Broadband spectrum monitoring system based on dynamic channel allocation

Abstract

The invention relates to the technical field of broadband spectrums, and discloses a broadband spectrum monitoring system based on dynamic channel allocation, which comprises a data acquisition unit, a data identification unit, a region division unit, a data alignment unit and a channel allocation unit.The system dynamically adjusts the coverage area through beacon nodes, accurately calculates the coverage radius in combination with the urban environment, and improves the coverage efficiency. Meanwhile, a channel allocation strategy is optimized by a game theory, the change of a frequency spectrum environment is responded, and frequency spectrum state parameters are constructed by fusing multi-dimensional characteristics, so that communication smoothness is guaranteed, blind areas of monitoring nodes are eliminated, data space-time alignment is realized, the monitoring sensitivity and the resource utilization rate are effectively improved, and stable frequency spectrum and reliable communication under large-scale activities in cities are ensured; and the dynamic and reasonable distribution of the channels is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of broadband spectrum, and particularly relates to a broadband spectrum monitoring system based on dynamic channel allocation. Background Art

[0002] When the existing broadband spectrum monitoring system is used in cities, in the face of the spectrum challenges brought by large-scale urban activities, it mainly adopts a combination of prediction based on historical data and distributed spectrum monitoring to achieve monitoring and channel allocation. Usually, it will first collect the spectrum usage data of historical activities in the area, and predict the spectrum requirements in different time periods and different regions during the activity through machine learning algorithms. At the same time, through a distributed spectrum monitoring network, multiple monitoring nodes are deployed in the city, and these nodes can perform real-time scanning of the spectrum in the area where they are located, collect data such as spectrum occupancy and signal strength to achieve the purpose of activity monitoring.

[0003] However, in complex large-scale urban activity scenarios, blind spots are prone to appear in the range of distributed monitoring nodes, and there is a lack of an effective coordination mechanism during data fusion among nodes, resulting in data inconsistency problems. Furthermore, the monitoring sensitivity to the sharp changes in spectrum usage in local areas is reduced, and spectrum anomalies cannot be detected in a timely and accurate manner, thus leading to unreasonable channel allocation. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a broadband spectrum monitoring system based on dynamic channel allocation to solve the above problems.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] A broadband spectrum monitoring system based on dynamic channel allocation, comprising:

[0007] A data acquisition unit, used to set up multiple monitoring nodes in the city, divide the city into multiple monitoring areas, divide the monitoring nodes into beacon nodes and ordinary nodes. The beacon nodes obtain the spectrum monitoring data and historical spectrum monitoring data of the monitoring area where they are located in real time, and dynamically adjust the transmission power of the beacon nodes according to the real-time spectrum monitoring data, synchronously calculate the effective coverage radius parameter of the beacon nodes, and calculate the signal strength and occupancy duration data of each frequency band in the current monitoring area collected by the ordinary nodes in real time to obtain a set of frequency band characteristic parameters;

[0008] A data identification unit, used to adjust the monitoring range of the ordinary nodes according to the effective coverage radius parameter, extract the set of frequency band characteristic parameters, and generate a shared parameter set including the positions and occupancy rates of spectrum holes;

[0009] The area division unit is used to divide the monitoring area into multiple game areas. Taking the beacon nodes in each area as the game entities, it calculates the channel allocation revenue parameters of the shared parameter set to obtain the final channel allocation strategy parameters for each game area;

[0010] The data alignment unit is used to perform spatio-temporal alignment processing on the real-time spectrum monitoring data of the monitoring nodes in each game area according to the final channel allocation strategy parameters, and generate global spectrum state parameters including signal intensity gradient distribution and spectrum state mutation probability;

[0011] The channel allocation unit is used to judge whether the current channel allocation strategy fails according to the global spectrum state parameters. If it fails, it immediately triggers a dynamic allocation instruction.

[0012] Furthermore, it dynamically adjusts the transmission power of the beacon nodes according to the real-time spectrum monitoring data, and synchronously calculates the effective coverage radius parameters of the beacon nodes, including:

[0013] For each frequency band in the real-time spectrum monitoring data, calculate the absolute value of the difference in signal intensity between time and time, divide the absolute value by the dynamic noise floor of this frequency band, and then sum over all frequency bands to obtain the total sum parameter of the change in signal intensity relative to the noise floor for each frequency band;

[0014] For each frequency band, first calculate its signal intensity time derivative and multiply it by and then divide by the average signal intensity of the entire frequency band to obtain the normalized signal intensity time derivative parameter for the frequency band;

[0015] After squaring the normalized signal intensity time derivative parameter for the frequency band, sum over all frequency bands to obtain the comprehensive parameter of the relationship between the square of the signal intensity change rate and the average value of the entire frequency band;

[0016] Divide the total sum parameter by the comprehensive parameter, and then perform an operation through the hyperbolic tangent function to obtain the dynamic interference compensation coefficient;

[0017] Fuse the dynamic interference compensation coefficient with the basic transmission power of the beacon node to obtain the current actual transmission power value;

[0018] Fuse and calculate the current actual transmission power value, the urban building density, and the wireless propagation loss to generate the effective coverage radius parameter.

[0019] Furthermore, calculate the signal intensity and occupancy duration data of each frequency band in the current monitoring area collected by the ordinary nodes in real time to obtain the frequency band characteristic parameter set, including:

[0020] Cluster the signal strength data collected by adjacent ordinary nodes within the same time period to generate a spatio-temporal dynamic feature matrix;

[0021] Analyze the spatio-temporal dynamic feature matrix to obtain the signal strength fluctuation variance, multipath fading factor, and Doppler shift rate of the wireless channel;

[0022] Calculate the ratio of the standard deviation to the mean of the signal strength in the frequency band, calculate the ratio of the Doppler shift rate to the maximum Doppler shift rate supported by the system, and multiply the two ratios to obtain the fluctuation-frequency shift coupling factor;

[0023] Take the square root of the multipath fading factor to obtain the multipath fading adjustment factor;

[0024] Divide the fluctuation-frequency shift coupling factor by the multipath fading adjustment factor to obtain the signal dynamic characteristic index;

[0025] Then calculate the ratio of the maximum value to the minimum value of the signal strength in the frequency band in the monitoring area to obtain the signal strength extreme value ratio, and multiply the signal strength extreme value ratio by the signal dynamic characteristic index to obtain the signal quality factor;

[0026] Perform a weighted calculation on the traditional occupancy duration of the specific frequency band monitored by ordinary nodes according to the signal quality factor to generate an adaptive occupancy rate;

[0027] Calculate the spatio-temporal dynamic feature matrix, the adaptive occupancy rate, and the dynamic interference compensation coefficient of the beacon node to generate a three-dimensional feature vector;

[0028] Reduce the dimension of the principal components of the three-dimensional feature vector to obtain a triple of feature parameters including frequency band activity, stability, and interference sensitivity;

[0029] Dynamically adjust the feature screening threshold according to the current network load, calculate the local density and distance of the feature parameters in the triple of feature parameters, and generate a set of frequency band feature parameters.

[0030] Furthermore, adjust the monitoring range of ordinary nodes according to the effective coverage radius parameter, including:

[0031] Calculate the monitoring range adjustment coefficient of ordinary nodes according to the effective coverage radius parameter of the beacon node and the requirement of monitoring area overlap;

[0032] Combine the original monitoring range of ordinary nodes with the monitoring range adjustment coefficient to generate a new monitoring range. Specifically, perform a multiplication operation on the initial monitoring range of ordinary nodes and the monitoring range adjustment coefficient to obtain the adjusted monitoring radius. Through a three-dimensional space buffer analysis algorithm, with the coordinates of ordinary nodes as the center and the adjusted monitoring radius as the new radius, generate a spherical basic monitoring area, which is the new monitoring range.

[0033] Further, extract the frequency band characteristic parameter set to generate a shared parameter set including the positions and occupancy rates of spectrum holes, including:

[0034] Extract the signal strength and occupancy duration data of each frequency band in the dynamic monitoring area from the frequency band characteristic parameter set according to the new monitoring range to generate spatio-temporal feature correlation parameters;

[0035] Based on the spatio-temporal feature correlation parameters, set the signal strength threshold and the occupancy duration threshold, and filter out the areas where the signal strength is lower than the signal strength threshold and the occupancy duration is lower than the occupancy duration threshold to obtain the spectrum hole position parameters;

[0036] For the monitoring area corresponding to the spectrum hole position parameters, calculate the proportion of the occupancy duration of each frequency band in this monitoring area to the total monitoring duration to obtain the spectrum hole occupancy rate parameters, specifically including: through the spatial overlay analysis algorithm, spatially match the node geographic coordinates with the continuous area of the spectrum hole, extract the node IDs and corresponding frequency bands falling within this area, group them by frequency band, summarize the weighted occupancy duration of each frequency band in all time windows, calculate the total occupancy duration of this frequency band in the monitoring area, the total monitoring duration is the total duration of the same period of the spatio-temporal dynamic feature matrix, and through the data aggregation algorithm, divide the total occupancy duration of each frequency band by the total monitoring duration to obtain the spectrum hole occupancy rate parameters;

[0037] Integrate the spectrum hole position parameters and the spectrum hole occupancy rate parameters to generate a shared parameter set including the positions and occupancy rates of spectrum holes.

[0038] Further, calculate the channel allocation revenue parameters of the shared parameter set to obtain the final channel allocation strategy parameters for each game area, including:

[0039] Based on the positions and occupancy rates of the spectrum holes in the shared parameter set, determine the availability of each frequency band to generate frequency band availability parameters;

[0040] Combine the frequency band availability parameters with the spectrum demands of each game area and analyze them to generate a preliminary channel allocation strategy;

[0041] Calculate the revenue of the preliminary channel allocation strategy to obtain the channel allocation revenue parameters;

[0042] Calculate the channel allocation revenue parameters to obtain the final channel allocation strategy parameters for each game area.

[0043] Further, perform spatio-temporal alignment processing on the real-time spectrum monitoring data of the monitoring nodes in each game area according to the final channel allocation strategy parameters to generate global spectrum state parameters including the signal strength gradient distribution and the spectrum state mutation probability, including:

[0044] Based on the time - slice division in the final channel allocation strategy parameters, an adaptive sliding time window is generated for each monitoring node. By using the time - scale signal transmitted by the beacon node and the received signal strength of the ordinary node, the signal propagation delay is calculated and the time - window boundary is dynamically adjusted to generate spatio - temporal synchronization calibration parameters;

[0045] According to the distribution density of the monitoring nodes and the effective coverage radius parameter of the beacon node, the weight index of the ordinary monitoring nodes is dynamically adjusted to generate the signal - strength gradient distribution parameter;

[0046] Analyze the spatio - temporal synchronization calibration parameters and the spectrum - state difference between adjacent time slices to obtain the spectrum - state mutation probability;

[0047] Analyze the signal - strength gradient distribution parameter and the spectrum - state mutation probability to generate the global spectrum - state parameter including the signal - strength gradient distribution and the spectrum - state mutation probability.

