Fault identification method for heat supply system of reclaimed water source heat pump

Abstract

The invention relates to the technical field of image processing, and relates to a reclaimed water source heat pump heat supply system fault identification method comprising the following steps: obtaining an infrared image of a key part in a reclaimed water source heat pump heat supply system, and determining a local hot spot or cold spot in the infrared image of the key part according to an image processing algorithm; performing temperature distribution detection by taking the hot spot as a center, and determining a temperature gradient of a surrounding area of the hot spot and a temperature difference value of the hot spot and an adjacent area; determining a first fault type and a first fault degree according to the change rate and the temperature difference value of the temperature gradient of the hot spot and the adjacent area; and responding to the condition that the first fault degree is greater than a first fault threshold, generating a corresponding fault report according to the position of the key part, the fault type and the fault degree, and starting alarm. According to the scheme, accurate alarm of faults is achieved, and the safety and reliability of a heat supply system can be improved.

Description

Technical Field

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

[0002] A grey water source heat pump is a heat pump device that uses grey water as a heat source and uses heat pump technology to increase the low-temperature heat energy of grey water to achieve indoor heating and cooling. Grey water refers to domestic sewage used in buildings for bathing, laundry, and dishwashing. Since the temperature of grey water is usually above 20°C, it has good thermal energy utilization potential. A grey water source heat pump is a heat pump device that uses grey water, a high-quality heat source, to convert it into hot water for building air conditioning.

[0003] The working principle of the grey water source heat pump is basically the same as that of the traditional water source heat pump, but the grey water source heat pump is equipped with a grey water heat exchanger outdoors. The heat exchanger is used to exchange heat between the grey water and the refrigerant, so that the refrigerant absorbs the low-temperature heat of the grey water to form high-temperature and high-pressure steam. The high-temperature and high-pressure steam is then exchanged with the indoor device to release the heat energy into the room, thereby realizing indoor heating and cooling.

[0004] Therefore, the water source heat pump is an excellent heat pump equipment, which can not only achieve high efficiency and energy saving, but also take into account the requirements of environmental protection, health and operation stability. However, the application of water source heat pump is still in its infancy, and there are still many potential failures for the equipment. How to effectively monitor the operation status of the equipment and detect abnormalities in time is important for improving the application level of the equipment and extending the life of the system.

[0005] The Chinese patent application document with publication number CN110108509A provides an intelligent fault diagnosis method for sewage source heat pump units. First, the historical data collected by the sensor installed on the sewage source heat pump is used to obtain the historical data of fault-free and faulty operation, build a BP neural network model, and optimize the weights and thresholds of the BP neural network through a genetic algorithm (GA). However, although this method can achieve fault diagnosis of sewage source heat pumps, the results of sensor detection still have the problem of inaccurate analysis, which makes it impossible to determine system faults in a timely and accurate manner.

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

[0007] The present invention aims to utilize image processing technology to perform real-time monitoring and fault diagnosis on the operating status of a medium-source water source heat pump heating system.

[0008] The present invention provides a method for identifying faults in a medium-sized water source heat pump heating system, comprising: acquiring infrared images of key parts in the medium-sized 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, and determining the temperature gradient of the area around the hot spot and the temperature difference between the hot spot and its adjacent areas; determining a first fault type and a first fault degree according to the rate of change of the temperature gradient and the temperature difference between the hot spot and its adjacent areas, wherein the first fault type is obtained based on the uniformity of the rate of change of the temperature gradient and the temperature difference, and the first fault degree is positively correlated with the rate of change of the temperature gradient and negatively correlated with the variance of the temperature difference; in response to the first fault degree being greater than a first fault threshold, generating a corresponding fault report and initiating an alarm according to the location of the key part, the fault type and the fault degree.

[0009] In the medium water source heat pump heating system, the present invention uses local hot spots to analyze and identify system faults. By analyzing and identifying the temperature distribution of thermal images, effective and accurate detection of faults in key parts is achieved. The degree of fault is calculated by the temperature gradient of the area around the hot spot and the temperature difference between the hot spot and its adjacent areas, thereby achieving accurate warning of faults, which is beneficial to improving the safety and reliability of the heating system.

[0010] Preferably, local hot spots or cold spots in infrared images of key parts are determined according to an image processing algorithm, including: 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 a hot spot, and taking the lowest temperature point in the local low temperature area as a cold spot, and based on the detected hot and cold spot positions, defining a window of a set size to capture the temperature distribution of the area adjacent to the hot or cold spot.

