Intelligent louver system and its regulation method

Abstract

The application discloses a kind of intelligent louver system and its regulation and control method, comprising: establishing building original model, establishing the parameterized louver model of variable lower edge height, the rotating angle of blade is controlled by constructing the 4 curve parameters of Bezier curve;The louver model is applied to the building original model, and a new building model is obtained;A plurality of indoor lighting design points are set according to indoor depth direction, and the optimization direction is set according to demand;Real-time meteorological data is obtained by networking, and real-time EPW meteorological file is obtained;According to the real-time EPW meteorological file, building model and louver model, the optimization direction is optimized according to the set louver lower edge height and blade rotating angle based on radiation intensity, daylight factor and indoor illuminance simulation, and the optimal solution of the lower edge height and 4 curve parameters is obtained;According to optimal solution, louver is regulated and controlled.The application solves the problem of real-time intelligent regulation and control of louver, so as to balance indoor radiation and lighting.

Description

Technical Field

[0001] This invention relates to the field of venetian blind technology, and in particular to an intelligent venetian blind system and its control method. Background Technology

[0002] High-rise, multi-span, large-space factory buildings are a design response to the spatial demands of high-tech manufacturing, driven by contemporary structural technology innovation, and an inevitable product of social development. This new type of industrial building combines the spatial characteristics of both large-span factories and high-rise buildings, both of which consume significant energy, posing new challenges to energy conservation in industrial buildings. Louvers, as an external shading component, function to regulate both indoor radiation and lighting, and hold great potential in high-rise, multi-span, large-space factory buildings. Traditional louvers rely on manual opening and closing, lacking convenience and struggling to balance radiation and lighting. With the rapid development of sensor technology, network transmission technology, and information processing technology, the concept of smart homes is entering people's daily lives, giving rise to intelligent louver systems. For high-rise, multi-span, large-space factory buildings with large spans and depths, it is difficult to intelligently control the shape of the louvers in real time to ensure sufficient and suitable lighting and radiation inside the factory. Furthermore, balancing indoor radiation and lighting requires consuming a large amount of energy to ensure indoor environmental comfort.

[0003] In existing technologies, precise consideration is generally not given to multi-point illumination over a large indoor area, and the control of smart blinds is usually achieved by real-time monitoring of indoor illumination. However, indoor illumination sensors are easily affected by various objects blocking them, resulting in unpredictable errors. Summary of the Invention

[0004] (a) Technical problems to be solved To address the aforementioned problems, this invention provides an intelligent louver system and its control method, which solves the problem of real-time intelligent control of louvers, thereby balancing indoor radiation and lighting and saving energy.

[0005] (II) Technical Solution To address the aforementioned technical problems, this invention provides a method for controlling an intelligent louver system, comprising the following steps: S1. Establish building model and louver model: Establish the original building model; establish a parameterized louver model with variable lower edge height, and control the louver rotation angle by constructing four curve parameters of the Betz curve; apply the louver model to the original building model to obtain a new building model, which includes a room model, a window model and a louver model. S2. Set multiple indoor lighting design points according to the indoor depth direction, and set the optimal direction for radiation intensity, daylight factor and indoor multi-point illuminance according to the needs. S3. Connect to the network to obtain real-time meteorological data and obtain real-time EPW meteorological files; S4. Based on the real-time EPW meteorological file, building model, and louver model, optimize the lower edge height of the louvers and the blade rotation angle according to the set optimization direction, based on radiation intensity, daylight factor, and indoor multi-point illuminance simulation, to obtain the optimal solution for the lower edge height and four curve parameters; including: S41. Radiation intensity simulation: Read the real-time EPW meteorological file, set the simulation time, generate the sky matrix, and use the solar radiation simulation calculator to simulate the solar radiation intensity based on the building model and the louver model. After outputting the solar radiation intensity at multiple points on the measuring network, calculate the average radiation intensity. S42. Daylight factor simulation: Establish a simulation model, generate a measurement network based on the indoor ground according to the simulation model, apply the measurement network to the simulation model, simulate the indoor daylight factor of the simulation model, output the daylight factor of multiple points on the measurement network, and calculate the average daylight factor. S43. Indoor Illuminance Simulation: Establish a simulation model, read the real-time EPW meteorological file, set the simulation time, generate a sky object, generate a measurement network with multiple indoor lighting design points as target measurement points, apply the measurement network to the simulation model, perform point matrix illuminance simulation of the simulation model, obtain the illuminance of multiple indoor lighting design points, calculate the difference between the illuminance of the multiple indoor lighting design points and the target illuminance, calculate and output the absolute value of the difference, and obtain the multi-point illuminance difference. S44. Multi-objective optimization: Connect the lower edge height and the four curve parameters to the input port of the multi-objective optimization calculator; connect the average radiation intensity, average daylighting coefficient, and multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator; run the multi-objective optimization in the set optimization direction through the multi-objective optimization calculator, perform multiple loops and iterations, generate the optimal solution set, and use the cracking calculator to output the optimal solution; S5. Adjust the venetian blinds according to the optimal solution of the lower edge height and the four curve parameters.

