Modular power management platform and method suitable for multi-standard base station

Abstract

The invention discloses a modular power management platform and method suitable for a multi-system base station, particularly relates to the field of multi-module power management, is used for solving the problems of low load feedback interference and efficient energy utilization, and improves the stability and energy efficiency of a system through spectrum feature analysis, frequency avoidance tuning and energy absorption of feedback current. Firstly, a feedback signal is recognized, and a basis is provided for follow-up adjustment and optimization; secondly, the power switching frequency and the current response frequency are optimized and adjusted through an avoidance strategy, and interference caused by feedback frequency band overlapping is avoided; besides, residual feedback energy is absorbed by a low-power super capacitor and is stably shunted to auxiliary power through a DC-DC voltage reduction unit, so that effective absorption and reuse of the feedback energy are realized. While ensuring the stable output of the main load, the long-term influence of the feedback effect on the module is reduced, the anti-interference capability of the system and the lasting stability of the operation are improved, and the efficient and reliable operation of the power supply management in the multi-module environment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of multi-module power management, and more specifically, to a modular power management platform and method applicable to multi-standard base stations. Background Art

[0002] With the rapid development of communication technology, the demand for the deployment of mobile communication base stations in multi-standard network environments is increasing. In particular, the 48V DC power supply system is widely used in base stations, micro data centers and other load scenarios with space constraints, providing stable DC power supply for different standards to ensure the continuity of network coverage and consistency of user experience. Based on this, the modular power supply system has gradually become an important form of power supply for multi-standard base stations. It realizes the combination of multiple functional modules with a compact and flexible modular structure, provides efficient and stable power conversion for a variety of loads, and adapts to the demand fluctuations of multi-band networks. However, in an environment where multiple modules are combined and operated, the power modules need to have both efficient current allocation and responsiveness to meet the rapid adaptability of the base station under complex load changes and ensure the continuity and efficiency of power supply.

[0003] In the low-load state of multi-module combination operation, subtle power feedback effects often occur between power modules, that is, when the modules are in low power or idle state, part of the energy is circulated and fed back between modules to form unstable reflux. This phenomenon may not have a significant impact when the power system is running for a short time, but in the long-term operation of the base station, the feedback current gradually accumulates, gradually causing problems such as increased local heat and increased current fluctuations, which in turn accelerates the aging of the power module and reduces the overall energy conversion efficiency of the system. At the same time, under high-frequency system switching and load fluctuations, the imbalance of the feedback effect poses a hidden danger to the output stability of the power module, which may cause fluctuations in the power supply quality of the base station equipment and affect the long-term reliability of the base station network. The traditional power module design fails to effectively suppress this feedback phenomenon and lacks a refined current feedback control mechanism, which reduces the operating efficiency of the module under low load conditions and threatens the stability of the system.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a modular power management platform and method suitable for multi-standard base stations. By real-time monitoring of the feedback current characteristics and accurately analyzing the frequency characteristics and energy distribution of the feedback frequency band, the system can quickly identify and isolate the interference source and reduce the cumulative impact of the feedback effect. During the operation of the power module, the feedback frequency band is avoided by actively adjusting the frequency to avoid the system instability caused by frequency resonance, thereby enhancing the reliability of the module operation. The remaining feedback energy is intelligently diverted to the supercapacitor and used for the system's low-power auxiliary equipment via the DC-DC step-down unit, achieving effective reuse of the feedback energy and reducing energy waste. The overall solution greatly improves the energy efficiency of the system, ensures the continuous and stable operation of the power module under variable loads, extends the life of the module, and provides a comprehensive solution for the safe and efficient operation of the multi-module power system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1, collects the tiny current signal of each module under low load conditions, and extracts the feedback frequency band signal through a differential amplifier and a bandpass filter;

[0008] S2, using fast Fourier transform to perform spectrum analysis on the extracted feedback frequency band signal, generate frequency peak, frequency stability and fluctuation amplitude characteristic data of the feedback signal, and determine whether to generate an avoidance signal by constructing a decision value;

[0009] S3, if the avoidance signal is obtained, the power switching frequency and current response frequency of the module are adjusted according to the frequency distribution characteristics of the feedback signal, so that the operating frequency band of each module avoids the feedback frequency band;

[0010] S4, the remaining feedback energy is absorbed and temporarily stored by the supercapacitor, and the stored energy is diverted to the system auxiliary power using the DC-DC step-down conversion unit.

