Child-friendly city space design method based on visual perception optimization

Abstract

The invention discloses a child-friendly city space design method based on visual perception optimization, and belongs to the technical field of city space design and environment perception. Comprising the following steps: S1, collecting a sunlight angle, illumination intensity, scattering characteristics, a reflecting surface state and related microclimate parameters of a site in real time, and constructing a light environment data basis; s2, according to the child development stage characteristics, differential light environment design parameters and threshold value standards are formulated; s3, based on a multi-level light environment regulation and control execution mechanism, precise intervention on illumination intensity, directivity, color temperature and glare control is realized; s4, dynamically adjusting light environment parameters based on the real-time environment monitoring data, and ensuring that optimal visual experience is maintained under different microclimate conditions; and S5, establishing a specific site light environment optimization knowledge base, and realizing digital accumulation and application iteration of design experience.

Description

Technical Field

[0001] This application relates to the technical field of urban space design and environmental perception. More specifically, it relates to a method for designing child-friendly urban spaces optimized based on visual perception. Background Art

[0002] With the continuous acceleration of the urbanization process, the frequency and scope of children's activities in urban spaces have increased significantly, and the concept of child-friendly cities has gradually received attention. As a special group of urban users, children have significant differences in physiological development, psychological cognition, and behavior patterns compared to adults. Especially in terms of visual perception, children are more sensitive to factors such as light intensity, color temperature change, and glare interference, which directly affect their mood, concentration, and enthusiasm for outdoor activities. Therefore, creating a safe, comfortable, and child-vision-needs-compliant urban light environment has become an important goal of urban space design.

[0003] Most existing urban space design methods set parameters based on adult aesthetics and functional requirements, lacking systematic consideration of the visual perception differences of children of different ages. Especially in light environment design, static lighting layouts or rough sunshade designs are often used, making it difficult to adapt to dynamic natural light changes and diverse usage behavior requirements. At the same time, in the traditional design process, the collection of on-site environmental parameters relies on manual experience, lacking fine-grained modeling of data such as microclimate, spectral characteristics, and reflective materials, resulting in problems such as excessive light, frequent glare, or uneven light distribution in the space during actual use, thus affecting children's activity comfort and space utilization rate.

[0004] In recent years, the development of sensor technology, image recognition, and intelligent control systems has provided a technical basis for the refined management of urban spaces. However, these technologies have not been deeply integrated with child visual behavior research, lacking targeted data collection, parameter optimization, and feedback mechanisms, nor have they formed a closed-loop system for light environment regulation for child users. At the same time, there is a lack of an effective experience knowledge accumulation mechanism in urban space design, which is not conducive to the transfer and reuse of optimization results between different sites, resulting in repeated and inefficient design processes and making it difficult to continuously optimize.

[0005] In summary, how to construct an optimization method for the urban space light environment oriented to children's visual perception characteristics, integrating real-time environment perception, intelligent regulation, and experience knowledge accumulation, and improving the adaptability and comfort of the space, has become a technical problem that urgently needs to be solved. Summary of the Invention

[0006] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a method for designing child-friendly urban spaces optimized based on visual perception for the above problems, including the following steps:

[0007] Step S1, collect the sunshine angle, light intensity, scattering characteristics, reflector status, and relevant microclimate parameters of the site in real time to build the basis of light environment data;

[0008] Step S2, formulate differential light environment design parameters and threshold standards according to the characteristics of children's development stages;

[0009] Step S3, based on the multi-level light environment regulation actuator, achieve precise intervention in light intensity, directionality, color temperature, and glare control;

[0010] Step S4, dynamically adjust the light environment parameters based on the real-time environmental monitoring data to ensure the best visual experience under different microclimate conditions;

[0011] Step S5, establish an optimization knowledge base for the light environment of specific sites to realize the digital accumulation of design experience and application iteration.

[0012] Furthermore, Step S1 includes the following steps:

[0013] Adopt a combination of spectrometers and illuminometers with different orientations to capture the intensity distribution, incident angle, and wavelength characteristics of visible light, ultraviolet light, and near-infrared light in real time, and establish the basic data layer of the full-spectrum energy distribution of the site;

[0014] Integrate micro-meteorological parameter collectors to build a microclimate monitoring network for the site and collect the microclimate parameters of the site in real time;

[0015] Based on laser reflection measurement and image analysis technology, evaluate the diffuse reflectivity, specular reflectivity, and spectral reflectivity of each surface material in the space, and generate a three-dimensional characteristic model of the space reflector surface;

[0016] Adopt infrared thermal imaging and computer vision technology to record the usage density, activity intensity, and space occupancy pattern of the site on the premise of protecting privacy, and establish a basic data set of human-environment interaction;

[0017] Perform real-time fusion and cleaning of multi-source sensing data, and use spatio-temporal interpolation algorithms to build a continuous light environment distribution model for the entire site. At the same time, combine neural network methods to identify the laws and abnormal patterns of light environment changes;

[0018] Develop a multi-dimensional light environment data visualization platform to convert optical parameters into intuitive visual charts, including light intensity heat maps, color temperature distribution maps, glare risk areas, and comfort score maps.

[0019] Furthermore, the spectrometer and illuminometer combination includes acquisition units in at least 6 directions. Each acquisition unit can capture the visible spectrum in the range of 380 - 780 nm with an accuracy of 2 - 5 nm, and simultaneously capture ultraviolet light in the range of 280 - 380 nm and near-infrared light in the range of 780 - 1400 nm, with a sampling frequency of once every 30 - 180 seconds;

[0020] The microclimate parameters include temperature, humidity, air pressure, wind speed, wind direction, precipitation, and PM2.5 concentration. Among them, the temperature monitoring range is -30°C to 50°C with an accuracy of ±0.3°C; the humidity monitoring range is 5 - 95% with an accuracy of ±3%; the wind speed monitoring range is 0 - 25 m / s with an accuracy of ±0.5 m / s.

