CN112128919A - Air conditioner health state evaluation method and device, air conditioner and storage medium - Google Patents
Air conditioner health state evaluation method and device, air conditioner and storage medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
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- F24F11/38—Failure diagnosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
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- F24F11/63—Electronic processing
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The application relates to an air conditioner health state evaluation method and device, an air conditioner and a storage medium. The method comprises the following steps: when the air conditioner is determined to have a fault, acquiring the fault type of the air conditioner; acquiring unit operation data and environmental data of an air conditioner; and evaluating the health state of the air conditioner according to the unit operation data, the environmental data and the fault type. By adopting the method, the whole operation state of the air conditioner can be integrally evaluated, and then the whole operation state is referred by maintenance personnel so as to improve the maintenance efficiency.
Description
Technical Field
The application relates to the technical field of computers, in particular to an air conditioner health state evaluation method and device, an air conditioner and a storage medium.
Background
With the improvement of consumption level, the air conditioner as a necessary product for life has entered thousands of households, and brings comfortable indoor environment temperature experience to vast consumers. Air conditioners have become indispensable home appliances in both hot summer and cold winter, and are closely related to the production life of people. However, as the air conditioner is installed and the service life of the air conditioner is increased, and according to different use environments, use conditions, maintenance conditions and quality grades of air conditioner products, the problems of bad smell, noise, poor refrigeration effect, increased power consumption, poor comfort and the like gradually occur, and the problems become key factors influencing normal use of users. Therefore, healthy, safe and stable operation of the air conditioner is the most basic and critical requirement.
The existing air conditioner maintenance mode is mainly a mode that a user reports after-sales personnel to get on the door for maintenance. However, after-sales maintenance personnel have different levels and experiences, and therefore, for personnel with poor maintenance level, the status of the air conditioning system cannot be comprehensively judged by means of knowledge and experience of the personnel. Moreover, after-sales personnel mainly concentrate on single maintenance of headache and foot pain, the whole operation state of the air-conditioning system cannot be completely detected, the problems cannot be comprehensively checked, the problem of repeated maintenance can be caused, and therefore the maintenance efficiency is reduced.
Disclosure of Invention
The invention provides an air conditioner health state evaluation method, an air conditioner health state evaluation device, an air conditioner and a storage medium, aiming at the problem that the health state of the air conditioner cannot be comprehensively detected.
An air conditioner health state evaluation method, comprising:
when the air conditioner is determined to have a fault, acquiring the fault type of the air conditioner; acquiring unit operation data and environment data of the air conditioner;
and evaluating the health state of the air conditioner according to the unit operation data, the environment data and the fault type.
In one embodiment, the evaluating the health status of the air conditioner according to the unit operation data, the environmental data and the fault type includes:
determining the air cleanliness, performance and service life of the air conditioner according to the unit operation data;
determining the somatosensory comfort level and the noise level according to the environment data;
determining a fault grade corresponding to the fault type;
and evaluating the health state of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level.
In one embodiment, the determining somatosensory comfort according to the environment data includes:
acquiring indoor environment temperature, indoor environment humidity and wind speed from the environment data;
and inputting the indoor environment temperature, the indoor environment humidity and the wind speed into a pre-trained evaluation model for evaluating the somatosensory comfort level, and outputting the somatosensory comfort level.
In one embodiment, the determining the air cleanliness of the air conditioner according to the unit operation data includes:
detecting indoor air cleanliness;
acquiring the pollution level of a filter screen and the running time of an indoor unit from the unit running data;
and determining the air cleanliness of the air conditioner according to the indoor air cleanliness, the pollution level of the filter screen and the running time of the indoor unit.
In one embodiment, the evaluating the health status of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level includes:
obtaining an evaluation constant value, and respectively obtaining evaluation coefficients corresponding to the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level; the evaluation constant value and each evaluation coefficient are obtained by multivariate linear regression modeling;
multiplying the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level by the corresponding evaluation coefficients respectively to obtain a fault evaluation value, a noise evaluation value, a performance evaluation value, a service life evaluation value, an air cleanliness evaluation value of the air conditioner and a somatosensory comfort level evaluation value respectively;
summing the evaluation constant value, the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner to obtain a health evaluation value;
and determining the health state of the air conditioner according to the health evaluation value.
In one embodiment, the evaluation constant and each of the evaluation coefficients are obtained by multiple linear regression modeling, including:
obtaining a modeling index and a standard health evaluation value; the modeling indexes comprise a modeling fault level, a modeling noise level, modeling performance determined according to modeling unit operation data, a modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to modeling environment data;
and performing multiple linear regression modeling according to the modeling index and the standard health evaluation value, and taking a regression coefficient when the sum of squares of residuals of the standard health evaluation value and the modeling index is minimum as an evaluation constant value and an evaluation coefficient.
In one embodiment, the method further comprises:
acquiring a preset important defect index and an evaluation threshold corresponding to the important defect index;
when the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value to determine that expert detection is needed, connecting a remote diagnosis platform to obtain an expert detection result;
and determining an air conditioner maintenance scheme according to the expert detection result.
