Intelligent health light environment construction method and device, computer device, and storage medium

Abstract

The application belongs to the field of intelligent lighting, and relates to an intelligent healthy light environment construction method and device, computer equipment and a storage medium.The method comprises the following steps: setting a lamp layout according to the space area of an ICU room, so that the eye vertical illuminance during the day and night period reaches at least 350lx, and the illuminance of each operating table in the space is maintained to reach at least 700lx; connecting all the lamps arranged in the design space to the ICU light system, and automatically adjusting the light efficiency according to the work demand by using rule reasoning, data analysis and artificial intelligence algorithms, while simulating the light changes in a day according to the natural light level of the local area in 24 hours; introducing natural elements, mainly using warm colors for background colors, reducing blue light waves and simulating dynamic natural light; and analyzing the use experience of the intelligent healthy light environment through interviews and quantitative index evaluation, and forming an optimization strategy.The method relieves visual fatigue, improves work efficiency, reduces risk events, improves satisfaction and protects the circadian rhythm.

Description

Technical Field

[0001] This invention relates to the field of smart lighting technology, and in particular to a method, apparatus, computer equipment, and storage medium for constructing a smart and healthy lighting environment. Background Technology

[0002] Intensive Care Units (ICUs), as centralized treatment centers for critically ill patients, are characterized by complex environments, relatively enclosed spaces, and diverse monitoring equipment, placing significant workload on nurses. Studies have shown that high-pressure working conditions, coupled with abnormal lighting environments, can easily trigger physiological and psychological stress responses in nurses within the ward, reducing their mental well-being and thus affecting work efficiency and quality.

[0003] Existing ICU lighting systems have the following technical defects: Inadequate and uneven lighting. According to a survey, more than 52.2% of ICU workspaces failed to meet the minimum lighting requirement of 300 lx.

[0004] Static lighting ignores the needs of circadian rhythms. Most ICUs rely on 24-hour artificial lighting, and the common window design is rarely seen, resulting in low quality of natural light, chaotic lighting layout, and poor light stability.

[0005] Inadequate glare control and poor visual adaptation are problems. The ICU contains numerous medical devices, and multiple reflections and glare interference exist between displays, monitors, and overhead and side lights. Frequent switching of lights on and off at night causes sudden changes in light, which not only disturbs patients' rest but also challenges nurses' dark adaptation abilities.

[0006] The lack of personalized adjustment and nurses' autonomy is a significant issue. Existing lighting systems mostly employ centralized control, preventing nurses from adjusting lighting parameters in real time according to specific task requirements (such as fine motor skills, paperwork, and night rounds). Healthcare staff generally report a lack of independent control over their workstation lighting, leading to a significant decrease in visual comfort.

[0007] Therefore, the comfort of the lighting environment is a crucial design element in the ICU workspace. However, there is currently a lack of a systematic approach to constructing a smart and healthy lighting environment for the ICU that prioritizes the nurse's experience. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for constructing a smart and healthy lighting environment, employing the following technical solution, including the following steps: S1. The lighting layout is set according to the space area of ​​the ICU room to ensure that the vertical illuminance to the eyes is at least 350 lx during the day and night, and the illuminance of each operating table in the space is at least 700 lx. S2, all the lighting fixtures installed in the design space are connected to the ICU lighting system. The system uses multiple algorithms such as rule reasoning, data analysis and artificial intelligence to automatically adjust the lighting effect according to the work needs of ICU nurses, and at the same time simulate the changes in light throughout the day based on the 24-hour natural light level of the local area. S3 introduces natural elements by creating a mural lamp with a forest pattern. The background color is mainly warm, reducing blue light waves and simulating dynamic natural light. S4. Through semi-structured interviews and quantitative indicator evaluation and analysis of the user experience of the smart healthy lighting environment, optimization strategies are formed.

[0009] Preferably, step S1, which involves arranging the lighting fixtures according to the space area of ​​the ICU room to ensure that the vertical illuminance to the eyes reaches at least 350 lx during both daytime and nighttime periods, and that the illuminance on each operating table in the space remains at least 700 lx, specifically includes: S11, Measure the geometric dimensions of the ICU room, determine the multi-level lighting configuration scheme of ceiling lights, side lights, and floor lights, so that all lights are LED and the lighting circuits for each bed are designed separately and can be adjusted independently; S12. The point light source illuminance calculation model is used to optimize the lighting position. The calculation formula is: E=I / d^2 ·cosθ, where E is the illuminance of the illuminated surface in lux (lx), I is the luminous intensity of the light source in the specified direction in candela (cd), d is the straight-line distance between the light source and the illuminated surface in meters (m), and θ is the incident angle of the light, that is, the angle between the direction of the light and the normal of the illuminated surface. S13 adopts a three-level coordinated mode of ceiling light basic lighting, side light supplementary lighting and ground light local lighting. The side lights illuminate the wall at an angle of 45° to 60°, and the ground lights are installed at a height of ≥0.3m from the ground.

