Fluorine deficiency detection method and device, electronic equipment and storage medium

Abstract

The application provides a fluorine deficiency detection method and device, electronic equipment and a storage medium. The method comprises the following steps: adopting a constructed power prediction model, determining a predicted power value corresponding to each collection time when an air conditioner normally operates based on outdoor environment temperature, indoor environment temperature and indoor humidity included in first environment parameters collected at the preset operating condition, determining an actual power value corresponding to each collection time according to an outdoor unit voltage and current at each collection time, and determining a fluorine deficiency detection result of the air conditioner according to the difference between the predicted power value and the actual power value corresponding to multiple collection times. In the application, the predicted power value corresponding to the non-fluorine deficiency state can be determined based on the environment parameters under the preset operating condition. The actual power value of the air conditioner operating in the fluorine deficiency state is reduced. Therefore, whether the air conditioner has the fluorine deficiency condition can be determined based on the difference between the predicted power value and the actual power value, and whether the air conditioner has the fluorine deficiency can be accurately determined.

Description

Technical Field

[0001] This application relates to the field of air conditioner technology, and in particular to a method, apparatus, electronic device, and storage medium for detecting refrigerant deficiency. Background Technology

[0002] During actual operation, air conditioners may experience minor refrigerant leaks in valves and other parts, potentially leading to refrigerant shortage. When running with insufficient refrigerant, the compressor windings will not be cooled properly, causing the winding temperature to exceed the reliability threshold, impacting compressor lifespan and potentially causing it to burn out.

[0003] Because of the high degree of concealment of fluorine deficiency problems, current fluorine deficiency detection logic has difficulty accurately determining whether a system is deficient in fluorine. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, this application proposes a method, apparatus, electronic device, and storage medium for detecting fluoride deficiency, which improves the accuracy of fluoride deficiency detection.

[0006] One embodiment of this application proposes a method for detecting fluoride deficiency, including:

[0007] In response to the air conditioner's operating parameters meeting preset conditions, the system acquires first environmental parameters collected at multiple acquisition times and first electrical parameters of the air conditioner.

[0008] Using the constructed power prediction model, based on the first environmental parameters including outdoor ambient temperature, indoor ambient temperature and indoor humidity at each of the acquisition times, the predicted power value corresponding to the normal operation of the air conditioner at each of the acquisition times is determined;

[0009] Based on the first electrical parameters, including the outdoor unit voltage and outdoor unit current, at each of the acquisition times, the actual power value of the air conditioner at each of the acquisition times is determined.

[0010] The refrigerant deficiency detection result of the air conditioner is determined based on the difference between the predicted power value and the actual power value corresponding to the multiple collection times.

[0011] Another embodiment of this application proposes a fluoride deficiency detection device, comprising:

[0012] The acquisition module is used to acquire first environmental parameters and first electrical parameters of the air conditioner at multiple acquisition times in response to the air conditioner's operating parameters meeting preset conditions.

[0013] The first determining module is configured to determine, based on the outdoor environment temperature, the indoor environment temperature and the indoor humidity included in the first environment parameter of each of the collection time points, a predicted power value corresponding to each of the collection time points when the air conditioner is normally operated by using the constructed power prediction model;

[0014] The second determining module is configured to determine, according to the outdoor unit voltage and the outdoor unit current included in the first electric parameter of each of the collection time points, an actual power value corresponding to each of the collection time points when the air conditioner is operated.

[0015] The detecting module is configured to determine a fluorine deficiency detection result of the air conditioner according to the difference between the predicted power value and the actual power value corresponding to the plurality of collection time points.

[0016] Another aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method of the foregoing aspect.

[0017] Another aspect of the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program is executable by a processor to implement the method of the foregoing aspect.

[0018] Another aspect of the present application provides a computer program product having a computer program stored thereon, and the program is executable by a processor to implement the method of the foregoing aspect.

[0019] The fluorine deficiency detection method, device, electronic device and storage medium provided by the present application, in response to the operating parameters of the air conditioner satisfying the preset condition, the first environment parameters collected at a plurality of collection time points and the first electric parameters of the air conditioner are obtained, based on the outdoor environment temperature, the indoor environment temperature and the indoor humidity included in the first environment parameter of each collection time point, the predicted power value corresponding to each collection time point when the air conditioner is normally operated is determined by using the constructed power prediction model, according to the outdoor unit voltage and the outdoor unit current included in the first electric parameter of each collection time point, the actual power value corresponding to each collection time point when the air conditioner is operated is determined, and the difference between the predicted power value and the actual power value corresponding to the plurality of collection time points is determined to determine the fluorine deficiency detection result of the air conditioner. In the present application, the predicted power value corresponding to the non-fluorine deficiency state can be determined based on the environment parameters under the preset operating condition, and the actual power value of the air conditioner under the fluorine deficiency state will be reduced, that is, the power values of the compressors of the air conditioner under the non-fluorine deficiency state and the fluorine deficiency state are different. Therefore, whether the air conditioner has fluorine deficiency can be determined based on the difference between the predicted power value and the actual power value, and the accuracy of the determination of whether the air conditioner has fluorine deficiency is improved.

