Transformer substation intelligent inspection robot based on large language model driving
By integrating tracked mobile platforms and multimodal perception systems, the intelligent diagnosis and response needs of substation inspection robots in complex scenarios have been addressed, enabling comprehensive intelligent inspection of substation equipment and improving inspection efficiency and reliability.
Patent Information
- Application Number
- CN202511577211.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-31
- Publication Date
- 2026-01-09
AI Technical Summary
Existing substation inspection robots lack deep understanding and adaptive decision-making capabilities in complex scenarios, making it difficult to meet the needs of intelligent diagnosis and response. Furthermore, relying on manual inspection is inefficient and risky, especially at night and in adverse weather conditions where continuous operation is difficult.
The substation intelligent inspection robot, driven by a large language model, integrates a tracked mobile platform, a multimodal perception system, an embedded control system, a remote communication system, a data analysis and diagnosis system, and an energy management system. Combined with a dual-spectrum gimbal camera, an infrared thermal imaging sensor, a Raspberry Pi host computer, an STM32 slave computer, a 4G module, a cloud server, and a solar charging module, it achieves comprehensive intelligent inspection.
It enables stable walking and precise path tracking in complex terrain, provides rich data sources, supports efficient motion control and data acquisition, ensures the stability and reliability of robot operation, realizes intelligent device status recognition and report generation, ensures long-term continuous operation, and improves the flexibility and coverage of inspection.
Smart Images
Figure CN121290412A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent operation and maintenance and robotics technology for power systems, and in particular to a smart substation inspection robot driven by a large language model. Background Technology
[0002] With the improvement of the intelligence level of power systems, the unmanned and minimally staffed operation of substations has become a development trend.
[0003] Currently, substations of 35kV and below still mainly rely on manual inspection, which has problems such as low efficiency, high risk, and difficulty in continuous operation at night and in bad weather. Existing inspection robots mostly use a combination of preset paths and simple sensors, lacking a deep understanding of equipment status and adaptive decision-making capabilities, making it difficult to meet the needs of intelligent diagnosis and response in complex scenarios.
[0004] To address this, we propose a smart substation inspection robot driven by a large language model. Summary of the Invention
[0005] The present invention mainly addresses the technical problems existing in the prior art and provides a substation intelligent inspection robot driven by a large language model.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: a substation intelligent inspection robot driven by a large language model, comprising a tracked mobile platform, a multimodal perception system, an embedded control system, a remote communication system, a data analysis and diagnosis system, and an energy management system. The tracked mobile platform, the multimodal perception system, the embedded control system, the remote communication system, the data analysis and diagnosis system, and the energy management system are connected by electrical signals. The tracked mobile platform includes a tracked chassis and an electromagnetic line-following module. The tracked mobile platform is used to achieve stable walking and path tracking in complex terrain.
[0007] Preferably, the multimodal sensing system includes a dual-spectral gimbal camera and an infrared thermal imaging sensor.
[0008] Preferably, the multimodal sensing system is used to acquire visible light images and device temperature data.
[0009] Preferably, the embedded control system includes a Raspberry Pi host computer and an STM32 slave computer.
[0010] Preferably, the embedded control system is used to realize motion control, data acquisition and communication scheduling.
[0011] Preferably, the remote communication system includes a 4G module and a cloud server.
[0012] Preferably, the remote communication system uses a 4G module to communicate with a cloud server to transmit remote images and control signals, and supports intranet penetration and remote login.
[0013] Preferably, the data analysis and diagnostic system includes a large language model and a convolutional neural network.
[0014] Preferably, the data analysis and diagnostic system is used to perform in-depth analysis and defect identification of image and temperature data, so as to realize intelligent identification of equipment status and report generation.
[0015] Preferably, the large language model is used to parse inspection task instructions, generate inspection reports, and combine with a CNN vision model to identify power equipment defects in infrared images.
[0016] Preferably, the energy management system includes a solar charging module and an automatic recharge mechanism.
[0017] Preferably, the energy management system is used to ensure the robot can operate continuously for a long time.
[0018] This invention provides a smart substation inspection robot driven by a large language model. It has the following advantages: 1. This intelligent substation inspection robot, driven by a large language model, integrates a tracked mobile platform, a multimodal perception system, an embedded control system, a remote communication system, a data analysis and diagnostic system, and an energy management system. It achieves comprehensive and intelligent inspection of substation equipment. The tracked mobile platform design enables the robot to move stably in complex terrain, and the electromagnetic line-following module achieves precise path tracking, greatly improving the flexibility and coverage of the inspection. The multimodal perception system integrates a dual-spectrum gimbal camera and an infrared thermal imaging sensor, enabling simultaneous acquisition of visible light images and equipment temperature data, providing a rich data source for subsequent in-depth analysis.
[0019] 2. This substation intelligent inspection robot, driven by a large language model, is equipped with an embedded control system. The embedded control system uses a combination of a Raspberry Pi host computer and an STM32 slave computer to achieve efficient motion control, data acquisition and communication scheduling, ensuring the stability and reliability of the robot's operation.
