Electric heater detection system and method based on big data image information

Abstract

The invention belongs to the technical field of electric heater detection, and particularly relates to an electric heater detection system and method based on big data image information. According to the method, by integrating the infrared thermogram, the appearance image and the environment background information, misjudgment caused by single-dimensional data is avoided, the reliability of the detection result is improved, the change of the working state of the electric heater can be mastered in real time, abnormity can be rapidly recognized, the problem can be rapidly positioned, and the accuracy of the detection result is improved according to the multi-dimensional data. Interference of ambient temperature fluctuation on detection is reduced, objectivity and consistency of detection results are ensured, thermal performance of the electric heater is concerned, appearance thermal radiation abnormity is analyzed, and double qualification of the electric heater in appearance and function is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric heater detection, and in particular relates to an electric heater detection system and method based on big data image information. Background Art

[0002] With the rapid development of modern science and technology, big data technology has been widely used in various fields. In the field of heating equipment, electric heaters, as an efficient and convenient way of heating, have gradually been favored by the majority of users. However, the operating status and use effect of electric heaters are often affected by many factors, such as ambient temperature, equipment aging, power supply fluctuations, etc. Traditional detection methods mostly rely on manual inspections and simple sensor monitoring, which are not only inefficient, but also difficult to accurately judge the actual working status of electric heaters.

[0003] In recent years, detection technology based on big data image information has gradually emerged. This technology can realize real-time monitoring and abnormality detection of the working status of electric heaters by collecting and analyzing image information during the working process of electric heaters, combined with big data processing technology. Specifically, through equipment such as high-definition cameras or infrared thermal imagers, the temperature distribution, working status and other information on the surface of the electric heater can be captured in real time, and this information can be converted into digital image data. After preprocessing and feature extraction, these image data can be input into the big data processing platform for further analysis and mining. Big data processing technology can use advanced algorithms and models to efficiently process massive image data, thereby realizing real-time monitoring of the working status of electric heaters and abnormal warning.

[0004] However, the current electric heater detection method based on big data image information still faces some challenges. For example, traditional detection methods often rely on a single data source (such as temperature sensors, appearance inspections, etc.), which easily ignores the complex anomalies that may exist during the operation of the equipment, resulting in insufficient accuracy and comprehensiveness of the detection results. It is usually impossible to process multiple different types of data at the same time, and it is impossible to fully analyze the working status of the electric heater, and there are certain blind spots in judgment. Summary of the invention

[0005] The purpose of the present invention is to provide an electric heater detection method based on big data image information, which can overcome the limitations of traditional detection technology, improve the accuracy, real-time and reliability of electric heater fault diagnosis, and ensure that users obtain safer and more stable electric heater performance during use.

[0006] The technical solution adopted by the present invention is as follows: An electric heater detection method based on big data image information, comprising: Acquire first image data of the electric heater during rated operation, wherein the first image data includes a first infrared thermal image, first appearance image information, and first environmental background information; Determine whether the first infrared thermal image meets the first preset condition, if not, determine that the electric heater is working abnormally, and establish a detection period; Acquire second image data within the detection period, and acquire a temperature distribution trend value according to the second image data; Acquire environmental background thermal radiation map information according to the second image data, and acquire environmental thermal radiation value according to the environmental background thermal radiation map information; Acquire second appearance image information according to the second image data, and acquire appearance thermal radiation value according to the second appearance image information; Acquire first temperature distribution information according to the first infrared thermal image, and acquire first temperature distribution value according to the first temperature distribution characteristic information; A detection value is obtained according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value, and a corresponding detection result is obtained according to the detection value.

[0007] In a preferred embodiment, the step of determining whether the first infrared thermal image meets the first preset condition, and if not, determining that the electric heater is working abnormally, and constructing a detection period includes: Obtain standard infrared thermal images; Overlapping the standard infrared thermal imaging image with the first infrared thermal imaging image to obtain an overlapped infrared thermal imaging image; Construct a plane rectangular coordinate system in the coincident infrared thermal images; Acquire the coordinates of the inflection points of the contours of a plurality of standard temperature regions and the coordinates of the inflection points of the contours of a plurality of first temperature regions in the coincident infrared thermal imaging images according to a plane rectangular coordinate system; Acquire multiple deviation temperature regions and multiple deviation temperature region contour inflection point coordinates of each deviation temperature region according to multiple standard temperature region contour inflection point coordinates and multiple first temperature region contour inflection point coordinates; Obtaining the area of ​​the deviation temperature region according to the coordinates of the inflection points of the deviation temperature region contours of each deviation temperature region; Get the deviation temperature area threshold; Determine whether the area of ​​the deviation temperature region exceeds the deviation temperature region threshold; If the area of ​​the deviation temperature region exceeds the deviation temperature region threshold, it is determined that the electric heater is operating abnormally, and a detection period is established.

