Electronic rectifier capable of automatically matching voltage of lamp tube

Abstract

The invention relates to the field of electronic rectifiers, and discloses an electronic rectifier capable of automatically matching the voltage of a lamp tube. The feature extraction and recognition module is connected with the electrical feature acquisition module; the driving parameter mapping module is connected with the feature extraction and recognition module; the driving parameter optimization and prediction module is connected with the output end of the electrical characteristic acquisition module and the output end of the driving parameter mapping module; the power output regulation module is connected with the driving parameter optimization and prediction module; the electrical characteristic acquisition module comprises a transient signal acquisition unit, a steady-state signal acquisition unit and a sensor interface and conditioning unit. According to the invention, the transient electrical characteristics of the lamp tube are collected at the initial starting stage of the lamp tube, unique electrical fingerprints are extracted by using signal processing methods such as continuous wavelet transform and the like, initial parameter mapping is carried out in combination with a neural network, and high-precision automatic identification and rapid adaptation of the type of the accessed lamp tube are realized.

Description

Technical Field

[0001] The invention relates to the technical field of electronic rectifiers, and in particular to an electronic rectifier capable of automatically matching lamp tube voltage. Background Art

[0002] Electronic rectifiers are an indispensable component of modern lighting systems and are widely used to drive various types of gas discharge lamps, such as fluorescent lamps, high-intensity gas discharge lamps (HID lamps), and increasingly popular solid-state lighting sources, such as light-emitting diode (LED) arrays. Its main function is to convert the mains power into high-frequency AC or specific DC power suitable for the startup and stable operation of specific lamps. In order to meet the electrical requirements of different lamps, existing electronic rectifiers usually integrate certain control circuits to try to match the corresponding driving strategy by detecting certain electrical parameters of the lamp after it is connected, such as voltage, current or power. Some designs also consider monitoring the working status of the lamp, and adjust the output according to the preset logic to ensure the basic operation of the lamp and the safety of the system.

[0003] However, with the continuous advancement of lighting technology and the continuous enrichment of lamp types, new challenges have been raised to the performance of electronic rectifiers. For example, in terms of the accuracy and breadth of lamp type identification, it is difficult to distinguish lamps with subtle electrical characteristics but significant differences in driving requirements by relying solely on steady-state parameters. There is also room for improvement in the automatic adaptation capability for new or non-standard lamps. In terms of the optimization of driving parameters, how to find a more ideal balance between multiple dimensions such as light efficiency, energy consumption, lamp life, and grid compatibility, so as to give full play to the potential of lamps, is a direction that the industry continues to explore. In addition, the dynamic changes in the performance parameters of lamps during long-term use, such as characteristic drift caused by aging, how to be more timely perceived and compensated in a forward-looking manner, so as to maintain the lighting quality and extend the effective working time of lamps as much as possible, is also an issue that designers need to pay attention to. At the same time, in the face of rapidly developing lamp technology and evolving energy efficiency standards, the flexibility of the electronic rectifier's own control strategy and compatibility with future technologies have also become important considerations for measuring its advancement. Summary of the invention

[0004] The purpose of the present invention is to provide an electronic rectifier that automatically matches the voltage of a lamp tube, thereby solving the problems of limited recognition capability, insufficiently precise parameter setting and insufficient dynamic adaptability of existing electronic rectifiers in automatically, accurately and optimally matching different lamp tubes.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: An electronic rectifier for automatically matching lamp tube voltage, comprising: An electrical characteristic acquisition module, configured to collect electrical signals of an access lamp tube and convert them into electrical characteristic data; A feature extraction and recognition module, connected to the electrical characteristic acquisition module, configured to extract feature information characterizing the lamp tube from the electrical characteristic data and identify the type of the lamp tube; A drive parameter mapping module, connected to the feature extraction and recognition module, configured to map and generate a set of initial drive parameters based on the feature information and the recognized lamp tube type; A drive parameter optimization and prediction module, connected to the output ends of both the electrical characteristic acquisition module and the drive parameter mapping module, configured to perform multi-objective optimization according to the electrical characteristic data and the initial drive parameters, combined with the predicted future state of the lamp tube, to obtain the final drive parameters; A power output regulation module, connected to the drive parameter optimization and prediction module, configured to control a power conversion circuit according to the final drive parameters and output regulated electric energy to the lamp tube.

[0006] Preferably, the electrical characteristic acquisition module includes: A transient signal acquisition unit, configured to acquire instantaneous current and voltage waveforms of the lamp tube at high speed during the startup stage of the lamp tube; A steady-state signal acquisition unit, configured to periodically acquire real-time current and voltage waveforms of the lamp tube during the stable operation stage of the lamp tube; A sensor interface and conditioning unit, configured to filter, amplify, and perform analog-to-digital conversion on the acquired waveforms to generate electrical characteristic data.

[0007] Preferably, the feature extraction and recognition module includes: An electrical fingerprint extraction unit, configured to perform time-frequency analysis on the transient signals during the startup stage of the lamp tube to extract an electrical fingerprint feature vector of the lamp tube; A real-time electrical feature calculation unit, configured to calculate real-time electrical features such as effective value, power factor, and total harmonic distortion according to the real-time current and voltage waveforms during the stable operation stage of the lamp tube; A lamp tube type recognition unit, configured to compare the extracted electrical fingerprint feature vector with a pre-stored lamp tube standard fingerprint database, and combine with the real-time electrical features to identify the type of the lamp tube or mark it as an unknown type.

