CN119147104A - Fault self-diagnosis early warning method, equipment and system for intelligent electric box - Google Patents
Fault self-diagnosis early warning method, equipment and system for intelligent electric box Download PDFInfo
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Abstract
本发明设备故障诊断技术领域，具体涉及智慧电箱的故障自诊断预警方法、设备及系统，包括实时获取智慧电箱内部的红外热成像图像；对当前时刻的红外热成像图像进行轮廓提取，得到至少一个轮廓区域；根据每个轮廓区域内的温度差异筛选老化疑似区域；根据老化疑似区域在历史时间段内的发热表现筛选得到老化区域；根据老化区域对应的坐标信息计算得到老化区域的面积和位置；将在当前时刻老化区域的温度平均值、老化区域的面积和位置发送至预设终端。本发明通过获取红外热成像图像中的轮廓区域，并筛选出老化疑似区域。然后，依据老化区域的发热特征进行验证，以确定其是否为老化区域，从而实现智慧电箱内部异常温度区域的识别。
The technical field of equipment fault diagnosis of the present invention specifically relates to a fault self-diagnosis warning method, equipment and system for a smart electric box, including real-time acquisition of an infrared thermal imaging image inside the smart electric box; contour extraction of the infrared thermal imaging image at the current moment to obtain at least one contour area; screening suspected aging areas according to the temperature difference within each contour area; screening the aging area according to the heating performance of the suspected aging area in the historical time period; calculating the area and position of the aging area according to the coordinate information corresponding to the aging area; sending the average temperature of the aging area at the current moment, the area and position of the aging area to a preset terminal. The present invention obtains the contour area in the infrared thermal imaging image and screens out the suspected aging area. Then, verification is performed based on the heating characteristics of the aging area to determine whether it is an aging area, thereby realizing the identification of abnormal temperature areas inside the smart electric box.
Description
Technical Field
The invention relates to the technical field of equipment fault diagnosis, in particular to a fault self-diagnosis early warning method, equipment and a system of an intelligent electric box.
Background
The intelligent electric box is an electric power management system based on the internet of things technology, and has the core functions of realizing real-time monitoring, accurate control and efficient management of electric energy through an integrated sensor, communication equipment and an intelligent algorithm. The main aim of the equipment is to ensure reasonable distribution and use of electric power resources, thereby improving the energy utilization efficiency and reducing the energy waste. Specifically, the intelligent electric box can monitor various key parameters including current, voltage and power in real time, so that a user can comprehensively grasp the electricity consumption condition, and corresponding adjustment and optimization are made. Through the real-time data, a user can better know the mode and trend of power consumption, and further take measures to reduce unnecessary power waste. In addition, wisdom electronic box still possesses unusual detection function, can in time discover electric current overload, voltage abnormality and other potential electric power problems to send out the alarm rapidly, so that the user can in time take measures, avoid equipment damage or the emergence of electric power accident. Such preventive measures greatly improve the safety and reliability of the power system.
However, although the smart box has many advantages in terms of power management, problems may occur when the internal circuit thereof is aged. Although the current, voltage and power parameters may appear superficially normal without reaching the triggering conditions for the anomaly detection function, the aging of such lines may actually have hidden the device from view. The existing technical means often cannot accurately diagnose and early warn about the specific locations and potential fault risks of these aged lines.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a fault self-diagnosis early warning method, equipment and a system for an intelligent electric box. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The application provides a fault self-diagnosis early warning method of an intelligent electric box, which comprises the steps of acquiring infrared thermal imaging images in the intelligent electric box in real time, carrying out contour extraction on the infrared thermal imaging images at the current moment to obtain at least one contour area, screening an ageing suspected area according to temperature differences in each contour area, screening an ageing suspected area according to heating performance of the ageing suspected area in a historical time period, calculating area and position of the ageing area according to coordinate information corresponding to the ageing area, and sending a temperature average value of the ageing area at the current moment and the area and position of the ageing area to a preset terminal, wherein the temperature average value is calculated by gray values of the infrared thermal imaging images of the ageing area at the current moment.
With reference to the first aspect, in one possible implementation manner, the contour extraction is performed on the infrared thermal imaging image at the current moment to obtain at least one contour area, and the contour extraction comprises the steps of performing edge detection on the infrared thermal imaging image at the current moment by using an edge detection mathematical model to obtain a plurality of edge pixel points, and performing area extraction on the infrared thermal imaging image at the current moment based on all the edge pixel points to obtain at least one contour area.
With reference to the first aspect, in one possible implementation manner, the edge detection mathematical model is a Sobel edge detection algorithm.
In combination with the first aspect, in one possible implementation manner, the method includes that the step of screening the suspected aging areas according to the temperature difference in each contour area includes the steps of converting gray values contained in each contour area into temperature values one by one according to a preset temperature mapping formula, calculating the temperature difference of each contour area according to the temperature values in the contour areas, and judging the temperature difference of each contour area one by one according to a preset first threshold value to obtain the suspected aging areas.
