Fresh air equipment and air conditioning system

Abstract

The invention provides fresh air equipment which comprises a fresh air opening, an air supply opening, an air return opening, an exhaust opening, a fan assembly, an indoor carbon dioxide concentration sensor and an outdoor carbon dioxide concentration sensor. The fresh air equipment comprises a generation part which is configured to establish a prediction model based on a detected indoor carbon dioxide concentration time sequence, and the prediction model can generate a predicted change rate of the indoor carbon dioxide concentration; a calculation section configured to calculate a concentration difference between the detected indoor carbon dioxide concentration and outdoor carbon dioxide concentration; and a control part configured to control the rotating speed of the fan assembly based on the predicted change rate and the concentration difference, including: for the same predicted change rate, increasing the rotating speed of the fan assembly as the concentration difference increases; for the same concentration difference, the rotating speed of the fan assembly is increased along with the increase of the predicted change rate; and for the decreasing predicted change rate, when the concentration difference is within the set stable difference value interval, the fresh air assembly is closed. The invention further provides an air conditioning system. The problem of fresh air interference can be effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to a fresh air device and an air conditioning system. Background Art

[0002] The indoor air quality is closely related to people's lives. With the improvement of the sealing of modern buildings, the natural ventilation effect between indoors and outdoors becomes worse, and the pollutants generated by indoor pollution sources are difficult to effectively diffuse, which in turn leads to the deterioration of air quality. The carbon dioxide (CO2) concentration is a key air quality parameter. In the case of poor room ventilation and people present, the carbon dioxide concentration rises rapidly. The carbon dioxide concentration can make people feel fatigued, affect sleep quality, reduce learning and work efficiency, and even endanger human health.

[0003] Using a fresh air device can effectively improve the indoor air quality. A fresh air device refers to a type of device used to introduce fresh air and improve air quality, mainly used in indoor ventilation systems to ensure the circulation of indoor air, reduce the accumulation of pollutants, and provide a healthier and more comfortable indoor environment. According to investigations, the energy consumption of fresh air devices is approximately 10% to 30% of the energy consumption of air conditioning systems. For commercial environments such as hotels and office buildings, the energy consumption can be as high as 40%. In addition, during the application of current fresh air devices, problems such as excessive fresh air and ineffective operation often occur. Excessive fresh air means that the amount of fresh air introduced exceeds the actual required amount of air, resulting in the indoor air circulation volume exceeding the design standard; ineffective operation means that although the fresh air device is in operation, it cannot improve the indoor air quality, causing waste of electric energy. Reducing the fresh air energy consumption is one of the key goals for achieving green and low-energy buildings, which helps to achieve the "dual carbon" goal. Summary of the Invention

[0004] In view of the problems such as excessive fresh air and ineffective operation that are likely to occur during the application of fresh air devices, the first aspect of the present application designs and provides a fresh air device.

[0005] The fresh air device includes: a housing having a fresh air inlet located outdoors; a supply air outlet located indoors, and the supply air outlet is connected to the fresh air inlet through a supply air duct; a return air inlet located indoors; an exhaust air outlet located outdoors, and the exhaust air outlet is connected to the return air inlet through an exhaust air duct; and a fan assembly for guiding the air flow in the supply air duct and the exhaust air duct.

[0006] In one or more embodiments of the present application, the fresh air device further includes an indoor carbon dioxide concentration sensor for detecting the indoor carbon dioxide concentration and an outdoor carbon dioxide concentration sensor for detecting the outdoor carbon dioxide concentration.

[0007] In one or more embodiments of the present application, the fresh air device further includes: a generating unit configured to establish a prediction model based on the detected indoor carbon dioxide concentration time series, the prediction model being capable of generating a predicted change rate of the indoor carbon dioxide concentration; a calculating unit configured to calculate the concentration difference between the detected indoor carbon dioxide concentration and the outdoor carbon dioxide concentration; and a control unit configured to control the rotational speed of the fan assembly based on the predicted change rate and the concentration difference.

[0008] In one or more embodiments of the present application, controlling the rotational speed of the fan assembly based on the predicted change rate and the concentration difference includes: for the same predicted change rate, increasing the rotational speed of the fan assembly as the concentration difference increases; for the same concentration difference, increasing the rotational speed of the fan assembly as the predicted change rate increases; and for a decreasing predicted change rate, when the concentration difference is within a set stable difference interval, turning off the fresh air assembly.

[0009] In one or more embodiments of the present application, the prediction model is capable of generating a predicted indoor carbon dioxide concentration at a set moment.

[0010] In one or more embodiments of the present application, automatic startup can be achieved based on the prediction model. When the predicted change rate increases and the predicted carbon dioxide concentration exceeds a set concentration threshold, the control unit can control the fan assembly to switch from the stopped operating state to the operating state.

[0011] In one or more embodiments of the present application, after the control unit controls the fan assembly to switch to the operating state, it starts to estimate whether the number of indoor carbon dioxide emission sources increases.

[0012] In one or more embodiments of the present application, if the number of indoor carbon dioxide emission sources does not increase within a set period, the control unit reduces the credibility of the prediction model for the corresponding time period through a penalty factor, and at the same time controls the fan assembly to switch from the operating state to the stopped operating state.

[0013] In one or more embodiments of the present application, automatic shutdown can be achieved based on the prediction model. When the predicted carbon dioxide concentration does not exceed the set concentration threshold within a set time period and the predicted change rate is a decreasing predicted change rate, the control unit can control the fan assembly to switch from the operating state to the stopped operating state.

[0014] In one or more embodiments of the present application, after the control unit controls the fan assembly to switch to the stopped operating state, it starts to estimate whether the number of indoor carbon dioxide emission sources increases; if the number of indoor carbon dioxide emission sources increases within a set period, the control unit reduces the credibility of the prediction model for the corresponding time period through a penalty factor, and at the same time controls the fan assembly to switch from the stopped operating state to the operating state.

[0015] In one or more embodiments of the present application, the prediction model is established through the following steps: collecting the indoor carbon dioxide concentration detected by the indoor carbon dioxide concentration sensor, and establishing a time series of the detected values of the indoor carbon dioxide concentration, the time series of the detected values including a plurality of observation values, and each observation value being an indoor carbon dioxide concentration detection value; verifying whether the plurality of observation values in the time series of the detected values are stationary; if the plurality of observation values have non-stationarity, performing a differencing operation on the time series of the detected values until the plurality of observation values have stationarity; recording the number of times of the differencing operation to obtain a verified time series; calculating the average value of the verified time series; calculating the difference between each observation value and the average value to obtain a deviation series; performing a convolution operation of the deviation series with itself to obtain an autocovariance series; normalizing the autocovariance series to obtain an autocorrelation coefficient; plotting an autocorrelation graph and a partial autocorrelation graph, establishing an autoregressive integrated moving average model, and using the autoregressive integrated moving average model as the prediction model.

