Air conditioning system capable of intelligently sensing user requirements

Abstract

The invention relates to the technical field of energy-saving air conditioners, and provides an air conditioning system capable of intelligently sensing user requirements, which comprises a server, a mobile terminal, a data acquisition module, an intelligent evaluation module, an environment regulation module and an interaction module, and is characterized in that the data acquisition module is used for acquiring state data of an internal user; the intelligent evaluation module analyzes a state index COM of the user according to the state data; the interaction module collects personalized demand data submitted by a user, analyzes a preference index Preference (S) corresponding to a user specific setting S according to the personalized data of the user, and analyzes a predicted preference index Liket of the user at a t moment according to historical data and the personalized data of the user; and the environment adjusting module comprehensively adjusts the temperature of the air conditioner loaded in the air conditioner according to the state index COM, the preference index Preference (S) of the S specifically set by the user and the predicted preference index Liket at the t moment.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving air conditioners, and particularly to an air conditioning system that intelligently senses user needs. Background Art

[0002] Most existing air conditioning systems can only adjust the ambient temperature through manual control or simple automatic temperature control functions. These systems often fail to accurately sense the actual needs of users, especially in terms of emotions and personalized comfort, and thus have obvious deficiencies in providing truly personalized and dynamically responsive environmental regulation.

[0003] For example, Chinese Patent No. CN119189607A discloses an intelligent control method and control system for air conditioners based on passenger comfort, which intelligently and automatically controls and adjusts the air conditioner according to the distribution of passengers in the vehicle. By obtaining the comfort temperature of passengers and quickly adjusting the air conditioner corresponding to the passengers according to their body temperatures, the body temperatures of passengers can quickly reach the comfort temperature, enabling passengers to be in a good and comfortable environment and greatly improving the driving and riding experience. At the same time, this intelligent and automatic control and adjustment of the air conditioner, however, ignores the personalization of user needs, resulting in poor comfort for users.

[0004] In addition, the following defects also exist in the prior art:

[0005] 1. Traditional systems cannot understand or adapt to the specific needs of individual users, such as emotional changes, physiological states (such as body temperature fluctuations), or personal preferences.

[0006] 2. Existing systems mostly rely on user input or very limited environmental factors and are difficult to respond in real time to the immediate changes of users, such as changes in temperature preferences caused by emotions.

[0007] 3. Existing air conditioning systems offer limited interaction options.

[0008] In order to solve the problems commonly existing in this field, such as the inability to meet personalized needs, the inability to set preferences, the inability to dynamically adjust the air conditioner, poor automatic adjustment ability, weak interaction ability, and poor energy-saving ability, the present invention is made. Summary of the Invention

[0009] The object of the present invention is to propose an air conditioning system that intelligently senses user needs in view of the current deficiencies.

[0010] In order to overcome the deficiencies of the prior art, the present invention adopts the following technical solutions:

[0011] An air conditioning system that intelligently senses user needs, the air conditioning system that intelligently senses user needs includes a server and a mobile terminal. The air conditioning system that intelligently senses user needs further includes a data collection module, an intelligent evaluation module, an environment adjustment module, and an interaction module. The data collection module collects the status data of the user in the usage scenario; the intelligent evaluation module analyzes the status index COM of the user according to the status data; the interaction module collects the personalized demand data submitted by the user, and analyzes the preference index Preference(S) of the corresponding user-specific setting S according to the personalized data of the user, and analyzes the predicted preference index Like of the user at time t according to the historical data and personalized data of the user t ; The environment adjustment module comprehensively adjusts the temperature of the air conditioner installed in the usage scenario according to the status index COM, the preference index Preference of the user, and the predicted preference index Like at time t t ;

[0012] The interaction module includes an interaction sniffing unit, an emotion perception unit, and a personality prediction unit. The interaction sniffing unit senses the connection request of the mobile terminal and establishes a binding relationship to collect the personalized demand data submitted by the mobile terminal; the emotion perception unit evaluates the personalized demand data to form the preference index Preference(S) of the specific setting S; the personality prediction unit predicts the past preferences of the user according to the personalized demand data and historical data to form the predicted preference index Like at time t t ;

[0013] Wherein, after the mobile terminal approaches the interaction sniffing unit and enters the recognition range, the personalized demand data of the mobile terminal is interactively transmitted to the interaction sniffing unit.

[0014] Optionally, the interaction sniffing unit includes an interaction panel, an identifier, and a memory. The identifier collects the mobile terminal in the recognition range. The identifier is arranged on the interaction panel to form a recognition part. The memory stores the personalized demand data collected by the identifier.