[0021] Furthermore, step S2 includes the following steps:

[0022] Precisely identify the age distribution range of children through human body proportion characteristics, movement patterns, and interaction behavior characteristics, and generate a dynamic distribution map of the age composition within the venue;

[0023] Construct a visual perception parameter library related to age, and precisely quantify the differentiated needs of children of different ages in terms of light intensity threshold, color temperature preference, contrast sensitivity, and glare tolerance;

[0024] According to the detected main age composition ratio within the venue, automatically adjust the weight coefficients of light intensity, uniformity, color temperature, and contrast indicators to accurately reflect the visual comfort needs of the current main user group;

[0025] Evaluate the emotional response and comfort experience of children to the current light environment through facial expression recognition, dwell time analysis, and activity area selection behavior, and verify and fine-tune the actual applicability of the perception evaluation indicators;

[0026] Adjust the light environment evaluation criteria according to different time periods of the day to ensure that the vitality awakening in the morning, anti-glare protection at noon, and gentle transition needs in the evening are fully considered.

[0027] Furthermore, step S3 includes the following steps:

[0028] According to the real-time sunlight intensity and the visual comfort needs of children, dynamically adjust the light transmission characteristics of the variable light transmittance intelligent glass to ensure that the illuminance is maintained within the optimal range for children's learning activities;

[0029] Use a micro-motor to control the angle of the electro-controlled spectral reflector and electrochromic technology to adjust the reflectivity, achieve precise guidance of the indirect light source, effectively eliminate strong contrast shadows within the line of sight area of children, and ensure that the color rendering index is not less than Ra95;

[0030] Integrated automatic shading device, which combines meteorological data with a spatial usage prediction model, presets the angles and extensions of the shading louvers, achieves precise filtering of direct sunlight, while retaining the diffused light component, and controls the UGR value of glare not exceeding 16;

[0031] Adopts a full-spectrum LED light source array, precisely controls the color temperature and brightness through PWM dimming technology. When natural light is insufficient, it intelligently supplements the light source, simulates the changing rhythm of natural sunlight, and simultaneously optimizes the lighting parameters for visual tasks;

[0032] Based on real-time sensing data, coordinates the linkage responses of each actuator, balances the energy consumption efficiency and visual comfort, and achieves precise shaping of the overall light flow in the space.

[0033] Furthermore, the variable transmittance intelligent glass has 3 - 5 light transmittance levels, the light transmittance range is 20% - 80%, the response time is 15 - 30 seconds, and the energy consumption is 5 - 10W / m 2 ;

[0034] The angle adjustment range of the electronically controlled spectral reflector is 0 - 170°, the accuracy is ±2°, and it uses a combination of 2 - 3 coating materials, which can achieve selective reflection in the wavelength range of 450 - 700nm, and the reflectance can be adjusted within the range of 20% - 85%;

[0035] The automatic shading device adopts a honeycomb structure design, has an angle adjustment range of 30 - 40°, a shading efficiency of 80 - 90%, the light transmittance can be continuously adjusted between 15% - 60%, and has a wind resistance of 7 - 8 levels;

[0036] The full-spectrum LED light source array contains LED units of 5 - 7 wavelengths, the color temperature adjustment range is 2700K - 6300K, the color rendering index Ra≥90, the stroboscopic coefficient is lower than 1%, and the unit power consumption illumination efficiency is 100 - 130lm / W.

[0037] Furthermore, step S4 includes the following steps:

[0038] Establish a multi-source meteorological data integration platform, and at the same time integrate the historical environmental parameter records of the site to construct a database containing the key factors affecting lighting;

[0039] Process multi-dimensional meteorological data and historical light environment records to generate a light environment parameter prediction matrix for the next 24 to 72 hours, including direct sunlight intensity, diffused light ratio, dominant light source direction, and color temperature change index;

[0040] Based on precise three-dimensional modeling and parametric optical characteristics of materials, combined with predictive meteorological data, simulate the sunlight projection path and reflection characteristics at different times, generate a dynamic visual preview of the site light environment changes, and identify potential discomfort areas and optimal utilization periods;

[0041] Automatically plan the equipment control schedule for the next 72 hours according to the prediction data, including the change curve of the light transmittance of smart glass, the deployment timing and angle setting of sunshade devices, and the activation threshold and intensity of the supplementary lighting system, and respond in advance to the expected changes in the light environment;

[0042] Based on the predicted light change pattern, calculate the optimal layout plan of the movable environmental components, generate component reconfiguration guidance, maximize the utilization efficiency of natural light resources, and ensure the visual comfort of the children's activity area at the same time;

[0043] By comparing the deviation between the actual observed data and the prediction results, dynamically adjust the prediction period and confidence interval, and initiate an emergency response plan for unexpected weather mutation events.

[0044] Furthermore, the light environment parameter prediction matrix is constructed based on a hybrid model combining deep learning and time series analysis, adopting a neural network structure of 6-8 layers. Under standard meteorological conditions, the prediction accuracy for different time scales is: 90-95% within 6 hours, 80-90% within 24 hours, and 70-75% within 72 hours.

[0045] Furthermore, step S5 includes the following steps:

[0046] Adopt a combination of high-precision infrared sensors and computer vision algorithms to anonymously record the activity paths, stay durations, and interaction frequencies of children under different light environment conditions, and establish an associated mapping between activity preferences and light environment parameters at the same time;

[0047] Collect children's subjective experience data through graphical expressions, simple gestures, or voices, and combine physiological index monitoring to construct a comprehensive comfort evaluation model to quantify the impact of the light environment on children's visual experience;

[0048] Take the children's active activity patterns and comfort feedback as reward signals, and use a method combining Q-learning and policy gradient to realize the autonomous optimization of the light environment control strategy and seek the optimal balance between energy efficiency and usage experience;

[0049] Structurally store the optimization experience of specific sites in terms of time and space dimensions, establish a parameter association network, and form a knowledge system for closed-loop optimization;

[0050] Through longitudinal comparative analysis of the time series data of children's behavior changes and environmental parameter adjustments, evaluate the optimization effect and environmental adaptation period, verify the effectiveness of the reinforcement learning strategy, and adjust the algorithm parameters and knowledge base update mechanism accordingly.