[0020] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings.

[0022] Figure 1 A flowchart of a fluorine deficiency detection method provided by an embodiment of the present application;

[0023] Figure 2 A flowchart of another fluorine deficiency detection method provided by an embodiment of the present application;

[0024] Figure 3 A flowchart of another fluorine deficiency detection method provided by an embodiment of the present application;

[0025] Figure 4 A structural diagram of a fluorine deficiency detection device provided by an embodiment of the present application;

[0026] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below with reference to the accompanying drawings, in which the same or similar elements or elements having the same or similar functions are denoted by the same or similar reference numerals throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0028] The fluorine deficiency detection method, device, electronic device and storage medium of the embodiments of the present application are described below with reference to the accompanying drawings.

[0029] Figure 1 A flowchart of a fluorine deficiency detection method provided by an embodiment of the present application;

[0030] The execution subject of the fluorine deficiency detection method of the embodiments of the present application is a fluorine deficiency detection device, which can be arranged in an electronic device having temperature control requirements, such as an air conditioner. In the present embodiment, no limitation is made.

[0031] As shown in FIG. 1, the method can include the following steps: Figure 1

[0032] Step 101: In response to the operating parameters of the air conditioner satisfying the preset conditions, the first environmental parameters collected at a plurality of collection time points and the first electrical parameters of the air conditioner are acquired.

[0033] ​The running parameters include at least one of a running frequency of the air conditioner and a fan rotating speed. The preset conditions include a set interval corresponding to the running frequency and a set interval corresponding to the fan rotating speed. As an example, the set interval corresponding to the running frequency is [62, 64] in HZ, and the set interval corresponding to the fan rotating speed is [1000, 1200] in revolutions,

[0034] The multiple collection time points can be multiple collection time points in a set time length, and the intervals between the collection time points can be the same or different. The set time length can include the current collection time point or not include the current collection time point. The first environmental parameters include an outdoor environmental temperature, an indoor environmental temperature, and an indoor humidity. The first electrical parameters include an outdoor unit voltage and an outdoor unit current, and the outdoor unit current is an equivalent current.

[0035] In an implementation manner of the embodiment of the application, in a case where the fluorine deficiency detection instruction is detected, the main control of the air conditioner is controlled to adjust the running parameters of the air conditioner to meet the preset conditions.

[0036] As another implementation manner, according to a set detection period, when any detection period is reached, the main control of the air conditioner is controlled to adjust the running parameters of the air conditioner to meet the preset conditions.

[0037] In the embodiment of the application, the running parameters of the air conditioner are controlled, and the running frequency and the fan rotating speed in the running parameters of the air conditioner are controlled to meet the corresponding preset conditions, so as to avoid the influence of additional factors on the running power of the air conditioner.

[0038] In step 102, based on the outdoor environmental temperature, the indoor environmental temperature, and the indoor humidity included in the first environmental parameters of each collection time point, the predicted power value corresponding to each collection time point in which the air conditioner is normally running is determined by using the constructed power prediction model.

[0039] The power prediction model is a machine learning model, and the model has learned the corresponding relationship between the environmental parameters of the air conditioner and the predicted power value corresponding to the normal running of the air conditioner in advance. The predicted power value corresponding to each collection time point in which the air conditioner is normally running means that, at each collection time point, the air conditioner is normally running, i.e., the air conditioner is running without fluorine deficiency.

[0040] In the embodiment of the application, for each collection time point, the outdoor environmental temperature, the indoor environmental temperature, and the indoor humidity collected at the collection time point are input into the power prediction model to obtain the predicted power value of the evaporator corresponding to the collection time point.

[0041] In step 103, according to the outdoor unit voltage and the outdoor unit current included in the first electrical parameters of each collection time point, the actual power value corresponding to each collection time point in which the air conditioner is running is determined.

[0042] In the embodiments of the present application, for each collection time, the outdoor unit voltage and the outdoor unit current corresponding to the collection time are multiplied to obtain the actual power value corresponding to the collection time, i.e., the actual power value is a measured value based on the running measurement of the air conditioner.

[0043] In step 104, the fluorine deficiency detection result of the air conditioner is determined according to the difference between the predicted power value and the actual power value corresponding to the plurality of collection times.

[0044] In the embodiments of the present application, since the air conditioner is in a fluorine deficiency state, the refrigerant is insufficient, which will cause the running power of the compressor of the air conditioner to decrease. Therefore, under the same running parameters, the predicted power value corresponding to the plurality of collection times and the actual power value corresponding to the plurality of collection times are compared in size to determine the difference between the predicted power value and the actual power value corresponding to the plurality of collection times, and according to the difference, the fluorine deficiency detection result of the air conditioner is determined to be fluorine deficiency or not fluorine deficiency.