An air conditioner state of health evaluation device, the device comprising:
the fault acquisition module is used for acquiring the fault type of the air conditioner when the air conditioner is determined to have a fault;
the data acquisition module is used for acquiring unit operation data and environment data of the air conditioner;
and the evaluation module is used for evaluating the health state of the air conditioner according to the unit operation data, the environment data and the fault type.
An air conditioner comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the air conditioner health state evaluation method when executing the computer program.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the air conditioner health status evaluation method of any one of the above.
According to the air conditioner health state evaluation method and device, the air conditioner and the storage medium, when the air conditioner is determined to have a fault, the fault type of the air conditioner is obtained, and the unit operation data and the environment data of the air conditioner are further obtained; therefore, the health state of the air conditioner is evaluated according to the unit operation data, the environment data and the fault type. When the air conditioner breaks down, the method further comprehensively evaluates the health state of the air conditioner by combining the unit operation data and the environment data, can realize the overall evaluation of the whole operation state of the air conditioner, and further provides the maintenance personnel with reference to improve the maintenance efficiency.
Drawings
FIG. 1 is a diagram illustrating an exemplary environment in which the method for evaluating the health status of an air conditioner is applied;
FIG. 2 is a schematic flow chart illustrating a method for evaluating the health status of an air conditioner according to an embodiment;
FIG. 3 is a schematic flow chart illustrating the steps for evaluating the health status of an air conditioner according to the unit operational data, environmental data, and fault type in one embodiment;
FIG. 4 is a block diagram showing the structure of an air conditioner health evaluation device according to an embodiment;
fig. 5 is an internal structure view of an air conditioner in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The air conditioner health state evaluation method provided by the application can be applied to the application environment shown in fig. 1. The air conditioner 102 communicates with the air conditioner health state evaluation device 104 via a network. When it is determined that the air conditioner 102 has a fault, the air conditioner 102 may implement the air conditioner health state evaluation method alone, or the air conditioner health state evaluation device 104 may implement the air conditioner health state evaluation method alone. Specifically, the air conditioner health status evaluation device 104 or the air conditioner 102 obtains the fault type of the air conditioner 102; the air conditioner health state evaluation device 104 or the air conditioner 102 acquires unit operation data and environmental data of the air conditioner 102; the air conditioner health state evaluation device 104 or the air conditioner 102 evaluates the health state of the air conditioner according to the unit operation data, the environmental data, the fault type and the noise decibel.
In one embodiment, as shown in fig. 2, an air conditioner health status evaluation method is provided, which is described by taking the method as an example applied to the air conditioner 102 in fig. 1, and includes the following steps:
and step S202, acquiring the fault type of the air conditioner when the air conditioner is determined to be in fault.
The fault types refer to different types of faults of the air conditioner, and comprise an emergency stop fault, a non-emergency stop fault, a compressor related temperature sensing bulb fault, a key device fault, a communication fault, a common temperature sensing bulb fault and other faults.
Specifically, when the air conditioner malfunctions, the type of malfunction of the malfunctioning is determined by the analog semaphore of the operation. Or when the user or the maintenance personnel determine that the air conditioner has a fault, determining the current fault type of the air conditioner according to the fault condition of the actual overhaul. For example, if the air conditioner is suddenly stopped, the corresponding fault type is a sudden stop type fault. Then, the user or the serviceman inputs the type of the failure to the air conditioner.
And step S204, acquiring unit operation data and environment data of the air conditioner.
The unit operation data refers to operation data generated by operation of the air conditioner, and includes air conditioner fault information (fault name, occurrence time, frequency and the like), air conditioner state information (operation mode, wind speed, set temperature, outlet pipe temperature, press frequency, fan frequency, high and low pressure and the like), and historical operation information of the air conditioner (accumulated operation time, single operation time, switch time and duration, pollution level of a filter screen, cleaning interval time of the filter screen, power consumption information and the like).
The environment data is data highly relevant to the user experience, and includes: air conditioning engineering information (including installation time and room area), air conditioning attribute information (model, refrigerating capacity/heating capacity, energy efficiency ratio, noise DB value and the like), indoor environment temperature, indoor environment humidity, noise decibel and the like. The indoor environment temperature and the indoor environment humidity can be detected by a sensor of the air conditioner when the air conditioner is started, and the noise decibel refers to the noise decibel of the environment where the air conditioner is located, and is a numerical value for measuring the noise of the air conditioner, wherein the noise decibel includes indoor unit noise decibels and outdoor unit noise decibels. A self-contained noise detection module may be activated. And carrying out noise detection on the air conditioner through a noise detection module so as to obtain the noise decibel.
Specifically, when the air conditioner is determined to be in fault and the comprehensive health state evaluation of the air conditioner is needed, the air conditioner acquires unit operation data and environment data generated by the operation of the air conditioner.
And step S206, evaluating the health state of the air conditioner according to the unit operation data, the environment data and the fault type.
Specifically, after the unit operation data, the environment data, the fault type and the noise decibel are obtained, the health state of the air conditioner is evaluated in a multi-dimensional mode according to the unit operation data, the environment data, the fault type and the noise decibel. And then, weighting and summing the evaluation values of the evaluation of each dimension to obtain a health evaluation value of the air conditioner, and determining the health state of the air conditioner according to the health evaluation value. And after the health state of the air conditioner is determined, the health state can be displayed through different colors to prompt a user or a maintenance person. The specific value of credit for each item may also be displayed at the same time as it is displayed.
