A resource dynamic monitoring and control method and system based on quota tracking

Abstract

The present application provides a method and system for dynamic resource monitoring and control based on quota tracking. Among them, the pipeline corridor deformation monitoring and resource optimization system based on optical fiber and acoustic wave sensing obtains the stress field propagation data of the underground pipeline corridor structure through the optical fiber sensor array, analyzes the energy attenuation characteristics of the stress wave at the intersection of the reinforcement, and establishes a dynamic mapping model between the structural deformation risk and the resource demand of the monitoring node. At the same time, the acoustic wave sensors arranged at the expansion joints are used to capture the frequency domain propagation characteristics of the abnormal vibration signal to form a resource allocation strategy matrix. Construct a quota tracking database, compare the actual consumption rate of the spectrum quota with the predicted value, generate a resource utilization efficiency report, and perform spectrum redistribution of frequency hopping interval optimization and transmission power adjustment for nodes in high stress concentration areas based on this. The technical solution provided by the present application realizes the dynamic optimization of resources of the underground pipeline corridor structure and improves the accuracy of risk warning and communication efficiency.

Description

Technical Field

[0001] The present application relates to the field of resource monitoring technology, and in particular to a method and system for dynamic resource monitoring and control based on quota tracking. Background Art

[0002] In the long-term safety monitoring scenario of urban underground integrated pipeline corridors, it is necessary to achieve high-precision real-time perception of structural deformation, stress concentration and abnormal vibration, and dynamic optimization management of resources. The sensors must have corrosion resistance, anti-interference ability and long-term stability; it is necessary to integrate the multi-dimensional data of fiber optic sensor arrays and acoustic wave sensors to analyze the risk of structural deformation; under limited spectrum resources, the data transmission cycle and spectrum quota of the monitoring nodes must be dynamically adjusted according to the degree of stress concentration and vibration anomalies to balance real-time performance and energy consumption.

[0003] At present, long-gauge fiber optic sensors are deployed in longitudinal / transverse arrays to analyze concrete cracking and displacement changes through distributed strain monitoring. Based on distributed acoustic wave sensing technology, the Rayleigh scattering principle is used to capture pipeline vibration signals, locate leakage points or third-party interference, use a fixed data transmission cycle and fixed transmission power, and rely on manual experience to configure spectrum resources.

[0004] However, static spectrum quotas cannot adapt to the dynamic changes in the stress field of the tunnel, resulting in insufficient data sampling in high-stress areas and redundant resources in low-risk areas; traditional fiber optic sensors have a long data processing cycle and are not linked to the phase offset parameters of the acoustic vibration signal, making it difficult to trigger resource reallocation in a timely manner; fixed transmission power is prone to channel congestion, and the frequency hopping mechanism lacks interval optimization driven by historical data, resulting in spectrum fragmentation. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for dynamic resource monitoring and control based on quota tracking, so as to solve the problem of poor dynamic resource monitoring and control effect in the prior art.

[0006] In a first aspect, embodiments of the present application provide a method for dynamic resource monitoring and control based on quota tracking, including:

[0007] Obtaining stress field propagation data generated by an optical fiber sensor array in an underground pipe gallery structure, the stress field propagation data including the energy attenuation characteristics of stress waves at the intersection of reinforcement bars, and establishing a correlation mapping table between structural deformation risk and monitoring node resource requirements based on the energy attenuation characteristics;

[0008] The frequency domain propagation characteristics of abnormal vibration signals are captured by an acoustic wave sensor array arranged at the expansion joints of the underground pipe gallery. The frequency domain propagation characteristics include the phase offset parameters of the reflected waves at the pipe joints.

[0009] Dynamically adjusting the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter, and generating a resource allocation strategy matrix including a channel occupancy prediction value;

[0010] Establishing a quota tracking database for IoT resources, the quota tracking database records historical spectrum quota usage data of monitoring nodes, and generating a resource utilization efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix;

[0011] Spectrum quota reallocation is performed on monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, where the reallocation includes frequency hopping interval optimization and transmission power adjustment based on historical spectrum quota usage data.

[0012] Optionally, after generating the resource allocation strategy matrix including the channel occupancy prediction value, the method further includes:

[0013] Perform cross-band coherent detection on adjacent monitoring areas to obtain time-varying interference distribution maps of authorized and unauthorized frequency bands;

[0014] Adaptively modifying the transmit power quota in the resource allocation strategy matrix according to the time-varying interference distribution map, wherein the modification includes adjusting a spectrum switching interval and a power compensation coefficient based on the stress wave propagation path length.

[0015] Optionally, dynamically adjusting the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter to generate a resource allocation strategy matrix including a channel occupancy prediction value includes:

[0016] Real-time collection of phase offset parameters and channel status indicators of monitoring nodes to form a pre-processing data set;

[0017] Calculating the phase offset change rate of each monitoring node based on the preprocessed data set, and generating a priority weight coefficient for each node using a dynamic weighting algorithm in combination with the current channel interference threshold;

[0018] According to the priority weight coefficient, the data transmission cycle quota in the association mapping table is incrementally adjusted, and the data transmission cycle quota of the monitoring node is proportionally shortened for nodes with a priority weight coefficient higher than a preset threshold;

[0019] The channel occupancy prediction model is constructed using the adjusted periodic quota. The historical channel occupancy data and real-time phase offset parameters are input. The channel occupancy prediction value is iteratively updated through the sliding window mechanism, and a multi-dimensional resource allocation strategy matrix is ​​output.

[0020] Optionally, the resource utilization efficiency report is used to report high stress

[0021] The monitoring nodes in the centralized area perform spectrum quota reallocation, which includes frequency hopping interval optimization and transmit power adjustment based on historical spectrum quota usage data, including:

[0022] parsing the analysis results of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource utilization efficiency report, extracting the spectrum efficiency deviation parameter of the high stress concentration area, correlating it with historical frequency hopping data, and generating a spectrum efficiency deviation correlation parameter;

[0023] Based on the spectrum efficiency deviation correlation parameter, the channel occupancy fluctuation characteristics of each frequency band in the historical usage period are calculated, and the frequency hopping interval decision parameter is generated by combining the signal propagation attenuation characteristics of the high stress concentration area;

[0024] According to the frequency switching priority in the frequency hopping interval decision parameter, historical signal strength attenuation data of the high stress concentration area is matched, the transmission power compensation gradient is calculated and superimposed with the reference value to generate a dynamic power compensation parameter;

[0025] The frequency hopping interval decision parameter and the dynamic power compensation parameter are integrated to reallocate the spectrum quota according to the priority weight, and generate a spectrum quota reallocation strategy table after verification;

[0026] The spectrum quota reallocation strategy table is loaded into the resource scheduling module of the monitoring node, the spectrum usage data after reallocation is recorded in real time and updated to the quota tracking database, and the spectrum quota reallocation is executed.

[0027] Optionally, the frequency hopping interval decision parameter and the dynamic power compensation parameter are integrated, and the spectrum quota is reallocated according to the priority weight. After verification, a spectrum quota reallocation strategy table is generated, including:

[0028] Performing a multi-dimensional parameter superposition operation on the frequency band switching priority and the dwell time proportional coefficient in the frequency hopping interval decision parameter and the transmit power compensation gradient in the dynamic power compensation parameter to generate an initial strategy parameter set for spectrum quota reallocation;

[0029] Detecting and correcting spectrum quota allocation conflicts in different frequency bands in a high stress concentration area based on the spectrum quota reallocation initial strategy parameter set, and outputting corrected spectrum quota reallocation intermediate strategy parameters;

[0030] Loading the priority weight as a constraint condition into the spectrum quota reallocation intermediate strategy parameter, calculating the maximum allocatable spectrum quota threshold of each frequency band under the priority weight constraint, and generating the spectrum quota reallocation boundary parameter;

[0031] Perform a cross-layer parameter comparison between the spectrum quota reallocation boundary parameter and the channel occupancy prediction value in the resource allocation strategy matrix. After verification, generate the spectrum quota reallocation verification parameter.

[0032] Based on the spectrum quota reallocation verification parameters, a spectrum quota reallocation strategy table including frequency band identification, quota threshold and power compensation gradient is generated according to the combination rule of frequency band switching priority and dwell time ratio coefficient.

[0033] Optionally, load the priority weights as constraints to the

[0034] Describe the intermediate strategy parameters for spectrum quota reallocation, calculate the maximum allocatable spectrum quota threshold for each frequency band under the priority weight constraint, and generate the spectrum quota reallocation boundary parameters, including:

[0035] Extracting priority weight constraint parameters from spectrum quota reallocation intermediate strategy parameters to generate an initial constraint parameter set including frequency band identifiers and priority weights;

[0036] Based on the priority weights in the initial constraint parameter set and in combination with the historical spectrum demand baseline values ​​of each frequency band, the maximum allocatable spectrum quota threshold of each frequency band under the weight constraint is calculated to generate a spectrum quota extreme value parameter set;

[0037] Comparing the spectrum quota extreme value parameter set with the physical layer load limit parameters for compliance, eliminating extreme value parameters that exceed the physical layer load capacity, and generating a spectrum quota reallocation boundary parameter set;

[0038] The spectrum quota reallocation boundary parameter set and the priority weight constraint parameter set are integrated to generate spectrum quota reallocation boundary parameters including frequency band identifier, priority weight and quota threshold.

[0039] Optionally, a quota tracking database for IoT resources is established, and a resource utilization efficiency report is generated based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix, including:

[0040] Establishing a quota tracking database for IoT resources, wherein the quota tracking database records historical spectrum quota usage data of monitoring nodes, and dynamically aggregating the actual spectrum quota consumption rate according to a preset time window based on the quota tracking database to generate a dynamic aggregated data set;

[0041] Correlate the node identifiers in the dynamic aggregation dataset across time windows, extract the temporal fluctuation characteristics and spatial distribution characteristics of the spectrum consumption rate, and generate a spectrum feature vector containing the node spectrum fluctuation index and spatial correlation degree;

[0042] Match the spectrum feature vector with the channel occupancy rate prediction value in the resource allocation strategy matrix, calculate the deviation and convergence index between the actual spectrum quota consumption rate and the prediction value, and generate a spectrum comparison index set;

[0043] Based on the spectrum comparison index set, a mapping relationship between node identification and resource allocation strategy matrix is ​​constructed. By dynamically adjusting the priority weights in the strategy matrix, an optimized strategy mapping table including node priority coefficients and strategy adaptability is generated.

[0044] Based on the priority coefficient and policy adaptability in the optimization strategy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison indicator set, a resource utilization efficiency report that can be used to evaluate and provide optimization suggestions is generated.

