Energy consumption optimization control method for tunnel lighting systems based on wireless networking

Abstract

This invention relates to the field of wireless power transmission and intelligent tunnel lighting control technology, specifically a method for optimizing energy consumption control of a tunnel lighting system based on wireless networking. The method includes: collecting real-time electrical operation data from the wireless power transmission end and the nonlinear load end; extracting the load current waveform characteristics of the nonlinear load end based on the real-time electrical operation data; calculating the reflected impedance drift corresponding to a transient dimming request, which is issued by the wireless networking control center to each lighting node and obtained after parsing via a communication protocol; importing the reflected impedance drift as input data into a source-load impedance dynamic collaborative model to generate a resonant parameter reconstruction command for the transmission end; comparing the impedance matching index with the energy efficiency stability threshold of the transmission link to obtain an energy efficiency evaluation result; adaptively adjusting the operating parameters of the wireless power transmission end to ensure the collaborative stability of the wireless networking communication link and the energy transmission link, thereby maintaining a zero-voltage switching state while meeting the transient dimming request; this invention effectively prevents overcurrent or frequency jitter, ensuring the safe operation of the tunnel lighting system under extreme conditions.

Description

Technical Field

[0001] This invention relates to the field of wireless power transmission and intelligent tunnel lighting control technology, specifically to a method for optimizing energy consumption control of a tunnel lighting system based on wireless networking. Background Technology

[0002] In the current operating environment of wireless transmission-driven tunnel lighting systems, the system needs to drive nonlinear loads such as LEDs through wireless power transmission terminals and respond to lighting needs in real time based on traffic flow and external light intensity at the tunnel entrance. To adapt to the complex driving environment, the system often needs to perform high-frequency and large-amplitude transient dimming operations, which directly causes changes in the current waveform characteristics at the load end, thereby causing drastic fluctuations in equivalent impedance and reflected impedance. Especially in large-scale tunnel lighting scenarios based on wireless networking, the centralized control commands issued by the network layer often have asynchronous behavior with the energy response of the underlying drive circuit due to communication delays. For the control of such dynamic loads, existing technical solutions mostly rely on traditional steady-state feedback regulation or single parameter matching logic. When faced with rapid drift of reflected impedance caused by transient dimming, existing solutions often struggle to predict and reconstruct the resonant parameters at the transmission end before the physical changes in the load occur, leading to detuning in the wireless transmission link. This mismatch between supply and demand impedance forces the system to deviate from the optimal zero-voltage switching soft-switching operating area, resulting in a severe decrease in transmission efficiency, a significant increase in switching losses, and even, under extreme conditions, oscillations or uncontrolled jitter in the input current waveform. This makes it difficult for the system to maintain efficient and stable energy transmission while meeting real-time dimming commands. Therefore, establishing a dynamic coordination relationship between source and load impedances and achieving precise impedance matching through adaptive adjustment during transient dimming to maintain the zero-voltage switching state of the system and optimize overall energy consumption, thereby resolving the contradiction between network command response lag and energy efficiency mismatch in wireless networked lighting systems, has become an urgent technical problem to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides an energy consumption optimization and control method for a tunnel lighting system based on wireless networking. Specifically, the technical solution of this invention includes: S1. Collect real-time electrical operation data of wireless power transmission end and nonlinear load end, establish a dynamic cooperative model of source-load impedance, and set the energy efficiency stability threshold of transmission link. S2. Based on the real-time electrical operation data, extract the load current waveform characteristics of the nonlinear load end, and calculate the reflection impedance drift corresponding to the transient dimming request. The transient dimming request is sent from the wireless network control center to each lighting node and obtained after being parsed by the communication protocol. S3. The reflected impedance drift is used as input data and imported into the source-load impedance dynamic cooperative model to generate a resonant parameter reconstruction command at the transmission end and calculate the impedance matching index under the current operating state. S4. The impedance matching index is compared with the transmission link energy efficiency stability threshold to obtain the energy efficiency evaluation result. In response to the energy efficiency evaluation result and the transmission end resonance parameter reconstruction command, the operating parameters of the wireless power transmission end are adaptively adjusted to ensure the coordinated stability of the wireless networking communication link and the power transmission link, so as to maintain the zero voltage switching state while meeting the transient dimming request.

[0004] Preferably, step S1 specifically includes: S11. Use voltage and current sensors to obtain the output voltage and output current of the wireless power transmission terminal, as well as the load voltage and load current of the nonlinear load terminal; S12. Obtain the dimming control signal at the nonlinear load end, analyze the duty cycle change rate of the dimming control signal, and use it as a parameter to characterize the dynamic change of the load. S13. Based on historical operating data, construct a dynamic collaborative model of source-load impedance. This model is essentially a data set containing multiple sets of mapping relationships, used to describe the correspondence between the resonant frequency of the wireless power transmission end, the matching network capacitance parameters, and the equivalent impedance of the nonlinear load end.

