Intelligent oil leakage detection system and method for large oil charging equipment

Abstract

The invention relates to the field of equipment detection, in particular to an intelligent oil leakage detection system and method for large oil charging equipment. The system is provided with a separated guide rail moving platform, so that all-directional automatic inspection is realized. The platform is composed of a source tracking module and a diffusion monitoring module, the source tracking module and the diffusion monitoring module are combined to operate when oil stains are not found, automatic separation is carried out once the oil stains are detected, and tasks of tracking oil stain sources and monitoring diffusion of the oil stains are executed respectively. The method comprises the following steps: starting the guide rail mobile platform to carry out omnibearing inspection, collecting an oil stain image by using the multi-mode sensor, processing data and identifying the oil stain by using the intelligent analysis module, automatically separating the source tracking and diffusion monitoring platform once the oil stain is detected, and planning a path to track the source and diffusion trend of the oil stain. And meanwhile, the information is transmitted to a remote monitoring system through the wireless communication module. According to the invention, the oil stain source can be detected and tracked in an omnibearing and automatic manner, and the oil stain diffusion trend can be monitored.

Description

Technical Field

[0001] The invention relates to the field of equipment detection, and in particular to an intelligent oil leakage detection system and method for large-scale oil-filled equipment. Background Art

[0002] Oil leakage from large oil-filled equipment will affect the appearance quality of large oil-filled equipment, affect equipment management, and pollute the environment. It will also change the equipment from a sealed state to an unsealed state, causing moisture to enter, affecting the safe and stable operation of the equipment, or even shutting it down. Therefore, the oil leakage phenomenon of large oil-filled equipment must be taken seriously, and the problems found must be carefully analyzed and handled correctly to ensure the safe operation of the equipment.

[0003] At present, most of the oil leakage detection of large oil-filled equipment relies on manual on-site inspections. Some key parts, such as casing, top of the body, top of the radiator, outlet device and other high positions are not easy to find with the naked eye. This greatly increases the possibility of continued development of oil leakage and increases the risk of equipment operation. At the same time, due to the colorless nature of transformer oil, it is more difficult to detect oil leakage defects.

[0004] With the development of technology, some automated detection technologies have been introduced into oil leakage detection, such as using fixed-position sensors or cameras for monitoring. For example, existing patents CN115808271A and CN210014883U have limitations. For example, the monitoring range of the sensor is limited, and it is difficult to cover all possible leakage areas of the equipment; although the camera can provide visual images, it is greatly affected by ambient light and has limited ability to identify oil stains. In addition, once the oil stain is detected, determining the source of the oil stain and tracking its diffusion trend still requires manual analysis, which not only takes a long time to respond, but also makes it difficult to ensure accuracy.

[0005] Therefore, how to improve the automation level, accuracy and response speed of oil leakage detection in large oil-filled equipment has become a technical problem that needs to be solved urgently. There is an urgent need for an intelligent system that can fully and automatically detect, track the source of oil stains and monitor the spread trend of oil stains. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent oil leakage detection system and method for large oil-filled equipment to solve the problem that the prior art cannot fully and automatically detect, track the source of oil stains and monitor the trend of oil stain diffusion.

[0007] To achieve the above purpose, the following technical solution is adopted.

[0008] An intelligent oil leakage detection system for large oil-filled equipment, comprising:

[0009] The detachable guide rail mobile platform is used for all-round mobile inspection along the preset guide rail on large oil-filled equipment; the detachable guide rail mobile platform includes a source tracking platform and a diffusion monitoring platform. When no oil stains are detected, the source tracking platform and the diffusion monitoring platform are combined and spliced ​​as a whole for mobile inspection. When oil stains are detected, the source tracking platform and the diffusion monitoring platform are automatically separated. The source tracking platform is used to identify oil stains, send out alarm signals and track the source of oil stains, and the diffusion monitoring platform is used to identify oil stains, send out alarm signals, and track and monitor the diffusion trend of oil stains.

[0010] Optionally, the source tracing platform and the diffusion monitoring platform are respectively provided with a multimodal sensing detection module, a power module, an alarm module and a combination separation module, and the source tracing platform and / or the diffusion monitoring platform are provided with a data processing unit, an intelligent analysis module and an intelligent path planning module;

[0011] The multimodal sensing detection module is used to collect images of oil stains on the surface of large oil-filled equipment;

[0012] The data processing unit is electrically connected to the multimodal sensing detection device and is used to receive and process the image data collected by the multimodal sensing detection module;

[0013] The intelligent analysis module is used to identify oil stain information and determine the leakage location based on the processed image data;

[0014] A combination and separation module, used to control the source tracing platform and the diffusion monitoring platform to be combined or separated;

[0015] The alarm module is used to send out an alarm signal after identifying the oil trace;

[0016] A real-time oil stain tracking algorithm, embedded in the intelligent analysis module, is used to dynamically track the oil stain using the position of the obtained oil stain image and update the position information and source data;

[0017] The intelligent path planning module is used to automatically adjust the moving paths of the source tracking platform and the diffusion monitoring platform according to the location information and source data updated by the real-time oil trace tracking algorithm;

[0018] The power module is used to control the source tracking platform and the diffusion monitoring platform to move along the guide rail according to the preset inspection instructions or the moving path sent by the intelligent path planning module.

[0019] Optionally, a self-charging and energy management system is also included, which is connected to the separate guide rail mobile platform and is used to monitor the energy status of the separate guide rail mobile platform and charge the separate guide rail mobile platform when necessary.

[0020] Optionally, also include,

[0021] The wireless communication module is used to transmit the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

[0022] Optionally, the multimodal sensing detection module includes:

[0023] UV imaging sensor, used to detect the fluorescence reaction of oil stains under UV irradiation;

[0024] Infrared thermal imaging sensor to detect temperature anomalies in the oil stain area;

[0025] An optical imaging sensor for capturing images of oil stains in the visible light range;

[0026] The data fusion module is used to fuse the ultraviolet imaging sensor data, the infrared thermal imaging sensor data, and the optical imaging sensor data.

[0027] Optionally, the intelligent analysis module includes:

[0028] An image preprocessing unit, used for denoising and contrast enhancement of image data collected by the multimodal sensing detection module;

[0029] A deep learning network is used to extract features and identify oil stains from preprocessed images;

[0030] A decision logic unit to determine the presence and location of oil stains based on the output of the deep learning network.

