Air treatment equipment control method and device, storage medium and air treatment equipment

Abstract

The invention discloses an air treatment equipment control method and device, a storage medium and air treatment equipment, and belongs to the technical field of air conditioners. The method comprises the following steps: acquiring current environment state parameters; the fresh air fan rotating speed of the fresh air fan is determined according to the current environment state parameters; and the air treatment equipment is controlled to operate based on the rotating speed of the fresh air fan, different use working conditions and scenes can be coped with through the mode, the optimal rotating speed of the fresh air fan is automatically determined, and the operation energy consumption of the multi-split air conditioner and the fresh air system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioners, and particularly to a control method, device, storage medium and air treatment device for an air treatment device. Background Art

[0002] In a multi-connected air conditioner with fresh air system, when the outdoor temperature is lower than the indoor temperature, the means to reduce the system energy consumption include: 1) reducing the rotational speed of the fresh air fan, resulting in a decrease in the fan energy consumption; 2) increasing the supply air volume of the low-temperature fresh air, resulting in a decrease in the energy consumption of the multi-connected air conditioner. However, these two means are contradictory. To increase the fresh air supply volume, the rotational speed of the fresh air fan must be increased. Currently, the rotational speed of the fresh air fan depends on manual adjustment by the user, so it is impossible to find the optimal rotational speed of the fresh air fan between the fan energy consumption and the energy consumption of the multi-connected air conditioner.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main object of the present invention is to provide a control method, device, storage medium and air treatment device for an air treatment device, aiming to solve the technical problem that the prior art cannot find the optimal rotational speed of the fresh air fan between the fan energy consumption and the energy consumption of the multi-connected air conditioner.

[0005] To achieve the above object, the present invention provides a control method for an air treatment device, the control method for the air treatment device comprising the following steps:

[0006] Obtain current environmental state parameters, the current environmental state parameters including indoor environmental parameters and outdoor environmental parameters;

[0007] Determine the rotational speed of the fresh air fan according to the current environmental state parameters; and,

[0008] Control the air treatment device to operate based on the rotational speed of the fresh air fan.

[0009] Optionally, the determining the rotational speed of the fresh air fan according to the current environmental state parameters includes:

[0010] Obtain the expected reward table corresponding to the air treatment device; and,

[0011] Select the rotational speed of the fresh air fan from the expected reward table based on the current environmental state parameters.

[0012] Optionally, after controlling the air treatment device to operate based on the rotational speed of the fresh air fan, further includes:

[0013] Obtain the operating power consumption and the indoor carbon dioxide concentration when the operating duration of the air treatment device reaches a preset duration;

[0014] Calculate a reward value based on the operating power consumption and the indoor carbon dioxide concentration; and,

[0015] Update the expected reward table based on the reward value.

[0016] Optionally, the calculating the reward value based on the operating power consumption and the indoor carbon dioxide concentration includes:

[0017] Compare the indoor carbon dioxide concentration with a set concentration;

[0018] Determine an additional penalty value based on the comparison result; and,

[0019] Calculate the reward value according to the weight coefficients corresponding to the operating power consumption and the additional penalty value and preset parameters.

[0020] Optionally, the obtaining the operating power consumption and the indoor carbon dioxide concentration when the operating duration of the air handling device reaches a preset duration includes:

[0021] Judge whether a learning condition is satisfied; and,

[0022] If the learning condition is satisfied, obtain the operating power consumption and the indoor carbon dioxide concentration when the operating duration of the air handling device reaches a preset duration.

[0023] Optionally, the judging whether the learning condition is satisfied includes:

[0024] Compare the outdoor temperature during the operation of the air handling device with a temperature threshold, where the temperature threshold is determined by a set temperature and / or the indoor temperature during the operation of the air handling device;

[0025] When the air handling device is in the cooling mode, if the outdoor temperature is less than the temperature threshold, it is determined that the learning condition is satisfied; and,

[0026] When the air handling device is in the heating mode, if the outdoor temperature is greater than the temperature threshold, it is determined that the learning condition is satisfied.

