LED bulb control system based on user behavior

Abstract

The application relates to the technical field of LED bulb control, in particular to an LED bulb control system based on user behavior, which comprises the following modules: a behavior acquisition module, which is used for acquiring the behavior mode of a target user, determining the initial light operation parameter of the target user under the corresponding behavior mode, and the behavior mode further comprises the position, scene mode and light sequence of the target user; a behavior analysis module, which is used for acquiring the behavior data of the target user under the behavior mode, determining the coincidence evaluation coefficient between the user behavior and the light, and setting the moving guide path of the light according to the coincidence evaluation coefficient; a light preference module, which is used for determining the preference range and preference color temperature of the light according to the behavior data of the user, and determining the adjustment parameter of the light under the behavior data; and a scene verification module, which is used for verifying the feedback consistency of the user behavior and the light feedback under different scene modes, and determining the feedback result related to the current light feedback; the control efficiency and accuracy of the LED bulb are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of LED bulb control, in particular to an LED bulb control system based on user behavior. BACKGROUND

[0002] In traditional LED bulb control systems, the switching and brightness adjustment of light usually require manual operation by users or are achieved through preset timing programs. However, this approach often fails to accurately capture and respond to users' actual needs, resulting in poor lighting effects, high energy consumption, and poor user experience, among other issues.

[0003] With the continuous development of Internet of Things technology and artificial intelligence technology, intelligent lighting systems have gradually become the mainstream trend in the market. These systems can capture and analyze user behavior data in real time and intelligently adjust light parameters based on the analysis results to meet users' individual needs. However, most intelligent lighting systems on the market currently have single functions and insufficient intelligence, and cannot fully meet users' needs for comfortable, energy-saving, and efficient lighting environments.

[0004] For example, Chinese Patent Publication No. CN117354986A discloses an intelligent control method and system for multifunctional LED lamp beads, aiming to solve the problem of existing lighting systems that cannot intelligently adapt to user needs. The method first identifies the target user behavior pattern and combines it with the LED lamp bead operating parameters to obtain the behavior-light operating parameters of the target user. It identifies environmental factors that affect users' control of light and derives the environment-light operating parameters of the user's environment. It constructs a target user light operating prediction model to predict future LED lamp bead operating parameters and forms an automatic adjustment scheme accordingly. Finally, it develops an LED lamp bead control scheme.

[0005] Existing technologies analyze user behavior and related data, but ignore the need to adjust the intensity, color temperature, and on-off of current light according to user behavior when controlling light, so as to adjust the range of current light to meet the needs of the current user and adapt to users' usage needs in different scenarios. SUMMARY

[0006] To solve the above technical problems, the technical solution adopted by the present application is: an LED bulb control system based on user behavior, comprising: a behavior acquisition module for acquiring the behavior pattern of a target user and determining the initial light operating parameters of the target user under the corresponding behavior pattern; the behavior pattern also includes the position, scene mode, and light sequence of the target user.

[0007] The behavior analysis module is configured to acquire behavior data of the target user in a behavior mode, determine a coincidence evaluation coefficient between the user behavior and the light, and set a moving guide path of the light according to the coincidence evaluation coefficient.

[0008] The light preference module is configured to determine a preference range and a preference color temperature of the light according to the behavior data of the user, and determine an adjustment parameter of the light in the behavior data.

[0009] The scene verification module is configured to verify a feedback consistency degree of the user behavior and the light feedback in different scene modes, and determine a feedback result related to the current light feedback.

[0010] The parameter adjustment module is configured to adjust a light sequence of the light according to the behavior mode of the target user, and determine a distribution adjustment result of the light.

[0011] The parameter adjustment module comprises an environmental factor unit, a behavior trend unit and a distribution adjustment unit. The environmental factor unit is configured to acquire an external temperature, weather and illumination, set an environmental factor, and output an environmental factor coefficient in combination with the adjustment parameter.

[0012] The behavior trend unit is configured to acquire the moving guide path of the light, and set a behavior trend coefficient in combination with a moving frequency and a moving interval of the current target user.

[0013] The distribution adjustment unit is configured to comprehensively process the environmental factor coefficient, the behavior trend coefficient and the feedback result, acquire a distribution adjustment coefficient of the target user in different behavior modes and positions, and output the distribution adjustment coefficient as the distribution adjustment result.

[0014] The present application has the advantages that the present application can provide a more comfortable and personalized lighting environment for the user by accurately capturing the user behavior and intelligently adjusting the light parameter, and improve the user experience. The present application can intelligently adjust the light brightness and color temperature according to the change of the user behavior and the external environment, effectively reduce the energy consumption, and achieve the goal of energy saving and emission reduction. The present application can automatically complete the switching, brightness adjustment and color temperature adjustment of the light without manual adjustment of the user by the intelligent light control system, and improve the management efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0015] The present application will be further described below in combination with the drawings and embodiments.

