A management and control method, device and system for smart air conditioning

Abstract

The present invention relates to the field of equipment control technology, and specifically to a management and control method, device and system for smart air conditioners. The method includes: generating a control strategy and an energy-saving strategy for air conditioners in a preset area according to the operation requirements of the air conditioner; controlling the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy; acquiring environmental data and the operation data of the air conditioner; analyzing the environmental data and the operation data to obtain analysis results; processing the analysis results using a machine learning algorithm to generate management suggestions for the air conditioner, and managing the operation of the air conditioner based on the management suggestions. In the present invention, the operation of the air conditioner is controlled by generating a control strategy and an energy-saving strategy based on the operation requirements, and at the same time, a machine learning algorithm is used to analyze the corresponding data to generate management suggestions. Therefore, the method can improve the efficiency of the air conditioner, reduce energy consumption, and bring a more intelligent and energy-saving experience to users. At the same time, it has broad application prospects and significant promotion value.

Description

Technical Field

[0001] The present invention relates to the field of equipment control technology, and in particular to a management and control method, device and system for a smart air conditioner. Background Art

[0002] With the rapid development of science and technology and the continuous improvement of people's requirements for quality of life, air conditioners, as indispensable temperature control equipment in modern buildings, have become an important trend in the development of the industry through intelligent management. In large buildings such as parks, office buildings, and commercial complexes, the operation and management of air conditioning systems not only affects the comfort of the indoor environment, but also directly affects energy consumption and operating costs. Therefore, developing an efficient and intelligent air conditioning control management method is of great significance for improving the overall management level of the park, optimizing energy utilization, and reducing operating costs. Summary of the invention

[0003] In view of this, the present invention provides a management and control method, device and system for a smart air conditioner to solve the problem that there is a lack of intelligent and efficient control and management methods for air conditioners in the prior art.

[0004] In a first aspect, the present invention provides a management and control method for a smart air conditioner, the method comprising: generating a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operation requirements of the air conditioner; controlling the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy; acquiring environmental data and operating data of the air conditioner; analyzing the environmental data and the operating data to obtain analysis results; processing the analysis results using a machine learning algorithm to generate management suggestions for the air conditioner, and managing the operation of the air conditioner based on the management suggestions.

[0005] In the present invention, the air conditioner operation is controlled by generating control strategies and energy-saving strategies based on operation demand, and a machine learning algorithm is used to analyze the corresponding data to generate management suggestions. Therefore, the method can improve the efficiency of the air conditioner, reduce energy consumption, and bring users a more intelligent and energy-saving experience. It also has broad application prospects and significant promotion value.

[0006] In an optional embodiment, a control strategy and an energy-saving strategy for the air conditioner in a preset area are generated according to the operation requirements of the air conditioner, including: generating an operation date and an operation time period within the operation date for the air conditioner in the preset area according to the operation requirements of the air conditioner; generating prohibited operation time periods and prohibited shutdown time periods for the air conditioner in multiple preset areas according to the operation requirements of the air conditioner, and the operation date, operation time period, prohibited operation time period and prohibited shutdown time period constitute a control strategy; generating a temperature limit strategy, an unmanned shutdown strategy, a timeout shutdown strategy, a window closing reminder strategy, a temperature control strategy and a door opening reminder strategy for the air conditioner in the preset area according to the operation requirements of the air conditioner, and the temperature limit strategy, the unmanned shutdown strategy, the timeout shutdown strategy, the window closing reminder strategy, the temperature control strategy and the door opening reminder strategy constitute an energy-saving strategy.

[0007] In the present invention, by setting the operating date, operating time period, prohibited operating time period and prohibited closing time period in the control strategy, the opening and closing of the air conditioner can be accurately controlled, and the timed opening and closing of the air conditioner is realized. At the same time, multiple strategies such as temperature limit strategy, unattended closing strategy, timeout closing strategy, window closing prompt strategy, temperature control strategy and door opening prompt strategy are set in the energy-saving strategy, thereby achieving energy saving on the basis of meeting the operating requirements and user experience.

[0008] In an optional embodiment, the operation of the air conditioner in the preset area is controlled according to the control strategy, including: controlling the start-up of the air conditioner in the preset area according to the operation date and the operation time period; during the prohibited operation time period, determining whether the air conditioner in the corresponding preset area is running, and turning off the running air conditioner; during the prohibited shutdown period, determining whether the air conditioner in the corresponding preset area is turned off, and turning on the turned-off air conditioner.