[0027] Optionally, each set of environmental parameters in the expected reward table corresponds to multiple fresh air fan speeds, and the selection probability of each fresh air fan speed is related to the magnitude of the corresponding reward value.

[0028] In addition, to achieve the above object, the present invention also proposes a control device for an air handling device, and the control device for the air handling device includes:

[0029] A detection module for obtaining current environmental state parameters, where the current environmental state parameters include indoor environmental parameters and outdoor environmental parameters;

[0030] A reading module, configured to determine the fresh air fan speed of the fresh air fan according to the current environmental state parameter; and,

[0031] A control module, configured to control the air handling device to operate based on the fresh air fan speed.

[0032] In addition, to achieve the above object, the present invention further provides an air handling device, which includes: a memory, a processor, and an air handling device control program stored on the memory and running on the processor. The air handling device control program is configured to implement the air handling device control method as described above.

[0033] In addition, to achieve the above object, the present invention further provides a storage medium, on which an air handling device control program is stored. When the air handling device control program is executed by a processor, it implements the air handling device control method as described above.

[0034] The present invention obtains the current environmental state parameter; determines the fresh air fan speed of the fresh air fan according to the current environmental state parameter; and controls the air handling device to operate based on the fresh air fan speed. Through the above method, it can cope with different usage conditions and scenarios, automatically determine the optimal speed of the fresh air fan, and reduce the operating energy consumption of the multi-split air conditioner and the fresh air system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic structural diagram of an air handling device in a hardware operating environment related to the embodiment of the present invention;

[0036] Figure 2 is a schematic flowchart of the first embodiment of the air handling device control method of the present invention;

[0037] Figure 3 is a schematic flowchart of the overall solution of the air handling device control method of the present invention;

[0038] Figure 4 is a schematic flowchart of the second embodiment of the air handling device control method of the present invention;

[0039] Figure 5 is a schematic block diagram of the first embodiment of the air handling device control device of the present invention.

[0040] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] Reference Figure 1 , Figure 1 is a schematic structural diagram of an air treatment device for the hardware operating environment involved in the solution of the embodiment of the present invention.

[0043] As Figure 1 shown, the air treatment device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0044] Those skilled in the art can understand that Figure 1 the structure shown in

[0045] does not constitute a limitation on the air treatment device, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Figure 1 As

[0046] shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and an air treatment device control program. Figure 1 In the air treatment device shown in

[0047] the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the air treatment device of the present invention may be provided in the air treatment device. The air treatment device calls the air treatment device control program stored in the memory 1005 through the processor 1001 and executes the air treatment device control method provided by the embodiment of the present invention. Figure 2 , Figure 2Schematic flowchart of the first embodiment of a control method for an air handling device according to the present invention.

[0048] In this embodiment, the air handling device control method includes the following steps:

[0049] Step S10: Obtain the current environmental state parameters.

[0050] In this embodiment, the execution subject of this embodiment can be the air handling device. This air handling device has functions such as data processing, data communication, and program operation. The air handling device can be a controller inside an air conditioner. Of course, it can also be other devices with similar functions, and this embodiment does not limit this. For ease of explanation, this embodiment is described by taking the air handling device as an example.

[0051] It should be noted that in a multi-split air conditioner with fresh air system, when the outdoor temperature is lower than the indoor temperature, the means to reduce the system energy consumption include 1) reducing the rotational speed of the fresh air fan, resulting in a reduction in fan energy consumption, and 2) increasing the supply air volume of low-temperature fresh air, resulting in a reduction in the energy consumption of the multi-split air conditioner. However, these two means are contradictory. To increase the fresh air supply volume, the rotational speed of the fresh air fan must be increased. And the rotational speed of the existing fresh air fan depends on manual adjustment by the user. Therefore, it is impossible to find the optimal rotational speed of the fresh air fan between the fan energy consumption and the multi-split air conditioner energy consumption.

