Intelligent inspection method of anti-explosion inspection robot

Abstract

The invention relates to the technical field of inspection equipment, and discloses an intelligent inspection method of an explosion-proof inspection robot, which comprises the following steps: determining main tasks and targets of the explosion-proof inspection robot, determining a specific area or place to be inspected, formulating a map or a layout map to help to determine an inspection path and area, and meanwhile, according to the danger of a target area, determining the inspection area or the target area. Hardware meeting the anti-explosion requirement is designed, the designed robot is provided with a sensor so as to ensure the safety and effectiveness when the inspection task is executed in the dangerous environment, the designed robot adopts a lithium battery as a power source of the robot, and meanwhile an intelligent power source management strategy is implemented. Power consumption of the battery is dynamically managed according to requirements of robot tasks. By implementing the power supply management strategy, the use amount of the battery can be well planned, more inspection tasks can be completed with the minimum electric quantity, and the charging time can be well planned, so that accidents can be avoided, economic loss can be avoided, and meanwhile, potential safety hazards can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of inspection equipment, and particularly to an intelligent inspection method for an explosion-proof inspection robot. Background Art

[0002] The intelligent inspection method for an explosion-proof inspection robot aims to perform inspection tasks safely and efficiently, especially in environments with potential explosion hazards, such as chemical plants or oil and gas facilities. This intelligent inspection method combines sensor technology, autonomous navigation, image processing, and data analysis to ensure safe and efficient inspection operations. It helps prevent potential hazards, improve production efficiency, and reduce personnel risks.

[0003] In the intelligent inspection method of the existing explosion-proof inspection robot, during the inspection, the inspection task often returns to charge due to the difficulty in maintaining the battery power, which not only delays the progress of the task, but also easily causes accidents due to the inability to perform inspections during the charging time of the robot, resulting in economic losses and even threatening personal safety. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent inspection method for an explosion-proof inspection robot, which solves the problem that the inspection task cannot be completed due to battery problems when the inspection robot performs the inspection task. Therefore, during the charging period of the robot, accidents occur due to the robot not performing inspections, resulting in economic losses and even threatening personal safety.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent inspection method for an explosion-proof inspection robot, comprising the following steps:

[0006] Step 1: Determine the main tasks and objectives of the explosion-proof inspection robot, determine the specific areas or locations to be inspected, and formulate a map or layout diagram to help determine the inspection path and area. At the same time, according to the danger level of the target area, design hardware that meets explosion-proof requirements;

[0007] Step 2: Equip the robot designed in Step 1 with sensors to ensure the safety and effectiveness of performing inspection tasks in a dangerous environment;

[0008] Step 3: Use a lithium battery as the power source for the robot designed in Step 1. At the same time, implement an intelligent power management strategy to dynamically manage the battery power consumption according to the requirements of the robot's tasks. When the robot is inactive, it can enter the low-power sleep mode. Equip it with an automatic charging pile and use fast charging technology to minimize the time the robot is in the charging state. Develop an energy-saving control algorithm to minimize the battery consumption of the robot when performing tasks. Implement regular maintenance and monitoring to ensure the performance and lifespan of the battery;

[0009] Step 4: Adopt the Simultaneous Localization and Mapping (SLAM) technology to enable the robot to locate its position and build a map in an unknown environment. Therefore, the robot can perform localization and map building even in places where satellite signals cannot be received;

[0010] Step 5: Select the MQTT communication protocol as the communication protocol for the robot. Deploy edge computing devices near the robot, which can perform data processing and decision-making close to the robot, reducing the amount of data that needs to be transmitted to the remote server, thereby reducing communication latency. Use the 4G network or a self-organized local area network within the oilfield to ensure timely data transmission. At the same time, install a local storage device on the robot. When communication is unavailable, data can be cached on the robot and transmitted in batches when communication is restored. Then use multiple communication channels to back up the communication link. When one communication method has problems, it can be switched to another method;

[0011] Step 6: Implant a decision-making system into the robot in Step 1 to analyze the information collected by the sensors in Step 2, enabling the robot to autonomously respond to the discovered situations, send out signals, and also be able to give solutions and send them to the monitoring personnel.

[0012] Preferably, the sensors in Step 2 include a gas detection sensor, a temperature sensor, a humidity sensor, a camera, an image sensor, and a lidar.

[0013] Preferably, the gas detection sensor is used to detect the concentration of harmful gases, the temperature sensor is used to monitor the environmental temperature, the humidity sensor can be used to monitor the humidity level, the camera and the image sensor are used to collect environmental images for visual inspection and monitoring, and the lidar is used to measure distances and detect obstacles to ensure that the robot can avoid obstacles or perform localization and navigation.

