An intelligent lighting control method and system for a smart nursing home

Abstract

The present invention relates to the technical field of intelligent lighting control, and specifically discloses an intelligent lighting control method and system for a smart nursing home. The method includes: collecting sensing data in the smart nursing home; performing one or more rounds of iterative updates on a preset knowledge graph based on the sensing data to obtain a target knowledge graph; inputting the target knowledge graph into a graph processing model to generate a lighting control instruction; and adjusting the lighting settings in a target area based on the lighting control instruction. The intelligent lighting control method for a smart nursing home disclosed by the present invention combines a knowledge graph to control the lighting in the smart nursing home, can meet the health conditions and living habits of different elderly people, and realizes lighting control management that takes into account both individuals and groups.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent lighting control, and particularly to an intelligent lighting control method and system for a smart nursing home. Background Art

[0002] With the increasing degree of social aging, the problem of elderly care has become increasingly prominent. As a new model of elderly care, smart nursing homes have greatly improved the quality and efficiency of elderly care services by introducing advanced technologies such as the Internet of Things and artificial intelligence. Lighting, as an important part of the nursing home environment, the intelligent transformation of its control system is of great significance for improving the quality of life of the elderly.

[0003] Therefore, the present invention provides an intelligent lighting control method and system for a smart nursing home to better control the lighting in the smart nursing home. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent lighting control method for a smart nursing home, and solve the following technical problems: The existing lighting control systems mostly rely on manual operation or simple induction control, and cannot be dynamically adjusted according to the individual needs of the elderly and environmental changes, resulting in problems such as inconvenient use and high energy consumption.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An intelligent lighting control method for a smart nursing home, comprising: collecting sensing data in the smart nursing home; the sensing data includes light sensing data, sound sensing data, and motion sensing data;

[0007] Based on the sensing data, perform one or more rounds of iterative updates on a preset knowledge graph to obtain a target knowledge graph; the nodes of the preset knowledge graph include installed lights, activity areas, and rest areas, the edges of the knowledge graph include action paths between nodes, the attribute features of the nodes include lamp types, lamp installation positions, light intensities, and light ray intensities, and the attribute features of the edges include path attributes and path usage frequencies;

[0008] Input the target knowledge graph into a graph processing model to generate lighting control instructions;

[0009] Based on the lighting control instructions, adjust the lighting settings in the target area;

[0010] Wherein, one round of iteration includes:

[0011] Based on the light sensing data, determine the light intensity map of the smart nursing home;

[0012] Based on the sound sensing data and the motion sensing data, determine the distribution map of the people in the intelligent nursing home;

[0013] Based on the light intensity map and the distribution map of the people, determine the light area map;

[0014] Based on the motion sensing data, determine the motion trend distribution map;

[0015] Determine whether the overall coverage of the light area map on the motion trend distribution map meets the judgment condition through the following formula:

[0016]

[0017] where C represents the light coverage, I i represents the light intensity of the i-th area, A i represents the area or weight of the i-th area, n is the number of areas for parameter calculation; D represents the motion trend coverage, T j represents the motion trend value of the j-th motion area, W j represents the area or weight of the j-th motion area, and E represents the overall coverage of the light area distribution on the motion trend;

[0018] If so, extract data from the preset knowledge graph based on the light area map and construct a subgraph as the target knowledge graph;

[0019] If not, based on the motion trend map, update the distribution map of the people, and based on the updated distribution map of the people, re-determine the light area map and perform the next round of iteration.

[0020] As a further solution of the present invention: the extracting data from the preset knowledge graph based on the light area map and constructing a subgraph includes:

[0021] Based on the light area map, determine the target positions where the lights need to be turned on;

[0022] Obtain the installation positions of the installed lights in the preset knowledge graph;

[0023] Calculate the distances between the installation positions of the installed lights and the target positions where the lights need to be turned on respectively;

[0024] Extract the installed lights corresponding to the installation positions with distances less than the preset value as the nodes of the subgraph;

[0025] Based on the nodes of the subgraph, extract the edges between the nodes corresponding to the nodes of the subgraph from the preset knowledge graph as the edges of the subgraph.

