TWI666410B - Air conditioning system and control method for the air conditioning system - Google Patents

Abstract

The air-conditioning system of the present invention includes at least one air-conditioning device, an air-conditioning controller, and at least one photographing device. The air conditioner is installed in the space. Each air conditioner includes a temperature sensor to detect the current temperature of the space. The air-conditioning controller is connected to a temperature sensor and stores a target temperature. The air-conditioning controller determines a first temperature difference according to the current temperature and the target temperature. The camera is installed in the space and detects the flow of people in the space. The air-conditioning controller determines a temperature adjustment time of one of the air-conditioning apparatuses according to the first temperature difference and the flow information transmitted by the photographing apparatus.

Description

Air conditioning system and air conditioning control method using the same

The present invention relates to an air-conditioning system and an air-conditioning control method using the same. Specifically, the present invention relates to an air-conditioning system with an energy-saving design and an air-conditioning control method using the same.

As energy demand increases, it may face a situation of insufficient energy supply. In addition, excessive power consumption not only causes energy waste and increased electricity costs, but also places a burden on the environment. Therefore, energy management has always been one of the important issues in life. As far as air-conditioning systems are concerned, there is still a general lack of effective energy-saving measures, whether at home or at commercial establishments. Although some energy-saving solutions have been proposed in the prior art, it is easy to produce uncomfortable feelings. Therefore, the existing air-conditioning system still needs to be improved.

It is an object of the present invention to provide an air conditioning system and an air conditioning control method, which can adjust the energy saving ratio according to environmental changes.

The air-conditioning system includes at least one air-conditioning device, an air-conditioning controller, and at least one photographing device. The air conditioner is installed in the space. Each air conditioner includes a temperature sensor to detect the current temperature of the space. The air-conditioning controller is connected to a temperature sensor and stores a target temperature. The air-conditioning controller determines a first temperature difference according to the current temperature and the target temperature. Photography device installed in space And detect the flow of people in the space. The air-conditioning controller determines a temperature adjustment time of one of the air-conditioning apparatuses according to the first temperature difference and the flow information transmitted by the photographing apparatus.

The air-conditioning control method includes the following steps: (A) using a temperature sensor of an air-conditioning device to detect a current temperature in a space; (B) using an air-conditioning controller to determine a first temperature based on the current temperature and a target temperature Temperature difference; (C) detecting a person's flow information in the space by a photographing device; (D) determining a temperature adjustment time of an air conditioner according to the first temperature difference and the flow information.

1‧‧‧air conditioning system

10,10A‧‧‧Air conditioner

20,20A‧‧‧Air-conditioning controller

30, 30A, 30B ‧ ‧ ‧ Camera

30C, 30D, 30E‧‧‧Photographic devices

30F‧‧‧Photographic device

40‧‧‧ relay device

50‧‧‧host

60‧‧‧Internet

70‧‧‧ Neural Network

110,110A‧‧‧Temperature sensor

701‧‧‧input layer

702‧‧‧hidden layer

703‧‧‧Output layer

704‧‧‧Content layer

R‧‧‧Temperature area

FIG. 1 is a schematic diagram of an embodiment of an air conditioning system according to the present invention.

Figure 2 is a schematic diagram of an air conditioning system configuration.

FIG. 3 is a flowchart of an embodiment of an air conditioning control method according to the present invention.

FIG. 4A is a characteristic diagram of a first temperature difference and an assigned value.

FIG. 4B is a characteristic diagram of the flow information and the attribution value.

FIG. 4C is a characteristic diagram of the temperature adjustment time and the belonging value.

FIG. 5 is a flowchart of an embodiment of an air conditioning control method according to the present invention.

FIG. 6 is a characteristic diagram of the second temperature difference and the assigned value.

FIG. 7 is a schematic diagram of an embodiment for obtaining a predicted temperature.

FIG. 8 is a flowchart of an embodiment of an air conditioning control method according to the present invention.

FIG. 9 is a schematic diagram of another embodiment of an air conditioning system according to the present invention.

FIG. 10 is a schematic configuration diagram of an air conditioning system.

FIG. 11 is a flowchart of an embodiment of an air conditioning control method according to the present invention.

