Intelligent light control method and device and storage medium

Abstract

The invention discloses an intelligent light control method and device and a storage medium, and relates to the technical field of intelligent light control, the intelligent light control method comprises the steps that user identification information sent by user terminal equipment through near field communication is received, and corresponding behavior habit information is determined based on the user identification information; the user terminal equipment is positioned through a sensor network, and the position of a user is determined; according to the position of the user and the behavior habit information, light control parameters of a functional area where the user is located currently are determined; according to the light control parameters, a control instruction is sent to target light equipment, and an instruction execution result fed back by the target light equipment is received in real time. According to the method and the device, the behavior habits and the real-time position information of the user are combined, so that intelligent light dynamic adjustment based on personalized requirements is realized, and the accuracy and the response speed of light control are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent lighting control technology, and in particular to intelligent lighting control methods, devices and storage media. Background Art

[0002] With the development of smart homes, intelligent lighting control systems are gradually shifting from traditional manual control to intelligent systems. While current lighting control systems can be remotely controlled via mobile apps or voice assistants, most are limited to simple on / off operations and brightness adjustment, lacking the ability to deeply learn and dynamically respond to user behavior patterns. For example, as users move around a room, current lighting control systems are unable to proactively detect their identity and behavior, making it difficult to adapt lighting preferences to specific functional areas.

[0003] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide an intelligent lighting control method, device and storage medium, aiming to solve the technical problem of being unable to generate adaptive lighting parameters based on the user's real-time location and behavioral habits.

[0005] To achieve the above objectives, an embodiment of the present application provides an intelligent lighting control method, which includes: Receiving user identification information sent by a user terminal device via near field communication, and determining corresponding behavior habit information based on the user identification information; Positioning the user terminal device through a sensor network to determine the user's location; Determining lighting control parameters for the functional area where the user is currently located based on the user's location and the behavioral habit information; According to the lighting control parameters, a control instruction is sent to the target lighting device, and the instruction execution result fed back by the target lighting device is received in real time.

[0006] In one embodiment, the step of receiving user identification information sent by a user terminal device via near field communication and determining corresponding behavior habit information based on the user identification information includes: Obtaining the user's lighting parameter adjustment record and lighting usage time through the user identification information; Determining lighting parameter settings corresponding to the functional areas in different time periods according to the lighting parameter adjustment records and the lighting usage time; The behavioral habit information of the user is determined according to the lighting parameter settings.

[0007] In one embodiment, the step of determining the lighting control parameters of the functional area where the user is currently located based on the user location and the behavioral habit information includes: Determining the functional area where the user is currently located according to the user's location; Obtain basic lighting parameters and real-time ambient lighting data corresponding to the functional area; According to the behavioral habit information and the real-time ambient lighting data, the basic lighting parameters are adjusted to generate the lighting control parameters corresponding to the functional area.

[0008] In one embodiment, the step of adjusting the basic lighting parameters according to the behavioral habit information and the real-time ambient lighting data to generate the lighting control parameters corresponding to the functional area includes: According to the behavioral habit information, obtaining the lighting parameter settings of the functional area corresponding to the current time period; Calculating a lighting parameter deviation value between the lighting parameter setting and the basic lighting parameter; Calculating ambient lighting compensation according to the real-time ambient lighting data; Performing weighted fusion on the lighting parameter difference value and the ambient light compensation to determine an adjustment coefficient of the basic lighting parameter; The basic lighting parameters are adjusted based on the adjustment coefficient to generate the lighting control parameters corresponding to the functional area.

[0009] In one embodiment, the step of determining the lighting control parameters of the functional area where the user is currently located based on the user location and the behavioral habit information includes: Obtaining historical movement trajectory data of the user; Predicting the user's expected movement path and target functional area based on the user's location, the behavioral habit information, and / or the historical movement trajectory data; The lighting control parameters of the target lighting device corresponding to the expected movement path and the target functional area are determined according to the behavioral habit information.

[0010] In one embodiment, the step of predicting the user's expected movement path and target functional area based on the user's location, the behavioral habit information, and / or the historical movement trajectory data includes: obtaining the activity type of the user in the functional area through a sensor network; generating a state feature vector of the user according to the user location and the activity type; Determining the user's historical behavior pattern based on the behavior habit information and / or the historical movement trajectory data; Comparing the state feature vector with the historical behavior pattern to determine a historical movement trajectory that matches the current state of the user; The expected movement path of the user and the target functional area are determined based on the extension direction of the historical movement trajectory and the probability distribution of the target functional area.

[0011] In one embodiment, after the step of determining the expected movement path of the user and the target functional area based on the extension direction of the historical movement trajectory and the probability distribution of the target functional area, the intelligent lighting control method further includes: When the user moves, the target lighting device on the expected movement path and corresponding to the target functional area is adjusted, and the adjustment includes: adjusting the change rate and change amplitude of the lighting control parameters corresponding to the target lighting device according to the user's movement speed and the distance from the target lighting device.

[0012] In one embodiment, the intelligent lighting control method further includes: Acquiring biological data collected by a wearable device, and identifying the user's current physiological state and emotional state based on the biological data; Determining the adjusted lighting control parameters according to the physiological state and the emotional state in combination with a corresponding lighting control parameter mapping table; The adjusted lighting control parameters are sent to the target lighting device.

[0013] An embodiment of the present application further provides an intelligent lighting control device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent lighting control method described above.