[0026] As a further solution of the present invention: updating the distribution map of the people based on the movement trend map includes:

[0027] Based on the movement trend map, determining the target population with a movement trend;

[0028] Obtaining the portrait of the target population through an image sensor;

[0029] Identifying the person object in the portrait and obtaining the attribute characteristics of the person object;

[0030] Based on the attribute characteristics and the current action behavior of the person object, determining the movement vector of each person object;

[0031] Based on the movement vector of each person object, determining the movement end position of each person object;

[0032] Based on the end positions of each person object, determining the updated distribution map of the people.

[0033] As a further solution of the present invention: determining the movement vector of each person object based on the attribute characteristics and the current action behavior of the person object includes:

[0034] Based on the attribute characteristics, determining the movement speed of the person object;

[0035] Based on the current action behavior of the person object, determining the movement direction;

[0036] Based on the movement speed and the movement direction, determining the movement vector.

[0037] As a further solution of the present invention: inputting the target knowledge graph into a graph processing model to generate a lighting control instruction includes:

[0038] Based on the target knowledge graph, obtaining the living habits and physical state data of the corresponding person object;

[0039] Converting the living habits and the physical state data of the person object into a first feature vector;

[0040] Inputting the feature vector and the target knowledge graph into the graph processing model to generate the lighting control instruction.

[0041] As a further solution of the present invention: the sensing data further includes biometric sensing data, and the biometric sensing data includes a facial image; the method further includes:

[0042] Based on the facial image of the person object, determining the facial micro-expression of the person object by means of image recognition;

[0043] Convert the facial micro-expression into a second feature vector;

[0044] Use the feature vector as input data for the image processing model to generate the lighting control instruction.

[0045] As a further solution of the present invention: The method further includes:

[0046] Based on the living habits and physical condition data of the person object, assign a weight coefficient to each person object; the weight coefficient reflects the acceptance degree of the person object to the lighting intensity;

[0047] Use the weight coefficient as input data for the image processing model to generate the lighting control instruction.

[0048] An intelligent lighting control system for a smart nursing home, the system includes:

[0049] A data acquisition module for collecting sensing data in the smart nursing home; the sensing data includes light sensing data, sound sensing data, and motion sensing data;

[0050] An iteration module for performing one or more rounds of iterative updates on a preset knowledge graph based on the sensing data to obtain a target knowledge graph; the nodes of the preset knowledge graph include installed lights, activity areas, and rest areas, the edges of the knowledge graph include action paths between nodes, the attribute features of the nodes include lamp types, lamp installation positions, lighting intensities, and light intensities, and the attribute features of the edges include path attributes and path usage frequencies;

[0051] An instruction generation module for inputting the target knowledge graph into a graph processing model to generate a lighting control instruction;

[0052] A lighting control module for adjusting the lighting settings of a target area based on the lighting control instruction;

[0053] Wherein, one round of iteration includes:

[0054] Determine the light intensity map of the smart nursing home based on the light sensing data;

[0055] Determine the person distribution map in the smart nursing home based on the sound sensing data and the motion sensing data;

[0056] Determine the lighting area map based on the light intensity map and the person distribution map;

[0057] Determine the motion trend distribution map based on the motion sensing data;

[0058] Determine whether the overall coverage of the light area map for the motion trend distribution map meets the judgment condition through the following formula:

[0059]

[0060] Where C represents the light coverage, I i represents the light intensity of the i-th area, A i represents the area or weight of the i-th area, n is the number of areas for parameter calculation; D represents the motion trend coverage, T j represents the motion trend value of the j-th motion area, W j represents the area or weight of the j-th motion area, and E represents the overall coverage of the light area distribution for the motion trend;

[0061] If so, extract data from the preset knowledge graph based on the light area map and construct a sub-graph as the target knowledge graph;

[0062] If not, based on the motion trend map, update the person distribution map, and re-determine the light area map based on the updated person distribution map and perform the next iteration.