FIG. 1 is a schematic diagram of an embodiment of an air conditioning system 1 according to the present invention. As shown in Figure 1, The air-conditioning system 1 includes an air-conditioning apparatus 10, an air-conditioning controller 20, and a photographing apparatus 30. The air conditioner 10 is, for example, a separate type air conditioner and heater, and includes a temperature sensor 110 for detecting the current temperature of the space. The air-conditioning controller 20 is connected (wired or wirelessly) with a temperature sensor 110 and stores a target temperature. The air-conditioning controller 20 is, for example, a programmable integrated circuit in an embedded platform, and has an algorithm for determining a temperature difference and a required output variable. The air-conditioning controller 20 determines a first temperature difference according to the current temperature and the target temperature. The photographing device 30 detects the flow of people in the space. The flow information includes, for example, the number of people in the space. The air-conditioning controller 20 determines the temperature-adjusting time of the air-conditioning apparatus 10 according to the first temperature difference and the flow of people information transmitted by the photographing apparatus 30. The temperature adjustment time refers to a time for adjusting the air volume, the wind speed, or the air temperature of the air conditioning device 10 to change the average temperature in the space.

In an embodiment, the air-conditioning controller 20 is disposed outside the air-conditioning device 10, and the air-conditioning device 10 corresponding to each temperature sensor 110 is determined according to the information transmitted by the photographing device 30 and the temperature sensors 110 in different air-conditioning devices 10. Temperature adjustment time. In another embodiment, the air-conditioning controller 20 is disposed in the air-conditioning device 10, that is, the air-conditioning device 10 may include a temperature sensor 110 and an air-conditioning controller 20. The temperature adjustment time of the air-conditioning apparatus 10 is determined according to the information transmitted by the temperature sensor 110 and the imaging device 30.

FIG. 2 is a plan view of an indoor space showing a configuration of an air conditioning system. As shown in FIG. 2, the air-conditioning apparatus 10 is installed in a space. In addition, a photographing device 30, a photographing device 30A, and a photographing device 30B are provided in the space. The air-conditioning apparatus 10 has a temperature-adjusting area R. The aforementioned photographing devices (30, 30A, 30B) are disposed in the temperature-adjusting area R. Further, the shooting range of the photographing device (30, 30A, 30B) corresponds to the temperature-adjusting area R. Detects changes in the flow of people in the temperature regulation area R. For example, image recognition is used to obtain the number of people in the space corresponding to the temperature adjustment region R, thereby obtaining the flow of people information.

FIG. 3 is a flowchart of an embodiment of an air conditioning control method according to the present invention. As shown in FIG. 3, the air conditioning control method includes steps S10 to S70. In step S10, the temperature of the air conditioner is sensed. The device detects the current temperature in the space. In step S30, the air conditioner controller determines a first temperature difference according to the current temperature and the target temperature. For example, target temperature Ta, and get the current temperature as Tn. A first temperature difference E1 (having E1 = Tn-Ta) can be obtained from the target temperature Ta and the current temperature Tn, so that the first temperature difference can be known. In one embodiment, the target temperature may be set according to regulations (for example, 26 degrees). In other embodiments, the target temperature can be further adjusted according to the season, thereby improving the accuracy of the temperature adjustment time. As shown in FIG. 3, in step S50, the spatial flow information is detected by the photographing device. In step S70, the temperature adjustment time of the air-conditioning apparatus is determined according to the first temperature difference and the flow of people information.

4A to 4C show an example of determining the temperature adjustment time. Regarding the level of the first temperature difference, the number of people, and the length of the temperature adjustment time, it has imprecise meaning. Fuzzy control can be used to determine the control method of the temperature adjustment time of the air conditioning device (such as changing the temperature adjustment time length). For the air conditioning control method in FIG. 3, the main variables are the first temperature difference, the flow of people information, and the temperature adjustment time. The first temperature difference and the flow of people information are input variables, and the temperature adjustment time is an output variable. For the above variables, a fuzzy set can be set separately, and the belonging function of the fuzzy set can be defined.