In combination with the first aspect, in one possible implementation manner, the calculating the temperature difference of each contour area according to the temperature values in the contour areas includes counting the total number of contour areas in an infrared thermal imaging image at the current moment and the maximum temperature value in each contour area, calculating the average value of the temperatures corresponding to each contour area based on the temperature values in each contour area, and calculating the temperature difference of each contour area according to a first preset formula, the total number of contour areas in the infrared thermal imaging image, the maximum temperature value in each contour area and the average value of the temperatures corresponding to each contour area.
With reference to the first aspect, in a possible implementation manner, the preset first threshold value is 0.8.
According to the method, in a possible implementation manner, an aging area is obtained through screening according to the heating performance of the aging suspected area in a historical time period, the method comprises the steps of converting gray values contained in each aging suspected area into temperature values one by one according to a preset temperature mapping formula, carrying out mean value calculation on the basis of the temperature values in each aging suspected area to obtain a temperature average value corresponding to each aging suspected area, counting the operation days of the intelligent electric box and the temperature values corresponding to each historical time of each aging suspected area in the historical time period, carrying out calculation on the basis of the operation days of the intelligent electric box, the temperature average value corresponding to each aging suspected area and the temperature values in each historical time to obtain an auxiliary judging value corresponding to each aging suspected area, and judging whether the aging suspected area is the aging area or not based on the auxiliary judging value corresponding to each aging suspected area.
In combination with the first aspect, in one possible implementation manner, determining whether the obtained aging suspected region is an aging region based on the auxiliary discrimination value corresponding to each aging suspected region further includes counting the number of temperature values in the aging suspected region, performing average calculation based on the temperature values in the aging suspected region to obtain a temperature average value corresponding to the aging suspected region, performing variance calculation based on the temperature values in the aging suspected region to obtain a variance corresponding to the aging suspected region, calculating to obtain a temperature confusion degree corresponding to the aging suspected region according to the number of temperature values corresponding to the aging suspected region, the temperature average value and the variance, and updating the auxiliary discrimination value based on the temperature confusion degree corresponding to the aging suspected region.
The application further provides a fault self-diagnosis early warning system of the intelligent electric box, which comprises an acquisition module, a contour extraction module, a first screening module, a second screening module and an information calculation module, wherein the acquisition module is used for acquiring an infrared thermal imaging image in the intelligent electric box in real time, the contour extraction module is used for carrying out contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area, the first screening module is used for screening an ageing suspected area according to temperature difference in each contour area, the second screening module is used for screening the ageing suspected area according to heating performance of the ageing suspected area in a historical time period, the information calculation module is used for calculating the area and the position of the ageing area according to coordinate information corresponding to the ageing area, and the early warning module is used for sending a temperature average value of the ageing area at the current moment and the area and the position of the ageing area to a preset terminal, and the temperature average value is calculated by the gray value of the infrared thermal imaging image of the ageing area at the current moment.
In a third aspect, the application also provides fault self-diagnosis early-warning equipment of the intelligent electric box, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for realizing the steps of the fault self-diagnosis early-warning method of the intelligent electric box when executing the computer program.
The invention has the following beneficial effects:
in the invention, firstly, a plurality of contour areas in an image are identified and extracted by acquiring an infrared thermal imaging image at the current moment. And then, carrying out detailed temperature difference analysis and calculation on each contour region according to the temperature differences of all contour regions in the infrared thermal imaging image at the current moment. In this way, several possible suspected areas of aging can be screened out efficiently. Then, according to the heating characteristics of the aging area in the real situation, further screening and verifying are carried out on each suspected aging area to determine whether the suspected aging area really belongs to the aging area. The area, the position and the average temperature of the aging region can be calculated by using the information because the feasibility of the aging region obtained by the steps is high. Finally, the accurate and reliable aging area information is utilized to provide powerful support and guarantee for the fault self-diagnosis early warning system, ensure the accuracy and timeliness of early warning and effectively avoid high-temperature damage caused by circuit aging.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a fault self-diagnosis early warning method of an intelligent electric box according to an embodiment 1 of the present invention;
Fig. 2 is a flow chart of step S2 provided in embodiment 1 of the present invention;
FIG. 3 is a flowchart of step S3 according to embodiment 1 of the present invention;
FIG. 4 is a flowchart of step S4 provided in an embodiment 1 of the present invention;
fig. 5 is a schematic structural diagram of a fault self-diagnosis and early-warning system of an intelligent electric box according to embodiment 2 of the present invention.
Fig. 6 is a schematic structural diagram of a fault self-diagnosis and early-warning device for an intelligent electric box according to embodiment 3 of the present invention.