[0015] Optionally, the emotion perception unit obtains the personalized demand data, extracts the decisive features of the personalized demand data, and calculates the preference index Preference(S) of the specific setting S according to the following formula:

[0016]

[0017] In the formula, f(S) is the proportion of the number of times the specific setting S is selected to the total number of selections, and c(S) is the proportion of the longest sequence of consecutive selections of the specific setting S by the user during the observation period to the total number of selections.

[0018] Optionally, the data acquisition module includes an environmental sensor unit, a user physiological monitoring unit, and a user activity monitoring unit. The environmental sensor unit monitors the environmental conditions within the usage scenario; the user physiological monitoring unit collects the physiological data of the user; the user activity monitoring unit monitors the behavior data of the user;

[0019] Among them, the behavior data includes the sitting posture adjustment frequency and the activity frequency.

[0020] Optionally, the intelligent evaluation module calculates the user's state index COM according to the state data and the following formula:

[0021]

[0022] In the formula, Normalize(Sync) is the normalized synchronization index, and Normalize(Anomaly) is the normalized anomaly index.

[0023] Among them, Normalize(Sync) is calculated according to the following formula for the synchronization index:

[0024]

[0025] In the formula, Sync is the value of the synchronization index, min(Sync) is the minimum value of the synchronization index in the historical data, and max(Sync) is the maximum value of the synchronization index in the historical data.

[0026] Optionally, the environmental regulation module includes a temperature control unit and a data processing unit. The data analysis unit calculates the temperature change rate dT / dt according to the state index COM, the preference index Preference(S) of the specific setting S, and the predicted preference index Like at time t. The temperature control unit controls the air conditioner within the usage scenario according to the temperature change rate dT / dt calculated by the data analysis unit to comprehensively adjust the temperature of the air conditioner in the usage scenario or the usage environment. t The temperature control unit controls the air conditioner within the usage scenario according to the temperature change rate dT / dt calculated by the data analysis unit to comprehensively adjust the temperature of the air conditioner in the usage scenario or the usage environment.

[0027] Optionally, the user activity monitoring unit includes an accelerometer and a data collector. The data collector collects the micro-motion data of the user collected by the accelerometer.

[0028] Optionally, the user physiological monitoring unit includes a detection board, a heart rate detector, and an infrared body temperature sensor. The heart rate detector is arranged on the detection board. The heart rate sensor collects the heart rate data of the user, and the infrared body temperature sensor collects the body temperature data of the user;

[0029] Among them, the detection board is arranged at the seat board and the backrest position of the seat in the use scenario, and enables the user's body to be in close contact.

[0030] Optionally, the interaction panel is arranged on the wall or the seat in the use scenario.

[0031] Optionally, the interaction panel is provided with an interaction prompt light, and is set to be always on in the working state.

[0032] The beneficial effects achieved by the present invention are as follows:

[0033] 1. Through the mutual cooperation of the data acquisition module and the intelligent evaluation module, the system can capture and analyze the user's real-time state and environmental data in real time, ensuring that the entire system has a powerful data-driven decision-making and prediction adjustment ability;

[0034] 2. Through the mutual cooperation of the intelligent evaluation module and the environmental adjustment module, the system can automatically adjust the air-conditioning output according to the evaluation data, ensuring that the entire system has a high efficient adaptive adjustment ability and good energy-saving effect;

[0035] 3. Through the mutual cooperation of the interaction module and the data acquisition module, the system can collect the user's feedback and preference settings, ensuring that the entire system has strong responsiveness and interaction ability;

[0036] 4. Through the mutual cooperation of the interaction module and the environmental adjustment module, the system can feedback the adjustment result to the user in real time, ensuring that the entire system has a highly transparent and user-friendly operation interface. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0038] Figure 1 It is a schematic overall block diagram of the present invention.

[0039] Figure 2 It is a schematic block diagram of the interaction module and the environmental adjustment module of the present invention.

[0040] Figure 3 It is a schematic block diagram of the data acquisition module and the user of the present invention.

[0041] Figure 4 It is a schematic block diagram of the temperature control module of the present invention.

[0042] Figure 5 It is a schematic structural diagram of the air outlet and the air supply adjustment unit of the present invention.

[0043] Figure 6 This is a top view schematic diagram of the shape memory alloy strip and the swing blade of the present invention.

[0044] Explanation of reference numerals: 1, air supply outlet; 2, shape memory alloy strip; 3, fixed seat; 4, deformation part; 5, first rotating part; 6, swing blade; 7, second rotating part. Detailed implementation manners

[0045] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to actual dimensions. The following implementation manners will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention.