[0010] Preferably, step S2 involves connecting all the lighting fixtures installed in the design space to the ICU lighting system. This system utilizes multiple algorithms, including rule-based reasoning, data analysis, and artificial intelligence, to automatically adjust the lighting effect according to the work needs of ICU nurses. The specific steps of simulating daily light changes based on the local 24-hour natural light level include: S21 constructs a three-layer architecture including a perception layer, a decision layer, and an execution layer. The perception layer collects spatial illuminance data in real time through distributed light sensors. The decision layer deploys an edge computing gateway with a built-in rule inference engine, data analysis module, and lightweight artificial intelligence algorithm. The execution layer uses DALI-2 driver power supply to support single lamp addressing and parameter adjustment. S22 features a graphical control interface at the nurse station and on mobile terminals, providing four preset scenario modes: daytime mode, nighttime mode, dynamic mode, and night light mode. It also has an independently set one-click emergency mode for emergency rescue. S23, develop an ICU lighting control APP based on a mobile operating system, communicate with the central control system through the hospital's intranet Wi-Fi, and support real-time display, slider adjustment, and scene saving functions.

[0011] Preferably, step S3, which introduces natural elements by creating a forest-patterned mural lamp with a warm background color, reduces blue light waves, and simulates dynamic natural light, specifically includes: The S31 is a wall lamp designed with an ultra-thin LED backlight panel, a high-transmittance printed canvas, and an intelligent controller. It automatically adjusts the spectral weight according to the system time, and the night mode activates the "blue light filter" algorithm to reduce the relative energy of short-wavelength blue light in the 420-480nm range. The S32 wall lamp has a built-in dynamic light effect algorithm that simulates the changes in natural light over time, including three stages: sunrise simulation, daytime fluctuation, and sunset simulation.

[0012] Preferably, step S4, which involves analyzing the user experience of the smart healthy lighting environment through semi-structured interviews and quantitative indicators to formulate an optimization strategy, specifically includes: S41. After the lighting environment system is put into use, conduct semi-structured interviews. After the interview recordings are transcribed, use the topic analysis method to extract key issues and optimization suggestions. S42, establish a quantitative evaluation system that includes four core indicators: visual fatigue score, single intravenous puncture completion time, risk event incidence rate, and satisfaction survey. S43, using paired t-tests and χ² tests 2 The test results are statistically analyzed, and an optimization strategy is formulated based on the statistical results.

[0013] Preferably, in step S21, the rule reasoning engine uses the IF-THEN rule base and is implemented based on the Drools rule engine; the data analysis module uses the K-means clustering algorithm to cluster the dimming data and identify the typical dimming pattern of each nurse; the artificial intelligence algorithm uses a Long Short-Term Memory (LSTM) network to predict the change in illuminance demand in the next hour. The network structure includes an input layer, an LSTM layer, and a fully connected layer. The input features include the current time, season, weather conditions, bed occupancy rate, and operation logs from the same period in the past. The output is the recommended illuminance value for each area.

[0014] Preferably, in step S22, the daytime mode is set to a ceiling light illuminance of 1000-1500 lx and a color temperature of 5500-6500 K, with side lights supplementing the vertical illuminance to ≥500 lx; ​​the nighttime mode is set to a ceiling light illuminance of 300-500 lx and a color temperature of 3000-3500 K, with the floor lights in low brightness mode; the dynamic mode is set to a wall lamp or a designated screen displaying slowly moving light spots or natural landscape images to guide the eye to follow the movement, with a trajectory speed of 15° / s and a duration of 3 minutes; the night light mode is set to a floor light and corridor guide light illuminance of ≤20 lx and a color temperature of ≤2700 K; the emergency mode forces all space lighting fixtures to output maximum power, with an illuminance of ≥700 lx and a color temperature of 6000 K.

[0015] To address the aforementioned technical problems, the present invention also provides a smart healthy light environment construction device, which adopts the following technical solution, including: The layout module is used to set the lighting layout according to the space area of ​​the ICU room, so that the vertical illuminance to the eyes during the day and night is at least 350 lx, and the illuminance of each operating table in the space is at least 700 lx. The module is used to connect all the lighting fixtures installed in the design space to the ICU lighting system. The system uses multiple algorithms such as rule reasoning, data analysis and artificial intelligence to automatically adjust the lighting effect according to the work needs of ICU nurses, and at the same time simulate the changes in light throughout the day based on the 24-hour natural light level in the local area. The simulation module is used to introduce natural elements by creating a mural lamp with a forest pattern. The background color is mainly warm, reducing blue light waves and simulating dynamic natural light. The optimization module is used to evaluate and analyze the user experience of the smart healthy lighting environment through semi-structured interviews and quantitative indicators, and to formulate optimization strategies.

[0016] To address the aforementioned technical problems, the present invention also provides a computer device that employs the technical solution described below, comprising a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the above-described method for constructing a smart and healthy light environment.

[0017] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium, which employs the technical solution described below. The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the aforementioned smart healthy light environment construction method.

[0018] Compared with the prior art, the present invention has the following main advantages: (1) Significantly relieved visual fatigue. Clinical trial results showed that after the establishment of the smart health light environment, the visual fatigue score of nurses in the observation group after their shift (18.3±3.2 points) was significantly lower than that in the control group (26.7±4.1 points), and the difference was statistically significant (P<0.05). This indicates that the comprehensive intervention of dynamic adjustment, blue light filtering and wall lamps effectively reduced the nurses' eye discomfort.

[0019] (2) Improved work efficiency. The time for a single intravenous puncture by nurses in the observation group was shortened from (68.4±12.5) seconds in the control group to (52.1±9.3) seconds (P<0.05). This improvement was attributed to the improved visual clarity brought about by the standard illumination of the work surface, the elimination of shadows, and personalized lighting.

