An adaptive radio frequency energy capture method and system

Abstract

The application relates to the field of information technology and discloses a self-adaptive radio frequency energy capturing method and system, which captures radio frequency signals in an environment, separates independent components of each frequency band signal from the radio frequency signals, determines the power density distribution and main lobe direction of each frequency band signal, and further determines the deviation of each frequency band signal from the main direction; the reference value of the antenna element spacing and phase adjustment is determined in combination with the expected gain requirement of the current environment, the antenna array configuration data is determined according to the reference value, the uncovered range of each frequency band signal is controlled by scanning the antenna array, the full / directional capturing mode is dynamically switched, the energy collection efficiency data in the current capturing mode is evaluated to determine the target frequency band with side lobe interference, the reference value of the target frequency band is adjusted, the signal focusing effect is determined according to the adjusted reference value, and the capturing priority of each frequency band signal is determined, so that the radio frequency energy collection efficiency in a complex electromagnetic environment is effectively improved, and efficient capturing and utilization of multi-frequency band weak radio frequency signals are realized.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an adaptive radio frequency energy harvesting method and system. Background Technology

[0002] Against the backdrop of rapid development in wireless communication and the Internet of Things (IoT), radio frequency (RF) energy harvesting technology, as a sustainable energy solution, has demonstrated crucial application value. It converts environmental RF signals into electrical energy, supporting low-power devices and becoming a key technology driving the widespread adoption of green communication and smart devices. Currently, most RF energy harvesting relies on fixed antenna designs and static energy harvesting patterns, which struggle to cope with fluctuations in environmental signal strength and changes in direction. This limitation leads to a significant decrease in energy harvesting efficiency when signals are unevenly distributed or the dominant direction is offset, especially in multi-band signal environments.

[0003] Therefore, improving the efficiency of radio frequency energy capture when the distribution of environmental signals changes has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides an adaptive radio frequency energy capture method and system, which solves the problem of how to improve the efficiency of radio frequency energy capture when the distribution of environmental signals changes.

[0005] To address the aforementioned technical problems, the first aspect of this invention provides an adaptive radio frequency energy harvesting method, comprising:

[0006] Real-time acquisition of radio frequency signal data in the current environment to obtain environmental feature data, and separation of independent components of each frequency band signal from the environmental feature data to generate a standardized frequency band signal distribution map;

[0007] Based on the standardized frequency band signal distribution map, the power density distribution and main lobe direction of each frequency band signal are determined, and the deviation from the main lobe direction of each frequency band signal is determined according to the main lobe direction.

[0008] Reference values ​​for antenna element spacing and phase adjustment are determined based on the deviation from the main direction and the desired gain requirements. When it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold, antenna array configuration data is determined using the reference values.

[0009] The antenna array configuration data is used to control the antenna array to scan the uncovered areas of each frequency band signal, and to dynamically switch between omnidirectional acquisition mode and directional acquisition mode according to the uncovered areas, so as to evaluate the energy harvesting efficiency data of the current acquisition mode;

[0010] Based on the energy harvesting efficiency data, target frequency bands with sidelobe interference are determined, and reference values ​​for the target frequency bands are adjusted. The signal focusing effect is then determined using the adjusted reference values ​​to determine the acquisition priority of signals in each frequency band.

[0011] A second aspect of the present invention provides an adaptive radio frequency energy harvesting system, comprising:

[0012] The signal spectrum generation module is used to collect radio frequency signal data in the current environment in real time, obtain environmental feature data, and separate the independent components of each frequency band signal from the environmental feature data to generate a standardized frequency band signal distribution spectrum.

[0013] The deviation direction determination module is used to determine the power density distribution and main lobe direction of each frequency band signal based on the standardized frequency band signal distribution map, and to determine the deviation direction of each frequency band signal from the main lobe direction according to the main lobe direction.

[0014] The antenna array configuration module is used to determine reference values ​​for antenna element spacing and phase adjustment based on the deviation from the main direction and the desired gain requirements, and to determine antenna array configuration data through the reference values ​​when it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold.

[0015] The energy collection efficiency evaluation module is used to control the antenna array to scan the uncovered range of each frequency band signal through the antenna array configuration data, and dynamically switch between omnidirectional acquisition mode and directional acquisition mode according to the uncovered range, so as to evaluate the energy collection efficiency data of the current acquisition mode.

[0016] The priority determination module is used to determine the target frequency band with sidelobe interference based on the energy harvesting efficiency data, adjust the reference value of the target frequency band, and determine the signal focusing effect through the adjusted reference value to determine the acquisition priority of each frequency band signal.

[0017] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0018] (1) Through precise signal analysis, we can fully and accurately understand the distribution of signals in different frequency bands in the current environment, providing detailed and reliable basic information for the subsequent configuration of antenna arrays and signal acquisition; based on the power density distribution, main lobe direction and deviation from the main direction of the frequency band signal, combined with the expected gain requirements, we can determine the reference values ​​for antenna element spacing and phase adjustment, and accurately determine the antenna array configuration data through these reference values, thereby optimizing the layout and parameter settings of the antenna array and improving the antenna's signal reception performance;

[0019] (2) By controlling the uncovered range of the scanning frequency band signal of the antenna array and dynamically switching between omnidirectional acquisition mode and directional acquisition mode according to the uncovered range, it can flexibly adapt to different signal distribution conditions and improve the efficiency and comprehensiveness of signal acquisition; Based on the energy harvesting efficiency data, the target frequency band with sidelobe interference is determined and the relevant reference values ​​are adjusted. The signal focusing effect is determined by the adjusted reference values, and then the acquisition priority of each frequency band signal is determined. This can effectively reduce the impact of sidelobe interference on signal acquisition, effectively improve the radio frequency energy harvesting efficiency in complex electromagnetic environments, and realize the efficient acquisition and utilization of weak radio frequency signals in multiple frequency bands. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an adaptive radio frequency energy harvesting method provided in a certain embodiment of the present invention;

[0022] Figure 2 This is a structural diagram of an adaptive radio frequency energy harvesting system provided in a certain embodiment of the present invention;

[0023] Figure label:

[0024] Among them, 10 is the signal spectrum generation module; 20 is the deviation direction determination module; 30 is the antenna array configuration module; 40 is the collection efficiency evaluation module; and 50 is the priority determination module. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0027] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the system or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0028] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is merely for describing specific embodiments and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0029] In one embodiment, such as Figure 1 As shown, the first aspect of the present invention provides an adaptive radio frequency energy harvesting method, comprising:

[0030] S1. Real-time acquisition of radio frequency signal data in the current environment to obtain environmental feature data, and separation of independent components of each frequency band signal from the environmental feature data to generate a standardized frequency band signal distribution map;

[0031] In one embodiment, the real-time acquisition of radio frequency signal data in the current environment to obtain environmental feature data includes:

[0032] The radio frequency signal data in the current environment is collected in real time by multiple antenna arrays, and the radio frequency signal data is processed by fast Fourier transform to divide the radio frequency signal data into multiple frequency band signals according to the frequency range.