[0009] In an optional embodiment, the operation of the air conditioner in the preset area is controlled according to the energy-saving strategy, including: judging whether the temperature of the air conditioner operating in the preset area is within a preset temperature range according to the temperature limit strategy, and adjusting the air conditioner temperature when it exceeds the preset temperature range; judging whether there is anyone in the preset area according to the unattended shutdown strategy, and shutting down the running air conditioner when no one is there; judging whether the air conditioner running time reaches a first preset time according to the timeout shutdown strategy, and shutting down the running air conditioner when the first preset time is reached; judging whether the temperature change is less than a preset degree after the air conditioner runs for a second preset time according to the window closing prompt strategy, and generating a window closing prompt when the temperature change is less than the preset degree; judging whether the temperature of the preset area meets the preset conditions when the air conditioner is in cooling or heating mode according to the temperature control strategy, and shutting down the running air conditioner when the preset conditions are met; judging whether the door is opened for a third preset time when the air conditioner is running according to the door opening prompt strategy, and generating a door opening prompt after the third preset time is reached.

[0010] In an optional embodiment, the environmental data includes indoor temperature, indoor humidity, outdoor temperature and light intensity, and the environmental data and operating data are analyzed to obtain analysis results, including: analyzing the environmental data based on a correlation analysis algorithm to obtain a first analysis result; analyzing the impact of the environmental data on the operating data based on a covariance analysis algorithm to obtain a second analysis result; performing time series analysis on the environmental data and operating data to obtain a third analysis result.

[0011] In the present invention, by analyzing the correlation, covariance, time series and other aspects of environmental data and operating data, comprehensive and accurate data support is provided for the generation of subsequent management suggestions.

[0012] In an optional embodiment, a machine learning algorithm is used to process the analysis results to generate management suggestions for the air conditioner, including: using an integrated learning algorithm to analyze the environmental data, predicting the environmental data for future time periods, and generating management suggestions for air conditioning adjustment; using a deep learning algorithm to process the third analysis result to generate management suggestions for air conditioning operating modes corresponding to different environmental modes; using an association rule mining algorithm to process the first analysis result and the second analysis result to generate air conditioning adjustment management suggestions or energy consumption reduction management suggestions.

[0013] In the present invention, a more in-depth analysis of environmental data, operating data and corresponding analysis results is performed by adopting machine learning algorithms such as ensemble learning algorithms, deep learning algorithms and association mining rule algorithms, so as to generate targeted management suggestions, namely energy efficiency optimization suggestions.

[0014] In an optional implementation, the method further includes: calculating energy-saving benefits based on air-conditioning operation data before and after the implementation of the control strategy, energy-saving strategy, and management suggestions.

[0015] In the present invention, by calculating the energy-saving benefit, it is achieved to provide the management control result for the management control method in a digitized manner, thereby enabling the user to more intuitively determine the energy-saving effect of the management control method.

[0016] In a second aspect, the present invention provides a management and control device for a smart air conditioner, the device comprising: a strategy generation module, for generating a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operation requirements of the air conditioner; an operation control module, for controlling the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy; a data acquisition module, for acquiring environmental data and operating data of the air conditioner; a data analysis module, for analyzing the environmental data and operating data to obtain analysis results; a management module, for processing the analysis results using a machine learning algorithm, generating management suggestions for the air conditioner, and managing the operation of the air conditioner based on the management suggestions.

[0017] In a third aspect, the present invention provides a management and control system for a smart air conditioner, the system comprising: a controller, for generating a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operating requirements of the air conditioner; controlling the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy; a sensor, for acquiring environmental data and operating data of the air conditioner; the controller is also used to analyze the environmental data and operating data to obtain analysis results; using a machine learning algorithm to process the analysis results, generate management suggestions for the air conditioner, and manage the air conditioner operation based on the management suggestions.

[0018] In an optional embodiment, when the air conditioner is a multi-split architecture consisting of one air conditioner outdoor unit and multiple air conditioner indoor units, the controller includes a control system, a wireless terminal device, and a centralized control gateway arranged on the air conditioner outdoor unit, and the control system is used to send control commands generated according to the control strategy and the energy-saving strategy to the multiple air conditioner indoor units in sequence through the wireless terminal device, the centralized control gateway, and the air conditioner outdoor unit;

[0019] When the air conditioner is a single-unit or wall-mounted air conditioner, the controller includes a control system and an air conditioner controller installed on the air conditioner plug-in board, and the control system is used to send the control command generated according to the control strategy and the energy-saving strategy to the air conditioner through the air conditioner controller;

[0020] Alternatively, the controller includes a control system, a control terminal, a collector and an intelligent micro-breaker, and the control system is used to send control commands generated according to the control strategy and energy-saving strategy to the air conditioner through the control terminal, the collector and the intelligent micro-breaker.

