Intelligent control system and method for courtyard electric window based on environmental data

Abstract

The invention discloses an intelligent control system and method for a courtyard electric window based on environmental data, relates to the technical field of intelligent home control, and solves the technical problems that when the courtyard electric window is controlled based on multiple control requirements, control of the courtyard electric window is likely to be contradictory, and the courtyard electric window is difficult to accurately control. When the basic data is analyzed and extracted to obtain the indoor real-time value, the influence of the outdoor data and the control variable corresponding to the courtyard electric window is comprehensively considered; constructing a Lagrangian function, and solving the Lagrangian function to obtain an optimal control variable under a plurality of target items; the objective function constructed by the method also comprises the control variable, so that the control demand can be directly converted into the solution of the control variable, and the optimal control variable is determined by constructing and solving the Lagrange function, so that the optimal control of the courtyard electric window can be realized on the basis of multiple control demands, and the control accuracy of the courtyard electric window is improved. And effective control failure caused by control demand conflict is avoided.

Description

Technical Field

[0001] The present application belongs to the field of smart home control and relates to patio electric window control technology, specifically an intelligent control system and method for patio electric windows based on environmental data. Background Art

[0002] Electric patio windows are a solution to modern building lighting and ventilation, and are widely used in residential, commercial buildings, industrial plants, etc. Electric patio windows are usually installed on the top of a building or in the patio area. Due to their special installation location, they have certain advantages in achieving ventilation, lighting, and temperature regulation functions.

[0003] In the existing control process of electric patio windows, control parameters of indoor and outdoor buildings are first collected through various types of pre-set sensors, and the differences in indoor and outdoor control parameters are compared to generate control instructions, and the electric patio windows are controlled according to the control instructions. However, each control instruction is generated separately according to the function type. For example, the control instruction generated by analyzing the control parameters related to ventilation is mainly used to realize the ventilation function, and the control instruction generated by analyzing the control parameters related to lighting is mainly used to realize the lighting function. If multiple fusion functions need to be realized through the electric patio windows, the electric patio window controls corresponding to each function may contradict each other, making it difficult to achieve precise control of the electric patio windows, thereby affecting the environmental control effect inside the building.

[0004] The present application provides an intelligent control system and method for patio electric windows based on environmental data to solve the above-mentioned technical problems. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes an intelligent control system and method for ceiling electric windows based on environmental data, which is used to solve the technical problem that when controlling the ceiling electric windows based on multiple control requirements, the control of the ceiling electric windows may be contradictory, making it difficult to accurately control the ceiling electric windows.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides an intelligent control system for electric patio windows based on environmental data, comprising: a data processing module, and a data acquisition module connected thereto;

[0007] Data acquisition module: used to collect basic data corresponding to target items in real time through data acquisition devices installed inside and outside the building, and obtain weather forecast data; wherein the target items include at least one of ventilation control, lighting control and temperature control, and the weather forecast data include temperature, air pressure and precipitation;

[0008] Data processing module: used to obtain the indoor real-time value corresponding to the target project according to the basic data analysis, calculate the data difference between the indoor real-time value and the standard value corresponding to the target project; perform weighted optimization calculation on each data difference to obtain the fusion control score; and,

[0009] Used to generate basic instructions based on fusion control scores; optimize the basic instructions based on weather forecast data to obtain control instructions; and control the patio electric windows based on the control instructions.

[0010] Preferably, obtaining the indoor real-time value corresponding to the target project according to the basic data analysis includes:

[0011] Extract the indoor data and outdoor data of the building from the basic data and mark them as outdoor data and indoor data respectively; the basic data includes light intensity, temperature and air quality;

[0012] Extract the real-time control variables of the patio electric windows, construct the indoor data of the target project under the control variables and outdoor data, and mark them as indoor real-time values.

[0013] Preferably, calculating the data difference between the indoor real-time value and the standard value corresponding to the target item includes:

[0014] Extract the standard values ​​corresponding to the target items; wherein the standard values ​​include light intensity standard values, temperature standard values ​​and air quality standard values;

[0015] Subtract the indoor real-time value from the standard value of the corresponding target item and take the absolute value to obtain the data difference; wherein the data difference includes the light intensity difference, temperature difference and air quality difference.

