CN110715401B - Defrosting control method and device for air conditioning equipment, medium and air conditioning equipment - Google Patents
Defrosting control method and device for air conditioning equipment, medium and air conditioning equipment Download PDFInfo
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- CN110715401B CN110715401B CN201910807909.0A CN201910807909A CN110715401B CN 110715401 B CN110715401 B CN 110715401B CN 201910807909 A CN201910807909 A CN 201910807909A CN 110715401 B CN110715401 B CN 110715401B
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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
- F24F11/41—Defrosting; Preventing freezing
- F24F11/42—Defrosting; Preventing freezing of outdoor units
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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
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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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
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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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
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
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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
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
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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
- F24F2140/00—Control inputs relating to system states
- F24F2140/20—Heat-exchange fluid temperature
Abstract
The application provides a defrosting control method and device of air conditioning equipment, a medium and the air conditioning equipment, wherein the method comprises the following steps: collecting frosting state data of the air conditioning equipment and uploading the frosting state data to a server; receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness; and executing defrosting operation according to the defrosting instruction. The frosting thickness can be determined by the neural network algorithm according to the frosting state data, and whether frosting is needed or not is judged according to the frosting thickness, so that the calculation accuracy of the frosting thickness is improved, the air conditioning equipment executes frosting according to the frosting thickness, and the frosting effect is improved.
Description
Technical Field
The application relates to the technical field of vehicles, in particular to a defrosting control method and device of air conditioning equipment, a medium and the air conditioning equipment.
Background
In the operation and heating process of the air conditioner, heat is absorbed from the outside and supplied to the inside, so that the temperature of the heat exchanger on the outer side is lower than the ambient temperature in the operation process of the air conditioner, and frost is continuously formed on the outer side in a long-time process.
The existing defrosting control method of the air conditioner comprises the following steps: a heat sensor is arranged at the opposite position, after a certain temperature is detected, the heat is converted into the cold, and the condensed frost layer on the condenser is melted by entering from the top of the condenser through a high-temperature refrigerant. And when the defrosting condition is met, the system is heated to continue running.
However, this method is not accurate enough to judge the frosting condition. Especially when snow cover caused by snowy days directly covers the two devices, the frosting condition cannot be detected by applying the method. In addition, when the accumulated snow is too thick, the existing defrosting control method cannot be used for defrosting completely, and the defrosting effect is influenced.
Disclosure of Invention
The application provides a defrosting control method and a corresponding device of air conditioning equipment, which mainly achieve the purposes of determining the frosting thickness by utilizing a neural network algorithm according to frosting state data and judging whether defrosting is needed or not according to the frosting thickness, so that the calculation accuracy of the frosting thickness is improved, and the defrosting effect is improved.
The present application also provides an air conditioner and a readable storage medium for performing the defrosting control method of the air conditioner of the present application.
In order to solve the above problems, the present application adopts the following technical solutions:
in a first aspect, the present application provides a defrosting control method of an air conditioning apparatus, the method including:
collecting frosting state data of the air conditioning equipment and uploading the frosting state data to a server;
receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness;
and executing defrosting operation according to the defrosting instruction.
Specifically, the collecting frosting state data of the air conditioning equipment and uploading the frosting state data to a server includes:
acquiring a current frosting picture on a condenser coil of the air conditioning equipment by utilizing camera equipment;
detecting the current coil temperature of a condenser coil of the air conditioning equipment by using a temperature sensor;
detecting the current outdoor ambient temperature by using a temperature sensor;
and uploading the current frosting picture, the current coil temperature and the current environment temperature to a server as frosting state data.
Specifically, the defrosting instruction is that the server compares the current frosting picture, the current coil temperature and the current environment temperature with reference data in a pre-stored defrosting parameter database by using a neural network algorithm to obtain a frosting thickness, and determines the frosting thickness according to the frosting thickness.
Specifically, the executing the defrosting operation according to the defrosting instruction includes:
extracting a defrosting mode in the defrosting instruction;
and controlling the direction of a four-way valve of the air conditioning equipment according to the defrosting mode to perform defrosting.
In a second aspect, the present application provides a defrosting control apparatus of an air conditioning device, the apparatus including:
the collecting module is used for collecting frosting state data of the air conditioning equipment;
the uploading module is used for uploading the frosting state data to a server;
and the execution module is used for executing defrosting operation according to the defrosting instruction when the defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm is received.
In a third aspect, the application provides a remote control method for defrosting of an air conditioning device, which comprises the following steps:
receiving frosting state data uploaded by air conditioning equipment;
obtaining frosting thickness by utilizing a neural network algorithm according to the frosting state data, and judging whether the air conditioning equipment needs to be frosted or not according to the frosting thickness;
and if so, generating a defrosting instruction according to the frosting thickness and sending the defrosting instruction to the air conditioning equipment, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction.
Specifically, the frosting status data includes a current frosting picture on a condenser coil of the air conditioning equipment, a current coil temperature of the condenser coil of the air conditioning equipment, and an outdoor current ambient temperature.
