CN116839682B - Cable processing and manufacturing real-time monitoring system based on Internet of things - Google Patents
Cable processing and manufacturing real-time monitoring system based on Internet of things Download PDFInfo
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Abstract
The invention discloses a real-time monitoring system for cable processing and manufacturing based on the Internet of things, which relates to the technical field of cable processing and monitoring. Meanwhile, the system can automatically process abnormal conditions, such as shutdown and environment adjustment, promote the optimization of the production process, reduce energy consumption and realize a more environment-friendly production mode.
Description
Technical Field
The invention relates to the technical field of cable processing monitoring, in particular to a real-time monitoring system for cable processing and manufacturing based on the Internet of things.
Background
The internet of things creates a highly interconnected environment by connecting physical devices, sensors and networks, thereby bringing many benefits to various fields. Cable manufacture is an important part of the modern industry, requiring products that maintain high quality and stability during manufacture. However, cable manufacture involves a number of complex process steps, such as insulation, braiding, extrusion, etc., each of which may be affected by environmental factors, thereby affecting the performance of the final product. In particular, during the cable insulation process, environmental parameters are an important regulatory factor, which may have a significant impact on the product quality. The application based on the Internet of things can improve user experience.
The moisture of the insulation material can have a significant effect on the performance of the insulation layer during the cable insulation process. Humidity can lead to reduced performance of the insulation material, affecting the electrical performance and durability of the cable. The high humidity environment may lead to a decrease in the dielectric strength of the insulating material, increasing the risk of breakdown of the cable during use. On the other hand, a low humidity environment may cause the insulating material to become fragile, affecting the flexibility and durability of the cable. In addition, in the processing process, the cable may be non-uniformly woven due to non-uniformity of tension and pressure.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a cable processing and manufacturing real-time monitoring system based on the Internet of things, which can timely detect temperature anomalies such as temperature, humidity, tension, pressure value and vibration value applied to a cable and humidity difference before and after cable insulation by collecting sensor data and image information in real time, can timely detect and evaluate problems such as abnormal temperature, thermal crack distribution, density gap change, environmental influence and the like, effectively prevent potential risks, and the analysis result of an evaluation module provides basis for decision making, helps to determine priority and take proper measures, thereby reducing quality problems of defective products and uneven weaving in production and improving the product qualification rate. Meanwhile, the system can automatically process abnormal conditions, such as shutdown, environment adjustment and the like, and further improves the production efficiency. Through continuous monitoring and timely intervention, the system can also optimize the production process, reduce energy consumption and realize a more environment-friendly production mode.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the cable processing and manufacturing real-time monitoring system based on the Internet of things comprises an Internet of things acquisition unit, an identification and calculation unit, an evaluation unit and an early warning unit;
The Internet of things acquisition unit is used for acquiring Internet of things data of a sensor group deployed in the cable processing process in real time and transmitting the Internet of things data to the cloud platform through Internet of things communication equipment; the Internet of things acquisition unit comprises an infrared imaging acquisition unit and a sensor unit;
the infrared imaging acquisition unit is used for acquiring infrared imaging of the surface of the cable in real time through the infrared thermal imaging visual sensor in the cable processing process to obtain a multi-frame image A, and acquiring a ray imaging diagram of the interior of the cable through the X-ray visual sensor to obtain a multi-frame image B; the sensor unit is used for monitoring environmental impact data in the cable processing process in real time; the environmental impact data includes temperature, humidity, tension, pressure value and vibration value applied to the cable;
the recognition computing unit is used for receiving the multi-frame image and the environmental impact data, establishing a recognition model, extracting the local temperature abnormal characteristics and the surface thermal crack distribution characteristics in the multi-frame image A, extracting the gap characteristics in the multi-frame image B, and obtaining the recognition model through calculation: an abnormal temperature coefficient YCw, a thermal crack distribution coefficient RLw, and a density gap abnormal coefficient MDyc; and the environmental impact data are identified and analyzed, and the environmental impact data are obtained by calculation: an environmental impact coefficient HJ; the environmental impact coefficient HJ is obtained by calculation according to the following formula:
Wherein R is represented as a temperature and humidity influence factor,representing the value of the temperature in real time,representing a real-time humidity value before insulation in the cable processing process;indicating the real-time humidity value in the insulation during the cable processing,、andrespectively represent、Andand (2) weight value of (2)，，And is also provided with；
When (when)Obtaining the humidity difference before and during the insulation step of the real-time cable processing,indicating a humidity difference threshold whenWhen the humidity difference before and during the insulation step of the real-time cable processing is greater than the difference thresholdIt means that the insulation material absorbs too much moisture, which results in affecting the insulation performance,a weight coefficient that affects insulation performance;
expressed as a tension value of the insulation material in the cable insulation production process,the pressure value is expressed as the pressure value of the insulating material braiding or stranded wire in the cable insulation production process;expressed as tension valueAnd pressure valueWeight value of (2); c represents a correction constant;
the evaluation unit is used for comparing the abnormal temperature coefficient YCw, the thermal cracking distribution coefficient RLw, the density gap abnormal coefficient MDyc and the environment influence coefficient HJ with an abnormal temperature threshold value Q1, a thermal cracking threshold value Q2, a density gap threshold value Q3 and an environment influence threshold value Q4 respectively to obtain an evaluation result;
the early warning unit is used for carrying out corresponding early warning and processing according to the evaluation result.
Preferably, the internet of things acquisition unit comprises a deployment unit, the deployment unit is used for installing various sensors on nodes and equipment of the cable processing winding area and the insulation area, and wireless communication technology is adopted, and the wireless communication technology comprises Wi-Fi, bluetooth, loRa or NB-IoT internet of things communication equipment is transmitted to the cloud platform.