The figure shows 800 fault self-diagnosis early warning equipment of an intelligent electric box, 801, a processor, 802, a memory, 803, a multimedia component, 804, an I/O interface, 805 and a communication component.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a fault self-diagnosis early warning method, device and system for an intelligent electric box according to the invention, which are specific embodiments, structures, features and effects thereof, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Example 1:
the invention provides a specific scheme of a fault self-diagnosis early warning method for an intelligent electric box, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a fault self-diagnosis early warning method of an intelligent electric box according to an embodiment of the invention is shown, wherein steps S1-S6 are included in the embodiment.
S1, acquiring an infrared thermal imaging image of the inside of an intelligent electric box in real time;
In this embodiment, the acquisition of the infrared thermal imaging image is achieved by an infrared sensor disposed inside the intelligent electronic box. The infrared sensor is responsible for capturing the temperature distribution condition inside the electric box and converting the captured information into image data. In order to ensure the continuity and accuracy of the data, the intelligent electronic box can automatically acquire the infrared thermal imaging image data once every second in the operation process. In this way, the present embodiment can obtain a series of infrared thermal imaging images at the historical time, and an infrared thermal imaging image corresponding to the latest current time. These image data will be used for subsequent quantitative data analysis of the aged areas in order to more accurately evaluate the temperature variations inside the electric box.
To ensure the quality of the infrared thermographic images, each of the infrared thermographic images is preprocessed in this embodiment. In the preprocessing process, a median filtering (MEDIAN FILTERING) algorithm is adopted to remove noise points in the image. Median filtering is a nonlinear filtering technique that is effective in removing sharp noise from an image while preserving edge information of the image, which is critical for subsequent image analysis and accurate capture of temperature changes. Through obtaining and preprocessing the infrared thermal imaging image in real time, the temperature distribution condition inside the intelligent electric box can be monitored in real time, and any abnormal temperature change can be captured in time, so that the accuracy and the reliability of data are ensured.
S2, carrying out contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area;
In this embodiment, the contour region extraction is achieved by adopting a combination of an edge detection algorithm and a contour extraction algorithm. Specifically, referring to fig. 2, step S2 is shown to include steps S21-S22.
S21, performing edge detection on the infrared thermal imaging image at the current moment by using an edge detection mathematical model to obtain a plurality of edge pixel points;
It should be noted that, in this embodiment, the edge detection mathematical model is preferably a Sobel edge detection algorithm. Specifically, in the step, according to the gray value of the pixel point in the infrared thermal imaging image at the current moment, a Sobel edge detection algorithm is used for carrying out edge detection on the image. The Sobel algorithm is a classical image processing algorithm, and extracts edges in an image by calculating gradients of gray values of the image. Meanwhile, for those skilled in the art, other edge detection algorithms, such as Canny edge detection algorithm, prewitt edge detection algorithm, roberts edge detection algorithm, etc., may be used in addition to the Sobel edge detection algorithm, and those skilled in the art may choose according to the actual situation, and the embodiment does not make any specific limitation.
In this embodiment, in order to achieve efficient edge detection, a Sobel edge detection algorithm is preferable as a mathematical model of edge detection. Specifically, in the process of infrared thermal imaging image processing at the current moment, according to the gray value of each pixel point in the image, a Sobel edge detection algorithm is used to execute edge detection. The Sobel algorithm is a classical algorithm widely applied to the field of image processing, and effectively extracts edge information in an image by calculating the gray value gradient of each pixel point in the image.
It will be apparent to those skilled in the art that there are a number of other edge detection algorithms in addition to the Sobel edge detection algorithm, such as the Canny edge detection algorithm, the Prewitt edge detection algorithm, the Roberts edge detection algorithm, etc. The algorithms have the characteristics and the applicable scenes, and technicians can flexibly select the most suitable edge detection algorithm according to specific application requirements and actual conditions. The edge detection algorithm selected for use is not particularly limited in this embodiment.
S22, carrying out region extraction in the infrared thermal imaging image at the current moment based on all the edge pixel points to obtain at least one contour region.
Further, region extraction is performed in this step based on the use of a contour extraction algorithm. Specifically, the contour extraction algorithm in this embodiment adopts, for example, a Douglas-Peucker algorithm, a snap model, or the like. By means of accurate edge detection and contour extraction, boundaries and shapes in an infrared image can be accurately identified, and clear region division is provided for subsequent temperature difference analysis.
In this step, a contour extraction algorithm is further used to perform region extraction to ensure that boundaries and shapes in the infrared image can be accurately identified. Specifically, in this embodiment, a Snake model is selected for contour extraction, and the Snake model dynamically adjusts contour lines according to an energy minimization principle, so as to adapt to complex shapes and edges in an image, further improve precision and reliability of contour extraction, and provide clear region division for subsequent temperature difference analysis.