[0021] In the present invention, the management and control system realizes seamless integration between air-conditioning equipment of different brands, models and architectures, that is, users can realize remote monitoring, centralized control and unified dispatch of all air-conditioning equipment in the park, which greatly improves management efficiency and convenience.

[0022] In a fourth aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the management and control method of the smart air conditioner according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0023] In a fifth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the management and control method for a smart air conditioner according to the first aspect or any corresponding embodiment thereof.

[0024] In a sixth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the management and control method of the smart air conditioner according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 is a flowchart of a management and control method of a smart air conditioner according to an embodiment of the present invention;

[0027] Figure 2 is a structural block diagram of a management and control device for a smart air conditioner according to an embodiment of the present invention;

[0028] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] According to an embodiment of the present invention, an embodiment of a management and control method for a smart air conditioner is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In this embodiment, a management and control method of a smart air conditioner is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 is a flow chart of a management and control method of a smart air conditioner according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0032] Step S101, generating a control strategy and energy-saving strategy for the air conditioner in a preset area according to the operation requirements of the air conditioner. Specifically, the management and control method can be applied to scenarios such as smart parks, for example, it can be applied to scenarios such as office buildings, large shopping malls, campuses, and industrial parks, that is, the management and control method can be used to manage and control the air conditioners installed in the corresponding scenarios. The operation requirements here may include the operation time, operation temperature, and other requirements of the air conditioner.

[0033] In addition, for any scenario, it includes multiple air conditioners installed in different locations, and the operating requirements of air conditioners in different locations may be the same or different. For example, for a campus scenario, the operating requirements of the air conditioners installed in the classroom and the air conditioners installed in the cafeteria are different. For an industrial park scenario, the operating requirements of the air conditioners installed in the factory area and the dormitory area are also different. Therefore, air conditioners (including one or more) with the same operating requirements can be divided into the same area, and the same control strategy can be generated. Therefore, the preset area here can be an area with the same operating requirements, and the control strategy can be understood as an operating strategy for controlling the start and shutdown of the air conditioner.

[0034] In addition, in order to reduce energy consumption during the operation of the air conditioner, corresponding energy-saving strategies can be generated according to the operation requirements of the air conditioner. For example, for the factory area in the industrial park, the operating temperature of the equipment cannot be higher than 24 degrees, so the upper and lower limits of the operating temperature of the air conditioner can be set as an energy-saving strategy. In addition, other energy-saving strategies can be generated according to operation requirements. Therefore, in actual applications, users can customize the control strategy and energy-saving strategy of the air conditioner according to actual needs and usage scenarios, thereby realizing flexible and diverse air conditioning strategy configuration solutions.

[0035] Step S102, controlling the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy. Specifically, after generating the corresponding control strategy and energy-saving strategy for each area, the operation of the air conditioner in the corresponding area can be controlled according to the control strategy and the energy-saving strategy. For example, the air conditioner in the corresponding area is turned on and off according to the control strategy and the energy-saving strategy.

[0036] Step S103, obtain environmental data and air conditioner operation data. Specifically, multiple sensors can be deployed in the scene where the air conditioner is installed to obtain environmental data in the corresponding scene. For example, humidity sensors, temperature sensors, etc. can be installed in the room where the air conditioner is installed. At the same time, parameters such as outdoor temperature and light intensity can be obtained according to the weather forecast. The operation data of the air conditioner includes data such as the operating time and power consumption of the air conditioner. In this way, key environmental parameters such as indoor and outdoor temperature, humidity, air quality, light intensity, etc. can be collected in real time, and the data can be uploaded to the cloud server or controller through the Internet of Things technology. At the same time, a weather forecast interface is integrated to obtain future weather change information to provide comprehensive and accurate data support for air conditioning control.

[0037] Step S104, analyzing the environmental data and the operation data to obtain analysis results. Specifically, before analyzing the data, the data may be preprocessed, for example, the data may be cleaned, for example, outliers may be removed and missing values ​​may be filled. In addition, the data may be normalized or standardized, for example, data of different dimensions may be converted into the same dimension to facilitate subsequent analysis.

