Self-adaptively adjustable intelligent bathroom light environment and energy consumption management system

Abstract

The invention relates to the technical field of power distribution management, and discloses a self-adaptively adjustable intelligent bathroom light environment and energy consumption management system, which comprises a signal acquisition module, a load driving module and an energy consumption management controller, and is characterized in that a first power distribution loop is opened in a transparent mode, and power is adjusted according to illumination data; switching to a second power distribution loop in a water mist mode, extracting a current feedback value and retrieving a photoelectric incidence matrix, converting a current characteristic into equivalent illumination to maintain constant power distribution, and updating a mapping weight parameter through a historical sample in a zero interference window, power distribution error adjustment caused by medium interference is eliminated through logic switching of a feedback path, load aging drift is compensated by adopting a time sequence reuse mechanism, and energy efficiency management of a power distribution system is realized on the premise of ensuring the stability of a light environment.

Description

TECHNICAL FIELD

[0001] The present application relates to a self-adaptive adjustable intelligent bathroom light environment and energy consumption management system, belonging to the technical field of power distribution management. BACKGROUND

[0002] The current lighting load regulation of bathroom space usually adopts a closed-loop control strategy based on optical feedback. The controller collects environmental illumination data and adjusts the output power of the driving circuit according to the preset logic to maintain a constant light environment quality. The power distribution accuracy of the lighting load is constrained by the environmental transmission medium and the physical characteristics of the load.

[0003] Under ideal working conditions, there is a certain conversion relationship between the driving current and the target illumination. The power distribution system adjusts the power accordingly. The high-density water mist particles generated in the bathroom space during use scatter the light path, causing the feedback signal captured by the optical sensor to deviate from the true illumination value. The control unit continuously increases the output power to counteract the feedback drop, resulting in invalid energy loss. To alleviate the interference of the environmental medium on the lighting system, existing technologies focus on improving the physical structure of the lighting device. For example, the utility model patent with the authorization announcement number CN211399483U discloses an intelligent bathroom indoor induction lighting device. By setting water-absorbing fibers on the top of the reflector cup and using a mesh black chrome coating at the bottom of the lens, the lens surface and the light path transparency are maintained by physical moisture absorption and light-heat conversion dehumidification methods. However, relying on passive physical structure protection methods, in extreme working conditions of high humidity saturation or water mist transient burst in the bathroom, the moisture-absorbing medium saturation and heat dehumidification lag physical bottleneck cannot be avoided. Once the environmental water mist concentration exceeds the physical defense threshold of the device, light scattering is inevitable. If there is no adaptive switching or correction mechanism at the control logic level, the system will fall into a vicious cycle of blindly increasing power due to the inability to identify the authenticity of the signal, and cannot achieve all-weather energy efficiency control. By increasing detection hardware to reduce interference, the deployment cost and integration difficulty of the system will increase, and the load entity will undergo irreversible aging over time, resulting in control deviation of the static power distribution model in the later stage of the full life cycle, and the regulation consistency cannot be maintained.

[0004] Therefore, how to construct a regulation strategy that can dynamically identify environmental modalities and has load characteristic self-calibration capability under the premise of utilizing the existing circuit structure of the system, so as to achieve precise power control of the lighting load across modalities and the full life cycle, has become a technical problem to be solved by the present application. SUMMARY

[0005] To solve the problems raised in the background art, the technical solution of the present application is as follows: a self-adaptive adjustable intelligent bathroom light environment and energy consumption management system, the system comprising:

[0006] The signal acquisition module is configured to acquire real-time illuminance data, a space occupancy signal, and a medium light attenuation parameter in the bathroom. The load driving module is configured to output a driving current to the lighting load according to the power distribution instruction. The energy consumption management controller is connected to the signal acquisition module and the load driving module. The energy consumption management controller performs the following operations: In step S101, the medium light attenuation parameter is compared with a preset transparency threshold. When it is determined that the mode is transparent, the first power distribution circuit is started, and the output power of the driving current is closed-loop adjusted according to the real-time illuminance data. In step S102, when it is determined that the mode is water mist, the second power distribution circuit is started, the current feedback value of the load driving module is extracted, the light-electricity correlation matrix containing the mapping weight parameter is retrieved, the current feedback value is converted into an equivalent illuminance value, and constant-power power distribution is performed based on the equivalent illuminance value. In step S103, the environmental state evolution is monitored. When the preset zero-interference calibration constraint is met, the historical samples of the real-time illuminance data stored during the running of the first power distribution circuit are called, and the mapping weight parameter in the light-electricity correlation matrix is updated.

