Unmanned aerial vehicle inspection method and equipment based on smart rod, and storage medium

Abstract

The invention discloses an unmanned aerial vehicle inspection method and device based on a smart rod and a storage medium, and relates to the technical field of smart city infrastructure, and the unmanned aerial vehicle inspection method based on the smart rod comprises the steps that the smart rod determines a target unmanned aerial vehicle and an inspection scheme of the target unmanned aerial vehicle according to a triggered inspection task, the inspection scheme is sent to the target unmanned aerial vehicle; controlling the target unmanned aerial vehicle to execute the inspection scheme to collect inspection data, and returning the inspection data to the smart rod; the smart rod uploads the received inspection data to the cloud; the cloud carries out data comparison based on the inspection data and city reference data of an area where the intelligent rod is located, and determines a data comparison result; and if the data comparison result meets the abnormal condition, generating a recheck instruction and / or prompt information according to the data comparison result. The technical defect that the inspection efficiency is low due to the fact that the problems in the whole urban area cannot be responded in time is overcome, and the urban inspection efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of smart city infrastructure, and in particular to a smart pole-based drone inspection method, equipment and storage medium. Background Art

[0002] Drone inspections have the advantages of being efficient, flexible, and safe, and can quickly and accurately obtain information about the inspection area. In related technologies, the inspection path of the drone is usually set manually, and then the drone is controlled to perform small-scale inspections along the set path. However, the drone in the above solution can only inspect within a single area strictly according to the planned path, and cannot respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a smart pole-based drone inspection method, equipment and storage medium, aiming to solve the technical problem of inability to respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency.

[0005] To achieve the above objectives, the present application proposes a drone inspection method based on a smart pole, which is applied to an intelligent inspection system. The intelligent inspection system includes a smart pole, a drone and a cloud. The method includes: The smart pole determines the target drone and the inspection plan of the target drone according to the triggered inspection task, and sends the inspection plan to the target drone; Controlling the target drone to execute the inspection plan to collect inspection data, and returning the inspection data to the smart pole; The smart pole uploads the received inspection data to the cloud; The cloud performs data comparison based on the inspection data and the city benchmark data of the area where the smart pole is located to determine the data comparison result; If the data comparison result meets the abnormal condition, a re-inspection instruction and / or prompt information is generated according to the data comparison result.

[0006] In one embodiment, the method further comprises: The smart pole receives the inspection task sent by the cloud; Alternatively, the smart pole senses the presence of abnormal environmental data and generates the inspection task according to the abnormal environmental data.

[0007] In one embodiment, the method for determining a target drone includes: Based on the inspection task, determine the task area and the number of the pre-dispatched drone; The target drone is determined according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number.

[0008] In one embodiment, after the step of determining the target drone according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number, the step further includes: Determine the task area and inspection key locations according to the inspection tasks; An inspection plan for the target UAV is determined based on the task area marked with the inspection key position and in combination with the state of the target UAV itself.

[0009] In one embodiment, the step of determining the target drone according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number comprises: Determining the pre-dispatched drone according to the pre-dispatched drone number; Based on the state of the pre-dispatched drone, determining whether the state of the pre-dispatched drone is normal; If it is determined that the state of the pre-dispatched drone is abnormal, a nearby drone is selected for replacement, and the state of the pre-dispatched drone after the replacement is determined again; If it is determined that the state of the pre-dispatched UAV is normal, it is determined as the target UAV.

[0010] In one embodiment, the cloud performs data comparison based on the inspection data and the city benchmark data of the area where the smart pole is located, and the step of determining the data comparison result includes: The cloud determines the city benchmark data corresponding to the inspection area according to the point cloud data in the inspection data; The inspection data is compared with the city benchmark data, and the data comparison result is determined.

[0011] In one embodiment, if the data comparison result satisfies the abnormal condition, the step of generating a recheck instruction and / or prompt information according to the data comparison result includes: If the cloud determines that the inspection data has data defects, it generates the re-inspection instruction; If it is determined that there is a problem in the inspection area based on the data comparison result, the prompt information is generated; If it is determined that the inspection data has data defects and there are problems in the inspection area, the re-inspection instruction and the prompt information are generated.

[0012] In one embodiment, after the step of generating a recheck instruction and / or prompt information according to the data comparison result if the data comparison result meets the abnormal condition, the step further includes: The cloud generates the prompt information and sends it to the control platform; The control platform displays the information content in the prompt information and notifies relevant departments based on the danger information in the prompt information.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a drone inspection device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the smart pole-based drone inspection method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the smart pole-based drone inspection method as described above are implemented.