[0031] Optionally, the real-time oil trace tracking algorithm includes:

[0032] A position tracking unit, used to record the current position of the oil stain identified by the intelligent analysis module and update the oil stain position information in real time;

[0033] A dynamic prediction unit, used to use the data recorded by the position tracking unit to predict the possible position of the oil mark at the next moment through a filtering algorithm such as a Kalman filter;

[0034] Source tracing analysis unit, used to analyze the oil stain diffusion path and speed, and infer the source of the oil stain based on the oil stain characteristics and equipment structure;

[0035] A reverse tracing mechanism, for reversely tracing and verifying the accuracy of the oil stain source according to the oil stain source determined by the source tracing and analysis unit;

[0036] The updating mechanism is used to update the location information and source data of the oil stain in real time according to the output of the dynamic prediction unit and the source tracing and analysis unit.

[0037] Optionally, the intelligent path planning module includes:

[0038] A path generation unit, used to generate a preliminary moving path of the diffusion monitoring platform according to the position information of the real-time oil trace tracking algorithm, and to generate a preliminary moving path of the source tracking platform according to the source data of the real-time oil trace tracking algorithm;

[0039] The optimization algorithm unit is used to optimize the two groups of preliminary paths by using a genetic algorithm, minimize the detection time and cover all potential leakage areas, and obtain two groups of optimized moving paths.

[0040] Optionally, the source tracing platform and the diffusion monitoring platform further include a multi-degree-of-freedom telescopic arm, and the multimodal sensing detection module is disposed on the multi-degree-of-freedom telescopic arm for tracking and identifying hidden locations of large oil-filling equipment.

[0041] A method for intelligent oil leakage detection of large oil-filled equipment comprises the following steps:

[0042] Start the separate rail mobile platform, so that the source tracking platform and the diffusion monitoring platform can move along the preset rails on the large oil-filled equipment in a combined state for all-round inspection through the power module;

[0043] The oil stain image on the surface of the equipment is collected through a multimodal sensing detection module;

[0044] Processing the collected image data through a data processing unit;

[0045] The intelligent analysis module analyzes the processed image data, identifies oil stains and determines the location of the leak;

[0046] If the intelligent analysis module identifies oil traces, the alarm module is activated to send out an alarm signal, and the source tracking platform and the diffusion monitoring platform are automatically separated by controlling the combined separation module;

[0047] The intelligent path planning module generates a moving path of the diffusion monitoring platform according to the position information updated by the real-time oil trace tracking algorithm of the intelligent analysis module; the intelligent path planning module generates a moving path of the source tracking platform according to the source data updated by the real-time oil trace tracking algorithm of the intelligent analysis module;

[0048] The power module of the source tracing platform controls the source tracing platform to track the source of the oil stain according to the moving path of the source tracing platform generated by the intelligent path planning module;

[0049] The power module of the diffusion monitoring platform controls the diffusion monitoring platform to track and monitor the oil stain diffusion trend according to the moving path of the diffusion monitoring platform generated by the intelligent path planning module;

[0050] The wireless communication module transmits the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention realizes automated source tracking and diffusion monitoring through the innovative design of a separate guide rail mobile platform. When oil stains are detected, the automatic separation mechanism of the source tracking platform and the diffusion monitoring platform can quickly and independently perform tracking tasks, significantly improving the response speed and processing efficiency of oil leakage problems. The all-round mobility of the source tracking platform and the diffusion monitoring platform ensures that every area of ​​the large oil-filled equipment can be effectively detected, covering potential leakage points without omission, and improving the comprehensiveness and reliability of detection. The multimodal sensing detection module enhances the ability to recognize oil stains through the fusion technology of ultraviolet imaging, infrared thermal imaging and optical imaging. It can accurately detect leaking oil even in insufficient light or when the oil stains are not obvious, thereby improving the accuracy of detection. The real-time oil stain tracking algorithm can not only update the oil stain location information in real time, but also predict the diffusion trend of the oil stain and infer the source, providing a scientific basis for timely measures and effectively preventing potential safety risks; the intelligent path planning module optimizes the algorithm unit and dynamically adjusts the moving path according to the real-time tracking data, ensuring the efficiency of the detection process, minimizing the detection time and cost, and maximizing the detection coverage.

[0053] The application of the multi-degree-of-freedom telescopic arm of the present invention enables the sensing detection module to reach areas of the device that are difficult to reach, such as the top, bottom or internal structure of the device, ensuring that there are no blind spots in the detection.

[0054] The wireless communication module of the present invention realizes the real-time transmission of detection information, so that the remote monitoring system can instantly receive oil stain information and monitor the status of the platform, thereby improving the convenience and real-time performance of management.

[0055] The self-charging and energy management system of the present invention ensures the energy supply of the separate guide rail mobile platform, and through autonomous monitoring and timely charging, ensures the continuous operation of the system, thereby enhancing the stability and reliability of the system.

[0056] The intelligent analysis module of the present invention provides efficient image processing and oil stain recognition capabilities through a deep learning network and a decision logic unit, provides accurate decision support for operation and maintenance personnel, and reduces the possibility of misjudgment and missed judgment.

[0057] The present invention realizes the automation of the entire process from detection, analysis to response through highly integrated module design and collaborative work between modules, greatly improving the overall performance and intelligence level of oil leakage detection of large-scale oil-filled equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the combined structure of an embodiment of an intelligent oil leakage detection system for large oil-filled equipment according to the present invention;

[0059] Figure 2 It is a schematic diagram of the separated structure of an embodiment of an intelligent oil leakage detection system for large oil-filled equipment according to the present invention;

[0060] Figure 3 is a structural schematic diagram from another perspective of an embodiment of an intelligent oil leakage detection system for large oil-filled equipment according to the present invention;

[0061] Figure 4 It is a schematic diagram of the steps of an embodiment of an intelligent oil leakage detection method for large oil-filled equipment according to the present invention;

[0062] Among them, 1. Separate guide rail mobile platform; 11. Source tracking platform; 12. Diffusion monitoring platform; 2. Multimodal sensing detection module; 3. Power module; 4. Alarm module; 5. Combined separation module; 6. Multi-degree-of-freedom telescopic arm. DETAILED DESCRIPTION

[0063] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0064] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0065] like Figure 1-Figure 3 As shown, an intelligent oil leakage detection system for large oil-filled equipment includes:

[0066] The separate guide rail mobile platform 1 is used for all-round mobile inspection along the preset guide rail on the large oil-filled equipment; the separate guide rail mobile platform 1 includes a source tracking platform 11 and a diffusion monitoring platform 12. When no oil stains are detected, the source tracking platform 11 and the diffusion monitoring platform 12 are combined and spliced ​​as a whole for mobile inspection. When oil stains are detected, the source tracking platform 11 and the diffusion monitoring platform 12 are automatically separated. The source tracking platform 11 is used to identify oil stains, send out alarm signals and track the source of oil stains. The diffusion monitoring platform 12 is used to identify oil stains, send out alarm signals, and track and monitor the diffusion trend of oil stains.