[0007] Preferably, when the energy consumption management controller performs step S102, it further performs the following operations: the time variation rate of the real-time illuminance data and the deduced variation rate converted from the driving current are calculated; the variation directions of the time variation rate and the deduced variation rate are compared; when it is determined that the absolute value of the time variation rate is greater than the deduced variation rate and the variation trends of the two rates are opposite, the current state is identified as a physical obstruction state, and a power locking instruction is issued to the load driving module.

[0008] Preferably, the zero-interference calibration constraint includes that the medium light attenuation parameter is lower than a preset lower limit, the space occupancy signal is zero, and the environmental background illuminance is lower than 0.5 lx. When the zero-interference calibration constraint is met, the energy consumption management controller controls the load driving module to output a preset step current sequence, and synchronously acquires a measured value of the real-time illuminance data. The mapping weight parameter is corrected by calculating the deviation between the measured value and the theoretically predicted value of the light-electricity correlation matrix.

[0009] Preferably, the signal acquisition module includes an illuminance sensor and an infrared backscatter sensor. The infrared backscatter sensor calculates the medium light attenuation parameter by detecting the echo intensity of an infrared light beam in the space medium.

[0010] Preferably, during the running of the first power distribution circuit, the energy consumption management controller calculates a mapping gain coefficient : ​Wherein, L is the real-time illumination data obtained by the signal acquisition module, I is the real-time driving current of the load driving module, and f(I) is the predicted illumination value calculated based on the photoelectric correlation matrix; the energy consumption management controller corrects the power distribution ratio in the second power distribution circuit by using the mapping gain coefficient η.

[0011] Preferably, the load driving module comprises a PWM dimming driver and a current sampling branch, and the energy consumption management controller adjusts the output power by adjusting the duty cycle of the PWM dimming driver and obtains the measured value of the driving current through the current sampling branch.

[0012] Preferably, the energy consumption management controller stores a safe power distribution limit value, and during the operation of the second power distribution circuit, if the equivalent illumination value continuously falls below the target threshold value and the input power of the load driving module reaches the safe power distribution limit value, the energy consumption management controller locks the current value of the driving current and outputs an energy consumption overload warning signal.

[0013] Preferably, the system further comprises a data interaction terminal, and the energy consumption management controller pushes the change trajectory of the photoelectric correlation matrix to the data interaction terminal through the network communication interface to generate the life prediction data of the lighting load.

[0014] Preferably, the energy consumption management controller executes a power smoothing algorithm at the switching moment when the environment returns from the water mist mode to the transparent mode, uses the last equivalent illumination value before switching as the calculation starting value of the first power distribution circuit, and controls the driving current to transition to the illumination feedback adjustment state according to a preset slope.

[0015] Preferably, the signal acquisition module further comprises a humidity sensor, the energy consumption management controller corrects the measurement accuracy of the infrared backscattering sensor by using the relative humidity value obtained by the humidity sensor, and increases the sampling frequency of the driving current of the energy consumption management controller when the relative humidity is higher than 95%.

[0016] Compared with the prior art, the beneficial effects of the present application are: 1. In the intelligent bathroom light environment, through the deep cooperation of medium state recognition and power feedback path switching, the power distribution misadjustment problem caused by optical feedback failure in a specific environment is eliminated, when the air medium is physically changed and causes the light path to be blocked, the system actively disconnects the distorted brightness feedback signal, and instead extracts the current characteristics inside the lighting execution unit driving circuit as the reference benchmark for power distribution. The operation of this logic closed loop ensures that the power supply system still maintains stable power output in the case of medium interference leading to optical perception failure, and physically cuts off the redundant power loss caused by distorted feedback sources.

[0017] 2. Based on the establishment of cross-modal time window alignment logic, using high confidence perception data in the transparent and idle state of the system, dynamically updating the power inference model for complex working conditions, through functional reuse in time sequence, making the power distribution system have the ability to perceive and compensate for the physical aging drift of the load unit due to long-term operation, without adding additional physical detection modules, the system can adjust the power distribution strategy according to the actual physical attenuation state of the actuator, thereby maintaining the accuracy and adjustment consistency of the power distribution model throughout the life cycle of the load unit.

