New ntn iot terminal resource management system based on 5g space-ground integration

Abstract

This invention relates to the field of resource management technology and discloses a novel NTN IoT terminal resource management system based on 5G satellite-ground fusion. The system includes: constructing uplink continuous energy demand using a cascaded orbital mechanics algorithm; extracting microscopic anomalous ion dissipation within a carbon electrode micro-energy storage device; fusing the energy demand and ion dissipation to generate a resource metric matrix with isomorphic dissipation attributes; driving the resource metric matrix to perform time-frequency resource optimization to obtain the optimal scheduling boundary value; subsequently acquiring curvature parameters to perform continuous topology power injection on the transmit power, generating a precoding matrix in the algebraic transformation space and applying it to the antenna array to correct phase distortion; and finally calculating the flux integral value and updating the basic dissipation. This invention effectively avoids mapping link breakage caused by local congestion and improves the connectivity stability of the satellite-ground link under extreme transmission conditions.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, and more specifically, to a novel NTN IoT terminal resource management system based on 5G satellite-ground convergence. Background Technology

[0002] With the development of 5G mobile communication technology, satellite-terrestrial converged non-terrestrial networks play a crucial role in scenarios such as ocean current monitoring and IoT in remote areas. In non-terrestrial network IoT communication scenarios, the visible window for low-orbit satellite transits is often extremely short, and the satellite-terrestrial link suffers from extremely high free-space path loss, requiring IoT terminals to report massive amounts of status data in bursts of high power within a limited time. To meet the stringent requirements of these instantaneous high-current bursts, new micro-terminals heavily rely on built-in high-rate carbon electrode micro-energy storage devices for power supply. Existing resource management protocols mainly tend to allocate continuous two-dimensional rectangular time-frequency grid resources to terminals in order to maximize transmission efficiency. However, in actual operation, when radio frequency devices face high-current pumping of long-period continuous time-domain resources, the micro-energy storage devices are limited by their intricate nanopore structure. Electrolyte ions are prone to severe steric hindrance congestion in the narrow intersecting channels, leading to a sharp increase in transmission resistance. This microscopic ion dissipation causes the discharge process of the energy storage device to deviate from a linear and stable evolution path, with internal resistance surging in milliseconds and causing severe voltage dips. The existing smooth continuous resource allocation mechanism based on pure digital logic fails to take into account the congestion limitations of terminal underlying devices at the nanoscale. As a result, when the terminal consumes continuous resources in the latter half, it often drops the network directly due to the nonlinear collapse of power output. The time slot originally allocated by the base station becomes a dead zone with no signal transmission, which in turn causes a serious problem of broken resource authorization mapping. Summary of the Invention

[0003] This invention provides a novel NTN IoT terminal resource management system based on 5G satellite-ground convergence, which solves the technical problems mentioned in the background art.

[0004] This invention provides a novel NTN IoT terminal resource management system based on 5G space-ground convergence, applicable to terminals including power amplifiers, temperature-compensated crystal oscillators, carbon electrode micro-energy storage devices, and multi-antenna arrays, configured to execute: Cascaded orbital mechanics algorithms are used to construct uplink continuous energy requirements to characterize macroscopic space loss; Extract the microscopic anomalous ion dissipation within the carbon electrode micro energy storage device to characterize the physical channel blockage state. The uplink continuous energy demand is fused with the microscopic anomalous ion dissipation to generate a resource metric matrix with isomorphic dissipation characteristics, so as to map the cross-domain coupling relationship between the orbital communication demand and the underlying microscopic steric hindrance. Based on the principle of least action, the resource metric matrix is ​​driven to perform time-frequency resource optimization, and the optimal scheduling boundary value for avoiding nonlinear energy depletion is obtained. Obtain the curvature feature value of the resource metric array, perform continuous topology power watering on the initial transmit power of the multi-antenna array, and obtain the accurate transmit power value of dynamically adapting the physical channel transmission capability. A spatial precoding matrix matching the precise transmit power value is generated in the algebraic transformation space and applied to the multi-antenna array to correct the radio frequency phase distortion of the power amplifier caused by power abrupt changes. The flux integral value of outward radiation is calculated and subtracted from the current microscopic anomalous ion dissipation along the reverse path to update the next cycle's basic dissipation, which includes topological memory capabilities.