Please refer to FIG. 4A. FIG. 4A is a characteristic diagram of a first temperature difference and an assigned value. As shown in FIG. 4A, the different temperature ranges for the first temperature difference can have values such as "above" (greater than 0 degrees), "close" (-2 degrees to 2 degrees), and "below" (less than 0 degrees) ), And define the belonging functions of these three sets. In the example of FIG. 4A, the z function, triangle function, and s function are respectively used as the attribution functions of the three sets “above”, “close”, and “below”. The magnitude of the belonging value indicates the degree of correspondence between the variable (for example, the first temperature difference value) and the fuzzy set. It should be understood that the number of the set of fuzzy sets and the kind of the belonging function are not limited thereto.

FIG. 4B is a characteristic diagram of the flow information and the attribution value. As shown in FIG. 4B, when the number of people is used as the flow information, the different value ranges for the number of people can be such as "more" (greater than 4), "several" (2 to 6), "less" (less than 4) fuzzy sets, and define these three sets respectively Combined attribution function. In the example of FIG. 4B, the z function, the triangle function, and the s function are respectively used as the attribution functions of the three sets “more”, “several”, and “less”.

FIG. 4C is a characteristic diagram of the temperature adjustment time and the belonging value. As shown in FIG. 4C, for different ranges of temperature adjustment time, there may be fuzzy sets such as "long" (greater than 20 minutes), "medium" (10 to 30 minutes), and "short" (less than 20 degrees), And define the belonging functions of these three sets. In the example of FIG. 4C, the z function, triangle function, and s function are used as the assignment functions of the three sets of "long", "medium", and "short", respectively.

In addition, according to the fuzzy sets in the above figures, a series of fuzzy rules connecting input variables and output variables can be established as a rule base, and inference rules for fuzzy rules are set (for example, using Mamdani's max-min inference method) . In other words, a fuzzy rule including the first temperature difference, the number of people, and the temperature adjustment time is established in the rule base. Fuzzy rules can be expressed in the form of if-then, for example, "if the first temperature difference is higher than the number of people, and then the temperature adjustment time is long." Another example is "if the first temperature difference is close and there are several people, then the temperature adjustment time is long." The values obtained by each variable may correspond to different fuzzy sets, which triggers multiple fuzzy rules. For example, obtaining a first temperature difference of 1.5 degrees according to the target temperature and the current temperature may correspond to two fuzzy sets of "above" (attribution value 0.6) and "close" (attribution value 0.4). The flow information also finds the corresponding fuzzy sets in the same way (for example, corresponding to "multi" and "several"). Based on this, the fuzzy rules corresponding to "above", "close", "multiple", and "several" in the rule base are found.

The conclusion is obtained according to the attribute value of the fuzzy rule, and the temperature adjustment time is obtained. Further, each of the fuzzy rules triggered as described above can obtain different attribution values regarding the temperature adjustment time. The inference result is to integrate the attribution values of each fuzzy rule and take it as a conclusion. Then perform a defuzzification operation (for example, using the center of gravity method) to obtain the exact temperature adjustment time. For example, for the case where the current temperature is close to the target temperature (the first temperature difference is close), the case where there are several people can output less temperature adjustment time than the case where there are many people to avoid power consumption. . That is, without affecting comfort, According to the number of people according to the flow of people information and the first temperature difference, the energy saving time can be distinguished to achieve the energy saving effect.

In other embodiments, in addition to the temperature adjustment time, a duty cycle may be added as another output variable, which is defined as the ratio of the on time to the operation cycle time. For example, for different temperature adjustment times, there may be fuzzy sets such as "high" (greater than 50%), "medium" (25% to 75%), and "low" (less than 50%), and sets corresponding to these fuzzy sets Attribution function. In the rule base, fuzzy rules including the first temperature difference, flow information, temperature adjustment time, and duty cycle are established. The conclusion is obtained according to the attribution value of the fuzzy rule, and the temperature adjustment time and working cycle are obtained. This achieves energy saving effects.

