Lighting optimization control methods, lighting systems, electronic devices and media

Abstract

This application provides a lighting optimization control method, lighting system, electronic device, and medium. The method includes: acquiring a user's emotional state parameters; generating a target spectral feature vector that meets photobiological safety constraints based on the emotional state parameters and in conjunction with preset photobiological safety limits; acquiring the spectral response matrix of the lighting device; constructing a constrained optimization problem based on the target spectral feature vector and the spectral response matrix; the constrained optimization problem aims to minimize the deviation between the target spectral feature vector and the actual spectral feature vector obtained after spectral feature extraction of the channel synthesized spectrum; solving the constrained optimization problem under preset constraints to obtain a channel control vector; and generating lighting control commands based on the channel control vector to drive the lighting device to output a target light environment. This application improves the accuracy of light environment adjustment, has strong dynamic adaptability, and provides higher actual lighting output comfort.

Description

Technical Field

[0001] This application belongs to the field of intelligent lighting technology and relates to a lighting optimization control method, lighting system, electronic device and medium. Background Technology

[0002] As intelligent lighting systems evolve from basic dimming and color adjustment to more emotional, contextualized, and adaptive experiences, lighting equipment is no longer limited to providing visual illumination. Instead, it is gradually transforming into an environmental interaction terminal capable of responding to users' psychological states, spatial activities, and daily rhythms. Under this trend, achieving precise matching and dynamic adaptation between the lighting environment and users' emotional states has become a key technical problem that urgently needs to be solved in this field.

[0003] Currently, existing mood lighting control technologies mainly employ two implementation paths: one is a preset mapping scheme based on emotion state recognition, which directly calls preset lighting configurations after identifying the user's emotional state through sensors or algorithms; the other is a command generation scheme based on generative models or rule engines, which generates lighting control commands in real time based on the user's actively input needs, scene descriptions, or emotional expressions using generative models or rule engines. However, the above schemes still have the following significant shortcomings in practical applications:

[0004] First, the mapping between emotional states and lighting parameters is simplistic and lacks continuous representation. Existing systems often simplify complex emotional states into discrete semantic labels (such as "relaxed" or "focused"), mechanically mapping each label to fixed parameters like brightness, hue, saturation, or rhythm of change. This approach ignores the continuous dynamic characteristics of emotions in terms of intensity, duration, and changing trends, resulting in lighting responses that cannot subtly follow the micro-fluctuations of the user's emotions, making it difficult to achieve true adaptive emotional regulation.

[0005] Secondly, existing dynamic spectrum generation schemes often lack sufficient consideration of the physical characteristics and hardware constraints of real light sources, easily leading to discrepancies between the target control effect and the actual output. Some schemes primarily generate dynamic lighting parameters based on preset templates, parameter superposition, or oscillator models. Their control process remains largely at the numerical algorithm level, failing to fully consider practical factors such as the spectral power distribution characteristics of multi-channel light sources, channel coupling relationships, drive current limitations, achievable chromaticity range, and luminance variation constraints. Therefore, in actual lighting systems, there may be significant deviations between the target chromaticity, luminance, or spectral distribution generated by the algorithm and the final output of the luminaire. Furthermore, during dynamic changes, abrupt transitions, color jumps, unreasonable proportions of short-wavelength blue light, or decreased visual comfort may occur. This not only weakens the mood-regulating effect but may also affect the user's visual experience and long-term comfort. Summary of the Invention

[0006] This application provides a lighting optimization control method, lighting system, electronic device, and medium to solve the technical problems of low light environment adjustment accuracy, weak dynamic adaptation capability, and insufficient actual lighting output comfort in the prior art.

[0007] In a first aspect, this application provides a lighting optimization control method, comprising: acquiring a user's emotional state parameters; generating a target spectral feature vector that satisfies photobiological safety constraints based on the emotional state parameters and in conjunction with preset photobiological safety limits; acquiring a spectral response matrix of a lighting device, wherein the spectral response matrix is ​​composed of the spectral power distribution of each light source channel in the lighting device at a preset wavelength sampling point, used to characterize the spectral output characteristics of each light source channel, and serving as a linear transformation basis for constructing the channel composite spectrum, so as to establish a mapping relationship between the spectral output of each light source channel and the channel composite spectrum; constructing a constrained optimization problem based on the target spectral feature vector and the spectral response matrix; the constrained optimization problem having the optimization objective of minimizing the deviation between the target spectral feature vector and the actual spectral feature vector obtained after spectral feature extraction of the channel composite spectrum; solving the constrained optimization problem under preset constraints to obtain a channel control vector; and generating a lighting control command based on the channel control vector to drive the lighting device to output a target light environment.

[0008] In one implementation of the first aspect, obtaining the user's emotional state parameters and generating a target spectral feature vector that satisfies photobiological safety constraints based on the emotional state parameters and in conjunction with preset photobiological safety limits includes: obtaining the user's multimodal behavioral cues; the multimodal behavioral cues include user behavior cues, temporal rhythm cues, spatial and device combination cues, and content type cues; inferring the user's emotional state parameters based on at least two of the multimodal behavioral cues; the emotional state parameters include at least emotion type, emotion intensity, emotion persistence parameter, emotion confidence level, and emotion change trend; selecting a spectral basis vector corresponding to the emotion type from a preset emotional spectral basis vector library; the emotional spectral basis vector... A vector library is used to characterize the mapping relationship between different emotion types and corresponding spectral adjustment parameters. The spectral basis vector includes one or more spectral anchor points associated with the emotion type. The spectral anchor points include color purity, brightness, short-wavelength energy ratio, spectral change smoothing coefficient, spectral retention duration, spectral transition speed, spectral shift direction, and spectral update ratio. Based on the emotion intensity, the emotion persistence parameter, the emotion confidence level, and the emotion change trend, the parameters of the spectral anchor points in the spectral basis vector are adjusted to obtain the adjusted spectral feature vector. The adjusted spectral feature vector is projected onto the safe and feasible region determined by the photobiological safety limit to obtain the target spectral feature vector.

