Screw rust degree in-situ measurement method and system
The screw rust detection method, which combines infrared thermal imaging and machine learning algorithms, solves the accuracy and efficiency problems of screw rust detection in existing technologies, and realizes high-precision and high-efficiency screw rust measurement and remote monitoring.
Patent Information
- Application Number
- CN202511520993.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-23
- Publication Date
- 2026-01-23
AI Technical Summary
Current technologies for detecting screw corrosion rely on manual observation or sampling analysis, which is inaccurate, inefficient, and destructive, making it impossible to achieve efficient and accurate measurement of screw rust.
By combining infrared thermal imaging technology with machine learning algorithms, the system heats the screws and monitors the surface temperature distribution in real time to identify rusted areas. It then uploads the rust level via voice prompts and a cloud platform, enabling non-contact detection.
It achieves high-precision, high-speed measurement of screw rust, provides visualized data and remote early warning, and reduces maintenance costs and manual intervention.
Smart Images
Figure CN121385031A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of metal corrosion detection, and in particular to a screw rust degree in-situ measurement method and system. BACKGROUND
[0002] Screws are widely used in the connection of metal structures, and long-term exposure to harsh environments, such as high humidity, high salinity or high temperature, can cause corrosion. It is crucial to detect corroded screws in a timely manner in engineering practice.
[0003] The evaluation of screw in-situ corrosion mainly relies on manual observation or sampling analysis. Manual observation results are greatly influenced by personnel experience and have low precision. Sampling analysis requires the screw to be removed, which is destructive and time-consuming.
[0004] Therefore, the prior art urgently needs a method that can ensure high screw rust degree measurement accuracy while improving measurement efficiency. SUMMARY
[0005] Therefore, it is necessary to provide a screw rust degree in-situ measurement method and system to solve the above technical problems. The method ensures high screw rust degree measurement accuracy and high measurement efficiency.
[0006] The present application adopts the following technical solutions: The present application provides a screw rust degree in-situ measurement method, comprising: In response to a screw rust degree in-situ measurement instruction, heating the screw and monitoring the surface temperature of the screw in real time; Obtaining a screw surface temperature distribution image; different temperature levels in the screw surface temperature distribution image are marked with different colors; For each low-temperature region on the screw surface temperature distribution image, according to the surface temperature of the screw, calculating the temperature difference between the low-temperature region and its neighborhood, and determining the low-temperature region corresponding to the temperature difference greater than or equal to a preset temperature difference as the corrosion region of the screw; Determining the corrosion level of the corrosion region of the screw by a machine learning algorithm.
[0007] Preferably, the method further comprises: When the temperature of the screw is higher than or equal to a preset temperature threshold, stop heating the screw; When the temperature of the screw is lower than the preset temperature threshold, continue heating the screw.
[0008] Preferably, the method further comprises: Displaying the corrosion level of the corrosion region of the screw and voice prompting whether the screw needs to be replaced.
[0009] Preferably, the voice prompt whether the screw needs to be replaced, specifically comprising: When the rust level of the rusted area of the screw is greater than or equal to the preset rust level, the voice prompts to replace the screw.
[0010] Preferably, the method further comprises: When the distance between the screw and the cloud platform is less than the preset distance, the rust level of the screw is uploaded to the cloud platform through WiFi; When the distance between the screw and the cloud platform is greater than or equal to the preset distance, the rust level of the screw is uploaded to the cloud platform through 5G, and the position of the screw is located.
[0011] A screw rust degree in-situ measurement system, the system is used to realize the method of any one of claims 1-5, the system comprises a thermal imaging module, a temperature detection module, a microcontroller, a heating module, a display module, a voice module WiFi module and a 5G module; The thermal imaging module is used to obtain a screw surface temperature distribution image; different temperature levels in the screw surface temperature distribution image are marked as different colors; The temperature detection module is used to monitor the surface temperature of the screw in real time; The microcontroller is used to output a rust degree in-situ measurement instruction; for each low-temperature area on the screw surface temperature distribution image, according to the surface temperature of the screw, the temperature difference between the neighborhood of the low-temperature area and the low-temperature area is calculated, and when the temperature difference is greater than or equal to a preset temperature difference, the corresponding low-temperature area is determined as a rusted area of the screw; the rust level of the rusted area of the screw is determined by a machine learning algorithm; The heating module is used to heat the screw in response to the rust degree in-situ measurement instruction of the screw; The display module is used to display the rust level of the rusted area of the screw; The voice module is used to voice prompt whether the screw needs to be replaced; The WiFi module is used to upload the rust level of the screw to the cloud platform; The 5G module is used to upload the rust level of the screw to the cloud platform and locate the position of the screw.