FIG. 5 is a flowchart of an embodiment of an air conditioning control method according to the present invention. As shown in FIG. 5, the air conditioning control method includes steps S10 to S70. The difference from the foregoing embodiment is that a step of adding a predicted temperature and a second temperature difference during the input variable collection stage is added. As shown in FIG. 5, in steps S10 and S30, the air-conditioning controller determines a first temperature difference according to the current temperature and the target temperature. In step S20, the air-conditioning controller determines a predicted temperature based on the historical temperature data. For example, the predicted temperature can be obtained statistically or machine learning. In step S40, the air-conditioning controller determines a second temperature difference according to the current temperature and the predicted temperature. For example, the current temperature Tn is obtained, and the predicted temperature is Tp. A second temperature difference E2 (having E2 = Tp-Tn) can be obtained from the current temperature Tn and the predicted temperature Tp, and thus the second temperature difference can be known.

As shown in FIG. 5, in step S50, the spatial flow information is detected by the photographing device. The foregoing step of obtaining the predicted temperature may be performed before obtaining the first temperature difference, but is not limited thereto. In other examples, the method of obtaining the input variables such as the first temperature difference, the flow information, and the second temperature difference may be processed in parallel. In step S70, the air-conditioning controller determines the temperature adjustment time according to the first temperature difference, the flow information, and the second temperature difference.

FIG. 6 is a characteristic diagram of the second temperature difference and the assigned value. As shown in Figure 6, The different second temperature differences may have fuzzy sets such as "walking up", "flat", and "walking down", and an assignment function corresponding to these fuzzy sets is set. A fuzzy rule including a first temperature difference value, flow information, a second temperature difference value, and a temperature adjustment time is established in the rule base. For example, "if the first temperature difference is higher than the number of people and the second temperature difference is rising, then the temperature adjustment time is long." When the rising second temperature difference is obtained, the relevant fuzzy rule is triggered, a conclusion is obtained according to the attribution value of the fuzzy rule, and the temperature adjustment time is obtained. For example, for the case where the current temperature is close to the target temperature (the first temperature difference is close), the case where the second temperature difference is flat may output fewer adjustments than the case where the second temperature difference is rising. Warm time to avoid power consumption. That is, without affecting the comfort, the energy saving time is further differentiated according to the second temperature difference obtained from the predicted temperature to achieve the energy saving effect.

The air-conditioning controller determines the predicted temperature according to the currently input historical temperature data and the processing result of the historical temperature data input at the previous moment. FIG. 7 is a schematic diagram of an embodiment for obtaining a predicted temperature. As shown in FIG. 7, the neural network 70 has a feedback design, which includes an input layer 701, a hidden layer 702, an output layer 703, and a content layer 704. Generally speaking, historical temperature data is transmitted to neurons in the input layer 701, and then transmitted to the output layer 703 via the hidden layer 702. In the example of FIG. 7, the currently obtained predicted temperature is related to the processing result of the currently input historical temperature data and the historical temperature data input at the previous time. For example, the currently input historical temperature data is then transmitted to the output layer 703 via the input layer 701 and the hidden layer 702. On the other hand, the historical temperature data input at the previous moment is then transmitted to the content layer 704 via the input layer 701 and the hidden layer 702, and after receiving the content layer 704's processing feedback, the hidden layer 702 is then transmitted to the output layer 703. This determines the predicted temperature. The feedback neural network used for predicting the temperature may be, for example, an Elman neural network, a Jordan neural network, or other feedback-like neural network architectures. A feedback neural network can be used to obtain the predicted temperature based on the relevance of temperature data before and after, thereby improving the accuracy of the temperature adjustment time.

FIG. 8 is a flowchart of an embodiment of an air conditioning control method according to the present invention. As shown in FIG. 8, the air conditioning control method includes steps S10 to S70. The difference from the foregoing embodiment is that a step of predicting temperature and temperature variable is added in the input variable collection stage. As shown in FIG. 8, in steps S10 and S30, the air-conditioning controller determines a first temperature difference according to the current temperature and the target temperature. In step S20, the air-conditioning controller determines a predicted temperature based on the historical temperature data. In step S42, the air-conditioning controller determines a plurality of predicted temperatures within a predetermined period (for example, 10 minutes) according to the historical temperature data, and determines a temperature variable according to the difference between the current temperature and each predicted temperature.