[0045] As an implementation manner, the first group of the predicted power values corresponding to the plurality of collection times and the second group of the actual power values corresponding to the plurality of collection times are compared to determine a target actual power value in the first group, wherein the target actual power value is less than the predicted power value of the collection time corresponding to the target actual power value; and in response to the proportion of the target actual power value in the first group being greater than a set threshold, it is considered that the air conditioner is fluorine deficient, for example, the set threshold is 80%, and the actual power values and the predicted power values of the 10 collection times, if the actual power values of 8 collection times are greater than the corresponding predicted power values, it is considered that the actual power values of the 10 collection times are significantly less than the predicted power values of the 10 collection times, i.e., it is considered that the actual power values of the 10 collection times indicate that the air conditioner has a fluorine deficiency problem, otherwise, it is considered that there is no fluorine deficiency problem.

[0046] As another implementation manner, the predicted power values of the plurality of collection times and the actual power values of the plurality of collection times are subjected to variance analysis to determine whether the difference between the actual power values of the plurality of collection times and the predicted power values of the plurality of collection times is significant. The method for determining the significance of the difference between two groups of data based on variance analysis can refer to the explanation in the related art, and the embodiments of the present application will not be described in detail.

[0047] In one scenario, in response to the difference being significant, it is determined that the air conditioner is short of fluorine. When the air conditioner is running in a normal state, for the same operating parameter conditions, i.e., in the same working condition, the measured power of the air conditioner corresponds to a first power value, and if the air conditioner is short of fluorine, the measured power of the air conditioner corresponds to a second power value which is less than the first power value, i.e., the air conditioner power is reduced due to the lack of fluorine. Therefore, for each collection time, if the predicted power value obtained by using the first environment parameter collected at the collection time and the power prediction model constructed in advance is compared with the actual power value actually measured and determined at the collection time, since the refrigerant can only be reduced but cannot be increased, the difference indicates that the actual power value is less than the predicted power value, and the actual power value is reduced, which indicates that the refrigerant is reduced, i.e., there is a lack of fluorine.

[0048] On the contrary, in another scenario, in response to the difference not being significant, i.e., there is no difference, it is determined that there is no lack of fluorine, and it is determined that the air conditioner is not short of fluorine.

[0049] In the fluorine shortage detection method of the embodiments of the present application, in response to the operating parameters of the air conditioner satisfying the preset conditions, the first environment parameters collected at a plurality of collection times and the first electrical parameters of the air conditioner are obtained, the power prediction model constructed is used, the outdoor environment temperature, the indoor environment temperature and the indoor humidity included in the first environment parameters at each collection time are used to determine the predicted power value of the air conditioner running normally at each collection time, the outdoor unit voltage and the outdoor unit current included in the first electrical parameters at each collection time are used to determine the actual power value of the air conditioner at each collection time, and the difference between the predicted power value and the actual power value at each collection time is used to detect the fluorine shortage of the air conditioner. In the present application, the environment parameters under the preset operating conditions can be used to determine the predicted power value under the non-shortage-of-fluorine state, and the actual power value of the air conditioner running in the shortage-of-fluorine state will be reduced, i.e., the power values of the compressors of the air conditioner in the non-shortage-of-fluorine state and the shortage-of-fluorine state are different. Therefore, the difference between the predicted power value and the actual power value can be used to determine whether the air conditioner is short of fluorine, and the accuracy of the determination of whether the air conditioner is short of fluorine is improved.

[0050] Based on the above embodiments, Figure 2 Another fluorine shortage detection method provided by the embodiments of the present application is shown in the flowchart as Figure 2 The method comprises the following steps:

[0051] Step 201, in response to the operating parameters of the air conditioner satisfying the preset conditions, the first environment parameters collected at a plurality of collection times and the first electrical parameters of the air conditioner are obtained.

[0052] At step 202, the constructed power prediction model is used to determine a predicted power value corresponding to each collection time based on the outdoor environment temperature, the indoor environment temperature and the indoor humidity included in the first environment parameter of each collection time.

[0053] At step 203, the outdoor unit voltage and the outdoor unit current included in the first electric parameter of each collection time are used to determine an actual power value corresponding to each collection time.

[0054] At step 204, variance analysis is performed on the predicted power values of the plurality of collection times and the actual power values of the plurality of collection times to determine whether the difference between the actual power values of the plurality of collection times and the predicted power values of the plurality of collection times is significant.

[0055] At step 205, in response to the difference being significant, it is determined that the air conditioner is deficient in fluorine.

[0056] The steps 201 to 205 can refer to the explanations in the foregoing embodiments for the same principles, which will not be described here again.

[0057] At step 206, a predicted power mean corresponding to the predicted power values of the plurality of collection times is determined, and an actual power mean corresponding to the actual power values of the plurality of collection times is determined.

[0058] In the embodiments of the present application, the predicted power mean is obtained by averaging the predicted power values of the plurality of collection times, and the actual power mean is obtained by averaging the actual power values of the plurality of collection times.

[0059] At step 207, the type of fluorine deficiency of the air conditioner is determined based on the predicted power mean and the actual power mean.

[0060] The type of fluorine deficiency includes a first type of fluorine deficiency, a second type of fluorine deficiency and a third type of fluorine deficiency. The first type of fluorine deficiency indicates that the amount of fluorine deficiency of the air conditioner is greater than a first set value, which is considered as severe fluorine deficiency. The second type of fluorine deficiency indicates that the amount of fluorine deficiency of the air conditioner is greater than or equal to a second set value and less than the first set value, which is considered as moderate fluorine deficiency. The third type of fluorine deficiency indicates that the amount of fluorine deficiency of the air conditioner is less than the second set value, which is considered as mild fluorine deficiency.