[0014] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent lighting control method described above are implemented.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application receives user identification information sent by a user terminal device through near-field communication, and determines the corresponding behavioral habit information based on the identification information, so that the intelligent lighting control system can quickly identify the user's identity and obtain the user's personalized lighting preference data. At the same time, the user terminal device is located using a sensor network to determine the user's location, thereby realizing real-time perception of the user's functional area. On this basis, the user's location is combined with behavioral habit information to accurately determine the lighting control parameters of the user's current functional area, effectively solving the problem that traditional lighting control systems cannot adaptively adjust lighting parameters based on the user's real-time location and personalized behavioral habits. Finally, by sending control instructions to the target lighting device and receiving the instruction execution results in real time, the accuracy and effectiveness of the lighting control are ensured. In the above manner, this application realizes the dynamic adjustment of lighting parameters according to the user's real-time location status, providing users with a personalized lighting experience, while improving the intelligence of lighting control and reducing the frequency of manual adjustment by users. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the first embodiment of the intelligent lighting control method involved in the embodiment of the present application; Figure 2 This is a flow chart of a second embodiment of the intelligent lighting control method according to an embodiment of the present application; Figure 3 This is a flow chart of a third embodiment of the intelligent lighting control method according to an embodiment of the present application; Figure 4 This is a flow chart of a fourth embodiment of the intelligent lighting control method according to an embodiment of the present application; Figure 5 This is a flow chart of a fifth embodiment of the intelligent lighting control method according to an embodiment of the present application; Figure 6 This is a flow chart of a sixth embodiment of the intelligent lighting control method according to an embodiment of the present application; Figure 7 This is a schematic diagram of the structure of the intelligent lighting control device involved in the embodiment of the present application.

[0017] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0019] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0020] With the development of smart homes, intelligent lighting control systems are gradually shifting from traditional manual control to intelligent systems. While current lighting control systems can be remotely controlled via mobile apps or voice assistants, most are limited to simple on / off operations and brightness adjustment, lacking the ability to deeply learn and dynamically respond to user behavior patterns. For example, as users move around a room, current lighting control systems are unable to proactively detect their identity and behavior, making it difficult to adapt lighting preferences to specific functional areas.

[0021] In view of the above problems, the present application proposes an intelligent lighting control method, which receives user identification information sent by a user terminal device through near-field communication, and determines corresponding behavioral habit information based on the user identification information; locates the user terminal device through a sensor network to determine the user's position; determines the lighting control parameters of the functional area where the user is currently located based on the user's position and the behavioral habit information; sends control instructions to the target lighting device based on the lighting control parameters, and receives the instruction execution results fed back by the target lighting device in real time.

[0022] It should be noted that the execution subject of this embodiment can be an intelligent lighting control device with data processing, network communication, and program execution functions, such as a smart home central control host, a cloud server, etc., or an electronic device capable of implementing the above functions, an intelligent lighting control system, etc. The following uses the intelligent lighting control system as an example to illustrate this embodiment and the following embodiments.

[0023] The intelligent lighting control method of the first embodiment proposed in this application, please refer to Figure 1 The method includes steps S10 to S40: Step S10: receiving user identification information sent by the user terminal device via near field communication, and determining corresponding behavior habit information based on the user identification information.

[0024] It's important to note that Near Field Communication (NFC) is a short-range, high-frequency wireless communication technology that allows electronic devices to conduct contactless, point-to-point data transmission over relatively short distances. User identification information, such as the device ID and serial number, is used to uniquely identify a user. This information can be stored in the NFC module of a mobile phone or a smart wearable device.

[0025] Additionally, it should be noted that behavioral habit information refers to a user's pre-set lighting preferences, such as brighter, warmer light for reading and dimmer, cooler light for resting. Alternatively, machine learning can be used to analyze a user's historical lighting preferences. This behavioral habit information can be stored in a cloud server or on a local edge computing device.

[0026] In one feasible embodiment, the intelligent lighting control system includes a near-field communication (NFC) receiving module for establishing a NFC connection with a user terminal device. When a user enters a room with a smartphone, smart wearable device, or other terminal device equipped with a built-in NFC chip, the user terminal device automatically triggers the NFC function and transmits pre-stored user identification information to the intelligent lighting control system. Upon receiving the user identification information, the intelligent lighting control system queries a pre-established user information database to match the user's behavioral habits with the user identification information, thereby triggering adaptive adjustment of the lighting control parameters.

[0027] Step S20: Positioning the user terminal device through a sensor network to determine the user's location.

[0028] It's important to note that a sensor network consists of multiple sensor nodes distributed throughout an indoor space. These nodes can locate user devices using various sensor technologies (such as Bluetooth positioning, Wi-Fi positioning, infrared positioning, and radar positioning). These sensor nodes sense the signals emitted by user devices and determine their location by calculating parameters such as signal strength and arrival time, ultimately determining the user's location.

[0029] In one feasible implementation, a sensor network consisting of multiple sensor nodes is deployed indoors. User devices, with Bluetooth, NFC sensing, or Wi-Fi enabled, continuously transmit signals. Upon receiving the signals, the sensor network calculates parameters such as signal strength and arrival time, and uses algorithms such as triangulation to locate the user's device, thereby determining the user's location.

[0030] In another feasible implementation, the indoor space is divided into multiple functional areas, such as living room, bedroom, kitchen, study, bathroom, and hallway, based on its purpose and layout. Each functional area is assigned a unique area identifier. An indoor space map is created, and a sensor network is used to scan and model the indoor space, acquiring its geometric structure and feature information to construct a three-dimensional map of the indoor space. The location and boundary coordinates of each functional area are then marked on the indoor space map. The functional areas are then associated with the indoor space map to determine the mapping relationship between the functional areas and the indoor space coordinates.