[0015] The present application provides a drone inspection method based on a smart pole, wherein the smart pole determines the target drone and the inspection plan of the target drone according to the triggered inspection task, and sends the inspection plan to the target drone; controls the target drone to execute the inspection plan to collect inspection data, and returns the inspection data to the smart pole; the smart pole uploads the received inspection data to the cloud; the cloud performs data comparison based on the inspection data and the city benchmark data of the area where the smart pole is located to determine the data comparison result; if the data comparison result meets the abnormal condition, generates a re-inspection instruction and / or prompt information according to the data comparison result. Based on the synergy between the smart pole and the drone, and then processing the data through the cloud server, the relevant inspection problems in the city can be solved quickly and efficiently, and the operation efficiency of the city is improved.

[0016] To summarize, this application generates an inspection plan through a smart pole module and provides inspection equipment in combination with a drone module. The two work together and finally coordinate and compare data through a cloud server. This overcomes the technical defect that the urban drone inspection plan of the related technology is based on manually controlled drones to inspect in a single area according to a preset planned path, and cannot respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency, thereby improving the efficiency of urban inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 This is a flow chart of the first embodiment of the drone inspection method based on the smart pole of the present application; Figure 2 This is a flow chart of the second embodiment of the drone inspection method based on the smart pole of the present application; Figure 3 This is a flow chart of the third embodiment of the drone inspection method based on the smart pole of the present application; Figure 4 This is a flowchart of a fourth embodiment of a drone inspection method based on a smart pole of the present application; Figure 5 This is a flowchart of a fifth embodiment of the drone inspection method based on a smart pole of the present application; Figure 6 This is a flowchart of a sixth embodiment of the smart pole-based drone inspection method of the present application; Figure 7 This is a flow chart of the seventh embodiment of the drone inspection method based on the smart pole of the present application; Figure 8 This is a flowchart of an eighth embodiment of the drone inspection method based on a smart pole of the present application; Fig. 9 This is a schematic diagram of the results of the drone inspection equipment used in this application.

[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] The urban drone inspection solution of the related technology is based on manually controlling the drone to inspect in a single area according to a preset planned path. It is unable to respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency.

[0023] This application provides a solution: when the smart pole triggers an inspection command, the system will determine the target drone and its inspection plan according to the command, and send the plan to the target drone. Subsequently, the drone will perform tasks according to the inspection plan, collect inspection data and return it to the smart pole. The smart pole will upload the inspection data to the cloud, and the cloud will compare the inspection data with the city benchmark data associated with the smart pole to obtain the data comparison result. If the comparison result meets the abnormal conditions, the cloud will generate a re-inspection instruction and / or prompt information according to the result. It overcomes the technical problem of being unable to respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency, and improves the efficiency of urban inspection.

[0024] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions, an unmanned aerial vehicle inspection device, an intelligent inspection system, etc. The following takes the intelligent inspection system as an example to illustrate this embodiment and the following embodiments.

[0025] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The present application embodiment provides a drone inspection method based on a smart pole, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the drone inspection method based on the smart pole of the present application.

[0027] In this embodiment, the smart pole-based drone inspection method includes steps S10 to S50: In step S10, the smart pole determines the target drone and the inspection plan of the target drone according to the triggered inspection task, and sends the inspection plan to the target drone.

[0028] In this embodiment, the intelligent inspection system includes a smart pole, a drone and a cloud. The intelligent inspection system is based on the synergy of the smart pole and the drone, and then processes data through the cloud server, so that the relevant inspection problems in the city can be solved quickly and efficiently. The smart pole is a multifunctional smart pole. It integrates multiple sensors, communication equipment, power supply system and other modules to realize the comprehensive management and data collection of multiple functions such as urban environment, transportation, security, lighting, etc. It is not only an upgraded version of urban lighting facilities, but also a comprehensive intelligent sensing terminal. The smart poles cooperate with each other to coordinate several drones in each area and issue inspection tasks, and cooperate with drones to complete urban inspection tasks. Drones can complete various flight operations and perform specific tasks according to preset flight tasks or real-time instructions. The cloud is a cloud computing platform, which is a mode of providing computing resources and services through the Internet. It can issue inspection tasks, and engineers can understand and control the entire intelligent inspection system through the cloud, and can also integrate data, compare data and issue prompt information. The inspection instruction is triggered by receiving an inspection task or the smart pole sensing an abnormal environmental parameter. The inspection plan is a plan generated by inspection instructions to ensure that each target drone completes the inspection task. The inspection plan includes the inspection area and the pre-dispatched drone number.

[0029] As an optional implementation, the smart pole triggers an inspection command based on receiving an inspection task sent from the cloud or sensing an abnormality in environmental parameters, determines the target drone and the inspection plan for each drone based on the inspection command, and sends the inspection plan to the target drone.

[0030] Step S20, controlling the target drone to execute the inspection plan to collect inspection data, and returning the inspection data to the smart pole.

[0031] In this embodiment, inspection data refers to data collected through regular or irregular inspections of urban infrastructure, environment, transportation, public facilities, etc. by drone inspections. These data are an important basis for urban management and operation, and can help city managers to promptly discover and solve problems in urban operations and improve the safety, efficiency and livability of the city. Inspection data includes point cloud data collected by drones.