[0052] In this embodiment, to solve the above technical problems, by obtaining the current environmental state parameters; determining the rotational speed of the fresh air fan based on the current environmental state parameters; and controlling the air handling device to operate based on the rotational speed of the fresh air fan, through the above method, it is possible to cope with different usage conditions and scenarios, automatically determine the optimal rotational speed of the fresh air fan, and reduce the operating energy consumption of the multi-split air conditioner and the fresh air system. Through the above method, it is possible to cope with different usage conditions and scenarios, automatically determine the optimal rotational speed of the fresh air fan, and reduce the operating energy consumption of the multi-split air conditioner and the fresh air system. Specifically, it can be implemented in the following manner.

[0053] In this embodiment, the fresh air fan of this embodiment includes a fresh air duct and a fresh air fan. The fresh air duct communicates the indoor space and the outdoor environment. The fresh air fan is arranged in the fresh air duct and is used to drive outdoor fresh air into the indoor space. In addition, the fresh air fan of this embodiment may further include an exhaust duct and an exhaust fan. The exhaust duct communicates the indoor space and the outdoor environment. The exhaust fan is arranged in the exhaust duct and is used to drive indoor air out to the outdoor environment. The exhaust fan can also be adjusted in the same way, or the rotational speed of the exhaust fan and the rotational speed of the fresh air fan can be adjusted. Further, in this embodiment, Figure 3 is taken as an example to illustrate the overall process of this solution. Refer to Figure 3As shown, Step 1: The algorithm obtains the environmental state S, including at least one of indoor temperature, indoor humidity, indoor enthalpy value, outdoor temperature, outdoor humidity, outdoor enthalpy value, indoor set temperature, indoor set humidity, and indoor set enthalpy value. Step 2: Based on the environmental state S, it is judged whether to enter the learning algorithm. Step 3: The algorithm selects and executes the best rotational speed action A of the fresh air fan in the current experience library. Step 4: Obtain the feedback reward and penalty data of the algorithm. Step 5: Update the experience database according to the machine learning algorithm. Step 6: Judge whether the current environmental state S meets the condition for continuing to execute the algorithm. If so, repeat to Step 2. If not, exit the learning algorithm. Among them, in Step 1, the environmental state can also include the change rate of indoor and outdoor temperatures, indoor and outdoor humidity, the current moment, the frequency of the multi-connected unit compressor, the cooling and heating states of the multi-connected unit, the on / off state of the indoor unit of the multi-connected unit, weather forecast information, indoor personnel information, indoor CO2 concentration, etc. In Step 2, the judgment conditions include that under the cooling condition, the outdoor temperature is lower than a certain threshold of the indoor temperature; under the heating condition, the outdoor temperature is higher than a certain threshold of the indoor temperature. In Step 4, the reward data of the algorithm is a formula related to the energy consumption Q of the fresh air and multi-connected system within the time period t, or related to the energy consumption Q' at the previous and subsequent moments t'. The penalty data of the algorithm is a formula related to the indoor CO2 concentration. In Step 5, the machine learning algorithm used is the reinforcement learning algorithm.

[0054] In a specific implementation, in this embodiment, it is necessary to first obtain the current environmental state parameters, and the current environmental state parameters at least include indoor environmental parameters and outdoor environmental parameters, such as the change rate of indoor and outdoor temperatures, indoor and outdoor humidity, the current moment, the frequency of the multi-connected unit compressor, the cooling and heating states of the multi-connected unit, the on / off state of the indoor unit of the multi-connected unit, weather forecast information, indoor personnel information, and indoor CO2 concentration.

[0055] Step S20: Determine the rotational speed of the fresh air fan according to the current environmental state parameters.