According to the air conditioner health state evaluation method, when the air conditioner is determined to have a fault, the fault type of the air conditioner is obtained, and unit operation data and environment data of the air conditioner are further obtained; therefore, the health state of the air conditioner is evaluated according to the unit operation data, the environment data and the fault type. When the air conditioner breaks down, the method further comprehensively evaluates the health state of the air conditioner by combining the unit operation data and the environment data, can realize the overall evaluation of the whole operation state of the air conditioner, and further provides the maintenance personnel with reference to improve the maintenance efficiency.
In one embodiment, as shown in fig. 3, step S206 includes:
and S302, determining the air cleanliness, performance and service life of the air conditioner according to the unit operation data.
The air cleanliness generally refers to the amount of dust contained in the air environment in the working area of the air conditioner, and in this embodiment, the air cleanliness of the air conditioner can be understood as an evaluation index for evaluating the quality of air cleaning performed on the working area by the air conditioner. Performance refers to how well the air conditioner has its functions, including but not limited to cooling and heating performance. The life refers to the life cycle of the air conditioner, and may be a remaining life cycle or a used life cycle, etc.
Specifically, when the unit operation data of the air conditioner is acquired, the operation parameters related to the air conditioner cleanliness are acquired from the unit operation data, and the air cleanliness of the air conditioner is determined according to the operation parameters. And similarly, acquiring operation parameters related to the performance and the service life from the operation data of the unit, and calculating the performance and the service life of the air conditioner according to the operation parameters. For example, when the cooling/heating performance of the air conditioner needs to be calculated, the operation parameter related to the calculation of the cooling/heating performance is acquired. Then, the operation parameters are input into a refrigerating/heating performance calculation method to calculate the refrigerating/heating performance. The refrigerating/heating performance calculation method can adopt any one of the existing mature calculation methods. When the service life is calculated, the accumulated operation time of the air conditioner can be obtained from the unit operation data because the service life is generally related to the service life of the equipment. And then the remaining life cycle of the air conditioner is estimated by combining the capacity of the air conditioner and the accumulated running time.
And step S304, determining the somatosensory comfort level and the noise level according to the environmental data.
The somatosensory comfort level refers to the appropriate degree of subjective feeling of a human body. The noise level refers to the noise level corresponding to different noise decibels. It should be understood that since air conditioners generally include multiple dampers, the noise generated by each of the dampers is different in magnitude. Therefore, the noise level can be adaptively adjusted according to different wind ranges.
Specifically, after the environmental data is acquired, a parameter affecting the somatosensory fitness is acquired from the data environment as a parameter for determining the somatosensory comfort level. For example, including but not limited to, indoor ambient temperature, indoor ambient humidity, and air speed of an air conditioner, etc. And then, determining the somatosensory comfort level according to the indoor environment temperature, the indoor environment humidity and the air speed of the air conditioner. A relational mapping table comprising the somatosensory comfort degrees corresponding to different temperatures, humidity and wind speeds can be established in advance through experiments. When the somatosensory comfort level is determined according to the indoor environment temperature, the indoor environment humidity and the wind speed, the somatosensory comfort level is determined by inquiring a pre-established relation mapping table.
And meanwhile, acquiring noise decibels obtained by starting a noise detection module from the environmental parameters, and inquiring the noise level corresponding to the noise decibels from a noise level table which is divided based on different noise decibels in advance. For example, referring to table 1 below, table 1 provides a noise level classification table of the indoor unit in a low wind level. When the decibel of noise is 20 in low wind, the corresponding noise level is 8.
TABLE 1
| Decibel dB noise | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| Noise level | 10 | 9 | 8 | 7 | 5 | 3 | 1 |
And step S306, determining the fault grade corresponding to the fault type.
The fault level refers to a fault level corresponding to different fault types, and the following table 2 is referred to, and table 2 provides a fault level division table.
Specifically, after the fault type and the noise decibel are obtained, a fault level table which is divided based on different fault types in advance is obtained. Then, a fault level corresponding to the fault type of the air conditioner is searched from the fault level table. For example, when the fault type is other faults, the corresponding fault level is level 1.
TABLE 2
And step S308, evaluating the health state of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness and the body feeling comfort level of the air conditioner.
Specifically, after rating indexes such as a fault level, a noise level, performance, a service life, air cleanliness and a feeling comfort of the air conditioner are obtained, the health state of the air conditioner is evaluated based on each evaluation index, and an evaluation value corresponding to each evaluation index is obtained. That is, a failure level evaluation value, a noise level evaluation value, a performance evaluation value, a lifetime evaluation value, an air cleanliness evaluation value of an air conditioner, and a sensory comfort evaluation value are obtained. Then, the respective evaluation values, that is, the failure level evaluation value, the noise level evaluation value, the performance evaluation value, the life evaluation value, the air cleanliness evaluation value of the air conditioner, and the sensory comfort evaluation value are weighted and summed to obtain a health evaluation value.