[0031] In this implementation, when a user enters an indoor space, the user's terminal device is located via a sensor network to determine the user's location. The user's location coordinates are then matched with the boundary coordinates of the functional area. Based on the matching results, the functional area in which the user is located is determined. Finally, corresponding lighting control parameters are generated based on the lighting requirements of the functional area.

[0032] Step S30: determining the lighting control parameters of the functional area where the user is currently located according to the user's location and the behavioral habit information.

[0033] It should be noted that functional areas refer to different indoor spaces, such as the living room, bedroom, and study, and each area has corresponding lighting requirements. Lighting control parameters include but are not limited to light brightness, color, lighting effects, and on / off status. For example, if a user is in the living room and tends to dim the lights when watching TV, the lights in the living room will be dimmed based on the user's behavioral information when the user is in the living room.

[0034] It is understandable that since the lighting requirements of different functional areas are different, determining the lighting control parameters based on the user's location and behavioral habits information can avoid the lighting control parameters not meeting the user's personalized needs and the current functional area, and improve the adaptability and comfort of lighting control.

[0035] In a feasible implementation, after determining the user's location and the user's behavioral habit information, the functional area in which the user is currently located is first determined based on the user's location. Then, based on the purpose of the functional area and the user's behavioral habit information in the functional area, the lighting control parameters are determined. For example, if the user is currently in the bedroom and the current time period is the evening rest time, based on the user's behavioral habit information, it is known that the user prefers darker lights when resting, the intelligent lighting control system will set the brightness in the lighting control parameters to a lower value. If the user is currently in the study and reading, based on the user's behavioral habit information, it is known that the user prefers higher brightness and warm colors when reading, the intelligent lighting control system will increase the brightness in the lighting control parameters and set the light to a warm color, so as to ensure that the lighting adjustment achieves the expected effect and meets the user's personalized needs when reading in the study.

[0036] Step S40: sending a control instruction to the target lighting device according to the lighting control parameters, and receiving the instruction execution result fed back by the target lighting device in real time.

[0037] It should be noted that the target lighting device refers to the lighting device that needs to be controlled in the functional area where the user is currently located. The control instruction is a signal generated according to the lighting control parameters, which is used to instruct the lighting device to perform corresponding actions, such as adjusting the brightness, changing the color, or changing the on / off status of the lighting device.

[0038] In this embodiment, the target lighting device executes the operation after receiving the control instruction and feeds back the instruction execution result so that the intelligent lighting control system can timely understand the operating status of the lighting device, avoiding poor lighting control effect due to the inability to know whether the lighting device has correctly executed the control instruction, thereby improving the stability of the intelligent lighting control system.

[0039] In one feasible implementation, control instructions are generated based on the determined lighting control parameters and transmitted to target lighting devices in the corresponding functional areas via a wireless communication module (e.g., ZigBee, Wi-Fi, etc.). Upon receiving the control instructions, the target lighting devices adjust parameters such as brightness and color temperature according to the instructions and provide feedback (e.g., successful adjustment, current lighting status, etc.) to the intelligent lighting control system via the wireless communication module. The intelligent lighting control system receives feedback on the instruction execution results in real time, analyzes, and processes them. If the instruction execution results do not meet expectations, the intelligent lighting control system can resend the control instructions and adjust the current lighting parameters to ensure that the target lighting devices operate as expected.

[0040] Based on the above embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 2 In the intelligent lighting control method, step S10 includes steps S11 to S13: Step S11: Obtain the user's lighting parameter adjustment record and lighting usage time through the user identification information.

[0041] It should be noted that lighting parameter adjustment records refer to the historical records of lighting parameter adjustments made by users in various functional areas during the use of indoor lighting. Light usage time refers to the specific time period during which users used lights in various functional areas, including daily active hours and time spent in each functional area.

[0042] Daily active hours refer to the time period during which users use lights throughout the day. Taking into account the differences in user habits, daily active hours may be continuous or multiple discrete time periods (for example, light usage on weekdays is fragmented). Dwell time in each functional area refers to the specific time and duration of light use in different functional areas.

[0043] By analyzing the daily active hours and the stay periods in each functional area, we can understand the user's activity habits at different times and in different functional areas, so that we can adaptively adjust the lighting parameters based on the user's current location and behavioral habit information.

[0044] Step S12: determining the lighting parameter settings corresponding to the functional areas in different time periods according to the lighting parameter adjustment records and the lighting usage time.

[0045] It should be noted that the lighting parameter settings are determined based on the user's behavior habits and time patterns. Since the user's lighting usage habits vary with time periods, step S12 can avoid the singleness of lighting control and improve the personalization of lighting control.

[0046] In this embodiment, by analyzing the user's lighting parameter adjustment records and lighting usage time in the past, the user's preference settings for lighting parameters such as light brightness and color temperature in each functional area in different time periods can be determined.

[0047] In a feasible implementation, a cluster analysis algorithm is used to classify the lighting parameter adjustment records of each functional area in a similar time period, and the lighting parameter setting values that are more concentrated in the corresponding functional area in each time period are calculated, so as to determine the lighting parameter settings corresponding to the corresponding functional area in different time periods.