[0005] The beneficial effects of this invention are as follows: By integrating the macroscopic requirements of satellite orbit operation with the microscopic ion dissipation law of terminal-level energy storage devices, a resource metric array with dissipation properties is constructed and irregular time-frequency scheduling boundary optimization is performed. This effectively avoids the transient nonlinear power depletion and radio frequency network drop risks caused by congestion of small confined channels in the traditional fixed rectangular grid allocation mode. At the same time, by combining topology power water injection and spatial precoding matrix dynamic correction mechanism, the dynamic transmission evolution trajectory of ions in confined space is adapted, improving the communication resource utilization and beam pointing accuracy during the reporting of sudden large-capacity data between satellite and ground, and ensuring the self-consistent closed loop of satellite-ground authorized mapping link. Attached Figure Description

[0006] Figure 1 This is a flowchart of the new NTN IoT terminal resource management system based on 5G satellite-ground convergence according to the present invention. Detailed Implementation

[0007] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0008] refer to Figure 1 , Figure 1The process flowcharts in the flowcharts correspond to the following steps in sequence: constructing the uplink continuous energy demand to characterize macroscopic spatial loss, extracting the microscopic anomalous ion dissipation to characterize the physical channel congestion state, generating a resource metric matrix with isomorphic dissipation characteristics, deriving the optimal scheduling boundary value to avoid nonlinear energy depletion, deriving the accurate transmit power value to dynamically adapt to the transmission capacity of the physical channel, generating the spatial precoding matrix, and updating the next cycle basic dissipation including topological memory capabilities.

[0009] like Figure 1 As shown, the novel NTN IoT terminal resource management system based on 5G space-ground convergence is applied to a terminal including a power amplifier, a temperature-compensated crystal oscillator, a carbon electrode micro-energy storage device, and a multi-antenna array. The power amplifier is used to perform uplink RF transmission according to a precise transmit power value; the temperature-compensated crystal oscillator is used to provide a stable clock for calculations in the time and frequency domains; the carbon electrode micro-energy storage device is used to provide burst energy to the power amplifier and the multi-antenna array; and the multi-antenna array is used to form an uplink beam toward the low-Earth orbit satellite according to the spatial precoding matrix.

[0010] Figure 1 The process shown is executed periodically by the processor in the terminal. The processor obtains the time reference from the temperature-compensated crystal oscillator, the orbital distance of the low-Earth orbit satellite transient transit from the low-Earth orbit satellite ephemeris and terminal position information, the voltage fluctuation from the carbon electrode micro-energy storage device, the initial transmit power and the inherent nonlinear distortion mapping of the power amplifier from the power amplifier, the initial coding array from the multi-antenna array, and outputs the optimal scheduling boundary value, the accurate transmit power value, the spatial precoding matrix, and the basic dissipation for the next cycle in each scheduling cycle.

[0011] In this implementation, all continuous calculations can be achieved through discrete sampling. Calculations in the time and frequency domains are first mapped to a normalized coordinate system to avoid ambiguity in the same formula when the physical sampling step size changes. The normalized coordinate system is defined as follows: ; in, For the current physical time, The current physical frequency, This is the start time of the scheduling cycle. This represents the starting frequency of the frequency domain integration interval. As the normalized benchmark corresponding to the scheduling period, This serves as the normalized reference for the frequency domain integration interval. and These are the normalized coordinates in the time and frequency domains, respectively. Subsequent processes involving double integrals, global curvature integral aggregation, and surface closure integrals in the time and frequency domains will all be performed in the above-mentioned normalized coordinate system.

[0012] like Figure 1As shown, the processor first acquires the received power sensed by the base station, the orbital distance of the transient low-Earth orbit satellite, the satellite gain, the terminal gain, the tropospheric loss, the ionospheric loss, and the frequency domain reference quantity. The received power sensed by the base station is determined by the base station's reception conditions of the terminal's uplink signal. The orbital distance of the transient low-Earth orbit satellite is calculated from the transient position of the low-Earth orbit satellite and the terminal's position. The satellite gain is the gain of the low-Earth orbit satellite antenna in the direction of the terminal, and the terminal gain is the gain of the multi-antenna array in the direction of the low-Earth orbit satellite. The tropospheric loss and ionospheric loss characterize the additional losses when the uplink signal crosses the troposphere and ionosphere, respectively. The frequency domain reference quantity is used to map frequency domain resources into an energy calculation reference.

[0013] The cascaded orbital mechanics algorithm includes the sequential execution of: determining the transient position of the low-Earth orbit (LEO) satellite, determining the terminal position, determining the orbital distance for the LEO satellite's transient transit, determining the satellite gain, and compensating for atmospheric transmission distortion. The processor obtains the LEO satellite's transient position based on its ephemeris, obtains the terminal position based on the terminal positioning results, and then calculates the orbital distance for the LEO satellite's transient transit. ; in, This indicates the orbital distance of a low-Earth orbit satellite transiently passing overhead. This represents the position vector of a low-Earth orbit satellite in normalized time coordinates. This represents the terminal's position vector in the normalized time coordinate system. This represents the L2 norm.

[0014] The processor combines the received power sensed by the base station, the orbital distance of the transient transit of low-Earth orbit satellites, satellite gain, and terminal gain to remove free-space propagation attenuation interference and extract the basic link loss parameters. ; in, This represents the basic link loss parameter. This indicates the received power sensed by the base station. Indicates the carrier frequency. Indicates the speed of electromagnetic wave propagation. Indicates satellite gain. Indicates terminal gain.

[0015] The processor uses tropospheric and ionospheric losses to compensate for atmospheric transmission distortion in the basic link loss parameters, thus deriving the space barrier attenuation compensation amount: ; in, This indicates the amount of space barrier attenuation compensation. Indicates tropospheric loss. This indicates ionospheric loss.