[0009] In one implementation of the first aspect, the parameters of the spectral anchor point in the spectral basis vector are adjusted based on the emotion intensity, the emotion persistence parameter, the emotion confidence level, and the emotion change trend to obtain the adjusted spectral feature vector, including: adjusting the color purity, the brightness, and the short-wavelength energy ratio based on the emotion intensity; adjusting the spectral change smoothing coefficient or the spectral retention duration based on the emotion persistence; adjusting the spectral transition speed or the spectral shift direction based on the emotion change trend; and adjusting the spectral update ratio based on the emotion confidence level.

[0010] In one implementation of the first aspect, obtaining the spectral response matrix of the lighting device includes: acquiring the spectral power distribution of each light source channel in the lighting device within a preset wavelength range; discretely sampling the spectral power distribution of each light source channel according to a preset wavelength interval to obtain the sampled spectral power distribution corresponding to each light source channel; and constructing the spectral response matrix based on the sampled spectral power distribution corresponding to each light source channel, wherein each row of the spectral response matrix corresponds to a wavelength sampling point, and each column corresponds to the sampled spectral power distribution of a light source channel at multiple wavelength sampling points.

[0011] In one implementation of the first aspect, generating lighting control commands based on the channel control vector to drive the lighting device to output a target light environment includes: determining target light state parameters of the lighting device according to the channel control vector; the target light state parameters include target light chromaticity parameters, target light intensity parameters, and target duty cycle parameters corresponding to each light source channel; obtaining current light state parameters of the lighting device; the current light state parameters include current light chromaticity parameters, current light intensity parameters, and current duty cycle parameters corresponding to each light source channel; and constructing a transition envelope function between the current light state parameters and the target light state parameters. The transition envelope function is any one of a cubic smoothing function, an exponential smoothing function, or a piecewise Bessel function; based on the transition envelope function, the current light state parameters and the target light state parameters are interpolated temporally to generate intermediate light state parameters; the intermediate light state parameters include intermediate light chromaticity parameters, intermediate light brightness parameters, and intermediate duty cycle parameters corresponding to each light source channel; the intermediate light state parameters are arranged to form a dynamic transition trajectory; the dynamic transition trajectory is used to characterize the time-continuous control path during the change from the current light state to the target light state; the dynamic transition trajectory is converted into the lighting control command.

[0012] In one implementation of the first aspect, the method further includes: determining the transition duration corresponding to the transition envelope function based on the emotion persistence parameter in the emotion state parameters; determining the transition slope of the transition envelope function based on the emotion intensity and emotion change trend in the emotion state parameters; the transition slope is used to limit the amount of chromaticity change, luminance change, and duty cycle change of each light source channel per unit time.

[0013] In one implementation of the first aspect, the method further includes hysteresis update control of the dynamic transition trajectory, wherein the step of hysteresis update control of the dynamic transition trajectory includes: determining the magnitude of change of the user's current emotional state parameter relative to the emotional state parameter at the previous moment; determining whether the magnitude of change exceeds a preset hysteresis threshold, and whether the state of the magnitude of change exceeding the preset hysteresis threshold is continuously maintained for more than a preset confirmation time; if so, regenerating the target spectral feature vector based on the current emotional state parameter; resolving the channel control vector based on the regenerated target spectral feature vector; and generating a new dynamic transition trajectory based on the resolved channel control vector; otherwise, maintaining the dynamic transition trajectory unchanged.

[0014] In a second aspect, this application provides a lighting system comprising: one or more lighting devices, each of the lighting devices including a plurality of light source channels; and a lighting control device connected to the lighting devices for performing the method as described in any of the preceding claims.

[0015] Thirdly, this application provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory to cause the electronic device to perform the method described in any of the preceding claims.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0017] As described above, the lighting optimization control method, lighting system, electronic device, and medium described in this application have the following beneficial effects:

[0018] (1) By constructing a constrained optimization problem, the safety, stability and comfort of the lighting control process can be improved while ensuring that the actual spectral output is close to the target spectral feature vector;

[0019] (2) By generating a transition envelope between the current light state parameters and the target light state parameters, the chromaticity difference, brightness difference and duty cycle change of each light source channel between consecutive moments are all less than the corresponding perception threshold, which effectively reduces the user's abruptness to changes in the light environment and reduces visual discomfort caused by rapid changes.

[0020] (3) Through the above-mentioned delayed update control mechanism, the system only triggers the replanning of the light environment when it confirms that the user's emotions have undergone a real and stable state transition, which effectively avoids frequent changes in lighting caused by emotion recognition noise or instantaneous fluctuations, and significantly improves the smoothness of lighting control and user experience.

[0021] (4) Role-based collaborative control among multiple lighting devices is realized, enabling multiple lighting devices to form a consistent, hierarchical and stable spatial light environment without relying on the light field diagram of the lamp cluster or on generative artificial intelligence to perform cross-modal semantic understanding of user lighting needs. Attached Figure Description

[0022] Figure 1 The flowchart shown is a lighting optimization control method according to an embodiment of this application.

[0023] Figure 2 The flowchart shown is a process for generating the target spectral feature vector in one embodiment of this application.

[0024] Figure 3 The diagram shown is a flowchart of the adjustment of the spectral anchor point in one embodiment of this application.

[0025] Figure 4 The flowchart shown is a process for obtaining the spectral response matrix according to an embodiment of this application.

[0026] Figure 5 The flowchart shown is a lighting optimization control method according to another embodiment of this application.

[0027] Figure 6 The diagram shown illustrates the generation flowchart of lighting control instructions according to an embodiment of this application.

[0028] Figure 7 The flowchart shown is a lighting optimization control method according to another embodiment of this application.

[0029] Figure 8 The flowchart shown is a hysteresis update control according to an embodiment of this application.

