CN114877493A - Combined air conditioner energy-saving control system and method based on edge algorithm deep learning - Google Patents
Combined air conditioner energy-saving control system and method based on edge algorithm deep learning Download PDFInfo
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- CN114877493A CN114877493A CN202210580916.3A CN202210580916A CN114877493A CN 114877493 A CN114877493 A CN 114877493A CN 202210580916 A CN202210580916 A CN 202210580916A CN 114877493 A CN114877493 A CN 114877493A
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- 238000013135 deep learning Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 67
- 238000005265 energy consumption Methods 0.000 claims abstract description 58
- 238000007726 management method Methods 0.000 claims abstract description 39
- 238000004378 air conditioning Methods 0.000 claims abstract description 24
- 238000013523 data management Methods 0.000 claims abstract description 14
- 238000002372 labelling Methods 0.000 claims abstract 2
- 238000012549 training Methods 0.000 claims description 17
- 230000001105 regulatory effect Effects 0.000 claims description 10
- 230000004927 fusion Effects 0.000 claims description 9
- 238000013500 data storage Methods 0.000 claims description 7
- 238000011161 development Methods 0.000 claims description 7
- 230000001276 controlling effect Effects 0.000 claims description 3
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- 238000010586 diagram Methods 0.000 description 6
- 230000001143 conditioned effect Effects 0.000 description 1
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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
- F24F11/46—Improving electric energy efficiency or saving
- F24F11/47—Responding to energy costs
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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
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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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Abstract
The invention relates to an air conditioner control system, in particular to a combined type air conditioner energy-saving control system and a combined type air conditioner energy-saving control method based on edge algorithm deep learning, which comprises a user management unit, a temperature management unit, an energy consumption management unit, a data management unit and an equipment monitoring unit, wherein the equipment monitoring unit is used for monitoring the running state of air conditioning equipment, and generating operation state data, a user management unit for managing users and labeling the operation state data with corresponding user labels, a temperature management unit for monitoring temperature in the operation state of the device monitoring unit, and generates temperature data under the corresponding user tag, the data management unit is used for receiving the temperature data and the operation state data, and the energy consumption management unit is used for calling the corresponding energy consumption adjusting instruction under the corresponding user label and adjusting the energy consumption of the air-conditioning equipment.
Description
Technical Field
The invention relates to an air conditioner control system, in particular to a combined type air conditioner energy-saving control system and method based on edge algorithm deep learning.
Background
The air conditioner is an indispensable part of people in modern life, provides cool and warm for people, and with the continuous improvement of living standard, many consumers put higher demands on the air conditioner from the aspects of comfort, energy conservation, environmental protection and the like. Compared with a constant speed air conditioner, the variable frequency air conditioner not only improves the comfort degree and reduces the noise, but also greatly saves energy. However, with the continuous development of the internet, the reduction of the energy consumption of the air conditioner from the aspect of hardware has not met the demands of more users. Specific users have different preferences of using air conditioners, and how to save energy consumption to the maximum extent while meeting the temperature preference of the users becomes a problem to be solved urgently according to the preference of the specific users.
Disclosure of Invention
The invention aims to solve the defects in the background technology by providing a combined type air conditioner energy-saving control system and method based on edge algorithm deep learning.
The technical scheme adopted by the invention is as follows:
the combined type air conditioner energy-saving control system and method based on the edge algorithm deep learning comprises a user management unit, a temperature management unit, an energy consumption management unit, a data management unit and an equipment monitoring unit, wherein the equipment monitoring unit is used for monitoring the running state of air conditioning equipment, and generating operation state data, the user management unit is used for managing users and marking corresponding user labels on the operation state data, the temperature management unit is used for monitoring the temperature under the operation state of the equipment monitoring unit, and generates temperature data under a corresponding user tag, the data management unit is used for receiving the temperature data and the operation state data, and after the classification processing is carried out on the energy consumption adjusting instructions, the energy consumption management unit is used for calling the corresponding energy consumption adjusting instructions under the corresponding user tags and adjusting the energy consumption of the air-conditioning equipment.
