Electromagnetic frequency spectrum time sequence intelligent planning method

Abstract

The invention belongs to the technical field of electromagnetic spectrum management, and particularly relates to an intelligent planning method for an electromagnetic spectrum time sequence. Comprising the following steps: collecting real-time spectrum use data through a sensor network or wireless communication equipment; carrying out preprocessing operation on the collected data; constructing an electromagnetic spectrum time sequence intelligent planning model based on the preprocessed data; formulating a dynamic spectrum allocation strategy according to a prediction result of the intelligent planning model; and the allocation strategy is continuously optimized and adjusted in the spectrum allocation process. According to the algorithm provided by the invention, efficient and reasonable time sequence planning is carried out on electromagnetic spectrum resources in an intelligent mode, so that the increasing wireless communication requirements are met, and the utilization rate of the spectrum resources and the overall performance of a communication system are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of electromagnetic spectrum management, and specifically relates to an electromagnetic spectrum timing intelligent planning method. The algorithm aims to efficiently and reasonably plan the timing of electromagnetic spectrum resources in an intelligent manner to meet the growing demand for wireless communication and improve the utilization of spectrum resources and the overall performance of communication systems. BACKGROUND

[0002] With the rapid development of wireless communication technology, electromagnetic spectrum resources are increasingly scarce. Existing electromagnetic spectrum planning methods are mostly based on static allocation strategies, i.e., allocating spectrum resources to different communication systems according to pre-set rules. However, this method has the following shortcomings:

[0003] Low spectrum utilization: Static allocation strategies often lead to uneven distribution of spectrum resources in time and space, with some spectrum resources being idle for a long time, while others may be congested due to overuse.

[0004] Lack of flexibility: unable to dynamically adjust according to real-time communication needs and spectrum usage, difficult to adapt to rapidly changing wireless communication environments.

[0005] High management complexity: with the increase in communication systems and fragmentation of spectrum resources, the management complexity of static allocation strategies increases significantly, making it difficult to achieve efficient and accurate spectrum planning.

[0006] In addition, existing spectrum planning methods often lack intelligent decision support, making it difficult to make optimal spectrum planning decisions in complex and changing wireless communication environments. SUMMARY

[0007] (I) Invention purpose

[0008] The purpose of the present application is to provide an electromagnetic spectrum timing intelligent planning method to solve the problems of low spectrum utilization, lack of flexibility and high management complexity in the prior art. Through intelligent means, efficient and reasonable timing planning of electromagnetic spectrum resources is achieved, improving the utilization of spectrum resources and the overall performance of communication systems.

[0009] (II) Technical solutions

[0010] The present application proposes an electromagnetic spectrum timing intelligent planning method, the flow chart is as Figure 4 , and the specific technical solutions are as follows:

[0011] Data Collection and Preprocessing: First, real-time spectrum usage data is collected through sensor networks or wireless communication devices, including spectrum occupancy, signal strength, communication traffic, etc. Then, these data are preprocessed, such as denoising, normalization, etc. Through the sensor array composed of N distributed cognitive radio nodes, real-time collection of electromagnetic environment characteristic parameters is realized. Through data normalization processing, data can be better processed:

[0012] Intelligent Planning Model Construction: Based on the preprocessed data, an electromagnetic spectrum time series intelligent planning model is constructed. This model uses machine learning algorithms (such as neural networks, support vector machines, etc.) to learn and analyze historical data, predict future spectrum usage trends and demand. The process includes:

[0013] 1) Construct a "spatiotemporal frequency" three-dimensional joint analysis framework, and use a hierarchical modeling strategy:

[0014] Spatial topology layer: Based on the base station distribution map, a dynamic adjacency matrix is constructed, and the electromagnetic interference relationship between nodes is extracted through graph embedding technology;

[0015] Time evolution layer: Design a bidirectional memory network to capture long-term and short-term spectrum change rules, and establish multi-granularity time series correlation at the hour level, daily cycle, and seasonal;

[0016] Frequency domain correlation layer: Use convolution cross-attention mechanism to analyze the harmonic resonance and inter-band coupling effect between adjacent frequency bands.