[0032] As an optional implementation method, the target drone is determined in combination with the status of the pre-dispatched drone, the target drone is controlled to execute the corresponding inspection plan, and the inspection data in the inspection area is collected and transmitted back to the smart pole.

[0033] In step S30, the smart pole uploads the received inspection data to the cloud.

[0034] In this embodiment, drones in various areas coordinate and cooperate through various smart poles to jointly complete the inspection tasks.

[0035] As an optional implementation method, the inspection data collected by each drone is first collected on each smart pole, and then the smart pole organizes the inspection data and transmits it back to the cloud.

[0036] In step S40, the cloud performs data comparison based on the inspection data and the city benchmark data of the area where the smart pole is located to determine the data comparison result.

[0037] In this embodiment, urban benchmark data refers to a data set used to describe basic information such as urban space, geography, economy, and environment during urban planning, construction, and management. The urban benchmark data of each area of ​​the city is associated with the smart poles of each area, so that the cloud can quickly and accurately find the corresponding urban benchmark data based on the inspection data sent back by the smart poles.

[0038] As an optional implementation, the cloud determines the city benchmark data associated with the smart pole based on the point cloud data in the inspection data, and obtains the data comparison result by comparing the inspection data with the city benchmark data.

[0039] Step S50: if the data comparison result meets the abnormal condition, generate a re-inspection instruction and / or prompt information according to the data comparison result.

[0040] In this embodiment, the data comparison result meets the abnormal condition when the inspection data collected by the drone is different from the corresponding city benchmark data, or the collected inspection data has data defects, resulting in the inability to perform comparison and identification.

[0041] As an optional implementation, when it is determined through the data comparison result that there are data defects in the inspection data and / or problems in the inspection area, a corresponding re-inspection instruction and / or prompt information is generated according to the data comparison result.

[0042] As an optional implementation method for determining the inspection plan, in the process of determining the inspection plan, when the number of drones that can be regulated normally in the area where the smart pole that triggers the inspection task is located is sufficient, the smart pole will regulate the drones in the area to perform the inspection task. When the similarity of the inspection path in the inspection task is greater than a certain threshold, the smart pole will merge the inspection task to the drone that is performing the inspection task, so that the drone can perform the original inspection task, and the smart pole will plan the path to assist the drone to complete the merged inspection task at the same time. When the inspection key positions of multiple drones intersect, the smart pole will reduce the dispatch of drones according to the urgency of the inspection task, and realize the inspection task of the inspection key position with fewer drones. When the number of drones that can be regulated normally in the area where the smart pole that triggers the inspection task is located is insufficient, the smart pole will coordinate with the smart poles in other areas to control the drones in other areas to perform the inspection work. If other areas are performing non-urgent inspection tasks, the drone can be controlled to perform the inspection task across the nearby area. When the inspection task is urgent, the smart poles can coordinate with each other, split the inspection task, and arrange multiple drones nearby to complete the inspection task together, thereby improving the efficiency of completing emergency inspection tasks.

[0043] For example, when abnormal environmental data in a specific area is monitored, such as air quality exceeding the standard or abnormal traffic flow, the smart pole will automatically trigger an inspection instruction. The inspection instruction includes the inspection area, target objects, such as roads, buildings, public facilities, etc., inspection time window and data collection requirements, such as image resolution, data type, etc. The system selects a suitable target drone from the drone library according to the size, complexity and task requirements of the inspection area. Based on the inspection instruction, the system generates a detailed inspection plan for each target drone, including flight path planning, data collection point setting, sensor configuration, etc. The flight path planning will consider avoiding obstacles, reducing energy consumption and ensuring the comprehensiveness of data collection. After receiving the inspection plan, the target drone will fly autonomously along the preset path and use the onboard sensors, such as high-definition cameras and lidars, to collect inspection data. During the flight, the drone transmits the collected data back to the smart pole in real time. The urban point cloud data generated by the laser radar can accurately present the three-dimensional structure of buildings and roads. The smart pole collects the inspection data and uploads it to the cloud. The cloud platform compares and analyzes the inspection data and the urban benchmark data to quickly discover abnormal situations, such as facility damage, environmental changes, etc. If the comparison results meet the abnormal conditions, the cloud platform will generate a re-inspection instruction based on the severity and nature of the data abnormality. The re-inspection instruction will detail the areas and objects that need further inspection, as well as specific re-inspection requirements. For some urgent or important abnormal situations, the system will generate prompt information to promptly notify the relevant management departments or personnel. For example, if a structural safety hazard is detected in a certain building, the system will immediately send an early warning message to the city management department so that timely measures can be taken.

[0044] Due to the synergy between smart poles and drones, and data processing through cloud servers, relevant inspection issues in the city can be resolved quickly and efficiently, overcoming the technical problem of inability to respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency, and improving the efficiency of urban inspections.