[0048] Furthermore, according to the global spectrum - state parameter, determine whether the current channel allocation strategy fails. If it fails, immediately trigger the dynamic allocation instruction, including:

[0049] Slice the historical spectrum monitoring data by k time slices and frequency bands, calculate the gradient mean of the signal strength distribution in space and the change rate of the spectrum occupancy rate in the time dimension for each slice, and form a dynamic feature sequence containing spatio - temporal variation rules;

[0050] Group the period data corresponding to the historical channel allocation according to the game area, and respectively count the median of the signal - strength gradient and the extreme value of the change rate of the spectrum occupancy rate in the dynamic feature sequence of each monitoring area to generate the preliminary regional gradient reference parameter and the regional mutation reference parameter;

[0051] Based on the building density of the current monitoring area, correct the preliminary regional gradient reference parameter and the regional mutation reference parameter to obtain the signal - strength gradient threshold and the mutation - probability threshold that finally adapt to the current monitoring environment.

[0052] Furthermore, according to the global spectrum - state parameter, determine whether the current channel allocation strategy fails. If it fails, immediately trigger the dynamic allocation instruction, and it also includes:

[0053] If the signal - strength gradient in the global spectrum - state parameter is greater than the signal - strength gradient threshold or the mutation probability continuously exceeds the mutation - probability threshold within k consecutive time slices, it is determined that the previous channel allocation strategy fails, and the dynamic allocation instruction is immediately triggered.

[0054] Furthermore, the dynamic allocation instruction includes:

[0055] Mark the area where the signal strength gradient exceeds the signal strength gradient threshold as an abnormal area, calculate the spatio-temporal correlation parameters between the abnormal area and adjacent areas, and generate a regional abnormal propagation factor;

[0056] Analyze the signal strength gradient distribution of each monitoring node in the abnormal area to obtain the spectral state chaos degree;

[0057] According to the regional abnormal propagation factor and the spectral state chaos degree, dynamically adjust the boundary of the game area to generate a temporary spectrum management area;

[0058] Analyze the monitoring nodes in the temporary spectrum management area to obtain the channel resource demand vector;

[0059] Extract the regional abnormal propagation factor, spectral state chaos degree, and channel resource demand vector to obtain multi-dimensional risk assessment parameters;

[0060] When the evaluation value of any dimension in the multi-dimensional risk assessment parameters exceeds the multi-dimensional risk threshold, generate a dynamic allocation instruction including the optimal channel combination, power adjustment, and time slice allocation strategy.

[0061] In summary, the present invention mainly has the following beneficial effects:

[0062] By setting up multiple monitoring nodes in the city, including beacon nodes and ordinary nodes, and adjusting the monitoring range of ordinary nodes according to the effective coverage radius parameter of the beacon nodes, the problem that distributed monitoring nodes are prone to blind spots can be effectively solved. In the prior art, distributed monitoring nodes act independently, and it is easy to have areas that cannot be monitored. In this system, the beacon nodes obtain real-time spectrum monitoring data and historical spectrum monitoring data, dynamically adjust their own transmission power, and then calculate the effective coverage radius in combination with the urban building density and wireless propagation loss, providing a basis for the accurate adjustment of the monitoring range of ordinary nodes, enabling the entire monitoring network to cooperate closely, expanding the effective monitoring area, and reducing blind spots. At the same time, after the data recognition unit adjusts the monitoring range of ordinary nodes according to the effective coverage radius parameter, it extracts the set of frequency band characteristic parameters to generate a shared parameter set including the position and occupancy rate of spectrum holes. This enables the data collected by each node to be processed and integrated within the framework of a unified monitoring range, avoiding the problem of data inconsistency caused by inconsistent node monitoring ranges during previous data fusion, improving the accuracy and reliability of the data, and thus enhancing the monitoring sensitivity to the rapid changes in spectrum usage in a local area and being able to detect spectrum anomalies in a timely and accurate manner.

[0063] By dividing the monitoring area into multiple game areas, with the beacon nodes in the area as the game entities, calculating the channel allocation revenue parameters of the shared parameter set, and obtaining the final channel allocation strategy parameters for each game area, it fully considers the actual spectrum usage and potential benefits in different areas, avoiding the unreasonable channel allocation problem caused by the lack of an effective cooperation mechanism in traditional systems. The data alignment unit performs spatio-temporal alignment processing on the real-time spectrum monitoring data of the monitoring nodes in each game area according to the final channel allocation strategy parameters, generating global spectrum state parameters including signal strength gradient distribution and spectrum state mutation probability, providing comprehensive and accurate spectrum state information for the channel allocation unit. The channel allocation unit determines whether the current channel allocation strategy fails based on this information. If it fails, it immediately triggers a dynamic allocation instruction to achieve dynamic optimization allocation of channel resources, ensuring that the spectrum resources can be flexibly adjusted according to the actual usage and requirements, improving the utilization rate of spectrum resources, meeting the changing spectrum requirements in complex scenarios such as large urban events, and ensuring smooth and efficient communication.

[0064] By considering various factors such as signal strength changes, dynamic noise floor, and signal strength time derivative in real-time spectrum monitoring data, it can accurately reflect the interference situation in the current spectrum environment, and accordingly dynamically adjust the transmission power of beacon nodes and calculate the effective coverage radius, enabling the entire monitoring system to adapt to the changes in the spectrum environment in a timely manner. During the generation process of the frequency band characteristic parameter set, multi-dimensional information such as spatio-temporal dynamic characteristic matrix, adaptive occupancy rate, and dynamic interference compensation coefficient of beacon nodes is considered. Through operations such as dimensionality reduction and feature screening, a characteristic parameter triple including frequency band activity, stability, and interference sensitivity is obtained, constructing a comprehensive and accurate frequency band characteristic description, providing strong support for the subsequent optimization of the channel allocation strategy. When the global spectrum state parameters are abnormal, such as the signal strength gradient being greater than the threshold or the mutation probability continuously being higher than the threshold, the system can quickly determine that the channel allocation strategy fails and trigger a dynamic allocation instruction. The fast response mechanism enhances the adaptability and flexibility of the system in the face of sudden spectrum changes and complex dynamic spectrum environments, ensuring that the system can quickly and accurately respond to the spectrum challenges in large urban event scenarios and maintain the stability and reliability of spectrum usage. Brief Description of the Drawings

[0065] Figure 1 It is a block diagram of the broadband spectrum monitoring system based on dynamic channel allocation of the present invention. Detailed Embodiments

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0067] Reference Figure 1 , a broadband spectrum monitoring system based on dynamic channel allocation, comprising:

[0068] A data acquisition unit, configured to set up a plurality of monitoring nodes in a city, divide the city into a plurality of monitoring areas, divide the monitoring nodes into beacon nodes and ordinary nodes, the beacon nodes to acquire in real time the spectrum monitoring data and historical spectrum monitoring data of the monitoring area where they are located, and dynamically adjust the transmission power of the beacon nodes according to the real-time spectrum monitoring data, synchronously calculate the effective coverage radius parameter of the beacon nodes, and calculate the data of the signal strength and occupancy duration of each frequency band in the current monitoring area collected by the ordinary nodes in real time to obtain a set of frequency band characteristic parameters;

[0069] A data recognition unit, configured to adjust the monitoring range of the ordinary nodes according to the effective coverage radius parameter, extract the set of frequency band characteristic parameters, and generate a set of shared parameters including the spectrum hole position and occupancy rate;

[0070] A region division unit, configured to divide the monitoring area into a plurality of game regions, take the beacon nodes in each region as game subjects, calculate the channel allocation revenue parameters of the set of shared parameters, and obtain the final channel allocation strategy parameters of each game region;

[0071] A data alignment unit, configured to perform spatio-temporal alignment processing on the real-time spectrum monitoring data of the monitoring nodes in each game region according to the final channel allocation strategy parameters, and generate global spectrum state parameters including the signal strength gradient distribution and the spectrum state mutation probability;

[0072] A channel allocation unit, configured to determine whether the current channel allocation strategy fails according to the global spectrum state parameters, and if it fails, immediately trigger a dynamic allocation instruction.

[0073] By setting up multiple monitoring nodes and dividing them into beacon nodes and ordinary nodes, the spectrum monitoring data of each region in the city can be accurately obtained. The beacon nodes dynamically adjust the transmission power according to the data to determine the effective coverage radius, providing a data basis for subsequent monitoring. Secondly, the data recognition unit adjusts the monitoring range and extracts the frequency band characteristics to generate a shared parameter set to assist in spectrum hole recognition and occupancy rate analysis. The region division unit introduces game theory and calculates the final channel allocation strategy with the beacon nodes as the main body to improve the rationality of spectrum resource allocation. The data alignment unit performs spatio-temporal alignment processing to generate global spectrum state parameters, enhancing the accuracy of spectrum state perception. The channel allocation unit judges and triggers dynamic allocation in a timely manner based on the global parameters to ensure the efficient and stable operation of the communication system, effectively improving the spectrum utilization rate, providing strong support for broadband spectrum monitoring and channel allocation, and ensuring smooth urban communication.