[0016] A second aspect of the present application provides an air conditioning system, including a fresh air device, and further including a refrigeration cycle air conditioning device for adjusting indoor air parameters. The air conditioning system further includes: an indoor carbon dioxide concentration sensor for detecting the indoor carbon dioxide concentration, and an outdoor carbon dioxide concentration sensor for detecting the outdoor carbon dioxide concentration; a generating unit configured to establish a prediction model based on the detected time series of the indoor carbon dioxide concentration, the prediction model being capable of generating a predicted change rate of the indoor carbon dioxide concentration; a calculating unit configured to calculate the concentration difference between the detected indoor carbon dioxide concentration and the outdoor carbon dioxide concentration; and a control unit configured to control the rotational speed of the fan assembly based on the predicted change rate and the concentration difference, and when the fan assembly is operating, control the air conditioning device to be in an operating state and increase the capacity output of the air conditioning device; wherein controlling the rotational speed of the fan assembly based on the predicted change rate and the concentration difference includes: for the same predicted change rate, increasing the rotational speed of the fan assembly as the concentration difference increases; for the same concentration difference, increasing the rotational speed of the fan assembly as the predicted change rate increases; for a decreasing predicted change rate, when the concentration difference is within a set stable difference interval, turning off the fresh air assembly.

[0017] In one or more embodiments of the present application, the prediction model can generate a predicted indoor carbon dioxide concentration at a set time; when the predicted change rate increases and the predicted indoor carbon dioxide concentration exceeds a first set concentration threshold, the control unit can control the fan assembly to switch from the stopped operating state to the operating state and maintain at a set rotational speed until the detected indoor carbon dioxide concentration is lower than the first set concentration threshold; estimate whether the number of indoor carbon dioxide emission sources increases; when the number of indoor carbon dioxide emission sources increases, the control unit controls the fan assembly to maintain at the set rotational speed, controls the air conditioning device to be in the operating state and increases the capacity output of the air conditioning device until a set target temperature and / or a set target humidity is reached; estimate whether the detected indoor carbon dioxide concentration is lower than the first set concentration threshold; when the detected indoor carbon dioxide concentration is lower than the first set concentration threshold, control the fan assembly to switch from the operating state to the stopped operating state and abort the control of increasing the capacity output of the air conditioning device; when the detected indoor carbon dioxide concentration is not lower than the first set concentration threshold, estimate whether the predicted change rate is increasing; if the predicted change rate is increasing, control the rotational speed of the fan assembly based on the predicted change rate and the concentration difference, control the air conditioning device to be in the operating state and maintain the control of increasing the capacity output of the air conditioning device until a set target temperature and / or a set target humidity is reached; estimate whether the detected indoor carbon dioxide concentration is lower than a second set concentration threshold; when the detected indoor carbon dioxide concentration is lower than the second set concentration threshold, control the fan assembly to operate at the set rotational speed until the detected indoor carbon dioxide concentration is lower than the first set concentration threshold; wherein the second set concentration threshold is higher than the first set concentration threshold.

[0018] In one or more embodiments of the present application, when the predicted carbon dioxide concentration does not exceed the first set concentration threshold during the set period and the predicted change rate is a decreasing predicted change rate, the control unit can control the fan assembly to switch from the operating state to the stopped operating state.

[0019] The present invention can effectively improve the problem of excessive fresh air.

[0020] After reading the specific embodiments of the present invention in conjunction with the drawings, other features and advantages of the present invention will become clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 Structural schematic diagram of the fresh air device provided by one or more embodiments of the present application;

[0023] Figure 2 Structural schematic block diagram of the fresh air device provided by one or more embodiments of the present application;

[0024] Figure 3 Schematic curve diagram of carbon dioxide concentration;

[0025] Figure 4 Schematic table of the control unit controlling the rotation speed of the fan assembly based on the predicted change rate and concentration difference;

[0026] Figure 5 Flowchart of the fresh air device provided by one or more embodiments of the present application;

[0027] Figure 6 Flowchart of the fresh air device provided by one or more embodiments of the present application;

[0028] Figure 7 Flowchart of the fresh air device provided by one or more embodiments of the present application;

[0029] Figure 8 Flowchart of the fresh air device provided by one or more embodiments of the present application;

[0030] Figure 9 Flowchart of the fresh air device provided by one or more embodiments of the present application;

[0031] Figure 10 Structural schematic block diagram of the air conditioning system provided by one or more embodiments of the present application;

[0032] Figure 11 Structural schematic block diagram of the air conditioning device in the air conditioning system provided by one or more embodiments of the present application;

[0033] Figure 12 Flowchart of the air conditioning system provided by one or more embodiments of the present application;

[0034] Figure 13 Flowchart of the air conditioning system provided by one or more embodiments of the present application;

[0035] In the figure: 10, fresh air equipment; 100, housing; 101, fresh air inlet; 102, air supply outlet; 103, air return inlet; 104, exhaust outlet; 105, air supply duct; 106, exhaust duct; 107, fan assembly; 108, indoor carbon dioxide concentration sensor; 109, outdoor carbon dioxide concentration sensor; 110, generation unit; 111, calculation unit; 112, control unit; 20, air conditioning equipment; 201, compressor; 202, condenser; 203, expansion valve; 204, evaporator. Detailed implementation manner

[0036] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0037] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0038] The terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "plurality" is two or more.

[0039] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0040] In the present invention, unless otherwise clearly specified or limited, the first feature being "on" or "under" the second feature may include direct contact between the first and second features, or may include the first and second features not being in direct contact but in contact through additional features therebetween. Moreover, the first feature being "above", "over" and "on top of" the second feature includes the first feature being directly above and obliquely above the second feature, or merely indicating that the horizontal height of the first feature is higher than that of the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly below and obliquely below the second feature, or merely indicating that the horizontal height of the first feature is less than that of the second feature.

[0041] The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, the components and settings of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. In addition, the present invention may repeat reference numerals and / or reference letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides examples of various specific processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0042] Referring to Figure 1 and Figure 2 , the structure of the fresh air device provided by one or more embodiments of the present invention is described.

[0043] As Figure 1 and Figure 2 shown, the fresh air device 10 includes a housing 100, and the housing 100 has a fresh air inlet 101, an air supply outlet 102, a return air inlet 103 and an exhaust outlet 104. An air supply duct 105 from the fresh air inlet 101 to the air supply outlet 102 is provided in the housing 100, and an exhaust duct 106 from the return air inlet 103 to the exhaust outlet 104, that is, the air supply outlet 102 is communicated with the fresh air inlet 101 through the air supply duct 105, and the exhaust outlet 104 is communicated with the return air inlet 103 through the exhaust duct 106. The fresh air device 10 includes a fan assembly 107, and the fan assembly 107 includes an air supply fan disposed in the air supply duct 105, and the air supply fan is used to guide the air flow in the air supply duct 105. The fan assembly 107 further includes an exhaust fan disposed in the exhaust duct 106 for guiding the air flow in the exhaust duct 106; wherein the fresh air inlet 101 and the exhaust outlet 104 are located outdoors, and the air supply outlet 102 and the return air inlet 103 are located indoors.

[0044] In one or more embodiments of the present invention, a return air duct may also be constructed inside the housing 100. One end of the return air duct is connected to the return air inlet 103, and the other end is connected to the supply air duct 105. Through the return air duct, the internal circulation of indoor air can be achieved.