[0016] Preferably, weighted optimization calculation is performed on each data difference, including:

[0017] An objective function is constructed based on the data difference and the corresponding weight coefficient, and constraints are set; wherein the constraints include temperature constraints, air quality constraints, and electric window opening and closing constraints;

[0018] Introduce Lagrangian multipliers; construct Lagrangian functions based on Lagrangian multipliers and objective functions; solve the Lagrangian functions through numerical optimization methods to solve the control variables corresponding to the objective functions; wherein the control variables include the opening and closing angles or the opening areas of the patio electric windows; the numerical optimization methods include the gradient descent method.

[0019] Preferably, constructing an objective function based on the data difference and the corresponding weight coefficient includes:

[0020] The extracted light intensity difference, temperature difference and air quality difference are marked as GC, WC and KC respectively, and the corresponding weight coefficients are marked as w1, w2 and w3 respectively; where GC = |L(u, L外 )-L 目标 |, WC = |T(u, T 外 )-T 目标 |, KC = |AQ(u, AQ 外 )-AQ 目标 |;L(u,L 外 )、T(u,T 外 )、AQ(u,AQ 外 ) are the indoor real-time values ​​of light intensity, temperature and air quality respectively;

[0021] The objective function is constructed as J(u) = w1·GC + w2·WC 2 +w3·KC; where J(u) is the fusion control score and u is the control variable.

[0022] Preferably, constructing a Lagrangian function based on the Lagrangian multiplier and the objective function includes:

[0023] Introduce Lagrange multipliers λ1, λ2, λ3, λ4, and construct the Lagrange function as follows:

[0024] Preferably, setting constraints includes:

[0025] Lighting Constraint: L min ≤L(u,L 外 )≤L max ; Among them, [L min , L max ] is the target range of light intensity;

[0026] Temperature constraint: T min ≤T(u,T 外 )≤T max ; Among them, [T min , T max ] is the target range of temperature;

[0027] Air quality constraints: AQ(u, AQ 外 )≥AQ 目标 ;

[0028] Physical constraints: 0≤u≤u max ; Among them, u max is the maximum value of the control variable u.

[0029] Preferably, generating basic instructions according to the fusion control score includes:

[0030] Extract the control variables corresponding to the fusion control score; predict the corresponding control duration when the patio electric window is controlled according to the control variables;

[0031] Combine the control variables and the corresponding control duration to generate basic instructions.

[0032] Preferably, the basic instructions are optimized based on the weather forecast data, including:

[0033] Determine the environmental factors that affect the opening of the patio electric windows;

[0034] Identify whether there are environmental factors that affect the control instructions in the meteorological forecast data; if yes, optimize the basic instructions according to the period of existence of the environmental factors to obtain the control instructions; if not, use the basic instructions as the control instructions.

[0035] Preferably, the basic instructions are optimized according to the existence period of the environmental influencing factors, including:

[0036] Determine whether the indoor real-time value corresponding to each target item exceeds the corresponding safety threshold during the period of existence of environmental influencing factors;

[0037] If yes, the basic instruction is used as the control instruction;

[0038] If not, the control duration in the basic instruction is circumvented according to the existing period, and the processed control duration is combined with the control variable to generate a control instruction.

[0039] A second aspect of the present application provides a method for intelligently controlling a patio electric window based on environmental data, comprising:

[0040] The basic data corresponding to the target project is collected in real time through data collection equipment installed inside and outside the building, and the weather forecast data is obtained; wherein the target project includes at least one of ventilation control, lighting control and temperature control, and the weather forecast data includes temperature, air pressure and precipitation;

[0041] Obtain the indoor real-time value corresponding to the target project based on the basic data analysis, and calculate the data difference between the indoor real-time value and the standard value corresponding to the target project; perform weighted optimization calculation on each data difference to obtain the fusion control score;

[0042] Generate basic instructions based on the fusion control score; optimize the basic instructions based on the weather forecast data to obtain control instructions; and control the patio electric window based on the control instructions.

