A microwave induction lamp control method and system based on artificial intelligence

Abstract

The present invention proposes a microwave induction lamp control method and system based on artificial intelligence, the method comprising: deploying multiple light sensors to collect environmental data of indoor and outdoor environments; establishing an illumination matrix for the area to be identified, and using the illumination uniformity method to analyze the illumination value in the illumination matrix in combination with the index of the area to be identified; using the improved PSO algorithm to calculate the optimal brightness and the optimal brightness average value of the microwave induction lamp according to the result of the step S2 analysis; classifying the area to be identified according to the environmental data, and combining the user's preferences, adaptively correcting the optimal brightness and the optimal brightness average value of the microwave induction lamp according to the scene classification; transmitting the corrected optimal brightness and the optimal brightness average value to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time environmental brightness. The present invention realizes that the microwave induction lamp automatically adjusts the brightness under different environmental conditions, achieving energy saving and comfort effects.

Description

Technical Field

[0001] The present invention belongs to the technical field of microwave induction lamp control, and in particular relates to a microwave induction lamp control method and system based on artificial intelligence. Background Art

[0002] Traditional microwave induction lamp brightness adjustment systems usually adjust brightness based on simple ambient brightness detection and user manual settings. Relying only on a small number of light sensors to collect environmental data results in incomplete data coverage and cannot accurately reflect the brightness distribution of the entire area to be identified. At the same time, when establishing an illumination matrix, traditional systems may only use basic lux metering illumination without considering factors such as illumination uniformity, resulting in inaccurate and incomplete illumination analysis of the area to be identified. Traditional systems may use relatively simple algorithms to calculate the optimal brightness and the average optimal brightness, which lacks accuracy and flexibility. At the same time, these systems often do not have adaptive correction capabilities and cannot make real-time adjustments based on environmental changes and user preferences. Scene classification may be relatively rough and fail to fully consider the differences in brightness requirements in different scenes. At the same time, these systems often ignore user personalized preferences and cannot provide customized lighting experiences.

[0003] Due to the limitations of traditional systems in data collection, analysis and adjustment, their ability to adjust the brightness of microwave sensor lights in real time is also limited, and they are unable to respond quickly to changes in the environment and changes in user needs. Summary of the invention

[0004] The purpose of the present invention is to propose a microwave induction lamp control method and system based on artificial intelligence, which requires a new automatic control means, can adapt to the dynamic surrounding environment and meet user preferences in real time, through the close interaction between the microwave induction lamp and the mobile sensor to continuously adjust the illumination configuration in response to the deviation of environmental parameters and user behavior. On the other hand, after receiving these measurements, the microwave induction lamp coordinates with each other to determine the appropriate local illumination configuration to meet the user's preferences.

[0005] In order to achieve the above object, a first aspect of the present invention provides a microwave induction lamp control method based on artificial intelligence, the method comprising:

[0006] S1. Deploy multiple light sensors to collect environmental data from indoor and outdoor environments, perform preprocessing, and transmit the preprocessed data to the controller;

[0007] S2, using a lux meter to establish an illumination matrix according to the measured illumination of the area to be identified, and using an illumination uniformity method to analyze the illumination values ​​in the illumination matrix combined with the index of the area to be identified, and transmitting the analysis results to the controller;

[0008] S3, using the improved PSO algorithm to calculate the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the analysis results of step S2;

[0009] S4, classifying the scene of the area to be identified according to the environmental data, and combining the user's preferences, adaptively correcting the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the scene classification to obtain the corrected optimal brightness and the average value of the optimal brightness;

[0010] S5. The corrected optimal brightness and the optimal brightness average are transmitted to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time ambient brightness.

[0011] Furthermore, the S4 method further includes:

[0012] Use infrared sensors to identify human posture, determine whether there is someone in the area of ​​the current illumination matrix, and transmit the recognition results to the controller:

[0013] If there is no one in the area of ​​the current illumination matrix, the signal of the microwave sensor light is adjusted to a shutdown signal;

[0014] If there is someone in the area of ​​the current illumination matrix, execute S5;

[0015] The controller is connected to the user's smart device, and the user controls the execution of commands for the microwave sensor lamp by giving instructions to the smart device;

[0016] Calculate the deviation of indoor and outdoor light sources based on the data of indoor and outdoor light sensors, and set a threshold to control the indoor microwave sensor light and execute an on / off signal, wherein the threshold is adaptively adjusted according to user preferences;