[0038] When analyzing data, the relationship between data can be analyzed, for example, the relationship between multiple environmental data can be analyzed, and the relationship between environmental data and operating data can also be analyzed. By analyzing the relationship between data, a data basis is provided for the generation of subsequent management suggestions.

[0039] Step S105, using a machine learning algorithm to process the analysis results, generate management suggestions for the air conditioner, and manage the air conditioner operation based on the management suggestions. Specifically, a machine learning algorithm is used to further process and analyze the environmental data, the operation data, and the corresponding analysis results, so as to determine the operation status of the air conditioner in different environments, thereby enabling more refined management of the air conditioner.

[0040] The management and control method of the smart air conditioner provided in the embodiment of the present invention generates control strategies and energy-saving strategies to control the operation of the air conditioner through operation demand, and uses machine learning algorithms to analyze the corresponding data to generate management suggestions. Therefore, the method can improve the efficiency of the air conditioner, reduce energy consumption, and bring users a more intelligent and energy-saving experience. It also has broad application prospects and significant promotion value.

[0041] In this embodiment, a management and control method for a smart air conditioner is provided, and the method comprises the following steps:

[0042] Step S201: generating a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operating requirements of the air conditioner.

[0043] Specifically, the above step S201 includes:

[0044] Step S2011, generating the operating date of the air conditioner in the preset area and the operating time period within the operating date according to the operating demand of the air conditioner; wherein the operating date of the air conditioner in the preset area can be determined by the operating demand. For example, the operating date can be operated throughout the year, or a few weeks, or non-holiday operation, or operation during the week, etc. The operating time period within the operating date is the specific working time of the air conditioner during the operating date, for example, the operating time period during the week is from 8 am to 17 pm. At the same time, in order to prevent people from forgetting to turn off the air conditioner during overtime work at night, the air conditioner can be set to turn off at 20 / 22 / 24 pm to save energy.

[0045] Step S2012, based on the operation requirements of the air conditioner, a plurality of prohibited operation time periods and prohibited shutdown time periods of the air conditioner in the preset areas are generated, wherein the operation date, operation time period, prohibited operation time period and prohibited shutdown time period constitute a control strategy; wherein, different prohibited operation time periods or prohibited shutdown time periods may be set for different areas. For example, for a certain smart park, the air conditioner in the activity room is prohibited from starting at 0-9 o'clock and 21-24 o'clock, but is allowed to be turned on; the air conditioner in the cafeteria is prohibited from starting outside the meal time period; and the air conditioner in the exhibition hall visiting channel is prohibited from being turned off during the visiting time period.

[0046] Step S2013, generates a temperature limit strategy, an unmanned shutdown strategy, a timeout shutdown strategy, a window closing reminder strategy, a temperature control strategy and a door opening reminder strategy for the air conditioner in a preset area according to the operating requirements of the air conditioner, and the temperature limit strategy, the unmanned shutdown strategy, the timeout shutdown strategy, the window closing reminder strategy, the temperature control strategy and the door opening reminder strategy constitute an energy-saving strategy.

[0047] Step S202: Control the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy.

[0048] Specifically, the above step S202 includes:

[0049] Step S2021, according to the operation date and operation time period, the air conditioner in the preset area is controlled to start. Thus, the timed on / off of the air conditioner is realized through the operation date and operation time period.

[0050] Step S2022: during the prohibited operation period, determine whether the air conditioner in the corresponding preset area is running, and turn off the running air conditioner.

[0051] Step S2023, during the prohibited shutdown period, determine whether the air conditioner in the corresponding preset area is turned off, and turn on the turned-off air conditioner.

[0052] Step S2024, judging whether the temperature of the air conditioner running in the preset area is within the preset temperature range according to the temperature limit strategy, and adjusting the air conditioner temperature when it exceeds the preset temperature range; wherein the preset temperature range can be determined according to actual operation requirements.

[0053] Step S2025, determine whether there is anyone in the preset area according to the no-person shutdown strategy, and turn off the running air conditioner when no one is there; specifically, a human presence sensor can be set in the air conditioner installation area, and the sensor is used to detect whether there is anyone in the corresponding area during the operation of the air conditioner, and report once every preset time. When the results of multiple reports are that there is no one, the running air conditioner can be turned off.