[0045] A fresh air damper is optionally provided at the fresh air inlet 101. When the fresh air damper is open, outdoor air can enter the supply air duct 105 from the fresh air inlet 101. When the fresh air damper is closed, the fresh air damper prevents outdoor air from entering the supply air duct 105 from the fresh air inlet 101.

[0046] In one or more embodiments of the present invention, a return air damper may also be provided at the connection between the return air duct and the supply air duct 105. The return air damper can change and guide the flow of air in the exhaust air duct 106 or in the return air duct.

[0047] An air purification component is provided in the supply air duct 105. In one or more embodiments of the present application, the air purification component includes one or more primary filters, high-efficiency filters, activated carbon filters, ultraviolet disinfection systems, negative ion generators, etc. Among them, the primary filter is used to capture large particles, dust, and other impurities in the air; the high-efficiency filter is used to capture smaller particles, such as viruses, bacteria, pollen, and other fine dust; the activated carbon filter is used to adsorb and remove odors, harmful gases, and volatile organic compounds in the air; the ultraviolet disinfection system is used to kill bacteria, viruses, and other microorganisms in the air; the negative ion generator releases negative ions into the air, and these ions can adsorb the particles in the air and make them settle to the ground, thereby purifying the air.

[0048] The fresh air device 10 further includes an indoor carbon dioxide concentration sensor 108 and an outdoor carbon dioxide concentration sensor 109. The indoor carbon dioxide concentration sensor 108 is used to detect the indoor carbon dioxide concentration, and the outdoor carbon dioxide concentration sensor 109 is used to detect the outdoor carbon dioxide concentration. In one or more embodiments of the present application, the indoor carbon dioxide concentration sensor 108 can be disposed at the return air inlet 103 located indoors, and the outdoor carbon dioxide concentration sensor 109 can be disposed at the fresh air inlet 101 located outdoors.

[0049] The supply air fan, the exhaust air fan, and the optional fresh air damper and return air damper can all be controlled by the main control chip. The main control chip includes components such as a processor, a storage unit, an input / output interface, and a communication interface. The processor can be a dedicated processor, a central processing unit (CPU), etc. The processor can access the storage unit to execute instructions or application programs stored in the storage unit to implement related functions. The storage unit can include volatile memory and / or non-volatile memory. The input / output interface can be communicatively connected to various sensors in the fresh air device 10, such as the indoor carbon dioxide concentration sensor 108 and the outdoor carbon dioxide concentration sensor 109, to receive the detection values of the sensors; the input / output interface can also be communicatively connected to the supply air fan, the exhaust air fan, and the motors driving the fresh air damper and the return air damper to output the control instructions generated by the processor to them. The communication interface can support different wireless communication protocols, such as Wi-Fi, Bluetooth, near field communication, NB-loT, etc., so that the fresh air device 10 can be communicatively connected to other electronic devices, including but not limited to cloud servers, computers (host computers), smart phones, tablet computers, PDAs, intelligent control tools, wearable devices, and in-vehicle devices, etc.

[0050] A total heat exchange core can also be provided in the fresh air device 10.

[0051] As Figure 2 shown, in one or more embodiments of the present invention, the fresh air device 10 further includes a generating unit 110, a calculating unit 111, and a control unit 112.

[0052] The generating unit 110 is configured to establish a prediction model based on the detected indoor carbon dioxide concentration time series, and the prediction model can generate a predicted change rate of the indoor carbon dioxide concentration. The "predicted change rate" refers to the estimation of the change trend of the indoor carbon dioxide concentration by the prediction model in a future set time period, and the duration of the set time period can be 1 hour, several hours, 24 hours, or several days, etc.

[0053] The calculating unit 111 is configured to calculate the concentration difference between the detected indoor carbon dioxide concentration and the outdoor carbon dioxide concentration. More specifically, the calculating unit 111 is configured to calculate the concentration difference between the indoor carbon dioxide concentration and the outdoor carbon dioxide concentration at the corresponding time point.

[0054] The control unit 112 is configured to control the rotational speed of the fan assembly 107 based on the predicted change rate output by the generation unit 110 and the concentration difference calculated by the calculation unit 111. In one or more embodiments of the present application, the control unit 112 controlling the rotational speed of the fan assembly 107 based on the predicted change rate and the concentration difference includes: for the same predicted change rate, as the concentration difference increases, increasing the rotational speed of the fan assembly 107; for the same concentration difference, as the predicted change rate increases, increasing the rotational speed of the fan assembly 107; and for a decreasing predicted change rate, when the concentration difference is within a set stable difference range, turning off the fresh air assembly.

[0055] In the control of the fresh air device 10, if only the detected indoor carbon dioxide concentration is used to control the rotational speed of the fan assembly 107 to further achieve the control of the fresh air volume, since it takes a certain amount of time to circulate the indoor air, introduce fresh air into the room and discharge the indoor air, even if the rotational speed of the fan assembly 107 is adjusted, it takes a certain amount of time for the carbon dioxide concentration in the indoor air to be effectively diluted and reduced, and the control effect will be relatively lagging; especially when the emission amount of the indoor carbon dioxide emission source is stable (for example, the number of people in the room remains unchanged, and the rate of carbon dioxide released by their breathing can be regarded as unchanged), the fresh air introduced by the fresh air device 10 cannot quickly change the composition of the entire indoor air, and the indoor-outdoor carbon dioxide concentration difference will tend to be stable and remain at a stable and low level as the working time of the fresh air device 10 increases. At this time, a dynamic equilibrium state is formed, and even if the fresh air device 10 continues to work, it will not reduce the indoor carbon dioxide concentration, that is, it causes energy waste, that is, fresh air surplus, as Figure 3 shown.

[0056] To solve this problem, in one or more embodiments of the present application, the control unit 112 is configured to control the rotational speed of the fan assembly 107 based on the predicted change rate and the concentration difference, that is, it includes: for the same predicted change rate, as the concentration difference increases, increasing the rotational speed of the fan assembly 107; for the same concentration difference, as the predicted change rate increases, increasing the rotational speed of the fan assembly 107; and, for a decreasing predicted change rate, when the concentration difference is within a set stable difference range, turning off the fresh air assembly.

[0057] The decreasing predicted change rate reflects that the growth of the indoor carbon dioxide concentration gradually slows down, and the operation of the fresh air device 10 inhibits the increase in the indoor carbon dioxide concentration, while the stable indoor-outdoor carbon dioxide concentration difference reflects that the gas exchange between the indoor and outdoor has reached a balanced state. At this time, the fresh air device 10 can be turned off to solve the problem of fresh air surplus.

[0058] In one or more embodiments of the present application, it is assumed that the hardware design of the fan assembly 107 limits its own rotational speed to be divided into three rotational speed modes from low to high: rotational speed 1, rotational speed 2, and rotational speed 3, and rotational speed 3 is the highest rotational speed.