[0045] Optionally, a mapping relationship between node identifiers and a resource allocation strategy matrix is ​​constructed based on a spectrum comparison indicator set. By dynamically adjusting the priority weights in the strategy matrix, an optimized strategy mapping table including node priority coefficients and strategy adaptability is generated, including:

[0046] Determining an initial mapping relationship between a node identifier and a resource allocation strategy matrix based on a spectrum comparison indicator set, wherein the resource allocation strategy matrix includes an initial value of a node priority coefficient and an initial value of a strategy adaptability;

[0047] Establishing a dynamic feedback loop in which priority weights and policy adaptation parameters in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a node priority coefficient correction value;

[0048] Inputting the node priority coefficient correction value into the strategy fitness calculation module, and generating a strategy fitness update value in combination with the strategy fitness initial value in the resource allocation strategy matrix;

[0049] The resource allocation strategy matrix is ​​reconstructed according to the node priority coefficient correction value and the strategy adaptation degree update value, and an optimization strategy mapping table including multi-dimensional weight constraints is generated.

[0050] Optionally, a dynamic feedback loop is established in which priority weights and policy adaptation parameters in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a node priority coefficient correction value, including:

[0051] When establishing a dynamic feedback loop, a dynamic feedback parameter set is generated by aggregating the node execution state data under the initial mapping relationship and the input and output deviation of the strategy matrix;

[0052] Incrementally calculating the priority weights of the policy matrix based on the dynamic feedback parameter set, and iteratively updating the priority weight distribution by superimposing the historical weight deviation compensation value and the real-time feedback weight offset;

[0053] Reconstructing the strategy adaptation parameter space according to the priority weight distribution, eliminating the coupling interference between parameters through multi-dimensional orthogonal projection operation, and generating the strategy adaptation parameter correction;

[0054] Performing a tensor fusion operation on the policy adaptation parameter correction amount and the dynamic feedback parameter set, completing parameter synchronization alignment based on the topological constraints of the initial mapping relationship, and outputting a dynamic adjustment factor of the policy matrix;

[0055] The strategy matrix dynamic adjustment factor is injected into the node priority coefficient calculation channel, and the node priority coefficient correction value is formed through nonlinear normalization processing and feedback loop self-calibration mechanism.

[0056] In a second aspect, embodiments of the present application provide a resource dynamic monitoring and control system based on quota tracking, including:

[0057] An analysis module is used to obtain stress field propagation data generated by an optical fiber sensor array in the underground pipeline corridor structure. The stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection of reinforcement bars, and to establish a correlation mapping table between structural deformation risks and monitoring node resource requirements based on the energy attenuation characteristics;

[0058] A detection module is used to capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array deployed at the expansion joints of the underground pipe gallery. The frequency domain propagation characteristics include the phase offset parameters of the reflected waves at the pipe joints.

[0059] A generating module, configured to dynamically adjust the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including a channel occupancy prediction value;

[0060] A tracking module is used to establish a quota tracking database for IoT resources, the quota tracking database records the historical spectrum quota usage data of the monitoring node, and generates a resource utilization efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix;

[0061] An optimization module is used to perform spectrum quota reallocation on monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, wherein the reallocation includes frequency hopping interval optimization and transmission power adjustment based on historical spectrum quota usage data.

[0062] In an embodiment of the present application, stress field propagation data generated by an optical fiber sensor array in an underground tunnel structure is obtained, wherein the stress field propagation data includes energy attenuation characteristics of stress waves at intersections of reinforcement ribs, so as to establish an association mapping table between structural deformation risks and resource requirements of monitoring nodes based on the energy attenuation characteristics; the frequency domain propagation characteristics of abnormal vibration signals are captured by an acoustic wave sensor array arranged at expansion joints of the underground tunnel, wherein the frequency domain propagation characteristics include phase offset parameters of reflected waves at pipe joints; the data transmission cycle quota of the monitoring nodes in the association mapping table is dynamically adjusted based on the phase offset parameters, and a resource allocation strategy matrix including channel occupancy prediction values ​​is generated; a quota tracking database for Internet of Things resources is established, wherein the quota tracking database records historical spectrum quota usage data of the monitoring nodes, and a resource utilization efficiency report including a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy prediction values ​​in the resource allocation strategy matrix is ​​generated based on the quota tracking database; spectrum quota reallocation is performed on monitoring nodes in high stress concentration areas based on the resource utilization efficiency report, wherein the reallocation includes frequency hopping interval optimization and transmission power adjustment based on historical spectrum quota usage data.

[0063] The technical solution of this application has the following beneficial effects:

[0064] This solution uses an array of fiber optic sensors to capture the energy attenuation characteristics of the stress field in underground pipeline corridors, establishes a mapping table linking structural deformation risks and resource requirements, and dynamically adjusts the data transmission cycle quota of monitoring nodes based on the phase offset parameters captured by acoustic sensors. It also generates a resource allocation strategy matrix and efficiency report based on historical spectrum quota data, ultimately achieving intelligent redistribution of spectrum quotas in high-stress areas through frequency hopping interval optimization and transmit power adjustment. This solution can accurately identify structural deformation risk areas, dynamically optimize monitoring resource allocation, improve data transmission efficiency and network stability, and effectively reduce channel interference and extend equipment life through historical data-driven frequency hopping and power adjustments, thus realizing adaptive resource management and risk warning capabilities for underground pipeline corridor monitoring systems.

[0065] Furthermore, through cross-band coherent detection, a time-varying interference distribution map of the licensed and unlicensed bands is constructed in real time, accurately quantifying the dynamic characteristics of multi-band electromagnetic interference. The spectrum switching interval and power compensation coefficient are adaptively optimized based on the stress wave propagation path length, forming a closed-loop feedback mechanism for interference perception and power control. This technology makes real-time corrections to the transmit power quota in the resource allocation strategy matrix based on the dynamic interference distribution. By coordinating propagation path loss compensation with band switching timing, it significantly reduces the risk of cross-band signal conflicts and power overflow, while enhancing the flexibility and robustness of spectrum resource allocation in complex electromagnetic environments. Its core value lies in establishing a full-link dynamic adaptation system of "interference detection-path modeling-power correction." Through the combined control of band switching and power compensation, it effectively improves the utilization rate and communication stability of the unlicensed band. While ensuring low-interference transmission in the licensed band, it achieves efficient coordination and interference self-suppression of multi-domain heterogeneous network resources, providing a low-latency, highly reliable adaptive spectrum sharing solution for high-density wireless scenarios.

[0066] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0068] Figure 1 A flowchart of a resource dynamic monitoring and control method based on quota tracking provided by the present application is shown;

[0069] Figure 2 The schematic diagram shows the structure of a resource dynamic monitoring and control system based on quota tracking provided by the present application. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0071] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0072] To address the problems of low resource utilization, severe channel interference, and short equipment life caused by static resource allocation in underground pipeline corridor monitoring scenarios, this solution integrates multi-source data from fiber optic sensor arrays and acoustic sensor arrays to establish a dynamic correlation model between structural deformation risk and resource requirements of monitoring nodes. A resource allocation strategy matrix is ​​generated based on historical spectrum quota usage data. On this basis, a quota tracking database is constructed. Real-time perception data and historical consumption deviation analysis are used to dynamically adjust the data transmission period, frequency hopping interval, and transmission power of nodes in high-stress areas, forming a closed-loop control mechanism. Ultimately, the comprehensive goals of on-demand resource allocation, channel interference suppression, and equipment energy consumption optimization are achieved, significantly improving the adaptability and operation and maintenance efficiency of the underground pipeline corridor safety monitoring system.

[0073] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0074] Figure 1 The present invention provides a flow chart of a method for dynamic resource monitoring and control based on quota tracking, as shown in FIG. Figure 1 As shown, the method includes:

[0075] 101. Obtain stress field propagation data generated by an optical fiber sensor array in an underground pipe gallery structure, wherein the stress field propagation data includes energy attenuation characteristics of stress waves at intersections of reinforcement bars, and establish a correlation mapping table between structural deformation risks and monitoring node resource requirements based on the energy attenuation characteristics;

[0076] In this step, the fiber optic sensor array refers to a network composed of multiple fiber optic sensors, which monitors the stress and strain distribution of the underground tunnel structure in real time by detecting the wavelength offset, intensity change or phase difference of the optical signal in the optical fiber.

[0077] Stress field propagation data refers to the propagation characteristic data of stress waves in underground pipeline corridor structures captured by fiber optic sensor arrays. It contains information such as wave velocity, amplitude attenuation, and spectrum characteristics, and is used to evaluate the stress state of the structure.

[0078] Stress waves refer to mechanical fluctuations caused by external loads in the tunnel structure. Their propagation path and energy attenuation are related to the material elastic modulus and structural defects.

[0079] The reinforcement intersection refers to the intersection node of steel bars or composite reinforcement bars in the concrete structure of the underground pipeline corridor, where stress concentration areas are easily formed due to geometric discontinuity.

[0080] Energy attenuation characteristics refer to the energy reduction characteristics of stress waves caused by material damping, friction loss or cracks when propagating at the intersection of stiffeners. It is usually quantified in decibels (dB) or attenuation coefficient (α).

[0081] Structural deformation risk refers to the possibility of cracks, displacement or collapse of the pipeline corridor due to stress concentration, material fatigue or foundation settlement. The risk level is assessed through energy attenuation characteristic modeling.

[0082] In the embodiment of the present application, first, the optical fiber sensor array captures the optical wavelength offset data at a sampling rate of 1kHz, uses the MOI SM130 demodulator to achieve high-precision wavelength demodulation (resolution ±1pm), and switches the multi-channel signal through an optical switch. Next, the original signal is decomposed by wavelet transform to extract the energy attenuation characteristics (A attenuation), and the attenuation rate of each reinforcement intersection is calculated after eliminating temperature interference. Finally, a random forest algorithm is used to construct a risk prediction model, with input including the A attenuation value, the sensor position coordinates and historical deformation data, and outputting the deformation risk probability P (0≤P≤1). The risk level is divided according to the P value (such as P>0.8 is high risk), and a "risk level-resource requirement" mapping table is generated. For example, high-risk nodes need to be allocated 50MHz bandwidth and 100Hz sampling rate.

[0083] An array of 32 FBG sensors was deployed at key nodes in the concrete structure of an underground utility tunnel, such as the intersection of reinforcement bars. During one monitoring session, sensor No. 3 detected an A attenuation value of 35 dB / m (threshold: 30 dB / m). The model calculated P=0.85, indicating a high risk. The system automatically updated the mapping table, increasing the resource requirements for this node from the default 20 MHz bandwidth and 50 Hz sampling rate to 60 MHz bandwidth and 200 Hz sampling rate. Furthermore, a temperature compensation module was implemented to eliminate the ±0.5 pm wavelength offset error caused by ambient temperature differences, ensuring data accuracy. This adjustment improved the real-time data transmission in this area by four times, providing a high-precision foundation for subsequent abnormal vibration monitoring.

[0084] 102. Capturing the frequency domain propagation characteristics of abnormal vibration signals using an acoustic wave sensor array deployed at the expansion joints of the underground pipe gallery, wherein the frequency domain propagation characteristics include a phase offset parameter of the reflected wave at the pipe joint connection;

[0085] In this step, the acoustic wave sensor array refers to a group of piezoelectric or MEMS acoustic wave sensors arranged at the expansion joints of the pipe gallery, which is used to collect the time domain waveform and frequency domain components of the vibration signal.

[0086] Frequency domain propagation characteristics refer to the characteristics after the vibration signal is converted from the time domain to the frequency domain through fast Fourier transform (FFT), including the main frequency, harmonic distribution, phase spectrum, etc.