[0008] In a preferred embodiment, the step of constructing a detection period includes: Obtaining a duration table, wherein the duration table includes a plurality of deviation temperature region interval areas and a corresponding detection duration of each deviation temperature region interval area; Obtain the target deviation temperature region area according to the deviation temperature region area; Obtain the corresponding detection duration from the duration table according to the target deviation temperature zone interval area; Obtain the time node when the electric heater is judged to be working abnormally and mark it as the start time; Get the end time based on the start time and detection duration; Get the detection period based on the start time and end time.

[0009] In a preferred embodiment, the step of obtaining second image data within the detection period and obtaining the temperature distribution trend value according to the second image data includes: Acquiring second image data within the detection period; Acquire a plurality of second infrared thermal images according to the second image data; Acquire a corresponding second temperature distribution vector according to a plurality of second infrared thermal images; The corresponding temperature distribution trend values ​​are acquired according to the plurality of second temperature distribution vectors.

[0010] In a preferred embodiment, the step of obtaining the environmental background thermal radiation map information according to the second image data and obtaining the environmental thermal radiation value according to the environmental background thermal radiation map information includes: Get the detection duration, start time and end time of the detection period; Acquire environmental background thermal radiation map information according to the second image data; Acquire the starting area of ​​the environmental background heat radiation corresponding to the starting time of the detection period according to the environmental background heat radiation map information; Acquire the end area of ​​the environmental background heat radiation corresponding to the end time of the detection period according to the environmental background heat radiation map information; The ambient thermal radiation value is obtained according to the ambient background thermal radiation start graph, the ambient background thermal radiation end graph and the detection time.

[0011] In a preferred solution, the step of obtaining the second appearance image information according to the second image data and obtaining the appearance thermal radiation value according to the second appearance image information includes: Acquire second appearance image information according to the second image data; Acquire a plurality of corresponding second appearance vectors according to the second appearance image information; Obtaining standard appearance image information, and obtaining corresponding multiple standard appearance vectors according to the standard appearance image information; The appearance thermal radiation value is obtained according to the plurality of second appearance vectors and the plurality of standard appearance vectors.

[0012] In a preferred embodiment, the step of obtaining first temperature distribution information according to the first infrared thermal image and obtaining a first temperature distribution value according to the first temperature distribution characteristic information includes: Acquire multiple temperature range intervals of the first infrared thermal image; Constructing a plane rectangular coordinate system according to the first infrared thermal image; Acquire multiple temperature range interval contour inflection point coordinates of each temperature range interval according to a plane rectangular coordinate system, and mark them as first temperature distribution information; Get the compensation coefficient for each temperature range; The first temperature distribution value is obtained according to the compensation coefficient of each temperature range interval and the coordinates of a plurality of temperature range interval contour inflection points of each temperature range interval.

[0013] In a preferred embodiment, the step of obtaining a detection value according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value, and obtaining a corresponding detection result according to the detection value includes: Acquire a detection value according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value; Obtaining a test table, wherein the test table includes a plurality of test interval values ​​and a test result corresponding to each test interval value; Obtain a target detection interval value according to the detection value; Obtain the corresponding test result from the test table according to the target detection interval value.

[0014] The present invention also provides an electric heater detection system based on big data image information, which is used in the above-mentioned electric heater detection method based on big data image information, comprising: A rated working module, used for obtaining first image data during the rated working process of the electric heater, wherein the first image data includes a first infrared thermal image, first appearance image information and first environmental background information; A temperature judgment module, used to judge whether the first infrared thermal image meets the first preset condition, and if not, judge that the electric heater is working abnormally, and establish a detection period; A distribution trend module, used to obtain second image data within a detection period, and obtain a temperature distribution trend value according to the second image data; An environmental thermal radiation module, used to obtain environmental background thermal radiation map information according to the second image data, and obtain an environmental thermal radiation value according to the environmental background thermal radiation map information; An appearance thermal radiation module, used to obtain second appearance image information according to the second image data, and obtain an appearance thermal radiation value according to the second appearance image information; A temperature distribution module, used to obtain first temperature distribution information according to the first infrared thermal image, and obtain a first temperature distribution value according to the first temperature distribution characteristic information; The detection result module is used to obtain detection values ​​according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value and the appearance thermal radiation value, and obtain corresponding detection results according to the detection values.

[0015] And, an electric heater detection terminal based on big data image information, comprising: one or more processors; a storage device having one or more programs stored thereon; When one or more programs are executed by one or more processors, the one or more processors implement an electric heater detection method based on big data image information.