[0012] Preferably, the 5G module and the cloud platform establish communication through a cellular network; the 5G module locates the position of the screw through the signal strength, time difference or angle of the base station signal.
[0013] Preferably, the heating module comprises a high-frequency heating module and a temperature feedback control system; the temperature feedback control system is used to ensure that the temperature is controlled during the heating of the screw.
[0014] Preferably, the system further comprises a power module for providing voltage for the microcontroller and the heating module.
[0015] The application provides a computer readable storage medium, which stores a computer program, and the computer program realizes the screw rust degree in-situ measurement method when executed by a processor.
[0016] The application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the screw rust degree in-situ measurement method when executing the program.
[0017] The application adopts the above at least one technical scheme to achieve the following beneficial effects. The method comprises the following steps: in response to a rust degree in-situ measurement instruction of a screw, the screw is heated, and the surface temperature of the screw is monitored in real time, the oxidation reaction of the rusted area is activated by heating the screw, the temperature difference between the rusted part and the non-rusted part is more obvious, and subsequent detection is facilitated; a screw surface temperature distribution image is acquired, the thermal imaging technology can directly display the temperature distribution of the screw surface, the rusted area will present abnormal temperature characteristics due to the difference in thermal conductivity, and a visual basis is provided for rust recognition; for each low-temperature area on the screw surface temperature distribution image, the temperature difference between the neighborhood of the low-temperature area and the low-temperature area is calculated according to the surface temperature of the screw, and the low-temperature area corresponding to the temperature difference greater than or equal to a preset temperature difference is determined as the rusted area of the screw; and the rust grade of the rusted area of the screw is determined through a machine learning algorithm. The method can guarantee the measurement accuracy and efficiency of the screw rust degree. BRIEF DESCRIPTION OF DRAWINGS
[0018] The accompanying drawings, which are included to provide a further understanding of the application, constitute a part of the application and illustrate the illustrative embodiments of the application and their description serve to explain the application, and do not constitute improper limitations on the application. In the drawings:
[0019] Figure 1 A screw rust degree in-situ measurement method flowchart is provided for the application; Figure 2 A screw rust degree in-situ measurement method flowchart is provided for the application; Figure 3 A screw rust degree in-situ measurement system schematic diagram is provided for the application; Figure 4 A computer device schematic diagram for realizing the screw rust degree in-situ measurement method is provided for the application. DETAILED DESCRIPTION
[0020] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0021] Devices such as desktop computers, servers, notebook computers, etc. that can execute the solutions of the present application. For the convenience of description, only the server is taken as the execution subject for description below.
[0022] The problems existing in the traditional in-situ measurement of screw rust degree are: (1) the artificial observation result is greatly influenced by the experience of personnel and is difficult to quantify; (2) sampling analysis needs to disassemble the screw, which is destructive and time-consuming; (3) the uploading of screw rust degree data cannot be realized.
[0023] There are differences in the radiation of the surface temperature of an object. All objects will emit infrared radiation at room temperature, and the intensity of these radiations is directly related to the surface temperature of the object. The infrared thermal imager generates a temperature distribution image by capturing the infrared radiation emitted by the surface of the object. In the temperature distribution image, the areas with higher temperature are displayed in red or yellow, and the areas with lower temperature are displayed in blue or purple. Due to the difference in thermal conductivity between metal and metal oxide (such as rust), the temperature distribution of the rusted part and the non-rusted part of the screw surface is different, and usually the thermal conductivity of the rusted area is poorer, showing higher thermal resistance and lower temperature response. By analyzing these temperature differences, the infrared thermal imaging method can effectively identify the rust degree and distribution of the screw surface.
