CN116085937B - Intelligent central air conditioner energy-saving control method and system - Google Patents
Intelligent central air conditioner energy-saving control method and system Download PDFInfo
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- CN116085937B CN116085937B CN202310380267.7A CN202310380267A CN116085937B CN 116085937 B CN116085937 B CN 116085937B CN 202310380267 A CN202310380267 A CN 202310380267A CN 116085937 B CN116085937 B CN 116085937B
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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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- 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
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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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
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
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Abstract
The invention discloses an intelligent central air conditioner energy-saving control method and system, relates to the technical field of energy-saving control, and solves the technical problem that in the prior art, only the measured temperature in a room is used as a control basis of a central air conditioner, so that the control of the central air conditioner is delayed and the energy consumption is increased; according to the invention, simulation control is performed based on standard input data under the optimal working condition of the central air conditioner, and standard output data is obtained according to a simulation result; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; the invention determines the air conditioner control duration of the central air conditioner under various conditions through the simulation process, and ensures the accuracy and applicability of the duration prediction model; the method comprises the steps of inputting a duration prediction sequence into a duration prediction model to obtain corresponding air conditioner control duration; the method predicts the operation time of the central air conditioner under the optimal working condition under each practical condition based on the time prediction model, and realizes accurate and efficient control of the central air conditioner.
Description
Technical Field
The invention belongs to the field of energy-saving control, relates to an energy-saving control technology of a central air conditioner, and particularly relates to an intelligent central air conditioner energy-saving control method and system.
Background
The central air conditioner consists of one or more cold and heat source systems and a plurality of air conditioning systems, wherein a plurality of devices are involved, the thermal efficiency models of the devices are different, and the energy-saving optimization control variables are also more, so that the central air conditioner is difficult to operate under the most efficient working condition.
The control system of the present central air conditioner collects real-time temperature in a room through a temperature sensor, compares the real-time temperature with a set temperature and adjusts the temperature through a refrigerating module or a heating module; the real-time temperature acquisition points are not distributed comprehensively, so that a certain error exists between the feedback numerical value and the actual value in the room, delay exists in control of the central air conditioner, and the energy consumption is further improved; therefore, there is a need for an intelligent central air conditioner energy-saving control method and system.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides an intelligent central air conditioner energy-saving control method and system, which are used for solving the technical problem that the control of the central air conditioner is delayed and the energy consumption is increased because the actual measured temperature in a room is taken as a control basis of the central air conditioner in the prior art.
In order to achieve the above object, a first aspect of the present invention provides an intelligent central air conditioner energy saving control system, which includes a central processing module, and a data acquisition module and an air conditioner control module connected with the central processing module; the central processing module sets standard input data, performs simulation control based on the standard input data under the optimal working condition of the central air conditioner, obtains optimal time length according to a simulation result, and marks the optimal time length as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; the standard input data comprise control temperature differences of each simulated room and pipeline lengths; the central processing module acquires control temperature differences of target rooms through a data sensor connected with the data acquisition module, calculates the length of a pipeline according to the position of each target room, and generates a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration; the central processing module generates an air conditioner control instruction according to the air conditioner control time length; the air conditioner control module controls the central air conditioner according to the received air conditioner control instruction; the air conditioner control command is periodically generated and sent.
The central processing module is respectively communicated and/or electrically connected with the data acquisition module and the air conditioner control module; the central processing module is mainly responsible for simulation and data analysis, and performs data interaction with the data acquisition module and the air conditioner control module. The data acquisition module acquires indoor and outdoor temperatures through a data sensor according to a set period (such as five minutes or one minute) and takes the indoor and outdoor temperatures as a data base for controlling a central air conditioner; the data sensor is mainly a temperature sensor and also comprises parameters which can be regulated by other central air conditioners, such as humidity and the like, if necessary. The air conditioner control module controls the central air conditioner according to the series of analysis results, and the parameters controlled by the air conditioner control module are mainly the operation working condition and the operation time length.
