A Fault Identification Method for a Central Water Source Heat Pump Heating System

Abstract

The present invention relates to the field of image processing technology. The present invention relates to a method for identifying faults in a medium water source heat pump heating system, which includes obtaining infrared images of key parts in the medium water source heat pump heating system, and determining local hot spots or cold spots in the infrared images of the key parts according to an image processing algorithm; detecting the temperature distribution with the hot spot as the center to determine the temperature gradient in the area around the hot spot and the temperature difference value between the hot spot and its adjacent area; determining the first fault type and the first fault degree according to the change rate of the temperature gradient between the hot spot and its adjacent area and the temperature difference value; in response to the first fault degree being greater than the first fault threshold, generating a corresponding fault report according to the position, fault type and fault degree of the key part and starting an alarm. According to the solution of the present invention, accurate alarm of faults is achieved, which is beneficial to improving the safety and reliability of the heating system.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing. More specifically, the present invention relates to a method for identifying faults in a medium water source heat pump heating system. Background Art

[0002] A medium water source heat pump is a heat pump device that uses reclaimed water as a heat source and improves the low-temperature heat energy of reclaimed water through heat pump technology to achieve functions such as indoor heating and cooling. Reclaimed water refers to domestic sewage used in buildings for bathing, laundry, dishwashing, etc. Since the temperature of reclaimed water is usually above 20°C, it has good potential for heat energy utilization. The medium water source heat pump is a heat pump device that utilizes this high-quality heat source of reclaimed water and converts it into building air-conditioning hot water.

[0003] The working principle of a medium water source heat pump is basically the same as that of a traditional water source heat pump. However, the medium water source heat pump is equipped with a reclaimed water heat exchanger outdoors. Through the heat exchanger, heat exchange is carried out between reclaimed water and refrigerant, so that the refrigerant absorbs the low-temperature heat of reclaimed water to form high-temperature and high-pressure steam. Then, the high-temperature and high-pressure steam is further heat-exchanged through an indoor device to release heat energy indoors, realizing indoor heating and cooling.

[0004] Therefore, the medium water source heat pump is an excellent heat pump device that can not only achieve high energy efficiency, but also meet the requirements of environmental protection, health, and operation stability. However, at present, the application of medium water source heat pumps is still in its infancy, and there are still many potential fault situations for the equipment. How to effectively monitor the operation status of the equipment and timely detect abnormalities plays an important role in improving the application level of the equipment and extending the system life.

[0005] The Chinese patent application document with the publication number CN110108509A provides an intelligent fault diagnosis method for a sewage source heat pump unit. First, historical data of fault-free and fault operation are obtained through the historical data collected by sensors installed on the sewage source heat pump, and a BP neural network model is constructed. The weights and thresholds of the BP neural network are optimized through a genetic algorithm (GA). However, although this method realizes the fault diagnosis of the sewage source heat pump, there are still problems with inaccurate analysis of the results detected by the sensors, resulting in the inability to accurately determine system faults in a timely manner.

[0006] Therefore, how to solve the problems of poor accuracy and low efficiency in fault identification of the current medium water source heat pump heating system is an important part of the current development of the medium water source heat pump system. Summary of the Invention

[0007] The present invention aims to use image processing technology to monitor the operation status of a medium water source heat pump heating system in real time and diagnose faults.

[0008] The present invention provides a method for identifying faults in a medium water source heat pump heating system, including: obtaining infrared images of key parts in the medium water source heat pump heating system, and determining local hot spots or cold spots in the infrared images of the key parts according to an image processing algorithm; performing temperature distribution detection with the hot spot as the center to determine the temperature gradient in the area around the hot spot and the temperature difference value between the hot spot and its adjacent area; determining the first fault type and the first fault degree according to the change rate of the temperature gradient between the hot spot and its adjacent area and the temperature difference value, wherein the first fault type is obtained based on the uniformity of the change rate of the temperature gradient and the temperature difference value, the first fault degree is positively correlated with the change rate of the temperature gradient, and negatively correlated with the variance value of the temperature difference value; in response to the first fault degree being greater than the first fault threshold, generating a corresponding fault report according to the position, fault type and fault degree of the key part and starting an alarm.