[0087] The reflected wave phase offset refers to the phase difference between the reflected wave and the incident wave at the pipe joint (unit: radian or angle), which reflects the degree of looseness of the structural connection or expansion of the gap.

[0088] In the embodiment of the present application, first, the acoustic wave sensor collects the original vibration signal at a sampling rate of 10kHz and uses a Butterworth bandpass filter (0.1-2kHz) to remove low-frequency noise and high-frequency interference. Subsequently, a 1024-point FFT transform is performed on the filtered signal to extract the main frequency component (such as the amplitude peak at 800Hz) and calculate its phase spectrum. The Hilbert transform is used to obtain instantaneous phase information, and the phase difference Δφ (unit: radian) between the incident wave and the reflected wave is calculated using a cross-correlation algorithm. Finally, combined with the structural parameters of the pipe joint (such as the bolt preload threshold), Δφ>1.0rad is set as the abnormal threshold to trigger dynamic adjustment of resource demand.

[0089] During subsequent monitoring of Node 3 of an underground utility corridor, an acoustic sensor array detected a vibration signal at the pipe joint with a dominant frequency of 820 Hz (significantly higher than the normal operating value of 650 Hz ± 50 Hz). Simultaneously, the phase offset Δφ continued to climb to 1.2 rad, far exceeding the preset safety threshold of 0.2 rad. By comparing spectral characteristics and analyzing them in conjunction with finite element simulation models, the system determined that this area presented risks of loose bolts and sealing structure failure. To accurately identify potential safety hazards, the system immediately activated a dynamic resource scheduling mechanism, compressing the data transmission cycle of the monitoring units associated with Node 3 from the conventional 2 seconds to 0.5 seconds (using the LoRaWAN protocol's fast transmission mode). Simultaneously, the edge computing node coordinated the increase in the acoustic sensor sampling rate from 1 kHz to 5 kHz, and activated a high-frequency vibration modal analysis algorithm (HHT transform) to extract detailed vibration characteristics in the 0.5-8 kHz frequency band.

[0090] 103. Dynamically adjust the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including a channel occupancy prediction value;

[0091] In this step, the phase offset parameter refers to a quantized phase offset value extracted by a cross-correlation algorithm or Hilbert transform, for example, Δφ=30° represents a characteristic of an abnormal vibration signal.

[0092] The data transmission cycle quota refers to the data upload time interval dynamically allocated to the monitoring node, for example, the cycle for high-risk nodes is 0.5 seconds and for low-risk nodes is 5 seconds.

[0093] The channel occupancy forecast value refers to the wireless channel usage rate for a future period predicted using a time series model (such as ARIMA or LSTM), for example, "occupancy ≥ 75% from 10:00 to 10:15."

[0094] The resource allocation strategy matrix refers to a two-dimensional matrix data structure, where rows represent monitoring node IDs, columns represent time slices, and matrix elements are parameters such as allocated spectrum bandwidth and transmit power.

[0095] In this embodiment of the present application, the Δφ parameter is first input into the priority weight calculation model (formula: W = Δφ / Δφ_max + P / P_max), where Δφ_max is the maximum phase offset threshold and P is the risk probability in step 101. Next, an LSTM model (64 hidden units, 60-minute time step) is used to predict future channel occupancy, inputting historical 1-hour data and outputting an occupancy curve for the next 5 minutes. Finally, a greedy algorithm is used to allocate spectrum resources based on the priority weights and predicted values, and a strategy matrix is ​​constructed. For example, high-risk nodes are allocated 80MHz bandwidth during peak occupancy periods, while low-risk nodes are reduced to 20MHz.

[0096] 104. Establish a quota tracking database for IoT resources, and generate a resource utilization efficiency report based on the quota tracking database, including a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix;

[0097] In this step, the quota tracking database refers to a storage system built based on a time series database (such as InfluxDB), which records fields such as the spectrum usage, timestamp, and location coordinates of each node. The quota tracking database records the historical spectrum quota usage data of the monitoring node.

[0098] The actual spectrum quota consumption rate refers to the percentage of spectrum resources actually used out of the allocated quota. For example, if 20MHz is allocated and 18MHz is actually used, the consumption rate is 90%.

[0099] Resource efficiency reports are performance analysis documents generated by comparing predicted values ​​with actual consumption rates. They include deviation statistics (such as mean square error), hotspot area markings, and optimization suggestions.

[0100] In the embodiment of the present application, first, InfluxDB is used to record the allocated bandwidth, actual usage, timestamp, and location coordinates of each node, with a data writing speed of ≥100,000 records / second. Next, the η value of each node is calculated, and the deviation between the predicted value and the actual value is evaluated by the mean square error (MSE). Finally, a visual report is generated: a heat map marks nodes with η < 50% or > 120%, a TOP10 list sorts the nodes with the largest deviation, and makes targeted suggestions (such as "node 3 η = 65%, it is recommended to reduce the bandwidth to 60MHz").

[0101] System statistics revealed that node 3's actual bandwidth utilization was only 65% ​​(allocated 80 MHz), with a deviation mean square error (MSE) of 18.7. The report indicated significant resource waste and recommended adjusting the bandwidth to 60 MHz, allocating the remaining 20 MHz to the adjacent node 7 (η = 115%). Correlation analysis also revealed that node 7's phase offset, Δφ = 0.9 rad, was approaching the threshold, triggering an increase in its risk level from "medium" to "high." This closed-loop feedback loop increased overall spectrum utilization from 72% to 89% and reduced redundant data transmission by 15%.

[0102] 105. Perform spectrum quota reallocation on monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, wherein the reallocation includes frequency hopping interval optimization and transmission power adjustment based on historical spectrum quota usage data.

[0103] In this step, frequency hopping interval optimization refers to dynamically adjusting the FHSS frequency hopping interval according to channel conflict history data.

[0104] Transmit power adjustment refers to dynamically adjusting the power based on the received signal strength (RSSI).

[0105] In the embodiment of the present application, first, high-deviation nodes are screened according to the efficiency report, and the spectrum quota is reallocated using a greedy algorithm to give priority to meeting the needs of high-risk nodes. Subsequently, the frequency hopping interval is calculated based on the number of historical conflicts: for example, the number of node conflicts N=8 times, T_hop=5 / (1+3)=1.25ms. At the same time, the transmit power is dynamically adjusted according to RSSI feedback: if the signal strength at the receiving end is <-80dBm, the power is increased in 3dB steps until it meets the standard. Finally, the updated parameters are sent down to the node to complete the closed-loop optimization.

[0106] At Node 3 in an underground utility corridor, a high channel collision rate (18%) triggered a system adjustment mechanism. Using real-time spectrum analysis, the system compressed Node 3's frequency hopping interval from 5ms to 2ms. It also optimized the frequency band switching strategy using a collision detection algorithm (N collisions = 12) and reduced the transmit power from 20dBm to 15dBm based on measured signal strength (RSSI = -75dBm). This adjustment significantly reduced Node 3's channel collision rate to 6%, lowering daily power consumption by 22% and freeing up 5MHz of available bandwidth. The system dynamically allocated this 5MHz of bandwidth to the heavily loaded Node 7, increasing its data transmission success rate from 78% to 95%, effectively alleviating local network congestion. Furthermore, through continuous monitoring and machine learning predictive models, the system completed three rounds of dynamic adjustments within 24 hours: first, optimizing Node 3's frequency hopping parameters and transmit power; then, allocating the freed-up bandwidth to Node 7; and finally, reallocating time slot resources based on overall network load. Statistics show that this series of adjustments improved overall network stability by 40%, reduced daily energy consumption by 18%, and ensured the real-time and integrity of key monitoring data. This case demonstrates the efficiency and reliability of dynamic resource allocation mechanisms in complex scenarios, providing a reusable technical paradigm for intelligent operation and maintenance of large-scale infrastructure.

[0107] In summary, 101 to 105 obtain the stress field propagation data of the underground tunnel optical fiber sensor array, combine it with the frequency domain propagation characteristics of the abnormal vibration signal captured by the acoustic sensor array, establish a correlation mapping table between the structural deformation risk and the resource requirements of the monitoring node, and realize accurate monitoring of the health status of the tunnel structure; dynamically adjust the data transmission cycle quota based on the phase offset parameter, generate a resource allocation strategy matrix with the channel occupancy rate prediction value, and optimize the network resource utilization efficiency; by establishing a quota tracking database, generate a resource utilization efficiency report, and compare and analyze the actual spectrum quota consumption rate with the predicted value to provide data support for resource allocation; finally, perform spectrum quota reallocation on the monitoring nodes in the high stress concentration area, including frequency hopping interval optimization and transmission power adjustment, significantly improving the real-time performance, reliability and resource utilization efficiency of the monitoring system, and providing intelligent and dynamic technical guarantees for the safe operation of the underground tunnel.

[0108] In some embodiments, after generating the resource allocation strategy matrix including the channel occupancy prediction value in step 103, the following steps may be further included:

[0109] 201. Perform cross-band coherent detection on adjacent monitoring areas to obtain time-varying interference distribution maps of authorized and unauthorized frequency bands;

[0110] In step 201, cross-band coherent detection refers to a technology that implements cross-band interference correlation analysis by jointly processing phase and amplitude information of multiple frequency band signals.

[0111] Authorized and unlicensed frequency bands refer to frequency bands that are strictly allocated by regulations (such as operator-dedicated frequency bands) and open shared frequency bands (such as WiFi bands).

[0112] The time-varying interference distribution map refers to the dynamic interference intensity heat map generated by time-frequency domain signal energy scanning and spatial gridding processing.

[0113] In this embodiment, multi-band joint signal processing technology is implemented, using a wide-band receiving array to synchronously sample signals from licensed bands (e.g., 3.5 GHz) and unlicensed bands (e.g., 5.8 GHz) in adjacent monitoring areas. Adaptive filtering algorithms (e.g., LMS or RLS filtering) are used to separate the target frequency band signal, and coherent accumulation techniques are used to extract the time-frequency characteristics of the interference signal. Cross-band interference phase differences are calculated using a cross-correlation function, and frequency-domain energy detection algorithms (e.g., Welch periodogram estimation) are combined to locate the spatial distribution of the interference source. Key parameters include the sampling rate (determined by the frequency band bandwidth), the coherent accumulation time (dynamically optimized based on the time-varying characteristics of the interference), and the dynamic threshold (calibrated using historical interference data and real-time signal-to-noise ratio). Finally, a millisecond-updated time-varying interference distribution map is generated through multi-dimensional data fusion (including frequency, time, and spatial domains). This map stores the interference intensity, delay, and coherence indicators of each frequency band within a spatial grid cell in matrix form.

[0114] 202. Perform adaptive correction on the transmit power quota in the resource allocation strategy matrix according to the time-varying interference distribution map, where the correction includes adjusting a spectrum switching interval and a power compensation coefficient based on a stress wave propagation path length.

[0115] In step 202, the resource allocation strategy matrix refers to a multi-dimensional resource scheduling decision table constructed with frequency band, time slot, and power as dimensions.