[0046] Example 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, this example provides an air conditioning system that intelligently senses user needs. The air conditioning system that intelligently senses user needs includes a server and a mobile terminal. The air conditioning system that intelligently senses user needs further includes a data collection module, an intelligent evaluation module, an environment adjustment module, and an interaction module. The data collection module collects the status data of the user in the usage scenario; the intelligent evaluation module analyzes the status index COM of the user according to the status data; the interaction module collects the personalized demand data submitted by the user, and analyzes the preference index Preference(S) of the user-specific settings S corresponding to the personalized data of the user, and analyzes the predicted preference index Like of the user at time t according to the historical data and personalized data of the user t ; the environment adjustment module comprehensively adjusts the temperature of the air conditioner installed in the usage environment or usage scenario according to the status index COM, the preference index Preference(S) of the specific setting S, and the predicted preference index Like of time t t ;

[0047] Among them, the specific setting S includes the air conditioner temperature, humidity, and wind speed.

[0048] The interaction module includes an interaction sniffing unit, an emotion perception unit, and a personality prediction unit. The interaction sniffing unit senses the connection request of the mobile terminal and establishes a binding relationship to collect the personalized demand data submitted by the mobile terminal. The emotion perception unit evaluates the personalized demand data to form a preference index Preference(S) for a specific setting S. The personality prediction unit predicts the user's past preferences based on the personalized demand data and historical data to form a predicted preference index Like at time t t ;

[0049] Among them, after the mobile terminal approaches the interaction sniffing unit and enters the recognition range, the personalized demand data of the mobile terminal is interactively transmitted to the interaction sniffing unit.

[0050] Optionally, the interaction sniffing unit includes an interaction panel, a recognizer, and a memory. The recognizer collects the mobile terminals in the recognition range. The recognizer is arranged on the interaction panel to form a recognition part. The memory stores the personalized demand data collected by the recognizer.

[0051] Optionally, the interaction panel is arranged on the wall or seat in the usage scenario. By arranging the interaction panel on the wall or seat in the usage scenario, the user can perform quick interactions and submit personalized demand data.

[0052] In this embodiment, the interaction panel can also be arranged at a position in the usage scenario space that is convenient to touch.

[0053] Optionally, an interaction prompt light is provided on the interaction panel and is set to be always on during the working state.

[0054] In this embodiment, when the user enters the usage scenario environment, the mobile terminal (such as a smart phone or other intelligent device) carried by the user enters the recognition range of the interaction panel. The recognizer on the interaction panel, which may be a device based on Bluetooth, NFC (Near Field Communication), Wi-Fi or other wireless communication technologies, is activated and starts to search for nearby mobile terminals. After the recognizer detects the mobile terminal, it establishes a connection with it through a wireless communication protocol.

[0055] Once the mobile terminal is recognized and connected, the interaction prompt light (set to be always on during the working state) on the interaction panel will light up, prompting the user that the interaction system is ready. The user can input or adjust temperature preferences and other comfort settings by touching the interaction panel or using the corresponding application on the mobile terminal.

[0056] The personalized demand data input by the user through the interaction panel is captured by the recognizer.

[0057] These data are then transmitted (in a wireless manner) to the memory of the system for storage. The memory is responsible for recording the user's preference data so that the system can learn and adapt to the user's long-term needs.

[0058] In addition, the interaction process is not a one-time thing; the user can adjust the settings again at any time according to the current comfort level.

[0059] In this embodiment, the entire system can continuously learn and optimize based on the user's adjustment history and feedback to provide more precise comfort control.

[0060] Optionally, the emotion perception unit obtains the personalized demand data, extracts the decisive features of the personalized demand data, and calculates the preference index Preference(S) of a specific setting S according to the following formula:

[0061]

[0062] In the formula, f(S) is the proportion of the number of times a specific setting S is selected to the total number of selections, and c(S) is the proportion of the longest sequence in which the user continuously selects a specific setting S during the observation period to the total number of selections.

[0063] Among them, the proportion f(S) of the number of times a specific setting S is selected to the total number of selections is calculated according to the following formula:

[0064]

[0065] In the formula, n S represents the number of times a specific setting S is selected, and N is the total number of selections;

[0066] In this embodiment, an example is provided: If the user adjusts the air conditioner settings a total of 20 times during the observation period or sampling period, and the temperature of 22°C is selected 8 times, then f(22°C) is 8 / 20 = 0.4.

[0067] The proportion c(S) of the longest sequence in which the user continuously selects a specific setting S during the observation period to the total number of selections is calculated according to the following formula:

[0068]

[0069] In the formula, L S is the number of times of the longest sequence in which a specific setting S is continuously selected, and N is the total number of selections.

[0070] After the user interacts with the interaction module through the mobile terminal, the emotion perception unit needs to extract key indicators from the specific setting S of the user interaction.

[0071] The process of extracting indicators can be roughly divided into the following steps:

[0072] S11. Collect data: Through the system's interaction interface (which may be a touch screen, mobile application, voice command, etc.), record each user's selection of setting S.

[0073] S12. Count the number of selections n S : Statistically count the total number of times the user selects this specific setting S within a certain period of time. This number shows the degree of the user's preference for this setting.

