Intelligent shutter system and regulation and control method thereof

Abstract

The invention discloses an intelligent shutter system and a regulation and control method thereof. The regulation and control method comprises the steps that an original building model is established, a parameterized shutter model with the height of the lower edge being variable is established, and the rotation angle of blades is controlled by constructing four curve parameters of a Betz curve; applying the shutter model to the original building model to obtain a new building model; a plurality of indoor lighting design points are arranged in the indoor depth direction, and the optimization direction is set according to requirements; networking to obtain real-time meteorological data, and obtaining a real-time EPW meteorological file; according to the real-time EPW meteorological file, the building model and the shutter model, optimizing the lower edge height of the shutter and the rotation angle of the shutter in the set optimizing direction based on radiation intensity, a lighting coefficient and indoor illumination simulation to obtain the optimal solution of the lower edge height and four curve parameters; and regulating and controlling the shutter according to the optimal solution. According to the invention, the problem of real-time intelligent regulation and control of the shutter is solved, so that indoor radiation and lighting are balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of blinds, and in particular to an intelligent blind system and a control method thereof. Background Art

[0002] High-rise superimposed large-space factory buildings are a design response to the space needs of high-tech manufacturing industry in the innovation of contemporary structural technology, and are also an inevitable product of social development. This new type of industrial building form combines the spatial characteristics of two types of buildings with high energy consumption, large-span factory buildings and high-rise buildings, and poses new challenges to energy conservation in industrial buildings. As a building external sunshade component, blinds have the functions of regulating indoor radiation and lighting, and have great potential in the application of high-rise superimposed large-space factory buildings. Traditional blinds rely on users to manually switch, lack convenience and are difficult to balance radiation and lighting. With the rapid development of sensor technology, network transmission technology and information processing technology, the concept of smart home has begun to enter people's daily production and life, and smart blinds systems have emerged. For high-rise superimposed large-space factory buildings with large spans and depths, it is difficult to intelligently control the form of blinds in real time to ensure that the interior of the factory obtains sufficient and appropriate light and radiation. In order to balance indoor radiation and lighting, a large amount of energy needs to be consumed to ensure the comfort of the indoor environment.

[0003] In the prior art, generally, multi-point lighting in a large indoor area is not accurately considered, and the control of smart blinds is generally achieved by relying on real-time monitoring of indoor lighting. However, indoor lighting sensors are easily affected by various objects, resulting in unpredictable errors. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] Based on the above problems, the present invention provides an intelligent blinds system and a control method thereof to solve the problem of real-time intelligent control of blinds, thereby balancing indoor radiation and lighting and saving energy consumption.

[0006] (II) Technical solution

[0007] Based on the above technical problems, the present invention provides a control method for an intelligent blind system, comprising the following steps:

[0008] S1. Establishing a building model and a shutter model: establishing an original building model; establishing a parameterized shutter model with a variable lower edge height, and controlling the blade rotation angle by constructing four curve parameters of a Bezier curve; applying the shutter model to the original building model to obtain a new building model, wherein the building model includes a room model, a window model and a shutter model;

[0009] S2. Set multiple indoor lighting design points according to the indoor depth direction, and set the optimal direction of radiation intensity, lighting factor and indoor multi-point illumination according to needs;

[0010] S3, obtain real-time meteorological data through Internet and obtain real-time EPW meteorological files;

[0011] S4, according to the real-time EPW meteorological file, the building model and the blinds model, based on the radiation intensity, the daylight factor and the indoor multi-point illumination simulation, the blinds lower edge height and the blade rotation angle are optimized in the set optimization direction to obtain the optimal solution of the lower edge height and the four curve parameters; including:

[0012] S41, radiation intensity simulation: read the real-time EPW meteorological file, set the simulation time, generate a sky matrix, use the solar radiation simulation operator to simulate the solar radiation intensity based on the building model and the blinds model, output the solar radiation intensity of multiple points on the measurement network, and calculate the average radiation intensity;

[0013] S42, daylight factor simulation: establishing a simulation model, generating a measurement network based on the indoor ground according to the simulation model, applying the measurement network to the simulation model, simulating the indoor daylight factor of the simulation model, outputting the multi-point daylight factor on the measurement network, and calculating the average daylight factor;

[0014] S43, indoor illumination 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, simulate the indoor lighting factor of the simulation model, obtain the illumination of multiple indoor lighting design points, calculate the difference between the illumination of the multiple indoor lighting design points and the target illumination, calculate and output the absolute value of the difference, and obtain the multi-point illumination difference;

[0015] S44, multi-objective optimization: connect the lower edge height and four curve parameters to the input port of the multi-objective optimization operator; connect the average radiation intensity, average daylight factor, and multi-point illumination difference obtained by simulation calculation to the target interface of the multi-objective optimization operator, run the multi-objective optimization in the set optimization direction through the multi-objective optimization operator, perform multiple cycles and iterations, generate an optimal solution set, and use the cracking operator to output the optimal solution;

[0016] S5. Adjust the blinds according to the optimal solution of the bottom edge height and the four curve parameters.