[0056] In a specific implementation, the current environmental state parameters determine different operating conditions and scenarios of the air handling equipment. Based on the current environmental state parameters, the rotational speed of the fresh air fan can be obtained, that is, the best rotational speed of the fresh air fan under different operating conditions and scenarios. Specifically, the rotational speed of the fresh air fan can be retrieved from the expected reward table according to the obtained current environmental state parameters. Among them, the expected reward table contains the reward values corresponding to different rotational speeds of the fresh air fan under different environmental state parameters. When selecting, the rotational speed of the fresh air fan with the largest reward value can be selected. Further, the expected reward table can also set the selection probability for different rotational speeds of the fresh air fan. Based on this selection probability, the rotational speed of the fresh air fan is automatically selected. The selection probability is related to the corresponding reward value, and the larger the reward value, the higher the corresponding selection probability. The specific corresponding relationship can be set according to actual needs, and this embodiment does not limit this.

[0057] Step S30: Control the air handling device to operate based on the fresh air fan speed.

[0058] In a specific implementation, after obtaining the fresh air fan speed from the expected reward table, controlling the air handling device to operate according to the fresh air fan speed can reduce the operating energy consumption of the multi-connected unit and the fresh air system. It should be noted that in this embodiment, the speed of the fresh air fan includes the air supply speed and the exhaust speed of the fresh air fan.

[0059] This embodiment obtains the current environmental state parameters; determines the fresh air fan speed of the fresh air fan according to the current environmental state parameters; and controls the air handling device to operate based on the fresh air fan speed. Through the above method, it can cope with different usage conditions and scenarios, automatically determine the optimal speed of the fresh air fan, and reduce the operating energy consumption of the multi-connected unit and the fresh air system.

[0060] Reference Figure 4 , Figure 4 is a schematic flowchart of the second embodiment of a control method for an air handling device according to the present invention.

[0061] Based on the above first embodiment, in the control method of the air handling device in this embodiment, the step S20 specifically includes:

[0062] Step S201: Obtain the expected reward table corresponding to the air handling device.

[0063] In a specific implementation, the optimal speed of the fresh air fan can be queried from the expected reward table. Therefore, in this embodiment, it is necessary to first obtain the expected reward table. The expected reward table contains the reward values corresponding to different fresh air fan speeds under different environmental state parameters. When selecting, the fresh air fan speed with the largest reward value can be selected. Further, the expected reward table can also set the selection probability for different fresh air fan speeds, and the fresh air fan speed can be automatically selected based on the selection probability. The selection probability is related to the corresponding reward value, and the larger the reward value, the higher the corresponding selection probability. The specific corresponding relationship can be set according to actual needs, and this embodiment does not limit this.

[0064] Step S202: Select the fresh air fan speed of the fresh air fan from the expected reward table based on the current environmental state parameters.

[0065] In a specific implementation, after obtaining the expected reward table, the fresh air fan speed can be obtained from the expected reward table.

[0066] Furthermore, based on different working conditions and application scenarios, the optimal speed of the fresh air fan will change accordingly. To ensure that the fresh air fan can always operate at the optimal speed, the expected reward table will be updated in real time in this embodiment.

[0067] In a specific implementation, in this embodiment, during the process of controlling the air handling equipment to operate at the obtained fresh air fan speed, the running duration of the air handling equipment is recorded in real time. When the running duration of the air handling equipment reaches the preset duration, the running power consumption of the air handling equipment from the start of operation to the running to the preset duration is obtained. Then, based on the running power consumption and carbon dioxide concentration during this period, the reward value can be calculated. This reward value is the reward value corresponding to the above-mentioned fresh air fan speed, and the expected reward table is updated based on this reward value.