Preferably, the sensor unit is used for monitoring environmental impact data in the cable processing process in real time; the environmental impact data passes through a temperature sensor, a humidity sensor, a tension sensor, a pressure sensor and a vibration sensor;
the temperature sensor is used for being deployed in the cable insulation area and collecting temperature data;
the humidity sensor is used for respectively installing the humidity sensor in the area to be insulated and the insulating area so as to monitor and acquire the real-time humidity value before insulation in the cable processing processAnd real-time humidity value in insulation during cable processing；
The tension sensor is used for installing the tension sensor in the cable insulation area and testing the tension value based on the strain principle;
the pressure sensor is used for installing a piezoresistive sensor in the cable insulation area to calculate the pressure value applied to the cable;
the vibration sensor is used for monitoring vibration conditions in the cable insulation process and obtaining vibration values.
Preferably, the identification computing unit comprises an image preprocessing unit, a local temperature anomaly extraction unit, a surface thermal crack distribution characteristic extraction unit and an anomaly temperature coefficient YCw computing unit;
the image preprocessing unit is used for preprocessing the multi-frame image A and the multi-frame image B, and comprises denoising, enhancement and correction;
the local temperature anomaly extraction unit uses an image processing technology to extract local temperature anomaly characteristics in a multi-frame image A; marking out the region with abnormal temperature in the image by adopting a threshold segmentation and edge detection method;
the abnormal temperature coefficient YCw calculating unit sets I (x, y) as the temperature of the data points in the image a, wherein (x, y) represents the pixel coordinates in the image; to extract the local temperature anomaly characteristic, the anomaly temperature coefficient YCw is calculated using the following formula:
wherein Σ represents summing all pixels of the image a, μ represents the average temperature of the image a, σ represents the standard deviation of the temperature of the image a; the abnormal temperature coefficient YCw represents the degree of deviation of the temperature of each pixel point in the image from the average temperature.
Preferably, the identification calculation unit further includes the surface thermal crack distribution feature extraction unit and a thermal crack distribution coefficient RLw calculation unit;
The surface hot crack distribution feature extraction unit is used for extracting surface hot crack distribution features in the multi-frame image A by using an image processing technology; adopting texture analysis and morphological operation methods to identify crack shapes and distribution in the image;
the thermal crack distribution coefficient RLw calculating unit is configured to set k (x, y) as an image a thermal crack characteristic, that is, a single-channel gray scale image, according to the extracted surface thermal crack distribution characteristic, and calculate and obtain the thermal crack distribution coefficient RLw by adopting the following formula to extract the surface thermal crack distribution characteristic:
where Σ represents summing all thermal cracking pixels of image a, normalizing the sum to obtain thermal cracking distribution coefficients RLw, ∇ ≡2 represents the laplacian of image a; the thermal crack distribution coefficient RLw represents the quadratic square of the gray value change rate of each pixel in the image.
Preferably, the identification calculation unit further comprises a density gap feature extraction unit and a density gap anomaly coefficient MDyc calculation unit;
the density gap feature extraction unit uses edge detection and straight line detection methods to determine the positions of the density gaps on the inner and outer boundaries of the cable in the multi-frame image B, detects the straight line features of the inner structure of the cable, including the positions of conductors, and marks the density change gap features;
The density gap abnormal coefficient MDyc calculating unit is used for setting O (x, y) as an image B density gap single-channel gray level image according to density change gap characteristics, and for extracting the density gap abnormal characteristics in the cable, calculating and obtaining the density gap abnormal coefficient MDyc by adopting the following formula:
where, Σ represents summing all pixels of image B,representing the average density or thickness of the image B,standard deviation of the density gap value of the image B; the density gap anomaly coefficient MDyc represents the degree of deviation of the density of each pixel point in the image from the average density.
Preferably, the evaluation unit comprises an abnormal temperature evaluation module and a thermal cracking evaluation module;
the abnormal temperature evaluation module is used for comparing the abnormal temperature coefficient YCw with an abnormal temperature threshold Q1;
if the abnormal temperature coefficient YCw is smaller than the abnormal temperature threshold Q1, the abnormal temperature coefficient YCw is in a safety range, and the evaluation result is normal; if the abnormal temperature coefficient YCw is more than or equal to the abnormal temperature threshold Q1, the evaluation result is abnormal, the evaluation result is marked and the abnormal cable temperature position is positioned, and a first evaluation result is generated and sent to the early warning unit;
the thermal cracking evaluation module is used for comparing the thermal cracking distribution coefficient RLw with a thermal cracking threshold Q2;
If the thermal cracking distribution coefficient RLw is less than or equal to the thermal cracking threshold Q2, the evaluation result is normal or qualified;
if the thermal cracking distribution coefficient RLw is greater than the thermal cracking threshold Q2, the evaluation result is abnormal or unqualified; and marking and positioning the abnormal or unqualified evaluation result on the position of the abnormal hot crack cable, generating a second evaluation result and sending the second evaluation result to the early warning unit.
Preferably, the evaluation unit further comprises a density gap evaluation module and an environmental impact evaluation module;
the density gap evaluation module is used for comparing the density gap abnormal coefficient MDyc with a density gap threshold value Q3;
if the density gap anomaly coefficient MDyc < the density gap threshold value Q3: indicating that the density gap is normal, and the evaluation result is "normal" or "qualified";
if the density gap anomaly coefficient MDyc is greater than or equal to the density gap threshold value Q3: indicating that there is a density gap abnormality, the evaluation result is "abnormality" or "disqualification"; marking and positioning the abnormal or unqualified evaluation result on the cable position with abnormal density gap, and generating a third evaluation result to be sent to the early warning unit;
the environmental impact evaluation module is used for comparing the impact coefficient HJ with an environmental impact threshold Q4;
If the impact coefficient HJ is less than or equal to the environmental impact threshold Q4: indicating that the environmental impact is within an acceptable range, the evaluation result is normal;
if the impact coefficient HJ > the environmental impact threshold Q4: and indicating that the environment abnormality exists, marking and positioning the evaluation result of the environment abnormality in the cable environment area, generating a fourth evaluation result and sending the fourth evaluation result to the early warning unit.