[0011] The present invention utilizes image processing technology to detect hot spots or cold spots in key parts, identifies areas in grayscale images by setting temperature threshold ranges, and finds potential fault points therein. Detecting hot spots and cold spots can achieve accurate capture of fault areas.

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

[0013] The present invention performs temperature distribution detection with a local hot spot as the center, determines whether a first fault type such as poor contact, electrical fault, and mechanical friction has occurred, and evaluates the degree of the fault.

[0014] Preferably, the first fault type is determined based on the rate of change of the temperature gradient and the temperature difference between the hot spot and its adjacent areas, including: in response to the rate of change of the temperature gradient being greater than a first threshold and the temperature difference being unevenly distributed, determining that poor contact has occurred; in response to the rate of change of the temperature gradient being less than the first threshold and greater than the second threshold and the temperature difference being evenly distributed, determining that an electrical fault has occurred; in response to the rate of change of the temperature gradient being less than the second threshold and the temperature difference being evenly distributed, determining that mechanical friction has occurred.

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

[0016] Preferably, the calculation formula of the first fault degree is: ; In the formula, Indicates The degree of failure at each hot spot, represents the temperature gradient at the hot spot, Indicates In the area around the hotspot The temperature gradient at each pixel is Indicates the number of the farthest pixels in the vicinity of the hotspot. Indicates The hotspot and its surrounding area The temperature difference between pixels, 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.

[0017] Preferably, it also includes: extracting equipment operation images with the cold spot as the center, and performing fog detection based on the image features of the equipment operation image to determine whether a second fault type has occurred; determining the second fault degree according to the size of the fog area and the local low-temperature area centered on the cold spot in the equipment operation image, wherein the second fault degree is positively correlated with the area of ​​the fog area and the area of ​​the local low-temperature area; in response to the second fault degree being greater than the second fault threshold, generating a corresponding fault report and initiating an alarm based on the location of the key parts, the fault type and the fault degree.

[0018] The present invention performs image analysis on the cold spot area, detects the fog area according to the image characteristics, calculates the fault degree based on the size of the fog area and the low-temperature area around the cold spot, thereby achieving accurate identification of system faults and effectively ensuring the safety of the working process of the heating system.

[0019] Preferably, the second fault type includes a refrigerant leak or other cooling system fault.

[0020] Preferably, fog detection is performed based on image features of the equipment operation image to determine whether the second fault type has occurred, including: preprocessing the operation image and extracting fog features in the preprocessed equipment operation image, wherein 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 a connected domain analysis algorithm; in response to the area being greater than a set area, determining that the second fault type has occurred.

[0021] The present invention utilizes image processing technology to perform real-time monitoring of the cold spot area of ​​the greywater heat pump heating system. By extracting image features and applying machine learning algorithms, automatic identification and early warning of system faults are achieved, effectively improving the accuracy of heating system fault identification.

[0022] Preferably, the calculation formula for the second fault degree is:

[0023] In the formula, Indicates the second fault level, Indicates The area of ​​fog at each cold spot, Represents the area of ​​the local low temperature region, represents the total number of cold spots, Indicates the total area of ​​the device operation image. Represents the weight coefficient of the fog area, The weight coefficient of the low temperature area, and .

[0024] Preferably, preprocessing the running image includes: denoising the running image and enhancing the contrast.

[0025] The beneficial effects of the present invention are as follows: the present invention utilizes infrared images to obtain local hot spots in the heating system, and performs temperature distribution detection with the local hot spots as the center, and utilizes the rate of change of the temperature gradient and the temperature difference between the hot spots and their adjacent areas to perform fault detection, thereby effectively improving the accuracy of fault detection in the heating system. At the same time, it does not require complex algorithm models, thereby effectively improving the efficiency of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart schematically illustrating a method for identifying a fault of a water source heat pump heating system according to an embodiment of the present invention; Figure 2 is a flow chart schematically illustrating a method for determining a local hot spot or cold spot according to an embodiment of the present invention; Figure 3 is a flow chart schematically illustrating a method for detecting a cold spot fault according to an embodiment of the present invention; Figure 4 is a flow chart schematically illustrating a method for determining a second fault type according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In water source heat pump systems, image processing technology can be used to identify a variety of potential fault conditions. By installing equipment such as cameras and thermal imagers, combined with advanced image analysis algorithms, the operating status of the system can be effectively monitored and abnormalities can be detected in a timely manner.