[0068] Specifically, when calculating the reward value based on the running power consumption and the indoor carbon dioxide concentration, in this embodiment, the indoor carbon dioxide concentration needs to be compared with the set concentration, and then the additional penalty value is obtained based on the magnitude relationship between the indoor carbon dioxide concentration and the set concentration. Finally, the reward value can be calculated according to the respective weight coefficients of the running power consumption and the additional penalty value and the preset parameters. In this embodiment, the above parameters can be substituted into the reward function to obtain the reward value, and the reward function is as follows:

[0069] r t =σ - β1P tot - 100β2penalty1,

[0070] In the above reward function, r t represents the reward value, σ is a relatively large positive integer to ensure that the final reward is positive; β1 and β2 are the respective weight coefficients of the running power consumption and the additional penalty value, and the emphasis on energy conservation and comfort can be selected by adjusting the weight coefficient ratio; P tot is the system energy consumption. When the preset duration is set to 30 minutes, it represents the power consumption of the system running for thirty minutes; penalty1 is the additional penalty term to prevent the temperature from exceeding the user-set temperature range, and 100 is to balance the effect of the previous energy consumption term on the reward. penalty1 = γ, γ is a relatively large positive integer. When the environmental carbon dioxide concentration exceeds the set value, it will seriously affect comfort, so the maximum penalty is given, where 1000 ppm represents the set concentration.

[0071] After obtaining the above reward value, the expected reward table can be updated by combining the current environmental state parameters and this reward value. The update process can be based on the following formula:

[0072] Q(St,At)=Q(St,At)+α[R t+1+γ·maxQ(S t+1 , a) - Q(St, At)],

[0073] where St and S t+1 represent the environmental state parameters, At and a represent the action range of the fresh air unit, that is, the rotational speed of the fresh air fan. A can be set to three gears, namely 1%, 50%, and 100%, corresponding to the lowest value, the middle value, and the highest value of the rotational speed of the fresh air unit. Therefore, the action range of the fresh air unit is A = [a1, a2, a3], where a1 = 1%, a2 = 50%, a3 = 100%. The above gear settings are only for illustrative purposes and can be adjusted accordingly in actual applications. This embodiment does not limit this.

[0074] Furthermore, in this embodiment, the learning of the air treatment device needs to meet certain learning conditions. Specifically, in this embodiment, the outdoor temperature during the operation of the air treatment device can be compared with the temperature threshold. If the air treatment device is in the cooling mode and the outdoor temperature is less than the temperature threshold, it is determined that the learning condition is met. Or when the air treatment device is in the heating mode and the outdoor temperature is greater than the temperature threshold, it is determined that the learning condition is met. In other cases, the air treatment device does not perform self-learning. Among them, the temperature threshold can be the indoor temperature, and the learning judgment is based on the magnitude relationship between the outdoor temperature and the indoor temperature. The temperature threshold can also be a temperature threshold set by the user. In the cooling mode, the temperature threshold represents the lowest temperature set by the user. In the heating mode, the temperature threshold represents the highest temperature set by the user. The temperature threshold can also be the maximum or minimum value of the indoor temperature and the temperature set by the user. This embodiment does not limit the determination method of the temperature threshold, and it can be selected according to actual needs.

[0075] For ease of understanding, in this embodiment, an example is given for the above process. For example, at 11 pm at night, the outdoor temperature is 24°C, the indoor temperature is 27°C, the set temperature is 28°C, and the air conditioner is in the cooling mode. Meeting the entry conditions, it enters the machine learning program. Based on the current state values (indoor and outdoor temperatures, humidity, set temperature, and the status of people at home transmitted by the radar sensor), search the Q table, and obtain the fan speed with the lowest energy consumption corresponding to the current state in the Q table. Execute the current fresh air fan supply air speed of 1220 and the exhaust fan speed of 780. (3) After executing the fresh air fan speed action, record the energy consumption of the fresh air fan and the air conditioning system for 30 minutes, and subtract the 30-minute system energy consumption from a very large number as the reward. (4) Record the indoor CO2 concentration after 30 minutes. If the CO2 concentration does not exceed 1000 ppm, the penalty is 0. If the CO2 concentration exceeds 1000 ppm, the penalty is negative infinity. (5) Use the indoor and outdoor temperatures, set temperature, indoor personnel status, and the time at that moment when the action is executed as the state values, add the above rewards and penalties, calculate the Q value, and update the Q table. (5) Check whether the current state has changed. If so, search the Q table again and formulate the fresh air fan speed. (6) After running the above function for 4 hours, it is detected that the current outdoor temperature is higher than the indoor temperature, meeting the exit conditions, exiting the machine learning mode, and returning to the fresh air fan speed before entry.