[0005] Preferably, step S2 specifically includes: S21. Perform time-frequency domain analysis on the acquired load current waveform to extract waveform distortion feature data and phase offset data; S22. Based on the duty cycle change rate of the dimming control signal, identify the dimming depth and dimming rate values ​​at the nonlinear load end. S23. Based on the dimming depth value and dimming rate value, use a preset impedance characteristic curve lookup table to calculate the equivalent resistance change value and equivalent reactance change value of the nonlinear load terminal at the next moment. S24. Using the change in equivalent resistance as the real part and the change in equivalent reactance as the imaginary part, perform vector synthesis to calculate the magnitude and obtain the reflection impedance drift.

[0006] Preferably, step S3 specifically includes: S31. Input the reflection impedance drift as a feedforward variable into the source-load impedance dynamic cooperative model. S32. Using the source-load impedance dynamic cooperative model, query the optimal operating frequency point and the optimal matching capacitor value that can maintain the inductive input impedance of the wireless power transmission terminal under the current reflection impedance drift. S33. Generate a transmission end resonance parameter reconstruction instruction containing the optimal operating frequency point value and the optimal matching capacitor value; S34. Calculate the total load impedance after superimposing the actual output impedance of the wireless power transmission terminal and the reflected impedance drift at the current moment. Calculate the absolute value of the difference between the total load impedance and the system rated characteristic impedance, and divide the absolute value by the system rated characteristic impedance. The resulting ratio is used as the impedance matching index.

[0007] Preferably, in step S4, the transmission link energy efficiency stability threshold includes a first stability threshold and a second stability threshold, and the value of the first stability threshold is strictly less than the value of the second stability threshold. Step S4 specifically includes comparing the impedance matching index with the first stability threshold and the second stability threshold, respectively, and generating corresponding energy efficiency assessment result labels: If the impedance matching index is less than or equal to the first stability threshold, the system is determined to be in a deep resonance state, and a superior energy efficiency evaluation result label is generated. If the impedance matching index is greater than the first stability threshold and less than or equal to the second stability threshold, the system is determined to be in a slightly detuned state, and a good energy efficiency assessment result label is generated. If the impedance matching index is greater than the second stability threshold, the system is determined to be in a severely detuned state, and a differential energy efficiency assessment result label is generated.

[0008] Preferably, step S4 further includes adjustment strategies for different energy efficiency assessment result labels: S41. When the energy efficiency assessment result is labeled as excellent, keep the resonant network parameters of the current wireless power transmission terminal unchanged, and only fine-tune the duty cycle of the driving voltage according to the output voltage feedback signal. S42. When the energy efficiency assessment result label is good, in response to the resonant parameter reconstruction command of the transmission end, only the driving frequency of the wireless power transmission end is dynamically tracked and adjusted. The adjustment direction is to make the phase difference between the input voltage and the input current approach zero, so as to compensate for the change in the imaginary part caused by the reflection impedance drift. S43. When the energy efficiency assessment result label is poor, in response to the resonant parameter reconstruction command of the transmission end, the driving frequency of the wireless power transmission end and the capacitance value of the variable impedance matching network are reconstructed and adjusted to force the system operating point to re-enter the zero-voltage switching region.

[0009] Preferably, step S4 further includes logic to prevent oscillation: S44. During the adaptive adjustment of the operating parameters of the wireless power transmission terminal, the input current waveform of the wireless power transmission terminal is monitored in real time. S45. If the amplitude of the input current waveform exceeds the preset safety limit, or if the waveform frequency exhibits uncontrolled jitter, immediately lock the current resonant parameters and reduce the dimming rate response level at the nonlinear load end until the input current waveform returns to stability.