S3, screening the suspected aging areas according to the temperature difference in each contour area;
In this embodiment, it is considered that the aging of the internal circuit of the smart box causes a local temperature rise, and a significant temperature difference is formed with other normal areas. Therefore, in the present embodiment, a method based on temperature difference analysis is proposed, by detecting and analyzing the temperature distribution inside the smart box, the suspected aging region can be effectively identified. The method is beneficial to early warning potential line aging problems in advance and helping maintainers to quickly locate problem areas, so that corresponding maintenance measures are taken, and safe and stable operation of the intelligent electric box is ensured. Specifically, referring to fig. 3, steps S31 to S33 included in the present step S3 are shown.
S31, converting gray values contained in each contour area into temperature values one by one according to a preset temperature mapping formula;
It should be noted that, since the infrared sensor directly outputs a gray-scale image, the gray-scale value of each pixel represents the radiation intensity of the point, not the direct temperature value. Therefore, in this step, it is also necessary to analyze the temperature value from the gray value corresponding to each pixel point in the contour area. The temperature mapping formula mentioned in this step is a mapping table or conversion formula of gray values and temperatures provided by the infrared sensor manufacturer. As it relates to the infrared sensor calibration parameters, the atmospheric environment in which the infrared sensor is located. Therefore, the specific formula content is listed in the present embodiment, and a person skilled in the art can determine the specific formula content according to the actual use situation of the infrared sensor, and the specific limitation is not made in the present embodiment.
The infrared sensor has only a function of directly outputting a grayscale image. The gray value of each pixel point in the image represents the radiation intensity of that point. However, these gray values do not directly reflect the temperature values. Therefore, in the data processing stage, the temperature value must be resolved from the gray value.
To achieve this, a mapping table or conversion formula between gray values and temperature provided by the infrared sensor manufacturer is required. These maps or formulas take into account the calibration parameters of the infrared sensor, the atmospheric environment in which the sensor is located, and other factors. In view of the above influencing factors, no specific temperature mapping formula is provided in the present embodiment. Those skilled in the art should decide the appropriate temperature mapping method by referring to calibration parameters and conversion formulas provided by the manufacturer according to the specific situation of the actually used infrared sensor. In this way, the technician can flexibly adjust and optimize the accuracy of the temperature measurement according to the actual requirements and environmental conditions.
S32, calculating the temperature difference of each contour area according to the temperature value in the contour area;
specifically, step S32 includes steps S321 to S323 in the present embodiment.
S321, counting the total number of contour areas in an infrared thermal imaging image at the current moment and the maximum temperature value in each contour area;
S322, carrying out average value calculation based on the temperature value in each contour area to obtain a temperature average value corresponding to each contour area;
S323, calculating the temperature difference of each contour area according to a first preset formula, the total number of the contour areas in the infrared thermal imaging image, the maximum temperature value in each contour area and the corresponding temperature average value of each contour area.
For the convenience of understanding of those skilled in the art, the present step uses the infrared thermal imaging image at the current time to form the first imageThe first preset formula for each contour region illustrates:
Wherein, Infrared thermal imaging image representing current timeTemperature differences of the individual profile areas; representing a linear normalization function; Representing a maximum function; infrared thermal imaging image representing current time The maximum of all temperature values in the individual profile areas; Representing a total number of contour regions in the infrared thermographic image at the current time; representing the first in the IR thermographic image at the current time Average value of all temperature values in each contour region; infrared thermal imaging image representing current time An average of all temperature values of the individual profile areas; representing an absolute value function.
In the calculation formula, the first infrared thermal imaging image at the current momentThe larger the maximum value of the temperature value in each contour area is, the infrared thermal imaging image of the current moment isThe greater the likelihood of temperature differences between the individual profile areas and the other areas. Recombined with the firstIndividual contour regions and other contour regionsThe sum of the absolute values of the differences of (a), i.eThe larger the difference between each contour area and other contour areas is, the first infrared image of the intelligent electronic box at the current moment is representedThe greater the temperature difference of the individual profile areas. Therefore, the first aspect can be better represented by the above calculationTemperature differences in individual profile areas.
S33, judging the temperature difference of each contour area one by one according to a preset first threshold value to obtain an ageing suspected area.
Note that, in this embodiment, the preset first threshold value is 0.8. Meanwhile, for those skilled in the art, different first thresholds may be selected according to practical situations, and the specific limitation is not made in this embodiment.
In the present embodiment, it is considered that when aging occurs inside the smart box, a local temperature rises, which is greatly different from other normal areas. Therefore, the gray value of each pixel point in the infrared thermal imaging image at the current moment is extracted, the gray value is converted into a temperature value by using a temperature mapping formula, and the temperature difference of each contour area is calculated according to a first preset formula. The region with larger temperature difference, namely the contour region with the temperature difference larger than the first threshold value, is the suspected aging region. Through temperature difference analysis, areas which may have aging problems can be initially identified, so that preparation is provided for further screening and verification to follow-up.
