Unmanned aerial vehicle management method based on intelligent robot and unmanned aerial vehicle honeycomb station

Abstract

The invention provides an unmanned aerial vehicle management method based on an intelligent robot and an unmanned aerial vehicle honeycomb station. The method comprises the following steps: carrying out initialization check on a honeycomb box; information matching and verification are carried out on the unmanned aerial vehicle and the honeycomb box, and the unmanned aerial vehicle is placed at a take-off point of the honeycomb station after verification is passed; the flight task is sent to the unmanned aerial vehicle through the remote management platform, and the unmanned aerial vehicle returns after completing the flight task; the intelligent robot guides landing of the unmanned aerial vehicle and performs secondary inspection on the unmanned aerial vehicle; uploading, collating and maintaining data of the unmanned aerial vehicle; and generating an unmanned aerial vehicle maintenance log, recovering the unmanned aerial vehicle to a standby state, and waiting for executing the next task. Through cooperation among the unmanned aerial vehicle, the intelligent robot, the honeycomb station and the remote management platform, automatic and intelligent monitoring management and maintenance of the unmanned aerial vehicle are realized, the labor cost is reduced, and the management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle management, and in particular to an unmanned aerial vehicle management method based on an intelligent robot and an unmanned aerial vehicle honeycomb station. Background Art

[0002] An unmanned aerial vehicle (UAV) is an aircraft that does not require a human pilot. It can fly autonomously via remote control or pre-programmed flight procedures and is typically managed by a control station, either remotely or autonomously. Currently, unmanned aerial vehicles (UAVs) are primarily drones, and their management is primarily manual. This leads to problems such as low human oversight efficiency and limited management coverage. For example: 1) After a UAV returns from a mission, it must be assessed and operated by professionals. Manual assessment of the wear and tear of the UAV's fuselage and propulsion system results in a high error rate, making proper maintenance and management impossible. 2) It is impossible to automatically and promptly purge the UAV, replace its battery, or relocate it, resulting in high labor costs. 3) In hazardous environments or those with significant operator health risks (e.g., high dust levels), personnel are unable to maintain 24 / 7 operation, making it impossible to effectively manage the UAV in a timely manner and potentially causing harm to the operator. This makes it impossible to effectively monitor and manage the UAV in a timely manner. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide an unmanned machine management method based on an intelligent robot and an unmanned machine hive station. Through the collaboration between the unmanned machine-intelligent robot-hive station-remote management platform, the unmanned machine is automated and intelligently monitored, managed and maintained, reducing labor costs and improving management efficiency.

[0004] To achieve the above objectives, the present invention provides the following solution: a method for managing unmanned machines based on intelligent robots and unmanned machine cellular stations, comprising the following steps:

[0005] Using an intelligent robot to scan and inspect the beehive boxes used to store unmanned machines, and uploading the scanned data to a remote management platform for digital management of the unmanned machines and initial inspection of the beehive boxes;

[0006] The intelligent robot reads the serial numbers on the unmanned machine and the beehive box, performs information matching and verification, and after verification, removes the unmanned machine from the beehive box, checks the battery and pulp of the unmanned machine, and places it at the take-off point of the beehive station, thereby completing the operation and management of the intelligent robot; the unmanned machine includes but is not limited to drones, unmanned aerial vehicles, unmanned vehicles, and unmanned ships;

[0007] Through the remote management platform, according to the mission objectives and requirements, the collaborative control model is used to coordinate the scheduling between the unmanned machine, the intelligent robot and the honeycomb box, and the flight control model is used to carry out the mission planning, mission control and mission management of the unmanned machine. Then, the data management model is used to implement the algorithm of the remote management platform and store information;

[0008] The intelligent robot receives return and landing signals, detects the take-off and landing area and the surrounding environment, and guides the unmanned vehicle to land. After the unmanned vehicle lands, the intelligent robot performs surface inspection, condition inspection, and dust cleaning operations on the unmanned vehicle to complete secondary inspection and maintenance.