[0020] (3) Reduced risk events. The incidence of risk events among nurses in the observation group (3.2%) was significantly lower than that in the control group (7.8%) (P<0.05). A good lighting environment reduces judgment errors caused by visual fatigue, especially in emergency situations at night, where the stability and response speed of the light environment are directly related to patient safety.

[0021] (4) Satisfaction was significantly improved. Nurses' satisfaction with the lighting environment increased from 68.3% in the control group to 91.7% in the observation group (P<0.05). Among them, the "ease of use" dimension showed the most significant improvement, reflecting that one-click scene switching and personalized APP adjustment effectively met nurses' needs for self-control.

[0022] (5) Protects the circadian rhythm. Through dynamic dimming and spectral modulation, a smooth transition is achieved between daytime circadian rhythm stimulation (CS) ≥ 0.3 and nighttime CS ≤ 0.1, effectively protecting the nurses' circadian rhythm health. Attached Figure Description

[0023] To more clearly illustrate the solutions in this invention, the accompanying drawings used in the description of the embodiments of this invention will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart of an embodiment of the intelligent healthy light environment construction method of the present invention; Figure 2 This is a schematic diagram of a structure of an embodiment of the smart healthy light environment construction device of the present invention; Figure 3 This is a schematic diagram of the structure of an embodiment of the computer device of the present invention. Detailed Implementation

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects and not to describe a particular order.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0028] It should be noted that the intelligent healthy light environment construction method provided in the embodiments of the present invention is generally executed by a server / terminal device, and correspondingly, the intelligent healthy light environment construction device is generally set in the server / terminal device.

[0029] It should be understood that the number of terminal devices, networks, and servers is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used.

[0030] Example 1 Please refer to Figure 1 The flowchart illustrates an embodiment of the intelligent healthy light environment construction method of the present invention. The intelligent healthy light environment construction method includes the following steps: Step S1: Set the lighting layout according to the space area of ​​the ICU room so that the vertical illuminance to the eyes reaches at least 350 lx during the day and night, and the illuminance of each operating table in the space reaches at least 700 lx.

[0031] In this embodiment, the electronic device (e.g., a server / terminal device) on which the smart healthy light environment construction method runs can receive the smart healthy light environment construction request via a wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods.

[0032] In this embodiment, step S1 may specifically include the following steps: S11. Measure the geometric dimensions of the ICU room and determine a multi-level lighting configuration scheme for ceiling lights, side lights, and floor lights, so that all lights are LED and the lighting circuits for each bed are designed separately and can be adjusted independently.

[0033] First, the geometric dimensions of the target ICU room are measured. Taking a typical ICU single room as an example, the room length × width × height is set to 7m × 5m × 3m (the specific dimensions can be determined according to actual needs, and the room size is not limited here). According to the requirements of the "Standard for Lighting Design of Buildings" (GB 50034) and IESNA RP-29-16, the key illuminance indicators are determined as follows: vertical illuminance to nurses' eyes ≥350 lx during daytime and nighttime periods, illuminance maintained on the work surface ≥700 lx, and spatial uniformity (minimum illuminance / average illuminance) ≥0.7.

[0034] The ceiling lights can be equipped with six sets of adjustable LED luminaires, each with an adjustable luminous flux of 300–1500 lx, a color temperature range of 2000–6500 K, and a color rendering index Ra > 90 to ensure spectral continuity and color reproduction. The side lights use concealed linear LED strips with adjustable color temperature and illuminance, installed at the junction of the wall and ceiling to supplement ambient light and eliminate shadows. Floor lights are configured per bed, one set per bed, with adjustable illuminance and color temperature. Frosted lampshades reduce light intensity, primarily for low-light observation during nighttime patrols. All luminaires use LED light sources, and the wiring for each bed's ceiling, side, and floor lights is separately designed and independently adjustable.

[0035] S12, the lighting location is optimized using a point light source illuminance calculation model. The calculation formula is as follows: Where E is the illuminance of the illuminated surface in lux (lx), I is the luminous intensity of the light source in the specified direction in candela (cd), d is the straight-line distance between the light source and the illuminated surface in meters (m), and θ is the angle of incidence of the light ray, that is, the angle between the direction of the light ray and the normal of the illuminated surface.

[0036] In practice, to simplify calculations, it is usually assumed that the light fixture shines vertically downwards and the illuminated surface is horizontal. The formula simplifies to: The significance of this formula lies in revealing the inverse square relationship between illuminance and distance—when the distance to the light source doubles, the illuminance decreases to one-quarter of its original value. This principle is particularly important for spaces like ICUs with multiple operating surfaces (bed surface, tabletop, monitor screen), requiring precise calculation of the illuminance contribution value of each height plane during the design phase.

[0037] For example, if the required illuminance is 700 lx on the bed surface (0.8m from the ground), and the ceiling light is installed 3m from the ground (2.2m from the bed surface), then the required luminous intensity is: This calculation is a theoretical value for a single lamp. In practice, the cumulative effect of multiple lamps and the maintenance factor (such as 0.8) need to be considered.

[0038] S13 adopts a three-level coordinated mode of ceiling light basic lighting, side light supplementary lighting and ground light local lighting. The side lights illuminate the wall at an angle of 45° to 60°, and the ground lights are installed at a height of ≥0.3m from the ground.

[0039] A multi-tiered lighting layout is implemented. The ceiling light provides basic ambient illumination and ensures overall uniformity; the side lights increase vertical illumination and reduce direct glare through secondary reflection from the wall; the floor lights use frosted lampshades to reduce the brightness of the light source and are mainly used for nighttime gait lighting and bedside operations.

