Multi-task cooperation method, device and equipment of inspection robot and storage medium

Abstract

The invention discloses a multi-task cooperation method, device and equipment of an inspection robot and a storage medium, and relates to the technical field of intelligent inspection robots, and the method comprises the steps that multi-sensor data is acquired, and the multi-sensor data comprises visible light image data, infrared thermal imaging data and gas concentration data; performing data processing on the multi-sensor data according to a Kalman filtering strategy to obtain target feature data; judging the target feature data according to a preset task scheduling strategy to obtain a danger level; and controlling a robot to execute a corresponding inspection task according to the danger level, wherein the inspection task comprises fire suppression, temperature abnormity early warning and execution of a preset inspection path. According to the method, the task priority is intelligently distinguished, the emergency situation is quickly responded, the problem of multi-task cooperative processing is effectively solved, and the safety and the inspection efficiency of a high-risk industrial scene are improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent inspection robots, and particularly to a multi-task collaboration method, device, equipment, and storage medium for inspection robots. Background Art

[0002] With the continuous improvement of industrial automation and intelligence levels, intelligent inspection robots are increasingly widely used in high-risk industrial scenarios such as petrochemical and power energy. These robots effectively reduce personnel safety risks and improve inspection efficiency and accuracy by replacing humans to perform inspection tasks. However, existing inspection robots still have significant deficiencies in handling multi-tasks and emergencies. On the one hand, traditional inspection robots mostly use a fixed polling mechanism to handle tasks and cannot dynamically adjust priorities according to the urgency of tasks, resulting in response delays in emergencies such as fires, potentially missing the best handling opportunities and increasing accident risks. On the other hand, although existing multi-robot collaboration schemes aim to improve inspection efficiency, they often face communication delays and device conflict problems in actual applications, affecting the collaboration effect. In addition, single-robot inspection robots also lack intelligent multi-task collaboration processing capabilities when facing complex and changing industrial environments and are difficult to handle multiple potential threats simultaneously.

[0003] Therefore, how to improve the safety and inspection efficiency of inspection robots in the face of multi-task conflicts is an urgent problem to be solved currently. Summary of the Invention

[0004] The main purpose of this application is to provide a multi-task collaboration method, device, equipment, and storage medium for inspection robots, aiming to solve the technical problem of how to improve the safety and inspection efficiency of inspection robots in the face of multi-task conflicts.

[0005] To achieve the above object, this application proposes a multi-task collaboration method for inspection robots, and the method includes: Obtain multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; Perform data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data; Judge the target feature data according to a preset task scheduling strategy to obtain a danger level; Control the robot to perform corresponding inspection tasks according to the danger level, and the inspection tasks include fire suppression, temperature anomaly warning, and execution of a preset inspection path.

[0006] In one embodiment, the step of judging the target feature data according to a preset task scheduling strategy to obtain a danger level includes: Obtain the preset lower explosive limit and the preset temperature threshold according to the preset task scheduling strategy; Obtain the target gas concentration and the target area temperature according to the target feature data; When the target gas concentration is greater than or equal to the preset lower explosive limit and the target area temperature is greater than or equal to the preset temperature threshold, determine that the danger level is the first danger level; When the target area temperature is greater than or equal to the preset temperature threshold, determine that the danger level is the second danger level; When the target gas concentration is less than the preset lower explosive limit and the target area temperature is less than the preset temperature threshold, determine that the danger level is the third danger level, and the first danger level is greater than the second danger level, and the second danger level is greater than the third danger level.

[0007] In one embodiment, the step of controlling the robot to perform corresponding inspection tasks according to the danger level includes: When the danger level is the first danger level, generate an interrupt signal to control the robot to stop the current task, and generate a first navigation path to reach the fire area according to the preset first path planning algorithm; Control the robot to move to the fire suppression point through the first navigation path, and activate the water spraying device of the robot to perform the fire suppression operation.

[0008] In one embodiment, the step of controlling the robot to perform corresponding inspection tasks according to the danger level includes: When the danger level is the second danger level, generate pan-tilt control parameters and lens control parameters according to the preset dynamic adjustment strategy, and obtain the current coordinates; Re-collect visible light image data and infrared thermal imaging data according to the pan-tilt control parameters and the lens control parameters, and transmit the visible light image data, the infrared thermal imaging data, and the current coordinates to the background to obtain an audit result; When the audit result is normal, perform the inspection task according to the preset inspection path; When the audit result is abnormal, raise the second danger level to the first danger level so that the robot performs the fire suppression operation.

[0009] In one embodiment, the step of controlling the robot to perform corresponding inspection tasks according to the danger level includes: When the danger level is the third danger level, control the robot to perform the inspection task according to the preset inspection path; During the execution of the inspection task, when new multi-sensor data is received, return to execute the step of obtaining target feature data from the multi-sensor data according to the Kalman filtering strategy to obtain an updated danger level.

[0010] In one embodiment, the step of processing the multi-sensor data according to the Kalman filtering strategy to obtain target feature data includes: Preprocess the visible light image data, infrared thermal imaging data, and gas concentration data to obtain target visible light image data, initial infrared thermal imaging data, and target gas concentration data; Perform anti-reflection processing on the initial infrared thermal imaging data according to the target visible light image data to obtain target infrared thermal imaging data; Filter the target infrared thermal imaging data and the target gas concentration data according to the Kalman filtering strategy to obtain target feature data.

[0011] In one embodiment, after the step of controlling the robot to perform corresponding inspection tasks according to the danger level, the following is further included: When the robot completes the inspection task or the battery power is lower than the preset threshold, generate a second navigation path to the charging point according to the preset second path planning algorithm; Control the robot to move to the charging point for charging through the second navigation path.