[0016] Figure 1 It is a system framework diagram of a LED ball control system based on user behavior.

[0017] Figure 2 It is a system framework diagram of a parameter adjustment module of a LED ball control system based on user behavior.

[0018] Figure 3 It is a system diagram of an LED bulb control system based on user behavior.

[0019] Figure 4 The present invention is a system diagram of a parameter adjustment module of an LED bulb control system based on user behavior.

[0020] Figure 5 The present invention is a flow chart of a behavior analysis module of an LED bulb control system based on user behavior.

[0021] Figure 6 The present invention is a flow chart of a scene verification module of an LED bulb control system based on user behavior. DETAILED DESCRIPTION

[0022] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0023] See Figure 1 、 Figure 2 , an LED bulb control system based on user behavior, including: a behavior acquisition module, a behavior analysis module, a lighting preference module, a scene verification module and a parameter adjustment module; the behavior acquisition module outputs the user's behavior pattern to the behavior analysis module, and the behavior analysis module transmits the analyzed data to the lighting preference module after analyzing the behavior data; the lighting preference module analyzes the corresponding lighting under the behavior data and transmits the corresponding analyzed data to the scene verification module, the scene verification module verifies the current user's scene and transmits the data to the parameter adjustment module; the parameter adjustment module adjusts and analyzes all data to complete the processing of the overall data; the parameter adjustment module also includes an environmental factor unit, a behavior trend unit, and a distribution adjustment unit to complete the processing and evaluation of the overall data.

[0024] like Figure 3 、 Figure 4 As shown, the behavior acquisition module is used to obtain the behavior pattern of the target user and determine the initial lighting operating parameters of the target user under the corresponding behavior pattern; the behavior pattern also includes the target user's location, scene mode and lighting sequence.

[0025] The behavior analysis module is used to obtain the behavior data of the target user under the behavior mode, determine the overlap evaluation coefficient between the user behavior and the light, and set the movement guidance path of the light based on the overlap evaluation coefficient.

[0026] The light preference module is configured to determine a preference range and a preference color temperature of the light according to the behavior data of the user, and determine an adjustment parameter of the light under the behavior data.

[0027] The scene verification module is configured to verify a feedback consistency degree of the user behavior and the light feedback under different scene modes, and determine a feedback result related to the current light feedback.

[0028] The parameter adjustment module is configured to adjust a light sequence of the light according to the behavior mode of the target user, and determine a distribution adjustment result of the light.

[0029] The parameter adjustment module includes an environmental factor unit, a behavior trend unit, and a distribution adjustment unit. The environmental factor unit is configured to obtain external temperature, weather, and illumination, set an environmental factor, and output an environmental factor coefficient in combination with the adjustment parameter.

[0030] The behavior trend unit is configured to obtain a moving guide path of the light, and set a behavior trend coefficient in combination with a moving frequency and a moving interval of the current target user.

[0031] The distribution adjustment unit is configured to comprehensively process the environmental factor coefficient, the behavior trend coefficient, and the feedback result, obtain a distribution adjustment coefficient of the target user under different behavior modes and positions, and output the distribution adjustment coefficient as the distribution adjustment result.

[0032] The behavior mode refers to the repetitive behavior or activity mode of the user at a specific time and place. These modes can be identified through data collected by various sensors and smart devices, such as: activity time: the user's wake-up time, work time, rest time, etc.; activity type: the activities performed by the user within a certain time period, such as reading, working, resting, and entertainment; activity frequency: the activity frequency of the user within a certain period of time, such as frequent entry and exit of a certain room; activity intensity: the intensity of the user's activity, such as intense exercise, quiet reading, etc.

[0033] The currently set light needs to be adjusted according to the different current user states to meet the light intensity required by the user under different behavior modes; the behavior mode of the user can be identified by using a camera or other devices to identify the current user behavior, and the content performed by the user under the behavior mode can be determined.

[0034] The scene mode refers to a series of lighting configurations pre-set by the system according to the behavior mode of the user and the environmental conditions.

[0035] For example, a common scene mode example is: welcome mode: trigger condition: user comes home or enters a room; response action: the light gradually turns on, creating a warm atmosphere.

[0036] Working mode: Trigger condition: user sits at desk or opens computer; Response action: light adjusts to higher brightness, cold color temperature, helps improve focus.

[0037] Reading mode: Trigger condition: user picks up book or e-reader; Response action: light adjusts to moderate brightness, warm color temperature, reduces eye strain.

[0038] Relaxation mode: Trigger condition: user sits on sofa or plays soft music; Response action: light adjusts to lower brightness, warm color temperature, creates a relaxed atmosphere.