In the embodiment, the problem of local temperature rise caused by the aging phenomenon in the intelligent electric box is considered, and gray value extraction is carried out on each pixel point in an image by acquiring an infrared thermal imaging image at the current moment. And converting the gray value into a temperature value by using a temperature mapping formula, and calculating the temperature difference of each contour area according to a first preset formula. By this means, regions of large temperature variation, which are generally considered as suspected regions of aging, can be effectively identified.
S4, screening to obtain an aging region according to the heating performance of the aging suspected region in the historical time period;
In this embodiment, considering that the amount of heat generated by the smart box may increase due to sudden load increase during operation, the smart box may be misjudged as an aged suspected area. However, the aged region is a process in which the heat generation amount and the heat generation area steadily increase with time. Therefore, in this embodiment, the screening is also performed according to the heat generation performance of each suspected aging region in the history period.
Referring to fig. 4, the steps further include step S41 to step S45:
S41, carrying out average value calculation based on the temperature value in each suspected aging area to obtain a temperature average value corresponding to each suspected aging area;
It should be noted that the temperature value in the suspected aging region mentioned in this step is already mentioned in step S31 as to how the temperature value is converted. Therefore, the description is omitted in this step.
S42, counting the operation days of the intelligent electric box, and counting the temperature value corresponding to each historical moment of each suspected aging area in a historical time period;
It should be noted that, the temperature value corresponding to each historical time of the suspected aging region in the historical time period mentioned in the step is according to the pixel coordinate information of the suspected aging region at the current time, and all the temperature values corresponding to the pixel coordinate information are correspondingly queried in the infrared thermal imaging image at each historical time.
The historical time period refers to a time period from the operation time of the intelligent electric box to the time of the previous data acquisition of the current time, the time included in the historical time period is called the historical time, the infrared thermal imaging images corresponding to the plurality of historical times are divided into a plurality of time-series arranged infrared thermal imaging images by taking a day as a basic unit in the implementation, and each infrared thermal imaging image in each historical time is also converted into a temperature value through the gray value of the step S31, and the specific implementation process of the method is described above. Therefore, the description is omitted in this step.
S43, calculating an auxiliary discrimination value corresponding to each suspected aging area according to the running days of the intelligent electric box, the average temperature value corresponding to each suspected aging area and the temperature value in each historical moment;
The performance characteristics of the aging region in the history are taken into account in this embodiment. In particular, these aged areas may exhibit significant heat generation phenomena over time, including expansion of the heat generation area and increase in the amount of heat generation. In other words, in the same region range, if the temperature average value corresponding to the aged suspected region at the present time is larger than the temperature average value in the history time, the probability that this aged suspected region really belongs to the aged region is higher. This phenomenon indicates that the temperature of the aged zone gradually increases over time, thereby making its behavior in temperature monitoring more pronounced. By monitoring and analyzing the temperature change, the real aging area can be identified more accurately, so that corresponding maintenance and repair measures are taken, and the normal operation and safety of the equipment are ensured. Therefore, based on the above derived features, the present step uses the present time to image the first infrared thermal imaging image The auxiliary discrimination calculation for each suspected aged region is illustrated by:
Wherein, Infrared thermal imaging image representing current timeAuxiliary discrimination values corresponding to the suspected aging areas; representing the running days of the intelligent electric box up to the current moment; infrared thermal imaging image representing current time A temperature average value corresponding to each suspected aging region; Indicating the first time before the current time The number of ir thermal imaging images acquired on the day; representing the first time from the current time Day acquired firstZhang Gongwai th thermal imaging imageAnd (5) carrying out average value calculation on the temperatures corresponding to the suspected aging areas to obtain a temperature average value.
Further, in this embodiment, in order to further improve screening accuracy of the aging area. The present embodiment contemplates that under normal load conditions, the heat distribution of a circuit will generally exhibit a uniform state. However, when the aging phenomenon occurs in the wiring, a significant temperature concentration phenomenon is exhibited in some specific areas, and the temperature difference in these areas is significantly increased. Based on this observation, the present embodiment proposes to assist in judging the aging condition by calculating the degree of temperature confusion of each suspected aging region. Specifically, the method utilizes the characteristic that the temperature of the aging circuit is concentrated in a specific area, and corrects the auxiliary discrimination value through quantitative analysis of the degree of temperature confusion, so that the aging area is more accurately identified. The method not only improves the screening accuracy, but also provides more reliable basis for subsequent maintenance and repair work. Therefore, the embodiment further includes a step S44 of updating the auxiliary discrimination value based on the temperature value distribution in the aged region. Specifically, step S44 includes step S441 to step S445 to achieve the above object.