[0012] In addition, to achieve the above object, the present application also proposes a multi-task cooperation device for an inspection robot, and the device includes: A data acquisition module for acquiring multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; A data processing module for processing the multi-sensor data according to the Kalman filtering strategy to obtain target feature data; A danger determination module for judging the target feature data according to a preset task scheduling strategy to obtain a danger level; A task cooperation module for controlling the robot to perform corresponding inspection tasks according to the danger level, and the inspection tasks include fire suppression, temperature anomaly warning, and executing a preset inspection path.

[0013] In addition, to achieve the above object, the present application also proposes a multi-task cooperation device for an inspection robot, and the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the multi-task cooperation method of the inspection robot as described above.

[0014] In addition, to achieve the above object, the present application also proposes a storage medium, the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the multi-task cooperation method of the inspection robot as described above are implemented.

[0015] In addition, to achieve the above object, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the multi-task cooperation method of the inspection robot as described above are implemented.

[0016] The present application provides a multi-task cooperation method for an inspection robot. The method of the present application includes: obtaining multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; performing data processing on the multi-sensor data according to a Kalman filtering strategy to obtain target feature data; judging the target feature data according to a preset task scheduling strategy to obtain a danger level; and controlling the robot to perform corresponding inspection tasks according to the danger level. The inspection tasks include fire suppression, temperature anomaly warning, and execution of a preset inspection path. In summary, it can be seen that the present application effectively solves the problem of multi-task cooperation processing by intelligently distinguishing task priorities and quickly responding to emergencies, and improves the safety and inspection efficiency of high-risk industrial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings here are incorporated into the description and form a part of this description, showing embodiments consistent with the present application and used together with the description to explain the principles of the present application.

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart provided for the first embodiment of the multi-task cooperation method of the inspection robot of the present application; Figure 2 It is a schematic flowchart provided for the second embodiment of the multi-task cooperation method of the inspection robot of the present application; Figure 3 It is a schematic flowchart of the entire process of an embodiment of the multi-task cooperation method of the inspection robot of the present application; Figure 4 It is a schematic module structure diagram of the multi-task cooperation device of the inspection robot in the embodiment of the present application; Figure 5 It is a schematic device structure diagram of the hardware operating environment involved in the multi-task cooperation method of the inspection robot in the embodiment of the present application.

[0020] The implementation, functional features, and advantages of the object of the present application will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0022] To better understand the technical solutions of the present application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0023] The main solution of the embodiment of the present application is: obtaining multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; performing data processing on the multi-sensor data according to a Kalman filtering strategy to obtain target feature data; judging the target feature data according to a preset task scheduling strategy to obtain a danger level; and controlling the robot to perform corresponding inspection tasks according to the danger level, where the inspection tasks include fire suppression, temperature anomaly warning, and executing a preset inspection path.

[0024] With the continuous improvement of the level of industrial automation and intelligence, intelligent inspection robots are increasingly widely used in high-risk industrial scenarios such as petrochemical and power energy. These robots effectively reduce the personnel safety risk and improve the inspection efficiency and accuracy by replacing humans to perform inspection tasks. However, there are still significant deficiencies in the existing inspection robots when dealing with multiple tasks and emergencies. On the one hand, traditional inspection robots mostly use a fixed polling mechanism to handle tasks and cannot dynamically adjust the priority according to the urgency of the tasks, resulting in a delay in response in case of emergencies such as fires, which may miss the best treatment opportunity and increase the accident risk. On the other hand, although the existing multi-robot cooperation solutions aim to improve the inspection efficiency, they often face communication delays and device conflicts in actual applications, affecting the cooperation effect. In addition, single-robot inspection robots also lack the intelligent multi-task cooperation processing ability when facing complex and changeable industrial environments and are difficult to handle multiple potential threats simultaneously. Therefore, how to improve the safety and inspection efficiency of inspection robots in the face of multi-task conflicts is an urgent problem to be solved at present.

[0025] The present application effectively solves the problem of multi-task cooperation processing by intelligently distinguishing task priorities and quickly responding to emergencies, and improves the safety and inspection efficiency of high-risk industrial scenarios.

[0026] It should be noted that the execution subject of this embodiment can be a multi-task cooperation system of an inspection robot, or a computing service device with data processing, network communication, and program running functions, or an electronic device capable of realizing the multi-task cooperation function of the above inspection robot, etc. This embodiment does not specifically limit this. The following takes the multi-task cooperation system of an inspection robot as an example to illustrate this embodiment and the following embodiments.

[0027] Based on this, the embodiments of the present application provide a multi-task collaboration method for an inspection robot. Refer to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the multi-task collaboration method for the inspection robot of the present application.

[0028] In this embodiment, the multi-task collaboration method of the inspection robot includes steps S10 to S40: Step S10: Obtain multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data.

[0029] It should be noted that the inspection robot is equipped with a variety of sensors, including a visible light camera, an infrared thermal imaging sensor, and a gas concentration detection sensor. The multi-sensor data refers to various types of data such as images, temperatures, and gas concentrations collected by devices such as the visible light camera, infrared thermal imaging sensor, and gas sensor equipped on the inspection robot.