[0063] Advantages of the present invention: By introducing a variety of sensors and intelligent algorithms, and using the method of knowledge graph to achieve a close combination with the installation of lamps in the nursing home and the activities of the elderly, the automatic adjustment of the lights in the intelligent nursing home is realized, and the automatic adjustment of the lights can take into account the activities of individuals and multiple people, achieving the purpose of improving the comfort and safety of the elderly's living environment. Moreover, the present invention can intelligently adjust the light settings in the nursing home through continuous iteration and optimization, improve the comfort and safety of living, and at the same time achieve the effect of energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below with reference to the accompanying drawings.

[0065] Figure 1 is a schematic flow chart of the intelligent light control method for the intelligent nursing home of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] Please refer to Figure 1 as shown, the present invention is an intelligent light control method for an intelligent nursing home,Figure 1 FIG. Figure 1 is a schematic flow chart of the intelligent lighting control method for a smart nursing home according to the present invention. In some embodiments, process 100 may be executed by a processor or a lighting intelligent control system. Process 100 may include the following operations.

[0068] Step 101, collect sensing data in the smart nursing home.

[0069] Among them, the sensing data includes light sensing data, sound sensing data, and motion sensing data.

[0070] The light sensing data refers to the data obtained by detecting the illumination intensity in the room through a light sensor.

[0071] The sound sensing data refers to the data obtained by detecting the sound signal in the room through a sound sensor.

[0072] The motion sensing data refers to the data obtained by detecting the activities of people in the room through a motion sensor.

[0073] In some embodiments, the sensing data in the smart nursing home may further include other sensing data, such as temperature and humidity sensing data, infrared sensing data, biometric sensing data, and image sensing data, etc.

[0074] Step 102, perform one or more rounds of iterative updates on the preset knowledge graph based on the sensing data to obtain a target knowledge graph.

[0075] The preset knowledge graph refers to a pre-constructed knowledge graph. A knowledge graph is a semantic network that reveals the relationships between entities (or called objects). The nodes in the graph represent entities. The nodes can have multiple types, called node types, which are used to indicate various types of entities. The edges in the graph represent relationships, and the edges can also have multiple types, called edge types, which are used to indicate various types of relationships. Entities can refer to things in the real world, such as people, geographical locations (e.g., a certain position in the room), devices (e.g., lights), etc. Relationships can be used to express the connections between different entities. For example, a person moves from a certain position in the room to another position; or, for example, the action path between two positions in the room, etc.

[0076] In some embodiments, the nodes of the preset knowledge graph include installed lights, activity areas, and rest areas. The edges of the knowledge graph include the action paths between the nodes. The attribute features of the nodes include lamp types, lamp installation positions, light intensities, and light ray intensities. The attribute features of the edges include path attributes and path usage frequencies.

[0077] Update refers to updating the nodes and edges of the preset knowledge graph, including adding, deleting, or changing node attributes, etc.

[0078] Among them, one round of iteration may include the following operations.

[0079] S10. Determine the light intensity map of the intelligent nursing home based on the light sensing data.

[0080] The light sensing data may include the light intensity of each position area in the intelligent nursing home, and add a light intensity indication on the basis of the floor plan of the entire intelligent nursing home, so as to obtain the light intensity map.

[0081] S11. Determine the personnel distribution map in the intelligent nursing home based on the sound sensing data and the motion sensing data.

[0082] In some embodiments, data preprocessing can be performed on the sound sensing data and the motion sensing data, including operations such as data cleaning and noise filtering, which can ensure the accuracy and reliability of the data.

[0083] After that, useful features can be extracted from the processed sound and motion data, such as the frequency, intensity, and duration of the sound, and the speed and direction of the motion. These features can help better identify and understand the activity status of the personnel.

[0084] Then, through machine learning and artificial intelligence algorithms, the extracted features can be analyzed and learned to identify different personnel identities. And according to the personnel activity trajectories in the sound and motion data, combined with the layout map of the nursing home, determine the specific positions of each person in the hospital. For example, the precise positioning of personnel can be achieved by matching the sensor data with the building structure of the nursing home.