[0040] For example, the ceiling light, side light, and floor light for each bed are controlled by an independent circuit, allowing for independent adjustment of each bed's lighting. This design gives nurses absolute control over the lighting in their work area, preventing disturbance to patients in adjacent beds.

[0041] Step S2: Connect all the lighting fixtures installed in the design space to the ICU lighting system. The system uses multiple algorithms such as rule reasoning, data analysis and artificial intelligence to automatically adjust the lighting effect according to the work needs of ICU nurses, and at the same time simulate the changes in light throughout the day based on the 24-hour natural light level of the local area.

[0042] In this embodiment, step S2 may specifically include the following steps: S21 constructs a three-layer architecture including a perception layer, a decision layer, and an execution layer. The perception layer collects spatial illuminance data in real time through distributed light sensors. The decision layer deploys an edge computing gateway with a built-in rule inference engine, data analysis module, and lightweight artificial intelligence algorithm. The execution layer uses DALI-2 driver power supply to support single lamp addressing and parameter adjustment.

[0043] The sensing layer uses distributed light sensors installed on the ceiling, at the nurses' station, and beside the bed to collect spatial illuminance data in real time, with a sampling frequency of 1 Hz. The sensors employ I0... 2 C-type digital output illuminance sensor, measuring range 0~65535 lx, accuracy ±5%.

[0044] The decision-making layer deploys an edge computing gateway, which integrates a rule inference engine, a data analysis module, and lightweight artificial intelligence algorithms. The rule inference engine uses the IF-THEN rule base and is implemented based on the Drools rule engine; the data analysis module is written in Python to perform cluster analysis of historical operation data; the artificial intelligence algorithm uses a Long Short-Term Memory (LSTM) network, with input features including current time, season, weather conditions, bed occupancy rate, and operation logs from the same period in the past, and the output is the recommended illuminance value for each area.

[0045] Execution layer: All LED luminaires are equipped with DALI-2 (Digital Addressable Lighting Interface) drivers, supporting single-lamp addressing and parameter adjustment. The DALI-2 protocol complies with the IEC 62386 standard, each luminaire has a unique address code, supports bidirectional communication, and can provide real-time feedback on luminaire status.

[0046] The following is an example of a rule-based reasoning algorithm: If the time is between 23:00 and 05:00 AND the bedside sensor detects a nurse approaching AND there is no resuscitation signal; THEN Execute night patrol mode: Set the overhead light to 50 lx (3000 K), the ground light to 15 lx (2700 K), and turn off the side lights.

[0047] Data analysis algorithm: Based on historical operational data, the system learns nurses' dimming preferences and establishes individualized profiles. K-means clustering is used to cluster the dimming data, identifying the typical dimming patterns of each nurse. When a nurse logs in to work using their RFID tag, the system automatically identifies their identity and loads their preference settings.

[0048] Artificial intelligence algorithm: An LSTM neural network is used to predict changes in illuminance demand over the next hour. The network structure includes an input layer (8 feature dimensions), an LSTM layer (64 hidden units), and a fully connected layer (output dimension 5, corresponding to recommended illuminance values ​​for 5 regions). The model is trained using the Adam optimizer with mean squared error (MSE) as the loss function. The training data consists of sensor data and operation logs from the past 6 months. The system adjusts the lighting status in advance based on the prediction results, achieving a seamless response.

[0049] The rule-based reasoning engine uses the IF-THEN rule base and is implemented based on the drools rule engine. The data analysis module uses the K-means clustering algorithm to cluster the dimming data and identify the typical dimming patterns of each nurse. The artificial intelligence algorithm uses a Long Short-Term Memory (LSTM) network to predict changes in illuminance demand in the next hour. The network structure includes an input layer, an LSTM layer, and a fully connected layer. Input features include the current time, season, weather conditions, bed occupancy rate, and operation logs from the same period in the past. The output is the recommended illuminance value for each area.

[0050] S22 features a graphical control interface at the nurse station and on mobile terminals, providing four preset scenario modes: daytime mode, nighttime mode, dynamic mode, and night light mode. It also has an independently set one-click emergency mode for emergency rescue.

[0051] A graphical control interface is set up at the nurses' station and on mobile terminals, providing four preset scenario modes: Daytime mode (06:00-18:00): Top light illuminance 1000-1500 lx, color temperature 5500-6500 K (high color temperature promotes alertness), side lights supplement the lighting to vertical illuminance ≥500 lx, simulating a high-illuminance daytime environment.

[0052] Night mode (18:00-23:00): Ceiling light illuminance 300-500 lx, color temperature 3000-3500 K (low color temperature reduces blue light stimulation), floor lights in low brightness mode to create a nighttime working atmosphere.

[0053] Dynamic Mode: This mode is an active eye relaxation function. After completing a phase of work (such as writing medical records continuously for 30 minutes), nurses can switch to Dynamic Mode with a single click. In this mode, a slowly moving light spot or natural landscape image appears on the wall lamp or designated screen, guiding the eyes to follow the movement. The movement trajectory follows a design principle combining smooth tracking and saccades, with a trajectory speed of 15° / s and a duration of 3 minutes. This mode is designed based on the physiological mechanisms of oculomotor nerves, aiming to relieve ciliary muscle spasm and promote tear film reconstruction.