[0017] Furthermore, in S1, the original building model is established based on the architectural drawings and on-site measurement results, and the original building model includes walls, floors and windows; the establishment of the shutter model includes: setting the bottom edge height of the shutter, generating blades based on the base line and the bottom edge height of the shutter, and then moving and copying multiple blades according to the blade height series to form a parameterized shutter model with a variable bottom edge height.

[0018] Furthermore, in S1, the controlling of the blade rotation angle by constructing four curve parameters of a Bezier curve includes: constructing a Bezier curve based on the four parameters, finding the intersection of the blade YZ plane and the Bezier curve in a unit spacing, multiplying the y coordinate of the intersection by 90 degrees as the rotation angle of each blade of the blinds from top to bottom, and rotating each blade according to the rotation angle.

[0019] Furthermore, in S2, the optimization direction includes: the average daylight coefficient is maximized, the average radiation intensity in spring, summer and autumn is minimized, the average radiation intensity in winter is maximized, and the difference between the illumination of the multiple indoor lighting design points and the target illumination is minimized.

[0020] Further, the S3 includes:

[0021] S31, triggering once at a fixed time interval to obtain real-time meteorological data from the National Meteorological Science Data Center website, and grabbing the current time for setting the replacement time;

[0022] S32, reading the original EPW meteorological data, and replacing the corresponding part in the original EPW meteorological file with the meteorological data obtained in real time at the set replacement time;

[0023] S33. After replacement, create a modified epw object and rewrite the EPW file to obtain a real-time EPW meteorological file.

[0024] Furthermore, the fixed time interval is 15 minutes.

[0025] Furthermore, in S42 and S43, the establishing of the simulation model includes: generating a room based on the room model, generating a window based on the window model, generating a sunshade component based on the blind model, applying the window to the room, integrating the room and the sunshade component, and establishing the simulation model.

[0026] Furthermore, in S44, the average radiation intensity, average daylight factor and multi-point illumination difference obtained by simulation operation are connected to the target interface of the multi-objective optimization operator, and the multi-objective optimization operator is used to run the multi-objective optimization with the set optimization direction, including: connecting the power operation result of the average radiation intensity, the reciprocal operation result of the average daylight factor and the multi-point illumination difference obtained by simulation operation to the target interface of the multi-objective optimization operator; taking the minimum value of the value of the target interface as the optimization direction, and automatically performing multi-objective optimization through the multi-objective optimization operator.

[0027] Furthermore, the exponent of the power operation is +1 in spring, summer and autumn, and -1 in winter.

[0028] The present invention also discloses an intelligent blind system, comprising:

[0029] at least one processor; and at least one memory in communication with the processor, wherein:

[0030] 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.

[0031] (III) Beneficial effects

[0032] The above technical solution of the present invention has the following advantages:

[0033] (1) The present invention is developed for the large space of the emerging high-rise composite large-space factory buildings. Relying on the Rhino-Grasshopper integrated parameterization platform, the Ladybug plug-in and various input, judgment, change and generation operators are called to simulate and optimize the lower edge height of the blinds and the blade rotation angle based on real-time meteorological files and indoor lighting radiation requirements, and the blinds are controlled by motors to reach the optimal state, thereby providing an excellent lighting and radiation environment for high-rise composite large-space factories; the three environmental indicators of radiation intensity, daylighting factor and multi-point illumination are optimized simultaneously under different seasons and weather conditions, thereby maximizing the indoor thermal comfort and light environment through efficient radiation and illumination control, which is conducive to improving the convenience of regulation, reducing energy consumption, improving the overall operation efficiency of the factory building, and protecting the health of users;

[0034] (2) The blinds of the present invention are designed with fully adjustable blades. According to the differentiated lighting requirements at different locations in the room, targets are set respectively, and the illumination of each lighting design point in the room is made as close to the lighting target as possible by optimizing, so as to meet the differentiated lighting requirements of multiple points in the room and provide a customized natural light environment for special users or special usage scenarios;

