Automatic monitoring method for temperature of busbar of each phase for infrared temperature measurement of busbar joint

Abstract

The invention relates to the technical field of bus joint temperature measurement, and discloses a method for automatically monitoring the temperature of each phase busbar for infrared temperature measurement of a bus joint, and the method comprises the steps: collecting the surface temperature data and real-time images of each phase busbar of the bus joint in real time through installing an infrared temperature measurement sensor and image collection equipment. After preprocessing, temperature data is converted into an image form, and the image form and a bus bar real-time image are superposed to generate a fusion image, so that the actual form and the temperature distribution condition of the bus bar can be observed at the same time. Through presetting a high temperature early warning threshold value and a temperature gradient change threshold value, a temperature gradient direction is calculated and a change trend is analyzed so that prediction and early warning of temperature abnormity are realized. And when the temperature or temperature gradient change exceeds a preset threshold value, an early warning mechanism is triggered, an alarm signal is sent out, and an abnormal position and related information are displayed through a visual interface. According to the invention, the use safety and reliability of the bus duct are improved, and the fault risk caused by abnormal temperature is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of busbar joint temperature measurement, and in particular to a method for automatically monitoring the temperature of each phase busbar for infrared temperature measurement of busbar joints. Background Art

[0002] With the emergence of modern engineering facilities and equipment, the electricity consumption of various industries has increased rapidly, especially with the emergence of numerous high-rise buildings and large factories and workshops. Traditional cables as transmission conductors can no longer meet the requirements in high-current transmission systems. The parallel use of multiple cables has brought many inconveniences to on-site installation and construction. Plug-in bus ducts have emerged as a new type of distribution conductor. Compared with traditional cables, they fully demonstrate their superiority in high-current transmission. However, with the popularization of bus ducts, some problems often occur in production and use.

[0003] The insulation between the bus duct and the ground is damaged. The insulation of the wire is reduced due to some reasons (wind, rain, sun, moisture, collision, scratch, friction, corrosion, etc.) at a certain place in the system line, resulting in partial current passing between the wire and the ground. There are hidden dangers to personal safety, and in serious cases, it may cause fire.

[0004] The contact resistance of the connector is too large, improper installation, such as loose connection, long-term use or corrosion caused by environmental factors, resulting in excessive contact resistance of the connector at the joint, which in turn causes temperature rise problems, and in severe cases, causes the connector to burn.

[0005] The phase-to-phase short circuit of the bus duct, aging, dirt and other factors lead to damage of the insulating material, improper installation leads to poor phase-to-phase insulation and sparking, and in severe cases, direct short circuit breakdown causes high-temperature burning. Summary of the invention

[0006] The object of the present invention is to provide a method for automatically monitoring the temperature of each phase busbar for infrared temperature measurement of busbar joints, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for automatically monitoring the temperature of each phase busbar for infrared temperature measurement of busbar joints, the method comprising:

[0008] Install infrared temperature sensors and image acquisition equipment suitable for the working environment of the busbar, which are used to measure the temperature of each phase busbar and capture the real-time image of the busbar respectively;

[0009] The infrared temperature sensor is used to regularly collect the surface temperature data of each phase busbar of the busbar joint, and the image acquisition device is used to regularly capture the real-time image of the busbar;

[0010] Preprocess the collected temperature data, including denoising and calibration operations;

[0011] The preprocessed temperature data is converted into an image form, and the temperature difference is displayed using color mapping to form a temperature distribution image;

[0012] The temperature distribution image is superimposed on the real-time image of the busbar to generate a fused image, wherein the fused image is used to simultaneously observe the actual shape and temperature distribution of the busbar;

[0013] According to the normal operating temperature range and historical data of the bus duct, preset high temperature warning threshold and temperature gradient change threshold;

[0014] Calculate the temperature gradient direction, analyze the temperature change trend over time, and predict the development path of temperature anomalies by comparing the temperature gradient changes at different time points;

[0015] Monitor temperature data in real time and trigger an early warning mechanism when the temperature or temperature gradient exceeds a preset threshold;

[0016] An alarm signal is issued, and the abnormal location and related information are displayed through a visual interface so that users can take timely measures to deal with the abnormality.