[0067] Specifically, the present invention relates to an intelligent oil leakage detection system for large-scale oil-filled equipment, which is designed to perform all-round mobile inspection on large-scale oil-filled equipment along a preset guide rail to achieve automated oil trace detection.

[0068] The core component of the system is a separate rail mobile platform 1, which includes a source tracking platform 11 and a diffusion monitoring platform 12. The two sub-platforms exist as a whole when no oil traces are detected, so as to carry out efficient mobile inspections. The source tracking platform 11 and the diffusion monitoring platform 12 are combined and spliced ​​by a precise mechanical connection structure or magnetic adsorption to ensure stability and reliability during movement.

[0069] When the sensors in the system detect oil stains, the source tracking platform 11 and the diffusion monitoring platform 12 are automatically separated. The source tracking platform 11 can be equipped with highly sensitive sensors related to oil stain detection, which can quickly identify the presence of oil stains and send out an alarm signal. The platform can also be equipped with a tracking algorithm to analyze the diffusion path of the oil stains, thereby tracing the source of the oil stains.

[0070] The diffusion monitoring platform 12 focuses on the diffusion trend analysis of the oil stain. It can be equipped with a sensor array to monitor the diffusion of the oil stain in real time and send out corresponding alarm signals. The diffusion monitoring platform 12 can automatically adjust its moving path on the guide rail according to the diffusion characteristics of the oil stain to achieve continuous monitoring of the oil stain diffusion area.

[0071] The system may also include a central control unit for receiving detection data and status information from the source tracking platform 11 and the diffusion monitoring platform 12 to monitor and manage the entire detection process. The central control unit may communicate with the remote monitoring system via wired or wireless means to ensure that the operation and maintenance personnel can understand the oil leakage of the equipment in real time and take corresponding measures in a timely manner.

[0072] In addition, both sub-platforms can adopt a modular design for easy maintenance and upgrades, maintain stability during mobile inspections, and operate independently after separation.

[0073] The system can also have an adaptive adjustment function, which can adjust the moving speed and inspection route according to the appearance and structural characteristics of large oil-filled equipment to meet the needs of different equipment.

[0074] The separate guide rail mobile platform 1 can also be designed with an autonomous power management system to ensure energy supply during long-term inspections, and can be integrated with an energy recovery module to improve energy utilization efficiency.

[0075] Through the above-mentioned specific implementation methods, the system of the present invention can not only realize intelligent oil leakage detection of large-scale oil-filled equipment, but also improve the automation level and response speed of detection, reduce the cost and risk of manual inspections, track the source of oil stains and monitor the trend of oil stain diffusion, and has high practical value and market application prospects.

[0076] As a preferred example, the source tracing platform 11 and the diffusion monitoring platform 12 are respectively provided with a multimodal sensing detection module 2, a power module 3, an alarm module 4 and a combination separation module 5, and the source tracing platform 11 and / or the diffusion monitoring platform 12 are provided with a data processing unit, an intelligent analysis module and an intelligent path planning module;

[0077] Multimodal sensing detection module 2, used to collect oil stain images on the surface of large oil-filled equipment;

[0078] A data processing unit, electrically connected to the multimodal sensing detection device, for receiving and processing image data collected by the multimodal sensing detection module 2;

[0079] Intelligent analysis module, used to identify oil stain information and determine the leakage location based on the processed image data;

[0080] A combination and separation module 5 is used to control the source tracking platform 11 and the diffusion monitoring platform 12 to be combined or separated;

[0081] An alarm module 4 is used to send out an alarm signal after identifying an oil stain;

[0082] Real-time oil stain tracking algorithm, embedded in the intelligent analysis module, is used to dynamically track the oil stain using the position of the obtained oil stain image and update the location information and source data;

[0083] An intelligent path planning module, which is used to automatically adjust the moving paths of the source tracking platform 11 and the diffusion monitoring platform 12 according to the location information and source data updated by the real-time oil trace tracking algorithm;

[0084] The power module 3 is used to control the source tracking platform 11 and the diffusion monitoring platform 12 to move along the guide rail according to the preset inspection instructions or the moving path sent by the intelligent path planning module.

[0085] Specifically, in a large-scale oil-filled equipment intelligent oil leakage detection system of the present invention, the source tracing platform 11 and the diffusion monitoring platform 12 are both equipped with a series of key modules to achieve automated oil trace detection and processing.

[0086] Both the source tracing platform 11 and the diffusion monitoring platform 12 are integrated with a multimodal sensing detection module 2. The multimodal sensing detection module 2 includes at least an ultraviolet imaging sensor, an infrared thermal imaging sensor, and an optical imaging sensor. For example, the ultraviolet imaging sensor can detect the fluorescence of the oil stain under ultraviolet irradiation of a specific wavelength, while the infrared thermal imaging sensor can capture thermal anomalies in the oil stain area.

[0087] The source tracking platform 11 and the diffusion monitoring platform 12 are each equipped with a power module 3, including a servo motor and a precision reducer, to ensure that the platform moves smoothly along the guide rail. The power module 3 can control the moving speed and direction of the platform according to the instructions of the intelligent path planning module or the preset inspection route.

[0088] Both the source tracing platform 11 and the diffusion monitoring platform 12 are equipped with an alarm module 4. When the multimodal sensing detection module 2 of any platform detects oil stains, the alarm module 4 of the platform can be triggered to emit an audible and visual alarm signal, and the alarm information can be sent to the remote monitoring center through the wireless communication module.

[0089] The source tracing platform 11 and the diffusion monitoring platform 12 are connected via the combined separation module 5. The combined separation module 5 uses an electronically controlled mechanical structure, such as an electromagnet or an electric push rod, to achieve rapid separation and combination of the two platforms. For example, when the system detects oil stains, the combined separation module 5 can trigger the electric push rod to separate the two platforms.