[0054] Step S2026, determine whether the air conditioner operation time reaches the first preset time according to the timeout shutdown strategy, and shut down the running air conditioner when the first preset time is reached; wherein the first preset time represents the maximum operation time of the air conditioner, and the first preset time can be determined according to actual conditions.

[0055] Step S2027, judging whether the temperature change is less than a preset degree after the air conditioner runs for a second preset time according to the window closing prompt strategy, and generating a window closing prompt when the temperature change is less than the preset degree; wherein, the window closing prompt strategy can be understood as that after the air conditioner is started and runs for a period of time, if the indoor temperature is slightly different from the indoor temperature before the air conditioner is turned on, the indoor window may be in an open state, and a window closing prompt is generated. For example, if the indoor temperature is 30 degrees before the air conditioner is turned on, and the indoor temperature is 29 degrees half an hour after the air conditioner is started and runs, that is, the indoor temperature is slightly different from the indoor temperature before the air conditioner is turned on, the indoor window may be in an open state, and a window closing prompt is generated.

[0056] Step S2028, judging whether the temperature of the preset area meets the preset conditions when the air conditioner is in cooling or heating mode according to the temperature control strategy, and turning off the running air conditioner when the preset conditions are met. The preset conditions can be determined according to actual conditions. For example, when the air conditioner is in cooling mode at 24 degrees, and the current indoor temperature is lower than 20 degrees, it is judged that the temperature meets the preset conditions, and no cooling is required at this time, and the air conditioner in the cooling state can be turned off. When the air conditioner is in heating mode at 26 degrees, and the current indoor temperature is higher than 28 degrees, it is judged that the temperature meets the preset conditions, and no heating is required at this time, and the air conditioner in the heating state can be turned off.

[0057] Step S2029, judging whether the door is open for a third preset time when the air conditioner is running according to the door opening prompt strategy, and generating a door opening prompt after the third preset time is reached. Specifically, a door magnetic sensor can be set in the room where the air conditioner is installed. After the air conditioner is started, if the door magnetic sensor senses that the door is open for a preset time, a door opening prompt is generated. For example, if the door magnetic sensor senses that the door is open for thirty minutes after the air conditioner is started, a door opening prompt is generated, which is used to remind the user that the door is open and needs to be turned off for energy saving.

[0058] Step S203, obtain environmental data and air conditioner operation data; see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0059] Step S204: Analyze the environmental data and operation data to obtain analysis results. When analyzing the data, the acquired data can be analyzed in real time by using stream processing technology, so as to quickly respond to environmental changes.

[0060] Specifically, the above step S204 includes:

[0061] Step S2041, analyze the environmental data based on the correlation analysis algorithm to obtain a first analysis result; specifically, the correlation between environmental factors such as temperature, humidity, and light intensity can be analyzed by using methods such as the Pearson correlation coefficient and the Spearman rank correlation coefficient to identify which factors have significant mutual influence.

[0062] Step S2042, analyzing the impact of environmental data on operating data based on a covariance analysis algorithm to obtain a second analysis result; specifically, the impact of a certain environmental factor (such as temperature) in the environmental data on any operating variable (such as air conditioning energy consumption) in the operating data can be analyzed under the condition of other variables to more accurately understand the relationship between the variables.

[0063] Step S2043, performing time series analysis on the environmental data and the operating data to obtain a third analysis result. Specifically, when any environmental factor or operating variable in the environmental data or the operating data is connected collected data, a time series analysis is performed on it, such as time averaging or smoothing the data.

[0064] Step S205, the analysis results are processed using a machine learning algorithm to generate management suggestions for the air conditioner. Specifically, the generated analysis results and management suggestions can be intuitively displayed in the form of charts or dashboards to facilitate user understanding.

[0065] Specifically, the above step S205 includes:

[0066] Step S2051, using an integrated learning algorithm to analyze the environmental data, predict the environmental data for the future period, and generate air conditioning adjustment management suggestions; specifically, the integrated learning algorithm can be an integrated learning algorithm such as random forest, gradient boosting tree (GBDT), XGBoost, etc. These algorithms can significantly improve the accuracy and stability of environmental state classification (such as comfort, overheating, overhumidity, etc.) and regression prediction by combining the prediction results of multiple weak learners. In practical applications, these algorithms can accurately judge the current environmental state and predict key parameters such as temperature and humidity in the future, thereby realizing intelligent adjustment of the air conditioning system. For example, when it is predicted that a high temperature and high humidity environment is about to appear, the air conditioning system can start the dehumidification and cooling mode in advance to ensure the comfort of the indoor environment.

