Quay crane digital twin detection system and method using unmanned aerial vehicle

Abstract

The invention discloses a quay crane digital twin detection system using an unmanned aerial vehicle, and the system comprises a website management platform which is used for achieving task management, user management, customer management and synchronous data storage and reading; and the hangar is arranged on a wharf and establishes communication with the website management platform, a plurality of unmanned aerial vehicles can be arranged in the hangar in a matched mode, or a plurality of hangars and unmanned aerial vehicles are arranged on the port wharf. The invention further discloses a quay crane digital twinning detection method using the unmanned aerial vehicle. According to the invention, the VT detection efficiency is improved, the safety of quay crane equipment and the use safety and reliability are improved, the image information and the corresponding digital information of important parts of the quay crane can be quickly known in real time, so that a correct decision can be made, and the production efficiency is also improved.

Description

Technical Field

[0001] The present invention relates to quay crane detection technology, and more specifically, to a quay crane digital twin detection system and method using an unmanned aerial vehicle. Background Art

[0002] The shore container crane, also known as the quay crane, is a tall mechanical equipment with a static height of more than 90 meters and a total length of more than 150 meters for the front and rear beams. It can move along the track pre-laid at the dock. Recently, drone technology has been used to detect and study it, but due to various factors, the detection work often takes a long time, and many times it is difficult to obtain accurate high-definition images, making it difficult to make accurate judgments, or using full-HD video shooting methods, which generates a large amount of video data at a time, and the later processing work is also particularly difficult. Many times, we focus on a key area and a key connection point, which requires immediate detection. There is no need to spend a particularly high price and several days to do some detection work that is not actually needed. The effectiveness and efficiency of the current detection method of drones for shore cranes need to be improved.

[0003] Digital twin technology has been developed for many years, and some terminals have also established digital twin systems. The central control rooms of many terminals have realized large-screen monitoring of equipment and operation of equipment functions. Some digital twin technologies have been applied in terminals, but they have not been applied to the detection of actual quay cranes and other equipment, especially since many quay crane products are products from many years ago. Summary of the invention

[0004] In view of the defects existing in the prior art, the purpose of the present invention is to provide a digital twin detection system and method of a quay crane using drones to solve the problems of detection accuracy and operating efficiency, and to quickly and in real time know the image display status of important parts of the quay crane so as to make correct decisions.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] The first aspect of the present invention provides a digital twin detection system for a quay crane using an unmanned aerial vehicle, comprising:

[0007] Website management platform, used to realize task management, user management, customer management and synchronous storage and reading of data;

[0008] A hangar is provided with one or more units, all arranged on the dock, and establishes communication with the website management platform, and the hangar is equipped with multiple drones;

[0009] Preferably, the website management platform includes:

[0010] A UAV flight control management unit, used to plan the flight path and operation actions of the UAV;

[0011] Modeling unit, using digital twin technology to build a three-dimensional model of the inspection area;

[0012] a diagnostic unit for diagnosing data transmitted by the drone;

[0013] Database, used to store and read data synchronously.

[0014] Preferably, the hangar and the website management platform establish communication using a wired network or a wireless network.

[0015] Preferably, the UAV is a four-wing UAV.

[0016] Preferably, the quay crane digital twin detection system further includes:

[0017] The central control room is provided with a display screen which uses a split-screen display of multiple scenes.

[0018] The second aspect of the present invention provides a digital twin detection method for a quay crane using an unmanned aerial vehicle, and the digital twin detection system for a quay crane using an unmanned aerial vehicle provided by the first aspect of the present invention is used to perform the following steps:

[0019] S1, using the drone to scan and take images of the wharf and port machinery equipment that need to be inspected, and using the modeling unit to build a three-dimensional model, in which the movable equipment and the wharf and its fixed objects are built separately, and given a constraint relationship that conforms to the actual situation, and the built model is imported into the drone flight control management unit; if there is already a model and the position of the port machinery equipment has changed, the port machinery equipment model can be moved in the virtual environment according to the actual positioning signal of the port machinery equipment to correspond to the actual position;

