Lighting system with self-learning optimization mechanism and method thereof

Abstract

A lighting system with a self-learning optimization mechanism and a method thereof. The lighting system comprises a plurality of lighting devices, a control device and a brightness adjustment device. The control device is connected with the plurality of lighting devices and stores a human factor curve. The human factor curve is a total brightness-time curve of the plurality of lighting devices in a default time interval. The brightness adjustment device communicates with the control device and is used to generate a dimming signal. The control device receives the dimming signal at a first time point, adjusts the total brightness of the plurality of lighting devices to a first brightness according to the dimming signal, and determines the total brightness corresponding to a second time point before the first time point and the total brightness corresponding to a third time point after the first time point according to the human factor curve. The control device performs curve fitting according to the first brightness, the total brightness corresponding to the second time point and the total brightness corresponding to the third time point to generate an adjustment curve, and updates the human factor curve according to the adjustment curve.

Description

Technical Field

[0001] This invention relates to a lighting system, and more particularly to a lighting system with a self-learning optimization mechanism. The invention also relates to a self-learning optimization method for this lighting system. Background Technology

[0002] With advancements in technology, people's demands for lighting systems are increasing. Therefore, human-centered lighting systems are gradually gaining attention. Existing human-centered lighting systems can control total brightness (or total color temperature) based on default luminance (or color temperature) curves to provide more user-friendly lighting functions.

[0003] However, existing human-caused lighting systems lack energy-saving control mechanisms, resulting in high energy consumption and failing to meet energy-saving requirements.

[0004] Furthermore, existing human-centric lighting systems can only control the total brightness (or total color temperature) based on a fixed luminance curve (or color temperature curve), lacking an effective optimization mechanism to optimize lighting for different applications. Therefore, existing human-centric lighting systems cannot meet the needs of different applications. Summary of the Invention

[0005] According to an embodiment of the present invention, a lighting system with a self-learning optimization mechanism is proposed, comprising multiple lighting devices, a control device, and a brightness adjustment device. The control device is connected to the multiple lighting devices and stores a human factor curve. The human factor curve is the total brightness-time curve of the multiple lighting devices within a default time interval. The brightness adjustment device communicates with the control device and is used to generate a dimming signal. The control device receives the dimming signal at a first time point and adjusts the total brightness of the multiple lighting devices to a first brightness according to the dimming signal. It then determines the total brightness at a second time point before the first time point and the total brightness at a third time point after the first time point based on the human factor curve. The control device performs curve fitting based on the first brightness, the total brightness at the second time point, and the total brightness at the third time point to generate an adjustment curve, and updates the human factor curve based on the adjustment curve.

[0006] In one embodiment, the control device replaces a portion of the human factor curve corresponding to the adjustment curve with an adjustment curve. The time period of this portion of the adjustment curve corresponds to the time period of the adjustment curve.

[0007] In one embodiment, the time length between the second time point and the first time point is equal to the time length between the third time point and the first time point.

[0008] In one embodiment, the curve fitting is either a linear fit or a curvilinear fit.

[0009] In one embodiment, the control device cyclically executes the human factor curve to control the total brightness of the plurality of lighting devices.

[0010] According to another embodiment of the present invention, a self-learning optimization method for a lighting system is proposed, comprising the following steps: storing a human factor curve by a control device, the human factor curve being the total brightness-time curve of the plurality of lighting devices in a default time interval; generating a dimming signal via a brightness adjustment device; receiving the dimming signal at a first time point by the control device and adjusting the total brightness of the plurality of lighting devices to a first brightness according to the dimming signal; determining, by the control device, the total brightness corresponding to a second time point before the first time point and the total brightness corresponding to a third time point after the first time point based on the human factor curve; performing curve fitting by the control device based on the first brightness, the total brightness corresponding to the second time point, and the total brightness corresponding to the third time point to generate an adjustment curve; and updating the human factor curve by the control device based on the adjustment curve.