[0024] The method provided by the present application is based on the low heat capacity of metal, and the temperature rises quickly after heating. The heat capacity of metal oxide is high, and the relative temperature rise is slow. In thermal imaging, the temperature of metal and metal oxide is different, and this difference can be captured by thermal imaging.
[0025] The method is based on the screw rust degree in-situ measurement system, realizes non-contact screw rust detection, solves the problems of low efficiency, large error and inconvenience of traditional detection methods. The scheme provided by the method can monitor the screw state in real time, provide visual data and rust grade analysis, early warning of potential risks, and avoid equipment failure or safety accidents. Through voice prompt and cloud platform data uploading, remote monitoring and predictive maintenance are realized, and maintenance cost is reduced.
[0026] The technical solutions provided by the embodiments of the present application will be described in detail below in combination with the drawings.
[0027] Figure 1 It is a flowchart of a screw rust degree in-situ measurement method in the present application, which specifically comprises the following steps: S101: In response to the rust degree in-situ measurement instruction of the screw, the screw is heated, and the surface temperature of the screw is monitored in real time.
[0028] In one exemplary embodiment, the method further comprises: when the temperature of the screw is higher than or equal to the preset temperature threshold, stopping heating the screw; when the temperature of the screw is lower than the preset temperature threshold, continuing to heat the screw.
[0029] Specifically, the preset temperature threshold is set according to specific engineering practice.
[0030] Specifically, the microcontroller outputs a PWM signal to control the heating module, and according to the heating requirement (the difference between the target temperature and the current temperature), the microcontroller controls the output power of the heating module through the PWM signal.
[0031] (1) Set the frequency of the PWM signal: set a fixed frequency (usually between 1 kHz and 10 kHz) to ensure signal stability and avoid excessive high-frequency noise interference.
[0032] (2) Set the duty cycle adjustment: control the working power of the heater by adjusting the ratio of high and low levels of the PWM signal.
[0033] (3) Control the heating intensity of the heater: a higher duty cycle (such as 70%) means that the heating module will be in working state for a longer time, and the heating intensity is higher, which is suitable for situations that require rapid heating. A lower duty cycle (such as 30%) means that the heating module is in working state for a shorter time, and the heating intensity is lower, which is suitable for fine-tuning the temperature or maintaining the temperature.
[0034] (4) Heating module response: after receiving the microcontroller module signal, the heating module adjusts the transmission amount of current according to the duty cycle adjustment, and then adjusts the heating intensity. In the case of low temperature or rapid heating, the duty cycle of the microcontroller module signal is high, the electric heater works for a long time, and the temperature rises rapidly.
[0035] (5) Constant temperature control: when the temperature of the screw reaches the predetermined value, the duty cycle of the microcontroller module gradually decreases to maintain a constant temperature. The microcontroller module will continue to adjust the duty cycle according to the feedback of the real-time temperature to ensure that the temperature fluctuates within the target range.
[0036] The temperature feedback and heating control adjustment steps include real-time feedback and control and heating stop.
[0037] (1) Real-time feedback and control: The temperature monitoring module constantly feeds back the surface temperature of the screw to the microcontroller module. The microcontroller module adjusts the duty cycle of the PWM signal in real-time according to these feedbacks to maintain the heating module in the best working state. If the temperature is close to the upper limit, the microcontroller module will automatically reduce the PWM duty cycle to reduce the output power of the heating module to prevent the screw from being damaged by overheating. If the temperature does not reach the set target, the microcontroller module will increase the PWM duty cycle to increase the heating power and speed up the heating process.
[0038] (2) Heating stop: When the temperature of the screw reaches the target value or the temperature control module detects that the temperature is too high, the microcontroller module will stop heating and the duty cycle of the PWM signal will be adjusted to 0% (i.e. the heating module is turned off). At this time, the system will continue to monitor the temperature, and if the temperature drops below the target value, the system will start heating again and adjust the PWM signal.
[0039] S102: Obtain a screw surface temperature distribution image; different temperature levels in the screw surface temperature distribution image are marked with different colors.
[0040] When the screw is heated, the data of the screw scanned by the thermal imaging module is executed, and a screw surface temperature distribution image is generated.
