level; determining high-risk areas based on the dynamic environment model; generating inspection paths based on the high-risk areas; assigning inspection tasks to the inspection personnel based on the workload, the current location and the skill level, and determining the target inspection personnel who performs the inspection tasks; and generating the inspection strategy based on the inspection path and the target inspection personnel.

[0015] By adopting the above technical solution, by obtaining the workload, current location and skill level of the inspectors, it is possible to ensure that the task allocation is more personalized and reasonable. Dynamic consideration of the status of personnel and the requirements of the inspection tasks can avoid waste of resources and overwork, and improve the work efficiency and safety of personnel. Through intelligent task allocation, inspectors can complete more suitable tasks according to their own status and capabilities, reduce the randomness of task allocation, and improve the work enthusiasm and overall efficiency of personnel. According to the real-time analysis of high-risk areas in the dynamic environment model, ensure that high-risk areas are inspected first. By incorporating high-risk areas into the inspection strategy, it can effectively prevent inspectors from ignoring potential danger points and improve the safety of inspections. According to the environmental data in the dynamic environment model, the generated inspection path can effectively prevent inspectors from entering high-risk areas or areas with hidden dangers, while ensuring that all areas that need to be inspected are covered. Optimizing the path can reduce inspection time and cost and improve work efficiency. Combined with the status of the inspectors and the task requirements, the system can assign the most suitable personnel to specific tasks to avoid work inefficiency or safety risks caused by unsuitable personnel status. The accuracy of task allocation and optimization of inspection routes not only improves the work efficiency of individual inspectors, but also makes team collaboration smoother and avoids coordination problems caused by improper personnel allocation.

[0016] Optionally, the method also includes: obtaining the real-time location data of the target patrol personnel; based on the high-risk area, if it is determined that the real-time location data overlaps with the high-risk area, generating risk warning data; marking the risk warning data in the dynamic environment model, and displaying it to the target patrol personnel through the AR glasses.

[0017] By adopting the above technical solution, by obtaining the real-time location data of the target inspector, it is possible to determine in real time whether the inspector is approaching or entering a high-risk area. When the inspector enters a high-risk area, the system will immediately generate risk reminder data and display it through AR glasses to ensure that the inspector can detect potential safety hazards at the first time. This real-time feedback mechanism can enhance the inspectors' sensitivity to environmental risks, allowing them to respond immediately and avoid unnecessary safety accidents. By monitoring the overlap between the inspectors' real-time location and the high-risk area, the system can effectively prevent the inspectors from entering the dangerous area, thereby reducing the probability of accidents. The system can not only monitor the inspectors' location in real time, but also dynamically adjust the alarm according to the predicted high-risk area. In this way, even if the inspectors have not fully entered the high-risk area, the system can also remind them in advance to ensure that the inspectors have enough time to make decisions. By automatically reminding the inspectors at the boundary of the dangerous area, it effectively prevents people from entering the high-risk area and reduces the potential risk of injury and accidents. Through real-time location data, the system can quickly identify whether the inspectors are in a high-risk area, quickly generate risk reminders and notify personnel. This quick response allows inspectors to make action decisions in a short period of time, improving the speed of emergency response during inspection work.

[0018] Optionally, the method also includes: determining an initial task level according to the inspection duration corresponding to the inspection task; obtaining working status data of the AR glasses; determining the working duration according to the working status data; correcting the initial task level by the working duration to obtain a target task level; and assigning inspection tasks to the inspection personnel according to the target task level.

[0019] By adopting the above technical solution, by determining the initial task level according to the duration of the inspection task, the priority of the task can be pre-set according to the complexity or importance of the task. The work status data of the inspectors, especially the working hours, can reflect their workload. If the inspectors have been working at high intensity for a long time, their work fatigue may affect the execution efficiency and accuracy of the inspection tasks. By monitoring and analyzing the working hours, the level of the task can be dynamically adjusted to avoid letting the fatigued inspectors continue to undertake too heavy tasks. By dynamically adjusting the task level, the inspectors can avoid the decline in efficiency or safety problems due to fatigue or excessive workload, and ensure that the task allocation is more scientific and reasonable. Correcting the task level according to the working hours of the inspectors can avoid too many high-intensity tasks being assigned to people who work for a long time. Matching the load of the inspection task with the working hours of the inspectors helps to avoid fatigue accumulation and thus improve work efficiency. By correcting the task level, it is possible to avoid all tasks being concentrated on a few people, thereby reducing the safety risks caused by excessive concentration of work. By correcting and dynamically allocating the inspection tasks, the system can reasonably arrange tasks according to the workload of the inspectors and avoid the waste of personnel resources.