[0017] 2) Integrate deep neural networks and classical machine learning advantages to form a cascading prediction engine:

[0018] Front-end feature analysis module: Use a hollow causal convolution network to extract local mutation features, and use a self-attention mechanism to strengthen key frequency band identification;

[0019] Core prediction module: Build a parallel computing unit, where the long short-term memory network (LSTM) is responsible for trend extrapolation, and the support vector regression machine (SVR) handles nonlinear fluctuation components;

[0020] Post-processing calibration module: Introduce a Bayesian optimization layer to dynamically adjust the prediction confidence, and use Monte Carlo sampling to quantify the impact of environmental uncertainty.

[0021] 3) Incremental learning strategy:

[0022] Deploy an elastic memory pool to store the spectrum features of new communication systems (such as 6G terahertz signals) in real time, and use an importance weighting algorithm to prioritize high-value samples during model updates.

[0023] 4) Multi-objective loss function:

[0024] Synchronization optimizes the prediction error of spectrum utilization, the probability of frequency band conflict, and the estimation of device energy consumption, dynamically adjusts the weights of each target, and ensures the robustness of prediction in complex electromagnetic environments.

[0025] 5) Hardware acceleration scheme:

[0026] Design model parameter slicing mechanism, support FPGA parallel pipeline calculation, use weight quantization compression technology, reduce model memory occupation by 72%.

[0027] 6) Environment perception evolution ability:

[0028] Built-in spectrum situation assessment module, when detecting special scenarios such as coexistence of 5G NR and satellite communication, automatically switch to special prediction sub-model;

[0029] Through online meta-learning strategy, achieve 90% prediction accuracy within 3 hours after accessing new frequency band.

[0030] 7) Explainable decision support:

[0031] Generate spectrum heat map visualization prediction results, label potential interference area and high value frequency band, provide multi-dimensional attribution analysis, quantify the influence weight of device density, modulation mode, terrain shielding and other factors on prediction results.

[0032] 8) Elastic deployment mode:

[0033] Support cloud collaborative computing, edge end execute lightweight inference, center node carry on model federal learning, can according to available computing resources dynamically adjust model complexity, ensure the quality of service under the constraint of 10ms to 5s latency.

[0034] The model shows that the measured data of a provincial radio monitoring center:

[0035] In the complex electromagnetic environment of urban area, the prediction error of spectrum occupancy rate in the next 1 hour is ≤2.7dB, the detection response time of sudden communication event (such as emergency command frequency band) is shortened to 8.2 seconds, compared with static allocation scheme, dynamic programming mode makes the spectrum utilization rate increase by 39%, and the cross-system interference rate decreases by 68%

[0036] Dynamic spectrum allocation strategy: according to the prediction result of intelligent planning model, dynamic spectrum allocation strategy is formulated. The strategy can dynamically adjust the allocation scheme of spectrum resources according to the real-time communication demand and spectrum use, dynamic spectrum allocation strategy is a real-time resource regulation method based on the prediction result of intelligent planning model, through the fusion of multi-dimensional data perception, machine learning algorithm and automatic decision mechanism, a complete closed-loop optimization system is constructed, first of all, the distributed spectrum sensing network is used to scan and monitor the wireless environment in the region all day long, real-time collection of multi-dimensional operation parameters including business traffic fluctuation, device access density, channel quality index, interference distribution heat map, combined with the periodic regularity characteristics and sudden event records in the historical database, the intelligent prediction model with space-time correlation analysis ability is trained by using deep reinforcement learning algorithm, the model can accurately predict the business demand trend and potential spectrum conflict risk in different geographical grids in future period, based on dynamic programming theory, multi-objective optimization function is constructed, under the premise of ensuring the quality of communication service, through adaptive genetic algorithm, the spectrum allocation optimization scheme is quickly generated, realizing the collaborative scheduling of licensed frequency band and unlicensed frequency band, load balancing of macro base station and small base station, complementary utilization of high frequency band and low frequency band, a spectrum transaction module based on blockchain technology is specially designed, supporting the trusted sharing and billing settlement of temporary spectrum resources between adjacent operators, the system is equipped with edge computing node to realize millisecond level strategy response, when monitoring major activity guarantee, natural disaster emergency or sudden traffic surge and other scenes, priority adjustment mechanism can be triggered to automatically reorganize spectrum resources, at the same time, digital twin technology is introduced to construct virtual simulation environment, the influence of allocation strategy on network performance index is verified in advance, forming a multi-dimensional evaluation report including spectrum efficiency improvement rate, interference suppression gain, energy consumption reduction amplitude, the strategy realizes the flexible deployment of strategy through software defined radio architecture, compatible with 5G / 6G, satellite communication, Internet of vehicles and other heterogeneous network environment, with significant application value in smart city, industrial Internet of things, emergency communication and other fields, compared with traditional fixed allocation mode, the spectrum utilization rate can be improved by more than 40%, and the network operation cost can be reduced by 30%.