[0041] S103: For each low-temperature area on the screw surface temperature distribution image, calculate the temperature difference between the low-temperature area and its neighborhood according to the surface temperature of the screw, and determine the low-temperature area corresponding to the temperature difference greater than or equal to the preset temperature difference as the rust area of the screw.
[0042] Specifically, different temperature areas in the screw surface temperature distribution image are displayed in different colors, usually high-temperature areas are displayed in red or white, and low-temperature areas are displayed in blue or green. The sensor detects the temperature distribution. The temperature of a specific area on the surface of the screw is lower, and there is a larger temperature difference compared with the adjacent area. At this time, the temperature of the rust area may be 20°C or more lower than the surrounding non-rust area. The microcontroller module analyzes these temperature differences to identify the location of the rust area. The area with a larger temperature difference is marked as a rust area that needs attention and the degree of rust is calculated.
[0043] The rust level of the screw is divided into slight, moderate, severe rust, and the higher the rust level, the greater the edge blur and local temperature difference in the thermal image. The rust level is determined by machine learning method.
[0044] S104: Determine the rust level of the rust area of the screw by machine learning algorithm.
[0045] The surface temperature distribution image of the sample screw and the surface temperature of the sample screw are obtained as a training set, and a machine learning algorithm is used to predict the corrosion grade of the corrosion area of the screw. The machine learning algorithm is trained by the training set to minimize the error between the predicted value and the true value, and a trained machine learning algorithm is obtained.
[0046] The corrosion grade of the corrosion area of the screw is predicted by the trained machine learning algorithm.
[0047] In an exemplary embodiment, the corrosion grade of the corrosion area of the screw is displayed, and a voice prompt is given as to whether the screw needs to be replaced.
[0048] Specifically, the screw rust grade and heating state are displayed in real time by the OLED display module, such as heating, temperature reaching the target, etc. When the corrosion grade exceeds the threshold, the voice module will issue a prompt sound to remind the operator to take further measures.
[0049] In an exemplary embodiment, when the corrosion grade of the corrosion area of the screw is greater than or equal to a preset corrosion grade, a voice prompt is given to replace the screw.
[0050] Specifically, the voice module is used to prompt whether the screw needs to be replaced, which specifically includes: when the corrosion grade of the screw is greater than or equal to a preset corrosion grade, the voice module is used to prompt to replace the screw; and when the corrosion grade of the screw is less than the preset corrosion grade, the voice module is not used to prompt to replace the screw.
[0051] In an exemplary embodiment, when the distance between the screw and the cloud platform is less than a preset distance, the corrosion grade of the screw is uploaded to the cloud platform through WiFi; and when the distance between the screw and the cloud platform is greater than or equal to the preset distance, the corrosion grade of the screw is uploaded to the cloud platform through 5G, and the position of the screw is located.
[0052] Specifically, the WiFi module uses a low-power communication protocol, such as the MQTT protocol, to ensure real-time data upload for short-distance communication.
[0053] The 5G module communicates with the cloud platform through a cellular network, and the 5G module uses base station signals for positioning through base station signal strength, time difference, or angle positioning systems.
[0054] Specifically, data upload and device positioning are achieved by uploading screw rust data using the WiFi module or the 5G module, querying historical data through the cloud platform, etc. The 5G module uses base station signals for device positioning.
[0055] In an exemplary embodiment, the present application provides a screw rust in-situ measurement method flow chart as shown in Figure 2 , and a screw rust in-situ measurement device as shown in Figure 2As shown, based on the heating instruction of the microcontroller module, the screw is heated by the heating module, and the surface temperature of the screw is monitored in real time by the temperature detection module; the screw surface temperature distribution image is obtained by the thermal imaging module; the screw surface temperature distribution image output by the thermal imaging module is analyzed by the microcontroller module to identify the rust area of the screw and calculate the rust grade of the screw; it is judged whether the rust grade exceeds the threshold value, and the voice prompt is given to replace the screw when the threshold value is exceeded; the modification grade of the screw is uploaded in a short distance through the WiFi module; the rust grade of the screw is uploaded remotely and the system position is positioned through the 5G module. The system position refers to the position of the screw.