[0008] Preferably, the drive parameter mapping module includes: A combined feature construction unit, configured to combine the electrical fingerprint feature vector and the real-time electrical features into a comprehensive feature vector; A neural network mapping unit, adopting a neural network model, receiving the comprehensive feature vector as an input, and outputting initial drive parameters, The initial drive parameters include initial recommended values of target voltage, switching frequency, and duty cycle; A network model management unit, which is used to store the structure and parameters of a neural network model, and adjust and update the model according to the system self-learning results.

[0009] Preferably, the drive parameter optimization and prediction module includes: A lamp tube state prediction unit, which uses a state estimation algorithm to predict the key performance states of the lamp tube in a future period based on historical and current electrical characteristic data and drive parameters; A multi-objective optimization function construction unit, which is used to define a comprehensive optimization objective function including multiple performance indicators such as power loss, luminous efficiency, harmonic distortion, and lamp tube life; An iterative optimization solution unit, which uses a heuristic optimization algorithm to iteratively search and determine the final drive parameters based on the initial drive parameters, combined with the predicted future state of the lamp tube and the comprehensive optimization objective function.

[0010] Preferably, the power output regulation module includes: A PWM signal generation unit, which is used to generate a pulse width modulation signal for controlling a power switch device according to the final drive parameters; A power stage drive and conversion unit, which includes a power switch device and corresponding drive circuits and power conversion topologies, and performs chopping, inversion, and frequency conversion of the input electrical energy according to the PWM signal; A closed-loop feedback control unit, which is used to monitor the voltage and current output to the lamp tube in real time and compare them with the target values in the final drive parameters.

[0011] Preferably, the time-frequency analysis is performed using continuous wavelet transform, and its calculation formula includes: where, i transient (t) is the instantaneous current signal during the lamp tube startup stage; Ψ(t) is the mother wavelet function; a is the scale parameter; b is the translation parameter; ψ * (.) is the complex conjugate of the mother wavelet function; Ψ CWT (a, b) is the wavelet transform coefficient, which is used to construct an electrical fingerprint feature vector.

[0012] Preferably, the neural network model uses a radial basis function neural network model, and its calculation formula for outputting the initial drive parameter P initial includes: where, F combined is the comprehensive feature vector; N hidden is the number of hidden layer nodes; W j is the weight vector from the jth hidden layer node to the output layer; C j is the center vector of the jth hidden layer node; σj is the width parameter of the j-th hidden layer node.

[0013] Preferably, the heuristic optimization algorithm adopts a quantum particle swarm optimization algorithm, and its particle position update formula includes: where X i (k) is the position of the i-th particle at the k-th iteration, representing a set of driving parameters; p i (k) is the individual attractor of the i-th particle; C mbest (k) is the mean of the historical optimal positions of all particles; α qpso (k) is the contraction-expansion coefficient; u ij (k) is a random number in the interval (0, 1).

[0014] Preferably, the key performance state of the prediction lamp tube in the future for a period of time adopts a Kalman filter algorithm, and its state prediction update formula at least includes: One-step state prediction: State update estimation: where is the state prediction value at time k; is the state estimation value at time; u(k - 1) is the control input at time k - 1, that is, the driving parameter; A is the state transition matrix; B is the input matrix; is the state update estimation value at time k; K(k) is the Kalman gain; y(k) is the observed value at time k, that is, part of the real-time electrical characteristics; H is the observation matrix.

[0015] In summary, the present invention includes at least one of the following beneficial technical effects: 1. By collecting the transient electrical characteristics of the lamp tube at the initial stage of startup, using signal processing methods such as continuous wavelet transform to extract unique electrical fingerprints, and then combining with a neural network for initial parameter mapping, the present invention realizes high-precision automatic identification and rapid adaptation of the connected lamp tube type. Compared with the existing methods that rely on steady-state parameters or simple threshold judgment, this solution solves the problems of insufficient ability to distinguish lamp tubes with similar characteristics and difficulty in identifying emerging lamp tube types, and significantly improves the "plug and play" compatibility of the rectifier.

[0016] 2. The present invention uses advanced multi-objective optimization algorithms such as quantum particle swarm optimization to finely optimize the driving parameters initially mapped by the neural network around multi-dimensional performance indicators such as light efficiency, energy consumption, harmonics, and lifespan. This strategy enables the rectifier to find a more balanced driving solution for a specific lamp tube. In contrast, traditional technologies often use fixed parameters or empirical look-up tables, making it difficult to effectively and dynamically balance multiple performance objectives for different lamp tubes. The present invention overcomes this limitation and helps to optimize the comprehensive working performance of the lamp tube.

[0017] 3. By introducing a lamp tube state prediction mechanism based on Kalman filtering, the present invention can estimate and predict in real time the state drift of the lamp tube caused by factors such as aging and environmental changes. Based on this prediction result, the system can proactively adjust the driving parameters for compensation. Compared with the existing technologies that mostly use lag compensation or fixed aging curves, the present invention solves the problems of untimely response and inaccurate compensation, thus being able to more effectively maintain the long-term working stability of the lamp tube and having the potential to extend its actual service life.