[0030] Figure 9 The diagram shown is a structural schematic of a lighting system according to an embodiment of this application.

[0031] Figure 10 The diagram shown is a structural schematic of an electronic device according to an embodiment of this application. Detailed Implementation

[0032] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0033] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0034] The following embodiments of this application provide a lighting optimization control method, lighting system, electronic device, and medium, effectively solving the shortcomings of existing technologies that rely solely on fixed lighting effect templates, simple emotion tag mapping, or simply user-demand-generated compilation. Compared to traditional solutions, this application provides more precise light environment adjustment accuracy, stronger dynamic adaptation capabilities, and higher actual lighting output comfort.

[0035] The lighting optimization control method described in this application can be widely applied to various scenarios such as full-color mood lighting in the home, immersive entertainment lighting, healthy and comfortable lighting, and the generation of personalized light environments in multiple spaces.

[0036] The following will describe in detail the principles and implementation methods of a lighting optimization control method, lighting system, electronic device and medium of this embodiment, so that those skilled in the art can understand the lighting optimization control method, lighting system, electronic device and medium of this embodiment without creative effort.

[0037] Please see Figure 1 The above is a flowchart of a lighting optimization control method in one embodiment of this application.

[0038] like Figure 1 As shown, this embodiment provides a lighting optimization control method, including the following steps S100 to S500.

[0039] In step S100, the user's emotional state parameters are obtained, and based on the emotional state parameters and in combination with preset photobiological safety limits, a target spectral feature vector that meets photobiological safety constraints is generated.

[0040] To reduce system hardware complexity and minimize the direct collection of user privacy information, this embodiment does not directly collect raw images, audio, or physiological signals such as facial images, raw voice content, heart rate, respiration, body temperature, and skin conductance. Instead, it receives structured emotional state parameters output by the upstream inference module. This reduces reliance on highly complex sensors and minimizes the transmission and storage of raw, sensitive personal data within the system.

[0041] In one embodiment of this application, the emotional state parameters include at least emotional type, emotional intensity, emotional duration parameter, emotional confidence level, and emotional change trend.

[0042] Specifically, the emotional state parameters can be represented as the following emotional state vector:

[0043] E_t=[e_t,a_t,τ_t,ρ_t,q_t];

[0044] Where E_t represents the emotional state vector corresponding to time t; e_t represents the emotional type, which can be represented by discrete categories or semi-continuous categories, such as calm, relaxed, focused, happy, excited, tense, tired, or depressed; a_t represents the emotional intensity, which can be normalized to a continuous value in the interval [0, 1], used to characterize the strength of the corresponding emotional type; τ_t represents the emotional persistence parameter, which characterizes the stability, duration, or cumulative characteristics of the current emotional state within a preset time window; ρ_t represents the emotional change trend, which characterizes the rate and direction of change of emotional intensity or emotional type over time, used to reflect the dynamic evolution of the emotional state; q_t represents the emotional confidence level, used to characterize the reliability of the upstream inference module's identification result of the current emotional state.

[0045] Please see Figure 2 The above is a flowchart illustrating the generation process of the target spectral feature vector in one embodiment of this application.

[0046] like Figure 2 As shown, obtaining the user's emotional state parameters, and generating a target spectral feature vector that meets photobiological safety constraints based on the emotional state parameters and in combination with preset photobiological safety limits, includes the following steps S101 to S105.

[0047] In step S101, the user's multimodal behavior cues are obtained.

[0048] In one embodiment of this application, the multimodal behavioral cues are used to characterize the user's behavioral state, usage scenario, and environmental context information from multiple dimensions, including user behavior cues, time rhythm cues, space and device combination cues, and content type cues.

[0049] Specifically, the user behavior cues are used to characterize the user's behavior patterns at the current moment or within a preset time window; the time rhythm cues are used to characterize the current time, day-night rhythm, work-rest cycle, or historical usage patterns; the space and device combination cues are used to characterize the user's space, the distribution of lighting devices, the status of terminal devices, or the device linkage relationship; and the content type cues are used to characterize the type of content the user is currently accessing or using, such as work content, entertainment content, learning content, rest content, or exercise content.

[0050] In step S102, the user's emotional state parameters are inferred based on at least two of the multimodal behavioral cues.

[0051] It should be noted that this application does not limit the specific inference method for the emotional state parameters. As mentioned above, in one optional embodiment, the emotional state parameters can be output by the upstream emotional state inference module. In another optional embodiment, the emotional state parameters can also be output by the non-intrusive behavioral cue fusion module. The non-intrusive behavioral cue fusion module can fuse the multimodal behavioral cues based on rule models, statistical models, machine learning models, or a combination thereof to output structured emotional state parameters.

[0052] In step S103, a spectral basis vector corresponding to the emotion type is selected from a preset emotion spectral basis vector library.

[0053] The emotional spectral basis vector library is used to characterize the mapping relationship between different emotion types and corresponding spectral adjustment parameters. For each emotion type, at least one corresponding spectral basis vector can be pre-configured in the preset emotional spectral basis vector library. The spectral basis vector is used to provide the basic spectral adjustment direction and initial spectral control parameters that match the corresponding emotion type.

[0054] Specifically, the spectral basis vector includes one or more spectral anchor points associated with the emotion type. These anchor points define the basic spectral modulation characteristics for the corresponding emotion type. The spectral anchor points include color purity, brightness, shortwave energy ratio, spectral change smoothing coefficient, spectral retention duration, spectral transition speed, spectral shift direction, and spectral update ratio.

[0055] For example, when the emotion type is calm or relaxed, a spectral basis vector with lower color purity, warmer base correlated color temperature, lower short-wavelength energy ratio, and slower dynamic change rate can be selected; when the emotion type is pleasant or excited, a spectral basis vector with higher color purity, higher dynamic change amplitude, and shorter spectral transition time can be selected.