[0074] S13. Observe that the total number of selections N is the total number of all setting selections made by the user within the same time period, including the selection of setting S and the selection of all other settings.

[0075] S14. Determine the number of consecutive selections L S : Find out the longest sequence in which the user continuously selects setting S, that is, the record of the user continuously selecting setting S multiple times without interruption.

[0076] In this embodiment, an example is provided. Suppose that during a trip, a certain user adjusted the air - conditioning temperature 10 times, and the specific temperature selections are as follows:

[0077] 22°C: 4 times (3 of which are consecutive)

[0078] 24°C: 3 times (2 consecutive times)

[0079] 20°C: 3 times (1 consecutive time)

[0080] Calculate the preference index Preference(S)(22°C) of this user for the 22°C temperature setting.

[0081] Among them, for 22°C, n 22℃ = 4, L 22℃ = 3 (the longest consecutive selection), then the ratio c(22°C) of the longest sequence in which the user continuously selects the specific setting S to the total number of selections during the observation period is 3 / 10 = 0.3;

[0082] In this embodiment, collect historical data and calculate the average prediction preference index Ma of m time - window lengths in the historical data t-1 :

[0083]

[0084] In the formula, m is the time - window length, and its value is set by the system according to the actual situation. Like t-i is the preference index at time point t - i;

[0085] The personalized prediction unit estimates the difference between the most recent time point and the moving average and uses it as an adjustment term Trend t-1 :

[0086] Trend t-1 = Like t-1 - MA t-1 ;

[0087] Wherein, MA t-1 is the average predicted preference index of m time window lengths in historical data, and Like t-i is the preference index at time point t - i;

[0088] The personalized prediction unit predicts the past preferences of the user based on the personalized demand data and historical data to form the predicted preference index Like at time t t ;

[0089] Like t = MA t-1 + Trend t-1 ;

[0090] Wherein, MA t-1 is the average predicted preference index of m time window lengths in historical data, and Trend t-1 is the adjustment term.

[0091] Optionally, the data acquisition module includes an environmental sensor unit, a user physiological monitoring unit, and a user activity monitoring unit. The environmental sensor unit monitors the environmental conditions in the usage scenario; the user physiological monitoring unit collects the physiological data of the user; the user activity monitoring unit monitors the behavior data of the user;

[0092] Among them, the behavior data includes the sitting posture adjustment frequency and the activity frequency.

[0093] The environmental sensor unit includes a temperature sensor, a humidity sensor, and a gas sensor. The temperature sensor is used to detect the temperature data at the air outlet of the air conditioner and in the usage scenario. The humidity sensor collects the humidity data at the air outlet of the air conditioner and in the usage scenario. The gas sensor collects the air quality of the usage scenario and the air outlet of the air conditioner.

[0094] Optionally, the user physiological monitoring unit includes a detection board, a heart rate detector, and an infrared body temperature sensor. The heart rate detector is arranged on the detection board. The heart rate sensor collects the heart rate data of the user. The infrared body temperature sensor collects the body temperature data of the user;

[0095] Among them, the detection board is arranged on the seat board and the backrest of the seat in the usage scenario or the usage environment, and enables the user's body to be in close contact.

[0096] In addition, the infrared body temperature sensor is arranged facing the seats in the usage scenario or the usage environment, so as to collect the body temperature data of the user.

[0097] Optionally, the user activity monitoring unit includes an accelerometer and a data collector, and the data collector collects the micro-motion data of the user collected by the accelerometer.

[0098] Wherein, the accelerometer is installed on the seat to detect the micro-motion of the seat, so as to obtain the micro-motion data of the user.

[0099] Through the mutual cooperation of the interaction module and the data collection module, the system can collect the feedback and preference settings of the user, ensuring that the whole system has strong responsiveness and interaction ability;

[0100] Optionally, the intelligent evaluation module calculates the user's state index COM according to the state data and the following formula:

[0101]

[0102] In the formula, Normalize(Sync) is the normalized synchronization index, and Normalize(Anomaly) is the normalized anomaly index.

[0103] Wherein, Normalize(Sync) is calculated according to the following formula for the synchronization index:

[0104]

[0105] In the formula, Sync is the value of the synchronization index, min(Sync) is the minimum value of the synchronization index in the historical data, and max(Sync) is the maximum value of the synchronization index in the historical data.

[0106] Environmental data Env = [E t , H t , where E t and H t are the temperature and humidity data at time t respectively; Physiological data Physio = [P t , T t , where P t and T t are the heart rate and body temperature data at time t respectively;

[0107] Behavioral data Behavior = [A t , S t , where A t and S t are the activity frequency and sitting posture adjustment frequency at time t respectively.