[0048] In another feasible implementation, a time-based lighting parameter change model is constructed using a time series analysis algorithm, combined with users' lighting parameter adjustment records and usage time. This model considers the impact of different day types (weekdays, weekends, etc.), seasonal changes, and weather changes on lighting usage habits, dynamically predicting users' lighting parameter preferences in various functional areas and at different time periods. For example, based on historical data, users prefer higher lighting brightness on rainy days than on sunny days. Therefore, the intelligent lighting control system can automatically adjust the lighting brightness to a higher value on rainy days, achieving more intelligent lighting parameter settings.

[0049] Step S13: Determine the behavioral habit information of the user according to the lighting parameter settings.

[0050] It should be noted that the lighting parameter settings reflect the user's preferences for various lighting parameters at different times and in different functional areas. By integrating these lighting parameter settings, the user's lighting usage patterns are obtained, which is the user's behavioral habit information.

[0051] In one feasible implementation, a user behavior feature matrix is constructed, with different functional areas serving as the row dimension and different time periods (e.g., set to one hour) serving as the column dimension. Each cell is populated with the lighting parameter settings for the corresponding functional area and time period, such as brightness and color temperature. Furthermore, a weighting mechanism can be introduced to assign higher weights to frequently occurring lighting parameter settings, highlighting user preferences.

[0052] In another feasible implementation, a user behavior habit model is constructed by first collecting the user's lighting parameter settings for each functional area and time period, including specific values for lighting parameters such as brightness and color temperature. These lighting parameters are then categorized and organized by functional area and time period to generate lighting parameter data. Next, a machine learning algorithm, such as a decision tree, support vector machine, or neural network, is used to train the lighting parameter data. During the training process, the functional area and time period are used as input features, and the corresponding lighting parameter settings are used as output labels. Through multiple iterations of training, the model learns the user's lighting preference patterns in different scenarios, ultimately resulting in a trained user behavior habit model. This predicts the user's lighting parameter preferences based on the user's current time period and functional area. Optionally, in addition to the basic functional area and time period information, environmental time variables such as date type, season, and weather can be introduced as additional input features during the training process to further improve the prediction accuracy of the user behavior habit model and enable it to more comprehensively reflect the user's lighting usage habits under different environmental conditions.

[0053] Based on the above embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 3 In the intelligent lighting control method, step S30 includes steps S31 to S33: Step S31: Determine the functional area where the user is currently located according to the user's location.

[0054] In this embodiment, the functional area where the user is currently located can be determined by matching the user's position with the boundary coordinates of the predefined functional area.

[0055] It is understandable that, since the user's position indoors changes dynamically, step S31 can avoid the problem of mismatch between lighting control and the user's actual position, thereby improving the accuracy of lighting control.

[0056] Step S32: Obtain basic lighting parameters and real-time ambient lighting data corresponding to the functional area.

[0057] It should be noted that basic lighting parameters are pre-set default lighting parameter settings based on the purpose and needs of the functional area. Real-time ambient lighting data refers to the current ambient light intensity and can be obtained by light sensors installed indoors. Light sensors can monitor changes in ambient light in real time and transmit this data to the intelligent lighting control system.

[0058] It is understandable that, since the ambient lighting conditions will affect the actual effect of the lighting, performing step S32 can avoid the singleness of the lighting control, thereby improving the adaptability and comfort of the lighting control.

[0059] Step S33: adjusting the basic lighting parameters according to the behavioral habit information and the real-time ambient lighting data, and generating the lighting control parameters corresponding to the functional area.

[0060] In this embodiment, the intelligent lighting control system comprehensively considers the user's behavioral habits and real-time ambient light data to adjust basic lighting parameters. For example, if the ambient light is dim, the intelligent lighting control system will appropriately increase the light brightness based on the user's behavioral habits.

[0061] It should be noted that the adjustment of lighting control parameters is achieved through an algorithm, which can dynamically adjust the lighting parameters according to the user's behavior habits and changes in ambient light.

[0062] It is understandable that, since the user's behavior habits and ambient lighting conditions are dynamically changing, performing step S33 can avoid the fixation of lighting control, thereby improving the flexibility and personalization of lighting control.

[0063] In a feasible implementation, step S33 includes steps S331 to S335: Step S331: according to the behavioral habit information, obtain the lighting parameter settings of the functional area corresponding to the current time period.

[0064] In this embodiment, the current time and the functional area where the user is located are first obtained, and then the lighting parameter settings of the functional area in the current time period are determined based on the behavioral habit model or in combination with the user's historical lighting parameter adjustment records.

[0065] Step S332: Calculating a lighting parameter deviation value between the lighting parameter setting and the basic lighting parameter.

[0066] In this embodiment, the lighting parameter deviation value is determined by comparing the basic lighting parameters corresponding to the functional area with the lighting parameter settings. Specifically, the basic lighting parameters are compared with each lighting parameter item in the lighting parameter settings to calculate the deviation value of each lighting parameter, including parameters such as brightness and color temperature. For the brightness parameter, the user's preferred brightness is obtained from the lighting parameter settings and compared with the basic brightness in the basic lighting parameters to determine the direction and degree of brightness deviation. For the color temperature parameter, the user's preferred color temperature is obtained from the lighting parameter settings and compared with the basic color temperature in the basic lighting parameters to determine the direction and degree of color temperature deviation.

[0067] Through the above methods, the intelligent lighting control system can quantify the differences between user preferences and basic settings, providing a basis for adjusting lighting parameters.

[0068] Step S333: Calculate ambient lighting compensation according to the real-time ambient lighting data.