[0017] The step of calculating the deviation of indoor and outdoor light sources according to the data of indoor and outdoor light sensors further includes:

[0018] When daylight illuminates the room, the feedback loop dims the microwave sensor light as shown below:

[0019] ;

[0020] in, represents the error signal, Indicates the indoor illumination level, Indicates the reference illuminance set by the user. represents the room illumination level detected by the sensor, represents the PI controller, represents the transfer coefficient of the microwave induction lamp, represents the window transfer function of sunlight passing through the surface of objects in the room, represents the transmission function of the light sensor in the smartphone, t represents the time t;

[0021] get: .

[0022] Furthermore, the step S2 specifically includes:

[0023] Use a lux meter to measure the illuminance in the area to be identified. Assume that the area is divided into m×n grids, and each grid represents a measurement point:

[0024] Step 1.1: Measure the current illuminance value at each grid point using a lux meter where i and j represent the row and column indices of the grid, respectively. ;

[0025] Step 1.2: Record the illumination value of each measurement point to form an m×n illumination matrix E;

[0026] Analyze illumination uniformity:

[0027] Step 2.1: Calculate the average illumination value of the illumination matrix E , which is expressed as follows:

[0028] ;

[0029] Step 2.2: Find the minimum illumination value in the illumination matrix E ;

[0030] Step 2.3: Calculate the illumination uniformity U, expressed as follows: ;

[0031] in, represents the minimum illumination value in the illumination matrix E, represents the average illumination value in the illumination matrix E;

[0032] The calculated analysis results are packaged into data packets, and the data packets are sent to the controller through the communication protocol.

[0033] Furthermore, the improved PSO algorithm specifically includes:

[0034] Step 3.1: Initialize all parameters of the PSO algorithm;

[0035] Step 3.2: Randomly generate a set of n particle positions and determine the illumination value of the particle position Is it satisfied? If it is satisfied, execute step 3.3. If it is not satisfied, re-generate a set of n particle positions randomly;

[0036] Step 3.3: Calculate the dimming level, average illumination uniformity and average dimming level of the brightness of the current particle position, determine whether the dimming level meets the preset dimming threshold, and determine whether the average illumination uniformity and average dimming level meet the visual comfort index. If the dimming level, average illumination uniformity and average dimming level all meet the requirements, execute step 3.4. If the dimming level, average illumination uniformity or average dimming level do not meet the requirements, randomly generate particles that do not meet the requirements and return to step 3.2.

[0037] Step 3.4: Calculate the optimal local position of each particle and the global optimal position ;

[0038] Step 3.4: Minimize the average dimming level of the microwave sensor lamp, expressed as follows:

[0039] ;

[0040] Where d represents the dimming level, represents the average dimming level and is calculated as follows: ;

[0041] Where N represents the total number of particles, Indicates the dark light level of the area where the current particle is located;

[0042] Step 3.5: Update the particle's velocity and position;

[0043] Step 3.6: Determine whether the particle update meets the maximum iteration amount. If so, execute step 3.7. If not, return to execute step 3.2. The speed of updating the particle and location , which is expressed as follows:

[0044] ;

[0045] in, represents the new velocity of particle i, represents the velocity of particle i, represents the inertia weight, and represents the acceleration constant, and Represents a random number between 0 and 1. represents the position of particle i, represents the new position of particle i; wherein, represents the inertia weight, which is expressed as follows:

[0046] ;

[0047] in, represents the maximum inertia weight, represents the minimum inertia weight, represents the maximum number of iterations, Indicates the current iteration number;

[0048] Step 3.7: Output the optimal illumination value and the optimal average dimming level .

[0049] Among them, the comfort index shown is a constraint condition, including:

[0050] The dimming capacity limit of a luminaire is the minimum and maximum values ​​that the brightness can be dimmed. Typical values ​​for the dimming capacity limit of a luminaire are in the range of 0-1 (full dimming);

[0051] The average illumination level is one of the indicators of visual comfort. The recommended average illumination level is 500 lux as specified in EN12464-1.

[0052] In addition to the average illuminance level, illuminance uniformity can also be considered as an indicator of visual comfort. The minimum illuminance uniformity EN12464-1 stipulates a value of 0.6.