[0010] Preferably, the method is applied to tunnel lighting scenarios, and the real-time electrical operation data in step S1 further includes: S14. Traffic flow and external light intensity values ​​at the tunnel entrance section collected by environmental sensing equipment. S15. The source-load impedance dynamic coordination model also incorporates a driving brightness requirement mapping relationship generated based on traffic flow value and external light intensity value. This mapping relationship defines the minimum lighting brightness value required under different combinations of traffic flow and external light intensity. S16. Based on the driving brightness demand mapping relationship, correct the transient dimming request in step S2. The correction logic is: under the premise of ensuring that the lighting brightness value is not lower than the minimum lighting brightness value, select the dimming scheme that minimizes the reflection impedance drift.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a dynamic cooperative model of source and load impedance, and solves the problem of lag in response of traditional feedback regulation through a feedforward control mechanism; the system uses the characteristics of the dimming control signal to pre-calculate the reflection impedance drift and generates a resonant parameter reconstruction command before the load undergoes physical changes; this mechanism ensures that the wireless transmission end can respond quickly and maintain a zero-voltage switching state when performing high-frequency, large-amplitude transient dimming on a nonlinear load, realizing nanosecond-level energy response under wireless networking control commands, significantly reducing switching losses and improving transmission energy efficiency; 2. This invention uses a vector synthesis method of resistance and reactance to quantify impedance changes, which greatly improves the matching accuracy of the system under complex working conditions. For nonlinear load characteristics, it combines dimming depth and rate to analyze current waveform distortion and phase shift, and accurately calculates the reflection impedance drift. This method effectively corrects the deviation caused by existing technology that only considers resistive changes, ensuring that the impact of load disturbance on the transmission link can be accurately assessed, and providing reliable data support for the accurate reconstruction of subsequent parameters. 3. This invention implements an adaptive adjustment strategy based on energy efficiency classification, achieving both flexibility and stability in system control. By calculating the impedance matching index and comparing it with a stability threshold, the operating state is divided into three levels: excellent, good, and poor. Differentiated methods such as voltage duty cycle fine-tuning, drive frequency tracking, or matching network capacitor reconstruction are adopted for each level. This avoids over-adjustment caused by slight disturbances and forces the system back to the soft-switching region in the event of severe detuning, ensuring stable operation. 4. This invention integrates tunnel driving environment perception and anti-vibration protection logic, taking into account lighting needs, energy saving effect and physical safety; the system determines the minimum brightness requirement based on traffic flow and external light intensity, selects the dimming scheme with the least reflection impedance drift, and dynamically allocates the power redundancy of each node according to the network topology of the wireless network, realizing global optimization of energy consumption and lighting quality; at the same time, when an abnormal current is detected, the parameters are immediately locked and the dimming response level is reduced, effectively preventing overcurrent or frequency jitter, and ensuring the safe operation of the tunnel lighting system under extreme conditions. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A method for optimizing energy consumption control of a tunnel lighting system based on wireless networking, comprising the following steps: S1. Collect real-time electrical operation data of wireless power transmission end and nonlinear load end, establish a dynamic cooperative model of source-load impedance, and set the energy efficiency stability threshold of transmission link. S2. Based on real-time electrical operation data, extract the load current waveform characteristics of the nonlinear load end, and calculate the reflection impedance drift corresponding to the transient dimming request. The transient dimming request is sent from the wireless network control center to each lighting node and obtained after being parsed by the communication protocol. S3. Input the reflection impedance drift as input data into the source-load impedance dynamic cooperative model, generate the resonant parameter reconstruction command at the transmission end, and calculate the impedance matching index under the current operating state. S4. Compare the impedance matching index with the transmission link energy efficiency stability threshold to obtain the energy efficiency assessment result. In response to the energy efficiency assessment result and the transmission end resonant parameter reconstruction command, adaptively adjust the operating parameters of the wireless power transmission end to ensure the coordinated stability of the wireless networking communication link and the energy transmission link, so as to maintain the zero voltage switching state while meeting the transient dimming request.

[0015] This embodiment provides an energy consumption optimization control method for a tunnel lighting system based on wireless networking. In view of the problem that the existing tunnel lighting system suffers from wireless transmission link detuning and energy efficiency collapse due to drastic fluctuations in load impedance when dealing with high-frequency and large-amplitude transient dimming, this invention constructs a feedforward control mechanism based on dynamic coordination of source and load impedance. In step S1, real-time electrical operation data of the wireless power transmission end and the nonlinear load end are collected to establish a source-load impedance dynamic coordination model and set the transmission link energy efficiency stability threshold. The source-load impedance dynamic coordination model is not a simple numerical storage, but a set of multi-dimensional data mapping relationships. This model is used to describe the coupling relationship between the electrical parameters of the wireless power transmission end and the nonlinear impedance of the LED lighting load end under different operating conditions. The construction of this model needs to be completed through offline calibration, and the specific calibration process will be detailed in subsequent embodiments. The setting of the transmission link energy efficiency stability threshold is based on the soft-switching working area boundary of the system hardware and is used to define whether the system is in a high-efficiency state. In step S2, based on real-time electrical operation data, the load current waveform characteristics at the nonlinear load end are extracted, and the reflection impedance drift corresponding to the transient dimming request is calculated. This transient dimming request is issued by the wireless network control center to each lighting node and obtained after being parsed through the communication protocol, thereby completing the decoupling conversion from the network protocol domain to the electrical physical domain locally at the lighting node; the reflection impedance drift... This is the core control variable of the present invention, and its physical meaning is: the equivalent impedance change caused by LED load dimming, which is mapped to the equivalent impedance disturbance value of the wireless transmission transmitting side through the magnetic coupling mechanism; since LED is a nonlinear device, its impedance change includes resistive and reactive components. In step S3, the reflection impedance drift is used as input data to import the source-load impedance dynamic cooperative model, generate the resonant parameter reconstruction command at the transmission end, and calculate the impedance matching index under the current operating state. This step employs a feedforward control strategy, pre-calculating the required compensation parameters before the load undergoes physical changes; the transmission end resonant parameter reconstruction instruction includes specific adjustment values ​​for the driving frequency or switching instructions for the matching network capacitor. In step S4, the impedance matching index is numerically compared with the transmission link energy efficiency stability threshold to obtain the energy efficiency assessment result. In response to the energy efficiency assessment result and the resonant parameter reconstruction command at the transmission end, the operating parameters of the wireless power transmission end are adaptively adjusted. The goal of this adjustment is to force the transmitter parameters to match the new load state while satisfying the transient dimming request, maintaining a zero-voltage switching state. .

