Intelligent lighting control method and system combined with user analysis

Abstract

The present invention discloses an intelligent lighting control method and system combined with user analysis, and relates to the technical field of lighting control. The method comprises: obtaining a user acquisition module, acquiring a first user according to the user acquisition module, and acquiring a real-time sensor image and a real-time sensor signal; acquiring a synchronous sensor image and a synchronous sensor signal after synchronization processing; obtaining a pre-trained pleasure matching model, and inputting the synchronous sensor image and the synchronous sensor signal into the pleasure matching model; obtaining a user pleasure index according to the pleasure matching model, and inputting the user pleasure index into a lighting control module for optimization, obtaining lighting control parameters, and controlling a lighting lamp group based on the lighting control parameters, thereby solving the problem in the prior art that the lighting control work is difficult to break the traditional single detection due to the inability to perfectly and systematically combine the environment with human psychological detection, and realizing the combination of lighting and human mental health.

Description

Technical Field

[0001] The present invention relates to the technical field of lighting control, and in particular to an intelligent lighting control method and system combined with user analysis. Background Art

[0002] With the development of science and technology and the improvement of people's living standards, the concept of smart home has gradually gained popularity. Among them, smart lighting control, as an important part of smart home, is of great significance to improving the quality of life and energy conservation and emission reduction. However, the existing smart lighting control methods often only consider the physical parameters of lighting equipment, and cannot break the phenomenon of traditional general lighting. If we want to truly realize the combination of lighting and human mental health, realize more value of lighting, and improve the social human happiness and safety index, the smart lighting control method and system combined with user analysis are of great significance.

[0003] The lighting control work existing in the existing technology is difficult to break the phenomenon of traditional single detection because it cannot perfectly and systematically combine the environment with human psychological detection, making it impossible to ultimately combine lighting with human mental health. Summary of the invention

[0004] The present application provides an intelligent lighting control method and system combined with user analysis, which solves the problem that the lighting control work in the prior art is difficult to break through the traditional single detection due to the inability to perfectly and systematically combine the environment with human psychological detection, and realizes the combination of lighting and human mental health.

[0005] In view of the above problems, the present application provides an intelligent lighting control method combined with user analysis.

[0006] In a first aspect, the present application provides an intelligent lighting control method combined with user analysis, the method comprising: obtaining a user acquisition module, the user acquisition module comprising an image acquisition device and an infrared detector; acquiring a first user according to the user acquisition module to acquire a real-time sensor image and a real-time sensor signal; synchronously processing the real-time sensor image and the real-time sensor signal to acquire a synchronized sensor image and a synchronized sensor signal after the synchronized processing; obtaining a pre-trained pleasure matching model, inputting the synchronized sensor image and the synchronized sensor signal into the pleasure matching model, wherein the pleasure matching model establishes a synchronized mapping sample set based on sensor image samples and sensor signal samples, and is trained and acquired by identification information identifying a user pleasure index; acquiring a user pleasure index according to the pleasure matching model, inputting the user pleasure index into a lighting control module for optimization, acquiring lighting control parameters, and controlling a lighting group based on the lighting control parameters.