[0043] Compared with the prior art, the beneficial effects of this application are:

[0044] 1. The present application collects the basic data corresponding to the target items in real time through the data collection equipment arranged inside and outside the building. When analyzing and extracting the basic data to obtain the indoor real-time value, the influence of the outdoor data and the control variables corresponding to the electric windows of the patio are comprehensively considered; then, the objective function is constructed according to the data difference corresponding to each target item, and the Lagrangian function is constructed after introducing the Lagrangian multiplier, and the Lagrangian function is solved to obtain the optimal control variables under multiple target items; the present application comprehensively considers the outdoor data and the control variables when calculating the indoor real-time value, and the constructed objective function also includes the control variables, so that the control demand can be directly converted into the solution of the control variables, and by constructing and solving the Lagrangian function to determine the optimal control variables, it can be ensured that the optimal control of the electric windows of the patio is achieved on the basis of multiple control demands, thereby improving the overall control efficiency and avoiding the conflict of control demands and the inability to achieve effective control.

[0045] 2. When generating control instructions based on the optimal control variables obtained by solving, the present application introduces a control duration, which is the control duration required to achieve the control requirements under the optimal control variables. The patio electric window can be automatically closed after the requirements are met; moreover, the control duration can also be optimized by identifying environmental influencing factors in meteorological forecast data to ensure that the control of the patio electric window according to the control instructions is more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 This is a schematic diagram of the process principle of the intelligent control method of the patio electric window in the first embodiment of the present application;

[0048] Figure 2 This is a schematic diagram of the system principle of the patio electric window intelligent control system in Example 1 of the present application;

[0049] Figure 3 This is a schematic diagram of the process of solving the control variables in the first embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0051] Because of its irreplaceable role, the patio is widely used in buildings. However, due to the particularity of its location, it also brings certain inconveniences. For example, on rainy days or when the outdoor air quality is poor, the patio windows need to be closed in time, and when the indoor air quality is poor or the lighting is insufficient, the patio windows need to be opened. If all the above operations are performed manually, it will be troublesome and laborious, so patio electric windows are also widely used. At present, in the control process of patio electric windows, generally, the server and its connected smart home devices are used for collaborative control. For example, if the smoke sensor detects smoke in the room, it can send a control command to open the patio electric window to achieve ventilation effect. However, the current control logic is relatively simple and can only solve a single problem. It is impossible to make effective decisions for multiple problems that may conflict. In many cases, manual intervention is still required, which will affect the overall control effect of the patio electric window. The present application provides an intelligent control system for patio electric windows based on environmental data, which is mainly used to solve decision-making problems in conflicting scenarios to improve the overall environmental control effect.

[0052] Embodiment 1:

[0053] See also Figure 1-Figure 2 , the first aspect of the present application provides an intelligent control system for electric patio windows based on environmental data, including: a data processing module, and a data acquisition module connected thereto;

[0054] Data collection module: used to collect basic data corresponding to the target project in real time through data collection devices installed inside and outside the building, and obtain meteorological forecast data;

[0055] Data processing module: used to obtain the indoor real-time value corresponding to the target project based on basic data analysis, calculate the data difference between the indoor real-time value and the standard value corresponding to the target project; perform weighted optimization calculation on each data difference to obtain a fusion control score; and, used to generate basic instructions based on the fusion control score; optimize the basic instructions based on meteorological forecast data to obtain control instructions; and control the patio electric windows based on the control instructions.

[0056] The data processing module is mainly responsible for data processing, and the data acquisition module is mainly responsible for data acquisition. The data processing module and the data acquisition module interact with each other through electrical connection or communication connection. Moreover, the data processing module and the data acquisition module can be integrated into the micro server, and interact with the data acquisition equipment and the electric windows of the patio through the existing data transmission protocol.