[0018] 3. Using the consistency verification logic of optical brightness change rate and current inference change rate, the system can accurately identify the pseudo-dark state induced by physical obstruction. When the change trend of the two signal sources deviates, the system determines that it is a local physical obstruction rather than a decrease in ambient light intensity, and executes a power retention instruction to prohibit the system from increasing output power based on distorted signals. This logic design solves the problem of unnecessary power supply increase caused by human activity blocking the sensor in a narrow power distribution scene, which not only suppresses instantaneous power waste, but also maintains the stability of the user's visual environment. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flow chart of the adaptive adjustment and calibration control of the dual-mode light environment of the present application; Figure 2 The hardware topology structure and signal transmission link diagram of the system of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0021] The embodiments of the present application provide an intelligent bathroom light environment and energy consumption management system that can be adaptively adjusted.

[0022] An intelligent bathroom light environment and energy consumption management system that can be adaptively adjusted, the system comprising: A signal acquisition module for acquiring real-time illuminance data, space occupancy signals and medium light attenuation parameters in the bathroom; A load driving module for outputting driving current to the lighting load according to the power distribution instruction; An energy consumption management controller connected with the signal acquisition module and the load driving module, respectively; The energy consumption management controller performs the following operations: Step S101, compare the medium light attenuation parameter with the preset transparency threshold, when determining the transparent mode, start the first power distribution circuit, and close-loop adjust the output power of the driving current according to the real-time illuminance data; Step S102, when determining the water mist mode, start the second power distribution circuit, extract the current feedback value of the load driving module, retrieve the light-electricity correlation matrix containing the mapping weight parameter, convert the current feedback value into the equivalent illuminance value, and execute the constant power distribution based on the equivalent illuminance value. Step S103, monitor the environmental state evolution, when satisfying the preset zero-interference calibration constraint, call the historical samples of the real-time illuminance data stored during the running of the first power distribution circuit, and update the mapping weight parameter in the light-electricity correlation matrix.

[0023] Preferably, when executing step S102, the energy consumption management controller further executes the following operations: calculating the time variation rate of the real-time illuminance data and the deduced variation rate obtained by converting the driving current ; comparing the variation directions of the time variation rate and the deduced variation rate; when determining that the absolute value of the time variation rate is greater than the deduced variation rate and the variation trends of the two rates are opposite, identifying the current state as the physical obstruction state, and issuing the power locking instruction to the load driving module.

[0024] Preferably, the zero-interference calibration constraint includes: the medium light attenuation parameter being lower than the preset lower limit, the space occupancy signal being zero, and the environmental background illuminance being lower than 0.5 lx; when satisfying the zero-interference calibration constraint, the energy consumption management controller controls the load driving module to output a preset step current sequence, and synchronously collects the measured value of the real-time illuminance data, and completes the correction of the mapping weight parameter by calculating the deviation between the measured value and the theoretically predicted value of the light-electricity correlation matrix.

[0025] Preferably, the signal acquisition module includes an illuminance sensor and an infrared backscattering sensor, and the infrared backscattering sensor calculates the medium light attenuation parameter by detecting the echo intensity of the infrared light beam in the space medium.

[0026] Preferably, during the running of the first power distribution circuit, the energy consumption management controller calculates the mapping gain coefficient : , wherein L is the real-time illuminance data obtained by the signal acquisition module, I is the real-time driving current of the load driving module, and f(I) is the predicted illuminance value calculated based on the light-electricity correlation matrix; the energy consumption management controller corrects the power distribution ratio in the second power distribution circuit by using the mapping gain coefficient η.

[0027] Preferably, the load driving module includes a PWM dimming driver and a current sampling branch, and the energy consumption management controller adjusts the output power by adjusting the duty cycle of the PWM dimming driver, and obtains the measured value of the driving current through the current sampling branch.

[0028] Preferably, the energy management controller stores safe power distribution limits. During the operation of the second power distribution circuit, if the equivalent illuminance value is continuously lower than the target threshold and the input power of the load drive module reaches the safe power distribution limit, the energy management controller locks the current value of the drive current and outputs an energy overload warning signal.

[0029] Preferably, the system also includes a data interaction terminal, whereby the energy management controller pushes the change trajectory of the photoelectric correlation matrix to the data interaction terminal through a network communication interface to generate lifetime prediction data for lighting load.

[0030] Preferably, at the instant the environment switches from the water mist mode back to the transparent mode, the energy management controller executes a power smoothing algorithm, using the last equivalent illuminance value before the switch as the calculation starting value of the first power distribution circuit, and controls the drive current to transition to the illuminance feedback adjustment state according to a preset slope.