[0016] Example 2: Step S1 specifically includes: S11. Use voltage and current sensors to obtain the output voltage and output current of the wireless power transmission terminal, as well as the load voltage and load current of the nonlinear load terminal; S12. Obtain the dimming control signal at the nonlinear load end, analyze the duty cycle change rate of the dimming control signal, and use it as a parameter to characterize the dynamic change of the load. S13. Based on historical operating data, construct a dynamic collaborative model of source-load impedance. This model is essentially a data set containing multiple sets of mapping relationships, used to describe the correspondence between the resonant frequency of the wireless power transmission end, the matching network capacitance parameters, and the equivalent impedance of the nonlinear load end.

[0017] This embodiment is a further specification of step S1 in embodiment 1, focusing on the accurate acquisition of data and the offline calibration method of the model; In step S11, based on a wireless power transfer system architecture including a frequency-tunable inverter source and a reconfigurable impedance matching network, such as a switched capacitor array, a high-frequency voltage and current sensor is used to acquire the output voltage of the wireless power transfer terminal. and output current and the load voltage at the nonlinear load terminal and load current ; The sensor sampling frequency is set to more than 10 times the wireless power transfer switching frequency to ensure that transient waveform details can be captured. In step S12, the dimming control signal at the nonlinear load end is acquired, and the duty cycle change rate of the dimming control signal is analyzed and used as a parameter characterizing the dynamic changes of the load. In order to quantify the intensity of dimming, this embodiment defines the duty cycle change rate. The calculation formula is as follows: In the specific implementation of the digital control system, this differential operation is discretized using a first-order backward difference equation: in, This is the duty cycle sample value for the current control cycle. The sampled value from the previous period. The sampling period for the dimming control loop is 10ms in this embodiment; in, The duty cycle function of the dimming PWM is acquired in real time; the duty cycle change rate. The unit is defined as ; The introduction of this parameter aims to distinguish between steady-state dimming and transient shocks, and this parameter directly determines the weight of subsequent impedance prediction. In step S13, a dynamic coordinated model of source-load impedance is constructed based on historical operating data. To ensure the independence and accuracy of the model parameters, the model is constructed through the following offline calibration experiments: In a laboratory setting, a set of calibration duty cycle sequences covering the entire measurement range was selected. in For each At different typical operating temperatures For example, at 25℃, 50℃, and 85℃, measure and record the calibrated equivalent impedance at the nonlinear load terminal. And the optimal calibration frequency required to maintain the inductive input impedance at the wireless power transmission end. and optimal calibration matching capacitor ; The calibration data above were analyzed using the least squares method. A multivariate regression fitting is performed to generate a mathematical function describing the mapping relationship between the resonant frequency of the wireless power transmission terminal, the matching network capacitance parameters, and the equivalent impedance of the nonlinear load terminal. Specifically, this mathematical function adopts a second-order polynomial fitting structure. To obtain the optimal frequency and optimal capacitance respectively, two independent sub-models need to be constructed, namely the frequency prediction model. With capacitance prediction model Their common form is: in, The duty cycle sample value represents the dimming control signal; The measured value of the equivalent impedance at the nonlinear load end; For constant terms; , , , , These are collectively referred to as the fitting coefficients, which together constitute the coefficient matrix. , and It reflects the degree of linear influence of the independent variable on the output. and It reflects the nonlinear influence trend of the independent variable on the output. This reflects the mutual coupling effect between the dimming duty cycle and the load impedance; For frequency prediction models , Represents the optimal output frequency of the target. coefficient matrix These are the frequency fitting coefficients; for the capacitance prediction model , Representative of the best capacitor coefficient matrix The coefficients are the capacitance fitting coefficients; the two sub-models together constitute the source-load impedance dynamic cooperative model.

[0018] Example 3: Step S2 specifically includes: S21. Perform time-frequency domain analysis on the acquired load current waveform to extract waveform distortion feature data and phase offset data; S22. Combine the duty cycle change rate of the dimming control signal to identify the dimming depth and dimming rate values ​​at the nonlinear load end. S23. Based on the dimming depth and dimming rate values, use the preset impedance characteristic curve lookup table to calculate the equivalent resistance change and equivalent reactance change of the nonlinear load terminal at the next moment. S24. Perform vector synthesis with the change in equivalent resistance as the real part and the change in equivalent reactance as the imaginary part, and calculate the magnitude to obtain the reflection impedance drift.