[0053] Furthermore, the dark light level of the area where the current particle is located is expressed as follows:

[0054] ;

[0055] in, Indicates the dark light level of the area where the current particle is located. represents the weight of the current position, represents the average illumination level, represents the optimal illumination value of the ith area, Indicates the dimming value for the current horizontal set point.

[0056] Furthermore, the scenes include a living room scene, a bedroom scene and a whole house scene.

[0057] Furthermore, the S4 specifically includes:

[0058] The user's heterogeneous illumination preference is defined as , according to the scene data and the user's heterogeneous illumination preference, the personalized definition value is obtained, and the deviation between the personalized definition value and the actual value is minimized according to the personalized definition value, which is expressed as follows:

[0059] ;

[0060] in, Indicates the brightness level of the updated microwave sensor lamp i, represents the brightness level of the current microwave sensor lamp i, M represents the total number of microwave sensor lamps, K represents sensor data, k represents the sensor currently closest to the microwave sensor lamp i, f represents the comprehensive correction function, and represents the weight of heterogeneous illumination preference and scene, k represents the kth sensor, and K represents the number of sensors;

[0061] Construct the deviation Lagrangian decomposition function, which is expressed as follows:

[0062] ;

[0063] in, represents the Lagrangian function, x represents the illumination level of the microwave induction lamp, , and represents the Lagrange multiplier, and Represents the constraints of illumination;

[0064] Define the optimal illumination error function , which is expressed as follows:

[0065] ;

[0066] in, Indicates the current optimal illumination value;

[0067] Defining the optimal average dimming level error function , which is expressed as follows:

[0068] ;

[0069] in, Indicates the current optimal average dimming level;

[0070] Construct the correction function, which is expressed as follows: ;

[0071] in, represents the adaptive weight;

[0072] Find the current optimal illumination value by taking the derivative The correction direction is expressed as follows:

[0073] ;

[0074] Get the corrected optimal illumination value and the optimal average dimming level , which is expressed as follows:

[0075] ;

[0076] in, Represents the learning rate.

[0077] Furthermore, the S5 specifically includes:

[0078] Transmitting the corrected optimal brightness and the optimal brightness average to the controller via wired or wireless means;

[0079] After receiving the data, the controller processes and analyzes it;

[0080] The controller detects the brightness of the current environment in real time through the connected light sensor;

[0081] Based on the received optimal brightness and optimal brightness average, as well as the ambient brightness detected in real time, the controller formulates a brightness adjustment strategy;

[0082] According to the formulated strategy, the controller adjusts the power output and duty cycle of the microwave induction lamp to adjust the brightness in real time. During the adjustment process, the controller continuously receives feedback from the sensor and fine-tunes the brightness according to the actual situation;

[0083] If the brightness is adjusted beyond the preset range or in an abnormal situation, the controller can automatically adjust to a safe state or issue an alarm.

[0084] In a second aspect of the present invention, a microwave induction lamp control system based on artificial intelligence is provided, the system comprising:

[0085] The light sensing deployment module is used to deploy multiple light sensors to collect environmental data from indoor and outdoor environments, perform preprocessing, and transmit the preprocessed data to the controller;

[0086] An illumination recognition module is used to establish an illumination matrix for the area to be recognized according to the measured illumination using a lux meter, and to analyze the illumination values ​​in the illumination matrix in combination with the index of the area to be recognized using an illumination uniformity method, and transmit the analysis results to a controller;

[0087] An optimization model building module, used to calculate the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the analysis result of step S2 using an improved PSO algorithm;

[0088] A parameter correction module is used to classify the scene of the area to be identified according to the environmental data, and to perform adaptive correction operations on the optimal brightness and the optimal brightness average of the microwave induction lamp according to the scene classification in combination with the user's preferences, so as to obtain the corrected optimal brightness and the optimal brightness average;

[0089] The instruction execution module is used to transmit the corrected optimal brightness and the optimal brightness average to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time ambient brightness.

[0090] Furthermore, the scenes include a living room scene, a bedroom scene and a whole house scene.