[0028] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Figure 1 FIG. 1 is a flow chart schematically showing a method 100 for identifying a fault in a water source heat pump heating system according to an embodiment of the present invention.

[0030] like Figure 1 As shown, at step S101, hot spots or cold spots are detected. Specifically, infrared images of key parts in the water source heat pump heating system are obtained, and local hot spots or cold spots in the infrared images of key parts are determined according to the image processing algorithm. In the water source heat pump system, image processing technology can be used to identify a variety of potential fault conditions. By installing equipment such as cameras and thermal imagers, combined with advanced image analysis algorithms, the operating status of the system can be effectively monitored and abnormalities can be detected in a timely manner.

[0031] In some embodiments, industrial cameras (visible light) and infrared thermal imagers (infrared) are installed at key locations such as heat pump units, pipelines, and valves to ensure coverage of all areas that need to be monitored and to collect image data of the system's operating status in real time. Infrared thermal imagers generate infrared images, which are usually pseudo-color images, with each pixel value representing the temperature information at that location.

[0032] Perform preprocessing operations such as denoising, enhancement, and segmentation on the collected images to improve image quality and facilitate subsequent feature extraction. Extract features that can reflect the operating status of the system from the preprocessed images, such as temperature distribution features: use infrared thermal imaging technology to obtain the surface temperature distribution of the equipment, and extract features such as temperature gradients and hot spots. The temperature distribution features may include, for example, temperature gradients and hot spots.

[0033] a. Temperature gradient: reflects the severity of the temperature change on the equipment surface. Abnormal temperature gradient may indicate local overheating or blockage.

[0034] b. Hot spot area: An area where the surface temperature of the equipment is significantly higher than the surrounding area, which may indicate equipment overload or failure.

[0035] Furthermore, it is also possible to continuously monitor the temperature changes over multiple time periods to analyze whether there is a trend change. Specifically, by drawing a graph of temperature changes over time, and determining whether there is a trend of sudden temperature drop or temperature rise based on special positions such as inflection points in the graph, it is possible to determine whether there is a hot spot or cold spot.

[0036] At step S102, the temperature distribution is detected with the hot spot as the center. Specifically, the temperature distribution is detected with the hot spot as the center to determine the temperature gradient of the area around the hot spot and the temperature difference 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 to facilitate temperature analysis. The pixel values ​​in the grayscale image are mapped to the actual temperature value, and a reasonable temperature threshold is set. The area exceeding the threshold is regarded as abnormal, and then the connected domain analysis algorithm (such as Blob detection) is used to mark the hot spot or cold spot area of ​​all connected domains.

[0037] In step S103, a first fault type and a first fault degree are determined according to a change rate and a temperature difference of a temperature gradient between a hot spot and its adjacent area.

[0038] The first fault type can be obtained based on the rate of change 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 rate of change of the temperature gradient and negatively correlated with the variance value of the temperature difference value. In response to the rate of change of the temperature gradient being greater than the first threshold and the temperature difference value being unevenly distributed, it is determined that poor contact occurs. In response to the rate of change of the temperature gradient being less than the first threshold and greater than the second threshold, and the temperature difference value being evenly distributed, it is determined that an electrical fault occurs. In response to the rate of change of the temperature gradient being less than the second threshold and the temperature difference value being evenly distributed, it is determined that mechanical friction occurs.

[0039] In step S104, a fault report is generated and an alarm is initiated. Specifically, in response to the first fault degree being greater than the first fault threshold, a corresponding fault report is generated and an alarm is initiated according to the location of the key part, the fault type and the fault degree.

[0040] In some embodiments, the calculation formula of the first fault degree is: ; In the formula, Indicates The degree of failure at each hot spot, represents the temperature gradient at the hot spot, Indicates In the area around the hotspot The temperature gradient at each pixel is Indicates the number of the farthest pixels in the vicinity of the hotspot. Indicates The hotspot and its surrounding area The temperature difference between pixels, 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.

[0041] Next, one implementation method of the above steps will be described in detail.

[0042] Figure 2 FIG. 2 is a flow chart schematically illustrating a method 200 for determining a local hot spot or a cold spot according to an embodiment of the present invention.

[0043] like Figure 2 As 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 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.