[0011] In one embodiment, the step of updating the human factors curve by the control device according to the adjustment curve includes the following steps: replacing a portion of the human factors curve corresponding to the adjustment curve with the adjustment curve via the control device, wherein the time period of this portion of the adjustment curve corresponds to the time period of the adjustment curve.

[0012] In one embodiment, the time length between the second time point and the first time point is equal to the time length between the third time point and the first time point.

[0013] In one embodiment, the curve fitting is either a linear fit or a curvilinear fit.

[0014] In one embodiment, the method further includes the following step: cyclically executing human factor curves via a control device to control the total brightness of the plurality of lighting devices.

[0015] As described above, the lighting system and method with a self-learning optimization mechanism according to embodiments of the present invention may have one or more of the following advantages:

[0016] (1) In one embodiment of the present invention, the lighting system includes multiple lighting devices, a control device, and a brightness adjustment device. The control device is connected to the multiple lighting devices and stores a human factor curve. The human factor curve is the total brightness-time curve of the multiple lighting devices in a default time interval. The brightness adjustment device communicates with the control device and is used to generate a dimming signal. The control device receives the dimming signal at a first time point and adjusts the total brightness of the multiple lighting devices to a first brightness according to the dimming signal. It also determines the total brightness corresponding to a second time point before the first time point and the total brightness corresponding to a third time point after the first time point based on the human factor curve. The control device performs curve fitting based on the first brightness, the total brightness corresponding to the second time point, and the total brightness corresponding to the third time point to generate an adjustment curve, and updates the human factor curve based on the adjustment curve. Through the above-mentioned special integrated curve fitting self-learning optimization mechanism, the human factor curve of the lighting system can be flexibly adjusted according to the user's usage habits. Therefore, the total brightness of the lighting system (the total brightness of the multiple lighting devices) can be automatically reduced when the user does not need lighting to meet the needs of energy saving.

[0017] (2) In one embodiment of the present invention, the lighting system has a self-learning optimization mechanism for integrated curve fitting, which allows the human factors curve of the lighting system to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system can be appropriately optimized according to the usage habits of different users, rather than using a fixed human factors curve. Therefore, the self-learning optimization mechanism of the lighting system can effectively optimize the lighting function of the lighting system, enabling the lighting system to meet the needs of different applications.

[0018] (3) In one embodiment of the present invention, the lighting system has a self-learning optimization mechanism for integrated curve fitting, which allows the human factors curve of the lighting system to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system can be appropriately optimized according to the usage habits of different users, enabling the lighting system to provide more human-centered lighting functions. Therefore, the lighting system can not only meet the needs of different users, but also effectively improve the user experience.

[0019] (4) In one embodiment of the present invention, each lighting device of the lighting system may have a moving object detector, enabling it to detect moving objects. Thus, the lighting system can be activated only when a moving object is detected to provide lighting. Therefore, the energy consumption of the lighting system can be further reduced to meet the requirements of energy saving.

[0020] (5) In one embodiment of the present invention, the control device of the lighting system can cyclically execute the human factor curve to control the total brightness of the above-mentioned multiple lighting devices, and can continuously update the human factor curve through the above-mentioned self-learning optimization mechanism. In this way, the lighting system can not only achieve a highly automated lighting function, but also automatically optimize the human factor curve. Therefore, the lighting system can be applied to various intelligent systems, such as smart homes, smart factories, smart parking lots, etc., so that the lighting system can conform to the future development trend.

[0021] (6) In one embodiment of the present invention, the lighting system is designed simply, so the desired effect can be achieved without significantly increasing the cost. In this way, the practicality of the lighting system can be greatly improved, making its application more extensive and its use more flexible. Attached Figure Description

[0022] Figure 1 This is a block diagram of a lighting system with a self-learning optimization mechanism according to an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the self-learning optimization mechanism of a lighting system with a self-learning optimization mechanism according to an embodiment of the present invention.

[0024] Figure 3 This is a flowchart of a self-learning optimization method for a lighting system according to an embodiment of the present invention.