[0045] Based on any of the above embodiments, in Embodiment 2 of the present application, refer to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the drone inspection method based on the smart pole of this application. The method also includes steps A11-A12: Step A11, the smart pole receives the inspection task sent from the cloud.

[0046] In this embodiment, the inspection task refers to regular or irregular inspection and evaluation of specific objects, such as equipment, facilities, environment, etc., to ensure their normal operation, safety and reliability, and to timely discover potential problems so that corresponding measures can be taken. The inspection task is applied to urban infrastructure management, industrial production, environmental monitoring, security and other fields.

[0047] As an optional implementation method, when engineers generate inspection tasks through the cloud based on the city's operating status or preset inspection plans, they send the inspection task instructions to the smart pole. After receiving the instructions, the smart pole will perform task analysis to clarify key information such as the inspection area, target object, and data collection requirements.

[0048] Step A12, alternatively, the smart pole senses the existence of abnormal environmental data and generates the inspection task according to the abnormal environmental data.

[0049] In this embodiment, environmental parameters refer to various physical, chemical and biological parameters used to characterize environmental conditions, including temperature, humidity, wind speed, precipitation, light, atmospheric pressure, electromagnetic interference, air quality and noise level.

[0050] As an optional implementation, when the smart pole senses that the environmental parameters in the area are not within the normal range, the corresponding inspection instructions are triggered based on the abnormality of the environmental parameters and the location of the area.

[0051] For example, the smart pole receives an inspection task for area A issued by the cloud, and then the smart pole triggers the corresponding inspection instruction. The task area is determined to be area A through the inspection instruction. The key inspection locations include intersections, bridges and rivers. The pre-dispatched drone numbers are "WRJ0001, WRJ0002...", and the key inspection locations are marked on the navigation map of the task area, and then the inspection plan is generated in combination with the pre-dispatched drone numbers.

[0052] Since tasks are issued based on the cloud, through the coordination of smart poles and the execution of drone inspections, a fully automatic mode of urban inspection is realized, thereby improving the efficiency of urban inspections.

[0053] Based on any of the above embodiments, in Embodiment 3 of the present application, refer to Figure 3 , Figure 3 This is a flow chart of the third embodiment of the drone inspection method based on the smart pole of the present application. The method for determining the target drone includes steps B11-B12: Step B11, based on the inspection task, determine the task area and the number of the pre-dispatched drone.

[0054] In this embodiment, the mission area refers to a specific urban area that needs to be inspected as determined by the inspection instruction. The pre-dispatched drone number refers to the number corresponding to the drone preliminarily determined to be dispatched in the inspection.

[0055] As an optional implementation, an inspection instruction is triggered according to the inspection task, and the inspection task area and the pre-dispatched drone number are determined by the inspection instruction, and the target drone is determined by judging the state of the pre-dispatched drone.

[0056] Step B12: determining the target drone according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number.

[0057] In this embodiment, the drone's own state includes its own position state, battery state, and operation state, etc. By checking the drone's own state, it can be determined whether the drone can operate normally.

[0058] As an optional implementation, the pre-dispatched drone is determined according to the pre-dispatched drone number, and then the battery status and operating status of the pre-dispatched drone are detected by the system to determine that the pre-dispatched drone in normal operation is the target drone.

[0059] For example, the smart pole receives an inspection task for area B with abnormal environmental parameters issued by the cloud, and then the smart pole triggers the corresponding inspection instruction. The abnormal area is determined to be area B through the inspection instruction. The key abnormal locations include serious abnormalities in environmental parameters and key inspection locations. The pre-dispatched drone numbers are "WRJ2001, WRJ2002...", and the key abnormal locations are marked on the navigation map of the mission area, and then the inspection plan is generated in combination with the pre-dispatched drone numbers.

[0060] Due to the smart pole's perception of the environment and the drone's inspection capabilities, the two work together to achieve rapid investigation and inspection of abnormalities in the city, improving the city's ability to respond quickly to dangerous situations.

[0061] Based on any of the above embodiments, in Embodiment 4 of the present application, refer to Figure 4 , Figure 4 This is a flowchart of the fourth embodiment of the drone inspection method based on the smart pole of the present application. After step B12, it also includes steps C11~C12: Step C11, determining the task area and key inspection locations according to the inspection task.

[0062] In this embodiment, the inspection key positions refer to the inspection positions mentioned in the inspection instructions, which need to be inspected in a focused manner. The inspection key positions also include areas where environmental parameters are seriously abnormal.

[0063] As an optional implementation, according to the inspection task triggered by the smart pole, the task area and the key inspection positions are determined from the inspection task, and the key inspection positions are marked on the task area.

[0064] Step C12, determining the inspection plan of the target UAV according to the task area marked with the inspection key position and in combination with the own state of the target UAV.