[0056] In step S104, based on the emotion intensity, the emotion persistence parameter, the emotion confidence level, and the emotion change trend, the spectral anchor point in the spectral basis vector is adjusted to obtain the adjusted spectral feature vector.

[0057] Please see Figure 3 The diagram shows a flowchart of the adjustment of the spectral anchor point in one embodiment of this application.

[0058] like Figure 3 As shown, based on the emotion intensity, the emotion persistence parameter, the emotion confidence level, and the emotion change trend, the parameters of the spectral anchor points in the spectral basis vector are adjusted to obtain the adjusted spectral feature vector, which includes:

[0059] (1) Adjust the color purity, the brightness and the shortwave energy ratio based on the emotional intensity.

[0060] Specifically, the amplitudes of the color purity adjustment, brightness adjustment, or shortwave energy ratio adjustment can be positively correlated with the emotional intensity. This embodiment adaptively adjusts the spectral adjustment amplitude according to the strength of the user's emotional response, avoiding excessive spectral changes when the emotional intensity is low.

[0061] (2) Based on the duration of the emotion, adjust the smoothing coefficient of the spectral change or the duration of the spectral retention.

[0062] Specifically, when the emotion persistence parameter is high, it indicates that the current emotional state has high stability or long duration within the past preset time window. In this case, the smoothing coefficient of the spectral change can be increased, or the duration of the target spectrum can be extended to improve the stability of the spectral output. When the emotion persistence parameter is low, it indicates that the current emotional state fluctuates greatly or has a short duration. In this case, the duration of the target spectrum can be reduced, or the flexibility of the spectrum update can be increased to respond to changes in the user's emotional state in a timely manner.

[0063] (3) Adjust the spectral transition speed or the spectral shift direction based on the emotional change trend.

[0064] Specifically, when the emotional trend indicates that the user's emotional state is rapidly increasing, the spectral transition speed can be appropriately increased to make the target spectrum approach the spectral anchor point corresponding to the current emotional state more quickly; when the emotional trend indicates that the user's emotional state is stabilizing or weakening, the spectral transition speed can be decreased, and the spectral shift direction can gradually move towards a neutral, gentle, or preset safe and comfortable range. This reduces the lag between the spectral output and the dynamic changes in the user's emotions.

[0065] (4) Adjust the spectral update ratio based on the emotional confidence level.

[0066] Specifically, when the emotion confidence level is high, it indicates that the emotion state parameters output by the upstream inference module have high reliability. In this case, the spectral update ratio can be increased so that the adjusted spectral feature vector reflects the current emotion state parameters more accurately. When the emotion confidence level is low, it indicates that the reliability of the current emotion state parameters is insufficient. In this case, the spectral update ratio can be reduced so that the adjusted spectral feature vector retains more of the spectral parameters from the previous moment. Alternatively, smoothing, delayed update, or anomaly filtering can be used to avoid frequent spectral jumps due to misjudgment of the emotion state.

[0067] In this implementation, by using emotion intensity, emotion persistence parameters, emotion change trends, and emotion confidence as continuous adjustment parameters in the generation process of the target spectral feature vector, the target spectral parameters can be continuously and smoothly adjusted according to changes in the user's emotional state, thereby improving the precision of spectral adjustment and the consistency of user experience.

[0068] In step S105, the adjusted spectral feature vector is projected onto the safe and feasible region determined by the photobiological safety limit to obtain the target spectral feature vector.

[0069] The preset photobiological safety limits are used to define the safety boundaries of the target light environment during actual output. These safety boundaries may include at least one constraint related to correlated color temperature, brightness, illuminance, radiance, short-wavelength energy ratio, blue light hazard weighted radiance, exposure time, multi-channel light source driving ratio, or spectral change rate. Based on the preset photobiological safety limits, a safe and feasible region can be determined in a predetermined optical parameter space. This safe and feasible region characterizes the range of spectral parameter values ​​that satisfy photobiological safety constraints.

[0070] The target spectral feature vector is a set of parameters of the target light environment in a predetermined light parameter space, which may include at least one of the following: correlated color temperature, chromaticity shift, brightness, color purity, short-wavelength energy ratio, and multi-channel light source ratio.

[0071] In one embodiment of this application, the target spectral feature vector can be represented as:

[0072] ;

[0073] Where T_t represents the target spectral feature vector at time t; B_e represents the spectral basis vector corresponding to the emotion type e_t; W_e represents the emotion regulation coefficient matrix corresponding to the emotion type e_t; a_t represents the emotion intensity; τ_t represents the emotion persistence parameter; and ρ_t represents the emotion change trend. The safe feasible region projection operator Π is used to constrain the spectral feature vector generated based on the emotional state parameters to the safe feasible region, so that the projected target spectral feature vector satisfies the preset photobiological safety limit.

[0074] In this implementation, personalized spectral adjustment based on emotional state parameters can be achieved while the adjusted spectral feature vector is restricted to a safe and feasible domain that meets photobiological safety requirements, thereby taking into account the emotional adaptation effect, spectral output stability and photobiological safety.

[0075] In step S200, the spectral response matrix of the lighting device is obtained.

[0076] The spectral response matrix is ​​composed of the spectral power distribution of each light source channel in the lighting device at a preset wavelength sampling point. It is used to characterize the spectral output characteristics of each light source channel and serves as a linear transformation basis for constructing the channel composite spectrum, so as to establish the mapping relationship between the spectral output of each light source channel and the channel composite spectrum.

[0077] Please see Figure 4 The above is a flowchart illustrating the process of obtaining the spectral response matrix according to an embodiment of this application.

[0078] like Figure 4 As shown, obtaining the spectral response matrix of a lighting device includes the following steps S201 to S203.

[0079] In step S201, the spectral power distribution of each light source channel in the lighting device within a preset wavelength range is collected.

[0080] Specifically, during the factory calibration phase, installation and commissioning phase, or periodic calibration phase of the lighting equipment, each light source channel in the lighting equipment can be lit up, and the spectral power distribution of each light source channel within a preset wavelength range can be collected.