[0057] The electric patio window in this embodiment is used in conjunction with the patio in the building. The functions of the patio can be realized by controlling the closing and opening of the electric patio window, such as providing lighting for the building and realizing ventilation. There are many types of electric patio windows, which can be divided into hanging window electric windows and casement window electric windows according to the opening method. Hanging window electric windows can be divided into upper hanging window electric windows, lower hanging window electric windows and middle hanging window electric windows, which can be understood as controlling the angle of window opening. Casement window electric windows control the area of ​​the window. Although the two have different structures and control methods, they can both be electrically controlled through control instructions.

[0058] The data acquisition device is installed inside and outside the building, preferably inside and outside the patio (with the patio plane as the dividing line), and of course other better installation methods can be used to more accurately control the patio electric window. It is worth noting that the basic data and standard values ​​collected in this embodiment are all based on the installation position of the data acquisition device as a reference. If other locations are required, it is necessary to convert to the installation position of the data acquisition device of the corresponding data type according to the structure of the building.

[0059] For example, if the user sets a standard temperature in the bedroom, based on the structure of the building, without considering other temperature influencing measures, the standard temperature is converted to the indoor temperature sensor (at the patio), that is, if the user's bedroom needs to reach the standard temperature, the location of the indoor temperature sensor needs to reach the converted value. Other data are also converted to ensure data consistency and computability.

[0060] In some other preferred embodiments, the data acquisition equipment can also be installed in other places. Of course, some data acquisition equipment can be installed in the patio and others in user-defined locations. For example, the data acquisition equipment corresponding to harmful gases that may threaten the health of the user can be installed in the user's bedroom, and the data can be directly used to calculate the real-time value of the room; and the light intensity sensor can be installed in the patio, and the data collected by the data acquisition equipment in other places such as the bedroom can be converted according to the location.

[0061] The target items in this embodiment include at least one of ventilation control, lighting control and temperature control. Ventilation control corresponds to the ventilation function of the electric patio window, lighting control corresponds to the lighting function of the electric patio window, and temperature control corresponds to the temperature adjustment function of the electric patio window. The target item can be one or more, and can also include other functions that can be realized by the electric patio window. The target item is pre-set by the user according to his or her own needs. Meteorological forecast data includes temperature, air pressure and precipitation. These data will affect the function of the electric patio window. For example, in the case of precipitation, snowfall, etc., the electric patio window may be considered to be closed, providing a data basis for the generation of control instructions.

[0062] In this embodiment, after the basic data is collected, it is necessary to immediately parse the basic data to obtain the indoor real-time value of the building interior. The indoor real-time value corresponding to the target project is obtained according to the basic data parsing, including:

[0063] The indoor and outdoor data of the building are extracted from the basic data and marked as outdoor data and indoor data respectively; the real-time control variables of the patio electric windows are extracted, and the indoor data of the target project under the control variables and outdoor data are constructed and marked as indoor real-time values.

[0064] The above basic data include light intensity, temperature and air quality (score), etc. Due to the influence of the layout of the rooms inside the building, the propagation rules of light intensity, temperature and air quality in the basic data are different in the building. Therefore, the light intensity can be based on the data collected at the patio, and the temperature and air quality can be based on the data collected in the user's bedroom or custom location. It should be noted that the calculation of the air quality score needs to consider not only the content of gases such as oxygen and carbon dioxide, but also the content of harmful gases such as carbon monoxide and sulfur dioxide.

[0065] The indoor real-time value can be understood as a functional relationship between the outdoor data and the control variable, which can be obtained through data simulation. The indoor real-time value is conditional data for subsequent calculations. For example, the light intensity in the basic data is L, and the corresponding indoor (light intensity) real-time value is L(u, L 外 ), the L(u,L 外 ) corresponds to the outdoor light intensity of L 外 , when the control variable (opening angle or opening area) is u, the indoor (light intensity) real-time value is L. In other preferred embodiments, the indoor value in the basic data can be directly used as the indoor real-time value.