[0108] In this embodiment, the change rate of consecutive time points is calculated:

[0109]

[0110] In the formula, △Env t is the environmental data change rate vector at time t, and △Physio t is the physiological data change rate vector at time t; △Env t is the behavior data change rate vector at time t;

[0111] In this embodiment, n pairs of data points ΔEnv = [e 1 , e 2 , …, e n and ΔPhysio = [p 1 , p 2 , …, p n are collected, where each e t and p t are the environmental and physiological change rates at the corresponding time points, respectively.

[0112] That is: e t = Δenv t = env t - env t-1 ;

[0113] p t = ΔPhysio t = Physio t - Physio t-1 ;

[0114] Next, the average values of the two data sets are calculated:

[0115]

[0116] Then, the covariance of ΔEnv and ΔPhysio is calculated:

[0117]

[0118] Next, the standard deviations of the two data sets are calculated:

[0119]

[0120] Combining the above formulas, the synchrony index value Sync is calculated according to the following formula (i.e., calculating the correlation coefficient between the environmental change rate vector △Env and the physiological data change rate vector △Physio):

[0121]

[0122] In the formula, Corr(△Env, △Physio) is the Pearson correlation coefficient between the environmental change rate vector △Env and the physiological data change rate vector △Physio.

[0123] In addition, in this embodiment, the anomaly index Normalize(Anomaly) is calculated according to the following formula:

[0124]

[0125] In the formula, Anomaly is the current anomaly index value, min(Anomaly) is the minimum anomaly index value in the historical data, and max(Anomaly) is the maximum anomaly index value in the historical data.

[0126] The current anomaly index value Anomaly is calculated according to the following formula:

[0127]

[0128] where μ ΔBehavior and σ ΔBehavior are the mean and standard deviation of the change rate ΔBehavior t of the behavioral data respectively.

[0129]

[0130] In the formula, n is the number of data points, that is, the total number of ΔBehavior t values, and ΔBehavior t is the change rate of the behavioral data.

[0131]

[0132] In the formula, n is the number of data points, that is, the total number of ΔBehavior t values, ΔBehavior t is the change rate of the behavioral data, and μ ΔBehavior is the average value (mean) of all ΔBehavior t values.

[0133] Optionally, the environmental regulation module includes a temperature control unit and a data processing unit, and the data analysis unit is based on the state index COM, the preference index Preference(S) of the user-specific setting S, and the predicted preference index Like at time t tCalculate the temperature change rate dT / dt, and the temperature control unit controls the air conditioner in the usage scenario or the usage environment according to the temperature change rate dT / dt calculated by the data analysis unit, so as to comprehensively adjust the temperature of the air conditioner in the usage scenario or the usage environment.

[0134] In this embodiment, the temperature change rate dT / dt:

[0135]

[0136] In the formula, α is an adjustment constant, β is a proportional constant, T is the current temperature, T set is the target set temperature, k is the sensitivity coefficient, COM is the state index, Preference(S) is the user's preference index, Like t is the predicted preference index at time t.

[0137] In this embodiment, an example of the values of the adjustment constant α, the proportional constant β, and the sensitivity coefficient k is provided:

[0138] 1) In the scenario of an office environment (in an open office environment, maintaining a stable and comfortable temperature is the key to reducing energy consumption and increasing employee comfort), then:

[0139] Adjustment constant α = 1.5;

[0140] Proportional constant β = 0.5;

[0141] Sensitivity coefficient k = 1.0;

[0142] 2) In the scenario of a shopping mall and retail environment (in a shopping mall and retail environment, it is necessary to quickly respond to the temperature changes caused by a large number of customers coming in and out to ensure the customer experience), then:

[0143] Adjustment constant α = 3.0;

[0144] Proportional constant β = 1.0;

[0145] Sensitivity coefficient k = 2.5;

[0146] 3) In the scenario of a residential environment (in a family residence, especially at night, it is important to maintain a warm and comfortable environment, especially in winter or colder regions), then:

[0147] Adjustment constant α = 2.0;

[0148] Proportional constant β = 0.7;

[0149] Sensitivity coefficient k = 1.5;

[0150] In this embodiment, specific values are selected according to the specific scenario and input from the human-computer interaction interface, which is a well-known technical means in the art. Therefore, in this embodiment, it will not be elaborated one by one.

[0151] The temperature control unit realizes the control of temperature according to the following steps:

[0152] S1. Read the current air conditioner temperature T;

[0153] S2. The temperature change rate dT / dt under the current conditions;

[0154] S3. Update the current temperature T according to the temperature change rate dT / dt and the size of the time step new :

[0155]

[0156] In the formula, △T is the time step.

[0157] For example: 1) Urban living environment. For example, in urban families or office environments, people may come and go frequently, resulting in temperature changes, similar to the frequent start and stop of urban driving. The time step is set to 15 seconds: in high-traffic home or office environments, such as apartments or open-plan offices, a shorter time step can enable the HVAC system to quickly respond to temperature changes caused by the opening and closing of doors and windows, the use of electrical appliances, etc.