[0025] Explanation of reference numerals in the attached figures:

[0026] 1-Lighting system; 11-Lighting device; 12-Control device; 13-Brightness adjustment device; Ds-Dimming signal; T1-First time point; T2-Second time point; T3-Third time point; T4-Fourth time point; T5-Fifth time point; T6-Sixth time point; T-Time length; Ch-Human factor curve; Ca, Ca'-Adjustment curve; S31~S37-Step flow.

[0027] The following detailed description of the features and advantages of the present invention is sufficient to enable anyone skilled in the art to understand the technical content of the present invention and implement it accordingly. Based on the content disclosed in this specification, the claims and drawings, anyone skilled in the art can easily understand the purpose and advantages of this creation. Detailed Implementation

[0028] The following description, with reference to the accompanying drawings, illustrates embodiments of the lighting system and method with a self-learning optimization mechanism according to the present invention. For clarity and convenience, the dimensions and proportions of the components in the drawings may be exaggerated or reduced. In the following description and / or claims, when a component is referred to as "connected" or "coupled" to another component, it may be directly connected or coupled to that other component or there may be an intervening component; when a component is referred to as "directly connected" or "directly coupled" to another component, there is no intervening component. Other terms used to describe the relationship between components or layers should be interpreted in the same manner. For ease of understanding, the same components in the following embodiments are indicated by the same symbols.

[0029] Please see Figure 1 This is a block diagram of a lighting system with a self-learning optimization mechanism according to an embodiment of the present invention. As shown in the figure, the lighting system 1 includes multiple lighting devices 11, a control device 12, and a brightness adjustment device 13.

[0030] The control device 12 is connected to the plurality of lighting devices 11 via wired or wireless means and stores human factor curves. In one embodiment, the control device 12 may be a control panel, microcontroller (MCU), central processing unit (CPU), application-specific integrated circuit chip (ASIC), field-programmable gate array (FPGA), personal computer, laptop computer, or other similar component designed for the lighting system 1. The human factor curve is the total brightness-time curve of the plurality of lighting devices 11 over a default time interval. The preset time interval may be one day (00:00-24:00), and each time point of the human factor curve has a corresponding total brightness.

[0031] The brightness adjustment device 13 communicates with the control device 12 and is used to generate a dimming signal. In one embodiment, the brightness adjustment device 13 may be a remote control, smartphone, smartwatch, tablet computer, or other similar component.

[0032] The user can operate the brightness adjustment device 13 to dim the light at a first time point to generate a dimming signal Ds. The control device 12 receives the dimming signal Ds at the first time point and adjusts the total brightness of the plurality of lighting devices 11 to a first brightness according to the dimming signal Ds.

[0033] Then, the control device 12 determines the total brightness corresponding to the second time point before the first time point and the total brightness corresponding to the third time point after the first time point based on the human factor curve. The first time point can be located at the center of the second and third time points, so that the time length between the second and first time points is equal to the time length between the third and first time points.

[0034] Next, the control device 12 performs curve fitting based on the first brightness, the total brightness corresponding to the second time point, and the total brightness corresponding to the third time point to generate an adjustment curve, and updates the human factors curve based on the adjustment curve. In one embodiment, the curve fitting is a linear fitting or a curvilinear fitting (quadratic polynomial fitting); curve fitting should be well known to those skilled in the art, and therefore will not be described in detail here. In this embodiment, the control device 12 replaces a portion of the human factors curve corresponding to the adjustment curve with the adjustment curve. The time period of this portion of the adjustment curve corresponds to the time period of the adjustment curve.

[0035] The control device 12 can cyclically execute the human factor curve to control the total brightness of the multiple lighting devices 11. Simultaneously, the control device 12 can continuously update the human factor curve through the aforementioned self-learning optimization mechanism. Thus, the lighting system 1 not only achieves highly automated lighting functions but also automatically optimizes the human factor curve. Therefore, the lighting system 1 can be applied to various intelligent systems, such as smart homes, smart factories, and smart parking lots, enabling it to align with future development trends.