[0035] (3) The blinds of the present invention obtain the latest meteorological data conditions through real-time networking for simulation, without the need to use sensors to detect environmental conditions. Therefore, problems such as sensor obstruction can be avoided, thereby improving simulation accuracy and improving indoor thermal comfort and light environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0037] Figure 1 The flow chart of the control method of the intelligent blinds system according to the embodiment of the present invention is as follows Figure 1 ;

[0038] Figure 2 The flow chart of the control method of the intelligent blinds system according to the embodiment of the present invention is as follows Figure 2 ;

[0039] Figure 3 A schematic diagram of a computing unit for establishing a parameterized blinds model in S12 of an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of a calculator for controlling the blade rotation angle using the S13 curve parameters according to an embodiment of the present invention;

[0041] Figure 5 A schematic diagram of controlling the rotation angle of a blade according to a Bezier curve according to an embodiment of the present invention;

[0042] Figure 6 An axonometric view of a building model according to an embodiment of the present invention;

[0043] Figure 7 An axonometric diagram of a shutter model according to an embodiment of the present invention;

[0044] Figure 8 A schematic diagram of setting the lighting optimization design points according to an embodiment of the present invention;

[0045] Fig. 9 This is a schematic diagram of a computing unit for obtaining a real-time EPW weather file in S3 of an embodiment of the present invention;

[0046] Fig.10 A schematic diagram of an operator for simulating the S41 radiation intensity according to an embodiment of the present invention;

[0047] Fig.11 A schematic diagram of a radiation intensity simulation result according to an embodiment of the present invention;

[0048] Fig.12 A schematic diagram of a computing unit for simulating the daylight factor in S42 according to an embodiment of the present invention;

[0049] Fig.13A schematic diagram of a simulation result of a daylight factor according to an embodiment of the present invention;

[0050] Fig.14 Schematic diagram of a computing unit for indoor illumination simulation in S43 according to an embodiment of the present invention;

[0051] Fig.15 It is a schematic diagram of indoor illumination simulation results according to an embodiment of the present invention;

[0052] Fig.16 A schematic diagram of a computing unit for multi-objective optimization in S44 of an embodiment of the present invention;

[0053] Fig.17 A schematic diagram of the multi-objective optimization settings and result interface of an embodiment of the present invention;

[0054] In the figure: 1: shutter; 2: outer glass window; 3: ventilation cavity; 4: inner glass window; 5: height control device; 6: blade rotation axis. DETAILED DESCRIPTION

[0055] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0056] The control method of an intelligent blind system of the present invention is based on the Rhino-Grasshopper platform and uses the Ladybug plug-in. It is suitable for intelligently controllable blinds in high-rise composite large-space factories, so that the interior of the factory can obtain sufficient and appropriate light and radiation, thereby ensuring the internal environment required by high-tech industries, and saving energy consumption to the greatest extent, thereby improving economic benefits. Figure 1-2 As shown, the following steps are included:

[0057] S1. Build building model and shutter model

[0058] S11. Establishing the original building model: According to the architectural drawings and on-site measurement results, establish the original building model in Rhino software, including room model and window model, and the building elements include walls, floors and windows;

[0059] S12. Establish a parameterized louver model with variable bottom height

[0060] Set the height of the lower edge of the blinds, use the Move operator to move the base line upwards, use the Extrude operator to generate blades based on the base line and height, then use the Series component to construct a blade height series, use the Move operator to move and copy multiple blades according to the blade height series, and form a parametric blind model with variable lower edge height; the operator process is as follows Figure 3shown.

[0061] S13, control the blade rotation angle by constructing four curve parameters of the Bezier curve

[0062] Use the Bezier Span operator to construct a Bezier curve based on four parameters; use the Curve|plane operator to find the intersection of the YZ plane of each blade and the Bezier curve, and multiply the y coordinate of the intersection by 90 degrees as the rotation angle of each blade from top to bottom of the blinds; finally, use the Rotate operator to rotate each blade according to the rotation angle to obtain the blinds model after the blades are rotated, thereby achieving the indirect control of the rotation angle of all blinds by constructing a Bezier curve with four parameters. The operator process is as follows Figure 4 As shown in Figure 2, the rotation angles of all louver blades are controlled according to the Bezier curve. Figure 5 shown.