[0009] After the secondary inspection and maintenance are completed, the unmanned machine uploads its own flight data and status information to the remote management platform, and the intelligent robot puts the unmanned machine back into the beehive box, completing the placement and maintenance of the unmanned machine;

[0010] The operation of the intelligent robot on the unmanned machine is recorded in the remote management platform to obtain the unmanned machine maintenance log, and the intelligent robot charges the unmanned machine, and then restores the unmanned machine to a standby state to wait for the next task.

[0011] Optionally, an intelligent robot is used to scan and inspect the beehive boxes used to store unmanned machines, and the scanned data is uploaded to a remote management platform for digital management of the unmanned machines and initial inspection of the beehive boxes, including:

[0012] Using the remote management platform, a box inspection task is sent to the intelligent robot. Based on the box inspection task, the intelligent robot performs a daily scheduled inspection and analysis of the beehive box in combination with the optical sensor on the intelligent robot and the environmental sensor, vibration sensor, and force sensor in the beehive box to obtain the inspection results and self-inspection checklist;

[0013] The test results and the self-test list are transmitted to the remote management platform in real time for storage, and simple maintenance and power detection are performed according to the test results;

[0014] The detection results include the status of the mechanical device, the status of the environmental indicators and the cleaning status, and the self-inspection list includes the hive box number, the detection time, the detection results and image data.

[0015] Optionally, the optical sensor is used to scan the inner wall and bottom plate of the honeycomb box and identify dust, stains or accumulated debris, the environmental sensor is used to monitor the temperature, humidity and dust particle concentration in the box, the vibration sensor is used to record the smoothness of mechanical operation, and the force sensor is used to record the opening force value and abnormal fluctuations, and identify jams or mechanical failures.

[0016] Optionally, the intelligent robot reads the serial numbers on the unmanned machine and the beehive box, performs information matching and verification, and after verification, the intelligent robot takes out the unmanned machine from the beehive box, checks the battery and pulp of the unmanned machine, and places it at the take-off point of the beehive station, completing the operation and management of the intelligent robot, including:

[0017] Using an RFID scanner or a QR code camera on the intelligent robot to read the serial numbers of the beehive box and the unmanned machine, uploading the read serial numbers to the remote management platform for matching verification, and upon successful verification, allowing the intelligent robot to open the beehive box;

[0018] The robotic arm of the intelligent robot is used to grasp the unmanned machine. During the grasping process, the metal contact interface on the robotic arm is used to read the battery power and voltage of the unmanned machine, and the intelligent camera is used to identify whether the propeller is cracked or damaged, thereby completing the battery and propeller inspection;

[0019] When the battery and propulsion test results are normal, the unmanned machine is placed at the take-off point of the honeycomb station. When the battery and propulsion test results are abnormal, the backup equipment is called and placed at the take-off point of the honeycomb station.

[0020] Optionally, the remote management platform utilizes a collaborative control model to perform collaborative scheduling among the unmanned machine, the intelligent robot, and the honeycomb box according to mission objectives and requirements, utilizes a flight control model to perform mission planning, mission control, and mission management for the unmanned machine, and utilizes a data management model to implement algorithms and store information for the remote management platform, including:

[0021] Generate a flight mission using a flight control model through the remote management platform, and send the flight mission to the unmanned vehicle. Based on the flight mission, the unmanned vehicle calculates the distance between the current position and the target position through GPS and IMU, and generates a real-time map in combination with SLAM to complete path planning and real-time positioning;

[0022] According to the path planning, the flight control model is used to control the unmanned vehicle to start executing the mission, and to take pictures of the target area point by point to generate inspection data. The inspection data is then analyzed and marked to obtain normal areas and abnormal areas. After the unmanned vehicle reaches the destination of the target area, it takes pictures and transmits them back;

[0023] Combined with the inspection data, a Grafana dashboard is integrated into the remote management platform to visualize the path progress line chart, power consumption dynamic bar chart, and camera image video stream to monitor the flight status in real time;

[0024] After completing the flight mission, the unmanned machine sends a return signal to the intelligent robot, and the unmanned machine returns home. The collaborative control model is used to perform collaborative scheduling between the unmanned machine, the intelligent robot and the honeycomb box to prepare for returning home; wherein the return signal includes the mission completion status, current location, remaining power and estimated arrival time.