[0051] Furthermore, the parameter correlation network contains 80 - 120 nodes and 200 - 300 connections, constructs a knowledge graph using graph neural network technology, supports multi - dimensional queries and similar scenario matching, and the matching accuracy is 75 - 85%.

[0052] Compared with the prior art, the present application has the following beneficial effects:

[0053] The present application combines real - time light environment data collection with children's development characteristics, uses an intelligent control system and deep learning technology to dynamically optimize the light environment of child - friendly urban spaces, ensures the balance between visual comfort and energy efficiency, and realizes the continuous iteration and optimization of design experience through a knowledge base and reinforcement learning strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic flowchart of a method for designing a child - friendly urban space based on visual perception optimization disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0056] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0057] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0058] As Figure 1 shown, a method for designing a child - friendly urban space based on visual perception optimization includes the following steps:

[0059] Step S1, collect the sunlight angle, light intensity, scattering characteristics, reflector state, and related micro - climate parameters of the site in real time to construct a light environment data basis;

[0060] Step S2, formulate differential light environment design parameters and threshold standards according to the characteristics of children's development stages;

[0061] Step S3, based on a multi - level light environment control actuator, achieve precise intervention in light intensity, directionality, color temperature, and glare control;

[0062] Step S4: Dynamically adjust the light environment parameters based on real-time environmental monitoring data to ensure the best visual experience under different microclimate conditions;

[0063] Step S5: Establish an optimization knowledge base for the light environment of a specific site to achieve digital accumulation and application iteration of design experience.

[0064] This child-friendly urban space design method based on visual perception optimization realizes the precise optimization and dynamic adaptation of the urban space light environment through the integration of real-time environmental perception, analysis of children's physiological characteristics, intelligent regulation, and knowledge base construction, and has significant technical effects. First, by establishing a light environment data foundation in Step S1, key visual parameters such as solar altitude angle, light intensity, scattering, and reflection characteristics are systematically collected and integrated, providing high-resolution and quantifiable environmental data support for subsequent interventions and designs. Secondly, in Step S2, according to the physiological and psychological differences in the light environment of children at different developmental stages, a multi-dimensional design parameter system is constructed to make the spatial light environment more in line with the growth rhythm and visual safety needs of children. Further, in Step S3, a multi-level light environment regulation execution mechanism is introduced to achieve controllable adjustment of indicators such as light intensity, directionality, color temperature, and glare at the technical level, improving the fineness and adaptability of design responses. By performing feedback regulation on real-time environmental monitoring data in Step S4, the light environment can actively adapt to changes in different weather, seasons, and time periods, continuously ensuring that children obtain a comfortable and healthy visual experience. Finally, in Step S5, the structured precipitation and intelligent invocation of light environment optimization experience are realized by constructing a knowledge base, providing technical support for the rapid design of future similar scenarios and cross-project knowledge transfer.

[0065] Furthermore, Step S1 includes the following steps:

[0066] Use spectrometers and illuminometers with different orientations to capture the intensity distribution, incident angle, and wavelength characteristics of visible light, ultraviolet light, and near-infrared light in real time, and establish a basic data layer for the full-spectrum energy distribution of the site;

[0067] Integrate miniature meteorological parameter collectors to construct a microclimate monitoring network for the site and collect the microclimate parameters of the site in real time;

[0068] Based on laser reflection measurement and image analysis technologies, evaluate the diffuse reflectivity, specular reflectivity, and spectral reflectivity of various surface materials in the space, and generate a three-dimensional characteristic model of the space reflection surface;

[0069] Use infrared thermal imaging and computer vision technologies to record the usage density, activity intensity, and space occupancy pattern of the site while protecting privacy, and establish a basic dataset for human-environment interaction;

[0070] Perform real-time fusion and cleaning of multi-source sensing data, and use spatio-temporal interpolation algorithms to construct a continuous light environment distribution model for the entire site. At the same time, combine neural network methods to identify the changing patterns and abnormal modes of the light environment;

[0071] Develop a multi-dimensional light environment data visualization platform to convert optical parameters into intuitive visual charts, including light intensity heat maps, color temperature distribution maps, glare risk areas, and comfort score maps.

[0072] In summary, through a series of advanced sensing technologies and data processing means, a comprehensive and accurate light environment monitoring and optimization process has been established. First, through the first item of step S1, a combination of spectrometers and illuminometers with different orientations is used to capture the intensity distribution, incident angle, and wavelength characteristics of visible light, ultraviolet light, and near-infrared light in real time, and a basic data layer of the full-spectrum energy distribution of the site is successfully constructed, providing accurate optical data support for subsequent light environment regulation. Second, by integrating micro-meteorological parameter collectors and constructing a microclimate monitoring network, real-time collection of environmental conditions is ensured, enabling the light environment design to be highly consistent with climate change. Based on laser reflection measurement and image analysis technologies, the reflection characteristics of various surface materials in the space are accurately evaluated, generating a three-dimensional reflection surface model, which provides key data for optimizing the light distribution. Further, the combination of infrared thermal imaging and computer vision technologies records the usage density, activity intensity, and space occupancy patterns of the site, constructing a detailed interaction dataset and enhancing the understanding of the human-environment relationship. The real-time fusion and cleaning of multi-source sensing data, as well as the application of spatio-temporal interpolation algorithms, have successfully realized the construction of a continuous distribution model of the light environment for the entire site. At the same time, combined with neural network methods, the changing patterns and potential abnormal modes of the light environment are identified, effectively improving the accuracy of light environment prediction and regulation. Finally, the developed multi-dimensional data visualization platform converts complex optical data into easy-to-understand visual charts through intuitive methods such as heat maps and color temperature distribution maps, greatly enhancing the designers' perception and decision-making efficiency of light environment changes.

