A data processing method based on an artificial intelligence chip

Abstract

The application discloses a kind of data processing methods based on artificial intelligence chip, including central processing module, equipment self-checking module, autonomous inspection module, flight control module, wireless communication module, data analysis module and data collection module;The application, unmanned aerial vehicle is detected by equipment self-checking module, systematic detection avoids omission, to avoid false detection and miss detection leading to damage of unmanned aerial vehicle;Through autonomous inspection module and flight control module cooperate navigation positioning module accurately position each point of unmanned aerial vehicle, without manual operation, it is convenient to use and reduces artificial cost;Through data collection module and data analysis module collect the image, sound wave and thermal imaging collected by unmanned aerial vehicle, complete photoacoustic thermal integrated inspection work, and the cooperative control module can make multiple unmanned aerial vehicles can be cooperated to carry out inspection, can be applicable to various environmental scenes, without individually customizing inspection system, effectively improve the practicability of inspection system.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a data processing method based on an artificial intelligence chip. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are unmanned aircraft controlled by radio remote control equipment and their own program control devices. UAVs can be divided into military and civilian applications according to their application fields. Civilian UAVs are used in aerial photography, agriculture, express delivery, outdoor observation, power line inspection and other fields, which expands the uses of UAVs. Inspection is a crucial task across various fields, aiming to ensure the safe, stable, and efficient operation of equipment, facilities, and the environment. Unmanned aerial vehicles (UAVs) serve as advanced inspection equipment, combining their high-altitude flight capabilities, flexibility, and various onboard sensors to achieve comprehensive, rapid, and accurate inspections of target areas or equipment. An integrated collaborative inspection system is particularly important. While existing inspection systems generally meet user needs, they still have certain shortcomings. First, current UAV inspection systems still require manual inspection of the UAV's status, potentially leading to mis-inspections or missed inspections of components, which can damage the UAV and increase unnecessary costs. Second, existing UAV inspection systems often require manual adjustments to flight paths in complex terrain environments, which is inconvenient and increases labor costs. Third, existing UAV inspection systems cannot handle diverse inspection tasks, requiring customized solutions for various inspection needs, thus affecting the system's practicality. Therefore, designing a data processing method based on an artificial intelligence chip is essential. Summary of the Invention

[0003] The purpose of this invention is to provide a data processing method based on an artificial intelligence chip to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a data processing method based on an artificial intelligence chip, comprising a central processing module, a device self-test module, an autonomous inspection module, a flight control module, a wireless communication module, a data analysis module, and a data collection module. The central processing module controls and connects to the device self-test module, the autonomous inspection module, the flight control module, the wireless communication module, the data analysis module, and the data collection module respectively. The autonomous inspection module and the flight control module are mutually controlled and connected, and the data analysis module and the data collection module are mutually controlled and connected.

[0005] As a further technical solution of the present invention, the data analysis module and the data collection module are both connected to the data storage module, the data transmission module and the data backup module.

[0006] As a further technical solution of the present invention, the data analysis module controls and connects to the image recognition module, the sound wave recognition module and the thermal imaging recognition module respectively, and the image recognition module, the sound wave recognition module and the thermal imaging recognition module all control and connect to the anomaly judgment module.

[0007] As a further technical solution of the present invention, the anomaly judgment module controls the connection of the anomaly alarm module and the anomaly recording module respectively, and the anomaly recording module controls the connection of the data collection module.

[0008] As a further technical solution of the present invention, the device self-test module controls and connects to the power monitoring module, the power monitoring module controls and connects to the power warning module, and the power warning module controls and connects to the timed return module.

[0009] As a further technical solution of the present invention, the device self-test module controls the connected hardware detection module, communication detection module and capacity detection module respectively.

[0010] As a further technical solution of the present invention, the wireless communication module controls and connects to the signal transmission module and the signal interaction module respectively, and both the signal transmission module and the signal interaction module control and connect to the collaborative control module.

[0011] As a further technical solution of the present invention, the autonomous inspection module and the flight control module are both connected to the equipment positioning module, the route preset module and the route change module, and the equipment positioning module, the route preset module and the route change module are all connected to the navigation positioning module.

[0012] As a further technical solution of the present invention, the navigation and positioning module controls and connects the starting point positioning module, the inspection point module and the ending point positioning module respectively.

