Intelligent dimming control method and device, electronic equipment and storage medium

Abstract

The invention discloses an intelligent dimming control method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent illumination, and the method comprises the steps: obtaining environment illumination change data in real time, and generating an initial illumination distribution data set based on the environment illumination change data, processing the initial illumination distribution data set by using a time sequence analysis model to obtain an illumination dynamic change parameter, inputting the illumination dynamic change parameter into a preset scene demand database, generating an illumination adjustment scheme adapted to a scene, and optimizing a dimming parameter according to the illumination adjustment scheme to determine a dimming control signal; and finally, a dimming control signal is sent to the lighting equipment through the Internet of Things, the environment change can be responded in real time, a personalized lighting scheme is generated through accurate matching of actual scene lighting requirements, the lighting experience of a user is improved, and meanwhile, the energy utilization efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent lighting technology, specifically to an intelligent dimming control method, device, electronic device, and storage medium. Background Technology

[0002] With the rapid development of IoT technology and smart city construction, IoT-based intelligent dimming control for individual lamps has become an important technical means to improve lighting efficiency and reduce energy consumption and emissions. It not only concerns the optimization of energy utilization but also directly affects people's quality of life and environmental comfort. Achieving intelligent management of individual lamps through IoT technology can provide flexible lighting solutions for different scenarios, possessing broad application prospects and profound practical significance.

[0003] In related technologies, a fixed threshold dimming method is mainly used for single-lamp management. The output power of the lamp is adjusted according to the ambient light intensity by preset static brightness curves or simple photosensitive triggering mechanisms.

[0004] However, some lighting systems rely on simple preset rules or fixed parameters, which cannot adapt to complex and ever-changing environmental needs. This results in a mismatch between the lighting effect and the actual scene requirements, affecting the user experience and causing energy waste. Summary of the Invention

[0005] In view of the above problems, the present invention provides an intelligent dimming control method, device, electronic device and storage medium, which can cope with complex and ever-changing environmental requirements, generate personalized lighting solutions to improve the user's lighting experience, and improve energy utilization efficiency.

[0006] In a first aspect, embodiments of the present invention provide an intelligent dimming control method, the intelligent dimming control method comprising: Acquire ambient light change data and generate an initial light distribution dataset based on the ambient light change data; The initial illumination distribution dataset was processed using a time series analysis model to obtain dynamic illumination change parameters; The dynamic lighting change parameters are input into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme. Based on the aforementioned illumination adjustment scheme, the dimming parameters are optimized, and the dimming control signal is determined; The dimming control signal is sent to the lighting device via the Internet of Things (IoT), wherein the dimming control signal is used to instruct the lighting device to adjust the brightness and color temperature.

[0007] In some embodiments, the intelligent dimming control method further includes: Acquire brightness feedback data and match the brightness feedback data with scene requirements to obtain matching results; When the matching result indicates that the brightness feedback data does not match the scene requirements, the brightness correction amount is calculated using a pre-established brightness correction model; The corrected brightness value is determined based on the brightness correction amount, and the final dimming parameters are generated based on the corrected brightness value; A secondary dimming control signal is generated based on the final dimming parameters and sent to the lighting device.

[0008] In some embodiments, the intelligent dimming control method further includes: The ambient light data after the second dimming is obtained and compared with the final dimming parameters to obtain the applicability result of the dimming parameters; The applicability results of the dimming parameters are input into a pre-established natural light interference prediction model to obtain the natural light interference corresponding to the ambient light data. If the natural light interference exceeds the threshold, then the updated ambient light dynamic change data is obtained; The updated ambient light dynamic change data are analyzed to obtain an updated light adjustment scheme.

[0009] In some embodiments, acquiring the ambient light data after secondary dimming and comparing it with the final dimming parameters to obtain the applicability result of the dimming parameters includes: The ambient light data after the secondary dimming is verified to obtain the verified light data. The applicability of the verified illumination data is compared with the final dimming parameters to obtain the deviation rate. If the deviation rate is within the threshold range, the applicability result of the dimming parameters is generated.

[0010] In some embodiments, generating the final dimming parameters based on the corrected brightness value includes: The corrected brightness value is input into the energy consumption control model to calculate the predicted energy consumption value. The optimal dimming parameters are calculated using the optimal prediction model based on the energy consumption prediction values. The optimal dimming parameters are verified in multiple dimensions and dynamically adapted to the environment to determine the final dimming parameters.