[0033] Calculate the power spectral density value of each frequency band signal, combine it with the phase difference between each antenna element in the antenna array, quantify the directional angle data of each frequency band signal, and determine the signal variation parameters of each frequency band signal based on the directional angle data.

[0034] Based on the signal variation parameters, the K-means clustering algorithm is used to group the signals of each frequency band, and the Pearson correlation coefficient of each frequency band signal within the group is calculated as the time correlation coefficient.

[0035] Based on the time correlation coefficient, the frequency of each frequency band signal occurring within a unit space is used as the spatial distribution density, which is then combined with each signal change parameter and each direction angle data to form environmental feature data describing the dynamic change law of radio frequency signal data in the current environment.

[0036] Specifically, this invention deploys multiple radio frequency receiving antenna arrays within the target monitoring area corresponding to the current environment. Each antenna array contains at least four directional antenna elements, arranged in a quaternary or octet circular array layout. Adjacent antenna elements maintain a spacing of λ / 2, where λ is the center wavelength of the monitoring frequency band. This layout ensures that the signals received by each antenna element have a clear phase difference relationship, providing basic data for subsequent direction estimation. The antenna elements are arranged at preset angular intervals, and the raw radio frequency signals received by each antenna element are synchronously acquired through software-defined wireless equipment. The time-domain waveform data and reception timestamp information of the signals are obtained as radio frequency signal data. The acquired time-domain waveform data includes the amplitude and phase information of the signal, while the timestamp information is used for data synchronization between different antenna arrays. This synchronization accuracy needs to reach the microsecond level to ensure the accuracy of subsequent phase difference calculation. The radio frequency signal data is processed by Fast Fourier Transform to obtain frequency domain signal data. Based on the frequency range of the frequency domain signal data, the radio frequency signal data received by the antenna is divided into multiple frequency bands, namely low frequency band (30MHz to 300MHz), mid frequency band (300MHz to 3GHz) and high frequency band (3GHz to 30GHz).

[0037] The power spectral density (PSD) values ​​of signals in each frequency band are calculated using the Fourier transform method. PSD describes the distribution of signal power in the frequency domain, and calculating the PSD values ​​reveals the energy distribution characteristics of signals in each frequency band. The phase difference of signals received from different antenna elements in the same frequency band is compared, and the angle of arrival (AHA) of each frequency band is calculated based on the relationship between the phase difference and the antenna spacing, thus obtaining the directional angle data for each frequency band. It should be noted that the estimation of the signal AHA is based on the principle of phase difference measurement: when a plane wave signal arrives at the antenna array, antenna elements at different positions will have a path difference, leading to a phase difference in the received signal. This invention calculates the AHA by measuring the phase difference between adjacent antenna elements, combined with the antenna spacing and signal wavelength. This method can achieve a direction-finding accuracy within 5 degrees under high signal-to-noise ratio conditions. Next, based on the directional angle data and PSD values ​​of each frequency band signal, a two-dimensional spectrum and direction distribution matrix is ​​constructed for the current moment, providing an intuitive representation for the visualization of the signal environment. In this matrix, rows represent different frequency band divisions, columns represent 360-degree directional angle divisions, and each matrix element stores the signal strength value of the corresponding frequency band in a specific direction. Subtracting the corresponding elements of the distribution matrix at the current time from the distribution matrix at the previous time gives the signal strength change value and the signal direction change value. Dividing these two change values ​​by the time interval gives the signal change parameters, including the rate of change of signal strength and the rate of change of signal direction.

[0038] Based on the signal variation parameters of each frequency band, the K-means clustering algorithm is used to group frequency bands with similar variation patterns. It uses the rate of change of signal intensity and the rate of change of direction as feature vectors and finds the optimal cluster center through iterative calculation. The Pearson correlation coefficient of the signal intensity variation of each frequency band within the group is calculated as the time correlation coefficient. The calculation of the Pearson correlation coefficient quantifies the time correlation between signals of different frequency bands. A correlation coefficient close to 1 indicates that the signal changes of the two frequency bands are highly synchronized and may come from different frequency components of the same signal source.

[0039] Based on the calculated time correlation coefficient, the frequency of each frequency band signal occurrence within a unit space is used as the spatial distribution density. This density is then combined with the signal variation parameters and directional angle data of each frequency band signal to form environmental characteristic data describing the dynamic changes of radio frequency signals in the current environment. The spatial distribution density can also be obtained by statistically analyzing the frequency of signal occurrence within a unit solid angle.

[0040] This invention, by dividing radio frequency signals, facilitates a deeper analysis of the characteristics and differences of signals in each frequency band. Calculating the power spectral density of each frequency band and combining it with the phase difference quantization of direction angle data between antenna elements allows for accurate determination of the source direction of each signal. The K-means clustering algorithm is used to group signals in each frequency band, grouping signals with similar characteristics together for unified signal processing and analysis, and also reflecting the temporal variation patterns of the signals. Combining spatial distribution density, signal variation parameters, and direction angle data to form environmental characteristic data comprehensively describes the dynamic changes of radio frequency signal data in the current environment, providing rich and accurate information for subsequent signal processing, analysis, and decision-making, thus improving the system's adaptability and processing effectiveness in complex radio frequency environments.

[0041] In one embodiment, separating the independent components of each frequency band signal from the environmental feature data and generating a standardized frequency band signal distribution map includes:

[0042] The environmental feature data is subjected to noise characteristic identification and denoising to obtain denoised feature data. Filtering parameters are then set to perform bandpass filtering on the denoised feature data to obtain the effective components of the signals in each frequency band.

[0043] The correlation between each effective component is calculated to construct a correlation matrix, and the correlation matrix is ​​decomposed into eigenvalues. The eigenvectors with eigenvalues ​​greater than a preset eigenvalue threshold are extracted from the decomposition results as principal components to obtain the independent components of each frequency band signal.

[0044] Based on each independent component, the amplitude of each frequency band signal is normalized, and the normalization result is arranged according to the frequency of each frequency band signal to generate a standardized frequency band signal distribution map.

[0045] Specifically, this invention identifies and denoises environmental feature data by calculating the mean, variance, and kurtosis statistical characteristic values ​​of signals in each frequency band. The mean reflects the DC component of the signal, the variance characterizes the power distribution of the signal, and the kurtosis describes the sharpness of the signal distribution. When the kurtosis value is in the range of 2.8 to 3.2, it indicates that the noise conforms to the Gaussian distribution characteristics and is determined to be Gaussian noise. When the kurtosis value is less than 2.5 or greater than 3.5, it indicates the presence of impulse noise or other non-Gaussian interference and is determined to be non-Gaussian noise. Based on the noise type, the corresponding denoising method is selected to denoise the environmental feature data corresponding to each frequency band signal: for Gaussian noise, a wavelet threshold denoising method is used to process it, and for non-Gaussian noise, a nonlinear filtering method is used to process it, thereby obtaining denoised feature data. For the denoising feature data, filtering parameters are set according to the frequency distribution range of each frequency band signal. The passband frequency is set to 30MHz to 300MHz for the low frequency band, 300MHz to 3GHz for the mid frequency band, and 3GHz to 30GHz for the high frequency band. The bandpass filtering method is used to filter each frequency band signal and its denoising feature data separately, thereby extracting the effective components in each frequency band signal.