[0031] Preferably, the signal acquisition module also includes a humidity sensor. The energy management controller uses the relative humidity value obtained by the humidity sensor to correct the measurement accuracy of the infrared backscatter sensor, and increases the sampling frequency of the drive current by the energy management controller when the relative humidity is higher than 95%.

[0032] Example 1: This example describes the operating logic of an adaptive and adjustable intelligent bathroom lighting environment and energy consumption management system in a closed bathroom space. This application scenario includes high humidity interference and non-directional physical shading. A dynamic balance between lighting environment and energy consumption is maintained through multi-modal logic switching and a multi-source information arbitration mechanism. The signal acquisition module hardware architecture integrates an illuminance sensor, an infrared backscatter sensor, and a 5.8GHz microwave Doppler radar or passive infrared detection unit as the space occupancy signal source. The detection unit outputs a logic zero level only when no characteristic frequency shift or pyroelectric signal change is extracted within a continuous 3-minute sliding time window. The first and second power distribution circuits refer to two independent closed-loop feedback control algorithms operating in the non-volatile storage space of the energy consumption management controller, physically sharing the same PWM dimming drive hardware. Based on the medium light attenuation parameter α mode determination result, the controller uses software pointer operations to compare the PID regulator feedback input variable with the physical illuminance sample value L and the algorithm-derived equivalent illuminance value. In terms of switching between different feedback sources, the system achieves seamless operation of power regulation logic based on different feedback sources in a single physical circuit topology. Regarding the construction of key control thresholds and the iterative procedure of mapping parameters, an adaptive calibration method based on the statistical distribution of the field environment is adopted. For the analog voltage signal output by the infrared backscatter sensor, the controller has a built-in 12-bit ADC sampling module for analog-to-digital conversion, with an input range of 0 mV to 3300 mV. The system defines the medium light attenuation parameter as the normalized ratio of the current environmental infrared echo to the total reflection mirror echo.

[0033] During the factory calibration phase, the sensor is aligned with a standard diffuse gray board, and the reference voltage value is recorded as 2800 mV, with the corresponding attenuation parameter defined as 0.00. The sensor is then placed in a completely dark room, and the noise floor voltage value is recorded as 50 mV, with the corresponding attenuation parameter defined as 1.00. During operation, the controller reads the real-time voltage value and calculates the current attenuation parameter using linear interpolation: if the real-time voltage is greater than 2800 mV, the parameter is forcibly set to 0.00; if it is less than 50 mV, the parameter is forcibly set to 1.00; intermediate values ​​are mapped proportionally. During the initial deployment phase, the controller continuously acquires the infrared backscatter sensor echo signal at a frequency of 20 Hz in a ventilated and unmanned reference environment to construct a Gaussian distribution model. The sum of the distribution mean and three times the standard deviation is selected as the transparency threshold. Quantization avoids the inherent background noise of the sensor. When the zero-interference calibration constraint is met, the controller calls the accumulated data pairs of drive current I and illuminance L in the transparent mode in the buffer, constructs the Vandermonde matrix, and uses the recursive least squares method to solve for the second-order polynomial coefficient vector. The calculation uses the root mean square value of the fitted residuals as the convergence criterion. The photoelectric correlation matrix coefficient register is overwritten and updated only when the residual value is below the 0.02 confidence interval. During system operation, the signal acquisition module continuously acquires real-time illuminance data L, space occupancy signal, and medium light attenuation parameter α in the bathroom. The energy management controller compares the received medium light attenuation parameter α with a preset transparency threshold. When α is below the transparency threshold, the controller determines that the environment is in transparent mode and opens the first power distribution loop. In this loop, the controller uses the real-time illuminance data L collected by the optical illuminance sensor as the feedback variable to adjust the occupancy of the PWM dimming driver in the load drive module in a closed loop. The energy management controller adjusts the drive current I output to the lighting load to maintain the preset illuminance. When the density of water mist particles in the space increases, causing the medium light attenuation parameter α obtained by the signal acquisition module to exceed the preset transparency threshold, the energy management controller executes mode switching logic. Based on the Mie scattering effect caused by water mist particles, the real-time illuminance data L undergoes nonlinear attenuation. The controller blocks the closed-loop feedback path of the first power distribution circuit and activates the second power distribution circuit. In the operating state of the second power distribution circuit, the energy management controller extracts the feedback value of the drive current I output by the load drive module. The controller retrieves the preset photoelectric correlation matrix and converts the drive current I into an equivalent illuminance value. It is used as a virtual feedback variable to perform constant power distribution. Since the transmission path of the drive current I is not affected by the external space medium, the equivalent illuminance value is... It can characterize the actual radiant power of the lighting load, thus avoiding the power overstatement caused by the influence of medium scattering on the readings of external optical sensors.

