Intelligent window and indoor tail end linkage control method, device and equipment

Abstract

The invention discloses a linkage control method, device and equipment for an intelligent window and an indoor tail end. The linkage control method comprises the steps that multi-source environment parameters and intelligent window characteristic parameters are collected; according to the multi-source environment parameters and the intelligent window characteristic parameters, respectively simulating and calculating a thermal sensation voting index, an unsatisfaction percentage, a brightness distribution image of each indoor position, a glare possibility index and a circadian rhythm stimulation index; determining a first control impulse vector according to the circadian rhythm stimulation threshold; screening a second control impulse vector from the first control impulse vectors according to a thermal sensation voting threshold value, an unsatisfaction percentage threshold value, a glare possibility threshold value, a color temperature threshold value and a horizontal illumination threshold value; and selecting the target adjusting quantity combination with the minimum energy consumption of the indoor tail end from the second control impulse vector to control the intelligent window and the indoor tail end. According to the invention, linkage control of the intelligent window and the indoor terminal is realized, and unified optimization of indoor health, comfort and energy efficiency target is realized.

Description

Technical Field

[0001] This application relates to the field of optoelectronic intelligent control technology, and in particular to an intelligent window and an indoor terminal linkage control method, device, equipment and computer-readable storage medium. Background Technology

[0002] Smart windows are able to passively or actively change their light and heat characteristics according to the outdoor environment or indoor needs, resisting the adverse effects of dynamically changing outdoor weather conditions on the relatively stable indoor environment, maintaining a relatively comfortable light and heat environment, and potentially reducing building energy consumption.

[0003] Currently, building smart window control strategies typically rely on rule-based control methods. These methods aim to reduce building energy consumption, prevent glare, and maintain a comfortable indoor light and heat environment by controlling the window's transmittance. Decision variables usually include solar radiation intensity, indoor work surface illuminance, and heat flux across the window surface. While rule-based control of smart windows using these decision variables can reduce building energy consumption, prevent glare, or increase comfort hours to some extent, solar radiation intensity also affects the indoor light and heat environment, thus impacting building energy demand. The indoor natural light environment directly affects artificial lighting needs, and the ratio of natural to artificial light and its duration of operation influences circadian rhythms and sleep patterns. Current control strategies often focus on the smart window itself, with independent operation of the indoor terminal. If the indoor terminal fails to respond promptly to changes in the indoor environment, it can negatively impact occupants' health, causing discomfort from light and heat, and leading to energy waste.

[0004] In summary, how to effectively address the problems of current control strategies affecting human health, causing discomfort from light and heat, and leading to energy waste is an urgent issue that needs to be resolved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a smart window and indoor terminal linkage control method, which realizes the linkage control of smart window and indoor terminal, and achieves unified optimization of indoor health, comfort and energy efficiency goals; another purpose of this application is to provide a smart window and indoor terminal linkage control device, equipment and computer-readable storage medium.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0007] A smart window and indoor terminal linkage control method, comprising:

[0008] Collect multi-source environmental parameters and intelligent window characteristic parameters;

[0009] The thermal comfort model is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and the smart window characteristic parameters.

[0010] The lighting model is used to simulate the brightness distribution image, glare probability index, color temperature index and horizontal illuminance at various locations in the room based on the multi-source environmental parameters and the intelligent window characteristic parameters.

[0011] The circadian rhythm stimulation index is simulated using the multi-source environmental parameters and the intelligent window characteristic parameters;

[0012] Obtain preset circadian rhythm stimulation threshold, thermal sensation voting threshold, dissatisfaction percentage threshold, glare probability threshold, color temperature threshold, and horizontal illuminance threshold;

[0013] A first control impulse vector is determined based on the circadian rhythm stimulation threshold; wherein, the first control impulse vector contains multiple combinations of adjustment quantities that make the circadian rhythm stimulation index meet the circadian rhythm stimulation threshold, and each combination of adjustment quantities consists of smart window adjustment quantity and indoor terminal adjustment quantity.

[0014] The first control impulse vector is filtered by combining adjustment amounts based on the thermal perception voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold to obtain a second control impulse vector composed of the selected combinations of adjustment amounts; wherein, each combination of adjustment amounts in the second control impulse vector satisfies the following conditions: the thermal perception voting index satisfies the thermal perception voting threshold, the dissatisfaction percentage satisfies the dissatisfaction percentage threshold, the glare probability index satisfies the glare probability threshold, the color temperature index satisfies the color temperature threshold, and the horizontal illuminance satisfies the horizontal illuminance threshold;

[0015] Select the target adjustment combination that minimizes the energy consumption of the indoor terminal from the second control impulse vector, and control the smart window and the indoor terminal according to the target adjustment combination.

[0016] In one specific embodiment of this application, the collection of multi-source environmental parameters includes:

[0017] Collect multi-source environmental parameters, including outdoor environmental parameters, indoor usage and spatial information, and individual physiological perception parameters.

[0018] In one specific embodiment of this application, the process of acquiring the outdoor environmental parameters is further included, the process of acquiring the outdoor environmental parameters including:

[0019] Obtain the set of most recent historical meteorological variables for a first preset duration;

[0020] The outdoor environmental parameters are obtained by using a long short-term memory network to predict the weather for a second preset duration based on the set of most recent historical meteorological variables.

[0021] In one specific embodiment of this application, after collecting multi-source environmental parameters and smart window characteristic parameters, the method further includes:

[0022] Acquire outdoor spectral input;

[0023] The indoor natural light spectrum is calculated based on the intelligent window's characteristic parameters and the outdoor spectral input.

[0024] In one specific embodiment of this application, after controlling the smart window and the indoor terminal according to the target adjustment combination, the method further includes:

[0025] Obtain the control results and find the target reference spectral power distribution from the control results;

[0026] Calculate the spectral power at the first compensation wavelength point based on the target reference spectral power distribution and the indoor natural light spectrum;

[0027] Obtain an indoor heat map of people distribution, and determine key activity areas indoors based on the indoor heat map of people distribution;

[0028] Determine the solar radiation coverage path using a solar incidence analysis algorithm;

[0029] The spectral power of the first compensation wavelength point is adjusted according to the key activity area and the sunshine coverage path to obtain the spectral power of the second compensation wavelength point;

[0030] The lighting output is adjusted based on the spectral power at the second compensation wavelength point.