[0066] In this embodiment, after obtaining the indoor real-time value, it is necessary to calculate the difference between the indoor real-time value and the corresponding standard value, that is, to calculate the data difference. Calculating the data difference between the indoor real-time value and the standard value corresponding to the target item includes: extracting the standard value corresponding to the target item; subtracting the indoor real-time value from the standard value corresponding to the target item and taking the absolute value to obtain the data difference.

[0067] Exemplarily, the standard values ​​corresponding to the target items include light intensity standard values, temperature standard values ​​and air quality standard values, and these standard values ​​are based on the installation location of the data acquisition equipment corresponding to the target items. Subtract the absolute value of the light intensity real-time value from the light intensity standard value in the indoor real-time value to obtain the light intensity difference; such as GC = | L (u, L 外 )-L 目标 |, L 目标 is the standard value of light intensity. The temperature difference and air quality difference can be calculated by referring to this method.

[0068] If the light intensity difference, temperature difference, and air quality difference are not 0, it means that the light intensity, temperature, and air quality scores do not meet the user's requirements and need to be adjusted. According to the existing solution, the electric patio window is adjusted when the light intensity is insufficient, the temperature is deviated, and the air quality is poor. However, if the light intensity is insufficient and the window needs to be opened, and the temperature deviation requires the window to be closed, there will be a conflict in the control process of the electric patio window. How to make the control effect of the electric patio window meet multiple requirements is the main problem to be solved in this embodiment.

[0069] In this embodiment, weighted optimization calculation is performed on each data difference. Figure 3 , specifically including the following steps:

[0070] Step 1: Define the objective function. Considering the lighting, temperature and ventilation requirements, the objective function is defined as: J(u) = w1·GC + w2·WC 2 +w3·KC; where J(u) is the fusion control score, u is the control variable, w1, w2 and w3 are the weight coefficients corresponding to lighting, temperature and ventilation respectively;

[0071] Referring to the above, GC=|L(u,L 外 )-L 目标 |, L 目标 is the standard value of light intensity; WC=|T(u,T 外 )-T 目标 |, T 外 is the outdoor temperature, T 目标 is the temperature target value; KC=|AQ(u,AQ 外 )-AQ 目标 |, AQ 外 is the outdoor air quality fraction, AQ 目标 is the air quality target score;

[0072] Step 2: Set constraints, set constraints corresponding to each target project;

[0073] 1. Temperature constraint: T min ≤T(u,T 外 )≤T max ; Among them, [T min , T max ] is the target range of temperature; the target range of temperature is set by the user or is the default range;

[0074] 2. Lighting constraints: L min ≤L(u,L 外 )≤L max ; Among them, [L min , L max] is the target range of light intensity; the target range of light intensity is set by the user or the default range;

[0075] 3. Air quality constraints: AQ(u, AQ 外 )≥AQ 目标 ;

[0076] 4. Physical constraints: 0≤u≤u max ; Among them, u max is the maximum value of the control variable u; when the control variable is the opening angle of the patio electric window, u max is the maximum opening angle; when the control variable is the opening area of ​​the patio electric window, u max is the maximum opening area;

[0077] Step 3: Construct the Lagrangian function, introduce the Lagrangian multipliers λ1, λ2, λ3, λ4, and construct the Lagrangian function as follows:

[0078] Step 4: Solve for the partial derivative and set it to 0:

[0079] A numerical optimization method (such as the gradient descent method) is used to solve the control variable u. The gradient descent method for parameter optimization is disclosed in detail in the existing scheme, and the specific calculation steps are not repeated here.

[0080] It should be noted that in the Lagrange multiplier method, the Lagrange multiplier λ is used to embed the constraint conditions into the objective function to form a Lagrange function. For inequality constraints, the Lagrange multiplier usually needs to satisfy non-negativity, that is, λ ≥ 0, which is used to punish the violation of the constraint conditions. For equality constraints, the Lagrange multiplier has no non-negativity requirement.

[0081] After solving the control variables, basic instructions can be generated based on the control variables. Specifically, the control variables corresponding to the fusion control score are extracted; the corresponding control duration when the patio electric window is controlled according to the control variables is predicted; and the basic instructions are generated by combining the control variables and the corresponding control duration.