For example, the current temperature Tn is obtained, and the predicted temperatures in a predetermined period are Tp1 and Tp2. A second temperature difference E21 (with E21 = Tp1-Tn) can be obtained from the current temperature Tn and the predicted temperature Tp1, and a second temperature difference E22 (with E22 = Tp2-Tn) can be obtained from the current temperature Tn and the predicted temperature Tp2. From the second temperature difference E21 and the second temperature difference E22, a temperature variable dE2 (with dE2 = E21-E22) can be obtained. This shows the rate of change in temperature. After the end of the predetermined period, the next calculation of the predicted temperature can be performed.

As shown in FIG. 8, in step S50, the spatial flow information is detected by the photographing device. The foregoing step of obtaining the predicted temperature may be performed before obtaining the first temperature difference, but is not limited thereto. In step S70, the air-conditioning controller determines the temperature adjustment time according to the first temperature difference, the flow information, and the temperature variable.

For example, different temperature variables may have fuzzy sets such as "strong rise", "slow rise", "flat", "strong fall", and "slow fall", and an assignment function corresponding to these fuzzy sets may be set. A fuzzy rule including the first temperature difference, the flow information, the temperature variable, and the temperature adjustment time is established in the rule base. For example, "if the first temperature difference is higher than the number of people and the temperature variable is a strong rise, then the temperature adjustment time is long." When a temperature variable with a large rate of rise is obtained, the relevant fuzzy rule is triggered, a conclusion is obtained according to the attribution value of the fuzzy rule, and the temperature adjustment time is obtained. For example, in the case where the temperature adjustment time is longer than 25 minutes, the case where the temperature variable is slowly rising is stronger than the temperature variable It can output less temperature adjustment time to avoid power consumption. That is, without affecting the comfort, the energy saving time is further differentiated according to the temperature variable to achieve the energy saving effect.

FIG. 9 is a schematic diagram of another embodiment of the air conditioning system 1 of the present invention. As shown in FIG. 9, the air-conditioning system 1 includes an air-conditioning device (10, 10A), an air-conditioning controller (20, 20A), and a photographing device (30, 30C, 30F). The air-conditioning apparatus 10 and the air-conditioning apparatus 10A may be installed in different areas in a space, for example. Taking the air-conditioning apparatus 10 as an example, the air-conditioning apparatus 10 includes a temperature sensor 110 for detecting the current temperature of the space. In the embodiment of FIG. 9, the air-conditioning controller 110 is provided in the air-conditioning apparatus 10. The air-conditioning controller 20 is connected to the temperature sensor 110 and stores a target temperature. The air-conditioning controller 20 determines a first temperature difference according to the current temperature and the target temperature. On the other hand, the photographing device 30 detects the flow of people in the space. The photographing device 30, the photographing device 30C, and the photographing device 30F are installed in different areas in a space, for example. For example, the number of persons in different regions in a space is obtained by using a photographing device in different regions. The air-conditioning controller 20 determines the temperature-adjusting time of the air-conditioning apparatus 10 according to the first temperature difference and the flow of people information transmitted by the photographing apparatus 30.

In FIG. 9, the photographing devices located in different areas can detect changes in the flow of people in different areas and send them to the corresponding air-conditioning device. As shown in FIG. 9, the air conditioning system 1 includes a relay device 40 and a host 50. The aforementioned air-conditioning device (10, 10A) and photographing device (30, 30C, 30F) are connected to the relay device 40 and the host computer 50 through the network 60. The relay device 40 is a data forwarding device, and the host 50 is, for example, a server installed at a remote end. After the flow information obtained by the photographing device (30, 30C, 30F) can be transmitted to the relay device 40 through the network 60, the relay device 40 transmits the flow information to the corresponding air-conditioning device. In addition, the execution results of the air conditioner (10, 10A) and the camera (30, 30C, 30F) can also be transmitted to the host 50 for storage or data analysis via the network 60.

