Substation secondary equipment detection method and system based on infrared image

Abstract

The invention discloses a transformer substation secondary equipment detection method and system based on an infrared image, and relates to the field of equipment detection.The method comprises the following steps that a terminal digital model including terminal three-dimensional position coordinates, functional attributes, a temperature upper limit threshold value and a temperature lower limit threshold value is established according to equipment space distribution in a secondary screen cabinet, and a terminal digital model is established according to the terminal three-dimensional position coordinates, the functional attributes, the temperature upper limit threshold value and the temperature lower limit threshold value; carrying out equipment source positioning based on hierarchical modeling of terminal installation height parameters; configuring three task modes of periodic inspection, active trigger inspection and passive response inspection, importing the terminal inspection model into a task system, and setting a task starting time point, inspection period duration and inspection interval parameters; full-automatic and real-time detection of a secondary screen cabinet area is realized, instant threshold judgment and localization processing are performed on temperature data by means of field edge calculation, remote communication load is effectively reduced, reliability and response speed of the system are improved, and accurate and efficient online monitoring capability is provided for operation and maintenance personnel.

Description

Technical Field

[0001] This invention relates to the field of equipment testing technology, specifically to a method and system for testing secondary equipment in substations based on infrared images. Background Technology

[0002] With the global emphasis on renewable and clean energy utilization, the power industry is developing rapidly. Substations, as a crucial component of the power system, are responsible for converting high-voltage electricity into low-voltage electricity suitable for user needs. The reliability and safety of their equipment directly affect the stability of the power supply. The secondary circuits of substations, as an important part of the power system, directly impact the safety and stability of the power grid.

[0003] However, due to the complexity of substation secondary circuits, problems such as overheating defects in substation secondary equipment terminal blocks occur frequently, potentially leading to power grid accidents and severely impacting the normal operation of the power system. More critically, when overheating defects occur in secondary equipment terminals and components, the lack of real-time monitoring and early warning systems often prevents operators from detecting them in time. This can lead to the deterioration of the overheating point, posing a serious threat to power grid safety.

[0004] Therefore, existing technologies are no longer sufficient to meet the requirements for the safe and stable operation of substations, and a new type of solution is urgently needed. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method and system for detecting secondary equipment in substations based on infrared images, which can effectively solve the problems of the existing technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention discloses a method for detecting secondary equipment in substations based on infrared images, comprising the following steps:

[0008] Step 1: Based on the spatial distribution of equipment in the secondary cabinet, establish a digital model of the terminals, including the three-dimensional position coordinates of the terminals, functional attributes, upper temperature threshold, and lower temperature threshold. Based on the terminal installation height parameter, perform layered modeling to locate the equipment source.

[0009] Step 2: Configure three task modes: periodic inspection, active trigger inspection, and passive response inspection. Import the terminal inspection model into the task system and set the task start time, inspection cycle duration, and inspection interval parameters.

[0010] Step 3: Build an edge computing architecture at the substation site to perform threshold judgment on the collected terminal temperature data in real time, extract the highest temperature value of the temperature anomaly point and generate an alarm signal;

[0011] Step 4: Deploy a short-focal-length infrared imaging device with a field of view ≥120° on the door of the secondary screen cabinet. Its infrared detector and optical lens meet the requirements for close-range imaging at 30-50cm. Optimize the monitoring angle through the thermal imager tilt adjustment mechanism.

[0012] Step 5: Use a rack and pinion guide transmission device to drive the infrared acquisition device to move vertically, with the movement stroke covering the entire height area of ​​the secondary cabinet;

[0013] Step 6: The infrared acquisition device acquires terminal temperature distribution images according to a preset trajectory and transmits them synchronously to the edge computing node;

[0014] Step 7: Perform pixel-level analysis of the temperature image through edge computing nodes. When the terminal temperature exceeds the set threshold, mark the location of the abnormal terminal and output alarm information.

[0015] Step 8: Upload the edge computing alarm data to the central monitoring system and generate a test report by associating it with the equipment ledger information.

[0016] Furthermore, the terminal digitization model in step 1 includes the longitudinal layer coordinate information of the terminal block, with a height difference of 50±5mm between each layer of terminals, and is associated with the equipment nameplate parameters and electrical circuit numbers.