[0009] In the medium water source heat pump heating system of the present invention, local hot spots are used to analyze and identify system faults. By analyzing and identifying the temperature distribution of the thermal image, effective and accurate detection of faults in key parts is realized. The fault degree is calculated through the temperature gradient in the area around the hot spot and the temperature difference value between the hot spot and its adjacent area, and accurate alarm of the fault is realized, which is beneficial to improving the safety and reliability of the heating system.

[0010] Preferably, determining local hot spots or cold spots in the infrared images of key parts according to an image processing algorithm includes: converting the infrared image into a grayscale image; setting a temperature threshold range, and using an edge detection algorithm to identify local high-temperature areas or local low-temperature areas in the grayscale image as potential fault points; taking the highest temperature point in the local high-temperature area as the hot spot, taking the lowest temperature point in the local low-temperature area as the cold spot, and defining a window of a set size according to the detected positions of the hot spot and the cold spot to capture the temperature distribution in the area adjacent to the hot spot or the cold spot.

[0011] The present invention uses image processing technology to detect hot spots or cold spots that appear in key parts. By setting a temperature threshold range to identify areas in the grayscale image, potential fault points are found, and the accurate capture of the fault area can be realized by detecting the hot spot and the cold spot.

[0012] Preferably, the first fault type includes poor contact, electrical fault and mechanical friction.

[0013] The present invention performs temperature distribution detection with the local hot spot as the center, determines whether the first fault types such as poor contact, electrical fault and mechanical friction have occurred, and evaluates the fault degree at the same time.

[0014] Preferably, the first type of fault is determined according to the change rate of the temperature gradient between the hot spot and its adjacent area and the temperature difference value, including: determining that there is poor contact in response to the change rate of the temperature gradient being greater than the first threshold and the temperature difference value being unevenly distributed; determining that there is an electrical fault in response to the change rate of the temperature gradient being less than the first threshold and greater than the second threshold, and at the same time the temperature difference value being evenly distributed; determining that there is mechanical friction in response to the change rate of the temperature gradient being less than the second threshold and the temperature difference value being evenly distributed.

[0015] The present invention divides the fault types by combining the system characteristics, and identifies the fault types according to the change rate of the temperature gradient and the distribution of the temperature difference value, effectively improving the accuracy of fault identification.

[0016] Preferably, the calculation formula for the first degree of fault is:

[0017] ;

[0018] In the formula, represents the degree of fault at the th hot spot, represents the temperature gradient at the hot spot, represents the temperature gradient at the th pixel point in the area around the th hot spot, represents the number of the farthest pixel points in the area adjacent to the hot spot, represents the th hot spot and the th pixel point in its surrounding area, represents the variance function, α represents the weight coefficient of the change rate of the temperature gradient, and β represents the weight coefficient of the temperature difference value.

[0019] Preferably, it further includes: extracting the device operation image centered on the cold spot, and detecting fog based on the image features of the device operation image to determine whether the second type of fault occurs; determining the second degree of fault according to the fog area in the device operation image and the size of the local low-temperature area centered on the cold spot, where the second degree of fault is positively correlated with the fog area and the local low-temperature area; in response to the second degree of fault being greater than the second fault threshold, generating a corresponding fault report according to the position, fault type, and degree of fault of the key part and starting an alarm.

[0020] The present invention analyzes the image of the cold spot area, detects the fog area according to the image features, and calculates the degree of fault by combining the fog area and the size of the low-temperature area around the cold spot, realizing the accurate identification of system faults and effectively ensuring the safety of the heating system during operation.

[0021] Preferably, the second type of fault includes refrigerant leakage or other cooling system faults.

[0022] Preferably, fog detection is performed based on the image features of the device operation image to determine whether the second type of fault occurs, including: preprocessing the operation image, and extracting the fog features in the preprocessed device operation image, where the fog features include color features and texture features; determining the fog area according to the fog features, and determining the area of the fog pixel area according to the connected domain analysis algorithm; in response to the area being greater than the set area, determining that the second type of fault occurs.

[0023] The present invention uses image processing technology to monitor the cold spot area of the intermediate water heat pump heating system in real time. By extracting image features and applying machine learning algorithms, automatic identification and early warning of system faults are realized, effectively improving the accuracy of fault identification in the heating system.