[0019] This embodiment is a further specification of step S2 in embodiment 1, and elaborates in detail the calculation logic of the reflection impedance drift. In step S21, time-frequency domain analysis is performed on the acquired load current waveform to extract waveform distortion feature data and phase offset data; the waveform distortion feature data is used to calculate the total harmonic distortion of the load current. Phase offset data is obtained by comparing the time difference between the zero-crossing points of the load voltage and the zero-crossing points of the load current. In step S22, the dimming depth and dimming rate values ​​at the nonlinear load end are identified by combining the duty cycle change rate of the dimming control signal; the dimming depth value... Defined as the absolute value of the difference between the target duty cycle and the current duty cycle; the dimming rate value is the value calculated in step S12. It represents the rate of change of duty cycle over time. In step S23, based on the dimming depth value, dimming rate value, and the current load temperature monitoring value or the temperature rise estimate based on running time, the calculation of the temperature rise estimate based on running time follows the logic of the thermal circuit model: First, obtain the thermal resistance coefficient and thermal capacity coefficient of the system, and set the initial ambient temperature; then, use the integral algorithm to calculate the cumulative temperature rise of the heat dissipation generated by the load current over time; the specific calculation logic is: the current estimated temperature value is equal to the initial ambient temperature value, plus the product of the steady-state temperature rise value and the transient thermal response factor; its mathematical expression is: in, For the current estimated temperature, The initial ambient temperature, For the real-time active power of the load, The thermal resistance coefficient, The thermal time constant; The above formula satisfies the principle of dimensional consistency: the left side of the equal sign... With the first item on the right All dimensions are in temperature units; in the second item on the right, the real-time active power of the load. With thermal resistance coefficient The product dimension, after conversion, is also in temperature units; the running time in the exponential term With thermal time constant The ratio is a dimensionless number; Wherein, real-time power is the active power at the load end, and its value is equal to the load voltage within one switching cycle. With load current The integral average of the product of instantaneous values; The steady-state temperature rise is calculated by multiplying the thermal resistance coefficient by the real-time power. The transient thermal response factor is 1 minus a power function with the natural constant as the base and the negative operating time divided by the thermal time constant as the exponent. Using a pre-defined impedance characteristic curve lookup table with an introduced temperature correction coefficient, the equivalent resistance change and equivalent reactance change at the nonlinear load terminal at the next moment are calculated. The specific calculation logic for the temperature correction coefficient is as follows: a temperature drift function is set. ,in This represents the temperature coefficient of resistance of the load material; for example, 0.004 for copper. For the current monitored temperature, For the calibration temperature, such as 25℃; the corrected equivalent resistance change value. Corrected equivalent reactance change value ,in and The base values ​​are obtained from the lookup table; the impedance characteristic curve lookup table is based on the calibration data in step S13. The real part obtained by decomposition and the virtual part Constructed; querying this table yields the predicted equivalent resistance change at the nonlinear load terminal at the next time step. and predicted equivalent reactance change value ; In step S24, vector synthesis is performed using the change in equivalent resistance as the real part and the change in equivalent reactance as the imaginary part to calculate the magnitude and obtain the reflection impedance drift. In this embodiment, the reflection impedance drift is... The calculation follows the following vector composition formula: in, : Impedance disturbance magnitude referred to the primary side by load change, unit: ohms; : Predicted load resistance variation component, unit: ohms; : Predicted load reactance variation component, unit: ohms; This formula ensures that when quantifying load changes, it takes into account both the real part of energy consumption and the imaginary part of reactive power, correcting the deviation of traditional techniques that only consider resistive changes.

[0020] Example 4: Step S3 specifically includes: S31. Obtain the actual output impedance of the wireless power transmission terminal at the current moment, and decompose it into resistance and reactance components. Combine this with the equivalent impedance change of the load terminal calculated in step S24, and pre-calculate the reflected impedance change component of the primary side of the wireless power transmission terminal using the mutual inductance coupling mapping model, and perform vector synthesis to obtain the predicted total equivalent impedance magnitude at the next moment. Use this predicted total equivalent impedance magnitude at the next moment as the input variable. The input is fed into the source-load impedance dynamic cooperative model; S32. By using the source-load impedance dynamic cooperative model, query the optimal operating frequency point and the optimal matching capacitor value that can maintain the inductive input impedance of the wireless power transmission terminal under the current reflection impedance drift. S33. Generate a transmission end resonant parameter reconstruction instruction containing the optimal operating frequency point value and the optimal matching capacitor value. S34. Calculate the total load impedance after superimposing the actual output impedance of the wireless power transmission terminal and the reflected impedance drift at the current moment. Calculate the absolute value of the difference between the total load impedance and the system's rated characteristic impedance, and divide the absolute value by the system's rated characteristic impedance. The resulting ratio is used as the impedance matching index.