It should be noted that, the simulated room in the invention is modeled according to the actual room, that is, the building responsible for the central air conditioner and the room in the loophole are acquired first, and the three-dimensional model is constructed according to the relative positions of the rooms and the heating module of the central air conditioner refrigerating module. The optimal working condition of the central air conditioner is a state that the energy consumption and the temperature control efficiency are balanced, so that the refrigeration or heating efficiency can be ensured, and the energy consumption can be controlled.
Preferably, the hub processing module sets standard input data, including: extracting the pipeline length of each simulated room covered by the central air conditioner, and setting a plurality of groups of control temperature differences; randomly selecting a plurality of simulated rooms, randomly matching control temperature differences and calculating pipeline lengths for the selected simulated rooms, and generating a room data set; several room data sets are integrated into standard input data.
The standard input data is the basis of simulation, and comprises randomly selected simulation rooms, and corresponding characteristic parameters of each simulation room, such as pipeline length and control temperature difference. The control temperature difference is to set multiple groups of data according to the set region of the central air conditioner and is used for expressing the difference value (sign is needed to distinguish) between the corresponding set temperature of the simulated room and the external temperature. The pipe length refers to the length of the pipe that is fed to each simulated room after the cooling medium or heating medium is processed by the central air conditioner, because the pipe length affects the control fineness of the simulated room to some extent, and it is required to take it into consideration in temperature control.
The central air conditioner can regulate the temperature of a plurality of simulated rooms at the same time, so that different room combinations (the simulated rooms can be replaced by unique identifiers) are arranged, and the simulated rooms in each room combination are correspondingly provided with control temperature differences to simulate actual conditions. It should be noted that the pipe length may be the pipe length corresponding to each simulated room, or may be the non-coincident pipe total length corresponding to all the selected simulated rooms.
The selected simulated rooms (unique identifiers) are integrated and spliced with the control temperature difference randomly matched for each simulated room and the set pipeline length to generate a room data set. Each room data set represents one control situation of the central air conditioner, so it is necessary to randomly set as many room data sets as possible to ensure that most or all situations of the central air conditioner can be covered.
Preferably, the performing simulation control based on standard input data under the optimal working condition of the central air conditioner, obtaining an optimal time length according to a simulation result, and marking the optimal time length as standard output data includes: extracting room data groups in standard input data one by one; the simulated central air conditioner operates under the optimal working condition, and temperature control is carried out on each simulated room in the room data set according to the control temperature difference; judging whether the temperature distribution characteristics of each simulated room in the room data set meet the requirements; if yes, stopping simulation, extracting simulation time length and acquiring standard output data; and if not, continuing simulation.
And (3) performing primary simulation on each room data set in the standard input data, namely extracting one room data set, identifying a plurality of simulated rooms and corresponding control temperature differences in the room data set, simulating the control of the central air conditioner on the plurality of simulated rooms according to the control temperature differences, and recording the simulation time length corresponding to the room data set as the optimal time length, namely the component part of the standard output data when the temperature distribution characteristics of all the simulated rooms meet the requirements.
According to the invention, a plurality of groups of standard input data and corresponding standard output data can be obtained in the mode, so that training of the artificial intelligent model can be realized.
Preferably, the determining whether the temperature distribution characteristics of each simulated room in the room data set meet the requirements includes: acquiring a temperature average value of a simulated room; judging whether the temperature average value is consistent with the target temperature; if yes, the next step is carried out; if not, judging that the temperature distribution characteristics do not meet the requirements; acquiring a maximum value and a minimum value of the temperature in the simulated room; respectively taking the maximum temperature value and the minimum temperature value as centers to define a maximum temperature region and a minimum temperature region; judging whether the average temperature difference of the two areas is smaller than a temperature difference threshold value or not; if yes, the temperature distribution characteristics are judged to be satisfactory.
The invention judges whether the temperature distribution characteristics of the simulated room meet the requirements mainly from two angles, one is whether the temperature average value in the simulated room has smaller difference with the target temperature, and the other is whether the temperature distribution in the simulated room has larger difference. The temperature distribution differentiation is mainly judged by analyzing the temperature average value of the temperature maximum area and the temperature minimum area in the simulated room, so that the influence on the user experience caused by the overlarge temperature difference of the two areas in the room is avoided.