[0074] In one case of this embodiment, the transmission power of the beacon nodes is dynamically adjusted according to the real-time spectrum monitoring data, and the effective coverage radius parameters of the beacon nodes are synchronously calculated, including:

[0075] For each frequency band in the real-time spectrum monitoring data, calculate its signal strength difference absolute value at time and time, divide the absolute value by the dynamic noise floor of this frequency band, and then sum over all frequency bands to obtain the total parameter of the signal strength change relative to the noise floor for each frequency band;

[0076] For each frequency band, first calculate its signal strength time derivative and multiply it by and then divide by the average signal strength of the entire frequency band to obtain the normalized signal strength time derivative parameter for the frequency band;

[0077] After squaring the normalized signal strength time derivative parameter for the frequency band, sum over all frequency bands and then 1 to obtain the comprehensive parameter of the relationship between the square of the signal strength change rate and the average value of the entire frequency band;

[0078] Divide the total parameter by the comprehensive parameter and then perform an operation through the hyperbolic tangent function to obtain the dynamic interference compensation coefficient;

[0079] When specifically applied, the specific calculation formula of the above dynamic interference compensation coefficient is as follows: ;

[0080] In the formula, represents the dynamic interference compensation coefficient, represents the hyperbolic tangent function, represents the frequency band at time instantaneous signal strength, represents the frequency band Dynamic noise floor Indicates the dynamic time window length , Indicates the time derivative of signal strength Indicates the average value of the full-band signal strength

[0081] Fuse the dynamic interference compensation coefficient with the basic transmission power of the beacon node to obtain the current actual transmission power value, which specifically includes: after normalizing the dynamic interference compensation coefficient, multiply the dynamic interference compensation coefficient by the dynamic interference compensation coefficient to obtain the current actual transmission power value;

[0082] Fuse and calculate the current actual transmission power value, urban building density, and wireless propagation loss to generate an effective coverage radius parameter, which specifically includes: based on the current actual transmission power value, perform spatial interpolation on the urban building density data to generate a continuously distributed three-dimensional density cloud map, convert the three-dimensional density cloud map into a signal propagation obstacle grid map through a rasterization projection algorithm, assign a corresponding attenuation weight value to each grid, use the Monte Carlo simulation algorithm to randomly generate a large number of signal propagation paths in the grid map, statistically calculate the cumulative attenuation values of each path, and based on the receiving device sensitivity, filter out the paths whose attenuation values do not exceed the difference between the transmission power and the receiving sensitivity, and take the statistical average of the maximum propagation distances of these paths as the effective coverage radius parameter.

[0083] Dynamically adjust the transmission power of the beacon node through real-time spectrum monitoring data and synchronously calculate the effective coverage radius parameter. Generate the dynamic interference compensation coefficient by extracting real-time spectrum data and fuse it with the basic transmission power, enabling the transmission power of the beacon node to adapt to the changes in the spectrum environment in real time, improving the monitoring sensitivity to the sharp changes in the local area spectrum usage, being able to detect spectrum anomalies in a timely and accurate manner, thus providing a strong basis for reasonable channel allocation, further enhancing the utilization efficiency of spectrum resources, and ensuring the stability and reliability of wireless communication in urban activity scenarios.

[0084] A method of calculating the actual transmission power by fusing the dynamic interference compensation coefficient and the basic transmission power, and generating the effective coverage radius parameter by fusing the urban building density and the wireless propagation loss can effectively improve the communication performance of beacon nodes in complex urban environments. On the one hand, through the precise compensation of dynamic interference, the transmission power adaptability of beacon nodes is improved, enabling them to maintain good communication quality in a changing spectrum environment. On the other hand, by comprehensively considering the urban building density and the wireless propagation loss, the generated effective coverage radius parameter is more in line with the actual propagation environment, providing an accurate reference for the layout and optimization of beacon nodes, thereby reasonably planning the wireless communication network, avoiding resource waste and interference problems caused by insufficient coverage or over-coverage, and further improving the overall performance and stability of the wireless communication network in urban activity scenarios.

[0085] In one case of this embodiment, the signal strength and occupancy duration data of each frequency band in the current monitoring area collected by ordinary nodes in real time are calculated to obtain a set of frequency band characteristic parameters, including:

[0086] Cluster the signal strength data collected by adjacent ordinary nodes within the same time period to generate a spatio-temporal dynamic feature matrix, specifically including: determining the set of adjacent ordinary nodes based on geographical coordinates, aligning the signal strength data within the same time period through timestamps, segmenting the data according to a fixed time window, for the signal strength sequences of adjacent nodes within each window, using a spatio-temporal clustering algorithm to identify spatio-temporal clusters with similar signal strengths, extracting the mean, variance, number of nodes covered by the cluster, and proportion of cluster duration within the spatio-temporal cluster, arranging them in chronological order, and generating a spatio-temporal dynamic feature matrix with node ID, frequency band, and timestamp as indexes;

[0087] Analyze the spatio-temporal dynamic feature matrix to obtain the signal strength fluctuation variance, multipath fading factor, and Doppler shift rate of the wireless channel, specifically including: For the spatio-temporal dynamic feature matrix, perform double-dimensional grouping according to node ID and frequency band, extract the signal strength mean sequence of each node in different time windows under each frequency band, calculate the variance of this sequence on the time axis, and use it as the signal strength fluctuation variance. Based on the statistical distribution characteristics of the signal strength in the spatio-temporal dynamic feature matrix, use the maximum likelihood estimation algorithm to fit the distribution parameters. By calculating the direct path power (calibrated from the square of the signal strength mean combined with the noise power) and the scattered path power (obtained by subtracting the noise power from the signal strength variance to get the power ratio of the two), the scattered path power can be used as the multipath fading factor. Perform a fast Fourier transform on the signal data of adjacent time windows to convert the time-domain signal into a frequency-domain spectrum. Use the spectral analysis method to detect the main peak frequency position in the spectrum, calculate the offset of the main peak frequency between adjacent windows and combine it with the real-time geographical coordinates of the node to calculate the relative motion speed between the node and the signal source. Associate the frequency offset with the relative motion speed through the frequency shift estimation algorithm, and directly obtain the Doppler shift rate through the preset signal carrier frequency parameter (i.e., the fixed signal operating frequency at the transmitting end);

[0088] Calculate the ratio of the standard deviation to the mean of the signal strength in the frequency band, calculate the ratio of the Doppler shift rate to the maximum Doppler shift rate supported by the system, and multiply the two ratios to obtain the fluctuation-frequency shift coupling factor;

[0089] Take the square root of the multipath fading factor to obtain the multipath fading adjustment factor;

[0090] Divide the fluctuation-frequency shift coupling factor by the multipath fading adjustment factor to obtain the signal dynamic characteristic index;

[0091] Then calculate the ratio of the maximum value to the minimum value of the signal strength in the frequency band in the monitoring area to obtain the signal strength extreme ratio, and multiply the signal strength extreme ratio by the signal dynamic characteristic index to obtain the signal quality factor;

[0092] When specifically applied, the specific calculation formula of the above signal quality factor is as follows: ;

[0093] In the formula, represents the signal quality factor, represents the frequency band the standard deviation of the signal strength, represents the frequency band the mean of the signal strength, represents the Doppler shift rate, represents the maximum Doppler shift rate supported by the system, represents the multipath fading factor, Indicates the extreme value ratio of signal strength within the monitoring area;

[0094] Based on the signal quality factor, perform weighted calculation on the traditional occupancy duration of a specific frequency band monitored by ordinary nodes to generate an adaptive occupancy rate, specifically including: Based on the signal quality factor, divide the traditional occupancy duration of the specific frequency band into time windows (with the same period as the spatio-temporal dynamic feature matrix), extract the signal quality factor values within each window as the weighting coefficients, accumulate the occupancy durations of each window multiplied by the corresponding signal quality factor values, obtain the total weighted occupancy duration, and through normalization processing, take the ratio of the weighted total duration to the total monitoring duration of this frequency band (the sum of all window times) as the adaptive occupancy rate;

[0095] Calculate the spatio-temporal dynamic feature matrix, the adaptive occupancy rate, and the dynamic interference compensation coefficient of beacon nodes to generate a three-dimensional feature vector, specifically including: For the spatio-temporal dynamic feature matrix, extract the core features such as the signal strength mean, variance, and number of cluster-covered nodes according to the node ID, frequency band, and time window, use the principal component analysis method to reduce the dimension of the high-dimensional features, generate the spatio-temporal feature vector, and splice the spatio-temporal feature vector, the adaptive occupancy rate, and the dynamic interference compensation coefficient in a fixed order to form a three-dimensional feature vector including spatio-temporal distribution, occupancy efficiency, and interference impact;

[0096] Reduce the dimension of the principal components of the three-dimensional feature vector to obtain a triple of characteristic parameters including frequency band activity, stability, and interference sensitivity, specifically including: Normalize the data of each dimension of the three-dimensional feature vector, calculate the covariance matrix of the standardized data of each dimension through calculation, perform eigenvalue decomposition (in the eigenvectors obtained by eigenvalue decomposition, each element corresponds to the linear correlation coefficient between the original feature and the principal component, that is, the principal component load weight), obtain the principal components and their corresponding eigenvalues, sort them from large to small according to the eigenvalues, select the principal components with a cumulative contribution rate of more than 85%, combine the number of cluster-covered nodes and the signal strength mean in the spatio-temporal feature vector with the adaptive occupancy rate, map them to the frequency band activity, map the features such as signal strength variance and multipath fading factor that characterize signal fluctuation and transmission stability to the stability, map the features affected by the beacon node interference in the dynamic interference compensation coefficient and the Doppler frequency shift rate to the interference sensitivity, and generate an ordered triple of characteristic parameters including frequency band activity, stability, and interference sensitivity according to the principal component load weight to allocate the contribution degrees of the frequency band activity parameter, the stability parameter, and the interference sensitivity parameter;

[0097] Dynamically adjust the feature screening threshold according to the current network load, calculate the local density and distance of the feature parameters in the feature parameter triple, and generate a set of frequency band feature parameters, specifically including: According to the current network load (real-time throughput and concurrent connection number of beacon nodes), adopt a dynamic threshold algorithm to adjust the feature screening criteria: when the load is high, set the stability threshold (calculate the 90th percentile as the threshold through historical data), and set a lower tolerance value for interference sensitivity (take 80% of the recent minimum value as the tolerance value). When the load is low, adopt a loose threshold (mean standard deviation), after standardizing the feature parameter triple, use the density-reachable algorithm to calculate the local density: use a dynamic radius (dynamic radius initial value (1 current load maximum load)) to delimit the neighborhood, take the number of points within the dynamic radius neighborhood of each frequency band in three-dimensional space as the local density, calculate the feature distance between frequency bands through the Manhattan distance algorithm, and screen effective features according to the local density load-related density threshold, stability dynamic noise floor, and interference sensitivity load adjustment coefficient, and integrate the screened triple with the statistics of local density and average distance to generate a set of frequency band feature parameters including frequency band active state, stability characteristics, and interference response.

[0098] By proposing a dual calculation method of signal quality factor and adaptive occupancy rate, the problem of inconsistent spectrum data fusion in traditional monitoring systems in complex scenarios is effectively solved. The calculation formula of the signal quality factor integrates multi-dimensional parameters such as standard deviation, mean, and Doppler frequency shift rate, making the spectrum quality assessment no longer limited to a single signal strength index and being able to more comprehensively reflect the transmission stability and anti-interference ability of the channel. Secondly, the weighted calculation method of adaptive occupancy rate dynamically adjusts the weight of the frequency band occupancy duration according to the signal quality, avoiding the deviation generated by the traditional fixed-duration statistical method in blind areas or data conflict areas, enabling the system to accurately capture the sudden changes of local spectrum even when the monitoring nodes are unevenly distributed or there are blind areas, and improving the perception sensitivity to spectrum anomalies.