[0091] The beneficial technical effects of the present invention are at least as follows:

[0092] The present invention uses a lux meter to establish an illumination matrix, which can quantify the light intensity distribution of the area to be identified. By analyzing the illumination matrix, the uniformity information of the illumination in the area can be obtained, which provides a basis for the brightness adjustment of the microwave induction lamp. Using the improved particle swarm optimization (PSO) algorithm, the optimal brightness and the optimal brightness average value of the microwave induction lamp can be efficiently calculated according to the analysis results. The PSO algorithm can quickly find the best solution, improving the response speed and accuracy of the system. By classifying the recognition area by scene using environmental data, the needs of the current environment can be better understood. Adaptive correction is performed in combination with the user's preferences, which improves the personalization of the system and enhances the user experience. The controller adjusts the brightness of the microwave induction lamp in real time according to the corrected optimal brightness and the optimal brightness average value. In this way, the brightness can be automatically adjusted under different environmental conditions to achieve energy saving and comfort effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0094] Figure 1 This is a flow chart of a microwave induction lamp control method based on artificial intelligence of the present invention.

[0095] Figure 2 This is a framework diagram of a microwave induction lamp control system based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0096] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0097] like Figure 1 As shown, an embodiment of the present invention provides a microwave induction lamp control method based on artificial intelligence, and the method includes the following steps S1-S5.

[0098] S1. Deploy multiple light sensors to collect environmental data from indoor and outdoor environments, perform preprocessing, and transmit the preprocessed data to the controller;

[0099] S2, using a lux meter to establish an illumination matrix according to the measured illumination of the area to be identified, and using an illumination uniformity method to analyze the illumination values ​​in the illumination matrix combined with the index of the area to be identified, and transmitting the analysis results to the controller;

[0100] S3, using the improved PSO algorithm to calculate the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the analysis results of step S2;

[0101] S4, classifying the scene of the area to be identified according to the environmental data, and combining the user's preferences, adaptively correcting the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the scene classification to obtain the corrected optimal brightness and the average value of the optimal brightness;

[0102] S5. The corrected optimal brightness and the optimal brightness average are transmitted to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time ambient brightness.

[0103] The preprocessing includes: data cleaning, data calibration, data standardization (converting all light intensity data into lux) and data fusion, etc.

[0104] Furthermore, the S4 method further includes:

[0105] Use infrared sensors to identify human posture, determine whether there is someone in the area of ​​the current illumination matrix, and transmit the recognition results to the controller:

[0106] If there is no one in the area of ​​the current illumination matrix, the signal of the microwave sensor light is adjusted to a shutdown signal;

[0107] If there is someone in the area of ​​the current illumination matrix, execute S5;

[0108] The controller is connected to the user's smart device, and the user controls the execution of commands for the microwave sensor lamp by giving instructions to the smart device;

[0109] Calculate the deviation of indoor and outdoor light sources based on the data of indoor and outdoor light sensors, and set a threshold to control the indoor microwave sensor light and execute an on / off signal, wherein the threshold is adaptively adjusted according to user preferences;

[0110] The step of calculating the deviation of indoor and outdoor light sources according to the data of indoor and outdoor light sensors further includes:

[0111] When daylight illuminates the room, the feedback loop dims the microwave sensor light as shown below:

[0112] ;

[0113] in, represents the error signal, Indicates the indoor illumination level, Indicates the reference illuminance set by the user. represents the room illumination level detected by the sensor, represents the PI controller, represents the transfer coefficient of the microwave induction lamp, represents the window transfer function of sunlight passing through the surface of objects in the room, represents the transmission function of the light sensor in the smartphone, t represents the time t;

[0114] get: .

[0115] Furthermore, the step S2 specifically includes:

[0116] Use a lux meter to measure the illuminance in the area to be identified. Assume that the area is divided into m×n grids, and each grid represents a measurement point:

[0117] Step 1.1: Measure the current illuminance value at each grid point using a lux meter where i and j represent the row and column indices of the grid, respectively. ;

[0118] Step 1.2: Record the illumination value of each measurement point to form an m×n illumination matrix E;

[0119] Analyze illumination uniformity:

[0120] Step 2.1: Calculate the average illumination value of the illumination matrix E , which is expressed as follows:

[0121] ;

[0122] Step 2.2: Find the minimum illumination value in the illumination matrix E ;

[0123] Step 2.3: Calculate the illumination uniformity U, expressed as follows: ;

[0124] in, represents the minimum illumination value in the illumination matrix E, represents the average illumination value in the illumination matrix E;

[0125] The calculated analysis results are packaged into data packets, and the data packets are sent to the controller through the communication protocol.