[0069] Real-time ambient light data can be collected by light sensors installed indoors, including information such as light intensity and color temperature. Ambient light compensation is calculated based on the difference between the real-time ambient light data and the preset ideal lighting conditions.

[0070] Specifically, the expected light intensity range and expected color temperature range are first pre-set. Then, the real-time light intensity and color temperature decibels collected are compared with the expected light intensity range and expected color temperature range. If the real-time light intensity is lower than the expected light intensity range, the light intensity compensation value that needs to be increased is calculated; if the real-time light intensity is higher than the expected light intensity range, the light intensity compensation value that needs to be reduced is calculated. Similarly, if the real-time color temperature is lower than the expected color temperature range, the color temperature compensation value that needs to be increased is calculated; if the real-time color temperature is higher than the expected color temperature range, the color temperature compensation value that needs to be reduced is calculated.

[0071] Step S334: performing weighted fusion on the lighting parameter difference value and the ambient light compensation to determine the adjustment coefficient of the basic lighting parameter.

[0072] In this embodiment, weighted fusion is achieved by assigning different weights to the lighting parameter deviation value and the ambient light compensation. Different weights are set for different lighting parameters based on user preferences and the importance of ambient light conditions to lighting control.

[0073] Specifically, based on pre-set lighting parameter weights, each lighting parameter deviation and ambient light compensation value is multiplied by the corresponding weight coefficient, and the weighted results are then combined. For the brightness parameter, the brightness deviation value is multiplied by the brightness weight, and the light intensity compensation is multiplied by the ambient light weight. Finally, the weighted brightness and light intensity compensation results are added together to obtain the brightness adjustment coefficient. The same method is used to calculate the corresponding adjustment coefficients for other lighting parameters.

[0074] Step S335: adjusting the basic lighting parameters based on the adjustment coefficients to generate the lighting control parameters corresponding to the functional areas.

[0075] In this embodiment, the basic lighting parameters are adjusted based on the adjustment coefficients for each lighting parameter obtained in step S334. In specific implementations, each lighting parameter in the basic lighting parameters is calculated with the corresponding adjustment coefficient to obtain the final lighting control parameters. For example, for brightness parameters, the basic brightness is added or subtracted from the brightness adjustment coefficient to obtain the adjusted brightness value; for color temperature parameters, the basic color temperature is added or subtracted from the color temperature adjustment coefficient to obtain the adjusted color temperature value. Ultimately, the adjusted lighting parameters are transmitted to the target lighting device as the lighting control parameters for the functional area, achieving precise lighting control.

[0076] In this way, the intelligent lighting control system can dynamically adjust lighting parameters according to the user's personalized preferences and real-time ambient lighting conditions, providing users with a more comfortable and intelligent lighting environment.

[0077] Based on the above embodiments of the present application, in the fourth embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 4 , the intelligent lighting control method, step S30 further includes steps S34 to S36: Step S34: Obtain the historical movement trajectory data of the user.

[0078] In this embodiment, historical movement trajectory data refers to information such as a user's movement paths and dwell time between different functional areas within a given time period. Specifically, multiple sensor nodes are deployed in the indoor space. These sensor nodes receive real-time location signals from user terminal devices (such as mobile phones and smart wearable devices) and convert these location signals into precise coordinate data. This coordinate data is continuously recorded and stored, forming the user's historical movement trajectory data. By analyzing this historical trajectory data, we can understand the user's activity patterns and corresponding movement paths over different time periods.

[0079] Step S35: predicting the user's expected movement path and target functional area based on the user's location, the behavior habit information and / or the historical movement trajectory data.

[0080] In this embodiment, the user's current location and behavioral habits are first analyzed to determine the user's current functional area and current activity type. Then, combined with the user's historical movement trajectory data, common movement patterns of the user in similar scenarios are identified. For example, if the historical movement trajectory shows that the user moves from the living room to the bedroom at 9 pm, and the user's current movement direction is toward the bedroom, the intelligent lighting control system can predict in advance that the user's movement direction is likely to be toward the bedroom and control the target lighting devices along the movement path from the living room to the bedroom in advance.

[0081] In one feasible implementation, a spatiotemporal prediction framework based on a hidden Markov model is employed, treating user location as the observed state and the latent state corresponding to the user's possible intentions and target functional areas. First, pattern mining is performed on historical mobility trajectory data to identify common movement path segments and their corresponding contextual features (such as time period, date type, and currently active functional area). Then, combining current user location and behavioral information, a state transition probability matrix and a time evolution model are constructed. The prediction process employs the Viterbi algorithm to search for the optimal path, while also incorporating an attention mechanism to dynamically adjust the weights of different historical trajectory segments to accommodate the time-varying nature of user behavior.

[0082] To further improve prediction accuracy, a graph-based navigation model can be integrated to model the indoor space as a topological graph, with nodes representing functional areas and edges representing accessible paths. The Dijkstra algorithm can then be used to calculate the shortest expected path. Furthermore, the deviation between the prediction results and the user's actual movement trajectory is continuously monitored, and the prediction model parameters are updated in real time through an online learning mechanism.

[0083] In another feasible implementation, step S35 includes steps S351 to S353: Step S351: Acquire the activity type of the user in the functional area through a sensor network.

[0084] It should be noted that activity type refers to the specific behavior performed by users in functional areas, such as reading, resting, cooking, etc.

[0085] In this embodiment, the sensor network is used not only to locate the user's position but also to identify the user's activity type within the functional area. Specifically, multiple sensors, such as infrared sensors, are deployed within the functional area. Infrared sensors can detect changes in human body thermal radiation to determine whether the user is in motion. Sensor feedback data, combined with the user's functional area, can be used to identify the user's activity type within the functional area, such as reading, resting, etc.