[0011] In a preferred embodiment, step S1 includes the following contents:

[0012] S1.1, using a current sensor array to collect the current of each module in real time at a millisecond sampling interval; the sensor array will synchronously record the current fluctuations between modules and transmit them to the differential amplifier for preliminary signal processing;

[0013] S1.2, the differential amplifier inputs the current signal of each module in the form of voltage and calculates the voltage difference between the modules;

[0014] S1.3, after the differential signal is amplified, the bandpass filter performs frequency-selective filtering on it to match the feedback signal frequency, ensuring that only the current fluctuation signal within the feedback frequency band is retained, filtering out the current signal interference of other frequencies, and finally obtaining the feedback frequency band signal.

[0015] In a preferred embodiment, step S2 includes the following contents:

[0016] S2.1, storing the feedback frequency band signal data extracted from step S1 into a discrete time sequence according to a preset sampling time interval; after the feedback frequency band signal data is stored in sequence, removing the high-frequency noise and DC components in the acquisition to prevent deviation in spectrum analysis;

[0017] S2.2, the pre-processed feedback frequency band signal data enters the fast Fourier transform module, performs frequency domain conversion, and obtains spectrum data;

[0018] S2.3, calculate the energy distribution of the spectrum data to determine the strength of each frequency component; define the energy at the frequency as the square of the amplitude;

[0019] S2.4, in order to quantitatively analyze the frequency stability, the frequency stability is constructed. The concentration of energy in the dominant frequency band is measured by comparing the energy integral value of the signal in the dominant frequency band with the total energy integral value of the whole frequency band to judge the frequency stability;

[0020] S2.5, based on the spectrum analysis results, determine the dominant frequency band of the feedback signal, that is, the main frequency range where the energy is concentrated, to ensure that the calculation of the fluctuation amplitude is focused on the main feedback component; within the selected dominant frequency band, the fluctuation amplitude is obtained by calculating the square root value of the energy.

[0021] In a preferred embodiment, step S2 further includes the following contents:

[0022] S2.6, to determine whether the unstable feedback current has reached the level that requires the adjustment module of step S3 to be activated, a decision value is obtained by comprehensive calculation based on the frequency peak value, stability and fluctuation amplitude; when the decision value is greater than or equal to the corresponding threshold, an avoidance signal is generated;

[0023] S2.7, when the avoidance signal is obtained, the main characteristic parameters of the frequency peak, stability and fluctuation amplitude of each collected signal are grouped through a clustering algorithm to form different feedback mode categories; for each type of feedback mode, its dominant frequency, energy distribution and frequency stability characteristics are stored to form a feedback feature database.

[0024] In a preferred embodiment, step S3 includes the following contents:

[0025] S3.1, if an avoidance signal is obtained, extract the frequency peak value f corresponding to the current avoidance signal from the feature database generated in step S2 peak , stability S f and fluctuation range A f ; Determine the required avoidance frequency band based on the frequency peak and fluctuation amplitude [f low ,f high ], where: f low =f peak -k·(1-S f )·A f ;f high =f peak +k·(1-S f )·A f ; Where k is the gain factor;

[0026] S3.2, according to the calculated avoidance frequency band, adjust the power switching frequency f of the module switch , so that it deviates from the avoidance frequency band range; set the target switching frequency f new The following relationship is satisfied: new =f switch ±m·(f high -f low ), where m is the avoidance factor;

[0027] S3.3, in order to ensure the stability of the current response after the power switch frequency is adjusted, the current response frequency f of the module is I Perform synchronous regulation; according to the new switching frequency f new , calculate the current response frequency so that it maintains a specific multiple relationship with the new switching frequency, the formula is as follows: in, is the current response coefficient;

[0028] S3.4, after adjusting the power switch frequency and current response frequency, re-evaluate the impact of frequency change on voltage output and perform voltage swing correction; the new voltage swing V new The adaptive calculation is performed according to the following formula: Among them, V base is the initial voltage swing, A small adjustment factor.

[0029] In a preferred embodiment, step S4 includes the following contents:

[0030] A low-power supercapacitor is used as a temporary energy storage element and connected to the output of the power module through a diode isolator to ensure unidirectional energy transfer. At the same time, the charging current of the supercapacitor is separated from the module output current to ensure the voltage stability of the module when the main load changes.

[0031] A parallel switch controller is configured in the energy storage circuit to dynamically control the charging process of the supercapacitor to avoid interference with the main load during the energy storage process; the parallel switch controller monitors the charging voltage and charging current of the supercapacitor in real time. When the feedback current enters the energy storage circuit, the switch controller adjusts the charging rate through pulse width modulation to ensure that the supercapacitor gradually absorbs the feedback energy within a stable voltage range;

[0032] When the supercapacitor is charged to a preset full charge threshold, the full charge detection circuit triggers a transfer signal to start the DC-DC buck conversion unit;

[0033] After the voltage reduction conversion, the energy in the supercapacitor is gradually released to the auxiliary power channel of the system in the form of a small current.