FIG. 10 is a plan view of an indoor space showing a configuration of an air conditioning system. As shown in FIG. 10, the air-conditioning apparatus 10 and the air-conditioning apparatus 10A are installed in different areas in a space. In addition, a photographing device (30, 30A to 30F) is installed in the space. Camera (30, 30A, 30B) installed in air conditioner The temperature adjustment region R of the device 10, and the photographing devices (30C, 30D, 30E) are provided in the temperature adjustment region R of the air conditioning device 10A. Further, the shooting range of the photographing device (30, 30A, 30B) corresponds to the temperature adjustment area R of the air conditioning device 10, and the shooting range of the photographing device (30C, 30D, 30E) corresponds to the temperature adjustment area R of the air conditioning device 10A . As a result, the photographing devices located in different temperature-regulated areas detect changes in the flow of people in different areas and transmit them to the corresponding air-conditioning devices.

In addition, as shown in FIG. 10, the photographing device 30F in the space is set away from the temperature adjustment region R (that is, outside the range of the temperature adjustment region R), and can obtain the flow of people information different from other photographing devices (30, 30A ~ 30E). For example, in the step of obtaining the flow of people information, the number of people in the space detected by the camera set in the temperature adjustment area is used as the flow of people, and the flow rate of the space detected by the camera 30F is used as the flow of people. The temperature adjustment time can be adjusted according to different flow information. For example, the host computer (refer to Figure 9) stores the flow data of visitor characteristics of the store. The flow data describes the movement pattern of visitors in different periods. For example, after entering a store, a proportion of people move to a certain area, and a proportion of people move to Another area.

Refer to FIGS. 9 and 10. The interior space shown in FIG. 10 is taken as an example of the interior of the store. The photographing device (30, 30A, 30B) is installed in the store corresponding to the temperature adjustment area R of the air-conditioning device 10 and detects the number of people in the area. The camera device (30C, 30D, 30E) is set in another temperature-adjusting area R of the air-conditioning device 10A in the store and detects the number of people in the area. The photographing device 30F is set near the doorway in the store and detects the flow rate of people entering and leaving the doorway. According to the flow rate obtained by the photographing device 30F, the number of people in each temperature-adjusted area after a period of time can be known in advance according to the flow data stored in the host. The air-conditioning controller 20 can determine the temperature adjustment time of the air-conditioning device 10 as an initial output value according to the first temperature difference and the number of people obtained by the photographing device (30, 30A, 30B), and then change the number of people obtained based on the photographing device 30F. Fine-tune the temperature adjustment time as the final output value. Similarly, the temperature adjustment time of the air conditioner 10A can be adjusted in the above manner to provide a better temperature adjustment effect. In other words, the relay device 40 can use the flow information obtained by the photographing device 30F It is transmitted to the air-conditioning apparatus 10 and the air-conditioning apparatus 10A. This allows for comfort while providing energy savings.

In another embodiment, the photographing device includes a thermal camera. For example, the imaging device is a camera having an optical imaging function and a camera having a thermal image detection function. The space is detected by a thermal camera to generate a flat temperature to show the temperature distribution. The air-conditioning control method includes detecting the plane temperature of the space, and the air-conditioning controller adjusts the wind direction of the air-conditioning device according to the plane temperature. For example, in FIG. 10, the photographing device 30 may generate a plane temperature corresponding to the area where the air-conditioning device 10 is located. The temperature on one side of the display area is higher than other locations. . In other words, in addition to obtaining the temperature adjustment time, the air conditioning control method can obtain a wind direction adjustment amount through a thermal imaging camera to provide a better temperature adjustment effect.

FIG. 11 is a flowchart of an embodiment of an air conditioning control method according to the present invention. As shown in FIG. 11, the air conditioning control method includes steps S10 to S70. The difference from the foregoing embodiment is the step of adding humidity information during the input variable collection stage. For example, the air conditioning system also includes a hygrometer. The hygrometer is set in the space and detects the humidity information in the space. As shown in FIG. 11, in steps S10 and S30, the air-conditioning controller determines a first temperature difference according to the current temperature and the target temperature. In steps S20 and S40, the air-conditioning controller determines a second temperature difference according to the current temperature and the predicted temperature. In step S50, the spatial flow information is detected by the camera. In step S60, the humidity information in the space is detected. The foregoing step of obtaining the predicted temperature may be performed before obtaining the first temperature difference, but is not limited thereto. In other examples, the method of obtaining the input variables of the first temperature difference value, the flow information, the second temperature difference value, and the humidity information may be processed in parallel. In step S70, the air conditioning controller determines the temperature adjustment time according to the first temperature difference value, the flow information, the second temperature difference value, and the humidity information.