[0031] In one specific embodiment of this application, after controlling the smart window and the indoor terminal according to the target adjustment combination, the method further includes:

[0032] Eyelid opening and closing degree was measured for each person in the room to obtain the set of eyelid opening and closing degrees;

[0033] When an indoor person experiencing glare discomfort is identified based on the eyelid opening and closing degree set, the first location information of the indoor person experiencing glare discomfort is obtained;

[0034] The target lighting device is determined based on the first location information;

[0035] The lighting output of the target lighting device is regulated.

[0036] In one specific embodiment of this application, after controlling the smart window and the indoor terminal according to the target adjustment combination, the method further includes:

[0037] Facial infrared temperature analysis was performed on the individuals in each room to obtain a set of facial infrared temperatures.

[0038] When it is determined from the facial infrared temperature set that there is an indoor person in a heat load sensitive state, the second location information of the indoor person in the heat load sensitive state is obtained;

[0039] The target temperature control device is determined based on the second location information;

[0040] The temperature output of the target temperature control device is regulated.

[0041] A smart window and an indoor terminal linkage control device, comprising:

[0042] The parameter acquisition module is used to collect multi-source environmental parameters and intelligent window characteristic parameters;

[0043] The thermal sensation voting and dissatisfaction percentage simulation module is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and the smart window characteristic parameters using a thermal comfort model.

[0044] The simulation module is used to simulate the brightness distribution image, glare probability index, color temperature index and horizontal illuminance of various locations in the room based on the multi-source environmental parameters and the intelligent window characteristic parameters using the lighting model.

[0045] The circadian rhythm stimulation simulation module is used to simulate circadian rhythm stimulation indicators based on the multi-source environmental parameters and the intelligent window characteristic parameters using a rhythm model.

[0046] The threshold acquisition module is used to acquire preset circadian rhythm stimulation threshold, thermal sensation voting threshold, dissatisfaction percentage threshold, glare probability threshold, color temperature threshold, and horizontal illuminance threshold.

[0047] The first control impulse vector determination module is used to determine the first control impulse vector according to the circadian rhythm stimulation threshold; wherein, the first control impulse vector includes multiple adjustment quantity combinations that make the circadian rhythm stimulation index meet the circadian rhythm stimulation threshold, and each adjustment quantity combination is composed of smart window adjustment quantity and indoor terminal adjustment quantity.

[0048] The second control impulse vector acquisition module is used to filter the first control impulse vector by adjusting the combination of the thermal sensation voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold, to obtain a second control impulse vector composed of the filtered combinations of adjustment amounts; wherein, each combination of adjustment amounts in the second control impulse vector satisfies the following conditions: the thermal sensation voting index satisfies the thermal sensation voting threshold, the dissatisfaction percentage satisfies the dissatisfaction percentage threshold, the glare probability index satisfies the glare probability threshold, the color temperature index satisfies the color temperature threshold, and the horizontal illuminance satisfies the horizontal illuminance threshold.

[0049] The smart window and indoor terminal control module is used to select the target adjustment combination with the minimum energy consumption of the indoor terminal from the second control impulse vector, and control the smart window and the indoor terminal according to the target adjustment combination.

[0050] A smart window and indoor terminal linkage control device, comprising:

[0051] Memory, used to store computer programs;

[0052] A processor is used to execute the computer program to implement the steps of the smart window and indoor terminal linkage control method as described above.

[0053] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned smart window and indoor terminal linkage control method.

[0054] The intelligent window and indoor terminal linkage control method provided in this application includes: collecting multi-source environmental parameters and intelligent window characteristic parameters; using a thermal comfort model to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and intelligent window characteristic parameters; using a daylighting model to simulate brightness distribution images, glare probability indicators, color temperature indicators, and horizontal illuminance at various locations indoors based on the multi-source environmental parameters and intelligent window characteristic parameters; using a rhythm model to simulate diurnal rhythm stimulation indicators based on the multi-source environmental parameters and intelligent window characteristic parameters; obtaining preset diurnal rhythm stimulation thresholds, thermal sensation voting thresholds, dissatisfaction percentage thresholds, glare probability thresholds, color temperature thresholds, and horizontal illuminance thresholds; and determining a first control impulse vector based on the diurnal rhythm stimulation thresholds; wherein, the first control impulse vector includes a function that makes the diurnal rhythm stimulation indicators satisfy the diurnal rhythm... Multiple adjustment combinations of the stimulation threshold are used, each combination consisting of the smart window adjustment and the indoor terminal adjustment. The first control impulse vector is filtered based on the thermal perception voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold to obtain a second control impulse vector composed of the selected adjustment combinations. Each adjustment combination in the second control impulse vector satisfies the following criteria: thermal perception voting index meets the thermal perception voting threshold, dissatisfaction percentage meets the dissatisfaction percentage threshold, glare probability index meets the glare probability threshold, color temperature index meets the color temperature threshold, and horizontal illuminance meets the horizontal illuminance threshold. The target adjustment combination with the lowest indoor terminal energy consumption is selected from the second control impulse vector, and the smart window and the indoor terminal are controlled according to this target adjustment combination.

[0055] As can be seen from the above technical solutions, by prioritizing non-visual health needs and constructing a rhythm-oriented intelligent lighting environment, the ability of indoor spaces to support the human body's diurnal rhythm is effectively improved. Multi-objective collaborative optimization is achieved, enhancing visual comfort and thermal environment quality. By establishing a dynamic linkage between the lighting model and the thermal comfort model, and jointly controlling smart windows and indoor terminals, multiple comfort indicators such as illuminance, color temperature, glare, and thermal sensation voting indicators are coordinated and adjusted to meet the combined visual and thermal needs of users. The intelligent windows and indoor terminals are linked for control, with the control strategy following a hierarchical control logic of prioritizing health, optimizing comfort, and providing a safety net for energy conservation, achieving unified optimization of indoor health, comfort, and energy efficiency goals.

[0056] Accordingly, this application also provides an intelligent window and indoor terminal linkage control device, equipment and computer-readable storage medium corresponding to the above-mentioned intelligent window and indoor terminal linkage control method, which have the above-mentioned technical effects, and will not be repeated here. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart illustrating one implementation of the intelligent window and indoor terminal linkage control method in this application.

[0059] Figure 2 This is a structural block diagram of an intelligent window and indoor terminal linkage control system according to an embodiment of this application;

[0060] Figure 3 This is a structural block diagram of a smart window and an indoor terminal linkage control device according to an embodiment of this application;

[0061] Figure 4 This is a structural block diagram of a smart window and an indoor terminal linkage control device according to an embodiment of this application;

[0062] Figure 5 This is a schematic diagram of the specific structure of an intelligent window and an indoor terminal linkage control device provided in an embodiment of this application. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0065] See Figure 1 , Figure 1 This is a flowchart illustrating an implementation of the smart window and indoor terminal linkage control method in this application. The method may include the following steps:

[0066] S101: Collects multi-source environmental parameters and smart window characteristic parameters.