[0065] In this embodiment, marking refers to marking and recording the inspection key locations in the map or geographic information system of the mission area, which helps the drone to accurately identify and locate the areas that need to be inspected when performing the mission, ensuring the efficient execution of the inspection mission. The drone's own status includes its own position status, battery status, and operating status. By checking the drone's own status, it can be determined whether the drone can operate normally and determine the drone's current location information.

[0066] As an optional implementation, the inspection plan for the target drone is determined based on the inspection key locations marked in the mission area, combined with the pre-dispatched drone number and the drone's own status.

[0067] In this embodiment, the inspection path refers to the inspection process route planned by combining the drone's own position status with the inspection area.

[0068] As an optional implementation, based on the scope of the inspection area and combined with the location status of the target drone itself, an inspection route is planned on the navigation map.

[0069] As an optional inspection task evaluation method, the drone receives the inspection task issued by the smart pole, and evaluates the inspection task based on its own position status. If the inspection path planned by the inspection task does not conflict with the inspection path currently executed by itself and the distance is less than a certain threshold, the drone accepts the inspection task. If the inspection path planned by the inspection task is significantly different from the drone's own position status and the distance is greater than a certain threshold, or there is a conflict in the current inspection path or the currently executed inspection task is more urgent, the drone continues to complete the current inspection plan and rejects the new inspection task.

[0070] For example, the city needs to inspect the infrastructure in the downtown area, including roads, bridges, street lights, etc. The cloud platform generates inspection tasks according to the task requirements and sends them to the designated smart poles. After receiving the inspection tasks sent by the cloud, the smart poles parse the task content to trigger the inspection instructions, and generate inspection plans for each pre-dispatched drone based on the inspection instructions. Then, the pre-dispatched drones’ own position status, battery status, and operating status are detected. When the pre-dispatched drone can inspect normally, it is determined as the target drone, and the inspection route of the target drone is determined based on its own position status and inspection area.

[0071] Since the target UAV can be selected and the inspection route can be planned based on the UAV’s own status, the failure of the inspection mission caused by UAV problems can be avoided. The autonomous target UAV can be determined and the autonomous route can be planned, which improves the efficiency of urban inspection.

[0072] Based on any of the above embodiments, in Embodiment 5 of the present application, refer to Figure 5 , Figure 5 This is a flowchart of the fifth embodiment of the drone inspection method based on the smart pole of this application. Step B12 includes steps D11 to D14: Step D11, determining the pre-dispatched drone according to the pre-dispatched drone number.

[0073] In this embodiment, the pre-dispatched drone refers to a drone prepared for dispatch that is selected by the smart pole based on the inspection plan.

[0074] As an optional implementation, during the preparation stage of the drone inspection mission, the system identifies and selects the corresponding drone individual from the drone resource pool based on the drone number pre-assigned by the cloud or task management system.

[0075] Step D12: based on the state of the pre-dispatched UAV itself, determine whether the state of the pre-dispatched UAV is normal.

[0076] In this embodiment, whether the status of the pre-dispatched UAV is normal refers to judging based on the status of the pre-dispatched UAV itself, such as the battery status and the running status, to confirm whether the status of the pre-dispatched UAV is normal or abnormal.

[0077] As an optional implementation, the system detects the battery status and operating status of the pre-dispatched UAV. If the battery status and operating status are normal, it is determined to be the target UAV. If the battery status or operating status is abnormal, another UAV is selected to replace the pre-dispatched UAV.

[0078] In step D13, if it is determined that the status of the pre-dispatched drone is abnormal, a nearby drone is selected for replacement, and the status of the pre-dispatched drone after the replacement is determined again.

[0079] In this embodiment, selecting a nearby drone for replacement means that when the state of the pre-dispatched drone becomes abnormal, a nearby drone is selected to replace the pre-dispatched drone according to the position state of the pre-dispatched drone.

[0080] As an optional implementation, when the battery status and operating status of the pre-dispatched UAV are abnormal, based on the pre-dispatched UAV's own position status, a nearby UAV is selected to replace the pre-dispatched UAV, and then the replaced pre-dispatched UAV's own status is detected.

[0081] Step D14: If it is determined that the status of the pre-dispatched UAV is normal, it is determined as the target UAV.

[0082] In this embodiment, the target UAV refers to a pre-dispatched UAV whose own status has been detected to be normal and can operate normally, and it is determined as the target UAV.

[0083] As an optional implementation method, after confirming that the various status indicators of the pre-dispatched UAV meet the mission requirements, it is officially selected as the target UAV to perform this inspection mission.

[0084] For example, the various status indicators of the pre-dispatched drones are first monitored and evaluated in real time to ensure that they have the ability to perform tasks, and the evaluation standards for various status indicators are set according to the task requirements. For example, the battery power must be higher than a certain threshold, etc. In a long-term inspection task, the battery status of the "WRJ3001 drone" in the pre-dispatched drones is not good and cannot meet the inspection task requirements. It is replaced by the "WRJ3003 drone". After testing, the "WRJ3003 drone" meets the inspection task requirements and is identified as the target drone.