[0036] Through the aforementioned self-learning optimization mechanism of integrated curve fitting, the human factors curve of lighting system 1 can be flexibly adjusted according to the user's usage habits. Therefore, the total brightness of lighting system 1 (the total brightness of the multiple lighting devices) can be automatically reduced when the user does not need lighting, in order to meet the needs of energy saving.

[0037] Furthermore, in this embodiment, the lighting system 1 has a self-learning optimization mechanism that integrates curve fitting, allowing the human factors curve of the lighting system 1 to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system 1 can be appropriately optimized based on the usage habits of different users, rather than using a fixed human factors curve. Therefore, the self-learning optimization mechanism of the lighting system 1 can effectively optimize the lighting function of the lighting system 1, enabling the lighting system 1 to meet the needs of different applications.

[0038] Furthermore, in this embodiment, the lighting system 1 has a self-learning optimization mechanism that integrates curve fitting, allowing the human factors curve of the lighting system 1 to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system 1 can be appropriately optimized according to the usage habits of different users, enabling the lighting system 1 to provide more human-centered lighting functions. Therefore, the lighting system 1 not only meets the needs of different users but also effectively enhances the user experience.

[0039] On the other hand, each lighting device 11 of the lighting system 1 may also have a moving object detector (such as an infrared detector, a microwave detector, etc.) to enable it to detect moving objects. In this way, the lighting system 1 can be turned on only when a moving object is detected to provide lighting. Therefore, the energy consumption of the lighting system 1 can be further reduced to meet the requirements of energy saving.

[0040] Of course, this embodiment is only for illustrative purposes and is not intended to limit the scope of the invention. Equivalent modifications or changes made to the lighting system 1 with a self-learning optimization mechanism according to this embodiment should still be included within the patent scope of the invention.

[0041] Please see Figure 2 This is a schematic diagram of the self-learning optimization mechanism of a lighting system with a self-learning optimization mechanism according to an embodiment of the present invention. As shown in the figure, the control device 12 stores the human factor curve Ch. The control device 12 receives the dimming signal Ds at the first time point T1 and adjusts the total brightness of the plurality of lighting devices 11 to a first brightness according to the dimming signal Ds.

[0042] Then, the control device 12 determines the total brightness corresponding to the second time point T2 before the first time point T1 and the total brightness corresponding to the third time point T3 after the first time point T1 based on the human factor curve Ch. The first time point T1 can be located at the center of the second time point T2 and the third time point T3. That is to say, the time length T1 between the second time point T2 and the first time point and the time length between the third time point T3 and the first time point T1 are both T (T2=T1-T; T3=T1+T).

[0043] Next, the control device 12 can perform curve fitting to generate an adjustment curve based on the first brightness, the total brightness corresponding to the second time point T2 (T1—T), and the total brightness corresponding to the third time point T3. For example, taking linear fitting as an example, the curve from T2 (T1—T) to T can be represented by the following equation (1):

[0044] ;

[0045] Where t represents any time point in the curve; St represents the total brightness at time point t; S T1 S represents the total brightness at time point T1; T1—T This represents the total brightness at time points T1–T(T2).

[0046] The curves for T1 to T3 (T1+T) can also be obtained in the same way. Combining the two curves above yields the adjustment curve Ca from the second time point T2 to the third time point T3. Thus, the control device 12 can adjust curve Ca to replace a portion of the human factor curve Ch corresponding to the adjustment curve.

[0047] Similarly, when the control device 12 receives the dimming signal Ds at the fourth time point T4, it adjusts the total brightness of the plurality of lighting devices 11 to the fourth brightness according to the dimming signal Ds.