[0020] S2, planning the operation trajectory and operation command actions of the UAV that need to be performed in the virtual simulation interface of the three-dimensional model, or importing the planned operation trajectory and related command actions after performing coordinate conversion according to the current coordinate positioning;

[0021] S3, performing simulation operations on the virtual simulation interface of the three-dimensional model to verify and determine the feasibility of the operation trajectory and operation instruction actions of the UAV;

[0022] S4, if feasible, execute step S5, if not feasible, return to step S2;

[0023] S5, the drone flies to the designated detection area according to the planned safe route;

[0024] S6, the UAV starts to operate according to the planned and verified trajectory and mission;

[0025] S7, the drone transmits the collected data to the website management platform through the communication transmission module;

[0026] S8, the website management platform calls the modeling function module to partially or completely update the three-dimensional model, instantly maps the current state of the equipment entity in the virtual environment, and calls the diagnosis system to diagnose the collected data, including historical data comparison and disease identification diagnosis;

[0027] S9, the website management platform issues conclusions and reports.

[0028] Preferably, the modeling unit performs three-dimensional simulation modeling or three-dimensional solid modeling on the wharf and the port machinery equipment respectively;

[0029] The fixed part and the movable part of the dock are modeled separately.

[0030] Preferably, the virtual scene in the three-dimensional model is established in a 1:1 ratio with the actual scene and is clearly displayed on the display screen.

[0031] Preferably, the operation task action of the drone includes a fully automatic mode and a semi-automatic mode;

[0032] The fully automatic mode is that after the UAV receives the operation task, the operator issues the start and take-off instructions and then automatically completes the subsequent data collection work;

[0033] The semi-automatic mode is that the UAV flies fully automatically and manually focuses and shoots to acquire data; or when the UAV flies automatically and waits to shoot and acquire data at the planned position, the position of the UAV is manually slightly adjusted and the data is manually focused and shot to acquire data.

[0034] Preferably, the drone uses a target recognition algorithm to collect data;

[0035] The UAV flight control management unit processes the images collected by the UAV using an image recognition algorithm and outputs a reference result;

[0036] An image algorithm model is arranged in the diagnosis unit.

[0037] The digital twin detection system and method of a quay crane using an unmanned aerial vehicle provided by the present invention combine the unmanned aerial vehicle technology with the digital twin technology, and also apply a target recognition algorithm and a refined obstacle avoidance trajectory planning method; by mapping the real-time status and historical data of the quay crane, the comparison of historical data and the combination of disease identification provide an intuitive image visual and digital display reference for the safety and reliability trend of the quay crane equipment, so that the equipment can truly meet the requirements of pre-use detection, improve the VT detection efficiency, and make correct decisions based on the detection data and results, thereby increasing the safety of the quay crane equipment and the safety and reliability of use, while also improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the digital twin detection method of the quay crane of the present invention. DETAILED DESCRIPTION

[0039] In order to better understand the above technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0040] The present invention provides a digital twin detection system for a quay crane using an unmanned aerial vehicle, comprising:

[0041] The website management platform is used to implement some basic functions such as task management, user management, customer management, and synchronous storage and reading of data, which are convenient for task management and allocation. At the same time, the website management platform is embedded with the UAV flight control unit, digital twin technology, and modeling unit, and the frequency division display function of each functional module.

[0042] The hangar is located on the dock and communicates with the website management platform. There are multiple drones in the hangar.

[0043] The above website management platform includes:

[0044] The UAV flight control management unit is used to plan the flight path and operation actions of the UAV;

[0045] Modeling unit, using digital twin technology to build a three-dimensional model of the inspection area;

[0046] A diagnostic unit to diagnose the data transmitted by the drone;

[0047] Database, used to store and read data synchronously.

[0048] The UAV flight control management unit is connected to the UAV in real time to transmit commands and return data. The UAV flight control management unit displays the UAV's first-person view image and the main camera view image in real time. The UAV flight control management unit is embedded with a three-dimensional model function, and the real-time digital twin model is imported into the UAV flight control management unit. The real-time position information of the UAV during operation is displayed in the digital twin model in real time and accurately, which is easy to observe.