[0082] It should be noted that when predicting the control duration, the meteorological forecast data can be referred to, that is, the meteorological forecast data and the artificial intelligence model can be combined to predict how long it will take to open the patio electric window with the solved control variable u so that each target project can meet the requirements. The predicted duration is the control duration. In some other preferred embodiments, the control duration is set to infinite, and then the control variable u is updated based on the PID feedback technology, and the control of the patio electric window is realized according to the updated control variable u.

[0083] There may be environmental factors that affect the opening of the electric patio windows in the weather forecast data, such as snowfall, rain, etc., which require closing the electric patio windows to avoid losses. At this time, the control duration in the basic instructions can be adjusted according to the weather forecast data, and the time period when the environmental factors exist is used as the closing period to generate the control instructions. It is understandable that the control duration in the control instructions may be a period of time to open the electric patio windows, and a period of time to close or reduce the electric patio windows due to environmental factors. In some other preferred embodiments, the closing or opening of the electric patio windows can also be controlled by sensors to monitor in real time whether there are environmental factors.

[0084] Embodiment 2: The difference from the embodiment is that no lighting constraint is set in the constraint condition. This is mainly because lighting optimization is an objective function, not a strict constraint condition. The goal of lighting optimization is to make the indoor light intensity as close to the target value as possible by adjusting the control variables (opening and closing angles) of the patio electric windows, rather than setting a hard constraint. On this basis, the lighting constraint in the constraint condition of embodiment 1 is deleted as this embodiment. Since there is a lot of overlap with embodiment 1, it will not be repeated here.

[0085] Embodiment 3: The difference from Embodiment 1 is that the functions of electric patio windows are becoming more and more abundant. For example, the lighting of some electric patio windows is not achieved by opening the windows, but by coatings or additional sunshades. In this case, when constructing the objective function, lighting control can be considered, and only temperature and ventilation need to be considered, and lighting control can be achieved separately.

[0086] Referring to the technical solution of Example 1, the specific steps of solving the control variables when only temperature and ventilation are considered are as follows:

[0087] Step 1: Define the objective function. Considering the lighting, temperature and ventilation requirements, the objective function is defined as: J(u) = w1·KC + w2·WC 2 ; Where J(u) is the fusion control score, u is the control variable, w1 and w2 are the weight coefficients corresponding to ventilation and temperature respectively;

[0088] Referring to the above, WC=|T(u,T 外 )-T 目标 |, T 外 is the outdoor temperature, T 目标 is the temperature target value; KC=|AQ(u,AQ 外 )-AQ 目标 |, AQ 外 is the outdoor air quality fraction, AQ 目标 is the air quality target score;

[0089] Step 2: Set constraints, set constraints corresponding to each target project;

[0090] 1. Temperature constraint: T min ≤T(u,T 外 )≤T max ; Among them, [T min , T max ] is the target range of temperature; the target range of temperature is set by the user or is the default range;

[0091] 2. Air quality constraints: AQ(u, AQ 外 )≥AQ 目标 ;

[0092] 3. Physical constraints: 0≤u≤u max ; Among them, u max is the maximum value of the control variable u; when the control variable is the opening angle of the patio electric window, u max is the maximum opening angle; when the control variable is the opening area of ​​the patio electric window, u max is the maximum opening area;

[0093] Step 4: Construct the Lagrangian function, introduce the Lagrangian multipliers λ1, λ2, λ3, and construct the Lagrangian function as follows:

[0094] Step 5: Solve for the partial derivative and set it to 0:

[0095]

[0096] A numerical optimization method (such as the gradient descent method) is used to solve the control variable u. The gradient descent method for parameter optimization is disclosed in detail in the existing scheme, and the specific calculation steps are not repeated here.

[0097] Embodiment 4: The difference from Embodiment 1 is that conditional intervention is performed on the weight coefficient corresponding to the target item. In Embodiment 1, the priority of each target item can be set according to the importance of the target item or the user's preference, and the weight coefficient can be set according to the priority.