[0030] In addition, it should be noted that during the inspection process, the visible light camera equipped on the inspection robot will continuously collect image data of the inspection area, and these image data are used to identify the general conditions of the inspection area, such as intuitive information such as the appearance, color, and shape of objects. The infrared thermal imaging sensor continuously detects the infrared radiation of the surrounding environment and converts it into infrared thermal imaging data, which reflects the temperature distribution on the surface of the object and helps to discover areas with abnormal temperatures, such as potential overheated equipment or fire sources. The gas concentration detection sensor will continuously monitor the specific gas concentration (such as combustible gas, toxic gas, etc.) in the inspection area. It can be understood that these data are mainly used to detect abnormally high-temperature areas and explosive areas, such as equipment overheating, potential fire sources, and combustible gas leakage.

[0031] Step S20: Perform data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data.

[0032] It should be noted that the target feature data refers to a data set that can more accurately and comprehensively reflect the characteristics of objects and the environment in the inspection area after the multi-sensor data is processed by the Kalman filtering strategy. In this embodiment, it includes temperature characteristics (such as average temperature) of the inspection area, gas concentration characteristics (such as concentration values), and characteristic information such as the position and size of objects in the visible light image. Specifically, in this step, the system will perform denoising processing on the visible light image to make the image clearer; perform temperature correction on the infrared thermal imaging data to eliminate sensor errors; perform moving average filtering on the gas concentration data to reduce data fluctuations. Then, use the Kalman filtering strategy to fuse these processed data to obtain target feature data.

[0033] In a feasible implementation manner, the step S20 specifically includes: Step S201: Preprocess the visible light image data, infrared thermal imaging data, and gas concentration data to obtain target visible light image data, initial infrared thermal imaging data, and target gas concentration data.

[0034] It should be noted that the target visible light image data refers to the visible light image data that can more clearly and accurately reflect the visual information such as the appearance, color, and shape of the objects in the inspection area after preprocessing operations such as denoising and image enhancement. The initial infrared thermal imaging data refers to the infrared thermal imaging data obtained after temperature correction and smoothing processing of the original infrared thermal imaging data, which preliminarily eliminates the sensor error and random noise but has not been subjected to anti-reflection processing. The target gas concentration data refers to the more stable and accurate gas concentration data obtained after preprocessing operations such as moving average filtering on the original gas concentration data.

[0035] In addition, it should be noted that for the visible light image data, denoising processing is first performed. During the inspection process, the visible light camera may be affected by factors such as environmental light changes and sensor noise, resulting in noise points in the image. Therefore, the median filtering algorithm is used to take the median of all pixel points in the neighborhood of each pixel point in the image as the new value of this pixel point, thereby effectively removing salt-and-pepper noise and the like in the image. Then, image enhancement processing is performed. Through histogram equalization technology, the gray distribution of the image is adjusted, and the contrast of the image is enhanced, making the objects in the inspection area clearer and more distinguishable, and obtaining the target visible light image data. When the infrared thermal imaging sensor collects data, it will be affected by factors such as its own thermal noise and environmental temperature interference. For the infrared thermal imaging data, temperature correction is performed. Using the pre-calibrated relationship curve between temperature and voltage, the voltage value output by the sensor is converted into an accurate temperature value to eliminate the error of the sensor itself. At the same time, the Gaussian filtering algorithm is used to smooth the image to reduce the random noise in the image and obtain the initial infrared thermal imaging data. The gas concentration detection sensor may be affected by factors such as environmental airflow and sensor zero drift during the measurement process, resulting in fluctuations and deviations in the measurement data. The gas concentration data needs to be subjected to moving average filtering processing. The measured values at consecutive multiple moments are taken, and their average value is calculated as the gas concentration value at the current moment, thereby reducing the random fluctuation of the data and obtaining the target gas concentration data.

[0036] Step S202: Perform anti-reflection processing on the initial infrared thermal imaging data according to the target visible light image data to obtain target infrared thermal imaging data.

[0037] It should be noted that in this step, the system will determine the boundaries and surface features of objects in the inspection area based on the target visible light image data. Through image segmentation algorithms, such as edge detection-based segmentation methods, the contour information of the objects is extracted from the target visible light image data to clarify the boundary positions of the objects. Then, according to the boundaries and surface features of the objects, anti-reflection processing is performed on the corresponding regions in the initial infrared thermal imaging data. For regions with strong reflection on the object surface, false high-temperature signals will be generated in the infrared thermal imaging data. By analyzing information such as the surface material and smoothness of the objects in the target visible light image data, corresponding algorithms are used to suppress the reflection interference in the initial infrared thermal imaging data. For example, for a smooth metal surface, strong reflection will occur, forming a false high-temperature region in the infrared thermal imaging data. By analyzing the characteristics of the metal surface in the target visible light image data, the high-temperature signals in this region of the infrared thermal imaging data are corrected to reduce the influence of reflection interference and obtain the target infrared thermal imaging data.

[0038] It can be understood that anti-reflection processing refers to the process of suppressing or correcting the interference signals generated by the surface reflection of objects in the initial infrared thermal imaging data by analyzing the boundaries and surface features of the objects in the target visible light image data. Its purpose is to eliminate the influence of reflection interference on the accuracy of the infrared thermal imaging data, so that the infrared thermal imaging data can more truly reflect the temperature conditions of the objects. The target infrared thermal imaging data refers to the infrared thermal imaging data that has undergone anti-reflection processing, eliminated the reflection interference, and can more accurately and truly reflect the temperature distribution of the objects in the inspection area.

[0039] Step S203: Filter the target infrared thermal imaging data and the target gas concentration data according to the Kalman filtering strategy to obtain target feature data.