[0021] This embodiment is a further specification of step S3 in embodiment 1, focusing on how to quantify the matching state of the system; In steps S31 to S33, the reflection impedance drift is input as a feedforward variable into the source-load impedance dynamic cooperative model to query and generate a model containing the optimal frequency. Matching capacitor values The command to reconstruct the resonant parameters at the transmission end; In step S34, the total load impedance after superimposing the actual output impedance of the wireless power transmission terminal and the reflection impedance drift at the current moment is calculated. First, based on the output voltage obtained in step S11... With output current And its phase difference, calculate the real part of the output impedance at the current moment. With the imaginary part Considering the vector characteristics of impedance, the predicted value of total load impedance It is not a direct superposition of scalars, but rather the modulus obtained by separately superimposing the real and imaginary parts, as calculated in detail below: in, The predicted magnitude of the total load impedance at the next moment; and These are the real and imaginary parts of the output impedance of the wireless power transmission terminal at the current moment, respectively. and These are the changes in the equivalent resistance of the secondary load, respectively. and equivalent reactance change value The equivalent changes in reflected resistance and reflected reactance, calculated by the magnetic coupling mechanism and referred to the primary side of the wireless power transmission terminal; Calculate the impedance matching index The calculation formula is revised as follows: It should be noted that this formula is based on the principle of impedance vector synthesis. It accurately reflects the impact of load changes on the total impedance modulus of the system, rather than a simple scalar summation; the formula aims to quickly estimate the deviation rate of the total impedance modulus of the system as an indicator to determine whether the system deviates from the energy efficiency classification of the ZVS region. in, : Indicates the degree to which the current operating condition deviates from the ideal optimal operating point, unit: dimensionless; : Reflection impedance drift, unit: ohms; : The predicted magnitude of the total load impedance at the next moment, in ohms; : The system's rated characteristic impedance, in ohms, is an inherent constant in the system design and corresponds to the point of highest transmission efficiency.

[0022] Example 5: In step S4, the transmission link energy efficiency stability threshold includes a first stability threshold and a second stability threshold, and the value of the first stability threshold is strictly less than the value of the second stability threshold. Step S4 specifically includes comparing the impedance matching index with the first stability threshold and the second stability threshold, respectively, and generating corresponding energy efficiency assessment result labels: If the impedance matching index is less than or equal to the first stability threshold, the system is determined to be in a deep resonance state, and a superior energy efficiency evaluation result label is generated. If the impedance matching index is greater than the first stability threshold and less than or equal to the second stability threshold, the system is determined to be in a slightly detuned state, and a good energy efficiency assessment result label is generated. If the impedance matching index is greater than the second stability threshold, the system is determined to be in a severely detuned state, and a differential energy efficiency assessment result label is generated.

[0023] Step S4 also includes adjustment strategies for different energy efficiency assessment result labels: S41. When the energy efficiency assessment result is labeled as excellent, keep the resonant network parameters of the current wireless power transmission terminal unchanged, and only fine-tune the duty cycle of the driving voltage according to the output voltage feedback signal. S42. When the energy efficiency assessment result is labeled as good, in response to the resonant parameter reconstruction command at the transmission end, only the driving frequency of the wireless power transmission end is dynamically tracked and adjusted. The adjustment direction is to stabilize the phase difference between the input voltage and the input current within the preset weak inductive range, such as 10°~15°, in order to compensate for the change in the imaginary part caused by the drift of the reflected impedance and ensure the soft switching condition. S43. When the energy efficiency assessment result label is poor, in response to the resonant parameter reconstruction command at the transmission end, the driving frequency of the wireless power transmission end and the capacitance value of the variable impedance matching network are reconstructed and adjusted to force the system operating point to re-enter the zero-voltage switching region.

[0024] This embodiment is a further specification of step S4 in embodiment 1, and details the graded adjustment strategy based on energy efficiency assessment results; Regarding Example 5, the logic for setting the transmission link energy efficiency stability threshold is as follows: Set the first stability threshold In this embodiment, the value is 0.10, corresponding to the critical point where the system transmission efficiency decreases by 2% and the second stable threshold. In this embodiment, the value is 0.30, corresponding to the system being decoupled. The critical point at which the region enters a hard-switching state; like It was determined to be a top-tier energy efficiency rating. like The energy efficiency assessment result was labeled as "good". like The result was labeled as a poor energy efficiency assessment result. Regarding Example 5, the graded adjustment strategy is implemented as follows: In step S41, when the energy efficiency assessment result is labeled as excellent, the resonant network parameters of the current wireless power transmission terminal are kept unchanged, and the duty cycle of the driving voltage is finely adjusted only according to the output voltage feedback signal; at this time, the system is in deep resonance and no additional impedance compensation is required. In step S42, when the energy efficiency assessment result is labeled as "good," in response to the resonant parameter reconstruction command at the transmission end, only the driving frequency of the wireless power transmission end is dynamically tracked and adjusted; the adjustment direction is to adjust the phase difference between the input voltage and the input current. Maintain within the weak sensitivity threshold range, i.e. ,and To compensate for the change in the imaginary part caused by the drift of the reflection impedance; In step S43, when the energy efficiency assessment result is labeled as poor, in response to the resonant parameter reconstruction command at the transmission end, the driving frequency of the wireless power transmission end and the capacitance value of the variable impedance matching network are simultaneously reconstructed and adjusted; at this point, single frequency adjustment is no longer sufficient. The condition is that the system operating point must be forced to re-enter the zero-voltage switching region by changing the capacitance value of the hardware topology parameter. Example 6: Step S4 also includes logic to prevent oscillation: S44. During the adaptive adjustment of the operating parameters of the wireless power transmission terminal, the input current waveform of the wireless power transmission terminal is monitored in real time. S45. If the amplitude of the input current waveform exceeds the preset safety limit, or if the waveform frequency exhibits uncontrolled jitter, immediately lock the current resonant parameters and reduce the dimming rate response level at the nonlinear load end until the input current waveform returns to stability.