Specifically, the obtaining of the frosting thickness by using a neural network algorithm according to the frosting state data and judging whether the air conditioning equipment needs to be frosted according to the frosting thickness includes:
calling pre-stored reference data in a frosting parameter database;
training by utilizing a neural network algorithm according to the reference data to obtain a frosting thickness prediction model, wherein the reference data comprises: the standard frosting picture on the condenser coil of the air conditioning equipment corresponding to different standard ambient temperatures, the standard coil temperature of the condenser coil of the air conditioning equipment and the standard frosting thickness.
Preferably, the obtaining of the frosting thickness by using a neural network algorithm according to the frosting state data and the judging whether the air conditioning equipment needs to be frosted according to the frosting thickness include:
comparing the current frosting picture, the current coil temperature and the current environment temperature with the reference frosting picture, the reference coil temperature and the reference environment temperature respectively by using the frosting thickness prediction model;
obtaining the frosting thickness according to the comparison result, wherein the frosting thickness comprises the current frosting thickness and the predicted frosting thickness in a preset time period;
and when the current frosting thickness is larger than the preset thickness and the current coil temperature is lower than the preset temperature, judging that the air conditioning equipment needs to be defrosted.
Or when the predicted frosting thickness is larger than the preset thickness, judging that the air conditioning equipment needs to be defrosted.
Specifically, if yes, a defrosting instruction is generated according to the frosting thickness and is sent to the air conditioning equipment, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction, and the method includes the following steps:
determining a defrosting mode according to the current frosting thickness;
and generating a defrosting instruction according to the defrosting mode and sending the defrosting instruction to the air conditioning equipment.
Preferably, if yes, a defrosting instruction is generated according to the frosting thickness and is sent to the air conditioning equipment, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction, and the method includes:
generating an instruction for adjusting the defrosting mode according to the predicted frosting thickness;
and sending the instruction for adjusting the defrosting mode to the air conditioning equipment, so that the air conditioning equipment adjusts the current defrosting mode.
In a fourth aspect, the present application provides a remote control device for defrosting an air conditioning apparatus, the method including:
the receiving module is used for receiving frosting state data uploaded by the air conditioning equipment;
the judging module is used for judging whether the air conditioning equipment needs defrosting or not by utilizing a neural network algorithm according to the frosting state data;
and the sending module is used for sending a defrosting instruction to the air conditioning equipment if the air conditioning equipment needs defrosting, so that the air conditioning equipment executes defrosting operation according to the defrosting instruction.
In a fifth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores thereon a computer program, which when executed by a processor, implements the steps of the defrosting control method of an air conditioning apparatus according to any one of the first aspect.
In a sixth aspect, the present application provides an air conditioning apparatus, characterized by comprising a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the defrosting control method of the air conditioning apparatus according to any one of the first aspect
Compared with the prior art, the technical scheme of the application has the following advantages:
1. the application provides a defrosting control method of air conditioning equipment, which comprises the steps of collecting frosting state data of the air conditioning equipment and uploading the data to a server; receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness; and executing defrosting operation according to the defrosting instruction. According to the defrosting method and the defrosting device, the defrosting of the air conditioning equipment is realized through interaction between the air conditioning equipment and the server. This application calculates the thickness of frosting based on neural network algorithm to whether change the frost according to the thickness of frosting judgement, improved the computational accuracy of thickness of frosting, make air conditioning equipment carry out the defrosting according to the thickness of frosting, even frosting too thickly, also can change the frost totally, improve the effect of changing the frost.
2. In this application, air conditioning equipment uploads to the server through gathering frosting state data, the server utilizes neural network algorithm right frosting state data handles and obtains frosting thickness, and basis frosting thickness judges whether air conditioning equipment needs the frost, works as the server judges when air conditioning equipment needs the frost, the formation instruction of defrosting send to air conditioning equipment, air conditioning equipment basis the instruction of defrosting carries out the defrosting. According to the defrosting method and the defrosting device, the defrosting judgment and execution are completed based on the interaction between the air conditioning equipment and the server, and the frosting recognition precision and the defrosting effect are improved.
3. In this application, obtaining the frosting thickness by using a neural network algorithm according to the frosting state data, and judging whether the air conditioning equipment needs to be frosted or not according to the frosting thickness includes: calling pre-stored reference data in a frosting parameter database; training by utilizing a neural network algorithm according to the reference data to obtain a frosting thickness prediction model, wherein the reference data comprises: the standard frosting picture on the condenser coil of the air conditioning equipment corresponding to different standard ambient temperatures, the standard coil temperature of the condenser coil of the air conditioning equipment and the standard frosting thickness. The method and the device train the pre-measured reference data based on the neural network algorithm to obtain the frosting thickness prediction model. The frosting thickness prediction model is used for calculating the subsequent frosting thickness, and can calculate the current frosting thickness and predict the frosting thickness in a specified time period, so that the frosting condition can be predicted in advance, and the frosting condition can be conveniently entered in advance.