[0098] For example, if the user sets the priorities of lighting control, ventilation control, and temperature control from high to low, the weight coefficient of lighting control can be set to 0.5, the weight coefficient of ventilation control can be set to 0.3, and the weight coefficient of temperature control can be set to 0.2.

[0099] However, this embodiment redefines the weight coefficient from the perspective of user security, and the definition rules are as follows:

[0100] 1. When any indoor real-time value corresponding to the target project exceeds the safety threshold, the corresponding weight coefficient is set to 1; if the indoor real-time values ​​of multiple target projects exceed the safety threshold, the corresponding weight coefficients are equal and the sum of the weights is 1.

[0101] 2. When the indoor real-time values ​​corresponding to the target items do not exceed the safety threshold, the weight coefficient is set according to the distance between each indoor real-time value and the safety threshold; that is, the ratio of the weight coefficient is equal to the inverse ratio of the distance, and the smaller the distance, the larger the weight coefficient.

[0102] It should be noted that the safety threshold of the above-mentioned target items is very different from the standard value. The safety threshold is the lower limit (or upper limit) of the value. When the real-time indoor value exceeds the safety threshold (including being lower than the lower limit, or greater than the upper limit), it may have a safety impact on the users in the building.

[0103] For Rule 2, an exemplary explanation is as follows:

[0104] Set the indoor real-time value to x, and the corresponding safety threshold to x 阈值 ; Calculate Euclidean distance Then the weight coefficient corresponding to the indoor real-time value x is Where c is a positive integer to prevent the denominator from being 0;

[0105] Then the calculated weight coefficients of each target item are normalized to ensure that the sum of the weight coefficients is 1.

[0106] The second aspect of the present application provides a method for intelligently controlling a patio electric window based on environmental data, including:

[0107] The basic data corresponding to the target project is collected in real time through data collection equipment installed inside and outside the building, and the weather forecast data is obtained; wherein the target project includes at least one of ventilation control, lighting control and temperature control, and the weather forecast data includes temperature, air pressure and precipitation;

[0108] Obtain the indoor real-time value corresponding to the target project based on the basic data analysis, and calculate the data difference between the indoor real-time value and the standard value corresponding to the target project; perform weighted optimization calculation on each data difference to obtain the fusion control score;

[0109] Generate basic instructions based on the fusion control score; optimize the basic instructions based on the weather forecast data to obtain control instructions; and control the patio electric window based on the control instructions.

[0110] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. An intelligent control system for electric windows in a patio based on environmental data, characterized in that: include: A data processing module, and a data acquisition module connected thereto; Data acquisition module: used to collect basic data corresponding to target items in real time through data acquisition devices installed inside and outside the building, and obtain weather forecast data; wherein the target items include at least one of ventilation control, lighting control and temperature control, and the weather forecast data include temperature, air pressure and precipitation; Data processing module: used to obtain the indoor real-time value corresponding to the target project according to the basic data analysis, calculate the data difference between the indoor real-time value and the standard value corresponding to the target project; perform weighted optimization calculation on each of the data differences to obtain a fusion control score; as well as, Used to generate basic instructions according to the fusion control score; optimize the basic instructions based on the weather forecast data to obtain control instructions; and control the patio electric window based on the control instructions.

2. The intelligent control system for electric patio windows based on environmental data according to claim 1, characterized in that: Obtaining the indoor real-time value corresponding to the target project according to the basic data analysis, including: Extracting indoor data and outdoor data of the building from the basic data and marking them as outdoor data and indoor data respectively; wherein the basic data includes light intensity, temperature and air quality; The real-time control variables of the patio electric window are extracted, and the indoor data of the target project under the control variables and the outdoor data are constructed and marked as indoor real-time values.

3. The intelligent control system for electric patio windows based on environmental data according to claim 2 is characterized in that: Calculating the data difference between the indoor real-time value and the standard value corresponding to the target item includes: Extracting standard values ​​corresponding to the target items; wherein the standard values ​​include light intensity standard values, temperature standard values ​​and air quality standard values; The indoor real-time value is subtracted from the standard value corresponding to the target item and the absolute value is taken to obtain a data difference; wherein the data difference includes a light intensity difference, a temperature difference and an air quality difference.