[0099] By constructing characteristic parameter triplets and frequency band characteristic parameter sets, the intelligence level and dynamic adaptability of spectrum management are significantly enhanced. On the one hand, the characteristic parameter triplets organically integrate spatiotemporal characteristics, interference sensitivity and stability, and realize efficient compression and accurate characterization of spectrum characteristics through principal component analysis dimensionality reduction and dynamic threshold screening. On the other hand, the mechanism of dynamically adjusting the screening criteria based on network load enables the system to give priority to retaining high-quality frequency band characteristics with high stability and low interference sensitivity when the load is high, and relax the screening conditions to explore potential available frequency bands when the load is low. The adaptive spectrum feature extraction method not only effectively deals with the consistency problem during distributed node data fusion, but also can flexibly adjust the spectrum monitoring strategy according to the load requirements of different scenarios, optimize the allocation of spectrum resources, and improve the overall performance and reliability of wireless communication networks in complex dynamic environments.

[0100] In one case of this embodiment, adjusting the monitoring range of the common node according to the effective coverage radius parameter includes:

[0101] According to the effective coverage radius parameters of the beacon node and the overlap requirements of the monitoring area, the monitoring range adjustment coefficient of the ordinary node is calculated, which specifically includes: determining the three-dimensional coordinates of the beacon node and the ordinary node, calculating the straight-line distance between adjacent beacon nodes, taking the coverage radius of the beacon node as the benchmark, combining the overlap requirements (taking the overlapping area of ​​adjacent monitoring areas not less than 30% of the coverage area of ​​a single node as the overlap threshold), deriving the theoretical boundary of the monitoring range of the ordinary node through the geometric projection algorithm, using the grid division method to discretize the monitoring area into grid units, counting the number of times each grid is covered by the beacon node and the ordinary node, and constructing a coverage density matrix. When the coverage density of the monitoring area is lower than the overlap threshold, the monitoring radius of the ordinary node is proportionally expanded, otherwise it is reduced. Through iterative calculation, the monitoring range of each ordinary node is effectively connected with the coverage area of ​​the beacon node in accordance with the overlap requirements, and finally the ratio of the adjusted monitoring radius to the initial radius is used as the monitoring range adjustment coefficient of the ordinary node;

[0102] The original monitoring range of the common node is combined with the monitoring range adjustment coefficient to generate a new monitoring range, specifically including: multiplying the initial monitoring range of the common node by the monitoring range adjustment coefficient to obtain the adjusted monitoring radius, and through the three-dimensional space buffer analysis algorithm, with the coordinates of the common node as the center and the adjusted monitoring radius as the new radius, generating a spherical basic monitoring area, which is the new monitoring range.

[0103] By dynamically adjusting the monitoring range of ordinary nodes, the problems of monitoring blind spots and inconsistent data fusion of distributed nodes in complex urban activity scenarios are effectively solved. First, based on the monitoring range adjustment mechanism of beacon node effective coverage radius and overlap requirements, using geometric projection and grid division algorithms, the monitoring boundaries of ordinary nodes are accurately deduced and a coverage density matrix is constructed, which can realize the adaptive optimization of the monitoring area during the iteration process, ensuring that the overlap of adjacent node monitoring areas is not less than 30%, eliminating monitoring blind spots from the root. Second, by regenerating the monitoring range through the three-dimensional space buffer analysis algorithm, the dynamic reconstruction of the monitoring area is realized, enhancing the multi-node collaborative monitoring ability. It not only significantly improves the monitoring sensitivity of local area spectrum changes, enabling the system to detect spectrum anomalies more timely and accurately, but also provides more accurate spectrum usage data for subsequent channel allocation, effectively avoiding unreasonable channel allocation problems caused by missing or conflicting monitoring data, thus significantly improving the reliability and efficiency of the distributed spectrum monitoring system in complex dynamic environments.

[0104] In one case of this embodiment, the frequency band characteristic parameter set is extracted to generate a shared parameter set including the spectrum hole position and occupancy rate, including:

[0105] According to the new monitoring range, the signal strength and occupancy duration data of each frequency band in the dynamic monitoring area are extracted from the frequency band characteristic parameter set to generate spatio-temporal characteristic correlation parameters, specifically including: according to the new monitoring range, the node IDs falling within the monitoring area are extracted through the spatial screening algorithm, and the mean and variance of the signal strength of the corresponding nodes are screened from the spatio-temporal dynamic characteristic matrix of the frequency band characteristic parameter set according to the frequency band and timestamp index. At the same time, the occupancy duration data required for adaptive occupancy rate calculation is extracted, segmented by a fixed time window, and the data within each window is spatio-temporally aligned to generate spatio-temporal characteristic correlation parameters with node ID, frequency band, and timestamp as keys, including signal strength statistics and weighted occupancy duration.

[0106] Based on the spatio-temporal characteristic correlation parameters, a signal strength threshold and an occupancy duration threshold are set, and the areas with signal strength lower than the signal strength threshold and occupancy duration lower than the occupancy duration threshold are screened to obtain the spectrum hole position parameters, specifically including: calculating the 10th percentile of the signal strength of each frequency band as the set signal strength threshold, setting 30% of the historical average occupancy duration as the occupancy duration threshold, and performing double-condition screening on the signal strength mean and weighted occupancy duration data of each node ID, frequency band, and timestamp, that is, the signal strength mean signal strength threshold and weighted occupancy duration occupancy duration threshold, extracting the geographical coordinates of the nodes and the corresponding frequency bands that meet the conditions, and fitting the continuous area of the spectrum hole through the spatial interpolation algorithm to generate spectrum hole position parameters including coordinate position, frequency band, and idle state;

[0107] For the monitoring area corresponding to the spectrum hole location parameters, calculate the proportion of the occupancy duration of each frequency band in this monitoring area to the total monitoring duration to obtain the spectrum hole occupancy rate parameter, which specifically includes: through the spatial overlay analysis algorithm, spatially match the node geographical coordinates with the continuous area of the spectrum hole, extract the node IDs and corresponding frequency bands falling within this area, group them by frequency band, summarize the weighted occupancy duration of each frequency band in all time windows, calculate the total occupancy duration of this frequency band in the monitoring area, and the total monitoring duration is the total duration of the same period of the spatio-temporal dynamic feature matrix (i.e., the sum of the times of all time windows). Through the data aggregation algorithm, divide the total occupancy duration of each frequency band by the total monitoring duration to obtain the normalized spectrum hole occupancy rate parameter;

[0108] Integrate the spectrum hole location parameters and the spectrum hole occupancy rate parameters to generate a shared parameter set containing the spectrum hole location and occupancy rate, which specifically includes: based on the coordinate position, frequency band, and idle status in the spectrum hole location parameters and the frequency band and occupancy rate value in the spectrum hole occupancy rate parameters, perform field association through the spatial join algorithm, use the frequency band and geographical coordinates as the common index, and integrate the coordinate position, frequency band number, idle status of each spectrum hole area with the corresponding occupancy rate parameter to construct a structured data set containing fields such as frequency band ID, geographical coordinates, idle status, and occupancy rate, and store it in a unified data format to generate a shared parameter set containing the spectrum hole location and occupancy rate.

[0109] By accurately locating the spectrum hole and determining its occupancy rate, the blind area problem of spectrum resource monitoring in complex scenarios is effectively solved. By extracting the key data of each frequency band in the dynamic monitoring area from the frequency band feature parameter set and generating spatio-temporal feature correlation parameters, a comprehensive and detailed understanding of the spectrum usage situation is achieved. And by setting reasonable signal strength and occupancy duration thresholds, the true spectrum hole locations are screened out, and the continuous area is fitted through the spatial interpolation algorithm, making the positioning of the spectrum hole more accurate and intuitive. This not only improves the utilization rate of spectrum resources but also provides accurate idle frequency band information for subsequent channel allocation, avoiding the problem of unreasonable channel allocation caused by inaccurate monitoring, thereby improving the performance and efficiency of the entire communication system.

[0110] By generating a shared parameter set containing the positions and occupancy rates of spectrum holes, the data collaboration among distributed monitoring nodes is greatly enhanced. On the one hand, by integrating the spectrum hole position parameters and occupancy rate parameters to construct a structured shared parameter set, the unified management and efficient sharing of data from different nodes are realized, eliminating the inconsistency problems during data fusion. On the other hand, storing in a unified data format facilitates the rapid exchange and reading of spectrum hole information among nodes, enabling the system to promptly and accurately detect sharp changes in spectrum usage in local areas, improving the sensitivity to spectrum anomalies. In complex large-scale urban activity scenarios, this efficient collaboration mechanism ensures the dynamic allocation and rational utilization of spectrum resources, providing strong support for addressing the challenges of spectrum resource tension and diverse communication requirements.