[0158] 2) In relatively stable living environments, such as suburban houses or office areas with less traffic, the temperature changes are not as frequent as in urban environments. In this relatively static environment, a longer time step helps to reduce the frequent adjustment of the air conditioning system, thereby improving energy efficiency, while still being able to effectively respond to the actual temperature change requirements, such as the increase in indoor temperature caused by direct sunlight in the afternoon. The time step can be set to 60 seconds.

[0159] 3) In occasionally used environmental usage scenarios, for environments that are used occasionally or are unoccupied for a long time, such as holiday vacation homes, closed exhibition rooms, or any other places that do not require frequent adjustment for a long time, using a longer time step can maximize the energy utilization efficiency, while reducing the wear of the equipment and extending its service life. Then the time step can be set to 300 seconds or longer.

[0160] Through the mutual cooperation of the intelligent evaluation module and the environmental regulation module, the system can automatically adjust the air conditioner output according to the evaluation data, ensuring that the entire system has high efficient adaptive adjustment ability and good energy-saving effect;

[0161] In this embodiment, by reasonably setting the time step, the air conditioning system can flexibly adapt to different driving and usage conditions while ensuring user comfort and system efficiency. This helps to optimize the environmental control system within the entire usage scenario or environment.

[0162] Through this method, the temperature is dynamically adjusted to gradually approach the target set temperature T set , while considering the actual experience and preferences of the user. This control strategy is both responsive and can ensure the smoothness of temperature changes and the stability of the system.

[0163] Through the mutual cooperation of the data acquisition module and the intelligent evaluation module, the system can capture and analyze the real-time status of the user and environmental data in real time, ensuring that the entire system has strong data-driven decision-making and predictive adjustment capabilities;

[0164] Through the mutual cooperation of the interaction module and the environmental adjustment module, the system can provide real-time feedback of the adjustment results to the user, ensuring that the entire system has a highly transparent and user-friendly operation interface.

[0165] Embodiment 2: This embodiment should be understood as including all the features of any one of the foregoing embodiments and is further improved on this basis. According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the air conditioning system that intelligently senses user needs further includes a air supply module. The air supply module determines the user distribution index DI at time t according to the user distribution data collected by the data acquisition module t , and adjusts the air supply angle and air supply strategy according to the user distribution index.

[0166] The air supply module includes an air supply evaluation unit, an air supply adjustment unit, and an air supply control unit. The air supply evaluation unit obtains the user distribution data collected by the acquisition module and calculates the user distribution index at time t according to the user distribution data; the air supply adjustment unit is arranged at the air supply outlet 1 of the air conditioner and guides the air supply outlet 1 of the air conditioner; the air supply control unit determines the air supply angle and air supply strategy according to the user distribution index, and controls the air supply adjustment unit according to the air supply angle and air supply strategy.

[0167] In this embodiment, the user distribution data includes the user distance, the number of users, and the user location (expressed as coordinates in this embodiment).

[0168] In addition, the position of each user is collected by an infrared sensor arranged in the usage scenario, and the position of each user is in the form of coordinates (x i , yi ) are recorded, where i is the index of the user.

[0169] Among them, the heat source distribution set in the usage scenario is detected by an infrared sensor to identify the heat of the human body, thereby determining the user location and quantity.

[0170] The air supply evaluation unit obtains the user distribution data and calculates the average distance AD between users according to the following formula:

[0171]

[0172] In the formula, N is the total number of users, (x i , y i ) and (x j , y j ) are the coordinates of the i-th and j-th users respectively.

[0173] The air supply evaluation unit obtains the user distribution data and calculates the distance variance DV between users according to the following formula:

[0174]

[0175] In the formula, N is the total number of users, (x i , y i ) and (x j , y j ) are the coordinates of the i-th and j-th users respectively, and AD is the average distance between users.

[0176] The air supply evaluation unit obtains the user distribution data and calculates the maximum distance MD between users according to the following formula:

[0177]

[0178] In the formula, N is the total number of users, (x i , y i ) and (x j , y j ) are the coordinates of the i-th and j-th users respectively, {…} represents a set that contains the distance calculation results between all user pairs that meet the condition i≠j. Each element in this set is the distance calculation result between two users; the max operation is used to find the maximum value from all calculated distance values.

[0179] The meaning of the above formula is: when given the position coordinates of a group of users, calculate and find the maximum value of the pairwise distances between all users.

[0180] The air supply evaluation unit obtains the average distance AD between users, the distance variance DV between users, and the maximum distance MD between users, and calculates the distribution index DI at time t according to the following formulat :

[0181]

[0182] Wherein, W is the dynamic weight factor, AD is the average distance between users, λ is the variance adjustment coefficient, DV is the distance variance between users, η is the maximum distance adjustment coefficient, and MD is the maximum distance between users.