[0085] Finally, generate the personnel distribution map by graphically displaying the position information of each person.

[0086] S12. Determine the lighting area map based on the light intensity map and the personnel distribution map.

[0087] When determining the lighting area map, if the current time is daytime, the ambient light can be considered to provide overall brightness, and the lights are used as key lighting to highlight important areas, while the auxiliary lighting creates a specific atmosphere; if the current time is night, the lights are needed to focus on recruiting to highlight important areas, and the auxiliary lighting supplements the areas with insufficient light.

[0088] In some embodiments, the areas corresponding to the personnel in the personnel distribution map can be used as important areas, and other areas, such as the movement path area, can be used as secondary areas, and other areas can be used as general areas.

[0089] S13. Determine the motion trend distribution map based on the motion sensing data.

[0090] Motion sensing data can include information such as the position, speed, and acceleration of a person. This information can reflect the movement trajectory, speed change, acceleration change, etc. of an object.

[0091] Based on this information, a motion trend distribution map can be generated.

[0092] S14. Determine whether the overall coverage of the lighting area map for the motion trend distribution map meets the judgment condition through the following formula:

[0093]

[0094] where C represents the lighting coverage, I i represents the lighting intensity of the i-th area, A i represents the area or weight of the i-th area, n is the number of areas for parameter calculation; D represents the motion trend coverage, T j represents the motion trend value of the j-th motion area, W j represents the area or weight of the j-th motion area, and E represents the overall coverage of the lighting area distribution for the motion trend.

[0095] C measures the average coverage intensity of the lighting area, taking into account the actual lighting intensity and importance of each area. D measures the overall importance or intensity of the motion trend distribution. E provides a ratio indicating the actual coverage of the lighting area for the motion trend distribution. The higher the value, the better the coverage effect. When the overall coverage of E is greater than 90% or 95%, it can be considered that the overall coverage meets the judgment condition.

[0096] In this embodiment, by effectively combining the lighting intensity and the motion trend, a comprehensive evaluation of the lighting area coverage is achieved, which can improve the rationality of lighting intelligent control and save resources as much as possible on the premise of meeting the lighting coverage requirements.

[0097] When the overall coverage meets the judgment condition, execute S15; otherwise, execute S16.

[0098] S15. If so, extract data from the preset knowledge graph based on the lighting area map and construct a subgraph as the target knowledge graph.

[0099] In some embodiments, the extracting data from the preset knowledge graph and constructing a subgraph based on the lighting area map includes:

[0100] Based on the lighting area map, determine the target positions where the lights need to be turned on; for example, the target positions can be important areas, secondary areas, and general areas in the lighting area map, etc.

[0101] Obtain the installation positions of the installed lights in the preset knowledge graph;

[0102] Calculate the distance between the installation positions of the installed lights and the target positions of the lights to be lit respectively; the calculation method can be pairwise calculation, that is, calculate the distance between the installation position of each installed light and the target position of each light to be lit.

[0103] Extract the installed lights corresponding to the installation positions where the distance is less than a preset value as the nodes of the subgraph; for example, the distance is less than 3m, 5m, etc.

[0104] Based on the nodes of the subgraph, extract the edges between the nodes corresponding to the nodes of the subgraph from the preset knowledge graph as the edges of the subgraph.

[0105] The subgraph of the preset knowledge graph can be constructed through the above steps.

[0106] S16, if not, based on the motion trend graph, update the person distribution graph, and based on the updated person distribution graph, re-determine the lighting area graph and perform the next round of iteration.

[0107] In some embodiments, the updating the person distribution graph based on the motion trend graph includes:

[0108] Based on the motion trend graph, determine the target population with a motion trend; the target population can be a part or all of the people in the motion trend graph.

[0109] Obtain the person portraits of the target population through an image sensor. The person portrait is an image including the facial images of the target population, such as a photo, etc.