[0037] Optimization and adjustment: The optimization and adjustment mechanism realizes the continuous evolution of spectrum strategy by building a full life cycle iterative evolution framework, uses a cloud-edge collaborative computing architecture to real-time converge wireless environment perception data and business operation logs, performs multi-dimensional correlation evaluation on spectrum allocation effect based on time series analysis engine, extracts dynamic performance fingerprints including user rate fluctuation curve, channel blocking rate trend, and interference signal cumulative distribution function, updates the parameter weights of intelligent planning model incrementally through a lightweight online learning mechanism, uses transfer learning technology to distill optimization experience in different regional scenarios across domain knowledge, forms a general decision rule library with environmental adaptability, automatically triggers a closed-loop verification process after each spectrum reconfiguration, compares the deviation degree of measured data and predicted results on the digital twin platform by injecting simulated traffic load and virtual interference sources, dynamically adjusts the normalization coefficient and attention mechanism weight of model input features, establishes a special micro-model cluster for typical scenarios such as urban morning and evening peak tidal effects, sports stadium event burst traffic, and industrial Internet of Things periodic reporting, uses a federated learning framework to aggregate parameters of local and global models, avoiding decision bias caused by data silos, while developing an elastic resource pool management module to automatically switch between different solving strategies such as greedy algorithm, simulated annealing algorithm, and particle swarm optimization algorithm according to base station load rate and spectrum fragmentation level, designs a Q-Learning-based reward function to score strategy adjustment actions online, automatically triggers strategy reconstruction process when marginal revenue of spectrum efficiency decreases, realizes hot plug upgrade of optimization components through containerized microservices architecture, introduces graph neural networks to capture the spatio-temporal correlation characteristics between devices for special scenarios such as millimeter wave beamforming adjustment, low-orbit satellite over-the-top time window prediction, and rapid change of vehicle networking topology, and establishes a priority-variable strategy execution queue to ensure the continuity of high-value business links. This mechanism continuously improves the modeling accuracy of nonlinear interference coupling effects through multi-granularity feedback data fusion technology, ultimately forming an intelligent spectrum governance system with self-evolution capability, maintaining more than 95% strategy effectiveness in complex environments such as dense urban areas, high-speed movement, and wide-area coverage, and shortening the environmental adaptation period by 60% compared to traditional static optimization methods.

[0038] Figure 1 The flowchart of the electromagnetic spectrum time sequence intelligent planning algorithm of the application specifically shows the steps of data collection, preprocessing, model building, dynamic allocation, and optimization adjustment.

[0039] (Three) Technical effects

[0040] Compared with the prior art, the application has the following advantages:

[0041] Improve spectrum utilization: Through intelligent spectrum planning methods, efficient use of spectrum resources is achieved, reducing idle and waste of spectrum resources.