[0006] Furthermore, in S1, the original building model is established based on architectural drawings and on-site measurement results. The original building model includes walls, floors, and windows. The establishment of the louver model includes: setting the lower edge height of the louver, generating blades based on the base line and the lower edge height of the louver, and then moving and copying multiple blades according to the blade height sequence to form a parameterized louver model with variable lower edge height.

[0007] Furthermore, in S1, controlling the blade rotation angle by constructing the four curve parameters of the Bézier curve includes: constructing the Bézier curve based on the four parameters, finding the intersection point of the blade YZ plane and the Bézier curve per unit spacing, multiplying the y coordinate of the intersection point by 90 degrees as the rotation angle of each blade of the louver from top to bottom, and rotating each blade according to the rotation angle.

[0008] Furthermore, in S2, the optimization direction includes: maximizing the average daylighting coefficient, minimizing the average radiation intensity in spring, summer, and autumn, maximizing the average radiation intensity in winter, and minimizing the difference between the illuminance of the multiple indoor daylighting design points and the target illuminance.

[0009] Furthermore, S3 includes: S31. Trigger a real-time acquisition of meteorological data from the National Meteorological Science Data Center website at fixed time intervals, and capture the current time for setting the replacement time; S32. Read the original EPW meteorological data, and replace the corresponding part of the original EPW meteorological file with the real-time acquired meteorological data at the set replacement time. S33. After replacement, create the modified EPW object and rewrite the EPW file to obtain the real-time EPW weather file.

[0010] Furthermore, the fixed time interval is 15 minutes.

[0011] Furthermore, in S42 and S43, the establishment of the simulation model includes: generating a room based on the room model, generating a window based on the window model, generating a shading component based on the louver model, applying the window to the room, integrating the room and the shading component, and establishing a simulation model.

[0012] Furthermore, in S44, the step of connecting the average radiant intensity, average daylight factor, and multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator, and running multi-objective optimization in the set optimization direction through the multi-objective optimization calculator, includes: connecting the power operation result of the average radiant intensity, the reciprocal operation result of the average daylight factor, and the multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator; using the minimum value of the target interface as the optimization direction, and automatically performing multi-objective optimization through the multi-objective optimization calculator.

[0013] Furthermore, the exponent of the exponentiation operation is +1 in spring, summer, and autumn, and -1 in winter.

[0014] This invention also discloses an intelligent louver system, comprising: At least one processor; and at least one memory communicatively connected to said processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the control method by calling the program instructions.

[0015] (III) Beneficial Effects The above-described technical solution of the present invention has the following advantages: (1) This invention is developed for the large space of the emerging high-rise composite large space factory buildings. It relies on the Rhino-Grasshopper integrated parametric platform, calls the Ladybug plugin and various input, judgment, change and generation calculators, and simulates and optimizes the lower edge height of the louvers and the rotation angle of the blades based on real-time meteorological files and indoor lighting and radiation requirements. The louvers are controlled by motors to achieve the optimal state, thereby providing excellent lighting and radiation environment for high-rise composite large space factory buildings. It realizes the simultaneous optimization of three environmental indicators: radiation intensity, light coefficient and multi-point illuminance under different seasons and weather conditions. Through efficient radiation and illuminance control, it maximizes the indoor thermal comfort and light environment, which is conducive to improving the convenience of regulation, reducing energy consumption, improving the overall operating efficiency of the factory building, and protecting the health of users. (2) The louvers of the present invention are designed with fully adjustable blades. They are designed to meet the differentiated lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the differentiated ... (3) The louvers of the present invention simulate the latest meteorological data conditions by real-time network connection, without the need to use sensors to detect environmental conditions. Therefore, the problems of sensor blockage can be avoided, thereby improving the simulation accuracy and making the indoor thermal comfort and light environment better. Attached Figure Description