[0081] The preset wavelength range can be determined based on the actual emission band of the lighting device, for example, it can cover the visible light band, or further cover the short-wave band related to photobiological safety evaluation. The light source channel can include at least one of the following: red light channel, green light channel, blue light channel, white light channel, warm white light channel, and cool white light channel.

[0082] In step S202, the spectral power distribution of each light source channel is discretely sampled according to a preset wavelength interval to obtain the sampled spectral power distribution corresponding to each light source channel.

[0083] The preset wavelength interval can be determined based on the spectral calculation accuracy, storage resources, and control real-time requirements. For example, it can be 5nm, 10nm, or other wavelength intervals suitable for lighting control.

[0084] In step S203, the spectral response matrix is ​​constructed based on the sampled spectral power distribution corresponding to each light source channel.

[0085] Specifically, the sampled spectral power distributions corresponding to each light source channel can be arranged according to a preset channel order to form the spectral response matrix. Each row of the spectral response matrix corresponds to a wavelength sampling point, and each column corresponds to the sampled spectral power distribution of a light source channel at multiple wavelength sampling points.

[0086] For example, for a lighting device with five light source channels—red (R), green (G), blue (B), warm white (WW), and cool white (CW)—the spectral power distribution of each of the five light source channels can be collected separately and sampled discretely at wavelength intervals of 5 nm or 10 nm. In this case, the constructed spectral response matrix A can include five columns, each corresponding to the sampled spectral power distribution of the R, G, B, WW, or CW channels at multiple wavelength sampling points.

[0087] In step S300, a constrained optimization problem is constructed based on the target spectral feature vector and the spectral response matrix.

[0088] In this embodiment, the constrained optimization problem aims to minimize the deviation between the target spectral feature vector and the actual spectral feature vector obtained after spectral feature extraction of the channel synthesized spectrum. The deviation may include a weighted sum of chromaticity error, brightness error, shortwave energy ratio error, and penalties for channel changes at adjacent time points.

[0089] Specifically, spectral features can be extracted from the channel synthesized spectrum using a spectral feature transformation operator to obtain the actual spectral feature vector.

[0090] In step S400, under preset constraints, the constraint optimization problem is solved to obtain the channel control vector.

[0091] Please see Figure 5 The above is a flowchart of a lighting optimization control method in another embodiment of this application.

[0092] like Figure 5 As shown, when solving the constrained optimization problem, the preset constraints used include at least one of the following: channel amplitude constraint, channel power constraint, chromaticity change rate constraint, stroboscopic safety constraint, and channel output difference constraint between adjacent time moments.

[0093] In one embodiment of this application, the constrained optimization problem can be expressed as:

[0094] ;

[0095] The constraints include: ;

[0096] Where u represents the channel control vector to be solved at the current time; u_{t−1} represents the channel control vector at the previous time; and A represents the spectral response matrix. This represents the transformation operator from the channel synthesized spectrum to the target optical parameter space; T_t represents the target spectral feature vector at the current moment. Represents the weighted spectral feature deviation term; W represents the weight matrix corresponding to different spectral feature errors; This represents the weighting coefficient of the channel change penalty term at adjacent time points; This represents the weighting coefficient of the safety penalty item; The function represents the safety penalty function; u_max represents the maximum allowable control quantity for each light source channel; Δu_max represents the maximum allowable change in the control quantity of the light source channel at adjacent times; P(u) represents the total power corresponding to the channel control vector; P_max represents the maximum allowable power; flicker(u) represents the flicker evaluation parameter corresponding to the channel control vector; This represents the safe and feasible region determined by constraints such as flicker, power, shortwave energy ratio, and visual comfort.

[0097] Specifically, the weighted spectral feature deviation term is used to constrain the consistency between the actual output light environment and the target spectral feature vector; the adjacent time-time channel change penalty term is used to suppress excessive jumps in the light source channel control quantity within adjacent control cycles; and the safety penalty function is used to penalize channel control quantities that may cause photobiological safety risks, visual discomfort, power over-limit, or flicker risks.

[0098] In this implementation, by using the above objective function and constraints, the safety, stability, and comfort of the lighting control process can be improved while ensuring that the spectral output is close to the target spectral eigenvector.

[0099] In step S500, a lighting control command is generated based on the channel control vector to drive the lighting device to output the target light environment.

[0100] Please see Figure 6 The diagram shows a flowchart of the generation of lighting control instructions according to an embodiment of this application.

[0101] like Figure 6 As shown, generating lighting control commands based on the channel control vector to drive the lighting device to output the target light environment includes the following steps S501 to S506.

[0102] In step S501, the target light state parameters of the lighting device are determined according to the channel control vector.

[0103] The target light state parameters are used to characterize the target optical state that the lighting equipment should theoretically achieve. The target light state parameters include target light chromaticity parameters, target light intensity parameters, and target duty cycle parameters corresponding to each light source channel.

[0104] Specifically, the light colorimetric parameters may include at least one of the following: XYZ tristimulus values, xyY chromaticity coordinates, CIE 1976 u′v′ chromaticity coordinates, correlated color temperature, or chromaticity offset; the light brightness parameters may include at least one of the following: brightness, illuminance, or dimming level; the duty cycle parameters may include the PWM duty cycle, drive current value, or digital dimming value corresponding to the red light channel, green light channel, blue light channel, warm white light channel, cool white light channel, or other light source channels.

[0105] In step S502, the current light state parameters of the lighting device are obtained.

[0106] The current light state parameters are used to characterize the actual optical state of the lighting device at the current moment. The current light state parameters include the current light chromaticity parameter, the current light intensity parameter, and the current duty cycle parameter corresponding to each light source channel.

[0107] It should be noted that the meaning of each parameter in the current light state parameters can be basically the same as the meaning of the corresponding parameter in the target light state parameters, so the meaning of the same parameters will not be elaborated here.