Preferably, the early warning unit comprises a priority module, an early warning module and an automatic processing module;
the priority module is used for carrying out second analysis on the first evaluation result, the second evaluation result, the third evaluation result and the fourth evaluation result, analyzing and obtaining an early warning level, and setting a first priority command, a second priority command, a third priority command and a fourth priority command aiming at the abnormality which seriously affects the production safety, the quality and the environment;
the early warning module is used for generating corresponding notification and report for the abnormality according to each priority, wherein the notification and report comprises abnormality description, position and time information; for helping the relevant personnel to know the situation and take appropriate action;
the automatic processing module is used for performing automatic processing measures according to the early warning levels of the first priority order, the second priority order, the third priority order and the fourth priority order, including shutdown, environment adjustment and automatic restoration.
Preferably, the early warning unit further comprises an automatic generation report module, and the automatic generation report module is used for automatically generating a report according to the first evaluation result, the second evaluation result, the third evaluation result, the fourth evaluation result, the first priority command, the second priority command, the third priority command and the fourth priority command, and sorting the monitoring result, the abnormal situation and the early warning record into reports for reference by the management layer.
(III) beneficial effects
The invention provides a cable processing and manufacturing real-time monitoring system based on the Internet of things. The beneficial effects are as follows:
(1) By monitoring various data in the cable processing and manufacturing process in real time, such as temperature, humidity, tension, pressure value and vibration value applied to the cable, the humidity difference value before and after cable insulation, and the system can timely detect and evaluate abnormal temperature, thermal crack distribution, density gap, environmental influence and other conditions. The method is favorable for finding potential safety hazards early, and corresponding early warning and treatment measures are adopted, so that the safety of the production process is improved.
(2) According to the cable processing and manufacturing real-time monitoring system based on the Internet of things, various parameters and characteristics in the cable processing process can be accurately acquired through infrared imaging, X-ray images, image processing and sensor data. By evaluating the abnormal temperature, the thermal crack distribution and the density gap, the system can discover the quality problem in advance, thereby reducing the generation of defective products and improving the qualification rate and quality of the products.
(3) The cable processing and manufacturing real-time monitoring system based on the Internet of things can analyze the influence degree of the environment by monitoring environmental influence data including factors such as temperature, humidity and the like in real time, and further adopts corresponding measures to adjust, so that the energy consumption and the environmental influence are reduced, and the aims of energy conservation and emission reduction are fulfilled.
(4) According to the cable processing and manufacturing real-time monitoring system based on the Internet of things, through the comprehensive evaluation module, the system can generate early warning and processing commands according to different priorities. The commands can automatically trigger corresponding actions, such as stopping, environment adjustment and automatic restoration, so that intelligent decision making and automatic processing are realized, and the efficiency and benefit of the production process are improved.
Drawings
Fig. 1 is a block diagram and flow diagram of a cable processing and manufacturing real-time monitoring system based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Cable manufacture is an important part of the modern industry, requiring products that maintain high quality and stability during manufacture. However, cable manufacture involves a number of complex process steps, such as insulation, braiding, extrusion, etc., each of which may be affected by environmental factors, thereby affecting the performance of the final product. In particular, during the cable insulation process, environmental parameters are an important regulatory factor, which may have a significant impact on the product quality.
The moisture of the insulation material can have a significant effect on the performance of the insulation layer during the cable insulation process. Humidity can lead to reduced performance of the insulation material, affecting the electrical performance and durability of the cable. The high humidity environment may lead to a decrease in the dielectric strength of the insulating material, increasing the risk of breakdown of the cable during use. On the other hand, a low humidity environment may cause the insulating material to become fragile, affecting the flexibility and durability of the cable. In addition, in the processing process, the cable may be non-uniformly woven due to non-uniformity of tension and pressure.
Example 1
The invention provides a cable processing and manufacturing real-time monitoring system based on the Internet of things, referring to fig. 1, which comprises an Internet of things acquisition unit, an identification and calculation unit, an evaluation unit and an early warning unit;
The Internet of things acquisition unit is used for acquiring Internet of things data of a sensor group deployed in the cable processing process in real time and transmitting the Internet of things data to the cloud platform through Internet of things communication equipment; the Internet of things acquisition unit comprises an infrared imaging acquisition unit and a sensor unit;
the infrared imaging acquisition unit is used for acquiring infrared imaging of the surface of the cable in real time through the infrared thermal imaging visual sensor in the cable processing process to obtain a multi-frame image A, and acquiring a ray imaging diagram of the interior of the cable through the X-ray visual sensor to obtain a multi-frame image B; the sensor unit is used for monitoring environmental impact data in the cable processing process in real time; the environmental impact data includes temperature, humidity, tension, pressure value and vibration value applied to the cable;
the recognition computing unit is used for receiving the multi-frame image and the environmental impact data, establishing a recognition model, extracting the local temperature abnormal characteristics and the surface thermal crack distribution characteristics in the multi-frame image A, extracting the gap characteristics in the multi-frame image B, and obtaining the recognition model through calculation: an abnormal temperature coefficient YCw, a thermal crack distribution coefficient RLw, and a density gap abnormal coefficient MDyc; and the environmental impact data are identified and analyzed, and the environmental impact data are obtained by calculation: an environmental impact coefficient HJ; the environmental impact coefficient HJ is obtained by calculation according to the following formula:
Wherein R is represented as a temperature and humidity influence factor,representing the value of the temperature in real time,representing a real-time humidity value before insulation in the cable processing process;indicating the real-time humidity value in the insulation during the cable processing,、andrespectively represent、Andand (2) weight value of (2)，，And is also provided with；
When (when)Obtaining the humidity difference before and during the insulation step of the real-time cable processing,indicating a humidity difference threshold whenWhen the humidity difference before and during the insulation step of the real-time cable processing is greater than the difference thresholdIt means that the insulation material absorbs too much moisture, which results in affecting the insulation performance,a weight coefficient that affects insulation performance;
the meaning of the formula is that describing the effect of humidity on the properties of the insulating material, a high humidity environment may reduce the dielectric strength of the insulating material, increase the risk of breakdown of the cable, and a low humidity environment may weaken the insulating material, thereby affecting the flexibility and durability of the cable. A series of parameters and formulas are introduced, for example, R represents a temperature and humidity influence factor,Andrepresenting humidity values at different stages,A humidity difference threshold value or the like is represented in order to quantitatively evaluate the influence of humidity on the insulation performance.