[0059] Correspondingly, several thresholds p0, p1, p2, p3, and p4 are selected from low to high for the predicted change rate of the indoor carbon dioxide concentration generated by the prediction model. Among them, p0 is the decreasing predicted change rate threshold, and p0 is set to 0. When the predicted change rate p' satisfies p' < p0, that is, p' is less than 0, it represents that the predicted change rate at this time is a decreasing predicted change rate. The predicted change rate is divided into several intervals from low to high, including (p0, p1), (p1, p2), (p2, p3), and (p3, p4).

[0060] On the other hand, it is set that when the concentration difference Δp is less than 100 ppm , the concentration difference Δp is within the set stable difference interval; and the concentration difference is further divided into (100 ppm , 200 ppm ), (200 ppm , 300 ppm ), (300 ppm , 400 ppm ), and (400 ppm , 500 ppm ) and other increasing intervals.

[0061] That is, the control strategy of the above control unit 112 can be represented as a table as shown Figure 4 . According to the above table, after obtaining the predicted change rate of the indoor carbon dioxide concentration generated by the generating unit 110 and the concentration difference generated by the calculating unit 111, the corresponding rotation speed of the fan assembly 107 can be obtained by looking up the table. That is, for the same predicted change rate, as the concentration difference increases, the rotation speed of the fan assembly 107 is increased until the maximum rotation speed; for the same concentration difference, as the predicted change rate increases, the rotation speed of the fan assembly 107 is increased until the maximum rotation speed; for the predicted change rate less than or equal to 0, that is, the decreasing predicted change rate, when the concentration difference is within the set stable difference interval, the fresh air assembly is closed. While improving the user's comfort, the situation of excessive fresh air is avoided, and the energy consumption of the fresh air device 10 is reduced.

[0062] In one or more embodiments of the present application, the prediction model established by the generating unit 110 based on the detected time series of the indoor carbon dioxide concentration can be a neural network model, an autoregressive neural network model, an exponential smoothing state space model, etc.

[0063] In one or more embodiments of the present application, the generating unit 110 may further establish an ARIMAX prediction model based on the detected indoor carbon dioxide concentration time series and the outdoor carbon dioxide concentration series. Taking the indoor carbon dioxide concentration and the outdoor carbon dioxide concentration as internal variables and the change of the carbon dioxide emission source as an external variable, an ARIMAX prediction model is constructed, and this ARIMAX prediction model can predict the change of the indoor-outdoor carbon dioxide concentration difference.

[0064] In one or more embodiments of the present application, the prediction model established by the generating unit 110 based on the detected indoor carbon dioxide concentration time series can be established through multiple steps as shown in the figure. In this embodiment, the prediction model is an ARIMA (AutoRegressive Integrated Moving Average) model, that is, an autoregressive integrated moving average model.

[0065] Step S101: Collect the indoor carbon dioxide concentration detected by the indoor carbon dioxide concentration sensor 108, and establish a time series of the detected values of the indoor carbon dioxide concentration. The time series of the detected values includes multiple observations, and each observation is a detected value of the indoor carbon dioxide concentration.

[0066] In one or more embodiments of the present application, collect the detection data of the indoor carbon dioxide concentration sensor 108 in the past 30 days, and establish a time series of the detected values in the order of time points. The time series of the detected values can be represented by X(t).

[0067] In one or more embodiments of the present application, it is also possible to collect the detection data of the indoor carbon dioxide concentration sensor 108 in the past 60 days, 90 days, 120 days, 150 days, and 180 days, and establish a time series of the detected values in the order of time points.

[0068] Step S102: Verify whether the multiple observations in the time series of the detected values are stationary.

[0069] Step S103: If the multiple observations are non-stationary, perform a differencing operation on the time series of the detected values until the new series after the differencing operation is stationary.

[0070] The ARIMA model assumes that the time series is stationary. Stationarity is one of the key assumptions for the effectiveness of the ARIMA model, that is, the statistical characteristics of the time series do not change significantly over time, including the mean, variance, and autocorrelation structure, etc. If the multiple observations in the time series of the detected values are non-stationary, then its statistical properties change over time, and the autocorrelation coefficient may not approach zero within a long time.

[0071] The differencing operation is to reduce or remove the trends and seasonality in the time series of the detection values to make it more stationary. After the differencing operation, a stationarity test is performed again. If it is still non-stationary, the differencing operation is continued until the new series after the differencing operation is stationary.

[0072] The differencing operation calculates the differences at adjacent time points. Exemplarily, a new series is obtained by subtracting the observed value at the previous moment. One differencing operation can be expressed as:

[0073] d(t) = x t - x t-1

[0074] where x t is the observed value of the time series of the detection values at time t, and x t-1 is the observed value of the time series of the detection values at time t - 1, and d(t) is the new series obtained after performing the first-order differencing on the time series of the detection values.

[0075] For the stationarity test, existing stationarity test algorithms such as the unit root test and the rolling statistic test can be selected, which will not be elaborated here.

[0076] Step S104: Record the number of differencing operations to obtain the verification time series.

[0077] Step S105: Calculate the average value of the verification time series.

[0078] The average value is the sum of all the observed values in the verification time series divided by the number of observed values.

[0079] Step S106: Calculate the difference between each observed value in the verification time series and the average value to obtain the deviation series.

[0080] Step S107: Perform a convolution operation on the deviation series with itself to obtain the autocovariance series.

[0081] Step S108: Standardize the autocovariance series to obtain the autocorrelation coefficient.

[0082] The autocorrelation coefficient ρ(h) can be expressed by the following formula:

[0083]

[0084] ρ(h) represents the autocorrelation coefficient of the time series at lag h. The autocorrelation coefficient measures the correlation of the autocovariance sequence with itself at different time points. If ρ(h) is close to 1, it indicates that the autocovariance sequence has strong autocorrelation at lag h. If ρ(h) is close to 0, it indicates that there is no strong correlation. γ(t+h, t) is the covariance of the sequence at times t+h and t. Covariance measures the linear relationship between two variables, with a positive value indicating positive correlation and a negative value indicating negative correlation. γ(t+h, t+h) is the covariance of the sequence at times t+h and t+h. γ(t, t) is the covariance of the sequence at times t and t. γ(h) is the autocovariance of the sequence at lag h, i.e., γ(t+h, t). γ(0) represents the variance of the time series at the same time point. Autocorrelation describes the degree of linear correlation between an observation at one moment in a time series and an observation at some past moment in the same series. The expression of the autocorrelation coefficient can also be called the autocorrelation function.

[0085] Step S109: Plot the autocorrelation graph and the partial autocorrelation graph, establish an autoregressive integrated moving average model, and use the autoregressive integrated moving model as the prediction model.

[0086] The autocorrelation graph (ACF, Autocorrelation Function) shows the direct relationship between a time series and its lagged version. In the autocorrelation graph, the horizontal axis represents the lag and the vertical axis represents the autocorrelation coefficient. The partial autocorrelation graph (PACF, Partial Autocorrelation Function) shows the autocorrelation relationship at lag k when considering intermediate lags. In the PACF graph, the horizontal axis also represents the lag and the vertical axis represents the partial autocorrelation coefficient. The autocorrelation graph and the partial autocorrelation graph can be plotted using functions in statistical software or programming languages (such as Python, R).