[0116] The transmit power quota refers to the maximum power limit allowed for a base station or terminal to transmit on a specific frequency band.

[0117] The stress wave propagation path length refers to the calculation model of the equivalent propagation distance of electromagnetic waves after diffraction and reflection in a complex environment.

[0118] The spectrum switching interval refers to the frequency band switching time slot reserved to avoid interference. Its duration is determined by the device switching delay and the interference fluctuation frequency.

[0119] The power compensation coefficient refers to the power scaling factor that is dynamically adjusted based on path loss and interference intensity to maintain the target signal-to-noise ratio.

[0120] In this embodiment of the present application, a dynamic resource allocation algorithm (such as reinforcement learning (Q-learning) or a convex optimization model) is used to input the time-varying interference map into a resource allocation strategy matrix. To modify the transmit power quota, the path loss compensation coefficient is first calculated based on the stress wave propagation path length (modeled using ray tracing or geometric diffraction theory). The path length is calculated in real time based on the geometric relationship between the base station and user equipment and the obstacle distribution (using LiDAR point clouds or digital maps). The spectrum switching interval is optimized using a Markov decision process, with its parameters jointly calibrated by the device switching latency (a hardware indicator) and the interference fluctuation frequency (extracted from the map). The power compensation coefficient is dynamically adjusted using a back-propagation neural network. The inputs include interference intensity, path loss index, and user QoS requirements, and the output is the power adjustment step size and direction. Finally, the correction parameters are mapped to the strategy matrix through nonlinear programming, forming an optimal power allocation scheme that balances spectrum efficiency and interference suppression. This scheme is then embedded in the base station controller for closed-loop control. Furthermore, after modifying the resource allocation strategy matrix, step 104 is executed.

[0121] Here's a specific example:

[0122] In an Industrial Internet of Things (IIoT) scenario, a smart factory deployed a hybrid 5G private network and WiFi 6 unlicensed frequency bands, requiring cross-band collaboration for real-time AGV control and AR remote inspection. Millisecond-level signals were collected using a distributed broadband receiving array (covering both the 3.5 GHz licensed and 5.9 GHz unlicensed bands). An improved LMS adaptive filtering algorithm was employed to isolate the target frequency band, and a spatial interference energy matrix was constructed based on weighted Welch periodic spectrum estimation. A ray tracing model was used to simulate the propagation path of electromagnetic waves in an environment densely populated with metal equipment. Cross-band phase offsets were calculated using a cross-correlation function, ultimately generating a three-dimensional time-varying spectrum (with a 10 ms update period) that includes interference intensity, delay spread, and coherence bandwidth. This spectrum was then used to identify 5.9 GHz periodic pulse interference caused by the high-frequency motion of the robotic arm. The resource allocation strategy matrix utilizes a two-layer reinforcement learning framework. First, based on the path loss index in the interference map (calculated using a stress wave diffraction model to show a 12dB attenuation of 3.5GHz signals by metal shelves), the AGV scheduling channel's spectrum switching interval is dynamically adjusted to 80μs (previously 200μs) to avoid time slot conflicts with Wi-Fi video streams. Second, a deep Q-network (DQN) calculates a power compensation factor, increasing transmit power by 6dB in edge regions to offset multipath fading while simultaneously reducing power by 4dB in the center region to prevent intermodulation interference. Field measurements show that this solution reduces AGV control command latency from 15ms to 5ms, reduces AR video stream freezes by 73%, and increases unlicensed frequency channel utilization from 58% to 82%.

[0123] In summary, steps 201 to 202 capture the dynamic interference distribution characteristics of licensed and unlicensed bands in real time through cross-band coherent detection, adaptively optimize the spectrum switching interval based on the stress wave propagation path length, and dynamically modify the power compensation coefficient based on the channel attenuation model, forming a closed-loop feedback mechanism for interference perception and resource control. This technology can accurately quantify the matching relationship between interference intensity and power quota. By dynamically adjusting the transmit power threshold and band switching strategy, it significantly improves the efficiency of spectrum resource allocation in time-varying interference environments, suppresses disordered power competition in unlicensed bands, reduces the risk of cross-band signal conflicts, and enhances the robustness and adaptability of network topologies in complex electromagnetic scenarios. Its core advantage lies in establishing a collaborative framework for multi-dimensional interference suppression and power compensation. This optimizes system energy efficiency and extends device endurance while ensuring communication reliability. Real-time closed-loop control enables electromagnetic environment self-healing and dynamic resource balancing, providing a low-latency, highly stable spectrum sharing solution for heterogeneous networks.

[0124] In some embodiments, the step 104 of dynamically adjusting the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter to generate a resource allocation strategy matrix including a channel occupancy prediction value includes:

[0125] 301. Collect phase offset parameters and channel status indicators of monitoring nodes in real time to form a preprocessing data set;

[0126] In step 301, the phase offset parameter refers to the phase change of the signal received by the monitoring node relative to the phase change of the signal transmitted, and is used to characterize the channel propagation characteristics.

[0127] Channel state indicators refer to a set of parameters that reflect the quality of the communication link, including signal-to-noise ratio, bit error rate and spectrum occupancy.

[0128] The preprocessed dataset refers to the set of original monitoring data that has undergone noise suppression, feature extraction, and format standardization.

[0129] In an embodiment of the present application, distributed sensor nodes and software-defined radio (SDR) modules are deployed to collect phase offsets (such as ±15° phase jitter caused by Doppler shift) and channel state indicators (such as signal-to-noise ratio and bit error rate) of monitoring nodes in a drone cluster in real time. The Kalman filter algorithm is used to suppress noise in the raw data, and key features (such as interference pulse width and spectrum occupancy) are extracted through adaptive threshold segmentation technology. The preprocessed data set is stored in the form of a time series, and the sampling rate is dynamically adjusted by the channel bandwidth (such as a 100ms sampling interval at a 20MHz bandwidth). Finally, the heterogeneous data (such as Beidou B1C band and 5G NR band signals) are spatiotemporally aligned and format standardized through edge computing nodes to generate a preprocessed data set containing the mean and variance of the phase offset and a channel state heat map.

[0130] 302. Calculate the phase offset change rate of each monitoring node based on the preprocessed data set, and generate a priority weight coefficient for each node using a dynamic weighting algorithm in combination with the current channel interference threshold;

[0131] In step 302, the phase offset change rate refers to the rate of change of the phase offset per unit time, which is used to evaluate the dynamic characteristics of the channel.

[0132] The channel interference threshold refers to the critical value for determining whether a channel is interfered with, and is dynamically calibrated through the historical data training model.

[0133] The priority weight coefficient refers to the resource allocation priority value dynamically calculated based on the node phase change rate and interference intensity.

[0134] In an embodiment of the present application, based on a pre-processed data set, the phase offset change rate (such as Δφ / Δt, unit: degree / second) is first calculated by a sliding window statistical method, combined with a real-time channel interference threshold (dynamically calibrated by a support vector machine model trained with historical data). A dynamic weighting algorithm is constructed using a deep reinforcement learning (DRL) framework: the phase change rate, interference intensity, and node geographic location are used as inputs, and weight coefficients are iteratively generated through a Q-learning strategy. For example, in an urban canyon scenario, the weight coefficient of a node that is affected by building reflections and causes a sudden phase change (such as a change rate of more than 30 degrees / second within 10ms) is increased to 1.8 times the baseline value. Finally, the priority weight matrix of each node is output through normalization processing, and the weight value range is limited to between 0.5 and 2.0 for subsequent resource scheduling decisions.

[0135] 303. Incrementally adjust the data transmission cycle quota in the association mapping table according to the priority weight coefficient. For nodes with a priority weight coefficient higher than a preset threshold, the data transmission cycle quota of the monitoring node is proportionally shortened.

[0136] In step 303, the association mapping table refers to a data structure that records the correspondence between nodes and resource allocation strategies.

[0137] The data transmission period quota refers to the length of the data transmission time window allocated to a node and is used to control resource usage.

[0138] In an embodiment of the present application, incremental optimization is performed on the data transmission cycle quota in the association mapping table based on the priority weight coefficient. A mixed integer linear programming (MILP) model is adopted, and the constraints include the total bandwidth upper limit (such as 100MHz) and the node minimum service level agreement (SLA). When the node weight coefficient exceeds the preset threshold (such as 1.5), its cycle quota is shortened according to an exponential function. For example, the cycle of a node with a weight of 1.8 is compressed from 200ms to 120ms. At the same time, a reverse protection mechanism is introduced to extend the cycle of low-weight nodes (such as 0.6) to 300ms to avoid resource starvation. The adjustment process is synchronized to all nodes through a distributed consensus algorithm (such as the Raft protocol) to ensure the consistency of the resource allocation strategy of the entire network.

[0139] 304. Utilize the adjusted periodic quota to construct a channel occupancy prediction model, input historical channel occupancy data and real-time phase offset parameters, iteratively update the channel occupancy prediction value through a sliding window mechanism, and output a multi-dimensional resource allocation strategy matrix.

[0140] In step 304, the channel occupancy prediction model refers to a mathematical model that predicts future channel usage based on historical data and real-time parameters.

[0141] The sliding window mechanism refers to a data processing method that dynamically updates the predicted value through a fixed time window.

[0142] The multi-dimensional resource allocation strategy matrix refers to a decision matrix that includes multi-dimensional resource scheduling solutions such as frequency bands, power, and time slots.

[0143] In an embodiment of the present application, a channel occupancy prediction model based on a long short-term memory network (LSTM) is constructed using the adjusted periodic quota. The input data includes historical occupancy (with a granularity of 5 minutes) and real-time phase offset parameters (frequency domain features extracted by wavelet transform). The predicted value is iteratively updated through a sliding window mechanism (window length 60 seconds, step length 10 seconds), and the key time point data is weighted using an attention mechanism. The model outputs a multi-dimensional resource allocation strategy matrix, including a frequency band switching sequence (such as a delay of less than 50ms from switching from 5.8GHz to 2.4GHz), a power adjustment gradient (such as a ±3dB step size), and a routing priority mapping table. The final strategy is distributed to each node through a federated learning framework, achieving a 22% improvement in global spectrum efficiency and a 35% reduction in end-to-end latency56.