Different health evaluation values correspond to different health states, and the health state of the air conditioner is determined according to the health evaluation values. For example, when the health evaluation value is 100 points, it indicates that the air conditioner is in a very healthy state, by way of example.
In the embodiment, the health state of the air conditioner is evaluated by determining different evaluation indexes through the unit operation data and the environmental parameters, so that the comprehensive evaluation of the air conditioner can be realized.
In one embodiment, step S304 includes: acquiring indoor environment temperature, indoor environment humidity and wind speed from the environment data; the indoor environment temperature, the indoor environment humidity and the wind speed are input into an evaluation model trained in advance and used for evaluating the somatosensory comfort level, and the somatosensory comfort level is output.
The evaluation model is trained in advance and used for outputting a model with somatosensory comfort. The evaluation model is obtained by training data such as different temperatures, humidity and wind speeds as training data, and somatosensory comfort levels corresponding to the different temperatures, humidity and wind speeds as model targets. The body feeling corresponding to each temperature, humidity and wind speed is comfort level, which can be understood as comfort level score given by subjective feeling of a human body in the environment of the temperature, humidity and wind speed.
Specifically, when the somatosensory comfort is determined according to the environmental data, the indoor ambient temperature, the indoor ambient humidity and the wind speed are acquired from the environmental data. And then, calling the trained evaluation model, inputting the indoor environment temperature, the indoor environment humidity and the wind speed into the evaluation model, and outputting the somatosensory comfort degree corresponding to the indoor environment temperature, the indoor environment humidity and the wind speed through the evaluation model.
In the embodiment, the somatosensory comfort degree is output through the pre-trained evaluation model, and the accuracy of the evaluation index of the somatosensory comfort degree is ensured.
In one embodiment, determining air cleanliness of an air conditioner according to unit operation data includes: detecting indoor air cleanliness; acquiring the pollution level of the filter screen and the running time of the indoor unit from the running data of the unit; and determining the air cleanliness of the air conditioner according to the indoor air cleanliness, the pollution level of the filter screen and the running time of the indoor unit.
The indoor air cleanliness refers to the degree of the amount of dust particles contained in the air environment in the working area of the air conditioner, and the dust particle concentration of the current indoor air can be determined through the indoor air cleanliness. The pollution level of the filter screen is the pollution degree of the filter screen in the air conditioner, and the running time of the indoor unit is the running time of the indoor unit.
Specifically, when the air cleanliness of the air conditioner is determined according to the unit operation data, firstly, the concentration of dust particles is detected in the air of the area where the air conditioner is located, and the indoor air cleanliness is determined. And then, acquiring the pollution level of the filter screen and the running time of the indoor unit from the environmental data, and determining the air cleanliness of the air conditioner by combining the indoor cleanliness. The expression is as follows: x3 is f (a1, a2 and a3), x3 is the air cleanliness of the air conditioner, a 1-bit filter screen pollution level, a 2-bit indoor air cleanliness, and a 3-bit indoor unit operation time.
In this embodiment, the air cleanliness of the air conditioner is determined through the indoor air cleanliness, the filter screen pollution level and the indoor unit operation time, and the accuracy of the evaluation index of the air cleanliness of the air conditioner is ensured.
In one embodiment, step S308 includes: obtaining an evaluation constant value, and respectively obtaining evaluation coefficients corresponding to a fault level, a noise level, performance, service life, air cleanliness of an air conditioner and somatosensory comfort; the evaluation constant value and each evaluation coefficient are obtained by modeling through multiple linear regression; multiplying the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level by corresponding evaluation coefficients respectively to obtain a fault evaluation value, a noise evaluation value, a performance evaluation value, a service life evaluation value, an air cleanliness evaluation value of the air conditioner and a somatosensory comfort level evaluation value respectively; summing the evaluation constant value, the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner to obtain a health evaluation value; and determining the health state of the air conditioner according to the health evaluation value.
The evaluation constant and the evaluation coefficient corresponding to each evaluation index are obtained by multivariate linear regression modeling, and can be understood as the regression coefficient in the multivariate linear regression model.
Specifically, when the health evaluation value of the air conditioner is determined, an evaluation constant value and an evaluation coefficient corresponding to each evaluation index are obtained by acquiring modeling. That is, the evaluation constant value a is acquired, and an evaluation coefficient k1 corresponding to the failure level x1, an evaluation coefficient k2 corresponding to the noise level x2, an evaluation coefficient k3 corresponding to the performance x3, an evaluation coefficient k4 corresponding to the lifetime x4, an evaluation coefficient k5 corresponding to the air cleanliness x5 of the air conditioner, and an evaluation coefficient k6 corresponding to the sensory comfort x6 are acquired. Then, the evaluation indexes x1, x2, x3, x4, x5, and x6 are multiplied by the corresponding evaluation coefficients k1, k2, k3, k4, k5, and k6, respectively, to obtain a failure evaluation value, a noise evaluation value, a performance evaluation value, a lifetime evaluation value, an air cleanliness evaluation value, and a sensory comfort evaluation value of the air conditioner. And then, summing the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner with the evaluation constant value A to obtain a health evaluation value f (x), and determining the health state of the air conditioner according to the health evaluation value f (x). The health evaluation value is calculated by the following formula:
f(x)＝A+k1x1+k2x2+k3x3+k4x4+k5x5+k6x6
in one embodiment, the evaluation constants and the evaluation coefficients are obtained by multiple linear regression modeling, including: obtaining a modeling index; the modeling indexes comprise modeling fault levels, modeling noise levels, modeling performance determined according to modeling unit operation data, modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to modeling environment data; and performing multiple linear regression modeling according to the modeling indexes, and taking a regression coefficient when the sum of squares of residuals of the modeling health evaluation value obtained by modeling and the modeling indexes is minimum as an evaluation constant value and an evaluation coefficient.