[0061] The determination methods of different types of fluorine deficiency will be described below.

[0062] In the embodiments of the present application, in order to calculate the power values corresponding to various working conditions in theory, a plurality of power attenuation coefficients are set. The power attenuation coefficient indicates the attenuation of the power value in the case of lack of fluorine relative to the case of no lack of fluorine. The more serious the lack of fluorine, the greater the value of the corresponding power attenuation coefficient. In order to distinguish different lack of fluorine conditions, the corresponding power attenuation coefficient is referred to as a first power attenuation coefficient and a second power attenuation coefficient, etc. The number of power attenuation coefficients can be set according to the needs of the divided levels. The first power attenuation coefficient is less than the second power attenuation coefficient.

[0063] As an implementation manner, the first power attenuation coefficient and the second power attenuation coefficient can be set values determined based on experience.

[0064] As another implementation manner, the power attenuation coefficient indicates the ratio between the power value in the case of lack of fluorine and the power value in the case of no lack of fluorine. The first power attenuation coefficient and the second power attenuation coefficient are determined based on the change rule of the measured power values in various working conditions. Optionally, for each amount of lack of fluorine, the power values measured in various working conditions can be averaged as the corresponding power value of the air conditioner running under the corresponding amount of lack of fluorine. Similarly, the corresponding power value of the air conditioner in the case of no lack of fluorine is determined, and then the corresponding power value of the air conditioner running under each amount of lack of fluorine is divided by the corresponding power value in the case of no lack of fluorine to obtain the power attenuation coefficient corresponding to each amount of lack of fluorine.

[0065] As an implementation manner, the first power attenuation coefficient is determined according to the ratio between the reference power value of the air conditioner when the amount of lack of fluorine is a first set value and the reference power value when the air conditioner is normally running. The second power attenuation coefficient is determined according to the ratio between the reference power value of the air conditioner when the amount of lack of fluorine is a second set value and the reference power value when the air conditioner is normally running. The first set value is greater than the second set value.

[0066] As an example, the more serious the lack of fluorine of the air conditioner, the greater the amount of lack of fluorine, and the more serious the power attenuation of the air conditioner, that is, the smaller the power value of the air conditioner in the case of more serious lack of fluorine. Table 1 shows the reference power values of the air conditioner in the case of normal running (the amount of lack of fluorine is 0), the amount of lack of fluorine is 20%, the amount of lack of fluorine is 40%, the amount of lack of fluorine is 60%, and the amount of lack of fluorine is 80% under the same allowable working conditions (the same indoor and outdoor environment temperature and indoor humidity).

[0067] Table 1

[0068]

[0069] As can be seen from Table 1, the greater the amount of lack of fluorine, the smaller the corresponding power value, indicating that the power attenuation is more serious, and the smaller the value of the corresponding power attenuation coefficient.

[0070] Thus, when the fluorine deficiency amount is 80%, the corresponding power attenuation coefficient is 100 / 900 = 0.11; when the fluorine deficiency amount is 60%, the corresponding power attenuation coefficient is 200 / 900 = 0.22; when the fluorine deficiency amount is 40%, the corresponding power attenuation coefficient is 400 / 900 = 0.44; and when the fluorine deficiency amount is 20%, the corresponding power attenuation coefficient is 700 / 900 = 0.77. As an example, since the first attenuation coefficient corresponds to severe fluorine deficiency, 80% is the first set value of the fluorine deficiency amount, and if the fluorine deficiency amount of 80% is considered as severe fluorine deficiency, the value of the first attenuation coefficient is 0.11. The second set value can be 60%, or 40%, or 20%.

[0071] In the first scenario, the predicted power average is multiplied by the set first power attenuation coefficient to obtain a first fluorine deficiency power value. In response to the actual power average being less than or equal to the first fluorine deficiency power value, it is determined that the air conditioner belongs to a first fluorine deficiency type, i.e., a severe fluorine deficiency type. This can be expressed by the following formula:

[0072] MP2≤t1*MP1;

[0073] wherein MP2 is the actual power average, MP1 is the predicted power average, and t1 is the first power attenuation coefficient.

[0074] The first fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is greater than or equal to a first set value. The fluorine deficiency amount is the ratio of the fluorine amount that the air conditioner lacks to the rated fluorine amount. For example, the first set value is 80%.

[0075] In the second scenario, the predicted power average is multiplied by the set second power attenuation coefficient to obtain a second fluorine deficiency power value. The first power attenuation coefficient is less than the second power attenuation coefficient. The actual power average is compared with the first fluorine deficiency power value and the second fluorine deficiency power value, respectively. In response to the actual power average being greater than the first fluorine deficiency power value and less than or equal to the second fluorine deficiency power value, it is determined that the air conditioner belongs to a second fluorine deficiency type. The second fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is greater than or equal to a second set value and less than the first set value.