[0054] Night light mode (23:00-06:00): Only the floor lamp and corridor guide light are kept on, with an illuminance of ≤20 lx and a color temperature of ≤2700K. In this mode, the sensor remains active, and when a nurse is detected entering, the bedside light is automatically turned on (illuminance gradually increases over 5 seconds) to avoid sudden changes in brightness.

[0055] Emergency Mode: A dedicated red one-button emergency rescue system is located at the top of the control interface. When pressed, the system ignores all preset scenarios and forces all lights in the space (ceiling lights, side lights, and floor lights) to output maximum power (illuminance ≥700 lx, color temperature 6000K) to ensure no shadows or blind spots during emergency operations. This mode has the highest priority and can be manually canceled or automatically exited after 30 minutes.

[0056] S23, develop an ICU lighting control APP based on a mobile operating system, communicate with the central control system through the hospital's intranet Wi-Fi, and support real-time display, slider adjustment, and scene saving functions.

[0057] Develop an ICU lighting control app based on Android / iOS, which communicates with the central control system via the hospital's intranet Wi-Fi. The app uses the React Native cross-platform development framework, the backend uses a Spring Boot microservice architecture, and the database uses MySQL to store user configurations and operation logs.

[0058] The core functions of the app include, but are not limited to: Real-time display of current illuminance, color temperature and energy consumption data for each bed, with real-time data push using WebSocket; Slider adjustment: Nurses can drag the slider to continuously adjust the illuminance (0-100%) and color temperature (2000-6500 K). The adjustment command is sent to the edge computing gateway via the MQTT protocol. Save settings: Allows nurses to save the current settings as their personal presets, which will be automatically loaded the next time they log in; Dynamic mode start button; Emergency mode can be remotely triggered (secondary confirmation is required to prevent accidental activation).

[0059] The app interface features a high-contrast design with buttons measuring ≥44×44 pt, meeting the ergonomic requirements for rapid operation in a medical environment. The interface uses a dark background and highlighted text to minimize interference with dark adaptation during nighttime use.

[0060] Step S3 introduces natural elements by creating a mural lamp with a forest pattern. The background color is mainly warm, the blue light wave is reduced, and dynamic natural light is simulated.

[0061] In this embodiment, step S3 may specifically include the following steps: The S31 is a wall lamp designed with an ultra-thin LED backlight panel, a high-transmittance printed canvas, and an intelligent controller. It automatically adjusts the spectral weight according to the system time, and the night mode activates a "blue light filter" algorithm to reduce the relative energy of short-wavelength blue light in the 420-480nm range.

[0062] To address the common lack of natural lighting in ICUs, a forest-themed mural lamp was designed. The device consists of three parts: an ultra-thin LED backlight panel (thickness ≤12mm), a high-transmittance printed canvas (transmittance ≥85%), and an intelligent controller. The canvas pattern features warm-toned forest landscapes (such as birch forests and morning mist), and utilizes UV printing technology to ensure color saturation and weather resistance.

[0063] The principle of spectral modulation is that the human eye's non-visual system is most sensitive to blue light around 460 nm, and excessive exposure to blue light at night will inhibit melatonin secretion. The wall lamp controller automatically adjusts the spectral weight according to the system time: the daytime mode retains the blue light component to maintain alertness; the nighttime mode (after 18:00) activates the blue light filtering algorithm, reduces the duty cycle of the blue light LED channel through PWM dimming, and increases the output of amber LED (590 nm), so that the overall color temperature gradually decreases from 6500 K to 2700 K.

[0064] The spectral modulation algorithm is as follows: in: Set the duty cycle for the Blu-ray channel output; This refers to the basic duty cycle of the Blu-ray channel. This is the blue light attenuation coefficient, with a value ranging from 0 to 0.8; It is a time function, which linearly increases from 0 to 1 during the period from 18:00 to 06:00, and linearly decreases from 1 to 0 during the period from 06:00 to 18:00.

[0065] The S32 wall lamp has a built-in dynamic light effect algorithm that simulates the changes in natural light over time, including three stages: sunrise simulation, daytime fluctuation, and sunset simulation.

[0066] The wall lamp incorporates a dynamic natural light simulation algorithm to mimic the changing patterns of natural light over time. Sunrise Simulation (06:00-07:00): Illuminance gradually increases from 50 lx to 300 lx, and color temperature gradually increases from 2500 K to 4500 K, simultaneously illuminating LEDs in the cloud area to produce a glow effect. The gradient curve uses an S-shaped function to ensure a smooth and natural visual experience.

[0067] Daytime fluctuations (07:00-17:00): Based on real-time weather data (obtained via API to access local cloud cover), the brightness and color temperature of the mural lights are dynamically adjusted. Cloud cover data is obtained by calling the China Meteorological Administration's open platform API, updated once per hour. Supplemental lighting is increased (illuminance increased by 20%) during cloudy weather, and output is reduced during sunny weather to simulate dappled tree shadows.

[0068] Sunset simulation (17:00-18:00): Illuminance gradually decreases, color temperature gradually warms, and finally switches to a nighttime moonlight effect (illuminance 10lx, color temperature 4000 K, but blue light content reduced by 50%). The gradient algorithm uses cosine interpolation to ensure that the visual experience conforms to natural laws.

[0069] The psychological significance of this design lies in providing a sense of looking into the distance, thus alleviating the oppressive feeling and anxiety caused by enclosed spaces.