[0017] Preferably, the method for denoising the collected temperature data is:

[0018] Using the wavelet denoising algorithm, the original temperature data is decomposed into multiple levels of wavelets to obtain wavelet coefficients at different scales.

[0019] Threshold processing is performed on the wavelet coefficients, and the wavelet coefficients below a certain threshold are set to zero or shrunk to remove or weaken the noise component;

[0020] The processed wavelet coefficients are inversely transformed to reconstruct the denoised temperature data set.

[0021] Preferably, the step of generating a temperature distribution image using the preprocessed temperature data comprises:

[0022] S1: Normalize the preprocessed temperature data and convert it to a fixed value range. The normalization formula is:

[0023]

[0024] Where T is the original temperature value, T min and T max are the minimum and maximum values ​​of the temperature data, T norm is the normalized temperature value;

[0025] S2: constructing a color mapping table according to the normalized temperature value range, wherein the color mapping table is an array containing multiple color entries, each entry corresponding to a normalized temperature value and a corresponding color RGB value;

[0026] S3: Traverse the preprocessed temperature data, find the corresponding color for each temperature value according to the normalization result and the color mapping table, and apply the color to the corresponding position in the image, and finally generate an image showing the temperature distribution.

[0027] Preferably, the method for generating the fused image is:

[0028] The size of the temperature distribution image is adjusted through an image scaling algorithm to match the real-time image of the busbar;

[0029] Set the transparency α for the temperature distribution image. The value range of transparency α is 0 to 1, where 0 means completely transparent and 1 means completely opaque.

[0030] The image superposition algorithm is used to superimpose the temperature distribution image with transparency onto the real-time image of the busbar. The specific algorithm is as follows: Assume that the real-time image of the busbar is I real , the temperature distribution image is I temp , then the fused image I fuse =(1-α)*I real +α*I temp .

[0031] Preferably, the following steps are performed on the collected temperature data to calculate the temperature gradient direction and analyze the temperature variation trend over time:

[0032] Step 1: Calculate the temperature gradient: For the temperature data at each time point, use the temperature gradient formula to calculate the temperature gradient vector; suppose the temperature data is T(x,y,t), where (x,y) is the spatial coordinate and t is the time, then the temperature gradient vector is

[0033] Step 2: Determine the direction of the temperature gradient: Determine the direction of the temperature gradient based on the calculated temperature gradient vector; the direction of the temperature gradient is the direction in which the temperature changes fastest, and the direction angle of the temperature gradient vector is given;

[0034] Step 3: Analysis of temperature change trend: For temperature data at different time points, repeat steps 1 and 2 to obtain the change of temperature gradient over time; analyze the change trend of the size and direction of the temperature gradient over time, and identify the temperature anomaly area and its development trend; when the direction of the temperature gradient remains consistent over time and the rate of change is large, it indicates that the temperature anomaly may continue to develop in this direction; use machine learning algorithms to establish a prediction model for the development path of temperature anomalies; based on the prediction model, predict the development path of temperature anomalies at future time points.

[0035] Preferably, the method further comprises: using an image edge detection algorithm to identify temperature abnormality areas in the fused image and mark abnormal locations.

[0036] Preferably, the method further comprises: designing a visual user interface for displaying temperature data, temperature gradient vectors and abnormal positions; providing the functions of zooming in, zooming out, rotating and translating images on the user interface so as to view the abnormal positions and actual on-site conditions in detail.

[0037] Preferably, suggested countermeasures and operation guides are displayed on the user interface to help users quickly handle exceptions.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] This method combines infrared temperature measurement and image processing technology to convert temperature data into intuitive image form and display temperature differences through color mapping. This enables operation and maintenance personnel to quickly and accurately identify temperature abnormal areas, thereby improving the efficiency and accuracy of fault detection.