[0014] Preferably, the intelligent power management strategy in step three includes the establishment of an energy prediction model, and its algorithm is: [E = \int_0^T P(t)dt], where E represents energy consumption, usually in joules or kilojoules, P(t) represents the power consumption of the device at time (t), usually in watts, (t) represents time, usually in seconds, minutes or hours, and (T) represents the total time within the entire time period, usually the same unit as (t).

[0015] Preferably, the intelligent power management strategy in step three also includes the algorithms of dynamic power management, and the algorithms of dynamic power management are divided into power adjustment algorithms and dynamic voltage and frequency adjustment algorithms.

[0016] Preferably, the power adjustment algorithm is: [P(t) = P_{\text{max}} \times \frac{t}{T}];

[0017] where (P(t)) is the power consumption at time (t), P_{\text{max}} represents the maximum allowable power, (t) represents the current time, and (T) represents the total task time.

[0018] Preferably, the dynamic voltage and frequency adjustment algorithm is: [P = C \times V^2 \times f];

[0019] where P represents power, C represents capacitive load, V represents voltage, and f represents clock frequency.

[0020] Preferably, the MQTT communication protocol in step five is a lightweight communication protocol based on the publish-subscribe model, aiming to achieve reliable and real-time data transmission between different devices and applications, and has the characteristics of lightweight, efficient, asynchronous communication, multiple quality levels and wide application fields.

[0021] Preferably, the intelligent algorithm in step six includes a path planning algorithm, and its formula is: [f(n) = g(n) + h(n)]; where f(n) represents the estimated total cost from the starting point to node (n), g(n) represents the actual cost from the starting point to node (n), and h(n) represents the estimated cost from node (n) to the target.

[0022] Preferably, the communication protocol in step five also includes the CoAP communication protocol. The CoAP communication protocol is a communication protocol specially designed for restricted environments, aiming to provide a lightweight, energy-saving and scalable communication mechanism so that these devices can communicate with each other and with the Internet.

[0023] The present invention provides an intelligent inspection method for an explosion-proof inspection robot. It has the following beneficial effects:

[0024] 1. By implementing the power management strategy, the present invention can well plan the battery usage, complete more inspection tasks with the least amount of power, and at the same time, cooperate with the design of various charging methods to optimize the battery, thereby preventing the task from being unable to be completed due to inspection power reasons, and being able to plan the charging time, thus avoiding accidents and economic losses, and at the same time being able to solve potential safety hazards.

[0025] 2. By using communication protocols, edge computing devices, 4G networks or self-organized internal oilfield local area networks and various communication channels, the present invention can maintain the stability and timeliness of communication, avoid the delay of monitoring personnel's decision-making caused by network latency, and therefore avoid the situation where abnormal data fails to be transmitted in time due to network latency and prevent accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of the present invention;

[0027] Figure 2 is an energy framework diagram of the robot in the present invention;

[0028] Figure 3 is a communication framework diagram of the robot in the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment:

[0031] Please refer to the attached Figure 1 - attached Figure 3 , the embodiments of the present invention provide an intelligent inspection method for an explosion-proof inspection robot, including the following steps:

[0032] Step 1: Determine the main tasks and objectives of the explosion-proof inspection robot, determine the specific areas or locations to be inspected, and formulate a map or layout diagram to help determine the inspection path and area. At the same time, according to the danger level of the target area, design hardware that meets the explosion-proof requirements;

[0033] Specifically, designing the hardware according to the usage environment can maximize the adaptation to the inspection environment, thereby increasing the service life of the inspection robot and reducing the inspection cost.

[0034] Step 2: Equip the robot designed in Step 1 with sensors to ensure the safety and effectiveness when performing inspection tasks in dangerous environments;

[0035] Specifically, by using various sensors to collect data, various data in the environment and equipment can be effectively collected and analyzed, thus facilitating the drawing of conclusions to assist users in making decisions.

[0036] Step 3: Use lithium batteries as the power source for the robot designed in Step 1, and at the same time implement an intelligent power management strategy to dynamically manage the battery power consumption according to the requirements of the robot's tasks. When the robot is inactive, it can enter the low-power sleep mode, and be equipped with an automatic charging pile. Use fast charging technology to minimize the time the robot is in the charging state, and develop energy-saving control algorithms to minimize the battery consumption of the robot when performing tasks. Implement regular maintenance and monitoring to ensure the performance and lifespan of the battery;

[0037] Specifically, through the combination of various methods and devices, the diversification of the robot's power source is achieved, which can effectively solve the problem of insufficient battery power of the robot, prevent the inspection task from being unable to be completed, and can plan the charging time, thus avoiding accidents and economic losses, and at the same time solving potential safety hazards.