[0144] Here's a specific example:

[0145] In a smart city vehicle-road collaboration scenario, a hybrid communication system supporting the 5.9GHz C-V2X dedicated frequency band and the 4.9GHz city emergency network was deployed on a main road. Cross-network coordination was required to ensure priority passage for emergency vehicles and real-time linkage with traffic signals. In step 301, the roadside unit (RSU) and the vehicle terminal collected phase offset (e.g., ±8° phase shift caused by sudden braking) and channel state indicators (including 12dB signal-to-noise ratio fluctuations in the 4.9GHz band due to interference from high-voltage transmission lines) in real time. An improved Kalman filter algorithm was used to remove road noise. Multi-source data alignment was then used to generate a preprocessed dataset containing timestamps, geographic coordinates, and spectrum occupancy thermals. Based on the phase offset change rate (e.g., the 30° / second instantaneous change caused by an ambulance accelerating) and a dynamic interference threshold (predicted and calibrated using a Gaussian process regression model), a dual-Q network reinforcement learning algorithm was used to generate node priority weights. The weight coefficient for emergency vehicles was increased to 2.2 times, while that for standard vehicles was reduced to 0.7 times. In step 303, the association mapping table is reconstructed based on the weight coefficients, compressing the data transmission cycle for emergency vehicles from 100ms to 45ms while extending the cycle for non-emergency vehicles to 180ms. A mixed integer programming model is used to ensure that total bandwidth utilization does not exceed 85%. An LSTM-TCN fusion prediction model is constructed based on the adjusted cycle quotas. The model inputs historical 24-hour channel occupancy and real-time phase parameters (multi-scale features extracted using wavelet packet transform), and the prediction values ​​are iteratively updated using a 30-second sliding window. The output strategy matrix guides the RSU to dynamically allocate the 5.9 GHz frequency band to emergency vehicle signal control commands (latency < 20ms) and the 4.9 GHz band to traffic light status broadcasts. This has resulted in a 55% increase in emergency vehicle traffic efficiency, a 68% reduction in multi-band conflicts, and a maintenance of a channel prediction error rate below 7.2% even in heavy rain.

[0146] In summary, steps 301 to 304 construct a dynamic preprocessing dataset by collecting node phase offsets and channel state indicators in real time. Combined with correlation analysis between the phase offset change rate and the channel interference threshold, a dynamic weighting algorithm is used to generate node priority weight coefficients, achieving precise quantitative mapping of multi-dimensional channel states. Data transmission cycle quotas are then adaptively and incrementally adjusted based on the weight coefficients, forming a dynamic resource scheduling mechanism centered around high-priority nodes. This technology iteratively updates the channel occupancy prediction model using a sliding window mechanism, integrating historical data with real-time parameters to establish a multi-dimensional resource allocation strategy matrix. This effectively mitigates channel congestion and signal conflicts, and improves the spatiotemporal consistency of spectrum resource allocation. Its core value lies in establishing a closed-loop optimization system of "data collection - weight calculation - quota adjustment - model prediction." Through the synergy of dynamic priority scheduling and channel state prediction, it significantly enhances the resilient responsiveness of network topologies in complex interference environments. While ensuring the timely transmission of critical nodes, it also achieves load balancing and interference mitigation across the entire network's channel resources, providing a low-latency, highly reliable adaptive resource management solution for heterogeneous communication systems.

[0147] In some embodiments, in step 105, spectrum quota reallocation is performed on monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, and the reallocation includes frequency hopping interval optimization and transmit power adjustment based on historical spectrum quota usage data, including:

[0148] 401. Analyze the analysis results of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource utilization efficiency report, extract the spectrum efficiency deviation parameter of the high stress concentration area, associate it with historical frequency hopping data, and generate a spectrum efficiency deviation association parameter;

[0149] In step 401, the actual spectrum quota consumption rate refers to the proportion of spectrum resources actually used by the monitoring node in a specific time period, reflecting the resource utilization efficiency.

[0150] The channel occupancy forecast value refers to the estimated value of future channel occupancy generated based on historical data and a prediction model (such as ARIMA or LSTM).

[0151] The spectrum efficiency deviation parameter refers to a quantitative indicator of the difference between the actual consumption rate and the predicted value, which is used to evaluate the efficiency of spectrum utilization.

[0152] Historical frequency hopping data refers to the historical records of the monitoring node switching between different frequency bands, including switching time, frequency band occupancy rate and other information.

[0153] The spectrum efficiency deviation correlation parameter refers to a comprehensive parameter generated by combining the spectrum efficiency deviation parameter with historical frequency hopping data, which is used to guide subsequent decision-making.

[0154] In an embodiment of the present application, by analyzing the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource utilization efficiency report, spectrum efficiency deviation parameters are extracted from high-stress concentration areas and then correlated with historical frequency hopping data to generate spectrum efficiency deviation correlation parameters. First, a time series analysis method (such as ARIMA or LSTM) is used to compare the actual spectrum quota consumption rate with the predicted value, and the deviation rate between the two is calculated as a quantitative indicator of the spectrum efficiency deviation parameter. Subsequently, by correlating historical frequency hopping data (including frequency band switching time, occupancy rate, etc.), the Pearson correlation coefficient or Spearman rank correlation coefficient is used to analyze the correlation between spectrum efficiency deviation and frequency hopping behavior. Finally, the spectrum efficiency deviation parameter is combined with historical frequency hopping data using a weighted fusion algorithm (such as the entropy weight method or the hierarchical analysis method) to generate a spectrum efficiency deviation correlation parameter, providing data support for subsequent frequency hopping decisions.

[0155] 402. Based on the spectrum efficiency deviation correlation parameter, calculate the channel occupancy fluctuation characteristics of each frequency band in the historical usage period, and combine the signal propagation attenuation characteristics of the high stress concentration area to generate a frequency hopping interval decision parameter;

[0156] In step 402, the channel occupancy fluctuation characteristics refer to the occupancy fluctuation of each frequency band in the historical usage period, reflecting the stability of the frequency band.

[0157] The signal propagation attenuation characteristic refers to the attenuation degree of the signal in different frequency bands in the high stress concentration area, which affects the frequency band selection.

[0158] The frequency hopping interval decision parameter refers to a parameter generated based on the channel occupancy fluctuation characteristics and signal propagation attenuation characteristics, and is used to optimize the frequency band switching strategy.

[0159] In an embodiment of the present application, the frequency hopping interval decision parameters are generated by calculating the channel occupancy fluctuation characteristics of each frequency band during the historical usage period and combining the signal propagation attenuation characteristics of the high stress concentration area. First, the occupancy fluctuation characteristics of each frequency band are analyzed by wavelet transform or Fourier transform, and its frequency and amplitude information are extracted to quantify the frequency band stability. Subsequently, the attenuation degree of different frequency bands is calculated in combination with the signal propagation attenuation characteristics of the high stress concentration area (such as the path loss model or the empirical attenuation formula). Finally, a multi-objective optimization algorithm (such as NSGA-II) is used to balance the frequency band stability and signal attenuation characteristics to generate the frequency hopping interval decision parameters and optimize the frequency band switching strategy.

[0160] 403. According to the frequency switching priority in the frequency hopping interval decision parameter, historical signal strength attenuation data of the high stress concentration area is matched, a transmit power compensation gradient is calculated, and a reference value is superimposed to generate a dynamic power compensation parameter;

[0161] In step 403, the frequency band switching priority refers to the switching order of each frequency band determined according to the frequency hopping interval decision parameter, and the frequency band with high stability is selected first.

[0162] The historical signal strength attenuation data refers to the historical records of signal strength attenuation in different frequency bands in high stress concentration areas.

[0163] The transmit power compensation gradient refers to the power adjustment amplitude calculated based on the signal strength attenuation data, which is used to compensate for signal attenuation.

[0164] The dynamic power compensation parameter refers to the power compensation parameter after the reference value is superimposed, which is used to dynamically adjust the transmit power.

[0165] In the embodiment of the present application, the transmission power is calculated by matching the historical signal strength attenuation data of the high stress concentration area.

[0166] The power compensation gradient is added to the baseline value to generate dynamic power compensation parameters. First, the target frequency band is determined based on the frequency switching priority, and its historical signal strength attenuation data (such as the received signal strength indicator (RSSI) or signal-to-noise ratio (SNR)) is extracted. Next, a gradient descent method or least squares method is used to calculate the transmit power compensation gradient to ensure that the signal strength meets coverage requirements. Finally, the compensation gradient is added to the baseline transmit power value to generate dynamic power compensation parameters, which are used to adjust the transmit power in real time.

[0167] 404. The frequency hopping interval decision parameter and the dynamic power compensation parameter are integrated to reallocate spectrum quotas according to priority weights, and a spectrum quota reallocation strategy table is generated after verification.

[0168] In step 404, the priority weight refers to the priority of each frequency band determined according to the spectrum efficiency deviation association parameter and the frequency hopping interval decision parameter.

[0169] The spectrum quota reallocation strategy table refers to a strategy table generated by integrating the frequency hopping interval decision parameter and the dynamic power compensation parameter, and is used to guide resource allocation.

[0170] In the embodiment of the present application, by integrating the frequency hopping interval decision parameter and the dynamic power compensation parameter, the priority weight

[0171] Spectrum quota is redistributed and, after verification, a spectrum quota redistribution strategy table is generated. First, a weighted fusion algorithm (such as weighted averaging or fuzzy analytic hierarchy process) is used to combine the frequency hopping interval decision parameter with the dynamic power compensation parameter to generate a comprehensive priority weight. Spectrum quota is then redistributed across frequency bands based on the priority weights, ensuring that high-priority bands receive more resources. Finally, the redistribution strategy is verified through Monte Carlo simulation or cross-validation, generating a spectrum quota redistribution strategy table to provide a basis for resource scheduling.

[0172] 405. Load the spectrum quota reallocation strategy table into the resource scheduling module of the monitoring node, record the spectrum usage data after reallocation in real time and update it to the quota tracking database, and execute spectrum quota reallocation.

[0173] In step 405, the resource scheduling module refers to a functional module in the monitoring node that is responsible for spectrum resource allocation and scheduling.

[0174] The quota tracking database refers to a database that records spectrum quota usage data for real-time updates and historical analysis.

[0175] Spectrum quota reallocation refers to adjusting the spectrum resource allocation of monitoring nodes according to the reallocation strategy table to optimize resource utilization efficiency.

[0176] In the embodiment of the present application, the spectrum usage data after reallocation is recorded in real time and updated to the quota tracking database.

[0177] Spectrum quota reallocation is performed. First, the reallocation policy table is loaded into the resource scheduling module. A dynamic scheduling algorithm (such as a greedy algorithm or a genetic algorithm) is used to adjust frequency band switching and transmit power in real time. Subsequently, spectrum usage data (such as occupancy rate and signal strength) after reallocation is recorded and updated to the quota tracking database for subsequent analysis and optimization. Finally, a closed-loop feedback mechanism ensures the real-time and effectiveness of the reallocation policy, improving system resource utilization efficiency and signal transmission stability.

[0178] Here's a specific example:

[0179] In the optimization scenario of urban rail transit wireless communication systems, the following complete implementation scheme addresses spectrum resource allocation requirements in high-stress areas (such as tunnels and underground stations): First, based on a wireless communication network resource utilization efficiency report for a particular subway line, the actual spectrum quota consumption rate and predicted channel occupancy rate are analyzed. The spectrum efficiency deviation parameter for the tunnel area is extracted and correlated with historical frequency hopping data. A spectrum efficiency deviation correlation parameter α = 0.78 is generated through Pearson correlation analysis. Subsequently, the channel occupancy fluctuation characteristics of each frequency band over the historical usage period are calculated based on α = 0.78. In combination with the signal propagation attenuation characteristics of the tunnel area, a frequency hopping interval decision parameter is generated using a wavelet transform and a multi-objective optimization algorithm to optimize the frequency band switching strategy. Next, based on the frequency band switching priority in the frequency hopping interval decision parameter, historical signal strength attenuation data for the tunnel area is matched, and the transmit power compensation gradient is calculated using the gradient descent method. A dynamic power compensation parameter β = 1.2 is then generated by superimposing the baseline value. Finally, the frequency hopping interval decision parameter and the dynamic power compensation parameter are combined to reallocate spectrum quota according to priority weights. Monte Carlo simulation is used to verify the generated spectrum quota reallocation strategy table. Finally, the reallocation strategy table is loaded into the resource scheduling module of the monitoring node. The reallocated spectrum usage data is recorded in real time and updated to the quota tracking database. Spectrum quota reallocation is then executed, significantly improving signal coverage quality and communication stability in the tunnel area. This solution achieves resource optimization and improved signal transmission stability in high-stress areas through the coordinated processing of spectrum efficiency deviation analysis, frequency hopping decision optimization, power compensation adjustment, and quota reallocation execution. This provides reliable technical support for optimizing wireless communication systems in complex environments.