Taking Figure 10 as an example, a hygrometer can be set in the space to detect the humidity information in the space. For different humidity values, there may be fuzzy sets such as "higher", "medium", and "lower", and an assignment function corresponding to these fuzzy sets is set. The rule base contains a first temperature difference Fuzzy rules of value, flow information, second temperature difference value, humidity information, temperature adjustment time. For example, "if the first temperature difference is lower than the number of people is small, the second temperature difference is lower than the humidity is lower, and then the temperature adjustment time is short." When a relatively low humidity (for example, 40%) is measured, and the relevant fuzzy rule is triggered, a conclusion is obtained according to the attribute value of the fuzzy rule, and the temperature adjustment time is obtained. For example, a situation where the humidity is low and the number of people is less than that when the humidity is high and the number of people is less. For another example, a situation where the humidity is low and no one can output less temperature adjustment time than a situation where the humidity is low and the number of people is small, avoiding power consumption. That is, under the condition of not affecting comfort, according to the humidity information to further explore the potential energy-saving time to achieve energy-saving effects.

As mentioned earlier, the target temperature can be adjusted according to the season. In this way, the seasonal temperature change is set to different target temperatures to improve the energy saving effect. Furthermore, in another embodiment, the air conditioning system may include a hygrometer. The hygrometer is set in the space and detects the humidity information of the space. The air conditioning controller adjusts the target temperature based on the humidity information. For example, the target temperature is set to 26 degrees, and when the humidity is lower than 50%, the target temperature is increased. In this way, energy saving effects can be achieved without affecting comfort. Further, the method of adjusting the target temperature based on the humidity information can further refer to the comfort range table provided by the American Air Conditioning Association. According to the comfort range table, the air conditioning controller enables the air conditioning system to automatically adjust in response to changes in ambient humidity, thereby achieving Energy saving purpose. Further, the manner of adjusting the target temperature according to the humidity information can further cooperate with the aforementioned fuzzy function set for the humidity information. In other words, the target temperature can be adjusted first according to the humidity information, and then the temperature adjustment time can be obtained by using the humidity information in the air conditioning control method. This improves the accuracy of the temperature adjustment time.

The present invention has been described by the above related embodiments, but the above embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the invention. On the contrary, modifications and equal settings included in the spirit and scope of the scope of patent application are all included in the scope of the present invention.

Claims (16)