The modeling unit operation data and modeling environment data refer to sample data used for modeling, and are unit operation data and environment data under different working conditions and conditions in a standard experimental environment. The standard health evaluation value refers to an evaluation score of the air conditioner in a standard health state.
Specifically, when training is performed to obtain the evaluation constant and the evaluation coefficient of each evaluation index, first, the modeling index corresponding to the evaluation index, including the modeling fault level, the modeling noise level, is obtained (refer to tables 1 and 2). And determining modeling performance, modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to the modeling environmental data by the collected sample data and the modeling unit operation data. Then, multiple linear regression modeling is performed based on each modeling index and the standard health evaluation value. Reverse training is carried out through known modeling indexes and standard health evaluation values, multiple groups of different evaluation constants and evaluation coefficients are obtained through the reverse training, and the final evaluation constants and evaluation coefficients are determined by using a least square method. That is, when the sum of squares of residuals of the standard health evaluation value and the modeling index is minimum, the corresponding evaluation constant and each evaluation coefficient are the evaluation constant finally obtained by training and the evaluation coefficient corresponding to each evaluation index.
In the embodiment, the regression coefficient obtained by modeling through multiple linear regression analysis can accurately determine the influence of different evaluation indexes on the health of the air conditioner, and then the health evaluation value of the air conditioner is calculated through the regression coefficient, so that the evaluation accuracy can be improved, and the evaluation method is more effective than the evaluation method using only one independent variable.
In one embodiment, after step S206, the method further includes: acquiring a preset important defect index and an evaluation threshold corresponding to the important defect index; when the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value to determine that expert detection is needed, connecting a remote diagnosis platform to obtain an expert detection result; and determining an air conditioner maintenance scheme according to the expert detection result.
The important defect index is a preset index which has a large influence on the health state of the air conditioner. The evaluation threshold is a critical value of the evaluation value of the preset important defect index and is used for comparing with the actual evaluation value of the evaluation index to judge whether serious abnormality occurs in a certain dimension of the air conditioner. The evaluation index is used for evaluating the health state of the air conditioner and comprises the indexes of faults, noise, performance, service life, air cleanliness of the air conditioner and body feeling comfort level. The remote diagnosis platform refers to terminal equipment corresponding to a remote overhaul expert.
Specifically, after the health state of the air conditioner is determined, a preset important defect index and a corresponding evaluation threshold value are obtained. Then, the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value. Whether serious abnormity occurs is determined through comparison, and when the serious abnormity is determined to occur, further detection of a connection expert can be determined. For example, when the important defect index is performance, a performance evaluation value of the performance is acquired, and the performance evaluation value is compared with a preset performance evaluation threshold. When the performance evaluation value is larger than the performance evaluation threshold value, it is determined that serious abnormality occurs in the performance.
When the expert is determined to be needed to detect, the remote diagnosis platform is connected with the remote diagnosis platform, and the field condition is remotely sent to the remote diagnosis platform in the modes of pictures, videos and the like for the expert to judge. Or the remote diagnosis platform is connected with an expert to communicate in real time to inform the field situation. And after the expert knows the field condition, receiving a detection result fed back by the expert through the remote diagnosis platform. And then, matching a corresponding air conditioner maintenance scheme according to a detection result given by an expert. The matching can be obtained by matching the keywords with a preset maintenance scheme, or can be obtained by a mapping relation established with a configured maintenance scheme in advance. For example, the system is diagnosed by expert to find out that there is no refrigerant or the electronic expansion valve is faulty, and then the maintenance scheme such as filling refrigerant, replacing the electronic expansion valve or cleaning the filter screen is obtained. Different detection results correspond to different maintenance schemes.
In the embodiment, the maintenance scheme is obtained by connecting the remote diagnosis platform for expert detection, and the high efficiency of maintenance is ensured.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided an air conditioner health status evaluation device including: a fault acquisition module 402, a data acquisition module 404, and an evaluation module 406, wherein:
a failure obtaining module 402, configured to obtain a failure type of the air conditioner when it is determined that the air conditioner fails.
And the data acquisition module 404 is configured to acquire unit operation data and environmental data of the air conditioner.
And the evaluation module 406 is used for evaluating the health state of the air conditioner according to the unit operation data, the environmental data and the fault type.
In one embodiment, the evaluation module 406 is further configured to determine air cleanliness, performance, and life of the air conditioner based on the unit operating data; determining the somatosensory comfort level and the noise level according to the environmental data; determining a fault grade corresponding to the fault type; and evaluating the health state of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness and the somatosensory comfort level of the air conditioner.