[0024] Preferably, the calculation formula for the second fault degree is:

[0025]

[0026] In the formula, represents the second fault degree, represents the th area of the fog area at the cold spot, represents the area of the local low-temperature area, represents the total number of cold spots, represents the total area of the device operation image, represents the weight coefficient of the fog area area, the weight coefficient of the low-temperature area area, and .

[0027] Preferably, preprocessing the operation image includes: denoising the operation image and enhancing the contrast.

[0028] The beneficial effects of the present invention are as follows: The present invention uses infrared images to obtain local hot spots in the heating system, and performs temperature distribution detection centered on the local hot spots. The rate of change and temperature difference value of the temperature gradient between the hot spot and its adjacent area are used for fault detection, effectively improving the accuracy of fault detection in the heating system. At the same time, no complex algorithm model is required, effectively improving the fault detection efficiency. Brief Description of the Drawings

[0029] Figure 1 is a flowchart schematically showing a method for identifying faults in a water source heat pump heating system according to an embodiment of the present invention;

[0030] Figure 2 is a flowchart schematically showing a method for determining local hot spots or cold spots according to an embodiment of the present invention;

[0031] Figure 3 is a flowchart schematically showing a method for detecting cold point detection failures according to an embodiment of the present invention;

[0032] Figure 4 is a flowchart schematically showing a method for determining a second type of failure according to an embodiment of the present invention. Detailed implementation manners

[0033] In a water source heat pump system, image processing technology can be used to identify various potential failure situations. By installing devices such as cameras and thermal imagers and combining advanced image analysis algorithms, the operation status of the system can be effectively monitored and abnormalities can be detected in a timely manner.

[0034] The following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings.

[0035] Figure 1 is a flowchart schematically showing a method 100 for identifying failures in a water source heat pump heating system according to an embodiment of the present invention.

[0036] As Figure 1 shown, at step S101, hot spot or cold point detection. Specifically, infrared images of key parts in the medium water source heat pump heating system are obtained, and local hot spots or cold points in the infrared images of the key parts are determined according to image processing algorithms. In a water source heat pump system, image processing technology can be used to identify various potential failure situations. By installing devices such as cameras and thermal imagers and combining advanced image analysis algorithms, the operation status of the system can be effectively monitored and abnormalities can be detected in a timely manner.

[0037] In some embodiments, industrial cameras (visible light) and infrared thermal imagers (infrared) are installed at key parts such as heat pump units, pipelines, and valves to ensure that all areas to be monitored are covered, and image data of the operation status of the system is collected in real time. The infrared thermal imager generates infrared images, which are usually pseudo-color images, and each pixel value represents the temperature information at that position.

[0038] Preprocessing operations such as denoising, enhancement, and segmentation are performed on the collected images to improve the image quality and facilitate subsequent feature extraction. Features that can reflect the operation status of the system are extracted from the preprocessed images. For example, temperature distribution features: the surface temperature distribution of the device is obtained using infrared thermal imaging technology, and features such as temperature gradient and hot spot areas are extracted. This temperature distribution feature may include, for example, temperature gradient and hot spot areas.

[0039] a. Temperature gradient: Reflects the severity of the temperature change on the surface of the device, and an abnormal temperature gradient may indicate local overheating or blockage.

[0040] b. Hot spot area: An area where the surface temperature of the device is significantly higher than the surrounding area, which may indicate device overload or malfunction.

[0041] Furthermore, the temperature change over multiple time periods can be continuously monitored to analyze whether there are trend changes. Specifically, a curve graph of temperature versus time can be plotted, and based on special positions such as inflection points in the curve graph, it can be determined whether there is a trend of sudden temperature drop or rise, so as to determine whether there are hot spots or cold spots.

[0042] At step S102, temperature distribution detection is performed with the hot spot as the center. Specifically, temperature distribution detection is performed with the hot spot as the center to determine the temperature gradient in the area around the hot spot and the temperature difference value between the hot spot and its adjacent area. If the above infrared image does not directly store temperature data in grayscale form, it can be converted into a grayscale image for more convenient temperature analysis. Map the pixel values in the grayscale image to actual temperature values, set a reasonable temperature threshold, and consider the area exceeding the threshold as abnormal. Then, using a connected component analysis algorithm (such as Blob detection), mark the hot spot or cold spot areas of all connected components.