[0018] 4. Through the entire process of intelligent driving parameter determination from feature acquisition, recognition mapping to optimization prediction, especially through an updatable neural network model and a potential mechanism for expanding the lamp tube DNA database, the present invention endows the electronic rectifier with certain adaptive and learning abilities. Compared with the rectifiers with fixed control logic and difficult to upgrade in the existing technologies, the present invention can better adapt to the continuous development of lamp tube technologies, handle more unknown or new lamp tubes, reduce the risk of frequent elimination of the rectifier due to lamp tube replacement, and enhance the long-term applicability and robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the system module architecture diagram of the present invention; Figure 2 is the schematic diagram of the electrical characteristic acquisition module of the present invention; Figure 3 is the schematic diagram of the feature extraction and recognition module of the present invention; Figure 4 is the schematic diagram of the driving parameter mapping module of the present invention; Figure 5 is the schematic diagram of the driving parameter optimization and prediction module of the present invention; Figure 6 is the schematic diagram of the power output regulation module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] The following is a more detailed description of the present invention in conjunction with the attached Figure 1 - attached Figure 6 ,.

[0021] Please refer to the attached Figure 1 - attachedFigure 6 , the present invention provides an electronic ballast for automatically matching the lamp voltage, comprising: An electrical characteristic acquisition module, configured to collect electrical signals of the connected lamp and convert them into electrical characteristic data; Specifically, an embodiment of the present invention provides an electronic ballast for automatically matching the lamp voltage. The electrical characteristic acquisition module in this electronic ballast is configured to collect electrical signals of the connected lamp and convert them into electrical characteristic data available for subsequent processing units.

[0022] In an exemplary embodiment, the electrical characteristic acquisition module includes a sensor interface and conditioning unit, a transient signal acquisition unit, and a steady-state signal acquisition unit.

[0023] The sensor interface and conditioning unit is used to directly sense the voltage and current signals of the lamp. This unit may include a voltage sensor and a current sensor.

[0024] Exemplarily, the voltage sensor may use a precision resistor voltage division network combined with an operational amplifier for buffering and isolation. The current sensor may preferably be a closed-loop Hall effect current sensor or a current detection circuit configured with a precision sampling resistor.

[0025] The sensor interface and conditioning unit further includes an analog signal conditioning circuit. This conditioning circuit may include an anti-aliasing filter, such as a second-order Butterworth low-pass filter. Its cut-off frequency f c is usually set to a fraction of the sampling frequency f s to meet the Nyquist sampling theorem. For example: f c = f s / N; wherein, N is usually greater than 2, and may be taken as 2.5 exemplarily. This conditioning circuit may further include a signal amplification circuit for amplifying the weak signals output by the sensor to an input range suitable for an analog-to-digital converter (ADC). One of the core components of the sensor interface and conditioning unit is the analog-to-digital converter (ADC). This ADC is responsible for converting the conditioned analog voltage and current signals into digital signals.

[0026] A transient signal acquisition unit, whose control logic is connected to the sensor interface and conditioning unit. This unit is used to control the ADC to perform high-speed data acquisition during the initial startup phase when it detects that the lamp is connected to the electronic ballast. The sampling frequency f s is relatively high during this phase. For example, f s ≥100 kHz. The acquisition duration T transient is preset to completely capture the transient process of lamp startup. For example, T transient = 10 ms. During this period, the digitized instantaneous current signal and / or instantaneous voltage signal obtained by the acquisition are stored. The number of acquired sample points It can be determined by the following formula: where is the total number of sampling points in the transient acquisition stage; f s is the sampling frequency in the transient acquisition stage; T transient is the transient acquisition duration. This series of high-frequency sampling data collected constitutes the transient electrical characteristic data, which is the basis for subsequent electrical fingerprint extraction.

[0027] The steady-state signal acquisition unit, whose control logic is also connected to the sensor interface and conditioning unit. This unit is activated after the lamp tube completes startup and enters the stable or quasi-stable working state. It controls the ADC to perform periodic data acquisition at a relatively low sampling frequency f s-steady For example, f s-steady can be set between 1 kHz and 10 kHz. The main data collected in this stage are the current and voltage waveform data when the lamp tube is in steady-state operation, usually covering one or more power frequency cycles. These data are used to calculate the real-time operating parameters of the lamp tube, such as the effective voltage value, effective current value, etc. This series of steady-state sampling data collected constitutes the steady-state electrical characteristic data.

[0028] During operation, when the lamp tube is connected to the electronic ballast, the system first determines the connection of the lamp tube by monitoring the changes in the electrical state of the lamp tube interface (such as current mutation or voltage establishment). Once the connection of the lamp tube is confirmed, the transient signal acquisition unit is triggered. It performs high-speed sampling and digital conversion on the current and / or voltage signals at the initial stage of lamp tube startup through the sensor interface and conditioning unit. The acquired digital sequence current signal and / or voltage signal are output as the transient electrical characteristic data. This transient electrical characteristic data is then transmitted to the subsequent feature extraction and recognition module for extracting the electrical fingerprint of the lamp tube.