S441, counting the number of temperature values in the suspected aging area;
S442, carrying out average value calculation based on the temperature value in the suspected aging region to obtain a temperature average value corresponding to the suspected aging region;
S443, calculating variance based on the temperature value in the suspected aging area to obtain a temperature variance corresponding to the suspected aging area;
S444, calculating according to the number, the temperature average value and the variance of the temperature values corresponding to the suspected aging areas to obtain the temperature confusion degree corresponding to the suspected aging areas;
for ease of understanding by those skilled in the art, the present embodiment continues with the present time of IR thermographic image For example, the suspected aging area is the first infrared image of the intelligent electronic box at the current momentThe specific calculation formula corresponding to the temperature confusion degree of each suspected aging area is as follows:
Wherein, Infrared thermal imaging image representing current timeThe degree of temperature confusion of the individual suspected aged areas; infrared thermal imaging image representing current time The number of temperature values in each suspected aging region; infrared thermal imaging image representing current time Temperature variances corresponding to the suspected aging areas; infrared thermal imaging image representing current time Within the suspected ageing regionA plurality of temperature values; infrared thermal imaging image representing current time A temperature average value corresponding to each suspected aging region; representing an absolute value function.
Through the calculation, the first infrared thermal imaging image at the current momentThe greater the difference of all the temperature values of the suspected aging areas, the more the infrared thermal imaging image at the current moment is indicatedThe greater the degree of temperature confusion for each suspected aged region; And (3) with In direct proportion to each other,The larger the value of (2), the more the current time is in the infrared thermal imaging imageThe higher the temperature non-uniformity of each suspected aged region, the greater the degree of temperature confusion.
S445, updating the auxiliary discrimination value based on the temperature confusion degree corresponding to the suspected aging region.
In the present embodiment, the greater the auxiliary discrimination value before updating and the greater the degree of temperature confusion are, the greater the likelihood that the suspected aged region is an aged region is considered. The auxiliary discrimination value is updated using the above features. See the following updated formula:
Wherein, Infrared thermal imaging image representing current timeUpdated auxiliary discrimination values corresponding to the suspected aging areas; representing a linear normalization function; infrared thermal imaging image representing current time Auxiliary discrimination values corresponding to the suspected aging areas; infrared thermal imaging image representing current time The degree of temperature confusion for each suspected aged region.
S45, judging whether the obtained aging suspected area is an aging area or not based on the auxiliary judging value corresponding to each aging suspected area.
In the current step, the present embodiment employs a method based on threshold judgment to confirm whether the suspected aging area actually belongs to the aging area. Specifically, in this example, the judgment is assisted by setting a second threshold value to 0.85. In other words, in the present embodiment, the suspected aged region having the auxiliary discrimination value of 0.85 or more is determined as the aged region. According to the embodiment, the aging area can be more accurately identified by analyzing the temperature data at the historical moment and combining the heating characteristics of the circuit, so that erroneous judgment is effectively reduced, and the reliability of the screening result is improved.
S5, calculating the area and the position of the aging area according to the coordinate information corresponding to the aging area;
In this embodiment, the specific method includes converting the coordinates of the pixel points of the contour area into actual physical coordinates, and calculating the area and the position. The area and the position of the aging area are accurately calculated, so that the problem part can be positioned, and specific operation guidance is provided for maintenance personnel.
In this embodiment, the specific method steps employed include converting the coordinates of each pixel point within the contour region to actual physical coordinates. This process involves mapping pixel coordinates in the image to specific locations in the actual object or scene, thereby enabling these coordinates to be described in physical units (e.g., millimeters, centimeters, etc.) to determine the specific locations of the aged regions in the overall object or scene. Then, by calculating the area of the area surrounded by these physical coordinates, the actual area size of the aged area can be obtained.
Through the calculation, the embodiment accurately locates the specific position of the problem part of the intelligent electric box, thereby providing more detailed and specific operation guidance for maintenance personnel. Such guidance includes not only location information of the aged areas. In this way, maintenance personnel can more efficiently and accurately perform maintenance or replacement work, thereby improving the overall work efficiency and the reliability of the equipment.
And S6, sending a temperature average value of an aging area at the current moment, and the area and the position of the aging area to a preset terminal, wherein the temperature average value is calculated by the gray value of an infrared thermal imaging image of the aging area at the current moment.
Specifically, in the present embodiment, the area, the position, and the average temperature of the aged area are transmitted to the maintenance personnel by using a preset terminal. In order to assist maintainer early warning in time can prevent the emergence of electric box trouble and conflagration, improves the security and the reliability of wisdom electric box.
In this embodiment, the preset terminal device may be a device directly connected to the smart box in a wireless manner, for example, a mobile device with preset alarm software installed therein, or a computer terminal device indirectly connected to the smart box through a cloud platform. In this embodiment, by fully utilizing the functions of the cloud platform and the mobile device, the area or specific position information of the aging area can be timely transmitted to the personnel responsible for maintenance. The main purpose of this approach is to help maintenance personnel to receive early warning information quickly, thereby effectively preventing the occurrence of electrical box faults and fires. By the mode, the safety and the reliability of the intelligent electric box can be remarkably improved, and the stable operation of the electric power system is ensured.