[0067] Collect multi-source environmental parameters and smart window characteristic parameters. Multi-source environmental parameters can include cross-domain data such as light, heat, airflow, occupancy, physiology, and equipment status. Smart window characteristic parameters can include the spectral transmittance, reflectance, absorptivity characteristics, and solar heat gain coefficient (SHGC) of the window material.

[0068] The smart window ontology model is used to describe the optical and thermal responses of electrochromic windows, thermochromic windows, or other window types with dynamic dimming capabilities under different environmental parameters and control signals.

[0069] S102: Using a thermal comfort model, thermal sensation voting indicators and dissatisfaction percentages are simulated based on multi-source environmental parameters and smart window characteristic parameters.

[0070] After collecting multi-source environmental parameters and smart window characteristic parameters, a thermal comfort model is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and smart window characteristic parameters. The thermal sensation voting indicators and dissatisfaction percentages are used to describe the subjective perception response of indoor personnel to environmental thermal conditions.

[0071] S103: Using a daylighting model, simulate the brightness distribution image, glare probability index, color temperature index, and horizontal illuminance at various locations indoors based on multi-source environmental parameters and smart window characteristic parameters.

[0072] After predicting the characteristic parameters of the smart window, a daylighting model is used to simulate the brightness distribution image, glare probability index, color temperature index, and horizontal illuminance at various locations indoors based on multi-source environmental parameters and the smart window's characteristic parameters. The brightness distribution images at each location allow for the determination of the indoor natural light distribution characteristics.

[0073] S104: Using a rhythm model to simulate diurnal rhythmic stimulation indicators based on multi-source environmental parameters and smart window characteristic parameters.

[0074] After collecting multi-source environmental parameters and smart window characteristic parameters, a rhythm model was used to simulate diurnal rhythmic stimulation indicators based on these parameters. The diurnal rhythmic stimulation indicators were then used to characterize the impact of indoor spectral characteristics on the human diurnal rhythm system and non-visual physiological functions.

[0075] S105: Obtain preset circadian rhythm stimulation threshold, thermal sensation voting threshold, dissatisfaction percentage threshold, glare probability threshold, color temperature threshold, and horizontal illuminance threshold.

[0076] Pre-set thresholds for circadian rhythm stimulation, thermal sensation voting, dissatisfaction percentage, glare probability, color temperature, and horizontal illuminance. These thresholds can be set as normalized thresholds. The preset thresholds for circadian rhythm stimulation, thermal sensation voting, dissatisfaction percentage, glare probability, color temperature, and horizontal illuminance are then retrieved.

[0077] S106: Determine the first control impulse vector based on the circadian rhythm stimulation threshold.

[0078] The first control impulse vector contains multiple combinations of regulation quantities that make the circadian rhythm stimulation index meet the circadian rhythm stimulation threshold. Each combination of regulation quantities consists of intelligent window regulation quantity and indoor terminal regulation quantity.

[0079] After obtaining the circadian rhythm stimulation threshold, a first control impulse vector is determined based on this threshold. This first control impulse vector contains multiple combinations of regulatory parameters that ensure the circadian rhythm stimulation indicators meet the circadian rhythm stimulation threshold. Each combination of parameters consists of a smart window regulation parameter and an indoor end-point regulation parameter. By prioritizing the circadian rhythm stimulation indicators to meet the circadian rhythm stimulation threshold, the health goals are ensured.

[0080] S107: Based on the thermal sensation voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold, the first control impulse vector is screened by the combination of adjustment amounts to obtain the second control impulse vector composed of the selected combination of adjustment amounts.

[0081] Among them, each combination of adjustment quantities in the second control impulse vector meets the following criteria: thermal sensation voting index meets thermal sensation voting threshold, dissatisfaction percentage meets dissatisfaction percentage threshold, glare probability index meets glare probability threshold, color temperature index meets color temperature threshold, and horizontal illuminance meets horizontal illuminance threshold.

[0082] After obtaining the thermal sensation voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold, and determining the first control impulse vector based on the diurnal rhythm stimulation threshold, the first control impulse vector is then filtered for adjustment combinations based on the thermal sensation voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold, resulting in a second control impulse vector composed of the selected adjustment combinations.

[0083] In the second control impulse vector, each combination of adjustment values ​​meets the following criteria: thermal perception voting index meets the thermal perception voting threshold, dissatisfaction percentage meets the dissatisfaction percentage threshold, glare probability index meets the glare probability threshold, color temperature index meets the color temperature threshold, and horizontal illuminance meets the horizontal illuminance threshold.

[0084] Based on meeting health goals, comfort was optimized by setting thresholds for thermal sensation voting, dissatisfaction percentage, glare probability, color temperature, and horizontal illuminance.

[0085] S108: Select the target adjustment combination with the minimum energy consumption of the indoor terminal from the second control impulse vector, and control the smart window and the indoor terminal according to the target adjustment combination.

[0086] After selecting the second control impulse vector composed of various adjustment combinations, the target adjustment combination with the minimum energy consumption at the indoor terminal is chosen from the second control impulse vector, and the smart window and the indoor terminal are controlled according to the target adjustment combination. By selecting the target adjustment combination with the minimum energy consumption at the indoor terminal from the second control impulse vector that satisfies each threshold constraint, energy can be effectively saved and energy waste can be avoided.

[0087] As can be seen from the above technical solutions, by prioritizing non-visual health needs and constructing a rhythm-oriented intelligent lighting environment, the ability of indoor spaces to support the human body's diurnal rhythm is effectively improved. Multi-objective collaborative optimization is achieved, enhancing visual comfort and thermal environment quality. By establishing a dynamic linkage between the lighting model and the thermal comfort model, and jointly controlling smart windows and indoor terminals, multiple comfort indicators such as illuminance, color temperature, glare, and thermal sensation voting indicators are coordinated and adjusted to meet the combined visual and thermal needs of users. The intelligent windows and indoor terminals are linked for control, with the control strategy following a hierarchical control logic of prioritizing health, optimizing comfort, and providing a safety net for energy conservation, achieving unified optimization of indoor health, comfort, and energy efficiency goals.

[0088] It should be noted that, based on the above embodiments, this application also provides corresponding improvement solutions. In subsequent embodiments, steps that are the same as or corresponding to those in the above embodiments can be referred to each other, and the corresponding beneficial effects can also be referred to each other. These improvements will not be elaborated upon in the following improved embodiments.