[0046] The independent component separation process is based on the principle of statistical independence of signals. This invention calculates the correlation between effective components within each frequency band signal, i.e., the Pearson correlation coefficient, and constructs a correlation matrix based on the calculated correlation. Each element in this matrix represents the degree of linear correlation between two corresponding frequency band signals. Then, eigenvalue decomposition is performed on the correlation matrix, transforming it into a diagonal form. Larger eigenvalues ​​correspond to the primary signal components, while smaller eigenvalues ​​are usually related to noise or minor components. Eigenvectors with eigenvalues ​​greater than a preset threshold are extracted as primary components. A linear transformation is then performed on the original signal based on these primary components to separate uncorrelated frequency band signal components, avoiding co-channel interference and intermodulation interference, thus obtaining the independent components of each frequency band signal. Alternatively, the selection of primary components can also adopt the cumulative contribution rate criterion; when the cumulative sum of the top few largest eigenvalues ​​reaches 95% of the total sum of eigenvalues, the corresponding eigenvector can represent the primary component of the signal.

[0047] Based on the independent components of each frequency band signal, the maximum and minimum amplitude values ​​of each band are calculated. The signal amplitude is then normalized to a standard range of 0 to 1 using a linear mapping. The normalized data for each frequency band are arranged in ascending order of frequency to construct a two-dimensional distribution relationship between frequency and amplitude, generating a standardized frequency band signal distribution map. This map visually reflects the spectrum occupancy of the electromagnetic environment. Each data point in the map represents the normalized signal strength at a specific frequency, and consecutive data points form a spectral envelope. The two-dimensional distribution relationship between frequency and amplitude can be constructed using a logarithmic frequency axis, which can clearly display the distribution characteristics of both low-frequency and high-frequency signals simultaneously.

[0048] This invention effectively removes interference noise from environmental feature data and improves signal purity by identifying and denoising the noise characteristics. By setting filtering parameters for bandpass filtering, it accurately extracts the effective signal components of each frequency band, avoiding mutual interference between different frequency bands. By calculating the correlation between effective components to construct a correlation matrix and performing eigenvalue decomposition to extract the main components, it accurately separates the independent components of each frequency band signal, facilitating a deeper understanding of the essential characteristics of each frequency band signal. Furthermore, it normalizes the amplitude of each frequency band signal and generates a standardized frequency band signal distribution map based on the frequency arrangement normalization results. This allows the amplitude and frequency information of different frequency band signals to be displayed and compared on a unified scale, intuitively presenting the distribution of each frequency band signal, facilitating subsequent analysis, evaluation, and decision-making regarding signal distribution.

[0049] S2. Based on the standardized frequency band signal distribution map, determine the power density distribution and main lobe direction of each frequency band signal, and determine the deviation from the main lobe direction of each frequency band signal according to the main lobe direction.

[0050] In one embodiment, step S2 includes:

[0051] Spatial filtering is performed on the standardized frequency band signal distribution map to perform beamforming analysis, and the weighting coefficients of each antenna element in the antenna array are determined based on the analysis results to calculate the array output power in each direction and obtain power distribution data.

[0052] The power density data and main lobe direction of each frequency band signal are determined based on the power distribution data.

[0053] Calculate the angle between each main lobe direction and the normal direction of the antenna array, and when the angle exceeds a preset deviation threshold, determine that the main lobe direction of the corresponding frequency band signal deviates from the main direction.

[0054] Specifically, this invention performs spatial filtering on signals in each frequency band of a standardized frequency band signal distribution map for beamforming analysis. The setting of the weighting coefficients directly determines the beamforming effect. When it is desired to receive a signal in a specific direction, the phase difference of the signal arriving at each antenna element in that direction needs to be calculated. Then, the weighting coefficients are set based on the reverse compensation value of this phase difference, so that the signals in the desired direction achieve in-phase superposition after weighted summation, while signals in other directions cancel each other out due to phase inconsistency. This invention sets the weighting coefficients of each antenna element in the antenna array according to the analysis results and the desired signal direction. These coefficients can be calculated based on the array manifold vector, which enables the array to form a main lobe in the desired direction, enhancing signal reception in that direction and aligning the phases of the signals in each element in the desired signal direction. Through weighted summation, coherent superposition of signals in a specific direction is achieved while signals in other directions cancel each other out. By traversing all possible directional angles and calculating the array output power in each direction, the power distribution data of each frequency band signal in different directions is obtained. This power distribution data reflects the strength of the signal in each direction. The process of traversing all possible directional angles is achieved by setting an angle scanning step size. Within the azimuth range of 0 degrees to 360 degrees, the array output power in that direction is calculated every 1 degree. For each direction, the corresponding weighting coefficient can also be calculated based on the geometric relationship between that direction and each antenna element. Then, the received signals of each antenna are multiplied by the corresponding weight and summed. This process is equivalent to filtering in the spatial domain, enhancing the signal in a specific direction while suppressing interference from other directions.

[0055] The power density data and main lobe direction are determined based on the power distribution data of signals in each frequency band. By comparing the power values ​​in all directions, the direction with the highest power can be found. This direction is the main lobe direction, which represents the actual direction of the signal source and is the direction with the best beamforming effect.

[0056] For the main lobe directions of the obtained signals in each frequency band, the angle between the main lobe direction of each signal and the normal direction of the antenna array is calculated. If the calculated angle exceeds a preset deviation threshold, the signal in that frequency band is determined to deviate from the main direction, and the deviation angle value and the corresponding frequency band identifier are recorded, thus completing the identification of signals deviating from the main direction. The definition of the antenna array normal direction is crucial for calculating the deviation angle. For a planar array, the normal direction is perpendicular to the array plane and points forward; for a circular array, the normal direction is radially outward from the center. The deviation angle is calculated using the vector angle formula: the dot product of the main lobe direction vector and the normal direction vector is performed, and then the angle value is obtained using the inverse cosine function. The preset deviation threshold needs to consider practical application requirements. In mobile communication base stations, a preset deviation threshold of 30 degrees is typically set, as signals exceeding this range may originate from interference from neighboring cells. In radar systems, the preset deviation threshold may be set to 5 degrees for accurate target tracking.

[0057] This invention performs spatial filtering on the signal distribution map of a standardized frequency band, enabling accurate calculation of the array output power in each direction and obtaining precise power distribution data. This helps to gain a deeper understanding of the energy distribution of the signal in different directions. Based on the power distribution data, the power density data and main lobe direction of each frequency band signal are determined, clearly identifying the direction in which the signal energy is most concentrated, which helps to improve the antenna's signal reception efficiency and accuracy. By calculating the angle between the main lobe direction and the antenna array normal direction and comparing it with a preset deviation threshold, the invention can promptly detect deviations in the main lobe direction of the frequency band signal from the main direction, helping to take measures in advance to adjust the antenna array configuration, ensuring that the signal can be effectively received, and improving the stability and reliability of the system.