[0020] In a second aspect of the present application, an AR inspection device applied to a waste incineration station is provided, the AR inspection device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire the station temperature data for the waste incineration station sent by the temperature acquisition sensor; the processing module is used to construct a thermal map based on the station temperature data; the acquisition module is also used to acquire the scanning data for the waste incineration station sent by the laser radar; the processing module is also used to construct an obstacle distribution map based on the scanning data; the acquisition module is also used to acquire the environmental data of the waste incineration station, the environmental data including gas concentration data and vibration data; the processing module is also used to generate a dynamic environment model of the waste incineration station based on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data; the processing module is also used to plan the inspection strategy of the inspection personnel corresponding to the waste incineration station according to the dynamic environment model, and the inspection personnel wear AR glasses; the processing module is also used to display the dynamic environment model and the inspection strategy to the inspection personnel through the AR glasses.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By combining the data of temperature sensors, lidar, gas sensors and vibration sensors, the environment and equipment status of the waste incineration station can be monitored in all directions, avoiding the limitations of a single data source. The heat map constructed by temperature data intuitively displays the high-temperature areas in the station, helping inspectors quickly identify high-risk areas around the incinerator. Using lidar scanning, garbage accumulation, equipment failure points or other physical obstacles can be accurately located to avoid unexpected obstructions in the inspection path. Combining gas sensors and vibration sensors can dynamically identify potential gas leakage risks and equipment abnormalities, further improving detection accuracy and coverage. Through comprehensive perception of data, inspection strategies are formulated based on actual conditions, significantly reducing the omission of problems caused by manual observation and improving the accuracy of inspections. The dynamic environment model generated based on multidimensional data can fully reflect the real-time status of the waste incineration station, including temperature distribution, obstacle location, gas diffusion and equipment operation abnormalities. The dynamic environment model can be updated in real time with the input of new data to ensure the immediacy and accuracy of the inspection strategy. The heat map and obstacle distribution map generated by the model can intuitively mark high-risk areas to help inspectors complete their tasks more safely and efficiently. The construction of dynamic models makes up for the inability of traditional inspections to perceive changes in real time, and helps to promptly discover and deal with potential hidden dangers. Based on the dynamic environmental model, the inspection strategy can automatically plan the inspection route according to the real-time risks, avoiding high temperature, gas leakage and physical obstacle areas, thereby reducing the time and energy waste of inspectors. Inspectors can intuitively see the heat map, obstacle distribution map and risk markers through AR glasses, which greatly reduces the difficulty of understanding and judgment. With the help of automated data analysis and intelligent planning, the dependence on the experience of inspectors is greatly reduced, promoting the standardization and intelligence of waste incineration station inspections. Therefore, it is easy to improve the inspection efficiency of waste incineration stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a flow chart of an AR inspection method applied to a waste incineration station provided in an embodiment of the present application; Figure 2 Another flowchart of an AR inspection method applied to a waste incineration station provided in an embodiment of the present application; Figure 3 A schematic diagram of a module of an AR inspection device applied to a waste incineration station provided in an embodiment of the present application; Figure 4A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] In industrial scenarios such as waste incineration stations, inspection is a key link in ensuring the normal operation of equipment and preventing safety hazards.

[0030] At present, traditional inspection methods mainly rely on manual visual observation and manual recording. However, this method has many limitations, especially in terms of the subjective judgment and visual range of inspectors, which cannot fully perceive the real-time changes in complex environments. Especially in waste incineration plants, manual inspections face greater challenges due to the presence of environmental factors such as high temperature, high humidity, and toxic gases. These harsh environments are not only prone to fatigue and negligence of inspectors, but may also significantly reduce inspection efficiency. Therefore, traditional inefficient inspection methods can no longer meet the safety and monitoring needs of waste incineration plants.

[0031] In order to solve the above technical problems, the present application provides an AR inspection method applied to a waste incineration station. Figure 1 , Figure 1 A flowchart of an AR inspection method applied to a waste incineration station provided in an embodiment of the present application. The AR inspection method is applied to a server and includes steps S110 to S180, which are as follows: S110: Acquire the temperature data in the waste incineration station sent by the temperature collection sensor.

[0032] Specifically, a server refers to a computer system for receiving, storing and processing data. In an embodiment of the present application, the main function of the server is to receive data from a temperature sensor and to further analyze or display it. Temperature acquisition sensors are installed at key locations of a waste incineration station, such as a furnace, flue, feed area, etc., for real-time monitoring of temperature changes. The temperature sensor can be a thermocouple, an infrared sensor or other type of temperature sensor, and appropriate equipment is selected as needed to ensure accurate temperature acquisition. The temperature data within the station refers to the temperature data at various locations within the waste incineration station. These data may include temperature changes in different areas, which can reflect the operating status of the equipment, potential overheating problems, etc. Through the network or other communication methods, the sensor transmits the collected temperature data to the server in real time. The server analyzes the received data, monitors the temperature conditions within the station in real time, and helps personnel understand the operating status of the equipment, detect anomalies, etc.

[0033] S120. Construct a thermal map based on the temperature data in the station.

[0034] Specifically, the server first needs to collect temperature data from various areas within the waste incineration station. These data usually come from temperature sensors installed in different locations, such as infrared sensors or thermal imaging sensors. Temperature sensors can monitor temperature changes within the station in real time, especially in areas with high temperatures and concentrated heat sources. It is crucial to obtain these data. Temperature data is obtained through temperature sensors within the station. These data generally exist in the form of two-dimensional or three-dimensional point data, and each point represents a location and a corresponding temperature value. These temperature data need to be processed to generate a thermal map.

[0035] In a possible implementation, a thermal map is constructed based on the temperature data in the station, specifically including: obtaining a two-dimensional image of the regional surface temperature sent by a thermal imaging sensor in a preset area in the waste incineration station; if it is determined that the area corresponding to the target area is greater than a preset area threshold, obtaining temperature point data sent by an infrared sensor corresponding to the target area, where the target area is any one of a plurality of preset areas; using an inverse distance weighted interpolation algorithm to insert the temperature point data into continuously distributed temperature data; based on the continuously distributed temperature data, combined with the two-dimensional image, removing redundant areas to obtain target temperature data; using a three-dimensional grid modeling algorithm to map the target temperature data to spatial grid units to obtain a thermal map.