[0090] Data processing unit, intelligent analysis module and intelligent path planning module: These modules can be set up on the source tracing platform 11 and the diffusion monitoring platform 12 respectively, and each can independently process the data of the platform; or they can be set up on only one platform, or set up outside the system, receive data from the two platforms through wireless communication, and send control instructions to the corresponding platform after centralized processing. For example, if the system configures the intelligent analysis module on the source tracing platform 11, the data collected by the multimodal sensor detection module 2 of the diffusion monitoring platform 12 can be sent to the source tracing platform 11 through wireless communication, and processed uniformly by the intelligent analysis module.

[0091] The real-time oil trace tracking algorithm is embedded in the intelligent analysis module. The real-time oil trace tracking algorithm can dynamically track the oil trace according to the location information of the oil trace image and update the location information and source data in real time. For example, the real-time oil trace tracking algorithm can use time series analysis methods to predict the diffusion trend of the oil trace.

[0092] The system can allow operation and maintenance personnel to monitor the system, process data and schedule tasks through a user interface. The user interface can be graphical, displaying oil stain detection results, system status and user command input.

[0093] The system can be connected to the remote monitoring system through the wireless communication module to achieve remote monitoring and maintenance. For example, the operation and maintenance personnel can receive real-time detection reports and alarms through the remote monitoring system and perform remote operations through the user interaction interface.

[0094] Through the above-mentioned specific implementation methods, the system of the present invention can realize automated and intelligent oil leakage detection of large-scale oil-filled equipment, improve detection efficiency and accuracy, and reduce the demand and risk of manual inspections.

[0095] As a preferred example, it also includes a self-charging and energy management system, which is connected to the separate guide rail mobile platform 1 and is used to monitor the energy status of the separate guide rail mobile platform 1 and charge the separate guide rail mobile platform 1 when necessary.

[0096] Specifically, the self-charging and energy management system is used to ensure that the separated guide rail mobile platform 1 has the ability to operate continuously, and may include an energy collection unit, an energy storage unit, and an energy management and control unit.

[0097] The energy harvesting unit may include a solar panel, a vibration energy harvester, or a radio frequency energy harvester to harvest energy from the environment. For example, a solar panel can provide power to the system under sunlight conditions.

[0098] Energy storage units usually include battery packs or supercapacitors to store the energy collected by the energy harvesting unit. These storage units should have high energy density and long cycle life.

[0099] The energy management and control unit is responsible for monitoring the energy status of the entire system, including the input power of the energy harvesting unit, the remaining energy of the energy storage unit, and the energy consumption of the separated guide rail mobile platform 1. The unit can automatically adjust the energy harvesting and use strategy based on real-time data.

[0100] The self-charging and energy management system can monitor the energy consumption and storage status of the separate guide rail mobile platform 1 in real time, ensuring that the system operates when the energy is sufficient and issuing an early warning when the energy is insufficient.

[0101] When it is monitored that the energy status of the separate guide rail mobile platform 1 is lower than a preset threshold, the self-charging and energy management system will start the charging program and use the electrical energy of the energy storage unit to charge the platform battery.

[0102] The system can intelligently schedule the charging time and charging rate according to the mission plan and energy status of the separated guide rail mobile platform 1 to optimize energy utilization efficiency. The self-charging and energy management system also includes safety functions such as overcharge protection, over-discharge protection and short-circuit protection to ensure the safe and stable operation of the system under various working conditions. The system can provide a user interaction interface, allowing operation and maintenance personnel to view the energy status, adjust charging parameters or receive system status notifications. The self-charging and energy management system can communicate with the remote monitoring system, enabling operation and maintenance personnel to monitor the energy status at a remote location and intervene when necessary.

[0103] Through the above-mentioned specific implementation methods, the self-charging and energy management system of the present invention can provide stable and reliable energy support for the separate guide rail mobile platform 1, significantly improving the self-sufficiency and operational reliability of the system.

[0104] As a preferred example, it also includes:

[0105] The wireless communication module is used to transmit the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

[0106] Specifically, in the intelligent oil leakage detection system for large oil filling equipment of the present invention, the wireless communication module is a key component for realizing remote monitoring and control. The module is responsible for transmitting the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

[0107] The wireless communication module is integrated into the data processing unit of the system, or connected to the data processing unit as an independent component, and exchanges data with the remote monitoring system through wireless signals.

[0108] The wireless communication module uses wireless data transmission technology, such as Wi-Fi, Bluetooth, ZigBee, cellular network (2G / 3G / 4G / 5G) or other dedicated wireless communication protocols to send oil trace information and moving path data to the remote monitoring system in real time.

[0109] The oil stain information processed by the intelligent analysis module includes the location, size, shape and diffusion speed of the oil stain, etc. This information is sent through the wireless communication module, enabling the remote monitoring system to understand the oil leakage status of the equipment in real time.

[0110] The moving path data generated by the intelligent path planning module includes the moving direction, speed and target position of the platform, etc. These data guide the source tracking platform 11 and the diffusion monitoring platform 12 of the separated guide rail mobile platform 1 to perform tracking and monitoring tasks.

[0111] To ensure the security of data transmission, the wireless communication module can use encryption technology, such as TLS / SSL, AES or other industry-standard security protocols to encrypt the transmitted data. The wireless communication module can provide a signal strength indication to help operation and maintenance personnel understand the stability of the current wireless connection so that measures can be taken to improve communication conditions when necessary. The wireless communication module is designed with the communication distance requirements in mind, and the transmission power can be adjusted according to the actual application scenario to meet the communication requirements of different distances. Considering that the system may run for a long time in an unattended environment, the wireless communication module adopts a low-power design to reduce the consumption of system energy. The wireless communication module can also support fault diagnosis functions. When there is a problem with the communication link, it can report to the remote monitoring system in time and take corresponding remedial measures. The wireless communication module allows users to configure communication parameters such as network selection, communication frequency, data transmission rate, etc. to adapt to different application requirements.

[0112] Through the above specific implementation methods, the wireless communication module of the present invention can ensure that the data of the intelligent analysis module and the intelligent path planning module are transmitted to the remote monitoring system in real time, safely and reliably, thereby realizing remote monitoring and management of the intelligent oil leakage detection system of large oil-filled equipment.