[0110] Identify the person objects in the person portraits, and obtain the attribute characteristics of the person objects. The attribute characteristics of the person objects include the age, gender, physical health status, vision condition, sensitivity to light, etc. of the person. These information can be obtained through the detection and diagnosis of each person object in advance and stored in the database in advance, and the relevant data can be directly called when needed.

[0111] Based on the attribute characteristics and the current action behaviors of the person objects, determine the motion vectors of each person object. The motion vector includes the motion direction and motion distance of the person object.

[0112] Based on the motion vectors of each person object, determine the motion end positions of each person object. Among them, the vector end position of the motion vector corresponding to each person object is the motion end position of the person object.

[0113] Based on the end positions of the respective person objects, determine an updated person distribution map. The updated person distribution map reflects the position areas where the people in the intelligent nursing home may move in the short term. In this embodiment, by predicting the motion vectors of each person in advance to update the person distribution map, it is possible to achieve the advance control of the lights, ensuring that the light intensity within the line of sight of the elderly during activities does not change suddenly, and improving the comfort of the light intensity in the intelligent nursing home.

[0114] In some embodiments, the determining the motion vector of each person object based on the attribute features and the current action behavior of the person object includes:

[0115] Based on the attribute features, determine the motion speed of the person object;

[0116] Based on the current action behavior of the person object, determine the motion direction;

[0117] Based on the motion speed and the motion direction, determine the motion vector.

[0118] Step 103, input the target knowledge graph into a graph processing model to generate a lighting control instruction.

[0119] In some embodiments, the inputting the target knowledge graph into a graph processing model to generate a lighting control instruction includes:

[0120] Obtain the living habits and physical condition data of the corresponding person object based on the target knowledge graph. Living habits include the person's exercise habits, light-switching habits, etc., and the physical condition data includes the person's exercise ability, physical function, whether there are eye diseases (such as presbyopia, glaucoma, etc.).

[0121] Convert the living habits and the physical condition data of the person object into a first feature vector. Specifically, the living habits and the physical condition data can be converted into a first feature vector by means of feature extraction.

[0122] Input the feature vector and the target knowledge graph into the graph processing model to generate the lighting control instruction.

[0123] The graph processing model can process the input feature vector and the target knowledge graph, and after processing, it can output a lighting control instruction.

[0124] The graph processing model can be pre-trained based on big data, and the graph processing model can be a graph neural network model or the like.

[0125] In this embodiment, by considering the living habits and physical condition data of the person object, the lighting control can be made more in line with the needs of the person. For example, if a person object included in the target knowledge graph has presbyopia and is used to reading at night, the graph processing model can generate a lighting control instruction to adjust the lighting brightness to adapt to the visual needs of the person, while ensuring sufficient light is provided during reading.

[0126] In some embodiments, in order to enable the graph processing model to output more accurate, or rather, lighting control instructions more suitable for the current environment of the smart nursing home, the sensing data may further include biometric sensing data, and the biometric sensing data includes facial images; the method further includes:

[0127] Based on the facial image of the person object, determine the facial micro-expression of the person object by means of image recognition. The facial micro-expression of the person object can reflect whether the current light intensity makes them feel comfortable. For example, when the light is too strong, there will be a micro-squinting expression in the person's eyes.

[0128] Convert the facial micro-expression into a second feature vector;

[0129] Use the feature vector as the input data of the image processing model to generate the lighting control instruction.

[0130] In this embodiment, in order to enable the graph processing model to output more accurate or more suitable lighting control instructions for the current environment of the smart nursing home, biometric sensing data including facial images is introduced. The purpose of this method is to judge whether the current light intensity makes people feel comfortable by analyzing the facial micro-expressions of people, so as to adjust the lighting control instructions and improve the comfort of the lighting environment in the nursing home.

[0131] In some embodiments, the method further includes:

[0132] Based on the living habits and physical condition data of the person object, assign a weight coefficient to each person object; the weight coefficient reflects the acceptance degree of the person object to the light intensity;

[0133] Use the weight coefficient as the input data of the image processing model to generate the lighting control instruction.