[0034] To address the drift phenomenon of lighting load physical characteristics over operating time, the energy management controller executes drift self-calibration logic. The controller monitors environmental conditions and, when simultaneously meeting zero-interference calibration constraints (medium light attenuation parameter α below a preset lower limit, zero space occupancy signal, and ambient background illuminance below 0.5 lx), controls the load drive module to output a preset step current sequence. The controller synchronously collects real-time illuminance data L as the physical truth value, calculates its deviation from the theoretical prediction value derived from the current photoelectric correlation matrix, and uses this deviation to calculate the aging correction factor and update the mapping weight parameters in the photoelectric correlation matrix to ensure the accuracy of subsequent water mist modes. The accuracy of the extrapolation depends on the controller's calculation of the time-varying rate of change ΔL of the real-time illuminance data L in transparent mode, as well as the extrapolated rate of change obtained from the driving current I. When the absolute value of ΔL is detected to be greater than Furthermore, the trends of the two changes diverge, that is, ΔL shows a negative decline. When maintaining a steady-state range, the controller determines the current state to be a physical occlusion state. To eliminate misjudgments caused by differences in the response speed of different sensors, the controller opens two first-in-first-out (FIFO) circular buffers with a depth of 10 in memory, which are used to store real-time illuminance data and extrapolated equivalent illuminance data, respectively. The system synchronously updates these two buffers with a heartbeat cycle of 100 milliseconds. When calculating the rate of change, the controller does not use the instantaneous difference, but extracts the earliest pushed data point and the latest data point in the two buffers respectively, and calculates the average slope of these two endpoints over a time span of 1000 milliseconds. Through this long-period sliding window averaging algorithm, ripple noise with a frequency higher than 10 Hz in the current signal is filtered out, and the inherent physical delay of about 200 milliseconds of the optical sensor is compensated, ensuring that the two rate of change values ​​participating in the comparison are aligned in the time dimension. In response to this determination, the controller issues a power lock command to the load drive module, prohibiting the increase of output power based on the feedback signal of the optical sensor, thereby eliminating false dimming caused by human occlusion.

[0035] Example 2: To verify the control accuracy and energy efficiency advantages of the adaptively adjustable intelligent bathroom lighting environment and energy management system of the present invention under dynamic and complex working conditions, a standardized closed test platform conforming to the G05B1502 standard definition and containing controlled environmental interference sources was constructed. Its core test space is a 12-cubic-meter sealed temperature and humidity control room, equipped with an adjustable-output-rate ultrasonic water mist generator to simulate the nonlinear medium light attenuation process in the bathroom environment. The platform integrates a high-precision luminous flux meter as a reference true value acquisition device, with its probe positioned close to the lighting load surface to avoid the influence of spatial medium attenuation. A high-frequency power analyzer is also configured to monitor the system's power consumption in real time. To reproduce the noise characteristics of a real electrical environment, a Gaussian white noise interference source with a signal-to-noise ratio of 20dB is coupled into the power supply circuit. The sampling period is a key control parameter in the system. The setting follows a decision logic based on signal frequency domain characteristics: the highest frequency component of the changes in the bathroom lighting environment is mainly generated by rapid human movement, and its characteristic frequency... Approximately 5Hz; based on the Nyquist sampling theorem, to ensure the signal is not distorted and to retain a certain engineering margin, the sampling period is set to... Should meet Based on this logic, this embodiment sets the sampling period to 80ms, which balances the transient response speed with the controller's computational load.