[0049] Establish a web-based management system. In addition to the normal web client and management functions, the web-based management system has a module for registering and managing multiple drones. It can deploy and instruct the drones registered in the management system. Each drone has a separate working function module for separate display and command control. The main page displays the status and location information of each drone in real time.

[0050] The interactive interface of the web page can display a synchronized virtual and real scene. When the drone is operating, the position of the drone can be displayed in real time in a virtual environment. Combined with the drone's first FPV perspective and the camera's perspective, the specific location of the drone's shooting equipment can be accurately known. The captured image data is also transmitted to the website management platform in a timely manner, and a picture display link is given at the corresponding location for real-time viewing.

[0051] Large movable steel structure equipment such as shore container cranes have communication facilities, wired or wireless networks, and high-precision locators. The real-time coordinate position information and status of the shore crane are reflected in the virtual scene through the locator. The shore crane to be tested needs to have a more accurate three-dimensional model, which can be generated by design software or by using modeling software after drone photography and scanning. The shore crane model and the scene model of the terminal have a relationship consistent with the actual situation. For example, the shore crane can move along a certain section of the track line of the terminal, and the position relationship between several shore crane equipment, etc., have corresponding constraints in the virtual and real scene. If the information sent by the locator is inconsistent with the constraint relationship or the deviation is large, the site scene needs to be confirmed. If the locator information is wrong, the locator needs to be calibrated, repaired or replaced. The communication network should be transmitted in real time, efficiently and accurately to ensure the accuracy of port machinery products, equipment to be tested and the surrounding environment.

[0052] The relationship between the quay crane equipment model to be inspected and the actual scene is reflected in real time in the virtual scene and displayed in three dimensions at a ratio of 1:1. It can be used for measurement and planning in the virtual scene, and used for global reference positioning in combination with the first-person perspective during actual operations to facilitate accurate decision-making. Secondly, the positioning device of the quay crane can feed back the position coordinate information and posture information to the management and control platform system in real time to ensure that the virtual environment is consistent with the actual environment of the terminal, especially the coordinate position information and action posture of the quay crane are consistent with the actual situation.

[0053] Wireless WIFI signals and cameras are also deployed on site. When the upper steel structure interferes with GPS and other satellite signals, and the drone is located in a weak GPS signal area, WIFI wireless positioning and visual Slam technology can be used for auxiliary positioning and calibration of relative position information to complete the task. Instant messaging technology is a major guarantee for convenient work.

[0054] The digital twin detection system of the quay crane also includes:

[0055] The central control room is equipped with a display screen that uses a multi-scene frequency division display.

[0056] Large-screen display and split-screen display technology are also very important. The digital twin model and different local and overall interfaces are displayed in split-screen. The drone's FPV first-person perspective and the mounted lens perspective are displayed synchronously in real time, combining local analysis with overall control. The working status of the drone, especially when multiple drones are working, can more clearly know the status of each drone and the status of the detected equipment, which is conducive to making correct decisions.

[0057] Combination Figure 1 As shown, the present invention also provides a quay crane digital twin detection method using a drone, and the quay crane digital twin detection system of the present invention is used to perform the following steps:

[0058] S1, using the drone to scan and take images of the wharf and port machinery equipment that need to be inspected, and using the modeling unit to build a three-dimensional model, in which the movable equipment and the wharf and its fixed objects are built separately, and given a constraint relationship that conforms to the actual situation, and the built model is imported into the drone flight control management unit; if there is already a model and the position of the port machinery equipment has changed, the port machinery equipment model can be moved in the virtual environment according to the actual positioning signal of the port machinery equipment to correspond to the actual position;

[0059] S2, planning the three-dimensional curve (or broken line) operation trajectory and operation instruction action of the UAV in the virtual simulation interface of the three-dimensional model, or importing the planned three-dimensional curve (or broken line) operation trajectory and related instruction actions after coordinate conversion according to the current coordinate positioning;

[0060] S3, performing simulation operation on the virtual simulation interface of the three-dimensional model to verify and judge the feasibility of the operation trajectory and the operation instruction action;

[0061] S4, if feasible, execute step S5, if not feasible, return to step S2;

[0062] S5, the drone flies to the designated detection area according to the planned safe route;

[0063] S6, the UAV starts to operate according to the planned and verified trajectory and mission;

[0064] S7, the drone transmits the collected data to the website management platform through the communication transmission module;

[0065] S8, the website management platform calls the modeling function module to partially or completely update the three-dimensional model, instantly maps the current state of the equipment entity in the virtual environment, and calls the diagnosis system to diagnose the collected data, including historical data comparison and disease identification diagnosis;

[0066] S9, the website management platform issues conclusions and reports.