[0017] Furthermore, in step 2, the active-triggered inspection is triggered by a change in equipment status, the passive-response inspection is activated by a remote control command, and the periodic inspection interval can be set to be adjustable from 1 to 24 hours.

[0018] Furthermore, the threshold determination in step 3 employs a dynamic compensation algorithm, the calculation formula of which is:

[0019] ;

[0020] In the formula, This represents the dynamically adjusted temperature offset value. The weighting factor representing the influence of ambient temperature has an empirical value of 0.2~0.5. Represents the real-time ambient temperature. Represents the reference ambient temperature. The weighting factor representing the rate of temperature change, with an empirical value of 10~30s. Represents the rate of temperature change. Represents the volatility sensitivity coefficient. This represents the standard deviation of temperature under the same operating conditions over the past 24 hours.

[0021] Furthermore, the infrared imaging device in step 4 operates in the 8-14μm band, has a spatial resolution of not less than 1.5mrad, and a temperature measurement accuracy of ±1℃ or ±1% of the reading value.

[0022] Furthermore, the positioning accuracy of the rack and pinion transmission device in step 5 is ≤0.1mm, the scanning speed is adjustable from 5-20mm / s, and a limit sensor is provided.

[0023] Furthermore, the pixel-level parsing process in step 7 includes:

[0024] The acquired terminal temperature distribution image is converted to grayscale, and the actual temperature value of each pixel is calculated based on the preset pixel-temperature mapping relationship.

[0025] Establish a spatial mapping relationship between the image coordinate system and the terminal digital model, and locate the temperature abnormal pixel to the specific terminal number;

[0026] Calculate the average temperature value of a continuous 5×5 pixel matrix within the abnormal terminal area. When the average value exceeds the upper temperature threshold of the corresponding terminal, trigger a primary alarm signal and mark the boundary coordinates of the abnormal area for temperature gradient analysis.

[0027] Furthermore, the steps of the temperature gradient analysis include:

[0028] Perform heat diffusion path analysis on the marked abnormal areas and extract the temperature gradient distribution curve within a radius of 10 pixels centered on the highest temperature point;

[0029] When the gradient change rate exceeds 0.8℃ / pixel and continues for 3 consecutive frames, a level 2 alarm signal containing the over-temperature development trend is generated.

[0030] Synchronous execution of dynamic threshold correction: Based on whether the absolute value of the temperature difference between adjacent terminals is greater than 15℃, the upper limit temperature threshold is automatically lowered by 5℃ for verification. If it still exceeds the limit, the final alarm information is output to the central monitoring terminal.

[0031] A substation secondary equipment detection system based on infrared imaging includes:

[0032] The terminal modeling unit is used to establish a digital model of the three-dimensional position coordinates, functional attributes, and upper and lower temperature thresholds of the terminals based on the spatial distribution of terminals in the secondary cabinet and the functional attributes of nameplates and circuit numbers. It performs vertical layered modeling based on the terminal installation height parameters to form a hierarchical coordinate system.

[0033] The task scheduling unit provides three task modes: periodic inspection, proactively triggered inspection, and passively responded inspection. It also imports the terminal digital model into the task system and allows setting the task start time, inspection cycle, and inspection interval.

[0034] The edge computing unit, deployed at the field edge node, performs threshold judgment on the collected terminal temperature data in real time, calls the dynamic compensation algorithm to calculate the temperature offset, and extracts the highest temperature of the temperature anomaly point to generate a primary alarm signal.

[0035] The transmission actuator is equipped with an infrared imaging device with a field of view ≥120°, whose infrared detector and optical lens support close-range imaging at 30-50cm.

[0036] The infrared imaging unit is used to enable the transmission actuator to perform adjustable scanning in the vertical direction at a speed of 5–20 mm / s, with the motion stroke covering the entire height of the cabinet, a positioning accuracy of ≤0.1 mm, and a limit sensor to prevent overtravel.

[0037] The image acquisition and transmission unit is used to synchronously acquire terminal temperature distribution images according to a preset trajectory and transmit the data to the edge computing node in real time via a high-speed bus.