[0111] In one case of this embodiment, the channel allocation revenue parameters of the shared parameter set are calculated to obtain the final channel allocation strategy parameters for each game area, including:

[0112] Based on the spectrum hole positions and occupancy rates in the shared parameter set, determine the availability of each frequency band and generate frequency band availability parameters, specifically including: Based on the shared parameter set, group by frequency band, and through a spatial join algorithm, associate the geographical coordinates, frequency band, and idle status of the spectrum hole position parameters with the frequency band and occupancy rate values of the occupancy rate parameters. For each frequency band, extract the continuous region coordinates and occupancy rate of its corresponding spectrum hole, use a data aggregation algorithm to calculate the average occupancy rate of this frequency band in the monitoring area, and combine with the idle status to set a threshold for the occupancy rate. If the occupancy rate is lower than the threshold and there is a continuous hole region, it is determined that this frequency band is available in the corresponding area, and generate frequency band availability parameters including frequency band ID, available area, and availability status;

[0113] Combine the frequency band availability parameters with the spectrum requirements of each game area and analyze them to generate a preliminary channel allocation strategy, specifically including: Based on the frequency band availability parameters, use a spatial overlay analysis algorithm to spatially match the geographical scope of each game area with the available areas of the frequency bands, extract the available area ranges and statuses of each frequency band within each game area, combine with the spectrum requirements of the game area (target frequency band list and minimum bandwidth requirements), use a multi-attribute decision-making algorithm with frequency band occupancy rate, signal stability, and interference sensitivity as evaluation dimensions to calculate a comprehensive priority score for each available frequency band. After sorting by score from high to low, use a greedy allocation strategy to preferentially allocate high-priority frequency bands to game areas with urgent spectrum requirements, and at the same time, through a neighborhood conflict detection algorithm, avoid co-channel interference between adjacent game areas (that is, check whether the same frequency band has been allocated to adjacent cells, and if so, select the sub-optimal frequency band), and finally generate a preliminary channel allocation strategy including frequency band ID, allocated game area, available time period, and power limit;

[0114] Calculate the benefits of the preliminary channel allocation strategy to obtain channel allocation benefit parameters, specifically including: based on the preliminary channel allocation strategy, for each game area and its allocated frequency band, extract three indicators of spectrum hole occupancy rate, signal stability, and interference sensitivity from the shared parameter set, and synchronously quantify the spectrum demand score of the game area, specifically: target frequency band matching degree (1 if the allocated frequency band is within the frequency band of the game area, otherwise 0) Bandwidth satisfaction degree (the ratio of the available bandwidth of the allocated frequency band to the minimum demand of the game area, normalized to 0 1), perform weighted calculation on the occupancy rate (weight 0.3), stability (weight 0.4), interference sensitivity (weight 0.2), and demand satisfaction degree (weight 0.1) to obtain the basic benefit value of each game area. Through the neighborhood conflict detection algorithm, compare the allocated frequency bands of adjacent game areas. If they are of the same frequency, based on the effective coverage radius parameter of the beacon node, combine the spatial screening algorithm to identify the signal overlapping area, and deduct the benefit of the involved area according to the proportion of the area (deduct 20% if the overlap rate > 10%, and so on in a 1:2 ratio). Finally, summarize the regional benefits according to the load weight of each game area (the proportion of the real-time throughput of the beacon node) to obtain the total benefit, and generate channel allocation benefit parameters including the total benefit, the contribution value of each indicator (spectrum hole occupancy rate, signal stability, interference sensitivity, spectrum demand score), and the basic benefit value of each game area;

[0115] Calculate the final channel allocation strategy parameters for each game area, specifically including: based on the channel allocation benefit parameters, take the maximization of the basic benefit value of each game area and the minimization of neighborhood conflict deduction as two objectives, and introduce a multi-objective optimization mechanism, specifically: combine the frequency band availability parameters (including available area, occupancy rate threshold, signal stability index) with the spectrum demand constraint (target frequency band list matching, allocated bandwidth ≥ minimum demand bandwidth), and conduct a per-region evaluation of the frequency band allocation in the preliminary strategy. For adjacent regions with co-channel interference, accurately locate the signal overlapping area through the neighborhood conflict detection algorithm, calculate the proportion of the overlapping area according to the effective coverage radius parameter of the beacon node, and quantify the conflict level (deduct 20% if the overlap rate > 10%, and so on in a 1:2 ratio). During the adjustment process, for the game areas that need to reallocate the frequency band, from its available frequency band set, screen the sub-optimal frequency band that meets the target frequency band included in the area demand list and the available bandwidth ≥ minimum demand bandwidth, and give priority to ensuring the basic benefits of the game areas with urgent spectrum demands. Update the allocation plan round by round through iterative dynamic allocation: after each round of adjustment, recalculate the basic benefits, conflict deduction values, and total benefits of each region until the fluctuation range of the total benefits between two adjacent iterations is lower than the convergence threshold (total benefit change rate < 5%), and all allocated frequency bands meet the target frequency band matching Bandwidth demand Without the triple constraints of adjacent co-frequency interference, the final channel allocation strategy parameters are finally generated.

[0116] Through accurate frequency band availability analysis and multi-dimensional channel allocation strategy generation, the problem of unreasonable channel allocation in complex scenarios is effectively solved. Based on the spectrum hole positions and occupancy rates in the shared parameter set, the availability of each frequency band can be accurately determined, and detailed frequency band availability parameters are generated. This step realizes the refined evaluation of frequency band availability through spatial connection algorithms and data aggregation algorithms, ensuring the scientificity and accuracy of frequency band allocation. Then, combined with the spectrum requirements of each game area, multi-attribute decision-making algorithms and greedy allocation strategies are used to generate a preliminary channel allocation strategy, fully considering multiple key factors such as frequency band occupancy rate, signal stability, and interference sensitivity, improving the rationality and efficiency of channel allocation, and effectively avoiding co-frequency interference between adjacent game areas through the neighborhood conflict detection algorithm, further enhancing the reliability of channel allocation. Finally, the generated channel allocation strategy can better meet the spectrum requirements of each game area, improve the utilization rate of spectrum resources, and provide a strong guarantee for the stable operation of the communication system.

[0117] Through comprehensive channel allocation benefit calculation and multi-objective optimization mechanisms, the comprehensive benefits of channel allocation are significantly improved. Based on the preliminary channel allocation strategy, by quantifying indicators such as spectrum hole occupancy rate, signal stability, and interference sensitivity, and combining spectrum requirement scores, the basic benefit values of each game area can be accurately calculated. At the same time, using the neighborhood conflict detection algorithm and beacon node effective coverage radius parameters, the benefits of co-frequency interference areas are reasonably deducted, making the total benefit calculation more reasonable. By introducing a multi-objective optimization mechanism, with the maximization of the basic benefit values of each game area and the minimization of neighborhood conflict deductions as the optimization objectives, the allocation scheme is iteratively updated round by round through dynamic allocation until the convergence conditions and constraint requirements are met. This process not only improves the global optimality of channel allocation but also ensures the efficient utilization of spectrum resources and the stable operation of the system. The finally generated final channel allocation strategy parameters can better adapt to the changes in spectrum requirements in complex dynamic environments, providing an efficient and reliable channel allocation solution for the communication system in large urban event scenarios, and significantly improving the overall performance and user experience of the system.

[0118] In one case of this embodiment, the real-time spectrum monitoring data of the monitoring nodes in each game area is processed for spatio-temporal alignment according to the final channel allocation strategy parameters to generate global spectrum state parameters including signal strength gradient distribution and spectrum state mutation probability, including:

[0119] Based on the time - slice division in the final channel allocation strategy parameters, an adaptive sliding time window is generated for each monitoring node. By using the time - scale signal transmitted by the beacon node and the received signal strength of the ordinary node, the signal propagation delay is calculated and the time - window boundary is dynamically adjusted to generate spatio - temporal synchronization calibration parameters, which specifically include: Based on the time - slice division of the final channel allocation strategy, an adaptive sliding time window is generated for each monitoring node. Among them, the beacon node periodically transmits a time - stamped time - scale signal, and the ordinary node receives and records the signal strength and arrival time. The two - way timestamp algorithm is used to calculate the signal propagation delay through the time difference between the sending and receiving times of the signal interaction between the beacon node and the ordinary node. According to the signal propagation delay, the time - window boundary of the ordinary node is dynamically adjusted. Based on the time - slice of the beacon node, the delay is compensated to the start and end times of the ordinary node's time window, so that the time window slides adaptively with the propagation delay, generating spatio - temporal synchronization calibration parameters including the node time - synchronization deviation;

[0120] The weight index of the ordinary monitoring node is dynamically adjusted according to the distribution density of the monitoring nodes and the effective coverage radius parameter of the beacon node to generate a signal - strength gradient distribution parameter, which specifically includes: calculating the spatial distribution density of the ordinary nodes in the monitoring area through the kernel density estimation algorithm, constructing a buffer area centered on the beacon node, and using the inverse - distance weighted algorithm to generate an initial weight index according to the ratio of the Euclidean distance from the ordinary node to the beacon node to the coverage radius. For the area where the distribution density is higher than the threshold (historical density mean standard deviation), the weight is attenuated according to the density ratio. For the low - density area, the weight is compensated according to the coverage radius. Finally, the signal strength of the ordinary node is spatially weighted and interpolated with the adjusted weight index to generate a signal - strength gradient distribution parameter;

[0121] Analyze the spatio - temporal synchronization calibration parameters and the spectral - state difference between adjacent time slices to obtain the spectral - state mutation probability. The specific process is as follows: For each time slice , divide the signal - strength change amount of the th time slice by the reference signal - strength benchmark and take the absolute value, then perform - power operation to obtain the non - linear amplification value of the signal - strength change in the m - th time slice. Multiply the amplification value by the sign function. If , then , if , then , if , then . Then sum up the results of all time slices to obtain the comprehensive mutation trend value. Sum up the amplification values of all time - slice signals to obtain the total sum of the non - linear amplification values of the signal - strength changes of all time slices. Take the maximum value between the total sum and the non - zero constant , and multiply the maximum value by the environmental attenuation factor , the total fluctuation intensity value after environmental adjustment is obtained, and the comprehensive mutation trend value is divided by the total fluctuation intensity value after environmental adjustment to obtain the spectrum state mutation probability;

[0122] When specifically applied, the specific calculation formula of the above spectrum state mutation probability is as follows: ;

[0123] In the formula, represents the spectrum state mutation probability, represents the change amount of the signal intensity in the th time slice, represents the reference signal intensity benchmark, represents the non-linear amplification index, , represents the environmental attenuation factor, , represents the anti-zero constant, , represents the sign function, is , is the total number of time slices;

[0124] Analyze the signal intensity gradient distribution parameter and the spectrum state mutation probability, and generate a global spectrum state parameter including the signal intensity gradient distribution and the spectrum state mutation probability. Specifically, based on the signal intensity gradient distribution parameter, the monitoring area is discretized into regular grid cells through a spatial grid algorithm, the signal intensity gradient values of each grid cell are extracted, the spectrum state mutation probability of each grid cell in adjacent time slices is calculated synchronously, and a spatio-temporal data association algorithm is used to match the gradient distribution value of the grid cell with the mutation probability at the corresponding position. Indexed by node ID, frequency band, and timestamp, a multi-dimensional data set containing spatial gradient features and time mutation characteristics is constructed. After being integrated in a unified data format, a global spectrum state parameter including the signal intensity gradient distribution and the spectrum state mutation probability is generated.