[0020] 3. This substation intelligent inspection robot, driven by a large language model, is equipped with a remote communication system. The remote communication system uses a 4G module to connect with a cloud server to achieve remote image transmission and control signal transmission. It supports intranet penetration and remote login, enabling operators to monitor the robot's status and remotely control it anytime and anywhere.
[0021] 4. This substation intelligent inspection robot, driven by a large language model, is equipped with a data analysis and diagnosis system. The data analysis and diagnosis system uses a large language model and convolutional neural network to perform in-depth analysis and defect identification of image and temperature data, realizing intelligent identification of equipment status and report generation, providing timely and accurate decision support for operation and maintenance personnel.
[0022] 5. This substation intelligent inspection robot, driven by a large language model, is equipped with an energy management system. The energy management system, through a solar charging module and an automatic recharging mechanism, ensures the robot's ability to operate continuously for a long time, further improving the efficiency and sustainability of inspections. Attached Figure Description
[0023] Figure 1 This is a schematic diagram of the device module of the present invention. Detailed Implementation
[0024] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.
[0025] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.
[0026] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0027] In the description of the embodiments of the present invention, it should be noted that the terms "center," "upper," "lower," "inner," "outer," and "side," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0028] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.
[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0030] Example 1: A smart substation inspection robot driven by a large language model, such as... Figure 1As shown, the system includes a tracked mobile platform, a multimodal sensing system, an embedded control system, a remote communication system, a data analysis and diagnostic system, and an energy management system. These components are connected via electrical signals. The tracked mobile platform comprises a tracked chassis and an electromagnetic line-following module, enabling stable movement and path tracking in complex terrain. The multimodal sensing system includes a dual-spectrum gimbal camera and an infrared thermal imaging sensor, used to acquire visible light images and equipment temperature data. The embedded control system includes a Raspberry Pi host computer and an STM32 slave computer, used for motion control, data acquisition, and communication scheduling. The remote communication system includes a 4G module and a cloud server, enabling remote image transmission and control signal transmission via the 4G module and cloud server, supporting intranet penetration and remote login. The data analysis and diagnostic system includes a large language model and a convolutional neural network. This system performs deep analysis and defect identification on image and temperature data, enabling intelligent identification and report generation of equipment status. The large language model parses inspection task instructions, generates inspection reports, and combines a CNN vision model to identify power equipment defects in infrared images. The energy management system includes a solar charging module and an automatic recharging mechanism, ensuring the robot can operate continuously for extended periods. By integrating a tracked mobile platform, a multimodal perception system, an embedded control system, a remote communication system, the data analysis and diagnostic system, and the energy management system, the system achieves comprehensive and intelligent inspection of substation equipment. The tracked mobile platform design allows the robot to move stably in complex terrain, and the electromagnetic line-following module enables precise path tracking, significantly improving the flexibility and coverage of the inspection. The multimodal perception system integrates a dual-spectrum gimbal camera and an infrared thermal imaging sensor, simultaneously acquiring visible light images and equipment temperature data, providing a rich data source for subsequent in-depth analysis.
[0031] Example 2: Based on Example 1, as follows Figure 1As shown, the tracked mobile platform includes a tracked chassis and an electromagnetic line-following module, enabling stable movement and path tracking in complex terrain. The multimodal perception system includes a dual-spectrum gimbal camera and an infrared thermal imaging sensor, used to acquire visible light images and equipment temperature data. The embedded control system includes a Raspberry Pi host computer and an STM32 slave computer, used for motion control, data acquisition, and communication scheduling. The remote communication system includes a 4G module and a cloud server, enabling remote image transmission and control signal transmission via the 4G module and cloud server, supporting intranet penetration and remote login. The data analysis and diagnostic system includes a large language model and a convolutional neural network, used for deep analysis and defect identification of image and temperature data, achieving intelligent identification and report generation of equipment status. The large language model is used to parse inspection task instructions, generate inspection reports, and combine with a CNN vision model to identify electrical equipment defects in infrared images. The energy management system includes a solar charging module and an automatic recharging mechanism, ensuring the robot's continuous operation over extended periods. By setting up an embedded control system, which uses a combination of a Raspberry Pi host computer and an STM32 slave computer, efficient motion control, data acquisition and communication scheduling are achieved, ensuring the stability and reliability of robot operation.