[0038] Step 4: Adopt the Simultaneous Localization and Mapping (SLAM) technology to enable the robot to locate its own position and build a map in an unknown environment, so that the robot can also perform positioning and map building in places where satellite signals cannot be received;

[0039] Specifically, the map established by using the SLAM technology can still perform inspections in places where satellite signals are weak or not received, without being restricted by satellite signals.

[0040] Step 5: Select the MQTT communication protocol as the communication protocol for the robot, and deploy edge computing devices near the robot. Data processing and decision-making can be carried out near the robot, reducing the amount of data that needs to be transmitted to the remote server, thereby reducing communication latency. Use the 4G network or a self-organized internal local area network in the oilfield to ensure timely data transmission. At the same time, install a local storage device on the robot. When communication is unavailable, data can be cached on the robot and transmitted in batches when communication is restored. Then use multiple communication channels to back up the communication link. When one communication method has problems, it can be switched to another method;

[0041] Specifically, by setting the communication protocol and cooperating with devices and networks to prevent signal instability, it is possible to avoid the disappearance or delay of the network, and thus avoid accidents caused by untimely information transmission.

[0042] Step Six: Implement a decision-making system for the robot in Step One, which is used to analyze the information collected by the sensors in Step Two, enabling the robot to autonomously respond to the discovered situations, send out signals, and also be able to provide solutions and send them to the monitoring personnel.

[0043] The sensors in Step Two include gas detection sensors, temperature sensors, humidity sensors, cameras, image sensors, and lidar.

[0044] The gas detection sensor is used to detect the concentration of harmful gases, the temperature sensor is used to monitor the environmental temperature, the humidity sensor can be used to monitor the humidity level, the cameras and image sensors are used to collect environmental images for visual inspection and monitoring, and the lidar is used to measure distances and detect obstacles to ensure that the robot can avoid obstacles or perform positioning and navigation.

[0045] The intelligent power management strategy in Step Three includes the establishment of an energy prediction model, and its algorithm is: [E = \int_0^T P(t) dt], where E represents the energy consumption, usually in joules or kilojoules, P(t) represents the power consumption of the device at time (t), usually in watts, (t) represents time, usually in seconds, minutes, or hours, and (T) represents the total time within the entire time period, usually in the same unit as (t).

[0046] The intelligent power management strategy in Step Three also includes the algorithms of dynamic power management. The algorithms of dynamic power management are divided into power adjustment algorithms and dynamic voltage and frequency adjustment algorithms.

[0047] The power adjustment algorithm is: [P(t) = P_{\text{max}} \times \frac{t}{T}];

[0048] where (P(t)) is the power consumption at time (t), P_{\text{max}} represents the maximum allowable power, (t) represents the current time, and (T) represents the total task time.

[0049] The dynamic voltage and frequency adjustment algorithm is: [P = C \times V^2 \times f];

[0050] where P represents power, C represents the capacitive load, V represents voltage, and f represents the clock frequency.

[0051] The MQTT communication protocol in Step Five is a lightweight communication protocol based on the publish-subscribe model, aiming to achieve reliable and real-time data transmission between different devices and applications, and has the characteristics of being lightweight, efficient, asynchronous communication, multiple quality levels, and a wide range of application fields.

[0052] In Step 6, the intelligent algorithm includes a path planning algorithm, and its formula is: [f(n) = g(n) + h(n)]; where f(n) represents the estimated total cost from the starting point to node (n), g(n) represents the actual cost from the starting point to node (n), and h(n) represents the estimated cost from node (n) to the target.

[0053] In Step 5, the communication protocol also includes the CoAP communication protocol. The CoAP communication protocol is a communication protocol specifically designed for constrained environments, aiming to provide a lightweight, energy-saving, and scalable communication mechanism so that these devices can communicate with each other and with the Internet.