[0076] In this embodiment, by obtaining the expected reward table corresponding to the air handling device; and, when the learning conditions are met, selecting the fresh air fan speed from the expected reward table based on the current environmental state parameters, and obtaining the operating power consumption and indoor carbon dioxide concentration when the operating duration of the air handling device reaches the preset duration; calculating the reward value according to the operating power consumption and the indoor carbon dioxide concentration; and, updating the expected reward table based on the reward value, by adopting a machine learning method, learning the power consumption of the fresh air fan and the multi-connected unit, so as to adjust the fresh air fan speed in real time, enabling the fresh air fan to always operate at the optimal point of system energy consumption.

[0077] In addition, an embodiment of the present invention also proposes a storage medium, on which an air handling device control program is stored. When the air handling device control program is executed by a processor, the steps of the air handling device control method as described above are implemented.

[0078] Since this storage medium adopts all the technical solutions of the above all embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, and will not be elaborated here one by one.

[0079] Refer to Figure 5 , Figure 5 which is the structural block diagram of the first embodiment of the air handling device control device of the present invention.

[0080] As Figure 5As shown in the figure, the air treatment equipment control device proposed in the embodiment of the present invention includes:

[0081] A detection module 10, configured to obtain current environmental state parameters.

[0082] It should be noted that in a multi-split air conditioner with fresh air system, when the outdoor temperature is lower than the indoor temperature, the means to reduce the system energy consumption include: 1) reducing the rotational speed of the fresh air fan, resulting in a reduction in the fan energy consumption; 2) increasing the supply air volume of the low-temperature fresh air, resulting in a reduction in the energy consumption of the multi-split air conditioner. However, these two means are contradictory. To increase the fresh air supply volume, the rotational speed of the fresh air fan must be increased. Currently, the rotational speed of the fresh air fan depends on manual adjustment by the user, so it is impossible to find the optimal rotational speed of the fresh air fan between the fan energy consumption and the multi-split air conditioner energy consumption.

[0083] In this embodiment, to solve the above technical problems, by obtaining the current environmental state parameters; determining the rotational speed of the fresh air fan according to the current environmental state parameters; and controlling the air treatment equipment to operate based on the rotational speed of the fresh air fan, the optimal rotational speed of the fresh air fan can be automatically determined to cope with different usage conditions and scenarios, reducing the operating energy consumption of the multi-split air conditioner and the fresh air system. Specifically, it can be implemented in the following manner.