[0029] After the lamp tube startup process ends, or after preliminary identification and application of initial drive parameters, the steady-state signal acquisition unit takes over. It performs periodic sampling on the current and voltage signals during the steady-state operation of the lamp tube through the sensor interface and conditioning unit. The acquired digital sequence is output as the steady-state electrical characteristic data. This steady-state electrical characteristic data is also transmitted to the feature extraction and recognition module for calculating the real-time electrical characteristics. And these steady-state data may also be used as the input for state observation by the drive parameter optimization and prediction module.

[0030] Through the above structure and working process, the electrical characteristic acquisition module can capture the key electrical information of the whole process of the lamp tube from startup to stable operation.

[0031] The feature extraction and recognition module, connected to the electrical characteristic acquisition module, is used to extract the characteristic information representing the lamp tube from the electrical characteristic data and identify the type of the lamp tube; Specifically, in the feature extraction and recognition module in the embodiments of the present invention, its input end is electrically connected to the output end of the aforementioned electrical characteristic acquisition module. The core function of this module is to extract information that can characterize the essential characteristics of the lamp tube from the acquired electrical characteristic data, and accordingly identify the specific type or category of the lamp tube. In an exemplary implementation manner, the feature extraction and recognition module includes an electrical fingerprint extraction unit, a real-time electrical feature calculation unit, and a lamp tube type recognition unit.

[0032] The electrical fingerprint extraction unit receives the transient electrical characteristic data output from the electrical characteristic acquisition module, particularly the instantaneous current signal or voltage signal during the lamp tube startup phase. This unit first preprocesses the input transient signal, such as digital filtering to eliminate the noise introduced during the acquisition process, or baseline correction. Subsequently, this unit processes the preprocessed transient signal using time-frequency analysis methods. Exemplarily, the continuous wavelet transform (CWT) is preferably used to analyze the transient current signal i transient (t). The calculation formula of the continuous wavelet transform is as follows: where, i transient (t) is the instantaneous current signal during the lamp tube startup phase; Ψ(t) is the mother wavelet function; a is the scale parameter; b is the translation parameter; ψ * (.) is the complex conjugate of the mother wavelet function; Ψ CWT (a, b) is the wavelet transform coefficient, which is used to construct the electrical fingerprint feature vector.

[0033] After obtaining the wavelet transform coefficient or scalogram, the electrical fingerprint extraction unit extracts a set of predefined feature quantities from it to form an electrical fingerprint feature vector. This feature vector can exemplarily include the energy distribution in multiple frequency bands, the dominant frequency component, the dominant frequency phase, and the standard deviation of the energy distribution, etc. For example, the feature E k represents the total energy in the kth preset frequency band: where, band k represents the kth scale (frequency) interval. The dominant frequency can be obtained by performing a fast Fourier transform (FFT) on the transient current i transient (t) and obtaining the frequency point with the largest amplitude from its spectrum. The dominant frequency of the finally formed electrical fingerprint feature vector is output.

[0034] A real-time electrical feature calculation unit that receives the steady-state electrical characteristic data output from the electrical characteristic acquisition module. This data is typically the current and voltage sampling values of one or more cycles when the lamp tube is operating stably. Based on these sampling values, this unit calculates a series of real-time electrical features that characterize the current operating state of the lamp tube. These features may include, but are not limited to, the effective value of voltage, the effective value of current, active power, apparent power, power factor, the modulus and phase angle of impedance, and the total harmonic distortion of current. The effective value of voltage and the effective value of current can be calculated by the following formula (taking voltage as an example): where, is the number of steady-state voltage sampling points within a calculation window; v steady [j] is the j-th voltage sampling value collected during steady-state operation. The power factor can be obtained from the ratio of the active power P active and the apparent power V rms ·I rms : where,

[0035] where, i steady [j] is the j-th current sampling value collected during steady-state operation; v steady [j] is the j-th voltage sampling value collected during steady-state operation.

[0036] The total harmonic distortion of current THD I is calculated as follows: where, I h is the effective value of the h-th harmonic current; I1 is the effective value of the fundamental current, and H max is the highest harmonic order considered. These calculated real-time electrical features form a real-time feature vector and are output.

[0037] A lamp tube type identification unit, whose input end is connected to the output end of the electrical fingerprint extraction unit. Exemplarily, it can also be connected to the output end of the real-time electrical feature calculation unit. There is a standard fingerprint database of lamp tubes pre-stored inside this unit. This database stores the standard electrical fingerprint feature vectors F database,j,fingerprint of multiple known lamp tube types, where j is the lamp tube type index. When receiving the electrical fingerprint feature vector F fingerprint of the current lamp tube, this unit compares it with each standard fingerprint in the database. The comparison process can adopt an affinity calculation method, such as a similarity metric based on weighted distance. An exemplary affinity Affinity j calculation formula is as follows: Among them, D fp is the dimension of the electrical fingerprint feature vector; F fingerprint,k is the kth component of the electrical fingerprint currently extracted; F database,j,fingerprint,k is the kth component of the standard fingerprint of the jth lamp in the database; w k is the preset weight of the kth feature component, reflecting its importance in recognition; scale k The normalization or scaling factor of the kth feature component is optional. The lamp type recognition unit selects the one with the highest affinity to the current fingerprint. max If Affinity max If the affinities exceed a preset recognition threshold, the corresponding lamp type is output as the recognition result. If all affinities are below the threshold, or below a lower "new type candidate" threshold, the current lamp can be marked as an unknown type or a new type candidate. The recognition result (lamp type or unknown label) is output.