[0086] Step S352: Generate a state feature vector of the user according to the user location and the activity type.

[0087] It should be noted that the state feature vector is a multi-dimensional vector that digitally represents information such as the user's location and activity type, and is used to describe the user's current state.

[0088] In this embodiment, the user's location information and activity type are converted into numerical features. For example, the user's location can be represented as a two-dimensional coordinate vector, and the activity type can be represented by a predefined code (such as reading as 1, resting as 2, etc.). The user's location, activity type, and time are then combined into a state feature vector. For example, if the user is reading in a study with the coordinates (3, 4), the state feature vector can be represented as [3, 4, 1].

[0089] Optionally, the coordinates of the user position are encoded, and the indoor space is divided into grid areas, each grid area corresponds to a unique coordinate index, thereby determining the two-dimensional coordinate vector corresponding to the user position.

[0090] Step S353: Determine the user's historical behavior pattern based on the behavior habit information and / or the historical movement trajectory data.

[0091] It should be noted that historical behavior patterns refer to regular patterns in a user's past behavior. By analyzing user behavior habits, we can extract user activity preferences and movement paths across different time periods and functional areas. Furthermore, by combining historical movement trajectory data, we can identify common user behavior patterns in specific scenarios. For example, data analysis shows that users tend to move from the living room to the bedroom after 9 p.m. to rest.

[0092] In this embodiment, a machine learning algorithm, such as cluster analysis or sequence pattern mining, can be used to extract and store historical behavior patterns of users from a large amount of historical data.

[0093] Step S354: Compare the state feature vector with the historical behavior pattern to determine a historical movement trajectory that matches the current state of the user.

[0094] It should be noted that the comparison process refers to calculating the similarity between the user's current state feature vector and the historical behavior pattern. In this embodiment, algorithms such as cosine similarity, Euclidean distance, or dynamic time warping (DTW) can be used for the comparison.

[0095] Exemplarily, the cosine similarity between the user's current state feature vector and the feature vectors corresponding to each historical behavior pattern is calculated, and the historical behavior pattern with the highest cosine similarity is selected as the matching result of the user's current state.

[0096] Step S355: Determine the expected movement path and the target functional area of the user based on the extension direction of the historical movement trajectory and the probability distribution of the target functional area.

[0097] In this embodiment, the user's likely next movement direction is predicted by analyzing the extension direction of historical movement trajectories that match the current state. Simultaneously, the probability distribution of target functional areas is combined to assess the likelihood of the user moving to each functional area. For example, if historical movement trajectories indicate a 70% probability of the user moving to the bedroom and a 30% probability of the kitchen after reading, the probability distribution of the corresponding functional areas is used to determine the user's most likely target functional area. Based on this prediction mechanism, the intelligent lighting control system can predict the user's movement path and target functional area in advance, enabling pre-adjustment of lighting parameters.

[0098] After step S355, the intelligent lighting control method may further include: when the user moves, adjusting the target lighting equipment on the expected movement path and corresponding to the target functional area, and the adjustment includes: adjusting the change rate and change amplitude of the lighting control parameters corresponding to the target lighting equipment according to the user's movement speed and the distance from the target lighting equipment.

[0099] In this embodiment, adaptive adjustment of lighting parameters is achieved through a dynamic response mechanism. Sensor networks, such as Bluetooth positioning, UWB (Ultra Wide Band) positioning, and radar positioning, collect real-time location information of user terminal devices. The user's movement velocity vector is calculated using continuous timestamps. Simultaneously, a distance formula is used to calculate the actual distance between the user's current location and the target lighting device along the expected movement path. When the user moves quickly and is far from the target device, an exponential function is used to accelerate the rate of change of lighting parameters, while proportionally amplifying the amplitude of changes in parameters such as brightness and color temperature. This ensures that lighting is quickly adjusted to the desired functional area before the user enters. As the user approaches the target lighting device or slows down, a linear attenuation function is used to smooth out changes in lighting parameters, preventing sudden changes that could impact the user experience.

[0100] In one feasible implementation, a fuzzy logic controller is introduced, using the user's movement speed (fast / medium / slow) and the distance range between the user's current location and the target lighting device (far / medium / near) as input variables. Predefined fuzzy rules are used to adjust the variation coefficient. For example, when a user approaches a target functional area at a medium speed, the brightness variation rate is automatically set to a medium level, keeping the color temperature variation within a comfortable range. Control commands are then pushed to the target lighting device in real time, and a feedback control mechanism is used to perform error correction based on the actual lighting parameters transmitted back by the target lighting device, ensuring accurate lighting adjustment.

[0101] Step S36: Determine the lighting control parameters of the target lighting device corresponding to the expected movement path and the target functional area according to the behavioral habit information.

[0102] In this embodiment, the lighting environment is preconfigured based on the user's predicted movement path and target functional area. Based on the user's behavioral information, the user's preferred lighting settings for the target functional area are obtained. Then, based on the predicted movement path, the lighting control parameters of the corresponding lighting devices along the predicted movement path are adjusted in advance to ensure a comfortable lighting experience for the user during movement. For example, if the user is predicted to move from the living room to the bedroom, the bedroom lights are pre-adjusted to the user's preferred lighting settings, and the lighting effects can be gradually transitioned as the user approaches the bedroom. This approach enables dynamic pre-adjustment of lighting, enhancing the user experience.

