Intelligent inspection system for safety production supervision

Abstract

The invention belongs to the technical field of intelligent inspection, and provides an intelligent inspection system for safety production supervision, which comprises an autonomous navigation module, a multi-sensor fusion sensing module, an intelligent analysis and diagnosis platform, a remote monitoring and emergency response system and a self-adaptive learning optimization mechanism, by integrating advanced technologies such as high-precision positioning, an SLAM technology, AI path planning, multi-sensor fusion sensing, deep learning analysis, remote monitoring and emergency response and adaptive learning optimization, autonomous and efficient inspection in a complex industrial environment is realized, potential safety hazards can be monitored in real time, an abnormal state can be quickly identified, potential risks can be predicted, and the safety of the industrial environment can be improved. According to the invention, the system can effectively monitor the safety production, provides a fault diagnosis report and a maintenance suggestion, supports remote monitoring and emergency response, continuously optimizes the inspection path and improves the recognition precision, thereby effectively improving the efficiency and accuracy of safety production supervision, and reducing the safety risk of manual inspection.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent inspection, in particular to an intelligent inspection system for production safety supervision. Background Art

[0002] In industrial production environments, safe production is a vital link. Traditional inspection methods mainly rely on manual labor, which has problems such as low inspection efficiency, untimely detection of safety hazards, and inaccurate data recording. With the development of industrial intelligence and information technology, although some inspection systems have been applied to safe production supervision, these systems often have single functions and low intelligence, making it difficult to achieve comprehensive, efficient and accurate monitoring of complex industrial environments; especially in some extreme environments, such as high temperature, high pressure, and harmful gas leakage, manual inspections are not only inefficient, but also pose great safety risks.

[0003] To this end, those skilled in the art have proposed an intelligent inspection system for production safety supervision to solve the problems raised by the background technology. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an intelligent inspection system for safe production supervision to solve the problem that the inspection system in the prior art is applied to safe production supervision, but these systems often have single functions and low intelligence, and it is difficult to achieve comprehensive, efficient and accurate monitoring of complex industrial environments; especially in some extreme environments, such as high temperature, high pressure, harmful gas leakage and other scenarios, manual inspections are not only inefficient, but also have great safety hazards and other problems.

[0005] An intelligent inspection system for production safety supervision, comprising:

[0006] Autonomous navigation module: Integrates high-precision GPS positioning, SLAM (simultaneous localization and mapping) technology and AI path planning algorithms, enabling the system to autonomously plan and execute inspection routes in complex industrial environments without human intervention;

[0007] Multi-sensor fusion perception module: Integrates infrared thermal imagers, high-definition cameras, gas leak detectors, and sound recognition sensors to collect on-site environmental data in real time, achieving multi-dimensional and all-round monitoring of safety hazards;

[0008] Intelligent analysis and diagnosis platform: Use deep learning algorithms to quickly analyze the collected data, automatically identify abnormal conditions, predict potential risks, and provide fault diagnosis reports and maintenance suggestions;

[0009] Remote monitoring and emergency response system: supports real-time data transmission to cloud servers. Managers can remotely monitor the inspection process through mobile devices or PCs. Once an emergency is detected, the system automatically triggers the early warning mechanism and mobilizes the nearest emergency resources for processing;

[0010] Adaptive learning optimization mechanism: The system continuously optimizes inspection paths and improves recognition accuracy based on historical inspection data and feedback, achieving continuous self-improvement.

[0011] Preferably, it also includes an energy management system that can dynamically adjust the working mode of each component according to the inspection task requirements and the energy consumption status of the equipment to extend the overall battery life.

[0012] Preferably, in the intelligent navigation module, an AI path planning algorithm is introduced for path planning, which can improve the navigation accuracy and efficiency in complex industrial environments.

[0013] Preferably, the multi-sensor fusion perception module further includes an environmental adaptability adjustment function, which automatically adjusts the sensor sensitivity according to the current environmental conditions to ensure accurate monitoring in various extreme environments.

[0014] Preferably, in the multi-sensor fusion perception module, the Kalman filtering algorithm is used to process the collected on-site environmental data, which can achieve smoothing and prediction of sensor data, improve data accuracy and reliability, and help achieve multi-dimensional and all-round monitoring of safety hazards.

[0015] Preferably, in the intelligent analysis and diagnosis platform, the deep learning algorithm includes a convolutional neural network (CNN).

[0016] Preferably, the intelligent analysis and diagnosis platform also has a historical data comparison function, which can compare and analyze the current monitoring data with the historical data of the same period, discover long-term trend changes, and provide a basis for preventive maintenance.