[0063] The shutter model is applied to the original building model to obtain a new building model, wherein the building model includes a room model, a window model and a shutter model. The axonometric drawing of the building model is as follows: Figure 6 As shown, the axonometric drawing of the louver model is as follows Figure 7 As shown, a shutter is provided at the ventilation cavity 3, and a shutter 1 is provided between the outer glass window 2 and the inner glass window 4. The height of the shutter 1 is controlled by a height control device 5, and the shutter 1 is adjusted in angle around a blade rotation axis 6;

[0064] S2. Set multiple indoor lighting design points according to the indoor depth direction, and set the optimal direction of radiation intensity, lighting factor and indoor multi-point illumination according to needs;

[0065] The optimization direction is to maximize the indoor average daylight coefficient; the optimization direction is to maximize the radiation in winter and minimize the radiation in spring, summer, and autumn; combined with the building plan and the internal use requirements of the building, set the lighting requirements for the indoor depth distribution, and set 4 lighting optimization design points A, B, C, and D to determine the illumination target value of each point, such as Figure 8 shown.

[0066] S3. Get real-time weather data online and obtain real-time EPW weather files (EPW, EnergyPlus Weather)

[0067] S31, triggering once at a fixed time interval to obtain real-time meteorological data from the National Meteorological Science Data Center website, and grabbing the current time for setting the replacement time;

[0068] Use Cpython's self-written operator to obtain real-time meteorological data from the National Meteorological Science Data Center website, use the current time capture operator Capture current time to capture the current time, use the trigger operator Trigger to trigger every 15 minutes to obtain real-time meteorological data and capture the current time, use the analysis time setting operator AnalysisiPeriod to set the replacement time, and input it as the replacement sequence number into the replacement data operator Replace Items;

[0069] S32, reading the original EPW meteorological data, and replacing the corresponding part in the original EPW meteorological file with the meteorological data obtained in real time at the set replacement time;

[0070] Use the epw file import operator ImportEPW to read the original EPW weather data (Energy PlusWeather), and replace the corresponding part in the original weather file with the real-time weather data through the replacement data operator Replace Items at the set replacement time;

[0071] S33, after replacement, create a modified epw object and rewrite the EPW file to obtain a real-time EPW meteorological file;

[0072] The modified epw object is created through the epw creation operator CreateEPW, and the EPW file is rewritten through the epw file writing operator Write EPW to obtain the real-time EPW meteorological file for use in simulation.

[0073] The operation process of step S3 is as follows: Fig. 9 shown.

[0074] S4. According to the real-time EPW meteorological file, the building model and the blinds model, based on the radiation intensity, the daylight factor and the indoor multi-point illumination simulation, the blinds lower edge height and the blade rotation angle are optimized in the set optimization direction to obtain the optimal solution of the lower edge height and the four curve parameters, specifically including:

[0075] S41. Radiation intensity simulation

[0076] Use the meteorological file reading operator Import epw to read the real-time EPW meteorological file, use the sky matrix generation operator SkyMatrix to generate the sky matrix, use the solar radiation simulation operator IncidentRadiation to simulate the radiation intensity, and output the solar radiation intensity of multiple points on the measurement network, and then use the average operator Average to calculate the average radiation intensity. The operator process is as follows Fig.10 The radiation intensity simulation results are shown in Fig.11 shown.

[0077] S42, daylight factor simulation

[0078] Based on the room model, use the room generation operator RoomSolid to generate a room. Based on the window model, use the window setting operator Apenture to generate a window. Use the window operator AddSubface to apply the window to the room. Based on the blinds model, use the sunshade addition operator Shade to generate a sunshade component. Use the simulation model establishment operator Model to integrate the room and the sunshade component. Establish a simulation model. Use the measurement grid generation operator GridRooms to generate a measurement grid based on the indoor ground. Use the measurement grid application operator AssignGridsViews to apply the measurement grid to the simulation model. Use the daylight factor simulation operator DaylightFactor to simulate the indoor daylight factor of the simulation model. After outputting the multi-point daylight factors on the measurement grid, use the average operator Average to calculate the average daylight factor. The operator process is as follows: Fig.12 The simulation results of the daylight factor are shown in Fig.13 shown.