[0073] Further, the combination of the spectrometer and the illuminometer includes acquisition units in at least 6 directions. Each acquisition unit can capture the visible spectrum in the range of 380 - 780 nm with an accuracy of 2 - 5 nm, and simultaneously capture ultraviolet light in the range of 280 - 380 nm and near-infrared light in the range of 780 - 1400 nm, with a sampling frequency of once every 30 - 180 seconds.

[0074] Further, the micro-meteorological parameters include temperature, humidity, air pressure, wind speed, wind direction, precipitation, and PM2.5 concentration. Among them, the temperature monitoring range is -30°C to 50°C with an accuracy of ±0.3°C; the humidity monitoring range is 5 - 95% with an accuracy of ±3%; the wind speed monitoring range is 0 - 25 m / s with an accuracy of ±0.5 m / s.

[0075] Furthermore, the three-dimensional characteristic model records the optical characteristics of the site surface materials at a grid density of 0.5 m × 0.5 m to 1 m × 1 m, including a diffuse reflectance range of 0.1 - 0.9, a specular reflectance range of 0.05 - 0.85, and records the spectral reflectance characteristics at 6 - 12 wavelength points.

[0076] Furthermore, step S2 includes the following steps:

[0077] Precisely identify the age distribution range of children through human proportion characteristics, movement patterns, and interaction behavior characteristics, and generate a dynamic distribution map of the age composition within the site;

[0078] Construct a visual perception parameter library related to age, and precisely quantify the different needs of children of different ages in terms of light intensity threshold, color temperature preference, contrast sensitivity, and glare tolerance;

[0079] Automatically adjust the weight coefficients of the light intensity, uniformity, color temperature, and contrast indicators according to the detected main age composition ratio within the site to accurately reflect the visual comfort needs of the current main user group;

[0080] Evaluate the emotional response and comfort experience of children to the current light environment through facial expression recognition, dwell time analysis, and activity area selection behavior, and verify and fine-tune the practical applicability of the perception evaluation indicators;

[0081] Adjust the light environment evaluation criteria according to different periods of the day to ensure that the vitality awakening in the morning, anti-glare protection at noon, and gentle transition needs in the evening are fully considered.

[0082] In summary, by introducing the analysis of behavior recognition and physiological perception differences related to children's age, the dynamic regulation of the adaptability of the light environment is realized, significantly improving the comfort and humanization level of children's urban spaces. First, through the comprehensive analysis of children's body proportion characteristics, movement patterns, and interaction behaviors, the accurate recognition of age distribution is achieved, and a dynamic distribution map of the age composition of children in the site is generated, enabling the spatial light environment design to respond to the actual presence of different age groups according to time and location. Further, a visual perception parameter library related to age is constructed, quantifying the specific sensitive thresholds and preference differences of children for visual factors such as light intensity, color temperature, contrast, and glare at different developmental stages, providing a scientific basis for adjusting light environment parameters. According to the proportion of the age composition of the main users detected in real time, the design weights of various optical parameters can be automatically and dynamically adjusted to ensure that the visual needs of the current main children group are the core of optimization, enhancing the personalization and accuracy of the spatial experience. In addition, through facial expression recognition, stay time, and activity behavior analysis methods, the subjective comfort experience of children towards the light environment is effectively evaluated, thereby verifying and fine-tuning the applicability of the light environment evaluation model, making the design feedback mechanism more intelligent in human-computer interaction. Finally, the light environment standards are also adjusted according to the time cycle to meet the functional and psychological light environment needs in different periods of morning, noon, and evening, ensuring that children can obtain a visually comfortable experience that is physiologically suitable and psychologically comfortable at different times.

[0083] Furthermore, among the differentiated requirements, the recommended illuminance range for children aged 0 - 3 is 250 - 350 lx, and the color temperature is 2700 - 3000 K; for children aged 4 - 6, the recommended illuminance range is 300 - 400 lx, and the color temperature is 3000 - 3500 K; for children aged 7 - 9, the recommended illuminance range is 350 - 450 lx, and the color temperature is 3500 - 4000 K; for children aged 10 - 12, the recommended illuminance range is 400 - 500 lx, and the color temperature is 4000 - 4500 K; for children aged 13 - 15, the recommended illuminance range is 450 - 550 lx, and the color temperature is 4500 - 5000 K.

[0084] Furthermore, step S3 includes the following steps:

[0085] According to the real-time sunlight intensity and the visual comfort requirements of children, dynamically adjust the light transmission characteristics of the variable light transmittance intelligent glass to ensure that the illuminance is maintained within the optimal range for children's learning activities;

[0086] Use a micro-motor to control the angle of the electro-controlled spectral reflector and electrochromic technology to adjust the reflectivity, achieve the precise guidance of indirect light sources, effectively eliminate strong contrast shadows in the line of sight area of children, and ensure that the color rendering index is not less than Ra95;

[0087] Integrated automatic adjustment sunshade device, combined with meteorological data and space usage prediction model, preset the angle and extension of sunshade louvers, achieve precise filtering of direct light, while retaining the component of scattered light, and control the glare value UGR not exceeding 16;

[0088] Adopt a full-spectrum LED light source array, precisely control the color temperature and brightness through PWM dimming technology. When natural light is insufficient, intelligently supplement the light source, simulate the change of natural sunlight rhythm, and optimize the visual task lighting parameters at the same time;

[0089] Based on real-time sensing data, coordinate the linkage response of each actuator, balance the energy consumption efficiency and visual comfort, and achieve precise shaping of the overall light flow in the space.