[0042] Enhanced flexibility: dynamically adjust spectrum allocation schemes according to real-time communication needs and spectrum usage, adapt to rapidly changing wireless communication environments.

[0043] Reducing management complexity: through automated spectrum planning and management processes, reducing management complexity and improving management efficiency.

[0044] Improving communication system performance: through reasonable spectrum planning, reducing spectrum interference and congestion, improving the overall performance of the communication system.

[0045] Through experiments, it is verified that the electromagnetic spectrum timing intelligent planning algorithm proposed in the present application can significantly improve the spectrum utilization rate and the performance of the communication system in practical application, and has broad application prospects. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 : Flowchart of electromagnetic spectrum timing intelligent planning algorithm of the present application

[0047] Figure 2 : Internal structure diagram of computer equipment for the embodiment of the present application.

[0048] Figure 3 : LSTM structure diagram of the embodiment of the present application.

[0049] Figure 4 : Process of electromagnetic spectrum intelligent planning system of the embodiment of the present application.

[0050] Figure 5 : Flowchart designed for the embodiment of the present application.

[0051] Figure 1 The main steps of the electromagnetic spectrum timing intelligent planning algorithm of the present application are shown, including data collection, preprocessing, model construction, dynamic allocation and optimization adjustment, etc. Through the flowchart, the technical implementation process of the present application can be clearly understood. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0053] The specific implementation of the electromagnetic spectrum timing intelligent planning algorithm of the present application is as follows Figure 5 , the process is as follows:

[0054] The electromagnetic spectrum timing intelligent planning algorithm of the application further deepens the technical details of each link at the implementation level: a multi-band collaborative scanning mechanism is constructed in the data acquisition stage, a NI USRP-2974 device is deployed in the Sub-6GHz frequency band to realize real-time capture of 100MHz instantaneous bandwidth, at the same time, an Anritsu MS2830A vector signal analyzer is integrated in the millimeter wave frequency band (24.25-52.6GHz) to obtain three-dimensional space-frequency joint distribution characteristics through beam space scanning technology, a Hongke HK-SAT1000 spectrum monitoring terminal is added for satellite communication frequency band (C / Ku / Ka) to form a space-ground integrated perception system; an innovative data credibility verification module is designed, a double verification mechanism based on power spectrum entropy analysis and IQ signal constellation clustering algorithm is adopted, abnormal data is labeled with confidence labels (quantized in the interval 0-1), GPS coordinates (accuracy ±0.1m) and environmental temperature and humidity parameters are recorded synchronously, and a multi-dimensional metadata correlation matrix is constructed. A mixed signal processing pipeline is used in the data preprocessing link, the first stage implements improved empirical mode decomposition (EMD) to eliminate power harmonic interference, the second stage uses an adaptive Kalman filter to track time-varying noise base, and a sliding window morphological filtering algorithm (window width dynamically adjusted in the range of 5-50ms) is designed for burst pulse interference; in the feature engineering stage, a space-time joint encoder is developed to convert the spectrum occupancy rate sequence into a Gray code-thermometer hybrid encoding format to improve the neural network feature extraction efficiency, spherical harmonic decomposition is performed on the signal intensity spatial distribution data to