Air conditioner dust deposition detection method, device, medium and system

Abstract

The invention provides an air conditioner dust deposition detection method, device, medium and system, and the method comprises the steps: obtaining the temperature data of a plurality of detection points in a preset area, and generating a thermodynamic diagram based on all the temperature data; determining a temperature band of the thermodynamic diagram according to the RGB values of the pixel points in the thermodynamic diagram, wherein the thermodynamic diagram represents the temperature distribution characteristics of the predetermined area in the running state of the air conditioner; the temperature distribution type of the preset area is determined according to the temperature band, the dust deposition condition of the air conditioner is determined according to the temperature distribution type, and an air conditioner dust deposition detection result is output and comprises dust deposition components in the air conditioner and the corresponding dust deposition degree. The problem of low air conditioner dust distribution detection efficiency in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of air conditioning testing technology, and more specifically, to an air conditioning dust accumulation detection method, an air conditioning dust accumulation detection device, a computer-readable storage medium, and an air conditioning dust accumulation detection system. Background Technology

[0002] Currently, the main methods for detecting dust accumulation inside air conditioners include visual inspection, pressure drop detection, and sensor monitoring. These methods require disassembly or the use of specialized equipment, resulting in long testing times and low efficiency in detecting dust distribution in air conditioners. Summary of the Invention

[0003] The main objective of this application is to provide a method, device, computer-readable storage medium, and system for detecting dust accumulation in air conditioners, so as to at least solve the problem of low efficiency in detecting the distribution of dust in air conditioners in the prior art.

[0004] To achieve the above objectives, according to one aspect of this application, an air conditioner dust accumulation detection method is provided, comprising: acquiring temperature data of multiple detection points within a predetermined area, and generating a heat map based on all the temperature data; determining temperature bands of the heat map based on the RGB values ​​of pixels in the heat map, wherein the heat map characterizes the temperature distribution characteristics of the predetermined area under air conditioner operation; determining the temperature distribution type of the predetermined area based on the temperature bands, and determining the dust accumulation status of the air conditioner based on the temperature distribution type, and outputting an air conditioner dust accumulation detection result, wherein the air conditioner dust accumulation detection result includes dust-accumulating components in the air conditioner and the corresponding degree of dust accumulation, and the temperature distribution type characterizes the temperature distribution status of the predetermined area.

[0005] Optionally, determining the temperature band of the heatmap based on the RGB values ​​of the pixels in the heatmap includes: traversing the pixels in the heatmap to extract the RGB values ​​of each pixel; grouping the pixels according to a preset RGB value difference threshold to obtain multiple pixel sets, wherein the RGB value difference threshold is a value used to determine whether the pixels in the heatmap belong to the same temperature band; and using an edge detection algorithm to extract the boundary of the pixel set to determine the temperature band.

[0006] Optionally, determining the temperature distribution type of the predetermined region based on the temperature bands includes: calculating a temperature index value for each temperature band based on the RGB values ​​of the pixels in each temperature band, wherein the temperature index value represents the degree of temperature; sorting the temperature bands according to the temperature index values ​​and a predetermined sorting rule to obtain a temperature band sequence; and determining the temperature distribution type based on the temperature band sequence.

[0007] Optionally, calculating the temperature index value of each temperature band based on the RGB values ​​of the pixels in each temperature band includes: obtaining the RGB values ​​of each pixel in each temperature band, and calculating the temperature index value based on T. score =a·R avg +b·G avg +c·B avg The temperature index values ​​for each temperature zone are obtained, where a, b, and c are preset constants, and T score R is the temperature index value. avg It is based on Certainly, G avg It is based on Definitely, B avg It is based on It is determined that N is the number of pixels in each of the temperature zones.

[0008] Optionally, after determining the temperature distribution type based on the temperature band sequence, the method further includes: processing the temperature bands using an image processing algorithm to obtain the area of ​​the temperature band corresponding to the highest temperature index value as a first temperature band area, and the area of ​​the temperature band corresponding to the lowest temperature index value as a second temperature band area; determining the ratio of the second temperature band area to the first temperature band area as a first ratio, and determining the dust accumulation area based on the first ratio.

[0009] Optionally, determining the dust accumulation status of the air conditioner includes: determining a second ratio as the ratio of the lowest temperature index value to the highest temperature index value, and determining the thickness of the dust accumulation on the air conditioner based on the second ratio, wherein the dust accumulation status includes the thickness of the dust accumulation on the air conditioner.

[0010] Optionally, acquiring temperature data from multiple detection points within a predetermined area and generating a heat map based on all the temperature data includes: acquiring temperature data from each temperature sensor within the predetermined area at the same time, and location data of each temperature sensor; constructing a temperature field data matrix based on the temperature data and the location data, and generating the heat map based on the temperature field data matrix, wherein the temperature field data matrix characterizes the temperature distribution at different locations within the predetermined area.

[0011] According to another aspect of this application, an air conditioner dust accumulation detection device is provided, comprising: an acquisition unit, configured to acquire temperature data of multiple detection points within a predetermined area, and generate a heat map based on all the temperature data; a first determination unit, configured to determine the temperature band of the heat map based on the RGB values ​​of pixels in the heat map, wherein the heat map characterizes the temperature distribution characteristics of the predetermined area under air conditioner operation; and a second determination unit, configured to determine the temperature distribution type of the predetermined area based on the temperature band, and determine the dust accumulation status of the air conditioner based on the temperature distribution type, and output an air conditioner dust accumulation detection result, wherein the air conditioner dust accumulation detection result includes dust-accumulating components in the air conditioner and the corresponding degree of dust accumulation, and the temperature distribution type characterizes the temperature distribution status of the predetermined area.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0013] According to another aspect of this application, a road traffic control system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0014] The technical solution of this application first acquires temperature data from multiple detection points within a predetermined area and generates a heat map based on all the temperature data. Then, it determines the temperature bands of the heat map based on the RGB values ​​of the pixels. Finally, it determines the temperature distribution type of the predetermined area based on the temperature bands, and determines the dust accumulation status of the air conditioner based on the temperature distribution type, outputting the air conditioner dust accumulation detection results. This solution, by acquiring temperature data from a predetermined area and generating a heat map, can more intuitively display the temperature distribution characteristics of the predetermined area. Dividing the temperature bands based on the RGB values ​​of the pixels in the heat map can accurately identify temperature differences between different areas within the predetermined area and determine the temperature distribution type based on these differences. Since the dust accumulation distribution of components inside the air conditioner has different characteristics, it is possible to accurately determine the air conditioner component and the degree of dust accumulation based on the temperature distribution type, solving the problem of low detection efficiency for air conditioner dust distribution in existing technologies. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1A hardware structure block diagram of a mobile terminal for performing an air conditioner dust accumulation detection method according to an embodiment of this application is shown;

[0017] Figure 2 A schematic flowchart of an air conditioner dust accumulation detection method according to an embodiment of this application is shown;

[0018] Figure 3 A schematic flowchart of another air conditioner dust accumulation detection method provided according to an embodiment of this application is shown;

[0019] Figure 4 A schematic diagram of a temperature distribution that is high in the middle and low at both ends, according to an embodiment of this application, is shown.

[0020] Figure 5 A schematic diagram of a temperature distribution that is low in the middle and high at both ends, according to an embodiment of this application, is shown.

[0021] Figure 6 A flowchart illustrating the determination of dust accumulation in an air conditioner component according to an embodiment of this application is shown.

[0022] Figure 7 A flowchart illustrating a method for detecting the degree of dust accumulation in an air conditioner, according to an embodiment of this application, is shown.

[0023] Figure 8 A structural block diagram of an air conditioner dust accumulation detection device provided according to an embodiment of this application is shown.

[0024] The above figures include the following reference numerals:

[0025] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] As described in the background section, the existing technology has low efficiency in detecting the distribution of dust in air conditioners. To solve the above-mentioned technical problems, the embodiments of this application provide an air conditioner dust accumulation detection method, an air conditioner dust accumulation detection device, a computer-readable storage medium, and an air conditioner dust accumulation detection system.

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0031] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for an air conditioner dust accumulation detection method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0032] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the air conditioner dust accumulation detection method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] This embodiment provides a method for detecting air conditioner dust accumulation that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0034] Figure 2 This is a flowchart of an air conditioner dust accumulation detection method according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0035] Step S101: Obtain temperature data from multiple detection points within a predetermined area, and generate a heat map based on all of the above temperature data;

[0036] Specifically, temperature data is acquired through temperature sensors, and the collected sensor data undergoes noise removal and data standardization. Since sensors may not cover the entire predetermined area, spatial interpolation is required to estimate the temperature at unmonitored locations. If the sensor acquisition frequency is insufficient to capture temperature changes over time, temporal interpolation methods, such as linear interpolation, polynomial interpolation, or wavelet interpolation, can be used to fill data gaps in the continuous time series.