[0016] The processor maps the spatial barrier attenuation compensation to the frequency domain reference quantity and performs double integration in both the time and frequency domains. First, the local energy demand values ​​corresponding to the uplink continuous energy demand in each time-frequency coordinate system are defined: ; in, This represents the local energy demand value corresponding to the uplink continuous energy demand in each time-frequency coordinate system. This represents the frequency domain reference value. The processor then... By performing a double integral, we can construct the uplink continuous energy requirement to overcome the interstellar barrier: ; in, This indicates the continuous energy demand for upward movement. This represents the normalized time integral interval in the time and frequency domains. This represents the normalized frequency integral interval in both the time and frequency domains. Represents the normalized time integral primitive. This represents the normalized frequency integral primitive. During discrete execution, the processor operates according to the time base and frequency domain resource granularity provided by the temperature-compensated crystal oscillator. Summing is performed to obtain the uplink continuous energy requirement consistent with the continuous double integral.

[0017] like Figure 1 As shown, the processor acquires the voltage fluctuation within the carbon electrode micro-energy storage device, the pore dimension of the confined channels inside the carbon electrode micro-energy storage device, and the gamma function value characterizing the integral continuity. The voltage fluctuation is obtained by sampling from the output terminal of the carbon electrode micro-energy storage device, the pore dimension of the confined channels inside the carbon electrode micro-energy storage device is obtained by calibrating the pore structure of the carbon electrode micro-energy storage device, and the gamma function value characterizing the integral continuity is calculated from the pore dimension value.

[0018] The processor extracts the rate of change of voltage fluctuations over time to characterize the transient fluctuations in the terminal's internal power supply, deriving the voltage derivative value. For discrete sampling points, the voltage derivative value is obtained using central difference. ; in, Indicates at the sampling point The voltage derivative value, and This indicates the voltage fluctuation at adjacent sampling points. The sampling interval represents the voltage fluctuation. For the first and last points of the sampling sequence, the processor uses forward differencing and backward differencing to obtain the voltage derivative values, respectively.

[0019] The processor constructs a fractional-order decay kernel function with historical memory hysteresis based on the pore dimension value of the confined channels inside the carbon electrode micro-energy storage device, in order to quantify the physical congestion effect of the fractal topology network on ions: ; in, This represents a fractional decay kernel function with historical memory lag. This represents the pore dimension value of the confined channels inside a carbon electrode micro-energy storage device. Indicates the current time. This represents the historical time integration variable.

[0020] The processor uses a fractional decay kernel function with historical memory hysteresis and the voltage derivative value to perform infinite historical time integration, and combines the gamma function value characterizing the continuity of the integration for continuity correction to extract the microscopic anomalous ion dissipation: ; in, This indicates the current microscopic anomalous ion dissipation. Indicates the first The base dissipation at the start of each scheduling cycle, This represents the gamma function value that characterizes the continuity of the integral. This represents the continuous correction constant obtained from the calibration of the carbon electrode micro-energy storage device. Representing historical time integral variables The voltage derivative value at that point.

[0021] In the first scheduling cycle Set it to zero; in subsequent scheduling cycles, The basic dissipation for the next cycle, including topological memory capability, is derived from the update of the previous scheduling cycle. Infinite history time integration is implemented using an effective history window during discrete execution. The processor determines the effective history window based on the decay result of a fractional decay kernel function with historical memory lag, ensuring that historical terms outside the effective history window do not participate in the current summation. ; in, Indicates sampling point Microscopic anomalous ion dissipation at the location, This indicates the number of historical sampling points within the valid historical window. The first is the number of kernels obtained by discretizing a fractional decay kernel function with historical memory lag. Each historical weight, Indicates the first The voltage derivative values ​​at each historical sampling point are processed to output a value that simultaneously reflects the current voltage fluctuation and the historical residual ion state.

[0022] like Figure 1 As shown, the processor acquires the flat substrate value, the scaling constant for cross-domain coupling, and the mapping matrix characterizing the internal fractal topology of the carbon electrode micro-energy storage device. The flat substrate value is used to describe the time and frequency domain infrastructure without the influence of microscopic anomalous ion dissipation. The scaling constant for cross-domain coupling is used to describe the coupling strength between the uplink continuous energy demand and the microscopic anomalous ion dissipation. The mapping matrix characterizing the internal fractal topology of the carbon electrode micro-energy storage device is used to map the congestion direction of the confined channels inside the carbon electrode micro-energy storage device to the time and frequency domain. ; in, Indicates the flat base value. Represents the Kronecker function. and Coordinate indices of a resource metric matrix representing isomorphic dissipation characteristics. and The values ​​correspond to the normalized time coordinate and the normalized frequency coordinate: ; in, The mapping matrix represents the internal fractal topology of the carbon electrode micro-energy storage device. The fractal topological mapping coefficients represent the normalized time coordinate direction. The fractal topological mapping coefficients represent the directions of the normalized frequency coordinates.