[0048] Then, the control device 12 determines the total brightness corresponding to the fifth time point T5 before the fourth time point T4 and the total brightness corresponding to the sixth time point T6 after the fourth time point T4 based on the human factor curve. In the same way, the control device 12 can obtain the adjustment curve Ca' from the fifth time point T5 to the sixth time point T6. In this way, the control device 12 can adjust the curve Ca' to replace the part of the human factor curve Ch corresponding to the adjustment curve Ca'.

[0049] In another embodiment, curve fitting can also be curvilinear fitting, which can achieve a similar effect.

[0050] Of course, this embodiment is only for illustrative purposes and is not intended to limit the scope of the invention. Equivalent modifications or changes made to the lighting system 1 with a self-learning optimization mechanism according to this embodiment should still be included within the patent scope of the invention.

[0051] It is worth noting that existing human-centric lighting systems lack energy-saving control mechanisms, resulting in high energy consumption and failing to meet energy-saving requirements. Furthermore, existing human-centric lighting systems can only control the total brightness (or total color temperature) based on a fixed brightness curve (or color temperature curve), lacking an effective optimization mechanism to optimize lighting for different applications. Therefore, existing human-centric lighting systems cannot meet the needs of various applications. In contrast, according to an embodiment of the present invention, the lighting system includes multiple lighting devices, a control device, and a brightness adjustment device. The control device is connected to the multiple lighting devices and stores the human-centric curve. The human-centric curve is the total brightness-time curve of the multiple lighting devices within a default time interval. The brightness adjustment device communicates with the control device and is used to generate a dimming signal. The control device receives the dimming signal at a first time point and adjusts the total brightness of the multiple lighting devices to a first brightness level according to the dimming signal. It then determines the total brightness corresponding to a second time point before the first time point and the total brightness corresponding to a third time point after the first time point based on the human-centric curve. The control device performs curve fitting based on the first brightness, the total brightness at the second time point, and the total brightness at the third time point to generate an adjustment curve, and updates the human factors curve based on the adjustment curve. Through this special integrated curve fitting self-learning optimization mechanism, the human factors curve of the lighting system can be flexibly adjusted according to the user's usage habits. Therefore, the total brightness of the lighting system (the total brightness of the multiple lighting devices) can be automatically reduced when the user does not need lighting to meet the needs of energy saving.

[0052] According to embodiments of the present invention, the lighting system has a self-learning optimization mechanism that integrates curve fitting, allowing the human factors curve of the lighting system to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system can be appropriately optimized based on the usage habits of different users, rather than using a fixed human factors curve. Therefore, the self-learning optimization mechanism of the lighting system can effectively optimize the lighting function of the lighting system, enabling the lighting system to meet the needs of different applications.

[0053] Furthermore, according to embodiments of the present invention, the lighting system has a self-learning optimization mechanism for integrated curve fitting, enabling the human factors curve of the lighting system to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system can be appropriately optimized according to the usage habits of different users, allowing the lighting system to provide more human-centered lighting functions. Therefore, the lighting system not only meets the needs of different users but also effectively enhances the user experience.

[0054] Furthermore, according to embodiments of the present invention, each lighting device in the lighting system can have a moving object detector, enabling it to detect moving objects. Thus, the lighting system can only activate to provide illumination when a moving object is detected. Therefore, the energy consumption of the lighting system can be further reduced to meet the requirements of energy conservation and power saving.

[0055] Furthermore, according to embodiments of the present invention, the control device of the lighting system can cyclically execute the human factor curve to control the total brightness of the multiple lighting devices, and can continuously update the human factor curve through the aforementioned self-learning optimization mechanism. Thus, the lighting system not only achieves highly automated lighting functions but also automatically optimizes the human factor curve. Therefore, the lighting system can be applied to various intelligent systems, such as smart homes, smart factories, and smart parking lots, enabling it to align with future development trends.

[0056] Furthermore, according to embodiments of the present invention, the lighting system is simple in design, thus achieving the desired effect without significantly increasing costs. This greatly enhances the practicality of the lighting system, making its application more widespread and its use more flexible. As can be seen from the above, the lighting system and method with a self-learning optimization mechanism according to embodiments of the present invention can indeed achieve excellent technical results.