[0113] As a preferred example, the multimodal sensing detection module 2 includes:

[0114] UV imaging sensor, used to detect the fluorescence reaction of oil stains under UV irradiation;

[0115] Infrared thermal imaging sensor to detect temperature anomalies in the oil stain area;

[0116] An optical imaging sensor for capturing images of oil stains in the visible light range;

[0117] The data fusion module is used to fuse the ultraviolet imaging sensor data, the infrared thermal imaging sensor data, and the optical imaging sensor data.

[0118] Specifically, in the intelligent oil leakage detection system for large oil-filled equipment of the present invention, the multimodal sensing detection module 2 is a key component, which is used to comprehensively collect oil stain images on the surface of the equipment. The module includes the following sensors and data processing units:

[0119] UV imaging sensors are used to detect the fluorescence reaction of oil stains under specific wavelength ultraviolet light. When oil stains are excited by ultraviolet light, characteristic fluorescence is generated, and UV imaging sensors can capture this phenomenon to identify the presence and distribution of oil stains.

[0120] Infrared thermal imaging sensors are used to detect temperature anomalies in the oil stain area. Since oil stains may cause local temperature changes, infrared thermal imaging sensors can help identify the location of oil stains by capturing these temperature differences.

[0121] Optical imaging sensors work in the visible light range to capture intuitive images of oil stains. Optical imaging sensors provide high-resolution color images that help analyze the color and morphological characteristics of oil stains.

[0122] The data fusion module is responsible for fusing the data collected by the ultraviolet imaging sensor, infrared thermal imaging sensor and optical imaging sensor. The data fusion module uses advanced image processing and data analysis algorithms to integrate information from different sensors to provide a more comprehensive and accurate oil stain image.

[0123] The data fusion module can pre-process the sensor data, including denoising, contrast enhancement and edge detection, to improve image quality and enhance oil trace features.

[0124] The data fusion module can also perform feature extraction operations to identify key features of oil stains, such as shape, size, and texture, from the fused image. The data fusion module includes a multi-sensor data calibration function to ensure the consistency of data from different sensors in space and time for accurate analysis. The data fusion module works in conjunction with the intelligent analysis module to provide the fused data to the intelligent analysis module for further oil stain identification and analysis. The data fusion module is designed as a real-time processing unit that can quickly respond to changes in sensor data and update oil stain information in a timely manner. The data fusion module allows users to customize the parameters and algorithms of data fusion according to specific application scenarios to meet different detection needs.

[0125] Through the above specific implementation methods, the multimodal sensing detection module 2 of the present invention can provide accurate and comprehensive oil trace detection, and provide a solid data foundation for the intelligent oil leakage detection system.

[0126] As a preferred example, the intelligent analysis module includes:

[0127] An image preprocessing unit, used for denoising and contrast enhancement of the image data collected by the multimodal sensing detection module 2; a deep learning network, used for feature extraction and oil stain recognition of the preprocessed image;

[0128] A decision logic unit to determine the presence and location of oil stains based on the output of the deep learning network.

[0129] Specifically, in a large-scale oil-filled equipment intelligent oil leakage detection system of the present invention, the intelligent analysis module is responsible for in-depth analysis of the image data collected by the multimodal sensing detection module 2 to identify and locate the oil trace. The module includes the following units:

[0130] The image preprocessing unit performs denoising and contrast enhancement on the raw image data collected by the multimodal sensing detection module 2. The denoising process can use Gaussian filtering, median filtering or other advanced image denoising algorithms to reduce random noise in the image. Contrast enhancement makes the oil stain feature more obvious by adjusting the image histogram or applying other contrast enhancement techniques.

[0131] The image data processed by the image preprocessing unit is input into the deep learning network, which is responsible for feature extraction and oil stain recognition. The deep learning network can adopt the convolutional neural network (CNN) architecture, which is particularly suitable for image recognition tasks. The network automatically learns the feature representation in the image through a series of convolutional layers, pooling layers, and fully connected layers, thereby achieving high-precision recognition of oil stains.

[0132] The output of the deep learning network is passed to the decision logic unit, which determines the presence and location of the oil stain based on the network's prediction results. The decision logic unit can use threshold judgment, probability analysis or other logical judgment methods to ensure the accuracy and reliability of oil stain identification.

[0133] The convolutional layers in the deep learning network automatically extract image features, such as edges, textures, and shapes, which are crucial for oil stain recognition. During the network training process, the network parameters are continuously optimized through a large amount of annotated image data to improve the effect of feature extraction.

[0134] In the deep learning network, the oil stain recognition algorithm learns how to distinguish oil stains from non-oil stain areas through training. The algorithm can adopt a multi-classification strategy to identify different types of oil stain features. The decision logic unit determines the exact location of the oil stain based on the output of the deep learning network and the characteristic position of the oil stain in the image. This can be achieved through methods such as image coordinate mapping and spatial transformation.

[0135] The intelligent analysis module is designed to process image data in real time to meet the system's demand for rapid response to oil stains. The module's real-time performance is achieved through optimization algorithms and hardware acceleration. The intelligent analysis module can adaptively adjust the preprocessing parameters and the parameters of the deep learning network according to the data characteristics collected by the multimodal sensing detection module 2 to adapt to different detection environments and oil stain characteristics. The intelligent analysis module can provide user interactive feedback, allowing operation and maintenance personnel to view the oil stain recognition results and perform manual corrections or confirmations when necessary. The module can also record historical data on oil stain recognition and perform statistical analysis to provide a reference for equipment maintenance and fault prediction.

[0136] Through the above specific implementation methods, the intelligent analysis module of the present invention can efficiently and accurately complete the task of identifying and locating oil traces, and provide a powerful analysis tool for intelligent oil leakage detection of large oil-filled equipment.

[0137] As a preferred example, the real-time oil trace tracking algorithm includes:

[0138] A position tracking unit, used to record the current position of the oil stain identified by the intelligent analysis module and update the oil stain position information in real time;

[0139] A dynamic prediction unit is used to use the data recorded by the position tracking unit to predict the possible position of the oil stain at the next moment through filtering algorithms such as Kalman filter;

[0140] Source tracing analysis unit, used to analyze the oil stain diffusion path and speed, and infer the source of the oil stain based on the oil stain characteristics and equipment structure;

[0141] A reverse tracing mechanism is used to reversely trace and verify the accuracy of the oil stain source according to the oil stain source determined by the source tracing analysis unit;

[0142] The updating mechanism is used to update the location information and source data of the oil trace in real time according to the output of the dynamic prediction unit and the source tracking analysis unit.