Preferably, the training the artificial intelligence model through the standard input data and the standard output data to obtain the duration prediction model includes: integrating the plurality of standard input data into model input data, and integrating the plurality of standard output data into model output data; constructing an artificial intelligent model based on the BP neural network model or the RBF neural network model; training the artificial intelligent model through model input data and model output data, marking the trained artificial intelligent model as a duration prediction model, and updating and storing in time.
Preferably, the inputting the duration prediction sequence to the duration prediction model to obtain the corresponding air conditioner control duration includes: acquiring control temperature differences of all target rooms according to a set period, and splicing and generating a duration prediction sequence by combining the lengths of the pipelines corresponding to all the target rooms; the duration prediction sequence is consistent with the content attribute of the standard input data; and inputting the duration prediction sequence into a duration prediction model to obtain an output value, and marking the output value as the air conditioner control duration.
Preferably, the central processing module generates an air conditioner control instruction according to the air conditioner control time length and sends the air conditioner control instruction to the air conditioner control module; the air conditioner control module controls the central air conditioner to run under the optimal working condition based on the air conditioner control instruction.
The air conditioner control command is generated regularly according to the set period, the air conditioner control module compares the air conditioner control command with the air conditioner control command which is being executed after receiving the new air conditioner control command, and if the air conditioner control command is inconsistent, the air conditioner control module controls the air conditioner control module according to the new air conditioner control command. If there is no executing air conditioner control command, the control is also performed according to the new air conditioner control command.
The second aspect of the invention provides an intelligent central air conditioner energy-saving control method, which comprises the following steps: setting standard input data, performing simulation control based on the standard input data under the optimal working condition of the central air conditioner, acquiring optimal time length according to a simulation result, and marking the optimal time length as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; acquiring control temperature differences of target rooms, calculating the length of a pipeline according to the positions of the target rooms, and generating a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration; and generating an air conditioner control instruction according to the air conditioner control time length, and controlling the central air conditioner according to the received air conditioner control instruction.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, simulation control is performed based on standard input data under the optimal working condition of the central air conditioner, and the optimal time length is obtained according to a simulation result and marked as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; the invention determines the air conditioner control duration of the central air conditioner under various conditions through the simulation process, and ensures the accuracy and applicability of the duration prediction model.
2. The method comprises the steps of obtaining control temperature differences of target rooms, calculating the length of a pipeline according to the positions of the target rooms, and generating a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration; the method predicts the operation time of the central air conditioner under the optimal working condition under each practical condition based on the time prediction model, and realizes accurate and efficient control of the central air conditioner.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system principle of the present invention;
FIG. 2 is a schematic diagram of the method steps of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, an embodiment of a first aspect of the present invention provides an intelligent central air conditioner energy saving control system, which includes a central processing module, and a data acquisition module and an air conditioner control module connected with the central processing module; the central processing module sets standard input data, performs simulation control based on the standard input data under the optimal working condition of the central air conditioner, obtains optimal time length according to a simulation result, and marks the optimal time length as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; the central processing module acquires control temperature differences of target rooms through a data sensor connected with the data acquisition module, calculates the length of a pipeline according to the position of each target room, and generates a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration; the central processing module generates an air conditioner control instruction according to the air conditioner control time length; the air conditioner control module controls the central air conditioner according to the received air conditioner control instruction.
Next, the present embodiment describes the technical solution of the present invention in detail with specific examples.
The first step in this embodiment is to set standard input data and to simulate the acquisition of corresponding standard output data. Extracting the pipeline length of each simulated room covered by the central air conditioner, and setting a plurality of groups of control temperature differences; randomly selecting a plurality of simulated rooms, randomly matching control temperature differences and calculating pipeline lengths for the selected simulated rooms, and generating a room data set; several room data sets are integrated into standard input data.