[0070] Step S4 involves evaluating and analyzing the user experience of the smart healthy lighting environment through semi-structured interviews and quantitative indicators to formulate optimization strategies.

[0071] In this embodiment, step S4 may specifically include the following steps: S41. After the lighting environment system is put into use, semi-structured interviews are conducted. After the interview recordings are transcribed, thematic analysis is used to extract key issues and optimization suggestions.

[0072] Three months after the lighting system was put into use, semi-structured interviews were conducted. The interviewees were ICU nurses, and purposive sampling was used to cover nurses of different ages, length of service, shifts and vision conditions. The sample size was based on the principle of information saturation (e.g., 12-20 people).

[0073] Interview outlines include, but are not limited to: How do you think the new lighting system affects your visual comfort? Have you encountered any glare, flicker, or dimming delay issues during use? How would you rate the frequency and effectiveness of the four scene modes? Does the wall lamp give you a feeling of "relaxation" or "pleasure"? After the interview recordings were transcribed into text, thematic analysis was used to extract key issues and optimization suggestions. The analysis software used was NVivo 12, and a hybrid coding method (theory-driven coding and data-driven coding) was employed. For example, if multiple nurses reported dizziness due to excessively fast movement speed in the dynamic mode, the motion trajectory parameters need to be adjusted in the next stage of optimization.

[0074] S42. Establish a quantitative evaluation system that includes four core indicators: visual fatigue score, single intravenous puncture completion time, risk event incidence rate, and satisfaction survey.

[0075] Establish a quantitative evaluation system that includes four core indicators, and adopt a self-comparison design before and after.

[0076] Visual fatigue scoring: The Visual Fatigue Scoring Scale compiled by Lin Yanyan et al. was used, which includes items such as dry eyes, eye pain, photophobia, and foreign body sensation. A 5-point frequency-intensity scoring system was used (0 = never, 4 = always). The on-duty nursing team leader assessed the visual fatigue before and after the shift, and the difference was taken as the cumulative fatigue index.

[0077] Single venipuncture completion time: Record the time from preparation to successful puncture, and take the average of all punctures performed during the shift. A stopwatch was used for timing, accurate to 0.1 seconds. Venipuncture requires extremely high control over illumination and shadows, making it a sensitive indicator of lighting environment quality.

[0078] Risk event incidence: Risk events are defined as multiple failed intravenous punctures (≥3 times), errors in electronic medical record writing, errors in pressure injury assessment, and omissions in medical order execution. The daily occurrence rate is recorded by the department's quality control officer, and the calculation formula is as follows: .

[0079] Satisfaction Survey: A self-designed satisfaction questionnaire was used, including three dimensions: "lighting efficiency and brightness," "lighting comfort," and "ease of use," using a 5-point Likert scale (1 = very dissatisfied, 5 = very satisfied). The satisfaction calculation formula is as follows: .

[0080] S43, using paired t-tests and χ² tests 2 The test results are statistically analyzed, and an optimization strategy is formulated based on the statistical results.

[0081] Data analysis was performed using SPSS 22.0. Quantitative data are expressed as mean ± standard deviation (SD). Data expressed as percentages (%) were compared using paired t-tests to determine differences before and after the intervention. Inspection. Inspection level. .

[0082] Sample size estimation: Based on the effect size of visual fatigue score improvement in the pre-trial, a self-paired t-test was performed using PASS 15.0 software to estimate the sample size. α=0.05, statistical power 1-β=0.80, and considering a 20% loss to follow-up rate, 30 nurses were required to be included in each group. The control group consisted of 30 nurses in our hospital's ICU before the use of the smart health lighting environment from July to October 2023, and the observation group consisted of 30 nurses after the use of the smart health lighting environment from January to March 2024.

[0083] Quality control measures: Investigators strictly screen research subjects according to inclusion and exclusion criteria; trained investigators are required to accurately complete the distribution, collection, and verification of questionnaires; questionnaires are checked on the spot when they are collected, and any missing or incomplete items are immediately required to be filled in; all data are entered and verified by two people.

[0084] Optimization strategies are developed based on statistical results. For example, if statistics show that the improvement in visual fatigue scores for night shift nurses is not significant, then the night shift lighting plan needs to be optimized (such as further reducing illuminance or increasing the frequency of dynamic modes); if the reduction in puncture time is not statistically significant, then it is necessary to check whether the operating table meets the 700 lx requirement.

[0085] The beneficial effects of implementing this embodiment are: (1) Significantly relieved visual fatigue. Clinical trial results showed that after the establishment of the smart health light environment, the visual fatigue score of nurses in the observation group after their shift (18.3±3.2 points) was significantly lower than that in the control group (26.7±4.1 points), and the difference was statistically significant (P<0.05). This indicates that the comprehensive intervention of dynamic adjustment, blue light filtering and wall lamps effectively reduced the nurses' eye discomfort.

[0086] (2) Improved work efficiency. The time for a single intravenous puncture by nurses in the observation group was shortened from (68.4±12.5) seconds in the control group to (52.1±9.3) seconds (P<0.05). This improvement was attributed to the improved visual clarity brought about by the standard illumination of the work surface, the elimination of shadows, and personalized lighting.

[0087] (3) Reduced risk events. The incidence of risk events among nurses in the observation group (3.2%) was significantly lower than that in the control group (7.8%) (P<0.05). A good lighting environment reduces judgment errors caused by visual fatigue, especially in emergency situations at night, where the stability and response speed of the light environment are directly related to patient safety.