[0057] Please see Figure 3 This is a flowchart of a self-learning optimization method for a lighting system according to an embodiment of the present invention. As shown in the figure, the method of this embodiment may include the following steps:

[0058] Step S31: The control device stores the human factor curve, which is the total brightness-time curve of the above-mentioned multiple lighting devices in a preset time interval.

[0059] Step S32: A dimming signal is generated via a brightness adjustment device.

[0060] Step S33: The control device receives the dimming signal at the first time point and adjusts the total brightness of the above-mentioned multiple lighting devices to the first brightness according to the dimming signal.

[0061] Step S34: The control device determines the total brightness corresponding to the second time point before the first time point and the total brightness corresponding to the third time point after the first time point based on the human factor curve.

[0062] Step S35: The control device performs curve fitting based on the first brightness, the total brightness corresponding to the second time point, and the total brightness corresponding to the third time point to generate an adjustment curve.

[0063] Step S36: The control device updates the human factors curve based on the adjustment curve. The control device replaces a portion of the human factors curve corresponding to the adjustment curve with the adjustment curve, where the time period of this portion of the adjustment curve corresponds to the time period of the adjustment curve.

[0064] Step S37: The human factor curve is executed cyclically by the control device to control the total brightness of the above-mentioned multiple lighting devices.

[0065] Of course, this embodiment is only for illustrative purposes and is not intended to limit the scope of the invention. Equivalent modifications or changes made to the self-learning optimization method of the lighting system according to this embodiment should still be included within the patent scope of the invention.

[0066] Although the steps of the methods described in this invention are shown and described in a specific order, the order of operation of each method may be changed, some steps may be performed in reverse order, or some steps may be performed simultaneously with other steps. In another embodiment, different steps may be implemented intermittently and / or alternately.

[0067] In summary, according to embodiments of the present invention, the lighting system includes multiple lighting devices, a control device, and a brightness adjustment device. The control device is connected to the multiple lighting devices and stores a human factor curve. The human factor curve is the total brightness-time curve of the multiple lighting devices within a default time interval. The brightness adjustment device communicates with the control device and is used to generate a dimming signal. The control device receives the dimming signal at a first time point and adjusts the total brightness of the multiple lighting devices to a first brightness according to the dimming signal. It then determines the total brightness at a second time point before the first time point and the total brightness at a third time point after the first time point based on the human factor curve. The control device performs curve fitting based on the first brightness, the total brightness at the second time point, and the total brightness at the third time point to generate an adjustment curve, and updates the human factor curve based on the adjustment curve. Through the aforementioned special integrated curve fitting self-learning optimization mechanism, the human factor curve of the lighting system can be flexibly adjusted according to the user's usage habits. Therefore, the total brightness of the lighting system (the total brightness of the multiple lighting devices) can be automatically reduced when the user does not need lighting, in order to meet the needs of energy saving.

[0068] According to embodiments of the present invention, the lighting system has a self-learning optimization mechanism that integrates curve fitting, allowing the human factors curve of the lighting system to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system can be appropriately optimized based on the usage habits of different users, rather than using a fixed human factors curve. Therefore, the self-learning optimization mechanism of the lighting system can effectively optimize the lighting function of the lighting system, enabling the lighting system to meet the needs of different applications.

[0069] Furthermore, according to embodiments of the present invention, the lighting system has a self-learning optimization mechanism for integrated curve fitting, enabling the human factors curve of the lighting system to be flexibly adjusted according to the user's usage habits. Thus, the human factors curve of the lighting system can be appropriately optimized according to the usage habits of different users, allowing the lighting system to provide more human-centered lighting functions. Therefore, the lighting system not only meets the needs of different users but also effectively enhances the user experience.

[0070] Furthermore, according to embodiments of the present invention, each lighting device in the lighting system can have a moving object detector, enabling it to detect moving objects. Thus, the lighting system can only activate to provide illumination when a moving object is detected. Therefore, the energy consumption of the lighting system can be further reduced to meet the requirements of energy conservation and power saving.