4. In this application, the frosting status data includes the current frosting picture on air conditioning equipment's the condenser coil the current coil temperature and the outdoor current ambient temperature of air conditioning equipment's condenser coil. The obtaining of the frosting thickness by utilizing a neural network algorithm according to the frosting state data and the judging of whether the air conditioning equipment needs to be frosted according to the frosting thickness comprise the following steps: respectively comparing the current frosting picture, the current coil temperature and the current environment temperature with the reference frosting picture, the reference coil temperature and the reference environment temperature by using the frosting thickness prediction model to obtain frosting thickness, wherein the frosting thickness comprises the current frosting thickness and the predicted frosting thickness in a preset time period; and when the current frosting thickness is larger than the preset thickness and the current coil temperature is lower than the preset temperature, judging that the air conditioning equipment needs to be defrosted. Or when the predicted frosting thickness is larger than the preset thickness, judging that the air conditioning equipment needs to be defrosted. According to the method and the device, the current frosting thickness and the predicted frosting thickness can be obtained according to a neural network algorithm, and whether defrosting is needed or not is judged according to the current frosting thickness and the predicted frosting thickness. In the application, when the air conditioning equipment is in the initial starting state, the predicted frosting thickness can be obtained through a neural network algorithm, and whether defrosting is needed or not is judged according to the predicted frosting thickness, so that the problem that defrosting cannot be detected in the initial starting state in the prior art is solved.
5. In the application, an instruction for adjusting the defrosting mode can be generated according to the predicted frosting thickness; and sending the instruction for adjusting the defrosting mode to the air conditioning equipment, so that the air conditioning equipment adjusts the current defrosting mode. This application can be according to predicting the current mode of changing the frost of thickness adjustment of frosting, when predicting the thickness of frosting when too thick, can in time adjust the mode of changing the frost to in time clear away the clean frost that is about to condense, solved prior art, owing to the thick problem that can't change the frost clean that leads to of frosting for a long time.
Drawings
FIG. 1 is a flow chart of a defrosting control method of an air conditioning device in one embodiment;
FIG. 2 is a block diagram of a defrosting control device of an air conditioning apparatus according to an embodiment;
FIG. 3 is a flow chart of a remote control method for defrosting of an air conditioning device in one embodiment;
FIG. 4 is a block diagram of a remote control device for defrosting an air conditioning device according to an embodiment;
fig. 5 is a block diagram showing an internal structure of an air conditioner according to an embodiment.
The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In some of the flows described in the specification and claims of this application and in the above-described figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being numbered, e.g., S11, S12, etc., merely to distinguish between various operations, and the order of the operations itself is not intended to represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those of ordinary skill in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The technical solutions in the present application will be described clearly and completely with reference to the drawings attached hereto, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, an embodiment of the present application provides a defrosting control method for an air conditioning apparatus, where the method is performed in the air conditioning apparatus. As shown in fig. 1, the method comprises the steps of:
and S11, collecting frosting state data of the air conditioning equipment and uploading the data to a server.
In this application, gather air conditioning equipment's frosting state data to upload to the server, specifically include:
the air conditioning equipment collects the frosting state data in real time and uploads the frosting state data to a server in real time;
or the air conditioning equipment collects the frosting state data every other preset time period and uploads the frosting state data to a server.
Herein, the frosting status data includes but is not limited to: a current frosting picture on a condenser coil of the air conditioning equipment, a current coil temperature of the condenser coil of the air conditioning equipment, and an outdoor current ambient temperature. And the frosting state data is used as an input parameter of the neural network algorithm, and the frosting thickness is finally output through the neural network algorithm.
In this application, gather air conditioning equipment's frosting state data to upload to the server, include:
acquiring a current frosting picture on a condenser coil of the air conditioning equipment by utilizing camera equipment; detecting the current coil temperature of a condenser coil of the air conditioning equipment by using a temperature sensor; detecting the current outdoor ambient temperature by using a temperature sensor; and uploading the current frosting picture, the current coil temperature and the current environment temperature to a server as frosting state data. The camera device may be a video monitoring device.
And S12, receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness.
In the application, after the server receives the frosting state data, the frosting state data is processed by using a neural network algorithm to obtain the frosting thickness, and a defrosting instruction is determined according to the frosting thickness.
In the application, the server constructs a defrosting parameter database in advance, and the defrosting parameter database comprises a plurality of datum data measured in the air conditioner development stage. The reference data includes, but is not limited to: the standard frosting picture on the condenser coil of the air conditioning equipment corresponding to different standard ambient temperatures, the standard coil temperature of the condenser coil of the air conditioning equipment and the standard frosting thickness.
The reference data are processed based on a neural network algorithm, so that a model capable of predicting frosting thickness, namely a frosting thickness prediction model, is trained. In one possible design, the method for training the model for predicting frost thickness of the present application is as follows:
constructing a prediction function by taking the reference environment temperature, the reference frosting picture and the reference coil pipe temperature as input quantities and the reference frosting thickness as an output quantity;
calculating a theoretical value of frosting thickness by taking the reference environment temperature, the reference frosting picture and the reference coil temperature as input quantities of the prediction function, and comparing the theoretical value with the reference frosting thickness to obtain an error value;
and continuously adjusting coefficients of all input quantities in the prediction function according to the error value to enable the obtained theoretical value of the frosting thickness and the error value of the reference frosting thickness to be within a preset error range, and obtaining the prediction function which is a function in the frosting thickness prediction model after multiple training and continuous adjustment.