[0039] Sleep mode: Trigger condition: user enters bedroom and lies down; Response action: light gradually dims until completely off, helps user fall asleep better.

[0040] Away mode: Trigger condition: user leaves room or house; Response action: all lights turn off, ensures safety and energy saving.

[0041] Entertainment mode: Trigger condition: user turns on TV or sound equipment; Response action: light adjusts to appropriate brightness and color temperature, enhances viewing or listening experience.

[0042] Party mode: Trigger condition: multiple people are active in living room or music is playing; Response action: light adjusts to warm color tone, moderate brightness, creates a happy atmosphere.

[0043] Safety mode: Trigger condition: detects abnormal sound or motion; Response action: some or all lights turn on, reminds user to pay attention to safety.

[0044] At this time, after recognizing the user's behavior pattern, the initial light running parameters are set according to the user's current scene mode and the target user's position, and the light sequence is also set according to the user's relative position, thereby completing the setting of the initial light running parameters. At this time, the purpose of identifying the light sequence after obtaining the behavior pattern is to adjust the set light line according to the current scene mode and the position of the target user. If the light sequence is consistent in two settings, no modification is made, which can reduce energy consumption when the light changes dynamically.

[0045] In an embodiment of the present application, the behavior acquisition module is used to acquire the behavior pattern of the target user and determine the initial light running parameters of the target user in the corresponding behavior pattern; the behavior pattern further includes the position of the target user, the scene mode, and the light sequence.

[0046] When the module is implemented, the behavior mode of the current target user can be identified by setting corresponding devices, for example, deploying sensors: installing human infrared sensors, photosensitive sensors, sound sensors, etc. in the room; connecting smart devices: ensuring that the user's smart phone, smart watch and other devices are connected to the central controller; environmental monitoring: using photosensitive sensors and temperature sensors to monitor the light intensity and temperature of the outside world; extracting user activity time, activity type, activity frequency, activity intensity and other features; using machine learning algorithms to identify user behavior patterns, such as getting up in the morning, working hours, and coming home at night; so that the user's behavior pattern can be obtained, and the position and scene mode of the target user are also obtained after the behavior pattern is identified. As for the light sequence, if the light is currently in use, the current light sequence is obtained, otherwise the light sequence is considered as the value in the default initial light operation parameter.

[0047] At this time, the video data can be obtained, and the video data and the historical human body model and the human body behavior annotation are input into the neural network to identify the behavior mode of the target user in action.

[0048] In an embodiment of the present application, the behavior analysis module implementation includes the following contents.

[0049] The moving guide path of the light in the behavior analysis module is generated according to the user movement path to help the user walk safely at night or in a low-light environment.

[0050] The behavior data obtained in the module mainly includes the activity time, activity type and activity frequency of the target user in the behavior mode, determines the coincidence between the light irradiation range and the user movement at this time, calculates the coincidence rate of the light irradiation range and the user movement path, to obtain the coincidence evaluation coefficient at this time; the light irradiation range can be determined by the irradiation angle of the current LED at this time, and the light irradiation range can also be represented by using the equal-illumination line diagram to represent the light intensity distribution of the light emitted at the current position, and the coincidence between the user position point and the corresponding light irradiation range under equal-illumination is identified according to the light intensity distribution.

[0051] As shown in Figure 5 The implementation of obtaining the coincidence evaluation coefficient at this time can be based on the behavior data of the target user to obtain the activity time, activity type and activity frequency of the target user; based on the activity time, activity type and activity frequency of the target user, the user movement path of the current target user is identified, the coincidence of the position points on the user movement path and the light irradiation range is calculated, the coincidence rate of the position points on the user movement path is obtained, and the coincidence evaluation coefficient is calculated according to the coincidence rate of the position points.

[0052] The user movement path can be obtained by the currently identified activity time, activity type and activity frequency, and the position points of the target user when moving are obtained, so that the specific movement of the user under different types can be determined, and the position points are regarded as the user movement path; the position points are clustered to obtain a plurality of clustering clusters; the clustering manner includes using the activity time, activity type and activity frequency as the clustering cluster, clustering the position points of the user, obtaining the features related to the activity time, activity type and activity frequency, calculating the feature data difference value of the feature of the center point of each clustering cluster compared with other features in the current clustering cluster, and matching the feature data difference value with the light irradiation range to obtain the user movement path; at this time, the matching manner is to cluster the position points under the corresponding light irradiation range according to the feature data difference value to obtain some position points that are most closely related to the behavior data of the user at this time, so as to evaluate what form of guide path the user generally needs under the related action mode, so as to improve the control condition of the LED bulb.