[0016] The technical effects achieved by the present invention are: The present invention avoids misjudgment caused by single-dimensional data by integrating infrared thermal images, appearance images and environmental background information, thereby improving the reliability of detection results. By detecting the temperature distribution trend value within the detection period, the changes in the working state of the electric heater can be grasped in real time, and the anomaly can be quickly identified and the problem can be located. According to multi-dimensional data, the interference of ambient temperature fluctuations on the detection is reduced, and the objectivity and consistency of the detection results are ensured. Not only the thermal performance of the electric heater is paid attention to, but also the abnormality of thermal radiation of the appearance is analyzed to ensure that the electric heater is qualified in both appearance and function. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method provided by the present invention; Figure 2 It is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0022] Please see attached Figure 1 As shown, a method for detecting electric heaters based on big data image information is provided, comprising: S1. Acquire first image data of the electric heater during rated operation, wherein the first image data includes a first infrared thermal image, first appearance image information, and first environmental background information; S2, judging whether the first infrared thermal image meets the first preset condition, if not, judging that the electric heater is working abnormally, and establishing a detection period; S3, obtaining second image data within the detection period, and obtaining a temperature distribution trend value according to the second image data; S4, obtaining environmental background thermal radiation map information according to the second image data, and obtaining environmental thermal radiation value according to the environmental background thermal radiation map information; S5, obtaining second appearance image information according to the second image data, and obtaining an appearance thermal radiation value according to the second appearance image information; S6. Acquire first temperature distribution information according to the first infrared thermal image, and acquire a first temperature distribution value according to the first temperature distribution characteristic information; S7. Obtain detection values ​​according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value, and obtain corresponding detection results according to the detection values.

[0023] As in the above steps S1 to S7, when the electric heater is in rated working state, first image data is collected by thermal imaging equipment and camera equipment, including infrared thermal image, appearance image information and environmental background information. The infrared thermal image provides surface temperature distribution information of the electric heater, the appearance image captures changes in the appearance or physical damage of the electric heater, and the environmental background information reflects the thermal radiation condition of the space where the electric heater is located. It is judged whether the first infrared thermal image meets the first preset condition, for example, the uniformity of the surface temperature distribution of the electric heater, the temperature range and other indicators. If an abnormality is detected (such as concentrated hot spots, excessive temperature difference, etc.), it is preliminarily determined that the electric heater is working abnormally, and a detection period is constructed. The second image data is collected during the detection period to obtain the temperature distribution trend. By comparing multiple frames of images within the period, the temperature distribution trend value is obtained, and the environmental background thermal radiation map information is extracted to quantify the thermal radiation value of the working environment of the electric heater. The external thermal radiation characteristics of the electric heater are identified through the appearance image information, including the shell Whether the body is heating abnormally, the shell is deformed or there is local overheating, etc., obtain the appearance thermal radiation value, extract the temperature distribution information in the first infrared thermal image, obtain the thermal distribution characteristics of the electric heater surface (such as uniformity and hot spot location), and quantify them as temperature distribution values, and combine the first temperature distribution value, temperature distribution trend value, environmental thermal radiation value and appearance thermal radiation value to calculate the detection value, and finally judge whether the working state of the electric heater is normal according to the detection value. By combining infrared thermal images, appearance images and environmental background information, misjudgment caused by single-dimensional data is avoided, and the reliability of the detection results is improved. Through the temperature distribution trend value within the detection period, the changes in the working state of the electric heater can be grasped in real time, anomalies can be quickly identified and problems can be located. According to multi-dimensional data, the interference of ambient temperature fluctuations on the detection is reduced, and the objectivity and consistency of the detection results are ensured. Not only the thermal performance of the electric heater is paid attention to, but also the abnormal appearance thermal radiation is analyzed to ensure that the electric heater is qualified in both appearance and function.

[0024] In a preferred embodiment, the step of determining whether the first infrared thermal image meets the first preset condition, and if not, determining that the electric heater is working abnormally, and constructing a detection period includes: S201, obtaining a standard infrared thermal image; S202, overlapping the standard infrared thermal imaging image with the first infrared thermal imaging image to obtain an overlapping infrared thermal imaging image; S203, constructing a plane rectangular coordinate system in the overlapping infrared thermal images; S204, obtaining the coordinates of the inflection points of the contours of a plurality of standard temperature regions and a plurality of inflection points of the contours of the first temperature regions in the overlapping infrared thermal imaging images according to a plane rectangular coordinate system; S205, acquiring a plurality of deviation temperature regions and a plurality of deviation temperature region contour inflection point coordinates of each deviation temperature region according to a plurality of standard temperature region contour inflection point coordinates and a plurality of first temperature region contour inflection point coordinates; S206, obtaining the area of ​​the deviation temperature region according to the coordinates of the multiple deviation temperature region contour inflection points of each deviation temperature region; S207, obtaining a deviation temperature region threshold; S208, determining whether the area of ​​the deviation temperature region exceeds the deviation temperature region threshold; If the area of ​​the deviation temperature region exceeds the deviation temperature region threshold, it is determined that the electric heater is operating abnormally, and a detection period is established.