When the reference frosting picture is taken as the input quantity of the prediction function, the LBP characteristic value of the reference frosting picture is specifically extracted, and the LBP characteristic value is taken as the input quantity.
And S13, executing defrosting operation according to the defrosting instruction.
In this application, executing the defrosting operation according to the defrosting instruction includes:
and extracting a defrosting mode in the defrosting instruction, and controlling the direction of a four-way valve of the air conditioning equipment according to the defrosting mode to execute defrosting. In the application, the server determines the defrosting mode according to the frosting thickness, and carries the defrosting mode in the defrosting instruction and sends the defrosting mode to the air conditioning equipment.
In one possible design, the determining, by the server, the defrosting mode according to the frosting thickness specifically includes:
when the frosting thickness is lower than a preset target value, defrosting by adopting a heating cycle defrosting mode; when the frosting thickness is higher than the target value, defrosting is performed by adopting a refrigeration cycle defrosting mode, and the target value can be set according to actual needs.
In this application, when adopting the heating cycle to change the white mode and changing frost, air conditioning equipment changes the white mode and keeps the direction of cross valve unchangeable according to the heating cycle to control interior fan and move in minimum wind-break, control electricity and assist hot operation, control outer fan stop operation. In this application, when the frost layer of frosting is thinner, through the decay that reduces the refrigerant temperature to the heat that utilizes the refrigerant to carry ization the defrosting layer can, can reduce the frequency of cross valve switching-over, reduced the fault rate of cross valve.
When the refrigeration cycle defrosting mode is adopted for defrosting, the direction of the four-way valve is changed by the air conditioning equipment according to the refrigeration cycle defrosting mode, and the inner fan and the outer fan are controlled to stop running. The direction of the four-way valve is changed, so that the air conditioning equipment is in a refrigerating state. The temperature of the refrigerant flowing into the indoor heat exchanger is reduced due to the entering of the refrigeration cycle, so that the descending amplitude of the indoor temperature can be reduced by controlling the inner fan to stop running. By controlling the external fan to stop running, the heat exchange between the refrigerant and the outside air can be reduced, so that the heat carried by the refrigerant can be used for removing a frost layer on the outdoor condenser.
Referring to fig. 3, an embodiment of the present application provides a remote control method for defrosting an air conditioning device, where the method is executed in a server. As shown in fig. 3, the method comprises the steps of:
and S21, receiving frosting state data uploaded by the air conditioning equipment.
In the application, the air conditioning equipment collects the frosting state data and uploads the frosting state data to the server, and the server receives the frosting state data and then analyzes and processes the frosting state data to obtain the frosting thickness. Wherein the frosting status data includes, but is not limited to, a current frosting picture on the air conditioning unit's condenser coil, a current coil temperature of the air conditioning unit's condenser coil, and a current outdoor ambient temperature.
Specifically, the air conditioning equipment acquires a current frosting picture on a condenser coil of the air conditioning equipment by utilizing camera equipment; detecting the current coil temperature of a condenser coil of the air conditioning equipment by using a temperature sensor; detecting the current outdoor ambient temperature by using a temperature sensor; and uploading the current frosting picture, the current coil temperature and the current environment temperature to a server as frosting state data. The camera device may be a video monitoring device.
And S22, obtaining the frosting thickness by utilizing a neural network algorithm according to the frosting state data, and judging whether the air conditioning equipment needs to be frosted according to the frosting thickness.
In this application, obtaining the frosting thickness by using a neural network algorithm according to the frosting state data, and judging whether the air conditioning equipment needs to be frosted or not according to the frosting thickness includes:
calling pre-stored reference data in a frosting parameter database; training by utilizing a neural network algorithm according to the reference data to obtain a frosting thickness prediction model, wherein the reference data comprises: the standard frosting picture on the condenser coil of the air conditioning equipment corresponding to different standard ambient temperatures, the standard coil temperature of the condenser coil of the air conditioning equipment and the standard frosting thickness.
In one possible design, the method for training the model for predicting frost thickness of the present application is as follows:
constructing a prediction function by taking the reference environment temperature, the reference frosting picture and the reference coil pipe temperature as input quantities and the reference frosting thickness as an output quantity; calculating a theoretical value of frosting thickness by taking the reference environment temperature, the reference frosting picture and the reference coil temperature as input quantities of the prediction function, and comparing the theoretical value with the reference frosting thickness to obtain an error value; and continuously adjusting coefficients of all input quantities in the prediction function according to the error value to enable the obtained theoretical value of the frosting thickness and the error value of the reference frosting thickness to be within a preset error range, and continuously training and adjusting to obtain the prediction function which is the function in the frosting thickness prediction model. When the reference frosted picture is used as the input quantity of the prediction function, the LBP characteristic value of the reference frosted picture is specifically extracted, and the LBP characteristic value is used as an input value.