[0134] In this embodiment, by assigning a weight coefficient to each person object and using the image processing model to generate the lighting control instruction, a more personalized and comfortable lighting environment can be provided for each person object.

[0135] Step 104, based on the lighting control instruction, adjust the lighting settings of the target area.

[0136] In some embodiments, the lighting control of the target area based on the lighting control instructions can be achieved in various ways, for example, a PLC-based control method.

[0137] The working principle of the present invention is as follows: by introducing a variety of sensors and intelligent algorithms, combined with the knowledge graph, the installation of lamps in the nursing home, the activities of the elderly, and the physical health status of the elderly are comprehensively considered, and the automatic adjustment of the lights in the smart nursing home is realized. The automatic adjustment of the lights can take into account the activities of individuals and multiple people, and achieve the purpose of improving the comfort and safety of the elderly's living environment. At the same time, it can meet the health conditions and living habits of different elderly people, and realize the lighting control management that takes into account both individuals and groups. In addition, the present invention can intelligently adjust the lighting settings in the nursing home through continuous iteration and optimization, improve the comfort and safety of living, and achieve energy-saving effects.

[0138] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent lighting control method for a smart nursing home, characterized in that: The method comprises: Collecting sensor data in the smart nursing home; the sensor data includes light sensor data, sound sensor data, and motion sensor data; Based on the sensor data, one or more rounds of iterative updates are performed on the preset knowledge graph to obtain a target knowledge graph; the nodes of the preset knowledge graph include installed lights, activity areas, and rest areas; the edges of the knowledge graph include action paths between nodes; the attribute characteristics of the nodes include lamp type, lamp installation location, light intensity, and light intensity; the attribute characteristics of the edges include path attributes and path usage frequency; Inputting the target knowledge graph into a graph processing model to generate lighting control instructions; Adjusting the lighting settings of the target area based on the lighting control instructions; Among them, one round of iteration includes: Determine a light intensity map of the smart nursing home based on the light sensing data; Determine a person distribution map in the smart nursing home based on the sound sensor data and the motion sensor data; Determine a lighting area map based on the light intensity map and the character distribution map; Determining a motion trend distribution map based on the motion sensing data; The following formula is used to determine whether the overall coverage of the light area map to the motion trend distribution map meets the judgment condition: Among them, C represents the lighting coverage, I i represents the light intensity of the ith area, A i represents the area or weight of the ith region, n is the number of regions for parameter calculation; D represents the coverage of the movement trend, T j represents the motion trend value of the jth motion area, W j represents the area or weight of the jth motion area, and E represents the overall coverage of the light area distribution to the motion trend; If yes, extract data from the preset knowledge graph based on the light area graph and construct a subgraph as the target knowledge graph; If not, based on the motion trend graph, the character distribution graph is updated, and based on the updated character distribution graph, the lighting area graph is re-determined and the next round of iteration is performed; The step of extracting data from the preset knowledge graph based on the light area graph and constructing a subgraph includes: Based on the light area map, determining a target location where a light needs to be lit; Obtaining the installation locations of installed lamps in the preset knowledge graph; respectively calculating the distance between the installation position of the installed lamp and the target position where the lamp needs to be lit; Extract the installed lights corresponding to the installation positions whose distance is less than the preset value as nodes of the subgraph; Based on the nodes of the subgraph, edges between nodes corresponding to the nodes of the subgraph are extracted from the preset knowledge graph as edges of the subgraph.

2. The intelligent lighting control method for a smart nursing home according to claim 1, characterized in that: The updating of the character distribution map based on the motion trend map includes: Based on the exercise trend graph, determining a target population with an exercise trend; Acquire the portraits of the target group through an image sensor; Identify the person object in the person portrait and obtain the attribute characteristics of the person object; Determine a motion vector of each character object based on the attribute feature and the current action behavior of the character object; Based on the motion vector of each of the character objects, determining the motion end point position of each of the character objects; Based on the endpoint positions of the various character objects, an updated character distribution map is determined.