[0079] S43, indoor illumination simulation

[0080] Use the room generation operator RoomSolid to generate a room, use the window setting operator Apenture to generate windows, use the window setting operator AddSubface to apply windows to the room, use the shading operator Shade to generate shading components, use the simulation model establishment operator Model to integrate the room and shading components, establish a simulation model, use the meteorological file reading operator Import epw to read the real-time EPW meteorological file, set the simulation time, use the sky generation operator ClimateBased to generate a sky object, use the measurement network generation operator SenorGird to generate a measurement network based on four target measurement points, the four target measurement points are four indoor lighting design points, use the measurement network application operator AssignGridsViews to apply the measurement network to the simulation model, use the dot matrix illumination simulation operator PitGrid to simulate the indoor lighting factor of the simulation model, obtain the illumination of the four indoor lighting design points, use the subtraction operator Subtraction to calculate the difference between it and the target illumination, and use the absolute value operator Absolute to calculate and output the absolute value of the difference. The operator process is as follows Fig.14 The illumination simulation results are shown in Fig.15 shown.

[0081] S44, multi-objective optimization: connect the lower edge height and the four curve parameters to the input port of the multi-objective optimization operator; connect the average radiation intensity, average daylight factor and multi-point illumination difference obtained by simulation operation to the target interface of the multi-objective optimization operator, run the multi-objective optimization operator with the set optimization direction, perform multiple cycles and iterations, generate the optimal solution set, and use the cracking operator to output the optimal solution.

[0082] Use the multi-objective optimization operator Wallacei to perform multi-objective optimization.

[0083] Connect the lower edge height of the blinds and the four curve parameters to the input ports Genes and Phenotype of the multi-objective optimization operator Wallacei (the slider needs to be connected to the Genes input port. Since the slider is distributed throughout the program interface, it is replaced by a numerical unit in the figure);

[0084] The average radiation intensity obtained by simulation operation is connected to the power operator Power for power operation. The exponent is provided by the list operator Value List. When spring, summer and autumn seasons are selected, the exponent is +1. When winter is selected, the exponent is -1. The power operation result is connected to the target interface objectives of the target optimization operator Wallacei.

[0085] Connect the average daylight factor to the reciprocal operator One Over X to calculate the reciprocal, and connect the reciprocal result to the target interface objectives of the target optimization operator Wallacei;

[0086] Connect the four-point illumination difference to the target interface objectives of the target optimization operator Wallacei;

[0087] Double-click the target optimization operator Wallacei to run multi-objective optimization. The operator automatically takes the minimum value of the target interface objectives as the optimization direction, performs multiple cycles and iterations, and gradually generates solutions that meet all objectives as much as possible, and generates the optimal solution set. After the optimization is completed, use the decryption operator Decode Phenotype to output the optimal solution. The operator process is as follows Fig.16 As shown in the figure, the multi-objective optimization settings and results interface are as follows Fig.17 shown.

[0088] S5. Adjust the blinds according to the optimal solution of the bottom edge height and the four curve parameters.

[0089] The motor is used to control the blinds to rotate the blades at the optimal angle and adjust the height of the blinds to achieve the optimal indoor light and heat environment.

[0090] The embodiment of the present invention may also be an intelligent blind system that runs the control method of the intelligent blind system described above.

[0091] Finally, it should be noted that the above-mentioned control method can be converted into software program instructions, which can be implemented by using a control system including a processor and a memory, or can be implemented by computer instructions stored in a non-transitory computer-readable storage medium. The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0092] In summary, the above-mentioned intelligent blinds system and its control method have the following advantages:

[0093] Beneficial effects:

[0094] (1) The present invention is developed for the large space of the emerging high-rise composite large-space factory buildings. Relying on the Rhino-Grasshopper integrated parameterization platform, the Ladybug plug-in and various input, judgment, change and generation operators are called to simulate and optimize the lower edge height of the blinds and the blade rotation angle based on real-time meteorological files and indoor lighting radiation requirements, and the blinds are controlled by motors to reach the optimal state, thereby providing an excellent lighting and radiation environment for high-rise composite large-space factories; the three environmental indicators of radiation intensity, daylighting factor and multi-point illumination are optimized simultaneously under different seasons and weather conditions, thereby maximizing the indoor thermal comfort and light environment through efficient radiation and illumination control, which is conducive to improving the convenience of regulation, reducing energy consumption, improving the overall operation efficiency of the factory building, and protecting the health of users;

[0095] (2) The blinds of the present invention are designed with fully adjustable blades. According to the differentiated lighting requirements at different locations in the room, targets are set respectively, and the illumination of each lighting design point in the room is made as close to the lighting target as possible by optimizing, so as to meet the differentiated lighting requirements of multiple points in the room and provide a customized natural light environment for special users or special usage scenarios;