[0126] Furthermore, the improved PSO algorithm specifically includes:

[0127] Step 3.1: Initialize all parameters of the PSO algorithm;

[0128] Step 3.2: Randomly generate a set of n particle positions and determine the illumination value of the particle position Is it satisfied? If it is satisfied, execute step 3.3. If it is not satisfied, re-generate a set of n particle positions randomly;

[0129] Step 3.3: Calculate the dimming level, average illumination uniformity and average dimming level of the brightness of the current particle position, determine whether the dimming level meets the preset dimming threshold, and determine whether the average illumination uniformity and average dimming level meet the visual comfort index. If the dimming level, average illumination uniformity and average dimming level all meet the requirements, execute step 3.4. If the dimming level, average illumination uniformity or average dimming level do not meet the requirements, randomly generate particles that do not meet the requirements and return to step 3.2.

[0130] Step 3.4: Calculate the optimal local position of each particle and the global optimal position ;

[0131] Step 3.4: Minimize the average dimming level of the microwave sensor lamp, expressed as follows:

[0132] ;

[0133] Where d represents the dimming level, represents the average dimming level and is calculated as follows: ;

[0134] Where N represents the total number of particles, Indicates the dark light level of the area where the current particle is located;

[0135] Step 3.5: Update the particle's velocity and position;

[0136] Step 3.6: Determine whether the particle update meets the maximum iteration amount. If so, execute step 3.7. If not, return to execute step 3.2. The speed of updating the particle and location , which is expressed as follows:

[0137] ;

[0138] in, represents the new velocity of particle i, represents the velocity of particle i, represents the inertia weight, and represents the acceleration constant, and Represents a random number between 0 and 1. represents the position of particle i, represents the new position of particle i; wherein, represents the inertia weight, which is expressed as follows:

[0139] ;

[0140] in, represents the maximum inertia weight, represents the minimum inertia weight, represents the maximum number of iterations, Indicates the current iteration number;

[0141] Step 3.7: Output the optimal illumination value and the optimal average dimming level .

[0142] Furthermore, the dark light level of the area where the current particle is located is expressed as follows:

[0143] ;

[0144] in, Indicates the dark light level of the area where the current particle is located. represents the weight of the current position, represents the average illumination level, represents the optimal illumination value of the ith area, Indicates the dimming value for the current horizontal set point.

[0145] Furthermore, the scene includes a living room scene, a bedroom scene and a whole house scene. The scene includes a living room scene, which may include three microwave induction lamps; a bedroom scene, which may include two microwave induction lamps; and a whole house scene, which may include seven microwave induction lamps.

[0146] Furthermore, the S4 specifically includes:

[0147] The user's heterogeneous illumination preference is defined as , according to the scene data and the user's heterogeneous illumination preference, the personalized definition value is obtained, and the deviation between the personalized definition value and the actual value is minimized according to the personalized definition value, which is expressed as follows:

[0148] ;

[0149] in, Indicates the brightness level of the updated microwave sensor lamp i, represents the brightness level of the current microwave sensor lamp i, M represents the total number of microwave sensor lamps, K represents sensor data, k represents the sensor currently closest to the microwave sensor lamp i, f represents the comprehensive correction function, and represents the weight of heterogeneous illumination preference and scene, k represents the kth sensor, and K represents the number of sensors;

[0150] Construct the deviation Lagrangian decomposition function, which is expressed as follows:

[0151] ;

[0152] in, represents the Lagrangian function, x represents the illumination level of the microwave induction lamp, , and represents the Lagrange multiplier, and Represents the constraints of illumination;

[0153] Define the optimal illumination error function , which is expressed as follows:

[0154] ;

[0155] in, Indicates the current optimal illumination value;

[0156] Defining the optimal average dimming level error function , which is expressed as follows:

[0157] ;

[0158] in, Indicates the current optimal average dimming level;

[0159] Construct the correction function, which is expressed as follows: ;

[0160] in, represents the adaptive weight;

[0161] Find the current optimal illumination value by taking the derivative The correction direction is expressed as follows:

[0162] ;

[0163] Get the corrected optimal illumination value and the optimal average dimming level , which is expressed as follows:

[0164] ;

[0165] in, Represents the learning rate.