Specifically, once the aging area is detected, the cloud platform or the mobile device with the preset alarm software can immediately send early warning information to maintenance personnel through the mobile device. After receiving the information, maintenance personnel can quickly locate a specific aging area and take corresponding maintenance measures, such as replacing aging parts or performing maintenance. The process not only improves the maintenance efficiency, but also effectively reduces the potential safety hazard caused by the fault and fire disaster of the electric box, and ensures the stable operation of the intelligent electric box.
Example 2:
as shown in fig. 5, the present embodiment provides a fault self-diagnosis early warning system of an intelligent electric box, the system includes:
The acquisition module is used for acquiring an infrared thermal imaging image of the inside of the intelligent electronic box in real time;
The contour extraction module is used for carrying out contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area;
the first screening module is used for screening the suspected aging area according to the temperature difference in each contour area;
the second screening module is used for screening and obtaining an aging area according to the heating performance of the aging suspected area in the historical time period;
The information calculation module is used for calculating the area and the position of the aging area according to the coordinate information corresponding to the aging area;
the early warning module is used for sending a temperature average value of an aging area at the current moment, the area and the position of the aging area to a preset terminal, and the temperature average value is calculated by the gray value of an infrared thermal imaging image of the aging area at the current moment.
It should be noted that, regarding the system in the above embodiment, the specific manner in which the respective modules perform the operations has been described in detail in the embodiment regarding the method, and will not be described in detail herein.
Example 3:
Corresponding to the above method embodiment, a fault self-diagnosis and early-warning device for a smart electric box is further provided in this embodiment, and the fault self-diagnosis and early-warning device for a smart electric box described below and the fault self-diagnosis and early-warning method for a smart electric box described above can be referred to correspondingly.
Fig. 6 is a block diagram of a fault self-diagnosis early warning apparatus 800 of a smart box according to an exemplary embodiment. As shown in fig. 6, the fault self-diagnosis and early-warning apparatus 800 of the smart box may include a processor 801 and a memory 802. The fault self-diagnosis and early-warning apparatus 800 of the smart box may further include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the fault self-diagnosis and early-warning device 800 of the smart electric box, so as to complete all or part of the steps in the fault self-diagnosis and early-warning method of the smart electric box. The memory 802 is used to store various types of data to support the operation of the fault self-diagnostic pre-warning device 800 at the smart box, which may include, for example, instructions for any application or method operating on the fault self-diagnostic pre-warning device 800 at the smart box, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the fault self-diagnosis and early-warning device 800 of the smart box and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G, or 4G, or a combination of one or more thereof, so the corresponding communication component 805 may include a Wi-Fi module, a bluetooth module, an NFC module.
In an exemplary embodiment, the fault self-diagnosis and early-warning apparatus 800 of the smart box may be implemented by one or more Application-specific integrated circuits (ASIC), digital signal processors (DIGITALSIGNAL PROCESSOR DSP), digital signal processing apparatus (DIGITAL SIGNAL Processing Device DSPD), programmable logic devices (Programmable Logic Device PLD), field programmable gate array (Field Programmable GATE ARRAY FPGA), controller, microcontroller, microprocessor or other electronic components for performing the fault self-diagnosis and early-warning method of the smart box.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the fault self-diagnosis and early-warning method of a smart electric box described above. For example, the computer readable storage medium may be the above-described memory 802 including program instructions executable by the processor 801 of the intelligent electric box fault self-diagnosis and early-warning apparatus 800 to perform the above-described intelligent electric box fault self-diagnosis and early-warning method.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (8)
1. The fault self-diagnosis early warning method of the intelligent electric box is characterized by comprising the following steps of:
acquiring an infrared thermal imaging image of the inside of the intelligent electronic box in real time;
Carrying out contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area;
screening the suspected aging area according to the temperature difference in each contour area;
screening to obtain an aging region according to the heating performance of the aging suspected region in the historical time period;
calculating the area and the position of the aging area according to the coordinate information corresponding to the aging area;
Transmitting a temperature average value of an aging area at the current moment, and the area and the position of the aging area to a preset terminal, wherein the temperature average value is obtained by calculating the gray value of an infrared thermal imaging image of the aging area at the current moment;
Performing contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area, wherein the contour extraction comprises the following steps:
performing edge detection on the infrared thermal imaging image at the current moment by using an edge detection mathematical model to obtain a plurality of edge pixel points;
performing region extraction in the infrared thermal imaging image at the current moment based on all the edge pixel points to obtain at least one contour region;
Screening to obtain an aging region according to the heating performance of the aging suspected region in the historical time period, wherein the method comprises the following steps:
Converting gray values contained in each suspected aging region into temperature values one by one according to a preset temperature mapping formula;
Carrying out average value calculation based on the temperature value in each suspected aging area to obtain a temperature average value corresponding to each suspected aging area;
counting the operation days of the intelligent electric box and the temperature value corresponding to each historical moment of each suspected aging area in a historical time period;
calculating to obtain an auxiliary discrimination value corresponding to each suspected aging area according to the operation days of the intelligent electric box, the average temperature value corresponding to each suspected aging area and the temperature value in each historical moment;
And judging whether the obtained aging suspected area is an aging area or not based on the auxiliary judging value corresponding to each aging suspected area.