[0034] A modular power management platform suitable for multi-standard base stations, including: acquisition module, analysis module, tuning module and energy storage module;

[0035] Acquisition module: collects tiny current signals of each module under low load conditions, extracts feedback frequency band signals through differential amplifiers and bandpass filters, and sends the feedback frequency band signals to the analysis module;

[0036] Analysis module: Use fast Fourier transform to perform spectrum analysis on the extracted feedback frequency band signal, generate frequency peak, frequency stability and fluctuation amplitude characteristic data of the feedback signal, and determine whether to generate an avoidance signal by constructing a decision value, and send the avoidance signal to the tuning module;

[0037] Tuning module: If an avoidance signal is obtained, the power switching frequency and current response frequency of the module are adjusted according to the frequency distribution characteristics of the feedback signal, so that the operating frequency band of each module avoids the feedback frequency band, and the remaining feedback energy is transmitted to the energy storage module;

[0038] Energy storage module: The remaining feedback energy is absorbed and temporarily stored through supercapacitors, and the stored energy is diverted to the system auxiliary power using the DC-DC step-down conversion unit.

[0039] The present invention is applicable to the technical effects and advantages of the modular power management platform and method for multi-standard base stations:

[0040] The multi-module power management method of the present invention realizes the dual improvement of system stability and operating efficiency under low load state through efficient feedback current control and energy consumption mechanism. First, by real-time acquisition and analysis of the frequency characteristics of the feedback signal, an accurate feedback signal characteristic database is established to provide a reliable basis for frequency regulation. When the feedback signal strength reaches the set threshold, an avoidance signal is generated, and the power switching frequency and current response frequency of the module are automatically adjusted accordingly, so that the working frequency band avoids the feedback frequency band, ensuring that the frequency distribution is reasonable and non-overlapping, thereby avoiding system instability caused by frequency resonance. On this basis, for the residual feedback energy that cannot be eliminated, low-power supercapacitors are used for absorption and temporary storage, and the stored energy is shunted to auxiliary equipment through the DC-DC step-down unit, realizing the reasonable distribution and efficient utilization of feedback energy. Under the synergistic effect of each step, comprehensive management of the feedback effect is achieved in a low-load environment, significantly improving the long-term stability and energy efficiency of the module operation, and providing efficient and safe operation guarantee for the multi-module power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure is a flow chart of a modular power management method applicable to multi-standard base stations of the present invention.

[0042] Figure 2 The schematic diagram is a structural diagram of a modular power management platform applicable to multi-standard base stations of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiment 1: Figure 1 The present invention provides a modular power management method applicable to a multi-standard base station, comprising:

[0045] S1, collects the tiny current signal of each module under low load conditions, and extracts the feedback frequency band signal through a differential amplifier and a bandpass filter;

[0046] S2, using fast Fourier transform to perform spectrum analysis on the extracted feedback frequency band signal, generate frequency peak, frequency stability and fluctuation amplitude characteristic data of the feedback signal, and determine whether to generate an avoidance signal by constructing a decision value;

[0047] S3, if the avoidance signal is obtained, the power switching frequency and current response frequency of the module are adjusted according to the frequency distribution characteristics of the feedback signal, so that the operating frequency band of each module avoids the feedback frequency band;

[0048] S4, the remaining feedback energy is absorbed and temporarily stored by the supercapacitor, and the stored energy is diverted to the system auxiliary power using the DC-DC step-down conversion unit.

[0049] In a power supply system with multiple modules, when operating under low load or idle state, current fluctuations between modules can easily cause subtle feedback effects. Although this feedback current is small in amplitude, its long-term accumulation will have a potential impact on the overall power conversion efficiency of the system and the life of the module, especially in a high-density module environment, where the superposition of feedback effects will further lead to instability in current conduction. Therefore, in order to accurately identify the feedback signal and suppress its cumulative impact in the early stage of system operation, a sophisticated signal acquisition and filtering process must be designed. By accurately capturing the feedback signal within a specific frequency band, reliable data support can be provided for subsequent regulation and energy management, so as to achieve stable operation between modules and improve the overall system efficiency.

[0050] Step S1 includes the following contents:

[0051] The purpose of collecting the tiny current of each module under low load or idle state is to identify and effectively suppress the feedback current between power modules. Under low load conditions, since the power output is not enough to fully drive the load, reverse feedback current is prone to occur between modules. This feedback phenomenon originates from the residual energy released by the module when it is not fully output, which often circulates between modules in the form of tiny current, forming an unstable reflux, affecting the overall efficiency and life of the module.