[0011] In some embodiments, inputting the dynamic illumination change parameters into a preset scene requirement database to generate a scene-adaptive illumination adjustment scheme includes: If the dynamic lighting parameters are higher than the reading scene threshold in the scene requirement database, a high brightness adjustment command is generated. If the dynamic lighting change parameter is lower than the rest scene threshold in the scene requirement database, a low brightness adjustment command is generated. The high brightness adjustment command and / or the low brightness adjustment command are logically integrated to obtain the illumination adjustment scheme.

[0012] In some embodiments, the step of processing the initial illumination distribution dataset using a time series analysis model to obtain dynamic illumination change parameters includes: Based on the initial illumination distribution dataset, the dynamic change characteristics of ambient illumination are obtained; By performing time series analysis on the dynamic changes in ambient light, the fluctuation range and trend parameters of light intensity were obtained: Based on the fluctuation range and the change trend parameters, the dynamic change parameters of illumination are output.

[0013] Secondly, embodiments of the present invention provide an intelligent dimming control device, the intelligent dimming control device comprising: The first generation module is used to acquire ambient light change data and generate an initial light distribution dataset based on the ambient light change data. The processing module is used to process the initial illumination distribution dataset using a time series analysis model to obtain dynamic illumination change parameters; The second generation module is used to input the dynamic lighting change parameters into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme. The determining module is used to optimize the dimming parameters and determine the dimming control signal according to the illumination adjustment scheme; The control module is used to send the dimming control signal to the lighting device via the Internet of Things, wherein the dimming control signal is used to instruct the lighting device to adjust the brightness and color temperature.

[0014] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores program code that can run on the processor, and when the program code is executed by the processor, it implements the intelligent dimming control method as described in any embodiment of the first aspect.

[0015] Fourthly, embodiments of this application provide a computer storage medium storing one or more programs, which can be executed by an electronic device as described in the third aspect to implement the intelligent dimming control method as described in any embodiment of the first aspect.

[0016] This invention provides an intelligent dimming control method, device, electronic device, and storage medium, comprising: acquiring ambient light change data in real time; generating an initial light distribution dataset based on the ambient light change data; processing the initial light distribution dataset using a time series analysis model to obtain dynamic light change parameters; inputting the dynamic light change parameters into a preset scene requirement database to generate a scene-adaptive light adjustment scheme; optimizing the dimming parameters according to the light adjustment scheme to determine the dimming control signal; and finally sending the dimming control signal to the lighting device via the Internet of Things. This method can respond to environmental changes in real time and generate personalized lighting schemes through precise matching of actual scene lighting needs, improving the user's lighting experience while also improving energy efficiency.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] The invention will now be described in more detail with reference to embodiments and the accompanying drawings.

[0019] Figure 1 A schematic flowchart of an exemplary intelligent dimming control method according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of an exemplary implementation environment according to an embodiment of the present invention is shown; Figure 3 A structural block diagram of an intelligent dimming control device according to an embodiment of the present invention is shown; Figure 4 A structural block diagram of an electronic device for performing an intelligent dimming control method according to an embodiment of this application is shown; Figure 5 A computer-readable storage medium for storing or carrying an implementation of an intelligent dimming control method according to an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0021] The intelligent dimming control method provided in this application embodiment can be applied to, for example... Figure 2In the implementation environment shown, terminal 101 communicates with lighting device 102 via Internet of Things (IoT) 103. Terminal 101 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Lighting device 102 can be, but is not limited to, various types of lamps.

[0022] The application scenarios of the embodiments of this application will be described in conjunction with the above implementation environment.

[0023] As an illustration, the intelligent dimming control method provided in this application embodiment includes, but is not limited to, at least one of the following scenarios.

[0024] In some implementations, this intelligent dimming control method can be applied to industrial safety lighting. For example, in an industrial work area, a safety threshold is set in a scene requirement database, and a dimming parameter optimization module switches the lighting mode according to the type of work. When maintenance work is being carried out, a high-brightness cool white light mode is switched, and when inspection work is being carried out, a low-brightness warm yellow light mode is switched.

[0025] In other implementations, the method can also be applied to commercial office spaces. For example, in a conference room setting, after the PPT projector is detected to be running, the color temperature sensor identifies the cool white light (6500K) on the screen, automatically reduces the brightness of the surrounding downlights to 200lx and simultaneously increases the color temperature to 5000K to eliminate screen reflection interference.