Assuming that the simulated rooms covered by the central air conditioner are F1, F2, … and Fn (n is a positive integer and can be understood as the serial number of the simulated room), and the lengths of the corresponding pipelines of the simulated rooms are G1, G2, … and Gn; it is also necessary to set several sets of control temperature differences, for example, in the range of 0 ℃ to 20 ℃, one control temperature difference is set for each 2 ℃, and several sets of control temperature differences can be obtained.
Selecting a plurality of simulated rooms, randomly matching a control temperature difference for each simulated room, and splicing the simulated rooms into basic data such as [ F1, (24, 22), G1], (24, 22) as the control temperature difference by combining the pipeline length G1, and particularly adjusting the temperature from 24 ℃ to 22 ℃; each simulated room can correspond to a plurality of pieces of basic data, a plurality of simulated rooms are integrated corresponding to one piece of basic data to form a room data set, and a plurality of room data sets are integrated into standard input data. Each room data set can be understood as a control case of the central air conditioner, and each control case at least comprises a plurality of variables such as the number of simulated rooms, control temperature difference, pipeline length and the like.
The working condition of the central air conditioner is simulated based on each room data set in the standard input data, for example, the central air conditioner simulates each simulated room under the optimal working condition, the requirement is that the temperature of each simulated room is respectively regulated according to the control temperature difference, and when the temperature distribution characteristics of all the simulated rooms meet the requirement, the simulated time length of the room data set, namely the optimal time length, is recorded, and the optimal time length is used as a part of the standard output data. It is noted that when all the room data sets in the standard input data are simulated to obtain the corresponding optimal time length, all the optimal time lengths are integrated under the condition of considering the association relation between the room data sets and the optimal time length, and the standard output data are obtained.
Training of the artificial intelligent model can be completed through the constructed standard input data and standard output data, and a duration prediction model is obtained. It should be noted that, to ensure stability and applicability of the duration prediction model, the number of standard input data and standard output data should be increased as much as possible.
The second step of this embodiment is to obtain control parameters of each target room according to the data sensor, and generate a duration prediction sequence according to the control parameters. Acquiring control temperature differences of all target rooms according to a set period, and splicing and generating a duration prediction sequence by combining the lengths of the pipelines corresponding to all the target rooms; and inputting the duration prediction sequence into a duration prediction model to obtain an output value, and marking the output value as the air conditioner control duration.
The target rooms are in one-to-one correspondence with the simulated rooms, and unique identifiers can be shared in the duration prediction sequence and the standard input data. And marking the room needing to be temperature-regulated as a target room, acquiring the difference value of the set temperature and the outdoor temperature of each target room, calculating the control temperature difference, simultaneously reading the length of the pipeline corresponding to each target room, and generating a duration prediction sequence according to the splicing sequence of the standard input data.
The control time length, namely the optimal time length corresponding to the current state, can be obtained by inputting the time length prediction sequence into the time length prediction model, and an air conditioner control instruction is generated according to the optimal time length, so that the temperature adjustment of each target room is completed.
In the embodiment, data is collected according to a set period, that is, an air conditioner control instruction is regenerated in each period of time; generating an air conditioner control command if 5 minutes; of course, in other preferred embodiments, it is also possible to detect whether the central air conditioner has completed the previous air conditioner control command, and if so, automatically execute the next air conditioner control command generation step.