[0036] Specifically, thermal imaging sensors can capture the temperature distribution on the surface of an object and generate a two-dimensional thermal image showing the temperature values ​​at different locations. For example, thermal imaging sensors at a waste incineration station may be located around the equipment, through which a two-dimensional image of the surface temperature of different areas can be obtained. Each pixel value in the two-dimensional image corresponds to a temperature data, showing the temperature distribution of a certain area. The target area is the preset area of ​​the waste incineration station, which is a pre-determined monitoring area. These areas may include the furnace, storage area, discharge pipe, etc. The temperature distribution in some areas may be large, or the change is more significant, exceeding the preset area threshold, so more detailed data is required. Infrared sensors can provide more accurate single-point temperature data, which is usually used to supplement thermal imaging data, especially for more detailed analysis of the target area. The temperature data provided by the infrared sensor is sent in the form of temperature points, which can help fill in the gaps in the thermal imaging image or provide more accurate temperature data. Inverse distance weighted interpolation is a spatial interpolation method. Its basic idea is that the closer the known data point is to the target point, the greater the interpolation weight of the target point. Through the IDW algorithm, discrete temperature point data can be converted into continuous temperature distribution data. The purpose is to fill the scattered data obtained by the infrared sensor into a continuous temperature field. In this way, the originally discrete temperature data will cover the entire area. Combining the thermal image and the interpolated temperature data, some redundant areas can be identified and removed, where the temperature changes are small or unimportant and do not require further attention. By removing redundancy, the validity of the data can be improved and the areas with more significant temperature changes can be highlighted. After removing redundancy, the target temperature data obtained is more accurate temperature distribution information, which can clearly reflect the thermal state of each area in the station. The three-dimensional grid modeling method maps the target temperature data to the grid cells in the three-dimensional space. In the thermal map of the waste incineration station, grid modeling can accurately represent the distribution of temperature in space, such as temperature data at different heights, depths or regions. Through three-dimensional grid modeling, the temperature data is mapped to a three-dimensional grid to form a detailed thermal map that can show the change of temperature with spatial distribution. This thermal map can help engineers or inspectors understand the temperature distribution in different areas more intuitively and find abnormal temperature areas in time.

[0037] S130: Obtain scanning data of the waste incineration station sent by the laser radar.

[0038] Specifically, LiDAR is a technology that uses laser beams to detect objects and measure distances. LiDAR emits a laser beam, waits for the laser beam to hit an object and then reflects back to the sensor, and then calculates the distance to the object based on the reflection time. LiDAR can quickly collect a large amount of distance data and generate three-dimensional point cloud data through precise spatial calculations. LiDAR can obtain spatial data within the station through rotating or fixed scanning. Each laser scan generates a series of data points, which represent the distance after the laser hits the surface of the object. Through multiple scans, complete scan data of the entire area or object can be obtained. In a complex and dynamic environment such as a waste incineration station, LiDAR can be used to monitor the spatial layout, obstacles, equipment and personnel positions within the station in real time. Waste incineration stations usually contain multiple components such as equipment, pipelines, storage areas, etc. These parts may sometimes be blocked or have environmental changes, and the high-precision scanning of LiDAR can help generate a three-dimensional model of each device and obstacle in the station. The scanning data sent by LiDAR can include spatial information such as the height and depth of each location in the station, which is used to construct obstacle distribution maps, equipment layout maps, etc. within the station.

[0039] S140: construct an obstacle distribution map based on the scanning data.

[0040] Specifically, after the server obtains the scanning data of the laser radar, it first needs to process the data into useful information. For example, by analyzing the scanned point cloud data, the parts related to the obstacles are extracted, and an obstacle distribution map is generated based on this information. The specific steps include: the server merges the scanning data of the laser radar to obtain a complete point cloud map. This is because the scanning of the laser radar is usually carried out in different areas, so it is necessary to splice the data of multiple scans to obtain detailed information of the entire environment. The server processes the point cloud data based on the height segmentation to distinguish the ground and possible obstacles in the station. For example, points below a certain height usually represent objects on the ground or on the ground, and points above this height may be equipment or other obstacles. The server uses algorithms to identify and mark the location and boundaries of obstacles to create a clear obstacle distribution map. The obstacle distribution map constructed by the server can show the distribution of objects such as equipment, walls, pipes, conveyor belts, etc. in the waste incineration station, helping managers and inspectors understand the spatial layout of the site, avoid collisions, improve inspection efficiency, and quickly locate obstacles or potential safety hazards in emergency situations.

[0041] In a possible implementation, an obstacle distribution map is constructed based on the scanning data, specifically including: determining the point cloud data corresponding to the static objects and dynamic objects in the waste incineration station based on the scanning data; using the ICP algorithm to splice multiple point cloud data to obtain a point cloud map; performing height-based point cloud segmentation on the point cloud map to obtain a feature point cloud between the ground and the obstacle; based on the feature point cloud, generating a bounding box for the obstacle and determining the obstacle distribution map.