[0037] Step S102: Determine the temperature zone of the heat map based on the RGB values ​​of the pixels in the heat map. The heat map represents the temperature distribution characteristics of the predetermined area under air conditioning operation.

[0038] Step S103: Determine the temperature distribution type of the predetermined area based on the temperature zone, determine the dust accumulation status of the air conditioner based on the temperature distribution type, and output the air conditioner dust accumulation detection result. The air conditioner dust accumulation detection result includes the dust accumulation components in the air conditioner and the corresponding degree of dust accumulation. The temperature distribution type characterizes the temperature distribution status of the predetermined area.

[0039] Specifically, temperature distribution types include: a hot-in-the-middle and cold-on-the-sides temperature distribution, where the temperature in the middle region is significantly higher than that in the two sides; a cold-in-the-middle and hot-on-the-sides temperature distribution; and a hot-on-one-and-cold-on-the-sides temperature distribution.

[0040] In the above embodiments, temperature data from multiple detection points within a predetermined area are first acquired, and a heatmap is generated based on all the temperature data. Next, temperature bands in the heatmap are determined according to the RGB values ​​of the pixels. Finally, the temperature distribution type of the predetermined area is determined based on the temperature bands, and the dust accumulation status of the air conditioner is determined based on the temperature distribution type, and the air conditioner dust accumulation detection result is output. This solution, by acquiring temperature data from a predetermined area and generating a heatmap, can more intuitively display the temperature distribution characteristics of the predetermined area. Dividing temperature bands according to the RGB values ​​of the pixels in the heatmap can accurately identify temperature differences between different areas within the predetermined area and determine the temperature distribution type based on these differences. Since the dust accumulation distribution of components inside the air conditioner has different characteristics, the air conditioner component and degree of dust accumulation can be accurately determined based on the temperature distribution type, solving the problem of low detection efficiency for air conditioner dust distribution in existing technologies.

[0041] In one optional approach, determining the temperature band of the heatmap based on the RGB values ​​of the pixels in the heatmap includes: traversing the pixels in the heatmap to extract the RGB values ​​of each pixel; grouping the pixels according to a preset RGB value difference threshold to obtain multiple pixel sets, wherein the RGB value difference threshold is a value used to determine whether the pixels in the heatmap belong to the same temperature band; and using an edge detection algorithm to extract the boundaries of the pixel sets to determine the temperature band.

[0042] In the above embodiments, by traversing every pixel in the heatmap and grouping them according to a preset RGB value difference threshold, pixels with similar temperature characteristics are ensured to be classified into the same temperature band. The grouped set of pixels represents a region within a specific temperature range, i.e., a temperature band. This grouping method based on pixel RGB values ​​can accurately reflect the details of temperature distribution. By employing an edge detection algorithm to accurately locate the boundaries between temperature bands, it is possible to effectively identify drastic changes in pixel values, i.e., abrupt changes in temperature distribution, which helps to clearly define the boundaries between different temperature bands, accurately identify the range of temperature bands, and help to accurately determine the location and extent of dust accumulation in air conditioners. In addition, automatically extracting temperature bands from the heatmap speeds up dust accumulation detection, making the detection process more efficient.

[0043] Specifically, heatmaps are generated based on temperature data, where the RGB value of each pixel is directly related to its corresponding temperature. The lower the temperature, the closer the pixel's color is to blue; the higher the temperature, the closer the color is to red. RGB values ​​are a visual representation of temperature. By setting an RGB value difference threshold, we can determine whether two adjacent pixels are similar enough to be considered to belong to the same temperature band. The number of temperature bands depends on the aforementioned RGB value difference threshold; the smaller the threshold, the more temperature bands, and vice versa. If the RGB value difference threshold is too small, although the temperature bands will be more detailed, it requires stronger data processing capabilities to analyze the temperature data, making it more difficult and potentially inaccurate; if the threshold is too large, it will not accurately reflect the temperature change patterns, also leading to inaccuracies. The specific RGB value difference threshold needs to be adjusted based on actual conditions. Once the pixels are grouped, the next step is to use edge detection algorithms, such as Canny Edge Detector (an edge detection algorithm used in image processing and computer vision) and Sobel Operator (an edge detection algorithm widely used in image processing and computer vision), to identify the boundaries of each pixel set, i.e., the temperature bands. Edge detection algorithms can identify the boundaries between different regions in an image, i.e., points of abrupt changes in color (in this case, temperature).

[0044] In another alternative approach, determining the temperature distribution type of the predetermined region based on the temperature bands includes: calculating a temperature index value for each temperature band based on the RGB values ​​of the pixels in each temperature band, wherein the temperature index value represents the degree of temperature; sorting the temperature bands according to the temperature index values ​​and a predetermined sorting rule to obtain a temperature band sequence; and determining the temperature distribution type based on the temperature band sequence.

[0045] In the above embodiments, by converting the RGB values ​​of pixels in each temperature band into temperature index values, a quantitative representation of the temperature level is achieved, reflecting the true temperature conditions of the temperature band. The temperature bands are sorted using a preset sorting rule, forming a temperature band sequence. This sorted sequence visually displays the temperature distribution from low to high within a predetermined area, making the temperature distribution type clearly visible. Based on the temperature band sequence, the temperature distribution type of the predetermined area can be accurately determined. Determining the temperature distribution type provides more specific guidance for dust accumulation detection, allowing the system to specifically analyze which components may accumulate dust and the degree of dust accumulation, thereby improving the accuracy and efficiency of detection.

[0046] Specifically, for each temperature band, all pixels within it are traversed, and the RGB value of each pixel is extracted. Since there is a mapping relationship between RGB values ​​and temperature (high-temperature areas correspond to red, and low-temperature areas correspond to blue), RGB values ​​can be converted into temperature values, or directly used as a representative of the temperature index. The average RGB values ​​of all pixels within the temperature band are calculated to reflect the average temperature level within that area; the higher the temperature, the higher the corresponding red channel value; the lower the temperature, the higher the corresponding blue channel value. Therefore, calculating the average RGB value as a temperature index can effectively reflect the degree of temperature. The sorting rule is based on the degree of temperature. For example, the calculated temperature index values ​​can be sorted in descending or ascending order. Descending order means starting from the temperature band with the highest temperature, while ascending order means starting from the temperature band with the lowest temperature. According to the selected sorting rule, all temperature bands are sorted to generate a temperature band sequence. Each position in the sequence corresponds to a temperature band, reflecting the distribution of temperature bands from the highest temperature to the lowest temperature (or vice versa) from the start point to the end point of the sequence. Once the temperature band sequence is obtained, the temperature distribution type can be determined by analyzing the arrangement pattern of the temperature bands in the sequence.

[0047] In some exemplary embodiments, calculating a temperature index value for each of the above-mentioned temperature bands based on the RGB values ​​of the above-mentioned pixels in each of the above-mentioned temperature bands includes: obtaining the RGB values ​​of each of the above-mentioned pixels in each of the above-mentioned temperature bands, and calculating the temperature index value based on T. score =a·R avg +b·G avg +c·B avg The temperature index values ​​for each of the aforementioned temperature zones are obtained, where a, b, and c are preset constants, and T score For the above temperature index values, R avg It is based on Certainly, G avg It is based on Definitely, B avg It is based on This is determined, where N is the number of pixels in each of the aforementioned temperature zones. The selection of preset constants a, b, and c needs to be based on experiments and analysis; by adjusting the values ​​of a, b, and c, T can be optimized. score It reflects the actual temperature, making it more consistent with the temperature information in the heat map.