[0183] In this embodiment, an example of the values of the dynamic weight factor W, the variance adjustment coefficient λ, and the maximum distance adjustment coefficient η is provided. Specifically:

[0184] 1) In the scenario of an urban living environment, such as an apartment or a city residence, the living space may experience rapid temperature changes due to opening and closing doors and windows or using household appliances. Especially in a small space, the activities of family members may be relatively limited. Then the dynamic weight factor W = 6, the variance adjustment coefficient λ = 0.2, and the maximum distance adjustment coefficient η = 0.1;

[0185] 2) In the scenario of a long-term living environment, for those living environments that require a long stay, such as a holiday home or a long-term rental apartment, maintaining consistent comfort is very important, especially during a long stay. Then the dynamic weight factor W = 4, the variance adjustment coefficient λ = 0.1, and the maximum distance adjustment coefficient η = 0.2;

[0186] 3) In the hot summer living scenario, in the hot summer, whether it is a family or other living environments, such as a summer rental house or a temporary residence, special attention needs to be paid to providing sufficient cooling to cope with the high temperature. Then the dynamic weight factor W = 8, the variance adjustment coefficient λ = 0.3, and the maximum distance adjustment coefficient η = 0.3;

[0187] In this embodiment, selecting the appropriate values through different scenarios and inputting them from the human-computer interaction interface are well-known technical means in the art, so they will not be elaborated one by one in this embodiment.

[0188] Among them, through these adjustments to the environment for different usage scenarios, it can ensure that the air conditioning system in the usage scenario or the usage environment can provide the optimal temperature control effect according to specific requirements and conditions, thereby greatly improving the comfort of users.

[0189] The air supply adjustment unit includes a current controller (not shown), a shape memory alloy (SMA) bar 2, a fixed seat 3, and at least two swing blades 6. The current controller controls the magnitude of the current passing through the shape memory alloy bar 2; at least two swing blades 6 are arranged at the air supply port 1 and are hinged to the inner walls on both sides of the air supply port 1 to form a first rotating part 5 and a second rotating part 7, as Figure 6As shown, the shape memory alloy strips 2 are symmetrically arranged on both sides of the first rotating part 5 and the second rotating part 7, and one end of the shape memory alloy strip 2 is connected to the first rotating part 5, and the other end of the shape memory alloy is connected to the fixed seat 3. The current controller is electrically connected to the deformation part 4 of the shape memory alloy and applies a current to the deformation part 4;

[0190] When the current controller applies a current to the shape memory alloy, the shape memory alloy deforms, and this deformation causes the swing blade 6 hinged thereto to rotate accordingly. Therefore, the deformation of the shape memory alloy enables the swing blade 6 to move in the desired direction, thereby precisely controlling the air supply direction. This setting ensures that the air outlet 1 can dynamically adjust the air supply angle according to the user's needs or changes in the environment within the usage scenario, improving the efficiency of the air conditioning system and the comfort of the user.

[0191] The swing blade 6 is arranged at the air outlet 1 of the air conditioner and is hinged to the inner walls on both sides of the air outlet 1 to form a first rotating part 5 and a second rotating part 7 that can be dynamically adjusted. When the shape memory alloy strip 2 pushes or pulls the swing blade 6, the swing blade 6 rotates according to the elongation or retraction of the shape memory alloy strip 2, thereby changing the air supply direction.

[0192] Among them, by adjusting the magnitude of the current, the controller can precisely control the heating degree of the shape memory alloy. When the shape memory alloy is heated by the current, the shape memory alloy begins to contract or expand according to the preset shape memory effect.

[0193] The air supply control unit obtains the distribution index DI at the calculated time t t and calculates the current amount I applied to the shape memory alloy strip 2 according to the following formula:

[0194] I = I 0 + k · (DI t - DI 0 ) ;

[0195] In the formula, I 0 is the base current value, that is, the current required at the minimum user distribution index value (usually when the users are evenly distributed) (unit: ampere, A), DI t is the currently calculated user distribution index, DI 0 is the reference value of the user distribution index. In this embodiment, it is set as the user distribution index value when the users are evenly distributed. k is a proportionality constant used to adjust the sensitivity of the change in the distribution index value to the current (unit: A (ampere)).

[0196] In this embodiment, it is set that for every 0.1 change in the user distribution index, the current needs to increase by 0.5 A, so k is set to 5 A;

[0197] The air supply control unit controls the current controller according to the calculated current I applied to the shape memory alloy strip 2, so as to adjust the cold air sent out by the air supply regulating unit of the air conditioner and send it to various places in the usage scenario.