[0067] In the digital twin detection method of the quay bridge of the present invention, it is necessary to perform three-dimensional simulation modeling or three-dimensional entity modeling on the wharf and the quay bridge respectively, and model the fixed part and the movable part of the wharf separately, and map and display the wharf scene in the virtual environment of the metaverse and reflect the mutual relationship between the movable devices and the fixed objects of the wharf in real time; there is accurate geographic location coordinate information, relatively accurate three-dimensional size information, accurate size information that can meet the detection requirements, and relatively accurate three-dimensional image display. In particular, the three-dimensional model of the quay bridge, as a large-scale steel structure production equipment that can be moved at the wharf, needs real-time location information and size information to meet the requirements of intelligent detection of equipment. The model information of the quay bridge should accurately map its three-dimensional image and size information, and reflect its relationship with the fixed place and fixed objects of the wharf in real time. When the technology allows, the digital twin technology is used to map other objects or people that change to the corresponding positions according to the actual situation, so as to realize the combination of virtual and real, which is more conducive to the obstacle avoidance and refinement requirements of the drone detection trajectory planning. The metaverse technology combines the virtual scenes of the wharf and the quay bridge with the specific location information of reality to accurately plan the operation path and tasks.

[0068] In a virtual environment, tasks are planned, operations are completed in simulation, and operation paths and methods are debugged to achieve the optimal solution for safe operations. The optimal solution can be directly pushed to the quay crane inspection task platform, and the actual operation drone is controlled to complete the operation according to the optimal solution. In the process of completing the operation, the position of the drone in the virtual environment is synchronized with the actual operation drone. The relative position of the actual drone can be accurately displayed in the virtual environment, and the first-person perspective of the actual drone is also displayed at the same time. The various indicator parameters of the drone and the quay crane (including position parameters) are displayed in real time. The operator and the experts in front of the screen can better understand the actual situation and make correct decisions based on this more comprehensive information.

[0069] Compared with the fixed model, the port machinery equipment has a relative constraint and motion relationship with the wharf, that is, the port machinery equipment can move on the track set by the port wharf.

[0070] The planned operation trajectory and action are stored in the form of reference digital coding, and the actual operation track is also stored in the format of digital coding. The storage content is relative coordinate information, that is, the position information and attitude information of the drone relative to the quay crane, and the relevant information of the camera. The storage information is related to the quay crane information. Each detection area is stored separately. When calling and reusing the existing track and operation information, the corresponding storage information can be accurately found. After the position of the on-site quay crane changes, or another quay crane with the same parameter information in the same batch of the terminal, for a specific detection point on the quay crane, the relative position of the drone detection operation can remain unchanged. If the drone uses GPS and other satellite positioning, the coordinate information and attitude information called can be converted according to the coordinate conversion formula. The absolute GPS coordinate information during the operation can be converted to reproduce the previous operation task according to the existing operation method. Avoid repeated planning of trajectories and repeated teaching of drone operations. When the quay crane detection operation that does not match the stored information is called, the display interface will pop up a window to prompt that the trajectory information does not match the equipment to be detected, and it is necessary to recheck, plan, adjust and save before use. After calling the existing historical track information, before the detection work is carried out, the track information can be graphically displayed in the relative position of the simulation model. It can even quickly simulate the detection work task in the simulation model. After confirmation, the track information converted according to the actual position and related action instructions are sent to the UAV to be carried out.

[0071] The drone carries high-definition cameras and multi-spectral cameras, and can also work with cameras integrated with AR technology. With the help of the virtual and real combination of the metaverse, it can quickly and accurately capture overall and local high-definition photos and multi-spectral images in real time, and transmit the pictures and images to the quayside diagnostic system and display system. The FPV perspective of the drone and the perspective of the camera lens used for shooting are displayed in real time on the control platform.