[0048] In the above embodiments, the temperature index value is calculated using the above formula, where R avg G avg And B avg These represent the average values ​​of all pixels in the red, green, and blue channels within each temperature band. a, b, and c are preset weighting constants, ensuring that the quantification of temperature characteristics for each temperature band considers both the intuitive color representation of temperature (red for high temperature, blue for low temperature) and the complexity of temperature distribution, making the temperature index values ​​more accurately reflect the actual temperature situation. The weighting constants can be optimized according to specific application scenarios to improve the calculation accuracy of the temperature index values. For example, in the scenario of air conditioner dust accumulation detection, if specific temperature distribution characteristics are found to be more critical in judging the degree of dust accumulation, the values ​​of the preset constants can be adjusted to give these characteristics higher weights, thereby more accurately identifying the dust accumulation situation. The calculation of temperature index values ​​provides a crucial basis for subsequent temperature band sorting and dust accumulation detection. By analyzing the temperature index values ​​of different temperature bands, it is possible to accurately locate which areas inside the air conditioner have severe dust accumulation and which areas have less dust accumulation, thereby improving the accuracy of dust accumulation detection.

[0049] Specifically, the RGB value is a triplet representing the intensity of the red (R), green (G), and blue (B) color channels, typically with each channel's value between 0 and 255. Next, the average red channel value R for the temperature band is calculated. avg Average green channel value G avg and the average blue channel value B avg .

[0050] In some other exemplary embodiments, after determining the temperature distribution type based on the temperature band sequence, the method further includes: processing the temperature bands using an image processing algorithm to obtain the area of ​​the temperature band corresponding to the highest temperature index value as a first temperature band area, and the area of ​​the temperature band corresponding to the lowest temperature index value as a second temperature band area; determining the ratio of the second temperature band area to the first temperature band area as a first ratio, and determining the dust accumulation area based on the first ratio.

[0051] In the above embodiments, image processing algorithms are used to calculate the areas of the temperature band corresponding to the highest temperature index value (first temperature band) and the temperature band corresponding to the lowest temperature index value (second temperature band), converting the visual information of temperature distribution into quantifiable area data. The area of ​​the first temperature band represents the size of the region with the most severe dust accumulation, while the area of ​​the second temperature band reflects the size of the region with no dust accumulation or light dust accumulation. The ratio (first ratio) of the area of ​​the second temperature band (low dust accumulation region) to the area of ​​the first temperature band (high dust accumulation region) is calculated as a criterion to help identify whether the dust accumulation inside the air conditioner has reached a level requiring cleaning. The lower the ratio, the larger the proportion of severely dusty areas; conversely, the higher the ratio, the lower the degree of dust accumulation or the absence of dust accumulation in most areas.

[0052] Specifically, if the first ratio is close to 1 (e.g., 0.75 to 1.25), it indicates that the area of ​​the dust-accumulated area is similar to that of the area without dust accumulation, and the degree of dust accumulation is moderate; if the first ratio is much less than 1 (e.g., less than 0.5), it indicates that the area of ​​the dust-accumulated area is large and the degree of dust accumulation is severe; if the first ratio is much greater than 1 (e.g., greater than 1.5), it may indicate that the dust accumulation inside the air conditioner is relatively light.

[0053] In some exemplary solutions of this application, determining the dust accumulation status of the air conditioner includes: determining the ratio of the lowest temperature index value to the highest temperature index value as a second ratio value, and determining the thickness of the dust accumulation on the air conditioner based on the second ratio value, wherein the dust accumulation status includes the thickness of the dust accumulation on the air conditioner.

[0054] In the above embodiments, a quantitative index is obtained to assess the thickness of dust accumulation in the air conditioner by calculating a second ratio, namely the ratio of the lowest temperature index value to the highest temperature index value. The temperature index value is calculated based on the RGB values ​​of the heat map, reflecting the temperature characteristics of different temperature zones, and thus reflecting the severity of dust accumulation. This quantitative assessment provides maintenance personnel with objective data support, improving the accuracy and consistency of dust accumulation judgment. Furthermore, because the calculation method of the second ratio is sensitive to changes in dust accumulation thickness, even slight changes in the degree of dust accumulation will correspondingly alter the difference between the highest and lowest temperature index values, causing a change in the second ratio, thereby enabling timely reflection of minute changes in the dust accumulation status. Compared with traditional manual inspection, this saves time, reduces labor costs, avoids errors in subjective judgment, ensures the efficiency and reliability of dust accumulation detection, and further improves the efficiency of air conditioner dust distribution detection.

[0055] Specifically, the closer the second ratio is to 1, the smaller the difference between the highest and lowest temperature index values, and the more uniform the ash thickness or the lighter the degree of ash accumulation. Conversely, the lower the ratio, the higher the highest temperature index value is than the lowest temperature index value, the greater the difference in ash thickness, and the existence of areas with thicker ash accumulation.

[0056] In some other exemplary solutions of this application, acquiring temperature data from multiple detection points within a predetermined area and generating a heat map based on all of the aforementioned temperature data includes: acquiring the temperature data of each temperature sensor within the predetermined area at the same time, and the location data of each of the aforementioned temperature sensors; constructing a temperature field data matrix based on the aforementioned temperature data and the aforementioned location data, and generating the aforementioned heat map based on the aforementioned temperature field data matrix, wherein the aforementioned temperature field data matrix characterizes the temperature distribution at different locations within the predetermined area.

[0057] In the above embodiments, the heat map can display temperature data in an intuitive visual form, mapping the temperature data of the detection points onto colors. Temperature differences at different locations can be accurately recorded and presented, making the temperature distribution clear at a glance. This helps to quickly identify areas with abnormal temperatures, such as components with severe dust accumulation, as these areas may experience uneven temperature distribution due to airflow obstruction. Furthermore, by automatically collecting temperature data and location information and generating heat maps in real time, the efficiency and accuracy of data collection are improved. Dust accumulation problems can be detected and resolved promptly, preventing the deterioration of dust accumulation from affecting air conditioning performance. The temperature distribution information provided by the heat map can serve as a basis for maintenance strategy formulation. For example, if the heat map shows abnormal temperatures in certain specific areas, it may indicate that the air conditioning components in those areas have severe dust accumulation and require priority cleaning. This allows for more targeted maintenance, reducing unnecessary comprehensive disassembly and inspection, saving time and costs, and further improving the efficiency of air conditioning dust distribution detection.

[0058] Specifically, by combining the location information of sensors within a predetermined area, a two-dimensional matrix matching the shape and size of the predetermined area is created. The rows and columns of this matrix correspond to the x and y directions in the spatial coordinate system, respectively. The processed temperature data is then filled into the matrix, with each matrix element corresponding to a location in space and its value being the temperature at that location.

[0059] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the air conditioner dust accumulation detection method of this application will be described in detail below with reference to specific embodiments.

[0060] This embodiment relates to a specific method for detecting dust accumulation in air conditioners, such as... Figure 3 As shown, it includes the following steps:

[0061] Step S201: Acquire temperature data and generate a heat map;

[0062] Step S202: Divide the temperature zones according to the heat map;

[0063] Step S203: Determine the components of the air conditioner that are accumulating dust and the degree of dust accumulation.