An air-conditioning system includes: at least one air-conditioning device disposed in a space, and each of the air-conditioning devices includes: a temperature sensor for detecting a current temperature of the space; and an air-conditioning controller connected to the temperature sensor. And a target temperature is stored, the air-conditioning controller determines a first temperature difference according to the current temperature and the target temperature; at least one photographing device is set in the space and detects the flow of people information in the space, wherein The air-conditioning controller obtains at least a first attribution value about the first temperature difference value and at least a second attribution value about the person flow information by using fuzzy control, and the air-conditioning controller obtains at least one first attribution value and The at least one second attribution value obtains at least one third attribution value regarding a temperature adjustment time from a fuzzy rule triggered by a rule base to determine the temperature adjustment time of the air conditioner. The air-conditioning system according to claim 1, wherein the air-conditioning controller determines a predicted temperature based on historical temperature data, and determines a second temperature difference based on the current temperature and the predicted temperature; the air-conditioning controller uses fuzzy control, The temperature regulation time is determined according to the fuzzy rule triggered by the first temperature difference value, the flow information, and the second temperature difference value. The air-conditioning system according to claim 2, wherein the air-conditioning controller uses a feedback-type neural network to obtain the current input historical temperature based on the currently input historical temperature data and the processing result of the historical temperature data input at the previous moment. The correlation between the data and the historical temperature data input at the previous moment to determine the predicted temperature. The air-conditioning system according to claim 2, further comprising a hygrometer set in the space and detecting a piece of humidity information in the space. The air-conditioning controller uses fuzzy control based on the first temperature difference and the flow of people. Information, the second temperature difference, and the fuzzy rule triggered by the humidity information to determine the temperature adjustment time. The air-conditioning system according to claim 1, further comprising a hygrometer set in the space and detecting a humidity information of the space, and the air-conditioning controller adjusts the target temperature according to the humidity information. The air-conditioning system according to claim 2, wherein the air-conditioning controller determines a plurality of predicted temperatures corresponding to different times within a predetermined period according to a historical temperature data, and obtains a plurality of corresponding temperatures at different times according to the current temperature and each of the predicted temperatures. The second temperature difference determines a temperature variable; the air-conditioning controller uses fuzzy control to determine the temperature adjustment time according to the first temperature difference, the flow information, and the fuzzy rule triggered by the temperature variable. The air-conditioning system according to claim 1, wherein the air-conditioning device has a temperature-adjusting area, and the at least one photographing device includes a first photographing device disposed in the temperature-adjusting area and a first photographing device disposed outside the temperature-adjusting area. Two cameras, the first camera detects the number of people in the space, and the second camera detects the flow rate of the space. The air-conditioning system according to claim 1, wherein the photographing device includes a thermal camera to detect a plane temperature of the space, and the air-conditioning controller adjusts a wind direction of the air-conditioning device according to the plane temperature. An air conditioning control method for the air conditioning system according to any one of claims 1 to 8. The method includes the following steps: (A) detecting a current temperature in a space with a temperature sensor of an air conditioning device (B) using an air-conditioning controller to determine a first temperature difference according to the current temperature and a target temperature; (C) detecting a flow of information in the space by a camera device; (D) the air-conditioning controller using fuzzy control Obtaining at least a first attribution value related to the first temperature difference value and at least a second attribution value related to the flow information, and the air conditioner controller according to the at least one first attribution value and the at least one second attribution value At least a third attribution value related to a temperature adjustment time is obtained from the fuzzy rules triggered by a rule base to determine the temperature adjustment time of an air conditioner. The air conditioning control method according to claim 9, further comprising: determining a predicted temperature according to a historical temperature data, and determining a second temperature difference value based on the current temperature and the predicted temperature; the step (D) uses fuzzy control , To determine the temperature adjustment time according to the fuzzy rule triggered by the first temperature difference, the flow information, and the second temperature difference. The air conditioner control method according to claim 10, wherein the predicted temperature is obtained by using a feedback-type neural network to obtain a current input history according to a currently input historical temperature data and a processing result of the historical temperature data input at a previous moment. The correlation between the temperature data and the historical temperature data entered at the previous moment is determined. The air conditioning control method according to claim 10, further comprising: detecting a humidity information of the space; the step (D) is to use fuzzy control, according to the first temperature difference, the flow information, and the second temperature difference Value, the fuzzy rule triggered by the humidity information to determine the temperature adjustment time. The air conditioning control method according to claim 9, further comprising: detecting a humidity information of the space, and the air conditioning controller adjusts the target temperature according to the humidity information. The air conditioning control method according to claim 10, further comprising: determining a plurality of predicted temperatures corresponding to different times within a predetermined period according to a historical temperature data, and obtaining a plurality of corresponding times corresponding to different times according to the current temperature and each of the predicted temperatures. The second temperature difference determines a temperature variable; step (D) uses fuzzy control to determine the temperature adjustment time according to the first temperature difference, the flow information, and the fuzzy rule triggered by the temperature variable. The air conditioning control method according to claim 9, wherein the air conditioning device has a temperature adjustment area, and the at least one photographing device includes a first photographing device disposed in the temperature adjustment area and one outside the temperature adjustment area. The second camera device, in step (C), the first camera device detects the number of people in the space, and the second camera device detects the flow rate of the space. The air conditioning control method according to claim 9, wherein the photographing device includes a thermal camera, and the air conditioning control method further includes: detecting a plane temperature of the space; and adjusting a wind direction of the air conditioning device according to the plane temperature.