[0084] In this embodiment, the fresh air fan of this embodiment includes a fresh air duct and a fresh air fan. The fresh air duct communicates the indoor space and the outdoor environment. The fresh air fan is arranged in the fresh air duct and is used to drive outdoor fresh air into the indoor space. In addition, the fresh air fan of this embodiment may further include an exhaust duct and an exhaust fan. The exhaust duct communicates the indoor space and the outdoor environment. The exhaust fan is arranged in the exhaust duct and is used to drive indoor air out to the outdoor environment. The exhaust fan can also be adjusted in the same way, or the rotational speed of the exhaust fan and the rotational speed of the fresh air fan can be adjusted. Further, in this embodiment, Figure 3 is taken as an example to illustrate the overall process of this solution. Refer to Figure 3As shown in the figure, Step 1: The algorithm obtains the environmental state S, including at least one of indoor temperature, indoor humidity, indoor enthalpy value, outdoor temperature, outdoor humidity, outdoor enthalpy value, indoor set temperature, indoor set humidity, and indoor set enthalpy value. Step 2: Based on the environmental state S, it is judged whether to enter the learning algorithm. Step 3: The algorithm selects and executes the best rotational speed action A of the fresh air fan in the current experience library. Step 4: Obtain the feedback reward and penalty data of the algorithm. Step 5: Update the experience database according to the machine learning algorithm. Step 6: Judge whether the current environmental state S meets the condition for continuing to execute the algorithm. If so, repeat to Step 2. If not, exit the learning algorithm. Among them, in Step 1, the change rate of indoor and outdoor temperatures, indoor and outdoor humidity, current time, frequency of the multi-connected compressor, refrigeration and heating states of the multi-connected unit, opening state of the indoor unit of the multi-connected unit, weather forecast information, indoor personnel information, indoor CO2 concentration, etc. can also be added to the environmental state. In Step 2, the judgment conditions include that under the refrigeration condition, the outdoor temperature is lower than a certain threshold of the indoor temperature; under the heating condition, the outdoor temperature is higher than a certain threshold of the indoor temperature. In Step 4, the reward data of the algorithm is a formula related to the energy consumption Q of the fresh air and multi-connected system within the time period t, or related to the energy consumption Q' at the previous and subsequent times t'. The penalty data of the algorithm is a formula related to the indoor CO2 concentration. In Step 5, the machine learning algorithm used is the reinforcement learning algorithm.

[0085] In a specific implementation, in this embodiment, it is necessary to first obtain the current environmental state parameters, and the current environmental state parameters at least include indoor environmental parameters and outdoor environmental parameters, such as the change rate of indoor and outdoor temperatures, indoor and outdoor humidity, current time, frequency of the multi-connected compressor, refrigeration and heating states of the multi-connected unit, opening state of the indoor unit of the multi-connected unit, weather forecast information, indoor personnel information, and indoor CO2 concentration.

[0086] The reading module 20 is used to determine the rotational speed of the fresh air fan according to the current environmental state parameters.

[0087] In specific implementation, the current environmental state parameters determine different operating conditions and scenarios of the air handling equipment. Based on the current environmental state parameters, the rotational speed of the fresh air fan can be obtained, that is, the optimal rotational speed of the fresh air fan under different operating conditions and scenarios. Specifically, the rotational speed of the fresh air fan can be retrieved from the expected reward table according to the obtained current environmental state parameters. The expected reward table contains the reward values corresponding to different rotational speeds of the fresh air fan under different environmental state parameters. When selecting, the rotational speed of the fresh air fan with the maximum reward value can be selected. Further, the expected reward table can also set the selection probability for different rotational speeds of the fresh air fan. The fresh air fan rotational speed is automatically selected based on this selection probability. The selection probability is related to the corresponding reward value, and the greater the reward value, the higher the corresponding selection probability. The specific corresponding relationship can be set according to actual requirements, and this embodiment does not limit it.

[0088] The control module 30 is configured to control the air handling equipment to operate based on the rotational speed of the fresh air fan.

[0089] In specific implementation, after obtaining the rotational speed of the fresh air fan from the expected reward table, controlling the air handling equipment to operate according to this rotational speed of the fresh air fan can reduce the operating energy consumption of the multi-split air conditioner and the fresh air system. It should be noted that in this embodiment, the rotational speed of the fresh air fan includes the air supply rotational speed and the exhaust rotational speed of the fresh air fan.

[0090] This embodiment obtains the current environmental state parameters; determines the rotational speed of the fresh air fan according to the current environmental state parameters; and controls the air handling equipment to operate based on the rotational speed of the fresh air fan. Through the above methods, it can cope with different operating conditions and scenarios, automatically determine the optimal rotational speed of the fresh air fan, and reduce the operating energy consumption of the multi-split air conditioner and the fresh air system.