As a preferred technical scheme of the invention: the user management unit comprises a user identification module, a user generation module and a label generation module, wherein the user identification module is used for judging the user identity according to the user characteristics and selecting the existing user, the user generation module is used for generating a new user when the existing user is not selected by the user identification module, and the label generation module is used for generating a user label according to the currently selected user and adopting the current user label to mark the current running state data.
As a preferred technical scheme of the invention: the temperature management unit comprises an external temperature monitoring module and an internal temperature monitoring module, wherein the external temperature monitoring module is used for acquiring an outdoor temperature value, and the internal temperature monitoring module is used for acquiring an indoor temperature value.
As a preferred technical scheme of the invention: the device monitoring unit comprises a start-stop monitoring module, a regulation and control temperature identification module, an energy consumption monitoring module and a clock module, wherein the start-up monitoring module is used for operating the regulation and control temperature identification module, the energy consumption monitoring module and the clock module after receiving a start signal and stopping operating the regulation and control temperature identification module, the energy consumption monitoring module and the clock module after receiving a stop signal, the clock module is used for continuously generating a time sequence according to external time data after starting, the regulation and control temperature identification module is used for acquiring a regulation and control temperature signal and generating temperature regulation data at a current time node, and the energy consumption monitoring module is used for acquiring energy consumption information and generating energy consumption data at the current time node.
As a preferred technical scheme of the invention: the data management unit comprises a data storage module, a data classification module, a data learning module and an instruction generation module, wherein the data storage module is used for receiving temperature data and running state data, the data classification module is used for classifying the temperature data and the running state data according to the user tags, the data learning module is used for performing fusion learning training on the temperature data and the running state data, and the instruction generation module is used for generating an energy consumption adjusting instruction according to the learning training result of the data learning module and the current temperature data and the running state data at a corresponding time node.
As a preferred technical scheme of the invention: the energy consumption management unit comprises an instruction receiving module and an intelligent control module, the instruction receiving module is used for receiving the instruction issued by the instruction generating module in real time and converting the instruction into a signal which can be identified by the intelligent control module, and the intelligent control module is used for regulating and controlling hardware in the air conditioning equipment according to the signal converted by the instruction receiving module.
A combined type air conditioner energy-saving control method based on edge algorithm deep learning comprises the following steps:
monitoring the running state of the air conditioning equipment, acquiring running state data, and marking a user label on the running state data according to user characteristics;
monitoring indoor and outdoor temperatures, and generating temperature data under corresponding user tags;
performing fusion training on the data according to the preference development of the running state data under the corresponding user label, and generating an energy consumption adjusting instruction according to a training result;
and adjusting the real-time energy consumption of the air conditioning equipment according to the energy consumption adjusting instruction.
As a preferred technical scheme of the invention: the fusion training of the data according to the preference development of the running state data under the corresponding user label specifically comprises the following steps:
and calculating similarity through the internal temperature regulation preference of the user under each external temperature condition, and predicting the preference temperature under the current user label by using the internal temperature regulation preference to obtain a temperature preference selection list.
As a preferred technical scheme of the invention: the calculation similarity specifically includes:
wherein t and f are the set of the user adjusted temperature at the current time and the current temperature, respectively, n (t) is the current time, and n (f) is the set of the user adjusted temperature at the current temperature.
As a preferred technical scheme of the invention: the predicting of the preferred temperature under the current user label is specifically as follows:
F(W tf )＝α 1 (W t1f1 )+α 2 (W t2f2 )+…+α n (W tnfn )
wherein alpha is n Representing the corresponding weight coefficients under different degrees of similarity.
According to the combined type air conditioner energy-saving control system and method based on the edge algorithm deep learning, various collected temperature and equipment data can be fused when a specific user starts air conditioner equipment through the cooperation of all modules, training is carried out under each user label, so that the adjustment preference of each user at various temperatures can be known, and the optimal adjustment instruction of the current equipment can be given. The running states of a compressor, a fan and the like of the air conditioning equipment can be adjusted through hardware and a circuit through the given optimal adjusting instruction, so that the energy consumption can be reduced greatly on the premise of meeting the preference of a user.