[0017] Preferably, in the adaptive learning optimization mechanism, a reinforcement learning algorithm (including Q-learning) is used to learn historical inspection data, which can continuously optimize the inspection path and improve the recognition accuracy.

[0018] A processor is configured to execute the above-mentioned intelligent inspection system for production safety supervision.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned intelligent inspection system for production safety supervision.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The present invention integrates high-precision GPS positioning, SLAM technology and AI path planning algorithm through an autonomous navigation module, so that the system can autonomously plan and execute inspection routes in complex industrial environments without human intervention, greatly improving inspection efficiency.

[0022] 2. The multi-sensor fusion perception module of the present invention integrates multiple sensors such as infrared thermal imagers, high-definition cameras, gas leak detectors, and sound recognition sensors. It can collect on-site environmental data in real time, realize multi-dimensional and all-round monitoring of safety hazards, and ensure that safety hazards are nowhere to hide.

[0023] 3. The intelligent analysis and diagnosis platform of the present invention uses deep learning algorithms to quickly analyze the collected data, automatically identify abnormal conditions, predict potential risks, and provide fault diagnosis reports and maintenance suggestions, providing strong technical support for safe production.

[0024] 4. The present invention supports real-time data transmission to the cloud server. Managers can remotely monitor the inspection process through mobile devices or PCs. Once an emergency is detected, the system automatically triggers the early warning mechanism and mobilizes the nearest emergency resources for processing, effectively improving the emergency response speed and processing capabilities.

[0025] 5. The adaptive learning optimization mechanism of the present invention can continuously optimize the inspection path and improve the recognition accuracy according to the historical inspection data and feedback, achieve continuous self-improvement, and ensure the long-term stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a framework diagram of the intelligent inspection system for safe production supervision of the present invention. DETAILED DESCRIPTION

[0027] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0028] Embodiment: The present invention provides an intelligent inspection system for production safety supervision, such as Figure 1 As shown, it includes an autonomous navigation module, a multi-sensor fusion perception module, an intelligent analysis and diagnosis platform, a remote monitoring and emergency response system, and an adaptive learning optimization mechanism, and the autonomous navigation module, the multi-sensor fusion perception module, the intelligent analysis and diagnosis platform, the remote monitoring and emergency response system, and the adaptive learning optimization mechanism are electrically connected in sequence:

[0029] Autonomous navigation module: Integrates high-precision GPS positioning, SLAM (simultaneous localization and mapping) technology and AI path planning algorithms, enabling the system to autonomously plan and execute inspection routes in complex industrial environments without human intervention;

[0030] Multi-sensor fusion perception module: Integrates infrared thermal imagers, high-definition cameras, gas leak detectors, and sound recognition sensors to collect on-site environmental data in real time, achieving multi-dimensional and all-round monitoring of safety hazards;

[0031] Intelligent analysis and diagnosis platform: Use deep learning algorithms to quickly analyze the collected data, automatically identify abnormal conditions, predict potential risks, and provide fault diagnosis reports and maintenance suggestions;

[0032] Remote monitoring and emergency response system: supports real-time data transmission to cloud servers. Managers can remotely monitor the inspection process through mobile devices or PCs. Once an emergency is detected, the system automatically triggers the early warning mechanism and mobilizes the nearest emergency resources for processing;

[0033] Adaptive learning optimization mechanism: The system continuously optimizes inspection paths and improves recognition accuracy based on historical inspection data and feedback, achieving continuous self-improvement.

[0034] From the above, we can see that by integrating advanced technologies such as high-precision positioning, SLAM technology, AI path planning, multi-sensor fusion perception, deep learning analysis, remote monitoring and emergency response, and adaptive learning optimization, autonomous and efficient inspections in complex industrial environments have been achieved. It can monitor safety hazards in real time, quickly identify abnormal conditions, predict potential risks, and provide fault diagnosis reports and maintenance suggestions. It also supports remote monitoring and emergency response, continuously optimizes inspection paths and improves recognition accuracy, thereby effectively improving the efficiency and accuracy of production safety supervision and reducing the safety risks of manual inspections.

[0035] Furthermore, it also includes an energy management system that can dynamically adjust the working mode of each component according to the inspection task requirements and equipment energy consumption status, thereby extending the overall battery life.

[0036] From the above, we can see that by adding an energy management system, the working mode of each component can be intelligently and dynamically adjusted according to the specific needs of the inspection task and the real-time energy consumption status of the equipment, thereby ensuring the inspection efficiency and quality while maximizing the overall battery life of the system, improving the system's continuous working ability, and ensuring the smooth progress of the inspection task.