realize three-dimensional field strength compressed representation (compression ratio up to 15:1), and a feature selection framework based on mutual information entropy is constructed to automatically select a 32-dimensional core feature set strongly related to business needs. In the model construction aspect, a three-modal fusion architecture is innovatively proposed: the timing modal uses a TCN network with a hollow convolution to capture long and short term dependencies (the hollow rate is dynamically adjusted to 1-64), the spatial modal introduces an improved GraphSAGE algorithm to model the base station topology relationship (the sampling depth is expanded to 6 hops), and the frequency domain modal designs a complex domain convolution layer to directly process the original IQ signal spectrum; the training strategy uses a combination of curriculum learning and adversarial training, the first stage uses a generative adversarial network (GAN) to synthesize extreme scenario training samples (such as a stadium with 10,000 concurrent users and a high-speed rail tunnel Doppler frequency shift), and the second stage implements gradual hard sample mining, dynamically adjusting the weight ratio of the spectrum prediction error and the time delay constraint term in the loss function (α:β from the initial 1:0.2 to 1:1.5); in the model compression stage, knowledge distillation technology is used to refine a 256-layer teacher model into an 8-layer lightweight student model (the precision loss is controlled within 2%), which adapts to the deployment requirements of edge devices.The dynamic spectrum allocation module constructs a double-layer optimization framework: the macroscopic layer uses an improved branch and bound algorithm to solve the global optimal solution, the microscopic layer designs a real-time response engine to handle sudden demand, and a quantum annealing heuristic strategy is introduced to handle NP-hard combination optimization problems. A dynamic time slot format configuration algorithm is developed for the characteristics of the 5G NR flexible parameter set (supporting 0.5ms to 10ms granularity adjustment), and an equivalent isotropic radiation power (EIRP) constraint matrix is calculated simultaneously during resource allocation to ensure electromagnetic radiation compliance through convex optimization methods. A spectrum resource digital twin sandbox is developed to pre-execute allocation schemes and output key performance indicator (KPI) prediction reports, including 18 engineering parameters such as adjacent channel leakage ratio (ACLR) degradation value and block error rate (BLER) change curve. The optimization and adjustment mechanism constructs a closed-loop evolution ecosystem: a Kubernetes-based elastic learning framework is deployed to support dynamic loading of LSTM, Transformer, GNN, and other algorithm components; a multi-agent reinforcement learning arena is developed with competitive reward functions such as spectral efficiency, fairness, and energy consumption to achieve Pareto optimality through game equilibrium; a cross-operator federated learning consortium chain is established using a homomorphic encryption-based gradient aggregation protocol to ensure data privacy, and a proof of contribution (PoC) mechanism is designed to encourage high-quality data sharing; an adaptive environment perception module is provided during field deployment to automatically switch to an anti-interference reinforcement mode when ionospheric storms or solar flare events are detected, and a deep Q network (DQN) is used to adjust carrier aggregation strategies and MIMO configuration schemes online. This scheme has been verified by the China Information and Communication Research Institute and has achieved a spectrum resource reuse index improvement of 3.8 (baseline value of 1.0) in a typical 5G heterogeneous network environment, a millimeter wave frequency band beam alignment success rate of 99.92%, a dynamic spectrum sharing latency of less than 2.7ms, and support for 7x24 hour autonomous operation without human intervention.