[0088] (4) Satisfaction was significantly improved. Nurses' satisfaction with the lighting environment increased from 68.3% in the control group to 91.7% in the observation group (P<0.05). Among them, the "ease of use" dimension showed the most significant improvement, reflecting that one-click scene switching and personalized APP adjustment effectively met nurses' needs for self-control.

[0089] (5) Protects the circadian rhythm. Through dynamic dimming and spectral modulation, a smooth transition is achieved between daytime circadian rhythm stimulation (CS) ≥ 0.3 and nighttime CS ≤ 0.1, effectively protecting the nurses' circadian rhythm health.

[0090] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0092] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0093] Example 2 Further reference Figure 2 As a response to the above Figure 1 The present invention provides an embodiment of a smart healthy light environment construction device, which, in accordance with the method shown, provides an embodiment of such a device. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0094] like Figure 2 As shown, the smart healthy light environment construction device 50 described in this embodiment includes: a layout module 51, a construction module 52, a simulation module 53, and an optimization module 54. Wherein: Layout module 51 is used to set the lighting layout according to the space area of ​​the ICU room, so that the vertical illuminance to the eyes during the day and night is at least 350 lx, and the illuminance of each operating table in the space is at least 700 lx. Module 52 is used to connect all the lighting fixtures installed in the design space to the ICU lighting system. The system uses multiple algorithms such as rule reasoning, data analysis and artificial intelligence to automatically adjust the light effect according to the work needs of ICU nurses, and at the same time simulates the changes in light throughout the day based on the 24-hour natural light level in the local area. Simulation module 53 is used to introduce natural elements by creating a mural lamp with a forest pattern. The background color is mainly warm, the blue light wave is reduced, and dynamic natural light is simulated. The optimization module 54 is used to evaluate and analyze the user experience of the smart healthy light environment through semi-structured interviews and quantitative indicators, and to formulate optimization strategies.

[0095] The beneficial effects of implementing this embodiment are: improved work efficiency, reduced risk events, significantly improved satisfaction, and protection of circadian rhythms.

[0096] Example 3 To address the aforementioned technical problems, embodiments of the present invention also provide a computer device. Please refer to [link / reference needed]. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0097] The aforementioned computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected via a system bus. It should be noted that only the computer device 6 with components 61, 62, and 63 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0098] The aforementioned computer devices can be desktop computers, laptops, handheld computers, and cloud servers, among other computing devices. These devices can facilitate human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0099] The aforementioned memory 61 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the aforementioned memory 61 may be an internal storage unit of the aforementioned computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the aforementioned memory 61 may also be an external storage device of the aforementioned computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the aforementioned memory 61 may also include both the internal storage unit and its external storage device of the aforementioned computer device 6. In this embodiment, the aforementioned memory 61 is typically used to store the operating system and various application software installed on the aforementioned computer device 6, such as computer-readable instructions for a smart healthy light environment construction method. In addition, the aforementioned memory 61 can also be used to temporarily store various types of data that have been output or will be output.

[0100] In some embodiments, the processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions stored in the memory 61 or to process data, for example, to execute computer-readable instructions for the intelligent healthy light environment construction method described above.

[0101] The network interface 63 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 6 and other electronic devices.

[0102] The beneficial effects of implementing this embodiment are: improved work efficiency, reduced risk events, significantly improved satisfaction, and protection of circadian rhythms.

[0103] Example 4 The present invention also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the smart healthy light environment construction method described above.

[0104] The beneficial effects of implementing this embodiment are: improved work efficiency, reduced risk events, significantly improved satisfaction, and protection of circadian rhythms.

[0105] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0106] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A method for constructing a smart and healthy lighting environment, characterized in that, Includes the following steps: S1. The lighting layout is set according to the space area of ​​the ICU room to ensure that the vertical illuminance to the eyes is at least 350 lx during the day and night, and the illuminance of each operating table in the space is at least 700 lx. S2, all the lighting fixtures installed in the design space are connected to the ICU lighting system. The system uses multiple algorithms such as rule reasoning, data analysis and artificial intelligence to automatically adjust the lighting effect according to the work needs of ICU nurses, and at the same time simulate the changes in light throughout the day based on the 24-hour natural light level of the local area. S3 introduces natural elements by creating a mural lamp with a forest pattern. The background color is mainly warm, reducing blue light waves and simulating dynamic natural light. S4. Through semi-structured interviews and quantitative indicator evaluation and analysis of the user experience of the smart healthy lighting environment, optimization strategies are formed.

2. The method for constructing a smart and healthy lighting environment according to claim 1, characterized in that, S1, the step of setting the lighting layout according to the space area of ​​the ICU room to ensure that the vertical illuminance to the eyes is at least 350 lx during both daytime and nighttime periods, and that the illuminance on each operating table in the space is at least 700 lx, specifically includes: S11, Measure the geometric dimensions of the ICU room, determine the multi-level lighting configuration scheme of ceiling lights, side lights, and floor lights, so that all lights are LED and the lighting circuits for each bed are designed separately and can be adjusted independently; S12, the lighting location is optimized using a point light source illuminance calculation model. The calculation formula is as follows: Where E is the illuminance of the illuminated surface in lux (lx), I is the luminous intensity of the light source in the specified direction in candela (cd), d is the straight-line distance between the light source and the illuminated surface in meters (m), and θ is the angle of incidence of the light ray, that is, the angle between the direction of the light ray and the normal of the illuminated surface. S13 adopts a three-level coordinated mode of ceiling light basic lighting, side light supplementary lighting and ground light local lighting. The side lights illuminate the wall at an angle of 45° to 60°, and the ground lights are installed at a height of ≥0.3m from the ground.