[0090] In summary, by integrating a variety of advanced light environment control technologies, precisely adjusting and optimizing the lighting conditions in the children's space, significantly improving the visual comfort and learning experience of children. First, based on the real-time sunlight intensity and the visual comfort requirements of children, dynamically adjust the light transmission characteristics of the variable light transmittance intelligent glass to ensure that the light intensity is always within the optimal range, providing an ideal learning environment for children. Second, use the electronically controlled spectral reflector controlled by a micro-motor and electrochromic technology to precisely adjust the reflectivity of light, optimize the guidance of indirect light sources, eliminate strong contrast shadows in the line of sight area, and ensure that the color rendering index reaches a high standard (above Ra95). In addition, the integrated automatic adjustment sunshade device combined with meteorological data and space usage prediction model can intelligently adjust the angle and extension of the sunshade louvers, precisely filter direct light and retain an appropriate amount of scattered light, thereby effectively controlling glare and keeping the UGR value not exceeding 16 to ensure visual comfort. The full-spectrum LED light source array combined with PWM dimming technology precisely controls the color temperature and brightness, and intelligently supplements when natural light is insufficient, simulating the change of natural sunlight rhythm and optimizing the visual task lighting for children. Through the acquisition of real-time sensing data, it is possible to coordinate the linkage response of each control device, balance the energy consumption efficiency while ensuring visual comfort, precisely shape the light flow in the space, and improve the overall comfort and energy efficiency of the space.

[0091] Furthermore, use a micro-motor to control the angle of the electronically controlled spectral reflector and electrochromic technology to adjust the reflectivity, achieve precise guidance of indirect light sources, effectively eliminate strong contrast shadows in the line of sight area of children, and ensure that the color rendering index is not less than Ra95, including the following steps:

[0092] Precisely adjust the reflector angle θ by a micro-motor and adjust the reflectivity ρ(λ, V) by electrochromic technology to construct a joint control model: ρ(λ, V) = ρ base (λ) + Δ ρEC (λ, V), and stipulate that the control variables move within a safe range, where ρ base(λ) is the reference reflectance spectrum of the reflector under the initial non-electrochromic effect, representing the inherent reflection ability of the material at wavelength λ; Δ ρEC (λ, V) is the change in reflectance at wavelength λ after applying voltage V caused by the electrochromic effect;

[0093] With the goal of minimizing the illuminance fluctuation in the child's line of sight area, design the objective function By jointly adjusting θ and V to achieve uniform light guidance and reduce strong contrast shadows, where E(x, y; θ, V) is the illuminance at point (x, y) when the reflector angle is θ and the reflectance is controlled by voltage V, representing the illumination intensity in the line of sight area; σ E (θ, V) is the standard deviation of illuminance, measuring the uniformity of light distribution. The smaller the value, the more uniform the illumination; A is the target illumination area within the child's line of sight area; dx and dy represent the lengths of the tiny spatial units within the area, used for integral calculation of the total light intensity;

[0094] Utilize the color rendering formula Set the constraint conditions to ensure that the output satisfies Ra ≥ 95 to guarantee high-quality color rendering effect, where Ra(ρ(λ, V)) is the color rendering index, calculated based on the reflectance ρ(λ, V), representing the fidelity of the light source color; k is a constant in the color rendering calculation, used to represent the deviation of the reference chromatogram, used to calculate the difference between the actual reflectance and the ideal reference light source; S(λ) is the spectral distribution function of the light source, representing the radiation intensity of the light source at each wavelength; R ref (λ) is the spectral distribution function of the reference color light source, representing the radiation intensity of sunlight, used as a reference for color rendering calculation; dλ is the tiny wavelength unit in the integral, representing the summation step for each wavelength when calculating the color rendering;

[0095] Integrate the objective function and the color rendering constraint into an optimization problem with constraints:

[0096]

[0097] s.t. Ra(ρ(λ, V)) ≥ 95

[0098] θ ∈ [θ min , θ max , V ∈ [V min , V max

[0099] Adopt real-time feedback and optimization algorithms to dynamically solve, and adjust the control parameters to achieve the best indirect light guidance, where θ min , θ max ​are the minimum and maximum values of the reflector angle, representing the controllable angle range for the micromotor to adjust the reflector; V min , V max are the minimum and maximum values of the electrochromic voltage, representing the control range of the voltage when adjusting the reflectivity.

[0100] Furthermore, an integrated automatic shading device is combined with a meteorological data and space usage prediction model to preset the shading louver angle and extension degree, achieving precise filtering of direct sunlight while retaining the scattered light component and controlling the glare value UGR not to exceed 16, including the following steps:

[0101] Calculate the initial louver angle and the extension degree where where H s is the solar altitude angle, representing the vertical angle of the sun relative to the horizon, used to judge the incident angle of sunlight; D is the vertical projection distance between the louver and the building opening, used to convert the shading angle corresponding to the incident angle; Δδ(I d , t) is the dynamic correction amount of the shading angle, which adjusts the louver angle in real time based on the current direct sunlight intensity I d and time t;

[0102] Adjust the shading strategy using the transmittance function and calculate the regulated direct sunlight and scattered light to ensure effective reduction of direct sunlight while retaining sufficient scattered light components. Among them, is the intensity of direct sunlight passing through the shading device after adjustment, that is, the part of direct sunlight actually received by the user; I s is the external scattered light intensity; is the intensity of scattered light actually entering the room after the shading device is adjusted; τ d (δ b , L b ) is the direct sunlight transmittance function at the current louver angle and extension degree, representing the filtering ability of the shading device for direct sunlight; τ s (δ b , L b ) is the scattered light transmittance function at the current louver angle and extension degree, representing the degree to which the shading device allows scattered light to pass through;

[0103] Use the standard formula to evaluate the glare of the current light environment and judge whether it meets the visual comfort standard of UGR ≤ 16. Among them, UGR is the unified glare value, used to measure the degree of visual discomfort, and the lower the value, the lighter the glare. The design goal is ≤ 16; L bk is the background brightness; L sL is the luminance of the glare source, i.e., the luminance of a certain light source or reflecting surface in the line-of-sight direction; ω is the projected solid angle of the glare source in the viewing angle, which affects its interference with vision; p is the distance from the center point of the glare source to the observer's line of sight.