In one embodiment, the evaluation module 406 is further configured to obtain the indoor ambient temperature, the indoor ambient humidity, and the wind speed from the environmental data; the indoor environment temperature, the indoor environment humidity and the wind speed are input into an evaluation model trained in advance and used for evaluating the somatosensory comfort level, and the somatosensory comfort level is output.
In one embodiment, the evaluation module 406 is also used to detect indoor air cleanliness; acquiring the pollution level of the filter screen and the running time of the indoor unit from the running data of the unit; and determining the air cleanliness of the air conditioner according to the indoor air cleanliness, the pollution level of the filter screen and the running time of the indoor unit.
In one embodiment, the evaluation module 406 is further configured to obtain an evaluation constant value, and obtain evaluation coefficients corresponding to a fault level, a noise level, performance, a service life, air cleanliness of the air conditioner, and a somatosensory comfort level, respectively; the evaluation constant value and each evaluation coefficient are obtained by modeling through multiple linear regression; multiplying the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level by corresponding evaluation coefficients respectively to obtain a fault evaluation value, a noise evaluation value, a performance evaluation value, a service life evaluation value, an air cleanliness evaluation value of the air conditioner and a somatosensory comfort level evaluation value respectively; summing the evaluation constant value, the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner to obtain a health evaluation value; and determining the health state of the air conditioner according to the health evaluation value.
In one embodiment, the evaluation module 406 is further configured to obtain a modeling indicator; the modeling indexes comprise modeling fault levels, modeling noise levels, modeling performance determined according to modeling unit operation data, modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to modeling environment data; and performing multiple linear regression modeling according to the modeling indexes, and taking a regression coefficient when the sum of squares of residuals of the modeling health evaluation value obtained by modeling and the modeling indexes is minimum as an evaluation constant value and an evaluation coefficient.
In one embodiment, the air conditioner health state evaluation device further comprises an expert diagnosis module, which is used for acquiring preset important defect indexes and evaluation threshold values corresponding to the important defect indexes; when the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value to determine that expert detection is needed, connecting a remote diagnosis platform to obtain an expert detection result; and determining an air conditioner maintenance scheme according to the expert detection result.
For specific limitations of the air conditioner health status evaluation device, reference may be made to the above limitations of the air conditioner health status evaluation method, which are not described herein again. All or part of the modules in the air conditioner health state evaluation device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the air conditioner, and can also be stored in a memory in the air conditioner in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, an air conditioner is provided, the internal structure of which may be as shown in fig. 5. The air conditioner comprises a processor, a memory, a communication interface, a display screen and an input device which are connected through a system bus. Wherein, the processor of the air conditioner is used for providing calculation and control capability. The memory of the air conditioner comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the air conditioner is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement an air conditioner health status evaluation method. The display screen of the air conditioner can be a liquid crystal display screen or an electronic ink display screen, and the input device of the air conditioner can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the air conditioner, an external keyboard, a touch pad or a mouse and the like.
It will be understood by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and does not constitute a limitation on the air conditioner to which the present application is applied, and a particular air conditioner may include more or less components than those shown in the drawings, or combine certain components, or have a different arrangement of components.
In one embodiment, an air conditioner is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
when the air conditioner is determined to have a fault, acquiring the fault type of the air conditioner;
acquiring unit operation data and environmental data of an air conditioner;
and evaluating the health state of the air conditioner according to the unit operation data, the environmental data and the fault type.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining the air cleanliness, performance and service life of the air conditioner according to the unit operation data; determining the somatosensory comfort level and the noise level according to the environmental data; determining a fault grade corresponding to the fault type; and evaluating the health state of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness and the somatosensory comfort level of the air conditioner.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring indoor environment temperature, indoor environment humidity and wind speed from the environment data; the indoor environment temperature, the indoor environment humidity and the wind speed are input into an evaluation model trained in advance and used for evaluating the somatosensory comfort level, and the somatosensory comfort level is output.