4. The intelligent control system for electric patio windows based on environmental data according to claim 3 is characterized in that: Performing weighted optimization calculation on each of the data differences, including: Constructing an objective function based on the data difference and the corresponding weight coefficient, and setting constraints; wherein the constraints include temperature constraints, air quality constraints, and electric window opening and closing constraints; Introduce Lagrangian multipliers; construct a Lagrangian function based on the Lagrangian multipliers and the objective function; solve the Lagrangian function through a numerical optimization method to solve the control variables corresponding to the objective function; wherein the control variables include the opening and closing angles or the opening areas of the patio electric windows; the numerical optimization method includes a gradient descent method.

5. The intelligent control system for electric patio windows based on environmental data according to claim 4 is characterized in that: Constructing an objective function based on the data difference and the corresponding weight coefficient includes: The extracted light intensity difference, temperature difference and air quality difference are marked as GC, WC and KC respectively, and the corresponding weight coefficients are marked as w1, w2 and w3 respectively; where GC = |L(u,L 外 )-L 目标 |,WC=|T(u,T 外 )-T 目标 |,KC=|AQ(u,AQ 外 )-AQ 目标 |;L(u,L 外 )、T(u,T 外 )、AQ(u,AQ 外 ) are the indoor real-time values ​​of light intensity, temperature and air quality respectively; The objective function is constructed as J(u) = w1·GC + w2·WC 2 +w3·KC; where J(u) is the fusion control score and u is the control variable.

6. The intelligent control system for electric patio windows based on environmental data according to claim 4, characterized in that: Constructing a Lagrangian function based on the Lagrangian multiplier and the objective function includes: Introduce Lagrange multipliers λ1,λ2,λ3,λ4 and construct the Lagrange function as follows:

7. The intelligent control system for electric patio windows based on environmental data according to claim 1, characterized in that: Generate basic instructions according to the fusion control score, including: Extracting the control variable corresponding to the fusion control score; predicting the corresponding control duration when the skylight electric window is controlled according to the control variable; The control variable and the corresponding control duration are combined to generate a basic instruction.

8. The intelligent control system for electric patio windows based on environmental data according to claim 7, characterized in that: Optimizing the basic instructions based on the weather forecast data includes: Determine the environmental factors that affect the opening of the patio electric windows; Identify whether there are environmental factors affecting the control instructions in the meteorological forecast data; if yes, optimize the basic instructions according to the existence period of the environmental factors to obtain the control instructions; if no, use the basic instructions as the control instructions.

9. The intelligent control system for electric patio windows based on environmental data according to claim 8, characterized in that: The basic instructions are optimized according to the existence period of the environmental influencing factors, including: Determine whether the indoor real-time value corresponding to each of the target items exceeds the corresponding safety threshold during the period of existence of the environmental influencing factors; If yes, the basic instruction is used as the control instruction; If not, the control duration in the basic instruction is circumvented according to the existence period, and the processed control duration is combined with the control variable to generate a control instruction.

10. A method for intelligent control of electric patio windows based on environmental data, based on the intelligent control system for electric patio windows based on environmental data as claimed in any one of claims 1 to 9, characterized in that: include: The basic data corresponding to the target project is collected in real time through data collection equipment installed inside and outside the building, and the weather forecast data is obtained; wherein the target project includes at least one of ventilation control, lighting control and temperature control, and the weather forecast data includes temperature, air pressure and precipitation; Obtaining the indoor real-time value corresponding to the target project according to the basic data analysis, calculating the data difference between the indoor real-time value and the standard value corresponding to the target project; performing weighted optimization calculation on each of the data differences to obtain a fusion control score; Generate basic instructions according to the fusion control score; optimize the basic instructions based on the weather forecast data to obtain control instructions; and control the patio electric window based on the control instructions.

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