[0038] In the workflow of the entire module, first, the electrical characteristic acquisition module collects transient and steady-state signals. Subsequently, the electrical fingerprint extraction unit processes the transient signal to generate an electrical fingerprint. At the same time or later, the real-time electrical characteristic calculation unit processes the steady-state signal to generate a real-time characteristic. Finally, the lamp type identification unit compares the electrical fingerprint (and optionally combines part of the real-time characteristic information as an auxiliary judgment) with the database and outputs the identified lamp type. The lamp type information, electrical fingerprint feature vector, and real-time electrical feature vector output by this module will serve as important inputs for the subsequent drive parameter mapping module.

[0039] A driving parameter mapping module is connected to the feature extraction and recognition module, and maps and generates a set of initial driving parameters based on the feature information and the recognized lamp type; Specifically, the input end of the driving parameter mapping module in this embodiment is electrically connected to the output end of the aforementioned feature extraction and recognition module. The module quickly generates a set of initial driving parameters suitable for the lamp tube based on the identified lamp tube feature information. The initial driving parameters provide a good starting point for subsequent fine optimization. In an exemplary embodiment, the driving parameter mapping module includes a combined feature construction unit, a neural network mapping unit, and a network model management unit.

[0040] The combined feature construction unit receives the electrical fingerprint feature vector output from the feature extraction and recognition module. The unit can also receive the real-time electrical feature vector output from the feature extraction and recognition module. The function of the combined feature construction unit is to integrate the feature information from these different sources into a unified comprehensive feature vector. For example, if the electrical fingerprint feature vector is F fingerprint , the real-time electrical characteristic vector is Frealtime The combined comprehensive feature vector F combined can be obtained through a vector concatenation operation: where: is the electrical fingerprint feature vector of the lamp tube; F realtime is the real-time electrical feature vector of the lamp tube; [.] T represents the transpose operation of the vector; F combined is the comprehensive feature vector formed by concatenation. This comprehensive feature vector is then transmitted to the neural network mapping unit.

[0041] The neural network mapping unit, whose input end is connected to the output end of the combined feature construction unit. The core of this unit is a neural network model that is pre-trained or can be updated online. This neural network model establishes a non-linear mapping relationship from the comprehensive feature vector to the initial driving parameters. Exemplarily, a radial basis function neural network (RBFNN) can preferably be used as this mapping model. When receiving the comprehensive feature vector, the RBFNN calculates and outputs a set of initial driving parameters. Its output calculation formula can be expressed as: The neural network model uses a radial basis function neural network model, and its output initial driving parameter P initial The calculation formula includes: where, F combined is the comprehensive feature vector; N hidden is the number of hidden layer nodes; W j is the weight vector from the j-th hidden layer node to the output layer; C j is the center vector of the j-th hidden layer node; σ j is the width parameter of the j-th hidden layer node.

[0042] The network model management unit is used to store and manage the model used by the neural network mapping unit. This includes storing the structural parameters of the neural network, such as the number of hidden layer nodes of the RBFNN. It also includes storing the weight parameters of the model, such as the center vector, width parameter, and output layer weight of the RBFNN. This unit can also support the model update mechanism. For example, in the case where the system has self-learning ability, when new training data or performance feedback is obtained, this unit can load the updated model parameters. Thus, the mapping accuracy and adaptability of the neural network mapping unit can be continuously improved.

[0043] In the overall workflow of the drive parameter mapping module: First, the combined feature construction unit receives and integrates the feature information from the previous module to form a comprehensive feature vector. Subsequently, the neural network mapping unit processes this comprehensive feature vector using the neural network model stored in the network model management unit. Finally, the neural network model outputs a set of initial drive parameters for the current lamp tube and operating conditions. This module quickly provides customized initial drive settings for different lamp tubes through non-linear mapping. This lays a good foundation for the subsequent drive parameter optimization process and helps improve the response speed and final effect of the overall control strategy. This embodiment ensures a fast and intelligent conversion from lamp tube characteristics to the preliminary drive strategy.

[0044] The drive parameter optimization and prediction module is respectively connected to the output ends of the electrical characteristic acquisition module and the drive parameter mapping module, and is used to perform multi-objective optimization based on the electrical characteristic data and the initial drive parameters, combined with the predicted future state of the lamp tube, to obtain the final drive parameters; Specifically, in this embodiment, the input end of the drive parameter optimization and prediction module is electrically connected to the output end of the aforementioned electrical characteristic acquisition module and the output end of the drive parameter mapping module. This module aims to finely optimize the initial drive parameters and incorporate the prediction of the future state of the lamp tube to generate the final drive instruction applied to the power output stage. In an exemplary embodiment, the drive parameter optimization and prediction module includes a lamp tube state prediction unit, a multi-objective optimization function construction unit, and an iterative optimization solution unit.