[0064] This solution provides a specific implementation scenario for air conditioner dust accumulation detection: S1: Room temperature field data extraction. Multiple sensors are deployed in the room to ensure coverage of the entire space. Typically (taking cooling as an example), the size of the room affects the cooling capacity of the air conditioner; therefore, a reasonable temperature data collection time point needs to be selected based on the room size. After the air conditioner is turned on, a suitable time (usually when the temperature tends to stabilize) is chosen to collect temperature data to reflect the overall temperature state of the room. The collected sensor data is preprocessed, interpolated, and combined with the sensor location information to generate a two-dimensional temperature field time series data matrix. S2: Dust distribution analysis. Without dust accumulation, airflow from the air outlet is usually unobstructed, and the overall room temperature change is relatively uniform. When dust accumulates at the air outlet, airflow is obstructed, and the airflow at the dusty area is reduced compared to the dust-free area, resulting in uneven temperature distribution at the air outlet. This solution uses Matplotlib (a widely used open-source data visualization library in Python) to plot the temperature field data matrix as a heatmap and map its values ​​into the RGB color space. Based on the temperature distribution, the dust accumulation status of the air conditioner is reflected, specifically including the following: S21 Indoor Temperature Zone Division: Indoor temperature field map is collected, and the indoor temperature zones are divided according to the RGB values ​​corresponding to the temperature field heat map. The division principle includes: color-coding the indoor temperature distribution, with the coldest temperature zone marked as pure blue and the hottest as pure red. Different temperatures will be marked with different colors, with higher temperatures closer to red. Based on the RGB values ​​of the temperature field, the color coding of each temperature zone within the temperature field is completed. In this invention, the method of separating different temperature zones can set a certain RGB threshold range (i.e., the difference in RGB values ​​(span) of the heat map belongs to different temperature zones; this logic is quite common, and the threshold range can also be determined according to actual conditions, which will not be discussed in detail here). Simultaneously, contour extraction and segmentation based on image processing are performed to divide the temperature field into color regions of different temperatures. Example of color regions is shown in the figure. Figure 4 As shown, temperature bands 1 and 4 are analyzed for sorting and distribution of the segmented color regions. To reiterate: S21 addresses the division of temperature bands. The use of pure blue or light blue colors to distinguish temperature bands is solely for differentiation. These colors do not directly reflect the temperature levels. Each temperature band corresponds to the RGB values ​​of the S2 heatmap, which represent the actual temperature levels within each band. S22 Temperature Band Sorting: To differentiate the temperatures of each band, they are sorted. Based on the RGB values ​​of the temperature bands mentioned in S21, the lowest temperature band (closest to blue) is labeled 1, the next lowest is labeled 2, and so on. All temperature bands are then sorted. Similarly, as shown... Figure 4As shown, this is a top view of the room. The indoor temperature is divided into 5 distinct temperature zones, numbered 1, 2, 3, 4, and 5 according to their temperature intensity. S23 Indoor Temperature Zone Distribution Judgment: Indoor temperature zone merging refers to determining the room's internal temperature distribution by analyzing the temperature intensity of each zone. The steps for distribution judgment are as follows: S231 Obtain the two lowest temperature zones: Under cooling conditions, first obtain the two lowest temperature zones. This is because the characteristics of the lowest temperature zones are obvious. By examining these two lowest temperature zones and whether other temperature zones are interspersed between them, it is easiest to determine whether the temperature distribution is high in the middle and low at both ends. The reason for not selecting temperature zone 1 alone is that when there is dust accumulation in the air conditioner, it is difficult to quickly determine the indoor temperature distribution and thus the characteristics of the dust accumulation. However, if there are no other temperature zones interspersed between temperature zones 1 and 2, it is necessary to use zone 1 as the center and judge the overall distribution of the temperature zones on both sides of zone 1. This is necessary to distinguish whether the room's temperature distribution is high on one side and low on the other, or low in the middle and high at both ends. S232 Room Temperature Distribution Judgment: Based on the principle of S231, the room temperature distribution is judged. For example... Figure 5 As shown, the lowest temperature zones 1 and 2 are obtained. At this time, temperature zones 1 and 2 are on the same side, without any temperature zones in between. Furthermore, the temperature on one side (2) of temperature zone 1 is lower than the temperature on the other side (3, 4, 5), meaning the highest temperature of one side of the room is lower (higher) than the other side. The room temperature distribution follows a pattern of high on one side and low on the other. Figure 4 As shown, temperature zones 1 and 2, which have the lowest temperatures, are obtained. Other temperature zones are interspersed between these two zones. Since temperature zones 1 and 2 are the two lowest-temperature zones, the room temperature distribution can be considered as high in the middle and low on both sides. Figure 5As shown, temperature zones 1 and 2, which have the lowest temperatures, are obtained. No other temperature zones are interspersed between temperature zones 1 and 2, and the temperatures on both sides of temperature zone 1 are higher than the overall temperature zone 1. This means that the lowest temperature in the room is sandwiched between higher temperatures, and the room temperature distribution pattern is low in the middle and high on both sides. S24 Specific dust accumulation component judgment in air conditioner: S241 Analysis of dust accumulation in air conditioner components: Components in the indoor unit of an air conditioner that are prone to dust accumulation include the fan, evaporator, and filter. Their characteristics are as follows: Fan position: Located behind the evaporator, its function is to push air outwards. The central part has a high-speed rotating airflow concentration, making it easier to disperse dust and resulting in less dust accumulation. The root and edges have weaker airflow, leading to more dust accumulation. Therefore, the dust accumulation distribution is characterized by less dust in the middle and more at the edges. Evaporator: The central part faces the air inlet, resulting in concentrated airflow and high air velocity, carrying more dust and easily accumulating dust. The airflow velocity on both sides is lower, carrying less dust and resulting in less dust accumulation. Therefore, the dust accumulation distribution is characterized by more dust in the middle and less at the edges. Filter: The filter is installed at the air inlet of the air conditioner. The central area is where airflow is concentrated and dust accumulates more, while the airflow on both sides is weaker and dust accumulates less. Therefore, the filter's dust accumulation is characterized by more dust in the middle and less on the sides. However, doors and windows often affect the airflow at the inlet, causing different air volumes on both sides of the inlet, resulting in uneven dust accumulation. This creates an asymmetrical distribution around the filter, with one side having more dust and the other less. S242 Dust Accumulation Component Judgment: Dust inside the air conditioner affects the airflow. During cooling, cold air cannot be completely released, and the temperature at the dust accumulation location will be higher than other locations. Dust accumulation affects the airflow from the indoor unit, further affecting the room's temperature distribution. Based on the room temperature distribution in S232 and the characteristics of the dust accumulation component in S241, the dust accumulation component of the air conditioner is determined. The overall process is as follows: Figure 6 As shown, specifically: When the room temperature distribution is hot in the middle and cold on both sides, it indicates that the flow of cold air in the middle of the air outlet is obstructed, affecting the cooling effect. This indicates that there is more dust accumulation in the middle of the air outlet, that is, the dust distribution is more in the middle and less on the sides. In this case, it can be considered that the evaporator and / or filter are dusty. When the room temperature is cold in the middle and hot on both sides, it indicates that the cooling effect on both sides of the air outlet is not good, and there is more dust accumulation on both sides. That is, the dust distribution is less in the middle and more on the sides. In this case, it can be considered that the fan is dusty. When the room temperature is cold on one side and hot on the other side, it indicates that there is more dust accumulation on one side and less dust accumulation on the other side. In this case, it can be considered that the filter is dusty. (Note: In this invention, "intermediate high" refers to a high intermediate temperature or a hot intermediate temperature; "high" and "hot" have the same meaning. Similarly, "below cold" has the same meaning.) S243 Determining the degree of dust accumulation on components: Based on the parameters of the coldest and hottest temperature zones, the area ratio of the coldest and hottest temperature zones reflects the overall area of ​​dust accumulation on the air conditioning components, and the area ratio of the coldest and hottest temperature zones reflects the thickness of the dust accumulation. Figure 7As shown below: S2431 Judgment of the ash accumulation area: Through S242, the components with ash accumulation can be judged. And according to the temperature zone division logic of S22, when the air conditioner is in refrigeration, the size of the ash accumulation area of the air conditioner components directly affects the size of the temperature zone area. Especially for the coldest temperature zone and the hottest temperature zone, that is, the area of the coldest temperature zone reflects the area where the air conditioner components have no ash accumulation (or less ash accumulation), and the area of the hottest temperature zone reflects the area where the air conditioner components have more ash accumulation. Since the ash accumulation components of the air conditioner are not necessarily the same and the size may also be different, the ratio of the area of the coldest temperature zone to the area of the hottest temperature zone is used, that is, the ratio is equivalent to standardization, to judge the degree of the ash accumulation area of the air conditioner (that is, the overall ash accumulation area of the air conditioner), and the threshold of the ratio can be selected according to the actual situation. For example: Extract the contour of the temperature zone and use the cv2.contourArea() function in Opencv to calculate the area of the contour. Taking Figure 5 as an example, calculate the areas of temperature zone 1 and temperature zone 5 as s1 and s5 respectively. Assume Figure 5 in which s1:s5 = a, and actually set a ratio threshold as b. If a < b, it means that the area of temperature zone 1 is much smaller than the area of temperature zone 5. Temperature zone 5 is the temperature zone with the highest temperature, indicating that there are more places where the air volume at the air outlet is blocked, and it can be considered that the area of the overall ash accumulation area of the air conditioner is larger; if a > b, it means that the area of temperature zone 1 is larger than the area of temperature zone 5. Temperature zone 1 is the temperature zone with the lowest temperature, indicating that more air volume is released at the air outlet, and it can be considered that the area of the overall ash accumulation area of the air conditioner is smaller. S2432 Judgment of the thickness of ash accumulation: Based on the original RGB values of the thermal map of temperature, according to the temperature zone division logic of S22, the thickness of the ash accumulation area of the air conditioner components directly affects the RGB values of the thermal map of the temperature zone. Especially for the coldest temperature zone and the hottest temperature zone, that is, the RGB value of the coldest temperature zone reflects the thickness of the air conditioner components with no ash accumulation (or less ash accumulation), and the RGB value of the hottest temperature zone reflects the thickness of the area where the air conditioner components have more ash accumulation. Since the refrigeration performance of each air conditioner is not necessarily the same and the set working temperature is not necessarily the same, the ratio of the RGB value of the coldest temperature zone to the RGB value of the hottest temperature zone is used, that is, the ratio is equivalent to standardization, to judge the thickness of the ash accumulation of the air conditioner. The temperature zone is divided according to the RGB values of the thermal map. The RGB values of the thermal map represent the high and low temperature. After extracting the contour of the temperature zone, traverse all the pixels inside the contour, calculate the mean values of its R, G, and B channels respectively, so as to obtain the RGB mean value of the temperature zone. After weighted averaging the RGB mean values, a single value can be obtained, and this value is used as the actual temperature value of this temperature zone. Taking Figure 5 as an example, calculate the RGB mean values inside temperature zone 1 and temperature zone 5 as T1 and T5 respectively. Assume Figure 5For T1:T5 = x in it, a threshold value y (such as 0.7) is actually set. When x >= y, it indicates that the RGB values between the two temperature zones are close, and the temperature values of the heat map are similar, that is, the temperature difference between the coldest temperature zone and the hottest temperature zone is not large, and it can be considered that the overall dust accumulation on the air-conditioning components is less; if x < y, it indicates that the temperature difference between the two temperature zones is large, and the temperature difference between the coldest temperature zone and the hottest temperature zone is large, and it can be considered that the overall dust accumulation on the air-conditioning components is thick. S3 completes the detection of air-conditioning dust accumulation: According to the detection of the dust-accumulating components and the degree of air-conditioning dust accumulation in S2, the result of air-conditioning dust accumulation is output, and the user is reminded of the dust-accumulation situation of the air conditioner in a timely manner.