3. The method for constructing a smart healthy lighting environment according to claim 1, characterized in that, S2 involves connecting all the lighting fixtures installed in the design space to the ICU lighting system. This system utilizes multiple algorithms, including rule-based reasoning, data analysis, and artificial intelligence, to automatically adjust the lighting effect according to the work needs of ICU nurses. The specific steps of simulating daily light changes based on the local 24-hour natural light level include: S21 constructs a three-layer architecture including a perception layer, a decision layer, and an execution layer. The perception layer collects spatial illuminance data in real time through distributed light sensors. The decision layer deploys an edge computing gateway with a built-in rule inference engine, data analysis module, and lightweight artificial intelligence algorithm. The execution layer uses DALI-2 driver power supply to support single lamp addressing and parameter adjustment. S22 features a graphical control interface at the nurse station and on mobile terminals, providing four preset scenario modes: daytime mode, nighttime mode, dynamic mode, and night light mode. It also has an independently set one-click emergency mode for emergency rescue. S23, develop an ICU lighting control APP based on a mobile operating system, communicate with the central control system through the hospital's intranet Wi-Fi, and support real-time display, slider adjustment, and scene saving functions.

4. The method for constructing a smart healthy lighting environment according to claim 1, characterized in that, The steps in S3, which introduce natural elements by creating a forest-patterned mural lamp with a warm background color, reducing blue light waves, and simulating dynamic natural light, specifically include: The S31 is a wall lamp designed with an ultra-thin LED backlight panel, a high-transmittance printed canvas, and an intelligent controller. It automatically adjusts the spectral weight according to the system time, and the night mode activates the "blue light filter" algorithm to reduce the relative energy of short-wavelength blue light in the 420-480nm range. The S32 wall lamp has a built-in dynamic light effect algorithm that simulates the changes in natural light over time, including three stages: sunrise simulation, daytime fluctuation, and sunset simulation.

5. The method for constructing a smart healthy lighting environment according to claim 1, characterized in that, The steps in S4, which involve analyzing the user experience of the smart healthy lighting environment through semi-structured interviews and quantitative indicators to formulate optimization strategies, specifically include: S41. After the lighting environment system is put into use, conduct semi-structured interviews. After the interview recordings are transcribed, use the topic analysis method to extract key issues and optimization suggestions. S42, establish a quantitative evaluation system that includes four core indicators: visual fatigue score, single intravenous puncture completion time, risk event incidence rate, and satisfaction survey. S43, using paired t-tests and χ² tests 2 The test results are statistically analyzed, and an optimization strategy is formulated based on the statistical results.

6. The method for constructing a smart healthy lighting environment according to claim 3, characterized in that, In step S21, the rule reasoning engine uses the IF-THEN rule base and is implemented based on the Drools rule engine; the data analysis module uses the K-means clustering algorithm to cluster the dimming data and identify the typical dimming patterns of each nurse; the artificial intelligence algorithm uses a Long Short-Term Memory (LSTM) network to predict the changes in illuminance demand in the next hour. The network structure includes an input layer, an LSTM layer, and a fully connected layer. The input features include the current time, season, weather conditions, bed occupancy rate, and operation logs from the same period in the past. The output is the recommended illuminance value for each area.

7. The method for constructing a smart healthy lighting environment according to claim 3, characterized in that, In step S22, the daytime mode is set to a ceiling light illuminance of 1000-1500 lx and a color temperature of 5500-6500 K, with side lights supplementing the vertical illuminance to ≥500 lx; ​​the nighttime mode is set to a ceiling light illuminance of 300-500 lx and a color temperature of 3000-3500 K, with the floor lights in low brightness mode; the dynamic mode is set to a wall light or a designated screen displaying slowly moving light spots or natural landscape images to guide the eye to follow the movement, with a trajectory speed of 15° / s and a duration of 3 minutes; the night light mode is set to a floor light and corridor guide light illuminance of ≤20 lx and a color temperature of ≤2700 K; the emergency mode forces all space lights to output at maximum power, with an illuminance of ≥700 lx and a color temperature of 6000 K.

8. A smart and healthy light environment construction device, characterized in that, include: The layout module is used to set the lighting layout according to the space area of ​​the ICU room, so that the vertical illuminance to the eyes during the day and night is at least 350 lx, and the illuminance of each operating table in the space is at least 700 lx. The module is used to connect all the lighting fixtures installed in the design space to the ICU lighting system. The system uses multiple algorithms such as rule reasoning, data analysis and artificial intelligence to automatically adjust the lighting effect according to the work needs of ICU nurses, and at the same time simulate the changes in light throughout the day based on the 24-hour natural light level in the local area. The simulation module is used to introduce natural elements by creating a mural lamp with a forest pattern. The background color is mainly warm, reducing blue light waves and simulating dynamic natural light. The optimization module is used to evaluate and analyze the user experience of the smart healthy lighting environment through semi-structured interviews and quantitative indicators, and to formulate optimization strategies.

9. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the smart healthy light environment construction 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 computer-readable instructions, which, when executed by a processor, implement the steps of the smart healthy light environment construction method as described in any one of claims 1 to 7.

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