[0072] There are two operating modes for drones, fully automatic mode and semi-automatic mode. In fully automatic mode, after receiving the operation task, the drone will automatically complete the subsequent data collection work after the operator issues the start work and take-off instructions. Semi-automatic mode can be fully automatic flight, manual focus shooting to obtain data; or semi-automatic mode is automatic flight in the planned position to shoot and obtain data, and it is found that adjusting the camera angle cannot achieve the best angle. Here, the position of the drone is slightly adjusted within the prescribed constraint range (such as within a diameter of 0.5 meters) to achieve the best viewing angle and focus, and obtain more effective color image data. Both fully automatic and semi-automatic are operation modes to ensure efficient and accurate data collection.

[0073] The constraints for fine-tuning the drone position in semi-automatic mode are formulated to ensure safety and are based on actual conditions, and range planning can be performed in the virtual simulation of the Metaverse.

[0074] The actual situation reflected by the first-person perspective is also directly transmitted to the management and control platform, and the perspective of the main camera is also transmitted to the management and control platform in real time, and photos are taken and stored for experts or diagnostic systems to make the next work instructions after diagnosis.

[0075] Deploying a drone hangar means placing the hangar close to the equipment to be tested or the environment to be tested. The hangar should be within the transmission distance between the drone and the hangar. The hangar is connected to the power supply and wired network; the hangar and the supporting drone (usually a four-wing aircraft) have a corresponding registration relationship with the website platform. The website management platform can manage and assign tasks to multiple hangars and multiple drones. The hangar has basic inspection and maintenance functions for drones, can protect against wind, rain and dust, and can charge or replace the battery for drones. Before takeoff and after landing, the hangar can perform appearance inspections on drones; more advanced hangars are equipped with X-ray flaw detection instruments, which can perform comprehensive inspections on drones before takeoff and after landing to ensure safer flights.

[0076] The newly collected photos by the drone have important attributes such as relative position information. The automatic modeling software can be used to effectively update the local part of the picture to the three-dimensional model of the quay crane. The new model and the historical model can be compared to show the historical change trend of the local structure, and predict the change and development trend of the local area in the future, so as to effectively make correct decisions. By updating the newly captured image data to the existing quay crane model, the digital model of the quay crane is kept consistent with the equipment on the actual terminal, which is conducive to the quay crane manager to know the status of the quay crane in real time. In a sense, a new model of digital twins has been achieved, especially for old quay cranes that have been in use for many years. This digital twin detection method and real-time status awareness have a more important safety role.

[0077] During the operation, the target recognition technology of edge technology assists in focusing to achieve more accurate operation results. As an edge computing technology, target recognition technology is applied to the front-end operation of drones. It can automatically find the key areas of concern and focus on shooting images or pictures to ensure efficient and accurate collection. When there are new areas that cannot be identified and need to be photographed, experts can intervene and direct the manual focus mode to collect and shoot image data, and at the same time update the recognition of this image to the target recognition algorithm.

[0078] After the image recognition is completed, the image is processed using image recognition technology and the reference result is output. Defect image recognition technology is applied to defect diagnosis system (disease diagnosis system). The trained image algorithm model is arranged in the diagnosis system. The pictures or images transmitted back in real time are diagnosed by this image diagnosis system and reviewed by professionals. The algorithm model training of image recognition technology includes high-precision photos and multi-spectral images. Many key parts are judged by combining multiple factors, including historical data, multi-spectral photos, stress status and other factors.

[0079] Example

[0080] This embodiment provides a digital twin detection system for a quay crane using a drone, as follows:

[0081] 1) Web-based website management platform, including:

[0082] Database, synchronous storage and reading of data;

[0083] Task management, user management, customer management and other functions are some basic functions that facilitate task management and allocation. The website management platform is embedded with drone flight control unit, digital twin technology, modeling unit, and frequency division display function of each functional module.