[0089] In one specific embodiment of this application, collecting multi-source environmental parameters may include:

[0090] Collect multi-source environmental parameters, including outdoor environmental parameters, indoor usage and spatial information, and individual physiological perception parameters.

[0091] Multi-source environmental parameters can include outdoor environmental parameters, indoor usage and spatial information, and individual physiological perception parameters. Collecting these multi-source environmental parameters provides a comprehensive reference for subsequent indicator simulations in various models, improving the accuracy of the simulation results.

[0092] In one specific embodiment of this application, a process for acquiring outdoor environmental parameters is also included, which includes:

[0093] Obtain the set of most recent historical meteorological variables for a first preset duration;

[0094] By using a long short-term memory network to predict the weather for a second preset duration based on the most recent set of historical meteorological variables, outdoor environmental parameters are obtained.

[0095] To address the simulation uncertainties in the collaborative control process between smart windows and indoor terminal devices, a Long Short-Term Memory (LSTM) neural network is introduced as a time-series simulation model for external environmental parameters. When acquiring outdoor environmental parameters, the most recent historical meteorological variable set for a first preset time period is obtained. The LSTM network is then used to predict the weather for a second preset time period based on this set of recent historical meteorological variables, thus obtaining the outdoor environmental parameters. By training on historical data of key meteorological variables such as solar radiation, temperature, and relative humidity, the LSTM network can achieve rolling predictions of meteorological conditions for the second preset time period, providing feedforward input for the control logic and improving the control efficiency of the smart window.

[0096] The outdoor environmental parameters for the second preset duration can be predicted using the following formula:

[0097] ;

[0098] in, The set of recent historical meteorological variables (such as solar radiation, temperature, etc. in the past n hours) is the first preset time period. Outdoor environmental parameters for a second preset duration in the future (such as variables for the next hour).

[0099] In one specific embodiment of this application, after step S101, the method may further include the following steps:

[0100] Step 1: Obtain outdoor spectral input;

[0101] Step 2: Calculate the indoor natural light spectrum based on the smart window's characteristic parameters and the outdoor spectral input.

[0102] For ease of description, the two steps above can be combined for explanation.

[0103] After collecting multi-source environmental parameters and smart window characteristic parameters, outdoor spectral input can also be obtained. Based on the smart window characteristic parameters and outdoor spectral input, the indoor natural light spectrum can be calculated, thus realizing the rapid and accurate calculation of the indoor natural light spectrum.

[0104] In one specific embodiment of this application, after controlling the smart window and the indoor terminal according to the target adjustment combination, the method may further include the following steps:

[0105] Step 1: Obtain the control results and find the target reference spectral power distribution from the control results;

[0106] Step 2: Calculate the spectral power at the first compensation wavelength point based on the target reference spectral power distribution and the indoor natural light spectrum;

[0107] Step 3: Obtain an indoor heat map of people distribution and determine key activity areas indoors based on the heat map;

[0108] Step 4: Determine the solar radiation path using a solar incidence analysis algorithm;

[0109] Step 5: Adjust the spectral power of the first compensation wavelength point according to the key activity area and the path of sunlight coverage to obtain the spectral power of the second compensation wavelength point;

[0110] Step 6: Adjust the illumination output based on the spectral power at the second compensation wavelength point.

[0111] For ease of description, the above six steps can be combined for explanation.

[0112] After controlling the smart window and indoor terminals according to the target adjustment combination, the control results can be obtained, and the target reference spectral power distribution can be found from the control results. The spectral power of the first compensation wavelength point is calculated based on the target reference spectral power distribution and the indoor natural light spectrum. An indoor occupant distribution heatmap is obtained, and key activity areas are identified based on this heatmap. A solar incidence analysis algorithm is used to determine the solar radiation path. The spectral power of the first compensation wavelength point is adjusted based on the key activity areas and the solar radiation path to obtain the spectral power of the second compensation wavelength point. Lighting output is then regulated based on the second compensation wavelength point spectral power. By regulating lighting output according to key activity areas, accurate regulation of lighting output is achieved, enabling full utilization of energy and avoiding resource waste.

[0113] In one specific embodiment of this application, after controlling the smart window and the indoor terminal according to the target adjustment combination, the method may further include the following steps:

[0114] Step 1: Measure the eyelid opening and closing degree of each person in the room to obtain the eyelid opening and closing degree set;

[0115] Step 2: When indoor occupants with glare discomfort are identified based on the eyelid opening and closing set, obtain the first location information of the indoor occupants with glare discomfort within the room;

[0116] Step 3: Determine the target lighting equipment based on the initial location information;

[0117] Step 4: Adjust the lighting output of the target lighting equipment.

[0118] For ease of description, the four steps above can be combined for explanation.

[0119] To achieve a personalized response mechanism, the platform introduces a multimodal physiological feature fusion and recognition algorithm. After controlling the smart window and indoor terminals according to the target adjustment combination, it can also detect the eyelid opening and closing degree of each person in the room, obtaining an eyelid opening and closing degree set. When an indoor person with glare discomfort is identified based on the eyelid opening and closing degree set, the platform obtains the first location information of the indoor person with glare discomfort, determines the target lighting device based on the first location information, and adjusts the lighting output of the target lighting device. By detecting eyelid opening and closing degree, the platform determines whether an individual has glare discomfort, thereby improving subjective comfort and response speed.

[0120] In one specific embodiment of this application, after controlling the smart window and the indoor terminal according to the target adjustment combination, the method may further include the following steps:

[0121] Step 1: Perform facial infrared temperature analysis on each person in the room to obtain a facial infrared temperature dataset;

[0122] Step 2: When it is determined from the facial infrared temperature set that there are indoor personnel in a heat-sensitive state, obtain the second location information of the indoor personnel in the heat-sensitive state;

[0123] Step 3: Determine the target temperature control device based on the second location information;

[0124] Step 4: Adjust the temperature output of the target temperature control device.

[0125] For ease of description, the four steps above can be combined for explanation.

[0126] After controlling the smart window and indoor terminals according to the target adjustment combination, facial infrared temperature analysis can be performed on each indoor occupant to obtain a facial infrared temperature set. When it is determined from the facial infrared temperature set that an indoor occupant is in a heat-sensitive state, the platform obtains the second location information of the occupant in the room. Based on the second location information, the target temperature control device is determined, and its temperature output is adjusted. The platform integrates a occupant distribution heat map and a solar incidence analysis algorithm to identify key activity areas and sunlight coverage paths in real time, and intelligently generates local dimming commands. This mechanism can effectively control the output of LED lighting in non-critical areas, minimize the range of artificial lighting compensation, and thus reduce system energy consumption while meeting visual tasks.