[0053] The coincidence rate is set according to the distance between the position points on the user movement path and the center point of the light irradiation range and the radius of the light irradiation range in sequence. When the distance between the position points on the user movement path and the center point of the light irradiation range is less than the radius of the light irradiation range, the logical value of the position point is set to 1, and when the distance is greater than the radius of the light irradiation range, the logical value is set to 0. The average value of the logical values is taken as the coincidence rate of the position points on the user movement path.

[0054] The coincidence evaluation coefficient is represented as the position matching coefficient and the time matching coefficient corresponding to the position points under the corresponding coincidence rate value are obtained, and the coincidence evaluation coefficient is obtained.

[0055] At this time, the position matching coefficient is to match the position of the position point, and the time matching coefficient is to match the matching value between the activity time of the target user and the expected time of the light. Finally, the position matching coefficient, the time matching coefficient and the coincidence rate are comprehensively evaluated to obtain the coincidence evaluation coefficient.

[0056] The position matching coefficient is represented as ; wherein represents the position matching coefficient of the i-th position point, represents the radius of the light irradiation range, represents the distance between the i-th position point and the center point of the light irradiation range;

[0057] The time matching coefficient is represented as ; wherein represents the time matching coefficient of the i-th position point, represents the difference value between the time stamp corresponding to the i-th position point and the expected time stamp, The time difference threshold value indicates that the time matching coefficient is used to determine whether the user is within the expected time range when the light is displayed according to the set path. The expected time stamp indicates the expected time of the user moving to the corresponding position. The time difference threshold value indicates the user's movement within a certain time period. The movement time length in a small area is used as the time difference threshold value at this time. The time difference threshold value is set from the average time required by the user to move in a certain area based on historical data.

[0058] The coincidence evaluation coefficient is represented as ; wherein, The coincidence evaluation coefficient is represented as The coincidence rate is represented as The number of position points is represented as i, and the value of i ranges from 1 to n.

[0059] The coincidence evaluation coefficient obtained at this time determines how the current LED bulb control system sets the movement of the light of the current LED bulb according to the user's movement when the user moves within the expected time or the relative distance between the user and the light. This light movement guide path can be dynamically achieved by adjusting the angle of the light emitted and the opening or closing of multiple groups of lights to obtain the final output light movement guide path.

[0060] The implementation of the light movement guide path is as follows: when the coincidence evaluation coefficient is greater than the first coincidence threshold value, it indicates that the overall setting is ideal and no significant adjustment is needed. The position points with a product value greater than the preset threshold value of the position matching coefficient and the time matching coefficient can be used as the light movement guide path at this time. The preset threshold value set here can be 0.6, and the points with a value greater than 0.6 are used as the main light irradiation position. When the coincidence evaluation coefficient is less than the second coincidence threshold value, it indicates that the overall setting of the light is not ideal, resulting in different light irradiation in some positions, which needs to be optimized and adjusted. For example, the position points with a product value less than the preset product value of the coincidence rate, the position matching coefficient and the time matching coefficient are used as the light movement guide path. The preset product value set at this time can be 0.3, which is used to express the part with obvious light irradiation shortage to adjust the switch and light irradiation range of the set LED bulb.

[0061] When the coincidence evaluation coefficient is less than the first coincidence threshold and greater than the second coincidence threshold, the position point corresponding to the average of the position matching coefficient and the time matching coefficient is obtained, and the user movement path where the position point corresponding to the average of the position matching coefficient and the time matching coefficient is located is taken as the movement guide path of the light; at this time, the adjustment is to indicate that the current light setting is in a relatively ordinary state, and at this time, the corresponding path can be evaluated according to the average, so the user movement path where the position point is located is selected as the movement guide path at this time, to complete the setting of the overall light.

[0062] At this time, the first coincidence threshold and the second coincidence threshold can be set to 0.5 and 0.3 respectively, and since the value of the product at this time is affected by multiple position points, the finally calculated value will be relatively small, and the threshold value set at this time will also be adjusted according to the range of the finally calculated value, and historical data can also be used to set the first coincidence threshold and the second coincidence threshold. The historical data is sorted from large to small according to the position matching coefficient and the time matching coefficient, and at this time, the position matching coefficient is taken as the first sorting field, and when the position matching coefficient is the same, the time matching coefficient value is sorted. The coincidence evaluation coefficient of the top ten percent of the historical data after sorting at this time is taken as the first coincidence threshold at this time, and the coincidence evaluation coefficient of the last thirty percent of the data is taken as the second coincidence threshold at this time, so as to complete the adjustment of the current light and behavior.

[0063] The following examples can be used to illustrate the implementation of the module.