[0038] The temperature image analysis unit is used to perform grayscale processing on the acquired image, restore the actual temperature of each pixel according to the pixel-temperature mapping relationship, and map the pixels to specific terminal numbers through the spatial mapping between the image coordinate system and the terminal digital model.

[0039] The temperature anomaly alarm module is used to calculate the average temperature of the marked abnormal terminal area using a 5×5 pixel matrix. When the temperature exceeds the upper limit threshold of the terminal, a first-level alarm is triggered, and heat diffusion path and gradient change analysis are performed. If the gradient change rate is >0.8 ℃ / pixel and lasts for 3 frames, a second-level alarm is generated, and the threshold is dynamically corrected and reviewed simultaneously.

[0040] The alarm data integration module is used to upload the alarm data generated by the temperature anomaly alarm module to the central monitoring system through a secure channel, and associate it with the equipment ledger information to automatically generate detection reports. It supports historical data backtracking, alarm statistics, and multi-dimensional report output.

[0041] Furthermore, the terminal modeling unit is interconnected with the task scheduling unit, the transmission execution unit, and the image acquisition and transmission unit via a wireless network; the transmission execution unit is interconnected with the infrared imaging unit via an electrical medium; the task scheduling unit is interconnected with the edge computing unit via a wireless network; the temperature image analysis unit is interconnected with the image acquisition and transmission unit and the temperature anomaly alarm module via a wireless network; and the temperature anomaly alarm module is interconnected with the alarm data integration module via a wireless network.

[0042] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0043] 1. By constructing a digital model of the terminals and integrating three inspection modes—periodic, active triggering, and passive response—fully automatic and real-time detection of the secondary cabinet area is achieved. With the help of on-site edge computing, temperature data is judged and processed locally in real time, effectively reducing the load on remote communication, improving the reliability and response speed of the system, and providing maintenance personnel with accurate and efficient online monitoring capabilities.

[0044] 2. By employing a short-focal-length infrared thermal imager with a field of view ≥120° and a precision rack and pinion guide transmission device, high-resolution imaging at close range of 30-50 cm and millimeter-level scanning positioning are achieved. Combined with pixel-level temperature, grayscale mapping, and one-to-one spatial correspondence between image coordinates and digital models, the location of abnormal terminals can be accurately locked, significantly improving the accuracy of temperature measurement and spatial positioning, and meeting the fine-grained detection requirements in complex environments.

[0045] 3. A dynamic compensation algorithm is used to correct the temperature threshold in real time, integrating the average temperature of a 5×5 pixel matrix, heat diffusion path, and temperature gradient change rate analysis to achieve multi-level dynamic assessment. When the gradient exceeds the limit and persists for multiple frames, it automatically escalates to a level two alarm. The upper and lower limit thresholds are verified by checking the temperature difference between adjacent terminals, significantly reducing the false alarm rate and providing early warning capabilities for fault development trends, thus providing strong support for the safe operation and maintenance of substations. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0047] Figure 1 This is a flowchart illustrating the substation secondary equipment testing method of the present invention;

[0048] Figure 2 This is a schematic diagram of the substation secondary equipment detection system of the present invention.

[0049] The labels in the diagram represent: 1. Terminal modeling unit; 2. Task scheduling unit; 3. Edge computing unit; 4. Transmission execution unit; 5. Infrared imaging unit; 6. Image acquisition and transmission unit; 7. Temperature image analysis unit; 8. Temperature anomaly alarm module; 9. Alarm data integration module. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] The present invention will be further described below with reference to embodiments.

[0052] Example 1

[0053] The substation secondary equipment detection method based on infrared images in this embodiment, such as... Figure 1 As shown, it includes the following steps:

[0054] Step 1: Based on the spatial distribution of equipment in the secondary cabinet, establish a digital model of the terminals, including the three-dimensional position coordinates of the terminals, functional attributes, upper temperature threshold, and lower temperature threshold. Based on the terminal installation height parameter, perform layered modeling to locate the equipment source. The digital model of the terminals includes the vertical layered coordinate information of the terminal blocks, with a height difference of 50±5mm between each layer of terminals, and is associated with the equipment nameplate parameters and electrical circuit numbers.