[0025] As in the above steps S201 to S208, the standard infrared thermal image is a reference image of the electric heater under normal working conditions, which contains standard distribution characteristics of each temperature zone, and is used to compare the actual temperature distribution of the first infrared thermal image. The first infrared thermal image is overlapped with the standard infrared thermal image to generate an overlapped infrared thermal image, so as to make an intuitive comparison of the temperature zone distributions of the two. A plane rectangular coordinate system is established on the overlapped infrared thermal image, and the temperature zone positions in the image are accurately quantified as coordinate points. The coordinates of the contour inflection points of the standard temperature zone and the first temperature zone are extracted respectively. These inflection points represent the boundaries of the temperature zones and are used to define the shape and position of the temperature distribution. By comparing the contour inflection points of the standard temperature zone and the first temperature zone, the deviation zone between the two is determined, and the contour inflection point coordinates of the deviation temperature zone are extracted. The area of ​​the deviation temperature zone is calculated to quantify the severity of the temperature distribution deviation. The larger the area, the more significant the deviation. The calculation formula for the area of ​​the deviation temperature zone is: , where S represents the area of ​​the deviation temperature region, i represents the number of multiple deviation temperature regions, i=1,2,3…n, h represents the number of the coordinates of the inflection points of the contours of multiple deviation temperature regions in each deviation temperature region, h=1,2,3…t, It is represented as the x-axis coordinate point of the inflection point of the h-th deviation temperature region contour, It is represented as the x-axis coordinate point of the inflection point of the h+1th deviation temperature region contour, It is represented as the y-axis coordinate point of the inflection point of the h-th deviation temperature region contour, It is represented as the y-axis coordinate point of the inflection point of the h+1th deviation temperature area contour, and a threshold value of the deviation temperature area area is set as the key criterion for judging whether the electric heater is working abnormally. If the area of ​​the deviation temperature area exceeds the set threshold, the electric heater is judged to be working abnormally, and a detection period is constructed. By overlapping the first infrared thermal image with the standard thermal image, the temperature distribution difference is accurately compared to avoid errors caused by subjective judgment, thereby improving the accuracy of judgment. Through mathematical coordinate analysis and deviation area area calculation, the degree of deviation of the temperature distribution is quantitatively evaluated. According to the shape and position of the deviation temperature area, the specific area of ​​the electric heater abnormality can be quickly located.

[0026] In a preferred embodiment, the step of constructing the detection period includes: S2081, obtaining a duration table, wherein the duration table includes a plurality of deviation temperature region interval areas and a corresponding detection duration of each deviation temperature region interval area; S2082, obtaining the target deviation temperature region interval area according to the deviation temperature region area; S2083, obtaining the corresponding detection duration from the duration table according to the target deviation temperature zone interval area; S2084, obtaining a time point at which the electric heater is judged to be working abnormally, and marking it as a start time; S2085, obtaining the end time according to the start time and the detection duration; S2086. Obtain the detection period according to the start time and the end time.

[0027] As in the above steps S2081 to S2086, the duration table includes multiple deviation temperature region interval areas and corresponding detection durations. Each interval area represents a certain range of temperature deviation, and the detection duration is the time required to monitor the deviation area. The association between the deviation area and the detection duration is achieved through the pre-defined duration table. According to the actual calculated deviation temperature region area, the target deviation temperature region interval to which the area belongs is determined. The selection of the target deviation interval area follows the proximity principle, that is, the deviation temperature region area belongs to the closest interval. According to the target deviation temperature region interval area, the corresponding detection time is found from the duration table. The detection duration reflects the time range that requires continuous monitoring of the abnormal state. The time node for determining that the electric heater is working abnormally is obtained, and the time node is marked as the start time. The start time is the starting point for constructing the detection period. According to the start time and the detection duration, the end time of the abnormal monitoring is inferred. The end time is the end point of the detection period. The start time and the end time are used to clarify the detection period, ensure that the length of the detection period is adapted to the actual deviation, and avoid too long or too short monitoring affecting the analysis quality. Different detection durations are set according to different degrees of deviation to optimize the allocation of detection resources, which not only ensures sufficient monitoring but also avoids waste of resources.

[0028] In a preferred embodiment, the step of acquiring the second image data within the detection period and acquiring the temperature distribution trend value according to the second image data includes: S301, acquiring second image data within a detection period; S302, acquiring a plurality of second infrared thermal images according to the second image data; S303, acquiring a corresponding second temperature distribution vector according to a plurality of second infrared thermal images; S304. Obtain corresponding temperature distribution trend values ​​according to the multiple second temperature distribution vectors.