[0013] As a further technical solution of the present invention, the flight control module controls and connects to the lift control module, the hover control module and the direction control module respectively, and the lift control module, the hover control module and the direction control module all control and connect to the fixed-point orbit module.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This data processing method based on an artificial intelligence chip utilizes a central processing module to control a device self-testing module to perform self-testing on the drone. The device self-testing module controls a power monitoring module to monitor the drone's remaining power in real time. When the drone's power reaches a pre-set power warning value, the power warning module issues an alarm. After a certain period of time, the drone is returned to its home position via a timed return-to-home module, preventing the drone from crashing and being damaged due to insufficient power. Simultaneously, a hardware detection module performs operational tests on various components of the drone, a communication detection module confirms the normal communication connection between the drone and the inspection system, and a capacity detection module checks whether the internal storage card capacity of the drone is sufficient. This systematic testing avoids omissions and prevents false positives and false negatives. Damage to drones can be addressed through an autonomous inspection module and flight control module that regulate the device's positioning module. The navigation and positioning module provides real-time drone location tracking, and a route preset module allows for pre-setting inspection routes. The starting point positioning module, inspection point module, and endpoint positioning module work in conjunction with the navigation and positioning module to precisely locate each point on the drone. This eliminates the need for manual operation, making it convenient and reducing labor costs. Furthermore, a data collection and analysis module gathers images, sound waves, and thermal images from the drone. An anomaly detection module then determines if any anomalies exist, completing an integrated optical, acoustic, and thermal inspection. Simultaneously, a signal transmission module and signal interaction module regulate the collaborative control module, enabling multiple drones to conduct inspections collaboratively. This system is applicable to various environmental scenarios, eliminating the need for a separately customized inspection system and significantly improving its practicality. Attached Figure Description

[0015] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a schematic diagram of the anomaly detection module in this invention; Figure 3 This is a schematic diagram of the device self-test module in this invention; Figure 4 This is a schematic diagram of the wireless communication module in this invention; Figure 5 This is a schematic diagram of the flight control module in this invention; In the diagram: 1. Central Processing Module; 2. Equipment Self-Test Module; 3. Autonomous Inspection Module; 4. Flight Control Module; 5. Wireless Communication Module; 6. Data Analysis Module; 7. Data Collection Module; 8. Data Storage Module; 9. Data Transmission Module; 10. Data Backup Module; 11. Image Recognition Module; 12. Acoustic Wave Recognition Module; 13. Thermal Imaging Recognition Module; 14. Anomaly Detection Module; 15. Anomaly Alarm Module; 16. Anomaly Recording Module; 17. Power Monitoring Module; 18. Power Warning Module; 19. 20. Timed return module; 21. Hardware detection module; 22. Communication detection module; 23. Capacity detection module; 24. Signal transmission module; 25. Signal interaction module; 26. Cooperative control module; 27. Lifting control module; 28. Hovering control module; 29. ​​Direction control module; 30. Fixed-point circling module; 31. Equipment positioning module; 32. Route preset module; 33. Route change module; 34. Navigation and positioning module; 35. Starting point positioning module; 36. Inspection point module; 37. End point positioning module. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see the appendix Figure 1 -Appendix Figure 5This invention provides an embodiment of a data processing method based on an artificial intelligence chip, comprising a central processing module 1, a device self-test module 2, an autonomous inspection module 3, a flight control module 4, a wireless communication module 5, a data analysis module 6, and a data collection module 7. The central processing module 1 controls and connects to the device self-test module 2, the autonomous inspection module 3, the flight control module 4, the wireless communication module 5, the data analysis module 6, and the data collection module 7. The autonomous inspection module 3 and the flight control module 4 are mutually controlled and connected. The data analysis module 6 and the data collection module 7 are mutually controlled and connected. Both the data analysis module 6 and the data collection module 7 control and connect to a data storage module 8, a data transmission module 9, and a data backup module 10. The data analysis module 6 controls and connects to an image recognition module 11, a sound wave recognition module 12, and a thermal imaging recognition module 13. These modules are all connected to an anomaly detection module 14. The anomaly detection module 14 controls and connects to an anomaly alarm module 15 and an anomaly recording module 16. The anomaly recording module 16 controls and connects to the data collection module 7. The device self-test module 2 controls and connects to... Power monitoring module 17 controls and connects to power warning module 18, which in turn controls and connects to timed return module 19. Equipment self-test module 2 controls and connects to hardware detection module 20, communication detection module 21, and capacity detection module 22. Wireless communication module 5 controls and connects to signal transmission module 23 and signal interaction module 24. Both signal transmission module 23 and signal interaction module 24 control and connect to collaborative control module 25. Autonomous inspection module 3 and flight control module 4 control and connect to equipment positioning module 30, route preset module 31, and... The route change module 32, equipment positioning module 30, route preset module 31, and route change module 32 all control and connect to the navigation and positioning module 33. The navigation and positioning module 33 controls and connects to the starting point positioning module 34, the inspection point module 35, and the ending point positioning module 36, respectively. The flight control module 4 controls and connects to the ascent and descent control module 26, the hovering control module 27, and the direction control module 28, respectively. The ascent and descent control module 26, the hovering control module 27, and the direction control module 28 all control and connect to the fixed-point orbiting module 29. The flight control module 4 is used to complete the automatic inspection flight of the UAV.