[0025] This embodiment adds protection logic to prevent system oscillation; In step S44, the input current waveform of the wireless power transmission terminal is monitored in real time during the adjustment process; In step S45, if the amplitude of the input current waveform exceeds the preset safety limit... , The value is set as the rated operating current of the wireless power transmission system. 1.2 to 1.5 times that, of which The physical meaning is clearly defined as: the effective value of the rated AC current on the output side of the inverter circuit at the wireless power transmission terminal; or, if uncontrolled jitter occurs in the waveform frequency, immediately lock the current resonant parameters; simultaneously, reduce the dimming rate response level at the nonlinear load terminal; specifically, change the execution time constant of the dimming command from... For example, 1ms extended to For example, 10ms, until the input current waveform returns to stability; this logic ensures that the physical safety of the electrical system is prioritized under extreme operating conditions.

[0026] Example 7: The method is applied to tunnel lighting scenarios, and the real-time electrical operation data in step S1 also includes: S14. Traffic flow and external light intensity values ​​at the tunnel entrance section collected by environmental sensing equipment. S15, the source-load impedance dynamic coordination model also incorporates the driving brightness requirement mapping relationship generated based on traffic flow value and external light intensity value. This mapping relationship defines the minimum lighting brightness value required under different combinations of traffic flow and external light intensity. S16. Based on the mapping relationship of driving brightness demand, correct the transient dimming request in step S2. The correction logic is: under the premise of ensuring that the lighting brightness value is not lower than the minimum lighting brightness value, select the dimming scheme that minimizes the reflection impedance drift.

[0027] This embodiment combines a specific application scenario of tunnel lighting; In step S14, the traffic flow data at the tunnel entrance section is collected by the environmental sensing device. and external light intensity values ; In step S15, the source-load impedance dynamic coordination model also incorporates a mapping relationship for driving brightness demand generated based on traffic flow data and external light intensity data; this mapping relationship is pre-defined according to industry standards such as the Highway Tunnel Lighting Design Guidelines, defining the driving brightness demand under specific conditions. and Minimum illumination brightness value under combination ; In step S16, the transient dimming request is corrected based on the driving brightness demand mapping relationship; the correction logic is: calculate the requirement to meet the requirements. Choose from all feasible dimming schemes for the given conditions to adjust the amount of reflection impedance shift. The minimum solution is used as the final execution instruction; this step achieves the globally optimal solution for lighting requirements and transmission energy efficiency.

[0028] 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. A method for energy consumption optimization control of a wireless mesh-based tunnel lighting system, characterized in that, The specific steps include: S1, collecting real-time electrical operation data of the wireless power transmission end and the nonlinear load end, establishing a source-load impedance dynamic collaboration model, and setting a transmission link energy efficiency stability threshold; S2, based on the real-time electrical operation data, extracting the load current waveform characteristics of the nonlinear load end, calculating the reflected impedance drift corresponding to the transient dimming request, which is issued by the wireless networking control center to each lighting node and obtained after being parsed through the communication protocol; S3, inputting the reflected impedance drift as input data into the source-load impedance dynamic collaboration model, generating a transmission end resonance parameter reconstruction instruction, and calculating an impedance matching degree index under the current operating state; S4, comparing the impedance matching degree index with the transmission link energy efficiency stability threshold, obtaining an energy efficiency evaluation result, and in response to the energy efficiency evaluation result and the transmission end resonance parameter reconstruction instruction, adaptively adjusting the operating parameters of the wireless power transmission end to ensure the collaborative stability of the wireless networking communication link and the energy transmission link, so as to meet the transient dimming request while maintaining the zero-voltage switching state. 2.The tunnel lighting system energy consumption optimization control method based on wireless networking of claim 1, wherein: The step S1 specifically includes: S11, using a voltage and current sensor to obtain the output voltage and output current of the wireless power transmission end, and the load voltage and load current of the nonlinear load end; S12, obtaining the dimming control signal of the nonlinear load end, parsing the dimming control signal duty cycle change rate, and taking it as a parameter representing the dynamic change of the load; S13, based on historical operation data, a source-load impedance dynamic collaboration model is constructed, which is essentially a data set containing multiple mapping relationships, used to describe the corresponding relationship between the wireless power transmission end resonance frequency, the matching network capacitance parameter and the nonlinear load end equivalent impedance. 3.The method of claim 2, wherein the method further comprises: determining a number of the wireless nodes in the wireless network; and determining the number of the wireless nodes in the wireless network based on the number of the wireless nodes in the wireless network and the number of the wireless nodes in the wireless network. The step S2 specifically includes: S21, time-frequency domain analysis is performed on the collected load current waveform to extract waveform distortion feature data and phase shift data; S22, combining the dimming control signal duty cycle change rate, the dimming depth value and the dimming rate value of the nonlinear load end are identified; S23, according to the dimming depth value and the dimming rate value, the preset impedance characteristic curve lookup table is used to calculate the equivalent resistance change value and the equivalent reactance change value of the nonlinear load end at the next moment; S24, vector synthesis is performed with the equivalent resistance change value as the real part and the equivalent reactance change value as the imaginary part, the modulus is calculated to obtain the reflected impedance drift.