[0042] Specifically, in a waste incineration plant, static objects include fixed equipment, pipes, walls, etc., while dynamic objects are moving objects, such as inspectors or robots. LiDAR scanning data provides point cloud data about these objects. By analyzing the changes in the point cloud data, static objects and dynamic objects can be distinguished. The point cloud data of static objects changes less, and their position and shape remain unchanged during the scanning process. The point cloud data of dynamic objects will change over time and may appear in different positions or change shape. The ICP algorithm is a commonly used point cloud registration method for aligning multiple point cloud data sets and splicing them into a complete point cloud map. In the actual application of a waste incineration plant, the LiDAR may collect data from multiple scanning positions. The ICP algorithm can splice the point cloud data obtained from scanning at these different positions together to form a comprehensive and complete environmental point cloud map. Once the complete point cloud map is obtained, it needs to be further processed. Height-based point cloud segmentation is the process of distinguishing the ground from obstacles by analyzing the vertical height information of the point cloud. Usually, the point cloud data of the ground is low, while the point cloud data of objects is high. By setting a height threshold, the ground point cloud can be separated from the obstacle point cloud. Based on the extracted obstacle feature point cloud, the bounding box algorithm can be used to determine the geometry and position of the obstacle. A bounding box is a simple geometric body that encloses all points of an obstacle by minimizing the circumscribed rectangle or circumscribed cube. This can clearly define the location of the obstacle and provide a basis for subsequent path planning, obstacle avoidance, etc. Finally, through the analysis of the feature point cloud and the generation of the bounding box, the server can draw an obstacle distribution map showing the locations of all static and dynamic obstacles in the waste incineration station. This distribution map can help inspectors clearly understand the spatial layout of the station and avoid potential collisions or dangers.

[0043] For example, suppose that in a waste incineration plant, the LiDAR device regularly scans the entire plant area and generates point cloud data. The LiDAR scans the entire waste incineration plant at different locations. The data obtained from each scan includes various points in the plant area, such as equipment, walls, pipes, and even point cloud data of inspectors. By analyzing the point cloud data, the server can identify static objects and dynamic objects. For example, the scan data shows a combustion furnace in the furnace area, and its point cloud data will not change, which is a static object; while the position of the inspector's point cloud data will change during the scanning process, which is a dynamic object. Suppose that the LiDAR scans data from multiple locations and uses the ICP algorithm to stitch these scan results into a complete point cloud map. At this time, the server obtains a three-dimensional point cloud data containing all the equipment, walls, pipes and other obstacles in the waste incineration plant. Suppose that the equipment in the waste incineration plant is high, while the ground area is low. By setting a threshold, such as 2 meters, the server divides the point cloud data into two parts: points below 2 meters represent the ground, and points above 2 meters represent equipment or other obstacles. For points whose height exceeds the threshold, the server will further analyze these points and generate one or more bounding boxes to enclose all points of each obstacle. For example, the point cloud data of the furnace equipment will be enclosed in a rectangular bounding box, and the conveyor belt area will be enclosed in a cuboid bounding box. Finally, the server will construct an obstacle distribution map based on the obstacle bounding boxes obtained by analysis. The map clearly shows the location and distribution of various obstacles in the waste incineration plant, such as equipment, pipes, walls, etc. Inspectors can plan inspection routes based on the distribution map to avoid unnecessary risks.

[0044] S150. Obtain environmental data of the waste incineration station, where the environmental data includes gas concentration data and vibration data.

[0045] Specifically, gas concentration data refers to the concentration level of certain specific gases in the waste incineration station, such as carbon dioxide, nitrogen oxides, carbon monoxide, hydrogen sulfide, etc. A large amount of waste gas is generated during the incineration process. These gases may contain harmful components and pose a threat to the environment and personnel health. Therefore, real-time monitoring of gas concentration is an important part of the safety management of the waste incineration station. Gas concentration is usually collected by gas sensors installed in various areas of the station. These sensors detect different gas components in the air in real time and send them to the server in the form of data. The server generates a monitoring chart of gas concentration based on the sensor data to help monitor air quality and judge potential risks. Vibration data refers to the degree of vibration of equipment, ground or structure in the waste incineration station. Incineration equipment may generate vibration during operation, especially large mechanical equipment, conveyor belts and combustion furnaces. If the vibration amplitude exceeds the normal range, it may indicate that the equipment is faulty or the structure is under pressure, which may cause equipment damage or safety accidents. Vibration data is generally collected by vibration sensors installed on equipment, ground or structure. Vibration sensors usually measure the vibration of objects based on acceleration or displacement, convert the collected vibration signals into data, and send them to the server for processing and analysis.

[0046] S160. Generate a dynamic environment model of the waste incineration station based on the thermal map, obstacle distribution map, gas concentration data, and vibration data.

[0047] Specifically, a heat map is a spatial temperature distribution map constructed by monitoring the temperature of different areas in the station. It is usually generated by a thermal imaging sensor or an infrared sensor, which can show the high and low changes in temperature. The heat map can help identify high-temperature areas, predict possible overheating problems of equipment, or help discover abnormal operation of equipment. The obstacle distribution map shows the location and distribution of all static and dynamic obstacles in the station. These obstacles include equipment, machines, personnel, or any objects that may affect inspections and equipment operations. Through the obstacle distribution map, inspectors can clearly know which areas are occupied by obstacles, the routes that need to be detoured, or the safety risks faced. Gas concentration data refers to the real-time monitoring data of the concentration of harmful gases in the station, such as carbon monoxide and sulfur dioxide. These data reflect the air quality in the working environment. With this data, the server can determine the potential toxic gas leaks in the waste incineration station in real time, help identify harmful environments in a timely manner, and take safety measures. Vibration data is collected by vibration sensors, showing the vibration of equipment, ground, or other structures. Vibration data is used to detect whether the equipment has abnormal vibrations, whether there is a possibility of failure or equipment damage, so as to predict the health of the equipment in advance.