Drawings
FIG. 1 is an overall system block diagram of a preferred embodiment of the present invention;
FIG. 2 is a block diagram of a device monitoring unit in accordance with a preferred embodiment of the present invention;
FIG. 3 is a block diagram of a subscriber management unit in a preferred embodiment of the present invention;
FIG. 4 is a block diagram of a temperature management unit in a preferred embodiment of the present invention;
FIG. 5 is a block diagram of a data management unit in a preferred embodiment of the present invention;
FIG. 6 is a block diagram of the energy management unit in the preferred embodiment of the present invention.
The meaning of each label in the figure is: 100. a device monitoring unit; 101. a start-stop monitoring module; 102. a regulation temperature identification module; 103. an energy consumption monitoring module; 104. a clock module; 200. a user management unit; 201. a user identification module; 202. a user generation module; 203. a tag generation module; 300. a temperature management unit; 301. an external temperature monitoring module; 302. an internal temperature monitoring module; 400. a data management unit; 401. a data storage module; 402. a data classification module; 403. a data learning module; 404. an instruction generation module; 500. an energy consumption management unit; 501. an instruction receiving module; 502. and an intelligent control module.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and the features in the embodiments may be combined with each other, and the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. 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 invention.
Referring to fig. 1 to 6, a preferred embodiment of the present invention provides a combined type air conditioner energy-saving control system based on deep learning of an edge algorithm, which includes a user management unit 200, a temperature management unit 300, an energy consumption management unit 500, a data management unit 400, and an equipment monitoring unit 100, wherein the equipment monitoring unit 100 is used for monitoring an operation state of an air conditioner, and generates operation state data, the user management unit 200 manages users and marks corresponding user tags on the operation state data, the temperature management unit 300 monitors temperature in an operation state of the device monitoring unit 100, and generates temperature data under a corresponding user tag, the data management unit 400 for receiving the temperature data and the operation state data, after the classification processing is performed on the energy consumption adjustment instructions, the energy consumption management unit 500 is configured to call the corresponding energy consumption adjustment instructions under the corresponding user tags and adjust the energy consumption of the air conditioning equipment.
Further: the user management unit 200 includes a user identification module 201, a user generation module 202, and a tag generation module 203, where the user identification module 201 is configured to determine a user identity according to a user characteristic and select an existing user, the user generation module 202 is configured to generate a new user when the user identification module 201 cannot select the existing user, and the tag generation module 203 is configured to generate a user tag according to the currently selected user and tag current running state data with the current user tag. In this embodiment, the user identification module 201 adopts a fingerprint identification mode of a remote controller, when one user uses the remote controller, the fingerprint acquisition module on the remote controller acquires a fingerprint, and simultaneously compares the fingerprint with the existing user identity, and when the fingerprint does not store a file, a new user file is generated through the user generation module 202. Otherwise, the existing user scheme matching the fingerprint is selected.
Further: the temperature management unit 300 comprises an external temperature monitoring module 301 and an internal temperature monitoring module 302, wherein the external temperature monitoring module 301 is used for acquiring an outdoor temperature value, and the internal temperature monitoring module 302 is used for acquiring an indoor temperature value. The external temperature monitoring module 301 and the internal temperature monitoring module 302 are mainly used for collecting current outdoor temperature and indoor temperature, and are convenient for judging the favorite adjusting mode of a current user when the outdoor temperature and the indoor temperature are respectively various values, so that the adjusting preference under various outdoor temperatures and various indoor temperatures can be completed through subsequent training.
Further: the device monitoring unit 100 includes a start-stop monitoring module 101, a regulated temperature identification module 102, an energy consumption monitoring module 103 and a clock module 104, the start-up monitoring module is configured to operate the regulated temperature identification module 102, the energy consumption monitoring module 103 and the clock module 104 after receiving a start signal, and stop operating the regulated temperature identification module 102, the energy consumption monitoring module 103 and the clock module 104 after receiving a stop signal, the clock module 104 is configured to continuously generate a time sequence according to external time data after starting, the regulated temperature identification module 102 is configured to acquire a regulated temperature signal and generate temperature regulation data at a current time node, and the energy consumption monitoring module 103 is configured to acquire energy consumption information and generate energy consumption data at the current time node. The appliance monitoring unit 100 monitors the operating conditions of the air conditioning appliance, such as the current size of the compressor and fan, and the expected temperature to which the appliance is currently being conditioned and the user's temperature control options for various temperature data. For example, a user often chooses to increase or decrease the temperature at 25 ℃.