[0143] Specifically, in a large-scale oil-filled equipment intelligent oil leakage detection system of the present invention, the real-time oil trace tracking algorithm includes the following units:

[0144] The position tracking unit is responsible for recording the current position of the oil stain on the large oil filling equipment identified by the intelligent analysis module and updating the position information of the oil stain in real time. The position tracking unit can use image processing technology, such as edge detection or pattern recognition, to determine the exact position of the oil stain.

[0145] The dynamic prediction unit uses the data recorded by the position tracking unit to predict the possible position of the oil track at the next moment through filtering algorithms such as the Kalman filter. The Kalman filter is an efficient recursive filter that can handle state estimation and data filtering of nonlinear systems.

[0146] The source tracing analysis unit analyzes the diffusion path and speed of the oil stain, and infers the source of the oil stain by combining the physical and chemical properties of the oil stain and the specific structure of the large oil-filled equipment. The source tracing analysis unit can use fluid dynamics models and material diffusion models to simulate the diffusion process of the oil stain.

[0147] The reverse tracking mechanism tracks the propagation path of the oil stain in reverse according to the source tracking analysis unit, and verifies the accuracy of the oil stain source. This mechanism can be implemented by using historical data analysis and path backtracking algorithm.

[0148] The update mechanism updates the location information and source data of the oil trace in real time based on the output of the dynamic prediction unit and the source tracking analysis unit. The update mechanism ensures that the system can continuously track the latest status of the oil trace and provide accurate input data for the intelligent path planning module.

[0149] The real-time oil tracking algorithm can use data fusion technology to combine data from different sensors to improve the accuracy and robustness of oil tracking. The algorithm can adaptively adjust its parameters according to the diffusion characteristics of the oil and the actual conditions of the equipment to achieve the best tracking effect. The system can provide a user interface to allow operation and maintenance personnel to monitor the oil tracking process and perform manual intervention or parameter adjustments when necessary. The real-time oil tracking algorithm can also record key data during the tracking process and generate reports to support equipment maintenance and fault analysis. The algorithm can identify abnormal conditions during the tracking process and issue alarms in time to alert operation and maintenance personnel to possible problems.

[0150] Through the above specific implementation methods, the real-time oil trace tracking algorithm of the present invention can accurately track the diffusion path of the oil trace, accurately infer the source of the oil trace, and provide strong technical support for intelligent oil leakage detection of large oil-filled equipment.

[0151] As a preferred example, the intelligent path planning module includes:

[0152] A path generation unit, used to generate a preliminary moving path of the diffusion monitoring platform 12 according to the position information of the real-time oil trace tracking algorithm, and to generate a preliminary moving path of the source tracking platform 11 according to the source data of the real-time oil trace tracking algorithm;

[0153] The optimization algorithm unit is used to optimize the two groups of preliminary paths by using a genetic algorithm, minimize the detection time and cover all potential leakage areas, and obtain two groups of optimized moving paths.

[0154] Specifically, the intelligent path planning module includes the following units:

[0155] The path generation unit is responsible for generating a preliminary moving path of the diffusion monitoring platform 12 according to the position information provided by the real-time oil trace tracking algorithm. At the same time, according to the source data of the real-time oil trace tracking algorithm, a preliminary moving path of the source tracking platform 11 is generated. The path generation unit uses a heuristic algorithm or a graph search-based algorithm, such as the A* algorithm, to determine the platform's movement order and direction.

[0156] The optimization algorithm unit uses a genetic algorithm to optimize the two groups of preliminary paths generated by the path generation unit. The genetic algorithm is a search algorithm that simulates the natural selection process. Through selection, crossover and mutation operations in an iterative process, the path plan is continuously improved to minimize the detection time and ensure that all potential leakage areas are covered.

[0157] The optimization algorithm unit considers multiple factors during the optimization process, including but not limited to the moving distance, estimated arrival time, oil spread speed and equipment structural characteristics. By combining these factors, the algorithm is able to generate an efficient moving path.

[0158] The intelligent path planning module can receive the updated data of the real-time oil trace tracking algorithm in real time, and adjust and optimize the moving path according to the new data to ensure that the source tracking platform 11 and the diffusion monitoring platform 12 always perform tasks in the optimal path. The optimization algorithm unit not only pursues minimization of detection time, but also considers maximizing the coverage and accuracy of oil trace detection to achieve multi-objective optimization. During the path optimization process, the intelligent path planning module can handle various constraints, such as the maximum moving speed of the platform, battery power limit, and equipment structural obstacles. The intelligent path planning module can provide user customization functions, allowing operation and maintenance personnel to adjust the parameters of the optimization algorithm according to specific needs, such as increasing or decreasing the detection frequency of certain areas. The system can provide a path visualization interface so that operation and maintenance personnel can intuitively view the moving paths and current positions of the two platforms. The intelligent path planning module also includes an exception handling mechanism, which can adjust the path in time to avoid failure when the platform encounters obstacles or deviates from the predetermined path during movement. The module can use historical data to analyze the law of oil trace diffusion and provide a reference for future path planning.

[0159] Through the above specific implementation methods, the intelligent path planning module of the present invention can provide efficient and accurate path planning for intelligent oil leakage detection of large oil-filled equipment, ensuring that the source tracking platform 11 and the diffusion monitoring platform 12 can respond in time and cover all potential leakage areas.

[0160] As a preferred example, the source tracking platform 11 and the diffusion monitoring platform 12 also include a multi-degree-of-freedom telescopic arm 6, and the multi-modal sensing detection module 2 is arranged on the multi-degree-of-freedom telescopic arm 6 for tracking and identifying the hidden location of large oil-filled equipment.

[0161] Specifically, in an intelligent oil leakage detection system for large oil-filling equipment of the present invention, the source tracking platform 11 and the diffusion monitoring platform 12 are both equipped with a multi-degree-of-freedom telescopic arm 6, which significantly improves the system's ability to track and identify hidden locations of large oil-filling equipment.