[0052] Collecting tiny currents and analyzing feedback characteristics under low load conditions helps to accurately identify the frequency and intensity characteristics of the feedback current, providing basic data for subsequent frequency avoidance and feedback energy absorption, thereby optimizing system operating efficiency and stability.

[0053] S1.1, use a microampere-level precision current sensor array to collect the current of each module in real time at a millisecond sampling interval. Suppose the current signal of each module is I m (t) (where m represents the module number and t represents the sampling time point), the sensor array will synchronously record the current fluctuations between modules and transmit them to the differential amplifier for preliminary signal processing.

[0054] S1.2, the differential amplifier inputs the current signal of each module in the form of voltage and calculates the voltage difference between the modules. This differential processing is achieved through the following formula: V d (t) = A d ·[I m1 (t)-Im2 (t)]; where V d (t) is the signal voltage after differential amplification, A d is the gain of the amplifier, I m1 (t) and I m2 (t) is the instantaneous current signal of two adjacent modules. The function of the amplifier is to expand the small voltage difference between the modules to a recognizable range, so that the small fluctuation of the feedback current can be enhanced at the signal level.

[0055] S1.3, after the differential signal is amplified, a bandpass filter is used to perform frequency-band selective filtering on it. Suppose the passband frequency of the filter is f r , the frequency range is [f min ,f max ]. This filter is specially designed to match the feedback signal frequency, ensuring that only the current fluctuation signal within the feedback frequency band is retained, filtering out the current signal interference of other frequencies, and finally obtaining the feedback frequency band signal. Feedback frequency band signal V f (t) is expressed as: V f (t)=H(f)·V d (t); where H(f) is the transfer function of the bandpass filter, defined as:

[0056] The bandpass filter ensures that the feedback frequency band signal only contains the frequency band signal related to the feedback current.

[0057] Through the precise collection and differential amplification of the current of each module under low load conditions, combined with the band-selective filtering of the bandpass filter, the tiny feedback current signal between modules can be effectively extracted. The filtered feedback signal is further separated into key feedback characteristic parameters through the time difference method to form a feedback signal model of the system under low load conditions. This precise signal collection and extraction process ensures the accurate capture of tiny feedback signals in complex environments, provides accurate basic data for subsequent steps, and thus helps the power system to operate efficiently and reliably in a multi-module combination environment.

[0058] In a power supply system with multiple modules, feedback current will frequently occur under low load conditions, forming an unstable feedback effect, affecting the overall stability and efficiency of the system. In order to deeply analyze and quantify the characteristics of these feedback signals, it is necessary to convert the feedback frequency band signal into frequency domain data and extract its key spectrum characteristics. Step S2 performs spectrum analysis on the extracted feedback signal to obtain characteristic data such as frequency peak, frequency stability, and fluctuation amplitude, and further constructs a decision value to determine the potential threat level of the feedback signal to the system. In this step, the feedback signal is preprocessed, frequency domain converted, feature extracted, and decision values ​​constructed to ensure that each key parameter can be used as a basis for system tuning, thereby providing scientific data information support for the operation of the power module.

[0059] Step S2 includes the following contents:

[0060] S2.1, the feedback frequency band signal data extracted from step S1 is stored into a discrete time series according to a preset sampling time interval. After the feedback frequency band signal data is stored in sequence, the high-frequency noise and DC components in the acquisition are removed to prevent deviations in the spectrum analysis. The preprocessing here ensures the purity of the signal input and helps to accurately extract the spectrum features.

[0061] S2.2, the pre-processed feedback frequency band signal data enters the fast Fourier transform module, performs frequency domain conversion, and obtains the spectrum data F(f), the formula of which is as follows: Where N is the number of sampling points, f is the frequency, j represents the imaginary unit, and n is the sampling sequence. The fast Fourier transform module converts the current fluctuation in the time domain to the frequency domain and generates spectrum data F(f), where the amplitude and phase information of each frequency component shows the frequency component of the feedback signal.

[0062] S2.3, calculate the energy distribution of the spectrum data to determine the strength of each frequency component. First, define the energy at the frequency as the square of the amplitude, and the calculation formula is: E(f) = |F(f)| 2 ; By calculating the energy E(f), we can obtain the main energy concentration frequency band and identify the frequency peak f peak and the corresponding peak energy E peak , thereby clarifying the dominant frequency component of the feedback current.