Further, the obtaining a frosting thickness according to the frosting state data by using a neural network algorithm, and judging whether the air conditioning equipment needs to be frosted according to the frosting thickness includes:
comparing the current frosting picture, the current coil temperature and the current environment temperature with the reference frosting picture, the reference coil temperature and the reference environment temperature respectively by using the frosting thickness prediction model; obtaining the frosting thickness according to the comparison result, wherein the frosting thickness comprises the current frosting thickness and the predicted frosting thickness in a preset time period; and when the current frosting thickness is larger than the preset thickness and the current coil temperature is lower than the preset temperature, judging that the air conditioning equipment needs to be defrosted. Or when the predicted frosting thickness is larger than the preset thickness, judging that the air conditioning equipment needs to be defrosted.
In this application, obtain behind the frosting thickness prediction model, will current frosting picture current coil pipe temperature and current ambient temperature input frosting thickness prediction model, through frosting thickness prediction model is right current frosting picture current coil pipe temperature and current ambient temperature handles and obtains frosting thickness. Referring to table 1 below, table 1 is a data structure table of the frosting database in an embodiment.
In one embodiment, the frosting database has a data structure table
| DatumData of | Data value | Data value | Data value |
| Reference ambient temperature | T1 | T2 | T3 |
| Reference coil temperature | t1 | t2 | t3 |
| Reference frosting picture | M1 | M2 | M3 |
| Base frosting thickness | H1 | H2 | H3 |
As shown in table 1, the frosting database includes the reference frosting picture, the reference coil temperature, and the corresponding relationship between the reference environmental temperature and each reference data. Included in table 1 are:
under the reference environment temperature T1, the corresponding reference coil temperature is T1, the reference frosting picture is M1, and the reference frosting thickness is H1;
under the reference environment temperature T2, the corresponding reference coil temperature is T2, the reference frosting picture is M2, and the reference frosting thickness is H2;
the corresponding reference coil temperature at the reference ambient temperature T3 is T3, the reference frosting picture is M3, and the reference frosting thickness is H3.
In one embodiment, assume that the current ambient temperature is T0, the current coil temperature is T0, and the current frosting picture is M0. When the server processes the frosting state data by using the frosting thickness prediction model, the frosting state data processing method comprises the following steps: comparing the T0 with T1, T2 and T3 respectively to obtain a reference ambient temperature which is closest to the temperature of T0 and is T2, comparing T0 with T2, comparing M0 with M2, and obtaining the frosting thickness according to the comparison result and H2.
Herein, the frost thickness includes a current frost thickness and a predicted frost thickness within a specified time period. The specified time period may be set according to time requirements, for example, to the thickness of frost formation within the future day. According to the defrosting method and the defrosting device, whether defrosting is needed or not and what defrosting mode is adopted can be judged according to the current frosting thickness. According to the method and the device, whether defrosting needs to be carried out in advance can be judged according to the predicted frosting thickness, so that the phenomenon that defrosting cannot be carried out thoroughly due to over-thick frosting is avoided.
In the application, the judgment condition for judging whether defrosting is needed according to the frosting thickness can be set according to the actual situation. In one embodiment, the determination condition may be set as: and when the current frosting thickness is larger than the preset thickness and the current coil temperature is lower than the preset temperature, judging that the air conditioning equipment needs to be defrosted.
In another embodiment, the determination condition may be set as: and when the predicted frosting thickness is larger than the preset thickness, judging that the air conditioning equipment needs to be defrosted. For example, although the current frosting thickness does not reach the defrosting thickness, if the frosting thickness H1 after three hours is predicted to exceed the preset defrosting thickness, the defrosting mode needs to be entered in advance, and the problem that in the prior art, due to the fact that the frosting thickness cannot be predicted, timely defrosting cannot be achieved is solved.
In another embodiment, when the air conditioning equipment is in an initial startup state, if it is detected that the current frosting thickness reaches a preset thickness, it is determined that defrosting is needed, and a defrosting instruction is sent to the air conditioning equipment, so that the problem that the frosting thickness cannot be detected when the air conditioning equipment is in the initial startup state in the prior art is solved.
And S23, if yes, generating a defrosting instruction according to the frosting thickness and sending the defrosting instruction to the air conditioning equipment, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction.
In this application, if, according to the frost thickness generates the instruction of changing frost and sends to air conditioning equipment makes air conditioning equipment basis the instruction of changing frost carries out the operation of changing frost, include:
determining a defrosting mode according to the current frosting thickness; and generating a defrosting instruction according to the defrosting mode and sending the defrosting instruction to the air conditioning equipment.
In one possible design, the determining, by the server, the defrosting mode according to the frosting thickness specifically includes:
when the frosting thickness is lower than a preset target thickness, defrosting by adopting a heating circulation defrosting mode; and when the frosting thickness is higher than the target thickness, defrosting by adopting a refrigeration cycle defrosting mode.