Further: the data management unit 400 includes a data storage module 401, a data classification module 402, a data learning module 403, and an instruction generation module 404, where the data storage module 401 is configured to receive temperature data and operating state data, the data classification module 402 is configured to classify the temperature data and the operating state data according to a user tag, the data learning module 403 is configured to perform fusion learning training on the temperature data and the operating state data, and the instruction generation module 404 is configured to generate an energy consumption adjustment instruction according to a learning training result of the data learning module 403 and according to current temperature data and operating state data at a corresponding time node. The data management unit 400 can fuse the previously collected various temperature and device data and train under each user label, so that the adjustment preference of each user under various temperatures can be known and the optimal adjustment instruction of the current device can be given.
Further: the energy consumption management unit 500 includes an instruction receiving module 501 and an intelligent control module 502, where the instruction receiving module 501 is configured to receive an instruction issued by the instruction generating module 404 in real time and convert the instruction into a signal that can be recognized by the intelligent control module 502, and the intelligent control module 502 is configured to regulate and control hardware in the air conditioning equipment according to the signal converted by the instruction receiving module 501. The running states of a compressor, a fan and the like of the air conditioning equipment can be adjusted through hardware and a circuit through the given optimal adjusting instruction, so that the energy consumption can be reduced greatly on the premise of meeting the preference of a user.
The embodiment also provides a combined type air conditioner energy-saving control method based on the edge algorithm deep learning, which comprises the following steps:
monitoring the running state of the air conditioning equipment, acquiring running state data, and marking a user label on the running state data according to user characteristics;
monitoring indoor and outdoor temperatures, and generating temperature data under corresponding user tags;
performing fusion training on the data according to the preference development of the running state data under the corresponding user label, and generating an energy consumption adjusting instruction according to a training result;
and adjusting the real-time energy consumption of the air conditioning equipment according to the energy consumption adjusting instruction.
As a preferred technical scheme of the invention: the fusion training of the data according to the preference development of the running state data under the corresponding user label specifically comprises the following steps:
and calculating the similarity through the internal temperature regulation preference of the user under each external temperature condition, and predicting the preference temperature under the current user label by using the internal temperature regulation preference to obtain a temperature preference selection list.
Preferably: the calculating similarity specifically comprises the following steps:
wherein t and f are the set of the user adjusted temperature at the current time and the current temperature, respectively, n (t) is the current time, and n (f) is the set of the user adjusted temperature at the current temperature.
Preferably: the predicting of the preferred temperature under the current user label is specifically as follows:
F(W tf )＝α 1 (W t1f1 )+α 2 (W t2f2 )+…+α n (W tnfn )
wherein alpha is n Representing the corresponding weight coefficients under different degrees of similarity.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. The utility model provides a combination formula air conditioner energy-saving control system based on marginal algorithm degree of deep learning which characterized in that: comprises a user management unit (200), a temperature management unit (300), an energy consumption management unit (500), a data management unit (400) and an equipment monitoring unit (100), wherein the equipment monitoring unit (100) is used for monitoring the running state of the air conditioning equipment, and generating operation state data, the user management unit (200) for managing users and labeling the operation state data with corresponding user labels, the temperature management unit (300) is used for monitoring the temperature under the operation state of the equipment monitoring unit (100), and generates temperature data under a corresponding user tag, the data management unit (400) for receiving the temperature data and the operation state data, and after the classification processing is carried out on the energy consumption adjusting instructions, the energy consumption management unit (500) is used for calling the corresponding energy consumption adjusting instructions under the corresponding user tags and adjusting the energy consumption of the air conditioning equipment.
2. The combined type air-conditioning energy-saving control system based on the edge algorithm deep learning of claim 1, characterized in that: the user management unit (200) comprises a user identification module (201), a user generation module (202) and a label generation module (203), wherein the user identification module (201) is used for judging the identity of a user according to user characteristics and selecting the existing user, the user generation module (202) is used for generating a new user when the existing user cannot be selected by the user identification module (201), and the label generation module (203) is used for generating a user label according to the currently selected user and marking the current running state data by adopting the current user label.