[0166] Furthermore, the S5 specifically includes:

[0167] Transmitting the corrected optimal brightness and the optimal brightness average to the controller via wired or wireless means;

[0168] After receiving the data, the controller processes and analyzes it;

[0169] The controller detects the brightness of the current environment in real time through the connected light sensor;

[0170] Based on the received optimal brightness and optimal brightness average, as well as the ambient brightness detected in real time, the controller formulates a brightness adjustment strategy;

[0171] According to the formulated strategy, the controller adjusts the power output and duty cycle of the microwave induction lamp to adjust the brightness in real time. During the adjustment process, the controller continuously receives feedback from the sensor and fine-tunes the brightness according to the actual situation;

[0172] If the brightness is adjusted beyond the preset range or in an abnormal situation, the controller can automatically adjust to a safe state or issue an alarm.

[0173] To better implement the above method, Figure 2 As shown, the present invention also proposes a microwave induction lamp control system based on artificial intelligence, the system comprising:

[0174] The light sensing deployment module 101 is used to deploy multiple light sensors to collect environmental data of indoor and outdoor environments, perform preprocessing, and transmit the preprocessed data to the controller;

[0175] The illumination recognition module 102 is used to establish an illumination matrix for the area to be recognized according to the measured illumination using a lux meter, and analyze the illumination values ​​in the illumination matrix in combination with the index of the area to be recognized using an illumination uniformity method, and transmit the analysis result to the controller;

[0176] The optimization model building module 103 is used to calculate the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the analysis result of step S2 using the improved PSO algorithm;

[0177] The parameter correction module 104 is used to classify the scene of the to-be-recognized area according to the environmental data, and to perform adaptive correction operations on the optimal brightness and the optimal brightness average of the microwave induction lamp according to the scene classification in combination with the user's preference, so as to obtain the corrected optimal brightness and the optimal brightness average;

[0178] The instruction execution module 105 is used to transmit the corrected optimal brightness and the optimal brightness average value to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time ambient brightness.

[0179] Furthermore, the scenes include a living room scene, including three microwave induction lamps; a bedroom scene, including two microwave induction lamps; and a whole house scene, including seven microwave induction lamps.

[0180] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0181] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0182] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is usually placed when in use. These are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0183] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A microwave induction lamp control method based on artificial intelligence, characterized in that: The method comprises: S1. Deploy multiple light sensors to collect environmental data from indoor and outdoor environments, perform preprocessing, and transmit the preprocessed data to the controller; S2, using a lux meter to establish an illumination matrix for the area to be identified according to the illumination value, and using an illumination uniformity method to analyze the illumination value in the illumination matrix combined with the illumination value of the area to be identified, and transmitting the analysis result to the controller; S3, using the improved PSO algorithm to calculate the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the analysis results of step S2; S4, classifying the scene of the area to be identified according to the environmental data, and combining the user's preferences, adaptively correcting the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the scene classification to obtain the corrected optimal brightness and the average value of the optimal brightness; S5, transmitting the corrected optimal brightness and the optimal brightness average to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time ambient brightness; The S4 specifically includes: The user's heterogeneous illumination preference is defined as According to the scene data and the user's heterogeneous illumination preference, a personalized definition value is obtained, and the deviation between the personalized definition value and the actual value is minimized according to the personalized definition value, which is expressed as follows: in, Indicates the brightness level of the updated microwave sensor lamp i, represents the brightness level of the current microwave sensor lamp i, M represents the total number of microwave sensor lamps, k represents the sensor currently closest to the microwave sensor lamp i, f represents the comprehensive correction function, α and β represent the weights of the heterogeneous illumination preference and the scene respectively, and K represents the number of sensors; Construct the deviation Lagrangian decomposition function, which is expressed as follows: Where L(x,λ,μ) represents the Lagrangian function, x represents the illumination level of the microwave induction lamp, and x=[x1,x2,...,x i ],λ i and μ i represents the Lagrange multiplier, g i (x)≤0 and h i (x) = 0 represents the constraint condition of illumination; Define the optimal illumination error function It is expressed as follows: in, represents the current optimal illumination value, and H(t) represents the room illumination level detected by the sensor; Defining the optimal average dimming level error function It is expressed as follows: in, represents the current optimal average dimming level, and Y(t) represents the indoor illumination level; Construct the correction function, which is expressed as follows: Among them, w(t) represents the adaptability weight; Find the current optimal illumination value by taking the derivative The correction direction is expressed as follows: Get the corrected optimal illumination value and the optimal average dimming level It is expressed as follows: Among them, η represents the learning rate.