[0029] As in the above steps S301 to S304, during the constructed detection period, the second image data is continuously collected, including multiple frames of infrared thermal images, which record the dynamic changes in the temperature distribution of the electric heater during the detection period. Multiple second infrared thermal images are separated from the second image data, each thermal image represents the temperature distribution at a certain time point during the detection period, and each second infrared thermal image is analyzed to convert its temperature distribution characteristics into a temperature distribution vector represented by mathematics. The temperature distribution vector is a quantitative representation method that can describe the temperature distribution of each thermal image. Combined with multiple second temperature distribution vectors, the temperature distribution trend value is calculated. The temperature distribution trend value calculation formula is: , where Q represents the temperature distribution trend value, j represents the number of multiple second temperature distribution vectors, j=1,2,3…r, It is represented as the rth second temperature distribution vector. By collecting multiple infrared thermal images during the detection period, the temporal dynamic characteristics of the temperature distribution of the electric heater can be fully reflected. The infrared thermal images can be converted into temperature distribution vectors, which can significantly reduce the complexity of data processing.

[0030] In a preferred embodiment, the step of obtaining the environmental background thermal radiation map information according to the second image data and obtaining the environmental thermal radiation value according to the environmental background thermal radiation map information includes: S401, obtaining the detection duration, start time and end time of the detection period; S402, obtaining environmental background thermal radiation map information according to the second image data; S403, acquiring the starting area of ​​the environmental background heat radiation corresponding to the starting time of the detection period according to the environmental background heat radiation map information; S404, obtaining the end area of ​​the environmental background heat radiation corresponding to the end time of the detection period according to the environmental background heat radiation map information; S405 , obtaining an ambient thermal radiation value according to an ambient background thermal radiation start graph, an ambient background thermal radiation end graph, and a detection duration.

[0031] As in the above steps S401 to S405, the time information of the detection period is obtained from the above steps, including the detection duration, the start time and the end time, and the environmental background thermal radiation map information is extracted from the second image data. At the start time point of the detection period, the thermal radiation area of ​​the background area is obtained according to the environmental background thermal radiation map information, and is marked as the starting area of ​​the environmental background thermal radiation. At the end time point of the detection period, the thermal radiation area of ​​the background area is obtained according to the environmental background thermal radiation map information, and is marked as the ending area of ​​the environmental background thermal radiation. According to the starting area, the ending area and the detection duration of the environmental background thermal radiation, the environmental thermal radiation value is comprehensively calculated. The calculation formula of the environmental thermal radiation value is: , where G represents the ambient thermal radiation value, It is expressed as the end area of ​​ambient background heat radiation, It is represented by the starting area of ​​environmental background heat radiation, and T is represented by the detection duration. It not only focuses on the environmental heat radiation at a single time point, but also based on the dynamic changes during the detection period, which helps to fully understand the impact of the environment on the operating status of the electric heater.

[0032] In a preferred embodiment, the step of obtaining the second appearance image information according to the second image data and obtaining the appearance thermal radiation value according to the second appearance image information includes: S501, obtaining second appearance image information according to second image data; S502, acquiring a plurality of corresponding second appearance vectors according to the second appearance image information; S503, obtaining standard appearance image information, and obtaining a plurality of corresponding standard appearance vectors according to the standard appearance image information; S504 : Acquire appearance thermal radiation values ​​according to the multiple second appearance vectors and the multiple standard appearance vectors.

[0033] As in the above steps S501 to S504, the appearance image information of the electric heater is separated and extracted from the second image data as the basic data for appearance thermal radiation analysis, the second appearance image information is converted into multiple feature vectors, and the temperature distribution, texture characteristics and regional contour information of the appearance area are extracted. These vectors can be used as parameters for quantitatively describing the appearance thermal radiation characteristics. A preset standard appearance image is used as a comparison benchmark, from which multiple standard appearance vectors are generated to characterize the appearance thermal radiation characteristics of the electric heater under normal working conditions. The second appearance vector is compared with the standard appearance vector, and then the thermal radiation value of the appearance area is derived. The calculation formula of the appearance thermal radiation value is: , where R represents the appearance thermal radiation value, q represents the numbers of multiple second appearance vectors and multiple standard appearance vectors, q=1,2,3…u, Denoted as the qth second appearance vector, It is represented as the i-th standard appearance vector. Through the generation and comparison of appearance vectors, the change of appearance thermal radiation is refined from the overall measurement to the vector level, which improves the detection accuracy and data interpretation ability. The characteristic changes of the appearance area are considered, and the influence of environmental interference (such as illumination and occlusion) on the detection results is effectively avoided, thereby enhancing the robustness of detection.