[0108] In step S503, a transition envelope function is constructed between the current optical state parameter and the target optical state parameter.

[0109] The transition envelope function is any one of a cubic smoothing function, an exponential smoothing function, or a piecewise Bessel function.

[0110] In this embodiment, the transition envelope function has the following specific functions:

[0111] First, it achieves a smooth transition. When the target light state parameters change, if the light jumps directly from the current brightness, chromaticity, and duty cycle to the target value, the user may perceive abrupt changes, flickering, or discomfort. The transition envelope is used to define the shape of the change process, allowing the light to gradually approach the target state.

[0112] Second, control the pace of change. Different emotional states correspond to different paces of light changes. For example, relaxed and calm emotions are suited to slow, gentle changes; excited and joyful emotions can tolerate faster changes. The transition envelope can express this difference in pace as different curve shapes.

[0113] Third, it satisfies the continuity of perception and safety constraints. By generating intermediate control values ​​through the transition envelope, the chromaticity difference, brightness difference, and channel duty cycle changes between adjacent control moments can be checked to ensure that these changes are less than the preset perception threshold, thereby reducing the risk of flicker, abrupt changes, and visual discomfort.

[0114] In this implementation, a transition envelope is generated between the current light state parameters and the target light state parameters, ensuring that the chromaticity difference, luminance difference, and duty cycle changes of each light source channel between consecutive moments are all less than the corresponding perception thresholds. This effectively reduces the user's perception of abrupt changes in the lighting environment and minimizes visual discomfort caused by rapid changes.

[0115] In step S504, based on the transition envelope function, the current optical state parameters and the target optical state parameters are interpolated temporally to generate intermediate optical state parameters.

[0116] The intermediate light state parameters include intermediate light chromaticity parameters, intermediate light brightness parameters, and intermediate duty cycle parameters corresponding to each light source channel.

[0117] It should be noted that the meaning of each parameter in the intermediate light state parameters can be basically the same as the meaning of the corresponding parameter in the target light state parameters, so the meaning of the same parameters will not be elaborated here.

[0118] In step S505, the intermediate light state parameters are arranged to form a dynamic transition trajectory.

[0119] The dynamic transition trajectory is used to characterize the time-continuous control path during the transition from the current light state to the target light state. This application does not directly control the lighting device to jump from the current light state to the new target light state, but rather constructs the dynamic transition trajectory between the current light state parameters and the target light state parameters. Therefore, the lighting device can sequentially output multiple intermediate light state parameters according to the dynamic transition trajectory, thereby achieving continuous and smooth switching of the target light environment.

[0120] In one embodiment of this application, the dynamic transition trajectory can be generated based on a cubic smoothing function. For example, the cubic smoothing function can be expressed as:

[0121] ;

[0122] Where s represents the normalization time, and s∈[0,1]; h(s) represents the transition weight corresponding to the normalization time s. As s gradually changes from 0 to 1, h(s) smoothly changes from 0 to 1, so that the optical state parameters gradually transition from the current optical state parameters to the target optical state parameters. Since this cubic smoothing function has a small rate of change at the beginning and end, it can reduce the abruptness at the start and end of the transition.

[0123] In step S506, the dynamic transition trajectory is converted into the lighting control command.

[0124] The lighting control commands can be used to control the driving state of each light source channel in the lighting equipment, so that the lighting equipment outputs a target light environment that matches the target spectral feature vector. The lighting control commands may include PWM duty cycle control commands, current control commands, digital dimming control commands, communication protocol control commands, or combinations thereof.

[0125] Please see Figure 7 The above is a flowchart of a lighting optimization control method according to another embodiment of this application.

[0126] like Figure 7 As shown, the lighting optimization control method described in this application further includes steps S507 and S508.

[0127] In step S507, the transition duration corresponding to the transition envelope function is determined based on the emotion persistence parameter in the emotion state parameters.

[0128] The transition duration characterizes the time required for a lighting device to transition from its current light state parameters to a target light state parameter. This transition duration can affect the speed of change in the target light environment and the user's perceived comfort level. A longer transition duration results in smoother changes in color intensity, brightness, and duty cycle of each light source channel, suitable for calm, relaxed, or fatigue-recovering emotional states requiring a gentle transition. Conversely, a shorter transition duration allows for a faster response from the lighting device to the target light state, suitable for scenarios involving reminders, entertainment, or pronounced emotional changes.

[0129] In another embodiment of this application, the transition duration can also be determined based on both the emotion persistence parameter and the emotion change trend. For example, the transition duration can be expressed as:

[0130] ;

[0131] Where T_trans represents the transition duration; T_min represents the preset minimum transition time; k_τ represents the adjustment coefficient corresponding to the emotion persistence parameter; τ_t represents the emotion persistence parameter; k_ρ represents the adjustment coefficient corresponding to the emotion change trend; ρ_t represents the emotion change trend, and |ρ_t| represents the amplitude of the emotion change trend.

[0132] In step S508, the transition slope of the transition envelope function is determined based on the emotional intensity and emotional change trend in the emotional state parameters.

[0133] The transition slope is used to characterize the rate of change of the transition envelope function per unit time, and is used to limit the amount of chromaticity change, luminance change, and duty cycle change of each light source channel per unit time. The larger the transition slope, the faster the lighting device changes from the current light state parameters to the target light state parameters; the smaller the transition slope, the smoother and gentler the change in light state of the lighting device, so that the chromaticity difference, luminance difference, and duty cycle change of each light source channel at consecutive moments are less than the corresponding preset perception thresholds, thus ensuring timely emotional response while reducing the risk of sudden changes in the light environment, visual discomfort, and flicker.

[0134] In one embodiment of this application, the lighting optimization control method further includes step S600.

[0135] In step S600, hysteresis update control is performed on the dynamic transition trajectory.

[0136] Please see Figure 8 The above is a flowchart of a hysteresis update control according to an embodiment of this application.