[0025] Optionally, the intelligent robot receives return and landing signals and detects the take-off and landing area and the surrounding environment to complete the landing guidance of the unmanned machine. After the unmanned machine lands, the intelligent robot performs surface inspection, condition inspection, and dust cleaning operations on the unmanned machine to complete secondary inspection and maintenance, including:

[0026] The intelligent robot receives the return signal and synchronously confirms the mission completion status of the unmanned vehicle with the remote management platform. The robot then uses a laser radar or camera to scan the take-off and landing area and the surrounding environment to determine whether it is safe. If so, a landing permission signal is sent to the unmanned vehicle. If not, an alternative landing area is provided to complete the landing guidance of the unmanned vehicle, and the unmanned vehicle lands.

[0027] The jet nozzle on the intelligent robot is used to purge the unmanned machine, and it is determined whether the current battery power and temperature of the unmanned machine are lower than the set battery safety threshold. If so, the battery is replaced. It is also determined whether the propeller has cracks or wear. If so, it is repaired or replaced, completing the secondary inspection and maintenance of the unmanned machine.

[0028] Optionally, after the secondary inspection and maintenance are completed, the unmanned machine uploads its own flight data and status information to the remote management platform, and the intelligent robot returns the unmanned machine to the beehive box, completing the placement and maintenance of the unmanned machine, including:

[0029] After the secondary inspection and maintenance are completed, the unmanned aircraft uploads its flight data and status information to the remote management platform. The remote management platform integrates the uploaded data, generates a mission report, performs an anomaly analysis on the mission report, obtains an anomaly result, triggers a maintenance process, and then performs a mission completion assessment based on the mission report.

[0030] The intelligent robot reads the serial number of the unmanned machine to place the unmanned machine in the corresponding beehive box, checks whether the placement position of the unmanned machine is correct, and determines whether the internal environment of the beehive box is safe. If so, the status of the unmanned machine is updated to "placed".

[0031] Optionally, the expression for evaluating the task completion is:

[0032] S2=0.4a+0.3b+0.3c

[0033] Among them, S2 is the task score, a is the data completeness, b is the power consumption efficiency, and c is the target coverage.

[0034] Optionally, the operation of the intelligent robot on the unmanned machine is recorded in the remote management platform to obtain an unmanned machine maintenance log, and the intelligent robot is used to charge the unmanned machine, and then the unmanned machine is restored to a standby state to wait for the next task, including:

[0035] Uploading all operations performed by the intelligent robot on the unmanned machine to the remote management platform, generating a maintenance frequency table on a daily basis based on the uploaded data, annotating the faulty equipment with a label and a maintenance time point, generating a fault log index, and combining the maintenance frequency table and the fault log index to obtain an unmanned machine maintenance log;

[0036] The intelligent robot is used to charge the unmanned machine, and then the intelligent robot is used to determine whether the unmanned machine meets the standby conditions. If so, the unmanned machine is restored to the standby state; wherein the standby conditions include that the power meets the takeoff threshold, the battery temperature is normal, and the propulsion state is normal.

[0037] The present invention discloses the following technical effects by providing an unmanned machine management method based on an intelligent robot and an unmanned machine hive station:

[0038] 1. Autonomous management of unmanned machines. This invention achieves one-to-one binding between the honeycomb box and the unmanned machine, ensuring greater traceability and compatibility with the box environment, avoiding potential equipment conflicts and disruptions caused by multiple unmanned machines sharing a single box. The honeycomb box also features a self-checking function. An intelligent robot routinely scans the box to check the lid mechanism and internal dust accumulation. Multiple environmental sensors collect real-time information about internal humidity, temperature, and dust levels to ensure a healthy storage environment for the unmanned machines.

[0039] 2. Efficient adaptive maintenance of unmanned machines. Based on the sensor data inside the unmanned machines and honeycomb boxes, the remote management platform can dynamically adjust the maintenance frequency and maintenance items, effectively extending the service life of the unmanned machines and honeycomb stations.