[0048] In a possible implementation, a dynamic environment model of a waste incineration station is generated based on a thermal map, an obstacle distribution map, gas concentration data, and vibration data, specifically including: performing data processing on the thermal map, obstacle distribution map, gas concentration data, and vibration data to obtain data to be fused, the data processing including denoising, filtering, time alignment, and normalization; according to the data to be fused, superimposing the obstacle detection data with the high temperature area of ​​the thermal imaging to generate a spatial feature map; according to the data to be fused, combining the gas diffusion model and wind direction information, predicting the diffusion path and source location of the harmful gas; according to the data to be fused, combining the vibration abnormality location and the temperature gradient, marking the high-risk equipment location; based on the spatial feature map, the diffusion path, the source location, and the high-risk equipment location, constructing a dynamic environment model, the dynamic environment model is used to represent the real-time environmental status and risk distribution of the waste incineration station.

[0049] Specifically, sensor data usually has noise, which may be caused by environmental interference, sensor errors or other factors. The purpose of denoising is to remove these invalid interference signals to make the data more accurate and reliable. Through filtering, the data can be smoothed, unnecessary high-frequency changes can be removed, and the data can be more stable. For example, high-frequency noise can be removed by a low-pass filter. Different sensors may have different acquisition times. In order to fuse these data, they need to be aligned to the same time axis first. For example, thermal maps, obstacle distribution maps, gas concentrations, and vibration data all need to be synchronized at the same time point. Different types of data have different dimensions such as temperature, gas concentration, vibration, etc. Normalization processing scales all data to a uniform scale for subsequent processing and comparison. First, the obstacle distribution map is obtained by scanning and sensors, and then these obstacle data are superimposed with thermal imaging data. In this way, it is possible to identify whether the high-temperature area is covered by obstacles and which areas have potential high-temperature hazards. The generated spatial feature map can show the relationship between the high-temperature area and the obstacles, helping patrol personnel to reasonably avoid risks. By monitoring the changes in gas concentration and combining information such as wind direction, it is possible to predict how harmful gases such as CO and CO2 spread within the station.

[0050] Secondly, the gas diffusion model takes into account the diffusion law of gas in space. For example, gas will expand with wind direction and air flow. Therefore, it is necessary to predict the path of gas diffusion based on the location of the gas source and wind direction data. In addition to the diffusion path, the model also identifies the source location of harmful gases, which is critical for timely handling of leakage problems. The server detects the vibration data of the equipment through sensors to identify whether the equipment has abnormal vibration. If the vibration amplitude of the equipment is too large, it may mean that the equipment has failed or damaged. Combined with temperature changes, if the temperature gradient around a certain device changes greatly, it may indicate that the device is at risk of overheating or failure. By combining temperature gradient with vibration data, the location of high-risk equipment can be marked more accurately. Finally, by combining all the data processed, a dynamic environmental model is generated. The model integrates the real-time environmental status and risk distribution of the waste incineration station. Through this model, managers and inspectors can intuitively see the environmental status and potential safety hazards, so as to formulate corresponding countermeasures.

[0051] S170. According to the dynamic environment model, an inspection strategy for the inspection personnel corresponding to the waste incineration station is planned, and the inspection personnel wear AR glasses.

[0052] Specifically, the dynamic environment model includes the real-time environmental status of the waste incineration station, such as temperature, gas concentration, vibration data, etc., and also includes risk distribution, such as high-temperature areas, harmful gas diffusion paths, equipment failure points, etc. The server uses this information to evaluate the different risks faced by each inspector when performing tasks. Inspection task planning Based on the risk analysis results of the dynamic environment model, the server can plan appropriate inspection tasks for each inspector. The server will mark the areas with high temperature, high humidity, gas leakage or abnormal vibration according to the environmental model. These areas will be included in the inspection tasks first. Based on the current location, workload, skill level and other information of the inspectors, the server can specify appropriate inspection areas and tasks for each inspector. For example, for inspectors with higher technical levels, more complex tasks can be assigned or high-risk areas can be handled. The inspection strategy will be adjusted in real time and flexibly adjusted according to dynamic environmental data, such as temperature changes, gas concentration, vibration, etc., to ensure that inspectors can respond to new risks in a timely manner. After the inspectors wear AR glasses, the server will show them the inspection task information in real time through the glasses, such as the current inspection location, high-risk areas, temperature distribution, gas leakage location, etc. AR glasses can display inspection routes and tasks in real time by overlaying virtual information. For example, when an inspector walks into a high-temperature area, AR glasses will automatically remind the inspector of the high temperature information in the area and provide safety advice, such as wearing protective equipment and avoiding staying for a long time. If the inspector approaches the source of a gas leak, AR glasses will issue a warning and instruct him to avoid the area or take emergency measures. If the inspector's task changes, such as adding a new high-temperature area, AR glasses will update the inspection route and tasks in real time to ensure that the inspector is aware of the latest task priorities and safety reminders.

[0053] In one possible implementation, based on the dynamic environment model, an inspection strategy for the inspection personnel corresponding to the waste incineration station is planned, specifically including: obtaining the personnel status data of the inspection personnel, the personnel status data including workload, current location and skill level; determining high-risk areas based on the dynamic environment model; generating inspection paths based on the high-risk areas; assigning inspection tasks to the inspection personnel based on the workload, current location and skill level, and determining the target inspection personnel who perform the inspection tasks; and generating an inspection strategy based on the inspection path and the target inspection personnel.