[0137] like Figure 8 As shown, the steps for hysteresis update control of the dynamic transition trajectory include steps S601 to S604.

[0138] In step S601, the magnitude of change of the user's current emotional state parameter relative to the emotional state parameter at the previous moment is determined.

[0139] The magnitude of change can be used to characterize the degree of change of at least one of the following: user emotion type, emotion intensity, emotion persistence parameter, emotion change trend, or emotion confidence level, between adjacent sampling periods.

[0140] Specifically, the magnitude of the change can be calculated using Euclidean distance, weighted distance, Manhattan distance, or other measures that can characterize differences in emotional states.

[0141] In step S602, it is determined whether the change amplitude exceeds a preset hysteresis threshold, and whether the state in which the change amplitude exceeds the preset hysteresis threshold is continuously maintained for more than a preset confirmation time.

[0142] Specifically, a preset hysteresis threshold δ_h can be set, and the change amplitude can be compared with the preset hysteresis threshold δ_h. When the change amplitude is less than or equal to δ_h, it indicates that the current emotional state change amplitude is small, which may be a short-term fluctuation or recognition noise. When the change amplitude is greater than δ_h, the system can further determine whether the threshold-exceeding state persists for multiple consecutive sampling periods, or whether it is continuously maintained for more than a preset confirmation time.

[0143] In step S603, if yes, then based on the emotional state parameters at the current moment, the target spectral feature vector is regenerated; the channel control vector is re-solved based on the regenerated target spectral feature vector; and a new dynamic transition trajectory is generated based on the re-solved channel control vector.

[0144] In this embodiment, the specific implementation process of generating the target spectral feature vector, solving the channel control vector, and constructing the dynamic transition trajectory has been described in detail in the previous embodiments, and will not be repeated here.

[0145] In step S604, otherwise the dynamic transition trajectory remains unchanged.

[0146] In this implementation, through the aforementioned delayed update control mechanism, the system only triggers the replanning of the lighting environment when it confirms that the user's emotions have undergone a real and stable state transition. This effectively avoids frequent changes in lighting caused by emotion recognition noise or instantaneous fluctuations, and significantly improves the smoothness of lighting control and user experience.

[0147] In one embodiment of this application, the lighting optimization control method further includes: constructing a role coordination matrix based on the spatial location, lighting role, and functional partition of the lighting devices; determining the spectral parameter allocation ratio, dynamic change sequence, dynamic change amplitude, and synchronization degree of each lighting role according to the role coordination matrix; and controlling multiple lighting devices to form a spatial light environment with consistent mood based on the spectral parameter allocation ratio, dynamic change sequence, dynamic change amplitude, and synchronization degree.

[0148] Specifically, when the lighting equipment includes multiple lighting devices, the system can classify the multiple lighting devices into different lighting roles based on their installation location in the space, illumination range, lighting purpose, and functional area. The lighting roles may include at least one of main lighting, ambient lighting, accent lighting, and auxiliary lighting, wherein the main lighting is used to provide basic illuminance for the space; the ambient lighting is used to express color and create emotional atmosphere; the accent lighting is used to highlight specific areas, furnishings, or interactive areas; and the auxiliary lighting is used to supplement local brightness or enhance spatial hierarchy.

[0149] The role coordination matrix is ​​used to characterize the coordination relationship between different lighting roles in terms of spectral parameter allocation, dynamic change process, and synchronization control. The matrix elements in the role coordination matrix can represent at least one of the following: spectral parameter allocation ratio, brightness intensity ratio, dynamic change sequence, dynamic change amplitude, transition time difference, and degree of synchronization between different lighting roles.

[0150] For example, when the mood type corresponds to a relaxing, calm, or fatigue-recovering emotional lighting environment, the system can, based on the role coordination matrix, control the main lighting to reduce brightness and maintain a low dynamic change amplitude, control the ambient lighting to take the lead in color expression and adopt a smoother transition trajectory, and control the auxiliary lighting to change synchronously with a smaller brightness amplitude or lower color purity. This creates a soft, stable, and spatially consistent relaxing lighting environment.

[0151] In this implementation, role-based collaborative control among multiple lighting devices is achieved, enabling multiple lighting devices to form a consistent, hierarchical, stable, and comfortable spatial light environment without relying on the light field diagram of the lamp cluster or on generative artificial intelligence to perform cross-modal semantic understanding of user lighting needs.

[0152] Please see Figure 9 The image shown is a schematic diagram of the structure of a lighting system according to an embodiment of this application.

[0153] like Figure 9 As shown in the figure, this application provides a lighting system, including one or more lighting devices and a lighting control device.

[0154] In one embodiment of this application, each of the lighting devices includes multiple light source channels.

[0155] The multiple light source channels may include a red light (R) channel, a green light (G) channel, a blue light (B) channel, a warm white light channel, a cool white light channel, or a multi-band light source channel. Different light source channels can emit light according to their corresponding channel control values ​​under the control of the lighting control device to synthesize the target lighting environment.

[0156] In one embodiment of this application, a lighting control device is connected to the lighting equipment and is used to perform the lighting optimization control method as described in any one of the above-described methods.

[0157] It should be noted that the principle of the lighting control device described above in this embodiment corresponds one-to-one with the steps in the lighting optimization control method described above, so it will not be repeated here.

[0158] Please see Figure 10 The image shown is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0159] like Figure 10 As shown, this application provides an electronic device, including:

[0160] A memory for storing computer programs.

[0161] A processor, the processor being configured to execute a computer program stored in the memory, so as to cause the electronic device to perform the method described in any of the preceding descriptions.