[0104] Build a feedback control mechanism to optimize the function [Objective, dynamically adjust δ b and L b , and achieve multi-objective optimization of direct light filtration, scattered light retention, and glare control, where: w1 is the priority weight for direct light filtration, which controls the degree of reducing strong light; w2 is the priority weight for the glare approaching the target value, which is used to precisely adjust comfort; w3 is the weight for scattered light retention, which encourages maintaining sufficient soft light illumination.

[0105] Furthermore, the variable transmittance intelligent glass has 3 - 5 light transmittance levels, the light transmittance range is 20% - 80%, the response time is 15 - 30 seconds, and the energy consumption is 5 - 10W / m 2 ;

[0106] The angle adjustment range of the electronically controlled spectral reflector is 0 - 170°, the accuracy is ±2°, and 2 - 3 coating materials are combined to achieve selective reflection in the wavelength range of 450 - 700nm, and the reflectance can be adjusted in the range of 20% - 85%;

[0107] The automatic adjustable sunshade device adopts a honeycomb structure design, has an angle adjustment range of 30 - 40°, a sunshade efficiency of 80 - 90%, the light transmittance can be continuously adjusted between 15% - 60%, and has a wind resistance of 7 - 8 levels;

[0108] The full-spectrum LED light source array includes LED units of 5 - 7 wavelengths, the color temperature adjustment range is 2700K - 6300K, the color rendering index Ra ≥ 90, the stroboscopic coefficient is lower than 1%, and the unit power consumption illumination efficiency is 100 - 130lm / W.

[0109] Furthermore, step S4 includes the following steps:

[0110] Establish a multi-source meteorological data integration platform, and at the same time integrate the historical environmental parameter records of the site to build a database containing the key factors affecting illumination;

[0111] Process multi-dimensional meteorological data and historical light environment records to generate a predicted matrix of light environment parameters for the next 24 to 72 hours, including direct light intensity, scattered light ratio, dominant light source direction, and color temperature change index;

[0112] Based on precise three-dimensional modeling and parametric optical properties of materials, combined with predictive meteorological data, simulate the sunlight projection paths and reflection characteristics at different times, generate a dynamic visual preview of the site light environment changes, and identify potential discomfort areas and optimal utilization periods;

[0113] Automatically plan the equipment control schedule for the next 72 hours according to the prediction data, including the change curve of the light transmittance of smart glass, the deployment time and angle setting of sunshade devices, and the activation threshold and intensity of the supplementary lighting system, and respond to the expected light environment changes in advance;

[0114] Based on the predicted light change pattern, calculate the optimal layout plan of movable environmental components, generate component reconfiguration guidance, maximize the utilization efficiency of natural light resources, and ensure the visual comfort of children's activity areas at the same time;

[0115] By comparing the deviation between the actual observed data and the predicted results, dynamically adjust the prediction period and confidence interval, and start the emergency response plan for unexpected weather mutation events.

[0116] In summary, by integrating multi-source meteorological data and historical light environment records, combined with precise modeling and prediction technologies, efficient prediction and precise control of future light environment changes have been achieved. First, by establishing a multi-source meteorological data integration platform and a database containing key factors affecting lighting, a rich data foundation is provided for light environment prediction. After processing multi-dimensional meteorological data and historical light environment records, a prediction matrix of light environment parameters for the next 24 to 72 hours is generated, covering key indicators such as direct light intensity, scattered light ratio, dominant light source direction, and color temperature change, providing a scientific basis for the dynamic adjustment of the space light environment. Based on precise three-dimensional modeling and parametric optical properties of materials, combined with meteorological prediction data, it is possible to simulate the sunlight projection paths and reflection characteristics at different times, generate a dynamic visual preview of the site light environment changes, identify potential discomfort areas in advance and find the best utilization periods, so as to optimize the space design. Combined with the prediction data, the equipment control schedule for the next 72 hours is automatically planned, including the change curve of the light transmittance of smart glass, the deployment time and angle setting of sunshade devices, and the activation threshold and intensity of the supplementary lighting system, etc., to ensure an early response to the expected light environment changes. Based on the light change pattern, it is also possible to calculate the optimal layout plan of movable environmental components, maximize the utilization efficiency of natural light resources, and ensure the visual comfort of children's activity areas at the same time. In addition, by comparing the deviation between the actual observed data and the predicted results, dynamically adjust the prediction period and confidence interval, and start the emergency response plan when unexpected weather mutation events occur, ensuring the flexibility and adaptability of light environment control.

[0117] Furthermore, the light environment parameter prediction matrix is constructed based on a hybrid model that combines deep learning and time series analysis. It adopts a neural network structure with 6 - 8 layers. Under standard meteorological conditions, the prediction accuracy for different time scales is as follows: within 6 hours, it is 90 - 95%; within 24 hours, it is 80 - 90%; within 72 hours, it is 70 - 75%.

[0118] Furthermore, the equipment regulation schedule is generated based on a multi - objective optimization algorithm, considering three dimensions: visual comfort, energy efficiency, and equipment service life. The weight ratio can be adjusted within the range of 40:40:20 to 50:30:20 according to season and site characteristics.

[0119] Furthermore, the movable environmental components include reflector plates, sunshades, intelligent barriers, and light art devices. Each type of component provides 3 - 5 configuration options. The optimal layout is calculated through an optimization algorithm, and the adjustment accuracy of the component position is 10 - 20 cm.

[0120] Furthermore, the high - precision infrared sensor has a resolution of 320×240 to 640×480 pixels, a sampling frequency of 15 - 30 Hz, an identification distance of 1 - 12 m, and can track 20 - 30 independent targets simultaneously.