[0032] Example 3: Based on Examples 1 and 2, as follows... Figure 1 As shown, the multimodal perception system includes a dual-spectrum gimbal camera and an infrared thermal imaging sensor, used to acquire visible light images and equipment temperature data. The embedded control system includes a Raspberry Pi host computer and an STM32 slave computer, used for motion control, data acquisition, and communication scheduling. The remote communication system includes a 4G module and a cloud server, enabling remote image transmission and control signal transmission via the 4G module and cloud server, supporting intranet penetration and remote login. The data analysis and diagnostic system includes a large language model and a convolutional neural network, used for deep analysis and defect identification of image and temperature data, achieving intelligent identification and report generation of equipment status. The large language model is used to parse inspection task instructions, generate inspection reports, and combine with a CNN vision model to identify power equipment defects in infrared images. The energy management system includes a solar charging module and an automatic recharging mechanism, used to ensure the robot's continuous operation for extended periods. By setting up a remote communication system, which uses a 4G module to communicate with a cloud server for remote image transmission and control signal transmission, and supports intranet penetration and remote login, operators can monitor the robot's status and remotely control it anytime, anywhere.
[0033] Example 4: Based on Examples 1, 2, and 3, as follows... Figure 1As shown, the embedded control system includes a Raspberry Pi host computer and an STM32 slave computer, used for motion control, data acquisition, and communication scheduling. The remote communication system includes a 4G module and a cloud server, enabling remote image and control signal transmission via the 4G module and cloud server, supporting intranet penetration and remote login. The data analysis and diagnostic system includes a large language model and a convolutional neural network, used for deep analysis and defect identification of image and temperature data, achieving intelligent identification and report generation of equipment status. The large language model is used to parse inspection task instructions, generate inspection reports, and combine with a CNN vision model to identify power equipment defects in infrared images. The energy management system includes a solar charging module and an automatic recharging mechanism, ensuring the robot's continuous operation for extended periods. By setting up the data analysis and diagnostic system, which utilizes a large language model and convolutional neural network for deep analysis and defect identification of image and temperature data, intelligent identification and report generation of equipment status are achieved, providing timely and accurate decision support for maintenance personnel.
[0034] Example 5: Based on Examples 1, 2, 3, and 4, as follows... Figure 1 As shown, the remote communication system includes a 4G module and a cloud server. The system uses the 4G module to transmit remote image and control signals to the cloud server, supporting intranet penetration and remote login. The data analysis and diagnostic system includes a large language model and a convolutional neural network. This system performs deep analysis and defect identification on image and temperature data, enabling intelligent identification and report generation of equipment status. The large language model parses inspection task instructions, generates inspection reports, and combines a CNN visual model to identify power equipment defects in infrared images. The energy management system includes a solar charging module and an automatic recharging mechanism. This system ensures the robot can operate continuously for extended periods. By implementing the energy management system, which uses the solar charging module and automatic recharging mechanism, the system guarantees the robot's ability to operate continuously for long periods, further improving the efficiency and sustainability of inspections.
[0035] The working principle of this invention is as follows: During operation, a tracked mobile platform first carries the robot to a pre-set inspection area. An electromagnetic line-following module ensures stable movement and accurate path tracking in complex terrain. Upon reaching the designated location, a dual-spectrum gimbal camera in the multimodal perception system acquires visible light images, and an infrared thermal imaging sensor collects equipment temperature data. This data is transmitted in real-time to the embedded control system. The Raspberry Pi host computer and STM32 slave computer in the embedded control system work together to perform preliminary processing of the acquired data and transmit the data to the cloud server via a 4G module in the remote communication system. This also supports intranet penetration and remote login, facilitating real-time monitoring and scheduling by operators. After receiving the data, the cloud server sends it to the data analysis and diagnostic system. A large language model parses the inspection task instructions, and a convolutional neural network performs in-depth analysis and defect identification of the image and temperature data, intelligently identifying the equipment status and generating an inspection report. When the robot's battery is low, the automatic recharging mechanism in the energy management system is activated, guiding the robot back to the charging area. The solar charging module replenishes its power, ensuring continuous operation of the robot for extended periods.
[0036] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.
Claims
1. A substation intelligent inspection robot driven by a large language model, characterized in that, It includes a tracked mobile platform, a multimodal sensing system, an embedded control system, a remote communication system, a data analysis and diagnostic system, and an energy management system. The tracked mobile platform, multimodal sensing system, embedded control system, remote communication system, data analysis and diagnostic system, and energy management system are connected by electrical signals. The tracked mobile platform includes a tracked chassis and an electromagnetic line-following module.
2. The intelligent substation inspection robot based on a large language model as described in claim 1, characterized in that: The multimodal sensing system includes a dual-spectrum gimbal camera and an infrared thermal imaging sensor.
3. The intelligent substation inspection robot based on a large language model as described in claim 1, characterized in that: The embedded control system includes a Raspberry Pi host computer and an STM32 slave computer.
4. The intelligent substation inspection robot driven by a large language model according to claim 1, characterized in that: The remote communication system includes a 4G module and a cloud server.
5. The intelligent substation inspection robot driven by a large language model according to claim 1, characterized in that: The data analysis and diagnostic system includes a large language model and a convolutional neural network.
6. The intelligent substation inspection robot based on a large language model as described in claim 1, characterized in that: The energy management system includes a solar charging module and an automatic recharge mechanism.