[0040] It should be noted that in this step, the system will establish a system state model and an observation model for the target infrared thermal imaging data and the target gas concentration data. For the target infrared thermal imaging data, the temperature distribution on the object surface is used as the system state, and the measurement value of the infrared thermal imaging sensor is used as the observation value; for the target gas concentration data, the concentration of a specific gas in the environment is used as the system state, and the measurement value of the gas concentration detection sensor is used as the observation value. Then, the Kalman filtering algorithm is used for recursive calculation. In the prediction step, the state at the current moment is predicted based on the state estimate at the previous moment and the system dynamic model; in the update step, the predicted state is corrected using the observation value at the current moment to obtain a more accurate state estimate. By continuously iterating the prediction and update steps, the target infrared thermal imaging data and the target gas concentration data are fused and filtered to obtain the target feature data. These target feature data include information such as the average temperature in the inspection area and the concentration change trend and average concentration of specific (such as flammable and explosive) gases.

[0041] Step S30: Judge the target feature data according to a preset task scheduling strategy to obtain a danger level.

[0042] It should be noted that the danger level refers to the level division used to measure the danger degree or task urgency of the inspection area after judging the target feature data according to the preset task scheduling strategy. In this embodiment, the danger level is divided into multiple levels. For example, the first-level danger level indicates a serious danger situation and immediate measures need to be taken; the second-level danger level indicates a certain danger and requires close attention and timely handling; the third-level danger level indicates that the inspection area is in a safe state. The preset task scheduling strategy refers to a preset three-level task priority scheduling strategy, which is used to guide the robot to perform corresponding inspection tasks according to different danger levels. Specifically, in this step, the system will judge the danger level of the inspection area according to the target feature data and in combination with the three-level task priority scheduling strategy to determine the danger level of the inspection area.

[0043] It can be understood that the function of this step is to accurately judge the danger degree of the inspection area according to the target feature data and use the preset task scheduling strategy, providing a basis for how the robot will perform inspection tasks when facing multiple tasks to be executed simultaneously in the future.

[0044] In a feasible implementation manner, the step S30 specifically includes: Step S301: Obtain a preset lower explosive limit and a preset temperature threshold according to the preset task scheduling strategy.

[0045] It should be noted that the preset lower explosive limit refers to the lowest concentration value at which a combustible gas mixed with air can explode. The preset temperature threshold is a temperature limit set according to the safety requirements of the industrial scenario. When the ambient temperature exceeds this limit, there may be dangerous situations such as fire.

[0046] In addition, it should be noted that in this step, according to the specific high-risk industrial scenarios applied by the inspection robot, such as petrochemical, power energy and other scenarios, the corresponding preset LEL (Lower Explosive Limit) and preset temperature threshold are extracted from the preset task scheduling strategy database. These preset values are determined based on a large amount of experimental data, industry standards and safety specifications. For example, in the petrochemical scenario, for common combustible gases such as methane, its preset lower explosive limit is usually set to 5%LEL (for the convenience of explanation here, to distinguish from the ≥LEL50% scenario mentioned below, the actual preset value is determined according to the specific gas and safety requirements); the preset temperature threshold is set according to the normal operating temperature range of the equipment and the temperature threshold that may cause a fire.

[0047] Step S302: Obtain the target gas concentration and the target area temperature based on the said target feature data.

[0048] It should be noted that the system extracts the target gas concentration and the target area temperature from the target feature data processed by the Kalman filtering strategy. The concentration of the target gas refers to the total concentration value of specific combustible gases in the inspection area, such as methane, hydrogen, etc.; the target area temperature refers to the average temperature of the inspection area. These data are collected by the multi-sensor fusion module and obtained after preprocessing, anti-reflection processing, and Kalman filtering processing, and have high accuracy and reliability.

[0049] Step S303: When the target gas concentration is greater than or equal to the preset lower explosion limit and the target area temperature is greater than or equal to the preset temperature threshold, determine that the danger level is the first danger level.

[0050] It should be noted that in this step, the system compares the extracted target gas concentration with the preset lower explosion limit, and at the same time compares the target area temperature with the preset temperature threshold. When the target gas concentration (i.e., the total concentration of combustible gases) is greater than or equal to the preset lower explosion limit (such as LEL50%), and the target area temperature is greater than or equal to the preset temperature threshold (such as 150 °C), it is determined that the inspection area is at the first danger level. It can be understood that the function of this step is to quickly identify the extremely dangerous situation where there are both high-concentration combustible gases and high temperature in the inspection area, and issue the highest-level danger alarm in time so as to take emergency measures to prevent accidents.

[0051] Step S304: When the target area temperature is greater than or equal to the preset temperature threshold, determine that the danger level is the second danger level.

[0052] It should be noted that in this step, when the target area temperature is greater than or equal to the preset temperature threshold (such as 150 °C), but the target gas concentration (i.e., the total concentration of combustible gases) is less than the preset lower explosion limit (LEL50%), it is determined that the inspection area is at the second danger level. The function of this step is to identify the situation where although there is no high-concentration combustible gas, the temperature has risen abnormally, which indicates that there are fire hazards or other abnormal situations in this area and need to be paid attention to and processed in time.

[0053] In addition, it should be noted that the second danger level indicates that there is an abnormal increase in temperature in the inspection area. Although it has not reached the direct conditions for causing a fire or explosion, corresponding measures need to be taken to prevent the situation from deteriorating.

[0054] Step S305: When the target gas concentration is less than the preset lower explosion limit and the temperature of the target area is less than the preset temperature threshold, determine that the danger level is the third danger level, where the first danger level is greater than the second danger level, and the second danger level is greater than the third danger level.

[0055] It should be noted that in this step, when the target gas concentration (i.e., the total concentration of combustible gases) is less than the preset lower explosion limit (such as LEL50%), and the temperature of the target area is less than the preset temperature threshold (such as 150°C), it is determined that the inspection area is at the third danger level. The danger level decreases as the number increases, that is, the third danger level is the lowest level, indicating that the inspection area is relatively safe, but regular inspections still need to be maintained to ensure timely discovery of potential safety hazards.