[0007] In a second aspect, the present application provides an intelligent lighting control system combined with user analysis, and the system includes: an image infrared module: obtaining a user acquisition module, the user acquisition module includes an image collector and an infrared detector; a real-time sensing module: acquiring a first user according to the user acquisition module, and acquiring a real-time sensing image and a real-time sensing signal; a synchronization processing module: synchronously processing the real-time sensing image and the real-time sensing signal, and acquiring a synchronized sensing image and a synchronized sensing signal after the synchronization processing; a matching model module: obtaining a pre-trained pleasure matching model, and inputting the synchronized sensing image and the synchronized sensing signal into the pleasure matching model, wherein the pleasure matching model establishes a synchronized mapping sample set based on sensor image samples and sensor signal samples, and is trained and acquired by identification information identifying the user pleasure index; a pleasure index module: obtaining a user pleasure index according to the pleasure matching model, and inputting the user pleasure index into the lighting control module for optimization, acquiring lighting control parameters, and controlling the lighting lamp group based on the lighting control parameters.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The intelligent lighting control method and system combined with user analysis provided in the embodiment of the present application obtains a user acquisition module, which includes an image acquisition device and an infrared detector, and then acquires the first user according to the user acquisition module to acquire a real-time sensor image and a real-time sensor signal, and synchronously processes the real-time sensor image and the real-time sensor signal to acquire a synchronized sensor image and a synchronized sensor signal after the synchronized processing, and then obtains a pre-trained pleasure matching model, inputs the synchronized sensor image and the synchronized sensor signal into the pleasure matching model, and finally obtains the user pleasure index according to the pleasure matching model, inputs the user pleasure index into the lighting control module for optimization, obtains lighting control parameters, and controls the lighting lamp group based on the lighting control parameters, thereby solving the problem that the lighting control work in the prior art is difficult to break the traditional single detection due to the inability to perfectly and systematically combine the environment with human psychological detection, and realizes the combination of lighting and human mental health. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of an intelligent lighting control method combined with user analysis is provided for this application;

[0011] Figure 2 A schematic diagram of the structure of an intelligent lighting control system combined with user analysis is provided for this application.

[0012] Explanation of the reference numerals: image infrared module 11 , real-time sensing module 12 , synchronization processing module 13 , matching model module 14 , pleasure index module 15 . DETAILED DESCRIPTION

[0013] The present application provides an intelligent lighting control method and system combined with user analysis, by obtaining a user acquisition module, the user acquisition module includes an image acquisition device and an infrared detector, and then collecting data from the first user according to the user acquisition module, obtaining real-time sensor images and real-time sensor signals, and synchronously processing the real-time sensor images and real-time sensor signals, obtaining synchronized sensor images and synchronized sensor signals after synchronization processing, and then obtaining a pre-trained pleasure matching model, inputting the synchronized sensor images and synchronized sensor signals into the pleasure matching model, and finally obtaining the user pleasure index according to the pleasure matching model, inputting the user pleasure index into the lighting control module for optimization, obtaining lighting control parameters, and controlling the lighting lamp group based on the lighting control parameters. This solves the problem that the lighting control work in the prior art is difficult to break the traditional single detection due to the inability to perfectly and systematically combine the environment with human psychological detection, realizes the combination of lighting and human mental health, realizes more value of lighting, and improves the social human happiness and safety index.

[0014] Embodiment 1

[0015] like Figure 1 As shown, the present application provides an intelligent lighting control method and system combined with user analysis, the method comprising:

[0016] Acquire a user acquisition module, wherein the user acquisition module includes an image collector and an infrared detector;

[0017] Collecting the first user according to the user collection module to obtain a real-time sensing image and a real-time sensing signal;

[0018] The user acquisition module includes an image collector and an infrared collector. The image collector mainly acquires the user's environment, and acquires image information such as the user's movements and behaviors through the image collector to obtain image data, and outputs the image data as a real-time sensor image. The human body will radiate different infrared waves according to its own mood, and present different wavelengths and fluctuations according to different behaviors. The infrared waves of the user are collected through the infrared collector to obtain infrared sensor signals, and the infrared sensor signals are output as real-time sensor signals, which provide a basis for the subsequent acquisition of the first user according to the user acquisition module to obtain real-time sensor images and real-time sensor signals.

[0019] Synchronously processing the real-time sensing image and the real-time sensing signal to obtain a synchronously processed synchronous sensing image and a synchronously processed synchronous sensing signal;

[0020] There is a corresponding relationship between the real-time sensor image and the real-time sensor signal, that is, the two types of data generated by the same user in the same time period will have certain errors in the process of acquiring the two types of data respectively, resulting in corresponding asynchrony between the two types of data, which is not conducive to the subsequent joint analysis of the two types of data. It is necessary to synchronize the real-time sensor image and the real-time sensor signal. Assign a timestamp to each real-time sensor image and real-time sensor signal to ensure that the data collected by each sensor is aligned in time. According to the result of timestamp alignment, the real-time sensor image and the real-time sensor signal are data-associated to ensure that each image frame matches its corresponding signal data, and a synchronized sensor image and a synchronized sensor signal are obtained, which is convenient for the subsequent synchronization of the sensor image and the sensor signal.