[0063] S2.4, in order to quantitatively analyze the frequency stability, construct the frequency stability S f , by comparing the energy integral value of the signal in the dominant frequency band with the total energy integral value of the entire frequency band, the concentration of energy in the dominant frequency band is measured to determine the frequency stability. The frequency stability calculation formula is: Where δ is the frequency tolerance range, f min and f max is the frequency band boundary. Frequency stability Sf The closer the value is to 1, the more the frequency is concentrated in the dominant frequency band and tends to be more stable.

[0064] S2.5, according to the spectrum analysis results, determine the dominant frequency band of the feedback signal [f peak -δ,f peak +δ], which is the main frequency range where the energy is concentrated, ensuring that the calculation of the fluctuation amplitude focuses on the main feedback components.

[0065] In the selected dominant frequency band, the fluctuation amplitude A is obtained by calculating the square root value of the energy f , the formula is: The fluctuation range A in this formula f It indicates the average amplitude of the feedback signal in the dominant frequency band, and is used to characterize the strength of the feedback signal. By selecting the dominant frequency band and integrating it, the interference items in the spectrum can be effectively filtered out, thereby obtaining the true fluctuation amplitude of the feedback signal.

[0066] S2.6, in order to determine whether the unstable feedback current has reached the level that requires the adjustment module of step S3 to be activated, it is necessary to use the frequency peak value f peak , stability S f and fluctuation range A f The decision value D is obtained by comprehensive calculation. Since these parameters have different units, they need to be normalized to make them dimensionless quantities to ensure the rationality of the calculation. Comprehensively consider the impact of each parameter on the feedback effect. For example, the decision value D can be constructed as: A n : Normalized fluctuation amplitude, reflecting the strength of the feedback signal. S f : Frequency stability, when the frequency stability S f When it is close to 1, it means the frequency is highly stable. n : Normalized frequency peak value, indicating the relative high or low of the feedback frequency. α, β: Exponential coefficients, positive integers greater than 1, used to enhance the influence weight of the corresponding parameters, which can be set according to system requirements (such as α = 2, β = 2). n : The larger the fluctuation amplitude, the higher the intensity of the feedback current and the greater the impact on the system, so it is a linear relationship in the decision value. When the frequency stability S f When it approaches 1 (i.e. the feedback frequency is very stable), Also close to 1, the denominator Approaching 0, the value of this item increases rapidly, highlighting the impact of frequency stability on the system. The logarithmic function and peace method are used to smooth the impact of frequency peaks on decision values ​​and avoid excessive deviation of calculation results due to high or low frequencies.

[0067] Set the threshold D th , when D ≥ Dth When the feedback effect reaches the level that requires the start of step S3, an avoidance signal is generated. th It can be set according to system experience and safety requirements.

[0068] The decision value is used to quantitatively measure the instability of the feedback current and the potential threat to the system stability. Specifically, the decision value reflects the combined effect of the intensity, frequency stability and frequency deviation characteristics of the feedback current. When the decision value exceeds the preset threshold, it indicates that the feedback signal has reached a critical value that may cause system interference. At this time, the adjustment measures in step S3 need to be initiated to adjust the power switch frequency and current response frequency to avoid the feedback frequency band and ensure the stable operation of the power module.

[0069] S2.7, when the avoidance signal is obtained, the frequency peak value f of each collected signal is peak , stability S f and fluctuation range A f The main characteristic parameters such as frequency, frequency and frequency stability are grouped by clustering algorithm to form different feedback mode categories. For each type of feedback mode, its dominant frequency, energy distribution and frequency stability are stored to form a feedback feature database. The database is then used for real-time identification of high-frequency feedback modes through an adaptive threshold screening mechanism to provide accurate feature data support for subsequent system tuning.

[0070] Step S2 realizes accurate spectrum analysis of the feedback signal through fast Fourier transform, obtains core parameters such as frequency peak, frequency stability and fluctuation amplitude, and can effectively identify and quantify the dominant frequency component of the feedback signal. At the same time, the construction of the decision value comprehensively considers factors such as the strength, stability and frequency deviation of the feedback signal, so that the system can accurately judge the interference threat degree of the feedback effect to the power module. When the decision value exceeds the threshold, an avoidance signal is generated, which provides a reliable tuning basis for subsequent steps. This processing method improves the dynamic response capability of the system and makes the operation of the power module in the feedback environment more stable and reliable.

[0071] When the power module is running under low load, the frequency component of the feedback current will interfere with the power switching frequency and current response frequency of the module, resulting in system instability. Therefore, after detecting that the feedback signal strength reaches the set threshold and generating an avoidance signal, it is necessary to avoid and optimize the power switching frequency and current response frequency of the power module according to the frequency distribution characteristics of the feedback signal. In step S3, by extracting parameters such as the frequency peak, frequency stability, and fluctuation amplitude of the feedback signal from the feature database, the avoidance frequency band is calculated and the frequency is adjusted, so that the operating frequency of the module deviates from the feedback frequency band, thereby avoiding interference caused by frequency overlap. In addition, the adjusted voltage swing correction further ensures stable output after frequency tuning. This process provides multiple layers of protection for the stable operation of the power module in a complex frequency interference environment.