[0056] Step S40: Control the robot to perform corresponding inspection tasks according to the danger level, and the inspection tasks include fire suppression, temperature anomaly warning, and execution of a preset inspection path.

[0057] It should be noted that the inspection tasks refer to the specific operations performed by the robot according to different danger levels, including fire suppression, temperature anomaly warning, and execution of a preset inspection path, etc. These tasks are aimed at ensuring the safety of the inspection area and taking corresponding measures in a timely manner when dangerous situations are detected. It can be understood that the fire suppression task means that when a fire occurs in the inspection area (i.e., when the danger level is relatively high), the robot uses its equipped fire extinguishing device to extinguish or control the fire source. The temperature anomaly warning task means that when there is a temperature anomaly in the inspection area (i.e., when the danger level is relatively low), the robot reminds relevant personnel by sending alarm signals, recording abnormal information, etc. The task of executing the preset inspection path means that the robot conducts routine inspections on equipment, environment, etc. within the inspection area according to the preset inspection route and inspection points.

[0058] In a feasible implementation manner, after the step S40, it further includes: Step S50: When the robot completes the inspection task or the battery level is lower than the preset threshold, generate a second navigation path to the charging point according to the preset second path planning algorithm.

[0059] It should be noted that when the inspection robot completes all preset inspection tasks, or detects that its own battery level is lower than the preset safety threshold, the robot will activate the preset second path planning algorithm. This algorithm calculates and generates an optimal navigation path, that is, the second navigation path, based on the current position of the robot, the position of the charging point, and the environmental map information, to guide the robot to safely and efficiently reach the charging point.

[0060] Additionally, it should be noted that the preset second path planning algorithm specifically refers to the Dijkstra algorithm in this embodiment. The Dijkstra algorithm is a classic algorithm for calculating the single-source shortest path in a weighted graph, which can efficiently find the shortest path from the starting point to the ending point.

[0061] Step S60: Control the robot to move to the charging point for charging through the second navigation path.

[0062] It should be noted that once the second navigation path is generated, the inspection robot will immediately activate the navigation system and move along this path towards the charging point. During the movement, the robot will continuously monitor the obstacles and dangerous areas on the path and adjust the moving speed and direction according to the actual situation to ensure safe arrival at the charging point. After arriving at the charging point, the robot will automatically dock with the charging device and start the charging process.

[0063] This embodiment provides a multi-task collaboration method for an inspection robot. The method of this embodiment includes: acquiring multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; performing data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data; judging the target feature data according to the preset task scheduling strategy to obtain a danger level; and controlling the robot to execute corresponding inspection tasks according to the danger level, where the inspection tasks include fire suppression, temperature anomaly warning, and executing a preset inspection path. In summary, it can be seen that this application effectively solves the problem of multi-task collaborative processing by intelligently distinguishing task priorities and quickly responding to emergencies, improving the safety and inspection efficiency of high-risk industrial scenarios.

[0064] Based on the first embodiment of this application, in the second embodiment of this application, for the same or similar content as the above-mentioned embodiment one, reference can be made to the above introduction and will not be elaborated hereinafter. On this basis, please refer to Figure 2 and Figure 3 , Figure 2 is a schematic flowchart of the second embodiment of the multi-task collaboration method for the inspection robot of this application, Figure 3 is a schematic flowchart of the entire process of an embodiment of the multi-task collaboration method for the inspection robot of this application. The specific steps of step S40 include: Step A10: When the danger level is the first danger level, generate an interrupt signal to control the robot to stop the current task, and generate a first navigation path to reach the fire area according to the preset first path planning algorithm.

[0065] It should be noted that the interruption signal refers to a signal used to control the robot to immediately stop the current task and switch to the emergency handling mode. The preset first path planning algorithm refers to an algorithm pre-programmed in the robot control system for generating an optimal navigation path based on the current position information and the target position information. In this embodiment, the algorithm is the A* algorithm, but other suitable path planning algorithms can also be selected according to actual requirements. In this step, when the inspection robot determines that the inspection area is at the first danger level according to the preset task scheduling strategy, an interruption signal is immediately generated. This interruption signal is used to control the robot to immediately stop the current task being executed, whether it is the preset inspection path or other low-priority tasks. Subsequently, the robot will call the preset first path planning algorithm (such as the A* algorithm), combine the current position information and the position information of the fire area, and generate an optimal first navigation path to ensure that the robot can quickly and accurately reach the fire area.

[0066] It can be understood that the function of this step is to ensure that in the event of an extreme danger situation, the inspection robot can respond quickly, interrupt the current task, and plan an efficient navigation path to reach the fire area as soon as possible for disposal.

[0067] Step A20: Control the robot to move to the fire extinguishing point through the first navigation path, and activate the water spraying device of the robot to perform the fire suppression operation.

[0068] It should be noted that the water spraying device refers to a fire extinguishing device installed on the inspection robot, including parts such as a water pump, a nozzle, and a control system. When performing the fire suppression operation, the robot will adjust the spraying angle and flow rate according to the size and position of the fire to ensure the maximization of the fire extinguishing effect. Specifically, in this step, the inspection robot will control itself to move to the fire extinguishing point through the autonomous navigation system according to the generated first navigation path. During the movement, the robot will continuously monitor the situation of the fire area to ensure the accuracy and safety of the path. After reaching the fire extinguishing point, the robot will immediately activate the water spraying device, adjust the spraying angle and flow rate according to the size and position of the fire, and perform the fire suppression operation until the fire is controlled or completely extinguished.