[0023] The processor performs time-cumulative integration on the microscopic anomalous ion dissipation and implements a spatial folding decay map to remove the ion steric hindrance effect that intensifies nonlinearly over time, deriving the spatial decay term: ; in, Represents the spatial decay term. This represents the first normalized reference quantity obtained from the calibration of the carbon electrode micro-energy storage device. This represents the second normalized reference quantity obtained from the ion memory time calibration of the carbon electrode micro-energy storage device. This represents the normalized starting coordinates of the current scheduling cycle. This represents the normalized time-cumulative variable. In this embodiment, the spatial folding decay mapping is accomplished jointly by the spatial decay term and the mapping matrix characterizing the internal fractal topology of the carbon electrode micro-energy storage device.

[0024] The processor integrates the cross-domain coupling scaling constant, uplink continuous energy demand, spatial decay term, and mapping matrix characterizing the internal fractal topology of the carbon electrode micro-energy storage device to construct a cross-domain additional matrix. To maintain local computability in the time and frequency domains, the cross-domain additional matrix uses the local energy demand values ​​corresponding to the uplink continuous energy demand in each time-frequency coordinate system for calculation: ; in, Represents a cross-domain appended matrix. This represents the proportionality constant for cross-domain coupling. This represents the energy normalization reference value obtained from the terminal calibration. This represents the local energy demand value corresponding to the uplink continuous energy demand in each time-frequency coordinate system. Represents the spatial decay term. The mapping matrix represents the internal fractal topology of the carbon electrode micro-energy storage device.

[0025] The processor superimposes the flat basis values ​​with the cross-domain additional matrix to reconstruct a resource metric matrix that eliminates errors in the smooth geometric assumption, thus reconstructing the isomorphic dissipative features. ; in, A resource metric matrix representing isomorphic dissipation characteristics. Indicates the flat base value. This represents the cross-domain additional matrix. Through the above processing, the output simultaneously carries macroscopic spatial loss and underlying microscopic steric hindrance information.

[0026] like Figure 1 As shown, the processor obtains the spatial geometric signal-to-interference-plus-noise ratio (SIR) and the curvature penalty term around the candidate boundary values. The spatial geometric SIR is used to describe the relationship between signal power, interference power, and noise power within the candidate boundary values. ; in, Indicates the spatial geometric signal-to-interference-plus-noise ratio. Indicates signal power. Indicates interference power. Indicates noise power.

[0027] The processor derives Gaussian curvature from a resource metric matrix of isomorphic dissipative features, and constructs a curvature penalty term around the candidate boundary values ​​using Gaussian curvature: ; in, The Gaussian curvature is represented by the resource metric matrix with isomorphic dissipative characteristics. The curvature components are represented by the first and second partial derivatives of the resource metric matrix with isomorphic dissipative characteristics. The determinant of the resource metric matrix representing the isomorphic dissipation characteristics. During discrete execution, the first and second partial derivatives are obtained by differencing the resource metric matrix of the isomorphic dissipation characteristics on adjacent normalized time and normalized frequency coordinates: ; in, This represents the curvature penalty term outside the candidate boundary value. This term only penalizes regions with negative Gaussian curvature and is used to quantify resource orthogonality consumption loss.

[0028] The processor calculates the logarithmic term of the theoretical information capacity based on the signal-to-interference-plus-noise ratio (SINR), and combines this with the curved spatiotemporal geometric volume element of the resource metric matrix with isomorphic dissipation characteristics to solve the channel capacity gain integral within the candidate boundary values: ; in, Represents the volume element of curved spacetime geometry. The volume correction factor is determined by the resource metric matrix with isomorphic dissipative characteristics. ; in, Indicates candidate boundary values Integral channel capacity gain within, This represents the normalized time-frequency region enclosed by the candidate boundary values. This represents the logarithmic term of the theoretical information capacity.

[0029] The processor performs a closed integral along the outer boundary of the candidate boundary values ​​on the curvature penalty term to obtain the curvature penalty value: ; in, This represents the curvature penalty value. This represents the curvature penalty weight obtained from the scheduling performance calibration. Indicates the outer boundary of the candidate boundary values. This represents a normalized closed integral line element.

[0030] The processor uses a resource metric matrix with isomorphic dissipative characteristics, driven by the principle of least action, to optimize time-frequency resources. To ensure consistency between the principle of least action and the channel capacity gain integral and curvature penalty, the action is the result of subtracting the channel capacity gain integral from the curvature penalty. The candidate boundary value that minimizes the action is the optimal scheduling boundary value. ; ; ; in, This represents the action quantity corresponding to the variational objective function. Indicates the total resource range allowed by the base station. This represents the normalized time-frequency region enclosed by the optimal scheduling boundary values. This represents the optimal scheduling boundary value used to avoid nonlinear energy exhaustion. During discrete execution, the processor uses the initial resource allocation of the base station to form initial candidate boundary values, and successively moves the boundary points on the candidate boundary values ​​until... It will no longer decrease or reach the preset number of iterations.