[0021] Acquire a pre-trained pleasure matching model, and input the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model is trained and acquired by establishing a synchronous mapping sample set based on the sensing image sample and the sensing signal sample, and identification information identifying the user pleasure index;

[0022] Inputting the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model comprises a loss recognition network layer based on an image mask and a loss recognition network layer based on a signal mask, the loss recognition network layer of the image mask comprises a first prediction mask value, and the loss recognition network layer of the signal mask comprises a second prediction mask value;

[0023] Using the loss identification network layer of the image mask and the loss identification network layer of the signal mask to perform identification respectively, to obtain a loss indicator based on the first predicted mask value and a loss indicator based on the second predicted mask value;

[0024] A user pleasure index is obtained according to the loss index of the first predicted mask value and the loss index of the second predicted mask value.

[0025] The sensor image samples, sensor signal samples and identification information of the user pleasure index are obtained in advance through big data, and the sensor image samples and sensor signal sample positivity data sets are constructed to obtain a synchronous mapping sample set. The sensor image samples and sensor signal samples are masked, that is, the interested parts in the corresponding data are processed. The pleasure matching model includes a loss recognition network layer based on image mask and a loss recognition network layer based on signal mask. The loss recognition network layer of image mask includes a first predicted mask value, which is the standard value for masking the sensor image samples. The loss recognition network layer of signal mask includes a second predicted mask value, which is the standard value for masking the sensor signal samples. The loss recognition network layer of image mask and the loss recognition network layer of signal mask are used for identification respectively to obtain a loss index based on the first predicted mask value, which indicates the degree of deviation between the value calculated by the network model and the first predicted mask value, and a loss index based on the second predicted mask value, which indicates the degree of deviation between the value calculated by the network model and the second predicted mask value. According to the loss index of the first predicted mask value and the loss index of the second predicted mask value, a comprehensive calculation is performed to obtain the total loss index, and the user pleasure index is obtained according to the standard pleasure index corresponding to the total loss index. By constructing a pleasure matching model, the efficiency and accuracy of obtaining user pleasure can be improved.

[0026] The user pleasure index is obtained according to the pleasure matching model, the user pleasure index is input into the lighting control module for optimization, the lighting control parameters are obtained, and the lighting lamp group is controlled based on the lighting control parameters.

[0027] Obtaining control parameter indicators of the lighting lamp group, wherein the control parameter indicators include the color temperature of the lamp group, the color rendering index of the lamp group, and the lighting brightness;

[0028] The color temperature of the lamp group, the color rendering index of the lamp group and the lighting brightness are used as an indicator set to generate a control solution set;

[0029] Obtaining the intervals of each control parameter, and generating an optimal space solution set according to the control solution set;

[0030] An objective function is established, and each solution set in the optimization space solution set is calculated according to the objective function to obtain an optimization result, where the optimization result is an optimization solution set based on the user pleasure index.

[0031] The controllable content of the lighting lamp group is obtained, and the controllable content includes the color temperature of the lamp group, the color rendering of the lamp group, and the lighting brightness. The controllable content is the control parameter index. The color temperature of the lamp group, the color rendering of the lamp group, and the lighting brightness in the control parameter index are taken as an index set to generate a control solution set. The interval of the control parameter of each control parameter index is obtained, and multiple optimal space solutions are generated according to the control solution set, and the multiple optimal space solutions are sorted into an optimal space solution set. An objective function is established, and each solution set in the optimal space solution set is calculated by the objective function to obtain the optimal space solution. There are multiple optimal space solutions, that is, the optimization result is an optimal solution set based on the user pleasure index. The lighting control parameters are obtained by the various control parameters in the optimal solution set, and the lighting lamp group is controlled according to the lighting control parameters. The lighting control effect that is more in line with the scene and the current state of the user can be obtained by controlling the lighting lamp group through the control parameters obtained after optimization.