[0064] For example, a human body infrared sensor detects that a user enters a house, and a smartphone positioning technology determines that the user is at the entrance; the coincidence evaluation coefficient of the user at different position points is calculated, and it is assumed that the coincidence evaluation coefficient at the entrance is low; a lamp can be added at the entrance to ensure that the user has enough lighting when entering the house, or the light at the entrance can be adjusted to the position where the user most often passes through, and then the light at the entrance is adjusted to a moderate brightness and warm color tone to create a warm welcome atmosphere. When the user moves from the entrance to the living room, the system adjusts the light in the living room in advance to ensure that the user is always in a suitable lighting environment, and dynamically adjusts the brightness and color temperature of the light according to the activity type of the user in the living room (such as watching TV, reading, etc.).

[0065] At this time, in addition to the implementation mode of using image recognition processing, a variety of sensors can also be used for setting to complete the intelligent adjustment of the LED ball control system.

[0066] In an embodiment of the present application, in the light preference module, it is necessary to determine that the range and color temperature of the light at this time can meet the needs of the user, for example, adjusting the illumination range of the light to ensure uniform illumination; learning the color temperature preference of the user in different scenes through the historical data and feedback of the user; and the preference range of the light refers to the preference range of the illumination intensity, which is used to describe the intensity of the current light.

[0067] At this time, the user's behavior data can be processed, so the way to obtain the adjustment parameter is to take the illumination intensity and color temperature of the light setting as the independent variable, and the user's behavior data as the dependent variable, analyze the regression coefficient of the user's behavior data and the data corresponding to the current light setting of the illumination intensity and color temperature, perform t-test analysis on the regression coefficient to obtain the significant coefficient, and set the adjustment parameter according to the significant coefficient.

[0068] At this time, the form of setting the light preference is mainly based on the preference range affected by the illumination intensity of the light and the color temperature setting value, for example, in the regression analysis, the probability of the illumination intensity and the probability of the color temperature of the light in each time period are taken as inputs to obtain the regression coefficient corresponding to the current illumination intensity and color temperature of the light; then the regression coefficient is subjected to t-test analysis to obtain the significant coefficient, and the significant coefficient is compared with the preset preference factor of the preference range and the preference color temperature to obtain the adjustment parameter; the comparison method is to compare the significant coefficient corresponding to the illumination intensity and color temperature at this time with the numerical value of the preset preference factor, and select the significant coefficient corresponding to the maximum difference value of the significant coefficient and the preset preference factor at this time to select the parameter that needs to be adjusted at this time, that is, the illumination intensity and color temperature corresponding to the maximum difference value of the significant coefficient and the preset preference factor; the preset preference factor is the average value of the corresponding significant coefficient of the illumination intensity and color temperature in the historical data when the user sets the light; according to the obtained illumination intensity and color temperature, the illumination intensity and color temperature that need to be set at present can be known to improve the accuracy of the adaptation of the preference range and the preference color temperature of the light to the user's behavior.

[0069] The ball control system set under the adjustment parameter is mainly inclined to control a single or multiple LED bulbs, controls in a scene without performing a corresponding precise pattern, and completes the control of the LED bulb to release light according to the illumination intensity and color temperature of the ball set at this time to realize the light demand of the user in a specific scene.

[0070] The adjustment parameter can be represented as the illumination intensity and color temperature of the light; the illumination intensity of the light is adjusted according to the significant coefficient and the preset preference factor; the color temperature of the light is adjusted according to the significant coefficient and the preset preference factor.

[0071] For example, if the significance coefficient indicates that the current light intensity is too high and the user prefers low brightness in relaxation mode, the light intensity is reduced; conversely, if the significance coefficient indicates that the current light intensity is too low and the user prefers high brightness in work mode, the light intensity is increased.

[0072] For example, if the significance coefficient indicates that the current color temperature is cool, and the user prefers warm color temperature in relaxation mode, the color temperature is adjusted to a warm tone; conversely, if the significance coefficient indicates that the current color temperature is warm, and the user prefers cool color temperature in work mode, the color temperature is adjusted to a cool tone.

[0073] In one embodiment of the present invention, the scene verification module is used to verify the feedback consistency between user behavior and lighting feedback in different scene modes, and determine the feedback result related to the current lighting feedback.

[0074] This module verifies the consistency and satisfaction of user behavior and lighting feedback in different scenarios and at different times through experiments and user feedback. For example, by identifying and analyzing current lighting data, we determine the similarity between user behavior and lighting feedback in scene mode and historical data, and then determine how to adjust the lighting in the current scenario.

[0075] At this time, it is also possible to evaluate the time it takes for the current control system to meet user needs when user behavior changes, or to judge whether the scene mode is consistent with the user feedback form when the scene mode changes with the user, so as to identify whether the current control system can meet user needs when making adaptive adjustments.

[0076] like Figure 6 As shown, at this time, the feedback consistency between user behavior and lighting feedback in different scene modes is verified, and the feedback result related to the current lighting feedback is determined by: obtaining the lighting setting data of the target user in the corresponding scene mode, and calculating the first similarity between the current lighting setting data and the behavior data regarding the user behavior.