[0031] like Figure 1 As shown, the processor obtains the upper limit of available energy within the scheduling cycle and the curvature eigenvalue derived from the resource metric matrix of isomorphic dissipation characteristics: ; in, Indicates the first The upper limit of available energy within a scheduling cycle. This indicates the energy release efficiency of a carbon electrode micro-energy storage device. This represents the equivalent energy storage parameters of a carbon electrode micro-energy storage device. Indicates the first The voltage fluctuation of the carbon electrode micro-energy storage device at the start of each scheduling cycle corresponds to the current voltage value. Indicates the minimum operating voltage allowed by the terminal; ; in, The curvature eigenvalues ​​are derived from the resource metric matrix of isomorphic dissipative characteristics. The Gaussian curvature is represented by the resource metric matrix with isomorphic dissipative characteristics.

[0032] The processor extracts the inverse norm of the curvature feature value at each point in spacetime to inversely calibrate the pore pore patency at different spacetime locations: ; in, The inverse norm is a characteristic measure. This represents the regularization constant used to prevent the denominator from being zero.

[0033] The processor aggregates the inverse norm representation in both time and frequency dimensions using global curvature integrals, and uses this as a baseline constraint to normalize and allocate the upper limit of available energy, thus deriving a baseline power value: ; ; in, This represents the aggregated result of the global curvature integral. Indicates the first The baseline power value for each scheduling cycle. Indicates the first The duration of each scheduling cycle.

[0034] The processor continuously and dynamically adjusts the baseline power value using the inverse norm, a characteristic mapped to each time-frequency coordinate system within the optimal scheduling boundary value, and performs power injection into the continuous topology in conjunction with the initial transmit power to obtain the accurate transmit power value. ; ; in, This represents the water injection power value obtained by power injection according to a continuous topology. This represents a precise transmit power value that dynamically adapts to the transmission capability of the physical aperture. This indicates the initial transmit power of the multi-antenna array. This represents the power water injection step factor for a continuous topology. Processor selection. This ensures that the precise transmit power value varies continuously within the optimal scheduling boundary value, and that the total energy corresponding to the precise transmit power value does not exceed the upper limit of available energy. If the precise transmit power value exceeds the allowable saturation value of the power amplifier, the processor will redistribute the excess portion according to the inverse norm representation in the unsaturated time-frequency coordinate system.

[0035] like Figure 1 As shown, the processor acquires the initial coding array of the multi-antenna array, the three-dimensional rotation generator, and the inherent nonlinear distortion mapping of the power amplifier. The initial coding array is used to control the transmission direction of the multi-antenna array without RF phase distortion correction. The three-dimensional rotation generator is used to represent the dynamic rotational distortion characteristics induced by power abrupt changes in the algebraic transformation space. The inherent nonlinear distortion mapping of the power amplifier is used to describe the relationship between the output phase of the power amplifier and the precise transmit power value.

[0036] In this embodiment, the algebraic transformation space is a phase distortion compensation calculation space spanned by three-dimensional rotation generators. The algebraic transformation space does not change the number of elements in the multi-antenna array; instead, it first calculates the cumulative radio frequency phase distortion in the three-dimensional direction, and then projects the cumulative radio frequency phase distortion onto each element according to the element positions of the multi-antenna array, thereby generating a spatial precoding matrix. ; ; in, In a multi-antenna array, the first... The guiding component of each array element, Represents the imaginary unit. Indicates the carrier wavelength. Indicates the first The position vectors of each array element This represents a unit vector representing the satellite orientation determined by the orbital distance of a transient low-Earth orbit satellite. and Indicates the satellite orientation angle parameter. Indicates by all The guiding vector formed express The conjugate transpose of . Indicates the initial coding matrix; ; in, , and This represents a three-dimensional rotation generator, corresponding to infinitesimal rotations in three orthogonal directions.

[0037] The processor extracts the temporal gradient vector of the precise transmit power value and fuses it with a three-dimensional rotation generator to capture the dynamic rotational distortion features excited in space by high-frequency power envelope fluctuations. ; in, This indicates dynamic rotational distortion characteristics. The time gradient vector representing the precise transmit power value. This represents the characteristic power gradient obtained from the power amplifier calibration. , and This represents the weights of the three directions determined by the unit vector of the low-orbit satellite direction.

[0038] The processor combines the inherent nonlinear distortion mapping of the power amplifier to perform time accumulation of the dynamic rotational distortion characteristics, quantifying the RF phase accumulation distortion phase caused by severe fluctuations in physical dissipation: ; in, Indicates the cumulative distortion phase of the radio frequency phase. This represents the value of the inherent nonlinear distortion mapping of the power amplifier at the precise transmit power value. This represents the normalized time-accumulated variable.

[0039] The processor projects the accumulated RF phase distortion onto each element of the multi-antenna array to obtain the inverse phase compensation phase for each element: ; in, Indicates the first The phase compensation of each array element in anti-phase, , and This represents the coefficients of the radio frequency phase cumulative distortion phase in the three three-dimensional rotating generator directions. , and Indicates the first The projection coefficients are corresponding to each array element. The projection coefficients are obtained by calibrating the array element positions and power amplifier phase response of the multi-antenna array.