[0032] Furthermore, the method further comprises:

[0033] Analyze each solution set in the optimization space solution set to obtain a lighting pleasure index set;

[0034] The matching degree is calculated by using the lighting pleasure index set and the user pleasure index to obtain a matching degree set. The objective function is used to identify the matching degree set, and the maximum matching degree is used as the optimization target to obtain an optimization result.

[0035] Obtain the user pleasure index of each solution set in the optimization space solution set, output the user pleasure index as the lighting pleasure index, obtain the lighting pleasure index, and sort out multiple lighting pleasure indices to obtain a lighting pleasure index set. Perform matching calculation on the lighting pleasure index set and the user pleasure index, determine the similarity between the lighting pleasure index and the user pleasure index, and obtain a matching set. The higher the matching degree, the more the lighting pleasure index set meets the user's current required lighting conditions. The objective function is used to identify the matching set, take the maximum matching degree as the optimization goal, obtain the lighting pleasure index set with the highest matching degree, and obtain the optimization result. Identification based on the matching set and taking the maximum matching degree as the optimization goal can make the obtained lighting pleasure index set more consistent with the user pleasure index.

[0036] Furthermore, the method further comprises:

[0037] According to the optimization space solution set, N initial solution sets are obtained, where N is the total number of initial solutions and N is a positive integer greater than 0;

[0038] By analyzing the N initial solution sets, N matching degrees are obtained;

[0039] Selecting the first M initial solution sets with larger matching degrees and the remaining NM initial solution sets from the N matching degrees, where M is a positive integer greater than 0 and less than N;

[0040] The remaining NM initial solution sets are randomly divided into M initial solution sets to obtain M optimization blocks;

[0041] Iteratively search for the M optimal blocks, and when a preset number of optimization times is reached, obtain the M optimal blocks after iteration;

[0042] Obtaining M matching degrees corresponding to the M optimal blocks after iteration, wherein the M matching degrees are the maximum matching degrees in each optimal block;

[0043] The M matching degrees are compared to obtain an optimization result.

[0044] According to the number of solution sets in the optimization space, N corresponding initial solution sets are obtained, where N is the total number of initial solutions. By performing matching degree analysis on the N initial solution sets, N matching degrees are obtained. The first M initial solution sets with larger matching degrees are selected from the N matching degrees, and the first M initial solution sets are used as the main solution sets, and the remaining NM initial solution sets are used as the secondary solution sets. The remaining NM initial solution sets are randomly divided into M initial solution sets, that is, the secondary solution sets are respectively assigned to the main solution sets, and the main solution sets of the secondary solution sets are obtained as the optimization blocks, and there are as many optimization blocks as there are main solution sets. Iterate the optimization of the M optimization blocks, and when the preset number of optimization times is reached, obtain the M optimization blocks after iteration, and obtain the maximum matching degree in each optimization block after iteration, obtain M matching degrees, compare the M matching degrees, and obtain the optimization result according to the maximum matching degree. By setting the main solution set and the secondary solution set for optimization, the optimization result can be made more reasonable.

[0045] Furthermore, the method further comprises:

[0046] Acquire a first optimizing block among the M optimizing blocks, and a first initial solution set corresponding to the first optimizing block;

[0047] Based on the first initial solution set as a target, assimilating the remaining initial solution sets divided in the first optimization block to obtain an assimilated solution set;

[0048] Obtain the matching degree corresponding to the assimilated solution set, and if the matching degree of any assimilated solution set is greater than the matching degree of the first initial solution set, replace the assimilated solution set with the first initial solution set.