[0198] Through the mutual cooperation of the air supply module and the data acquisition module, the system can accurately adjust the air supply direction and intensity according to the user distribution data collected in real time, ensuring that the whole system has an optimized air distribution efficiency and an enhanced user comfort experience.

[0199] Through the mutual cooperation of the air supply module and the data acquisition module, the air conditioner is allowed to not only reflect the current environmental state, but also dynamically adjust the air supply according to the actual position and density of the users, so as to ensure that each user area can obtain appropriate temperature and ventilation, improving the overall efficiency of the system and the satisfaction of the users.

[0200] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention accordingly. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, the elements therein can be updated with the development of technology.

Claims

1. An air conditioning system that intelligently senses user needs, the air conditioning system that intelligently senses user needs comprising a server and a mobile terminal, characterized in that: The air conditioning system for intelligently sensing user needs also includes a data acquisition module, an intelligent evaluation module, an environment adjustment module, and an interaction module. The data acquisition module collects the status data of the user in the usage scenario; the intelligent evaluation module analyzes the status index COM of the user according to the status data; the interaction module collects the personalized demand data submitted by the user, and analyzes the preference index Preference(S) corresponding to the user's specific setting S according to the user's personalized data, and analyzes the predicted preference index Like of the user at time t according to the user's historical data and personalized data t The environment adjustment module is based on the state index COM, the preference index Preference (S) of the user-specific setting S, and the predicted preference index Like at time t t Comprehensively adjust the temperature of the air conditioner installed in the usage scenario; The interaction module includes an interaction sniffing unit, an emotion perception unit, and a personality prediction unit. The interaction sniffing unit perceives the connection request of the mobile terminal and establishes a binding relationship to collect the personalized demand data submitted by the mobile terminal; The emotion perception unit evaluates the personalized demand data to form a preference index Preference(S) of a specific setting S; the personalized prediction unit predicts the user's past preferences based on the personalized demand data and historical data to form a predicted preference index Like at time t t ; Among them, after the mobile terminal approaches the interactive sniffing unit and enters the identification range, the personalized demand data of the mobile terminal is interactively transmitted to the interactive sniffing unit.

2. The air conditioning system according to claim 1, characterized in that: The interactive sniffing unit includes an interactive panel, an identifier, and a memory. The identifier collects mobile terminals within an identification range. The identifier is arranged on the interactive panel to form an identification part. The memory stores personalized demand data collected by the identifier.

3. The air conditioning system according to claim 2, characterized in that: The emotion perception unit obtains the personalized demand data, extracts the decisive features of the personalized demand data, and calculates the preference index Preference(S) of the specific setting S according to the following formula: Where f(S) is the ratio of the number of times a specific setting S is selected to the total number of selections, and c(S) is the ratio of the longest sequence of users continuously selecting a specific setting S to the total number of selections during the observation period.

4. The air conditioning system according to claim 3, characterized in that: The data acquisition module includes an environmental sensor unit, a user physiological monitoring unit and a user activity monitoring unit. The environmental sensor unit monitors the environmental conditions in the usage scene; the user physiological monitoring unit collects the user's physiological data; and the user activity monitoring unit monitors the user's behavior data; The behavioral data include sitting posture adjustment frequency and activity frequency.

5. The air conditioning system according to claim 4, characterized in that: The intelligent evaluation module calculates the user's state index COM according to the state data and the following formula: In the formula, Normalize(Sync) is the normalized synchronization index, and Normalize(Anomaly) is the normalized anomaly index.

6. The air conditioning system according to claim 5, characterized in that: The environment adjustment module includes a temperature control unit and a data processing unit. The data analysis unit calculates the predicted like index Like according to the state index COM, the user's preference index Preference, and the predicted like index Like at time t. t The temperature change rate dT / dt is calculated, and the temperature control unit controls the air conditioner according to the temperature change rate dT / dt calculated by the data analysis unit to comprehensively adjust the temperature of the air conditioner.

7. The air conditioning system according to claim 6, characterized in that: The user activity monitoring unit includes an accelerometer and a data collector, and the data collector collects micro-motion data of the user collected by the accelerometer.

8. The air conditioning system according to claim 7, characterized in that: The user physiological monitoring unit includes a detection board, a heart rate detector, and an infrared body temperature sensor, wherein the heart rate detector is arranged on the detection board, the heart rate sensor collects the heart rate data of the user, and the infrared body temperature sensor collects the body temperature data of the user; The detection plate is arranged on the seat and backrest of a chair arranged in the usage scenario, and enables the user's body to be in close contact with the detection plate.

9. The air conditioning system according to claim 8, characterized in that: The interactive panel is arranged on a wall or a seat in the usage scene.

10. The air conditioning system according to claim 9, characterized in that: The interactive panel is provided with an interactive prompt light, which is set to be always on in the working state.

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