[0040] 3. Intelligent robot-assisted management: Intelligent robots provide comprehensive follow-up from pre-flight to post-flight, addressing management blind spots in traditional manual processes and significantly improving efficiency and safety. Specifically: 1) A multifunctional robotic arm assists with physical operations such as opening and closing hive lids, transporting drones, and replacing batteries. 2) Self-checking capabilities: The robot's built-in sensors regularly scan the internal structure of the hive site, identifying any anomalies (such as damage, blockages, and excessive dust) and reporting them promptly. 3) Information exchange: Before landing, the drone performs an "environmental check" to ensure the landing area is safe and clear of obstacles.

[0041] 4. Real-time monitoring and intelligent placement management of drones: 1) Pre-flight inspection: The intelligent robot checks the drone's power-on status, battery level, propeller, camera, and other status before flight. 2) Post-flight maintenance: This includes clearing obstacles, replacing batteries or propellers, and the robot can also disassemble or repair them if necessary. 3) Placement records: After completing a flight, the drone automatically returns to its corresponding hive, recording the flight log to facilitate subsequent fault tracing.

[0042] 5. Scalability: When more drones are needed, simply add new hive boxes to the hive station (a hive station includes multiple hive boxes and intelligent robots). The scheduling logic of the intelligent robots can also be expanded in parallel. You can also choose a suitable location to set up the hive station based on the execution environment of the drone to better carry out the drone's flight mission.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A schematic diagram of a method flow chart provided in an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of a cellular station architecture provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the process of intelligent robot-assisted management provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1 As shown, the present invention provides an unmanned machine management method based on an intelligent robot and an unmanned machine hive station, comprising the following steps:

[0051] 1. Initialization inspection of the honeycomb box

[0052] like Figure 2 、 Figure 3 As shown, an intelligent robot is used to scan and inspect the beehive boxes used to store unmanned equipment, and the scanned data is uploaded to a remote management platform for digital management of the unmanned equipment and initial inspection of the beehive boxes to ensure that the beehive boxes are suitable for storing and using unmanned equipment. Specifically, it includes:

[0053] 1.1 Using the remote management platform, send a box inspection task to the intelligent robot. Based on the box inspection task, the intelligent robot combines the optical sensor and the environmental sensor, vibration sensor and force sensor in the beehive box to perform a daily scheduled inspection and analysis of the beehive box, and obtain the inspection results and self-inspection list.

[0054] Test results include:

[0055] Mechanical device status: A "health score" is given through mechanical operation stability analysis;

[0056] Environmental indicator status: compares temperature, humidity, and dust concentration with specified thresholds and marks whether they meet the standards;

[0057] Cleaning status: The degree of dust accumulation is automatically rated through optical comparison data, such as "qualified" or "needs cleaning".

[0058] The self-inspection checklist includes: hive box number, inspection time, inspection results and image data, and the latest hive box status is automatically uploaded to the remote management platform every day.

[0059] An optical sensor is used to scan the inner walls and floor of the beehive box and identify dust, stains or accumulated debris.

[0060] Environmental sensors are used to monitor environmental conditions such as temperature, humidity, and dust particle concentration (refer to PM2.5 and PM10 indicators) inside the box to determine whether the environment is unsuitable for storing drones.

[0061] Vibration sensors are used to record the smoothness of mechanical operation, that is, to analyze amplitude and speed fluctuations and determine gear wear.

[0062] Force sensor, used to record the opening force and abnormal fluctuations, and identify possible jamming or mechanical failure.

[0063] 1.2 The test results and the self-test list are transmitted to the remote management platform in real time for storage, and simple maintenance and power detection are performed based on the test results.

[0064] 2. Unmanned aerial vehicles are ready for deployment

[0065] like Figure 2 、 Figure 3 As shown, the intelligent robot reads the serial numbers on the unmanned machine and the beehive box, performs information matching and verification, and after verification, the intelligent robot takes out the unmanned machine from the beehive box, checks the battery and pulp of the unmanned machine, and places it at the take-off point of the beehive station, completing the operation and management of the intelligent robot; the unmanned machine includes but is not limited to drones, unmanned aerial vehicles, unmanned cars and unmanned ships. Specifically including:

[0066] 2.1 Use the RFID scanner or QR code camera on the intelligent robot to read the numbers of the beehive box and the unmanned machine, upload the read numbers to the remote management platform for matching verification, and after successful verification, allow the intelligent robot to open the beehive box.