In one embodiment, the processor, when executing the computer program, further performs the steps of: detecting indoor air cleanliness; acquiring the pollution level of the filter screen and the running time of the indoor unit from the running data of the unit; and determining the air cleanliness of the air conditioner according to the indoor air cleanliness, the pollution level of the filter screen and the running time of the indoor unit.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining an evaluation constant value, and respectively obtaining evaluation coefficients corresponding to a fault level, a noise level, performance, service life, air cleanliness of an air conditioner and somatosensory comfort; the evaluation constant value and each evaluation coefficient are obtained by modeling through multiple linear regression; multiplying the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level by corresponding evaluation coefficients respectively to obtain a fault evaluation value, a noise evaluation value, a performance evaluation value, a service life evaluation value, an air cleanliness evaluation value of the air conditioner and a somatosensory comfort level evaluation value respectively; summing the evaluation constant value, the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner to obtain a health evaluation value; and determining the health state of the air conditioner according to the health evaluation value.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining a modeling index; the modeling indexes comprise modeling fault levels, modeling noise levels, modeling performance determined according to modeling unit operation data, modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to modeling environment data; and performing multiple linear regression modeling according to the modeling indexes, and taking a regression coefficient when the sum of squares of residuals of the modeling health evaluation value obtained by modeling and the modeling indexes is minimum as an evaluation constant value and an evaluation coefficient.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a preset important defect index and an evaluation threshold corresponding to the important defect index; when the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value to determine that expert detection is needed, connecting a remote diagnosis platform to obtain an expert detection result; and determining an air conditioner maintenance scheme according to the expert detection result.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
when the air conditioner is determined to have a fault, acquiring the fault type of the air conditioner;
acquiring unit operation data and environmental data of an air conditioner;
and evaluating the health state of the air conditioner according to the unit operation data, the environmental data and the fault type.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the air cleanliness, performance and service life of the air conditioner according to the unit operation data; determining the somatosensory comfort level and the noise level according to the environmental data; determining a fault grade corresponding to the fault type; and evaluating the health state of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness and the somatosensory comfort level of the air conditioner.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring indoor environment temperature, indoor environment humidity and wind speed from the environment data; the indoor environment temperature, the indoor environment humidity and the wind speed are input into an evaluation model trained in advance and used for evaluating the somatosensory comfort level, and the somatosensory comfort level is output.
In one embodiment, the computer program when executed by the processor further performs the steps of: detecting indoor air cleanliness; acquiring the pollution level of the filter screen and the running time of the indoor unit from the running data of the unit; and determining the air cleanliness of the air conditioner according to the indoor air cleanliness, the pollution level of the filter screen and the running time of the indoor unit.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining an evaluation constant value, and respectively obtaining evaluation coefficients corresponding to a fault level, a noise level, performance, service life, air cleanliness of an air conditioner and somatosensory comfort; the evaluation constant value and each evaluation coefficient are obtained by modeling through multiple linear regression; multiplying the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level by corresponding evaluation coefficients respectively to obtain a fault evaluation value, a noise evaluation value, a performance evaluation value, a service life evaluation value, an air cleanliness evaluation value of the air conditioner and a somatosensory comfort level evaluation value respectively; summing the evaluation constant value, the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner to obtain a health evaluation value; and determining the health state of the air conditioner according to the health evaluation value.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a modeling index; the modeling indexes comprise modeling fault levels, modeling noise levels, modeling performance determined according to modeling unit operation data, modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to modeling environment data; and performing multiple linear regression modeling according to the modeling indexes, and taking a regression coefficient when the sum of squares of residuals of the modeling health evaluation value obtained by modeling and the modeling indexes is minimum as an evaluation constant value and an evaluation coefficient.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a preset important defect index and an evaluation threshold corresponding to the important defect index; when the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value to determine that expert detection is needed, connecting a remote diagnosis platform to obtain an expert detection result; and determining an air conditioner maintenance scheme according to the expert detection result.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An air conditioner health state evaluation method is characterized by comprising the following steps:
when the air conditioner is determined to have a fault, acquiring the fault type of the air conditioner;
acquiring unit operation data and environment data of the air conditioner;
and evaluating the health state of the air conditioner according to the unit operation data, the environment data and the fault type.
2. The method of claim 1, wherein the evaluating the health status of an air conditioner based on the unit operational data, the environmental data, and the fault type comprises:
determining the air cleanliness, performance and service life of the air conditioner according to the unit operation data;
determining the somatosensory comfort level and the noise level according to the environment data;
determining a fault grade corresponding to the fault type;
and evaluating the health state of the air conditioner according to the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level.
3. The method of claim 2, wherein determining somatosensory comfort from the environmental data comprises:
acquiring indoor environment temperature, indoor environment humidity and wind speed from the environment data;
and inputting the indoor environment temperature, the indoor environment humidity and the wind speed into a pre-trained evaluation model for evaluating the somatosensory comfort level, and outputting the somatosensory comfort level.
4. The method of claim 2, wherein said determining air cleanliness of said air conditioner from said unit operational data comprises:
detecting indoor air cleanliness;
acquiring the pollution level of a filter screen and the running time of an indoor unit from the unit running data;
and determining the air cleanliness of the air conditioner according to the indoor air cleanliness, the pollution level of the filter screen and the running time of the indoor unit.
5. The method of claim 2, wherein said evaluating the health status of the air conditioner based on the fault level, the noise level, and the performance, the lifetime, the air cleanliness of the air conditioner, and the somatosensory comfort comprises:
obtaining an evaluation constant value, and respectively obtaining evaluation coefficients corresponding to the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level; the evaluation constant value and each evaluation coefficient are obtained by multivariate linear regression modeling;
multiplying the fault level, the noise level, the performance, the service life, the air cleanliness of the air conditioner and the somatosensory comfort level by the corresponding evaluation coefficients respectively to obtain a fault evaluation value, a noise evaluation value, a performance evaluation value, a service life evaluation value, an air cleanliness evaluation value of the air conditioner and a somatosensory comfort level evaluation value respectively;
summing the evaluation constant value, the fault evaluation value, the noise evaluation value, the performance evaluation value, the service life evaluation value, the air cleanliness evaluation value and the somatosensory comfort evaluation value of the air conditioner to obtain a health evaluation value;
and determining the health state of the air conditioner according to the health evaluation value.