[0036] The experimental design included two parallel control groups: the control group adopted a traditional single optical feedback PID control strategy, while the experimental group of this invention adopted a dual-mode switching control strategy triggered by the medium light attenuation parameter α. The initial environment was set as a dry, transparent condition. Both systems stabilized the driving current I of the lighting load at 600mA, maintaining a corresponding reference illuminance of 300lx. The system power consumption was 24W. A water mist generator was activated, causing the medium light attenuation parameter α to increase linearly at a rate of 0.1 / min, simulating the accumulation of water mist concentration during showering. Experimental data recording showed that as the medium light attenuation parameter α gradually increased and exceeded the preset transparency... With a light threshold set to 0.15, the illuminance data received by the optical sensor in the control group exhibited nonlinear attenuation due to the Mie scattering effect, dropping from 300 lx to 180 lx. Its internal PID algorithm, responding to this negative deviation, erroneously increased the drive current I to an overload level of 850 mA, causing a surge in system power consumption to 34 W. Furthermore, at this point, the actual luminous flux output of the light source surface exceeded the rated value by 40%, resulting in glare and energy waste. In contrast, the sample of this invention, upon detecting that the medium light attenuation parameter α reached 0.15, immediately cut off the optical closed loop and instead converted the real-time acquired drive current I into an equivalent illuminance value based on the optical-electric correlation matrix. Constant power control was implemented; throughout the entire water mist concentration increase range, the driving current I of the sample group of the present invention was always locked in the steady state range of 600mA±5mA, the system power consumption was stable at 24W, and it was not affected by the scattering of the environmental medium, thus achieving the expected energy consumption management target.

[0037] Further verification of the physical occlusion recognition logic was conducted by introducing an artificial obstruction that quickly passed directly above the optical sensor in the transparent mode. Monitoring data showed that the real-time illuminance data L collected by the optical sensor experienced a sharp drop in slope of -150 lx / s within 200 ms, i.e., the time change rate ΔL was negative. Simultaneously, the extrapolated change rate calculated based on the driving current I... Maintaining a noise fluctuation range of 0.5 lx / s, the absolute value of ΔL identified by the sample group of this invention is much greater than... Furthermore, the trends of the two are completely opposite, indicating physical occlusion and triggering a power lock-in command. The drive current I remains unchanged. However, under the same operating conditions, the control group, lacking a heterogeneous information arbitration mechanism, misinterpreted a sudden darkening of the environment, causing a 150mA pulse-like surge in drive current I during the occlusion period, resulting in flickering of the light environment. Finally, for the device aging scenario, the light source aging was simulated by continuously running for 1000 hours and artificially introducing a 5% luminous flux attenuation. Without executing calibration logic, the optical-electric correlation matrix derivation... With a fixed deviation of 5% from the actual value, when the sample group of this invention detects that the zero-interference calibration constraints are met (medium light attenuation parameter α is less than 0.05, space occupancy signal is zero, and ambient background illuminance is less than 0.5 lx), it automatically performs a step current test, collects real illuminance data L to update the mapping weight parameters, and after calibration, when it re-enters the water mist mode... The error in extrapolating the actual luminous flux has converged from 5% to less than 0.8%.

[0038] Example 3: This example details the core operational logic within the energy management controller, particularly the construction mechanism of the photoelectric correlation matrix and the algorithm implementation path for drift self-calibration. In the energy management controller's storage unit, the photoelectric correlation matrix is ​​not a simple static lookup table, but a dynamic parameter model constructed based on the polynomial fitting principle. It describes the nonlinear mapping relationship between the driving current I of the lighting load and its luminous flux output. In the specific configuration of this example, this mapping relationship is defined as a second-order polynomial equation, where the mapping weight parameter is specifically embodied in the coefficient vector of this equation. The controller uses the real-time acquired driving current I as the input variable and calculates... Obtain the equivalent illuminance value , , and These are the mapping weight parameters, representing the quadratic nonlinear coefficient, the linear gain coefficient, and the basic bias under the cutoff current, respectively. This parameterized modeling method can cover the entire operating range of the lighting load from start-up to rated power with minimal storage overhead, and has the mathematical basis for adapting the aging characteristics of the components by adjusting the coefficients. To address the issue of luminous efficacy decay or drift caused by the lighting load over time, the system executes a parameter update procedure based on recursive least squares. When the environmental conditions meet the zero-interference calibration constraints of this invention, i.e., the dielectric light attenuation parameter α is below a preset lower limit, the space occupancy signal is zero, and the ambient background illuminance is below 0.5 lx, the controller initiates an active calibration sequence. The controller sends a command to the load drive module, causing it to output a set of preset step current sequences. The sequence contains at least five discrete current values ​​covering the linear and saturation zones of the lighting load.