[0162] The source tracking platform 11 and the diffusion monitoring platform 12 are each equipped with a multi-degree-of-freedom telescopic arm 6, which includes multiple rotating joints and telescopic sections, and can achieve complex spatial movements and reach difficult-to-reach areas of the equipment. The multi-modal sensing detection module 2 is installed at the end or appropriate position of the multi-degree-of-freedom telescopic arm 6, and the module includes an ultraviolet imaging sensor, an infrared thermal imaging sensor, an optical imaging sensor, and a data fusion module. The multi-degree-of-freedom telescopic arm 6 has precise spatial positioning capabilities and can guide the multi-modal sensing detection module 2 to the exact location where the oil stain occurs, including hidden areas such as the top, bottom or internal structure of the equipment. The design of the telescopic arm takes into account dynamic adjustment and stability to ensure that the sensor can stably capture images during movement, even when the surface of the equipment is uneven or there are obstacles. The multi-degree-of-freedom telescopic arm 6 can be operated by a remote control unit, which receives instructions from the intelligent path planning module and accurately controls the movement of the telescopic arm. During the movement of the telescopic arm, the data collected by the multi-modal sensing detection module 2 needs to be synchronized with the position and posture of the telescopic arm to ensure the accuracy and traceability of the data. The telescopic arm can be equipped with a mechanical locking mechanism to lock the current posture after reaching the target position, providing a stable detection platform for the sensor detection module. The multi-degree-of-freedom telescopic arm 6 is designed with good environmental adaptability and can resist the influence of external environmental factors such as temperature changes, humidity and corrosive substances. The design of the telescopic arm allows for simple maintenance and self-test procedures to ensure long-term stable operation and timely detection of potential mechanical failures. The operation of the telescopic arm follows safety features, including overload protection, emergency stop function and fault alarm to protect the safety of equipment and operators.

[0163] Through the above-mentioned specific implementation methods, the source tracing platform 11 and the diffusion monitoring platform 12 of the present invention significantly improve the detection capability of the hidden locations of large oil-filled equipment through the multi-degree-of-freedom telescopic arm 6, ensuring the comprehensiveness and accuracy of oil trace detection.

[0164] like Figure 4 As shown, a large-scale oil-filled equipment intelligent oil leakage detection method includes the following steps:

[0165] Start the separated guide rail mobile platform 1, so that the source tracking platform 11 and the diffusion monitoring platform 12 can move in all directions on the large oil-filled equipment in a combined state through the power module 3 along the preset guide rail;

[0166] The oil stain image on the surface of the equipment is collected by the multimodal sensing detection module 2;

[0167] Processing the collected image data through a data processing unit;

[0168] The intelligent analysis module analyzes the processed image data, identifies oil stains and determines the location of the leak;

[0169] If the intelligent analysis module identifies oil traces, the alarm module 4 is activated to send out an alarm signal, and the source tracking platform 11 and the diffusion monitoring platform 12 are automatically separated by controlling the combined separation module 5;

[0170] The intelligent path planning module generates a moving path of the diffusion monitoring platform 12 according to the location information updated by the real-time oil trace tracking algorithm of the intelligent analysis module; the intelligent path planning module generates a moving path of the source tracking platform 11 according to the source data updated by the real-time oil trace tracking algorithm of the intelligent analysis module;

[0171] The power module 3 of the source tracking platform 11 controls the source tracking platform 11 to track the source of the oil stain according to the moving path of the source tracking platform 11 generated by the intelligent path planning module;

[0172] The power module 3 of the diffusion monitoring platform 12 controls the diffusion monitoring platform 12 to track and monitor the diffusion trend of the oil trace according to the moving path of the diffusion monitoring platform 12 generated by the intelligent path planning module;

[0173] The wireless communication module transmits the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

[0174] Specifically, in step 1, the source tracking platform 11 and the diffusion monitoring platform 12 are started, and the two platforms are moved along the preset guide rails on the large oil-filled equipment in a combined state for all-round inspection through the power module 3. This step ensures that the system can cover all key areas of the equipment.

[0175] Step 2: Use the multi-modal sensing detection module 2 mounted on the separate guide rail mobile platform 1 to collect images of oil stains on the surface of the equipment. These images are the basis for subsequent analysis and processing.

[0176] Step three: perform necessary preprocessing on the collected image data through the data processing unit, such as denoising, contrast enhancement, etc., to improve the image quality and provide preparation for the recognition work of the intelligent analysis module.

[0177] Step 4: The intelligent analysis module receives and analyzes the processed image data, and uses the deep learning network and decision logic unit to identify oil stains and determine the leakage location.

[0178] Step 5: If the intelligent analysis module identifies oil traces, the alarm module 4 is activated to send out an alarm signal, and the source tracking platform 11 and the diffusion monitoring platform 12 are automatically separated through the combined separation module 5 so that each can perform different tasks.

[0179] Step six, the intelligent path planning module generates the moving path of the diffusion monitoring platform 12 according to the position information updated by the real-time oil trace tracking algorithm of the intelligent analysis module; at the same time, the moving path of the source tracking platform 11 is generated according to the source data.

[0180] Step 7: The power module 3 of the source tracking platform 11 controls the source tracking platform 11 to track the source of the oil trace according to the moving path generated by the intelligent path planning module. This step uses a real-time oil trace tracking algorithm.

[0181] Step 8: The power module 3 of the diffusion monitoring platform 12 controls the diffusion monitoring platform 12 to track and monitor the oil stain diffusion trend according to the moving path generated by the intelligent path planning module. This step ensures continuous monitoring of the oil stain diffusion.

[0182] Step nine, the wireless communication module transmits the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system to achieve remote monitoring and management.

[0183] Step 10: The remote monitoring system receives data from the wireless communication module, performs real-time monitoring, and takes corresponding response measures according to the situation.

[0184] Through the above steps, the method of the present invention can automatically detect the oil leakage of large oil-filled equipment, and realize the tracking of the oil source and the monitoring of the diffusion trend, thereby improving the efficiency and accuracy of the detection.

[0185] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. An intelligent oil leakage detection system for large oil-filled equipment, characterized in that: include, A separate guide rail mobile platform (1) is used for all-round mobile inspection along a preset guide rail on a large oil-filled device; the separate guide rail mobile platform (1) comprises a source tracing platform (11) and a diffusion monitoring platform (12); when no oil stain is detected, the source tracing platform (11) and the diffusion monitoring platform (12) are combined and spliced ​​as a whole for mobile inspection; when oil stains are detected, the source tracing platform (11) and the diffusion monitoring platform (12) are automatically separated; the source tracing platform (11) is used to identify oil stains, send out alarm signals and track the source of the oil stains; the diffusion monitoring platform (12) is used to identify oil stains, send out alarm signals and track and monitor the diffusion trend of the oil stains.