6. The method of claim 5, wherein the evaluation constants and the evaluation coefficients are obtained by multiple linear regression modeling, comprising:
obtaining a modeling index and a standard health evaluation value; the modeling indexes comprise a modeling fault level, a modeling noise level, modeling performance determined according to modeling unit operation data, a modeling service life, modeling air cleanliness and modeling somatosensory comfort determined according to modeling environment data;
and performing multiple linear regression modeling according to the modeling index and the standard health evaluation value, and taking a regression coefficient when the sum of squares of residuals of the standard health evaluation value and the modeling index is minimum as an evaluation constant value and an evaluation coefficient.
7. The method of claim 1, further comprising:
acquiring a preset important defect index and an evaluation threshold corresponding to the important defect index;
when the evaluation value of the evaluation index corresponding to the important defect index is compared with the evaluation threshold value to determine that expert detection is needed, connecting a remote diagnosis platform to obtain an expert detection result;
and determining an air conditioner maintenance scheme according to the expert detection result.
8. An air conditioner state of health evaluation device, characterized in that the device includes:
the fault acquisition module is used for acquiring the fault type of the air conditioner when the air conditioner is determined to have a fault;
the data acquisition module is used for acquiring unit operation data and environment data of the air conditioner;
and the evaluation module is used for evaluating the health state of the air conditioner according to the unit operation data, the environment data and the fault type.
9. An air conditioner comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112728739A (en) * | 2020-12-28 | 2021-04-30 | 博锐尚格科技股份有限公司 | Method and device for determining set value of air supply temperature of air conditioning box based on algorithm prediction |
| CN113606732A (en) * | 2021-08-04 | 2021-11-05 | 广东美的暖通设备有限公司 | Health degree evaluation method and device, computing equipment and storage medium |
| WO2023103355A1 (en) * | 2021-12-06 | 2023-06-15 | 青岛海尔空调电子有限公司 | Method and apparatus for determining refrigerant amount, and air conditioner and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2650619A1 (en) * | 2010-12-08 | 2013-10-16 | Mitsubishi Heavy Industries, Ltd. | Air conditioner |
| CN107560076A (en) * | 2017-08-31 | 2018-01-09 | 广东美的环境电器制造有限公司 | The determination method and device of filter screen service life, clarifier, computer-readable storage medium |
| JP2020020531A (en) * | 2018-08-01 | 2020-02-06 | パナソニックＩｐマネジメント株式会社 | Server for executing recommendation processing of air conditioner and recommendation processing system |
| CN110889515A (en) * | 2019-11-18 | 2020-03-17 | 珠海格力电器股份有限公司 | Data processing method, device and system |
| CN111426015A (en) * | 2020-04-29 | 2020-07-17 | 珠海格力电器股份有限公司 | Health degree monitoring method and device for air conditioning unit and air conditioning unit |
| CN111578447A (en) * | 2020-05-18 | 2020-08-25 | 珠海格力电器股份有限公司 | Air conditioner health degree diagnosis method and device, computer equipment and storage medium |
-
2020
- 2020-09-22 CN CN202011000466.3A patent/CN112128919A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2650619A1 (en) * | 2010-12-08 | 2013-10-16 | Mitsubishi Heavy Industries, Ltd. | Air conditioner |
| CN107560076A (en) * | 2017-08-31 | 2018-01-09 | 广东美的环境电器制造有限公司 | The determination method and device of filter screen service life, clarifier, computer-readable storage medium |
| JP2020020531A (en) * | 2018-08-01 | 2020-02-06 | パナソニックＩｐマネジメント株式会社 | Server for executing recommendation processing of air conditioner and recommendation processing system |
| CN110889515A (en) * | 2019-11-18 | 2020-03-17 | 珠海格力电器股份有限公司 | Data processing method, device and system |
| CN111426015A (en) * | 2020-04-29 | 2020-07-17 | 珠海格力电器股份有限公司 | Health degree monitoring method and device for air conditioning unit and air conditioning unit |
| CN111578447A (en) * | 2020-05-18 | 2020-08-25 | 珠海格力电器股份有限公司 | Air conditioner health degree diagnosis method and device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 付金会: "《统计学》", 30 April 2017, 中国矿业大学出版社 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112728739A (en) * | 2020-12-28 | 2021-04-30 | 博锐尚格科技股份有限公司 | Method and device for determining set value of air supply temperature of air conditioning box based on algorithm prediction |
| CN112728739B (en) * | 2020-12-28 | 2022-07-26 | 博锐尚格科技股份有限公司 | Method and device for determining set value of air supply temperature of air conditioning box based on algorithm prediction |
| CN113606732A (en) * | 2021-08-04 | 2021-11-05 | 广东美的暖通设备有限公司 | Health degree evaluation method and device, computing equipment and storage medium |
| WO2023103355A1 (en) * | 2021-12-06 | 2023-06-15 | 青岛海尔空调电子有限公司 | Method and apparatus for determining refrigerant amount, and air conditioner and storage medium |
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Application publication date: 20201225 |
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