[0077] Obtain a second similarity of response time in the light setting data when the scene mode is switched.

[0078] A third similarity between the lighting setting data and the behavior data regarding lighting feedback is obtained.

[0079] The first similarity, the second similarity, and the third similarity are combined and output as a feedback result related to the light feedback.

[0080] At this time, the feedback consistency between user behavior and light feedback can be expressed in three situations: whether the user behavior matches the light feedback, the response time of the light to the user behavior, and whether the light feedback reflects the user's intentions and needs.

[0081] The three cases correspond to the first similarity, the second similarity and the third similarity in turn, and the three similarities are integrated, so that whether the current light and the user feedback match can be known, so that whether the adjustment parameter and the moving guide path set by the current system can correspond to the user demand can be evaluated, after completing the verification, parameter adjustment is also needed, at this time, the module can also evaluate the content after parameter adjustment when being implemented, to perfect the implementation of the whole ball bubble control system.

[0082] The light setting data includes the illumination intensity, the color temperature value, the illumination range of the light and the response time corresponding to the scene mode switching.

[0083] The first similarity uses the calculation method of the Pearson correlation coefficient, compares the light setting data as input with the average values of the illumination intensity, the color temperature value and the illumination range of the light in the historical data, and calculates the Pearson correlation coefficient under the corresponding behavior data according to the behavior data as an index, the purpose of the Pearson correlation coefficient is to verify whether the parameters of the current light correspond to the behavior data, which can be judged by observing the value of the Pearson correlation coefficient under the corresponding behavior data, when the value is greater than 0.6, the corresponding behavior data and the light setting data are marked, and the ratio of the marked behavior data, light setting data and all behavior data, light setting data is regarded as the first similarity at this time.

[0084] The second similarity is the ratio of the standard deviation of the current response time and the standard deviation of the historical data.

[0085] The third similarity is to record the ratio of the current light feedback accurate data and the total data, and the feedback accurate data is obtained through the evaluation of the user.

[0086] The first similarity, the second similarity and the third similarity are finally weighted and averaged to obtain the feedback result related to the light feedback, and the feedback result also includes the values of the first similarity, the second similarity and the third similarity when being output, so that the external monitoring system can accurately identify the running condition of the current system.

[0087] In an embodiment of the present application, in the environmental factor unit, certain values are set for the external temperature, the weather and the light to represent the current environmental condition, for the adjustment parameter, the illumination intensity and the color temperature corresponding to the adjustment parameter are selected and integrated to obtain the environmental factor coefficient.

[0088] The environmental factor coefficient can be expressed as: obtaining an environmental factor and an adjustment parameter, the adjustment parameter including an illumination intensity and a color temperature, the environmental factor including a temperature coefficient, a weather coefficient, and an external light coefficient, sequentially normalizing the environmental factor and the adjustment parameter, and calculating the environmental factor coefficient according to an index value of the environmental factor and the adjustment parameter.

[0089] ; wherein, represents an environmental factor coefficient, represents a temperature coefficient, represents a weather coefficient, represents an external light coefficient, represents an index value of the illumination intensity, represents an index value of the color temperature. represents a weight factor of the temperature coefficient, represents a weight factor of the weather coefficient, represents a weight factor of the external light coefficient, represents a weight factor of the illumination intensity, represents a weight factor of the color temperature, represents an exponential constant; the weight factors set according to the order of the temperature coefficient, the weather coefficient, the external light coefficient, the illumination intensity, and the color temperature can be 0.2, 0.15, 0.15, 0.25, and 0.25.

[0090] For the temperature coefficient, the coefficient value is set according to the temperature value range, the temperature coefficient value range is 0-1, at this time, the maximum and minimum values of the temperature corresponding to the date can be divided into five temperature intervals, and the five temperature coefficients are set, for example, the coefficient values are set as 0.8, 0.9, 1, 0.9, and 0.8, or the average value of the coefficient value set in the historical data according to the current temperature is selected as the value of the temperature coefficient at this time.

[0091] For the weather coefficient and the external light coefficient, the value range is consistent with the temperature coefficient, which is 0-1, and the average value of the coefficient value set in the historical data according to the corresponding weather and illumination intensity can be used as the weather coefficient and the external light coefficient.

[0092] The behavior trend unit is used to obtain a moving guide path of the light, and set a behavior trend coefficient in combination with the moving frequency and the moving interval of the current target user.

[0093] At this time, the moving frequency and the moving interval are expressed as a ratio relative to the moving frequency and the moving interval in the historical data, and in combination with the coincidence evaluation coefficient obtained on the moving guide path, the corresponding trend situation when guiding the movement is determined.