[0040] By calculating the temperature gradient direction and analyzing the temperature change trend over time, the present invention can predict the development path of temperature anomalies, which helps operation and maintenance personnel take preventive measures in advance to avoid failures, thereby ensuring the stable operation of the power transmission system.

[0041] The infrared temperature sensor and image acquisition device used in this method are suitable for the working environment of the busbar, are easy to install and do not affect the normal operation of the busbar. At the same time, due to the use of automated monitoring technology, the frequency and difficulty of manual inspections are reduced, and maintenance costs are reduced.

[0042] When the temperature or temperature gradient changes exceed the preset threshold, the present invention will trigger the early warning mechanism and display the abnormal location and related information through a visual interface. This enables the operation and maintenance personnel to quickly locate the fault point and take corresponding measures, thereby effectively shortening the fault handling time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a step diagram of the method for automatically monitoring the temperature of each phase busbar according to the present invention;

[0044] Figure 2 A diagram of the steps for generating a temperature distribution image for the present invention;

[0045] Figure 3 This is a step diagram for analyzing the temperature change trend of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1-3 The present invention provides a technical solution: a method for automatically monitoring the temperature of each phase busbar for infrared temperature measurement of busbar joints, the method comprising:

[0048] Install hardware equipment: Select high-precision infrared temperature sensors and high-definition image acquisition equipment suitable for the working environment of the busbar. The infrared temperature sensor must have fast response, high sensitivity and anti-interference ability to ensure the accuracy of temperature measurement. The image acquisition equipment should have high resolution and wide-angle shooting functions to clearly capture the real-time image of the busbar. Install the infrared temperature sensor and image acquisition equipment in the appropriate position of the busduct to ensure that they can fully cover the busbars of each phase and avoid obstruction and interference.

[0049] Data collection: The infrared temperature sensor is used to collect the surface temperature data of each phase busbar of the busbar joint at regular intervals. The sampling frequency can be set according to actual needs, such as once per minute. At the same time, the image acquisition device is used to capture the real-time image of the busbar at regular intervals and record it synchronously with the temperature data for subsequent analysis.

[0050] Data preprocessing: De-noise the collected temperature data, use filtering algorithms or statistical methods to remove outliers and noise interference, and improve the accuracy of the data. Calibrate the temperature data and make corrections based on the characteristics of the sensor and environmental factors to ensure the accuracy of the measurement results.

[0051] Temperature data visualization: Convert the preprocessed temperature data into an image format, use color mapping technology to display the temperature difference, and form a temperature distribution image. Color mapping can be selected according to actual needs, such as using a heat map or rainbow map. Overlay the temperature distribution image with the real-time image of the busbar to generate a fused image. The fused image should be able to clearly display the actual shape and temperature distribution of the busbar, which is convenient for users to observe and analyze intuitively.

[0052] Warning mechanism setting: According to the normal operating temperature range and historical data of the bus duct, preset high temperature warning threshold and temperature gradient change threshold. The high temperature warning threshold can be set according to the rated temperature and safety margin of the bus duct, and the temperature gradient change threshold can be set according to the temperature change rate and abnormal conditions.

[0053] Calculate the temperature gradient direction and analyze the temperature change trend over time. By comparing the temperature gradient changes at different time points, predict the development path of temperature anomalies and provide a basis for early warning.

[0054] Real-time monitoring and early warning: Real-time monitoring of temperature data, when the temperature or temperature gradient changes exceed the preset threshold, the early warning mechanism is triggered. The early warning mechanism can use sound and light alarms, SMS notifications or email reminders to ensure that users can be informed of abnormal situations in a timely manner. After the alarm signal is issued, the abnormal location and related information, such as abnormal temperature values, temperature change trends and possible causes, are displayed through a visual interface. The visual interface should be concise and clear, so that users can quickly locate and handle abnormalities.