[0039] Within a 200ms sampling window during which each current step remains stable, the signal acquisition module synchronously acquires high-confidence real-time illuminance data for the current moment. The controller will collect multiple sets of data. The data pairs, used as a sample set, are input into the built-in least squares fitting algorithm module. This module uses minimizing the sum of squared prediction errors as the objective function to iteratively solve the coefficient vector of the second-order polynomial. The controller calculates the residual between the newly acquired sample data and the theoretical value calculated based on the current mapping weight parameters. If the root mean square value of this residual exceeds a preset drift tolerance, the model is determined to be inaccurate. At this point, the algorithm outputs the updated coefficients. , and The system overwrites the original mapping weight parameters in the storage unit. To prevent parameter oscillations caused by single sampling errors, the controller introduces a forgetting factor mechanism during the update process. Historical parameters and newly calculated parameters are weighted and fused according to preset weights, such as setting the weight of historical parameters to 0.8. This ensures the smoothness and stability of the evolution of the photoelectric correlation matrix throughout its entire lifecycle. Through this calibration mechanism based on a combination of physical models and data-driven approaches, the system ensures the equivalent illuminance value derived under the water mist mode. It is always anchored to the actual physical state of the lighting load, realizing high-precision closed-loop control under conditions without optical feedback. At the same time, all data generated in this calibration process is processed and stored only within the local controller, without uploading or retaining any user behavior characteristics, following the principle of data minimization.

[0040] Example 4: This example describes a standardized offline calibration and data filling procedure for systematically constructing the core photoelectric correlation matrix in an energy management controller. In a controlled anechoic chamber environment, the lighting load to be calibrated is placed at the geometric center of the integrating sphere system. Using a high-precision spectroradiometer as the reference measurement device, the controller drives the lighting load to perform equally spaced step scans within a preset current range (from the luminous threshold current to 1.2 times the rated current), with a step size set to 10mA. Thermal equilibrium is maintained at each current point. The system synchronously records the driving current I and the absolute luminous flux measured by the spectroradiometer at each step. A high-density benchmark dataset is generated, and the dataset is fitted with a second-order polynomial using the least squares method to extract the feature coefficients. , and These measured and traceable coefficients are then stored in the controller's non-volatile memory as the initial factory configuration of the opto-electric correlation matrix.

[0041] This embodiment further describes a field pre-calibration procedure for different deployment environments, solving the problem of missing procedures caused by differences in installation location and reflectivity. Upon initial power-on or after detecting a change in installation location, the system automatically enters an initialization self-test mode. The system controls the lighting load to output a series of specific light pulse signals. By analyzing the waveform characteristics returned by the signal acquisition module, it determines whether there is uncontrolled strong background light or dynamic interference in the current environment. If the environment meets the quiet conditions, the controller drives the lighting load to output a preset calibration light intensity sequence, while simultaneously acquiring the optical sensor readings at this time. Estimated values ​​of driving current By comparing multiple groups The system calculates the specific light transmission factor under the current environment based on the data pair. This factor is then used to normalize and correct the optical-electric correlation matrix, thereby establishing a personalized control baseline that adapts to the current physical space and ensuring the accuracy and stability of the mode switching threshold during subsequent operation.

[0042] Example 5: This example describes a standardized pre-deployment calibration and model building procedure to solve the control accuracy drift problem caused by batch differences in lighting loads, deviations in drive circuit characteristics, and individual sensor errors. This procedure is designed as an initialization process that is forcibly executed after the system is first installed or a key component is replaced, ensuring that all control parameters are generated based on the actual characteristics of the current physical entity. Before executing this procedure, the system needs to confirm that it is in a zero-interference darkroom environment, that is, the ambient background illuminance is less than 0.5 lx and there is no dynamic obstruction. The specific execution sequence of the procedure is divided into a background sniffing stage and an active excitation stage. The controller cuts off the drive current of the lighting load and enters a silent detection window that lasts for 2000 milliseconds. It continuously collects 40 illuminance data points with a period of 50 milliseconds. When the average value of these 40 points is less than 0.5 lux and the difference between any two adjacent points is less than 0.05 lux, the controller determines that the ambient background light meets the darkroom conditions and locks the background status flag, prohibiting the detection of background illuminance again in subsequent processes, thereby avoiding interference from active lighting on the judgment of background light.