An embodiment of a second aspect of the present invention provides an intelligent central air conditioner energy saving control method, including: setting standard input data, performing simulation control based on the standard input data under the optimal working condition of the central air conditioner, acquiring optimal time length according to a simulation result, and marking the optimal time length as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; acquiring control temperature differences of target rooms, calculating the length of a pipeline according to the positions of the target rooms, and generating a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration; and generating an air conditioner control instruction according to the air conditioner control time length, and controlling the central air conditioner according to the received air conditioner control instruction.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (5)
1. The intelligent central air-conditioning energy-saving control system comprises a central processing module, and a data acquisition module and an air-conditioning control module which are connected with the central processing module; the method is characterized in that:
the central processing module sets standard input data, performs simulation control based on the standard input data under the optimal working condition of the central air conditioner, obtains optimal time length according to a simulation result, and marks the optimal time length as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model; the standard input data comprise control temperature differences of each simulated room and pipeline lengths;
the central processing module acquires control temperature differences of target rooms through a data sensor connected with the data acquisition module, calculates the length of a pipeline according to the position of each target room, and generates a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration;
the central processing module generates an air conditioner control instruction according to the air conditioner control time length; the air conditioner control module controls the central air conditioner according to the received air conditioner control instruction; the air conditioner control instruction is periodically generated and sent;
the hub processing module sets standard input data, including:
extracting the pipeline length of each simulated room covered by the central air conditioner, and setting a plurality of groups of control temperature differences;
randomly selecting a plurality of simulated rooms, randomly matching control temperature differences and calculating pipeline lengths for the selected simulated rooms, and generating a room data set; integrating a plurality of room data sets into standard input data;
the simulation control is performed based on standard input data under the optimal working condition of the central air conditioner, the optimal time length is obtained according to the simulation result, and the optimal time length is marked as standard output data, and the method comprises the following steps:
extracting room data groups in standard input data one by one; the simulated central air conditioner operates under the optimal working condition, and temperature control is carried out on each simulated room in the room data set according to the control temperature difference;
judging whether the temperature distribution characteristics of each simulated room in the room data set meet the requirements; if yes, stopping simulation, extracting simulation time length and acquiring standard output data; if not, continuing simulation;
the determining whether the temperature distribution characteristics of each simulated room in the room data set meet the requirements comprises:
acquiring a temperature average value of a simulated room; judging whether the temperature average value is consistent with the target temperature; if yes, the next step is carried out; if not, judging that the temperature distribution characteristics do not meet the requirements;
acquiring a maximum value and a minimum value of the temperature in the simulated room; respectively taking the maximum temperature value and the minimum temperature value as centers to define a maximum temperature region and a minimum temperature region; judging whether the average temperature difference of the two areas is smaller than a temperature difference threshold value or not; if yes, the temperature distribution characteristics are judged to be satisfactory.
2. The intelligent central air conditioning energy saving control system according to claim 1, wherein the training the artificial intelligence model to obtain the duration prediction model by the standard input data and the standard output data comprises:
integrating the plurality of standard input data into model input data, and integrating the plurality of standard output data into model output data; constructing an artificial intelligent model based on the BP neural network model or the RBF neural network model;
training the artificial intelligent model through model input data and model output data, marking the trained artificial intelligent model as a duration prediction model, and updating and storing in time.
3. The intelligent central air conditioner energy saving control system according to claim 1, wherein the inputting the duration prediction sequence to the duration prediction model to obtain the corresponding air conditioner control duration comprises:
acquiring control temperature differences of all target rooms according to a set period, and splicing and generating a duration prediction sequence by combining the lengths of the pipelines corresponding to all the target rooms; the duration prediction sequence is consistent with the content attribute of the standard input data;
and inputting the duration prediction sequence into a duration prediction model to obtain an output value, and marking the output value as the air conditioner control duration.
4. The intelligent central air-conditioning energy-saving control system according to claim 1, wherein the central processing module generates an air-conditioning control instruction according to the air-conditioning control duration and sends the air-conditioning control instruction to the air-conditioning control module;
the air conditioner control module controls the central air conditioner to run under the optimal working condition based on the air conditioner control instruction.
5. An intelligent central air conditioner energy-saving control method based on the operation of the intelligent central air conditioner energy-saving control system as claimed in any one of claims 1 to 4, comprising:
setting standard input data, performing simulation control based on the standard input data under the optimal working condition of the central air conditioner, acquiring optimal time length according to a simulation result, and marking the optimal time length as standard output data; training an artificial intelligent model through standard input data and standard output data to obtain a duration prediction model;
acquiring control temperature differences of target rooms, calculating the length of a pipeline according to the positions of the target rooms, and generating a duration prediction sequence; inputting a duration prediction sequence into a duration prediction model to obtain a corresponding air conditioner control duration;
and generating an air conditioner control instruction according to the air conditioner control time length, and controlling the central air conditioner according to the received air conditioner control instruction.
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