[0069] In a feasible implementation manner, the step S40 further includes: Step B10: When the danger level is the second danger level, generate the pan-tilt control parameters and the lens control parameters according to the preset dynamic adjustment strategy, and obtain the current coordinates.

[0070] It should be noted that in this step, when the inspection robot determines that the inspection area is at the second danger level (such as abnormal temperature but not reaching the fire level) according to the preset task scheduling strategy, the robot will automatically generate pan-tilt control parameters and lens control parameters suitable for the current situation according to the preset dynamic adjustment strategy. The pan-tilt control parameters include the adjustment ranges of the pitch angle and the heading angle (such as the pitch angle from -30° to +90°, and the heading angle from -30° to +30°) to ensure that the robot can accurately aim at the abnormal area for observation; the lens control parameters involve the focal length adjustment of the zoom lens (such as 5-50mm zoom) to obtain clearer image data. At the same time, the robot will also obtain the current coordinates through the built-in positioning system for subsequent data transmission and path planning.

[0071] Step B20: Re-collect visible light image data and infrared thermal imaging data according to the pan-tilt control parameters and the lens control parameters, and transmit the visible light image data, the infrared thermal imaging data, and the current coordinates to the background to obtain an audit result.

[0072] It should be noted that in this step, the inspection robot will re-adjust the positions of the pan-tilt and the lens according to the generated pan-tilt control parameters and lens control parameters to collect visible light image data and infrared thermal imaging data. After the collection is completed, the robot will transmit these data and the current coordinates to the background system through the wireless communication module. After receiving the data, the background system will perform further analysis and processing to determine whether there is a real safety hazard. It can be understood that the function of this step is to provide more accurate and comprehensive information for the background system by re-collecting image data and thermal imaging data for accurate judgment and decision-making.

[0073] Step B30: When the audit result is normal, perform the inspection task according to the preset inspection path.

[0074] It should be noted that in this step, when the background system audits the data transmitted by the inspection robot and determines it to be normal (that is, there is no safety hazard), it will send an instruction to the robot to continue performing the inspection task according to the preset inspection path. It can be understood that the function of this step is to ensure that the inspection robot can continue to perform its preset inspection task to cover more inspection areas and ensure overall safety when it is confirmed that there is no safety hazard.

[0075] Step B40: When the audit result is abnormal, raise the second danger level to the first danger level to enable the robot to perform a fire suppression operation.

[0076] It should be noted that in this step, when the background system audits the data transmitted by the inspection robot and determines it to be abnormal (i.e., there are potential safety hazards that may escalate into fire risks), the background system will send an instruction to the robot to raise the current danger level from the second danger level to the first danger level. After receiving the instruction, the robot will immediately stop the current task and perform fire suppression operations according to the processing procedure of the first danger level. It can be understood that the function of this step is to ensure that when potential safety hazards are detected, the inspection robot can quickly upgrade the processing level, take more effective measures to respond, and prevent safety hazards from escalating into serious accidents such as fires.

[0077] In a feasible implementation manner, step S40 further includes: Step C10: When the danger level is the third danger level, control the robot to perform an inspection task according to a preset inspection path.

[0078] It should be noted that the third danger level means that no situation directly threatening safety is detected in the inspection area, such as no flame, the concentration of combustible gas not exceeding the standard, and the temperature not reaching the abnormal threshold, etc. At this level, the robot mainly performs routine inspection tasks to maintain continuous monitoring of the inspection area. In this step, when the inspection robot determines that the inspection area is at the third danger level according to the preset task scheduling strategy (i.e., no direct threats such as flame, excessive combustible gas, or significant temperature anomaly are detected), the robot will automatically perform the inspection task according to the preset inspection path. This path is pre-planned based on the layout of the inspection area, the positions of key equipment, and historical inspection data, aiming to comprehensively cover the inspection area and ensure timely detection of potential safety hazards.

[0079] Step C20: During the execution of the inspection task, when new multi-sensor data is received, return to perform the step of obtaining target feature data by performing data targeting on the multi-sensor data according to the Kalman filtering strategy, and obtain an updated danger level.

[0080] It should be noted that during the process of the inspection robot performing the inspection task according to the preset inspection path, the robot will continuously receive real-time data from multi-sensors such as visible light cameras, infrared thermal imagers, and gas sensors. Whenever new multi-sensor data is received, the robot will immediately return to perform the data fusion step based on the Kalman filtering strategy to generate more accurate and reliable target feature data, such as temperature distribution, gas concentration, etc. Subsequently, the robot will re-evaluate the danger level of the inspection area according to the updated target feature data. If the new evaluation result shows that the danger level has changed (such as escalating from the third danger level to the second or first danger level), the robot will immediately adjust the inspection task and perform corresponding emergency treatment measures, such as temperature anomaly warning or fire suppression operations.

[0081] In this embodiment, by setting response mechanisms for different risk levels (including interrupting the current task, planning a fire path, dynamically adjusting sensor parameters, auditing transmitted data, and updating the risk level through multi-sensor data fusion), a rapid response and effective handling of emergencies such as sudden fires are achieved, solving the problem of response delay caused by the inability of traditional inspection robots to distinguish task priorities, and improving the execution efficiency and safety of inspection tasks in high-risk industrial scenarios.