[0026] In some other implementations, the method can also be applied to medical rehabilitation wards. For example, a "day and night simulation curve" can be set according to the doctor's orders: the color temperature is gradually increased to 6000K at 6 am, maintained at 400lx / 4000K at noon, and gradually decreased to 10lx / 2700K at 8 pm. The color temperature is then finely adjusted in real time in conjunction with heart rate monitoring data. For example, when the patient is anxious, the warm yellow light of 3000K can be added to soothe them.

[0027] In one exemplary embodiment, such as Figure 1 As shown, an intelligent dimming control method is provided, which can be applied to... Figure 2 Taking the terminal in the example of this, the intelligent dimming control method can also be applied to other applications such as... Figure 3 The intelligent dimming control device 300 shown neutralizes Figure 4 The electronic devices 200 shown include computers, mobile terminals, etc. (See reference...) Figure 1 The intelligent dimming control method may include the following steps S110 to S150.

[0028] S110: Acquire ambient light change data and generate an initial light distribution dataset based on the ambient light change data.

[0029] In this embodiment, ambient light change data refers to dynamic parameters under mixed natural and artificial light illumination collected in real time by a light sensor, including light intensity, color temperature, and spatiotemporal variation characteristics. The initial light distribution dataset refers to a structured data set constructed based on the ambient light change data, including timestamps, spatial grid coding, light intensity matrix, and color temperature distribution heatmap.

[0030] For example, the terminal acquires ambient light change data in the area where the lighting device is located in real time through the light sensor of the Internet of Things, and generates a structured initial light distribution dataset after performing Kalman filtering noise reduction on the ambient light change data.

[0031] S120: The initial illumination distribution dataset is processed using a time series analysis model to obtain dynamic illumination change parameters.

[0032] In this embodiment of the application, the dynamic change parameters of illumination refer to the quantitative indicators extracted from the initial illumination distribution dataset through a time series model, including the range of illumination intensity fluctuations and periodic intensity.

[0033] For example, the terminal uses an LSTM (Long Short-Term Memory) time series model to perform sliding window standardization on the initial illumination distribution dataset, extract the illumination intensity trend term, periodic term and residual fluctuation, and finally output the dynamic change parameters of illumination.

[0034] S130: Input the dynamic lighting change parameters into the preset scene requirement database to generate a scene-adaptive lighting adjustment scheme.

[0035] In this embodiment, the preset scene requirement database refers to a structured database that stores preset lighting parameter requirements for different scenes, including scene type labels, priority weights, target illuminance ranges, color temperature ranges, and special constraints. The lighting adjustment scheme refers to an execution instruction set generated based on the matching of dynamic lighting change parameters with the scene database, including target luminaires, brightness setpoints, color temperature setpoints, dimming transition curves, and triggering conditions.

[0036] For example, the terminal inputs the obtained dynamic lighting change parameters into a pre-established scene requirement database, matches the dynamic lighting change parameters with the predefined rules in the scene requirement database in real time, outputs the matching results, and generates a lighting adjustment scheme adapted to the actual scene.

[0037] S140: Optimize the dimming parameters according to the lighting adjustment scheme and determine the dimming control signal.

[0038] In this embodiment of the application, the dimming control signal refers to the instruction format that encodes the optimized parameters into an executable format for the lighting device, and the dimming parameters refer to the specific instruction parameters required for the luminaire to perform control, including the brightness setting value, the color temperature setting value, and the fading time.

[0039] For example, based on the target brightness and color temperature values ​​in the illumination adjustment scheme, the terminal uses a constrained particle swarm optimization algorithm at the edge computing node, and fits the PWM (Pulse Width Modulation) duty cycle-illuminance formula (…). The initial dimming parameters are calculated using the luminaire coefficient (duty cycle) and the CCT-Bin (CCT: Correlated Color Temperature) mapping table (a key parameter mapping tool in luminaire dimming control). These parameters are then combined with a PID (Proportional-Integral-Derivative) controller (proportional coefficient). =0.8, integration time =5s) Based on the feedback from the real-time brightness sensor, the parameters are dynamically corrected, and finally a dimming control signal is generated.