[0054] Specifically, workload refers to the current work intensity or task burden of the inspector. For example, if an inspector has completed multiple tasks, he may be overloaded and need to be assigned lighter tasks to avoid excessive fatigue. Current location refers to the actual location of the inspector in the waste incineration plant. Based on its location, the server can optimize the inspection path, reduce unnecessary movement, and ensure the efficient execution of tasks. Skill level refers to the professional ability or experience level of the inspector. Inspectors with different skill levels can handle tasks of different complexity. The server will assign tasks according to the skills of the personnel to ensure that each task is completed by the right person. The dynamic environment model considers the real-time environmental data in the waste incineration plant, including temperature, gas concentration, vibration, etc., to identify high-risk areas. These high-risk areas include high-temperature areas, harmful gas leakage areas, abnormal vibration areas, etc., which need to be inspected first to prevent accidents. Once the high-risk areas are determined, the server will generate an inspection path, which will take into account factors such as the current location of the inspector, the priority of the task, the degree of risk, and the workload and skill level of the inspector. Path planning will try to avoid unnecessary routes, improve inspection efficiency, and ensure the safety of inspectors. The server will assign tasks to the most suitable inspectors based on their workload, current location, and skill level. If an inspector is near a high-risk area and has sufficient skills to handle tasks in that area, he will be assigned the corresponding tasks. The server will also make adjustments based on the workload of the personnel to avoid overwork. Based on the inspection path and the situation of the target inspectors, an inspection strategy is generated, which includes task scheduling, inspection sequence, and detailed content of the inspection tasks, such as inspection points, inspection equipment, precautions, etc. In this way, the server ensures that the inspection tasks are reasonably arranged in time and space, improving the efficiency and safety of the inspection.

[0055] S180, using AR glasses to display dynamic environment models and inspection strategies to inspection personnel.

[0056] Specifically, the dynamic environment model is displayed on AR glasses through graphics or icons, and inspectors can see real-time information such as heat maps, obstacle distribution maps, changes in gas concentration, etc., to help them quickly identify potential risk areas and make corresponding judgments. Inspection strategies are generated based on factors such as the priority of inspection tasks, personnel status, and environmental risks. Inspection strategies include inspection routes, the order of tasks, precautions, and prompts for high-risk areas. AR glasses will display the tasks that inspectors should perform, mark the inspection paths, various inspection points, and high-risk areas that require special attention. For example, AR glasses may display the equipment and locations that the inspector needs to check in the field of view, as well as related risk information, such as excessive temperature and abnormal vibration.

[0057] In one possible implementation, the real-time location data of the target inspection personnel is obtained; based on the high-risk area, if it is determined that the real-time location data overlaps with the high-risk area, risk warning data is generated; the risk warning data is marked in the dynamic environment model and displayed to the target inspection personnel through AR glasses.

[0058] Specifically, the actual location of the inspector can be obtained in a variety of ways, such as GPS positioning, indoor positioning servers, such as UWB, Bluetooth, and RFID technologies. These technologies can help the server understand the exact location of the inspector in the station and ensure the real-time nature of the data. The target inspector refers to the person who is currently performing the inspection task. The server can track the location of the inspector through positioning technology and monitor its dynamics in real time. High-risk areas are places where equipment, areas or environmental conditions are more dangerous, such as high temperature areas, harmful gas leakage areas, vibration abnormal areas, etc. In high-risk environments such as waste incineration stations, equipment failures, environmental abnormalities, etc. may lead to increased risks. When the real-time location of the inspector overlaps with the defined high-risk area, the server will determine that the current inspector is in the risk area, and the corresponding risk reminder data needs to be generated. For example, if the inspector is close to the high temperature area or the toxic gas leakage area, the server will trigger a warning. When the inspector enters the high-risk area, the server will mark the location as a high-risk area in the dynamic environment model and associate specific risk information, such as high temperature, gas leakage, vibration, etc. The AR glasses worn by inspectors can present the above-mentioned risk warning data in real time in their field of vision. For example, a flashing red frame may be displayed on the screen of AR glasses to identify the specific location of the high-risk area, or display relevant danger information, such as excessive temperature, excessive gas concentration, etc. The information seen by inspectors will include danger warnings of their current location and surrounding environment, as well as how to bypass high-risk areas or how to take necessary precautions.

[0059] In one possible implementation, refer to Figure 2 , Figure 2 Another flow chart of an AR inspection method applied to a waste incineration station provided in an embodiment of the present application. It includes steps S210 to S250, and the above steps are as follows: S210, determine the initial task level according to the inspection time corresponding to the inspection task; S220, obtain the working status data of the AR glasses; S230, determine the working time according to the working status data; S240, correct the initial task level by the working time to obtain the target task level; S250, assign inspection tasks to inspection personnel according to the target task level.

[0060] Specifically, each inspection task will have a predetermined inspection duration, that is, the inspector needs to complete the task within a specific time. The duration of the inspection task can be set according to factors such as the complexity of the task and the risk level. According to the duration and difficulty of the task, the server will set a preliminary level for the task. For example, if the task duration is long or the task itself is complex, the initial level of the task is high, indicating that the task is heavy; if the task is simple or short, the task level is low. Working status data of AR glasses: refers to the use of AR glasses, such as battery power, device operating status, usage time, etc. In addition, it can also include data on interaction with inspectors, such as whether the inspectors are in working state for a long time or frequently switch tasks. These data help to understand the workload of inspectors and the availability of equipment, and further help with task allocation and management. The server evaluates the current working status of the inspectors based on the working hours they have performed. If the inspectors have been working for a long time, the server can identify the possible fatigue risk. The evaluation of working hours can also be combined with other factors, such as the type of inspection task, the physical strength of the inspectors, the urgency of the task, etc. The server will adjust the task level based on the inspector's working hours. For example, if the inspector has been working for a long time and is close to fatigue, the server can appropriately shorten the difficulty or duration of the task and lower the task level. If the inspector has been working for a short time and is in good condition, the server can adjust the task level as needed to increase the difficulty or duration of the task. In this way, the distribution of tasks not only considers the requirements of the task itself, but also takes into account the status of the inspector to avoid overwork or uneven task distribution.