[0055] Step 2: Configure three task modes: periodic inspection, active trigger inspection, and passive response inspection. Import the terminal inspection model into the task system, set the task start time, inspection cycle duration, and inspection interval parameters, and perform fully automatic real-time inspection of the cabinet area. Active trigger inspection is triggered by equipment status change events, passive response inspection is activated by remote control commands, and the periodic inspection interval can be set to be adjustable from 1 to 24 hours.

[0056] Step 3: Build an edge computing architecture at the substation site to perform threshold judgment on the collected terminal temperature data in real time, extract the highest temperature value of the temperature anomaly point and generate an alarm signal, and realize localized data processing to reduce communication load;

[0057] Step 4: Deploy a short-focal-length infrared imaging device with a field of view ≥120° on the door of the secondary screen cabinet. Its infrared detector and optical lens meet the requirements for close-range imaging at 30-50cm. Optimize the monitoring angle through the thermal imager's tilt adjustment mechanism. The infrared imaging device operates in the 8-14μm band, with a spatial resolution of no less than 1.5mrad and a temperature measurement accuracy of ±1℃ or ±1% of the reading.

[0058] Step 5: The infrared acquisition device is driven vertically by a rack and pinion guide transmission device. The movement stroke covers the entire height area of ​​the secondary screen cabinet. The positioning accuracy is ≤0.1mm by stepper motor control. The positioning accuracy of the rack and pinion guide transmission device is ≤0.1mm, the scanning speed is adjustable from 5-20mm / s, and a limit sensor is set.

[0059] Step 6: The infrared acquisition device acquires terminal temperature distribution images according to a preset trajectory and transmits them synchronously to the edge computing node;

[0060] Step 7: Perform pixel-level analysis of the temperature image using edge computing nodes. When a terminal temperature exceeds a set threshold, mark the location of the abnormal terminal and output an alarm message. The pixel-level analysis process includes:

[0061] The acquired terminal temperature distribution image is converted to grayscale, and the actual temperature value of each pixel is calculated based on the preset pixel-temperature mapping relationship.

[0062] Establish a spatial mapping relationship between the image coordinate system and the terminal digital model, and locate the temperature abnormal pixel to the specific terminal number;

[0063] Calculate the average temperature value of a continuous 5×5 pixel matrix within the abnormal terminal area. When the average value exceeds the upper temperature threshold of the corresponding terminal, trigger a primary alarm signal and mark the boundary coordinates of the abnormal area for temperature gradient analysis.

[0064] The steps of temperature gradient analysis include:

[0065] Perform heat diffusion path analysis on the marked abnormal areas and extract the temperature gradient distribution curve within a radius of 10 pixels centered on the highest temperature point;

[0066] When the gradient change rate exceeds 0.8℃ / pixel and continues for 3 consecutive frames, a level 2 alarm signal containing the over-temperature development trend is generated.

[0067] Synchronous execution of dynamic threshold correction: Based on whether the absolute value of the temperature difference between adjacent terminals is greater than 15℃, the upper limit temperature threshold is automatically lowered by 5℃ for verification. If it still exceeds the limit, the final alarm information is output to the central monitoring terminal.

[0068] Step 8: Upload the edge computing alarm data to the central monitoring system and generate a test report by associating it with the equipment ledger information.

[0069] Compared with existing technologies, by establishing a digital model of the terminal and positioning it hierarchically, a one-to-one mapping between infrared imaging and specific terminals is achieved. It integrates three inspection modes: periodic, active triggering, and passive response. Combined with local edge computing, it performs real-time threshold judgment and pixel-level analysis of temperature data and images, which greatly reduces communication load and improves data processing efficiency. It adopts a toothed rail scanning device with a field of view ≥120°, high-resolution short-focal-length infrared imaging, and millimeter-level positioning accuracy, which can continuously acquire terminal temperature distribution maps.

[0070] Based on the average temperature of the 5×5 pixel matrix, temperature gradient analysis, and dynamic threshold correction strategy, multi-level alarms are accurately triggered and detection reports with associated equipment information are automatically generated, thereby significantly improving the accuracy, coverage, and response speed of inspections and effectively reducing blind spots and false alarm rates.