[0087] After plotting the autocorrelation graph and the partial autocorrelation graph, determine the order of the ARIMA model by observing the truncation structure in the ACF and PACF graphs. Specifically, the ARIMA model can be expressed as ARIMA(p, d, q), where p is the order of the autoregressive part, representing the number of past observations considered in the model; d is the number of differencing operations, representing the number of differencing operations performed to make the time series stationary; q is the order of the moving average part, representing the number of past prediction errors considered in the model. Among them, by observing the PACF graph, the lag value at which truncation occurs can be found, and further the order of the autoregressive term, i.e., the AR order (p), can be determined; by observing the ACF graph, the lag value at which truncation occurs can be found, and further the end of the moving average term, i.e., the MA order (q), can be determined; and d is the number of differencing operations.

[0088] Truncation refers to the phenomenon in the ACF graph or PACF graph where the coefficients of the lag terms rapidly approach zero and remain near zero. In the ACF graph, as the lag increases, the autocorrelation coefficient gradually decreases and approaches zero. Truncation means that at a certain lag, the autocorrelation coefficient becomes very small and tends to zero. This indicates that after this lag, there is basically no linear correlation between the observations in the time series. In the PACF graph, truncation is manifested as the partial autocorrelation coefficient rapidly approaching zero after a certain lag.

[0089] In one or more embodiments of the present application, the detected indoor carbon dioxide concentration becomes a stationary sequence after first-order differencing, so d = 1 is determined; according to the autocorrelation graph, it shows first-order truncation; according to the partial autocorrelation graph, it shows a trailing tail. In summary, the prediction model is ARIMA(0, 1, 1). Among them, first-order truncation refers to the situation in the ACF graph where as the number of lag steps increases, the graph of the ACF graph rapidly decreases or approaches zero after the first lag step. First-order truncation usually indicates the rapid decay of the time series, indicating that the time series is hardly correlated with the previous time points after the first lag step. A trailing tail refers to the situation where the PACF graph slowly decreases as the number of lag steps increases. If the PACF graph still maintains a significantly non-zero value when the number of lag steps is large, it indicates the existence of a trailing tail. A trailing tail can indicate that the sequence has a long memory in time, that is, there is still a certain degree of correlation between the observations at more distant time points and the observations at the current time point.

[0090] An ARIMA model is established using the determined orders p, d, and q.

[0091] In one or more embodiments of the present application, the ARIMA model can be represented by the following formula:

[0092]

[0093] Among them, represents the time series after d times of differencing, that is, the observations after d-order differencing; c is a constant term, representing the intercept of the model; Φ1, Φ2... Φ p represent the coefficients of the autoregressive terms, respectively representing the influence of the observations of the time series at times t - 1, t - 2,..., t - p on the current time t. respectively represent the values of the time series after d times of differencing at times t - 1, t - 2,..., t - p, ε t is a white noise error term, representing the random error of the time series at time t; θ1, θ2,..., θ q are the coefficients of the moving average terms, respectively representing the influence of the white noise at times t - 1, t - 2,..., t - q; ε t-1 , ε t-2 ..., εt-q respectively represent the values of white noise at times t-1, t-2, ..., t-q. The ARIMA model fits a given time series by adjusting the model parameters Φ1, Φ2...Φ p and θ1, θ2, ..., θ q to make the predictions generated by the model as close as possible to the actual observed values. The selection of the parameters in the above model is usually carried out by observing the ACF graph and PACF graph.

[0094] An ARIMA(0, 1, 1) model established by training with historical data.

[0095] Use the trained ARIMA(0, 1, 1) model to predict the indoor carbon dioxide concentration at future time points. The prediction results include the predicted value and the confidence interval.

[0096] Furthermore, the predicted change rate of the indoor carbon dioxide concentration within a certain period can be calculated based on the predicted value.

[0097] As Figure 6 shown, the prediction model can generate the predicted indoor carbon dioxide concentration at a set time. Therefore, the automatic startup of the fresh air device 10 can be realized, that is, when the predicted change rate increases (as shown in step S201 in Figure 6 ), which also means that the indoor carbon dioxide concentration will increase significantly in a short time in the future, and when the predicted carbon dioxide concentration exceeds the set concentration threshold (as shown in step S202 in Figure 6 ), the control unit 112 can control the fan assembly 107 to switch from the stopped operation state to the running state (as shown in step S203 in Figure 6 ), especially suitable for situations where it is necessary to respond in a timely manner to changes in indoor pollution sources or human activities.

[0098] As Figure 7 shown, after the control unit 112 controls the fan assembly 107 to switch to the running state, it starts to estimate whether the number of indoor carbon dioxide emission sources increases (as shown in step S304 in Figure 7 ). Indoor carbon dioxide emission sources include people and also carbon dioxide generated by fuel combustion, such as household appliances, heating systems, and kitchen cooking, etc. The main indoor carbon dioxide emission source is human breathing. Therefore, in one or more embodiments of the present application, after the control unit 112 controls the fan assembly 107 to switch to the running state, it starts to collect the detection results detected by the human presence detection module.

[0099] If the number of carbon dioxide emission sources does not increase within the set period, the deviation between the prediction result and the actual result exceeds the ideal range, and the control unit 112 reduces the credibility of the prediction model for the corresponding time period through a penalty factor (as shown in Figure 7as shown in step S305), and at the same time control the fan assembly 107 to switch from the running state to the stopped state (such as Figure 7 as shown in step S306).

[0100] In one or more embodiments of the present invention, by reducing the credibility of the prediction model for the corresponding time period through a penalty factor, the predicted value can be multiplied by a weight less than 1 to reduce the confidence of the model for this time period, which is equivalent to artificially penalizing the prediction of the prediction model during this time period.

[0101] In one or more embodiments of the present invention, the ARIMA model can also be transformed into an ARIMAX model, introducing external variables as factors, and introducing LASSO regularization on the basis of the ARIMAX model. The ARIMAX model can consider the dynamic characteristics of the time series, while LASSO regularization helps to reduce the complexity of the model and improve the generalization ability of the model. By combining these two methods, it is possible to better adapt to time series data with external influencing factors while avoiding the problem of overfitting.

[0102] Exemplarily, if it is predicted at time t0 that the predicted change rate of the indoor carbon dioxide concentration increases in the next 1 hour and the predicted carbon dioxide concentration exceeds the set concentration threshold (for example, higher than 650 ppm ), the control unit 112 can control the fan assembly 107 to switch from the stopped state to the running state, for example, maintaining operation at a rotational speed of 1, maintaining comfort and taking into account the low energy consumption working state. After the control unit 112 controls the fan assembly 107 to switch to the running state, for example, 20 minutes after the fan assembly 107 switches to the running state, it starts to receive the output result of the human presence detection module in real time; if no human detection signal generated by the human presence detection module is received within 10 minutes, it is determined that the prediction model has made a wrong prediction, a penalty factor is introduced to reduce the credibility of the prediction result for this corresponding time period, and at the same time the fan assembly 107 is switched from the running state to the stopped state.