[0043] At step S103, the first fault type and the first fault degree are determined. The first fault type and the first fault degree are determined according to the change rate of the temperature gradient between the hot spot and its adjacent area and the temperature difference value.

[0044] The first fault type can be obtained based on the change rate of the temperature gradient and the uniformity of the temperature difference value. In some embodiments, the first fault type includes poor contact, electrical fault, and mechanical friction. In an application scenario, the first fault degree is positively correlated with the change rate of the temperature gradient and negatively correlated with the variance value of the temperature difference value. In response to the change rate of the temperature gradient being greater than the first threshold and the temperature difference value being unevenly distributed, it is determined that there is poor contact. In response to the change rate of the temperature gradient being less than the first threshold and greater than the second threshold, and at the same time the temperature difference value being evenly distributed, it is determined that there is an electrical fault. In response to the change rate of the temperature gradient being less than the second threshold and the temperature difference value being evenly distributed, it is determined that there is mechanical friction.

[0045] At step S104, a fault report is generated and an alarm is activated. Specifically, in response to the first fault degree being greater than the first fault threshold, a corresponding fault report is generated based on the position, fault type, and fault degree of the key part and the alarm is activated.

[0046] In some embodiments, the calculation formula for the first fault degree is:

[0047] ;

[0048] where represents the fault degree at the th hot spot, Indicates the temperature gradient at the hot spot, Indicates the th temperature gradient at the th pixel in the area around the hot spot, Indicates the number of the farthest pixels in the area adjacent to the hot spot, Indicates the th temperature difference value between the hot spot and the th pixel in its surrounding area, Indicates the variance function, α represents the weight coefficient of the temperature gradient change rate, and β represents the weight coefficient of the temperature difference value.

[0049] Next, a specific implementation of the above steps will be described in detail.

[0050] Figure 2 is a flowchart schematically showing a method 200 for determining local hot spots or cold spots according to an embodiment of the present invention.

[0051] As Figure 2 shown, at step S201, the infrared image is converted into a grayscale image. In some embodiments, a high-resolution infrared thermal imager is used to scan key parts of the medium water source heat pump heating system, such as motors, compressors, electrical connection points, and pipe joints. The collected thermal image is converted into a grayscale image and noise is removed to more accurately detect hot spots.

[0052] At step S202, a temperature threshold range is set, and the edge detection algorithm is used to identify local high-temperature areas or local low-temperature areas in the grayscale image as potential fault points. In some embodiments, the threshold segmentation method can be used to identify local high-temperature areas in the image as potential fault points. As other embodiments, other edge detection algorithms can also be used to determine local high-temperature or low-temperature areas.

[0053] At step S203, the highest temperature point in the local high-temperature area is taken as the hot spot, and the lowest temperature point in the local low-temperature area is taken as the cold spot. According to the detected positions of the hot spot and the cold spot, a window of a set size is defined to capture the temperature distribution in the area adjacent to the hot spot or cold spot. In some embodiments, according to the detected position of the hot spot, a suitable window size can be defined to capture the surrounding temperature distribution. By calculating the temperature gradient in the area around the hot spot, how heat diffuses from the hot spot can be obtained. The window size can be, for example, a circle with a radius of 50 pixels.

[0054] Figure 3 is a flowchart schematically showing a method 300 for detecting cold spot faults according to an embodiment of the present invention.

[0055] In a water source heat pump system, gas leakage (such as refrigerant leakage) may lead to a decline in equipment performance, an increase in energy consumption, and even pose safety hazards. Using image processing technology for fog detection and temperature change analysis is one of the effective means to identify gas leakage. When gas leaks, especially when the leaked gas mixes with the surrounding ambient air and cools, visible fog or condensation often forms. By capturing these visual features with a camera, it is possible to preliminarily determine whether there is gas leakage.

[0056] As Figure 3 As shown, at step S301, device operation images are extracted with the cold spot as the center, and fog detection is performed based on the image features of the device operation images to determine whether the second type of failure occurs. In some embodiments, a high-resolution visible light camera can be used to ensure that details around the device can be clearly captured, and image data of key parts such as the heat pump unit, pipelines, and valves are collected in real time, thereby obtaining device operation images. Preprocessing operations such as denoising, enhancement, and segmentation are performed on the collected images to improve the image quality for subsequent feature extraction.