[0127] See Figure 2 , Figure 2This is a structural block diagram of an intelligent window and indoor terminal linkage control system according to an embodiment of this application. The architecture unfolds according to a closed-loop logic of "input-modeling-decision-execution," and is divided into four functional layers: input layer, model engine layer, control strategy layer, and terminal execution layer. These layers are organically linked through data and command flows. The input layer is responsible for collecting multi-source environmental parameters such as outdoor weather, indoor usage status, and individual physiological perceptions, serving as the basis for subsequent model calculations. The model engine layer performs structured modeling of the input information, dynamically constructing the causal response relationship between the "window-lighting-HVAC system" and "light-heat-human" based on the intelligent window's own model, thermal comfort model, lighting model, rhythm model, and long short-term memory network. It outputs physical and physiological evaluation indicators that can be directly invoked by the controller. Based on this, the control strategy layer follows a decision-making logic of prioritizing health, optimizing comfort, and ensuring energy conservation, combined with future environmental trend predictions, to continuously generate multi-terminal coordinated control schemes adapted to dynamic scenarios. Ultimately, the end-point execution layer translates the control strategy into specific operational instructions for the smart window, adjustable spectrum LED lighting system, and air conditioning / fresh air system, and constructs a closed-loop regulation mechanism by combining indoor end-point status feedback. Through this hierarchical linkage architecture, the system can achieve fusion perception of multimodal information, parallel optimization of multi-objective performance, and collaborative execution of multiple end-point devices, providing technical support for the integrated control of dynamic healthy lighting and thermal environment regulation.

[0128] The spectral power at wavelength λ after passing through the window can be calculated using the following formula:

[0129] ;

[0130] in, To be visible through the window at the wavelength Spectral power at that point ; For outdoor spectral input, ; is a wavelength- and incident angle-dependent spectral transmittance function.

[0131] The solar heat gain coefficient of window glass can be calculated using the following formula:

[0132] ;

[0133] Where g is the solar heat gain coefficient of the window glass; The direct transmittance of sunlight on the glass specimen; The coefficient of secondary heat transfer from the glass specimen to the indoor side is denoted as .

[0134] For window types exhibiting selective absorption across different bands of the solar spectrum, the model also considers the impact of color variations on light transmission uniformity and color temperature (CCT) variations. A computational strategy based on external environment perception and window material optical properties is proposed. By coupling outdoor solar radiation conditions with known window transmission characteristics in a modeling process, the composition of indoor natural light spectrum is deduced to provide input for subsequent perception models.

[0135] The color temperature index can be calculated using the following formula:

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] Where x and y are the chromaticity coordinates in the chromaticity diagram. , , It is a color matching function, where X, Y, and Z are tristimulus values.

[0144] The thermal comfort model describes the subjective perceived response of indoor occupants to environmental thermal conditions and serves as a basis for judging the coordinated control of smart windows and air conditioning. Based on the thermal comfort simulation thermal perception voting index (PMV) and percentage of dissatisfaction (PPD), the thermal comfort model couples factors such as the increase in solar radiation heat caused by smart windows and the adjustment of air conditioning cooling load into the overall thermal balance relationship. The thermal comfort model also introduces a time integral term or thermal adaptation hysteresis coefficient to describe the dynamic relationship between the transient thermal discomfort caused by changes in window control strategies and the human body's thermal buffering characteristics.

[0145] The following formula can be used to simulate the perceived voting index:

[0146] ;

[0147] Where M is the human metabolic rate, W / m²; W represents the human power output, W / m². This refers to the area coefficient of clothing. The surface heat transfer coefficient is W / (m²). 2 ·K); Temperature of the outer surface of the clothing, in K; The ambient air temperature, in K, is affected by the characteristics of the smart window. Let K be the average radiant temperature of the environment. The water vapor partial pressure in the air surrounding the human body is expressed in kPa.

[0148] The percentage of dissatisfaction can be calculated using the following formula:

[0149] .

[0150] The daylighting model is used to evaluate the indoor natural light distribution characteristics under different smart window conditions and their impact on lighting requirements. Based on the Radiance ray tracing method, the daylighting model combines window transmittance, spectral selective transmission, indoor surface reflectivity, and spatial geometric parameters to simulate the brightness distribution image and glare probability index at various locations. The daylighting model works in conjunction with the lighting system, dynamically adjusting LED output to meet visual task requirements by setting a minimum natural light illuminance threshold, maximizing the potential of daylight utilization, and reducing artificial lighting energy consumption.

[0151] The brightness distribution image at various locations within an indoor space can be simulated using the following formula:

[0152] ;

[0153] Where I is the illuminance at a certain measuring point or the brightness distribution image of a certain field of view; V is the view matrix, T is the transmission matrix, D is the sunlight matrix, and S is the sky matrix; It is a direct view matrix. It is a direct sunlight matrix; It is the direct solar coefficient matrix. It is a direct solar matrix.

[0154] The glare probability index for various locations indoors can be simulated using the following formula:

[0155] ;

[0156] in, The vertical illuminance to the eye produced by the light source, lx; Let be the brightness value of the i-th glare source, cd / m² 2 ; Let s be the solid angle corresponding to the i-th dazzling light source. r ; Let be the Guss position index of the i-th glare source; n is the number of glare sources.

[0157] The rhythm model is used to characterize the impact of indoor spectral characteristics on the human circadian rhythm system and non-visual physiological functions, aiming to achieve dynamic regulation of key wavelengths and health lighting compensation under natural light dominance. The rhythm model combines non-visual indicators such as equivalent melanopic illuminance (EML) and circadian rhythmic stimulation (CS), along with visual field distribution and temporal dynamics, to evaluate the effectiveness of smart window states in stimulating human circadian rhythms. The selective transmission behavior of natural light by the same window type will lead to the weakening or shift of energy in specific indoor wavelengths. Based on this, the model infers the actual indoor spectral distribution by combining sky spectral data and window material databases. Subsequently, spectral difference analysis is performed on rhythm-sensitive wavelengths, triggering adjustable spectral channels in the LED lighting system to compensate for specific wavelengths, ultimately achieving the synergistic regulation goal of natural light dominance and artificial light restoration.

[0158] The equivalent melanin illuminance can be calculated using the following formula:

[0159] ;

[0160] in, For the light source at wavelength Spectral power distribution under, ; The weighting function for melanops.