[0103] Similarly, as Figure 8 shown, if it is predicted that the carbon dioxide concentration does not exceed the set concentration threshold within the set time period (such as Figure 8 shown in step S401), and the predicted change rate is a decreasing predicted change rate (such as Figure 8 shown in step S402), the control unit 112 can control the fan assembly 107 to switch from the running state to the stopped state (such as Figure 8 shown in step S403).

[0104] As Figure 9 shown, after the control unit 112 controls the fan assembly 107 to switch to the stopped state, it starts to estimate whether the number of indoor carbon dioxide emission sources increases (such as Figure 9as shown in step S504 in [the above text]].

[0105] If the number of indoor carbon dioxide emission sources increases within a set period, the control unit 112 reduces the credibility of the prediction model for the corresponding time period through a penalty factor (as Figure 9 shown in step S505 in [the above text]), and at the same time controls the fan assembly 107 to switch from the stopped operating state to the operating state (as Figure 9 shown in step S506 in [the above text]).

[0106] In one or more embodiments of the present invention, by reducing the credibility of the prediction model for the corresponding time period through a penalty factor, the predicted value can also be multiplied by a weight less than 1 to reduce the confidence of the model for this time period, as opposed to artificially penalizing the prediction of the prediction model during this time period.

[0107] In one or more embodiments of the present invention, the ARIMA model can also be transformed into an ARIMAX model, introducing an external variable as a factor, and introducing LASSO regularization on the basis of the ARIMAX model. The ARIMAX model can consider the dynamic characteristics of the time series, while LASSO regularization helps to reduce the complexity of the model and improve the generalization ability of the model. By combining these two methods, it is possible to better adapt to time series data with external influencing factors while avoiding the problem of overfitting.

[0108] Exemplarily, if it is predicted at time t0 that the predicted change rate of the indoor carbon dioxide concentration is decreasing in the next k hours, and the predicted carbon dioxide concentration within the set time period does not exceed the set concentration threshold, the control unit 112 can control the fan assembly 107 to switch from the operating state to the stopped operating state. After the control unit 112 controls the fan assembly 107 to switch to the stopped operating state, for example, 20 minutes after the fan assembly 107 switches to the stopped operating state, it starts to receive the output result of the human presence sensing module in real time; if a human detection signal generated by the human presence sensing module is received, it is determined that the prediction model has made a wrong prediction, a penalty factor is introduced to reduce the credibility of the prediction result for the corresponding time period, and at the same time the fan assembly 107 is switched from the stopped operating state to the operating state.

[0109] In one or more embodiments of the present application, by combining the ARIMAX prediction model and the ARIMA prediction model, if it is predicted at time t0 that the indoor carbon dioxide concentration will not exceed the set concentration threshold within the next k hours, and at the same time the difference between the indoor and outdoor carbon dioxide concentrations will decrease, the control unit 112 can control the fan assembly 107 to switch from the operating state to the stopped state. After the control unit 112 controls the fan assembly 107 to switch to the stopped state, for example, 20 minutes after the fan assembly 107 switches to the stopped state, it starts to receive the output result of the human presence detection module in real time; if it receives the human detection signal generated by the human presence detection module, it is determined that the prediction model prediction is incorrect, a penalty factor is introduced, and the credibility of the prediction results of the RIMAX prediction model and the ARIMA prediction model for the corresponding period is reduced. At the same time, the fan assembly 107 is switched from the stopped state to the operating state.

[0110] The second aspect of the present application provides an air conditioning system. In addition to the fresh air device 10 provided in the above embodiments, the air conditioning system is further provided with an air conditioning device 20. The air conditioning device 20 is used to adjust indoor air parameters and has a refrigeration cycle. The air conditioning device 20 can independently perform refrigeration operation or independently perform heating operation.

[0111] As Figure 10 , in the present application, the air conditioning device 20 performs the refrigeration cycle of the air conditioning device 20 by using a compressor 201, a condenser 202, an expansion valve 203, and an evaporator 204. The refrigeration cycle includes a series of processes involving compression, condensation, expansion, and evaporation to cool or heat the indoor space.

[0112] The low-temperature and low-pressure refrigerant enters the compressor 201, and the compressor 201 compresses the refrigerant gas into a high-temperature and high-pressure state and discharges the compressed refrigerant gas. The discharged refrigerant gas flows into the condenser 202. The condenser 202 condenses the compressed refrigerant into a liquid phase, and heat is released to the surrounding environment through the condensation process.

[0113] The expansion valve 203 expands the high-temperature and high-pressure liquid-phase refrigerant formed by condensation in the condenser 202 into a low-pressure liquid-phase refrigerant. The evaporator 204 evaporates the refrigerant expanded in the expansion valve 203 and returns the refrigerant gas in the low-temperature and low-pressure state to the compressor 201. The evaporator 204 can achieve a refrigeration effect by using the latent heat of evaporation of the refrigerant to exchange heat with the material to be cooled. In the whole cycle, the air conditioning device 20 can adjust the temperature of the indoor space.

[0114] The outdoor unit of the air conditioning device 20 refers to the part of the refrigeration cycle including the compressor 201 and the outdoor heat exchanger. The indoor unit of the air conditioning device 20 includes an indoor heat exchanger, and the expansion valve 203 can be provided in the indoor unit or the outdoor unit.

[0115] The indoor heat exchanger and the outdoor heat exchanger serve as the condenser 202 or the evaporator 204. When the indoor heat exchanger serves as the condenser 202, the air conditioning device 20 serves as a heater in the heating mode, and when the indoor heat exchanger serves as the evaporator 204, the air conditioning device 20 serves as a cooler in the cooling mode.

[0116] As Figure 11 shown, the air conditioning system further includes an indoor carbon dioxide concentration sensor 108 for detecting the indoor carbon dioxide concentration and an outdoor carbon dioxide concentration sensor 109 for detecting the outdoor carbon dioxide concentration. In one or more embodiments of the present application, the indoor carbon dioxide concentration sensor 108 may be disposed at the indoor air return opening 103, and the outdoor carbon dioxide concentration sensor 109 may be disposed at the outdoor fresh air opening 101. In one or more embodiments of the present application, the indoor carbon dioxide concentration sensor 108 may be disposed in the indoor unit of the air conditioning device 20, and the outdoor carbon dioxide concentration sensor 109 may be disposed in the outdoor unit of the air conditioning device 20.

[0117] In one or more embodiments of the present invention, the air conditioning system further includes a generation unit 110, a calculation unit 111, and a control unit 112.

[0118] The generation unit 110 is configured to establish a prediction model based on the detected indoor carbon dioxide concentration time series, and the prediction model can generate a predicted change rate of the indoor carbon dioxide concentration. The "predicted change rate" refers to the estimation of the change trend of the indoor carbon dioxide concentration by the prediction model in a future set time period, and the duration of the set time period may be 1 hour, several hours, 24 hours, several days, etc.

[0119] The calculation unit 111 is configured to calculate the concentration difference between the detected indoor carbon dioxide concentration and the outdoor carbon dioxide concentration. More specifically, the calculation unit 111 is configured to calculate the concentration difference between the indoor carbon dioxide concentration and the outdoor carbon dioxide concentration at the corresponding time point.