[0049] Randomly obtain one of the M optimization blocks as the first optimization block, and obtain the first initial solution set corresponding to the first optimization block. Take the first initial solution set as the target, assimilate the remaining initial solution sets divided in the first optimization block, and obtain the assimilated solution set, that is, the secondary solution set in the first optimization block is brought closer to the main solution set, and the deviation between the secondary solution set and the main solution set is continuously reduced. Obtain the matching degree corresponding to the assimilated solution set. If the matching degree of any assimilated solution set is greater than the matching degree of the first initial solution set, replace the assimilated solution set with the first initial solution set, use the secondary solution set as the main solution set, and use the main solution set just now as the secondary solution set to complete the replacement, indicating that the matching degree of the secondary solution set is greater than the previous main solution set, and the previous main solution set can no longer be used as the main solution set. The secondary solution set with a higher matching degree should be used as the main solution set, which can ensure that the matching degree of the overall optimization space solution set is always maintained at a high level.

[0050] Furthermore, the method further comprises:

[0051] Obtain the real-time environmental parameters of the user's current environment according to the environment acquisition module;

[0052] Acquire a first fitness by identifying the user environment fitness of the real-time environment parameter;

[0053] When the first fitness does not meet the preset fitness, an environmental feedback parameter is obtained, and the environment acquisition module performs environmental control based on the environmental feedback parameter until the preset fitness is met.

[0054] The factors that affect the user's psychological pleasure are not only the light, but also the environment in which the user is located, including lighting conditions, temperature conditions, humidity conditions, wind speed, etc. The real-time environmental parameters of the user's current environment are obtained according to the environment acquisition module, and the user's environmental adaptability is identified by the real-time environmental parameters, that is, by implementing the matching degree analysis of the environmental parameters and the user's pleasure index, the real-time environmental matching degree is obtained, and the real-time environmental matching degree is output as the first fitness degree, and the preset fitness degree is set. When the first fitness degree does not meet the preset fitness degree, the environmental feedback parameters are obtained, and the environmental feedback instructions are sent to the environmental acquisition module. The environmental feedback instructions include environmental feedback parameters. After receiving the feedback instructions, the environmental acquisition module adjusts the environment according to the environmental feedback parameters until the preset fitness degree is met. By setting environmental feedback parameters to uniformly adjust the environment, the environmental control effect can be improved.

[0055] Embodiment 2

[0056] Based on the same inventive concept as the intelligent lighting control method combined with user analysis in the aforementioned embodiment, Figure 2 As shown, the present application provides an intelligent lighting control system combined with user analysis, the system comprising:

[0057] Image infrared module 11: The image infrared module 11 is used to obtain a user acquisition module, and the user acquisition module includes an image collector and an infrared detector;

[0058] Real-time sensing module 12: the real-time sensing module 12 is used to collect the first user according to the user collection module to obtain real-time sensing images and real-time sensing signals;

[0059] Synchronous processing module 13: The synchronous processing module 13 is used to synchronously process the real-time sensing image and the real-time sensing signal, and obtain the synchronously processed synchronous sensing image and synchronous sensing signal;

[0060] Matching model module 14: the matching model module 14 is used to obtain a pre-trained pleasure matching model, and input the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model is trained and acquired by establishing a synchronous mapping sample set based on the sensing image sample and the sensing signal sample, and the identification information identifying the user pleasure index;

[0061] Pleasure index module 15: The pleasure index module 15 is used to obtain the user pleasure index according to the pleasure matching model, input the user pleasure index into the lighting control module for optimization, obtain lighting control parameters, and control the lighting group based on the lighting control parameters.

[0062] Furthermore, the matching model module 14 includes the following execution steps:

[0063] Furthermore, the system further comprises:

[0064] Furthermore, the system further comprises:

[0065] Obtaining control parameter indicators of the lighting lamp group, wherein the control parameter indicators include the color temperature of the lamp group, the color rendering index of the lamp group, and the lighting brightness;

[0066] The color temperature of the lamp group, the color rendering index of the lamp group and the lighting brightness are used as an indicator set to generate a control solution set;

[0067] Obtaining the intervals of each control parameter, and generating an optimal space solution set according to the control solution set;

[0068] An objective function is established, and each solution set in the optimization space solution set is calculated according to the objective function to obtain an optimization result, where the optimization result is an optimization solution set based on the user pleasure index.