[0076] The formula is expressed as follows:

[0077] t1*MP1<MP2≤t2*MP1;

[0078] wherein t2 is the second power attenuation coefficient. For example, the second power attenuation coefficient is any value in the range of 0.22 to 0.44, or any value in the range of 0.22 to 0.77.

[0079] As an example, since the first attenuation coefficient corresponds to severe fluorine deficiency, if the fluorine deficiency amount of 80% is considered as severe fluorine deficiency, the value of the first attenuation coefficient is 0.11, and if the fluorine deficiency amount of 60% is considered as moderate fluorine deficiency, the value of the second attenuation coefficient is 0.22. Alternatively, if the fluorine deficiency amount of 40% is considered as moderate fluorine deficiency, the value of the second attenuation coefficient is 0.44; or alternatively, if the fluorine deficiency amount of 20% is considered as moderate fluorine deficiency, the value of the second attenuation coefficient is 0.77.

[0080] In the third scenario, in response to the actual power mean being greater than the second fluorine deficiency power value, it is determined that the air conditioner belongs to a third fluorine deficiency type, i.e., a mild fluorine deficiency type; wherein the third fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is less than the second set value.

[0081] The formula is as follows:

[0082] MP2>t2*MP1.

[0083] Step 208, in response to the difference not being significant, it is determined that the air conditioner does not lack fluorine.

[0084] Specifically, refer to the explanations in the foregoing embodiments for the same principles, which will not be repeated here.

[0085] In the fluorine deficiency detection method of the embodiments of the present application, the predicted power value obtained by prediction is multiplied by the first attenuation coefficient and the second attenuation coefficient corresponding to the respective fluorine deficiency amount to obtain the first fluorine deficiency power value and the second fluorine deficiency power value after attenuation corresponding to the respective fluorine deficiency amount, wherein the first fluorine deficiency power value and the second fluorine deficiency power value are the theoretical attenuation values corresponding to the respective fluorine deficiency amount, and then the actual power value is compared with the first fluorine deficiency power value and the second fluorine deficiency power value corresponding to the respective fluorine deficiency amount, and the type of fluorine deficiency is determined according to the comparison result, which realizes the comparison between the actual power value obtained by actual measurement and the theoretical fluorine deficiency amount value corresponding to each fluorine deficiency amount, accurately determines the fluorine deficiency type, improves the accuracy of the determination of the fluorine deficiency type, and thus can be used to guide the subsequent adjustment of the fluorine deficiency amount, limit the fluorine addition amount of the after-sales service engineer, avoid arbitrary fluorine addition in after-sales, and improve the rationality of after-sales charges.

[0086] Based on the above embodiments, another fluorine deficiency detection method is provided, Figure 3 The flowchart of another fluorine deficiency detection method provided by the embodiments of the present application specifically illustrates how to pre-construct a multiple linear regression model for predicting the power of an air conditioner when the power prediction model is a multiple linear regression model, as shown in Figure 3 The method comprises the following steps:

[0087] Step 301, acquiring the second environmental parameters and the second electrical parameters collected under the condition that the air conditioner is normally running and the running parameters meet the preset conditions.

[0088] In the embodiments of the present application, the normal operation of the air conditioner refers to that the air conditioner is not short of fluorine, for example, the installation time of the air conditioner is less than or equal to 3 months, and there is no fault, which is because the use time is short, and the air conditioner will not have the problem of short of fluorine within 3 months. The operating parameters can refer to the explanation and description in the foregoing embodiments, and the principles are the same, which will not be described here.

[0089] In step 302, a regression coefficient of a mapping relationship between the second environmental parameter and the second electrical parameter is determined according to the second environmental parameter and the second electrical parameter.

[0090] The second environmental parameter and the second electrical parameter can be different from the collection scenarios of the first environmental parameter and the first electrical parameter in the foregoing embodiments, and are referred to as “first” and “second” for identification. The explanation and description of the second environmental parameter and the second electrical parameter can refer to the explanation and description of the first environmental parameter and the first electrical parameter in the foregoing embodiments, and the principles are the same, which will not be described here.

[0091] As an implementation manner, in order to improve the accuracy of the construction of the multiple linear regression model, the second environmental parameter and the second electrical parameter are multiple, for each second electrical parameter, the actual power value corresponding to the second electrical parameter is determined according to the outdoor unit voltage and the outdoor unit current included in the second electrical parameter, the outdoor environmental temperature, the indoor environmental temperature and the indoor humidity included in the multiple second environmental parameters, and the actual power value corresponding to the multiple second electrical parameters are subjected to standard deviation normalization processing, linear transformation of the original data, so that the converted result falls into the interval [0, 1], and the conversion function is as follows:

[0092]

[0093] Wherein, X is the data before transformation, X * is the data after transformation, min is the minimum value of the data before transformation, and max is the maximum value of the data before transformation.

[0094] In step 303, a multiple linear regression model is constructed according to the regression coefficient.

[0095] As an implementation manner, the multiple linear regression model is constructed, the indoor environmental temperature, the indoor environmental humidity and the outdoor environmental temperature are input as independent variables, and the actual power value is input as dependent variable, the regression coefficient of the multiple linear regression model in the normal operation state is obtained, the multiple linear regression model in the normal operation state is constructed according to the value of the regression coefficient, and the corresponding predicted power value can be predicted according to the indoor environmental temperature, the indoor environmental humidity and the outdoor environmental temperature.