[0084] 2) Establish a UAV flight control unit

[0085] The drone connected to the UAV flight control unit can complete the task automatically or semi-automatically according to the planned flight path and operation action. The semi-automatic action and position adjustment are micro-motion, within the limited range, to ensure sufficient safety. The flight control platform technology is embedded in the management platform together with other technologies. A trajectory planning module is developed in the UAV flight control unit based on the existing UAV technology. The trajectory planning module defines the trajectory path and action instructions of the UAV according to the characteristics of the quay crane equipment and the detection requirements.

[0086] 3) Create or use 3D models

[0087] Using digital twin technology, a three-dimensional model is established in areas that require frequent inspections. The displacement constraint management between the fixed scene of the dock and the port machinery product equipment can update the position information of the three-dimensional model in real time according to the position changes of the port machinery quay crane, thereby ensuring that the image displayed on the virtual interface during operation is consistent with the actual situation. The drone's route to be operated is superimposed on this simulation model, and the position information of the drone's route to be operated relative to the equipment and the dock can be directly observed in real time, which is convenient for verifying the feasibility of the route. During the drone simulation operation and actual operation, the drone's real-time position mapping information is displayed in the simulation scene, and global macro-level information can be obtained intuitively.

[0088] 4) Display screen display in the central control room

[0089] Both simulation planning and actual operation require a larger screen to display the relative position of the drone and the quay crane to be inspected. The large-screen display is clearer and more intuitive, especially under the multi-scene frequency division display technology, each module can be displayed at the same time.

[0090] 5) Choose a suitable place at the dock to arrange the hangar. The hangar should be within the transmission distance of the drone and the product to be tested. The hangar has power supply and wired network; the hangar and the supporting drone (usually a four-wing aircraft) have a corresponding registration relationship with the website platform. The website management platform can manage and assign tasks to multiple hangars and multiple drones.

[0091] This embodiment provides a digital twin detection method for a quay crane using a drone, which is as follows:

[0092] After the website management platform and the UAV flight control unit are built, the images of the docks and port machinery equipment that need to be inspected are scanned by the UAV, and the 3D solid model is established through the modeling software. The 3D solid model is imported into the UAV flight control unit, and the 3D solid model meets the above requirements.

[0093] Plan the working track and action of the UAV of the quay crane to be inspected in the virtual simulation interface. Use the planned track to perform simulation verification on the simulation platform first. After confirming that the track is correct, save the track again and send it to the UAV to be inspected.

[0094] The drone waiting for work is located in a fixed hangar near the inspection area of ​​the dock. After receiving the command, the drone performs a self-inspection and the hangar conducts an external inspection of the drone. After confirming that it is normal, the hangar opens and the drone is ready to take off.

[0095] After the website management platform receives the information that the drone is ready, the operator confirms safety through the on-site images and environment on the large screen in the control room. The operator can click to send the task, and then take off with one click, and the drone will start the operation mode.

[0096] During the operation, the operator observes the operation status through the central control screen. When it is found that there is a visual deviation at a certain position and the camera angle needs to be adjusted manually, you can click to select the semi-automatic mode. When the adjustment of the camera angle cannot meet the requirements and the position of the drone needs to be fine-tuned, switch to another semi-automatic mode. In another semi-automatic mode, the operator can remotely control the drone through the joystick to fine-tune the position of the drone within the allowed safety range. Both the automatic mode and the semi-automatic mode are designed to improve the operation efficiency. The final operation track attitude information and action instructions are saved. When saving, the relative coordinate information and attitude information are saved with the reference coordinate information of the quay crane. When one of the detection points still cannot meet the requirements through the adjustment of the semi-automatic mode during operation, the drone detection waypoint at that point needs to be re-planned and simulated, and the detection is performed again until the requirements are met, and the final valid track attitude information and action instructions are saved.

[0097] If there are multiple quay cranes of the same batch with the same parameters, after the first one is tested, the final track attitude information and action instructions are saved. The track attitude information and action instructions of the first one can be reused for the second one. It should be noted that the track attitude information needs to be converted according to the coordinate information of the second quay crane when reused, and after the simulation and verification in the simulation system, the instructions are sent to the drone on site to automatically complete the inspection.

[0098] When the inspected quay crane equipment is moved, the same inspection needs to be carried out again, and the inspection method is the same as the inspection method of the second quay crane.