[0067] 2.2 Utilize the robotic arm on the intelligent robot. A 6-DOF robotic arm, such as the Dobot MG400, supports multi-directional movement and precise grasping. Use the robotic arm to grasp the unmanned vehicle. During the grasping process, the metal contact interface on the robotic arm reads the battery charge and voltage of the unmanned vehicle to determine its health status. A smart camera is used to identify propeller cracks and complete battery and propeller inspections.

[0068] 2.3 When the battery and propulsion test results are normal, the unmanned machine is placed at the take-off point of the honeycomb station. When the battery and propulsion test results are abnormal, the backup equipment is called and then placed at the take-off point of the honeycomb station.

[0069] 3. Unmanned aircraft performing flight missions

[0070] like Figure 2 、 Figure 3 As shown, through the remote management platform, according to the mission objectives and requirements, the collaborative control model is used to perform collaborative scheduling between the unmanned machine, the intelligent robot and the honeycomb box, and the flight control model is used to perform mission planning, mission control and mission management of the unmanned machine, and the data management model is used to implement the algorithm and store information of the remote management platform, including.

[0071] Specifically include:

[0072] 3.1 The remote management platform uses the flight control model to send flight missions to the unmanned aircraft. Based on the flight missions, the unmanned aircraft uses GPS and IMU to calculate the distance between the current position and the target position, and combines SLAM to generate a real-time map to avoid obstacles affecting the path, thereby completing path planning and real-time positioning.

[0073] The flight mission includes: the latitude and longitude of the starting and target locations, flight speed, expected altitude, additional mission requirements, navigation path, etc. Mission requirements include logistics delivery weight, image collection of inspection areas, etc.

[0074] 3.2 Based on the path planning, the flight control model is used to control the unmanned aerial vehicle to start executing the mission, and the target area is photographed point by point to generate inspection data (high-definition images or video streams). Then, combined with AI algorithms, such as CNN convolutional networks, the inspection data is analyzed and marked to obtain normal and abnormal areas. After the unmanned aerial vehicle reaches the destination of the target area, it takes photos and sends them back to confirm the correctness of the location.

[0075] 3.3 In combination with the inspection data, a Grafana dashboard is integrated into the remote management platform to graphically display the path progress line chart, power consumption dynamic bar chart and camera image video stream to monitor the flight status in real time.

[0076] The model dynamically monitors data such as attitude angle, position deviation, and battery status, and compares it to normal flight standards. If an anomaly occurs, such as sensor failure, excessive wind speed, or target loss, an alarm is triggered. For example, a minor anomaly will result in correction of the flight attitude or route, while a serious anomaly will result in an immediate emergency stop or return.

[0077] 3.4 After completing the flight mission, the unmanned aerial vehicle sends a return signal to the intelligent robot, and the unmanned aerial vehicle returns home. The collaborative control model is used to coordinate scheduling between the unmanned aerial vehicle, the intelligent robot, and the honeycomb box to prepare for return. The return signal includes the mission completion status, current location (latitude and longitude), remaining battery power, and estimated arrival time.

[0078] 4. Landing guidance and secondary check

[0079] like Figure 2 、 Figure 3 As shown, the intelligent robot receives return and landing signals, detects the take-off and landing area and the surrounding environment, and guides the unmanned vehicle to land. After the unmanned vehicle lands, the intelligent robot performs surface inspection, condition inspection, and dust cleaning operations on the unmanned vehicle, completing secondary inspection and maintenance. Specifically, it includes:

[0080] 4.1 The intelligent robot receives the return signal and synchronously confirms the mission completion status of the unmanned aircraft with the remote management platform. It then uses a lidar (for precise environmental mapping of the landing point) or a camera (for real-time capture of dynamic and static obstacles, such as small objects or pedestrians) to scan the takeoff and landing area to determine whether it is safe. If so, a landing permission signal is sent to the unmanned aircraft. If not, an alternative landing area is provided, and the unmanned aircraft is guided to land.