[0040] The processor constructs an inverse rotation operator that is strictly out of phase with the RF phase accumulation distortion phase and applies it to the initial coding array to generate a spatial precoding matrix that generates a deadlock satellite trajectory to eliminate RF beam deviation errors. Here, the deadlock satellite trajectory refers to the compensated state in which the spatial precoding matrix locks the transmission direction of the multi-antenna array to the predicted trajectory of the low-Earth orbit satellite within the current scheduling period. ; ; in, Indicates the reverse rotation operator. This indicates the number of elements in a multi-antenna array. Represents the spatial precoding matrix. This represents the initial coding matrix. The processor applies the spatial precoding matrix to the multi-antenna array to correct the radio frequency phase distortion of the power amplifier caused by power abrupt changes.

[0041] like Figure 1 As shown, the processor obtains the spatially defined topology backoff operator and the outward normal vector of the optimal scheduling boundary value. The spatially defined topology backoff operator is used to subtract the flux integral value radiated outward along the reverse path, and the outward normal vector of the optimal scheduling boundary value is used to determine the physical flux radiation path in the outward normal dimension of the scheduling surface boundary.

[0042] For the boundary parameters on the optimal scheduling boundary value The processor obtains the outward normal vector of the optimal scheduling boundary value based on the boundary tangential change: ; in, The outer normal vector representing the optimal scheduling boundary value. Normalized boundary parameters representing the optimal scheduling boundary values. and These represent the rates of change of the normalized time coordinate and the normalized frequency coordinate along the boundary parameters, respectively. The outward normal direction is chosen to point to the outside of the region enclosed by the optimal scheduling boundary values.

[0043] The processor extracts the directional projection matrix by fusing the resource metric matrix with isomorphic dissipation characteristics and the outward normal vector of the optimal scheduling boundary value, in order to calibrate the physical flux radiation path in the outward normal dimension of the scheduling surface boundary: ; in, Represents the directional projection matrix. Represents boundary parameters Resource metric matrix of isomorphic dissipative characteristics at corresponding locations. This represents the transpose of the outward normal vector of the optimal scheduling boundary value. This formula constructs a matrix using the outward normal vector and its transpose, avoiding the misinterpretation of the orientation projection matrix as a vector.

[0044] The processor combines the precise transmit power value with the directional projection matrix to calculate the geometric energy flux density of the surface, and performs a closed integral on the surface to obtain the flux integral value of the outward radiation remaining at the physical interface: ; ; in, This represents the geometric energy flux density of the curved surface. The Frobenius norm of the directional projection matrix is ​​represented. Indicates the first The integral value of outward radiation flux for each scheduling cycle. This represents the flux calibration coefficient obtained from the discharge recovery experiment of the carbon electrode micro-energy storage device. Indicates the optimal scheduling boundary value. This represents a normalized closed integral line element. The flux calibration coefficient is used to ensure that the integral value of the outward radiation flux can be calculated by subtracting the current microscopic anomalous ion dissipation.

[0045] The processor uses a spatially defined topological backoff operator to directionally strip out the flux integral value of outward radiation from the current microscopic anomalous ion dissipation, precisely continuing the residual ion state that was not fully reset, and transforming it into a gravitational benchmark for the next resource scheduling to derive the basic dissipation for the next cycle: ; ; in, The topological backoff operator represents the spatial definition. This represents the input value corresponding to the current microscopic anomalous ion dissipation. The input value represents the integral value of the outward radiated flux. Indicates the first The basic dissipation per scheduling cycle Indicates the first The current microscopic anomalous ion dissipation at the end of each scheduling cycle. The base dissipation for the next cycle, incorporating topological memory capabilities, is used as the basis for the next scheduling cycle. The calculation of microscopic anomalous ion dissipation is then performed, allowing the residual ion state to participate in the next resource scheduling.

[0046] like Figure 1 As shown, the new NTN IoT terminal resource management system based on 5G space-ground convergence operates in a closed-loop sequence within each scheduling cycle. The processor first constructs the uplink continuous energy demand to characterize macroscopic spatial loss using a cascaded orbital mechanics algorithm. Then, based on the voltage fluctuation within the carbon electrode micro-energy storage device, the pore dimension value of the confined pores inside the carbon electrode micro-energy storage device, and the gamma function value characterizing integral continuity, it extracts the microscopic anomalous ion dissipation to characterize the physical pore congestion state.

[0047] Subsequently, the processor merges the uplink continuous energy demand with the microscopic anomalous ion dissipation to generate a resource metric matrix with isomorphic dissipation characteristics, so as to map the cross-domain coupling relationship between the satellite communication demand and the underlying microscopic steric hindrance; then, based on the principle of minimum action, the resource metric matrix with isomorphic dissipation characteristics is driven to perform time-frequency resource optimization, and the optimal scheduling boundary value is obtained to avoid energy nonlinear exhaustion.