[0103] Based on the above embodiments of the present application, in the fifth embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 5 After step S40, the intelligent lighting control method includes steps S50 to S60: Step S50: When it is detected that the user leaves the functional area, a delay timer is started, and the delay timer counts according to a preset delay time.

[0104] Step S60: If the user is not detected returning to the functional area within the delay time, the target lighting equipment in the functional area is automatically turned off or dimmed to achieve energy-saving control.

[0105] In this embodiment, when the sensor network detects that a user has left the current functional area, the intelligent lighting control system triggers a delay timer. The delay timer operates based on a preset delay time, which can be determined based on the user's behavioral habits and the usage characteristics of the functional area. For example, the delay time can be appropriately extended in functional areas where users frequently enter and exit, while the delay time can be set to a relatively short time in functional areas where users only occasionally stop. After the timer starts, the intelligent lighting control system continuously monitors whether the user has returned to the functional area. If the sensor network does not detect the user's return to the functional area within the set delay time, the intelligent lighting control system automatically performs energy-saving operations, automatically turning off or dimming the target lighting devices in the functional area. This automatic turning off or dimming of lights minimizes unnecessary energy consumption without affecting normal user use.

[0106] Based on the above embodiments of the present application, in the sixth embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 6 The intelligent lighting control method may further include steps S70 to S90: Step S70: Acquire biological data collected by the wearable device, and identify the user's current physiological state and emotional state based on the biological data.

[0107] It should be noted that wearable devices (such as smartwatches and smart bracelets) can collect real-time biometric data from users, such as heart rate and body temperature. Physiological state refers to the user's current physical condition, including states such as wakefulness, fatigue, sleep, and activity. For example, changes in heart rate and respiratory rate can be used to determine whether the user is asleep; activity levels (such as step count and exercise intensity) can be used to determine whether the user is active. Emotional state refers to the user's current psychological state, including emotions such as joy, anxiety, relaxation, and tension. For example, a rapid increase in heart rate and an increase in skin conductance are often associated with anxiety or tension, while a stable heart rate and a normal body temperature may indicate a relaxed state.

[0108] In one feasible implementation, the biometric data collected by the wearable device is first preprocessed, including data cleaning, normalization, and feature extraction. The extracted biometric features are then input into a pre-trained multimodal state recognition model. The multimodal state recognition model can be a deep learning model, such as a convolutional neural network (CNN) or a long short-term memory network (LSTM), which is used to identify the user's physiological and emotional state. The model's training data includes a large number of biometric data samples labeled with physiological and emotional states. The output of the multimodal state recognition model can accurately identify the user's current physiological and emotional state, providing a basis for subsequent lighting control.

[0109] Step S80: determining the adjusted lighting control parameters according to the physiological state and the emotional state in combination with a corresponding lighting control parameter mapping table.

[0110] It should be noted that the lighting control parameter mapping table is a preset rule table that maps the user's emotional state to corresponding lighting control parameters (such as brightness, color temperature, etc.). For example, when the user is relaxed, the light can be adjusted to a softer, warmer color temperature.

[0111] In a feasible implementation, a lighting control parameter mapping table can be constructed based on industry standards and expert experience such as psychological research to determine appropriate lighting environments under different physiological and emotional states.

[0112] In another possible implementation, the lighting control parameter mapping table can be dynamically adjusted based on the user's long-term usage habits. By continuously monitoring the user's biometric data and lighting usage behavior, the lighting control parameter mapping table can be optimized using a machine learning algorithm. For example, if the user typically dims the lights to a lower brightness and warmer color temperature when entering the bedroom after 10 pm, the lighting control parameter mapping table can be automatically updated to map the "entering the bedroom after 10 pm" scenario to the "low brightness, warm color temperature" lighting parameter settings.

[0113] Step S90: sending the adjusted lighting control parameters to the target lighting device.

[0114] In this embodiment, by sending the adjusted lighting control parameters to the target lighting device, the lighting device can automatically adjust its brightness, color temperature, color and other parameters to match the user's emotional state, thereby providing the user with a more comfortable and personalized lighting environment.

[0115] An embodiment of the present application provides an intelligent lighting control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent lighting control method of the above-mentioned embodiment 1.

[0116] Reference below Figure 7 , which shows a schematic diagram of the structure of an intelligent light control device suitable for implementing the embodiment of the present application. The intelligent light control device in the embodiment of the present application may include various hardware and software components for implementing the intelligent light control method. Figure 7 The smart lighting control device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application.

[0117] like Figure 7As shown, the intelligent lighting control device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the intelligent lighting control device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007, such as a touchscreen, touchpad, or keyboard; output devices 1008, such as a liquid crystal display (LCD), speaker, or vibrator; storage device 1003, such as a magnetic tape or hard disk; and communication devices 1009. The communication device 1009 can allow the intelligent light control device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an intelligent light control device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or have alternatively.

[0118] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0119] The intelligent lighting control device provided in this application, utilizing the intelligent lighting control method described in the aforementioned embodiments, can resolve the technical issue of being unable to generate adaptive lighting parameters based on a user's real-time location and behavioral habits. Compared to the prior art, the beneficial effects of the intelligent lighting control device provided in this application are the same as those of the intelligent lighting control method described in the aforementioned embodiments. Other technical features of this intelligent lighting control device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0120] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0121] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0122] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the intelligent lighting control method in the above embodiment.

[0123] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0124] The computer-readable storage medium may be included in the intelligent lighting control device, or may exist independently without being incorporated into the intelligent lighting control device.