[0034] In a preferred embodiment, the steps of obtaining first temperature distribution information according to the first infrared thermal image and obtaining first temperature distribution value according to the first temperature distribution characteristic information include: S601, obtaining multiple temperature range intervals of a first infrared thermal image; S602, constructing a plane rectangular coordinate system according to the first infrared thermal image; S603, obtaining coordinates of multiple temperature range interval contour inflection points of each temperature range interval according to a plane rectangular coordinate system, and marking them as first temperature distribution information; S604, obtaining a compensation coefficient for each temperature range; S605 , obtaining a first temperature distribution value according to the compensation coefficient of each temperature range interval and the coordinates of a plurality of temperature range interval contour inflection points of each temperature range interval.

[0035] As in the above steps S601 to S605, multiple temperature range intervals are extracted from the first infrared thermal image. These intervals divide the areas with different temperature gradients and can reflect the heating characteristics and heat dissipation of the electric heater. A plane rectangular coordinate system is constructed on the first infrared thermal image to accurately locate the position and contour features of each temperature range interval. According to the constructed coordinate system, the contour inflection point coordinates of each temperature range interval are extracted and marked as the first temperature distribution information. These inflection point coordinates can accurately describe the shape and size of the temperature distribution area. According to the detection environment, equipment status and other factors, a compensation coefficient is allocated to each temperature range interval to correct the interference of environmental influences on the temperature distribution characteristics and improve the accuracy of the data. The first temperature distribution value is calculated by the compensation coefficient and the contour inflection point coordinates of the temperature range interval. The calculation formula of the first temperature distribution value is: , where F represents the first temperature distribution value, d represents the number of multiple temperature range intervals, d=1,2,3…v, It is expressed as the compensation coefficient of the dth temperature range interval, and f is expressed as the number of the coordinates of the inflection points of the multiple temperature range interval profiles in each temperature range interval, f=1,2,3…g, It is expressed as the x-axis coordinate point of the inflection point of the profile in the f-th temperature range. It is expressed as the x-axis coordinate point of the inflection point of the profile in the f+1th temperature range. It is expressed as the y-axis coordinate point of the inflection point of the profile in the f-th temperature range. It is expressed as the y-axis coordinate point of the inflection point of the contour of the f+1th temperature range, which can fully describe the temperature distribution characteristics in the infrared thermal image and improve the accuracy of detection. The introduction of the compensation coefficient corrects the influence of the detection environment and equipment status on the temperature distribution, ensuring the credibility and consistency of the temperature distribution value. The temperature range and compensation coefficient can be dynamically adjusted according to the specific application scenario to adapt to different types of equipment and environmental requirements.

[0036] In a preferred embodiment, the step of obtaining a detection value according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value, and obtaining a corresponding detection result according to the detection value includes: S701, obtaining a detection value according to a first temperature distribution value, a temperature distribution trend value, an ambient thermal radiation value, and an appearance thermal radiation value; S702, obtaining a detection table, wherein the detection table includes a plurality of detection interval values ​​and a detection result corresponding to each detection interval value; S703, obtaining a target detection interval value according to the detection value; S704. Obtain corresponding detection results from the detection table according to the target detection interval value.

[0037] As in the above steps S701 to S704, the detection value is calculated according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value and the appearance thermal radiation value. The calculation formula of the detection value is C=(F / Q)*G*R, where C represents the detection value, F represents the first temperature distribution value, Q represents the temperature distribution trend value, G represents the ambient thermal radiation value, and R represents the appearance thermal radiation value. A plurality of detection interval values ​​and their corresponding detection results are pre-stored in the detection table, and each interval value represents the range of different working states. According to the calculated detection value, it is matched with the detection interval value in the detection table to determine the target detection interval value to which the detection value belongs. According to the target detection interval value, the corresponding detection result is extracted from the detection table to evaluate the current working state of the electric heater. By integrating multiple key parameters (such as temperature distribution, thermal radiation value, etc.), the calculation of the detection value can fully reflect the working performance and state of the electric heater. The interval values ​​and results of the detection table can be flexibly defined, which is suitable for electric heater detection scenarios of different types and functions.

[0038] Please see attached Figure 2 As shown, the present invention also provides an electric heater detection system based on big data image information, which is used in the above-mentioned electric heater detection method based on big data image information, comprising: A rated working module, used for obtaining first image data during the rated working process of the electric heater, wherein the first image data includes a first infrared thermal image, first appearance image information and first environmental background information; A temperature judgment module, used to judge whether the first infrared thermal image meets the first preset condition, and if not, judge that the electric heater is working abnormally, and establish a detection period; A distribution trend module, used to obtain second image data within a detection period, and obtain a temperature distribution trend value according to the second image data; An environmental thermal radiation module, used to obtain environmental background thermal radiation map information according to the second image data, and obtain an environmental thermal radiation value according to the environmental background thermal radiation map information; An appearance thermal radiation module, used to obtain second appearance image information according to the second image data, and obtain an appearance thermal radiation value according to the second appearance image information; A temperature distribution module, used to obtain first temperature distribution information according to the first infrared thermal image, and obtain a first temperature distribution value according to the first temperature distribution characteristic information; The detection result module is used to obtain detection values ​​according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value and the appearance thermal radiation value, and obtain corresponding detection results according to the detection values.