2. The fault self-diagnosis and early warning method of the intelligent electric box according to claim 1, wherein the edge detection mathematical model is a Sobel edge detection algorithm.
3. The fault self-diagnosis and early-warning method of the intelligent electric box according to claim 1, wherein the step of screening the suspected areas of aging according to the temperature difference in each contour area comprises the steps of:
Converting gray values contained in each contour region into temperature values one by one according to a preset temperature mapping formula;
calculating the temperature difference of each contour area according to the temperature value in the contour area;
And judging the temperature difference of each contour area one by one according to a preset first threshold value to obtain an ageing suspected area.
4. The fault self-diagnosis and early-warning method of the intelligent electric box according to claim 3, wherein the calculating the temperature difference of each contour area according to the temperature value in the contour area comprises the following steps:
counting the total number of contour areas in an infrared thermal imaging image at the current moment and the maximum temperature value in each contour area;
calculating the average value based on the temperature value in each contour area to obtain a temperature average value corresponding to each contour area;
And calculating according to a first preset formula, the total number of contour areas in the infrared thermal imaging image, the maximum temperature value in each contour area and the corresponding temperature average value of each contour area to obtain the temperature difference of each contour area.
5. The fault self-diagnosis and early-warning method for the intelligent electric box according to claim 3, wherein the preset first threshold value is 0.8.
6. The fault self-diagnosis and early-warning method of a smart box according to claim 1, wherein the step of judging whether the obtained aging suspected region is an aging region based on the auxiliary discrimination value corresponding to each aging suspected region further comprises:
Counting the number of temperature values in the suspected aging area;
Calculating the average value based on the temperature value in the suspected aging area to obtain the temperature average value corresponding to the suspected aging area;
performing variance calculation based on the temperature value in the suspected aging region to obtain a variance corresponding to the suspected aging region;
calculating according to the number, the temperature average value and the variance of the temperature values corresponding to the suspected aging areas to obtain the temperature confusion degree corresponding to the suspected aging areas;
And updating the auxiliary discrimination value based on the temperature confusion degree corresponding to the suspected aging region.
7. A fault self-diagnosis early warning system of wisdom electronic box, characterized by comprising:
The acquisition module is used for acquiring an infrared thermal imaging image of the inside of the intelligent electronic box in real time;
The contour extraction module is used for carrying out contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area;
the first screening module is used for screening the suspected aging area according to the temperature difference in each contour area;
the second screening module is used for screening and obtaining an aging area according to the heating performance of the aging suspected area in the historical time period;
The information calculation module is used for calculating the area and the position of the aging area according to the coordinate information corresponding to the aging area;
The early warning module is used for sending a temperature average value of an aging area at the current moment, the area and the position of the aging area to a preset terminal, wherein the temperature average value is calculated by the gray value of an infrared thermal imaging image of the aging area at the current moment;
Performing contour extraction on the infrared thermal imaging image at the current moment to obtain at least one contour area, wherein the contour extraction comprises the following steps:
performing edge detection on the infrared thermal imaging image at the current moment by using an edge detection mathematical model to obtain a plurality of edge pixel points;
performing region extraction in the infrared thermal imaging image at the current moment based on all the edge pixel points to obtain at least one contour region;
Screening to obtain an aging region according to the heating performance of the aging suspected region in the historical time period, wherein the method comprises the following steps:
Converting gray values contained in each suspected aging region into temperature values one by one according to a preset temperature mapping formula;
Carrying out average value calculation based on the temperature value in each suspected aging area to obtain a temperature average value corresponding to each suspected aging area;
counting the operation days of the intelligent electric box and the temperature value corresponding to each historical moment of each suspected aging area in a historical time period;
calculating to obtain an auxiliary discrimination value corresponding to each suspected aging area according to the operation days of the intelligent electric box, the average temperature value corresponding to each suspected aging area and the temperature value in each historical moment;
And judging whether the obtained aging suspected area is an aging area or not based on the auxiliary judging value corresponding to each aging suspected area.
8. The utility model provides a trouble self-diagnosis early warning equipment of wisdom electronic box which characterized in that includes:
A memory for storing a computer program;
A processor for implementing the steps of the fault self-diagnosis and early-warning method of the intelligent electronic box according to any one of claims 1 to 6 when executing the computer program.
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