[0040] S150: Sends dimming control signals to lighting equipment via the Internet of Things, wherein the dimming control signals are used to instruct the lighting equipment to adjust the brightness and color temperature.

[0041] For example, the terminal encapsulates the dimming control signal as an instruction (including device ID, target brightness value, color temperature value, and fading time) through the Internet of Things, and sends it to the lighting device via the LoRaWAN (Long Range Wide Area Network) gateway using the MQTT (Message Queuing Telemetry Transport) protocol.

[0042] This application provides a single-lamp intelligent dimming control method. It acquires real-time ambient light change data and generates an initial light distribution dataset based on this data. Then, it processes the initial light distribution dataset using a time series analysis model to obtain dynamic light change parameters. These parameters are then input into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme. The dimming parameters are optimized according to the scheme to determine the dimming control signal. Finally, the dimming control signal is sent to the lighting device via the Internet of Things (IoT). This method can respond to environmental changes in real time and generate personalized lighting schemes through precise matching of actual scene lighting needs, improving the user's lighting experience while also increasing energy efficiency.

[0043] In some implementations, after sending a dimming control signal to the lighting device via the Internet of Things, the intelligent dimming control method further includes steps S210 to S240.

[0044] S210: Acquire brightness feedback data and match the brightness feedback data with scene requirements to obtain matching results.

[0045] S220: When the matching result is that the brightness feedback data does not match the scene requirements, the brightness correction amount is calculated using a pre-established brightness correction model.

[0046] The expression for the pre-established brightness correction model is as follows:

[0047] in, This is the brightness correction amount. For target brightness, This represents the actual brightness. This is the proportional gain coefficient. This is the integral gain coefficient. This is the differential gain coefficient.

[0048] S230: Determine the corrected brightness value based on the brightness correction amount, and generate the final dimming parameters based on the corrected brightness value.

[0049] S240: Generates a secondary dimming control signal based on the final dimming parameters and sends the secondary dimming control signal to the lighting equipment.

[0050] In this embodiment, brightness feedback data refers to the quantized value of the actual illumination state collected in real time by a light sensor after the dimming control signal is issued. Final dimming parameters refer to the execution parameters determined after closed-loop optimization, including the corrected PWM duty cycle, color temperature mixing ratio, and dimming transition curve parameters.

[0051] For example, after sending the dimming control signal, the terminal collects the actual brightness data of the illuminated area in real time through a light sensor and matches it with the target brightness in the scene requirement database to obtain a matching result. If the result shows that the brightness feedback data does not match the scene requirements, a brightness correction amount is calculated according to a formula, and then a corrected brightness value is generated based on the brightness correction amount. After verification by an energy consumption model, a secondary dimming control signal is generated to drive the lighting equipment to dynamically compensate for the brightness deviation. This embodiment achieves high-precision, fast-response closed-loop dimming control through a closed-loop feedback and dynamic correction mechanism, improving energy efficiency and user experience.

[0052] In some implementations, after sending a secondary dimming control signal to the lighting device, the intelligent dimming control method further includes steps S250 to S280.

[0053] S250: Acquire ambient light data after secondary dimming and compare it with the final dimming parameters to obtain the applicability result of the dimming parameters.

[0054] S260: Input the dimming parameter applicability results into the pre-established natural light interference prediction model to obtain the natural light interference corresponding to the ambient light data.

[0055] The expression for the pre-established natural light interference prediction model is as follows:

[0056] in, Natural light interference Let T be the standard deviation of the change in light intensity within the time window T. The average light intensity within the time window T. This represents the absolute value of the maximum change in light intensity within the time window T. The span of the time window T, , These are the weighting coefficients.

[0057] S270: If the natural light interference exceeds the threshold, obtain updated ambient light dynamic change data.

[0058] S280: Analyze the updated ambient light dynamic change data to obtain an updated light adjustment scheme.

[0059] In this embodiment, the dimming parameter suitability result refers to the quantified value of the degree of matching between the final dimming parameters and the actual lighting effect. Natural light interference refers to the quantified index of the interference intensity of sudden changes in ambient natural light on the dimming effect.

[0060] For example, after sending the secondary dimming control signal, the terminal collects ambient light data after secondary dimming in real time through a light sensor, compares the ambient light data after secondary dimming with the final dimming parameters, outputs the applicability result of the dimming parameters, and then calculates the natural light interference degree based on the light data within a time window T=30 seconds. If the natural light interference degree exceeds the threshold (0.8), the ambient light data is collected again and a new light adjustment scheme is generated. This embodiment improves the dimming failure response speed under sudden environmental changes and increases the success rate of dynamic scene adaptation through real-time deviation analysis and natural light interference quantification.