[0065] An embodiment of the present application also provides a specific usage scenario for determining the dust accumulation situation of an air conditioner according to temperature index values. First, multiple temperature sensors are used to obtain the temperature data of each detection point in a predetermined area of the air conditioner, and the position information of each sensor is recorded. Based on the collected temperature data, a temperature field data matrix is constructed, and then a plotting library such as Matplotlib is used to generate a heat map according to the position and temperature value of each point in the matrix. Through edge detection algorithms and RGB value analysis, the pixel points in the heat map are grouped according to temperature to form different temperature zones to reflect the temperature distribution. Traverse the pixel points in each temperature zone and extract their RGB values. According to T score = a·R avg + b·G avg + c·B avg , calculate the average temperature index value of the temperature zone, where R avg , G avg and B avg are the average values of the R, G, and B channels of the temperature zone respectively. And identify the temperature zones with the highest and lowest temperature index values, which represent the areas with the least and most dust accumulation respectively. Calculate the ratio of the temperature index values of these two temperature zones and record it as the second ratio. By analyzing the relationship between the second ratio and the standard threshold, the thickness of dust accumulation is quantitatively judged. For example, a ratio close to 1 indicates a lower degree of dust accumulation, and a ratio far from 1 indicates serious dust accumulation. This solution can provide a more accurate quantitative index for the thickness of dust accumulation compared with traditional dust accumulation detection methods. And the detection of the degree of dust accumulation can be carried out without disassembling the air conditioner, greatly reducing the maintenance cost and time, and improving the detection efficiency of the dust distribution of the air conditioner.

[0066] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0067] This application also provides an air conditioner dust accumulation detection device. It should be noted that this air conditioner dust accumulation detection device can be used to execute the air conditioner dust accumulation detection method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0068] The following describes the air conditioner dust accumulation detection device provided in the embodiments of this application.

[0069] Figure 8 This is a schematic diagram of an air conditioner dust accumulation detection device according to an embodiment of this application. Figure 3 As shown, the device includes:

[0070] The acquisition unit 10 is used to acquire temperature data of multiple detection points within a predetermined area and generate a heat map based on all of the above temperature data.

[0071] Specifically, temperature data is acquired through temperature sensors, and the collected sensor data undergoes noise removal and data standardization. Since sensors may not cover the entire predetermined area, spatial interpolation is required to estimate the temperature at unmonitored locations. If the sensor acquisition frequency is insufficient to capture temperature changes over time, temporal interpolation methods, such as linear interpolation, polynomial interpolation, or wavelet interpolation, can be used to fill data gaps in the continuous time series.

[0072] The first determining unit 20 is used to determine the temperature zone of the heat map based on the RGB values ​​of the pixels in the heat map, wherein the heat map characterizes the temperature distribution characteristics of the predetermined area under air conditioning operation.

[0073] The second determining unit 30 is used to determine the temperature distribution type of the predetermined area based on the temperature zone, and to determine the dust accumulation status of the air conditioner based on the temperature distribution type, and to output the air conditioner dust accumulation detection result. The air conditioner dust accumulation detection result includes the dust accumulation components in the air conditioner and the corresponding degree of dust accumulation. The temperature distribution type characterizes the temperature distribution status of the predetermined area.

[0074] Specifically, temperature distribution types include: a hot-in-the-middle and cold-on-the-sides temperature distribution, where the temperature in the middle region is significantly higher than that in the two sides; a cold-in-the-middle and hot-on-the-sides temperature distribution; and a hot-on-one-and-cold-on-the-sides temperature distribution.

[0075] Through the above embodiments, firstly, the acquisition unit acquires temperature data from multiple detection points within a predetermined area and generates a heat map based on all the temperature data; then, the first determination unit determines the temperature bands of the heat map based on the RGB values ​​of the pixels in the heat map; finally, the second determination unit determines the temperature distribution type of the predetermined area based on the temperature bands, and determines the dust accumulation status of the air conditioner based on the temperature distribution type, and outputs the air conditioner dust accumulation detection result. This solution, by acquiring temperature data of a predetermined area and generating a heat map, can more intuitively display the temperature distribution characteristics of the predetermined area. Dividing the temperature bands according to the RGB values ​​of the pixels in the heat map can accurately identify the temperature differences between different areas within the predetermined area and determine the temperature distribution type based on the temperature differences. Since the dust accumulation distribution of components inside the air conditioner has different characteristics, it is possible to accurately determine the air conditioner component and degree of dust accumulation based on the temperature distribution type, solving the problem of low detection efficiency of air conditioner dust distribution in the prior art.

[0076] As an optional solution, the first determining unit includes: a traversal module, used to traverse the pixels in the heat map to extract the RGB values ​​of each pixel; a grouping module, used to group the pixels according to a preset RGB value difference threshold to obtain multiple pixel sets, wherein the RGB value difference threshold is a value used to determine whether the pixels in the heat map belong to the same temperature zone; and an extraction module, used to extract the boundaries of the pixel sets using an edge detection algorithm to determine the temperature zone.

[0077] In the above embodiments, by traversing every pixel in the heatmap and grouping them according to a preset RGB value difference threshold, pixels with similar temperature characteristics are ensured to be classified into the same temperature band. The grouped set of pixels represents a region within a specific temperature range, i.e., a temperature band. This grouping method based on pixel RGB values ​​can accurately reflect the details of temperature distribution. By employing an edge detection algorithm to accurately locate the boundaries between temperature bands, it is possible to effectively identify drastic changes in pixel values, i.e., abrupt changes in temperature distribution, which helps to clearly define the boundaries between different temperature bands, accurately identify the range of temperature bands, and help to accurately determine the location and extent of dust accumulation in air conditioners. In addition, automatically extracting temperature bands from the heatmap speeds up dust accumulation detection, making the detection process more efficient.

[0078] Specifically, heatmaps are generated based on temperature data, where the RGB value of each pixel is directly related to its corresponding temperature. The lower the temperature, the closer the pixel's color is to blue; the higher the temperature, the closer the color is to red. RGB values ​​are a visual representation of temperature. By setting an RGB value difference threshold, we can determine whether two adjacent pixels are similar enough to be considered to belong to the same temperature band. The number of temperature bands depends on the aforementioned RGB value difference threshold; the smaller the threshold, the more temperature bands, and vice versa. If the RGB value difference threshold is too small, although the temperature bands will be more detailed, it requires stronger data processing capabilities to analyze the temperature data, making it more difficult and potentially inaccurate; if the threshold is too large, it will not accurately reflect the temperature change patterns, also leading to inaccuracies. The specific RGB value difference threshold needs to be adjusted based on actual conditions. Once the pixels are grouped, the next step is to use edge detection algorithms, such as Canny Edge Detector (an edge detection algorithm used in image processing and computer vision) and Sobel Operator (an edge detection algorithm widely used in image processing and computer vision), to identify the boundaries of each pixel set, i.e., the temperature bands. Edge detection algorithms can identify the boundaries between different regions in an image, i.e., points of abrupt changes in color (in this case, temperature).