[0067] Step S2052, using a deep learning algorithm to process the third analysis result, and generate management suggestions for air conditioning operation modes corresponding to different environmental modes; specifically, a convolutional network model or a recurrent network model, or an attention mechanism model such as Transformer can be used to capture long-term dependencies and complex patterns in the data, so as to more accurately identify data with similar environmental conditions and cluster them into different environmental modes (such as "high temperature and high humidity", "low temperature and low humidity", etc.). These pattern recognition results can provide targeted management suggestions for the air conditioning system. For example, when the current environment is identified as the "high temperature and high humidity" mode, the air conditioning system can automatically adjust to the dehumidification and strong cooling mode to quickly reduce the indoor temperature and humidity.

[0068] Step S2053, using an association rule mining algorithm to process the first analysis result and the second analysis result, and generating air conditioning adjustment management suggestions or energy consumption reduction management suggestions. Specifically, by exploring the correlation between different environmental factors in the first analysis result or the second analysis result, such as the changing pattern of outdoor temperature and indoor temperature, the relationship between light intensity and air conditioning energy consumption, etc., the optimization potential of the air conditioning system can be further explored. For example, when it is found that the outdoor temperature rises, the indoor temperature will also rise accordingly. At this time, the air conditioning system can start the cooling mode in advance to avoid the discomfort caused by the high indoor temperature. At the same time, through association rule mining, the correlation between light intensity and air conditioning energy consumption can also be found, so as to remind you to adjust the curtain angle when there is sufficient light, etc., to reduce air conditioning energy consumption.

[0069] In addition, for the models used in the above steps, the accuracy and efficiency can be evaluated regularly to continuously optimize the models. Moreover, when managing the air-conditioning operation according to the management suggestions, the user's feedback results can also be received, and the management suggestions can be adjusted according to the feedback results, so as to continuously optimize the generated management establishment. For example, if the current workshop humidity is too high, it is recommended to turn on the air-conditioning dehumidification mode; if the current outdoor and indoor temperatures are comfortable, it is recommended to turn off the air-conditioning or reduce the wind speed reminder.

[0070] Step S206, calculate the energy-saving benefit based on the air-conditioning operation data before and after the implementation of the control strategy, energy-saving strategy and management suggestions. For example, taking an air-conditioner that is not turned off at night as an example, if it is detected at 22:00 that no one is there but the air-conditioner is not turned off, the air-conditioner will be turned off, and the energy-saving benefit generated is the power consumption from 22:00 to 8:00 the next day, saving 10 hours. Then, based on the power consumption of all air-conditioners on that day divided by the time the air-conditioners are turned on, the average hourly power consumption of the air-conditioner on that day can be calculated, and multiplied by 10 hours of saved power consumption to get the power saved by the air-conditioner today, that is, the energy-saving benefit. The calculation of the energy-saving benefit provides a data basis for the adjustment and implementation of energy-saving strategies.

[0071] In this embodiment, a management and control system of a smart air conditioner is also provided, and the system includes: a controller, which is used to generate a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operation requirements of the air conditioner; control the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy; a sensor, which is used to obtain environmental data and operating data of the air conditioner; the controller is also used to analyze the environmental data and operating data to obtain analysis results; use a machine learning algorithm to process the analysis results, generate management suggestions for the air conditioner, and manage the operation of the air conditioner based on the management suggestions.

[0072] In an optional embodiment, when the air conditioner is a multi-split architecture consisting of one outdoor air conditioner and multiple indoor air conditioners, the controller includes a control system, a wireless terminal device, and a centralized control gateway arranged on the outdoor air conditioner. The control system is used to send control commands generated according to control strategies and energy-saving strategies to multiple indoor air conditioners in sequence through the wireless terminal device, the centralized control gateway, and the outdoor air conditioner.

[0073] Specifically, the control system can be used to generate control strategies and energy-saving strategies, and control the operation of the air conditioner based on the control strategies and energy-saving strategies and the data collected by the relevant sensors. In the process of controlling the air conditioner, the control system sends control commands to the wireless terminal device (DTU, Data Transfer unit) via 4G, and the wireless DTU sends the command to the centralized control gateway via 485 communication. After receiving the command, the centralized control gateway sends the control command to the air conditioner outdoor unit via 485 communication, and the air conditioner outdoor unit then sends the corresponding control command to the corresponding indoor unit, completing the multi-split architecture air conditioner control solution.