[0120] The control unit 112 is configured to control the rotational speed of the fan assembly 107 of the fresh air device 10 based on the predicted change rate output by the generation unit 110 and the concentration difference calculated by the calculation unit 111. Moreover, when the fan assembly 107 is operating, the control unit 112 controls the air conditioning device 20 to be in an operating state and increases the capacity output of the air conditioning device 20. In one or more embodiments of the present application, the control unit 112 controls the rotational speed of the fan assembly 107 of the fresh air device 10 based on the predicted change rate and the concentration difference, including: for the same predicted change rate, as the concentration difference increases, increasing the rotational speed of the fan assembly 107; for the same concentration difference, as the predicted change rate increases, increasing the rotational speed of the fan assembly 107; and for a decreasing predicted change rate, when the concentration difference is within a set stable difference range, turning off the fresh air assembly.

[0121] Increasing the capacity output of the air conditioning device 20 includes, but is not limited to: increasing the operating frequency of the compressor 201; that is, forcibly increasing the operating frequency of the compressor 201 based on the operating frequency output by a predetermined control algorithm (such as a PID algorithm or a fuzzy control algorithm).

[0122] The air conditioning system provided by the present invention can also reduce the indoor temperature change caused during the introduction of fresh air and improve comfort on the premise of avoiding an excess of fresh air.

[0123] In one or more embodiments of the present application, the prediction model is an ARIMA model, and the prediction model can generate the predicted indoor carbon dioxide concentration at a set time.

[0124] In one or more embodiments of the present invention, the generation unit 110 can also be configured to establish an ARIMAX model based on the detected indoor carbon dioxide concentration time series and the outdoor carbon dioxide concentration time series. The ARIMAX model can generate the predicted indoor carbon dioxide concentration and the predicted concentration difference between the indoor and outdoor carbon dioxide concentrations at a set time.

[0125] As Figure 12 shown, in one or more embodiments of the present invention, the air conditioning system can implement the control process as shown.

[0126] When the predicted change rate increases and the predicted indoor carbon dioxide concentration exceeds the first set concentration threshold (as Figure 12 shown in step S601), the control unit 112 can control the fan assembly 107 to switch from the stopped operating state to the operating state and maintain it at a set rotational speed until the detected indoor carbon dioxide concentration is lower than the first set concentration threshold (as Figure 12 shown in step S602).

[0127] That is, first, unmanned pre-start control can be executed. The fresh air device 10 switches to the operating state and maintains operation at a set speed (for example, speed 1), purifying the indoor air in advance while ensuring low energy consumption.

[0128] Presume whether the number of indoor carbon dioxide emission sources increases (as Figure 12 shown in step S603), for example, presume whether the human sensing module outputs a human detection signal, or whether the camera module outputs a heat source detection signal through an image recognition algorithm.

[0129] When the number of indoor carbon dioxide emission sources increases, the control unit 112 controls the fan assembly 107 to maintain at the set speed, controls the air conditioning device 20 to be in the operating state and increases the capacity output of the air conditioning device 20 until the set target temperature and / or set target humidity is reached, that is, the difference between the actually detected indoor temperature and the set target temperature approaches zero, and the difference between the actually detected indoor humidity and the set target humidity approaches zero (as Figure 12 shown in step S604).

[0130] Presume whether the detected indoor carbon dioxide concentration is lower than the first set concentration threshold (as Figure 12 shown in step S605).

[0131] When the detected indoor carbon dioxide concentration is lower than the first set concentration threshold, the control unit controls the fan assembly 107 to switch from the operating state to the stopped state, and aborts the control of increasing the capacity output of the air conditioning device 20 (as Figure 12 shown in step S606).

[0132] When the detected indoor carbon dioxide concentration is not lower than the first set concentration threshold, presume whether the predicted change rate is increasing (as Figure 12 shown in step S607); if the predicted change rate is increasing, control the speed of the fan assembly 107 based on the predicted change rate output by the generation unit 110 and the concentration difference calculated by the calculation unit 111, control the air conditioning device 20 to be in the operating state and maintain the control of increasing the capacity output of the air conditioning device 20 until the set target temperature and / or set target humidity is reached (as Figure 12 shown in step S608).

[0133] Presume whether the detected indoor carbon dioxide concentration is lower than the second set concentration threshold (as Figure 12 shown in step S609);

[0134] When the detected indoor carbon dioxide concentration is lower than the second set concentration threshold, control the fan assembly 107 to operate at the set speed until the detected indoor carbon dioxide concentration reaches the first set concentration threshold, control the fan assembly 107 to switch from the operating state to the stopped state, and abort the control of increasing the capacity output of the air conditioning device 20 (asFigure 12 as shown in step S610);

[0135] wherein the first set concentration threshold corresponds to the concentration level set to maintain the indoor air quality or ensure comfort, and the second set concentration threshold is higher than the first set concentration threshold, for example, 100 higher than the first set threshold ppm .

[0136] When the predicted carbon dioxide concentration does not exceed the first set concentration threshold within the set time period and the predicted change rate is lower than the set change rate threshold, the control unit 112 can control the fan assembly 107 to switch from the running state to the stopped state (such as Figure 13 shown in steps S701 to S703 therein).

[0137] That is to say, during the control process, the control of switching the fan assembly 107 from the running state to the stopped state is executed in all of the following three cases: 1. When the number of indoor carbon dioxide emission sources increases and the fan assembly 107 is controlled to run at the set speed so that the detected indoor carbon dioxide concentration is lower than the first set threshold; 2. When the predicted carbon dioxide concentration does not exceed the first set concentration threshold within the set time period and the predicted change rate is a decreasing predicted change rate; 3. Under the decreasing predicted change rate and when the concentration difference is within the set stable difference range. Thus, the situation of excessive fresh air can be avoided on the premise of ensuring comfort.

[0138] In the description of the above embodiments, the specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0139] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. Fresh air equipment, comprising: A housing, which has: A fresh air inlet located outdoors; A supply air outlet located indoors, and the supply air outlet is connected to the fresh air inlet through a supply air duct; A return air inlet located indoors; An exhaust air outlet located outdoors, and the exhaust air outlet is connected to the return air inlet through an exhaust air duct; And A fan assembly, which is used to guide the air flow in the supply air duct and the exhaust air duct; It is characterized in that it further includes: An indoor carbon dioxide concentration sensor for detecting the indoor carbon dioxide concentration, and an outdoor carbon dioxide concentration sensor for detecting the outdoor carbon dioxide concentration; A generating unit, which is configured to establish a prediction model based on the detected indoor carbon dioxide concentration time series, and the prediction model can generate a predicted change rate of the indoor carbon dioxide concentration; A calculating unit, which is configured to calculate the concentration difference between the detected indoor carbon dioxide concentration and the outdoor carbon dioxide concentration; and A control unit, which is configured to control the rotational speed of the fan assembly based on the predicted change rate and the concentration difference, including: For the same predicted change rate, as the concentration difference increases, increase the rotational speed of the fan assembly; For the same concentration difference, as the predicted change rate increases, increase the rotational speed of the fan assembly; For a decreasing predicted change rate, when the concentration difference is within a set stable difference range, turn off the fresh air assembly.