[0018] Based on the above, the advantages of this invention are as follows: The central processing module 1 controls the device self-test module 2 to perform self-testing on the drone. The device self-test module 2 controls the power monitoring module 17 to monitor the drone's remaining power in real time. When the drone's power reaches a pre-set power warning value, the power warning module 18 issues an alarm. After a certain period of time, the drone is returned unattended, and the timed return-to-home module 19 controls the drone to return to its starting point, preventing damage from insufficient power. Simultaneously, the hardware detection module 20 performs operational tests on each component of the drone, the communication detection module 21 confirms the normal communication connection between the drone and the inspection system, and the capacity detection module 22 checks the sufficiency of the drone's internal storage card. This systematic testing avoids omissions and prevents damage to the drone caused by false positives or false negatives. Furthermore, the autonomous inspection module 3 and... The flight control module 4 regulates the equipment positioning module 30, uses the navigation positioning module 33 to locate the UAV's position in real time, and uses the route preset module 31 to preset the inspection route in advance. The starting point positioning module 34, the inspection point module 35, and the ending point positioning module 36 work together with the navigation positioning module 33 to accurately locate each point of the UAV, eliminating the need for manual operation, making it convenient to use and reducing labor costs. The data collection module 7 and the data analysis module 6 collect the images, sound waves, and thermal images collected by the UAV, and then use the anomaly judgment module 14 to determine whether there are any anomalies, completing the integrated optical, acoustic, and thermal inspection work. At the same time, the signal transmission module 23 and the signal interaction module 24 regulate the collaborative control module 25, enabling multiple UAVs to conduct inspections collaboratively. It is applicable to various environmental scenarios, eliminating the need for a separately customized inspection system, and effectively improving the practicality of the inspection system.

[0019] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A data processing method based on an artificial intelligence chip, comprising a central processing module (1), a device self-test module (2), an autonomous inspection module (3), a flight control module (4), a wireless communication module (5), a data analysis module (6), and a data collection module (7), characterized in that: The central processing module (1) controls the self-test module (2), autonomous inspection module (3), flight control module (4), wireless communication module (5), data analysis module (6) and data collection module (7) respectively. The autonomous inspection module (3) and flight control module (4) are mutually controlled and connected, and the data analysis module (6) and data collection module (7) are mutually controlled and connected.

2. The data processing method based on an artificial intelligence chip according to claim 1, characterized in that: The data analysis module (6) and the data collection module (7) are both connected to the data storage module (8), the data transmission module (9), and the data backup module (10).

3. The data processing method based on an artificial intelligence chip according to claim 2, characterized in that: The data analysis module (6) controls the connection of the image recognition module (11), the sound wave recognition module (12) and the thermal imaging recognition module (13), respectively. The image recognition module (11), the sound wave recognition module (12) and the thermal imaging recognition module (13) all control the connection of the anomaly judgment module (14).

4. The data processing method based on an artificial intelligence chip according to claim 3, characterized in that: The anomaly judgment module (14) controls the connection of the anomaly alarm module (15) and the anomaly recording module (16), respectively, and the anomaly recording module (16) controls the connection of the data collection module (7).

5. The data processing method based on an artificial intelligence chip according to claim 1, characterized in that: The device self-test module (2) controls the power monitoring module (17), the power monitoring module (17) controls the power warning module (18), and the power warning module (18) controls the timed return module (19).

6. The data processing method based on an artificial intelligence chip according to claim 5, characterized in that: The device self-test module (2) controls the connected hardware detection module (20), communication detection module (21) and capacity detection module (22) respectively.

7. The data processing method based on an artificial intelligence chip according to claim 1, characterized in that: The wireless communication module (5) controls the connection of the signal transmission module (23) and the signal interaction module (24), respectively. Both the signal transmission module (23) and the signal interaction module (24) control the connection of the cooperative control module (25).

8. The data processing method based on an artificial intelligence chip according to claim 1, characterized in that: The autonomous inspection module (3) and flight control module (4) are both connected to the equipment positioning module (30), the route preset module (31) and the route change module (32), and the equipment positioning module (30), the route preset module (31) and the route change module (32) are all connected to the navigation positioning module (33).

9. A data processing method based on an artificial intelligence chip according to claim 8, characterized in that: The navigation and positioning module (33) controls the starting point positioning module (34), the inspection point module (35), and the ending point positioning module (36) respectively.

10. A data processing method based on an artificial intelligence chip according to claim 8, characterized in that: The flight control module (4) controls and connects to the lift control module (26), hover control module (27) and direction control module (28), respectively. The lift control module (26), hover control module (27) and direction control module (28) all control and connect to the fixed-point orbit module (29).

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