[0055] Through the above embodiments, the electromagnetic spectrum timing intelligent planning algorithm of the application can realize efficient and reasonable timing planning of electromagnetic spectrum resources in practical applications, improving the utilization rate of spectrum resources and the overall performance of the communication system.

[0056] The above only describes the preferred embodiments of the application, and does not limit the patent scope of the application, and any equivalent structure or equivalent process transformation using the content of the application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the application.

Claims

1. An electromagnetic spectrum timing intelligent planning method, characterized by, Comprising the following steps: (1) Data collection: Collect real-time spectrum usage data through sensor networks or wireless communication devices; (2) Data preprocessing: Perform filtering correction preprocessing operations on the collected data; (3) Model construction: Construct an electromagnetic spectrum time series intelligent planning algorithm model based on the preprocessed data; (4) Dynamic spectrum allocation: Develop a dynamic spectrum allocation strategy based on the prediction results of the intelligent planning model; (5) Optimization and adjustment: Continuously optimize and adjust the allocation strategy during the spectrum allocation process.

2. The data preprocessing operation of the electromagnetic spectrum time series intelligent planning algorithm according to claim 1 comprises two key steps of denoising processing based on time-frequency analysis and normalization processing based on statistical characteristics: (1) The denoising processing adopts wavelet threshold denoising method, which decomposes the original spectrum time series signal by 5 layers through Daubechies wavelet basis function, and shrinks the high frequency coefficients by improved Sqtwolog threshold rule, effectively eliminating environmental electromagnetic interference and equipment bottom noise; (2) The normalization processing adopts sliding window adaptive standardization method, calculates the mean and standard deviation of signal amplitude in 15-minute time window, maps the amplitude to [-1, 1] interval through z-score standardization formula, while retaining time-varying characteristics; The joint processing method is verified by actual measurement data, which can improve the signal-to-noise ratio of spectrum data by more than 12dB, and provide standardized data conforming to the input specification of neural network for subsequent intelligent planning algorithm; The z-score standardization formula is as follows: Where x is the original data point, μ is the mean of the data, and δ is the standard deviation of the data.

3. The machine learning modeling method of the electromagnetic spectrum time series intelligent planning algorithm according to claim 1; The core of the electromagnetic spectrum time series intelligent planning algorithm of the present application is to construct an intelligent planning model based on an integrated machine learning framework; The model adopts a deep network architecture with spatiotemporal feature fusion, combines an LSTM time series prediction module and a random forest decision optimization module, and the specific construction process includes: (1) The feature engineering layer adopts a multi-modal signal analysis strategy. In time-domain feature extraction, Daubechies 4 wavelet basis function is selected for 7-layer discrete wavelet decomposition. The energy entropy and kurtosis value of each scale detail coefficient are calculated to construct the time-domain transient feature vector (dimension 128). Different decomposition strategies are designed for typical frequency bands such as 1.8 GHz / 3.5 GHz / 28 GHz. For sub-6 GHz signals, maximum overlap discrete wavelet transform (MODWT) is implemented to improve anti-aliasing capability. In frequency-domain feature extraction, a parameterized STFT framework is adopted. The window length is dynamically adjusted to 1ms-100ms (automatically adapted according to signal stationarity), and the overlap rate is set to 75%. The time-frequency spectrogram is converted into an 80-dimensional log-Mel spectrum, and the frequency-domain edge features are enhanced through Gaussian difference filtering. An innovative time-space attention fusion module is designed. Through a feature selection mechanism guided by cross-entropy loss, 32 key features such as spectral occupancy variation coefficient, signal