[0071] Example 2

[0072] At other levels, this embodiment also provides another optimization mechanism based on Embodiment 1, specifically a substation secondary equipment detection system based on infrared images, such as... Figure 2 As shown, it includes:

[0073] Terminal modeling unit 1 is used to establish a digital model of the three-dimensional position coordinates, functional attributes, and upper and lower temperature thresholds of the terminals based on the spatial distribution of terminals in the secondary cabinet and the nameplate, circuit number, and functional attributes. It performs vertical layered modeling based on the terminal installation height parameters to form a hierarchical coordinate system.

[0074] Task scheduling unit 2 is used to provide three task modes: periodic inspection, active trigger inspection, and passive response inspection. It can also import the terminal digital model into the task system and set the task start time, inspection cycle, and inspection interval.

[0075] Edge computing unit 3 is deployed at the field edge node. It performs threshold judgment on the collected terminal temperature data in real time, calls the dynamic compensation algorithm to calculate the temperature offset, and extracts the highest temperature of the temperature anomaly point to generate a primary alarm signal.

[0076] The transmission actuator 4 is equipped with an infrared imaging device with a field of view ≥120°, whose infrared detector and optical lens support close-range imaging of 30-50cm.

[0077] Infrared imaging unit 5 is used to enable the transmission actuator 4 to perform adjustable scanning in the vertical direction at a speed of 5–20 mm / s, with the motion stroke covering the entire height of the cabinet, a positioning accuracy of ≤0.1 mm, and a limit sensor to prevent overtravel.

[0078] Image acquisition and transmission unit 6 is used to synchronously acquire terminal temperature distribution images according to a preset trajectory and transmit the data to the edge computing node in real time via a high-speed bus;

[0079] Temperature image analysis unit 7 is used to perform grayscale processing on the acquired image, restore the actual temperature of each pixel according to the pixel-temperature mapping relationship, and perform pixel-to-specific terminal number mapping through the spatial mapping of the image coordinate system and the terminal digital model.

[0080] Temperature anomaly alarm module 8 is used to calculate the average temperature of the marked abnormal terminal area using a 5×5 pixel matrix. When the temperature exceeds the upper limit threshold of the terminal, a first-level alarm is triggered, and heat diffusion path and gradient change analysis are performed. If the gradient change rate is >0.8 ℃ / pixel and lasts for 3 frames, a second-level alarm is generated, and the threshold is dynamically corrected and reviewed simultaneously.

[0081] The alarm data integration module 9 is used to upload the alarm data generated by the temperature anomaly alarm module 8 to the central monitoring system through a secure channel, and associate it with the equipment ledger information to automatically generate a detection report. It supports historical data backtracking, alarm statistics and multi-dimensional report output.

[0082] Terminal modeling unit 1 is interconnected with task scheduling unit 2, transmission execution unit 4 and image acquisition and transmission unit 6 via wireless network. Transmission execution unit 4 is interconnected with infrared imaging unit 5 via electrical medium. Task scheduling unit 2 is interconnected with edge computing unit 3 via wireless network. Temperature image analysis unit 7 is interconnected with image acquisition and transmission unit 6 and temperature abnormality alarm module 8 via wireless network. Temperature abnormality alarm module 8 is interconnected with alarm data integration module 9 via wireless network.

[0083] Example 3

[0084] This embodiment provides a threshold determination algorithm using dynamic compensation, the calculation formula of which is:

[0085] ;

[0086] In the formula, This represents the dynamically adjusted temperature offset value. The weighting factor representing the influence of ambient temperature has an empirical value of 0.2~0.5. Represents the real-time ambient temperature. Represents the reference ambient temperature. The weighting factor representing the rate of temperature change, with an empirical value of 10~30s. Represents the rate of temperature change. Represents the volatility sensitivity coefficient. This represents the standard deviation of temperature under the same operating conditions over the past 24 hours.

[0087] Compared with existing technologies, it automatically corrects for the effects of environmental temperature differences, eliminates baseline drift caused by seasonal and diurnal temperature variations, predicts degradation trends through temperature change rate (such as gradual temperature rise caused by loose terminals), is compatible with fluctuations in normal equipment operating conditions, and avoids false alarms caused by sudden load changes.