[0016] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings: Figure 1 This is a flowchart illustrating the control method of the intelligent louver system according to an embodiment of the present invention. Figure 1 ; Figure 2 This is a flowchart illustrating the control method of the intelligent louver system according to an embodiment of the present invention. Figure 2 ; Figure 3 A schematic diagram of the arithmetic unit for establishing the parameterized louver model in S12 of this embodiment of the invention; Figure 4 This is a schematic diagram of the calculator for controlling the blade rotation angle using the S13 curve parameters in an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the control of blade rotation angle based on the Betz curve according to an embodiment of the present invention. Figure 6 This is an axonometric drawing of the architectural model according to an embodiment of the present invention; Figure 7 This is an isometric view of the louver model according to an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the setting of illumination optimization design points according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the processor that obtains real-time EPW meteorological files in S3 of this embodiment of the invention; Figure 10 This is a schematic diagram of the arithmetic unit for simulating radiation intensity in embodiment S41 of the present invention; Figure 11 This is a schematic diagram of the radiation intensity simulation results according to an embodiment of the present invention; Figure 12 This is a schematic diagram of the arithmetic unit for simulating the S42 light-gathering coefficient according to an embodiment of the present invention; Figure 13 This is a schematic diagram of the simulation results of the daylighting coefficient in an embodiment of the present invention; Figure 14 This is a schematic diagram of the calculator for indoor illuminance simulation in embodiment S43 of the present invention; Figure 15 This is a schematic diagram of the indoor illuminance simulation results according to an embodiment of the present invention; Figure 16 This is a schematic diagram of the multi-objective optimization arithmetic unit in embodiment S44 of the present invention; Figure 17 This is a schematic diagram of the multi-objective optimization settings and results interface according to an embodiment of the present invention; In the diagram: 1: Louver; 2: Outer glass window; 3: Ventilation cavity; 4: Inner glass window; 5: Height control device; 6: Blade rotation shaft. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] This invention discloses a method for controlling an intelligent louver system. Based on the Rhino-Grasshopper platform and utilizing the Ladybug plugin, it is suitable for intelligently controllable louvers in high-rise, multi-level, large-space factory buildings. This allows the factory interior to obtain sufficient and suitable lighting and radiation, thereby ensuring the internal environment required for high-tech industries and maximizing energy savings and improving economic efficiency. An embodiment of this invention provides a method for controlling an intelligent louver system as follows: Figure 1-2 As shown, it includes the following steps: S1. Create architectural and louver models. S11. Establish the original building model: Based on the architectural drawings and on-site measurement results, establish the original building model in Rhino software, including room models and window models, which include architectural elements such as walls, floors and windows; S12. Establish a parameterized louver model with variable lower edge height. Set the bottom edge height of the louvers, use the Move function to move the base line upwards, use the Extrude function to generate blades based on the base line and height, then use the Series component to construct a blade height series, and use the Move function to move and copy multiple blades according to the blade height series, forming a parameterized louver model with a variable bottom edge height; the calculation process is as follows. Figure 3 As shown.

[0019] S13. Control the blade rotation angle by constructing four curve parameters of the Betz curve. Using the Bezier Span operator, a Bezier curve is constructed based on four parameters. The Curve|plane operator is then used to find the intersection points of the YZ plane of each blade with the Bezier curve. The y-coordinate of each intersection point is multiplied by 90 degrees to determine the rotation angle of each blade from top to bottom. Finally, the Rotate operator is used to rotate each blade according to the rotation angle, resulting in the rotated louver model. This allows for indirect control of the rotation angle of all louver blades using the Bezier curve constructed with four parameters. The operator process is as follows: Figure 4 As shown, the rotation angle of all louver blades is controlled according to the Betz curve. Figure 5 As shown.

[0020] The louver model is applied to the original building model to obtain a new building model, which includes a room model, a window model, and a louver model. The isometric drawing of the building model is shown below. Figure 6 As shown, the isometric view of the louvered model is as follows: Figure 7 As shown, a louver is installed in the ventilation cavity 3, and a louver 1 is installed between the outer glass window 2 and the inner glass window 4. The height of the louver 1 is controlled by the height control device 5, and the angle of the louver 1 is adjusted around the blade rotation axis 6. S2. Set multiple indoor lighting design points according to the indoor depth direction, and set the optimal direction for radiation intensity, daylight factor and indoor multi-point illuminance according to the needs. The optimization direction is to maximize the average indoor daylight factor; the optimization direction is to maximize radiation in winter and minimize radiation in spring, summer, and autumn; combining the building plan and internal usage requirements, the lighting requirements for the differential distribution of indoor depth are set, and four lighting optimization design points A, B, C, and D are set to determine the target illuminance value for each point, such as... Figure 8 As shown.