[0069] Furthermore, the system further comprises:

[0070] Analyze each solution set in the optimization space solution set to obtain a lighting pleasure index set;

[0071] The matching degree is calculated by using the lighting pleasure index set and the user pleasure index to obtain a matching degree set. The objective function is used to identify the matching degree set, and the maximum matching degree is used as the optimization target to obtain an optimization result.

[0072] Furthermore, the system further comprises:

[0073] According to the optimization space solution set, N initial solution sets are obtained, where N is the total number of initial solutions and N is a positive integer greater than 0;

[0074] By analyzing the N initial solution sets, N matching degrees are obtained;

[0075] Selecting the first M initial solution sets with larger matching degrees and the remaining NM initial solution sets from the N matching degrees, where M is a positive integer greater than 0 and less than N;

[0076] The remaining NM initial solution sets are randomly divided into M initial solution sets to obtain M optimization blocks;

[0077] Iteratively search for the M optimal blocks, and when a preset number of optimization times is reached, obtain the M optimal blocks after iteration;

[0078] Obtaining M matching degrees corresponding to the M optimal blocks after iteration, wherein the M matching degrees are the maximum matching degrees in each optimal block;

[0079] The M matching degrees are compared to obtain an optimization result.

[0080] Furthermore, the system further comprises:

[0081] Acquire a first optimizing block among the M optimizing blocks, and a first initial solution set corresponding to the first optimizing block;

[0082] Based on the first initial solution set as a target, assimilating the remaining initial solution sets divided in the first optimization block to obtain an assimilated solution set;

[0083] Obtain the matching degree corresponding to the assimilated solution set, and if the matching degree of any assimilated solution set is greater than the matching degree of the first initial solution set, replace the assimilated solution set with the first initial solution set.

[0084] Furthermore, the system further comprises:

[0085] Obtain the real-time environmental parameters of the user's current environment according to the environment acquisition module;

[0086] Acquire a first fitness by identifying the user environment fitness of the real-time environment parameter;

[0087] When the first fitness does not meet the preset fitness, an environmental feedback parameter is obtained, and the environment acquisition module performs environmental control based on the environmental feedback parameter until the preset fitness is met.

[0088] Furthermore, the matching model module 14 includes the following execution steps:

[0089] Inputting the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model comprises a loss recognition network layer based on an image mask and a loss recognition network layer based on a signal mask, the loss recognition network layer of the image mask comprises a first prediction mask value, and the loss recognition network layer of the signal mask comprises a second prediction mask value;

[0090] Using the loss identification network layer of the image mask and the loss identification network layer of the signal mask to perform identification respectively, to obtain a loss indicator based on the first predicted mask value and a loss indicator based on the second predicted mask value;

[0091] A user pleasure index is obtained according to the loss index of the first predicted mask value and the loss index of the second predicted mask value.