In this application, when adopting the heating cycle to change the white mode and changing frost, air conditioning equipment changes the white mode and keeps the direction of cross valve unchangeable according to the heating cycle to control interior fan and move in minimum wind-break, control electricity and assist hot operation, control outer fan stop operation. In this application, when the frost layer of frosting is thinner, through the decay that reduces the refrigerant temperature to the heat that utilizes the refrigerant to carry ization the defrosting layer can, can reduce the frequency of cross valve switching-over, reduced the fault rate of cross valve.
When the refrigeration cycle defrosting mode is adopted for defrosting, the direction of the four-way valve is changed by the air conditioning equipment according to the refrigeration cycle defrosting mode, and the inner fan and the outer fan are controlled to stop running. The direction of the four-way valve is changed, so that the air conditioning equipment is in a refrigerating state. The temperature of the refrigerant flowing into the indoor heat exchanger is reduced due to the entering of the refrigeration cycle, so that the descending amplitude of the indoor temperature can be reduced by controlling the inner fan to stop running. By controlling the external fan to stop running, the heat exchange between the refrigerant and the outside air can be reduced, so that the heat carried by the refrigerant can be used for removing a frost layer on the outdoor condenser.
Preferably, if yes, a defrosting instruction is generated according to the frosting thickness and is sent to the air conditioning equipment, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction, and the method includes:
generating an instruction for adjusting the defrosting mode according to the predicted frosting thickness; and sending the instruction for adjusting the defrosting mode to the air conditioning equipment, so that the air conditioning equipment adjusts the current defrosting mode.
Can in time adjust the mode of defrosting according to the prediction thickness of frosting in this application, when the speed of defrosting of current mode of defrosting is less than the speed of frosting, in time adjust the mode of frosting, solved prior art, because the thickness of frosting can't be predicted, the speed that leads to probably appearing frosting surpasss the speed of defrosting and the accumulation of frosting appears too thickly, the clean problem of unable defrosting.
Referring to fig. 3, in another embodiment, the present application provides a defrosting control device of an air conditioning apparatus, including:
the collecting module 11 is used for collecting frosting state data of the air conditioning equipment;
the uploading module 12 is used for uploading the frosting state data to a server;
and the execution module 13 is configured to execute a defrosting operation according to the defrosting instruction when receiving the defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm.
Specifically, the acquisition module 11 includes:
the collecting unit is used for collecting a current frosting picture on a condenser coil of the air conditioning equipment by utilizing camera equipment;
detecting the current coil temperature of a condenser coil of the air conditioning equipment by using a temperature sensor;
detecting the current outdoor ambient temperature by using a temperature sensor;
and uploading the current frosting picture, the current coil temperature and the current environment temperature to a server as frosting state data.
Specifically, the defrosting instruction is that the server compares the current frosting picture, the current coil temperature and the current environment temperature with reference data in a pre-stored defrosting parameter database by using a neural network algorithm to obtain a frosting thickness, and determines the frosting thickness according to the frosting thickness.
Specifically, the execution module 13 includes:
the execution unit is used for extracting a defrosting mode in the defrosting instruction;
and controlling the direction of a four-way valve of the air conditioning equipment according to the defrosting mode to perform defrosting.
Referring to fig. 4, in another embodiment, the present application provides a remote control device for defrosting an air conditioner, including:
the receiving module 21 is configured to receive frosting status data uploaded by the air conditioning equipment;
the judging module 22 is configured to judge whether the air conditioning equipment needs defrosting by using a neural network algorithm according to the frosting state data;
the sending module 23 is configured to send a defrosting instruction to the air conditioning equipment if it is determined that the air conditioning equipment needs defrosting, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction.
Specifically, the judging module 22 includes:
the training unit is used for calling reference data in a pre-stored frosting parameter database;
training by utilizing a neural network algorithm according to the reference data to obtain a frosting thickness prediction model, wherein the reference data comprises: the standard frosting picture on the condenser coil of the air conditioning equipment corresponding to different standard ambient temperatures, the standard coil temperature of the condenser coil of the air conditioning equipment and the standard frosting thickness.
Specifically, the judging module 22 includes:
the judging unit is used for respectively comparing the current frosting picture, the current coil pipe temperature and the current environment temperature with the reference frosting picture, the reference coil pipe temperature and the reference environment temperature by using the frosting thickness prediction model to obtain frosting thickness, and the frosting thickness comprises the current frosting thickness and the predicted frosting thickness in a preset time period;
and when the current frosting thickness is larger than the preset thickness and the current coil temperature is lower than the preset temperature, judging that the air conditioning equipment needs to be defrosted.
Or when the predicted frosting thickness is larger than the preset thickness, judging that the air conditioning equipment needs to be defrosted.
Preferably, the sending module 23 includes:
the sending unit is used for determining a defrosting mode according to the current frosting thickness;
and generating a defrosting instruction according to the defrosting mode and sending the defrosting instruction to the air conditioning equipment.
Preferably, the sending module 23 includes:
the adjusting unit is used for generating an instruction for adjusting the defrosting mode according to the predicted frosting thickness;
and sending the instruction for adjusting the defrosting mode to the air conditioning equipment, so that the air conditioning equipment adjusts the current defrosting mode.