2. According to the artificial intelligence-based microwave induction lamp control method of claim 1, it is characterized in that: The S4 further comprises: Use infrared sensors to identify human posture, determine whether there is someone in the area of ​​the current illumination matrix, and transmit the recognition results to the controller: If there is no one in the area of ​​the current illumination matrix, the signal of the microwave sensor light is adjusted to a shutdown signal; If there is someone in the area of ​​the current illumination matrix, execute S5; The controller is connected to the user's smart device, and the user controls the execution of commands for the microwave sensor lamp by giving instructions to the smart device; Calculate the deviation of indoor and outdoor light sources based on the data of indoor and outdoor light sensors, and set a threshold to control the indoor microwave sensor light and execute an on / off signal, wherein the threshold is adaptively adjusted according to user preferences; The step of calculating the deviation of indoor and outdoor light sources according to the data of indoor and outdoor light sensors further includes: When daylight illuminates the room, the feedback loop dims the microwave sensor light as shown below: e(t)=L(t)-H(t)Y(t) Y(t)=(e(t)c(t)l(t)+n(t)g(t)) Wherein, e(t) represents the error signal, Y(t) represents the indoor illumination level, L(t) represents the reference illumination set by the user, H(t) represents the room illumination level detected by the sensor, c(t) represents the PI controller, l(t) represents the transfer coefficient of the microwave sensor lamp, n(t) represents the window transfer function of sunlight passing through the surface of the object in the room, g(t) represents the transmission function of the light sensor in the smartphone, and t represents the time t; get:

3. According to the artificial intelligence-based microwave induction lamp control method of claim 1, it is characterized in that: The step S2 specifically includes: Use a lux meter to measure the illuminance in the area to be identified. Assume that the area is divided into m×n grids, and each grid represents a measurement point: Step 1.1: Use a lux meter to measure the current illuminance value E at each grid point i,j , where i and j represent the row and column indices of the grid, respectively, where i = 1, 2, ..., m, j = 1, 2, ..., n; Step 1.2: Record the illumination value of each measurement point to form an m×n illumination matrix E; Analyze illumination uniformity: Step 2.1: Calculate the average illumination value E of the illumination matrix E avg , which is expressed as follows: Step 2.2: Find the minimum illumination value E in the illumination matrix E min ; Step 2.3: Calculate the illumination uniformity U, expressed as follows: Among them, E min Represents the minimum illumination value in the illumination matrix E, E avg represents the average illumination value in the illumination matrix E; The calculated analysis results are packaged into data packets, and the data packets are sent to the controller through the communication protocol.

4. The method for controlling a microwave induction lamp based on artificial intelligence according to claim 1, characterized in that: The improved PSO algorithm specifically includes: Step 3.1: Initialize all parameters of the PSO algorithm; Step 3.2: Randomly generate a set of n particle positions and determine the illumination value E at the particle position i Does it meet E i，min ≤E i ≤E i，max , if it is satisfied, execute step 3.3, if it is not satisfied, re-randomly generate a set of n particle positions; Step 3.3: Calculate the dimming level, average illumination uniformity and average dimming level of the brightness of the current particle position, determine whether the dimming level meets the preset dimming threshold, and determine whether the average illumination uniformity and average dimming level meet the visual comfort index. If the dimming level, average illumination uniformity and average dimming level all meet the requirements, execute step 3.

4. If the dimming level, average illumination uniformity or average dimming level do not meet the requirements, randomly generate particles that do not meet the requirements and return to step 3.

2. Step 3.4: Calculate the optimal local position p for each particle best and the global optimal position g best ; Step 3.4: Minimize the average dimming level of the microwave sensor lamp, expressed as follows: Where d represents the dimming level, represents the average dimming level and is calculated as follows: Where N is the total number of particles, d i Indicates the dark light level of the area where the current particle is located; Step 3.5: Update the particle's velocity and position; Step 3.6: Determine whether the particle update meets the maximum iteration amount. If so, execute step 3.