[0085] Since the smart pole can select the target drone and plan the inspection route based on the drone’s own status, and has the function of replacing drones that do not meet the inspection mission nearby, it ensures the completion of the inspection mission and improves the effectiveness of urban inspections.

[0086] Based on any of the above embodiments, in Embodiment 6 of the present application, refer to Figure 6 , Figure 6 This is a flowchart of the sixth embodiment of the drone inspection method based on the smart pole of the present application. Step S40 includes steps E11-E12: Step E11, the cloud determines the city benchmark data corresponding to the inspection area based on the point cloud data in the inspection data.

[0087] In this embodiment, point cloud data refers to a set of points in a three-dimensional space obtained by laser scanning, photogrammetry and other technologies, each of which contains three-dimensional coordinates and possible additional attributes such as color, intensity, etc. Point cloud data is an important component of urban benchmark data, which is used to describe the city's topography, buildings, roads, vegetation and other geographic information.

[0088] As an optional implementation method, the cloud analyzes the point cloud data collected during the inspection mission, compares and integrates it with the city benchmark data, and determines the precise location and status of the inspection area through geometric features and semantic annotation information, reminds of abnormal area status, and updates normal area status.

[0089] Step E12, comparing the inspection data with the city benchmark data, and determining the data comparison result.

[0090] In this embodiment, the data comparison result refers to the comparison result generated after comparing the inspection data of the drone with the city benchmark data. Through the comparison, the difference between the current status and the benchmark status of urban infrastructure such as roads, bridges, buildings, etc. can be monitored, and potential problems can be discovered in time. Changes in the urban environment can also be detected, such as the construction and demolition of buildings, the reconstruction of roads, the growth of vegetation, etc., and the accuracy and reliability of the collected point cloud data can also be evaluated to ensure data quality.

[0091] As an optional implementation, the inspection data is compared with the city benchmark data to determine the data comparison result, and then generate a detailed difference report and change map details.

[0092] For example, suppose a city needs to conduct regular inspections of infrastructure in the downtown area to monitor the conditions of roads, bridges, buildings, etc. The cloud platform is responsible for managing and analyzing the inspection data and comparing it with the city benchmark data to identify any changes or anomalies. The cloud platform retrieves the city benchmark data of the corresponding area from the city benchmark database based on the geographic information of the inspection area. The drone inspects the corresponding area to obtain the inspection data, compares it with the city benchmark data of the area, and generates a detailed difference report, including geometric differences and semantic differences. The report lists the location, type and degree of each difference point in detail.

[0093] Since the inspection data collected by drones are summarized by smart poles and transmitted back to the cloud, the cloud independently compares the data and generates data comparison results, ensuring that engineers and relevant departments can respond quickly and improving the effectiveness of urban inspections.

[0094] Based on any of the above embodiments, in Embodiment 7 of the present application, refer to Figure 7 , Figure 7 This is a flowchart of the seventh embodiment of the drone inspection method based on the smart pole of the present application. Step S50 includes steps F11 to F13: Step F11: If the cloud determines that the inspection data has data defects, the re-inspection instruction is generated.

[0095] In this embodiment, data defects refer to situations where the data format is not uniform, other data is interspersed in the data, and the data does not exist. The re-inspection instruction is an instruction to re-inspect the inspection area corresponding to the inspection data, analyze the cause of the data defects, and optimize and adjust the original inspection instructions.

[0096] As an optional implementation, when the cloud checks the inspection data and determines that the inspection data has data defects, it generates corresponding re-inspection instructions based on the inspection data and sends the re-inspection instructions to the smart pole.

[0097] Step F12: If it is determined based on the data comparison result that there is a problem in the inspection area, the prompt information is generated.

[0098] In this embodiment, problems in the inspection area refer to differences in data comparison results and the occurrence of dangerous situations determined by the cloud.

[0099] As an optional implementation, after the inspection data is compared with the city benchmark data, the system evaluates the status of the inspection area based on the comparison results. If an abnormality or problem is found, the system will generate a prompt message to notify the relevant management personnel or system for further processing.

[0100] Step F13: If it is determined that the inspection data has data defects and the inspection area has problems, the re-inspection instruction and the prompt information are generated.

[0101] In this embodiment, when data defects and problems in the inspection area occur simultaneously, the cloud will simultaneously generate a re-inspection instruction and a prompt message.

[0102] As an optional implementation, after completing the comparison between the inspection data and the city benchmark data, the system not only evaluates the status of the inspection area, but also checks the quality of the inspection data. If defects are found in the inspection data, such as insufficient accuracy, missing data, etc., and there are abnormal problems in the inspection area, the system will simultaneously generate re-inspection instructions and prompt information to ensure the accuracy of the data and timely handling of the problem.