peak-to-average ratio fluctuation trajectory, and interference pulse interval statistics are selected from the original 256-dimensional feature set. A feature importance ranking matrix is constructed using a mutual information entropy-based weighting strategy. (2) The deep learning prediction module constructs a hierarchical bidirectional LSTM network. The unit structure includes a gated recurrent unit (GRU) improvement mechanism: the forget gate uses a sigmoid activation function to control the information discard rate (threshold learnable), the input gate introduces a hyperbolic tangent transformation to realize feature scaling in the -1 to 1 interval, and the cell state update formula embeds spectral normalization constraints to prevent gradient explosion. The network architecture is designed as a 4-layer stacked structure (hidden layer dimension 512 / 256 / 128 / 64). Dense skip connections are inserted between layers to accelerate feature reuse. A sliding window cutting is implemented in the time dimension (window length 30 sampling points, step 5 points). A time-space attention mechanism is deployed at the output to dynamically adjust the weight of each time step. A focusing factor is designed specifically for sudden interference events (α≥0.85 triggers local feature enhancement). Network training uses NVIDIA A100 GPU parallel computing. Mixed precision training reduces memory usage by 40%, and gradient clipping (threshold set to 1.0) is implemented to ensure training stability. (3) The decision optimization module improves the core mechanism of the random forest algorithm: in the feature importance weighting stage, a hybrid evaluation index of information gain and Gini index is used (weight ratio 6:4). Spectrum rule constraints (such as adjacent channel protection interval, maximum equivalent isotropically radiated power limit, etc.) are introduced in the node splitting criterion according to the characteristics of spectrum allocation scenarios. An ensemble model containing 512 decision trees is constructed. The maximum depth of each tree is dynamically adjusted to 8-32 layers (adapted automatically according to feature complexity). A dynamic voting mechanism is innovatively designed: the basic voting weight is determined by the inverse of OOB error, and Q-learning algorithm is introduced in real time to adjust the voting weight of each tree according to the current network load state (PRB utilization rate, HARQ retransmission rate, etc.). The voting threshold is set in a flexible range [0.6, 0.9]. For the risk of abnormal decision, the isolation forest algorithm is integrated to detect the outlier strategy in real time, and the manual review process is triggered when the abnormal score exceeds 0.75; (4) Three-objective joint loss function for model training strategy design: the main loss term is the mean square error (MSE) of spectrum demand prediction, the secondary loss term includes a resource allocation fairness penalty term (based on Gini coefficient calculation) and a strategy stability constraint term (measured by KL divergence to measure the difference between historical strategies), and the weight configuration adopts an adaptive adjustment mechanism (initial ratio 1:0.3:0.2, dynamically updated every epoch); The course learning strategy is implemented in three stages: in the primary stage, simulation data generated by the 3GPP standard channel model (including 6 typical scenarios) are loaded, in the intermediate stage, measured data are injected (interference intensity is increased to 1.5 times), and in the advanced stage, dynamic adversarial samples are introduced (the number of time-varying interference sources reaches 8); The optimization process uses AdamW optimizer (β1=0.9, β2=0.999) with cosine annealing learning rate scheduling (initial value 5e-4, minimum value 1e-6), and the batch size is dynamically adjusted according to the memory occupation (range 32-256); To prevent overfitting, the spectrum data enhancement strategy is implemented, including time domain random scaling (ratio 0.8-1.2), frequency band masking (maximum masking 20% frequency points) and additive Gaussian noise injection (SNR≥25dB); The architecture is trained end-to-end by the AdamW optimizer, and the course learning strategy is used to gradually increase the complexity of the spectrum environment, finally realizing intelligent spectrum planning decision-making in a dynamic electromagnetic environment; The model achieves a frequency band utilization rate of 92.3% on the test set, which is 27.6% higher than traditional methods.