[0045] The lamp tube state prediction unit receives the real-time electrical characteristic data output from the electrical characteristic acquisition module. This unit can also receive the currently applied or historical drive parameters as control inputs. The core function of this unit is to estimate the internal key state of the lamp tube and predict its short-term future trend based on the observed electrical characteristics and control inputs. Exemplarily, the Kalman filter algorithm can be preferably used to achieve state estimation and prediction. The state update and prediction process of the Kalman filter algorithm mainly includes the following recursive steps: One-step state prediction equation: Prediction error covariance matrix: P(k|k - 1) = AP(k - 1|k - 1)A T + Q; Kalman gain calculation: K(k) = P(k|k - 1)H T [HP(k|k - 1)H T + R] -1 ; State update estimation: Updated error covariance matrix: P(k|k) = [I - K(k)H]P(k|k - 1); Where: is the state prediction vector at time k based on the information at time k-1; is the optimal state estimation vector at time k-1, which may include the lamp aging factor, luminous efficacy index, etc.; u(k-1) is the drive parameter vector applied to the lamp at time k-1; A is the state transition matrix, which describes how the system state evolves over time; B is the input control matrix, which describes the influence of the control input on the state; P(k|k-1) is the prior error covariance matrix at time k; P(k-1|k-1) is the posterior error covariance matrix at time k-1; Q is the process noise covariance matrix; K(k) is the Kalman gain at time k; H is the observation matrix, which maps the state vector to the observation space; R is the observation noise covariance matrix; y(k) is the observation vector at time k, which can be composed of real-time electrical characteristic data; is the optimal state estimation vector at time k after fusing the observation information; I is the identity matrix. By iterating the above equations, the current state of the lamp can be obtained and the predicted states for several future steps. The predicted state information is output to the iterative optimization solving unit.

[0046] The multi-objective optimization function construction unit is used to define a comprehensive performance index for evaluating the quality of drive parameters. This comprehensive performance index is usually a weighted combination of multiple interrelated and even conflicting sub-objectives. Exemplarily, a comprehensive optimization objective function J total can be defined as: where P drive is a set of candidate drive parameter vectors; J loss is the power loss of the rectifier under the given drive parameters, and the goal is to minimize it; J efficiency is the luminous efficiency of the lamp, and the goal is to maximize it; J THD is the total harmonic distortion of the input current, and the goal is to minimize it; J lifetime is the expected life index evaluated based on the current drive parameters and the predicted lamp state , and the goal is to maximize it; w loss , w eff , w THD , w life are the weight coefficients of each sub-objective respectively, and their sum can be 1 or set according to the priority. The specific forms of these sub-objective functions can be established according to the lamp model and rectifier characteristics. This comprehensive optimization objective function J total is provided to the iterative optimization solving unit as the fitness function.

[0047] Iterative optimization solving unit, which receives the initial driving parameters from the driving parameter mapping module. It also receives the predicted state information from the lamp tube state prediction unit and the comprehensive optimization objective function from the multi-objective optimization function construction unit. This unit adopts a heuristic optimization algorithm to search for the parameter combination that optimizes the comprehensive optimization objective function within the feasible domain of the driving parameters. Exemplarily, the Quantum Particle Swarm Optimization (QPSO) algorithm can be preferably adopted. The QPSO algorithm explores the solution space by simulating the behavior of particles in a quantum system. Its core particle position update strategy is different from that of traditional PSO, and a simplified form can be expressed as: First, calculate the average optimal position of all particles: where p i,best (iter) represents the optimal position; N pop is the particle population size.

[0048] Then, the position of each particle is updated as follows: where X i (k) is the position of the i-th particle at the k-th iteration, representing a set of driving parameters; p i (k) is the individual attractor of the i-th particle; C mbest (k) is the mean of the historical optimal positions of all particles; α qpso (k) is the contraction-expansion coefficient; u ij (k) is a random number within the interval (0, 1). In each iteration, calculate the corresponding comprehensive optimization objective function value according to the new particle position. And update the individual historical optimal position and the global historical optimal position. The algorithm iterates until the termination condition is met, such as reaching the maximum number of iterations or the convergence of the objective function value. Finally, the driving parameters corresponding to the global historical optimal position are output as the final driving parameters. These final driving parameters are then transmitted to the power output regulation module.

[0049] In the overall workflow of the driving parameter optimization and prediction module: First, the lamp tube state prediction unit continuously estimates and predicts the lamp tube state. At the same time, the multi-objective optimization function construction unit defines the evaluation criteria. The iterative optimization solving unit starts with the initial parameters output by the driving parameter mapping module, combines the prediction information and the optimization objective, and iteratively searches through algorithms such as QPSO. Finally, a set of final driving parameters that are finely optimized and forward-looking are output. This module enables the rectifier to dynamically adjust the output through intelligent optimization and state prediction, in order to achieve the best comprehensive performance under complex constraints. It ensures that the driving strategy not only adapts to the present but also anticipates the future, thereby improving the overall working performance and lifespan of the lamp tube.