[0121] Furthermore, step S5 includes the following steps:

[0122] Adopt a combination of high - precision infrared sensors and computer vision algorithms to anonymously record the activity paths, stay durations, and interaction frequencies of children under different light environment conditions, and simultaneously establish an associated mapping between activity preferences and light environment parameters;

[0123] Collect children's subjective experience data through graphical expressions, simple gestures, or voices, and combine physiological index monitoring to construct a comprehensive comfort evaluation model to quantify the impact of the light environment on children's visual experience;

[0124] Take children's active activity patterns and comfort feedback as reward signals, and use a method that combines Q - learning and policy gradient to autonomously optimize the light environment regulation strategy and seek the optimal balance between energy efficiency and usage experience;

[0125] Structurally store the optimization experience of a specific site in terms of time and space dimensions, establish a parameter association network, and form a knowledge system for closed - loop optimization;

[0126] Through longitudinal comparative analysis of the time - series data of children's behavior changes and environmental parameter adjustments, evaluate the optimization effect and environmental adaptation period, verify the effectiveness of the reinforcement learning strategy, and accordingly adjust the algorithm parameters and knowledge base update mechanism.

[0127] In summary, through the combination of high-precision sensing technology and intelligent algorithms, the accurate tracking and dynamic optimization of children's activity behaviors and comfort levels under different light environment conditions have been achieved, greatly enhancing the personalization and adaptability of light environment design. First, high-precision infrared sensors and computer vision algorithms are used to anonymously record children's activity paths, stay durations, and interaction frequencies under different light environments, and establish an associated mapping between light environment parameters and activity preferences, providing behavioral data support. In addition, subjective experience data of children are collected through graphical expressions, simple gestures, or voices, and combined with physiological index monitoring to construct a comprehensive comfort evaluation model for quantifying the specific impact of the light environment on children's visual experiences. To further optimize the light environment control strategy, combining Q-learning and policy gradient methods, taking children's active activity patterns and comfort feedback as reward signals for autonomous optimization, and striving to find the best balance between energy efficiency and usage experience. The optimization experiences of specific sites are structured and stored in terms of time and space dimensions, forming a closed-loop optimization knowledge system based on a parameter association network to ensure the continuous progress of light environment design through continuous feedback and updates. Finally, through longitudinal comparative analysis of the time series data of children's behavior changes and environmental parameter adjustments, the optimization effect and adaptation period are evaluated, and the effectiveness of the reinforcement learning strategy is verified, providing data support for algorithm parameter adjustment and knowledge base update.

[0128] Further, the parameter association network contains 80 - 120 nodes and 200 - 300 connections, and uses graph neural network technology to construct a knowledge graph, supporting multi-dimensional queries and similar scenario matching, with a matching accuracy of 75 - 85%.

[0129] Further, the environmental adaptation period is quantitatively evaluated through 5 indicators, including the activity frequency change rate, stay duration growth ratio, interaction density increase value, positive mood deviation degree, and gaze comfort index. Each indicator is scored from 0 to 100 points, and a comprehensive score increase of 15 - 25% is required to determine effective optimization.

[0130] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for designing child-friendly urban spaces based on visual perception optimization, characterized in that, It includes the following steps: Step S1: Collect the sunshine angle, light intensity, scattering characteristics, reflector status and related microclimate parameters of the site in real time to build the basic light environment data. Step S2: Develop differential light environment design parameters and threshold standards according to the characteristics of children's development stages. Step S3: Based on the multi-level light environment regulation actuator, achieve precise intervention in light intensity, directionality, color temperature and glare control. Step S4: Dynamically adjust the light environment parameters based on the real-time environmental monitoring data to ensure the best visual experience under different microclimate conditions. Step S5: Establish an optimization knowledge base for the light environment of a specific site to realize the digital accumulation of design experience and application iteration.

2. The method for designing a child-friendly urban space optimized based on visual perception according to claim 1, wherein Step S1 includes the following steps: Use spectrometers and illuminometers in different orientations to capture the intensity distribution, incident angle and wavelength characteristics of visible light, ultraviolet light and near-infrared light in real time, and establish the basic data layer of the full-spectrum energy distribution of the site. Integrate micro-meteorological parameter collectors to build a microclimate monitoring network for the site and collect the microclimate parameters of the site in real time. Based on laser reflection measurement and image analysis technology, evaluate the diffuse reflectivity, specular reflectivity and spectral reflection characteristics of each surface material in the space, and generate a three-dimensional characteristic model of the space reflector surface. Use infrared thermal imaging and computer vision technology to record the usage density, activity intensity and space occupancy pattern of the site while protecting privacy, and establish a basic data set of human-environment interaction. Perform real-time fusion and cleaning of multi-source sensing data, and use spatio-temporal interpolation algorithms to build a continuous light environment distribution model for the entire site. At the same time, combine neural network methods to identify the changing rules and abnormal patterns of the light environment. Develop a multi-dimensional light environment data visualization platform to convert optical parameters into intuitive visual charts, including light intensity heat maps, color temperature distribution maps, glare risk areas and comfort score maps.

3. The method for designing a child-friendly urban space optimized based on visual perception according to claim 2, wherein The combination of the spectrometer and the illuminometer includes at least 6-direction acquisition units. Each acquisition unit can capture the visible spectrum in the range of 380-780nm with an accuracy of 2-5nm, and simultaneously capture ultraviolet light in the range of 280-380nm and near-infrared light in the range of 780-1400nm, with a sampling frequency of once every 30-180 seconds. The microclimate parameters include temperature, humidity, air pressure, wind speed, wind direction, precipitation and PM2.5 concentration. Among them, the temperature monitoring range is -30°C to 50°C with an accuracy of ±0.3°C; the humidity monitoring range is 5-95% with an accuracy of ±3%; the wind speed monitoring range is 0-25m / s with an accuracy of ±0.5m / s.

4. The method for designing a child-friendly urban space optimized based on visual perception according to claim 1, wherein, Step S2 includes the following steps: Precisely identify the age distribution range of children through human body proportion characteristics, movement patterns and interaction behavior characteristics, and generate a dynamic distribution map of the age composition in the site. Build a visual perception parameter library related to age to accurately quantify the differential requirements of children of different ages in terms of light intensity threshold, color temperature preference, contrast sensitivity and glare tolerance. According to the detected main age composition ratio in the site, automatically adjust the weight coefficients of light intensity, uniformity, color temperature and contrast indicators to accurately reflect the visual comfort requirements of the current main user group. Evaluate the emotional response and comfort experience of children to the current light environment through facial expression recognition, dwell time analysis, and activity area selection behavior, and verify and fine-tune the practical applicability of the perception evaluation indicators; Adjust the light environment evaluation criteria according to different time periods of the day to ensure that the vitality awakening in the morning, anti-glare protection at noon, and gentle transition needs in the evening are fully considered.