[0043] After the procedure is initiated, the energy management controller drives the lighting load to perform a linear scan across the full power range, gradually increasing the photocurrent to the rated current with a step size set to 1% of the rated current. During this process, the signal acquisition module synchronously records the correspondence between the drive current I and the real-time illuminance data L, constructing an original sample set. The controller then uses the Gauss-Newton iterative method to perform nonlinear least-squares fitting on this sample set, solving for the polynomial coefficients describing the photoelectric response characteristics of the current lighting load, i.e., the mapping weight parameters in the photoelectric correlation matrix. , and Simultaneously, the controller records power consumption data during the scanning process and establishes a mapping table between drive current and input power for subsequent energy consumption statistics and optimization. Regarding the threshold setting in the physical occlusion recognition logic, after completing the full-power scan, the controller maintains the lighting load at 50% of its rated power output and continuously collects illuminance data for one minute, calculating the standard deviation of the illuminance readings within this time window. The controller sets the physical occlusion detection threshold to 1. This ensures that occlusion detection is not falsely triggered under normal lighting fluctuations, while also guaranteeing a sensitive response to real occlusion events.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive and adjustable intelligent bathroom lighting environment and energy consumption management system, characterized in that, The system includes: The signal acquisition module is used to acquire real-time illuminance data, space occupancy signals, and medium light attenuation parameters in the bathroom. The load drive module is used to output drive current to the lighting load according to the power distribution command; The energy management controller is connected to both the signal acquisition module and the load drive module. The energy management controller performs the following operations: Step S101: Compare the dielectric light attenuation parameter with the preset transparency threshold. When the transparent mode is determined, open the first power distribution circuit and adjust the output power of the drive current in a closed loop according to the real-time illuminance data. Step S102: When the water mist mode is determined, the second power distribution circuit is opened, the current feedback value of the load drive module is extracted, and the optical-electric correlation matrix containing the mapping weight parameters is retrieved. The current feedback value is converted into an equivalent illuminance value, and constant power distribution is performed based on the equivalent illuminance value. Step S103: Monitor the evolution of the environmental state. When the preset zero-interference calibration constraint is met, call up historical samples of real-time illuminance data stored during the operation of the first power distribution circuit and update the mapping weight parameters in the optical-electric correlation matrix.

2. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, When executing step S102, the energy management controller further performs the following operations: calculates the time change rate of real-time illuminance data and the extrapolated change rate obtained from the conversion of drive current; compares the change direction of the time change rate and the extrapolated change rate; when it is determined that the absolute value of the time change rate is greater than the extrapolated change rate and the two change trends are divergent, it identifies the current state as a physical shading state and issues a power lock command to the load drive module.

3. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The zero-interference calibration constraints include: the dielectric light attenuation parameter is lower than the preset lower limit, the space occupancy signal is zero, and the ambient background illuminance is lower than 0.5 lx; when the zero-interference calibration constraints are met, the energy management controller controls the load drive module to output a preset step current sequence and simultaneously collects the measured values ​​of real-time illuminance data. The mapping weight parameters are corrected by calculating the deviation between the measured values ​​and the theoretical predicted values ​​of the photoelectric correlation matrix.

4. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The signal acquisition module includes an illuminance sensor and an infrared backscatter sensor. The infrared backscatter sensor calculates the light attenuation parameters of the medium by detecting the echo intensity of the infrared beam in the space medium.

5. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The energy management controller calculates the mapping gain coefficient during the operation of the first power distribution circuit. : Where L is the real-time illuminance data acquired by the signal acquisition module, I is the real-time drive current of the load drive module, and f(I) is the predicted illuminance value calculated based on the photoelectric correlation matrix; the energy management controller uses the mapping gain coefficient η to correct the power distribution ratio in the second power distribution circuit.

6. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The load drive module includes a PWM dimming driver and a current sampling branch. The power management controller adjusts the output power by adjusting the duty cycle of the PWM dimming driver and obtains the measured value of the drive current through the current sampling branch.

7. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The energy management controller stores safe power distribution limits. During the operation of the second power distribution circuit, if the equivalent illuminance value remains below the target threshold and the input power of the load drive module reaches the safe power distribution limit, the energy management controller locks the drive current. The current value is displayed and an energy overload warning signal is output.

8. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The system also includes a data interaction terminal. The energy management controller pushes the change trajectory of the light-electric correlation matrix to the data interaction terminal through the network communication interface to generate lifetime prediction data of lighting load.

9. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, When the environment switches from water mist mode to transparent mode, the energy management controller executes a power smoothing algorithm. It uses the last equivalent illuminance value before the switch as the calculation starting value of the first power distribution circuit and controls the drive current to transition to the illuminance feedback adjustment state according to a preset slope.

10. The adaptively adjustable intelligent bathroom lighting environment and energy consumption management system according to claim 1, characterized in that, The signal acquisition module also includes a humidity sensor. The energy management controller uses the relative humidity value obtained by the humidity sensor to correct the measurement accuracy of the infrared backscatter sensor, and increases the sampling frequency of the drive current when the relative humidity is higher than 95%.

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