[0071] Furthermore, according to embodiments of the present invention, the control device of the lighting system can cyclically execute the human factor curve to control the total brightness of the multiple lighting devices, and can continuously update the human factor curve through the aforementioned self-learning optimization mechanism. Thus, the lighting system not only achieves highly automated lighting functions but also automatically optimizes the human factor curve. Therefore, the lighting system can be applied to various intelligent systems, such as smart homes, smart factories, and smart parking lots, enabling it to align with future development trends.

[0072] Furthermore, according to embodiments of the present invention, the lighting system is simple in design, thus achieving the desired effect without significantly increasing costs. This greatly enhances the practicality of the lighting system, making its applications more widespread and its use more flexible.

[0073] It should be noted that although the above embodiments have been described herein, this does not limit the scope of patent protection for this invention. Therefore, any changes and modifications made to the embodiments described herein based on the innovative concept of this invention, or equivalent structural or procedural transformations made using the description and drawings of this invention, directly or indirectly applying the above technical solutions to other related technical fields, are all included within the scope of protection of this invention.

Claims

1. A lighting system with a self-learning optimization mechanism, characterized in that, include: Multiple lighting fixtures; A control device is connected to the plurality of lighting devices and stores human factor curves, which are the total brightness-time curves of the plurality of lighting devices in a default time interval; as well as A brightness adjustment device communicates with the control device and is used to generate a dimming signal; The control device is configured to receive the dimming signal at a first time point, adjust the total brightness of the plurality of lighting devices to a first brightness according to the dimming signal, and determine the total brightness corresponding to a second time point before the first time point and the total brightness corresponding to a third time point after the first time point according to the human factor curve. The time length between the second time point and the first time point is equal to the time length between the third time point and the first time point, so that the first time point is located at the center of the second time point and the third time point. The control device is configured to perform curve fitting to generate an adjustment curve according to the first brightness, the total brightness corresponding to the second time point, and the total brightness corresponding to the third time point. The control device is configured to replace a portion of the human factor curve corresponding to the adjustment curve with the adjustment curve to update the human factor curve. The time period of the human factor curve corresponding to the portion of the adjustment curve corresponds to the time period of the adjustment curve.

2. The lighting system with a self-learning optimization mechanism as described in claim 1, characterized in that, The curve fitting is either linear fitting or curvilinear fitting.

3. The lighting system with a self-learning optimization mechanism as described in claim 1, characterized in that, The control device is used to cyclically execute the human factor curve to control the total brightness of the plurality of lighting devices.

4. A self-learning optimization method for a lighting system, characterized in that, include: The human factor curve is stored by the control device. The human factor curve is the total brightness-time curve of multiple lighting devices in a default time interval. A dimming signal is generated via a brightness adjustment device; The control device receives the dimming signal at a first time point and adjusts the total brightness of the plurality of lighting devices to a first brightness according to the dimming signal; The control device determines the total brightness corresponding to the second time point before the first time point and the total brightness corresponding to the third time point after the first time point based on the human factor curve. The time length between the second time point and the first time point is equal to the time length between the third time point and the first time point, so that the first time point is located at the center of the second time point and the third time point. The control device performs curve fitting based on the first brightness, the total brightness corresponding to the second time point, and the total brightness corresponding to the third time point to generate an adjustment curve; as well as The control device updates the human factors curve by replacing a portion of the human factors curve corresponding to the adjustment curve with the adjustment curve, wherein the time period of the human factors curve corresponding to the portion of the adjustment curve corresponds to the time period of the adjustment curve.

5. The self-learning optimization method for a lighting system as described in claim 4, characterized in that, The curve fitting is either linear fitting or curvilinear fitting.

6. The self-learning optimization method for a lighting system as described in claim 4, characterized in that, Also includes: The control device cyclically executes the human factor curve to control the total brightness of the plurality of lighting devices.

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