[0081] 4.2 Use the jet nozzle on the intelligent robot to purge the unmanned machine and determine whether the current battery power and temperature of the unmanned machine are lower than the set battery safety threshold (30%). If so, replace the battery. Combined with AI image models such as YOLO or Mask R-CNN, determine whether the propeller has cracks or wear. If so, repair or replace it, completing the secondary inspection and repair of the unmanned machine.

[0082] 5. Data upload and placement maintenance

[0083] like Figure 2 、 Figure 3As shown, after the secondary inspection and maintenance are completed, the unmanned machine uploads its own flight data and status information to the remote management platform, and the intelligent robot puts the unmanned machine back into the beehive box, completing the placement and maintenance of the unmanned machine.

[0084] 5.1 After the secondary inspection and repair, the unmanned aircraft will upload its flight data and status information to the remote management platform. Flight data includes flight path information, mission completion status, flight speed, altitude, and inspection data. Status information includes abnormal status data, mission interruption reasons, route deviations, and battery life reports.

[0085] The remote management platform integrates the uploaded data, generates a task report, performs an abnormality analysis on the task report, obtains abnormal results, triggers a maintenance process, and then performs a task completion evaluation based on the task report; the expression for the task completion evaluation is:

[0086] S2=0.4a+0.3b+0.3c

[0087] Here, S2 represents the mission score, a represents data integrity, b represents power consumption efficiency, and c represents target coverage. Target coverage = successfully covered area / planned coverage area. Target coverage can be used to evaluate the performance of drones in specific missions. For example, in agricultural monitoring or search and rescue missions, assessing the drone's coverage of the target area directly reflects its efficiency and effectiveness, enabling more precise and efficient drone operations, thereby improving overall effectiveness and safety.

[0088] 5.2 The intelligent robot reads the serial number of the unmanned machine to place the unmanned machine in the corresponding beehive box, checks whether the placement position of the unmanned machine is correct, and determines whether the internal environment of the beehive box is safe. If so, the status of the unmanned machine is updated to "placed".

[0089] 6. Maintenance records and standby again

[0090] like Figure 2 、 Figure 3 As shown, the operation of the intelligent robot on the unmanned machine is recorded in the remote management platform to obtain the unmanned machine maintenance log, and the intelligent robot charges the unmanned machine, and then restores the unmanned machine to a standby state to wait for the next task. Specifically including:

[0091] 6.1 Upload all operations performed by the intelligent robot on the unmanned machine to the remote management platform. Based on the uploaded data, generate a maintenance frequency table on a daily basis, annotate the faulty equipment number and maintenance time, generate a fault log index, and combine the maintenance frequency table and the fault log index to obtain the unmanned machine maintenance log.

[0092] All operations performed by intelligent robots on unmanned machines include: cleaning operations, parts replacement operations, fault problem recording, environmental data recording, etc.

[0093] Cleaning operation: record the completion status of the cleaning action;

[0094] Replacement of parts: indicate the number and number of parts replaced, such as engine and battery;

[0095] Fault problem record: record the repaired or temporarily stored fault status.

[0096] 6.2 The intelligent robot charges the unmanned machine, and then uses the intelligent robot to determine whether the unmanned machine meets the standby conditions. If so, the unmanned machine is restored to the standby state; wherein the standby conditions include that the battery level meets the takeoff threshold (>70%), the battery temperature is normal, and the propulsion state is normal.

[0097] Therefore, the present invention provides an unmanned machine management method based on an intelligent robot and an unmanned machine hive station, and realizes automated and intelligent monitoring, management and maintenance of unmanned machines through collaboration among unmanned machines, intelligent robots, hive stations and remote management platforms, thereby reducing labor costs and improving management efficiency.