[0096] Predicted power value = a1*indoor environmental temperature + a2*indoor environmental humidity + a3*outdoor environmental temperature;

[0097] As an example, the regression coefficients a1 are -4.375, a2 are 1.082, and a3 are 29.3521.

[0098] In the refrigerant deficiency detection method of this application embodiment, multiple second environmental parameters and multiple second electrical parameters are collected when the air conditioner is running in a normal state without refrigerant deficiency. Based on the multiple second environmental parameters and multiple second electrical parameters, the regression coefficients of a multiple linear regression model that meets the set standards are determined to construct the corresponding multiple linear regression model. This allows the constructed multiple linear regression model to accurately determine the predicted power value based on the collected environmental parameters, thereby improving the accuracy of the predicted power value determination.

[0099] To achieve the above embodiments, this application also proposes a fluoride deficiency detection device.

[0100] Figure 4 This is a schematic diagram of a fluoride deficiency detection device provided in an embodiment of this application.

[0101] like Figure 4 As shown, the device may include:

[0102] The acquisition module 41 is used to acquire first environmental parameters and first electrical parameters of the air conditioner at multiple acquisition times in response to the air conditioner's operating parameters meeting preset conditions.

[0103] The first determining module 42 is used to determine the predicted power value of the air conditioner when it is operating normally at each of the acquisition times, based on the first environmental parameters including outdoor ambient temperature, indoor ambient temperature and indoor humidity at each of the acquisition times, using the constructed power prediction model.

[0104] The second determining module 43 is used to determine the actual power value of the air conditioner at each of the acquisition times based on the first electrical parameters, including the outdoor unit voltage and the outdoor unit current, at each of the acquisition times.

[0105] The detection module 44 is used to determine the refrigerant shortage detection result of the air conditioner based on the difference between the predicted power value and the actual power value corresponding to the multiple acquisition times.

[0106] Furthermore, in one implementation of this application embodiment, the detection module 44 is specifically used for:

[0107] An analysis of variance was performed on the predicted power values ​​and the actual power values ​​at the multiple acquisition times to determine whether the differences between the actual power values ​​and the predicted power values ​​at the multiple acquisition times were significant.

[0108] in response to the difference being significant, determining that the air conditioner is deficient in fluorine;

[0109] in response to the difference being insignificant, determining that the air conditioner is not deficient in fluorine.

[0110] Further, in an implementation form of the embodiment of the application, the apparatus further comprises a classification module.

[0111] The classification module is configured to determine a predicted power mean corresponding to the predicted power values at the plurality of collection time points, and determine an actual power mean corresponding to the actual power values at the plurality of collection time points; and determine a fluorine deficiency type of the air conditioner according to the predicted power mean and the actual power mean.

[0112] In an implementation form of the embodiment of the application, the classification module is specifically configured to:

[0113] multiply the predicted power mean by a set first power attenuation coefficient to obtain a first fluorine deficiency power value;

[0114] in response to the actual power mean being less than or equal to the first fluorine deficiency power value, determine that the air conditioner belongs to a first fluorine deficiency type; wherein the first fluorine deficiency type indicates that a fluorine deficiency amount of the air conditioner is greater than or equal to a first set value; the fluorine deficiency amount is a ratio of a fluorine deficiency amount of the air conditioner to a rated fluorine amount.

[0115] In an implementation form of the embodiment of the application, the classification module is specifically configured to:

[0116] multiply the predicted power mean by a set second power attenuation coefficient to obtain a second fluorine deficiency power value; wherein the first power attenuation coefficient is less than the second power attenuation coefficient;

[0117] compare the actual power mean with the first fluorine deficiency power value and the second fluorine deficiency power value respectively;

[0118] in response to the actual power mean being greater than the first fluorine deficiency power value and less than or equal to the second fluorine deficiency power value, determine that the air conditioner belongs to a second fluorine deficiency type; wherein the second fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is greater than or equal to a second set value and less than the first set value.

[0119] In an implementation form of the embodiment of the application, the classification module is specifically configured to:

[0120] in response to the actual power mean being greater than the second fluorine deficiency power value, determine that the air conditioner belongs to a third fluorine deficiency type; wherein the third fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is less than the second set value.

[0121] In an implementation form of the embodiment of the application, the first power attenuation coefficient is determined according to a ratio between a reference power value of the air conditioner when the fluorine deficiency amount is a first set value and a reference power value of the air conditioner when the air conditioner is normally running.

[0122] The second power attenuation coefficient is determined according to a ratio between a reference power value of the air conditioner when the fluorine deficiency amount is a second set value and a reference power value of the air conditioner when the air conditioner is normally running.

[0123] In an implementation form of the embodiment of the application, the power prediction model is a multiple linear regression model, and the power prediction model is constructed by a construction module.

[0124] The second environment parameters and second electric parameters collected under the condition that the air conditioner is normally running and the running parameters meet the preset condition are acquired.

[0125] The regression coefficient of the mapping relationship between the second environment parameters and the second electric parameters is determined according to the second environment parameters and the second electric parameters.