Expressed as tension value of insulating material in cable insulation production process ，The pressure value is expressed as the pressure value of the insulating material braiding or stranded wire in the cable insulation production process;expressed as tension valueAnd pressure valueWeight value of (2); c represents a correction constant; it is mentioned that uneven braiding of the cable may be caused by uneven distribution of tension and pressure during processing, further highlighting the importance of the processing environment.
The evaluation unit is used for comparing the abnormal temperature coefficient YCw, the thermal cracking distribution coefficient RLw, the density gap abnormal coefficient MDyc and the environment influence coefficient HJ with an abnormal temperature threshold value Q1, a thermal cracking threshold value Q2, a density gap threshold value Q3 and an environment influence threshold value Q4 respectively to obtain an evaluation result;
the early warning unit is used for carrying out corresponding early warning and processing according to the evaluation result.
In this embodiment, the recognition computing unit receives the multi-frame image and the environmental impact data, and establishes the recognition model. The method extracts local temperature abnormal characteristics and surface thermal crack distribution characteristics from a multi-frame image A, and simultaneously extracts gap characteristics from a multi-frame image B. By calculation, an abnormal temperature coefficient YCw, a thermal crack distribution coefficient RLw, and a density gap abnormal coefficient MDyc are obtained. In addition, the environmental impact data are identified and analyzed, and the environmental impact coefficient HJ is obtained through calculation, wherein the calculation of the HJ relates to weights such as temperature and humidity impact factors R. The evaluation unit compares the abnormal temperature coefficient YCw, the thermal crack distribution coefficient RLw, the density gap abnormal coefficient MDyc, and the environmental impact coefficient HJ with the preset thresholds Q1, Q2, Q3, and Q4 to obtain an evaluation result. By contrast, the evaluation unit can determine whether an abnormal situation exists.
Example 2
The embodiment is explained in embodiment 1, referring to fig. 1 specifically, the internet of things collection unit includes a deployment unit, where the deployment unit is configured to install various sensors on nodes and devices in a cable processing winding area and an insulating area, and transmit, to a cloud platform, a wireless communication technology including Wi-Fi, bluetooth, loRa or NB-IoT internet of things communication devices.
In this embodiment, the unit is described as transmitting sensor data to the cloud platform using wireless communication technology, such as Wi-Fi, bluetooth, loRa, or NB-IoT, etc. The application of the wireless communication can eliminate the wiring requirement and improve the convenience and the flexibility of data transmission. The installation mode of the deployment unit and the wireless communication technology used enable various sensors to transmit data from different positions and links to the cloud platform in real time. The comprehensive monitoring realizes the monitoring of a plurality of parameters in the cable processing process, and further enhances the controllability and consistency of the production quality.
Example 3
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the sensor unit is used to monitor environmental impact data in the cable processing process in real time; the environmental impact data passes through a temperature sensor, a humidity sensor, a tension sensor, a pressure sensor and a vibration sensor;
The temperature sensor is used for being deployed in the cable insulation area and collecting temperature data;
the humidity sensor is used for respectively installing the humidity sensor in the area to be insulated and the insulating area so as to monitor and acquire the real-time humidity value before insulation in the cable processing processAnd real-time humidity value in insulation during cable processing；
The tension sensor is used for installing the tension sensor in the cable insulation area and testing the tension value based on the strain principle;
the pressure sensor is used for installing a piezoresistive sensor in the cable insulation area to calculate the pressure value applied to the cable;
the vibration sensor is used for monitoring vibration conditions in the cable insulation process and obtaining vibration values.
In the embodiment, the sensor unit plays an important role in monitoring environmental impact data in the cable processing process in real time, and details the use and configuration positions of various sensors, so that key parameters in the cable manufacturing process are comprehensively monitored and controlled, and the product quality and stability are ensured.
Example 4
This embodiment is an explanation made in embodiment 1, referring to fig. 1, specifically, the recognition calculation unit includes an image preprocessing unit, a local temperature anomaly extraction unit, a thermal crack distribution feature extraction unit, and an anomaly temperature coefficient YCw calculation unit;
The image preprocessing unit is used for preprocessing the multi-frame image A and the multi-frame image B, and comprises denoising, enhancement and correction; this helps to improve image quality, reduce noise interference, and make subsequent image analysis more accurate and reliable.
The local temperature anomaly extraction unit uses an image processing technology to extract local temperature anomaly characteristics in a multi-frame image A; marking out the region with abnormal temperature in the image by adopting a threshold segmentation and edge detection method;
the abnormal temperature coefficient YCw calculating unit sets I (x, y) as the temperature of the data points in the image a, wherein (x, y) represents the pixel coordinates in the image; to extract the local temperature anomaly characteristic, the anomaly temperature coefficient YCw is calculated using the following formula:
wherein Σ represents summing all pixels of the image a, μ represents the average temperature of the image a, σ represents the temperature standard deviation of the image a; the abnormal temperature coefficient YCw represents the degree of deviation of the temperature of each pixel point in the image from the average temperature.