[0054] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent inspection method for an explosion-proof inspection robot, characterized in that, It includes the following steps: Step 1: Determine the main tasks and objectives of the explosion-proof inspection robot, identify the specific areas or locations to be inspected, formulate a map or layout diagram to assist in determining the inspection path and area, and at the same time, design hardware that meets explosion-proof requirements according to the danger level of the target area; Step 2: Equip the robot designed in Step 1 with sensors to ensure the safety and effectiveness when performing inspection tasks in a dangerous environment; Step 3: Use lithium batteries as the power source for the robot designed in Step 1, and at the same time implement an intelligent power management strategy to dynamically manage the battery power consumption according to the requirements of the robot's tasks. When the robot is inactive, it can enter a low-power sleep mode, and be equipped with an automatic charging pile. Use fast charging technology to minimize the time the robot is in the charging state, and develop energy-saving control algorithms to minimize the battery consumption of the robot during task execution. Implement regular maintenance and monitoring to ensure the performance and lifespan of the battery; Step 4: Adopt simultaneous localization and mapping technology to enable the robot to locate its own position and build a map in an unknown environment, so that the robot can also perform localization and map building in places where satellite signals cannot be received; Step 5: Select the MQTT communication protocol as the communication protocol for the robot. Deploy edge computing devices near the robot, which can perform data processing and decision-making close to the robot, reduce the amount of data that needs to be transmitted to the remote server, thereby reducing communication latency, and use the 4G network or a self-organized local area network within the oilfield to ensure timely data transmission. At the same time, install a local storage device on the robot. When communication is unavailable, data can be cached on the robot and batch-transmitted when communication is restored. Then use multiple communication channels to back up the communication link, and when one communication method has problems, it can be switched to another method; Step 6: Implant a decision-making system into the robot in Step 1 to analyze the information collected by the sensors in Step 2, enabling the robot to autonomously respond to the discovered situations, send out signals, and at the same time be able to give solutions and send them to the monitoring personnel.

2. The intelligent inspection method for an explosion-proof inspection robot according to claim 1, characterized in that, The sensors in Step 2 include gas detection sensors, temperature sensors, humidity sensors, cameras, image sensors, and lidar.

3. The intelligent inspection method for an explosion-proof inspection robot according to claim 2, characterized in that, The gas detection sensors are used to detect the concentration of harmful gases, the temperature sensors are used to monitor the ambient temperature, the humidity sensors can be used to monitor the humidity level, the cameras and image sensors are used to collect environmental images for visual inspection and monitoring, and the lidar is used to measure distances and detect obstacles to ensure that the robot can avoid obstacles or perform positioning and navigation.

4. The intelligent inspection method for an explosion-proof inspection robot according to claim 1, characterized in that, The intelligent power management strategy in step 3 includes the establishment of an energy prediction model, and its algorithm is: [E = \int_0^T P(t)dt], where E represents energy consumption, usually in joules or kilojoules, P(t) represents the power consumption of the device at time (t), usually in watts, (t) represents time, usually in seconds, minutes or hours, and (T) represents the total time within the entire time period, usually in the same unit as (t).

5. The intelligent inspection method for an explosion-proof inspection robot according to claim 1, characterized in that, The intelligent power management strategy in step 3 also includes the algorithm of dynamic power management. The algorithm of dynamic power management is divided into a power adjustment algorithm and a dynamic voltage and frequency adjustment algorithm.

6. The intelligent inspection method for an explosion-proof inspection robot according to claim 5, characterized in that, The power adjustment algorithm is: [P(t) = P_{\text{max}}\times\frac{t}{T}]; where (P(t)) is the power consumption at time (t), P_{\text{max}}) represents the maximum allowable power, (t) represents the current time, and (T) represents the total task time.

7. The intelligent inspection method for an explosion-proof inspection robot according to claim 6, characterized in that, The dynamic voltage and frequency adjustment algorithm is: [P = C\times V^2\times f]; where P represents power, C represents capacitive load, V represents voltage, and f represents clock frequency.

8. The intelligent inspection method for an explosion-proof inspection robot according to claim 1, characterized in that, The MQTT communication protocol in step 5 is a lightweight communication protocol based on the publish-subscribe model, aiming to achieve reliable and real-time data transmission between different devices and applications. It has the characteristics of being lightweight, efficient, asynchronous communication, multiple quality levels, and a wide range of application fields.

9. The intelligent inspection method for an explosion-proof inspection robot according to claim 1, characterized in that, The intelligent algorithm in step 6 includes a path planning algorithm, and its formula is: [f(n) = g(n) + h(n)]; where f(n) represents the estimated total cost from the starting point to node (n), g(n) represents the actual cost from the starting point to node (n), and h(n) represents the estimated cost from node (n) to the target.

10. The intelligent inspection method for an explosion-proof inspection robot according to claim 1, characterized in that, The communication protocol in step 5 also includes the CoAP communication protocol. The CoAP communication protocol is a communication protocol specifically designed for constrained environments, aiming to provide a lightweight, energy-saving, and scalable communication mechanism so that these devices can communicate with each other and with the Internet.

Images