[0061] In some implementations, S240 acquires ambient light data after secondary dimming and compares it with the final dimming parameters to obtain the applicability results of the dimming parameters, including S241 to S243.

[0062] S241: Verify the accuracy of the ambient lighting data after secondary dimming to obtain verified lighting data; S242: Compare the applicability of the verified illumination data with the final dimming parameters to obtain the deviation rate; S243: If the deviation rate is within the threshold range, generate the dimming parameter suitability result.

[0063] For example, the terminal uses a light sensor to collect ambient light data in real time after secondary dimming. It then performs a three-level verification process: removing ±3σ outliers, compensating for temperature drift (dynamically correcting data based on the sensor's temperature drift curve), and aligning the timestamps. This generates verified illumination data. The verified illumination data is then compared with the target value of the final dimming parameters for applicability, and the deviation rate is calculated. If the deviation rate is ≤5%, the applicability result of the dimming parameters is output; if the deviation rate exceeds 5%, the dimming process needs to be repeated. This embodiment reduces the misjudgment rate caused by environmental noise and improves the reliability of dimming decisions through three-level verification and dynamic deviation analysis.

[0064] In some implementations, S230 generates the final dimming parameters based on the corrected brightness value, including S231 to S233, wherein: S231: Input the corrected brightness value into the energy consumption control model to calculate the predicted energy consumption value.

[0065] The expression for the energy consumption control model is as follows:

[0066] in, This is the predicted energy consumption value. This is the base power of the lighting fixture. The rated power of the lamp at its maximum brightness. To the maximum brightness of the lamp, This refers to the actual brightness of the light fixture. This refers to the energy efficiency curve index of lighting fixtures. This is a correction term for environmental disturbances.

[0067] S232: Calculate the optimal dimming parameters using the optimal prediction model based on the energy consumption prediction value.

[0068] The expression for the optimal prediction model is:

[0069] in, For optimal dimming parameters, The target brightness is required for the scene. For reference energy consumption standards, This is the predicted energy consumption value. This refers to the actual brightness of the light fixture. This is the weighting coefficient for energy consumption error. This is the weighting coefficient for brightness error.

[0070] S233: Perform multi-dimensional verification and dynamic environment adaptation calibration of the optimal dimming parameters to determine the final dimming parameters.

[0071] In this embodiment, the optimal dimming parameter refers to the theoretically optimal solution generated under the constraints of brightness quality and energy consumption.

[0072] For example, the terminal inputs the corrected brightness value into the energy consumption control model, calculates the predicted energy consumption value using a formula, and then calculates the optimal dimming parameters based on the calculated predicted energy consumption value. After dynamic environmental calibration (compensating for natural light interference) and device protection constraints, the final dimming parameters are generated and finally sent to the lighting equipment via a communication protocol. This embodiment, through dual-objective optimization of energy consumption and quality and dynamic boundary constraints, reduces energy consumption while ensuring lighting accuracy, which helps to extend the service life of lighting equipment.

[0073] In some implementations, S130 inputs dynamic lighting change parameters into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme, including: S131 to S133, wherein: S131: If the dynamic lighting parameters are higher than the reading scene threshold in the scene requirement database, a high brightness adjustment command is generated.

[0074] S132: If the dynamic lighting parameters are lower than the rest scene threshold in the scene requirement database, a low brightness adjustment command is generated.

[0075] S133: Logically integrate the high brightness adjustment command and / or low brightness adjustment command to obtain a lighting adjustment scheme.

[0076] In this embodiment, after the terminal inputs the dynamic lighting change parameters into a preset scene requirement database, a high-brightness adjustment command is generated when the dynamic lighting change parameters are higher than the reading scene threshold (preset ≥500 Lux) in the scene requirement database; when the parameters are lower than the rest scene threshold (preset ≤100 Lux), a low-brightness adjustment command is generated. Then, a fuzzy logic controller performs weighted integration of conflicting commands (weighting coefficients: reading requirement 0.6, rest requirement 0.4), and outputs a lighting adjustment scheme. This embodiment improves the adaptation accuracy of complex lighting scenes and enhances the user's lighting experience by combining dynamic threshold determination with fuzzy logic integration.