[0126] The multiple linear regression model is constructed according to the regression coefficient.

[0127] It should be noted that the foregoing explanation and description of the method embodiments are also applicable to the device of this embodiment, which will not be described here.

[0128] The fluorine deficiency detection device provided in the application acquires first environment parameters and first electric parameters of the air conditioner collected at a plurality of collection time points in response to the running parameters of the air conditioner meeting the preset condition, determines the predicted power values of the air conditioner when the air conditioner is normally running at the collection time points based on the outdoor environment temperature, the indoor environment temperature and the indoor humidity included in the first environment parameters at the collection time points by using the constructed power prediction model, determines the actual power values of the air conditioner at the collection time points according to the outdoor unit voltage and the outdoor unit current included in the first electric parameters at the collection time points, and detects the fluorine deficiency of the air conditioner according to the difference between the predicted power values and the actual power values at the collection time points. In the application, the predicted power values corresponding to the non-fluorine deficiency state can be determined based on the environment parameters under the preset running condition, and the actual power values of the air conditioner when the air conditioner is running in the fluorine deficiency state will be reduced. Therefore, whether the air conditioner has fluorine deficiency can be determined based on the difference between the predicted power values and the actual power values, and the accuracy of the determination of whether the air conditioner has fluorine deficiency is improved.

[0129] In order to implement the above-mentioned embodiments, the application further provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method described in the foregoing method embodiments is implemented.

[0130] To achieve the above-mentioned embodiments, the application further provides a non-transitory computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method according to the above method embodiments.

[0131] To achieve the above-mentioned embodiments, the application further provides a computer program product, which stores a computer program. The computer program is executed by a processor to implement the method according to the above method embodiments.

[0132] Figure 5 A structural schematic diagram of an electronic device is provided for the embodiments of the application. For example, the electronic device 800 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0133] Referring to Figure 5 , the electronic device 800 can include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0134] The processing component 802 usually controls overall operations of the electronic device 800, such as operations associated with displaying, making phone calls, data communications, camera operations and recording operations. The processing component 802 can include one or more processors 820 to execute instructions to complete all or part of steps of the methods described above. In addition, the processing component 802 can include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0135] The memory 804 is configured to store various types of data to support operations of the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0136] The power component 806 provides power to the various components of the electronic device 800. The power component 806 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0137] The multimedia component 808 includes a screen to provide an output interface between the electronic device 800 and a user. In some embodiments, the screen can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes the touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense a touch, a slide, and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect duration and pressure related to the touching or sliding action. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0138] The audio component 810 is configured to output and / or input an audio signal. For example, the audio component 810 includes a microphone (MIC) to receive an external audio signal when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker to output an audio signal.

[0139] The I / O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which can be a keypad, a click wheel, buttons, and the like. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0140] The sensor component 814 includes one or more sensors for providing status assessments for various aspects of the electronic device 800. For example, the sensor component 814 can detect an open / closed position of the electronic device 800, relative positioning of components, such as a display and a keypad of the electronic device 800, a change in position of the electronic device 800 or a component of the electronic device 800, presence or absence of user contact with the electronic device 800, orientation or acceleration / deceleration / g-force and temperature of the electronic device 800. The sensor component 814 can include an accelerometer, a gyroscope, a magnetometer, a pressure sensor or a temperature sensor.

[0141] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 4G, or 5G, or a combination thereof. In an example embodiment, the communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an example embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) techniques, infrared data association (IrDA) techniques, ultra-wideband (UWB) techniques, Bluetooth (BT) techniques, and other techniques.

[0142] In an example embodiment, the electronic device 800 can be implemented using one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements for performing the above-described methods.

[0143] In an example embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 804 including instructions, is also provided that, when executed by the processor 820 of the electronic device 800, cause the electronic device 800 to perform the above-described methods. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disc, and an optical data storage device, and the like.

[0144] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Moreover, the usage of the terms "first", "second" or "third" does not limit the quantity or order of the specific features, structures, materials or characteristics, but rather the term "first", "second" or "third" can be used to distinguish the specific features, structures, materials or characteristics from one another. In addition, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples, without changing the scope of the application.

[0145] Furthermore, the terms "first", "second", or "third" are used herein only to describe a difference, and do not imply or suggest any relative importance or imply the number of the technical features indicated. Thus, the features defined with "first", "second", or "third" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise explicitly specified.

[0146] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more steps for implementing the processes described, and alternate implementations are possible. In some embodiments, the processes can be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as will be understood by those having ordinary skill in the art.

[0147] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable medium can specifically include the following, which are non-exhaustive list: electrical connection (electrical device having one or more wires), portable computer diskette (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fiber device, and portable compact disc read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium upon which the program is printed, as the program can be electronically captured, for example, by optically scanning the paper or other suitable medium, then electronically converted into a form that can be edited, compiled, or interpreted, or otherwise processed in electronic form into an executable form suitable for use in the instruction execution system, apparatus or device.

[0148] It should be understood that parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As in another embodiment, if implemented in hardware, any of the following technologies known in the art or their combination can be used: discrete logic circuit with logic gate circuit for implementing logic functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc.