[0125] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the intelligent lighting control device, the intelligent lighting control device: receives user identification information sent by the user terminal device through near-field communication, and determines corresponding behavioral habit information based on the user identification information; locates the user terminal device through a sensor network to determine the user's position; determines the lighting control parameters of the functional area where the user is currently located based on the user's position and the behavioral habit information; sends control instructions to the target lighting device based on the lighting control parameters, and receives the instruction execution results fed back by the target lighting device in real time.

[0126] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0128] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0129] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the intelligent lighting control method described above. This computer-readable storage medium can address the technical issue of being unable to generate adaptive lighting parameters based on a user's real-time location and behavioral habits. Compared to the prior art, the computer-readable storage medium provided in this application offers the same beneficial effects as the intelligent lighting control method provided in the aforementioned embodiments, and will not be further elaborated upon here.

[0130] An embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the above-mentioned intelligent lighting control method when executed by a processor.

[0131] The computer program product provided in this application solves the technical problem of being unable to generate adaptive lighting parameters based on a user's real-time location and behavioral habits. Compared to the prior art, the beneficial effects of the computer program product provided in this embodiment are similar to those of the intelligent lighting control method provided in the aforementioned embodiment, and are not further elaborated here.

[0132] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

[0133] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method.

[0135] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An intelligent lighting control method, characterized in that: The intelligent lighting control method comprises: Receiving user identification information sent by a user terminal device via near field communication, and determining corresponding behavior habit information based on the user identification information; Positioning the user terminal device through a sensor network to determine the user's location; Determining lighting control parameters for the functional area where the user is currently located based on the user's location and the behavioral habit information; According to the lighting control parameters, a control instruction is sent to the target lighting device, and the instruction execution result fed back by the target lighting device is received in real time.

2. The intelligent lighting control method according to claim 1, wherein: The step of receiving user identification information sent by a user terminal device via near field communication and determining corresponding behavior habit information based on the user identification information includes: Obtaining the user's lighting parameter adjustment record and lighting usage time through the user identification information; Determining lighting parameter settings corresponding to the functional areas in different time periods according to the lighting parameter adjustment records and the lighting usage time; The behavioral habit information of the user is determined according to the lighting parameter settings.

3. The intelligent lighting control method according to claim 1, wherein: The step of determining the lighting control parameters of the functional area where the user is currently located based on the user location and the behavioral habit information includes: Determining the functional area where the user is currently located according to the user's location; Obtain basic lighting parameters and real-time ambient lighting data corresponding to the functional area; According to the behavioral habit information and the real-time ambient lighting data, the basic lighting parameters are adjusted to generate the lighting control parameters corresponding to the functional area.

4. The intelligent lighting control method according to claim 3, wherein: The step of adjusting the basic lighting parameters according to the behavioral habit information and the real-time ambient lighting data to generate the lighting control parameters corresponding to the functional area includes: According to the behavioral habit information, obtaining the lighting parameter settings of the functional area corresponding to the current time period; Calculating a lighting parameter deviation value between the lighting parameter setting and the basic lighting parameter; Calculating ambient lighting compensation according to the real-time ambient lighting data; Performing weighted fusion on the lighting parameter difference value and the ambient light compensation to determine an adjustment coefficient of the basic lighting parameter; The basic lighting parameters are adjusted based on the adjustment coefficient to generate the lighting control parameters corresponding to the functional area.

5. The intelligent lighting control method according to claim 1, wherein: The step of determining the lighting control parameters of the functional area where the user is currently located based on the user location and the behavioral habit information includes: Obtaining historical movement trajectory data of the user; Predicting the user's expected movement path and target functional area based on the user's location, the behavioral habit information, and / or the historical movement trajectory data; The lighting control parameters of the target lighting device corresponding to the expected movement path and the target functional area are determined according to the behavioral habit information.

6. The intelligent lighting control method according to claim 5, wherein: The step of predicting the user's expected movement path and target functional area based on the user's location, the behavior habit information and / or the historical movement trajectory data includes: obtaining the activity type of the user in the functional area through a sensor network; generating a state feature vector of the user according to the user location and the activity type; Determining the user's historical behavior pattern based on the behavior habit information and / or the historical movement trajectory data; Comparing the state feature vector with the historical behavior pattern to determine a historical movement trajectory that matches the current state of the user; The expected movement path of the user and the target functional area are determined based on the extension direction of the historical movement trajectory and the probability distribution of the target functional area.

7. The intelligent lighting control method according to claim 6, wherein: After the step of determining the expected movement path of the user and the target functional area based on the extension direction of the historical movement trajectory and the probability distribution of the target functional area, the intelligent lighting control method further includes: When the user moves, the target lighting device on the expected movement path and corresponding to the target functional area is adjusted, and the adjustment includes: adjusting the change rate and change amplitude of the lighting control parameters corresponding to the target lighting device according to the user's movement speed and the distance from the target lighting device.

8. The intelligent lighting control method according to claim 1, wherein: The intelligent lighting control method further includes: Acquiring biological data collected by a wearable device, and identifying the user's current physiological state and emotional state based on the biological data; Determining the adjusted lighting control parameters according to the physiological state and the emotional state in combination with a corresponding lighting control parameter mapping table; The adjusted lighting control parameters are sent to the target lighting device.

9. An intelligent lighting control device, characterized in that: The intelligent lighting control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent lighting control method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent lighting control method according to any one of claims 1 to 8 are implemented.

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