[0048] After obtaining the optimal scheduling boundary value, the processor acquires the curvature feature value of the resource metric matrix with isomorphic dissipation characteristics, performs continuous topology power watering on the initial transmit power of the multi-antenna array, and obtains the accurate transmit power value that dynamically adapts to the transmission capability of the physical channel; then, it generates a spatial precoding matrix that matches the accurate transmit power value in the algebraic transformation space and applies it to the multi-antenna array to correct the radio frequency phase distortion of the power amplifier caused by power mutation.

[0049] Finally, the processor calculates the integral value of the outward radiation flux and subtracts it from the current microscopic anomalous ion dissipation along the reverse path, updating the next-cycle base dissipation to include topological memory capabilities. This next-cycle base dissipation is then incorporated into the calculation of the microscopic anomalous ion dissipation for the next scheduling cycle. Figure 1 The calculation of macroscopic spatial loss, physical channel congestion, time-frequency resource optimization, accurate transmission power value calculation, spatial precoding matrix correction, and basic dissipation update form a closed loop.

[0050] In an optional implementation, when any of the following inputs is not updated: the received power sensed by the base station, the orbital distance of the low-orbit satellite transiently passing over, the satellite gain, the terminal gain, the tropospheric loss, the ionospheric loss, the frequency domain reference quantity, the voltage fluctuation quantity, the pore dimension value, the gamma function value, the flat substrate value, the proportional constant of cross-domain coupling, the mapping matrix characterizing the internal fractal topology of the carbon electrode micro-energy storage device, the spatial geometric signal-to-interference-plus-noise ratio, the curvature penalty term of the candidate boundary value periphery, the upper limit of available energy, the initial coding matrix, the three-dimensional rotation generator, the nonlinear distortion mapping quantity inherent in the power amplifier, the spatially defined topology backoff operator, and the outward normal vector of the optimal scheduling boundary value, the processor uses the previous valid input value to execute the current scheduling cycle; when the previous valid input value exceeds the preset validity period, the processor stops updating the current scheduling cycle and maintains the spatial precoding matrix and the accurate transmit power value of the previous scheduling cycle until the input is recovered.

[0051] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A new type of NTN Internet of Things terminal resource management system based on 5G space-ground integration, applied to a terminal containing a power amplifier, a temperature compensation crystal oscillator, a carbon electrode micro energy storage device and a multi-antenna array, characterized in that, Configured for execution: Cascaded orbital mechanics algorithms are used to construct uplink continuous energy requirements to characterize macroscopic space loss; Extract the microscopic anomalous ion dissipation within the carbon electrode micro energy storage device to characterize the physical channel blockage state. The uplink continuous energy demand is fused with the microscopic anomalous ion dissipation to generate a resource metric matrix with isomorphic dissipation characteristics, so as to map the cross-domain coupling relationship between the orbital communication demand and the underlying microscopic steric hindrance. Based on the principle of least action, the resource metric matrix is ​​driven to perform time-frequency resource optimization, and the optimal scheduling boundary value for avoiding nonlinear energy depletion is obtained. Obtain the curvature feature value of the resource metric array, perform continuous topology power watering on the initial transmit power of the multi-antenna array, and obtain the accurate transmit power value of dynamically adapting the physical channel transmission capability. A spatial precoding matrix matching the precise transmit power value is generated in the algebraic transformation space and applied to the multi-antenna array to correct the radio frequency phase distortion of the power amplifier caused by power abrupt changes. The flux integral value of outward radiation is calculated and subtracted from the current microscopic anomalous ion dissipation along the reverse path to update the next cycle's basic dissipation, which includes topological memory capabilities.

2. The new NTN Internet of Things terminal resource management system based on 5G space-ground integration of claim 1, characterized in that, The construction of the uplink continuous energy demand used to characterize macroscopic spatial losses includes: Acquire the received power sensed by the base station, the orbital distance of low-orbit satellite transient transits, satellite gain, terminal gain, tropospheric loss, ionospheric loss, and frequency domain reference quantities; By combining the received power, the orbital distance, the satellite gain, and the terminal gain, free-space propagation attenuation interference is removed, and the basic link loss parameter is extracted. The atmospheric transmission distortion compensation amount of the basic link loss parameter is obtained by using the tropospheric loss and the ionospheric loss to compensate for the space barrier attenuation. The space barrier attenuation compensation amount is mapped to the frequency domain reference quantity and double integrated in the time and frequency domains to construct the uplink continuous energy requirement to overcome the interstellar space barrier.

3. The new NTN Internet of Things terminal resource management system based on 5G space-ground integration of claim 2, characterized in that, The extraction of microscopic anomalous ion dissipation used to characterize the physical pore blockage state includes: The voltage fluctuation within the carbon electrode micro-energy storage device, the pore dimension of the confined pores inside the carbon electrode micro-energy storage device, and the gamma function value characterizing the integral continuity are obtained. The rate of change of the voltage fluctuation is extracted in the time dimension to characterize the transient fluctuation characteristics of the power supply inside the terminal, and the voltage derivative value is obtained. A fractional decay kernel function with historical memory lag is constructed based on the pore dimension value to quantify the physical congestion effect of fractal topology network on ions; By integrating the decay kernel function and the voltage derivative over infinite historical time, and combining this with the gamma function value to eliminate continuity dimension deviation, the microscopic anomalous ion dissipation that reveals the true steric hindrance of ions with historical memory is extracted.