[0125] An adaptive sliding time window is generated for each monitoring node through the final channel allocation strategy, and the signal propagation delay is calculated using the bidirectional timestamp algorithm to dynamically adjust the time window boundary, generating spatio-temporal synchronization calibration parameters, realizing the spatio-temporal synchronization between monitoring nodes, and being able to monitor the spectrum state more accurately. On the other hand, considering factors such as the distribution density of monitoring nodes and the effective coverage radius of beacon nodes, the weight index of ordinary monitoring nodes is dynamically adjusted to generate the signal intensity gradient distribution parameter, making the spectrum monitoring data more representative in spatial distribution. Even in large urban activity scenarios with uneven node distribution, blind spots can be effectively reduced, and the monitoring accuracy of spectrum usage can be improved, thus providing a more reliable data basis for subsequent timely detection of spectrum anomalies and reasonable channel allocation.

[0126] By deeply analyzing the spatio-temporal synchronization calibration parameters and the spectral state differences of adjacent time slices, the spectral state mutation probability is obtained, and it is combined with the signal strength gradient distribution parameters to generate the global spectral state parameters, which significantly improves the monitoring sensitivity to the sharp changes in the spectral usage of local areas. By using methods such as non-linear amplification and sign function to process the signal strength change amounts of each time slice, the mutation trend of the spectral state can be effectively amplified, so that even in the early stage when the spectral change is relatively subtle, it can be detected in time, avoiding missing the discovery opportunity of spectral anomalies due to low monitoring sensitivity. Then, based on the signal strength gradient distribution parameters, a spatial grid algorithm and a spatio-temporal data association algorithm are used to combine the spatial gradient features and the time mutation characteristics to construct a multi-dimensional data set, which can more accurately present the dynamic changes of the spectrum and provide a more comprehensive and detailed decision-making basis for channel allocation, thus effectively avoiding the problem of unreasonable channel allocation caused by the inability to detect spectral anomalies in time and ensuring the efficient utilization of spectral resources in large urban activity scenarios.

[0127] In one case of this embodiment, according to the global spectral state parameters, it is judged whether the current channel allocation strategy fails. If it fails, a dynamic allocation instruction is immediately triggered, including:

[0128] The historical spectral monitoring data is sliced by k time slices and frequency bands, and the gradient mean of the signal strength distribution in space and the change rate of the spectral occupancy rate in the time dimension are calculated for each slice, forming a dynamic feature sequence containing spatio-temporal change rules, specifically including: the historical spectral monitoring data is divided into k time slices according to the minute-level time granularity and frequency-domain sliced according to the MHz-level frequency band accuracy. For each frequency band within each time slice, based on the geographical coordinates of the monitoring nodes and the signal strength data, the Kriging interpolation algorithm is used to perform spatial interpolation on the signal strength data of discrete monitoring nodes to obtain a continuous signal strength spatial distribution. The signal strength gradient value at each spatial position is calculated through the gradient operator, and the average value of the gradient values within the region is taken as the signal strength gradient mean of the frequency band for this time slice. For the spectral occupancy rate sequence of each frequency band in the time dimension, the first-order difference method is used to calculate the absolute change value of the occupancy rate between adjacent time slices, and it is divided by the time slice length to obtain the change rate in the time dimension, which is the mutation probability. Index the gradient mean and the change rate according to the time slice - frequency band to form a dynamic feature sequence containing spatio-temporal change rules;

[0129] Group the historical channel allocation corresponding time period data according to the game area, and separately count the median of the signal strength gradient and the extreme value of the spectrum occupancy rate change rate in the dynamic feature sequence of each monitoring area, and generate preliminary regional gradient reference parameters and regional mutation reference parameters, specifically including: according to the geographical scope of the game area, use the spatial connection algorithm to perform spatial matching between the historical channel allocation time period data and the game area polygon, first extract the dynamic feature sequence corresponding to the time period within each area, for each game area, group by time slice - frequency band dimension, calculate the median of the mean signal strength gradient within the group, and at the same time extract the maximum and minimum values of the spectrum occupancy rate change rate (extreme value of the mutation amplitude in the time dimension), take the median as the preliminary regional gradient reference parameter, and the extreme value of the mutation amplitude as the preliminary regional mutation reference parameter;

[0130] Based on the building density of the current monitoring area, correct the preliminary regional gradient reference parameters and regional mutation reference parameters to obtain the signal strength gradient threshold and mutation probability threshold finally adapted to the current monitoring environment, specifically including: based on the building density grid data of the monitoring area, use the partition statistics algorithm to obtain the average building density value of each game area, divide the area into three levels of high density, medium density and low density according to the density level, count the ratio of the gradient median to the average density in the historical data of each level area as the correction coefficient, multiply the gradient median of the corresponding level by the correction coefficient to obtain the signal strength gradient threshold adapted to the current density, for the extreme value of the spectrum occupancy rate change rate, according to the density classification result, use the historical data to count the average attenuation amplitude of the mutation extreme value under each density level, and perform a proportional adjustment in the same direction on the original extreme value through the hierarchical weighting algorithm to generate the final mutation probability threshold, and obtain the signal strength gradient threshold and mutation probability threshold finally adapted to the current monitoring environment.

[0131] By constructing a dynamic feature sequence containing spatio-temporal variation rules, the monitoring accuracy and sensitivity of the spectrum state in complex large urban activity scenarios are effectively improved. The historical spectrum monitoring data is finely sliced by time slice and frequency band, and the Kriging interpolation algorithm is used to realize the spatial continuity of the signal strength. Then, combined with the gradient operator and the first-order difference method, the dynamic features of each frequency band in terms of spatial gradient and time change rate are accurately extracted. This process can not only capture the subtle changes in the spectrum state, but also form a dynamic feature sequence with spatio-temporal dimensions, providing high-resolution data support for the subsequent evaluation of channel allocation strategies. In large urban activity scenarios, accurate dynamic feature extraction can effectively make up for the blind areas of distributed monitoring nodes, avoid monitoring deviations caused by insufficient data fusion and cooperation mechanisms, and thus significantly enhance the monitoring sensitivity to the rapid changes in spectrum usage. Through the generation of this dynamic feature sequence, the system can more sensitively detect spectrum anomalies, facilitating the system to timely adjust the channel allocation strategy to ensure the efficient utilization of spectrum resources.

[0132] By statistically analyzing the dynamic feature sequences in different game regions, preliminary regional gradient reference parameters and regional mutation reference parameters are obtained. Then, these reference parameters are hierarchically corrected according to the building density to generate the signal strength gradient threshold and mutation probability threshold that are finally adapted to the current environment. This parameter correction method based on environmental density fully considers the impact of buildings on signal propagation in large urban activity scenarios, enabling the channel allocation strategy to be dynamically adjusted according to the actual monitoring environment. In high building density areas, through the corrected thresholds, the system can more accurately identify changes in the spectrum state and avoid monitoring errors caused by environmental differences. In medium and low density areas, it can optimize resource allocation and reduce the situation of over-monitoring or under-monitoring. Through this precise parameter correction and adaptation, the system can timely determine whether the current channel allocation strategy fails and trigger dynamic allocation instructions when necessary, greatly improving the rationality of channel allocation and the efficiency of spectrum management, effectively solving the problem of unreasonable channel allocation caused by the lack of environmental adaptability in traditional methods, and providing a reliable solution for spectrum resource management in large urban activities.

[0133] In one case of this embodiment, according to the global spectrum state parameters, it is judged whether the current channel allocation strategy fails. If it fails, a dynamic allocation instruction is immediately triggered, and it further includes:

[0134] If the signal strength gradient in the global spectrum state parameters is greater than the signal strength gradient threshold or the mutation probability continuously exceeds the mutation probability threshold within k consecutive time slices, it is determined that the previous channel allocation strategy fails, and a dynamic allocation instruction is immediately triggered.

[0135] By setting clear threshold judgment criteria, the problem that it is difficult to detect the failure of the channel allocation strategy in complex large urban activity scenarios in a timely manner is effectively solved. When the signal strength gradient in the global spectrum state parameters exceeds the set threshold, or the mutation probability continuously exceeds the threshold within k consecutive time slices, the system can quickly determine that the current channel allocation strategy fails and immediately trigger a dynamic allocation instruction, improving the response speed to changes in the spectrum state and avoiding the late discovery of spectrum anomalies caused by insufficient monitoring sensitivity. In large urban activity scenarios, it can dynamically adjust channel allocation according to the actual usage of the spectrum in a timely manner, ensuring the efficiency and stability of communication, and effectively making up for the defect of unreasonable channel allocation caused by blind spots of monitoring nodes and inconsistent data fusion in traditional methods.

[0136] In one case of this embodiment, the dynamic allocation instruction includes:

[0137] Mark the area where the signal strength gradient exceeds the signal strength gradient threshold as an abnormal area, calculate the spatio-temporal correlation parameters between the abnormal area and the adjacent areas, and generate a regional anomaly propagation factor, specifically including: based on the discretized regular grid cells in the global spectrum state parameters, compare the signal strength gradient value of each grid cell with the signal strength gradient threshold adapted to the current monitoring environment. The grid cells with gradient values exceeding the corresponding threshold constitute the abnormal area. Use a spatial connection algorithm to identify the adjacent areas that are directly adjacent to the abnormal area in space. For the abnormal area and each adjacent area, extract the signal strength gradient change amount and the spectrum state mutation probability data of the two on the same frequency band dimension and adjacent time slice sequence. By calculating the correlation coefficient of the two sets of data, obtain the spatio-temporal correlation parameters. Based on the Euclidean distance between each adjacent area and the abnormal area, construct a spatial weight matrix, and perform weighted fusion of the spatio-temporal correlation parameters and the spatial weights to generate a regional anomaly propagation factor;

[0138] Analyze the signal strength gradient distribution of each monitoring node in the abnormal area to obtain the spectrum state chaos degree, specifically including: for each monitoring node in the abnormal area, extract the signal strength gradient value corresponding to its geographical coordinates to form a two-dimensional data set containing position information and gradient values. Normalize the gradient values, count the distribution of all node gradient values, calculate the occurrence frequency of each gradient value in the area (i.e., the ratio of the number of nodes with this gradient value to the total number of nodes in the abnormal area), generate a discrete probability density distribution. For each unique gradient value, regard its occurrence frequency as a probability, calculate the sum of the products of all gradient value probabilities and the corresponding natural logarithms, and then take the negative value. This value is the spectrum state chaos degree;