[0021] S3. Connect to the network to obtain real-time weather data and obtain real-time EPW weather files (EPW, EnergyPlus Weather). S31. Trigger a real-time acquisition of meteorological data from the National Meteorological Science Data Center website at fixed time intervals, and capture the current time for setting the replacement time; Using a self-written CPython calculator, real-time meteorological data is obtained from the National Meteorological Science Data Center website. The current time is captured by the Capture current time calculator, and the Trigger calculator is used to trigger the data every 15 minutes to obtain real-time meteorological data and capture the current time. The Replace Items calculator is set by the AnalysisiPeriod calculator and used as the replacement sequence number. S32. Read the original EPW meteorological data, and replace the corresponding part of the original EPW meteorological file with the real-time acquired meteorological data at the set replacement time. The ImportEPW file importer reads the original EPW weather data (Energy PlusWeather) using the EPW file importer, and at the set replacement time, the Replace Items data replaces the corresponding parts of the original weather file with real-time weather data using the Replace Items data replacementer. S33. After replacement, create the modified EPW object and rewrite the EPW file to obtain the real-time EPW weather file; The modified EPW object is created by creating the EPW operator, and the EPW file is rewritten by writing the EPW file to obtain the real-time EPW meteorological file for use in simulation.

[0022] The arithmetic unit process in step S3 is as follows: Figure 9 As shown.

[0023] S4. Based on the real-time EPW meteorological file, building model, and louver model, optimize the lower edge height of the louvers and the blade rotation angle according to the set optimization direction, based on radiation intensity, daylight factor, and indoor multi-point illuminance simulation, to obtain the optimal solution for the lower edge height and four curve parameters, specifically including: S41, Radiation Intensity Simulation The Import epw meteorological file reader reads the real-time EPW meteorological file, the SkyMatrix generator generates the sky matrix, the IncidentRadiation solar radiation simulator simulates the radiation intensity, outputs the solar radiation intensity at multiple points on the measurement network, and finally, the Average generator calculates the average radiation intensity. The generator process is as follows: Figure 10 As shown, the simulation results of radiation intensity are as follows: Figure 11 As shown.

[0024] S42, Daylight Factor Simulation Based on the room model, the RoomSolid generator is used to generate the room. Based on the window model, the Apenture generator is used to generate the window. The AddSubface generator is used to apply the window to the room. Based on the louver model, the Shade generator is used to generate shading components. The Model generator is used to integrate the room and shading components to build the simulation model. The GridRooms generator is used to generate a grid based on the indoor ground. The AssignGridsViews generator is used to apply the grid to the simulation model. The DaylightFactor generator is used to simulate the indoor daylight factor of the simulation model. After outputting the daylight factor at multiple points on the grid, the Average generator is used to calculate the average daylight factor. The generator process is as follows: Figure 12 As shown, the simulation results of the daylight factor are as follows: Figure 13 As shown.

[0025] S43, Indoor Illumination Simulation The RoomSolid generator generates rooms, the Apenture generator generates windows, the AddSubface generator applies windows to the room, the Shade generator generates shading components, the Model generator integrates the room and shading components to build a simulation model, the Import epw generator reads real-time EPW weather files and sets the simulation time, the ClimateBased generator generates sky objects, the SenorGird generator generates a measurement network based on four target measurement points (four indoor lighting design points), the AssignGridsViews generator applies the measurement network to the simulation model, the PitGrid generator simulates the illuminance of the simulation model, obtains the illuminance of the four indoor lighting design points, the Subtraction generator calculates the difference between the illuminance and the target illuminance, and the Absolute generator calculates and outputs the absolute value of the difference. The generator process is as follows: Figure 14 As shown, the illuminance simulation results are as follows: Figure 15 As shown.

[0026] S44. Multi-objective optimization: Connect the lower edge height and the four curve parameters to the input port of the multi-objective optimization calculator; connect the average radiation intensity, average daylighting coefficient, and multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator; run the multi-objective optimization in the set optimization direction through the multi-objective optimization calculator, perform multiple loops and iterations, generate the optimal solution set, and use the cracking calculator to output the optimal solution.