[0056] The present application provides a screw rust degree in-situ measurement system, which comprises a thermal imaging module, a temperature detection module, a microcontroller, a heating module, a display module, a voice module, a WiFi module and a 5G module; the thermal imaging module is used to obtain a screw surface temperature distribution image; different temperature grades in the screw surface temperature distribution image are marked with different colors; the temperature detection module is used to monitor the surface temperature of the screw in real time; the microcontroller is used to output a rust degree in-situ measurement instruction; for each low-temperature area on the screw surface temperature distribution image, the temperature difference between the low-temperature area and its neighborhood is calculated according to the surface temperature of the screw, and the low-temperature area corresponding to the temperature difference greater than or equal to a preset temperature difference is determined as the rust area of the screw; the rust grade of the rust area of the screw is determined by a machine learning algorithm; the heating module is used to heat the screw in response to the rust degree in-situ measurement instruction of the screw; the display module is used to display the rust grade of the rust area of the screw; the voice module is used to voice prompt whether the screw needs to be replaced; the WiFi module is used to upload the rust grade of the screw to the cloud platform; the 5G module is used to upload the rust grade of the screw to the cloud platform and locate the position of the screw.
[0057] In an exemplary embodiment, the 5G module establishes communication with the cloud platform through a cellular network; the 5G module locates the position of the screw through the signal strength, time difference or angle of the base station signal.
[0058] In an exemplary embodiment, the heating module comprises a high-frequency heating module and a temperature feedback control system; the temperature feedback control system is used to control the temperature during the heating process of the screw.
[0059] In an exemplary embodiment, the system further comprises a power module for providing voltage to the microcontroller and the heating module.
[0060] Specifically, the present application provides a screw rust degree in-situ measurement system as shown in Figure 3 the screw rust degree in-situ measurement system as shown in Figure 3As shown, the screw rust in-situ measurement system includes a thermal imaging module ⑤, a temperature detection module ④, a microcontroller module ②, a heating module ③, a display module ⑥, a voice module ⑦, a WiFi module ⑧, a 5G module ⑨, and a power module ①. Upon power-on, the screw in-situ measurement system initializes and performs self-tests on the thermal imaging module, temperature monitoring module, heating module, voice module, WiFi module, and 5G module.
[0061] The workflow of the in-situ screw rust measurement system includes: (1) Power module ① provides the corresponding voltage to microcontroller module ② and heating module ③ to ensure that the microcontroller module and heating module are heated normally.
[0062] (2) The microcontroller module ② communicates with the thermal imaging module ⑤ to obtain the original thermal imaging data of the screw and calculate the rust data of the screw. It sends a heating command to the heating module ③ and monitors the feedback of the temperature detection unit ④ in real time, and sends the processed rust data to the display module ⑥, WiFi module ⑧ and 5G module ⑨.
[0063] (3) Heating module ③ receives instructions from microcontroller module ② and works with temperature detection module ④ to accurately heat the screw.
[0064] (4) Temperature detection: Monitor the temperature of the screw. If the temperature is too high, stop heating; if the temperature is too low, continue heating to ensure that the screw heating process is stable and safe.
[0065] (5) The thermal imaging module ⑤ converts the surface temperature distribution of the object into a visual image and transmits the temperature data of the screw to the microcontroller module ②, providing a basis for subsequent data processing.
[0066] (6) The microcontroller module ② transmits the processed screw rust data to the display module ⑥, which will display the key screw data, including the screw diameter and rust level.
[0067] (7) Voice module ⑦ When the rust level of the screw exceeds the preset threshold, it prompts the staff to replace the screw.
[0068] (8) The WiFi module ⑧ uploads the screw rust data to the cloud platform over a short distance, supports remote data viewing and system monitoring, and realizes intelligent management.
[0069] (9) The 5G module uploads the data on the rust of the screws to the cloud platform over a long distance and determines the location of the system through the base station signal to realize the positioning of the equipment.
[0070] When applying the in-situ screw rust measurement method provided by this invention, it is not necessary to... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.
[0071] The specific limitations of the screw rust in-situ measurement device can refer to the limitations of the screw rust in-situ measurement method described above, which will not be repeated here. Each module in the screw rust in-situ measurement device described above can be implemented by software, hardware, and a combination thereof, in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor calls and executes the operations corresponding to each of the above modules.