7. If not, return to execute step 3.

2. The speed of updating the particle and location It is expressed as follows: in, represents the new velocity of particle i, represents the velocity of particle i, ω represents the inertia weight, c1 and c2 represent acceleration constants, Rand1 and Rand2 represent random numbers between 0 and 1, represents the position of particle i, represents the new position of particle i; wherein ω represents the inertia weight, which is expressed as follows: Among them, ω max represents the maximum inertia weight, ω min represents the minimum inertia weight, L max Indicates the maximum number of iterations, L j Indicates the current iteration number; Step 3.7: Output the optimal illumination value and the optimal average dimming level 5. The method for controlling a microwave induction lamp based on artificial intelligence according to claim 4, characterized in that: The dark light level of the area where the current particle is located is expressed as follows: Among them, d i Indicates the dark light level of the area where the current particle is located, γ indicates the weight of the current position, represents the average illumination level, e p represents the optimal illumination value of the ith area, Indicates the dimming value for the current horizontal set point.

6. The method for controlling a microwave induction lamp based on artificial intelligence according to claim 1, characterized in that: The scenes include a living room scene, a bedroom scene and a whole house scene.

7. The method for controlling a microwave induction lamp based on artificial intelligence according to claim 1, characterized in that: The S5 specifically includes: Transmitting the corrected optimal brightness and the optimal brightness average to the controller via wired or wireless means; After receiving the data, the controller processes and analyzes it; The controller detects the brightness of the current environment in real time through the connected light sensor; Based on the received optimal brightness and optimal brightness average, as well as the ambient brightness detected in real time, the controller formulates a brightness adjustment strategy; According to the established strategy, the controller adjusts the brightness in real time by adjusting the power output and duty cycle of the microwave induction lamp. During the adjustment process, the controller continuously receives feedback from the sensor and fine-tunes the brightness according to the actual situation. If the brightness is adjusted beyond the preset range or in an abnormal situation, the controller can automatically adjust to a safe state or issue an alarm.

8. A microwave induction lamp control system based on artificial intelligence, characterized in that: The system comprises: The light sensing deployment module is used to deploy multiple light sensors to collect environmental data from indoor and outdoor environments, perform preprocessing, and transmit the preprocessed data to the controller; An illumination recognition module is used to establish an illumination matrix for the area to be identified according to the illumination value using a lux meter, and analyze the illumination value in the illumination matrix combined with the illumination value of the area to be identified using an illumination uniformity method, and transmit the analysis result to the controller; An optimization model building module, used to calculate the optimal brightness and the average value of the optimal brightness of the microwave induction lamp according to the analysis result of step S2 using an improved PSO algorithm; A parameter correction module is used to classify the scene of the area to be identified according to the environmental data, and to perform adaptive correction operations on the optimal brightness and the optimal brightness average of the microwave induction lamp according to the scene classification in combination with the user's preferences, so as to obtain the corrected optimal brightness and the optimal brightness average; The instruction execution module is used to transmit the corrected optimal brightness and the optimal brightness average value to the controller, and the controller adjusts the microwave induction lamp by analyzing and receiving the real-time ambient brightness; The parameter correction module execution steps specifically include: The user's heterogeneous illumination preference is defined as According to the scene data and the user's heterogeneous illumination preference, a personalized definition value is obtained, and the deviation between the personalized definition value and the actual value is minimized according to the personalized definition value, which is expressed as follows: in, Indicates the brightness level of the updated microwave sensor lamp i, represents the brightness level of the current microwave sensor lamp i, M represents the total number of microwave sensor lamps, k represents the sensor currently closest to the microwave sensor lamp i, f represents the comprehensive correction function, α and β represent the weights of the heterogeneous illumination preference and the scene respectively, and K represents the number of sensors; Construct the deviation Lagrangian decomposition function, which is expressed as follows: Where L(x,λ,μ) represents the Lagrangian function, x represents the illumination level of the microwave induction lamp, and x=[x1,x2,...,x i ],λ i and μ i represents the Lagrange multiplier, g i (x)≤0 and h i (x) = 0 represents the constraint condition of illumination; Define the optimal illumination error function It is expressed as follows: in, represents the current optimal illumination value, and H(t) represents the room illumination level detected by the sensor; Defining the optimal average dimming level error function It is expressed as follows: in, represents the current optimal average dimming level, and Y(t) represents the indoor illumination level; Construct the correction function, which is expressed as follows: Among them, w(t) represents the adaptability weight; Find the current optimal illumination value by taking the derivative The correction direction is expressed as follows: Get the corrected optimal illumination value and the optimal average dimming level It is expressed as follows: Among them, η represents the learning rate.

9. The microwave induction lamp control system based on artificial intelligence according to claim 8, characterized in that: The scenes include a living room scene, a bedroom scene and a whole house scene.

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