[0161] The diurnal rhythm stimulation can be calculated using the following formula:

[0162] .

[0163] In terms of control structure, the system adopts a three-layer control logic architecture prioritizing health, optimizing comfort, and providing a safety net for energy conservation, implemented using intelligent control functions for dynamic scenarios. The upper decision layer focuses on non-visual effect indicators that ensure the health of biological rhythms, determining whether non-visual health needs should be prioritized. The middle execution layer aims at visual and thermal comfort, comprehensively optimizing illuminance, color rendering index, PMV, and PPD values. The bottom energy constraint layer uses an energy consumption assessment model to determine the impact of the current strategy on lighting and air conditioning loads.

[0164] Phase one involves ensuring health goals through the following formula:

[0165] ;

[0166] ;

[0167] in, : Represents the black-view response ratio of the D65 standard illuminant; subject to: .

[0168] Phase two involves optimizing comfort using the following formula:

[0169] ;

[0170] subject to: .

[0171] Phase three is energy minimization:

[0172] ;

[0173] subject to: ;

[0174] Where x is the control impulse vector, including the adjustment of equipment such as window zone light transmittance, lighting light, and air conditioning air volume; X is the set of equipment operation constraints, such as the maximum light transmittance of the smart window, the minimum light transmittance of the smart window, the maximum power of the LED, and the safe air volume of the air conditioner.

[0175] In the above three stages, , Let x be a function of the control variable x. By adjusting the control variable x, we can achieve the desired effect. , The equal values ​​all satisfy their respective threshold conditions.

[0176] In practical control, the relevant parameters of each system environment are first input to synthesize a candidate combination set of control impulse x. Then, the above three-stage optimization calculations are performed sequentially to ensure that high-priority constraints are satisfied first, and finally, the combination scheme with the lowest total energy consumption is selected. Simultaneously, this hierarchical control mechanism can be further embedded into a model predictive control framework, expanding the hierarchical target regulation logic of health, comfort, and energy saving into a dynamic optimization problem within each sliding time window. Within each control cycle, based on the predicted results of outdoor weather, solar radiation, and indoor environmental conditions for a future period, the system pre-plans the adjustment paths of devices such as smart windows, lighting, and air conditioning, thereby achieving a more predictive and responsive comprehensive control effect.

[0177] In terms of control mechanisms, the platform adopts a hierarchical linkage control method, dynamically allocating control weights for smart windows, lighting, air conditioning, and fresh air systems according to the order of health priority, comfort secondary, and energy saving as a safety net, to achieve timely switching of control strategies and multi-objective coordination. In the health priority layer, the system focuses on the non-visual stimulation effects of indoor spectral distribution on the human circadian rhythm system. Based on the window spectral attributes and external radiation prediction results, it dynamically assesses the illuminance levels of key indoor bands (such as the 460–490nm blue-green band) and actively compensates for rhythm-sensitive bands through adjustable spectral LEDs to maintain non-visual effect indicators within the physiologically recommended range, thereby achieving dynamic protection of circadian health goals.

[0178] The following formula can be used to actively compensate for rhythm-sensitive wavelengths using tunable spectral LEDs:

[0179] ;

[0180] in, For the spectral power at the wavelength point that needs compensation, ; For the target reference spectral power distribution, ; This represents the current actual indoor spectral distribution. .

[0181] Corresponding to the above method embodiments, this application also provides a smart window and indoor terminal linkage control device. The smart window and indoor terminal linkage control device described below can be referred to in correspondence with the smart window and indoor terminal linkage control method described above.

[0182] See Figure 3 , Figure 3 This is a structural block diagram of a smart window and an indoor terminal linkage control device according to an embodiment of this application. The method may include:

[0183] Parameter acquisition module 31 is used to acquire multi-source environmental parameters and intelligent window characteristic parameters;

[0184] The thermal sensation voting and dissatisfaction percentage simulation module 32 is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on multi-source environmental parameters and smart window characteristic parameters using a thermal comfort model.

[0185] The simulation module 33 is used to simulate the brightness distribution image, glare probability index, color temperature index and horizontal illuminance of various locations in the room based on the lighting model according to the multi-source environmental parameters and the characteristics of the smart window.

[0186] The circadian rhythm stimulation simulation module 34 is used to simulate circadian rhythm stimulation indicators based on multi-source environmental parameters and intelligent window characteristic parameters using a rhythm model.

[0187] The threshold acquisition module 35 is used to acquire preset circadian rhythm stimulation threshold, thermal sensation voting threshold, dissatisfaction percentage threshold, glare probability threshold, color temperature threshold and horizontal illuminance threshold;

[0188] The first control impulse vector determination module 36 is used to determine the first control impulse vector according to the circadian rhythm stimulation threshold; wherein, the first control impulse vector contains multiple adjustment quantity combinations that make the circadian rhythm stimulation index meet the circadian rhythm stimulation threshold, and each adjustment quantity combination is composed of intelligent window adjustment quantity and indoor terminal adjustment quantity.

[0189] The second control impulse vector acquisition module 37 is used to filter the adjustment amount combination of the first control impulse vector according to the thermal sensation voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold, to obtain the second control impulse vector composed of the selected adjustment amount combination; wherein, each adjustment amount combination in the second control impulse vector meets the following conditions: the thermal sensation voting index meets the thermal sensation voting threshold, the dissatisfaction percentage meets the dissatisfaction percentage threshold, the glare probability index meets the glare probability threshold, the color temperature index meets the color temperature threshold, and the horizontal illuminance meets the horizontal illuminance threshold.

[0190] The intelligent window and indoor terminal control module 38 is used to select the target adjustment quantity combination with the minimum energy consumption of the indoor terminal from the second control impulse vector, and control the intelligent window and the indoor terminal according to the target adjustment quantity combination.

[0191] As can be seen from the above technical solutions, by prioritizing non-visual health needs and constructing a rhythm-oriented intelligent lighting environment, the ability of indoor spaces to support the human body's diurnal rhythm is effectively improved. Multi-objective collaborative optimization is achieved, enhancing visual comfort and thermal environment quality. By establishing a dynamic linkage between the lighting model and the thermal comfort model, and jointly controlling smart windows and indoor terminals, multiple comfort indicators such as illuminance, color temperature, glare, and thermal sensation voting indicators are coordinated and adjusted to meet the combined visual and thermal needs of users. The intelligent windows and indoor terminals are linked for control, with the control strategy following a hierarchical control logic of prioritizing health, optimizing comfort, and providing a safety net for energy conservation, achieving unified optimization of indoor health, comfort, and energy efficiency goals.