In this embodiment, the abnormal temperature coefficient YCw is calculated by comparing the difference between the pixel temperature and the average temperature, so as to quantify the abnormal temperature condition of each pixel point in the image. The abnormal temperature condition in the image can be effectively identified and analyzed, so that beneficial information and data are provided for subsequent evaluation and early warning.
Example 5
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the identification calculating unit further includes the surface thermal crack distribution feature extracting unit and the thermal crack distribution coefficient RLw calculating unit;
the surface hot crack distribution feature extraction unit is used for extracting surface hot crack distribution features in the multi-frame image A by using an image processing technology; adopting texture analysis and morphological operation methods to identify crack shapes and distribution in the image;
the thermal crack distribution coefficient RLw calculating unit is configured to set k (x, y) as an image a thermal crack characteristic, that is, a single-channel gray scale image, according to the extracted surface thermal crack distribution characteristic, and calculate and obtain the thermal crack distribution coefficient RLw by adopting the following formula to extract the surface thermal crack distribution characteristic:
where Σ represents summing all thermal cracking pixels of image a, normalizing the sum to obtain thermal cracking distribution coefficients RLw, ∇ ≡2 represents the laplacian of image a; the thermal crack distribution coefficient RLw represents the quadratic square of the gray value change rate of each pixel in the image.
In this embodiment, the surface thermal crack distribution feature extraction unit uses an image processing technology, and adopts a texture analysis and morphological operation method to extract the distribution feature of the surface thermal crack from the multi-frame image a. This capability enables the system to capture the shape and distribution of cracks in the image, providing the necessary input for subsequent thermal crack distribution coefficient calculations. The thermal crack distribution coefficient RLw calculating unit calculates a thermal crack distribution coefficient RLw by using a formula based on the extracted surface thermal crack distribution characteristics. The coefficient is obtained by carrying out quadratic summation on the gray value change rate of all hot crack pixels in the image and carrying out normalization processing, so as to obtain the quantization result of the surface hot crack distribution. This helps to analyze and evaluate the extent of thermal crack distribution in the image.
Example 6
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the identification calculating unit further includes a density gap feature extracting unit and a density gap anomaly coefficient MDyc calculating unit;
the density gap feature extraction unit uses edge detection and straight line detection methods to determine the positions of the density gaps on the inner and outer boundaries of the cable in the multi-frame image B, detects the straight line features of the inner structure of the cable, including the positions of conductors, and marks the density change gap features;
the density gap abnormal coefficient MDyc calculating unit is used for setting O (x, y) as an image B density gap value, namely a single-channel gray scale image, according to the density change gap characteristics, and for extracting the density gap abnormal characteristics in the cable, calculating and obtaining the density gap abnormal coefficient MDyc by adopting the following formula:
where, Σ represents summing all pixels of image B,representing the average density of the image B,standard deviation of the density gap value of the image B; the density gap anomaly coefficient MDyc represents the degree of deviation of the density of each pixel point in the image from the average density.
In this embodiment, the density gap anomaly coefficient MDyc calculation unit can perform quantization calculation on the deviation degree of the density of all pixels in the image with respect to the average density, and generate the density gap anomaly coefficient MDyc. This quantitative value can provide a quantitative assessment of the abnormal condition of the density gap, enabling operators and systems to more accurately understand the condition of the density gap. This enables the system to respond quickly to changes, detecting density gap problems in time, thereby preventing potential quality problems and safety risks.
Example 7
This embodiment is explained in embodiment 1, referring to fig. 1, specifically, the evaluation unit includes an abnormal temperature evaluation module and a thermal crack evaluation module;
the abnormal temperature evaluation module is used for comparing the abnormal temperature coefficient YCw with an abnormal temperature threshold Q1;
if the abnormal temperature coefficient YCw is smaller than the abnormal temperature threshold Q1, the abnormal temperature coefficient YCw is in a safety range, and the evaluation result is normal; if the abnormal temperature coefficient YCw is more than or equal to the abnormal temperature threshold Q1, the evaluation result is abnormal, the evaluation result is marked and the abnormal cable temperature position is positioned, and a first evaluation result is generated and sent to the early warning unit;
the thermal cracking evaluation module is used for comparing the thermal cracking distribution coefficient RLw with a thermal cracking threshold Q2;
if the thermal cracking distribution coefficient RLw is less than or equal to the thermal cracking threshold Q2, the evaluation result is "normal" or "qualified".
If the thermal cracking distribution coefficient RLw is greater than the thermal cracking threshold Q2, the evaluation result is abnormal or unqualified; and marking and positioning the abnormal or unqualified evaluation result on the position of the abnormal hot crack cable, generating a second evaluation result and sending the second evaluation result to the early warning unit.
The evaluation unit further comprises a density gap evaluation module and an environmental impact evaluation module;
the density gap evaluation module is used for comparing the density gap abnormal coefficient MDyc with a density gap threshold value Q3;
if the density gap anomaly coefficient MDyc < the density gap threshold value Q3: indicating that the density gap is normal, and the evaluation result is "normal" or "qualified";
if the density gap anomaly coefficient MDyc is greater than or equal to the density gap threshold value Q3: indicating that there is a density gap abnormality, the evaluation result is "abnormality" or "disqualification"; marking and positioning the abnormal or unqualified evaluation result on the cable position with abnormal density gap, and generating a third evaluation result to be sent to the early warning unit;
the environmental impact evaluation module is used for comparing the impact coefficient HJ with an environmental impact threshold Q4;
if the impact coefficient HJ is less than or equal to the environmental impact threshold Q4: indicating that the environmental impact is within an acceptable range, the evaluation result is normal;
if the impact coefficient HJ > the environmental impact threshold Q4: and indicating that the environment abnormality exists, marking and positioning the evaluation result of the environment abnormality in the cable environment area, generating a fourth evaluation result and sending the fourth evaluation result to the early warning unit.