[0088] In summary, this invention combines three inspection modes—periodic, active triggering, and passive response—to achieve real-time monitoring of the temperature of secondary cabinet terminals. By leveraging edge computing and dynamic compensation algorithms to correct temperature thresholds in real time, and in conjunction with high-precision short-focus infrared imaging, toothed guide rail scanning, and pixel-level analysis, it can automatically issue multi-level alarms when the first over-temperature symptom appears. This significantly improves early warning capabilities and greatly reduces the workload of manual inspections, thereby enhancing the operational safety and maintenance efficiency of substation secondary equipment.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting secondary equipment in a substation based on infrared images, characterized in that, Includes the following steps: Step 1: Based on the spatial distribution of equipment in the secondary cabinet, establish a digital model of the terminals, including the three-dimensional position coordinates of the terminals, functional attributes, upper temperature threshold, and lower temperature threshold. Based on the terminal installation height parameter, perform layered modeling to locate the equipment source. Step 2: Configure three task modes: periodic inspection, active trigger inspection, and passive response inspection. Import the terminal inspection model into the task system and set the task start time, inspection cycle duration, and inspection interval parameters. Step 3: Build an edge computing architecture at the substation site to perform threshold judgment on the collected terminal temperature data in real time, extract the highest temperature value of the temperature anomaly point and generate an alarm signal; Step 4: Deploy a short-focal-length infrared imaging device with a field of view ≥120° on the door of the secondary screen cabinet. Its infrared detector and optical lens meet the requirements for close-range imaging at 30-50cm. Optimize the monitoring angle through the thermal imager tilt adjustment mechanism. Step 5: The infrared acquisition device is driven vertically by a rack and pinion guide transmission device, and the movement stroke covers the entire height area of ​​the secondary cabinet; Step 6: The infrared acquisition device acquires terminal temperature distribution images according to a preset trajectory and transmits them synchronously to the edge computing node; Step 7: Perform pixel-level analysis of the temperature image through edge computing nodes. When the terminal temperature exceeds the set threshold, mark the location of the abnormal terminal and output alarm information. Step 8: Upload the edge computing alarm data to the central monitoring system and generate a test report by associating it with the equipment ledger information.

2. The method for detecting secondary equipment in a substation based on infrared images according to claim 1, characterized in that, The terminal digitization model in step 1 includes the longitudinal layer coordinate information of the terminal block, with a height difference of 50±5mm between each layer of terminals, and is associated with the equipment nameplate parameters and electrical circuit numbers.

3. The method for detecting secondary equipment in a substation based on infrared images according to claim 1, characterized in that, The active-triggered inspection in step 2 is triggered by a change in equipment status, while the passive-response inspection is activated by a remote control command. The periodic inspection interval can be set to be adjustable from 1 to 24 hours.

4. The method for detecting secondary equipment in a substation based on infrared images according to claim 1, characterized in that, The threshold determination in step 3 uses a dynamic compensation algorithm, and its calculation formula is as follows: ； In the formula, This represents the dynamically adjusted temperature offset value. The weighting factor representing the influence of ambient temperature has an empirical value of 0.2~0.

5. Represents the real-time ambient temperature. Represents the reference ambient temperature. The weighting factor representing the rate of temperature change, with an empirical value of 10~30s. Represents the rate of temperature change. Represents the volatility sensitivity coefficient. This represents the standard deviation of temperature under the same operating conditions over the past 24 hours.

5. The method for detecting secondary equipment in a substation based on infrared images according to claim 1, characterized in that, The infrared imaging device in step 4 operates in the 8-14μm band, has a spatial resolution of not less than 1.5mrad, and a temperature measurement accuracy of ±1℃ or ±1% of the reading value.

6. The method for detecting secondary equipment in a substation based on infrared images according to claim 1, characterized in that, The positioning accuracy of the rack and pinion transmission device in step 5 is ≤0.1mm, the scanning speed is adjustable from 5-20mm / s, and a limit sensor is provided.

7. The method for detecting secondary equipment in a substation based on infrared images according to claim 1, characterized in that, The pixel-level parsing process in step 7 includes: The acquired terminal temperature distribution image is converted to grayscale, and the actual temperature value of each pixel is calculated based on the preset pixel-temperature mapping relationship. Establish a spatial mapping relationship between the image coordinate system and the terminal digital model, and locate the temperature abnormal pixel to the specific terminal number; Calculate the average temperature value of a continuous 5×5 pixel matrix within the abnormal terminal area. When the average value exceeds the upper temperature threshold of the corresponding terminal, trigger a primary alarm signal and mark the boundary coordinates of the abnormal area for temperature gradient analysis.