[0180] In summary, steps 401 to 405 achieve resource optimization and signal transmission stability improvement for monitoring nodes in high-stress concentration areas through spectrum quota reallocation technology. First, based on the analysis results of the actual spectrum quota consumption rate and the predicted channel occupancy rate, the spectrum efficiency deviation parameter is extracted and associated with historical frequency hopping data to generate spectrum efficiency deviation association parameters to quantify the degree of matching between spectrum utilization efficiency and channel occupancy rate. Second, by combining the channel occupancy fluctuation characteristics and signal propagation attenuation characteristics, the frequency hopping interval decision parameter is generated to optimize the frequency band switching strategy. Subsequently, the transmission power compensation gradient is calculated based on the signal strength attenuation data to generate dynamic power compensation parameters to ensure that the signal coverage range and strength meet the requirements. Finally, the frequency hopping interval and power compensation parameters are integrated to reallocate the spectrum quota according to the priority weight, generate and load the reallocation strategy table, and update the quota tracking database in real time. This design significantly improves the spectrum utilization efficiency and signal transmission quality in high-stress concentration areas by dynamically adjusting the frequency hopping interval and transmission power, providing a reliable solution for resource optimization in complex environments.

[0181] In some embodiments, the step 404 of fusing the frequency hopping interval decision parameter and the dynamic power compensation parameter, reallocating the spectrum quota according to the priority weight, and generating a spectrum quota reallocation strategy table after verification includes:

[0182] 501. Perform a multi-dimensional parameter superposition operation on the frequency band switching priority and the dwell time proportional coefficient in the frequency hopping interval decision parameter and the transmit power compensation gradient in the dynamic power compensation parameter to generate an initial strategy parameter set for spectrum quota reallocation;

[0183] In step 501, the frequency band switching priority refers to the switching order of each frequency band determined according to the frequency hopping interval decision parameter, and the frequency band with high stability is selected first.

[0184] The dwell time ratio coefficient refers to the proportion of the monitoring node's dwell time in a specific frequency band, reflecting the frequency of frequency band usage.

[0185] The transmit power compensation gradient refers to the power adjustment amplitude calculated based on the signal strength attenuation data, which is used to compensate for signal attenuation.

[0186] The multi-dimensional parameter superposition operation refers to an algorithm (such as weighted averaging or hierarchical analysis method) that performs weighted fusion of the frequency band switching priority, the dwell time ratio coefficient and the transmission power compensation gradient.

[0187] The spectrum quota reallocation initial policy parameter set refers to the initial policy parameter set generated after integrating multi-dimensional parameters, which is used for subsequent optimization.

[0188] In an embodiment of the present application, the frequency band switching priority and the dwell time proportional coefficient in the frequency hopping interval decision parameter are fused with the transmission power compensation gradient in the dynamic power compensation parameter through a multi-dimensional parameter superposition operation to generate a set of initial strategy parameters for spectrum quota redistribution. First, the analytic hierarchy process (AHP) or entropy weight method is used to determine the weights of the frequency band switching priority and the dwell time proportional coefficient, and the weighted fusion is performed in combination with the transmission power compensation gradient to generate the initial strategy parameters. Subsequently, normalization is performed to ensure that all parameters are in the same dimension, forming a high-quality data set that can be used for subsequent analysis. Finally, a multi-objective optimization algorithm (such as NSGA-II) is used to balance the frequency band stability and power compensation requirements to generate a set of initial strategy parameters for spectrum quota redistribution, providing data support for subsequent conflict detection and correction.

[0189] 502. Detect and correct spectrum quota allocation conflicts between different frequency bands in a high stress concentration area based on the spectrum quota reallocation initial strategy parameter set, and output corrected spectrum quota reallocation intermediate strategy parameters.

[0190] In step 502, spectrum quota allocation conflict refers to resource overlap or competition between different frequency bands during the resource allocation process.

[0191] The correction strategy refers to adjusting spectrum quota allocation through conflict detection algorithms (such as maximum matching or Hungarian algorithm) to eliminate conflicts.

[0192] The intermediate strategy parameters for spectrum quota reallocation refer to the set of strategy parameters after conflict correction, which are used for subsequent constraint optimization.

[0193] In an embodiment of the present application, based on the initial set of spectrum quota reallocation policy parameters, this step detects and corrects spectrum quota allocation conflicts in different frequency bands within high stress concentration areas, and outputs corrected intermediate policy parameters. First, a conflict detection algorithm (such as maximum matching or the Hungarian algorithm) is used to identify spectrum quota allocation conflicts and quantify the degree of conflict. Subsequently, a correction strategy (such as resource reallocation or priority adjustment) is used to eliminate conflicts and optimize spectrum quota allocation. Finally, the corrected policy parameters are verified through Monte Carlo simulation to ensure their rationality and effectiveness, and intermediate spectrum quota reallocation policy parameters are generated to provide reliable input for subsequent constraint optimization.

[0194] 503. Load the priority weight as a constraint condition into the spectrum quota reallocation intermediate strategy parameter, calculate the maximum allocatable spectrum quota threshold of each frequency band under the priority weight constraint, and generate the spectrum quota reallocation boundary parameter;

[0195] In step 503, the priority weight refers to the priority of each frequency band determined according to the spectrum efficiency deviation association parameter and the frequency hopping interval decision parameter.

[0196] The maximum allocatable spectrum quota threshold refers to the maximum amount of spectrum resources that can be allocated to each frequency band under the priority weight constraint.

[0197] The spectrum quota reallocation boundary parameter refers to the boundary parameter generated based on the priority weight and the maximum allocatable threshold, which is used to guide resource allocation.

[0198] In an embodiment of the present application, the priority weight is loaded as a constraint condition into the spectrum quota reallocation intermediate strategy parameter, the maximum allocatable spectrum quota threshold of each frequency band under the priority weight constraint is calculated, and the spectrum quota reallocation boundary parameter is generated. First, the Lagrange multiplier method or the gradient descent method is used to construct the objective function, and the priority weight and the spectrum quota requirement are combined to perform an optimization solution. Subsequently, the parameter set is iteratively updated to gradually approach the global optimal solution, and the maximum allocatable spectrum quota threshold of each frequency band is generated. Finally, the output is limited to a preset interval through the boundary condition projection method (such as the Simplex projection algorithm) to generate the spectrum quota reallocation boundary parameter, which provides a basis for subsequent cross-layer parameter comparison.

[0199] 504. Perform a cross-layer parameter comparison on the spectrum quota reallocation boundary parameter and the channel occupancy prediction value in the resource allocation strategy matrix. After verification, generate a spectrum quota reallocation verification parameter.

[0200] In step 504 , the channel occupancy prediction value refers to a future channel occupancy estimation value generated based on historical data and a prediction model (such as ARIMA or LSTM).

[0201] Cross-layer parameter comparison refers to an algorithm (such as KL divergence or Euclidean distance) that compares and verifies spectrum quota reallocation boundary parameters with channel occupancy prediction values.

[0202] Spectrum quota reallocation verification parameters refer to policy parameters that have been verified through comparison to ensure the rationality and effectiveness of resource allocation.

[0203] In the embodiment of the present application, spectrum quota redistribution verification parameters are generated by comparing cross-layer parameters with the channel occupancy prediction values ​​in the resource allocation strategy matrix. First, the difference between the boundary parameters and the channel occupancy prediction values ​​is calculated using KL divergence or Euclidean distance to quantify the verification results. Subsequently, the verification parameters are iteratively optimized through cross-validation or Monte Carlo simulation to ensure their accuracy and stability. Finally, spectrum quota redistribution verification parameters are generated, providing reliable data support for policy table generation.

[0204] 505. Based on the spectrum quota reallocation verification parameters, and in accordance with the combination rule of the frequency band switching priority and the dwell time ratio coefficient, a spectrum quota reallocation strategy table including the frequency band identifier, quota threshold and power compensation gradient is generated.

[0205] In step 505, the frequency band identifier refers to a unique number or name used to identify different frequency bands.

[0206] The quota threshold refers to the maximum amount of spectrum resources that can be allocated to each frequency band in the reallocation strategy.

[0207] The power compensation gradient refers to the power adjustment amplitude calculated based on the signal strength attenuation data, which is used to compensate for signal attenuation.

[0208] The spectrum quota reallocation strategy table refers to a strategy table including frequency band identifiers, quota thresholds, and power compensation gradients, and is used to guide resource scheduling.

[0209] In an embodiment of the present application, based on the spectrum quota reallocation verification parameters, a spectrum quota reallocation strategy table including frequency band identification, quota threshold and power compensation gradient is generated in accordance with the combination rule of frequency band switching priority and residence time ratio coefficient. First, a weighted fusion algorithm (such as weighted average or fuzzy analytic hierarchy process) is used to combine the verification parameters with the combination rule to generate a strategy table framework. Subsequently, normalization is performed to ensure that each parameter is in the same dimension, forming a strategy table that can be used for resource scheduling. Finally, the validity of the strategy table is verified through a closed-loop feedback mechanism, and a spectrum quota reallocation strategy table including frequency band identification, quota threshold and power compensation gradient is generated, providing a reliable basis for resource scheduling.

[0210] Here's a specific example:

[0211] In the wireless communication resource scheduling scenario of an industrial IoT smart factory, an automobile manufacturer deployed a dynamic spectrum reallocation system to resolve spectrum resource conflicts between AGVs, robotic arms, and environmental sensors. In specific implementation, the system multi-dimensionally superimposes the AGV's frequency hopping priority (band switching weight of 0.8), the dwell time ratio of the robotic arm control signal (0.65), and the power compensation gradient of the laser positioning device (+5dBm) to generate initial policy parameters. When a spectrum quota conflict is detected between the welding robot (2.4GHz band) and the AGV navigation system (5.8GHz band), the system dynamically reduces the welding robot's power gradient to +2dBm using the channel quality index (CQI). Based on the real-time priority of the AGV transport path (the weight of urgent material delivery is increased to 0.9), the system calculates the maximum allocatable threshold for each frequency band (for example, limiting the 5.8GHz band to 40%). After cross-layer verification, it generates a spectrum quota policy table that includes a dedicated frequency band identifier (5.8GHz / 20MHz dedicated to AGVs), dynamic power compensation (+3dBm for laser positioning), and dwell time (600ms for robotic arm control). This reduces the communication interruption rate on the production line by 58% and improves equipment collaboration efficiency by 33%.