In this embodiment, by comparing the abnormal temperature coefficient YCw with the abnormal temperature threshold Q1, it is possible to quickly determine whether the cable temperature is within the safety range, thereby avoiding degradation of the cable performance due to an excessive temperature. Comparing the thermal cracking distribution coefficient RLw to the thermal cracking threshold Q2 can identify whether there is a thermal cracking problem on the cable surface and whether it is outside of an acceptable range. By comparing the density gap abnormal coefficient MDyc with the density gap threshold value Q3, the density change gap condition in the cable can be accurately detected, and the quality of the cable internal structure is ensured. Comparing the impact coefficient HJ with the environmental impact threshold Q4 can determine whether the environment in the cable manufacturing process is within an acceptable range. The method can ensure the stability of environmental factors in the cable manufacturing process and prevent the cable quality from being influenced by environmental abnormality.
Example 8
In this embodiment, as explained in embodiment 1, referring to fig. 1, specifically, the early warning unit includes a priority module, an early warning module, and an automation processing module;
the priority module is used for carrying out second analysis on the first evaluation result, the second evaluation result, the third evaluation result and the fourth evaluation result, analyzing and obtaining an early warning level, and setting a first priority command, a second priority command, a third priority command and a fourth priority command aiming at the abnormality which seriously affects the production safety, the quality and the environment;
The early warning module is used for generating corresponding notification and report for the abnormality according to each priority, wherein the notification and report comprises abnormality description, position and time information; for helping the relevant personnel to know the situation and take appropriate action;
the automatic processing module is used for performing automatic processing measures according to the early warning levels of the first priority order, the second priority order, the third priority order and the fourth priority order, including shutdown, environment adjustment and automatic restoration.
In this embodiment, by performing the second analysis on the first evaluation result, the second evaluation result, the third evaluation result, and the fourth evaluation result, the system can more accurately determine the severity of the abnormal situation, and set a corresponding priority command for each abnormality. This facilitates more targeted actions in handling exceptions, thereby improving efficiency. For each priority anomaly, the early warning module can generate corresponding notifications and reports, including specific descriptions of anomalies, occurrence locations and time information. Thus, the related personnel can know the situation in time and take appropriate actions rapidly so as to prevent potential production safety, quality and environmental risks. Based on the early warning level, the automatic processing module can automatically take corresponding measures to cope with abnormal conditions. For example, for an anomaly determined to be of a first priority, the system may automatically trigger a shutdown measure to prevent further problem expansion. For different priorities, different measures may be taken, such as adjusting environmental parameters or automatic remediation. This helps to minimize production interruption, and to improve response speed and processing efficiency.
Example 9
In this embodiment, as explained in embodiment 1, referring to fig. 1, specifically, the early warning unit further includes an automatic generation report module, where the automatic generation report module is configured to automatically generate a report according to the first evaluation result, the second evaluation result, the third evaluation result, the fourth evaluation result, the first priority command, the second priority command, the third priority command, and the fourth priority command, and sort the monitoring result, the abnormal situation, and the early warning record into a report that can be referred by the management layer.
In the embodiment, the report is automatically created, and the tedious process of manually arranging and writing the report is omitted. This helps to improve the working efficiency and save time. By integrating the monitoring results, anomalies and pre-warning records into the report, the management layer can know the status and potential risk of the cable manufacturing process at a glance. The report provides a clear overview that helps make decisions quickly. The report records historical data of evaluation results, abnormal conditions and early warning commands, which is very useful for subsequent traceability, problem solving and improvement processes. Helping to achieve continued improvements in the process and quality management.
Let us assume that we have a real-time monitoring system for cable manufacturing, in which the parameters involved are as follows:
abnormal temperature threshold q1=50℃
Thermal cracking threshold q2=0.1
Density gap threshold q3=0.5
Environmental impact threshold q4=0.8
Assuming that the cable surface temperature acquired by infrared imaging is 55 ℃ during cable processing, the cable internal radiographic imaging acquired by X-ray imaging appears to be free of anomalies. The environmental impact data showed a temperature of 25 ℃, a humidity of 60%, a tension of 150N, a pressure value applied to the cable of 200N, and a vibration value of 0.05.
The calculation is performed according to the provided formula: calculating an abnormal temperature coefficient YCw: average temperature μ= (55+25)/2=40 ℃ standard deviation σ= v ((55-40)/(2+ (25-40)/(2) =21.21 abnormal temperature coefficient YCw = (55-40)/21.21≡0.71)
Thermal crack distribution coefficient RLw is calculated: assuming that the thermal cracking image is analyzed, the total number of thermal cracking pixels is 500, and the total number of image pixels is 5000. Thermal crack distribution coefficient RLw =500/5000=0.1
Calculating a density gap anomaly coefficient MDyc: assuming that the density gap image is analyzed, the total number of abnormal pixels in the density gap is 200, and the total number of pixels in the image is 4000. Density gap anomaly coefficient mdyc=200/4000=0.05
Calculating an environmental impact coefficient HJ: the temperature and humidity influence factor r=0.5 real-time temperature value, Real-time humidity value before insulation at =55℃=60% real-time humidity value in insulationAn environmental impact coefficient hj=0.5×55-0.33×60+60, with a weight value α=β=0.33
Evaluating according to the calculation result and the threshold value:
the abnormal temperature coefficient YCw < the abnormal temperature threshold Q1, and the evaluation result is normal.