4. The novel NTN IoT terminal resource management system based on 5G satellite-ground convergence according to claim 3, characterized in that, The resource metric matrix that generates isomorphic dissipative features includes: Obtain the flat substrate value, the proportionality constant of cross-domain coupling, and the mapping matrix characterizing the internal fractal topology of the carbon electrode micro-energy storage device; The microscopic anomalous ion dissipation is integrated over time and subjected to spatial folding decay mapping to remove the ion spatial steric hindrance effect that intensifies nonlinearly with time, thus obtaining the spatial decay term. By integrating the proportionality constant, the uplink continuous energy demand, the spatial attenuation term, and the mapping matrix, a cross-domain additional matrix is ​​constructed to quantify the degree of distortion induced by macroscopic communication energy demand in microscopic physical channels. The flat basis values ​​are superimposed with the cross-domain additional matrix to reconstruct a resource metric matrix of isomorphic dissipative features that eliminates errors in the smooth geometric assumption.

5. The novel NTN IoT terminal resource management system based on 5G satellite-ground convergence according to claim 4, characterized in that, The process of deriving the optimal scheduling boundary value for avoiding nonlinear energy depletion includes: Obtain the spatial geometric signal-to-interference-plus-noise ratio and the curvature penalty term around the candidate boundary value; Based on the signal-to-interference-plus-noise ratio, the theoretical information capacity logarithmic term is calculated, and combined with the curved spatiotemporal geometric volume element of the resource metric matrix, the channel capacity gain integral within the candidate boundary value is solved. The curvature penalty term is integrated along the outer boundary of the candidate boundary value to quantify the resource orthogonality swallowing loss caused by physical dissipation Gaussian negative curvature, and the curvature penalty value is obtained. A variational objective function is constructed by subtracting the curvature penalty value from the channel capacity gain integral, and the optimal scheduling boundary value that automatically avoids the deep valley of Gaussian curvature is obtained through functional extremum optimization.

6. The novel NTN IoT terminal resource management system based on 5G satellite-ground convergence according to claim 5, characterized in that, The method for obtaining the precise transmit power value that dynamically adapts to the physical channel transmission capability includes: Obtain the upper limit of available energy within the scheduling period and the curvature characteristic value derived from the resource metric matrix; The inverse norm of the curvature feature value at each point in spacetime is extracted to inversely calibrate the pore ion flow at different points in spacetime. The inverse norm representation is aggregated globally by curvature integral in both time and frequency dimensions, and used as a benchmark constraint to normalize and allocate the upper limit of available energy, thereby obtaining a benchmark power value. By using the inverse norm characterization quantity mapped to each time-frequency coordinate system within the optimal scheduling boundary value, the reference power value is continuously and dynamically adjusted to obtain the precise emission power value that resonates with the ion flow in the underlying carbon channels.

7. The novel NTN IoT terminal resource management system based on 5G satellite-ground convergence according to claim 6, characterized in that, The generated spatial precoding matrix includes: Obtain the initial coding array, three-dimensional rotation generator, and inherent nonlinear distortion mapping of the power amplifier for the multi-antenna array; The time gradient vector of the precise transmit power value is extracted and fused with the rotation generator to capture the dynamic rotational distortion features excited by the high-frequency power envelope fluctuation in space. By combining the nonlinear distortion mapping amount with the dynamic rotational distortion characteristics over time, the cumulative radio frequency phase distortion caused by the violent fluctuations of physical dissipation is quantified. Construct an inverse rotation operator that is strictly out of phase with the cumulative distortion phase and apply it to the initial coding matrix to generate the spatial precoding matrix that deadlocks satellite trajectories to eliminate radio frequency beam deviation errors.

8. The novel NTN IoT terminal resource management system based on 5G satellite-ground convergence according to claim 7, characterized in that, The update yields the next-cycle base dissipation, which includes topological memory capabilities, comprising: Obtain the spatially defined topological backoff operator and the outward normal vector of the optimal scheduling boundary value; The resource metric matrix and the external normal vector are fused to extract the directional projection matrix, so as to calibrate the physical flux radiation path in the external normal dimension of the scheduling surface boundary; The surface geometric energy flux density is calculated by combining the precise emission power value and the directional projection matrix, and the surface closed integral is performed to obtain the flux integral value of the outward radiation remaining at the physical interface; The flux integral value is selectively extracted from the current microscopic anomalous ion dissipation by the topological backoff operator, and the residual ion state that was not fully reset is precisely continued and transformed into the gravitational benchmark for the next resource scheduling to obtain the basic dissipation of the next cycle.

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