[0092] Through the above detailed description of the intelligent lighting control method combined with user analysis in this specification, those skilled in the art can clearly understand the intelligent lighting control method combined with user analysis in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0093] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent lighting control method combined with user analysis, characterized in that: The method is applied to an intelligent lighting control system, the system includes an environment acquisition module and a user acquisition module, and the method includes: Acquire a user acquisition module, wherein the user acquisition module includes an image collector and an infrared detector; Collecting the first user according to the user collection module to obtain a real-time sensing image and a real-time sensing signal; Synchronously processing the real-time sensing image and the real-time sensing signal to obtain a synchronously processed synchronous sensing image and a synchronously processed synchronous sensing signal; Acquire a pre-trained pleasure matching model, and input the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model is trained and acquired by establishing a synchronous mapping sample set based on the sensing image sample and the sensing signal sample, and identification information identifying the user pleasure index; Obtaining a user pleasure index according to the pleasure matching model, inputting the user pleasure index into a lighting control module for optimization, obtaining lighting control parameters, and controlling the lighting lamp group based on the lighting control parameters; The method further comprises: Obtaining control parameter indicators of the lighting lamp group, wherein the control parameter indicators include the color temperature of the lamp group, the color rendering index of the lamp group, and the lighting brightness; The color temperature of the lamp group, the color rendering index of the lamp group and the lighting brightness are used as an indicator set to generate a control solution set; Obtaining the intervals of each control parameter, and generating an optimal space solution set according to the control solution set; Establishing an objective function, calculating each solution set in the optimization space solution set according to the objective function, and obtaining an optimization result, wherein the optimization result is an optimization solution set based on the user pleasure index; The method for obtaining the optimization result includes: According to the optimization space solution set, N initial solution sets are obtained, where N is the total number of initial solutions and N is a positive integer greater than 0; By analyzing the N initial solution sets, N matching degrees are obtained; Selecting the first M initial solution sets with larger matching degrees and the remaining NM initial solution sets from the N matching degrees, where M is a positive integer greater than 0 and less than N; The remaining NM initial solution sets are randomly divided into M initial solution sets to obtain M optimization blocks; Iteratively search for the M optimal blocks, and when a preset number of optimization times is reached, obtain the M optimal blocks after iteration; Obtaining M matching degrees corresponding to the M optimal blocks after iteration, wherein the M matching degrees are the maximum matching degrees in each optimal block; Compare the M matching degrees to obtain an optimization result; Iteratively optimizing the M optimization blocks, wherein each iteration method includes: Acquire a first optimizing block among the M optimizing blocks, and a first initial solution set corresponding to the first optimizing block; Based on the first initial solution set as a target, assimilating the remaining initial solution sets divided in the first optimization block to obtain an assimilated solution set; Obtain the matching degree corresponding to the assimilated solution set, and if the matching degree of any assimilated solution set is greater than the matching degree of the first initial solution set, replace the assimilated solution set with the first initial solution set.

2. The method according to claim 1, characterized in that Calculating each solution set in the optimization space solution set according to the objective function, the method comprising: Analyze each solution set in the optimization space solution set to obtain a lighting pleasure index set; The matching degree is calculated by using the lighting pleasure index set and the user pleasure index to obtain a matching degree set. The objective function is used to identify the matching degree set, and the maximum matching degree is used as the optimization target to obtain an optimization result.

3. The method according to claim 1, characterized in that The method further comprises: Obtain the real-time environmental parameters of the user's current environment according to the environment acquisition module; Acquire a first fitness by identifying the user environment fitness of the real-time environment parameter; When the first fitness does not meet the preset fitness, an environmental feedback parameter is obtained, and the environment acquisition module performs environmental control based on the environmental feedback parameter until the preset fitness is met.

4. The method according to claim 1, characterized in that Inputting the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, the method comprising: Inputting the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model comprises a loss recognition network layer based on an image mask and a loss recognition network layer based on a signal mask, the loss recognition network layer of the image mask comprises a first prediction mask value, and the loss recognition network layer of the signal mask comprises a second prediction mask value; Using the loss identification network layer of the image mask and the loss identification network layer of the signal mask to perform identification respectively, to obtain a loss indicator based on the first predicted mask value and a loss indicator based on the second predicted mask value; A user pleasure index is obtained according to the loss index of the first predicted mask value and the loss index of the second predicted mask value.

5. Intelligent lighting control system combined with user analysis, characterized in that: The system for implementing the intelligent lighting control method combined with user analysis according to any one of claims 1 to 4 comprises: Image infrared module: obtain user acquisition module, the user acquisition module includes an image collector and an infrared detector; Real-time sensing module: collecting the first user according to the user collection module to obtain real-time sensing images and real-time sensing signals; Synchronous processing module: synchronously processes the real-time sensing image and the real-time sensing signal, and obtains the synchronously processed synchronous sensing image and synchronous sensing signal; Matching model module: obtaining a pre-trained pleasure matching model, inputting the synchronous sensing image and the synchronous sensing signal into the pleasure matching model, wherein the pleasure matching model is trained and acquired by establishing a synchronous mapping sample set based on the sensing image sample and the sensing signal sample, and identification information identifying the user pleasure index; Pleasure index module: obtains the user pleasure index according to the pleasure matching model, and The user pleasure index is input into the lighting control module to perform optimization and obtain lighting control parameters. The lighting group is controlled based on the lighting control parameter.

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