3. The combined type air-conditioning energy-saving control system based on the edge algorithm deep learning of claim 2 is characterized in that: the temperature management unit (300) comprises an external temperature monitoring module (301) and an internal temperature monitoring module (302), wherein the external temperature monitoring module (301) is used for acquiring an outdoor temperature value, and the internal temperature monitoring module (302) is used for acquiring an indoor temperature value.
4. The combined type air-conditioning energy-saving control system based on the edge algorithm deep learning of claim 3 is characterized in that: the equipment monitoring unit (100) comprises a start-stop monitoring module (101), a temperature regulation and control identification module (102), an energy consumption monitoring module (103) and a clock module (104), the starting monitoring module is used for operating the temperature regulating and controlling identification module (102), the energy consumption monitoring module (103) and the clock module (104) after receiving a starting signal, and the operation stop regulation temperature identification module (102), the energy consumption monitoring module (103) and the clock module (104) after receiving the stop signal, the clock module (104) is used for continuously generating a time sequence according to external time data after starting, the regulated temperature identification module (102) is configured to obtain a regulated temperature signal and generate temperature regulation data at a current time node, the energy consumption monitoring module (103) is used for acquiring energy consumption information and generating energy consumption data at the current time node.
5. The combined type air-conditioning energy-saving control system based on the edge algorithm deep learning is characterized in that: the data management unit (400) comprises a data storage module (401), a data classification module (402), a data learning module (403) and an instruction generation module (404), wherein the data storage module (401) is used for receiving temperature data and running state data, the data classification module (402) is used for classifying the temperature data and the running state data according to the user tags, the data learning module (403) is used for performing fusion learning training on the temperature data and the running state data, and the instruction generation module (404) is used for generating an energy consumption adjusting instruction according to a learning training result of the data learning module (403) and current temperature data and running state data at a corresponding time node.
6. The combined type air-conditioning energy-saving control system based on the edge algorithm deep learning of claim 5, is characterized in that: the energy consumption management unit (500) comprises an instruction receiving module (501) and an intelligent control module (502), wherein the instruction receiving module (501) is used for receiving an instruction issued by the instruction generating module (404) in real time and converting the instruction into a signal which can be identified by the intelligent control module (502), and the intelligent control module (502) is used for regulating and controlling hardware in the air conditioning equipment according to the signal converted by the instruction receiving module (501).
7. A combined type air conditioner energy-saving control method based on edge algorithm deep learning is characterized in that: the method comprises the following steps:
monitoring the running state of the air conditioning equipment, acquiring running state data, and marking a user label on the running state data according to user characteristics;
monitoring indoor and outdoor temperatures, and generating temperature data under corresponding user tags;
performing fusion training on the data according to the preference development of the running state data under the corresponding user label, and generating an energy consumption adjusting instruction according to a training result;
and adjusting the real-time energy consumption of the air conditioning equipment according to the energy consumption adjusting instruction.
8. The combined type air conditioner energy-saving control method based on the edge algorithm deep learning of claim 7, characterized in that: the fusion training of the data according to the preference development of the running state data under the corresponding user label specifically comprises the following steps:
and calculating similarity through the internal temperature regulation preference of the user under each external temperature condition, and predicting the preference temperature under the current user label by using the internal temperature regulation preference to obtain a temperature preference selection list.
9. The combined type air conditioner energy-saving control method based on the edge algorithm deep learning of claim 8, characterized in that: the calculation similarity specifically includes:
wherein t and f are the set of the user adjusted temperature at the current time and the current temperature, respectively, n (t) is the current time, and n (f) is the set of the user adjusted temperature at the current temperature.
10. The combined type air conditioner energy-saving control method based on the edge algorithm deep learning of claim 9 is characterized in that: the predicting of the preferred temperature under the current user label is specifically as follows:
F(W tf )＝α 1 (W t1f1 )+α 2 (W t2f2 )+…+α n (W tnfn )
wherein alpha is n Representing the corresponding weight coefficients under different degrees of similarity.
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