The thermal cracking distribution coefficient RLw is smaller than the thermal cracking threshold Q2, and the evaluation result is normal.
The density gap anomaly coefficient MDyc is less than the density gap threshold value Q3, and the evaluation result is normal.
The environmental impact coefficient HJ is less than or equal to an environmental impact threshold Q4, and the evaluation result is normal.
As all the evaluation results are normal, the system can not trigger early warning or processing measures.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. Real-time monitoring system is made in cable processing based on thing networking, its characterized in that: the system comprises an Internet of things acquisition unit, an identification calculation unit, an evaluation unit and an early warning unit;
the Internet of things acquisition unit is used for acquiring Internet of things data of a sensor group deployed in the cable processing process in real time and transmitting the Internet of things data to the cloud platform through Internet of things communication equipment; the Internet of things acquisition unit comprises an infrared imaging acquisition unit and a sensor unit;
The infrared imaging acquisition unit is used for acquiring infrared imaging of the surface of the cable in real time through the infrared thermal imaging visual sensor in the cable processing process to obtain a multi-frame image A, and acquiring a ray imaging diagram of the interior of the cable through the X-ray visual sensor to obtain a multi-frame image B; the sensor unit is used for monitoring environmental impact data in the cable processing process in real time; the environmental impact data includes temperature, humidity, tension, pressure value and vibration value applied to the cable;
the recognition computing unit is used for receiving the multi-frame image and the environmental impact data, establishing a recognition model, extracting the local temperature abnormal characteristics and the surface thermal crack distribution characteristics in the multi-frame image A, extracting the gap characteristics in the multi-frame image B, and obtaining the recognition model through calculation: an abnormal temperature coefficient YCw, a thermal crack distribution coefficient RLw, and a density gap abnormal coefficient MDyc; and the environmental impact data are identified and analyzed, and the environmental impact data are obtained by calculation: an environmental impact coefficient HJ; the environmental impact coefficient HJ is obtained by calculation according to the following formula:
，
wherein R is represented as a temperature and humidity influence factor,representing real-time temperature values, ">Representing a real-time humidity value before insulation in the cable processing process; / >Indicating the real-time humidity value in the insulation during the cable processing,/->、/>And->Respectively indicate->、/>And->Weight value of (2), and->，/>，/>And is also provided with；
When (when)Obtaining the humidity difference before and during the insulation step of the real-time cable processing,/for>Represents the humidity difference threshold, when->Obtaining a difference in humidity before and during the insulation step of the real-time cable processing greater than a difference threshold>It means that the insulation material is too hygroscopic, resulting in affecting the insulation properties +.>A weight coefficient that affects insulation performance;
expressed as tension value of insulation material during the production of cable insulation +.>The pressure value is expressed as the pressure value of the insulating material braiding or stranded wire in the cable insulation production process; />Expressed as tension value +.>And pressure value->Weight value of (2); c represents a correction constant;
the identification computing unit comprises an image preprocessing unit, a local temperature anomaly extraction unit, a surface thermal crack distribution characteristic extraction unit and an anomaly temperature coefficient YCw computing unit;
the image preprocessing unit is used for preprocessing the multi-frame image A and the multi-frame image B, and comprises denoising, enhancement and correction;
the local temperature anomaly extraction unit uses an image processing technology to extract local temperature anomaly characteristics in a multi-frame image A; marking out the region with abnormal temperature in the image by adopting a threshold segmentation and edge detection method;
The abnormal temperature coefficient YCw calculating unit sets I (x, y) as the temperature of the data points in the image a, wherein (x, y) represents the pixel coordinates in the image; to extract the local temperature anomaly characteristic, the anomaly temperature coefficient YCw is calculated using the following formula:
，
wherein Σ represents summing all pixels of the image a, μ represents the average temperature of the image a, σ represents the standard deviation of the temperature of the image a; the abnormal temperature coefficient YCw represents the degree of deviation of the temperature of each pixel point in the image from the average temperature;
the identification and calculation unit further comprises the surface thermal crack distribution characteristic extraction unit and a thermal crack distribution coefficient RLw calculation unit;
the surface hot crack distribution feature extraction unit is used for extracting surface hot crack distribution features in the multi-frame image A by using an image processing technology; adopting texture analysis and morphological operation methods to identify crack shapes and distribution in the image;
the thermal crack distribution coefficient RLw calculating unit is configured to set k (x, y) as an image a thermal crack characteristic, that is, a single-channel gray scale image, according to the extracted surface thermal crack distribution characteristic, and calculate and obtain the thermal crack distribution coefficient RLw by adopting the following formula to extract the surface thermal crack distribution characteristic:
，
Where Σ represents summing all thermal cracking pixels of image a, normalizing the sum to obtain thermal cracking distribution coefficients RLw, ∇ ≡2 represents the laplacian of image a; the thermal crack distribution coefficient RLw represents the quadratic square of the gray value change rate of each pixel in the image;
the identification computing unit further comprises a density gap characteristic extracting unit and a density gap abnormal coefficient MDyc computing unit;
the density gap feature extraction unit uses edge detection and straight line detection methods to determine the positions of the density gaps on the inner and outer boundaries of the cable in the multi-frame image B, detects the straight line features of the inner structure of the cable, including the positions of conductors, and marks the density change gap features;
the density gap abnormal coefficient MDyc calculating unit is used for setting O (x, y) as an image B density gap value, namely a single-channel gray scale image, according to the density change gap characteristics, and for extracting the density gap abnormal characteristics in the cable, calculating and obtaining the density gap abnormal coefficient MDyc by adopting the following formula:
，
where, Σ represents summing all pixels of image B,representing the average density of image B +.>Standard deviation of the density gap value of the image B; the density gap anomaly coefficient MDyc represents the degree of deviation of the density of each pixel point in the image from the average density;
The evaluation unit is used for comparing the abnormal temperature coefficient YCw, the thermal cracking distribution coefficient RLw, the density gap abnormal coefficient MDyc and the environment influence coefficient HJ with an abnormal temperature threshold value Q1, a thermal cracking threshold value Q2, a density gap threshold value Q3 and an environment influence threshold value Q4 respectively to obtain an evaluation result;
the early warning unit is used for carrying out corresponding early warning and processing according to the evaluation result.