[0094] The behavior trend coefficient is obtained by obtaining the coincidence evaluation coefficient, the moving frequency and the moving interval of the current moving guide path, and calculating the behavior trend coefficient after normalizing the coincidence evaluation coefficient, the moving frequency and the moving interval.

[0095] ; wherein, represents the behavior trend coefficient, represents the index value of the moving frequency, represents the index value of the moving interval, wherein the index values of the moving frequency and the moving interval represent normalized values; represents the coincidence evaluation coefficient, represents the standard value of the moving frequency, represents the standard value of the moving interval, wherein the standard values of the moving frequency and the moving interval are represented by the standard deviations of the moving frequency and the moving interval in the historical data; represents the weight factor of the moving frequency, represents the weight factor of the moving interval, represents the weight factor of the coincidence evaluation coefficient.

[0096] In the distribution adjustment unit, the distribution adjustment coefficient is matched by a value obtained by comprehensively processing the current environmental factor coefficient, the behavior trend coefficient and the feedback result, and the scheme corresponding to the current setting in the historical data is obtained to complete the rapid adjustment of the light.

[0097] The distribution adjustment coefficient is represented as, ; wherein, represents the distribution adjustment coefficient, represents the environmental factor coefficient, represents the behavior trend coefficient, represents the index value of the feedback result.

[0098] At this time, after obtaining the distribution adjustment coefficient, the required distribution adjustment result can be selected from the historical data according to the value of the current distribution adjustment coefficient, so that the user can always be in a suitable lighting environment.

[0099] When adjusting in the behavior trend unit, the following adjustment can be made.

[0100] Moving interval: The moving interval of the user is recognized by the camera to determine whether the user is ready to rest or go out, and the light setting is adjusted.

[0101] Moving frequency: The moving frequency of the user is monitored by the sensor to determine the current activity state of the user, and the brightness and color temperature of the light are adjusted.

[0102] The environmental factor unit can be adjusted in the following manner.

[0103] Temperature: Adjust the color temperature of the light in high or low temperature environments, providing a more comfortable lighting environment.

[0104] Weather: Adjust the brightness and color temperature of the light in rainy or sunny weather, compensating for changes in natural light.

[0105] Illumination: Automatically adjust the brightness of indoor lighting according to the intensity of external light, ensuring uniform and comfortable indoor lighting.

[0106] The distribution adjustment unit can be implemented in the following manner.

[0107] Step position: Adjust the distribution and irradiation range of the light according to the specific position of the user in different behavior patterns.

[0108] Dynamic adjustment: Dynamically adjust the distribution of the light when the user moves, ensuring that the user is always in a suitable lighting environment.

[0109] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application, which are still covered by the protection scope of the present application.

Claims

1. A LED bulb control system based on user behavior, characterized in that: include: The behavior acquisition module is used to obtain the behavior pattern of the target user and determine the initial lighting operating parameters of the target user under the corresponding behavior pattern; the behavior pattern also includes the target user's location, scene mode and lighting sequence; The behavior analysis module is used to obtain the behavior data of the target user under the behavior mode, determine the overlap evaluation coefficient between the user behavior and the light, and set the movement guidance path of the light based on the overlap evaluation coefficient; A lighting preference module is used to determine the lighting preference range and preferred color temperature based on the user's behavior data, and to determine the lighting adjustment parameters based on the behavior data; The scene verification module is used to verify the consistency between user behavior and lighting feedback in different scene modes and determine the feedback results related to the current lighting feedback; The parameter adjustment module is used to adjust the lighting sequence of the lights according to the behavior pattern of the target user and determine the distribution adjustment result of the lights; Parameter adjustment module, including environmental factor unit, behavior trend unit, and distribution adjustment unit; The environmental factor unit is used to obtain the external temperature, weather and light, set the environmental factor, and output the environmental factor coefficient in combination with the adjustment parameters; The behavior trend unit is used to obtain the movement guidance path of the light and set the behavior trend coefficient based on the movement frequency and movement interval of the current target user; A distribution adjustment unit is used to comprehensively process the environmental factor coefficient, the behavior trend coefficient, and the feedback result to obtain the distribution adjustment coefficient of the target user under different behavior patterns and locations, and output the distribution adjustment coefficient as a distribution adjustment result; The position matching coefficient is expressed as, ;in, represents the position matching coefficient of the i-th position point, Indicates the radius of the light's illumination range. Indicates the distance between the i-th position point and the center point of the light illumination range; The time matching coefficient is expressed as, ;in, represents the time matching coefficient of the i-th position point, Represents the difference between the timestamp corresponding to the i-th position point and the expected timestamp, Indicates the time difference threshold; The overlap assessment coefficient is expressed as, ;in, represents the coincidence evaluation coefficient, represents the overlap rate, Indicates the number of location points, the value of i ranges from 1 to n; The environmental factor coefficient is expressed as follows: obtain the environmental factor and adjustment parameters, the adjustment parameters include light intensity and color temperature, the environmental factor includes temperature coefficient, weather coefficient and external light coefficient, normalize the environmental factor and adjustment parameters in turn, and calculate the environmental factor coefficient according to the index values ​​of the environmental factor and adjustment parameters; ； in, represents the environmental factor coefficient, represents the temperature coefficient, represents the weather coefficient, represents the external illumination coefficient, An index value indicating light intensity. An index value indicating color temperature; represents the weighting factor of the temperature coefficient, represents the weight factor of the weather coefficient, Represents the weight factor of the external lighting coefficient, Represents the weight factor of light intensity, represents the weighting factor of color temperature, represents the exponential constant; The behavior trend coefficient is obtained by obtaining the overlap evaluation coefficient, movement frequency, and movement interval under the current movement guidance path, normalizing the overlap evaluation coefficient, movement frequency, and movement interval, and calculating the behavior trend coefficient. ； in, represents the behavioral trend coefficient, An indicator value indicating the frequency of movement, The index value representing the movement interval, in this case, the index values ​​of the movement frequency and the movement interval represent normalized values; represents the coincidence evaluation coefficient, Indicates the standard value of the moving frequency, Indicates the standard value of the moving interval, represents the exponential constant; represents the weight factor of the moving frequency, represents the weight factor of the moving interval, Represents the weight factor of the coincidence assessment coefficient; The distribution adjustment coefficient is expressed as: ； in, represents the distribution adjustment coefficient, represents the environmental factor coefficient, represents the behavioral trend coefficient, Indicates the indicator value of the feedback result, Represents an exponential constant.