5. The method for designing a child-friendly urban space optimized based on visual perception according to claim 1, wherein Step S3 includes the following steps: Dynamically adjust the light transmission characteristics of the variable light transmittance intelligent glass according to the real-time sunlight intensity and the visual comfort requirements of children to ensure that the illuminance is maintained within the optimal range for children's learning activities; Use a micro-motor to control the angle of the electro-controlled spectral reflector and electrochromic technology to adjust the reflectivity, achieve precise guidance of the indirect light source, effectively eliminate strong contrast shadows in the children's line of sight area, and ensure that the color rendering index is not less than Ra95; Integrate an automatic adjustable sunshade device, combine meteorological data with a space usage prediction model, preset the angle and extension of the sunshade louvers, achieve precise filtering of direct sunlight, retain the component of scattered light at the same time, and control the glare value UGR not to exceed 16; Adopt a full-spectrum LED light source array, precisely control the color temperature and brightness through PWM dimming technology, intelligently supplement the light source when natural light is insufficient, simulate the change rhythm of natural sunlight, and optimize the visual task lighting parameters at the same time; Based on real-time sensing data, coordinate the linkage response of each actuator, balance energy consumption efficiency and visual comfort, and achieve precise shaping of the overall light flow in the space.

6. The method for designing a child-friendly urban space optimized based on visual perception according to claim 5, characterized in that The variable transmittance intelligent glass has 3 - 5 light transmittance levels, with a light transmittance range of 20% - 80%, a response time of 15 - 30 seconds, and an energy consumption of 5 - 10 W / m 2 ; The angle adjustment range of the electro-controlled spectral reflector is 0-170°, the accuracy is ±2°, and 2-3 coating materials are combined to achieve selective reflection in the wavelength range of 450-700nm, and the reflectivity can be adjusted in the range of 20%-85%; The automatic adjustable sunshade device adopts a honeycomb structure design, has an angle adjustment range of 30-40°, a sunshade efficiency of 80-90%, the light transmittance can be continuously adjusted between 15%-60%, and has a wind resistance of 7-8 levels; The full-spectrum LED light source array contains 5-7 types of LED units with a color temperature adjustment range of 2700K-6300K, a color rendering index Ra≥90, a stroboscopic coefficient lower than 1%, and a unit power consumption illuminance efficiency of 100-130lm / W.

7. The method for designing a child-friendly urban space optimized based on visual perception according to claim 1, wherein Step S4 includes the following steps: Establish a multi-source meteorological data integration platform, and at the same time integrate the historical environmental parameter records of the site to construct a database containing the key factors affecting lighting; Process multi-dimensional meteorological data and historical light environment records to generate a light environment parameter prediction matrix for the next 24 to 72 hours, including direct light intensity, scattered light ratio, dominant light source direction, and color temperature change indicators; Based on precise three-dimensional modeling and parametric optical characteristics of materials, combined with predictive meteorological data, simulate the sunlight projection path and reflection characteristics at different times, generate a dynamic visual preview of the site light environment changes, and identify potential discomfort areas and optimal utilization periods; Automatically plan the equipment control schedule for the next 72 hours according to the prediction data, including the change curve of the light transmittance of smart glass, the deployment timing and angle setting of sunshade devices, and the turning-on threshold and intensity of the supplementary lighting system, and respond to the expected light environment changes in advance; Based on the predicted light change pattern, calculate the optimal layout plan of the movable environmental components, generate component reconfiguration guidance, maximize the utilization efficiency of natural light resources, and ensure the visual comfort of the children's activity area at the same time; By comparing the deviation between the actual observed data and the prediction results, dynamically adjust the prediction period and confidence interval, and start the emergency response plan for unexpected weather mutation events.

8. The method for designing a child-friendly urban space optimized based on visual perception according to claim 7, wherein, The light environment parameter prediction matrix is constructed based on a hybrid model combining deep learning and time series analysis, and adopts a 6-8 layer neural network structure. Under standard meteorological conditions, the prediction accuracy for different time scales is: 90-95% within 6 hours, 80-90% within 24 hours, and 70-75% within 72 hours.

9. The method for designing a child-friendly urban space optimized based on visual perception according to claim 1, characterized in that Step S5 includes the following steps: Anonymously record the activity paths, stay durations, and interaction frequencies of children under different light environment conditions by combining a high-precision infrared sensor and a computer vision algorithm, and establish an association mapping between activity preferences and light environment parameters at the same time; Collect the subjective experience data of children through graphical expressions, simple gestures, or voices, and combine physiological index monitoring to construct a comprehensive comfort evaluation model to quantify the impact of the light environment on children's visual experience; Use the positive activity patterns and comfort feedback of children as reward signals, and use a method combining Q-learning and policy gradient to realize the autonomous optimization of the light environment control strategy and seek the optimal balance between energy efficiency and usage experience; Structurally store the optimization experience of specific sites in terms of time and space dimensions, establish a parameter association network, and form a knowledge system for closed-loop optimization; Evaluate the optimization effect and environmental adaptation period by longitudinally analyzing the time series data of children's behavior changes and environmental parameter adjustments, verify the effectiveness of the reinforcement learning strategy, and adjust the algorithm parameters and knowledge base update mechanism accordingly.

10. The method for designing a child-friendly urban space optimized based on visual perception according to claim 9, characterized in that, The parameter association network contains 80-120 nodes and 200-300 connections, uses graph neural network technology to construct a knowledge graph, supports multi-dimensional queries and similar scene matching, and the matching accuracy is 75-85%.

Images