[0027] Multi-objective optimization is performed using the Wallacei multi-objective optimization arithmetic unit.

[0028] Connect the lower edge height of the louvers and the four curve parameters to the input ports Genes and Phenotype of the multi-objective optimization calculator Wallacei (the sliders need to be connected to the Genes input port; since the sliders are distributed throughout the program interface, they are represented by numerical units in the diagram). The average radiation intensity obtained from the simulation is fed into the power operator Power for power operation. The exponent is provided by the list operator Value List. When spring, summer, or autumn is selected, the exponent is +1. When winter is selected, the exponent is -1. The power operation result is fed into the target interface objectives of the target optimization operator Wallacei. The average daylight factor is input into the One Over X reciprocal calculator to calculate the reciprocal, and the reciprocal result is input into the target interface objective of the Wallacei target optimization calculator. Connect the four-point illumination difference values ​​to the target interface objectives of the target optimization calculator Wallacei; Double-click the Wallacei target optimization calculator to run multi-objective optimization. The calculator automatically uses the minimum value of the target interface (objectives) as the optimization direction, performing multiple loops and iterations to gradually generate solutions that satisfy as many objectives as possible, and then generates the optimal solution set. After optimization, use the Decode Phenotype calculator to output the optimal solution. The calculator process is as follows: Figure 16 As shown, the multi-objective optimization settings and results interface are as follows: Figure 17 As shown.

[0029] S5. Adjust the venetian blinds according to the optimal solution of the lower edge height and the four curve parameters.

[0030] By using a motor, the louvers are controlled to rotate at the optimal angle and adjust their height, thereby achieving the optimal indoor light and heat environment.

[0031] The embodiments of the present invention can also be intelligent louver systems that operate the above-described intelligent louver system control method.

[0032] Finally, it should be noted that the above-described control methods can be converted into software program instructions. These instructions can be implemented using a control system including a processor and memory, or by computer instructions stored in a non-transitory computer-readable storage medium. The integrated unit implemented as a software functional unit can be stored in a computer-readable storage medium. This software functional unit, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0033] In summary, the intelligent louver system and its control method described above have the following beneficial effects: (1) This invention is developed for the large space of the emerging high-rise composite large space factory buildings. It relies on the Rhino-Grasshopper integrated parametric platform, calls the Ladybug plugin and various input, judgment, change and generation calculators, and simulates and optimizes the lower edge height of the louvers and the rotation angle of the blades based on real-time meteorological files and indoor lighting and radiation requirements. The louvers are controlled by motors to achieve the optimal state, thereby providing excellent lighting and radiation environment for high-rise composite large space factory buildings. It realizes the simultaneous optimization of three environmental indicators: radiation intensity, light coefficient and multi-point illuminance under different seasons and weather conditions. Through efficient radiation and illuminance control, it maximizes the indoor thermal comfort and light environment, which is conducive to improving the convenience of regulation, reducing energy consumption, improving the overall operating efficiency of the factory building, and protecting the health of users. (2) The louvers of the present invention are designed with fully adjustable blades. They are designed to meet the differentiated lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the lighting requirements of different indoor locations. They are designed to meet the differentiated ... (3) The louvers of the present invention simulate the latest meteorological data conditions by real-time network connection, without the need to use sensors to detect environmental conditions. Therefore, the problems of sensor blockage can be avoided, thereby improving the simulation accuracy and making the indoor thermal comfort and light environment better.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it; although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for controlling an intelligent louver system, characterized in that, Includes the following steps: S1. Establish building model and louver model: Establish the original building model; establish a parameterized louver model with variable lower edge height, and control the louver rotation angle by constructing four curve parameters of the Betz curve; apply the louver model to the original building model to obtain a new building model, which includes a room model, a window model and a louver model. S2. Set multiple indoor lighting design points according to the indoor depth direction, and set the optimal direction for radiation intensity, daylight factor and indoor multi-point illuminance according to the needs. S3. Connect to the network to obtain real-time meteorological data and obtain real-time EPW meteorological files; S4. Based on the real-time EPW meteorological file, building model, and louver model, optimize the lower edge height of the louvers and the blade rotation angle according to the set optimization direction, based on radiation intensity, daylight factor, and indoor multi-point illuminance simulation, to obtain the optimal solution for the lower edge height and four curve parameters; including: S41. Radiation intensity simulation: Read the real-time EPW meteorological file, set the simulation time, generate the sky matrix, and use the solar radiation simulation calculator to simulate the solar radiation intensity based on the building model and the louver model. After outputting the solar radiation intensity at multiple points on the measuring network, calculate the average radiation intensity. S42. Daylight factor simulation: Establish a simulation model, generate a measurement network based on the indoor ground according to the simulation model, apply the measurement network to the simulation model, simulate the indoor daylight factor of the simulation model, output the daylight factor of multiple points on the measurement network, and calculate the average daylight factor. S43. Indoor Illuminance Simulation: Establish a simulation model, read the real-time EPW meteorological file, set the simulation time, generate a sky object, generate a measurement network with multiple indoor lighting design points as target measurement points, apply the measurement network to the simulation model, perform point matrix illuminance simulation of the simulation model, obtain the illuminance of multiple indoor lighting design points, calculate the difference between the illuminance of the multiple indoor lighting design points and the target illuminance, calculate and output the absolute value of the difference, and obtain the multi-point illuminance difference. S44. Multi-objective optimization: Connect the lower edge height and the four curve parameters to the input port of the multi-objective optimization calculator; connect the average radiation intensity, average daylighting coefficient, and multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator; run the multi-objective optimization in the set optimization direction through the multi-objective optimization calculator, perform multiple loops and iterations, generate the optimal solution set, and use the cracking calculator to output the optimal solution; S5. Adjust the venetian blinds according to the optimal solution of the lower edge height and the four curve parameters.