[0039] As mentioned above, the rated working module obtains the first image data generated by the electric heater during the rated working process. These data include the first infrared thermal image, the first appearance image information and the first environmental background information. These image information can provide the temperature distribution, appearance status and thermal radiation conditions of the surrounding environment when the electric heater is working. The temperature judgment module judges whether it meets the preset temperature standard or normal operating conditions based on the obtained first infrared thermal image. If the temperature distribution in the first infrared thermal image does not match the preset standard, the system will determine that the electric heater is working abnormally and start the detection process. The distribution trend module obtains the second image data within the detection period, and calculates the temperature distribution trend value based on these data. The environmental thermal radiation module obtains the thermal radiation conditions of the working environment of the electric heater based on the second image data. By obtaining the environmental background thermal radiation image information, the system can calculate the thermal radiation value of the environment. The appearance thermal radiation module obtains the second appearance image information based on the second image data, and calculates the The temperature distribution module uses the first infrared thermal image to obtain the first temperature distribution information, and extracts the first temperature distribution value according to the distribution information. The detection result module calculates a comprehensive detection value by comprehensively analyzing the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value and the appearance thermal radiation value. The detection value is compared with the preset detection table to obtain the detection result of whether there is any abnormality in the electric heater, such as normal operation, overheating, uneven heat dissipation, etc. By combining a variety of image information (infrared thermal images, appearance images, environmental background information, etc.), comprehensive and multi-dimensional electric heater detection is achieved. The comprehensive analysis of multiple parameters improves the accuracy and reliability of the detection, and can accurately diagnose the working status of the electric heater, identify temperature abnormalities, poor heat dissipation or appearance problems, ensure the safe operation of the equipment, and can adapt to different working environments and types of electric heaters. By adjusting the preset conditions and thresholds, it can be flexibly applied to the detection of electric heaters of different models or different usage conditions.

[0040] And, an electric heater detection terminal based on big data image information, comprising: one or more processors; a storage device having one or more programs stored thereon; When one or more programs are executed by one or more processors, the one or more processors implement an electric heater detection method based on big data image information.

[0041] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. An electric heater detection method based on big data image information, characterized in that: include: Acquire first image data of the electric heater during rated operation, wherein the first image data includes a first infrared thermal image, first appearance image information, and first environmental background information; Determine whether the first infrared thermal image meets the first preset condition, if not, determine that the electric heater is working abnormally, and establish a detection period; Acquire second image data within the detection period, and acquire a temperature distribution trend value according to the second image data; Acquire environmental background thermal radiation map information according to the second image data, and acquire environmental thermal radiation value according to the environmental background thermal radiation map information; Acquire second appearance image information according to the second image data, and acquire appearance thermal radiation value according to the second appearance image information; Acquire first temperature distribution information according to the first infrared thermal image, and acquire first temperature distribution value according to the first temperature distribution characteristic information; A detection value is obtained according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value, and a corresponding detection result is obtained according to the detection value.

2. The electric heater detection method based on big data image information according to claim 1 is characterized in that: The step of determining whether the first infrared thermal image meets the first preset condition, and if not, determining that the electric heater is working abnormally, and establishing a detection period includes: Obtain standard infrared thermal images; Overlapping the standard infrared thermal imaging image with the first infrared thermal imaging image to obtain an overlapped infrared thermal imaging image; Construct a plane rectangular coordinate system in the coincident infrared thermal images; Acquire the coordinates of the inflection points of the contours of a plurality of standard temperature regions and the coordinates of the inflection points of the contours of a plurality of first temperature regions in the coincident infrared thermal imaging images according to a plane rectangular coordinate system; Acquire multiple deviation temperature regions and multiple deviation temperature region contour inflection point coordinates of each deviation temperature region according to multiple standard temperature region contour inflection point coordinates and multiple first temperature region contour inflection point coordinates; Obtaining the area of ​​the deviation temperature region according to the coordinates of the inflection points of the deviation temperature region contours of each deviation temperature region; Get the deviation temperature area threshold; Determine whether the area of ​​the deviation temperature region exceeds the deviation temperature region threshold; If the area of ​​the deviation temperature region exceeds the deviation temperature region threshold, it is determined that the electric heater is operating abnormally, and a detection period is established.