2. The LED bulb control system based on user behavior according to claim 1, characterized in that: The overlap evaluation coefficient is implemented as follows: Based on the target user's behavioral data, the target user's activity time, activity type and activity frequency are obtained; based on the target user's activity time, activity type and activity frequency, the user movement path of the current target user is identified, and the overlap between the position points on the user's movement path and the lighting range is calculated to obtain the overlap rate of the position points on the user's movement path, and the position matching coefficient and time matching coefficient corresponding to the position points under the corresponding overlap rate value are obtained to obtain the overlap evaluation coefficient.

3. The LED bulb control system based on user behavior according to claim 2, characterized in that: The user movement path is represented as follows: obtaining the location points of the target user when moving, clustering the location points, and obtaining multiple clusters; the clustering method includes using activity time, activity type and activity frequency as clusters, clustering the user's location points, obtaining features related to activity time, activity type and activity frequency, calculating the feature data difference between the feature of the center point of each cluster and the feature data of other features in the current cluster, and matching the feature data difference with the lighting range to obtain the user movement path.

4. The LED bulb control system based on user behavior according to claim 2, characterized in that: The light's movement guidance path is implemented by, when the overlap evaluation coefficient is greater than a first overlap threshold, taking the position point where the product of the position matching coefficient and the time matching coefficient is greater than a preset threshold as the light's movement guidance path at that time; when the overlap evaluation coefficient is less than a second overlap threshold, taking the position point where the product of the overlap rate, the position matching coefficient, and the time matching coefficient is less than a preset product value as the light's movement guidance path; When the overlap evaluation coefficient is less than the first overlap threshold and greater than the second overlap threshold, the position point corresponding to the average value of the position matching coefficient and the time matching coefficient is obtained, and the user movement path where the position point corresponding to the average value of the position matching coefficient and the time matching coefficient is located is used as the movement guidance path of the light.

5. The LED bulb control system based on user behavior according to claim 1, characterized in that: The adjustment parameters are obtained by taking the light intensity and color temperature of the lighting settings as independent variables and the user's behavior data as the dependent variable, analyzing the regression coefficients of the user's behavior data and the data corresponding to the light intensity and color temperature of the current lighting settings, performing a t-test analysis on the regression coefficients, obtaining the significant coefficients, and setting the adjustment parameters according to the significant coefficients.

6. The LED bulb control system based on user behavior according to claim 1, characterized in that: Verify the consistency between user behavior and lighting feedback in different scene modes, and determine the implementation method of the feedback results related to the current lighting feedback: Obtaining lighting setting data of the target user in the corresponding scene mode, and calculating a first similarity between the current lighting setting data and the behavior data regarding the user behavior; Obtaining a second similarity of response time in the lighting setting data when the scene mode is switched; obtaining a third similarity between the lighting setting data and the behavior data regarding lighting feedback; The first similarity, the second similarity, and the third similarity are combined and output as a feedback result related to the light feedback.

Images