[0098] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0099] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for managing unmanned machines based on intelligent robots and unmanned machine honeycomb stations, characterized in that: The following steps are involved: Using an intelligent robot to scan and inspect the beehive boxes used to store unmanned machines, and uploading the scanned data to a remote management platform for digital management of the unmanned machines and initial inspection of the beehive boxes; The intelligent robot reads the serial numbers on the unmanned machine and the beehive box, performs information matching and verification, and after verification, removes the unmanned machine from the beehive box, checks the battery and pulp of the unmanned machine, and places it at the take-off point of the beehive station, thereby completing the operation and management of the intelligent robot; the unmanned machine includes but is not limited to drones, unmanned aerial vehicles, unmanned vehicles, and unmanned ships; Through the remote management platform, according to the mission objectives and requirements, the collaborative control model is used to coordinate the scheduling between the unmanned machine, the intelligent robot and the honeycomb box, and the flight control model is used to carry out the mission planning, mission control and mission management of the unmanned machine. Then, the data management model is used to implement the algorithm of the remote management platform and store information; The intelligent robot receives return and landing signals, detects the take-off and landing area and the surrounding environment, and guides the unmanned vehicle to land. After the unmanned vehicle lands, the intelligent robot performs surface inspection, condition inspection, and dust cleaning operations on the unmanned vehicle to complete secondary inspection and maintenance. After the secondary inspection and maintenance are completed, the unmanned machine uploads its own flight data and status information to the remote management platform, and the intelligent robot puts the unmanned machine back into the beehive box, completing the placement and maintenance of the unmanned machine; The operation of the intelligent robot on the unmanned machine is recorded in the remote management platform to obtain the unmanned machine maintenance log, and the intelligent robot charges the unmanned machine, and then restores the unmanned machine to a standby state to wait for the next task.

2. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 1, characterized in that: Utilize intelligent robots to scan and inspect beehive boxes used to store unmanned machines, and upload the scanned data to a remote management platform for digital management of unmanned machines and initial inspection of the beehive boxes, including: Using the remote management platform, a box inspection task is sent to the intelligent robot. Based on the box inspection task, the intelligent robot performs a daily scheduled inspection and analysis of the beehive box in combination with the optical sensor on the intelligent robot and the environmental sensor, vibration sensor, and force sensor in the beehive box to obtain the inspection results and self-inspection checklist; The test results and the self-test list are transmitted to the remote management platform in real time for storage, and simple maintenance and power detection are performed according to the test results; The detection results include the status of the mechanical device, the status of the environmental indicators and the cleaning status, and the self-inspection list includes the hive box number, the detection time, the detection results and image data.

3. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 2, characterized in that: The optical sensor is used to scan the inner wall and bottom plate of the honeycomb box and identify dust, stains or accumulated debris. The environmental sensor is used to monitor the temperature, humidity and dust particle concentration in the box. The vibration sensor is used to record the smoothness of mechanical operation. The force sensor is used to record the opening force value and abnormal fluctuations, and identify jams or mechanical failures.

4. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 3, characterized in that: The intelligent robot reads the serial numbers on the unmanned machine and the beehive box, performs information matching and verification, and after verification, takes the unmanned machine out of the beehive box, checks the battery and pulp of the unmanned machine, and places it at the take-off point of the beehive station, completing the operation and management of the intelligent robot, including: Using an RFID scanner or a QR code camera on the intelligent robot to read the serial numbers of the beehive box and the unmanned machine, uploading the read serial numbers to the remote management platform for matching verification, and upon successful verification, allowing the intelligent robot to open the beehive box; The robotic arm of the intelligent robot is used to grasp the unmanned machine. During the grasping process, the metal contact interface on the robotic arm is used to read the battery power and voltage of the unmanned machine, and the intelligent camera is used to identify whether the propeller is cracked or damaged, thereby completing the battery and propeller inspection; When the battery and propulsion test results are normal, the unmanned machine is placed at the take-off point of the honeycomb station. When the battery and propulsion test results are abnormal, the backup equipment is called and placed at the take-off point of the honeycomb station.

5. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 4, characterized in that: Through the remote management platform, according to the mission objectives and requirements, the collaborative control model is used to coordinate the scheduling between the unmanned machine, the intelligent robot and the honeycomb box, and the flight control model is used to perform mission planning, mission control and mission management of the unmanned machine. Then, the data management model is used to implement the algorithm of the remote management platform and store information, including: Generate a flight mission using a flight control model through the remote management platform, and send the flight mission to the unmanned vehicle. Based on the flight mission, the unmanned vehicle calculates the distance between the current position and the target position through GPS and IMU, and generates a real-time map in combination with SLAM to complete path planning and real-time positioning; According to the path planning, the flight control model is used to control the unmanned vehicle to start executing the mission, and to take pictures of the target area point by point to generate inspection data. The inspection data is then analyzed and marked to obtain normal areas and abnormal areas. After the unmanned vehicle reaches the destination of the target area, it takes pictures and transmits them back; Combined with the inspection data, a Grafana dashboard is integrated into the remote management platform to visualize the path progress line chart, power consumption dynamic bar chart, and camera image video stream to monitor the flight status in real time; After completing the flight mission, the unmanned machine sends a return signal to the intelligent robot, and the unmanned machine returns home. The collaborative control model is used to perform collaborative scheduling between the unmanned machine, the intelligent robot and the honeycomb box to prepare for returning home; wherein the return signal includes the mission completion status, current location, remaining power and estimated arrival time.

6. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 5, characterized in that: The intelligent robot receives return and landing signals, detects the take-off and landing area and the surrounding environment, and guides the unmanned vehicle to land. After the unmanned vehicle lands, the intelligent robot performs surface inspection, condition inspection, and dust cleaning operations on the unmanned vehicle to complete secondary inspection and maintenance, including: The intelligent robot receives the return signal and synchronously confirms the mission completion status of the unmanned vehicle with the remote management platform. The robot then uses a laser radar or camera to scan the take-off and landing area and the surrounding environment to determine whether it is safe. If so, a landing permission signal is sent to the unmanned vehicle. If not, an alternative landing area is provided to complete the landing guidance of the unmanned vehicle, and the unmanned vehicle lands. The jet nozzle on the intelligent robot is used to purge the unmanned machine, and it is determined whether the current battery power and temperature of the unmanned machine are lower than the set battery safety threshold. If so, the battery is replaced. It is also determined whether the propeller has cracks or wear. If so, it is repaired or replaced, completing the secondary inspection and maintenance of the unmanned machine.

7. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 6, characterized in that: After the secondary inspection and maintenance, the unmanned machine uploads its own flight data and status information to the remote management platform, and the intelligent robot returns the unmanned machine to the beehive box, completing the placement and maintenance of the unmanned machine, including: After the secondary inspection and maintenance are completed, the unmanned aircraft uploads its flight data and status information to the remote management platform. The remote management platform integrates the uploaded data, generates a mission report, performs an anomaly analysis on the mission report, obtains an anomaly result, triggers a maintenance process, and then performs a mission completion assessment based on the mission report. The intelligent robot reads the serial number of the unmanned machine to place the unmanned machine in the corresponding beehive box, checks whether the placement position of the unmanned machine is correct, and determines whether the internal environment of the beehive box is safe. If so, the status of the unmanned machine is updated to "placed".

8. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 7, characterized in that: The expression for task completion evaluation is: S2=0.4a+0.3b+0.3c Among them, S2 is the task score, a is the data completeness, b is the power consumption efficiency, and c is the target coverage.

9. The unmanned machine management method based on intelligent robots and unmanned machine cellular stations according to claim 8, characterized in that: Recording the operation of the intelligent robot on the unmanned machine to the remote management platform to obtain an unmanned machine maintenance log, charging the unmanned machine by the intelligent robot, and restoring the unmanned machine to a standby state to wait for the next task, including: Uploading all operations performed by the intelligent robot on the unmanned machine to the remote management platform, generating a maintenance frequency table on a daily basis based on the uploaded data, annotating the faulty equipment with a label and a maintenance time point, generating a fault log index, and combining the maintenance frequency table and the fault log index to obtain an unmanned machine maintenance log; The intelligent robot charges the unmanned machine, and then uses the intelligent robot to determine whether the unmanned machine meets the standby condition, and if so, restores the unmanned machine to the standby state; The standby conditions include that the battery level meets the takeoff threshold, the battery temperature is normal, and the propeller state is normal.

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