[0061] Finally, the server will assign tasks to inspectors based on the target task level. If the target task level is high, inspectors need to complete more complex or dangerous tasks; if the task level is low, the task may be a routine inspection task with relatively light workload. This step ensures that the task allocation is more reasonable, avoiding inspectors being fatigued due to heavy tasks or inefficient due to light tasks.

[0062] The present application also provides an AR inspection device for a waste incineration station, referring to Figure 3 , Figure 3A module schematic diagram of an AR inspection device applied to a waste incineration station provided in an embodiment of the present application. The AR inspection device is a server, and the server includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires the station temperature data for the waste incineration station sent by the temperature acquisition sensor; the processing module 32 constructs a thermal map based on the station temperature data; the acquisition module 31 acquires the scanning data for the waste incineration station sent by the laser radar; the processing module 32 constructs an obstacle distribution map based on the scanning data; the acquisition module 31 acquires the environmental data of the waste incineration station, and the environmental data includes gas concentration data and vibration data; the processing module 32 generates a dynamic environment model of the waste incineration station based on the thermal map, obstacle distribution map, gas concentration data and vibration data; the processing module 32 plans the inspection strategy of the inspection personnel corresponding to the waste incineration station according to the dynamic environment model, and the inspection personnel wear AR glasses; the processing module 32 displays the dynamic environment model and inspection strategy to the inspection personnel through the AR glasses.

[0063] In a possible implementation, the processing module 32 constructs a thermal map based on the temperature data in the station, specifically including: the acquisition module 31 acquires a two-dimensional image of the regional surface temperature sent by the thermal imaging sensor in a preset area in the waste incineration station; if the processing module 32 determines that the area corresponding to the target area is greater than the preset area threshold, the temperature point data sent by the infrared sensor corresponding to the target area is acquired, and the target area is any one of multiple preset areas; the processing module 32 adopts an inverse distance weighted interpolation algorithm to insert the temperature point data into continuously distributed temperature data; the processing module 32 removes redundant areas based on the continuously distributed temperature data in combination with the two-dimensional image to obtain the target temperature data; the processing module 32 adopts a three-dimensional grid modeling algorithm to map the target temperature data to the spatial grid unit to obtain a thermal map.

[0064] In a possible implementation, the processing module 32 constructs an obstacle distribution map based on the scanning data, specifically including: the processing module 32 determines the point cloud data corresponding to the static objects and dynamic objects in the waste incineration station based on the scanning data; the processing module 32 uses the ICP algorithm to splice multiple point cloud data to obtain a point cloud map; the processing module 32 performs height-based point cloud segmentation on the point cloud map to obtain a feature point cloud between the ground and the obstacle; the processing module 32 generates a bounding box for the obstacle based on the feature point cloud, and determines the obstacle distribution map.

[0065] In a possible implementation, the processing module 32 generates a dynamic environment model of the waste incineration station based on the thermal map, obstacle distribution map, gas concentration data and vibration data, specifically including: the processing module 32 performs data processing on the thermal map, obstacle distribution map, gas concentration data and vibration data to obtain data to be fused, and the data processing includes denoising, filtering, time alignment and normalization processing; the processing module 32 superimposes the obstacle detection data with the high temperature area of ​​the thermal imaging according to the data to be fused to generate a spatial feature map; the processing module 32 predicts the diffusion path and source location of the harmful gas according to the data to be fused, in combination with the gas diffusion model and wind direction information; the processing module 32 marks the high-risk equipment location according to the data to be fused, in combination with the vibration abnormality location and the temperature gradient; the processing module 32 constructs a dynamic environment model based on the spatial feature map, diffusion path, source location and high-risk equipment location, and the dynamic environment model is used to represent the real-time environmental status and risk distribution of the waste incineration station.

[0066] In a possible implementation, the processing module 32 plans the inspection strategy of the inspection personnel corresponding to the waste incineration station according to the dynamic environment model, specifically including: the acquisition module 31 acquires the personnel status data of the inspection personnel, and the personnel status data includes workload, current location and skill level; the processing module 32 determines the high-risk area according to the dynamic environment model; the processing module 32 generates the inspection path according to the high-risk area; the processing module 32 assigns inspection tasks to the inspection personnel according to the workload, current location and skill level, and determines the target inspection personnel who perform the inspection tasks; the processing module 32 generates the inspection strategy based on the inspection path and the target inspection personnel.

[0067] In one possible implementation, the acquisition module 31 acquires the real-time location data of the target patrol personnel; the processing module 32 generates risk warning data based on the high-risk area if it is determined that the real-time location data overlaps with the high-risk area; the processing module 32 marks the risk warning data in the dynamic environment model and displays it to the target patrol personnel through AR glasses.

[0068] In a possible implementation, the processing module 32 determines the initial task level according to the inspection duration corresponding to the inspection task; the acquisition module 31 acquires the working status data of the AR glasses; the processing module 32 determines the working duration according to the working status data; the processing module 32 corrects the initial task level according to the working duration to obtain the target task level; the processing module 32 assigns inspection tasks to inspection personnel according to the target task level.

[0069] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0070] The present application also provides an electronic device, referring to Figure 4 , Figure 4 The electronic device may include: at least one processor 41 , at least one network interface 44 , a user interface 43 , a memory 45 , and at least one communication bus 42 .

[0071] The communication bus 42 is used to realize the connection and communication between these components.