[0050] The power output regulation module is connected to the drive parameter optimization and prediction module, and is used to control the power conversion circuit according to the final drive parameters, and output the regulated electric energy to the lamp tube. Specifically, in this embodiment, the input end of the power output regulation module is electrically connected to the output end of the aforementioned drive parameter optimization and prediction module. The main responsibility of this module is to accurately control the power conversion circuit according to the received final drive parameters, so as to stably output the regulated electric energy to the connected lamp tube. In an exemplary embodiment, the power output regulation module includes a PWM signal generation unit, a power stage drive and conversion unit, and a closed-loop feedback control unit.

[0051] The PWM signal generation unit receives the final drive parameter vector output from the drive parameter optimization and prediction module. This parameter vector usually contains information such as the target output voltage, target switching frequency, target duty cycle, and current limit. This unit generates one or more sets of pulse width modulation (PWM) signals according to these parameters. These PWM signals are used to control the conduction and cut-off of the power switch devices in the subsequent power stage drive and conversion unit. The frequency of the PWM signal is determined by the target switching frequency, and its duty cycle is mainly determined by the target duty cycle and may be fine-tuned by the closed-loop feedback control unit. The generated PWM signals are output to the power stage drive and conversion unit.

[0052] The power stage drive and conversion unit has its control input end connected to the output end of the PWM signal generation unit. The core of this unit is the power conversion circuit topology and its drive circuit. The power conversion circuit topology can be selected according to application requirements. Exemplarily, it can be a half-bridge, full-bridge inverter circuit, or a DC / DC conversion circuit such as Buck or Boost. This unit includes power switch devices such as MOSFETs or IGBTs. It also includes a gate drive circuit that provides appropriate drive signals for these power switch devices, as well as passive components related to the topology, such as inductors, capacitors, and transformers. When the PWM signal is applied to the gate drive circuit, the power switch devices are periodically turned on and off according to the instructions of the PWM signal, so as to convert the input electric energy (such as the direct current after rectification and filtering from the mains) into electric energy with a specific voltage, current, and frequency suitable for the operation of the lamp tube. The converted electric energy is supplied to the lamp tube through the output end of this unit.

[0053] The closed-loop feedback control unit monitors the actual electrical parameters output from the power stage drive and conversion unit to the lamp tube. For example, the actual output voltage across the lamp tube and the actual current flowing through the lamp tube are sampled in real time through voltage sensors and current sensors. This unit compares the sampled actual values with the target values in the final drive parameter vector, and calculates the error signal. Exemplarily, if the control target is the output voltage, the error signal is: e V (t) = Vtarget (t) - V actual (t); where, e V (t) is the voltage error signal; V target (t) is the target output voltage value; V actual (t) is the actually measured output voltage value. This error signal is then input into a fast controller, preferably a proportional-integral-derivative (PID) controller. The output of the PID controller is used to adjust the parameters of the PWM signal generation unit, usually to finely adjust the duty cycle DD. The discrete form of the PID control law can be expressed as: where, u PID [k] is the output adjustment amount of the PID controller at time k; e[k] is the error signal at time k; k is the proportional gain; K i is the integral gain; K d is the derivative gain. This closed-loop feedback loop can quickly respond to output deviations caused by load changes, input voltage fluctuations, and model uncertainties, etc. It ensures that the electrical energy actually obtained by the lamp tube is highly consistent with the expected value.

[0054] In the overall working process of the power output regulation module: First, the PWM signal generation unit generates a reference PWM waveform according to the optimized final drive parameters. Subsequently, the power stage drive and conversion unit converts the input electrical energy according to this PWM waveform and outputs it to the lamp tube. At the same time, the closed-loop feedback control unit continuously monitors the actual output and feeds the error signal back to the PWM signal generation unit through the PID controller, thereby performing real-time and fast compensatory adjustments on the PWM waveform (mainly the duty cycle). This module can provide stable, efficient, and accurate power supply for lamp tubes with different characteristics through precise PWM control and fast closed-loop feedback. This is of great importance for ensuring the lighting quality of the lamp tube, extending its service life, and achieving the overall energy-saving goal of the system. This embodiment realizes the final fine regulation and stable output of the electrical energy for driving the lamp tube.

[0055] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electronic ballast for automatically matching the voltage of a lamp tube, characterized in that, Comprising: An electrical characteristic acquisition module, configured to collect electrical signals of an access lamp tube and convert them into electrical characteristic data; A feature extraction and recognition module, connected to the electrical characteristic acquisition module, configured to extract feature information characterizing the lamp tube from the electrical characteristic data and identify the type of the lamp tube; A drive parameter mapping module, connected to the feature extraction and recognition module, configured to map and generate a set of initial drive parameters based on the feature information and the recognized lamp tube type; A drive parameter optimization and prediction module, connected to the output ends of both the electrical characteristic acquisition module and the drive parameter mapping module, configured to perform multi-objective optimization according to the electrical characteristic data and the initial drive parameters, combined with the predicted future state of the lamp tube, to obtain the final drive parameters; A power output adjustment module, connected to the drive parameter optimization and prediction module, configured to control a power conversion circuit according to the final drive parameters and output adjusted electric energy to the lamp tube.