[0074] In an optional embodiment, when the air conditioner is a single-unit or wall-mounted air conditioner, the controller includes a control system and an air conditioner controller installed on the air conditioner plug-in board, and the control system is used to send control commands generated according to the control strategy and energy-saving strategy to the air conditioner through the air conditioner controller.

[0075] Specifically, the control system works in the same way as the control system in the above-mentioned embodiment. The air conditioner controller is a hardware device single-phase split air conditioner controller, which is a smart socket type installed on the air conditioner plug board. The top of the device has infrared and supports 4G communication. During the control process, the control system sends the communication command to the air conditioner controller via 4G, and the air conditioner controller sends the control command to the air conditioner via infrared to realize the single-body and wall-mounted architecture air conditioner control solution.

[0076] In an optional embodiment, the controller includes a control system, a control terminal, a collector and an intelligent micro-breaker, and the control system is used to send control commands generated according to control strategies and energy-saving strategies to the air conditioner through the control terminal, the collector and the intelligent micro-breaker.

[0077] Specifically, the control system works in the same way as the control system in the above embodiment. The control system is connected to the terminal via 4G and Ethernet, the terminal is connected to the collector via HPLC, and the collector is connected to the smart micro-breaker via Bluetooth, and the smart micro-breaker controls the power supply and the air conditioner.

[0078] In the present invention, the management and control system realizes seamless integration between air-conditioning equipment of different brands, models and architectures, that is, users can realize remote monitoring, centralized control and unified dispatch of all air-conditioning equipment in the park, which greatly improves management efficiency and convenience.

[0079] In this embodiment, a management and control device for a smart air conditioner is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0080] This embodiment provides a management and control device for a smart air conditioner. Figure 2 As shown, including:

[0081] A strategy generation module 21, for generating a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operation requirements of the air conditioner;

[0082] An operation control module 22, used to control the operation of the air conditioner in a preset area according to the control strategy and energy-saving strategy;

[0083] A data acquisition module 23 is used to acquire environmental data and air conditioner operation data;

[0084] The data analysis module 24 is used to analyze the environmental data and the operation data to obtain analysis results;

[0085] The management module 25 is used to process the analysis results using a machine learning algorithm, generate management suggestions for the air conditioner, and manage the operation of the air conditioner based on the management suggestions.

[0086] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0087] The embodiment of the present invention also provides a computer device having the above Figure 2 The management and control device of the smart air conditioner is shown.

[0088] See also Figure 3 , Figure 3 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 3As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0089] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0090] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0091] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of a computer device based on the presentation of a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0093] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0094] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0095] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0096] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A management and control system for a smart air conditioner, characterized in that: Applied to the smart park scenario, the system includes: A controller, configured to generate a control strategy and an energy-saving strategy for the air conditioner in a preset area according to the operation requirements of the air conditioner; and to control the operation of the air conditioner in the preset area according to the control strategy and the energy-saving strategy; Sensors for acquiring environmental data and air conditioner operation data; The controller is further used to analyze the environmental data and the operating data to obtain analysis results; process the analysis results using a machine learning algorithm to generate management suggestions for the air conditioner, and manage the operation of the air conditioner based on the management suggestions; When the air conditioner is a multi-split architecture consisting of one air conditioner outdoor unit and multiple air conditioner indoor units, the controller includes a control system, a wireless terminal device, and a centralized control gateway arranged on the air conditioner outdoor unit, and the control system is used to send control commands generated according to the control strategy and the energy-saving strategy to the multiple air conditioner indoor units in sequence through the wireless terminal device, the centralized control gateway, and the air conditioner outdoor unit; When the air conditioner is a single-unit or wall-mounted air conditioner, the controller includes a control system and an air conditioner controller installed on the air conditioner plug-in board, and the control system is used to send the control command generated according to the control strategy and the energy-saving strategy to the air conditioner through the air conditioner controller; Alternatively, the controller includes a control system, a control terminal, a collector and an intelligent micro-break, and the control system is used to send a control command generated according to a control strategy and an energy-saving strategy to the air conditioner through the control terminal, the collector and the intelligent micro-break; The environmental data includes indoor temperature, indoor humidity, outdoor temperature and light intensity. The environmental data and operation data are analyzed to obtain analysis results, including: Analyze the environmental data based on a correlation analysis algorithm to obtain a first analysis result; Analyze the impact of environmental data on operating data based on a covariance analysis algorithm to obtain a second analysis result; Performing time series analysis on the environmental data and the operating data to obtain a third analysis result; The analysis results are processed using a machine learning algorithm to generate management recommendations for the air conditioner, including: An integrated learning algorithm is used to analyze the environmental data, predict environmental data for future periods, and generate air conditioning adjustment management suggestions; Processing the third analysis result using a deep learning algorithm to generate management suggestions for air conditioning operation modes corresponding to different environmental modes; Using an association rule mining algorithm to process the first analysis result and the second analysis result to generate an air conditioning adjustment management suggestion or an energy consumption reduction management suggestion; The controller is also used to calculate energy-saving benefits based on air-conditioning operation data before and after the implementation of the control strategy, energy-saving strategy and management suggestions.