[0057] In some embodiments, fog features in the device operation images can be extracted, such as color features, texture features, etc. The second type of failure can be identified based on the key node positions, image feature information, and leakage conditions. The second type of failure includes refrigerant leakage or other cooling system failures.

[0058] At step S302, the second failure degree is determined according to the size of the fog area and the local low-temperature area centered on the cold spot in the device operation images. In some embodiments, the determination of the low-temperature area can be achieved through the color features, texture features, or morphological operations of the fog. For example, fog usually appears white or light gray, and a specific color range can be extracted through color space conversion (such as from RGB to HSV). By setting a threshold range, pixels that meet the fog color features are screened out to determine the fog (low-temperature) area.

[0059] Another example is that fog has unique texture features, and texture information can be extracted through methods such as local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), etc. In the present invention, the texture features of different regions can be calculated and compared with the reference value in the normal state to determine the fog (low-temperature) area.

[0060] Another example is to use morphological operations (such as dilation and erosion) to remove small-area noise interference and highlight the fog area. By first performing a dilation operation to expand the fog area and then performing an erosion operation to remove isolated small noise points, the specific fog (low-temperature) area can be obtained.

[0061] Furthermore, the abnormal temperature region can also be determined by analyzing the temperature changes around the cold spots. When gas leaks, especially refrigerant leaks, it will cause local temperature changes. By using an infrared thermal imager to capture the infrared images of the device surface, the abnormal temperature distribution can be detected, thereby determining whether there is a leak.

[0062] Use an infrared thermal imager to generate a temperature distribution map of the device surface, visually showing the temperature differences in each area. Convert the infrared image into a pseudo-color image for easy observation of temperature changes. Set the reference temperature for each area according to historical data or the temperature distribution under normal operating conditions. For example, collect multiple groups of temperature data under normal operating conditions, calculate the average value and standard deviation, and use them as reference values. For example, if the temperature in a certain area is more than 3°C lower than the reference value, it is regarded as abnormal.

[0063] In some embodiments, the second fault degree is positively correlated with the area of the fog region and the area of the local low-temperature region. In an application scenario, the calculation formula for the second fault degree is:

[0064]

[0065] In the formula, represents the second fault degree, represents the th area of the fog region at the cold spot, represents the area of the local low-temperature region, represents the total number of cold spots, represents the total area of the device operation image, represents the weight coefficient of the fog region area, the weight coefficient of the low-temperature region area, and .

[0066] Through the above formula, the second fault degree can be calculated according to the sizes of the fog region and the low-temperature region in the device operation image, and the fault level can be divided in combination with the threshold, which can intuitively reflect the operating state of the system and provide a quantitative basis for fault diagnosis.

[0067] At step S303, in response to the second fault degree being greater than the second fault threshold, generate a corresponding fault report and initiate an alarm according to the location, fault type, and fault degree of the key parts.

[0068] Figure 4 is a flowchart schematically showing a method 400 for determining a second fault type according to an embodiment of the present invention.

[0069] As Figure 4As shown, at step S401, the running image is preprocessed, and the fog features in the preprocessed device running image are extracted. The fog features include color features and texture features. In some embodiments, preprocessing the running image includes denoising the running image and enhancing the contrast.

[0070] At step S402, the fog area is determined according to the fog features, and the area of the fog pixel area is determined according to the connected component analysis algorithm. In the present invention, the connected component analysis algorithm (such as Blob detection) is used to identify the fog area in the image. By labeling all the pixels in the fog pixel area, features such as its area and shape are calculated.

[0071] At step S403, in response to the area being greater than the set area, it is determined that the second fault type has occurred. If a large and persistent fog area is detected, the alarm mechanism is triggered to notify the operator for inspection.

[0072] Through the above analysis of the temperature change in the hot or cold spot area and the analysis of the image features, the present invention realizes the accurate and effective identification of faults in the medium water source heat pump heating system, which is beneficial to improving the safety, stability and reliability of the operation of the medium water source heat pump heating system, promotes the development of the medium water source heat pump heating system, and helps to promote the development of the heating system in the direction of high efficiency, energy conservation, environmental protection, health and operation stability.

[0073] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the practice of the present invention.