In another embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the defrosting control method of an air conditioning apparatus according to any one of the above aspects. The computer-readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., a computer, a cellular phone), and may be a read-only memory, a magnetic or optical disk, or the like.
The computer-readable storage medium can be used for collecting frosting state data of the air conditioning equipment and uploading the frosting state data to a server; receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness; and executing defrosting operation according to the defrosting instruction. This application calculates the thickness of frosting based on neural network algorithm to whether change the frost according to the thickness of frosting judgement, improved the computational accuracy of thickness of frosting, make air conditioning equipment carry out the defrosting according to the thickness of frosting, even frosting too thickly, also can change the frost totally, improve the effect of changing the frost.
In addition, in yet another embodiment, the present application provides an air conditioning apparatus, as shown in fig. 5, comprising a processor 303, a memory 305, an input unit 307, and a display unit 309. It will be understood by those skilled in the art that the structural elements shown in fig. 5 do not constitute a limitation of all air conditioning units and may include more or fewer components than those shown, or some combination of components. The memory 305 may be used to store the application 301 and various functional modules, and the processor 303 executes the application 301 stored in the memory 305, thereby performing various functional applications of the device and data processing. The memory 305 may be an internal memory or an external memory, or include both internal and external memories. The memory may comprise read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, a floppy disk, a ZIP disk, a usb-disk, a magnetic tape, etc. The memories disclosed herein include, but are not limited to, these types of memories. The memory 305 disclosed herein is provided as an example only and not as a limitation.
The input unit 307 is used for receiving input of signals and receiving keywords input by a user. The input unit 307 may include a touch panel and other input devices. The touch panel can collect touch operations of a user on or near the touch panel (for example, operations of the user on or near the touch panel by using any suitable object or accessory such as a finger, a stylus and the like) and drive the corresponding connecting device according to a preset program; other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., play control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. The display unit 309 may be used to display information input by a user or information provided to the user and various menus of the air conditioner. The display unit 309 may take the form of a liquid crystal display, an organic light emitting diode, or the like. The processor 303 is a control center of the air conditioner, connects various parts of the entire computer using various interfaces and lines, and performs various functions and processes data by operating or executing software programs and/or modules stored in the memory 303 and calling data stored in the memory. The one or more processors 303 shown in fig. 4 are capable of executing, implementing the functions of the acquisition module 11, the reception module 12, and the execution module 13 shown in fig. 2.
In one embodiment, the air conditioner includes a memory 305 and a processor 303, wherein the memory 305 stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor 303 to execute the steps of the defrosting control method of the air conditioner according to the above embodiment.
The air conditioning equipment provided by the application and the embodiment can realize the collection of frosting state data of the air conditioning equipment and upload the data to the server; receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness; and executing defrosting operation according to the defrosting instruction. This application calculates the thickness of frosting based on neural network algorithm to whether change the frost according to the thickness of frosting judgement, improved the computational accuracy of thickness of frosting, make air conditioning equipment carry out the defrosting according to the thickness of frosting, even frosting too thickly, also can change the frost totally, improve the effect of changing the frost.
The computer-readable storage medium provided in this application and embodiment may implement the embodiment of the defrosting control method for an air conditioning device, and for specific function implementation, reference is made to the description in the embodiment of the method, which is not repeated herein.
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 a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as within the scope of the present disclosure 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 present application. 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 (7)
1. A defrosting control method of an air conditioning apparatus, characterized by comprising:
collecting frosting state data of the air conditioning equipment and uploading the frosting state data to a server;
receiving a defrosting instruction obtained by the server according to the frosting state data by using a neural network algorithm, wherein the defrosting instruction is obtained by the server according to the frosting state data to obtain a frosting thickness and is determined according to the frosting thickness;
executing defrosting operation according to the defrosting instruction;
the collection the frosting state data of air conditioning equipment to upload to the server, include:
acquiring a current frosting picture on a condenser coil of the air conditioning equipment by utilizing camera equipment;
detecting the current coil temperature of a condenser coil of the air conditioning equipment by using a temperature sensor;
detecting the current outdoor ambient temperature by using a temperature sensor;
uploading the current frosting picture, the current coil temperature and the current environment temperature to a server as frosting state data;
the defrosting instruction is that the server compares the current frosting picture, the current coil pipe temperature and the current environment temperature with reference data in a pre-stored defrosting parameter database by utilizing a neural network algorithm to obtain a frosting thickness, and the frosting thickness is determined according to the frosting thickness.
2. The defrosting control method of an air conditioning equipment according to claim 1, wherein the performing of the defrosting operation according to the defrosting instruction comprises:
extracting a defrosting mode in the defrosting instruction;
and controlling the direction of a four-way valve of the air conditioning equipment according to the defrosting mode to perform defrosting.