[0162] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0163] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0164] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0165] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0166] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the methods described in any of the above embodiments. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0167] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0168] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A lighting optimization control method, characterized in that, include: The user's emotional state parameters are obtained, and based on the emotional state parameters and in combination with preset photobiological safety limits, a target spectral feature vector that meets photobiological safety constraints is generated. Obtain the spectral response matrix of the lighting device, wherein the spectral response matrix is ​​composed of the spectral power distribution of each light source channel in the lighting device at a preset wavelength sampling point, which is used to characterize the spectral output characteristics of each light source channel and serve as a linear transformation basis for constructing the channel composite spectrum, so as to establish the mapping relationship between the spectral output of each light source channel and the channel composite spectrum. Based on the target spectral feature vector and the spectral response matrix, a constrained optimization problem is constructed; the constrained optimization problem aims to minimize the deviation between the target spectral feature vector and the actual spectral feature vector obtained after spectral feature extraction of the channel synthesized spectrum. Under preset constraints, the constraint optimization problem is solved to obtain the channel control vector; Based on the channel control vector, lighting control commands are generated to drive the lighting device to output the target light environment.

2. The method according to claim 1, characterized in that, Obtain the user's emotional state parameters, and based on these parameters and in conjunction with preset photobiological safety limits, generate a target spectral feature vector that meets photobiological safety constraints, including: Acquire user multimodal behavioral cues; the multimodal behavioral cues include user behavior cues, time rhythm cues, space and device combination cues, and content type cues; The user's emotional state parameters are inferred based on at least two of the multimodal behavioral cues; the emotional state parameters include at least emotion type, emotion intensity, emotion persistence parameter, emotion confidence level, and emotion change trend. Select a spectral basis vector corresponding to the emotion type from a preset emotion spectral basis vector library; the emotion spectral basis vector library is used to characterize the mapping relationship between different emotion types and corresponding spectral adjustment parameters; the spectral basis vector includes one or more spectral anchor points associated with the emotion type, and the spectral anchor points include color purity, brightness, short-wavelength energy ratio, spectral change smoothing coefficient, spectral retention duration, spectral transition speed, spectral shift direction, and spectral update ratio; Based on the emotion intensity, the emotion persistence parameter, the emotion confidence level, and the emotion change trend, the parameters of the spectral anchor point in the spectral basis vector are adjusted to obtain the adjusted spectral feature vector. The adjusted spectral feature vector is projected onto the safe and feasible region determined by the photobiological safety limit to obtain the target spectral feature vector.

3. The method according to claim 2, characterized in that, Based on the emotion intensity, the emotion persistence parameter, the emotion confidence level, and the emotion change trend, the parameters of the spectral anchor points in the spectral basis vector are adjusted to obtain the adjusted spectral feature vector, which includes: Based on the emotional intensity, adjust the color purity, the brightness, and the shortwave energy ratio; Based on the duration of the emotion, adjust the smoothing coefficient of the spectral change or the duration of the spectral retention; Based on the emotional change trend, adjust the spectral transition speed or the spectral shift direction; The spectral update ratio is adjusted based on the emotional confidence level.

4. The method according to claim 1, characterized in that, Obtaining the spectral response matrix of the lighting equipment includes: The spectral power distribution of each light source channel in the lighting device within a preset wavelength range is collected respectively; According to a preset wavelength interval, the spectral power distribution of each light source channel is discretely sampled to obtain the sampled spectral power distribution corresponding to each light source channel; Based on the sampled spectral power distribution corresponding to each light source channel, the spectral response matrix is ​​constructed, wherein each row of the spectral response matrix corresponds to a wavelength sampling point, and each column corresponds to the sampled spectral power distribution of a light source channel at multiple wavelength sampling points.

5. The method according to claim 1, characterized in that, Based on the channel control vector, generating lighting control commands to drive the lighting device to output the target light environment includes: Based on the channel control vector, the target light state parameters of the lighting device are determined; the target light state parameters include the target light chromaticity parameter, the target light brightness parameter, and the target duty cycle parameter corresponding to each light source channel; Obtain the current light state parameters of the lighting device; the current light state parameters include the current light chromaticity parameter, the current light intensity parameter, and the current duty cycle parameter corresponding to each light source channel; A transition envelope function is constructed between the current optical state parameters and the target optical state parameters; the transition envelope function is any one of a cubic smoothing function, an exponential smoothing function, or a piecewise Bessel function; Based on the transition envelope function, the current light state parameters and the target light state parameters are interpolated temporally to generate intermediate light state parameters; the intermediate light state parameters include intermediate light chromaticity parameters, intermediate light brightness parameters, and intermediate duty cycle parameters corresponding to each light source channel; The intermediate light state parameters are arranged to form a dynamic transition trajectory; the dynamic transition trajectory is used to characterize the time-continuous control path in the process of changing from the current light state to the target light state. The dynamic transition trajectory is converted into the lighting control command.

6. The method according to claim 5, characterized in that, Also includes: The transition duration corresponding to the transition envelope function is determined based on the emotion persistence parameter in the emotion state parameters. Based on the emotional intensity and emotional change trend in the emotional state parameters, the transition slope of the transition envelope function is determined; the transition slope is used to limit the amount of chromaticity change, luminance change, and duty cycle change of each light source channel per unit time.

7. The method according to claim 5, characterized in that, It also includes hysteresis update control of the dynamic transition trajectory, wherein the step of hysteresis update control of the dynamic transition trajectory includes: Determine the magnitude of change in the user's current emotional state parameter relative to the emotional state parameter at the previous moment; Determine whether the change amplitude exceeds a preset hysteresis threshold, and whether the state in which the change amplitude exceeds the preset hysteresis threshold is continuously maintained for more than a preset confirmation time; If so, then based on the emotional state parameters at the current moment, the target spectral feature vector is regenerated; based on the regenerated target spectral feature vector, the channel control vector is re-solved; and based on the re-solved channel control vector, a new dynamic transition trajectory is generated. Otherwise, the dynamic transition trajectory remains unchanged.

8. A lighting system, characterized in that, include: One or more lighting devices, each of which includes multiple light source channels; A lighting control device, connected to the lighting equipment, for performing the method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: The memory is used to store computer programs; A processor for executing a computer program stored in the memory to cause the electronic device to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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