[0037] Furthermore, in the intelligent navigation module, the AI ​​path planning algorithm is introduced for path planning, which can improve the navigation accuracy and efficiency in complex industrial environments. The formula of the AI ​​path planning algorithm is as follows:

[0038] f(n)=g(n)+h(n);

[0039] Among them, f(n) is the minimum cost estimate from the starting point to node n, g(n) is the actual cost from the starting point to node n, and h(n) is the heuristic estimated cost from node n to the end point.

[0040] From the above, we can see that by introducing the AI ​​path planning algorithm in the intelligent navigation module, the minimum cost estimate, actual cost and heuristic estimated cost from the starting point to each node can be accurately calculated, thereby significantly improving the navigation accuracy and efficiency in complex industrial environments, enabling the inspection system to plan and execute inspection routes more quickly and accurately, improving the efficiency and accuracy of inspections.

[0041] Furthermore, the multi-sensor fusion perception module further includes an environmental adaptability adjustment function, which automatically adjusts the sensor sensitivity according to the current environmental conditions to ensure accurate monitoring in various extreme environments.

[0042] From the above, it can be seen that through the environmental adaptability adjustment function in the multi-sensor fusion perception module, the sensor sensitivity can be intelligently and automatically adjusted according to the current environmental conditions, so that in various extreme environments, such as high temperature, high pressure, harmful gas leakage and other scenarios, the monitoring accuracy and stability can still be maintained, ensuring the reliability and effectiveness of the inspection system in various complex environments.

[0043] Furthermore, in the multi-sensor fusion perception module, the Kalman filter algorithm is used to process the collected on-site environmental data, which can realize the smoothing and prediction of sensor data, improve the accuracy and reliability of data, and help to achieve multi-dimensional and all-round monitoring of safety hazards. The formula of the Kalman filter algorithm is as follows:

[0044] x k =A k x k-1 +B k u k-1 +w k-1 ;

[0045] z k =H k x k +v k ;

[0046] Among them, x k is the state vector, A k is the state transfer matrix, B k is the control matrix, u k-1 is the control vector, w k-1 is the process noise, z k is the observation vector, H k is the observation matrix, v k is the observation noise.

[0047] From the above, it can be seen that by adopting the Kalman filtering algorithm in the multi-sensor fusion perception module, the collected on-site environmental data can be smoothed and predicted, noise interference can be effectively filtered out, and the accuracy and reliability of the data can be significantly improved, thereby further enhancing the system's multi-dimensional and all-round monitoring capabilities for safety hazards, and providing more accurate and reliable data support for safety production supervision.

[0048] Furthermore, in the intelligent analysis and diagnosis platform, the deep learning algorithm includes a convolutional neural network (CNN), and the formula of the convolutional neural network (CNN) is as follows:

[0049] z l+1 =W l *a l +b l ;

[0050] a l+1 =σ(z l+1 );

[0051] Among them, z l+1 is the input of the l+1th layer, W l is the weight matrix of the lth layer, a l is the output (activation value) of layer l, b l is the bias term of the lth layer, and σ is the activation function.

[0052] From the above, we can see that by using deep learning algorithms including convolutional neural networks (CNN) in the intelligent analysis and diagnosis platform, it is possible to efficiently process and analyze large amounts of data collected from the multi-sensor fusion perception module, accurately identify abnormal conditions and predict potential risks, and provide accurate fault diagnosis reports and maintenance suggestions, thereby greatly improving the intelligence level and efficiency of production safety supervision.

[0053] Furthermore, the intelligent analysis and diagnosis platform also has a historical data comparison function, which can compare and analyze the current monitoring data with the historical data of the same period, discover long-term trend changes, and provide a basis for preventive maintenance.

[0054] From the above, we can see that through the historical data comparison function of the intelligent analysis and diagnosis platform, the current monitoring data can be deeply compared and analyzed with the historical data of the same period, and long-term trend changes can be discovered in time, providing a scientific basis for preventive maintenance, which helps enterprises take measures in advance to eliminate potential safety hazards and avoid accidents, thereby ensuring production safety, reducing maintenance costs, and improving overall operational efficiency.

[0055] Furthermore, in the adaptive learning optimization mechanism, a reinforcement learning algorithm (including Q-learning) is used to learn historical inspection data, which can continuously optimize the inspection path and improve the recognition accuracy. The formula of the reinforcement learning algorithm is as follows:

[0056]

[0057] Where Q(s,a) is the expected return of taking action a in state s, α is the learning rate, r is the immediate reward, γ is the discount factor, s′ is the next state, and a′ is the next optional action.