[0099] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A digital twin detection system for a quay crane using an unmanned aerial vehicle, characterized in that: include: Website management platform, used to realize task management, user management, customer management and synchronous storage and reading of data; The hangar is arranged on the dock and establishes communication with the website management platform. The hangar is equipped with multiple drones.

2. The digital twin detection system for quay crane using drone according to claim 1 is characterized in that: The website management platform includes: A UAV flight control management unit, used to plan the flight path and operation actions of the UAV; Modeling unit, using digital twin technology to build a three-dimensional model of the inspection area; a diagnostic unit for diagnosing data transmitted by the drone; Database, used to store and read data synchronously.

3. The digital twin detection system for quay crane using drone according to claim 1 is characterized in that: The hangar and the website management platform establish communication via a wired network or a wireless network.

4. The digital twin detection system for quay crane using drone according to claim 1 is characterized in that: The UAV is a four-suspension-wing UAV.

5. The digital twin detection system for quay crane using drone according to claim 1 is characterized in that: The digital twin detection system of the quay crane also includes: The central control room is provided with a display screen which uses a frequency division display of multiple scenes.

6. A digital twin detection method for a quay crane using an unmanned aerial vehicle, characterized in that: The following steps are performed using the digital twin detection system for a quay crane using a drone as described in any one of claims 1 to 5: S1, using the drone to scan and take images of the wharf and port machinery equipment that need to be inspected, and using the modeling unit to build a three-dimensional model, in which the movable equipment and the wharf and its fixed objects are built separately, and given a constraint relationship that conforms to the actual situation, and the built model is imported into the drone flight control management unit; if there is already a model and the position of the port machinery equipment has changed, the port machinery equipment model can be moved in the virtual environment according to the actual positioning signal of the port machinery equipment to correspond to the actual position; S2, planning the three-dimensional curve (or broken line) operation trajectory and operation instruction action of the UAV in the virtual simulation interface of the three-dimensional model, or importing the planned three-dimensional curve (or broken line) operation trajectory and related instruction actions after coordinate conversion according to the current coordinate positioning; S3, performing simulation operation on the virtual simulation interface of the three-dimensional model to verify and judge the feasibility of the operation trajectory and the operation instruction action; S4, if feasible, execute step S5, if not feasible, return to step S2; S5, the drone flies to the designated detection area according to the planned safe route; S6, the UAV starts to operate according to the planned and verified trajectory and mission; S7, the drone transmits the collected data to the website management platform through the communication transmission module; S8, the website management platform calls the modeling function module to partially or completely update the three-dimensional model, instantly maps the current state of the equipment entity in the virtual environment, and calls the diagnosis system to diagnose the collected data, including historical data comparison and disease identification diagnosis; S9, the website management platform issues conclusions and reports.

7. The digital twin detection method of a quay crane using an unmanned aerial vehicle according to claim 6 is characterized in that: The modeling unit performs three-dimensional simulation modeling or three-dimensional solid modeling on the wharf and port machinery equipment respectively; The fixed part and the movable part of the dock are modeled separately.

8. The digital twin detection method of a quay crane using an unmanned aerial vehicle according to claim 6 is characterized in that: The virtual scene in the three-dimensional model is established in a 1:1 ratio with the actual scene and is displayed in three dimensions on the display screen.

9. The digital twin detection method of a quay crane using an unmanned aerial vehicle according to claim 6, characterized in that: The operation task actions of the UAV include fully automatic mode and semi-automatic mode; The fully automatic mode is that after the UAV receives the operation task, the operator issues the start and take-off instructions and then automatically completes the subsequent data collection work; The semi-automatic mode is that the UAV flies fully automatically and manually focuses and shoots to acquire data; or when the UAV flies automatically and waits to shoot and acquire data at the planned position, the position of the UAV is manually slightly adjusted, and then the UAV is manually focused and shot to acquire data.

10. The digital twin detection method of a quay crane using an unmanned aerial vehicle according to claim 6, characterized in that: The drone uses a target recognition algorithm to collect data; The website management platform calls an image recognition algorithm to process the image collected by the drone and outputs a reference result; An image algorithm model is arranged in the diagnosis unit.

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