[0058] In one embodiment, determining the power density data and main lobe direction of each frequency band signal based on the power distribution data includes:

[0059] Based on the power distribution data, the direction corresponding to the maximum power value in each frequency band signal is taken as the main lobe direction, and the angle position where the power drops to half of the maximum value is found from each main lobe direction to both sides, so as to determine the main lobe width of each frequency band signal.

[0060] The sum of the power values ​​in all directions within the width of each main lobe is calculated, and then divided by the solid angle corresponding to each main lobe direction to obtain the power density data of each frequency band signal.

[0061] Specifically, this invention uses power distribution data of signals in each frequency band as the direction corresponding to the maximum power value as the main lobe direction. It then searches outwards from the main lobe direction of each frequency band signal at angles where the power drops to half of the maximum value, and defines this angle range as the main lobe width of that frequency band signal. The main lobe width is determined using a power reduction method. Starting from the maximum power value in the main lobe direction, the search continues outwards to the positions where the power drops to half of the maximum value. The angle range between these two positions is the main lobe width. The main lobe width reflects the beam's directional resolution; a narrower width indicates higher directional resolution.

[0062] The power density data for all frequency band signals is obtained by summing the power values ​​in all directions within the main lobe width and then dividing by the solid angle corresponding to the main lobe. The calculation of power density involves the concept of solid angle, which describes the range of angles in three-dimensional space and is measured in steradian degrees. The solid angle corresponding to the main lobe can be approximated by multiplying the main lobe width by the azimuth and elevation angles. The total power is obtained by summing the power values ​​in all directions within the main lobe width and then dividing by the solid angle. Power density characterizes the power distribution per unit solid angle and is an important indicator for evaluating signal strength.

[0063] This invention determines the main lobe direction by searching for the direction corresponding to the maximum power of signals in each frequency band within the power distribution data. This accurately identifies the direction where signal energy is most concentrated, helping to improve the efficiency of signal reception and transmission. The main lobe width is determined by searching for angles where the power drops to half its maximum value from the main lobe direction. This visually reflects the spatial distribution range of signal energy, and the main lobe width information is crucial for evaluating signal coverage and anti-interference capabilities. Furthermore, by calculating power density data, the energy distribution of the signal in space can be described more accurately, aiding in the analysis of signal intensity differences in different regions.

[0064] S3. Determine the reference values ​​for antenna element spacing and phase adjustment based on the deviation from the main direction and the desired gain requirements, and determine the antenna array configuration data through the reference values ​​when it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold.

[0065] In one embodiment, step S3 includes:

[0066] The deviation angle of each frequency band signal is determined based on the deviation from the main direction, and combined with the desired gain requirement, the array aperture size and phase compensation amount required for each frequency band signal to reach the desired gain requirement are calculated as reference values ​​for the antenna element spacing and phase adjustment.

[0067] Based on the reference value, the synthesized radiation pattern of the adjusted array is calculated, and the actual gain of each deviation from the main direction is obtained from the synthesized radiation pattern to evaluate the power loss data of each frequency band signal;

[0068] If the deviation angle of a frequency band signal exceeds a preset angle threshold and its power loss data exceeds a preset loss threshold, it is determined that the geometry of the antenna array needs to be adjusted, and the antenna array configuration data is determined through the reference value.

[0069] Specifically, the relationship between the offset angle and gain attenuation is a core consideration in array antenna design. When a signal is incident from a direction offset from the main axis, the effective aperture of the array will decrease, resulting in a drop in gain. This relationship follows the cosine law, that is, the effective aperture is equal to the physical aperture multiplied by the cosine of the incident angle. So when the offset angle is 30 degrees, the effective aperture drops to 0.866 times its original size, and the corresponding gain loss is about 1.25dB. In practical applications, this loss needs to be compensated by increasing the array aperture.

[0070] This invention determines the deviation angle based on the difference between the actual direction of the signal and the main lobe direction in each frequency band, and calculates the array aperture size required to achieve the desired gain at that deviation angle using the correlation between the deviation angle and gain attenuation. The array aperture size is equal to the number of antenna elements multiplied by the element spacing. When the deviation angle is 30 degrees, the effective aperture drops to 0.866 times its original size, corresponding to a gain loss of approximately 1.25 dB. This loss needs to be compensated for by increasing the array aperture in practical applications. The calculation of the array aperture size is based on the fundamental relationship between gain and aperture; the gain is proportional to the ratio of the array aperture area to the square of the wavelength. The optimal spacing between antenna elements is determined to be an integer multiple of half the wavelength based on the wavelength of the signal frequency. If the desired gain is 20 dBi, the operating frequency is 2.4 GHz, and the corresponding wavelength is 12.5 cm, when the signal deviates by 45 degrees, to compensate for the 3 dB gain loss, the array aperture needs to be increased from the original 8 wavelengths to 11 wavelengths. This means that more antenna elements or a wider element spacing is required.

[0071] Simultaneously, the required phase compensation amount for each antenna element relative to the reference element is calculated based on the deviation angle, yielding reference values ​​for antenna element spacing and phase adjustment (i.e., including the number of array elements, element spacing, and phase compensation amount). The phase compensation amount is calculated based on the path difference principle. When a signal is incident at a deviation angle θ, the path difference between adjacent elements is d × sin(θ), where d is the element spacing. The phase difference corresponding to this path difference needs to be compensated by a phase shifter. If the deviation angle is 30 degrees and the element spacing is half a wavelength, the phase compensation amount is 90 degrees. Modern phased arrays typically use digital phase shifters, adjusting the phase in steps of 22.5 degrees or 11.25 degrees.

[0072] Based on the calculated reference values, the composite radiation pattern of the adjusted array is calculated by superimposing the radiation fields of each antenna element in the deviation direction. The actual gain value in the deviation direction is read from the composite radiation pattern, and the power loss data of each frequency band is evaluated by comparing the difference between the actual gain and the expected gain. The calculation of the composite radiation pattern involves the principle of vector superposition. The electric field intensity generated by the radiation field of each antenna element at a certain point in space has amplitude and phase characteristics. By adjusting the excitation phase of each element, the contributions of each element in the expected direction are superimposed in phase to form the main lobe. The actual gain is obtained by finding the gain value corresponding to the deviation direction in the radiation pattern, a process similar to reading the height value at a specific location in a contour map. The power loss assessment can be quantified using a decibel scale, and the difference between the actual gain and the expected gain directly reflects the degree of system performance degradation.

[0073] If the deviation angle of a frequency band signal exceeds a preset angle threshold and its power loss exceeds a preset loss threshold, it is determined that the antenna array geometry needs to be adjusted, and the antenna array configuration data is determined using calculated reference values. The threshold setting needs to comprehensively consider system requirements and actual constraints. The preset angle threshold is usually determined based on the system's coverage requirements; for example, if the horizontal half-power beamwidth of a base station antenna is typically 65 degrees, then the preset angle threshold can be set to 32.5 degrees. When both conditions are met simultaneously, it indicates that the existing array configuration can no longer meet performance requirements, and geometric adjustments are needed, such as changing the array arrangement or increasing the number of antenna elements.