4. The method of claim 3, wherein the method further comprises: The step S3 specifically includes: S31, inputting the reflected impedance drift as a feedforward variable into the source-load impedance dynamic collaboration model; S32, through the source-load impedance dynamic collaboration model, the best working frequency point value and the best matching capacitance value that can maintain the inductive input impedance of the wireless power transmission end under the current reflected impedance drift are queried; S33, a transmission end resonance parameter reconstruction instruction containing the best working frequency point value and the best matching capacitance value is generated; S34. Calculate the total load impedance after superimposing the actual output impedance of the wireless power transmission terminal and the reflected impedance drift at the current moment. Calculate the absolute value of the difference between the total load impedance and the system rated characteristic impedance, and divide the absolute value by the system rated characteristic impedance. The resulting ratio is used as the impedance matching index.

5. The method of claim 4, wherein the method further comprises: In step S4, the transmission link energy efficiency stability threshold includes a first stability threshold and a second stability threshold, and the value of the first stability threshold is strictly less than the value of the second stability threshold. Step S4 specifically includes comparing the impedance matching index with the first stability threshold and the second stability threshold, respectively, and generating corresponding energy efficiency assessment result labels: If the impedance matching index is less than or equal to the first stability threshold, the system is determined to be in a deep resonance state, and a superior energy efficiency evaluation result label is generated. If the impedance matching index is greater than the first stability threshold and less than or equal to the second stability threshold, the system is determined to be in a slightly detuned state, and a good energy efficiency assessment result label is generated. If the impedance matching index is greater than the second stability threshold, the system is determined to be in a severely detuned state, and a differential energy efficiency assessment result label is generated. 6.The method of claim 5, wherein the method further comprises: Step S4 also includes adjustment strategies for different energy efficiency assessment result labels: S41. When the energy efficiency assessment result is labeled as excellent, keep the resonant network parameters of the current wireless power transmission terminal unchanged, and only fine-tune the duty cycle of the driving voltage according to the output voltage feedback signal. S42. When the energy efficiency assessment result label is good, in response to the resonant parameter reconstruction command of the transmission end, only the driving frequency of the wireless power transmission end is dynamically tracked and adjusted. The adjustment direction is to make the phase difference between the input voltage and the input current approach zero, so as to compensate for the change in the imaginary part caused by the reflection impedance drift. S43. When the energy efficiency assessment result label is poor, in response to the resonant parameter reconstruction command of the transmission end, the driving frequency of the wireless power transmission end and the capacitance value of the variable impedance matching network are reconstructed and adjusted to force the system operating point to re-enter the zero-voltage switching region. 7.The method of claim 1, wherein the method further comprises: determining a number of the wireless nodes in the wireless network; and determining a number of the wireless nodes in the wireless network that are in the active state. Step S4 also includes logic to prevent oscillation: S44. During the adaptive adjustment of the operating parameters of the wireless power transmission terminal, the input current waveform of the wireless power transmission terminal is monitored in real time. S45. If the amplitude of the input current waveform exceeds the preset safety limit, or if the waveform frequency exhibits uncontrolled jitter, immediately lock the current resonant parameters and reduce the dimming rate response level at the nonlinear load end until the input current waveform returns to stability. 8.The method of claim 1, wherein the method further comprises: determining a number of the wireless nodes in the wireless network; and determining a number of the wireless nodes in the wireless network that are in the active state. The method is applied to tunnel lighting scenarios, and the real-time electrical operation data in step S1 further includes: S14. Traffic flow and external light intensity values ​​at the tunnel entrance section collected by environmental sensing equipment. S15. The source-load impedance dynamic coordination model also incorporates a driving brightness requirement mapping relationship generated based on traffic flow value and external light intensity value. This mapping relationship defines the minimum lighting brightness value required under different combinations of traffic flow and external light intensity. S16. Based on the driving brightness demand mapping relationship, correct the transient dimming request in step S2. The correction logic is: under the premise of ensuring that the lighting brightness value is not lower than the minimum lighting brightness value, select the dimming scheme that minimizes the reflection impedance drift.

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