2. The fresh air equipment according to claim 1, characterized in that: The prediction model can generate a predicted indoor carbon dioxide concentration at a set moment; When the predicted change rate increases and the predicted carbon dioxide concentration exceeds a set concentration threshold, the control unit can control the fan assembly to switch from the stop operation state to the operation state.

3. The fresh air equipment according to claim 2, characterized in that: After the control unit controls the fan assembly to switch to the operation state, it starts to estimate whether the number of indoor carbon dioxide emission sources increases; If the number of indoor carbon dioxide emission sources does not increase within a set period, the control unit reduces the credibility of the prediction model for the corresponding time period through a penalty factor, and at the same time controls the fan assembly to switch from the operation state to the stop operation state.

4. The fresh air equipment according to claim 2, characterized in that: When the predicted carbon dioxide concentration does not exceed the set concentration threshold within a set time period and the predicted change rate is a decreasing predicted change rate, the control unit can control the fan assembly to switch from the operation state to the stop operation state.

5. The fresh air equipment according to claim 4, characterized in that: After the control unit controls the fan assembly to switch to the stop operation state, it starts to estimate whether the number of indoor carbon dioxide emission sources increases; If the number of indoor carbon dioxide emission sources increases within a set period, the control unit reduces the credibility of the prediction model for the corresponding time period through a penalty factor, and at the same time controls the fan assembly to switch from the stop operation state to the operation state.

6. The fresh air equipment according to any one of claims 1 to 5, characterized in that: The prediction model is established through the following steps: Collect the indoor carbon dioxide concentration detected by the indoor carbon dioxide concentration sensor, and establish a time series of detected values of the indoor carbon dioxide concentration. The time series of detected values includes multiple observation values, and each observation value is an indoor carbon dioxide concentration detection value; Verify whether the multiple observation values in the time series of detected values are stationary; if the multiple observation values have non-stationarity, perform a differencing operation on the time series of detected values until the multiple observation values have stationarity; Record the number of differential operations to obtain a verification time series; Calculate the average value of the verification time series; Calculate the difference between each observation value and the average value to obtain a deviation sequence; Perform a convolution operation on the deviation sequence with itself to obtain an autocovariance sequence; Normalize the autocovariance sequence to obtain an autocorrelation coefficient; Plot an autocorrelation graph and a partial autocorrelation graph, establish an autoregressive integrated moving average model, and use the autoregressive integrated moving average model as the prediction model.

7. An air conditioning system, comprising: A fresh air device, which includes: A housing, which has: A fresh air inlet located outdoors; An air supply outlet located indoors, and the air supply outlet is connected to the fresh air inlet through an air supply duct; A return air inlet located indoors; An exhaust outlet located outdoors, and the exhaust outlet is connected to the return air inlet through an exhaust duct; and A fan assembly for guiding the air flow in the air supply duct and the exhaust duct; And An air conditioning device for adjusting indoor air parameters and having a refrigeration cycle; It is characterized in that it further includes: An indoor carbon dioxide concentration sensor for detecting the indoor carbon dioxide concentration and an outdoor carbon dioxide concentration sensor for detecting the outdoor carbon dioxide concentration; A generating unit configured to establish a prediction model based on the detected indoor carbon dioxide concentration time series, and the prediction model can generate a predicted change rate of the indoor carbon dioxide concentration; A calculating unit configured to calculate the concentration difference between the detected indoor carbon dioxide concentration and the outdoor carbon dioxide concentration; and A control unit configured to control the rotation speed of the fan assembly based on the predicted change rate and the concentration difference, and when the fan assembly is running, control the air conditioning device to be in an operating state and increase the capacity output of the air conditioning device; wherein, controlling the rotation speed of the fan assembly based on the predicted change rate and the concentration difference includes: For the same predicted change rate, as the concentration difference increases, increase the rotation speed of the fan assembly; For the same concentration difference, as the predicted change rate increases, increase the rotation speed of the fan assembly; For a decreasing predicted change rate, when the concentration difference is within a set stable difference interval, turn off the fresh air component.

8. The air conditioning system according to claim 7, characterized in that: The prediction model can generate a predicted indoor carbon dioxide concentration at a set moment; When the predicted change rate increases and the predicted indoor carbon dioxide concentration exceeds a first set concentration threshold, the control unit can control the fan assembly to switch from a stopped operating state to an operating state and maintain it at a set rotation speed until the detected indoor carbon dioxide concentration is lower than the first set concentration threshold; Estimate whether the number of indoor carbon dioxide emission sources increases; When the number of indoor carbon dioxide emission sources increases, the control unit controls the fan assembly to maintain a set rotation speed, controls the air conditioning device to be in an operating state and increases the capacity output of the air conditioning device until a set target temperature and / or a set target humidity is reached; Estimate whether the detected indoor carbon dioxide concentration is lower than the first set concentration threshold; When the detected indoor carbon dioxide concentration is lower than the first set concentration threshold, control the fan assembly to switch from an operating state to a stopped operating state and abort the control of increasing the capacity output of the air conditioning device; When the detected indoor carbon dioxide concentration is not lower than the first set concentration threshold, it is presumed whether the predicted change rate is increasing; if the predicted change rate is increasing, the rotation speed of the fan assembly is controlled based on the predicted change rate and the concentration difference, and the air conditioning equipment is controlled to be in an operating state and the control to increase the capacity output of the air conditioning equipment is maintained until the set target temperature and / or the set target humidity is reached; Presume whether the detected indoor carbon dioxide concentration is lower than the second set concentration threshold; When the detected indoor carbon dioxide concentration is lower than the second set concentration threshold, control the fan assembly to operate at a set rotation speed until the detected indoor carbon dioxide concentration is lower than the first set concentration threshold; Wherein the second set concentration threshold is higher than the first set concentration threshold.

9. The air conditioning system according to claim 8, characterized in that: When the predicted carbon dioxide concentration does not exceed the first set concentration threshold within the set time period and the predicted change rate is a decreasing predicted change rate, the control unit can control the fan assembly to switch from the operating state to the stopped operating state.

10. The air conditioning system according to any one of claims 7 to 9, characterized in that: The prediction model is established through the following steps: Collect the indoor carbon dioxide concentration detected by the indoor carbon dioxide concentration sensor, and establish a time series of the detected values of the indoor carbon dioxide concentration. The time series of the detected values includes a plurality of observed values, and each observed value is a detected value of the indoor carbon dioxide concentration; Verify whether the plurality of observed values in the time series of the detected values are stationary; if the plurality of observed values have non-stationarity, perform a differencing operation on the time series of the detected values until the plurality of observed values have stationarity; record the number of times of the differencing operation to obtain a verified time series; Calculate the average value of the verified time series; Calculate the difference between each observed value and the average value to obtain a deviation series; Perform a convolution operation of the deviation series with itself to obtain an autocovariance series; Standardize the autocovariance series to obtain an autocorrelation coefficient; Draw an autocorrelation graph and a partial autocorrelation graph, establish an autoregressive integrated moving average model, and use the autoregressive integrated moving average model as the prediction model.

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