[0055] Abnormal handling: After receiving the alarm signal, the user should immediately take measures to handle the abnormality, such as checking the insulation of the bus duct, whether the connector contact is good, whether there is a short circuit, etc. According to the abnormal situation and the handling results, the early warning mechanism and monitoring parameters should be adjusted in time to improve the accuracy and reliability of monitoring.

[0056] The present invention collects temperature data and real-time images of each phase busbar of the busbar joint by installing infrared temperature sensors and image acquisition equipment, and forms a fused image for observation and analysis after data preprocessing and visualization. At the same time, an early warning mechanism and real-time monitoring are set to ensure that early warnings can be triggered in time and measures can be taken to deal with abnormal temperatures. This method can improve the safety and reliability of the busbar duct and reduce failures and accidents caused by abnormal temperatures.

[0057] The present invention will be further described below in conjunction with Examples 1 to 3:

[0058] Embodiment 1:

[0059] The collected temperature data is denoised using a wavelet denoising algorithm. The specific steps are as follows:

[0060] i) Perform multi-level wavelet decomposition on the original temperature data. Select the Daubechies wavelet basis function, which has good time-frequency localization characteristics and is suitable for processing non-stationary signals. Determine the decomposition level based on the complexity of the temperature data and the denoising requirements. Too high a decomposition level may result in information loss, while too low a decomposition level may not fully remove noise.

[0061] The original temperature data is decomposed by wavelet using the selected wavelet basis function and decomposition series to obtain wavelet coefficients at different scales. These wavelet coefficients contain the low-frequency and high-frequency components of the temperature data, among which the high-frequency components mainly correspond to noise.

[0062] ii) Perform threshold processing on the obtained wavelet coefficients to remove or weaken the noise component, and determine the threshold according to the intensity of the noise and the characteristics of the temperature data.

[0063] The hard threshold method can be used to set the wavelet coefficients below a certain threshold to zero to completely remove the noise component; the soft threshold method can also be used to shrink the wavelet coefficients below the threshold to reduce the impact of noise. During the threshold processing, the sign of the wavelet coefficient needs to be kept unchanged to ensure that the reconstructed temperature data maintains the original trend and characteristics.

[0064] iii) Perform an inverse transformation on the processed wavelet coefficients to reconstruct the denoised temperature data set. The inverse transformation process is the inverse process of wavelet decomposition. Through the inverse transformation, the processed wavelet coefficients can be converted back to the original temperature data domain. During the inverse transformation process, it is necessary to ensure the accuracy and stability of the reconstruction to avoid distortion or error accumulation.

[0065] Assume that a set of collected original temperature data is {T1, T2, ..., Tn}, where n is the number of data points. Select Daubechies wavelet as the wavelet basis function, and the decomposition level is 3.

[0066] The original temperature data is decomposed by wavelet to obtain three-level wavelet coefficients {W1, W2, W3}, where W1 is the highest frequency coefficient and W3 is the lowest frequency coefficient.

[0067] Threshold processing is performed on the wavelet coefficients. Assuming that the selected threshold is β, the wavelet coefficients below β are set to zero or shrunk. The processed wavelet coefficients are inversely transformed to reconstruct the denoised temperature data set {T'1, T'2, ..., T'n}.

[0068] Through the above embodiments, the noise components in the temperature data can be effectively removed or weakened, thereby improving the accuracy and reliability of the data.

[0069] The steps of generating a temperature distribution image using the preprocessed temperature data include:

[0070] S1: Normalize the calibrated temperature data and convert it to a value range of 0 to 1. The normalization formula is:

[0071]

[0072] Where T is the original temperature value, Tmin and T max are the minimum and maximum values ​​of the temperature data, T norm is the normalized temperature value;

[0073] S2: Construct a color mapping table according to the normalized temperature value range. The color mapping table can be an array containing multiple color entries, each entry corresponding to a normalized temperature value and a corresponding color value (such as an RGB value). The selection of the color mapping table should take into account the human eye's sensitivity to color and the range of temperature data to ensure that the temperature difference can be clearly and accurately displayed in the image. For example, the Jet color mapping table is used, in which low temperature corresponds to blue and high temperature corresponds to red.