[0058] From the above, we can see that by adopting reinforcement learning algorithms (including Q-learning) in the adaptive learning optimization mechanism, it is possible to conduct in-depth learning and analysis of historical inspection data, continuously optimize inspection paths and improve recognition accuracy, so that the inspection system can more intelligently adapt to various complex environments and task requirements, further improve the efficiency and accuracy of inspections, and provide more powerful technical support for the company's production safety supervision.

[0059] Working principle: The system realizes autonomous and efficient inspection in complex industrial environments by integrating multiple modules such as autonomous navigation, multi-sensor fusion perception, intelligent analysis and diagnosis, remote monitoring and emergency response, and adaptive learning optimization. The system uses high-precision GPS positioning, SLAM technology and AI path planning algorithms to autonomously plan and execute inspection routes, collects on-site environmental data in real time through multiple sensors and performs fusion processing, and uses deep learning algorithms to quickly analyze data to identify abnormal conditions and predict potential risks, while supporting remote monitoring and emergency response. The system also has adaptive learning optimization capabilities, which can continuously optimize inspection paths and recognition accuracy based on historical inspection data and feedback to achieve continuous self-improvement.

[0060] Furthermore, the effects of the intelligent inspection system for production safety supervision of this embodiment and the current traditional inspection method (comparative example) are compared to obtain the following table:

[0061]

[0062]

[0063] It can be seen from the above table that the intelligent inspection system for production safety supervision in this embodiment shows significant advantages over traditional inspection methods in many aspects such as inspection efficiency, safety hazard discovery, data recording accuracy, adaptability to extreme environments, degree of intelligence, emergency response capability, continuous optimization capability and operating costs. It can more effectively improve the efficiency and accuracy of production safety supervision and reduce operating costs and safety risks.

[0064] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned intelligent inspection system for production safety supervision, including:

[0065] Memory, used to protect computer programs and data;

[0066] Processor, used to run system programs.

[0067] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned intelligent inspection system for production safety supervision, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0068] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0069] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0074] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0076] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent inspection system for production safety supervision, characterized in that: include: Autonomous navigation module: Integrates high-precision GPS positioning, SLAM technology and AI path planning algorithms, enabling the system to autonomously plan and execute inspection routes in complex industrial environments without human intervention; Multi-sensor fusion perception module: Integrates infrared thermal imager, high-definition camera, gas leak detector, and sound recognition sensor to collect on-site environmental data in real time; Intelligent analysis and diagnosis platform: Use deep learning algorithms to quickly analyze the collected data, automatically identify abnormal conditions, predict potential risks, and provide fault diagnosis reports and maintenance suggestions; Remote monitoring and emergency response system: supports real-time data transmission to cloud servers. Managers can remotely monitor the inspection process through mobile devices or PCs. Once an emergency is detected, the system automatically triggers the early warning mechanism and mobilizes the nearest emergency resources for processing; Adaptive learning optimization mechanism: The system continuously optimizes the inspection path and improves recognition accuracy based on historical inspection data and feedback.

2. An intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: It also includes an energy management system that can dynamically adjust the working mode of each component according to the inspection task requirements and equipment energy consumption status, thereby extending the overall battery life.

3. An intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: In the intelligent navigation module, the introduction of AI path planning algorithm for path planning can improve the navigation accuracy and efficiency in complex industrial environments.

4. The intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: The multi-sensor fusion perception module further includes an environmental adaptability adjustment function, which automatically adjusts the sensor sensitivity according to the current environmental conditions to ensure accurate monitoring in various extreme environments.

5. The intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: In the multi-sensor fusion perception module, the Kalman filter algorithm is used to process the collected on-site environmental data.

6. An intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: In the intelligent analysis and diagnosis platform, the deep learning algorithm includes a convolutional neural network.

7. An intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: The intelligent analysis and diagnosis platform also has a historical data comparison function, which can compare and analyze current monitoring data with historical data of the same period, discover long-term trend changes, and provide a basis for preventive maintenance.

8. An intelligent inspection system for production safety supervision as claimed in claim 1, characterized in that: In the adaptive learning optimization mechanism, the reinforcement learning algorithm is used to learn historical inspection data, which can continuously optimize the inspection path and improve the recognition accuracy.

9. A processor, characterized in that: The invention is configured to execute an intelligent inspection system for production safety supervision according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, an intelligent inspection system for production safety supervision as described in any one of claims 1 to 8 is implemented.

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