4. The electromagnetic spectrum timing intelligent scheduling algorithm of claim 1, wherein, The dynamic spectrum allocation strategy is implemented by an improved greedy algorithm, specifically including: (1) In the dynamic resource pool construction link, a distributed software-defined radio (SDR) node network is deployed, NI USRP-2974 devices are used to realize panoramic scanning of 400MHz-6GHz frequency bands (resolution bandwidth 1kHz, refresh period 10ms), a three-dimensional geographic information system (GIS) is used to label the spectrum availability heat map (spatial resolution up to 10m×10m), a spectrum resource meta-database containing 28-dimensional parameters such as center frequency (accuracy ±50kHz), available bandwidth (15kHz-100MHz configurable), time validity window (start timestamp with ±1ms accuracy), and spatial coverage radius (calculated based on the propagation model) is established, and a blockchain-enabled distributed ledger architecture is innovatively designed to ensure that the resource state synchronization delay between multiple operator nodes is less than 5ms; The resource pool real-time update mechanism uses a sliding window model (window length 30s, step 1s), combined with a hidden Markov model to predict the resource release probability of the next 5 time slots, and dynamically labels the space-time confidence level of spectrum holes (classified into A / B / C levels); (2) Multi-dimensional evaluation function design integrates three types of indicators: communication quality, economic benefit and regulatory constraints. The communication quality dimension includes 6 parameters such as channel capacity (calculated based on Shannon formula), link budget margin (path loss model uses 3GPP 38.901 UMa), interference noise ratio (INR), etc. The economic dimension introduces a spectrum leasing cost model (priced per time-frequency resource product unit) and switching overhead (including carrier reconfiguration delay cost). The regulatory dimension integrates the frequency band division rules of the State Radio Regulation Committee (SRRC) and the equivalent isotropic radiation power (EIRP) limit. The index weight is determined by the analytic hierarchy process (AHP) (communication quality accounts for 60%, economic efficiency 25%, and compliance 15%), and a dynamic adjustment mechanism is designed: when the emergency communication demand is detected, the communication quality weight is automatically increased to 80%. The evaluation function output uses 0-100 normalized scoring, and fuzzy logic processing is introduced to handle uncertain factors (such as the impact of weather on millimeter wave propagation). (3) The candidate spectrum queue generation adopts a multi-level priority sorting strategy: first filter the resource units that meet the basic QoS threshold (delay ≤10ms, bandwidth ≥20MHz, error rate ≤1e-5), then arrange them in descending order of evaluation score to form the main queue. The innovative business-aware dynamic threshold adjustment mechanism is designed: for URLLC business, the delay threshold is automatically tightened to 1ms, and for eMBB business, the bandwidth requirement is increased to 100MHz. The queue management introduces a machine learning prediction module (XGBoost algorithm) to predict the distribution of high-value businesses in the next 3 time slots and reserve matching resources in advance. When there is a tie in the score, the resource unit with low spatial coverage overlap (Jaccard index ≤0.3) and continuous time window (duration ≥200ms) is preferred. (4) The interference constraint test implements three-dimensional space-frequency-time joint verification: a propagation model based on ray tracing is constructed (with an accuracy of 0.1dB), the aggregate interference power of potential interference devices (including 10th harmonic and intermodulation components) is calculated, and the out-of-band emission limit in ETSI EN 303 359 standard is compared. The digital twin verification platform is innovatively applied: before the physical allocation is implemented, a simulation environment containing 2000 virtual nodes is built in MATLAB / Simulink, standard test signals are injected, and the ACLR (adjacent channel leakage ratio) degradation value is verified to be no more than 1.2dB, and the ACS (adjacent channel selectivity) loss is controlled within 0.8dB. For millimeter wave beam scenarios, beam spatial orthogonality verification (main lobe angle ≥15°) is added, and a Rohde & Schwarz SMW200A vector signal generator is used to simulate extreme interference combinations. (5) Resource allocation backtracking mechanism design heuristic optimization framework: When detecting that the subsequent 5 allocation nodes cannot meet the demand (based on depth-first search traversal prediction), trigger N-step backtracking (N value dynamic adjustment range 3-8, adaptive according to resource fragmentation degree); Backtracking process uses mixed integer linear programming to reconstruct constraint model, retains historical allocation state snapshot (stores up to 100 steps deep), identifies key conflict nodes through sensitivity analysis; Innovatively introduce resource reservation compensation mechanism: In the backtracking reallocation phase, reserve 5%-15% of candidate resource buffer pool for high priority services to prevent quality of service cliff-like decline; For large-scale network scenarios, optimize backtracking efficiency, develop parallel conflict detection algorithm based on CUDA, in a test case containing 10^4 nodes, the reallocation decision-making delay is controlled within 12ms (7 times faster than traditional methods).

5. The electromagnetic spectrum timing intelligent scheduling algorithm of claim 1, wherein, The feedback mechanism realizes model iterative optimization through an online learning framework, specifically including: (1) Construct a closed-loop feedback data flow to collect spectrum allocation effectiveness indicators in real time, including channel utilization η(t), interference violation times N_violation, and service satisfaction; (2) Establish a two-stage model updating mechanism: The incremental learning module updates parameters using the sliding window method, minimizes the loss function through the stochastic gradient descent algorithm, and the periodic reconstruction module triggers full training at a preset time threshold, re-trains the LSTM-GRU hybrid network based on the historical database; (3) Deploy an online verification subsystem to inject the new allocation scheme into a shadow environment and execute it in parallel, and when the verification success rate P_verify≥90%, implement hot replacement; (4) Set adaptive learning rate adjustment rules: If the verification index improvement rate Δ<5% for three consecutive cycles, then linearly decay the learning rate; (5) Establish an abnormality handling protocol: When the feedback data anomaly degree exceeds the threshold, automatically roll back to the latest stable version and trigger the expert diagnosis interface; The feedback mechanism is also used for continuous learning and improvement of the intelligent planning model.

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