3. The electric heater detection method based on big data image information according to claim 2 is characterized in that: The steps to construct a detection period include: Obtaining a duration table, wherein the duration table includes a plurality of deviation temperature region interval areas and a corresponding detection duration of each deviation temperature region interval area; Obtain the target deviation temperature region area according to the deviation temperature region area; Obtain the corresponding detection duration from the duration table according to the target deviation temperature zone interval area; Obtain the time node when the electric heater is judged to be working abnormally and mark it as the start time; Get the end time based on the start time and detection duration; Get the detection period based on the start time and end time.

4. The electric heater detection method based on big data image information according to claim 1 is characterized in that: The step of acquiring second image data within the detection period and acquiring a temperature distribution trend value according to the second image data comprises: Acquiring second image data within the detection period; Acquire a plurality of second infrared thermal images according to the second image data; Acquire a corresponding second temperature distribution vector according to a plurality of second infrared thermal images; The corresponding temperature distribution trend values ​​are obtained according to the multiple second temperature distribution vectors.

5. The electric heater detection method based on big data image information according to claim 1 is characterized in that: The steps of obtaining the environmental background thermal radiation map information according to the second image data and obtaining the environmental thermal radiation value according to the environmental background thermal radiation map information include: Get the detection duration, start time and end time of the detection period; Acquire environmental background thermal radiation map information according to the second image data; Acquire the starting area of ​​the environmental background heat radiation corresponding to the starting time of the detection period according to the environmental background heat radiation map information; Acquire the end area of ​​the environmental background heat radiation corresponding to the end time of the detection period according to the environmental background heat radiation map information; The ambient thermal radiation value is obtained according to the ambient background thermal radiation start graph, the ambient background thermal radiation end graph and the detection time.

6. The electric heater detection method based on big data image information according to claim 1 is characterized in that: The steps of acquiring second appearance image information according to the second image data and acquiring the appearance thermal radiation value according to the second appearance image information include: Acquire second appearance image information according to the second image data; Acquire a plurality of corresponding second appearance vectors according to the second appearance image information; Obtaining standard appearance image information, and obtaining corresponding multiple standard appearance vectors according to the standard appearance image information; The appearance thermal radiation value is obtained according to the plurality of second appearance vectors and the plurality of standard appearance vectors.

7. The electric heater detection method based on big data image information according to claim 1 is characterized in that: The step of obtaining first temperature distribution information according to the first infrared thermal image and obtaining a first temperature distribution value according to the first temperature distribution characteristic information includes: Acquire multiple temperature range intervals of the first infrared thermal image; Constructing a plane rectangular coordinate system according to the first infrared thermal image; Acquire multiple temperature range interval contour inflection point coordinates of each temperature range interval according to a plane rectangular coordinate system, and mark them as first temperature distribution information; Get the compensation coefficient for each temperature range; The first temperature distribution value is obtained according to the compensation coefficient of each temperature range interval and the coordinates of a plurality of temperature range interval contour inflection points of each temperature range interval.

8. The electric heater detection method based on big data image information according to claim 1 is characterized in that: The step of obtaining a detection value according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value, and obtaining a corresponding detection result according to the detection value includes: Acquire a detection value according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value, and the appearance thermal radiation value; Obtaining a test table, wherein the test table includes a plurality of test interval values ​​and a test result corresponding to each test interval value; Obtain a target detection interval value according to the detection value; Obtain the corresponding test result from the test table according to the target detection interval value.

9. An electric heater detection system based on big data image information, applied to the electric heater detection method based on big data image information as claimed in any one of claims 1 to 8, characterized in that: include: A rated working module, used for obtaining first image data during the rated working process of the electric heater, wherein the first image data includes a first infrared thermal image, first appearance image information and first environmental background information; A temperature judgment module, used to judge whether the first infrared thermal image meets the first preset condition, and if not, judge that the electric heater is working abnormally, and establish a detection period; A distribution trend module, used to obtain second image data within a detection period, and obtain a temperature distribution trend value according to the second image data; An environmental thermal radiation module, used to obtain environmental background thermal radiation map information according to the second image data, and obtain an environmental thermal radiation value according to the environmental background thermal radiation map information; An appearance thermal radiation module, used to obtain second appearance image information according to the second image data, and obtain an appearance thermal radiation value according to the second appearance image information; A temperature distribution module, used to obtain first temperature distribution information according to the first infrared thermal image, and obtain a first temperature distribution value according to the first temperature distribution characteristic information; The detection result module is used to obtain detection values ​​according to the first temperature distribution value, the temperature distribution trend value, the ambient thermal radiation value and the appearance thermal radiation value, and obtain corresponding detection results according to the detection values.

10. An electric heater detection terminal based on big data image information, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When one or more programs are executed by one or more processors, the one or more processors implement the electric heater detection method based on big data image information as described in any one of claims 1 to 8.

Images