[0072] The application also provides a computer readable storage medium storing a computer program, which can be used to execute the screw rust in-situ measurement method described above. Figure 1 The screw rust in-situ measurement method is provided.
[0073] The application also provides a computer readable storage medium storing a computer program, which can be used to execute the screw rust in-situ measurement method described above. Figure 4 The structure diagram of the computer device is shown in FIG. 1. Figure 4 As shown in FIG. 1, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and of course can also include other hardware required by the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the screw rust in-situ measurement method described above. Figure 1 The screw rust in-situ measurement method is provided.
[0074] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. In the embodiments provided by the application, any reference to the memory, storage, database, or other medium can include at least one of the non-volatile and volatile memories. The non-volatile memory can include a read-only memory (ROM), a tape, a floppy disk, a flash memory, or an optical memory. The volatile memory can include a random access memory (RAM) or an external cache memory. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0075] The technical features of the above embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered to be within the scope of the present application.
Claims
1. A method of measuring the degree of rust of a screw in situ, characterized by, The method comprises: in response to the rust in-situ measurement instruction of the screw, heating the screw and monitoring the surface temperature of the screw in real time; obtaining a screw surface temperature distribution image; different temperature levels in the screw surface temperature distribution image are marked with different colors; for each low-temperature region on the screw surface temperature distribution image, according to the surface temperature of the screw, calculating the temperature difference between the neighborhood of the low-temperature region and the low-temperature region, and determining the low-temperature region corresponding to the temperature difference greater than or equal to the preset temperature difference as the corrosion region of the screw; determining the corrosion level of the corrosion region of the screw through a machine learning algorithm.
2. The method of claim 1, wherein, The method further comprises: stopping heating the screw when the temperature of the screw is higher than or equal to the preset temperature threshold; continuing to heat the screw when the temperature of the screw is lower than the preset temperature threshold.
3. The method of claim 1, wherein, The method further comprises: displaying the corrosion level of the corrosion region of the screw and voice prompting whether the screw needs to be replaced.
4. The method of claim 3, wherein, The voice prompting whether the screw needs to be replaced specifically comprises: when the corrosion level of the corrosion region of the screw is greater than or equal to the preset corrosion level, voice prompting to replace the screw.
5. The method of claim 1, wherein, The method further comprises: when the distance between the screw and the cloud platform is less than the preset distance, uploading the corrosion level of the screw to the cloud platform through WiFi; when the distance between the screw and the cloud platform is greater than or equal to the preset distance, uploading the corrosion level of the screw to the cloud platform through 5G and positioning the position of the screw.
6. A system for in-situ measurement of screw thread galling, characterized by, The system is used to implement the method of any one of claims 1-5, and the system comprises a thermal imaging module, a temperature detection module, a microcontroller, a heating module, a display module, a voice module, a WiFi module and a 5G module; The thermal imaging module is used to obtain a screw surface temperature distribution image; different temperature levels in the screw surface temperature distribution image are marked with different colors; The temperature detection module is used to monitor the surface temperature of the screw in real time; The microcontroller is used to output a rust in-situ measurement instruction; for each low-temperature region on the screw surface temperature distribution image, according to the surface temperature of the screw, calculating the temperature difference between the neighborhood of the low-temperature region and the low-temperature region, and determining the low-temperature region corresponding to the temperature difference greater than or equal to the preset temperature difference as the corrosion region of the screw; determining the corrosion level of the corrosion region of the screw through a machine learning algorithm; The heating module is used to heat the screw in response to the rust in-situ measurement instruction of the screw; The display module is used to display the corrosion level of the corrosion region of the screw; The voice module is used to voice prompt whether the screw needs to be replaced; The WiFi module is used to upload the corrosion level of the screw to the cloud platform; The 5G module is used to upload the corrosion level of the screw to the cloud platform and position the position of the screw.
7. The system of claim 6, wherein, The 5G module and the cloud platform establish communication through a cellular network; the 5G module positions the position of the screw through the signal strength, time difference or angle of the base station signal.
8. The system of claim 6, wherein, The heating module comprises a high-frequency heating module and a temperature feedback control system; the temperature feedback control system is used to control the temperature during the heating process of the screw.
9. The system of claim 6, wherein, The system also includes a power module for providing voltage to the microcontroller and the heating module.