[0192] In one specific embodiment of this application, the parameter acquisition module 31 is specifically a module for acquiring multi-source environmental parameters, including outdoor environmental parameters, indoor usage and space information, and individual physiological perception parameters.

[0193] In one specific embodiment of this application, the device may further include a parameter acquisition module, which may include:

[0194] The meteorological variable set acquisition submodule is used to acquire the most recent historical meteorological variable set for a first preset time period;

[0195] The outdoor environmental parameter acquisition submodule is used to predict the weather for a second preset duration in the future using a long short-term memory network based on the most recent set of historical meteorological variables, and thus obtain the outdoor environmental parameters.

[0196] In one specific embodiment of this application, the device may further include:

[0197] The outdoor spectral input acquisition module is used to acquire outdoor spectral input after collecting multi-source environmental parameters and intelligent window characteristic parameters;

[0198] The indoor natural light spectrum calculation module is used to calculate the indoor natural light spectrum based on the smart window's characteristic parameters and the outdoor spectrum input.

[0199] In one specific embodiment of this application, the device may further include:

[0200] The reference spectral power distribution lookup module is used to acquire control results and look up the target reference spectral power distribution from the control results.

[0201] The first compensation wavelength point spectral power calculation module is used to calculate the first compensation wavelength point spectral power based on the target reference spectral power distribution and the indoor natural light spectrum.

[0202] The key activity area identification module is used to obtain an indoor heat map of people distribution and to identify key activity areas indoors based on the indoor people distribution heat map;

[0203] The solar coverage path determination module is used to determine the solar coverage path using a solar incidence analysis algorithm.

[0204] The second compensation wavelength point spectral power acquisition module is used to adjust the first compensation wavelength point spectral power according to the key activity area and the sunshine coverage path to obtain the second compensation wavelength point spectral power.

[0205] The first lighting output control module is used to control the lighting output based on the spectral power of the second compensation wavelength point.

[0206] In one specific embodiment of this application, the device may further include:

[0207] The eyelid opening and closing degree set acquisition module is used to detect the eyelid opening and closing degree of each person in the room and obtain the eyelid opening and closing degree set;

[0208] The first location information acquisition module is used to acquire the first location information of the indoor person experiencing glare discomfort when the indoor person is determined to have glare discomfort based on the set of eyelid opening and closing degrees.

[0209] A target lighting device determination module is used to determine the target lighting device based on the first location information;

[0210] The second lighting output control module is used to control the lighting output of the target lighting equipment.

[0211] In one specific embodiment of this application, the device may further include:

[0212] The facial infrared temperature set acquisition module is used to analyze the facial infrared temperature of each person in the room after controlling the smart window and the indoor terminal according to the target adjustment combination, and obtain the facial infrared temperature set.

[0213] The second location information acquisition module is used to acquire the second location information of an indoor person in a heat load sensitive state when it is determined from the facial infrared temperature set that there is an indoor person in a heat load sensitive state.

[0214] A target temperature control device determination module is used to determine the target temperature control device based on the second location information.

[0215] The temperature output control module is used to control the temperature output of the target temperature control device.

[0216] For the method embodiments described above, see [link to relevant documentation]. Figure 4 , Figure 4 This is a schematic diagram of the smart window and indoor terminal linkage control device provided in this application. The device may include:

[0217] Memory 332 is used to store computer programs;

[0218] The processor 322 is used to execute a computer program to implement the steps of the smart window and indoor terminal linkage control method of the above method embodiment.

[0219] For details, please refer to Figure 5 , Figure 5 This is a schematic diagram illustrating the specific structure of a smart window and indoor terminal linkage control device provided in this embodiment. The smart window and indoor terminal linkage control device can vary significantly due to different configurations or performance. It may include a processor (central processing unit, CPU) 322 (e.g., one or more processors) and a memory 332. The memory 332 stores one or more computer programs 342 or data 344. The memory 332 can be temporary or persistent storage. The program stored in the memory 332 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the data processing device. Furthermore, the processor 322 may be configured to communicate with the memory 332 and execute the series of instruction operations stored in the memory 332 on the smart window and indoor terminal linkage control device 301.

[0220] The smart window and indoor terminal linkage control device 301 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input / output interfaces 358, and / or one or more operating systems 341.

[0221] The steps in the intelligent window and indoor terminal linkage control method described above can be implemented by the structure of the intelligent window and indoor terminal linkage control device.

[0222] Corresponding to the above method embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the following steps:

[0223] Multi-source environmental parameters and smart window characteristic parameters are collected; a thermal comfort model is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and smart window characteristic parameters; a daylighting model is used to simulate brightness distribution images, glare probability indicators, color temperature indicators, and horizontal illuminance at various locations indoors based on the multi-source environmental parameters and smart window characteristic parameters; a circadian rhythm model is used to simulate diurnal rhythm stimulation indicators based on the multi-source environmental parameters and smart window characteristic parameters; preset diurnal rhythm stimulation thresholds, thermal sensation voting thresholds, dissatisfaction percentage thresholds, glare probability thresholds, color temperature thresholds, and horizontal illuminance thresholds are obtained; a first control impulse vector is determined based on the diurnal rhythm stimulation thresholds; wherein, the first control impulse vector contains multiple adjustment groups that make the diurnal rhythm stimulation indicators meet the diurnal rhythm stimulation thresholds. Each adjustment combination consists of an intelligent window adjustment and an indoor terminal adjustment. The first control impulse vector is filtered based on thermal perception voting thresholds, dissatisfaction percentage thresholds, glare probability thresholds, color temperature thresholds, and horizontal illuminance thresholds to obtain a second control impulse vector composed of these selected adjustment combinations. Each adjustment combination in the second control impulse vector satisfies the following criteria: thermal perception voting index meets the thermal perception voting threshold, dissatisfaction percentage meets the dissatisfaction percentage threshold, glare probability index meets the glare probability threshold, color temperature index meets the color temperature threshold, and horizontal illuminance meets the horizontal illuminance threshold. The target adjustment combination with the lowest indoor terminal energy consumption is selected from the second control impulse vector, and the intelligent window and indoor terminal are controlled according to this target adjustment combination.

[0224] The computer-readable storage medium may include 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.