[0044] At step S202, a temperature threshold range is set, and an edge detection algorithm is used to identify a local high temperature area or a local low temperature area in the grayscale image as a potential fault point. In some embodiments, a threshold segmentation method can be used to identify a local high temperature area in the image as a potential fault point. As other embodiments, other edge detection algorithms can also be used to determine the local high temperature or low temperature area.

[0045] 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, and a window of a set size is defined according to the detected hot spot and cold spot positions to capture the temperature distribution of the hot spot or the cold spot adjacent area. In some embodiments, a suitable window size can be defined according to the detected hot spot position to capture the surrounding temperature distribution, and by calculating the temperature gradient of the area around the hot spot, it can be obtained how the heat diffuses from the hot spot. The window size can be, for example, a circle with a radius of 50 pixels.

[0046] Figure 3 FIG. 3 is a flow chart schematically illustrating a method 300 for detecting a cold spot fault according to an embodiment of the present invention.

[0047] In a water source heat pump system, gas leaks (such as refrigerant leaks) may cause equipment performance degradation, increased energy consumption, and even safety hazards. Using image processing technology for fog detection and temperature change analysis is one of the effective means to identify gas leaks. When gas leaks, especially when the leaked gas mixes with the surrounding 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 a gas leak.

[0048] like Figure 3 As shown, at step S301, the equipment operation image is extracted with the cold spot as the center, and fog detection is performed based on the image features of the equipment operation image to determine whether the second fault type occurs. In some embodiments, a high-resolution visible light camera can be used to ensure that the details around the equipment can be clearly captured, and image data of key parts such as heat pump units, pipelines, valves, etc. can be collected in real time to obtain equipment operation images. The collected images are pre-processed such as denoising, enhancement, and segmentation to improve image quality and facilitate subsequent feature extraction.

[0049] In some embodiments, fog features in the equipment operation image may be extracted, such as color features, texture features, etc. The second fault type may be identified based on key node locations, image feature information, and leakage conditions. The second fault type includes refrigerant leakage or other cooling system failures.

[0050] At step S302, the second fault degree is determined based on the size of the local low temperature area centered on the fog area and the cold spot in the device operation image. In some embodiments, the low temperature area can be determined by 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 by color space conversion (such as from RGB to HSV). By setting a threshold range, pixels that meet the color features of the fog are screened out to determine the fog (low temperature) area.

[0051] For another example, 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 fog (low temperature) area can be determined by calculating the texture features of different areas and comparing them with the reference values ​​under normal conditions.

[0052] For another example, morphological operations (such as dilation and erosion) are used to remove small areas of noise interference and highlight the fog area. The fog area is first expanded by dilation operation, and then the isolated small noise points are removed by erosion operation, so as to obtain a specific fog (low temperature) area.

[0053] Furthermore, the abnormal temperature area can be determined by analyzing the temperature changes around the cold spot. When a gas leaks, especially a refrigerant leak, it will cause local temperature changes. By capturing infrared images of the surface of the equipment with a thermal imager, abnormal temperature distribution can be detected to determine whether there is a leak.

[0054] Use thermal imagers to generate temperature distribution maps on the equipment surface to intuitively display the temperature differences in each area. Convert infrared images into pseudo-color images to facilitate observation of temperature changes. Set the baseline temperature for each area based on historical data or temperature distribution under normal operating conditions. For example, collect multiple sets of temperature data under normal operating conditions, calculate the average and standard deviation as the baseline value. For example, set the temperature of a certain area to be abnormal if it is more than 3°C lower than the baseline value.

[0055] 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 one application scenario, the calculation formula for the second fault degree is:

[0056] In the formula, Indicates the second fault level, Indicates The area of ​​fog at each cold spot, Represents the area of ​​the local low temperature region, represents the total number of cold spots, Indicates the total area of ​​the device operation image. Represents the weight coefficient of the fog area, The weight coefficient of the low temperature area, and .

[0057] Through the above formula, the second fault degree can be calculated according to the size of the fog area and the low temperature area in the equipment operation image, and the fault level can be divided according to the threshold, which can intuitively reflect the operating status of the system and provide a quantitative basis for fault diagnosis.

[0058] In step S303, in response to the second fault degree being greater than the second fault threshold, a corresponding fault report is generated and an alarm is initiated according to the location of the key part, the fault type and the fault degree.

[0059] Figure 4 is a flow chart schematically illustrating a method 400 for determining a second fault type according to an embodiment of the present invention.