[0212] In summary, steps 501 to 505 achieve spectrum quota reallocation in high-stress concentration areas through multi-dimensional parameter fusion and constraint optimization technology, significantly improving resource utilization efficiency and signal transmission stability. First, the frequency band switching priority and dwell time ratio coefficient in the frequency hopping interval decision parameter are combined with the transmit power compensation gradient in the dynamic power compensation parameter to generate a set of initial spectrum quota reallocation strategy parameters. Second, spectrum quota allocation conflicts between different frequency bands in the high-stress concentration area are detected and corrected, and the corrected intermediate strategy parameters are output. Subsequently, the priority weight is loaded as a constraint condition into the intermediate strategy parameters, and the maximum allocable spectrum quota threshold for each frequency band is calculated to generate boundary parameters. Next, the boundary parameters are compared with the channel occupancy prediction value in the resource allocation strategy matrix through cross-layer parameters to generate verification parameters. Finally, according to the combination rule of the frequency band switching priority and the dwell time ratio coefficient, a spectrum quota reallocation strategy table containing frequency band identifiers, quota thresholds, and power compensation gradients is generated. Through multi-dimensional fusion and constraint optimization, this design significantly improves the rationality and adaptability of spectrum quota allocation, providing a reliable solution for resource scheduling in complex environments.

[0213] In some embodiments, the step 603 includes loading the priority weight as a constraint condition into the spectrum quota reallocation intermediate strategy parameter, calculating the maximum allocable spectrum quota threshold for each frequency band under the priority weight constraint, and generating the spectrum quota reallocation boundary parameter, including:

[0214] 601. Extract the priority weight constraint parameter from the spectrum quota reallocation intermediate strategy parameter to generate an initial constraint parameter set including a frequency band identifier and a priority weight;

[0215] In step 601, the priority weight constraint parameter refers to the priority weight of each frequency band determined based on the spectrum efficiency deviation association parameter and the frequency hopping interval decision parameter.

[0216] Frequency band identifier refers to the unique number or name used to identify different frequency bands.

[0217] The initial constraint parameter set refers to a parameter set including a frequency band identifier and a priority weight, which is used for subsequent calculation of the maximum allocatable threshold.

[0218] In an embodiment of the present application, an initial constraint parameter set including frequency band identifiers and priority weights is generated by extracting priority weight constraint parameters from the intermediate strategy parameters for spectrum quota reallocation. First, data parsing techniques (such as regular expressions or data mining algorithms) are used to extract priority weight constraint parameters from the intermediate strategy parameters to ensure the integrity and accuracy of the data. Subsequently, an initial constraint parameter set is constructed through the mapping relationship between frequency band identifiers and priority weights. Finally, normalization is performed to ensure that the weight distribution satisfies the constraint condition of Σw_i=1, and an initial constraint parameter set that can be used for subsequent calculations is generated. This step provides a clear data basis for the subsequent calculation of the maximum allocatable threshold.

[0219] 602. Based on the priority weights in the initial constraint parameter set and in combination with the historical spectrum demand baseline values ​​of each frequency band, calculate the maximum allocatable spectrum quota threshold of each frequency band under the weight constraint, and generate a spectrum quota extreme value parameter set;

[0220] In step 602, the historical spectrum demand baseline value refers to the average spectrum demand of each frequency band in the historical usage period, reflecting the frequency band resource usage.

[0221] The maximum allocatable spectrum quota threshold refers to the maximum amount of spectrum resources that can be allocated to each frequency band under the priority weight constraint.

[0222] The spectrum quota extreme value parameter set refers to a set of extreme value parameters generated based on priority weights and historical demand baseline values, and is used to guide resource allocation.

[0223] In an embodiment of the present application, by combining the historical spectrum demand baseline values ​​of each frequency band, the maximum allocatable spectrum quota threshold of each frequency band under the weight constraint is calculated, and a spectrum quota extreme value parameter set is generated. First, a time series analysis method (such as ARIMA or LSTM) is used to calculate the historical spectrum demand baseline value to quantify the resource usage of each frequency band. Subsequently, the objective function is constructed using the Lagrange multiplier method or the gradient descent method, and the optimization solution is performed by combining the priority weights with the historical demand baseline value. Finally, a spectrum quota extreme value parameter set is generated to provide data support for subsequent compliance comparisons. This step ensures the rationality of resource allocation through the coordinated optimization of weight constraints and historical demand.

[0224] 603. Compare the spectrum quota extreme value parameter set with the physical layer load limit parameter for compliance, and remove the exceeding

[0225] Determine the extreme value parameters of the physical layer carrying capacity and generate a spectrum quota redistribution boundary parameter set;

[0226] In step 603, the physical layer load limitation parameter refers to the maximum load capacity parameter of the physical layer hardware device (such as antenna, filter), which limits the upper limit of spectrum resource allocation.

[0227] Compliance comparison refers to an algorithm (such as threshold filtering or KL divergence) that compares and verifies the spectrum quota extreme value parameter set with the physical layer load limit parameters.

[0228] The spectrum quota reallocation boundary parameter set refers to the boundary parameter set generated after eliminating extreme value parameters that exceed the physical layer carrying capacity, ensuring the feasibility of resource allocation.

[0229] In an embodiment of the present application, a spectrum quota extreme value parameter set is compared with the physical layer carrying limit parameters for compliance, and extreme value parameters that exceed the physical layer carrying capacity are eliminated to generate a spectrum quota redistribution boundary parameter set. First, a threshold filtering algorithm or KL divergence is used to calculate the difference between the extreme value parameters and the physical layer carrying limit parameters to quantify the compliance results. Subsequently, a boundary parameter set is generated by eliminating extreme value parameters that exceed the carrying capacity. Finally, the rationality of the boundary parameter set is verified through Monte Carlo simulation to ensure that it meets the carrying capacity of the physical layer hardware equipment. This step ensures the feasibility of resource allocation through compliance comparison and parameter optimization.

[0230] 604. Integrate the spectrum quota reallocation boundary parameter set and the priority weight constraint parameter set to generate spectrum quota reallocation boundary parameters including a frequency band identifier, a priority weight, and a quota threshold.

[0231] In step 604, the priority weight constraint parameter set refers to a parameter set including frequency band identifiers and priority weights, and is used to guide resource allocation.

[0232] The spectrum quota reallocation boundary parameter refers to the parameter generated by integrating the boundary parameter set and the priority weight constraint set, which includes the frequency band identifier, priority weight and quota threshold, and is used to guide resource scheduling.

[0233] In an embodiment of the present application, spectrum quota reallocation boundary parameter set and priority weight constraint parameter set are integrated to generate spectrum quota reallocation boundary parameters including frequency band identifier, priority weight and quota threshold. First, a weighted fusion algorithm (such as weighted average or fuzzy analytic hierarchy process) is used to combine the boundary parameter set and the priority weight constraint set to generate a boundary parameter framework. Subsequently, normalization is performed to ensure that each parameter is in the same dimension, forming boundary parameters that can be used for resource scheduling. Finally, the validity of the boundary parameters is verified through a closed-loop feedback mechanism, and spectrum quota reallocation boundary parameters including frequency band identifier, priority weight and quota threshold are generated to provide a reliable basis for resource scheduling. This step ensures the accuracy and stability of resource allocation through parameter integration and verification.

[0234] Here's a specific example:

[0235] In the optimization scenario of a smart city IoT communication system, the following complete implementation scheme can be constructed to address spectrum resource allocation requirements in high-density device areas (such as smart transportation hubs and industrial parks): First, based on the resource utilization efficiency report of a smart city IoT communication network, priority weight constraint parameters are extracted from the intermediate strategy parameters for spectrum quota reallocation, generating an initial constraint parameter set consisting of frequency band identifiers and priority weights. Subsequently, based on the priority weights in the initial constraint parameter set and combined with the historical spectrum demand baseline values ​​for each frequency band (e.g., the historical average demand for the NB-IoT band is 50MHz), the maximum allocatable spectrum quota threshold for each frequency band under the weight constraint is calculated using a gradient descent method (e.g., a maximum quota of 60MHz for the NB-IoT band), generating a spectrum quota extreme value parameter set. Next, the spectrum quota extreme value parameter set is compared with the physical layer load limit parameters for compliance. Extreme value parameters that exceed the physical layer load capacity are eliminated using KL divergence calculation to generate a spectrum quota reallocation boundary parameter set. Finally, by integrating the spectrum quota reallocation boundary parameter set with the priority weight constraint parameter set, a spectrum quota reallocation boundary parameter containing frequency band identifiers, priority weights, and quota thresholds was generated, significantly improving the device connection quality and communication stability in the intelligent transportation hub area. This solution optimizes spectrum quota reallocation in high-density device areas through the coordinated optimization of priority weight constraints, historical demand baseline value analysis, and physical layer load constraints, providing reliable technical support for optimizing IoT communication systems in complex environments.

[0236] In summary, steps 601 to 604 achieve accurate generation of spectrum quota reallocation boundary parameters through the coordinated optimization of priority weight constraints and physical layer carrying restrictions. First, the priority weight constraint parameters in the spectrum quota reallocation intermediate strategy parameters are extracted to generate an initial constraint set including frequency band identifiers and priorities; secondly, based on the priority weights in the initial constraint set and combined with the historical spectrum demand baseline values ​​of each frequency band, the maximum allocable threshold of each frequency band under the weight constraint is calculated to generate a spectrum quota extreme parameter set; then, the extreme parameter set is compared with the physical layer carrying restriction parameters for compliance, and the extreme parameters that exceed the physical layer carrying capacity are eliminated to generate a spectrum quota reallocation boundary parameter set; finally, the boundary parameter set and the priority weight constraint set are integrated to generate spectrum quota reallocation boundary parameters including frequency band identifiers, priority weights and quota thresholds. This solution significantly improves the rationality of spectrum quota allocation and system stability through a dual optimization mechanism, providing reliable technical support for resource scheduling in complex environments.

[0237] In some embodiments, the establishment of a quota tracking database for IoT resources in step 104 and the generation of a resource utilization efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix, may include:

[0238] 701. Establish a quota tracking database for IoT resources, wherein the quota tracking database records historical spectrum quota usage data of monitoring nodes, and dynamically aggregate actual spectrum quota consumption rates according to a preset time window based on the quota tracking database to generate a dynamically aggregated data set.

[0239] In step 701, the quota tracking database refers to a database that records historical spectrum quota usage data of monitoring nodes and is used to store and analyze resource usage.

[0240] The preset time window refers to the time period used to divide historical data (such as a window every 10 minutes) to facilitate dynamic aggregation analysis.

[0241] The actual spectrum quota consumption rate refers to the proportion of spectrum resources actually used by the monitoring node within a specific time window.

[0242] The dynamic aggregated dataset refers to the data set generated by aggregating the actual consumption rate of spectrum quota by time window, which is used for subsequent feature extraction and analysis.