[0082] This application also provides a multi-task collaboration device for an inspection robot. Please refer to Figure 4 , and the multi-task collaboration device for the inspection robot includes: A data acquisition module 10, configured to acquire multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; A data processing module 20, configured to process the multi-sensor data according to a Kalman filtering strategy to obtain target feature data; A risk determination module 30, configured to determine a risk level based on a preset task scheduling strategy for the target feature data; A task collaboration module 40, configured to control the robot to execute corresponding inspection tasks according to the risk level, where the inspection tasks include fire suppression, temperature anomaly warning, and executing a preset inspection path.

[0083] The multi-task collaboration device for the inspection robot provided in this application adopts the multi-task collaboration method of the inspection robot in the above embodiment, and can solve the technical problem of how to improve the safety and inspection efficiency of the inspection robot in the face of multi-task conflicts. Compared with the prior art, the beneficial effects of the multi-task collaboration device for the inspection robot provided in this application are the same as those of the multi-task collaboration method of the inspection robot provided in the above embodiment, and other technical features in the multi-task collaboration device for the inspection robot are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0084] In one embodiment, the data processing module 20 is further configured to preprocess the visible light image data, infrared thermal imaging data, and gas concentration data to obtain target visible light image data, initial infrared thermal imaging data, and target gas concentration data; perform anti-reflection processing on the initial infrared thermal imaging data according to the target visible light image data to obtain target infrared thermal imaging data; and filter the target infrared thermal imaging data and the target gas concentration data according to a Kalman filtering strategy to obtain target feature data.

[0085] In one embodiment, the hazard determination module 30 is further configured to obtain a preset lower explosive limit and a preset temperature threshold according to a preset task scheduling strategy; obtain a target gas concentration and a target area temperature based on the target feature data; when the target gas concentration is greater than or equal to the preset lower explosive limit and the target area temperature is greater than or equal to the preset temperature threshold, determine that the hazard level is the first hazard level; when the target area temperature is greater than or equal to the preset temperature threshold, determine that the hazard level is the second hazard level; when the target gas concentration is less than the preset lower explosive limit and the target area temperature is less than the preset temperature threshold, determine that the hazard level is the third hazard level, where the first hazard level is greater than the second hazard level, and the second hazard level is greater than the third hazard level.

[0086] In one embodiment, when the hazard level is the first hazard level, the hazard determination module 30 is further configured to generate an interrupt signal to control the robot to stop the current task, and generate a first navigation path to the fire area according to a preset first path planning algorithm; control the robot to move to the fire suppression point through the first navigation path, and activate the water spraying device of the robot to perform a fire suppression operation.

[0087] In one embodiment, when the hazard level is the second hazard level, the hazard determination module 30 is further configured to generate pan-tilt control parameters and lens control parameters according to a preset dynamic adjustment strategy, and obtain the current coordinates; re-collect visible light image data and infrared thermal imaging data according to the pan-tilt control parameters and the lens control parameters, and transmit the visible light image data, the infrared thermal imaging data, and the current coordinates to the background to obtain an audit result; when the audit result is normal, perform an inspection task according to a preset inspection path; when the audit result is abnormal, raise the second hazard level to the first hazard level so that the robot performs a fire suppression operation.

[0088] In one embodiment, when the hazard level is the third hazard level, the hazard determination module 30 is further configured to control the robot to perform an inspection task according to a preset inspection path; during the execution of the inspection task, when new multi-sensor data is received, return to the step of obtaining target feature data by performing data targeting on the multi-sensor data according to the Kalman filtering strategy to obtain an updated hazard level.

[0089] In one embodiment, when the robot completes the inspection task or the battery power is lower than a preset threshold, the task cooperation module 40 is further configured to generate a second navigation path to the charging point according to a preset second path planning algorithm; control the robot to move to the charging point through the second navigation path to charge.

[0090] The present application provides a multi-task collaborative device for an inspection robot. The multi-task collaborative device for an inspection robot includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-task collaborative method for an inspection robot in Embodiment 1 above.

[0091] Reference is made below Figure 5 , which shows a schematic structural diagram of a multi-task collaborative device for an inspection robot suitable for implementing the embodiments of the present application. The multi-task collaborative device for an inspection robot in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions, tablet computers), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The multi-task collaborative device for an inspection robot shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0092] As Figure 5As shown in the figure, the multi-task collaboration device of the inspection robot may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can execute various appropriate actions and processes according to the program stored in the ROM (Read Only Memory) 1002 or the program loaded from the storage device 1003 into the RAM (Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the multi-task collaboration device of the inspection robot are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, an LCD (Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the multi-task collaboration device of the inspection robot to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a multi-task collaboration device of an inspection robot with various systems, it should be understood that it is not required to implement or have all the systems shown. Instead, more or fewer systems can be implemented or had.

[0093] Specifically, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed in the present application are executed.

[0094] The multi-task collaboration device of the inspection robot provided by the present application adopts the multi-task collaboration method of the inspection robot in the above-mentioned embodiment, and can solve the technical problem of how to improve the safety and inspection efficiency of the inspection robot when facing multi-task conflicts. Compared with the prior art, the beneficial effects of the multi-task collaboration device of the inspection robot provided by the present application are the same as those of the multi-task collaboration method of the inspection robot provided in the above-mentioned embodiment, and other technical features in the multi-task collaboration device of the inspection robot are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0095] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0096] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0097] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the multi-task cooperation method of the inspection robot in the above embodiments.

[0098] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or flash memory), optical fibers, CD-ROM (CD-Read Only Memory, portable compact disk read-only memory), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0099] The above computer-readable storage medium can be included in the multi-task cooperation device of the inspection robot; it can also exist separately and not be assembled into the multi-task cooperation device of the inspection robot.