[0103] Exemplarily, the cloud platform generates an inspection task, specifies an inspection area, and assigns a pre-dispatched drone number. The drone flies over the designated area according to the preset inspection plan and collects point cloud data. The collected point cloud data is uploaded to the smart pole, and then uploaded to the cloud platform by the smart pole. The cloud platform conducts a quality assessment on the collected point cloud data and finds the following data defects: the point cloud data is not accurate enough, there are some missing values ​​in the data, which affects the integrity of the data, and checks whether the data has missing or duplicate values. Then, the cloud platform compares the collected point cloud data with the city benchmark data, calculates the geometric differences and semantic differences, and finds the following problems: cracks appear on the road surface of a certain section of road, the geometric difference exceeds the set threshold, an unknown structure is added to the roof of a building, and the semantic annotation changes. Then, the system determines that the inspection data has defects, insufficient accuracy, missing data, and problems in the inspection area, such as cracks in the road surface and new structures. Re-inspection instruction content: The re-inspection area is a certain road section and a certain building. The re-inspection requirement is to use higher-precision laser scanning equipment to ensure that the data accuracy is within the set threshold. The re-inspection time is recommended to be completed within 3 days. Prompt information is generated: pavement cracks on a certain road section, location coordinates (X1, Y1, Z1), crack length 1 meter, width 0.1 meter, and an unknown structure added to the roof of a certain building.

[0104] Since the cloud can perform intelligent analysis of data comparison results, it ensures the authenticity of the data comparison results and the effectiveness of the prompt information, thereby improving the efficiency and response speed of urban management.

[0105] Based on any of the above embodiments, in Embodiment 8 of the present application, refer to Figure 8 , Figure 8 This is a flowchart of the eighth embodiment of the drone inspection method based on the smart pole of the present application. After step S50, it also includes steps G11~G12: Step G11, the cloud generates the prompt information and sends it to the control platform.

[0106] In this embodiment, the control platform is the core system for urban inspection task management and data analysis. It integrates multiple technical means to achieve efficient inspection and management of urban infrastructure, environment and equipment. The administrator can issue inspection tasks through the control platform and notify relevant departments based on prompt information.

[0107] As an optional implementation, after completing data processing and analysis, the cloud platform generates prompt information according to preset rules and conditions, and sends the prompt information to the control platform of the intelligent inspection system through the communication network.

[0108] In step G12, the control platform displays the information content in the prompt information and notifies relevant departments based on the danger information in the prompt information.

[0109] In this embodiment, the information content refers to the description of the inspection abnormal area in the prompt information. The dangerous situation information refers to the description of the situation of the dangerous situation area in the prompt information.

[0110] As an optional implementation method, after the control platform receives the prompt information sent from the cloud, the information content, such as problem description, suggested measures, etc., is visually displayed on the interface, and based on the dangerous information in the prompt information, the relevant responsible departments or personnel are promptly notified through the preset communication channels so that countermeasures can be taken quickly.

[0111] For example, the control platform receives the prompt information sent from the cloud and displays the details on the interface. According to the urgency of the prompt information, it triggers a sound alarm and a flashing prompt to alert the management personnel. The control platform extracts the dangerous information from the prompt information, determines the department that needs to be notified, and sends a notification to the relevant personnel of the city management department via SMS and email, including the problem description and urgency. It also sends an instant notification to the maintenance team via WeChat for Business to ensure the real-time delivery of information. The relevant departments or personnel view the details of the prompt information on the control platform, and assign tasks to the corresponding maintenance team or processing personnel according to the content of the prompt information. After receiving the task, the maintenance team goes to the site for inspection and repair, and feeds back the processing results to the control platform.

[0112] Due to the setting of the control platform, managers can quickly learn about the situation in abnormal areas and dangerous situations, and can quickly notify relevant departments of the dangerous situation information, thereby improving the efficiency and intelligence level of urban management.

[0113] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the drone inspection method based on smart poles of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0114] The present application provides a drone inspection device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the drone inspection method based on the smart pole in the above-mentioned embodiment one.

[0115] Reference below Fig. 9 , which shows a schematic diagram of the structure of the drone inspection device suitable for implementing the embodiment of the present application. The drone inspection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, personal digital assistants (PDA, Personal Digital Assistant), tablet computers (PAD, Portable Application Description), portable multimedia players (PMP, Portable Media Player:), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The drone inspection equipment shown is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present application.

[0116] like Fig. 9As shown, the drone inspection device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM) 1004. In the random access memory 1004, various programs and data required for the operation of the drone inspection device are also stored. The processing device 1001, the read-only memory 1002 and the random access memory 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the drone inspection device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a drone inspection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0117] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0118] The drone inspection equipment provided by the present application adopts the drone inspection method based on smart poles in the above embodiment, which can solve the technical problem of being unable to respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency. Compared with the prior art, the beneficial effects of the drone inspection equipment provided by the present application are the same as the beneficial effects of the drone inspection method based on smart poles provided in the above embodiment, and the other technical features of the drone inspection equipment are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0119] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0120] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0121] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the smart pole-based drone inspection method in the above-mentioned embodiment.