8. The method for detecting secondary equipment in a substation based on infrared images according to claim 7, characterized in that, The steps of the temperature gradient analysis include: Perform heat diffusion path analysis on the marked abnormal areas and extract the temperature gradient distribution curve within a radius of 10 pixels centered on the highest temperature point; When the gradient change rate exceeds 0.8℃ / pixel and continues for 3 consecutive frames, a level 2 alarm signal containing the over-temperature development trend is generated. Synchronous execution of dynamic threshold correction: Based on whether the absolute value of the temperature difference between adjacent terminals is greater than 15℃, the upper limit temperature threshold is automatically lowered by 5℃ for verification. If it still exceeds the limit, the final alarm information is output to the central monitoring terminal.

9. A substation secondary equipment detection system based on infrared images, the system being an implementation system of the substation secondary equipment detection method based on infrared images according to any one of claims 1-8, characterized in that, include: Terminal modeling unit (1) is used to establish a digital model of the three-dimensional position coordinates, functional attributes, and upper and lower temperature thresholds of the terminals based on the spatial distribution of terminals in the secondary cabinet and the functional attributes of nameplates and circuit numbers. It performs vertical layered modeling based on the terminal installation height parameters to form a hierarchical coordinate system. The task scheduling unit (2) is used to provide three task modes: periodic inspection, active trigger inspection, and passive response inspection. It also imports the terminal digital model into the task system and can set the task start time, inspection cycle, and inspection interval. The edge computing unit (3) is deployed at the field edge node. It performs threshold judgment on the collected terminal temperature data in real time, calls the dynamic compensation algorithm to calculate the temperature offset, and extracts the highest temperature of the temperature anomaly point to generate a primary alarm signal. The transmission actuator (4) is equipped with an infrared imaging device with a field of view ≥120°, whose infrared detector and optical lens support close-range imaging at 30-50cm. Infrared imaging unit (5) is used to enable the transmission actuator (4) to perform adjustable scanning in the vertical direction at a speed of 5–20 mm / s, with the motion stroke covering the entire height of the cabinet, positioning accuracy ≤0.1 mm, and a limit sensor is set to prevent overtravel. Image acquisition and transmission unit (6) is used to synchronously acquire terminal temperature distribution images according to a preset trajectory and transmit the data to the edge computing node in real time via a high-speed bus; Temperature image analysis unit (7) is used to perform grayscale processing on the acquired image, restore the actual temperature of each pixel according to the pixel-temperature mapping relationship, and perform pixel-to-specific terminal number mapping through the spatial mapping of the image coordinate system and the terminal digital model. The temperature anomaly alarm module (8) is used to calculate the average temperature of the marked abnormal terminal area using a 5×5 pixel matrix. When the temperature exceeds the upper limit threshold of the terminal, a first-level alarm is triggered, and the heat diffusion path and gradient change analysis are performed. If the gradient change rate is >0.8 ℃ / pixel and lasts for 3 frames, a second-level alarm is generated, and the threshold is dynamically corrected and reviewed synchronously. The alarm data integration module (9) is used to upload the alarm data generated by the temperature abnormality alarm module (8) to the central monitoring system through a secure channel, and associate it with the equipment ledger information to automatically generate a detection report. It supports historical data backtracking, alarm statistics and multi-dimensional report output.

10. The substation secondary equipment detection system based on infrared images according to claim 9, characterized in that, The terminal modeling unit (1) is interconnected with the task scheduling unit (2), the transmission execution unit (4) and the image acquisition and transmission unit (6) via a wireless network. The transmission execution unit (4) is interconnected with the infrared imaging unit (5) via an electrical medium. The task scheduling unit (2) is interconnected with the edge computing unit (3) via a wireless network. The temperature image analysis unit (7) is interconnected with the image acquisition and transmission unit (6) and the temperature abnormality alarm module (8) via a wireless network. The temperature abnormality alarm module (8) is interconnected with the alarm data integration module (9) via a wireless network.

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