3. A remote control method for defrosting of air conditioning equipment is characterized by comprising the following steps:
receiving frosting state data uploaded by air conditioning equipment;
obtaining frosting thickness by utilizing a neural network algorithm according to the frosting state data, and judging whether the air conditioning equipment needs to be frosted or not according to the frosting thickness;
if yes, generating a defrosting instruction according to the frosting thickness and sending the defrosting instruction to the air conditioning equipment, so that the air conditioning equipment executes a defrosting operation according to the defrosting instruction;
the frosting state data comprises a current frosting picture on a condenser coil of the air conditioning equipment, the current coil temperature of the condenser coil of the air conditioning equipment and the outdoor current environment temperature;
the obtaining of the frosting thickness by utilizing a neural network algorithm according to the frosting state data and judging whether the air conditioning equipment needs to be frosted or not according to the frosting thickness comprises the following steps:
calling pre-stored reference data in a frosting parameter database;
training by utilizing a neural network algorithm according to the reference data to obtain a frosting thickness prediction model, wherein the reference data comprises: the method comprises the following steps of obtaining different reference ambient temperatures, and reference frosting pictures on a condenser coil of the corresponding air-conditioning equipment at each reference ambient temperature, and reference coil temperatures and reference frosting thicknesses of the condenser coil of the air-conditioning equipment;
the obtaining of the frosting thickness by utilizing a neural network algorithm according to the frosting state data and the judging of whether the air conditioning equipment needs to be frosted according to the frosting thickness comprise the following steps:
comparing the current frosting picture, the current coil temperature and the current environment temperature with the reference frosting picture, the reference coil temperature and the reference environment temperature respectively by using the frosting thickness prediction model;
obtaining the frosting thickness according to the comparison result, wherein the frosting thickness comprises the current frosting thickness and the predicted frosting thickness in a preset time period;
when the current frosting thickness is larger than a preset thickness and the current coil temperature is lower than a preset temperature, judging that the air conditioning equipment needs to be defrosted;
or when the predicted frosting thickness is larger than the preset thickness, judging that the air conditioning equipment needs to be defrosted.
4. The remote control method for defrosting of air conditioning equipment according to claim 3, wherein if yes, a defrosting instruction is generated according to the frosting thickness and is sent to the air conditioning equipment, so that the air conditioning equipment performs a defrosting operation according to the defrosting instruction, and the method comprises the following steps:
determining a defrosting mode according to the current frosting thickness;
and generating a defrosting instruction according to the defrosting mode and sending the defrosting instruction to the air conditioning equipment.
5. The remote control method for defrosting of air conditioning equipment according to claim 4, wherein if yes, a defrosting instruction is generated according to the frosting thickness and is sent to the air conditioning equipment, so that the air conditioning equipment performs a defrosting operation according to the defrosting instruction, and the method comprises the following steps:
generating an instruction for adjusting the defrosting mode according to the predicted frosting thickness;
and sending the instruction for adjusting the defrosting mode to the air conditioning equipment, so that the air conditioning equipment adjusts the current defrosting mode.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, realizes the steps of the defrosting control method of an air conditioning apparatus according to any one of claims 1 to 2.
7. An air conditioning apparatus, comprising a memory and a processor, the memory having stored therein computer readable instructions, which, when executed by the processor, cause the processor to perform the steps of the defrosting control method of the air conditioning apparatus according to any one of claims 1 to 2.
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| CN112628942B (en) * | 2020-12-11 | 2021-11-30 | 珠海格力电器股份有限公司 | Defrosting control method and device, storage medium and terminal |
| CN112918214B (en) * | 2021-02-22 | 2022-05-31 | 一汽奔腾轿车有限公司 | Method and control device for realizing remote air conditioner control |
| CN113865009B (en) * | 2021-09-15 | 2022-11-22 | 珠海格力电器股份有限公司 | Defrosting method and system, air conditioner, storage medium and electronic equipment |
| DE102022113411A1 (en) * | 2022-05-27 | 2023-11-30 | Güntner Gmbh & Co. Kg | Method for operating a heat exchanger and device with a heat exchanger |
| DE102022113410A1 (en) * | 2022-05-27 | 2023-11-30 | Güntner Gmbh & Co. Kg | Method for operating a heat exchanger and system with a heat exchanger |
| CN115342585A (en) * | 2022-08-09 | 2022-11-15 | 海信冰箱有限公司 | Refrigerator and defrosting control method thereof |
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| CN106288158A (en) * | 2016-08-05 | 2017-01-04 | 珠海格力电器股份有限公司 | Air-conditioning and defrosting method thereof and device |
| CN107860099B (en) * | 2017-09-15 | 2018-11-30 | 珠海格力电器股份有限公司 | A kind of frosting detection method, device, storage medium and the equipment of air-conditioning |
| CN108386980B (en) * | 2018-03-05 | 2020-06-02 | 奥克斯空调股份有限公司 | Air conditioner defrosting control method and device |
| CN108679781B (en) * | 2018-05-03 | 2020-02-28 | 珠海格力电器股份有限公司 | Air conditioner defrosting control method and device, storage medium and air conditioner |
| CN109237727A (en) * | 2018-08-28 | 2019-01-18 | 青岛海尔空调电子有限公司 | defrosting control method for air conditioner |
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