2. The control method for the intelligent louver system according to claim 1, characterized in that, In S1, the original building model is established based on architectural drawings and on-site measurement results. The original building model includes walls, floors, and windows. The establishment of the louver model includes: setting the lower edge height of the louver, generating blades based on the base line and the lower edge height of the louver, and then moving and copying multiple blades according to the blade height sequence to form a parameterized louver model with variable lower edge height.

3. The control method for the intelligent louver system according to claim 1, characterized in that, In S1, controlling the blade rotation angle by constructing the four curve parameters of the Bézier curve includes: constructing the Bézier curve based on the four parameters, finding the intersection point of the blade YZ plane and the Bézier curve per unit spacing, multiplying the y coordinate of the intersection point by 90 degrees as the rotation angle of each blade of the louver from top to bottom, and rotating each blade according to the rotation angle.

4. The control method for the intelligent louver system according to claim 1, characterized in that, In S2, the optimization direction includes: maximizing the average daylighting coefficient, minimizing the average radiation intensity in spring, summer, and autumn, maximizing the average radiation intensity in winter, and minimizing the difference between the illuminance of the multiple indoor daylighting design points and the target illuminance.

5. The control method for the intelligent louver system according to claim 1, characterized in that, S3 includes: S31. Trigger a real-time acquisition of meteorological data from the National Meteorological Science Data Center website at fixed time intervals, and capture the current time for setting the replacement time; S32. Read the original EPW meteorological data, and replace the corresponding part of the original EPW meteorological file with the real-time acquired meteorological data at the set replacement time. S33. After replacement, create the modified EPW object and rewrite the EPW file to obtain the real-time EPW weather file.

6. The control method for the intelligent louver system according to claim 5, characterized in that, The fixed time interval is 15 minutes.

7. The control method for the intelligent louver system according to claim 1, characterized in that, In S42 and S43, establishing the simulation model includes: generating a room based on the room model, generating a window based on the window model, generating a shading component based on the louver model, applying the window to the room, integrating the room and the shading component, and establishing the simulation model.

8. The control method for the intelligent louver system according to claim 1, characterized in that, In S44, the step of connecting the average radiation intensity, average daylight factor, and multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator, and running multi-objective optimization in the set optimization direction through the multi-objective optimization calculator, includes: connecting the power operation result of the average radiation intensity, the reciprocal operation result of the average daylight factor, and the multi-point illuminance difference obtained from the simulation calculation to the target interface of the multi-objective optimization calculator; and automatically performing multi-objective optimization through the multi-objective optimization calculator with the minimum value of the target interface as the optimization direction.

9. The control method for the intelligent louver system according to claim 8, characterized in that, The exponent in the exponentiation operation is +1 in spring, summer, and autumn, and -1 in winter.

10. An intelligent louver system, characterized in that, include: At least one processor; and at least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the control method as described in any one of claims 1 to 9 by calling the program instructions.

Images