[0074] S3: Traverse the preprocessed temperature data, find the corresponding color for each temperature value based on the normalization result and the color mapping table, and apply the color to the corresponding position in the image. The specific steps are as follows:

[0075] S3.1. Initialize the image matrix: Initialize a blank image matrix according to the size and resolution of the temperature data. Each element of the image matrix corresponds to a temperature value in the temperature data.

[0076] S3.2, color filling: For each temperature value in the temperature data, find the corresponding color value according to the normalization result and the color mapping table, and fill the color value into the corresponding position of the image matrix.

[0077] S3.3, Image rendering: Use image rendering algorithms (such as nearest neighbor interpolation, bilinear interpolation, etc.) to render the image matrix to generate the final temperature distribution image. The image resolution and display effect can be considered during the rendering process to ensure the clarity and accuracy of the image.

[0078] Embodiment 2:

[0079] The temperature distribution image is superimposed with the real-time image of the busbar to generate a fused image. The specific method is as follows:

[0080] The busbar real-time image and temperature distribution image are registered using a method based on feature point matching. Feature points, such as corner points and edge points, are extracted from the two images. The corresponding feature points in the two images are found using a feature point matching algorithm (such as SIFT, SURF, etc.). The transformation matrix between the two images is calculated based on the matching results to achieve image registration. During the registration process, it should be ensured that the corresponding parts of the two images can be accurately aligned to avoid misalignment or ghosting. The registration accuracy should meet the requirements of subsequent fusion processing.

[0081] The registered temperature distribution image is fused with the busbar real-time image by using the transparency blending method. First, a transparency value α (ranging from 0 to 1) is set to control the transparency of the temperature distribution image in the fused image. Then, the value of each pixel in the fused image is calculated according to the transparency value. The specific calculation formula is: Let the busbar real-time image be I real , the temperature distribution image is I temp , then the fused image I fuse =(1-α)*I real +α*I temp .

[0082] During the fusion process, the transparency value can be adjusted according to actual needs to achieve the best fusion effect. The selection of the transparency value should take into account the clarity of the temperature distribution information and the visibility of the actual shape of the busbar.

[0083] For the collected temperature data, perform the following steps to calculate the temperature gradient direction and analyze the temperature change trend over time:

[0084] Step 1: Calculate the temperature gradient: For the temperature data at each time point, use the temperature gradient formula to calculate the temperature gradient vector; suppose the temperature data is T(x,y,t), where (x,y) is the spatial coordinate and t is the time, then the temperature gradient vector is The central difference method is used to approximate the components of the temperature gradient vector. For example, the partial derivative of the temperature data T(x,y,t) in the x direction can be calculated as Where Δx is a small displacement in the x-direction; similarly, the partial derivative in the y-direction can be calculated.

[0085] Step 2: Determine the direction of the temperature gradient: Determine the direction of the temperature gradient based on the calculated temperature gradient vector; the direction of the temperature gradient is the direction in which the temperature changes fastest, and the direction angle of the temperature gradient vector is Given; determine the direction of the temperature gradient according to the direction angle. For example, when the direction angle is 0 degrees, it means that the temperature gradient direction is along the positive direction of the x-axis; when the direction angle is 90 degrees, it means that the temperature gradient direction is along the positive direction of the y-axis.

[0086] Step 3, temperature change trend analysis: For temperature data at different time points, repeat steps 1 and 2 to obtain the change of temperature gradient over time; analyze the change trend of the size and direction of the temperature gradient over time, identify the temperature anomaly area and its development trend; when the temperature gradient direction remains consistent over time and the rate of change is large, it indicates that the temperature anomaly may continue to develop in this direction; use machine learning algorithms to establish a prediction model for the development path of temperature anomalies; based on the prediction model, predict the development path of temperature anomalies at future time points. For example, when the temperature gradient in a certain area continues to increase and the direction remains consistent, it indicates that there may be temperature anomalies in the area.