[0072] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0073] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0074] Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41, and it can be implemented separately through a chip.

[0075] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4 As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for an AR inspection method applied to a waste incineration station.

[0076] exist Figure 4 In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program stored in the memory 45 for an AR inspection method applied to a waste incineration station. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0077] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0078] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0079] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0083] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0084] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An AR inspection method applied to a waste incineration station, characterized in that: The method comprises: Obtain the temperature data of the waste incineration station sent by the temperature collection sensor; A thermal map is constructed based on the temperature data in the station; Obtaining scanning data of the waste incineration station sent by a laser radar; Constructing an obstacle distribution map according to the scanning data; Acquiring environmental data of the waste incineration station, wherein the environmental data includes gas concentration data and vibration data; Generate a dynamic environment model of the waste incineration station based on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data; According to the dynamic environment model, an inspection strategy of an inspector corresponding to the waste incineration station is planned, wherein the inspector wears AR glasses; The dynamic environment model and the inspection strategy are displayed to the inspection personnel through the AR glasses.

2. The AR inspection method applied to a waste incineration station according to claim 1 is characterized in that: The step of constructing a thermal map based on the station temperature data specifically includes: Acquire a two-dimensional image of the surface temperature of a region sent by a thermal imaging sensor in a preset region of the waste incineration station; If it is determined that the area corresponding to the target area is greater than the preset area threshold, the temperature point data sent by the infrared sensor corresponding to the target area is obtained, and the target area is any one of the multiple preset areas; Using an inverse distance weighted interpolation algorithm, the temperature point data is interpolated into continuously distributed temperature data; According to the continuously distributed temperature data, combined with the two-dimensional image, redundant areas are removed to obtain target temperature data; The target temperature data is mapped to spatial grid cells using a three-dimensional grid modeling algorithm to obtain the thermal map.

3. The AR inspection method applied to a waste incineration station according to claim 1 is characterized in that: The step of constructing an obstacle distribution map based on the scanning data specifically includes: Determine point cloud data corresponding to static objects and dynamic objects in the waste incineration station according to the scan data; Using the ICP algorithm, multiple point cloud data are spliced ​​to obtain a point cloud map; Performing height-based point cloud segmentation on the point cloud map to obtain a feature point cloud between the ground and the obstacle; A bounding box is generated for the obstacle according to the feature point cloud, and the obstacle distribution map is determined.

4. The AR inspection method applied to a waste incineration station according to claim 1 is characterized in that: The generating of the dynamic environment model of the waste incineration station based on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data specifically includes: Performing data processing on the thermal map, the obstacle distribution map, the gas concentration data, and the vibration data to obtain data to be fused, wherein the data processing includes denoising, filtering, time alignment, and normalization processing; According to the data to be fused, the obstacle detection data is superimposed with the high temperature area of ​​the thermal imaging to generate a spatial feature map; According to the data to be fused, combined with the gas diffusion model and wind direction information, the diffusion path and source location of the harmful gas are predicted; According to the data to be fused, the position of the high-risk equipment is marked in combination with the abnormal vibration position and the temperature gradient; Based on the spatial characteristic map, the diffusion path, the source location and the high-risk equipment location, the dynamic environment model is constructed, and the dynamic environment model is used to represent the real-time environmental status and risk distribution of the waste incineration station.

5. The AR inspection method applied to a waste incineration station according to claim 1 is characterized in that: The planning of the inspection strategy of the inspection personnel corresponding to the waste incineration station according to the dynamic environment model specifically includes: Acquiring personnel status data of the inspection personnel, the personnel status data including workload, current location, and skill level; determining high-risk areas according to the dynamic environmental model; Generate an inspection route based on the high-risk area; Allocate inspection tasks to the inspection personnel according to the workload, the current location, and the skill level, and determine target inspection personnel to perform the inspection tasks; The inspection strategy is generated based on the inspection path and the target inspection personnel.

6. The AR inspection method applied to a waste incineration station according to claim 5 is characterized in that: The method further comprises: Obtaining real-time location data of the target inspection personnel; According to the high-risk area, if it is determined that the real-time location data overlaps with the high-risk area, generating risk reminder data; The risk warning data is annotated in the dynamic environment model and displayed to the target inspection personnel through the AR glasses.

7. The AR inspection method applied to a waste incineration station according to claim 5 is characterized in that: The method further comprises: Determine the initial task level according to the inspection duration corresponding to the inspection task; Obtaining working status data of the AR glasses; Determine the working time according to the working status data; The initial task level is modified according to the working time to obtain a target task level; According to the target task level, inspection tasks are assigned to the inspection personnel.

8. An AR inspection device used in a waste incineration station, characterized in that: The AR inspection device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire the temperature data in the waste incineration station sent by the temperature acquisition sensor; The processing module (32) is used to construct a thermal map based on the temperature data in the station; The acquisition module (31) is also used to acquire scanning data sent by the laser radar for the waste incineration station; The processing module (32) is further used to construct an obstacle distribution map based on the scanning data; The acquisition module (31) is further used to acquire environmental data of the waste incineration station, wherein the environmental data includes gas concentration data and vibration data; The processing module (32) is further used to generate a dynamic environment model of the waste incineration station based on the thermal map, the obstacle distribution map, the gas concentration data and the vibration data; The processing module (32) is further used to plan, based on the dynamic environment model, an inspection strategy for an inspector corresponding to the waste incineration station, wherein the inspector wears AR glasses; The processing module (32) is also used to display the dynamic environment model and the inspection strategy to the inspection personnel through the AR glasses.

9. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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