2. An electronic ballast for automatically matching the voltage of a lamp tube according to claim 1, characterized in that, The electrical characteristic acquisition module includes: A transient signal acquisition unit, configured to rapidly acquire the instantaneous current and voltage waveforms of the lamp tube during the startup stage; A steady-state signal acquisition unit, configured to periodically acquire the real-time current and voltage waveforms of the lamp tube during the stable operation stage; A sensor interface and conditioning unit, configured to filter, amplify, and perform analog-to-digital conversion on the acquired waveforms to generate electrical characteristic data.

3. An electronic ballast for automatically matching the lamp tube voltage according to claim 1, characterized in that, The feature extraction and recognition module includes: An electrical fingerprint extraction unit, configured to perform time-frequency analysis on the transient signals during the startup stage of the lamp tube and extract the electrical fingerprint feature vector of the lamp tube; A real-time electrical feature calculation unit, configured to calculate the real-time electrical features such as the effective value, power factor, and total harmonic distortion according to the real-time current and voltage waveforms during the stable operation stage of the lamp tube; A lamp tube type recognition unit, configured to compare the extracted electrical fingerprint feature vector with a pre-stored lamp tube standard fingerprint database, and combine with the real-time electrical features to identify the type of the lamp tube or mark it as an unknown type.

4. An electronic ballast for automatically matching the voltage of a lamp tube according to claim 1, characterized in that, The drive parameter mapping module includes: A combined feature construction unit, configured to combine the electrical fingerprint feature vector and the real-time electrical features into a comprehensive feature vector; A neural network mapping unit, adopting a neural network model, receiving the comprehensive feature vector as input, and outputting the initial drive parameters, where the initial drive parameters include the initial recommended values of the target voltage, switching frequency, and duty cycle; A network model management unit, configured to store the structure and parameters of the neural network model, and adjust and update the model according to the system self-learning results.

5. An electronic ballast for automatically matching the lamp tube voltage according to claim 1, characterized in that, The drive parameter optimization and prediction module includes: A lamp tube state prediction unit, adopting a state estimation algorithm, based on the historical and current electrical characteristic data and drive parameters, predicting the key performance state of the lamp tube within a future period of time; A multi-objective optimization function construction unit, configured to define a comprehensive optimization objective function including multiple performance indexes such as power loss, luminous efficiency, harmonic distortion, and lamp tube life; An iterative optimization and solution unit, adopting a heuristic optimization algorithm, based on the initial drive parameters, combined with the predicted future state of the lamp tube and the comprehensive optimization objective function, iteratively searching and determining the final drive parameters.

6. An electronic ballast for automatically matching the voltage of a lamp tube according to claim 1, characterized in that, The power output adjustment module includes: A PWM signal generation unit, configured to generate a pulse width modulation signal for controlling a power switch device according to the final drive parameters; A power stage drive and conversion unit, including a power switch device, a corresponding drive circuit, and a power conversion topology, which performs chopping, inversion, and frequency conversion on the input electrical energy according to the PWM signal; A closed-loop feedback control unit, configured to monitor the voltage and current output to the lamp tube in real time and compare them with the target values in the final drive parameters.

7. An electronic ballast for automatically matching the lamp tube voltage according to claim 3, characterized in that, The time-frequency analysis is performed by continuous wavelet transform, and its calculation formula includes: where, i transient (t) is the instantaneous current signal in the lamp starting stage; Ψ(t) is the mother wavelet function; a is the scale parameter; b is the translation parameter; ψ * (.) is the complex conjugate of the mother wavelet function; Ψ CWT (a, b) is the wavelet transform coefficient, which is used to construct the electrical fingerprint feature vector.

8. An electronic ballast for automatically matching the lamp tube voltage according to claim 4, characterized in that, The neural network model adopts a radial basis function neural network model, and its output of the initial driving parameter P initial The calculation formula includes: Among them, F combined is the comprehensive feature vector; N hidden is the number of hidden layer nodes; W j is the weight vector from the j-th hidden layer node to the output layer; C j is the center vector of the j-th hidden layer node; σ j is the width parameter of the j-th hidden layer node.

9. An electronic ballast for automatically matching the voltage of a lamp tube according to claim 5, characterized in that, The heuristic optimization algorithm uses a quantum particle swarm optimization algorithm, and its particle position update formula includes: Among them, X i (k) is the position of the i-th particle at the k-th iteration, representing a set of driving parameters; p i (k) is the individual attractor of the i-th particle; C mbest (k) is the mean of the historical optimal positions of all particles; α qpso (k) is the contraction-expansion coefficient; u ij (k) is a random number within the interval (0, 1).

10. An electronic rectifier for automatically matching the voltage of a lamp tube according to claim 5, characterized in that, The key performance state of the predicted lamp tube within a certain period in the future is adopted by the Kalman filtering algorithm, and its state prediction update formula at least includes: one-step state prediction: Status update estimation: Among them, is the predicted value of the state at time k; is the estimated value of the state at time ; u(k - 1) is the control input at time k - 1, i.e., the driving parameter; A is the state transition matrix; B is the input matrix; is the updated estimated value of the state at time k; K(k) is the Kalman gain; y(k) is the observed value at time k, i.e., part of the real-time electrical characteristics; H is the observation matrix.

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