[0096] (3) The blinds of the present invention obtain the latest meteorological data conditions through real-time networking for simulation, without the need to use sensors to detect environmental conditions. Therefore, problems such as sensor obstruction can be avoided, thereby improving simulation accuracy and improving indoor thermal comfort and light environment.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A control method for an intelligent blinds system, characterized in that: The following steps are involved: S1. Establishing a building model and a shutter model: establishing an original building model; establishing a parameterized shutter model with a variable lower edge height, and controlling the blade rotation angle by constructing four curve parameters of a Bezier curve; applying the shutter model to the original building model to obtain a new building model, wherein the building model includes a room model, a window model and a shutter model; S2. Set multiple indoor lighting design points according to the indoor depth direction, and set the optimal direction of radiation intensity, lighting factor and indoor multi-point illumination according to needs; S3, obtain real-time meteorological data through Internet and obtain real-time EPW meteorological files; S4, according to the real-time EPW meteorological file, the building model and the blinds model, based on the radiation intensity, the daylight factor and the indoor multi-point illumination simulation, the blinds lower edge height and the blade rotation angle are optimized in the set optimization direction to obtain the optimal solution of the lower edge height and the four curve parameters; including: S41, radiation intensity simulation: read the real-time EPW meteorological file, set the simulation time, generate a sky matrix, use the solar radiation simulation operator to simulate the solar radiation intensity based on the building model and the blinds model, output the solar radiation intensity of multiple points on the measurement network, and calculate the average radiation intensity; S42, daylight factor simulation: establishing a simulation model, generating a measurement network based on the indoor ground according to the simulation model, applying the measurement network to the simulation model, simulating the indoor daylight factor of the simulation model, outputting the multi-point daylight factor on the measurement network, and calculating the average daylight factor; S43, indoor illumination 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, simulate the indoor lighting factor of the simulation model, obtain the illumination of multiple indoor lighting design points, calculate the difference between the illumination of the multiple indoor lighting design points and the target illumination, calculate and output the absolute value of the difference, and obtain the multi-point illumination difference; S44, multi-objective optimization: connect the lower edge height and four curve parameters to the input port of the multi-objective optimization operator; connect the average radiation intensity, average daylight factor, and multi-point illumination difference obtained by simulation calculation to the target interface of the multi-objective optimization operator, run the multi-objective optimization in the set optimization direction through the multi-objective optimization operator, perform multiple cycles and iterations, generate an optimal solution set, and use the cracking operator to output the optimal solution; S5. Adjust the blinds according to the optimal solution of the bottom edge height and the four curve parameters.

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

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

4. The control method of the intelligent blinds system according to claim 1, characterized in that: In S2, the optimization direction includes: the average daylight coefficient is maximized, the average radiation intensity in spring, summer and autumn is minimized, the average radiation intensity in winter is maximized, and the difference between the illumination of the multiple indoor lighting design points and the target illumination is minimized.

5. The control method of the intelligent blinds system according to claim 1, characterized in that: The S3 includes: S31, triggering once at a fixed time interval to obtain real-time meteorological data from the National Meteorological Science Data Center website, and grabbing the current time for setting the replacement time; S32, reading the original EPW meteorological data, and replacing the corresponding part in the original EPW meteorological file with the meteorological data obtained in real time at the set replacement time; S33. After replacement, create a modified epw object and rewrite the EPW file to obtain a real-time EPW meteorological file.

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

7. The control method of the intelligent blinds system according to claim 1, characterized in that: 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 sunshade component based on the blind model, applying the window to the room, integrating the room and the sunshade component, and establishing the simulation model.

8. The control method of the intelligent blinds system according to claim 1, characterized in that: In S44, the average radiation intensity, average daylight factor and multi-point illumination difference obtained by simulation operation are connected to the target interface of the multi-objective optimization operator, and the multi-objective optimization operator is used to run the multi-objective optimization with the set optimization direction, including: connecting the power operation result of the average radiation intensity, the reciprocal operation result of the average daylight factor and the multi-point illumination difference obtained by simulation operation to the target interface of the multi-objective optimization operator; taking the minimum value of the value of the target interface as the optimization direction, and automatically performing multi-objective optimization through the multi-objective optimization operator.

9. The control method of the intelligent blinds system according to claim 8, characterized in that: The exponent of the power operation is +1 in spring, summer and autumn, and -1 in winter.

10. An intelligent blinds system, characterized in that: include: at least one processor; and at least one memory in communication with the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the control method according to any one of claims 1 to 9.

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