[0074] In one embodiment, the calculation of antenna array configuration data can be performed through the following steps:

[0075] The compensation coefficient is calculated based on the deviation angle of the signal in each frequency band. This compensation coefficient is equal to the reciprocal of the cosine of the deviation angle. The new element spacing value is obtained by multiplying the element spacing of the antenna array in the reference value by the compensation coefficient. Simultaneously, based on the gain requirements in each direction of the power distribution requirements, array synthesis techniques (such as Chebyshev synthesis and Taylor synthesis) are used to determine the excitation amplitude ratio of each antenna element, resulting in an adjusted phase parameter reference value to meet specific radiation pattern requirements. The cosine relationship between the signal deviation angle and the effective aperture is the fundamental principle of array antenna compensation design. When an electromagnetic wave is incident on the antenna array at an angle deviating from the normal, the effective receiving area of ​​the array decreases according to the cosine of the incident angle. That is, if the signal deviates by 30 degrees, the cosine value is 0.866, meaning the effective aperture is only 86.6% of that under frontal incidence. The compensation coefficient, as the reciprocal of the cosine, needs to be 1.15 in this example, meaning the physical aperture needs to be increased by 15% to maintain the original receiving capability. The adjustment of the antenna element spacing directly affects the spatial resolution of the array. In the original design, using a half-wavelength spacing of 6.25cm for operation in the 2.4GHz band, the compensation coefficient reached 1.414 when compensating for the effects of a 45-degree deviation angle. Therefore, the new element spacing needed to be adjusted to 8.84cm. While this increased spacing compensates for the deviation angle, it also reduces the array's resolution in other directions, reflecting the trade-offs in antenna design. The determination of the excitation amplitude ratio is based on the specific requirements of the power distribution. Different application scenarios have different requirements for beamform: broadcast applications tend to have wide beam coverage, while point-to-point communication requires narrow beams. By adjusting the excitation amplitude of each antenna element, the main lobe width and sidelobe level of the beam can be controlled. For example, using Taylor distribution amplitude weighting, with the excitation amplitude of the center element set to 1 and gradually decreasing to 0.3 for the edge elements, the sidelobe level can be suppressed to 25dB below the main lobe.

[0076] Based on the adjusted phase parameter reference value, the new physical position coordinates of each antenna element are calculated with the array center as the origin. The required phase delay angle value for each element is obtained by dividing the distance difference between the new physical position coordinates and the origin by the signal wavelength and then multiplying by 360 degrees. The phase delay angle value is quantized into an integer multiple of the smallest step unit of the phase shifter, thus obtaining the physical position coordinates and phase delay setting value of each antenna element. It should be noted that the calculation of the phase delay angle value is based on the path difference principle: with the array center as the reference origin, the distance from each antenna element to the origin is different, resulting in a phase difference in the received signal. For every increase in distance difference by one wavelength, the phase difference increases by 360 degrees. In actual calculations, if an antenna element is 50cm from the origin and the operating wavelength is 12.5cm, the phase delay is 1440 degrees, equivalent to 4 complete cycles. This periodicity means that the actual phase shifter only needs to achieve a phase adjustment range of 0 to 360 degrees. Modern digital phase shifters typically use 5-bit or 6-bit control, corresponding to 32 or 64 discrete phase states. If a 5-bit phase shifter is used, the minimum step size is 11.25 degrees. When the calculated theoretical phase delay is 157 degrees, it needs to be quantized to the closest discrete value of 156.25 degrees, corresponding to 14 step units. Although this quantization error is unavoidable, it can be controlled within an acceptable range through proper design.

[0077] Based on the physical location coordinates and phase delay settings of each antenna element, as well as the new element spacing values, an array configuration parameter set is constructed, containing antenna element numbers, location coordinates, phase delay settings, and new element spacing values. By integrating the configuration parameters of all antenna elements and adding their operating frequency and array type identification information, antenna array configuration data is generated. The element number is used for unique identification, the location coordinates define the physical layout, the phase delay setting controls the beam pointing, the added operating frequency information ensures the configuration matches a specific frequency band, and the array type identifier distinguishes different configurations such as linear array, area array, or circular array. This systematic configuration data structure facilitates subsequent array control and optimization.

[0078] This invention determines reference values ​​for antenna element spacing and phase adjustment based on the deviation of signals from the main direction and the desired gain requirements of each frequency band. This allows for optimization tailored to the specific conditions of different frequency band signals, ensuring that the antenna achieves the desired gain effect in each frequency band and improving the overall performance of the antenna. Based on the reference values, the synthesized radiation pattern of the adjusted array is calculated, and the actual gain in each deviation direction is obtained to evaluate power loss data. This provides a clear understanding of the antenna's performance changes when deviating from the main direction, offering an accurate basis for subsequent adjustments and optimizations. When the deviation angle of a frequency band signal exceeds a preset angle threshold and the power loss data exceeds a preset loss threshold, the antenna array configuration data is determined using the reference values. This enables timely adjustments to the antenna array, resolving the problem of excessive power loss due to signal deviation from the main direction and ensuring stable signal transmission and reception.

[0079] S4. Control the antenna array to scan the uncovered range of each frequency band signal through the antenna array configuration data, and dynamically switch between omnidirectional acquisition mode and directional acquisition mode according to the uncovered range to evaluate the energy harvesting efficiency data of the current acquisition mode;

[0080] In one embodiment, step S4 includes:

[0081] Based on the antenna array configuration data, the radiation pattern is adjusted by phase control commands to scan the intensity of each frequency band signal point by point, thereby obtaining the spatial distribution data of the multi-frequency band signal.

[0082] Based on the spatial distribution data, spatial regions with signal strength lower than receiver sensitivity are identified as uncovered areas, and coverage integrity indices are calculated based on the uncovered areas.

[0083] The coverage integrity index is used to adaptively switch between omnidirectional capture mode and directional capture mode as the current capture mode.

[0084] Based on the current acquisition mode, the ratio of total received power to array aperture area is calculated to obtain energy harvesting efficiency data.

[0085] In one embodiment, adaptively switching between omnidirectional capture mode and directional capture mode as the current capture mode using the coverage integrity index includes:

[0086] Based on the coverage integrity index and signal strength distribution, determine whether to switch to directional capture mode. If the coverage integrity index is lower than the preset integrity threshold or the signal strength is concentrated in the preset direction, switch to directional capture mode as the current capture mode; otherwise, maintain omnidirectional capture mode as the current capture mode and generate capture mode selection results.