[0072] Step S3 includes the following contents:

[0073] If an avoidance signal is obtained, the power switching frequency and current response frequency of the power module are optimized based on the frequency peak, frequency stability and fluctuation amplitude of the feedback signal to ensure that the operating frequency band of each module avoids the feedback frequency band and reduce the impact of the feedback effect on system stability.

[0074] S3.1, if an avoidance signal is obtained, extract the frequency peak, frequency stability and fluctuation amplitude corresponding to the current avoidance signal from the feature database generated in step S2. Determine the required avoidance frequency band [f low ,f high ], where: f low =f peak -k·(1-S f )·A f ;f high =f peak +k·(1-S f )·A f ; Where k is the gain factor, which is used to adjust the sensitivity of the avoidance range and is set according to system requirements. The higher the frequency stability value, the more concentrated the frequency is, so the adjustment range is appropriately reduced; the larger the fluctuation amplitude, the higher the feedback signal strength is, and the avoidance range is appropriately increased.

[0075] S3.2, according to the calculated avoidance frequency band, adjust the power switching frequency f of the module switch , so that it deviates from the avoidance frequency band. Set the target switching frequency f new The following relationship is satisfied: new =f switch ±m·(f high -f low), where m is the avoidance factor, which is usually between 1.2 and 1.5 to ensure that the adjusted frequency does not overlap with the feedback frequency band. This calculation method ensures that the switching frequency is as far away from the feedback frequency band as possible, reducing interference caused by frequency overlap.

[0076] S3.3, in order to ensure the stability of the current response after the power switch frequency is adjusted, the current response frequency f of the module is I According to the new switching frequency f new , calculate the current response frequency so that it maintains a specific multiple relationship with the new switching frequency, the formula is as follows: in, is the current response factor, which is selected as an integer or decimal (such as 1.5 or 2.0) to avoid overlapping with the feedback frequency band. In this way, the current response frequency operates within the multiple range of the new switching frequency, reducing the response delay and stability problems caused by frequency mismatch.

[0077] S3.4, after adjusting the power switch frequency and current response frequency, re-evaluate the impact of frequency change on voltage output and perform voltage swing correction. The new voltage swing V new The adaptive calculation is performed according to the following formula: Among them, V base is the initial voltage swing, To adjust the coefficient slightly, ensure that the adjusted voltage swing can maintain the module's output stability and avoid voltage instability caused by frequency adjustment.

[0078] Step S3 dynamically tunes the power switch frequency and current response frequency through a frequency avoidance strategy, significantly reducing the impact of the feedback signal on the module stability in the case of frequency overlap. The adjusted power switch frequency and current response frequency effectively avoid the high-intensity feedback frequency band and avoid frequency resonance between modules, thereby improving the anti-interference performance of the system. At the same time, the adaptive correction of the voltage swing further stabilizes the output voltage, so that the module still maintains a steady-state output after the frequency is adjusted. This method achieves active avoidance of feedback interference in a high-density module environment, improving the overall reliability and efficiency of the system operation.

[0079] Step S4 includes the following contents:

[0080] In a multi-module power supply system, residual feedback energy will be generated under low load conditions. In order to avoid the long-term impact of this feedback energy on system stability and power efficiency, step S4 designs a process for absorbing and absorbing the feedback energy through supercapacitors to reduce the interference of the feedback energy on the main load and achieve diversion utilization. The following is a detailed processing process:

[0081] A low-power supercapacitor is used as a temporary energy storage element, and is connected to the output of the power module through a diode isolator to ensure unidirectional energy transfer. Diode isolation can prevent the supercapacitor from discharging in reverse to the power module, thereby ensuring that the normal operation of the module is not affected during the process of charging the feedback current into the supercapacitor. At the same time, the charging current of the supercapacitor is separated from the module output current, ensuring the voltage stability of the module when the main load changes.

[0082] A parallel switch controller is configured in the energy storage circuit to dynamically control the charging process of the supercapacitor to avoid interference with the main load during the energy storage process. The parallel switch controller monitors the charging voltage and charging current of the supercapacitor in real time. When the feedback current enters the energy storage circuit, the switch controller adjusts the charging rate through pulse width modulation (PWM) to ensure that the supercapacitor gradually absorbs the feedback energy within a stable voltage range. This process stabilizes the charging current waveform through switch control and reduces the impact of fluctuations on the output of the power module.