[0139] According to the regional anomaly propagation factor and the spectrum state chaos degree, dynamically adjust the boundary of the game area to generate a temporary spectrum management area, specifically including: taking the geometric center of the abnormal area as a reference, construct a spatial influence buffer area according to the numerical size of the regional anomaly propagation factor. The higher the propagation factor, the stronger the spatio-temporal influence potential of the abnormal area on the surrounding area. The buffer radius is dynamically expanded according to the proportionality coefficient (150 meters of buffer distance per unit propagation factor). Use the Delaunay triangulation algorithm to construct a spatial adjacency relationship network of the abnormal area and all adjacent areas, extract the directly adjacent polygon areas, and use the spectrum state chaos degree as the basis for judging regional stability. For the abnormal areas with chaos degree higher than 1.5 times the standard deviation of the historical mean, start the adjacent area merging mechanism, specifically: for each adjacent area, calculate the weighted comprehensive value of its propagation factor and the chaos degree of the abnormal area (the propagation factor weight accounts for 60% and the chaos degree weight accounts for 40%). When the comprehensive value exceeds the dynamic threshold (0.65), include this adjacent area in the temporary management scope through the polygon overlay algorithm. Based on the original boundary of the abnormal area, fuse the polygon areas of the eligible adjacent areas to generate a temporary spectrum management area including the abnormal influence range;

[0140] Analyze the monitoring nodes in the temporary spectrum management area to obtain the channel resource demand degree vector, specifically including: for the monitoring nodes in the temporary spectrum management area, extract the signal strength, spectrum occupancy rate, and channel conflict times of each node in the current and historical time slices, calculate the change rate of the spectrum occupancy rate of the node in the target frequency band (the difference in occupancy rates between adjacent time slices divided by the time slice length) as the demand urgency index, count the number of channel retransmissions of the node in the past 30 minutes as the congestion sensitivity index, set the priority weight according to the node service type (weight 0.6 for real-time services and weight 0.4 for non-real-time services), normalize the above indexes, and construct a multi-dimensional vector according to the frequency band dimension (10 MHz frequency bands). The component at the corresponding position of each frequency band is the change rate of the occupancy rate Priority weight Congestion sensitivity (1 Priority weight), to form a channel resource demand degree vector containing frequency band priority and demand intensity;

[0141] Extract the regional abnormal propagation factor, spectrum state chaos degree, and channel resource demand degree vector to obtain multi-dimensional risk assessment parameters, specifically including: perform cross-dimensional feature fusion on the regional abnormal propagation factor, spectrum state chaos degree, and channel resource demand degree vector. Specifically: standardize the propagation factor and chaos degree through the Z-score normalization method, arrange the demand components of each frequency band in the channel resource demand degree vector in the order of frequency bands (where 0-99 MHz is divided into 10 10 MHz frequency bands), and construct a multi-dimensional vector according to the fixed dimension order: the first dimension is the normalized regional abnormal propagation factor, the second dimension is the normalized spectrum state chaos degree, and the third to twelfth dimensions correspond to the channel resource demand components of 10 frequency bands in turn, finally forming multi-dimensional risk assessment parameters including spatial propagation risk, regional stability risk, and frequency band resource demand;

[0142] When the evaluation value of any dimension in the multi-dimensional risk assessment parameters exceeds the multi-dimensional risk threshold, generate a dynamic allocation instruction including the optimal channel combination, power adjustment, and time slice allocation strategy;

[0143] Among them, the multi-dimensional risk threshold is specifically:

[0144] Statistically analyze the distribution of propagation factors in all adjacent regions under the historical normal state, calculate its mean and standard deviation, and take the mean + 1.5 standard deviations as the regional abnormal propagation factor threshold;

[0145] Perform kernel density estimation on the chaos degree data of the historical stable region, take the chaos degree value corresponding to the peak of its probability density as the benchmark, and set the chaos degree value in combination with the service tolerance 1.2 as the spectrum state chaos degree threshold;

[0146] For each 10 MHz frequency band, calculate the mean value and 95% percentile of the demand component under normal service scenarios in the past 7 days, and take the 95% percentile as the threshold of the channel resource demand component;

[0147] For the frequency bands dominated by real-time services, lower the threshold of the channel resource demand component by 10%, and for non-real-time service frequency bands, raise the threshold of the channel resource demand component by 15%;

[0148] Among them, the dynamic allocation instruction is specifically:

[0149] The triggering condition is:

[0150] When the regional anomaly propagation factor is greater than or equal to the regional anomaly propagation factor threshold, or when the spectrum state chaos degree is greater than or equal to the spectrum state chaos degree threshold, or when the channel resource demand component of any frequency band is greater than or equal to the demand threshold of the corresponding frequency band;

[0151] The optimal channel combination strategy is:

[0152] When the channel resource demand component of any frequency band is greater than or equal to the demand threshold of the corresponding frequency band, for the frequency bands exceeding the threshold of the channel resource demand component, sort them in descending order of the demand component, and preferentially select the top 3 high-demand frequency bands as the core allocation frequency bands;

[0153] When the regional anomaly propagation factor is greater than or equal to the regional anomaly propagation factor threshold, mark the frequency band and its adjacent 1 frequency band (a total of 3 10 MHz frequency bands) as interference-sensitive frequency bands and temporarily disable them;

[0154] Generate a channel combination including high-demand frequency bands (by priority) and non-interference-sensitive frequency bands, ensuring that the proportion of real-time service frequency bands is ≥ 60%.

[0155] The power adjustment strategy is:

[0156] Core area of the abnormal area (within the buffer radius):

[0157] When the regional anomaly propagation factor is greater than or equal to the regional anomaly propagation factor threshold, lower the power of the monitoring nodes in the core area (the reduction is 10% of the current power) to reduce signal spillover interference;

[0158] When the spectrum state chaos degree is greater than or equal to the spectrum state chaos degree threshold, it indicates that the spectrum state in the area is unstable. Perform fine power adjustment (±5%) on the top 20% of the nodes with the highest gradient values to balance the signal strength distribution in the area;

[0159] Adjacent merged area (buffer extension area):

[0160] For adjacent areas included in the temporary management scope, the closer to the abnormal center, the greater the power increase (power per 100 meters distance). 2%, with an upper limit of 30%) to enhance the anti-interference capability of edge node signals.

[0161] The time slice allocation strategy is:

[0162] Real-time business nodes:

[0163] For nodes with high demand urgency (high occupancy rate change), short time slices are allocated (time slice length is shortened by 25%), and they are preferentially inserted into the first 50% of the time slice sequence to reduce transmission delay;

[0164] For nodes with high congestion sensitivity (>10 retransmissions in 30 minutes), an additional redundant time slice is allocated to reduce the probability of conflict.

[0165] Non-real-time business nodes:

[0166] For nodes with low demand urgency (low occupancy change rate), the time slice length is extended by 20% and scheduled during off-peak hours (the last 30% of the time slice in each cycle);

[0167] For low-demand frequency band nodes that are not affected by abnormal propagation, the time slice allocation interval is expanded by 50% to release resources for high-demand nodes.

[0168] Cross-regional collaborative allocation:

[0169] If the adjacent area merging mechanism is triggered (the degree of chaos is greater than 1.5 times the standard deviation), time slices are allocated to the nodes in the merged area in descending order of priority from the abnormal core area to the adjacent expansion area, and the proportion of time slice resources of the core area nodes is ≥ 40%;

[0170] When multiple dimensions exceed the standard at the same time (regional abnormal propagation factor, spectrum state disorder and channel resource demand components of any frequency band are all abnormal):

[0171] On the basis of the high-demand frequency band, the upper and lower adjacent frequencies of the frequency band where the abnormal area is located are additionally excluded to form an isolated protection frequency band;

[0172] Power reduction for the core area (15%) Bidirectional adjustment of the extended area power increase (20%) to build a signal strength gradient buffer zone;

[0173] The time slice priority of real-time business nodes is raised to the highest level, and one pre-allocated time slice is added to all nodes in the abnormal area to ensure the feedback of monitoring data.

[0174] By accurately identifying abnormal regions and deeply analyzing their propagation characteristics and the degree of spectral state chaos, it provides strong support for dynamically adjusting the spectrum management strategy. Based on the global spectrum state parameters, it can accurately mark the abnormal regions where the signal strength gradient exceeds the threshold, and generate regional abnormal propagation factors using spatio-temporal correlation parameters, clearly quantifying the potential influence range and intensity of the abnormal regions on the surrounding areas. At the same time, by analyzing the signal strength gradient distribution of each monitoring node within the abnormal region, the obtained spectral state chaos degree effectively reflects the disorder degree of spectrum usage within the region. According to these two key indicators, the boundary of the game region is dynamically adjusted to generate a temporary spectrum management region, realizing the rapid containment and precise control of abnormal situations, significantly improving the system's monitoring sensitivity and response ability to local spectrum anomalies, effectively solving the problem of delayed discovery of spectrum anomalies caused by monitoring blind spots and inconsistent data fusion in complex large-scale urban activity scenarios, and facilitating the subsequent reasonable allocation of channel resources and ensuring smooth communication.

[0175] By carefully analyzing the monitoring nodes within the temporary spectrum management region, generating a channel resource demand vector and combining it with multi-dimensional risk assessment parameters, the rationality of the channel allocation decision is optimized. On the one hand, the channel resource demand vector synthesizes multi-dimensional information such as the spectrum occupancy rate change rate of the node, the number of channel conflicts, and the service type priority, accurately quantifying the resource demand intensity of each frequency band, enabling the system to clearly know which frequency band resources are tense and which are relatively idle. On the other hand, by integrating the regional abnormal propagation factor, spectral state chaos degree, and channel resource demand vector to form multi-dimensional risk assessment parameters, comprehensively considering spatial propagation risks, regional stability risks, and resource demand factors. When the evaluation value of any dimension exceeds the threshold, the system can quickly generate dynamic allocation instructions including the optimal channel combination, power adjustment, and time slice allocation strategies, realizing the refined and dynamic management of channel resources. This dynamic allocation mechanism based on multi-dimensional risk assessment effectively avoids the problem of unreasonable channel allocation caused by the inability to detect spectrum anomalies in a timely and accurate manner in traditional methods, significantly improving the utilization efficiency of spectrum resources and ensuring the efficient operation and stable reliability of the communication system in complex large-scale urban activity scenarios.