[0225] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0226] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatuses, devices, and computer-readable storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0227] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A smart window and indoor terminal linkage control method, characterized in that, include: Collect multi-source environmental parameters and intelligent window characteristic parameters; The thermal comfort model is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and the smart window characteristic parameters. The lighting model is used to simulate the brightness distribution image, glare probability index, color temperature index and horizontal illuminance at various locations in the room based on the multi-source environmental parameters and the intelligent window characteristic parameters. The circadian rhythm stimulation index is simulated using the multi-source environmental parameters and the intelligent window characteristic parameters; Obtain preset circadian rhythm stimulation threshold, thermal sensation voting threshold, dissatisfaction percentage threshold, glare probability threshold, color temperature threshold, and horizontal illuminance threshold; A first control impulse vector is determined based on the circadian rhythm stimulation threshold; wherein, the first control impulse vector contains multiple combinations of adjustment quantities that make the circadian rhythm stimulation index meet the circadian rhythm stimulation threshold, and each combination of adjustment quantities consists of smart window adjustment quantity and indoor terminal adjustment quantity. The first control impulse vector is filtered by combining adjustment amounts based on the thermal perception voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold to obtain a second control impulse vector composed of the selected combinations of adjustment amounts; wherein, each combination of adjustment amounts in the second control impulse vector satisfies the following conditions: the thermal perception voting index satisfies the thermal perception voting threshold, the dissatisfaction percentage satisfies the dissatisfaction percentage threshold, the glare probability index satisfies the glare probability threshold, the color temperature index satisfies the color temperature threshold, and the horizontal illuminance satisfies the horizontal illuminance threshold; Select the target adjustment combination that minimizes the energy consumption of the indoor terminal from the second control impulse vector, and control the smart window and the indoor terminal according to the target adjustment combination.

2. The intelligent window and indoor terminal linkage control method according to claim 1, characterized in that, Collect multi-source environmental parameters, including: Collect multi-source environmental parameters, including outdoor environmental parameters, indoor usage and spatial information, and individual physiological perception parameters.

3. The intelligent window and indoor terminal linkage control method according to claim 2, characterized in that, It also includes the process of acquiring the outdoor environmental parameters, which includes: Obtain the set of most recent historical meteorological variables for a first preset duration; The outdoor environmental parameters are obtained by using a long short-term memory network to predict the weather for a second preset duration based on the set of most recent historical meteorological variables.

4. The intelligent window and indoor terminal linkage control method according to any one of claims 1 to 3, characterized in that, After collecting multi-source environmental parameters and smart window characteristic parameters, the following is also included: Acquire outdoor spectral input; The indoor natural light spectrum is calculated based on the intelligent window's characteristic parameters and the outdoor spectral input.

5. The intelligent window and indoor terminal linkage control method according to claim 4, characterized in that, After controlling the smart window and indoor terminal according to the target adjustment combination, the method further includes: Obtain the control results and find the target reference spectral power distribution from the control results; Calculate the spectral power at the first compensation wavelength point based on the target reference spectral power distribution and the indoor natural light spectrum; Obtain an indoor heat map of people distribution, and determine key activity areas indoors based on the indoor heat map of people distribution; Determine the solar radiation coverage path using a solar incidence analysis algorithm; The spectral power of the first compensation wavelength point is adjusted according to the key activity area and the sunshine coverage path to obtain the spectral power of the second compensation wavelength point; The lighting output is adjusted based on the spectral power at the second compensation wavelength point.

6. The intelligent window and indoor terminal linkage control method according to claim 1, characterized in that, After controlling the smart window and indoor terminal according to the target adjustment combination, the method further includes: Eyelid opening and closing degree was measured for each person in the room to obtain the set of eyelid opening and closing degrees; When an indoor person experiencing glare discomfort is identified based on the eyelid opening and closing degree set, the first location information of the indoor person experiencing glare discomfort is obtained; The target lighting device is determined based on the first location information; The lighting output of the target lighting device is regulated.

7. The intelligent window and indoor terminal linkage control method according to claim 1, characterized in that, After controlling the smart window and indoor terminal according to the target adjustment combination, the method further includes: Facial infrared temperature analysis was performed on the individuals in each room to obtain a set of facial infrared temperatures. When it is determined from the facial infrared temperature set that there is an indoor person in a heat load sensitive state, the second location information of the indoor person in the heat load sensitive state is obtained; The target temperature control device is determined based on the second location information; The temperature output of the target temperature control device is regulated.

8. A smart window and an indoor terminal linkage control device, characterized in that, include: The parameter acquisition module is used to collect multi-source environmental parameters and intelligent window characteristic parameters; The thermal sensation voting and dissatisfaction percentage simulation module is used to simulate thermal sensation voting indicators and dissatisfaction percentages based on the multi-source environmental parameters and the smart window characteristic parameters using a thermal comfort model. The simulation module is used to simulate the brightness distribution image, glare probability index, color temperature index and horizontal illuminance of various locations in the room based on the multi-source environmental parameters and the intelligent window characteristic parameters using the lighting model. The circadian rhythm stimulation simulation module is used to simulate circadian rhythm stimulation indicators based on the multi-source environmental parameters and the intelligent window characteristic parameters using a rhythm model. The threshold acquisition module is used to acquire preset circadian rhythm stimulation threshold, thermal sensation voting threshold, dissatisfaction percentage threshold, glare probability threshold, color temperature threshold, and horizontal illuminance threshold. The first control impulse vector determination module is used to determine the first control impulse vector according to the circadian rhythm stimulation threshold; wherein, the first control impulse vector includes multiple adjustment quantity combinations that make the circadian rhythm stimulation index meet the circadian rhythm stimulation threshold, and each adjustment quantity combination is composed of smart window adjustment quantity and indoor terminal adjustment quantity. The second control impulse vector acquisition module is used to filter the first control impulse vector by adjusting the combination of the thermal sensation voting threshold, the dissatisfaction percentage threshold, the glare probability threshold, the color temperature threshold, and the horizontal illuminance threshold, to obtain a second control impulse vector composed of the filtered combinations of adjustment amounts; wherein, each combination of adjustment amounts in the second control impulse vector satisfies the following conditions: the thermal sensation voting index satisfies the thermal sensation voting threshold, the dissatisfaction percentage satisfies the dissatisfaction percentage threshold, the glare probability index satisfies the glare probability threshold, the color temperature index satisfies the color temperature threshold, and the horizontal illuminance satisfies the horizontal illuminance threshold. The smart window and indoor terminal control module is used to select the target adjustment combination with the minimum energy consumption of the indoor terminal from the second control impulse vector, and control the smart window and the indoor terminal according to the target adjustment combination.

9. A smart window and indoor terminal linkage control device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the smart window and indoor terminal linkage control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the smart window and indoor terminal linkage control method as described in any one of claims 1 to 7.

Images