[0087] Specifically, phase control of the phase shifter is the core mechanism for realizing antenna array beam scanning. Each phase shifter adjusts the phase by changing the electrical length through which the signal passes, thereby controlling the beam pointing. This invention, based on antenna array configuration data and utilizing the phase control principle in array antenna theory, generates phase control commands. By sending these commands to the phase shifters of each antenna element, the radiation pattern is adjusted. The system then scans point-by-point within the azimuth and elevation angle ranges according to a preset scanning step size, recording the signal strength values ​​of each frequency band at each scanning point to obtain spatial distribution data of the multi-frequency band signals. The spatial scanning employs a rasterization method to cover the entire monitoring area: the azimuth angle ranges from 0 degrees to 360 degrees, with a scanning point set every 5 degrees; the elevation angle ranges from -60 degrees to 60 degrees, also in 5-degree increments, thus forming a spatial grid of 1800 scanning points (72×25). At each scanning point, the system remains stationary for a sufficient time to collect signal strength data for each frequency band. Due to the longer wavelength of the low-frequency band, a longer residence time is required to obtain stable measurement values; the high-frequency band can complete sampling quickly, resulting in the generated spatial distribution data of multi-frequency band signals exhibiting complex three-dimensional characteristics. Furthermore, the signal strength of each frequency band signal in different directions reflects the spatial distribution of the signal source in that frequency band. For example, mobile communication base station signals are mainly concentrated in the horizontal direction, while satellite signals come from the high elevation direction. By superimposing the spatial distribution data of each frequency band, a complete electromagnetic environment situation map can be obtained.

[0088] By analyzing the spatial distribution data of multi-band signals, spatial regions with signal strength lower than the receiver's sensitivity are identified as uncovered areas. The proportion of uncovered areas to the total scanned space is calculated as a coverage integrity index. It's important to note that receiver sensitivity is a key parameter for determining signal detectability; it defines the minimum signal power level that the receiver can reliably detect. Receiver sensitivity varies across different frequency bands. In the 2.4 GHz band, a typical receiver sensitivity is -90 dBm; while in the 28 GHz millimeter-wave band, due to increased noise, the sensitivity drops to -75 dBm. When the signal strength in a spatial region is lower than the receiver sensitivity for the corresponding frequency band, that region is considered uncovered.

[0089] When coverage integrity is above 80% and the signal is relatively uniformly distributed in all directions, the omnidirectional acquisition mode can effectively collect signal energy from all directions. Conversely, when the signal is mainly concentrated in a specific sector, such as when there is a strong signal only in the range of 90 to 180 degrees, the directional acquisition mode can obtain higher received power by concentrating the array gain in that direction. Therefore, this invention determines whether to switch to the directional acquisition mode based on the coverage integrity index and signal strength distribution. If the coverage integrity index is lower than a preset integrity threshold or the signal strength distribution is concentrated in a specific direction, the directional acquisition mode is switched to; otherwise, the omnidirectional acquisition mode is maintained, thus obtaining the current acquisition mode selection result.

[0090] Based on the current acquisition mode, the total received power is obtained by summing the received power of all frequency bands within the coverage area. The ratio of the total received power to the physical aperture area of ​​the antenna array is then calculated as the actual power density. The ratio of the actual power density to the electromagnetic wave power density in free space is defined as the energy harvesting efficiency, thus completing the calculation of the energy harvesting efficiency under the current mode. The total received power is obtained by integrating the power of all frequency bands within the coverage area. The electromagnetic wave power density in free space depends on the distribution of signal sources in the environment; a typical value is 100 mW / m² in urban environments (high-density signal areas), 25 mW / m² in suburban / township areas (medium signal density), and 2.5 mW / m² in rural / open areas (low signal density), which can be determined based on actual conditions.

[0091] This invention can accurately identify weak signal areas by scanning the signal strength of each frequency band point by point and determining the uncovered area. Then, the acquisition mode can be adjusted in a targeted manner to effectively improve the integrity of signal coverage, reduce signal blind spots, and ensure communication quality. By dynamically adjusting the acquisition mode and optimizing the array configuration, the energy harvesting efficiency can be significantly improved, and efficient utilization of complex electromagnetic environments can be achieved.

[0092] S5. Based on the energy harvesting efficiency data, determine the target frequency band with sidelobe interference, adjust the reference value of the target frequency band, and determine the signal focusing effect through the adjusted reference value to determine the acquisition priority of each frequency band signal;

[0093] In one embodiment, step S5 includes:

[0094] Calculate the power ratio of the main lobe to the side lobe in the energy harvesting efficiency data. If the power ratio exceeds a preset ratio threshold, it is determined that there is side lobe interference, and the frequency band signal with side lobe interference is taken as the target frequency band.

[0095] The reference values ​​of the target frequency band are adjusted using the Chebyshev weighting method, and the main lobe gain and side lobe suppression of the radiation pattern are calculated based on the adjusted reference values ​​to obtain optimized signal focusing effect data.

[0096] Based on the signal focusing effect data, the potential energy contribution value of each frequency band signal is calculated to determine the acquisition priority of each frequency band signal by sorting.

[0097] Specifically, this invention decomposes and analyzes energy harvesting efficiency data, calculating the received power in the main lobe direction and the side lobe direction separately. The power ratio is obtained by dividing the side lobe power by the main lobe power. If this ratio exceeds a preset threshold, side lobe interference is identified, and the frequency band causing the interference is recorded, with its corresponding signal used as the target frequency band. For example, in a 16-element linear array, if the received power in the main lobe direction is 100 milliwatts, and the total received power in all side lobe directions reaches 30 milliwatts, the power ratio is 0.3. When this ratio exceeds the preset threshold of 0.2, it indicates that side lobe interference has severely affected the directionality of energy harvesting. Side lobe interference manifests in various forms in real-world environments. In urban environments, reflected signals from tall buildings often enter the antenna system from the side lobe direction; near airports, radar signals from different directions may coexist, causing multi-source interference. This interference not only reduces the signal-to-noise ratio in the main lobe direction but also leads to energy dispersion, reducing overall harvesting efficiency.

[0098] For the target frequency bands with recorded sidelobe interference, the Chebyshev weighted method is used to adjust their reference values ​​to reduce sidelobe levels. Based on the adjusted reference values, the main lobe gain and sidelobe suppression of the radiation pattern are recalculated to obtain optimized signal focusing performance data. The Chebyshev weighted method suppresses all sidelobes to the same low level while maintaining a relatively constant main lobe gain. The weighting coefficients are calculated based on Chebyshev polynomials, and the amplitude weight of each antenna element is determined by setting the desired sidelobe level. For example, if the sidelobe level is required to be 30dB lower than the main lobe, the weight of the center element is 1, while the weight of the edge elements is reduced to 0.2. The radiation pattern is calculated using array antenna theory. The main lobe gain represents the signal amplification capability of the antenna array in the desired direction, while the sidelobe suppression quantifies the power difference between the main lobe and the highest sidelobe.