2. According to claim 1, a large-scale oil-filled equipment intelligent oil leakage detection system is characterized in that: The source tracing platform (11) and the diffusion monitoring platform (12) are respectively provided with a multimodal sensing detection module (2), a power module (3), an alarm module (4) and a combination separation module (5); the source tracing platform (11) and / or the diffusion monitoring platform (12) are provided with a data processing unit, an intelligent analysis module and an intelligent path planning module; The multimodal sensing detection module (2) is used to collect images of oil stains on the surface of large oil-filled equipment; The data processing unit is electrically connected to the multimodal sensing detection device and is used to receive and process the image data collected by the multimodal sensing detection module (2); The intelligent analysis module is used to identify oil stain information and determine the leakage location based on the processed image data; A combination and separation module (5), used for controlling the source tracing platform (11) and the diffusion monitoring platform (12) to be combined or separated; The alarm module (4) is used to send out an alarm signal after identifying the oil trace; A real-time oil stain tracking algorithm, embedded in the intelligent analysis module, is used to dynamically track the oil stain using the position of the obtained oil stain image and update the position information and source data; The intelligent path planning module is used to automatically adjust the moving paths of the source tracking platform (11) and the diffusion monitoring platform (12) according to the position information and source data updated by the real-time oil trace tracking algorithm; The power module (3) is used to control the source tracking platform (11) and the diffusion monitoring platform (12) to move along the guide rail according to a preset inspection instruction or a moving path sent by the intelligent path planning module.

3. According to claim 1, a large-scale oil-filled equipment intelligent oil leakage detection system is characterized in that: It also includes a self-charging and energy management system, which is connected to the separate guide rail mobile platform (1) and is used to monitor the energy status of the separate guide rail mobile platform (1) and charge the separate guide rail mobile platform (1) when necessary.

4. According to claim 2, a large-scale oil-filled equipment intelligent oil leakage detection system is characterized in that: Also includes, The wireless communication module is used to transmit the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

5. According to claim 2, a large-scale oil-filled equipment intelligent oil leakage detection system is characterized in that: The multimodal sensing detection module (2) comprises: UV imaging sensor, used to detect the fluorescence reaction of oil stains under UV irradiation; Infrared thermal imaging sensor to detect temperature anomalies in the oil stain area; An optical imaging sensor for capturing images of oil stains in the visible light range; The data fusion module is used to fuse the ultraviolet imaging sensor data, the infrared thermal imaging sensor data and the optical imaging sensor data.

6. According to claim 2, a large-scale oil-filled equipment intelligent oil leakage detection system is characterized in that: The intelligent analysis module includes: An image preprocessing unit, used for performing denoising and contrast enhancement on the image data collected by the multimodal sensing detection module (2); A deep learning network is used to extract features and identify oil stains from preprocessed images; A decision logic unit to determine the presence and location of oil stains based on the output of the deep learning network.

7. The intelligent oil leakage detection system for large oil-filled equipment according to claim 2 is characterized in that: The real-time oil track tracking algorithm includes: A position tracking unit, used to record the current position of the oil stain identified by the intelligent analysis module and update the oil stain position information in real time; A dynamic prediction unit, used to use the data recorded by the position tracking unit to predict the possible position of the oil mark at the next moment through a filtering algorithm such as a Kalman filter; Source tracing analysis unit, used to analyze the oil stain diffusion path and speed, and infer the source of the oil stain based on the oil stain characteristics and equipment structure; A reverse tracing mechanism, used to reversely trace and verify the accuracy of the oil stain source according to the oil stain source determined by the source tracing and analysis unit; The updating mechanism is used to update the location information and source data of the oil stain in real time according to the output of the dynamic prediction unit and the source tracing and analysis unit.

8. The intelligent oil leakage detection system for large oil-filled equipment according to claim 2 is characterized in that: The intelligent path planning module includes: A path generation unit, used to generate a preliminary moving path of the diffusion monitoring platform (12) according to the position information of the real-time oil trace tracking algorithm, and to generate a preliminary moving path of the source tracking platform (11) according to the source data of the real-time oil trace tracking algorithm; The optimization algorithm unit is used to optimize the two groups of preliminary paths by using a genetic algorithm, minimize the detection time and cover all potential leakage areas, and obtain two groups of optimized moving paths.

9. The intelligent oil leakage detection system for large oil-filled equipment according to claim 2 is characterized in that: The source tracking platform (11) and the diffusion monitoring platform (12) further include a multi-degree-of-freedom telescopic arm (6), and the multi-modal sensing detection module (2) is arranged on the multi-degree-of-freedom telescopic arm (6) for tracking and identifying the hidden location of large-scale oil-filled equipment.

10. A large-scale oil-filled equipment intelligent oil leakage detection method, based on a large-scale oil-filled equipment intelligent oil leakage detection system according to any one of claims 1 to 9, characterized in that: The following steps are included: The separated guide rail mobile platform (1) is started, so that the source tracking platform (11) and the diffusion monitoring platform (12) can move along the preset guide rail on the large oil-filled equipment in a combined state for all-round inspection through the power module (3); Collecting an image of oil stains on the surface of the equipment through a multimodal sensing detection module (2); Processing the collected image data by a data processing unit; The intelligent analysis module analyzes the processed image data, identifies oil stains and determines the location of the leak; If the intelligent analysis module identifies oil traces, the alarm module (4) is activated to send out an alarm signal, and the source tracking platform (11) and the diffusion monitoring platform (12) are automatically separated by controlling the combined separation module (5); An intelligent path planning module generates a moving path of the diffusion monitoring platform (12) according to the position information updated by the real-time oil trace tracking algorithm of the intelligent analysis module; and an intelligent path planning module generates a moving path of the source tracking platform (11) according to the source data updated by the real-time oil trace tracking algorithm of the intelligent analysis module; The power module (3) of the source tracing platform (11) controls the source tracing platform (11) to track the source of the oil stain according to the moving path of the source tracing platform (11) generated by the intelligent path planning module; The power module (3) of the diffusion monitoring platform (12) controls the diffusion monitoring platform (12) to track and monitor the diffusion trend of the oil stain according to the moving path of the diffusion monitoring platform (12) generated by the intelligent path planning module; The wireless communication module transmits the oil trace information identified by the intelligent analysis module and the moving path generated by the intelligent path planning module to the remote monitoring system.

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