[0079] In one optional scheme, the second determining unit includes: a calculation module, configured to calculate a temperature index value for each temperature band based on the RGB values ​​of the pixels in each temperature band, wherein the temperature index value represents the degree of temperature; a sorting module, configured to sort the temperature bands according to the temperature index values ​​and a predetermined sorting rule to obtain a temperature band sequence; and a first determining module, configured to determine the temperature distribution type based on the temperature band sequence.

[0080] In the above embodiments, by converting the RGB values ​​of pixels in each temperature band into temperature index values, a quantitative representation of the temperature level is achieved, reflecting the true temperature conditions of the temperature band. The temperature bands are sorted using a preset sorting rule, forming a temperature band sequence. This sorted sequence visually displays the temperature distribution from low to high within a predetermined area, making the temperature distribution type clearly visible. Based on the temperature band sequence, the temperature distribution type of the predetermined area can be accurately determined. Determining the temperature distribution type provides more specific guidance for dust accumulation detection, allowing the system to specifically analyze which components may accumulate dust and the degree of dust accumulation, thereby improving the accuracy and efficiency of detection.

[0081] Specifically, for each temperature band, all pixels within it are traversed, and the RGB value of each pixel is extracted. Since there is a mapping relationship between RGB values ​​and temperature (high-temperature areas correspond to red, and low-temperature areas correspond to blue), RGB values ​​can be converted into temperature values, or directly used as a representative of the temperature index. The average RGB values ​​of all pixels within the temperature band are calculated to reflect the average temperature level within that area; the higher the temperature, the higher the corresponding red channel value; the lower the temperature, the higher the corresponding blue channel value. Therefore, calculating the average RGB value as a temperature index can effectively reflect the degree of temperature. The sorting rule is based on the degree of temperature. For example, the calculated temperature index values ​​can be sorted in descending or ascending order. Descending order means starting from the temperature band with the highest temperature, while ascending order means starting from the temperature band with the lowest temperature. According to the selected sorting rule, all temperature bands are sorted to generate a temperature band sequence. Each position in the sequence corresponds to a temperature band, reflecting the distribution of temperature bands from the highest temperature to the lowest temperature (or vice versa) from the start point to the end point of the sequence. Once the temperature band sequence is obtained, the temperature distribution type can be determined by analyzing the arrangement pattern of the temperature bands in the sequence.

[0082] In one optional embodiment, the calculation module includes: an acquisition submodule, configured to acquire the RGB values ​​of each pixel in each of the aforementioned temperature bands, and calculate the RGB values ​​according to T. score =a·R avg +b·G avg +c·B avg The temperature index values ​​for each of the aforementioned temperature zones are obtained, where a, b, and c are preset constants, and T score For the above temperature index values, R avg It is based on Certainly, G avg It is based on Definitely, B avg It is based on It is determined that N is the number of the aforementioned pixels in each of the aforementioned temperature zones.

[0083] In the above embodiments, the temperature index value is calculated using the above formula. Ravg, Gavg, and Bavg represent the average values ​​of all pixels in the red, green, and blue channels of the temperature band, respectively. a, b, and c are preset weighting constants, ensuring that the quantification of the temperature characteristics of each temperature band considers both the intuitive color representation of temperature (red represents high temperature, blue represents low temperature) and the complexity of temperature distribution, making the temperature index value more accurately reflect the actual temperature situation. The setting of the weighting constants can be optimized according to specific application scenarios to improve the calculation accuracy of the temperature index value. For example, in the scenario of air conditioner dust accumulation detection, if it is found that specific temperature distribution characteristics are more critical to judging the degree of dust accumulation, the values ​​of the preset constants can be adjusted to give these characteristics higher weights, thereby more accurately identifying the dust accumulation situation. The calculation of the temperature index value provides a key basis for subsequent temperature band sorting and dust accumulation detection. By analyzing the temperature index values ​​of different temperature bands, it is possible to accurately locate which areas inside the air conditioner have severe dust accumulation and which areas have light dust accumulation, thereby improving the accuracy of dust accumulation detection.

[0084] Specifically, the RGB value is a triplet representing the intensity of the three color channels: red (R), green (G), and blue (B). Typically, the value for each channel is between 0 and 255. Next, the average red channel value Ravg, the average green channel value Gavg, and the average blue channel value Bavg for the temperature band are calculated.

[0085] As an optional solution, the second determining unit further includes: a processing module, used to process the temperature band using an image processing algorithm to obtain the area of ​​the temperature band corresponding to the highest temperature index value as the first temperature band area, and the area of ​​the temperature band corresponding to the lowest temperature index value as the second temperature band area; and a second determining module, used to determine the ratio of the second temperature band area to the first temperature band area as a first ratio, and determine the dust accumulation area based on the first ratio.

[0086] In the above embodiments, image processing algorithms are used to calculate the areas of the temperature band corresponding to the highest temperature index value (first temperature band) and the temperature band corresponding to the lowest temperature index value (second temperature band), converting the visual information of temperature distribution into quantifiable area data. The area of ​​the first temperature band represents the size of the region with the most severe dust accumulation, while the area of ​​the second temperature band reflects the size of the region with no dust accumulation or light dust accumulation. The ratio (first ratio) of the area of ​​the second temperature band (low dust accumulation region) to the area of ​​the first temperature band (high dust accumulation region) is calculated as a criterion to help identify whether the dust accumulation inside the air conditioner has reached a level requiring cleaning. The lower the ratio, the larger the proportion of severely dusty areas; conversely, the higher the ratio, the lower the degree of dust accumulation or the absence of dust accumulation in most areas.

[0087] Specifically, if the first ratio is close to 1 (e.g., 0.75 to 1.25), it indicates that the area of ​​the dust-accumulated area is similar to that of the area without dust accumulation, and the degree of dust accumulation is moderate; if the first ratio is much less than 1 (e.g., less than 0.5), it indicates that the area of ​​the dust-accumulated area is large and the degree of dust accumulation is severe; if the first ratio is much greater than 1 (e.g., greater than 1.5), it may indicate that the dust accumulation inside the air conditioner is relatively light.

[0088] In another alternative embodiment, the second determining unit further includes a third determining module, used to determine the ratio of the lowest temperature index value to the highest temperature index value as a second ratio value, and to determine the degree of dust accumulation on the air conditioner based on the second ratio value, wherein the dust accumulation condition includes the degree of dust accumulation on the air conditioner.

[0089] In the above embodiments, a quantitative index is obtained to assess the thickness of dust accumulation in the air conditioner by calculating a second ratio, namely the ratio of the lowest temperature index value to the highest temperature index value. The temperature index value is calculated based on the RGB values ​​of the heat map, reflecting the temperature characteristics of different temperature zones, and thus reflecting the severity of dust accumulation. This quantitative assessment provides maintenance personnel with objective data support, improving the accuracy and consistency of dust accumulation judgment. Furthermore, because the calculation method of the second ratio is sensitive to changes in dust accumulation thickness, even slight changes in the degree of dust accumulation will correspondingly alter the difference between the highest and lowest temperature index values, causing a change in the second ratio, thereby enabling timely reflection of minute changes in the dust accumulation status. Compared with traditional manual inspection, this saves time, reduces labor costs, avoids errors in subjective judgment, ensures the efficiency and reliability of dust accumulation detection, and further improves the efficiency of air conditioner dust distribution detection.

[0090] Specifically, the closer the second ratio is to 1, the smaller the difference between the highest and lowest temperature index values, and the more uniform the ash thickness or the lighter the degree of ash accumulation. Conversely, the lower the ratio, the higher the highest temperature index value is than the lowest temperature index value, the greater the difference in ash thickness, and the existence of areas with thicker ash accumulation.

[0091] In one optional embodiment, the acquisition unit includes: an acquisition module, configured to acquire temperature data of each temperature sensor within the predetermined area at the same time, and location data of each temperature sensor; and a construction module, configured to construct a temperature field data matrix based on the temperature data and the location data, and generate the heat map based on the temperature field data matrix, wherein the temperature field data matrix characterizes the temperature distribution at different locations within the predetermined area.