[0099] This invention extracts the main lobe gain value of each frequency band signal from the optimized signal focusing effect data and calculates its potential energy contribution value by combining it with the signal power density. The product of the main lobe gain value and the signal power density is used as the potential energy contribution value for each frequency band. Then, the acquisition priority of each frequency band signal is determined by sorting the potential energy contribution values ​​from high to low, prioritizing the allocation of antenna elements and processing time to high-contribution frequency bands. Finally, the sidelobe interference processing results, optimized signal focusing effect data, and frequency band acquisition priority configuration are integrated to form a complete configuration file containing antenna weight parameters, frequency band allocation schemes, and expected efficiency improvement values, thus determining the final energy harvesting efficiency improvement scheme. The antenna weight parameters include the amplitude and phase settings of each antenna element in different frequency bands (determined by the sidelobe interference processing results, i.e., the adjusted reference values); the frequency band allocation scheme clarifies the priority order and resource occupancy ratio of each frequency band signal; and the expected efficiency improvement value is obtained through simulation or theoretical calculation, providing a reference for actual deployment. In a typical case, by implementing this optimization scheme, the energy harvesting efficiency was increased from the initial 40% to 65%, significantly improving the system's energy utilization and ensuring optimal energy harvesting performance in complex electromagnetic environments.

[0100] This invention, as a scheme to improve energy harvesting efficiency, accurately identifies target frequency bands with sidelobe interference by calculating the power ratio of the main lobe and sidelobes in the energy harvesting efficiency data. This helps to address interference issues in a targeted manner, avoids unnecessary adjustments to the entire antenna system, and improves system optimization efficiency. By employing the Chebyshev weighting method to adjust the reference values ​​of the target frequency band, the main lobe gain and sidelobe suppression of the radiation pattern can be effectively improved, thereby optimizing signal focusing, reducing sidelobe interference in other directions, and improving signal reception quality. Based on the optimized signal focusing data, the potential energy contribution value of each frequency band is calculated, and the acquisition priority is determined accordingly. This helps the system, under limited resources, prioritize the acquisition of frequency band signals with high energy contribution, improving the overall energy harvesting efficiency and meeting the system's needs for signals in different frequency bands.

[0101] This application proposes an adaptive radio frequency energy harvesting method to address the challenge of improving radio frequency energy harvesting efficiency when environmental signal distribution changes. The method involves real-time separation of independent frequency band components of the environmental radio frequency signal to generate a standardized spectrum; extraction of power density and main lobe direction for each frequency band; calculation of signal source deviation angle; dynamic adjustment of antenna array spacing and phase based on deviation angle and gain requirements; real-time evaluation of energy harvesting efficiency by scanning signal blind zones through omnidirectional / directional mode switching; identification of sidelobe interference frequency bands; adjustment of parameters to optimize signal focusing; and dynamic setting of harvesting priority. This effectively improves radio frequency energy harvesting efficiency in complex electromagnetic environments, enabling efficient harvesting and utilization of weak radio frequency signals across multiple frequency bands.

[0102] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0103] In another embodiment, such as Figure 2 As shown, a second aspect of the present invention provides an adaptive radio frequency energy harvesting system, comprising:

[0104] The signal spectrum generation module 10 is used to collect radio frequency signal data in the current environment in real time, obtain environmental feature data, and separate the independent components of each frequency band signal from the environmental feature data to generate a standardized frequency band signal distribution spectrum.

[0105] The deviation direction determination module 20 is used to determine the power density distribution and main lobe direction of each frequency band signal based on the standardized frequency band signal distribution map, and to determine the deviation direction of each frequency band signal from the main lobe direction according to the main lobe direction.

[0106] The antenna array configuration module 30 is used to determine reference values ​​for the spacing and phase adjustment of antenna elements based on the deviation from the main direction and the desired gain requirements, and to determine antenna array configuration data through the reference values ​​when it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold.

[0107] The collection efficiency evaluation module 40 is used to control the antenna array to scan the uncovered range of each frequency band signal through the antenna array configuration data, and dynamically switch between omnidirectional acquisition mode and directional acquisition mode according to the uncovered range, so as to evaluate the energy collection efficiency data of the current acquisition mode.

[0108] The priority determination module 50 is used to determine the target frequency band with sidelobe interference based on the energy harvesting efficiency data, adjust the reference value of the target frequency band, and determine the signal focusing effect through the adjusted reference value to determine the acquisition priority of each frequency band signal.

[0109] It should be noted that the modules in the aforementioned adaptive radio frequency energy harvesting system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independently of the processor, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module. For specific limitations regarding an adaptive radio frequency energy harvesting system, please refer to the limitations regarding an adaptive radio frequency energy harvesting method above; both have the same function and role, and will not be repeated here.

[0110] In summary, this invention relates to the field of information technology and discloses an adaptive radio frequency energy harvesting method and system. By acquiring radio frequency signals from the environment and separating the independent components of each frequency band signal, the power density distribution and main lobe direction of each frequency band signal are determined, thereby determining the deviation of each frequency band signal from the main lobe direction. Combined with the desired gain requirements of the current environment, reference values ​​for antenna element spacing and phase adjustment are determined, thereby determining antenna array configuration data and controlling the antenna array to scan the uncovered areas of each frequency band signal. Then, omnidirectional / directional harvesting modes are dynamically switched, and the energy harvesting efficiency data under the current harvesting mode is evaluated to determine the target frequency band with sidelobe interference. The reference values ​​are adjusted, and the signal focusing effect is determined using the adjusted reference values ​​to determine the harvesting priority of each frequency band signal. This effectively improves the radio frequency energy harvesting efficiency in complex electromagnetic environments and achieves efficient harvesting and utilization of weak radio frequency signals across multiple frequency bands.

[0111] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0112] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. An adaptive radio frequency energy harvesting method, characterized in that, include: Real-time acquisition of radio frequency signal data in the current environment to obtain environmental feature data, and separation of independent components of each frequency band signal from the environmental feature data to generate a standardized frequency band signal distribution map; Based on the standardized frequency band signal distribution map, the power density distribution and main lobe direction of each frequency band signal are determined, and the deviation from the main lobe direction of each frequency band signal is determined according to the main lobe direction. Reference values ​​for antenna element spacing and phase adjustment are determined based on the deviation from the main direction and the desired gain requirements. When it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold, antenna array configuration data is determined using the reference values. The antenna array configuration data is used to control the antenna array to scan the uncovered areas of each frequency band signal, and to dynamically switch between omnidirectional acquisition mode and directional acquisition mode according to the uncovered areas, so as to evaluate the energy harvesting efficiency data of the current acquisition mode; Based on the energy harvesting efficiency data, target frequency bands with sidelobe interference are determined, and reference values ​​for the target frequency bands are adjusted. The signal focusing effect is then determined using the adjusted reference values ​​to determine the acquisition priority of signals in each frequency band.

2. The adaptive radio frequency energy harvesting method according to claim 1, characterized in that, The real-time acquisition of radio frequency signal data in the current environment to obtain environmental characteristic data includes: The radio frequency signal data in the current environment is collected in real time by multiple antenna arrays, and the radio frequency signal data is processed by fast Fourier transform to divide the radio frequency signal data into multiple frequency band signals according to the frequency range. Calculate the power spectral density value of each frequency band signal, combine it with the phase difference between each antenna element in the antenna array, quantify the directional angle data of each frequency band signal, and determine the signal variation parameters of each frequency band signal based on the directional angle data. Based on the signal variation parameters, the K-means clustering algorithm is used to group the signals of each frequency band, and the Pearson correlation coefficient of each frequency band signal within the group is calculated as the time correlation coefficient. Based on the time correlation coefficient, the frequency of each frequency band signal occurring within a unit space is used as the spatial distribution density, which is then combined with each signal change parameter and each direction angle data to form environmental feature data describing the dynamic change law of radio frequency signal data in the current environment.