[0077] In some implementations, S120 uses a time series analysis model to process the initial illumination distribution dataset to obtain dynamic illumination change parameters, including S121 to S123, wherein: S121: Based on the initial illumination distribution dataset, obtain the dynamic change characteristics of ambient illumination.

[0078] S122: Perform time series analysis on the dynamic changes in ambient light to obtain parameters of light intensity fluctuation range and trend.

[0079] S123: Output dynamic lighting change parameters based on fluctuation range and trend parameters.

[0080] In this embodiment, the terminal performs wavelet packet decomposition on the initial illumination distribution dataset to extract the dynamic change features of ambient illumination. Then, it analyzes the feature sequence using an LSTM time series model to output parameters of illumination intensity fluctuation range and trend, ultimately generating dynamic illumination change parameters. This embodiment enhances the ability to capture local abrupt changes in the illuminated area and improves the prediction accuracy of illumination change trends through multi-scale feature extraction and LSTM time series modeling.

[0081] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0082] Based on the same inventive concept, this application also provides an intelligent dimming control device for implementing the intelligent dimming control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more intelligent dimming control device embodiments provided below can be found in the limitations of the intelligent dimming control method described above, and will not be repeated here.

[0083] Please see Figure 3 , Figure 3 This invention provides a structural block diagram of an intelligent dimming control device, which includes: a first generation module 310, a processing module 320, a second generation module 330, a determining module 340, and a control module 350, wherein: The first generation module 310 is used to acquire ambient light change data and generate an initial light distribution dataset based on the ambient light change data. Processing module 320 is used to process the initial illumination distribution dataset using a time series analysis model to obtain dynamic illumination change parameters; The second generation module 330 is used to input the dynamic lighting change parameters into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme. The determination module 340 is used to optimize the dimming parameters and determine the dimming control signal according to the lighting adjustment scheme; The control module 350 is used to send dimming control signals to lighting equipment via the Internet of Things, wherein the dimming control signals are used to instruct the lighting equipment to adjust the brightness and color temperature.

[0084] It should be noted that the device embodiments in this invention correspond to the aforementioned method embodiments. The specific principles in the device embodiments can be found in the content of the aforementioned method embodiments, and will not be repeated here.

[0085] In the several embodiments provided in this example, the coupling between modules can be electrical, mechanical, or other forms of coupling.

[0086] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0087] Please see Figure 4 , Figure 4 This is a structural block diagram of an electronic device 200 that can perform the above-described intelligent dimming control method, provided in an embodiment of this application. The electronic device 200 may be a smartphone, tablet computer, computer, or portable computer.

[0088] The electronic device 200 also includes a processor 202 and a memory 204. The memory 204 stores programs that can execute the contents of the foregoing embodiments, and the processor 202 can execute the programs stored in the memory 204.

[0089] The processor 202 may include one or more cores for data processing and message matrix units. The processor 202 connects to various parts of the electronic device 200 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 204, and by calling data stored in the memory 204. Optionally, the processor 202 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 202 may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem / decoder. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem is used for wireless communication. It is understood that the modem / decoder may also not be integrated into the processor and may be implemented separately through a communication chip.

[0090] Memory 204 may include random access memory (RAM) or read-only memory (ROM). Memory 204 can be used to store instructions, programs, code, code sets, or instruction sets. Memory 204 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., instructions for a user to obtain random numbers), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data (e.g., random numbers) created by the terminal during use.

[0091] Electronic device 200 may also include a network module and a screen. The network module is used to receive and transmit electromagnetic waves, converting electromagnetic waves into electrical signals, thereby enabling communication with communication networks or other devices, such as audio playback devices. The network module may include various existing circuit elements used to perform these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, SIM cards, memory, etc. The network module can communicate with various networks such as the Internet, corporate intranets, and wireless networks, or communicate with other devices via wireless networks. The aforementioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The screen can display interface content and facilitate data interaction.

[0092] Please refer to Figure 5 , Figure 5 This diagram illustrates a structural block diagram of a computer-readable storage medium according to an embodiment of this application. The computer-readable storage medium 400 stores program code 410, which can be called by a processor to execute the methods described in the above method embodiments.

[0093] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. This program code 410 can be read from or written to one or more computer program products. The program code 410 may be compressed, for example, in a suitable form.