[0092] In the above embodiments, the heat map can display temperature data in an intuitive visual form, mapping the temperature data of the detection points onto colors. Temperature differences at different locations can be accurately recorded and presented, making the temperature distribution clear at a glance. This helps to quickly identify areas with abnormal temperatures, such as components with severe dust accumulation, as these areas may experience uneven temperature distribution due to airflow obstruction. Furthermore, by automatically collecting temperature data and location information and generating heat maps in real time, the efficiency and accuracy of data collection are improved. Dust accumulation problems can be detected and resolved promptly, preventing the deterioration of dust accumulation from affecting air conditioning performance. The temperature distribution information provided by the heat map can serve as a basis for maintenance strategy formulation. For example, if the heat map shows abnormal temperatures in certain specific areas, it may indicate that the air conditioning components in those areas have severe dust accumulation and require priority cleaning. This allows for more targeted maintenance, reducing unnecessary comprehensive disassembly and inspection, saving time and costs, and further improving the efficiency of air conditioning dust distribution detection.

[0093] Specifically, by combining the location information of sensors within a predetermined area, a two-dimensional matrix matching the shape and size of the predetermined area is created. The rows and columns of this matrix correspond to the x and y directions in the spatial coordinate system, respectively. The processed temperature data is then filled into the matrix, with each matrix element corresponding to a location in space and its value being the temperature at that location.

[0094] The aforementioned air conditioner dust accumulation detection device includes a processor and a memory. The aforementioned acquisition unit, the aforementioned first determination unit, and the aforementioned second determination unit are all stored as program units in the memory. The processor executes the aforementioned program units stored in the memory to achieve the corresponding functions. All of the aforementioned modules are located in the same processor; or, the aforementioned modules are located in different processors in any combination.

[0095] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can at least address the low efficiency of dust distribution detection in existing technologies.

[0096] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0097] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to perform the air conditioner dust accumulation detection method.

[0098] Specifically, methods for detecting dust accumulation in air conditioners include:

[0099] Step S101: Obtain temperature data from multiple detection points within a predetermined area, and generate a heat map based on all of the above temperature data;

[0100] Specifically, temperature data is acquired through temperature sensors, and the collected sensor data undergoes noise removal and data standardization. Since sensors may not cover the entire predetermined area, spatial interpolation is required to estimate the temperature at unmonitored locations. If the sensor acquisition frequency is insufficient to capture temperature changes over time, temporal interpolation methods, such as linear interpolation, polynomial interpolation, or wavelet interpolation, can be used to fill data gaps in the continuous time series.

[0101] Step S102: Determine the temperature zone of the heat map based on the RGB values ​​of the pixels in the heat map. The heat map represents the temperature distribution characteristics of the predetermined area under air conditioning operation.

[0102] Step S103: Determine the temperature distribution type of the predetermined area based on the temperature zone, determine the dust accumulation status of the air conditioner based on the temperature distribution type, and output the air conditioner dust accumulation detection result. The air conditioner dust accumulation detection result includes the dust accumulation components in the air conditioner and the corresponding degree of dust accumulation. The temperature distribution type characterizes the temperature distribution status of the predetermined area.

[0103] Specifically, temperature distribution types include: a hot-in-the-middle and cold-on-the-sides temperature distribution, where the temperature in the middle region is significantly higher than that in the two sides; a cold-in-the-middle and hot-on-the-sides temperature distribution; and a hot-on-one-and-cold-on-the-sides temperature distribution.

[0104] In one embodiment of this application, determining the temperature zone of the heatmap based on the RGB values ​​of the pixels in the heatmap includes: traversing the pixels in the heatmap to extract the RGB values ​​of each pixel; grouping the pixels according to a preset RGB value difference threshold to obtain multiple pixel sets, wherein the RGB value difference threshold is a value used to determine whether the pixels in the heatmap belong to the same temperature zone; and using an edge detection algorithm to extract the boundary of the pixel set to determine the temperature zone.

[0105] In one embodiment of this application, determining the temperature distribution type of the predetermined region based on the temperature bands includes: calculating a temperature index value for each temperature band based on the RGB values ​​of the pixels in each temperature band, wherein the temperature index value represents the degree of temperature; sorting the temperature bands according to the temperature index values ​​and a predetermined sorting rule to obtain a temperature band sequence; and determining the temperature distribution type based on the temperature band sequence.

[0106] In one embodiment of this application, calculating the temperature index value of each temperature band based on the RGB values ​​of the pixels in each temperature band includes: obtaining the RGB values ​​of each pixel in each temperature band, and calculating the temperature index value based on T... score =a·R avg +b·G avg +c·B avg The temperature index values ​​for each of the aforementioned temperature zones are obtained, where a, b, and c are preset constants, and T score For the above temperature index values, R avg It is based on Certainly, G avg It is based on Definitely, B avg It is based on It is determined that N is the number of the aforementioned pixels in each of the aforementioned temperature zones.

[0107] In one embodiment of this application, after determining the temperature distribution type based on the temperature band sequence, the method further includes: processing the temperature bands using an image processing algorithm to obtain the area of ​​the temperature band corresponding to the highest temperature index value as the first temperature band area, and the area of ​​the temperature band corresponding to the lowest temperature index value as the second temperature band area; determining the ratio of the second temperature band area to the first temperature band area as a first ratio, and determining the dust accumulation area based on the first ratio.

[0108] In one embodiment of this application, determining the dust accumulation status of the air conditioner includes: determining a second ratio value as the ratio of the lowest temperature index value to the highest temperature index value; and determining the thickness of the dust accumulation on the air conditioner based on the second ratio value, wherein the dust accumulation status includes the thickness of the dust accumulation on the air conditioner.

[0109] In one embodiment of this application, acquiring temperature data from multiple detection points within a predetermined area and generating a heat map based on all the aforementioned temperature data includes: acquiring temperature data from each temperature sensor within the predetermined area at the same time, and location data of each of the aforementioned temperature sensors; constructing a temperature field data matrix based on the aforementioned temperature data and the aforementioned location data, and generating the aforementioned heat map based on the aforementioned temperature field data matrix, wherein the aforementioned temperature field data matrix characterizes the temperature distribution at different locations within the predetermined area.

[0110] This invention provides an air conditioner dust accumulation detection system, which is used to run a program, wherein the program executes the air conditioner dust accumulation detection method during operation.

[0111] Specifically, methods for detecting dust accumulation in air conditioners include:

[0112] Step S101: Obtain temperature data from multiple detection points within a predetermined area, and generate a heat map based on all of the above temperature data;

[0113] Specifically, temperature data is acquired through temperature sensors, and the collected sensor data undergoes noise removal and data standardization. Since sensors may not cover the entire predetermined area, spatial interpolation is required to estimate the temperature at unmonitored locations. If the sensor acquisition frequency is insufficient to capture temperature changes over time, temporal interpolation methods, such as linear interpolation, polynomial interpolation, or wavelet interpolation, can be used to fill data gaps in the continuous time series.

[0114] Step S102: Determine the temperature zone of the heat map based on the RGB values ​​of the pixels in the heat map. The heat map represents the temperature distribution characteristics of the predetermined area under air conditioning operation.

[0115] Step S103: Determine the temperature distribution type of the predetermined area based on the temperature zone, determine the dust accumulation status of the air conditioner based on the temperature distribution type, and output the air conditioner dust accumulation detection result. The air conditioner dust accumulation detection result includes the dust accumulation components in the air conditioner and the corresponding degree of dust accumulation. The temperature distribution type characterizes the temperature distribution status of the predetermined area.

[0116] Specifically, temperature distribution types include: a hot-in-the-middle and cold-on-the-sides temperature distribution, where the temperature in the middle region is significantly higher than that in the two sides; a cold-in-the-middle and hot-on-the-sides temperature distribution; and a hot-on-one-and-cold-on-the-sides temperature distribution.

[0117] In one embodiment of this application, determining the temperature zone of the heatmap based on the RGB values ​​of the pixels in the heatmap includes: traversing the pixels in the heatmap to extract the RGB values ​​of each pixel; grouping the pixels according to a preset RGB value difference threshold to obtain multiple pixel sets, wherein the RGB value difference threshold is a value used to determine whether the pixels in the heatmap belong to the same temperature zone; and using an edge detection algorithm to extract the boundary of the pixel set to determine the temperature zone.

[0118] In one embodiment of this application, determining the temperature distribution type of the predetermined region based on the temperature bands includes: calculating a temperature index value for each temperature band based on the RGB values ​​of the pixels in each temperature band, wherein the temperature index value represents the degree of temperature; sorting the temperature bands according to the temperature index values ​​and a predetermined sorting rule to obtain a temperature band sequence; and determining the temperature distribution type based on the temperature band sequence.