[0176] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A broadband spectrum monitoring system based on dynamic channel allocation, characterized in that, Including: A data acquisition unit, which is used to set up multiple monitoring nodes in a city, divide the city into multiple monitoring areas, divide the monitoring nodes into beacon nodes and ordinary nodes. The beacon nodes obtain the spectrum monitoring data and historical spectrum monitoring data of the monitoring area where they are located in real time, dynamically adjust the transmission power of the beacon nodes according to the real-time spectrum monitoring data, synchronously calculate the effective coverage radius parameter of the beacon nodes, and calculate the signal strength and occupancy duration data of each frequency band in the current monitoring area collected by the ordinary nodes in real time to obtain a set of frequency band characteristic parameters; A data identification unit, which is used to adjust the monitoring range of the ordinary nodes according to the effective coverage radius parameter, extract the set of frequency band characteristic parameters, and generate a set of shared parameters including the spectrum hole position and occupancy rate; A region division unit, which is used to divide the monitoring area into multiple game regions, take the beacon nodes in each region as game subjects, calculate the channel allocation revenue parameters of the set of shared parameters, and obtain the final channel allocation strategy parameters of each game region; A data alignment unit, which is used to perform spatio-temporal alignment processing on the real-time spectrum monitoring data of the monitoring nodes in each game region according to the final channel allocation strategy parameters, and generate global spectrum state parameters including signal strength gradient distribution and spectrum state mutation probability; A channel allocation unit, which is used to judge whether the current channel allocation strategy fails according to the global spectrum state parameters, and if it fails, immediately trigger a dynamic allocation instruction.

2. The broadband spectrum monitoring system based on dynamic channel allocation according to claim 1, characterized in that Dynamically adjusting the transmission power of the beacon nodes according to the real-time spectrum monitoring data and synchronously calculating the effective coverage radius parameter of the beacon nodes includes: For each frequency band in the real-time spectrum monitoring data, calculate the absolute value of the difference in signal strength between the time and time, divide the absolute value by the dynamic noise floor of the frequency band, and then sum over all frequency bands to obtain the total parameter of the change in signal strength relative to the noise floor for each frequency band; For each frequency band, first calculate the time derivative of its signal strength and multiply it by and then divide it by the mean signal strength of the entire frequency band to obtain the time derivative parameter of the signal strength after normalization of the frequency band; After squaring the time derivative parameter of the signal strength after frequency band normalization, summing over all frequency bands to obtain a comprehensive parameter of the relationship between the square of the signal strength change rate and the mean value of the entire frequency band; Dividing the sum parameter by the comprehensive parameter and then performing an operation through the hyperbolic tangent function to obtain a dynamic interference compensation coefficient; Fusing the dynamic interference compensation coefficient with the basic transmission power of the beacon node to obtain the current actual transmission power value; Performing a fusion calculation on the current actual transmission power value, the urban building density, and the wireless propagation loss to generate an effective coverage radius parameter.

3. The broadband spectrum monitoring system based on dynamic channel allocation according to claim 2, characterized in that, Calculating the signal strength and occupancy duration data of each frequency band in the current monitoring area collected by the ordinary nodes in real time to obtain a set of frequency band characteristic parameters, including: Clustering the signal strength data collected by adjacent ordinary nodes within the same time period to generate a spatio-temporal dynamic characteristic matrix; Analyzing the spatio-temporal dynamic characteristic matrix to obtain the signal strength fluctuation variance, multipath fading factor, and Doppler frequency shift rate of the wireless channel; Calculating the ratio of the standard deviation to the mean value of the frequency band signal strength, calculating the ratio of the Doppler frequency shift rate to the maximum Doppler frequency shift rate supported by the system, and multiplying the two ratios to obtain a fluctuation-frequency shift coupling factor; Taking the square root of the multipath fading factor to obtain a multipath fading adjustment factor; Dividing the fluctuation-frequency shift coupling factor by the multipath fading adjustment factor to obtain a signal dynamic characteristic index; Then calculating the ratio of the maximum value to the minimum value of the frequency band signal strength in the monitoring area to obtain a signal strength extreme ratio, and multiplying the signal strength extreme ratio by the signal dynamic characteristic index to obtain a signal quality factor; Weight the traditional occupancy duration of the specific frequency band monitored by ordinary nodes according to the signal quality factor to generate an adaptive occupancy rate; Calculate the spatio-temporal dynamic feature matrix, the adaptive occupancy rate, and the dynamic interference compensation coefficient of beacon nodes to generate a three-dimensional feature vector; Reduce the dimension of the principal components of the three-dimensional feature vector to obtain a triple of feature parameters including frequency band activity, stability, and interference sensitivity; Dynamically adjust the feature screening threshold according to the current network load, calculate the local density and distance of the feature parameters in the triple of feature parameters, and generate a set of frequency band feature parameters.

4. A broadband spectrum monitoring system based on dynamic channel allocation according to claim 2, characterized in that, Adjust the monitoring range of ordinary nodes according to the effective coverage radius parameter, including: Calculate the monitoring range adjustment coefficient of ordinary nodes according to the effective coverage radius parameter of beacon nodes and the requirement of monitoring area overlap; Combine the original monitoring range of ordinary nodes with the monitoring range adjustment coefficient to generate a new monitoring range, specifically including: perform a multiplication operation on the initial monitoring range of ordinary nodes and the monitoring range adjustment coefficient to obtain the adjusted monitoring radius, and through a three-dimensional space buffer analysis algorithm, use the coordinates of ordinary nodes as the center and the adjusted monitoring radius as the new radius to generate a spherical basic monitoring area, which is the new monitoring range.

5. A broadband spectrum monitoring system based on dynamic channel allocation according to claim 3, characterized in that, Extract the set of frequency band feature parameters to generate a shared parameter set including the position and occupancy rate of spectrum holes, including: Extract the signal strength and occupancy duration data of each frequency band in the dynamic monitoring area from the set of frequency band feature parameters according to the new monitoring range to generate spatio-temporal feature correlation parameters; Based on the spatio-temporal feature correlation parameters, set the signal strength threshold and the occupancy duration threshold, and filter out the areas where the signal strength is lower than the signal strength threshold and the occupancy duration is lower than the occupancy duration threshold to obtain the spectrum hole position parameters; Through the spatial overlay analysis algorithm, spatially match the node geographical coordinates with the continuous area of spectrum holes, extract the node IDs and corresponding frequency bands falling within this area, group them by frequency band, summarize the weighted occupancy duration of each frequency band in all time windows, calculate the total occupancy duration of this frequency band in the monitoring area, the total monitoring duration is the total duration of the same period of the spatio-temporal dynamic feature matrix, and through the data aggregation algorithm, divide the total occupancy duration of each frequency band by the total monitoring duration to obtain the spectrum hole occupancy rate parameter; Integrate the spectrum hole position parameters and the spectrum hole occupancy rate parameters to generate a shared parameter set including the position and occupancy rate of spectrum holes.

6. The broadband spectrum monitoring system based on dynamic channel allocation according to claim 5, characterized in that, Calculate the channel allocation revenue parameters of the shared parameter set to obtain the final channel allocation strategy parameters for each game area, including: Based on the position and occupancy rate of spectrum holes in the shared parameter set, determine the availability of each frequency band to generate frequency band availability parameters; Combine the frequency band availability parameters with the spectrum requirements of each game area and analyze them to generate a preliminary channel allocation strategy; Calculate the revenue of the preliminary channel allocation strategy to obtain the channel allocation revenue parameters; Calculate the channel allocation revenue parameters to obtain the final channel allocation strategy parameters for each game area.

7. A broadband spectrum monitoring system based on dynamic channel allocation according to claim 6, characterized in that, Perform spatio-temporal alignment processing on the real-time spectrum monitoring data of the monitoring nodes in each game area according to the final channel allocation strategy parameters, and generate global spectrum state parameters including signal strength gradient distribution and spectrum state mutation probability, including: Based on the time slice division in the final channel allocation strategy parameters, generate an adaptive sliding time window for each monitoring node, calculate the signal propagation delay through the time scale signal transmitted by the beacon node and the received signal strength of the ordinary node, and dynamically adjust the time window boundary to generate spatio-temporal synchronization calibration parameters; Dynamically adjust the weight index of ordinary monitoring nodes according to the monitoring node distribution density and the effective coverage radius parameter of the beacon node to generate signal strength gradient distribution parameters; Analyze the spatio-temporal synchronization calibration parameters and the spectrum state differences between adjacent time slices to obtain the spectrum state mutation probability; Analyze the signal strength gradient distribution parameters and the spectrum state mutation probability to generate global spectrum state parameters including signal strength gradient distribution and spectrum state mutation probability.

8. A broadband spectrum monitoring system based on dynamic channel allocation according to claim 7, characterized in that, According to the global spectrum state parameters, determine whether the current channel allocation strategy fails. If it fails, immediately trigger a dynamic allocation instruction, including: Slice the historical spectrum monitoring data by k time slices and frequency bands, and calculate the gradient mean of the signal strength distribution in space and the change rate of the spectrum occupancy rate in the time dimension for each slice to form a dynamic feature sequence containing spatio-temporal change rules; Group the period data corresponding to the historical channel allocation by game area, and respectively count the median of the signal strength gradient and the extreme value of the change rate of the spectrum occupancy rate in the dynamic feature sequence of each monitoring area to generate preliminary regional gradient reference parameters and regional mutation reference parameters; Correct the preliminary regional gradient reference parameters and regional mutation reference parameters based on the building density of the current monitoring area to obtain the signal strength gradient threshold and mutation probability threshold that finally adapt to the current monitoring environment.

9. A broadband spectrum monitoring system based on dynamic channel allocation according to claim 8, characterized in that, According to the global spectrum state parameters, determine whether the current channel allocation strategy fails. If it fails, immediately trigger a dynamic allocation instruction, and also include: If the signal strength gradient in the global spectrum state parameters is greater than the signal strength gradient threshold or the mutation probability continuously exceeds the mutation probability threshold within k consecutive time slices, it is determined that the previous channel allocation strategy fails, and a dynamic allocation instruction is immediately triggered.

10. A broadband spectrum monitoring system based on dynamic channel allocation according to claim 9, characterized in that, The dynamic allocation instruction includes: Mark the area where the signal strength gradient exceeds the signal strength gradient threshold as an abnormal area, calculate the spatio-temporal correlation parameter between the abnormal area and the adjacent area, and generate a regional abnormal propagation factor; Analyze the signal strength gradient distribution of each monitoring node in the abnormal area to obtain the spectrum state confusion degree; Dynamically adjust the game area boundary according to the regional abnormal propagation factor and the spectrum state confusion degree to generate a temporary spectrum management area; Analyze the monitoring nodes in the temporary spectrum management area to obtain the channel resource demand vector; Extract the regional abnormal propagation factor, the spectrum state confusion degree, and the channel resource demand vector to obtain multi-dimensional risk assessment parameters; When the evaluation value of any dimension in the multi-dimensional risk assessment parameters exceeds the multi-dimensional risk threshold, generate a dynamic allocation instruction including the optimal channel combination, power adjustment, and time slice allocation strategy.

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