[0083] When the supercapacitor is charged to the preset full charge threshold (i.e. reaches the maximum rated voltage), the full charge detection circuit triggers the transfer signal and starts the DC-DC buck conversion unit. The DC-DC buck unit gradually reduces the storage voltage in the supercapacitor to meet the voltage requirements of the auxiliary power devices in the system (such as monitoring circuits, signal indicator lights and other low-power devices). The buck process is carried out at a lower current to prevent instantaneous discharge from affecting the overall voltage stability of the system.

[0084] After the step-down conversion, the energy in the supercapacitor is gradually released to the auxiliary power channel of the system in the form of a small current. The DC-DC unit uses constant current control to make the energy release smooth and divert it to the low power consumption requirements of each auxiliary module. Since the current load during the release process is small, this diversion utilization solution can maximize the consumption of feedback energy while maintaining the stability of the main load of the system, improve the overall energy efficiency of the system and extend the service life of the power module.

[0085] Embodiment 2: Figure 2 The present invention provides a modular power management platform suitable for multi-standard base stations, including: a collection module, an analysis module, a tuning module and an energy storage module.

[0086] Acquisition module: collects the tiny current signals of each module under low load conditions, extracts the feedback frequency band signals through differential amplifiers and bandpass filters, and sends the feedback frequency band signals to the analysis module.

[0087] Analysis module: Use fast Fourier transform to perform spectrum analysis on the extracted feedback frequency band signal to generate frequency peak, frequency stability and fluctuation amplitude characteristic data of the feedback signal, and determine whether to generate an avoidance signal by constructing a decision value, and send the avoidance signal to the tuning module.

[0088] Tuning module: If an avoidance signal is obtained, the power switching frequency and current response frequency of the module are adjusted according to the frequency distribution characteristics of the feedback signal, so that the operating frequency band of each module avoids the feedback frequency band, and the remaining feedback energy is transmitted to the energy storage module.

[0089] Energy storage module: The remaining feedback energy is absorbed and temporarily stored through supercapacitors, and the stored energy is diverted to the system auxiliary power using the DC-DC step-down conversion unit.

[0090] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0091] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0092] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A modular power management method applicable to multi-standard base stations, characterized in that: Includes steps: S1, collects the tiny current signal of each module under low load conditions, and extracts the feedback frequency band signal through a differential amplifier and a bandpass filter; S2, using fast Fourier transform to perform spectrum analysis on the extracted feedback frequency band signal, generate frequency peak, frequency stability and fluctuation amplitude characteristic data of the feedback signal, and determine whether to generate an avoidance signal by constructing a decision value; S3, if the avoidance signal is obtained, the power switching frequency and current response frequency of the module are adjusted according to the frequency distribution characteristics of the feedback signal, so that the operating frequency band of each module avoids the feedback frequency band; S4, the remaining feedback energy is absorbed and temporarily stored by the supercapacitor, and the stored energy is diverted to the system auxiliary power using the DC-DC step-down conversion unit.

2. The modular power management method applicable to multi-standard base stations according to claim 1, characterized in that: Step S1 includes the following contents: S1.1, using a current sensor array to collect the current of each module in real time at a millisecond sampling interval; the sensor array will synchronously record the current fluctuations between modules and transmit them to the differential amplifier for preliminary signal processing; S1.2, the differential amplifier inputs the current signal of each module in the form of voltage and calculates the voltage difference between the modules; S1.3, after the differential signal is amplified, the bandpass filter performs frequency-selective filtering on it to match the feedback signal frequency, ensuring that only the current fluctuation signal within the feedback frequency band is retained, filtering out the current signal interference of other frequencies, and finally obtaining the feedback frequency band signal.

3. The modular power management method applicable to multi-standard base stations according to claim 2, characterized in that: Step S2 includes the following contents: S2.1, storing the feedback frequency band signal data extracted from step S1 into a discrete time sequence according to a preset sampling time interval; after the feedback frequency band signal data is stored in sequence, removing the high-frequency noise and DC components in the acquisition to prevent deviation in spectrum analysis; S2.2, the pre-processed feedback frequency band signal data enters the fast Fourier transform module, performs frequency domain conversion, and obtains spectrum data; S2.3, calculate the energy distribution of the spectrum data to determine the strength of each frequency component; define the energy at the frequency as the square of the amplitude; S2.4, in order to quantitatively analyze the frequency stability, the frequency stability is constructed. The concentration of energy in the dominant frequency band is measured by comparing the energy integral value of the signal in the dominant frequency band with the total energy integral value of the whole frequency band to judge the frequency stability; S2.5, based on the spectrum analysis results, determine the dominant frequency band of the feedback signal, that is, the main frequency range where the energy is concentrated, to ensure that the calculation of the fluctuation amplitude is focused on the main feedback component; within the selected dominant frequency band, the fluctuation amplitude is obtained by calculating the square root value of the energy.