[0087] The specific method for predicting the development path of temperature anomaly is as follows:

[0088] Data comparison: Compare the temperature gradient changes at different time points, especially focusing on the consistency of the temperature gradient direction and the rate of change.

[0089] Prediction model establishment: A linear regression model is used to establish a prediction model for the development path of temperature anomalies. Assuming that the development path of temperature anomalies is linearly related to time, a prediction model can be obtained by fitting historical data.

[0090] Path prediction: Based on the prediction model, predict the possible location and development path of temperature anomalies at future time points. For example, assuming that at a certain time point, the temperature anomaly area is located at (x0, y0), and the temperature gradient direction is θ degrees, and the change rate is v units / time step, then after one time step in the future, the temperature anomaly area may be located at (x0+vcos(θ), y0+vsin(θ)).

[0091] Embodiment 3:

[0092] In order to further improve the intelligence and convenience of busbar joint temperature monitoring, this embodiment adds the application of image edge detection algorithm, the design of visual user interface, and the display of countermeasures and operation guide on the original basis. The specific implementation is as follows:

[0093] i) Application of image edge detection algorithm:

[0094] After the fused image is generated, an image edge detection algorithm (such as Canny edge detector, Sobel operator, etc.) is used to perform edge detection on the fused image to identify the boundary of the temperature abnormality area.

[0095] By setting a temperature threshold or gradient threshold, areas above the threshold are marked as temperature abnormal areas, and the boundaries of these areas are accurately extracted using an edge detection algorithm.

[0096] The identified temperature anomaly areas are marked, such as by highlighting them with frames of different colors or shapes, so that users can quickly locate the abnormal locations.

[0097] ii) Design of visual user interface:

[0098] Design an intuitive and easy-to-use visual user interface to display temperature data, temperature gradient vectors, and abnormal locations. The user interface should have good interactivity to facilitate user operation and viewing.

[0099] On the user interface, the temperature data is displayed in the form of a chart or heat map, allowing the user to clearly see the temperature distribution of each phase busbar.

[0100] Display the temperature gradient vector, and indicate the direction and magnitude of the temperature gradient through arrows or streamline diagrams, to help users understand the trend and law of temperature change.

[0101] It provides functions of zooming in, zooming out, rotating and panning images so that users can view the abnormal location and actual on-site conditions in detail. These functions can be realized through input devices such as mouse or touch screen, which is convenient for users to operate flexibly.

[0102] iii) Presentation of response measures and operational guidelines:

[0103] On the user interface, corresponding countermeasures and operation guidelines are displayed according to the type and severity of the identified temperature anomaly. These measures and guidelines can be presented in the form of text, pictures or videos, so that users can quickly understand and execute them.

[0104] For example, when the temperature anomaly caused by the insulation damage of the bus duct to the ground is identified, the user interface can show the steps and precautions for checking the insulation material, replacing the damaged parts and performing the insulation test. Emergency contact information and fault reporting functions are provided so that users can seek help and report faults in time when encountering an emergency.

[0105] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0106] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints, characterized in that: The method comprises: Install infrared temperature sensors and image acquisition equipment suitable for the working environment of the busbar, which are used to measure the temperature of each phase busbar and capture the real-time image of the busbar respectively; The infrared temperature sensor is used to regularly collect the surface temperature data of each phase busbar of the busbar joint, and the image acquisition device is used to regularly capture the real-time image of the busbar; Preprocess the collected temperature data, including denoising and calibration operations; The preprocessed temperature data is converted into an image form, and the temperature difference is displayed using color mapping to form a temperature distribution image; The temperature distribution image is superimposed on the real-time image of the busbar to generate a fused image, wherein the fused image is used to simultaneously observe the actual shape and temperature distribution of the busbar; According to the normal operating temperature range and historical data of the bus duct, preset high temperature warning threshold and temperature gradient change threshold; Calculate the temperature gradient direction, analyze the temperature change trend over time, and predict the development path of temperature anomalies by comparing the temperature gradient changes at different time points; Monitor temperature data in real time and trigger an early warning mechanism when the temperature or temperature gradient exceeds a preset threshold; An alarm signal is issued, and the abnormal location and related information are displayed through a visual interface so that users can take timely measures to deal with the abnormality.

2. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 1 is characterized in that: The method for denoising the collected temperature data is: Using the wavelet denoising algorithm, the original temperature data is decomposed into multiple levels of wavelets to obtain wavelet coefficients at different scales. Threshold processing is performed on the wavelet coefficients, and the wavelet coefficients below a certain threshold are set to zero or shrunk to remove or weaken the noise component; The processed wavelet coefficients are inversely transformed to reconstruct the denoised temperature data set.

3. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 1 is characterized in that: The steps of generating a temperature distribution image using the preprocessed temperature data include: S1: Normalize the preprocessed temperature data and convert it to a fixed value range. The normalization formula is: Where T is the original temperature value, T min and T max are the minimum and maximum values ​​of the temperature data, T norm is the normalized temperature value; S2: constructing a color mapping table according to the normalized temperature value range, wherein the color mapping table is an array containing multiple color entries, each entry corresponding to a normalized temperature value and a corresponding color RGB value; S3: Traverse the preprocessed temperature data, for each temperature value, find the corresponding color according to the normalization result and the color mapping table, and apply the color to the corresponding position in the image, and finally generate an image showing the temperature distribution.

4. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 3 is characterized in that: The method to generate the fused image is: The size of the temperature distribution image is adjusted through an image scaling algorithm to match the real-time image of the busbar; Set the transparency α for the temperature distribution image. The value range of transparency α is 0 to 1, where 0 means completely transparent and 1 means completely opaque. The image superposition algorithm is used to superimpose the temperature distribution image with transparency onto the real-time image of the busbar. The specific algorithm is as follows: Assume that the real-time image of the busbar is I real , the temperature distribution image is I temp , then the fused image I fuse =(1-α)*I real +α*I temp .

5. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 1 is characterized in that: For the collected temperature data, perform the following steps to calculate the temperature gradient direction and analyze the temperature change trend over time: Step 1: Calculate the temperature gradient: For the temperature data at each time point, use the temperature gradient formula to calculate the temperature gradient vector; suppose the temperature data is T(x,y,t), where (x,y) is the spatial coordinate and t is the time, then the temperature gradient vector is Step 2: Determine the direction of the temperature gradient: Determine the direction of the temperature gradient based on the calculated temperature gradient vector; the direction of the temperature gradient is the direction in which the temperature changes fastest, and the direction angle of the temperature gradient vector is given; Step 3: Analysis of temperature change trend: For temperature data at different time points, repeat steps 1 and 2 to obtain the change of temperature gradient over time; analyze the change trend of the size and direction of the temperature gradient over time, and identify the temperature anomaly area and its development trend; when the direction of the temperature gradient remains consistent over time and the rate of change is large, it indicates that the temperature anomaly may continue to develop in this direction; use machine learning algorithms to establish a prediction model for the development path of temperature anomalies; based on the prediction model, predict the development path of temperature anomalies at future time points.

6. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 1 is characterized in that: The method further comprises: using an image edge detection algorithm to identify an abnormal temperature area in the fused image and mark the abnormal position.

7. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 6 is characterized in that: The method also includes: designing a visual user interface for displaying temperature data, temperature gradient vectors and abnormal positions; providing functions of zooming in, zooming out, rotating and translating images on the user interface so as to view the abnormal positions and actual on-site conditions in detail.

8. The method for automatically monitoring the temperature of each phase busbar by infrared temperature measurement of busbar joints according to claim 7 is characterized in that: Display recommended countermeasures and operation guides on the user interface to help users quickly handle exceptions.

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