[0100] The above computer-readable storage medium carries one or more programs, which, when executed by the multi-task collaboration device of the patrol robot, cause the multi-task collaboration device of the patrol robot to: obtain multi-sensor data; perform data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data; judge the target feature data according to a preset task scheduling strategy to obtain a danger level; and control the robot to perform corresponding patrol tasks according to the danger level, where the patrol tasks include fire suppression, temperature anomaly warning, and execution of a preset patrol path.

[0101] Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partially on the user's computer, execute as a stand-alone software package, execute partially on the user's computer and partially on a remote computer, or execute entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0103] The modules described in the embodiments of the present application may be implemented in software or in hardware. Wherein, the name of the module does not constitute a limitation to the unit itself in some cases.

[0104] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the multi-task cooperation method of the above-mentioned inspection robot, and can solve the technical problem of how to improve the safety and inspection efficiency of the inspection robot when facing multi-task conflicts. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the multi-task cooperation method of the inspection robot provided in the above embodiments, and will not be elaborated here.

[0105] This application also provides a computer program product, including a computer program, and the steps of the multi-task cooperation method of the above-mentioned inspection robot are implemented when the computer program is executed by a processor.

[0106] The computer program product provided by this application can solve the technical problem of how to improve the safety and inspection efficiency of the inspection robot when facing multi-task conflicts. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as those of the multi-task cooperation method of the inspection robot provided in the above embodiments, and will not be elaborated here.

[0107] The above are only some embodiments of this application, and do not limit the patent scope of this application accordingly. Any equivalent structural transformation made by using the content of the specification and drawings of this application under the technical concept of this application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of this application.

Claims

1. A multi-task collaboration method for an inspection robot, characterized in that, The method includes: Obtaining multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; Performing data processing on the multi-sensor data according to a Kalman filtering strategy to obtain target feature data; Judging the target feature data according to a preset task scheduling strategy to obtain a danger level; Controlling the robot to execute corresponding inspection tasks according to the danger level, where the inspection tasks include fire suppression, temperature anomaly warning, and executing a preset inspection path.

2. The method according to claim 1, wherein The step of judging the target feature data according to a preset task scheduling strategy to obtain a danger level includes: Obtaining a preset lower explosion limit and a preset temperature threshold according to a preset task scheduling strategy; Obtaining a target gas concentration and a target area temperature according to the target feature data; When the target gas concentration is greater than or equal to the preset lower explosion limit and the target area temperature is greater than or equal to the preset temperature threshold, determining the danger level as the first danger level; When the target area temperature is greater than or equal to the preset temperature threshold, determining the danger level as the second danger level; When the target gas concentration is less than the preset lower explosion limit and the target area temperature is less than the preset temperature threshold, determining the danger level as the third danger level, where the first danger level is greater than the second danger level, and the second danger level is greater than the third danger level.

3. The method according to claim 1, characterized in that The step of controlling the robot to execute corresponding inspection tasks according to the danger level includes: When the danger level is the first danger level, generating an interrupt signal to control the robot to stop the current task, and generating a first navigation path to reach the fire area according to a preset first path planning algorithm; Controlling the robot to move to the fire extinguishing point through the first navigation path, and activating the water spraying device of the robot to perform fire suppression operations.

4. The method according to claim 1, wherein The step of controlling the robot to execute corresponding inspection tasks according to the danger level includes: When the danger level is the second danger level, generating pan-tilt control parameters and lens control parameters according to a preset dynamic adjustment strategy, and obtaining the current coordinates; Re-collecting visible light image data and infrared thermal imaging data according to the pan-tilt control parameters and lens control parameters, and transmitting the visible light image data, the infrared thermal imaging data, and the current coordinates to the background to obtain an audit result; When the audit result is normal, executing an inspection task according to a preset inspection path; When the audit result is abnormal, raising the second danger level to the first danger level so that the robot performs fire suppression operations.

5. The method according to claim 1, characterized in that, The step of controlling the robot to execute corresponding inspection tasks according to the danger level includes: When the danger level is the third danger level, controlling the robot to execute an inspection task according to a preset inspection path; During the execution of the inspection task, when new multi-sensor data is received, return to execute the step of performing data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data, and obtain an updated danger level.

6. The method according to claim 1, characterized in that The step of performing data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data includes: Preprocess the visible light image data, infrared thermal imaging data, and gas concentration data to obtain target visible light image data, initial infrared thermal imaging data, and target gas concentration data; Perform anti-reflection processing on the initial infrared thermal imaging data according to the target visible light image data to obtain target infrared thermal imaging data; Filter the target infrared thermal imaging data and the target gas concentration data according to the Kalman filtering strategy to obtain target feature data.

7. The method according to claim 1, wherein After the step of controlling the robot to perform corresponding inspection tasks according to the danger level, it further includes: When the robot completes the inspection task or the power is lower than the preset threshold, generate a second navigation path to the charging point according to the preset second path planning algorithm; Control the robot to move to the charging point for charging through the second navigation path.

8. A multi-task cooperation device for a patrol robot, characterized in that, The device includes: A data acquisition module for acquiring multi-sensor data, where the multi-sensor data includes visible light image data, infrared thermal imaging data, and gas concentration data; A data processing module for performing data processing on the multi-sensor data according to the Kalman filtering strategy to obtain target feature data; A danger determination module for judging the danger level according to the preset task scheduling strategy for the target feature data; A task coordination module for controlling the robot to perform corresponding inspection tasks according to the danger level, where the inspection tasks include fire suppression, temperature anomaly warning, and execution of a preset inspection path.

9. A multi-task collaborative device for a patrol robot, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the multi-task coordination method of the inspection robot according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, it implements the steps of the multi-task coordination method of the inspection robot according to any one of claims 1 to 7.

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