3. The intelligent lighting control method for a smart nursing home according to claim 2 is characterized in that: The step of determining the motion vector of each character object based on the attribute feature and the current action behavior of the character object comprises: Determining the movement speed of the person object based on the attribute feature; Determine a movement direction based on a current action of the character object; The motion vector is determined based on the motion speed and the motion direction.

4. The intelligent lighting control method for a smart nursing home according to claim 1, characterized in that: The step of inputting the target knowledge graph into a graph processing model to generate a lighting control instruction includes: Acquire the living habits and physical condition data of the corresponding person object based on the target knowledge graph; Converting the life habits of the character object and the body state data into a first feature vector; The feature vector and the target knowledge graph are input into the graph processing model to generate the lighting control instruction.

5. The intelligent lighting control method for a smart nursing home according to claim 4 is characterized in that: The sensor data further includes biometric sensor data, and the biometric sensor data includes a facial image; the method further includes: Based on the facial image of the person object, determining the facial micro-expression of the person object by image recognition; Converting the facial micro-expression into a second feature vector; The feature vector is used as input data of an image processing model to generate the light control instruction.

6. The intelligent lighting control method for a smart nursing home according to claim 4 is characterized in that: The method further comprises: Based on the life habits and physical condition data of the character object, a weight coefficient is assigned to each character object; the weight coefficient reflects the acceptance degree of the character object to the light intensity; The weight coefficient is used as input data of an image processing model to generate the light control instruction.

7. The intelligent lighting control method for a smart nursing home according to claim 1 is applied to an intelligent lighting control system for a smart nursing home, characterized in that: The system comprises: A data acquisition module, used to collect sensor data in the smart nursing home; the sensor data includes light sensor data, sound sensor data, and motion sensor data; An iteration module, configured to perform one or more rounds of iterative updates on a preset knowledge graph based on the sensor data to obtain a target knowledge graph; the nodes of the preset knowledge graph include installed lights, activity areas, and rest areas; the edges of the knowledge graph include action paths between nodes; the attribute characteristics of the nodes include lamp type, lamp installation location, light intensity, and light intensity; the attribute characteristics of the edges include path attributes and path usage frequency; An instruction generation module, used for inputting the target knowledge graph into a graph processing model to generate a lighting control instruction; A lighting control module, used to adjust the lighting settings of the target area based on the lighting control instructions; Among them, one round of iteration includes: Determine a light intensity map of the smart nursing home based on the light sensing data; Determine a person distribution map in the smart nursing home based on the sound sensor data and the motion sensor data; Determine a lighting area map based on the light intensity map and the character distribution map; Determining a motion trend distribution map based on the motion sensing data; The following formula is used to determine whether the overall coverage of the light area map to the motion trend distribution map meets the judgment condition: Among them, C represents the lighting coverage, I i represents the light intensity of the ith area, A i represents the area or weight of the ith region, n is the number of regions for parameter calculation; D represents the coverage of the movement trend, T j represents the motion trend value of the jth motion area, W j represents the area or weight of the jth motion area, and E represents the overall coverage of the light area distribution to the motion trend; If yes, extract data from the preset knowledge graph based on the light area graph and construct a subgraph as the target knowledge graph; If not, based on the motion trend graph, the character distribution graph is updated, and based on the updated character distribution graph, the lighting area graph is re-determined and the next round of iteration is performed; The step of extracting data from the preset knowledge graph based on the light area graph and constructing a subgraph includes: Based on the light area map, determining a target location where a light needs to be lit; Obtaining the installation locations of installed lamps in the preset knowledge graph; respectively calculating the distance between the installation position of the installed lamp and the target position where the lamp needs to be lit; Extract the installed lights corresponding to the installation positions whose distance is less than the preset value as nodes of the subgraph; Based on the nodes of the subgraph, edges between nodes corresponding to the nodes of the subgraph are extracted from the preset knowledge graph as edges of the subgraph.

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