2. The real-time monitoring system for cable processing and manufacturing based on the internet of things of claim 1, wherein: the Internet of things acquisition unit comprises a deployment unit, wherein the deployment unit is used for installing various sensors on nodes and equipment of a cable processing winding area and an insulation area, and wireless communication technology is adopted, and the wireless communication technology comprises Wi-Fi, bluetooth, loRa or NB-IoT Internet of things communication equipment which is transmitted to a cloud platform.
3. The real-time monitoring system for cable processing and manufacturing based on the internet of things of claim 1, wherein: the sensor unit is used for monitoring environmental impact data in the cable processing process in real time; the environmental impact data passes through a temperature sensor, a humidity sensor, a tension sensor, a pressure sensor and a vibration sensor;
the temperature sensor is used for being deployed in the cable insulation area and collecting temperature data;
The humidity sensor is used for respectively installing the humidity sensor in the area to be insulated and the insulating area so as to monitor and acquire the real-time humidity value before insulation in the cable processing processAnd the real-time humidity value in the insulation during the cable processing>；
The tension sensor is used for installing the tension sensor in the cable insulation area and testing the tension value based on the strain principle;
the pressure sensor is used for installing a piezoresistive sensor in the cable insulation area to calculate the pressure value applied to the cable;
the vibration sensor is used for monitoring vibration conditions in the cable insulation process and obtaining vibration values.
4. The real-time monitoring system for cable processing and manufacturing based on the internet of things of claim 1, wherein: the evaluation unit comprises an abnormal temperature evaluation module and a hot crack evaluation module;
the abnormal temperature evaluation module is used for comparing the abnormal temperature coefficient YCw with an abnormal temperature threshold Q1;
if the abnormal temperature coefficient YCw is smaller than the abnormal temperature threshold Q1, the abnormal temperature coefficient YCw is in a safety range, and the evaluation result is normal; if the abnormal temperature coefficient YCw is more than or equal to the abnormal temperature threshold Q1, the evaluation result is abnormal, the evaluation result is marked and the abnormal cable temperature position is positioned, and a first evaluation result is generated and sent to the early warning unit;
The thermal cracking evaluation module is used for comparing the thermal cracking distribution coefficient RLw with a thermal cracking threshold Q2;
if the thermal cracking distribution coefficient RLw is less than or equal to the thermal cracking threshold Q2, the evaluation result is normal or qualified;
if the thermal cracking distribution coefficient RLw is greater than the thermal cracking threshold Q2, the evaluation result is abnormal or unqualified; and marking and positioning the abnormal or unqualified evaluation result on the position of the abnormal hot crack cable, generating a second evaluation result and sending the second evaluation result to the early warning unit.
5. The real-time monitoring system for cable processing and manufacturing based on the internet of things of claim 1, wherein: the evaluation unit further comprises a density gap evaluation module and an environmental impact evaluation module;
the density gap evaluation module is used for comparing the density gap abnormal coefficient MDyc with a density gap threshold value Q3;
if the density gap anomaly coefficient MDyc < the density gap threshold value Q3: indicating that the density gap is normal, and the evaluation result is "normal" or "qualified";
if the density gap anomaly coefficient MDyc is greater than or equal to the density gap threshold value Q3: indicating that there is a density gap abnormality, the evaluation result is "abnormality" or "disqualification"; marking and positioning the abnormal or unqualified evaluation result on the cable position with abnormal density gap, and generating a third evaluation result to be sent to the early warning unit;
The environmental impact evaluation module is used for comparing the impact coefficient HJ with an environmental impact threshold Q4;
if the impact coefficient HJ is less than or equal to the environmental impact threshold Q4: indicating that the environmental impact is within an acceptable range, the evaluation result is normal;
if the impact coefficient HJ > the environmental impact threshold Q4: and indicating that the environment abnormality exists, marking and positioning the evaluation result of the environment abnormality in the cable environment area, generating a fourth evaluation result and sending the fourth evaluation result to the early warning unit.
6. The real-time monitoring system for cable processing and manufacturing based on the internet of things of claim 5, wherein: the early warning unit comprises a priority module, an early warning module and an automatic processing module;
the priority module is used for carrying out second analysis on the first evaluation result, the second evaluation result, the third evaluation result and the fourth evaluation result, analyzing and obtaining an early warning level, and setting a first priority command, a second priority command, a third priority command and a fourth priority command aiming at the abnormality which seriously affects the production safety, the quality and the environment;
the early warning module is used for generating corresponding notification and report for the abnormality according to each priority, wherein the notification and report comprises abnormality description, position and time information; for helping the relevant personnel to know the situation and take appropriate action;
The automatic processing module is used for performing automatic processing measures according to the early warning levels of the first priority order, the second priority order, the third priority order and the fourth priority order, including shutdown, environment adjustment and automatic restoration.
7. The real-time monitoring system for cable processing and manufacturing based on the internet of things of claim 6, wherein: the early warning unit further comprises an automatic generation report module, wherein the automatic generation report module is used for automatically generating a report according to the first evaluation result, the second evaluation result, the third evaluation result, the fourth evaluation result, the first priority command, the second priority command, the third priority command and the fourth priority command, and sorting the monitoring result, the abnormal situation and the early warning record into reports which can be used for reference by a management layer.
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