4. The modular power management method applicable to multi-standard base stations according to claim 3, characterized in that: Step S2 also includes the following contents: S2.6, to determine whether the unstable feedback current has reached the level that requires the adjustment module of step S3 to be activated, a decision value is obtained by comprehensive calculation based on the frequency peak value, stability and fluctuation amplitude; when the decision value is greater than or equal to the corresponding threshold, an avoidance signal is generated; S2.7, when the avoidance signal is obtained, the main characteristic parameters of the frequency peak, stability and fluctuation amplitude of each collected signal are grouped through a clustering algorithm to form different feedback mode categories; for each type of feedback mode, its dominant frequency, energy distribution and frequency stability characteristics are stored to form a feedback feature database.

5. The modular power management method applicable to multi-standard base stations according to claim 4, characterized in that: Step S3 includes the following contents: S3.1, if an avoidance signal is obtained, extract the frequency peak value f corresponding to the current avoidance signal from the feature database generated in step S2 peak , stability S f and fluctuation range A f ; Determine the required avoidance frequency band based on the frequency peak and fluctuation amplitude [f low ,f high ], where: f low =f peak -k·(1-S f )·A f ;f high =f peak +k·(1-S f )·A f ; Where k is the gain factor; S3.2, according to the calculated avoidance frequency band, adjust the power switching frequency f of the module switch , so that it deviates from the avoidance frequency band range; set the target switching frequency f new The following relationship is satisfied: new =f switch ±m·(f high -f low ), where m is the avoidance factor; S3.3, in order to ensure the stability of the current response after the power switch frequency is adjusted, the current response frequency f of the module is I Perform synchronous regulation; according to the new switching frequency f new , calculate the current response frequency so that it maintains a specific multiple relationship with the new switching frequency, the formula is as follows: in, is the current response coefficient; S3.4, after adjusting the power switch frequency and current response frequency, re-evaluate the impact of frequency change on voltage output and perform voltage swing correction; the new voltage swing V new The adaptive calculation is performed according to the following formula: Among them, V base is the initial voltage swing, A small adjustment factor.

6. The modular power management method applicable to multi-standard base stations according to claim 5, characterized in that: Step S4 includes the following contents: A low-power supercapacitor is used as a temporary energy storage element and connected to the output of the power module through a diode isolator to ensure unidirectional energy transfer. At the same time, the charging current of the supercapacitor is separated from the module output current to ensure the voltage stability of the module when the main load changes. A parallel switch controller is configured in the energy storage circuit to dynamically control the charging process of the supercapacitor to avoid interference with the main load during the energy storage process; the parallel switch controller monitors the charging voltage and charging current of the supercapacitor in real time. When the feedback current enters the energy storage circuit, the switch controller adjusts the charging rate through pulse width modulation to ensure that the supercapacitor gradually absorbs the feedback energy within a stable voltage range; When the supercapacitor is charged to a preset full charge threshold, the full charge detection circuit triggers a transfer signal to start the DC-DC buck conversion unit; After the voltage reduction conversion, the energy in the supercapacitor is gradually released to the auxiliary power channel of the system in the form of a small current.

7. A modular power management platform applicable to multi-standard base stations, used to implement the modular power management method applicable to multi-standard base stations according to any one of claims 1 to 6, characterized in that: include: Acquisition module, analysis module, tuning module and energy storage module; Acquisition module: collects tiny current signals of each module under low load conditions, extracts feedback frequency band signals through differential amplifiers and bandpass filters, and sends the feedback frequency band signals to the analysis module; Analysis module: Use fast Fourier transform to perform spectrum analysis on the extracted feedback frequency band signal, generate frequency peak, frequency stability and fluctuation amplitude characteristic data of the feedback signal, and determine whether to generate an avoidance signal by constructing a decision value, and send the avoidance signal to the tuning module; Tuning module: If an avoidance signal is obtained, the power switching frequency and current response frequency of the module are adjusted according to the frequency distribution characteristics of the feedback signal, so that the operating frequency band of each module avoids the feedback frequency band, and the remaining feedback energy is transmitted to the energy storage module; Energy storage module: The remaining feedback energy is absorbed and temporarily stored through supercapacitors, and the stored energy is diverted to the system auxiliary power using the DC-DC step-down conversion unit.

Images