[0094] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the intelligent dimming control method described in the various optional implementations above.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart dimming control method, characterized in that, The method includes: Acquire ambient light change data and generate an initial light distribution dataset based on the ambient light change data; The initial illumination distribution dataset was processed using a time series analysis model to obtain dynamic illumination change parameters; The dynamic lighting change parameters are input into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme. Based on the aforementioned illumination adjustment scheme, the dimming parameters are optimized, and the dimming control signal is determined; The dimming control signal is sent to the lighting device via the Internet of Things (IoT), wherein the dimming control signal is used to instruct the lighting device to adjust the brightness and color temperature.

2. The intelligent dimming control method according to claim 1, characterized in that, The intelligent dimming control method further includes: Acquire brightness feedback data and match the brightness feedback data with scene requirements to obtain matching results; When the matching result indicates that the brightness feedback data does not match the scene requirements, the brightness correction amount is calculated using a pre-established brightness correction model; The corrected brightness value is determined based on the brightness correction amount, and the final dimming parameters are generated based on the corrected brightness value; A secondary dimming control signal is generated based on the final dimming parameters and sent to the lighting device.

3. The intelligent dimming control method according to claim 2, characterized in that, The intelligent dimming control method further includes: The ambient light data after the second dimming is obtained and compared with the final dimming parameters to obtain the applicability result of the dimming parameters; The applicability results of the dimming parameters are input into a pre-established natural light interference prediction model to obtain the natural light interference corresponding to the ambient light data. If the natural light interference exceeds the threshold, then the updated ambient light dynamic change data is obtained; The updated ambient light dynamic change data are analyzed to obtain an updated light adjustment scheme.

4. The intelligent dimming control method according to claim 3, characterized in that, The process of acquiring ambient light data after secondary dimming and comparing it with the final dimming parameters to obtain the applicability result of the dimming parameters includes: The ambient light data after the secondary dimming is verified to obtain the verified light data. The applicability of the verified illumination data is compared with the final dimming parameters to obtain the deviation rate. If the deviation rate is within the threshold range, the applicability result of the dimming parameters is generated.

5. The intelligent dimming control method according to claim 2, characterized in that, The process of generating the final dimming parameters based on the corrected brightness value includes: The corrected brightness value is input into the energy consumption control model to calculate the predicted energy consumption value. The optimal dimming parameters are calculated using the optimal prediction model based on the energy consumption prediction values. The optimal dimming parameters are verified in multiple dimensions and dynamically adapted to the environment to determine the final dimming parameters.

6. The intelligent dimming control method according to claim 1, characterized in that, The step of inputting the dynamic lighting change parameters into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme includes: If the dynamic lighting parameters are higher than the reading scene threshold in the scene requirement database, a high brightness adjustment command is generated. If the dynamic lighting change parameter is lower than the rest scene threshold in the scene requirement database, a low brightness adjustment command is generated. The high brightness adjustment command and / or the low brightness adjustment command are logically integrated to obtain the illumination adjustment scheme.

7. The intelligent dimming control method according to claim 1, characterized in that, The initial illumination distribution dataset is processed using a time series analysis model to obtain dynamic illumination change parameters, including: Based on the initial illumination distribution dataset, the dynamic change characteristics of ambient illumination are obtained; By performing time series analysis on the dynamic changes in ambient light, the fluctuation range and trend parameters of light intensity were obtained: Based on the fluctuation range and the change trend parameters, the dynamic change parameters of illumination are output.

8. An intelligent dimming control device, characterized in that, The device includes: The first generation module is used to acquire ambient light change data and generate an initial light distribution dataset based on the ambient light change data. The processing module is used to process the initial illumination distribution dataset using a time series analysis model to obtain dynamic illumination change parameters; The second generation module is used to input the dynamic lighting change parameters into a preset scene requirement database to generate a scene-adaptive lighting adjustment scheme. The determining module is used to optimize the dimming parameters and determine the dimming control signal according to the illumination adjustment scheme; The control module is used to send the dimming control signal to the lighting device via the Internet of Things, wherein the dimming control signal is used to instruct the lighting device to adjust the brightness and color temperature.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores program code that can run on the processor. When the program code is executed by the processor, it implements the intelligent dimming control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by one or more processors to execute the intelligent dimming control method as described in any one of claims 1-7.

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