[0119] In one embodiment of this application, calculating the temperature index value of each temperature band based on the RGB values ​​of the pixels in each temperature band includes: obtaining the RGB values ​​of each pixel in each temperature band, and calculating the temperature index value based on T... score =a·R avg +b·G avg +c·B avg The temperature index values ​​for each of the aforementioned temperature zones are obtained, where a, b, and c are preset constants, and T score For the above temperature index values, R avg It is based on Certainly, G avg It is based on Definitely, B avg It is based on It is determined that N is the number of the aforementioned pixels in each of the aforementioned temperature zones.

[0120] In one embodiment of this application, after determining the temperature distribution type based on the temperature band sequence, the method further includes: processing the temperature bands using an image processing algorithm to obtain the area of ​​the temperature band corresponding to the highest temperature index value as the first temperature band area, and the area of ​​the temperature band corresponding to the lowest temperature index value as the second temperature band area; determining the ratio of the second temperature band area to the first temperature band area as a first ratio, and determining the dust accumulation area based on the first ratio.

[0121] In one embodiment of this application, determining the dust accumulation status of the air conditioner includes: determining a second ratio value as the ratio of the lowest temperature index value to the highest temperature index value; and determining the thickness of the dust accumulation on the air conditioner based on the second ratio value, wherein the dust accumulation status includes the thickness of the dust accumulation on the air conditioner.

[0122] In one embodiment of this application, acquiring temperature data from multiple detection points within a predetermined area and generating a heat map based on all the aforementioned temperature data includes: acquiring temperature data from each temperature sensor within the predetermined area at the same time, and location data of each of the aforementioned temperature sensors; constructing a temperature field data matrix based on the aforementioned temperature data and the aforementioned location data, and generating the aforementioned heat map based on the aforementioned temperature field data matrix, wherein the aforementioned temperature field data matrix characterizes the temperature distribution at different locations within the predetermined area.

[0123] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0124] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0129] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0130] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0133] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0134] 1) The air conditioner dust accumulation detection method of this application first acquires temperature data from multiple detection points within a predetermined area and generates a heat map based on all the temperature data; then, it determines the temperature bands of the heat map according to the RGB values ​​of the pixels; finally, it determines the temperature distribution type of the predetermined area based on the temperature bands, and determines the dust accumulation status of the air conditioner based on the temperature distribution type, and outputs the air conditioner dust accumulation detection result. This solution, by acquiring temperature data of a predetermined area and generating a heat map, can more intuitively display the temperature distribution characteristics of the predetermined area. Dividing the temperature bands according to the RGB values ​​of the pixels in the heat map can accurately identify the temperature differences between different areas within the predetermined area and determine the temperature distribution type based on the temperature differences. Since the dust accumulation distribution of dust-accumulating components inside the air conditioner has different characteristics, it is possible to accurately determine the air conditioner component and degree of dust accumulation based on the temperature distribution type, solving the problem of low detection efficiency of air conditioner dust distribution in the prior art.

[0135] 2) The air conditioner dust accumulation detection device of this application first acquires temperature data from multiple detection points within a predetermined area through an acquisition unit, and generates a heat map based on all the temperature data; then, a first determining unit determines the temperature bands of the heat map based on the RGB values ​​of the pixels in the heat map; finally, a second determining unit determines the temperature distribution type of the predetermined area based on the temperature bands, and determines the dust accumulation status of the air conditioner based on the temperature distribution type, and outputs the air conditioner dust accumulation detection result. This solution acquires temperature data from a predetermined area and generates a heat map, thereby more intuitively displaying the temperature distribution characteristics of the predetermined area. Dividing the temperature bands based on the RGB values ​​of the pixels in the heat map can accurately identify the temperature differences between different areas within the predetermined area and determine the temperature distribution type based on the temperature differences. Since the dust accumulation distribution of components inside the air conditioner has different characteristics, it is possible to accurately determine the air conditioner component and degree of dust accumulation based on the temperature distribution type, solving the problem of low detection efficiency of air conditioner dust distribution in the prior art.

[0136] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for detecting dust accumulation in air conditioners, characterized in that, include: Acquire temperature data from multiple detection points within a predetermined area, and generate a heat map based on all the temperature data; The temperature bands of the heat map are determined based on the RGB values ​​of the pixels in the heat map, and the heat map characterizes the temperature distribution characteristics of the predetermined area under air conditioning operation. The temperature distribution type of the predetermined area is determined based on the temperature zone, and the dust accumulation status of the air conditioner is determined based on the temperature distribution type. The air conditioner dust accumulation detection result is output, which includes the dust accumulation components in the air conditioner and the corresponding degree of dust accumulation. The temperature distribution type characterizes the temperature distribution status of the predetermined area.

2. The method according to claim 1, characterized in that, Determining the temperature band of the heatmap based on the RGB values ​​of the pixels in the heatmap includes: The pixels in the heatmap are traversed to extract the RGB values ​​of each pixel. The pixels are grouped according to a preset RGB value difference threshold to obtain multiple pixel sets. The RGB value difference threshold is a value used to determine whether the pixels in the heat map belong to the same temperature zone. An edge detection algorithm is used to extract the boundary of the pixel set in order to determine the temperature zone.

3. The method according to claim 1, characterized in that, Determining the temperature distribution type of the predetermined region based on the temperature zone includes: Based on the RGB values ​​of the pixels in each temperature band, calculate the temperature index value of each temperature band, whereby the temperature index value represents the degree of temperature. The temperature zones are sorted according to the temperature index values ​​and a predetermined sorting rule to obtain a temperature zone sequence. The temperature distribution type is determined based on the temperature zone sequence.

4. The method according to claim 3, characterized in that, Based on the RGB values ​​of the pixels in each temperature band, calculate the temperature index value for each temperature band, including: Obtain the RGB values ​​of each pixel in each of the temperature bands, and according to T score =a·R avg +b·G avg +c·B avg The temperature index values ​​for each temperature zone are obtained, where a, b, and c are preset constants, and T score R is the temperature index value. avg It is based on Certainly, G avg It is based on Definitely, B avg It is based on It is determined that N is the number of pixels in each of the temperature zones.

5. The method according to claim 3, characterized in that, After determining the temperature distribution type based on the temperature band sequence, the method further includes: The temperature band is processed using an image processing algorithm to obtain the area of ​​the temperature band corresponding to the highest temperature index value as the first temperature band area, and the area of ​​the temperature band corresponding to the lowest temperature index value as the second temperature band area. The ratio of the area of ​​the second temperature zone to the area of ​​the first temperature zone is determined as a first ratio, and the ash accumulation area is determined based on the first ratio.

6. The method according to claim 1, characterized in that, Determining the dust accumulation status of the air conditioner includes: The ratio of the lowest temperature index value to the highest temperature index value is determined as a second ratio. Based on the second ratio, the degree of dust accumulation on the air conditioner is determined, and the dust accumulation condition includes the degree of dust accumulation on the air conditioner.

7. The method according to claim 1, characterized in that, Acquire temperature data from multiple detection points within a predetermined area, and generate a heat map based on all the temperature data, including: Acquire the temperature data of each temperature sensor within the predetermined area at the same time, as well as the position data of each temperature sensor; Based on the temperature data and the location data, a temperature field data matrix is ​​constructed, and the heat map is generated based on the temperature field data matrix. The temperature field data matrix represents the temperature distribution at different locations within the predetermined area.

8. An air conditioner dust accumulation detection device, characterized in that, include: The acquisition unit is used to acquire temperature data from multiple detection points within a predetermined area and generate a heat map based on all the temperature data. The first determining unit is used to determine the temperature zone of the heat map based on the RGB values ​​of the pixels in the heat map, wherein the heat map characterizes the temperature distribution characteristics of the predetermined area under air conditioning operation. The second determining unit is used to determine the temperature distribution type of the predetermined area based on the temperature zone, and to determine the dust accumulation status of the air conditioner based on the temperature distribution type, and to output the air conditioner dust accumulation detection result. The air conditioner dust accumulation detection result includes the dust accumulation components in the air conditioner and the corresponding degree of dust accumulation. The temperature distribution type characterizes the temperature distribution status of the predetermined area.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the air conditioner dust accumulation detection method according to any one of claims 1 to 7.

10. An air conditioner dust accumulation detection system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing the air conditioner dust accumulation detection method according to any one of claims 1 to 7.

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