2. A management and control method for a smart air conditioner based on the management and control system of the smart air conditioner according to claim 1, characterized in that: Applied to the smart park scenario, the method includes: Generate control strategies and energy-saving strategies for air conditioners in preset areas according to the operating requirements of air conditioners; Controlling the operation of the air conditioner in the preset area according to the control strategy and energy-saving strategy; Obtain environmental data and air conditioning operation data; Analyze the environmental data and operating data to obtain analysis results; The analysis results are processed using a machine learning algorithm to generate management suggestions for the air conditioner, and the operation of the air conditioner is managed based on the management suggestions.

3. The method according to claim 2, characterized in that Generate control strategies and energy-saving strategies for air conditioners in preset areas based on air conditioner operation requirements, including: Generate the operation date of the air conditioner in the preset area and the operation time period within the operation date according to the operation demand of the air conditioner; Generate prohibited operation time periods and prohibited shutdown time periods of the air conditioners in multiple preset areas according to the operation requirements of the air conditioners, wherein the operation dates, operation time periods, prohibited operation time periods and prohibited shutdown time periods constitute a control strategy; A temperature limit strategy, an unmanned shutdown strategy, a timeout shutdown strategy, a window closing reminder strategy, a temperature control strategy and a door opening reminder strategy for the air conditioner in a preset area are generated according to the operating requirements of the air conditioner. The temperature limit strategy, the unmanned shutdown strategy, the timeout shutdown strategy, the window closing reminder strategy, the temperature control strategy and the door opening reminder strategy constitute an energy-saving strategy.

4. The method according to claim 3, characterized in that: Controlling the operation of the air conditioner in the preset area according to the control strategy includes: Controlling the start of the air conditioner in the preset area according to the operation date and operation time period; During the prohibited operation period, determine whether the air conditioner in the corresponding preset area is running, and turn off the running air conditioner; During the prohibited shutdown period, determine whether the air conditioner in the corresponding preset area is turned off, and turn on the turned-off air conditioner.

5. The method according to claim 3, characterized in that: Controlling the operation of the air conditioner in the preset area according to the energy-saving strategy includes: Determine whether the air conditioner temperature running in the preset area is within the preset temperature range according to the temperature limit strategy, and adjust the air conditioner temperature if it exceeds the preset temperature range; Determine whether there is anyone in the preset area according to the no-person shutdown strategy, and shut down the running air conditioner when no one is there; Determine whether the air conditioner operation time reaches a first preset time according to the timeout shutdown strategy, and shut down the running air conditioner when the first preset time is reached; Determining whether the temperature change is less than a preset degree after the air conditioner runs for a second preset time according to the window closing reminder strategy, and generating a window closing reminder when the temperature change is less than the preset degree; When the air conditioner is in cooling or heating mode, it is determined according to the temperature control strategy whether the temperature of the preset area meets the preset conditions, and the running air conditioner is turned off when the preset conditions are met; It is determined according to the door opening prompt strategy whether the door opening reaches a third preset time when the air conditioner is running, and a door opening prompt is generated after the third preset time is reached.

6. A management and control device for a smart air conditioner based on the management and control system of the smart air conditioner according to claim 1, characterized in that: Applied to the smart park scenario, the device includes: A strategy generation module, used to generate a control strategy and energy-saving strategy for air conditioners in a preset area according to the operation requirements of the air conditioners; An operation control module, used to control the operation of the air conditioner in a preset area according to the control strategy and energy-saving strategy; A data acquisition module, used to acquire environmental data and air conditioner operation data; A data analysis module, used to analyze the environmental data and operation data to obtain analysis results; A management module is used to process the analysis results using a machine learning algorithm, generate management suggestions for the air conditioner, and manage the operation of the air conditioner based on the management suggestions.

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