3. The adaptive radio frequency energy harvesting method according to claim 2, characterized in that, The step of separating the independent components of each frequency band signal from the environmental feature data and generating a standardized frequency band signal distribution map includes: The environmental feature data is subjected to noise characteristic identification and denoising to obtain denoised feature data. Filtering parameters are then set to perform bandpass filtering on the denoised feature data to obtain the effective components of the signals in each frequency band. The correlation between each effective component is calculated to construct a correlation matrix, and the correlation matrix is ​​decomposed into eigenvalues. The eigenvectors with eigenvalues ​​greater than a preset eigenvalue threshold are extracted from the decomposition results as principal components to obtain the independent components of each frequency band signal. Based on each independent component, the amplitude of each frequency band signal is normalized, and the normalization result is arranged according to the frequency of each frequency band signal to generate a standardized frequency band signal distribution map.

4. The adaptive radio frequency energy harvesting method according to claim 1, characterized in that, The step of determining the power density distribution and main lobe direction of each frequency band signal based on the standardized frequency band signal distribution map, and determining the deviation from the main lobe direction of each frequency band signal according to the main lobe direction, includes: Spatial filtering is performed on the standardized frequency band signal distribution map to perform beamforming analysis, and the weighting coefficients of each antenna element in the antenna array are determined based on the analysis results to calculate the array output power in each direction and obtain power distribution data. The power density data and main lobe direction of each frequency band signal are determined based on the power distribution data. Calculate the angle between each main lobe direction and the normal direction of the antenna array, and when the angle exceeds a preset deviation threshold, determine that the main lobe direction of the corresponding frequency band signal deviates from the main direction.

5. The adaptive radio frequency energy harvesting method according to claim 4, characterized in that, The determination of power density data and main lobe direction of each frequency band signal based on the power distribution data includes: Based on the power distribution data, the direction corresponding to the maximum power value in each frequency band signal is taken as the main lobe direction, and the angle position where the power drops to half of the maximum value is found from each main lobe direction to both sides, so as to determine the main lobe width of each frequency band signal. The sum of the power values ​​in all directions within the width of each main lobe is calculated, and then divided by the solid angle corresponding to each main lobe direction to obtain the power density data of each frequency band signal.

6. The adaptive radio frequency energy harvesting method according to claim 1, characterized in that, The process of determining reference values ​​for antenna element spacing and phase adjustment based on the deviation from the main direction and the desired gain requirements, and determining antenna array configuration data through the reference values ​​when it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold, includes: The deviation angle of each frequency band signal is determined based on the deviation from the main direction, and combined with the desired gain requirement, the array aperture size and phase compensation amount required for each frequency band signal to reach the desired gain requirement are calculated as reference values ​​for the antenna element spacing and phase adjustment. Based on the reference value, the synthesized radiation pattern of the adjusted array is calculated, and the actual gain of each deviation from the main direction is obtained from the synthesized radiation pattern to evaluate the power loss data of each frequency band signal; If the deviation angle of a frequency band signal exceeds a preset angle threshold and its power loss data exceeds a preset loss threshold, it is determined that the geometry of the antenna array needs to be adjusted, and the antenna array configuration data is determined through the reference value.

7. The adaptive radio frequency energy harvesting method according to claim 1, characterized in that, The step of controlling the antenna array to scan the uncovered areas of each frequency band signal through the antenna array configuration data, and dynamically switching between omnidirectional acquisition mode and directional acquisition mode according to the uncovered areas to evaluate the energy harvesting efficiency data in the current acquisition mode, includes: Based on the antenna array configuration data, the radiation pattern is adjusted by phase control commands to scan the intensity of each frequency band signal point by point, thereby obtaining the spatial distribution data of the multi-frequency band signal. Based on the spatial distribution data, spatial regions with signal strength lower than receiver sensitivity are identified as uncovered areas, and coverage integrity indices are calculated based on the uncovered areas. The coverage integrity index is used to adaptively switch between omnidirectional capture mode and directional capture mode as the current capture mode. Based on the current acquisition mode, the ratio of total received power to array aperture area is calculated to obtain energy harvesting efficiency data.

8. The adaptive radio frequency energy harvesting method according to claim 7, characterized in that, The step of adaptively switching between omnidirectional capture mode and directional capture mode as the current capture mode using the coverage integrity index includes: Based on the coverage integrity index and signal strength distribution, determine whether to switch to directional capture mode. If the coverage integrity index is lower than the preset integrity threshold or the signal strength is concentrated in the preset direction, switch to directional capture mode as the current capture mode; otherwise, maintain omnidirectional capture mode as the current capture mode and generate capture mode selection results.

9. The adaptive radio frequency energy harvesting method according to claim 1, characterized in that, The process of determining the target frequency band with sidelobe interference based on the energy harvesting efficiency data, adjusting the reference value of the target frequency band, and determining the signal focusing effect through the adjusted reference value to determine the acquisition priority of signals in each frequency band includes: Calculate the power ratio of the main lobe to the side lobe in the energy harvesting efficiency data. If the power ratio exceeds a preset ratio threshold, it is determined that there is side lobe interference, and the frequency band signal with side lobe interference is taken as the target frequency band. The reference values ​​of the target frequency band are adjusted using the Chebyshev weighting method, and the main lobe gain and side lobe suppression of the radiation pattern are calculated based on the adjusted reference values ​​to obtain optimized signal focusing effect data. Based on the signal focusing effect data, the potential energy contribution value of each frequency band signal is calculated to determine the acquisition priority of each frequency band signal by sorting.

10. An adaptive radio frequency energy harvesting system, characterized in that, include: The signal spectrum generation module is used to collect radio frequency signal data in the current environment in real time, obtain environmental feature data, and separate the independent components of each frequency band signal from the environmental feature data to generate a standardized frequency band signal distribution spectrum. The deviation direction determination module is used to determine the power density distribution and main lobe direction of each frequency band signal based on the standardized frequency band signal distribution map, and to determine the deviation direction of each frequency band signal from the main lobe direction according to the main lobe direction. The antenna array configuration module is used to determine reference values ​​for antenna element spacing and phase adjustment based on the deviation from the main direction and the desired gain requirements, and to determine antenna array configuration data through the reference values ​​when it is determined that the deviation of a frequency band signal from the main direction exceeds a preset angle threshold. The energy collection efficiency evaluation module is used to control the antenna array to scan the uncovered range of each frequency band signal through the antenna array configuration data, and dynamically switch between omnidirectional acquisition mode and directional acquisition mode according to the uncovered range, so as to evaluate the energy collection efficiency data of the current acquisition mode. The priority determination module is used to determine the target frequency band with sidelobe interference based on the energy harvesting efficiency data, adjust the reference value of the target frequency band, and determine the signal focusing effect through the adjusted reference value to determine the acquisition priority of each frequency band signal.

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