[0060] like Figure 4As shown, at step S401, the operation image is preprocessed, and fog features in the preprocessed device operation image are extracted. The fog features include color features and texture features. In some embodiments, preprocessing the operation image includes denoising the operation image and enhancing the contrast.

[0061] At step S402, the fog region is determined according to the fog features, and the area of ​​the fog pixel region is determined according to the connected domain analysis algorithm. In the present invention, the fog region in the image is identified by the connected domain analysis algorithm (such as Blob detection). All pixels in the fog pixel region are marked to calculate their area, shape and other features.

[0062] At step S403, in response to the area being larger than the set area, it is determined that the second fault type has occurred. If a large and persistent fog area is detected, an alarm mechanism is triggered to notify the operator to conduct an inspection.

[0063] The present invention realizes accurate and effective identification of faults in the grey water source heat pump heating system through the temperature change analysis and image feature analysis of the above-mentioned hot spot or cold spot areas, which is beneficial to improving the safety, stability and reliability of the operation of the grey water source heat pump heating system, promoting the development of the grey water source heat pump heating system, and helping to promote the development of the heating system towards high efficiency and energy saving while taking into account environmental protection, health and operational stability.

[0064] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for identifying faults in a water source heat pump heating system, characterized in that: include: Obtain infrared images of key parts in the water source heat pump heating system, and determine local hot spots or cold spots in the infrared images of key parts based on image processing algorithms; Carry out temperature distribution detection with the hot spot as the center to determine the temperature gradient of the area around the hot spot and the temperature difference between the hot spot and its adjacent areas; Determine a first fault type and a first fault degree according to the change rate of the temperature gradient and the temperature difference value between the hot spot and its adjacent area, 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, a corresponding fault report is generated and an alarm is initiated according to the location of the key part, the fault type and the fault degree.

2. A method for identifying faults in a water source heat pump heating system according to claim 1, characterized in that: Determine the local hot spots or cold spots in the infrared images of key parts based on the image processing algorithm, including: Convert infrared images to grayscale images; Set the temperature threshold range and use the edge detection algorithm to identify the local high temperature area or local low temperature area in the grayscale image as potential fault points; 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 hot and cold spot positions, a window of a set size is defined to capture the temperature distribution of the area near the hot or cold spot.

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

5. A method for identifying faults in a water source heat pump heating system according to claim 1, characterized in that: The calculation formula for the first fault degree is: ； In the formula, Indicates The degree of failure at each hot spot, represents the temperature gradient at the hot spot, Indicates In the area around the hotspot The temperature gradient at each pixel is Indicates the number of the farthest pixels in the vicinity of the hotspot. Indicates The hotspot and its surrounding area The temperature difference between pixels, 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. A method for identifying faults in a water source heat pump heating system according to claim 1, characterized in that: Also includes: Extracting equipment operation images with the cold spot as the center, and performing fog detection based on image features of the equipment operation images to determine whether the second fault type occurs; Determine the second fault degree according to the size of the local low temperature area centered on the fog area and the cold spot in the equipment operation image, wherein the second fault degree is positively correlated with the area of ​​the fog area and the area of ​​the local low temperature area; In response to the second fault degree being greater than the second fault threshold, a corresponding fault report is generated and an alarm is initiated according to the location of the key part, the fault type and the fault degree.

7. A method for identifying faults in a water source heat pump heating system according to claim 6, characterized in that: The second fault type includes a refrigerant leak or other cooling system malfunction.

8. A method for identifying faults in a water source heat pump heating system according to claim 6 or 7, characterized in that: Perform fog detection based on the image features of the equipment operation image to determine whether the second fault type occurs, including: Preprocessing the operation image, and extracting fog features in the preprocessed device operation image, wherein the fog features include color features and texture features; Determine the fog region according to the fog characteristics, and determine the area of ​​the fog pixel region according to a connected domain analysis algorithm; In response to the area being greater than a set area, it is determined that a second fault type has occurred.

9. A method for identifying faults in a water source heat pump heating system according to claim 6, characterized in that: The calculation formula for the second fault degree is: In the formula, Indicates the second fault level, Indicates The area of ​​fog at each cold spot, Represents the area of ​​the local low temperature region, represents the total number of cold spots, Indicates the total area of ​​the device operation image. Represents the weight coefficient of the fog area, The weight coefficient of the low temperature area, and .

10. A method for identifying faults in a water source heat pump heating system according to claim 8, characterized in that: Preprocessing the running image includes: The running image is subjected to denoising and contrast enhancement.

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