[0122] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM, Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM, CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, radio frequencies (RF, Radio Frequency), etc., or any suitable combination of the above.

[0123] The above-mentioned computer-readable storage medium may be included in the drone inspection device; or it may exist independently without being assembled into the drone inspection device.

[0124] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the drone inspection device, the drone inspection device: the smart pole determines the target drone and the inspection plan of the target drone according to the triggered inspection task, and sends the inspection plan to the target drone; controls the target drone to execute the inspection plan to collect inspection data, and returns the inspection data to the smart pole; the smart pole uploads the received inspection data to the cloud; the cloud performs data comparison based on the inspection data and the urban benchmark data of the area where the smart pole is located to determine the data comparison result; if the data comparison result meets the abnormal condition, generates a re-inspection instruction and / or prompt information according to the data comparison result.

[0125] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0127] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0128] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned smart pole-based drone inspection method, and can solve the technical problem of being unable to respond to problems in the entire urban area in a timely manner, resulting in low inspection efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the smart pole-based drone inspection method provided by the above-mentioned embodiment, and will not be repeated here.

[0129] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A drone inspection method based on a smart pole, characterized in that: Applied to an intelligent inspection system, the intelligent inspection system includes a smart pole, a drone and a cloud, and the method includes: The smart pole determines the target drone and the inspection plan of the target drone according to the triggered inspection task, and sends the inspection plan to the target drone; Controlling the target drone to execute the inspection plan to collect inspection data, and returning the inspection data to the smart pole; The smart pole uploads the received inspection data to the cloud; The cloud performs data comparison based on the inspection data and the city benchmark data of the area where the smart pole is located to determine the data comparison result; If the data comparison result meets the abnormal condition, a re-inspection instruction and / or prompt information is generated according to the data comparison result.

2. The smart pole-based drone inspection method according to claim 1, characterized in that: The method further comprises: The smart pole receives the inspection task sent by the cloud; Alternatively, the smart pole senses the presence of abnormal environmental data and generates the inspection task according to the abnormal environmental data.

3. The smart pole-based drone inspection method according to claim 2, characterized in that: The method for determining the target drone comprises: Based on the inspection task, determine the task area and the number of the pre-dispatched drone; The target drone is determined according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number.

4. The smart pole-based drone inspection method according to claim 3, characterized in that: After the step of determining the target drone according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number, the method further includes: Determine the task area and inspection key locations according to the inspection tasks; An inspection plan for the target UAV is determined based on the task area marked with the inspection key position and in combination with the state of the target UAV itself.

5. The smart pole-based drone inspection method according to claim 3, characterized in that: The step of determining the target drone according to the pre-dispatched drone number and the state of the drone corresponding to the pre-dispatched drone number comprises: Determining the pre-dispatched drone according to the pre-dispatched drone number; Based on the state of the pre-dispatched drone, determining whether the state of the pre-dispatched drone is normal; If it is determined that the state of the pre-dispatched drone is abnormal, a nearby drone is selected for replacement, and the state of the pre-dispatched drone after the replacement is determined again; If it is determined that the state of the pre-dispatched UAV is normal, it is determined as the target UAV.

6. The smart pole-based drone inspection method according to claim 1, characterized in that: The cloud performs data comparison based on the inspection data and the city benchmark data of the area where the smart pole is located, and the step of determining the data comparison result includes: The cloud determines the city benchmark data corresponding to the inspection area according to the point cloud data in the inspection data; The inspection data is compared with the city benchmark data, and the data comparison result is determined.

7. The inspection method of a drone based on a smart pole according to claim 6, characterized in that: If the data comparison result meets the abnormal condition, the step of generating a re-inspection instruction and / or prompt information according to the data comparison result includes: If the cloud determines that the inspection data has data defects, it generates the re-inspection instruction; If it is determined that there is a problem in the inspection area based on the data comparison result, the prompt information is generated; If it is determined that the inspection data has data defects and there are problems in the inspection area, the re-inspection instruction and the prompt information are generated.

8. The smart pole-based drone inspection method according to claim 7, characterized in that: After the step of generating a re-inspection instruction and / or prompt information according to the data comparison result if the data comparison result meets the abnormal condition, the method further includes: The cloud generates the prompt information and sends it to the control platform; The control platform displays the information content in the prompt information and notifies relevant departments based on the danger information in the prompt information.

9. An unmanned aerial vehicle inspection device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the smart pole-based drone inspection method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the smart pole-based drone inspection method according to any one of claims 1 to 8 are implemented.

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