Claims

1. A method for identifying faults in a medium water source heat pump heating system, characterized in that, Including: Obtain infrared images of key parts in the intermediate water source heat pump heating system, and determine local hot spots or cold spots in the infrared images of the key parts according to the image processing algorithm; Perform temperature distribution detection with the hot spot as the center, and determine the temperature gradient in the area around the hot spot and the temperature difference value between the hot spot and its adjacent area; Determine the first fault type and the first fault degree according to the change rate of the temperature gradient between the hot spot and its adjacent area and the temperature difference value, wherein the first fault type is obtained based on the uniformity of the change rate of the temperature gradient and the temperature difference value, and the first fault degree is positively correlated with the change rate of the temperature gradient and negatively correlated with the variance value of the temperature difference value; In response to the first fault degree being greater than the first fault threshold, generate a corresponding fault report based on the position, fault type, and fault degree of the key part and initiate an alarm; Extract the device operation image with the cold spot as the center, and perform fog detection based on the image features of the device operation image to determine whether the second fault type occurs; Determine the second fault degree according to the fog area in the device operation image and the size of the local low-temperature area centered on the cold spot, wherein the second fault degree is positively correlated with the fog area and the local low-temperature area; In response to the second fault degree being greater than the second fault threshold, generate a corresponding fault report based on the position, fault type, and fault degree of the key part and initiate an alarm.

2. The method for identifying faults in a medium water source heat pump heating system according to claim 1, wherein, Determine local hot spots or cold spots in the infrared image of the key part according to the image processing algorithm, including: Convert the infrared image into a grayscale image; Set the temperature threshold range, and use the edge detection algorithm to identify local high-temperature areas or local low-temperature areas in the grayscale image as potential fault points; Take the highest temperature point in the local high-temperature area as the hot spot, take the lowest temperature point in the local low-temperature area as the cold spot, and define a window of a set size according to the detected positions of the hot spot and the cold spot to capture the temperature distribution in the area adjacent to the hot spot or the cold spot.

3. A method for identifying faults in a medium-source heat pump heating system according to claim 1, characterized in that, The first fault type includes poor contact, electrical fault, and mechanical friction.

4. A method for identifying faults in a medium water source heat pump heating system according to claim 1 or 3, characterized in that, Determine the first fault type according to the change rate of the temperature gradient between the hot spot and its adjacent area and the temperature difference value, including: In response to the change rate of the temperature gradient being greater than the first threshold and the temperature difference value being unevenly distributed, determine that poor contact occurs; In response to the change rate of the temperature gradient being less than the first threshold and greater than the second threshold, and the temperature difference value being evenly distributed, determine that an electrical fault occurs; In response to the change rate of the temperature gradient being less than the second threshold and the temperature difference value being evenly distributed, determine that mechanical friction occurs.

5. A method for identifying faults in a medium water source heat pump heating system according to claim 1, characterized in that, The calculation formula for the first fault degree is: ； Wherein, represents the fault degree at the th hot spot, represents the temperature gradient at the hot spot, represents the temperature gradient at the th pixel in the area around the th hot spot, represents the number of the farthest pixels in the area adjacent to the hot spot, represents the temperature difference value between the th hot spot and the th pixel in its surrounding area, represents the variance function, α represents the weight coefficient of the temperature gradient change rate, and β represents the weight coefficient of the temperature difference value.

6. The fault identification method for a medium water source heat pump heating system according to claim 1, characterized in that, The second fault type includes refrigerant leakage or other cooling system faults.

7. A method for identifying faults in a medium water source heat pump heating system according to claim 1 or 6, characterized in that, Perform fog detection based on the image features of the device operation image to determine whether the second fault type occurs, including: Preprocess the operation image, and extract the fog features in the preprocessed device operation image, wherein the fog features include color features and texture features; Determine the fog area according to the fog features, and determine the area of the fog pixel area according to the connected domain analysis algorithm; In response to the area being greater than the set area, determine that the second fault type occurs.

8. A method for identifying faults in a medium-source heat pump heating system according to claim 1, characterized in that, The calculation formula for the second fault degree is: In the formula, represents the second degree of failure, represents the area of the fog region at the th cold point, represents the total number of cold points, represents the total area of the device operation image, represents the weight coefficient of the fog region area, is the weight coefficient of the low temperature region area, and .

9. A method for identifying faults in a medium water source heat pump heating system according to claim 7, characterized in that, Preprocess the operation image, including: Denoise the running image and enhance the contrast.

Images