[0091] It should be understood that the above is only an example for illustration and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not limit this.

[0092] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and this is not limited here.

[0093] In addition, for the technical details not described in detail in this embodiment, reference can be made to the air handling equipment control method provided in any embodiment of the present invention, which will not be elaborated here.

[0094] In addition, it should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or system including such an element.

[0095] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0096] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0097] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A control method for an air treatment device, characterized in that, The air handling device includes a fresh air unit, the fresh air unit includes a fresh air duct and a fresh air fan, the fresh air duct communicates with the indoor space and the outdoor environment, the fresh air fan is arranged in the fresh air duct and is used to drive outdoor fresh air into the indoor space, and the air handling device control method includes: Obtain the current environmental state parameters, where the current environmental state parameters include indoor environmental parameters and outdoor environmental parameters; Determine the fresh air fan speed of the fresh air fan according to the current environmental state parameters; and control the air handling device to operate based on the fresh air fan speed.

2. The air treatment equipment control method according to claim 1, characterized in that The determining the fresh air fan speed of the fresh air fan according to the current environmental state parameters includes: Obtain the expected reward table corresponding to the air handling device; and Select the fresh air fan speed of the fresh air fan from the expected reward table based on the current environmental state parameters.

3. The air treatment equipment control method according to claim 2, characterized in that After controlling the air handling device to operate based on the fresh air fan speed, it further includes: Obtain the operating power consumption and indoor carbon dioxide concentration when the operating duration of the air handling device reaches a preset duration; Calculate a reward value according to the operating power consumption and the indoor carbon dioxide concentration; and Update the expected reward table based on the reward value.

4. The air treatment equipment control method according to claim 3, characterized in that, The calculating the reward value according to the operating power consumption and the indoor carbon dioxide concentration includes: Compare the indoor carbon dioxide concentration with a set concentration; Determine an additional penalty value based on the comparison result; and Calculate a reward value according to the weight coefficients and preset parameters corresponding to the operating power consumption and the additional penalty value respectively.

5. The air treatment device control method according to claim 3, characterized in that, The obtaining the operating power consumption and indoor carbon dioxide concentration when the operating duration of the air handling device reaches a preset duration includes: Judge whether a learning condition is satisfied; and If the learning condition is satisfied, obtain the operating power consumption and indoor carbon dioxide concentration when the operating duration of the air handling device reaches a preset duration.

6. The air treatment equipment control method according to claim 5, characterized in that, The judging whether the learning condition is satisfied includes: Compare the outdoor temperature during the operation of the air handling device with a temperature threshold, where the temperature threshold is determined by a set temperature and / or the indoor temperature during the operation of the air handling device; When the air handling device is in the cooling mode, if the outdoor temperature is less than the temperature threshold, it is determined that the learning condition is satisfied; and When the air handling device is in the heating mode, if the outdoor temperature is greater than the temperature threshold, it is determined that the learning condition is satisfied.

7. The air treatment equipment control method according to claim 2, characterized in that, Each set of environmental parameters in the expected reward table corresponds to multiple fresh air fan speeds, and the selection probability of each fresh air fan speed is related to the size of the corresponding reward value.

8. A control device for an air treatment device, characterized in that, The air handling device control device includes: A detection module for obtaining the current environmental state parameters, where the current environmental state parameters include indoor environmental parameters and outdoor environmental parameters; A reading module for determining the fresh air fan speed of the fresh air fan according to the current environmental state parameters; and A control module for controlling the air handling device to operate based on the fresh air fan speed.

9. An air treatment device, characterized in that, The air handling device includes: a memory, a processor, and an air handling device control program stored on the memory and running on the processor, the air handling device control program being configured to implement the air handling device control method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, An air handling device control program is stored on the storage medium, and when the air handling device control program is executed by a processor, it implements the air handling device control method according to any one of claims 1 to 7.

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