[0149] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0150] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0151] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for detecting a deficiency in fluorine, characterized by, The method comprises: in response to the operating parameter of the air conditioner satisfying a preset condition, obtaining first environmental parameters collected at a plurality of collection time points and first electric parameters of the air conditioner; using the constructed power prediction model, based on the outdoor environment temperature, the indoor environment temperature and the indoor humidity included in the first environmental parameters of each of the collection time points, determining the predicted power value of the air conditioner at each of the collection time points corresponding to the normal operation; determining the actual power value of the air conditioner at each of the collection time points corresponding to the outdoor unit voltage and the outdoor unit current included in the first electric parameters of each of the collection time points; determining the fluorine deficiency detection result of the air conditioner according to the difference between the predicted power value and the actual power value corresponding to the plurality of collection time points; when the fluorine deficiency detection result of the air conditioner is fluorine deficiency, the method further comprises: determining the predicted power mean value corresponding to the predicted power value of the plurality of collection time points, and determining the actual power mean value corresponding to the actual power value of the plurality of collection time points; determining the fluorine deficiency type of the air conditioner according to the predicted power mean value and the actual power mean value.

2. The method of claim 1, wherein, The method further comprises: performing variance analysis on the predicted power value of the plurality of collection time points and the actual power value of the plurality of collection time points to determine whether the difference between the actual power value of the plurality of collection time points and the predicted power value of the plurality of collection time points is significant; in response to the difference being significant, determining that the air conditioner is deficient in fluorine; in response to the difference not being significant, determining that the air conditioner is not deficient in fluorine.

3. The method of claim 1, wherein, The method further comprises: multiplying the predicted power mean value by a set first power attenuation coefficient to obtain a first fluorine deficiency power value; in response to the actual power mean value being less than or equal to the first fluorine deficiency power value, determining that the air conditioner belongs to a first fluorine deficiency type; wherein the first fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is greater than or equal to a first set value; the fluorine deficiency amount is the ratio of the fluorine amount lacking to the rated fluorine amount.

4. The method of claim 3, wherein, The method further comprises: multiplying the predicted power mean value by a set second power attenuation coefficient to obtain a second fluorine deficiency power value; wherein the first power attenuation coefficient is less than the second power attenuation coefficient; comparing the actual power mean value with the first fluorine deficiency power value and the second fluorine deficiency power value, respectively; in response to the actual power mean value being greater than the first fluorine deficiency power value and less than or equal to the second fluorine deficiency power value, determining that the air conditioner belongs to a second fluorine deficiency type; wherein the second fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is greater than or equal to a second set value and less than a first set value.

5. The method of claim 4, wherein, The method further comprises: in response to the actual power mean value being greater than the second fluorine deficiency power value, determining that the air conditioner belongs to a third fluorine deficiency type; wherein the third fluorine deficiency type indicates that the fluorine deficiency amount of the air conditioner is less than the second set value.

6. The method of claim 4, wherein, The first power attenuation coefficient is determined according to a ratio between a reference power value of the air conditioner when the fluorine deficiency amount is a first set value and a reference power value of the air conditioner when the air conditioner is normally running. The second power attenuation coefficient is determined according to a ratio between a reference power value of the air conditioner when the fluorine deficiency amount is a second set value and a reference power value of the air conditioner when the air conditioner is normally running.

7. The method according to any one of claims 1 to 6, wherein The power prediction model is a multiple linear regression model, and the power prediction model is constructed by using the following method: The second environment parameter and the second electric parameter are acquired under the condition that the air conditioner is normally running and the running parameters meet the preset condition; A regression coefficient of a mapping relationship between the second environment parameter and the second electric parameter is determined according to the second environment parameter and the second electric parameter; The multiple linear regression model is constructed according to the regression coefficient.

8. A device for detecting a deficiency in fluorine, characterized in that The method comprises: An acquisition module is configured to acquire a first environment parameter and a first electric parameter of the air conditioner at a plurality of acquisition time points in response to the running parameters of the air conditioner meeting the preset condition; A first determination module is configured to determine a predicted power value of the air conditioner at each of the acquisition time points based on an outdoor environment temperature, an indoor environment temperature and an indoor humidity included in the first environment parameter at each of the acquisition time points by using the constructed power prediction model; A second determination module is configured to determine an actual power value of the air conditioner at each of the acquisition time points based on an outdoor unit voltage and an outdoor unit current included in the first electric parameter at each of the acquisition time points; A detection module is configured to determine a fluorine deficiency detection result of the air conditioner based on a difference between the predicted power value and the actual power value at each of the acquisition time points. The device further comprises a classification module. The classification module is configured to determine a predicted power mean value corresponding to the predicted power value at each of the acquisition time points and determine an actual power mean value corresponding to the actual power value at each of the acquisition time points when the fluorine deficiency detection result of the air conditioner is fluorine deficiency. The fluorine deficiency type of the air conditioner is determined according to the predicted power mean value and the actual power mean value.

9. An electronic device, comprising: The computer program is executed by the processor to implement the method in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method in any one of claims 1-7.

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