[0243] In this embodiment of the present application, a quota tracking database for IoT resources is established to record historical spectrum quota usage data for monitoring nodes. The actual spectrum quota consumption rate is dynamically aggregated according to preset time windows to generate a dynamically aggregated dataset. First, a distributed database technology (such as HBase or Cassandra) is used to construct the quota tracking database, recording the spectrum quota usage data of each node in real time. Subsequently, the historical data is dynamically aggregated by time window division (e.g., a 10-minute window), and the actual spectrum quota consumption rate within each window is calculated. Finally, through data cleaning and normalization, a dynamically aggregated dataset is generated, providing a data foundation for subsequent feature extraction and analysis.

[0244] 702. Correlate the node identifiers in the dynamic aggregated data set across time windows, extract the temporal fluctuation characteristics and spatial distribution characteristics of the spectrum consumption rate, and generate a spectrum feature vector including the node spectrum fluctuation index and spatial correlation degree;

[0245] In step 702, the node identifier refers to a unique number or name used to identify different monitoring nodes.

[0246] The time series fluctuation characteristics refer to the changing trend and periodic characteristics of the spectrum consumption rate in the time dimension, reflecting the dynamic changes in resource utilization.

[0247] Spatial distribution characteristics refer to the distribution characteristics of monitoring nodes in the spatial dimension, reflecting the resource usage correlation between nodes.

[0248] The node spectrum fluctuation index refers to an indicator that quantifies the temporal fluctuation characteristics of the spectrum consumption rate and reflects the stability of resource utilization.

[0249] Spatial correlation refers to an indicator that quantifies the spatial distribution characteristics of spectrum consumption rates between nodes, reflecting the regional characteristics of resource utilization.

[0250] The spectrum feature vector refers to the feature vector containing the node spectrum fluctuation index and spatial correlation, which is used for subsequent matching and analysis.

[0251] In this embodiment of the present application, node identifiers are correlated across time windows to extract the temporal fluctuation characteristics and spatial distribution characteristics of spectrum consumption rates, generating a spectrum feature vector containing the node spectrum fluctuation index and spatial correlation. First, a time series analysis method (such as ARIMA or LSTM) is used to extract the temporal fluctuation characteristics of spectrum consumption rates and quantify their changing trends and periodicity. Subsequently, a spatial clustering algorithm (such as K-means or DBSCAN) is used to analyze the spatial distribution characteristics between nodes and calculate the spatial correlation. Finally, the temporal fluctuation characteristics and spatial distribution characteristics are fused to generate a spectrum feature vector, providing feature data for subsequent matching and analysis.

[0252] 703. Match the spectrum feature vector with the channel occupancy rate prediction value in the resource allocation strategy matrix, calculate the deviation between the actual spectrum quota consumption rate and the prediction value and the convergence index, and generate a spectrum comparison index set;

[0253] In step 703, the resource allocation strategy matrix refers to a matrix containing channel occupancy prediction values, which is used to guide spectrum resource allocation.

[0254] Deviation refers to the quantitative indicator of the difference between the actual spectrum quota consumption rate and the predicted value, reflecting the accuracy of resource allocation.

[0255] The convergence index refers to the degree of convergence of the spectrum consumption rate over time, reflecting the stability of resource allocation.

[0256] The spectrum comparison index set refers to a parameter set that includes deviation and convergence indicators, which is used for subsequent strategy optimization.

[0257] In an embodiment of the present application, the spectrum feature vector is matched with the channel occupancy rate prediction value in the resource allocation strategy matrix, and the deviation and convergence index between the actual spectrum quota consumption rate and the prediction value are calculated to generate a spectrum comparison index set. First, a similarity calculation algorithm (such as cosine similarity or Euclidean distance) is used to match the spectrum feature vector with the channel occupancy rate prediction value to quantify the deviation between the two. Subsequently, the convergence index of the spectrum consumption rate is calculated through convergence analysis (such as sliding window mean or gradient descent method). Finally, the deviation and convergence index are integrated to generate a spectrum comparison index set, which provides a quantitative basis for subsequent strategy optimization.

[0258] 704. Construct a mapping relationship between the node identifier and the resource allocation strategy matrix based on the spectrum comparison indicator set, and generate an optimized strategy mapping table including node priority coefficients and strategy adaptability by dynamically adjusting the priority weights in the strategy matrix.

[0259] In step 704, the mapping relationship between the node identifier and the policy matrix refers to the association relationship between the node identifier and the resource allocation policy matrix, which is used to guide resource allocation.

[0260] Priority weight refers to the priority weight of each node determined based on a set of spectrum comparison indicators, and is used to optimize resource allocation.

[0261] The node priority coefficient refers to the parameter that quantifies the node priority and reflects the importance of resource allocation.

[0262] Strategy adaptability refers to the degree of match between resource allocation strategy and actual demand, reflecting the effectiveness of the strategy.

[0263] The optimization strategy mapping table refers to a parameter table containing node priority coefficients and strategy adaptability, which is used to guide resource scheduling.

[0264] In an embodiment of the present application, a mapping relationship between node identifiers and a resource allocation policy matrix is ​​constructed, and the priority weights in the policy matrix are dynamically adjusted to generate an optimized policy mapping table containing node priority coefficients and policy adaptability. First, a mapping algorithm (such as a hash map or a graph neural network) is used to construct a mapping relationship between node identifiers and the policy matrix to ensure data relevance. Subsequently, a dynamic adjustment algorithm (such as reinforcement learning or a genetic algorithm) is used to optimize the priority weights in the policy matrix to generate node priority coefficients and policy adaptability. Finally, the optimized parameters are integrated to generate an optimized policy mapping table, which provides a strategic basis for subsequent resource scheduling.

[0265] 705. Based on the priority coefficient and the policy adaptability in the optimization policy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison indicator set, a resource utilization efficiency report capable of evaluation and optimization suggestions is generated.

[0266] In step 705, the deviation threshold refers to the upper limit of the deviation between the actual spectrum quota consumption rate and the predicted value, which is used to evaluate resource utilization efficiency.

[0267] The convergence factor refers to a quantitative parameter of the spectrum consumption rate convergence indicator, which is used to evaluate the stability of resource allocation.

[0268] Resource efficiency reports are reports that include evaluation results and optimization recommendations, used to guide resource optimization and decision-making.

[0269] In an embodiment of the present application, based on the priority coefficient and policy adaptability in the optimization policy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison indicator set, a resource utilization efficiency report capable of evaluation and optimization recommendations is generated. First, a report generation framework (such as JasperReports or Tableau) is used to build a report template that integrates the priority coefficient, policy adaptability, and spectrum comparison indicators. Subsequently, the resource utilization efficiency is evaluated using the deviation threshold and convergence factor, and optimization recommendations are generated (such as adjusting the spectrum quota or optimizing the priority weight). Finally, the evaluation results and optimization recommendations are integrated through data visualization technology to generate a resource utilization efficiency report, providing clear technical support for decision makers.

[0270] Here is a specific example:

[0271] In the IoT spectrum resource management scenario for smart city intelligent transportation systems, a city transportation administration bureau deployed a LoRaWAN-based networked traffic signal control system to alleviate traffic congestion during morning and evening rush hours. The system installed monitoring nodes at 500 intersections in the central urban area, each of which transmits real-time traffic signal status, traffic flow video streams, and environmental sensor data. By establishing a quota tracking database, the system records historical spectrum usage data for each node in 15-minute windows. For example, the system found that the Jiefang South Road intersection node had an average spectrum usage rate of 98% during the morning rush hour (7:00-9:00), compared to only 35% during off-peak hours. Dynamic aggregation analysis across a 72-hour window identified pulsed fluctuations in spectrum usage at nodes near hospitals (e.g., bursts of data transmission when ambulances take priority). Furthermore, the spatial correlation of nodes in commercial areas reached a high of 0.83, indicating that adjacent nodes experience synchronized increases in spectrum usage during promotional events. When these feature vectors were matched with the preset "holiday traffic plan" strategy matrix, it was found that the actual consumption rate of the Jiefang South Road node deviated from the predicted value by 42%, and the convergence index was lower than 0.6, triggering the dynamic adjustment mechanism. The system increased the priority weights of the 10 nodes around the tertiary hospitals by 300%, marked them as red warning levels in the strategy mapping table, and configured elastic bandwidth pools for the business district nodes to divert non-real-time data through the NB-IoT channel. The final resource report showed that after optimization, the peak spectrum utilization rate in the core area of ​​the morning rush hour dropped by 28%, and the communication delay was shortened from 850ms to 210ms. It is recommended to deploy edge computing gateways at the nodes across the viaduct to realize local data processing, which is expected to reduce the spectrum load by another 15%. This embodiment achieves precise scheduling of city-level IoT resources through spatiotemporal feature mining and dynamic policy adaptation.

[0272] In summary, steps 701 to 705 achieve accurate assessment and optimization of IoT spectrum resource utilization efficiency through dynamic aggregation and feature analysis technology, significantly improving the rationality of resource allocation and system stability. First, a quota tracking database is established, and the actual spectrum quota consumption rate is dynamically aggregated according to a preset time window to generate a dynamic aggregation dataset. Second, the temporal fluctuation characteristics and spatial distribution characteristics of the spectrum consumption rate are extracted to generate a spectrum feature vector. Subsequently, the feature vector is matched with the channel occupancy prediction value, and the deviation and convergence indicators are calculated to generate a spectrum comparison indicator set. Next, a mapping relationship between node identifiers and the resource allocation strategy matrix is ​​constructed, and the priority weights are dynamically adjusted to generate an optimized strategy mapping table. Finally, based on the priority coefficient and strategy adaptability, combined with the deviation threshold and convergence factor, a resource utilization efficiency report is generated. Through the collaborative processing of multidimensional data analysis and strategy optimization, this design significantly improves the assessment accuracy and optimization effect of spectrum resource utilization efficiency, providing reliable technical support for IoT resource scheduling in complex environments.

[0273] In some embodiments, step 704 constructs a mapping relationship between node identifiers and a resource allocation strategy matrix based on a spectrum comparison indicator set, and dynamically adjusts priority weights in the strategy matrix to generate an optimized strategy mapping table including node priority coefficients and strategy adaptability, including:

[0274] 801. Determine an initial mapping relationship between a node identifier and a resource allocation strategy matrix based on a spectrum comparison indicator set, wherein the resource allocation strategy matrix includes an initial value of a node priority coefficient and an initial value of a strategy adaptability;

[0275] In step 801, the spectrum comparison index set refers to a parameter set including deviation and convergence indexes, which is used to guide resource allocation strategy.

[0276] Node ID refers to the unique number or name used to identify different monitoring nodes.

[0277] The resource allocation strategy matrix refers to a matrix containing the initial values ​​of node priority coefficients and the initial values ​​of strategy fitness, which is used to guide resource allocation.

[0278] The initial value of the node priority coefficient refers to the initial quantitative parameter of the node priority, which reflects the importance of resource allocation.

[0279] The initial value of strategy adaptation refers to the initial matching degree between resource allocation strategy and actual demand, reflecting the effectiveness of strategy.

[0280] The initial mapping relationship refers to the initial association relationship between the node identifier and the resource allocation strategy matrix, which is used to guide resource allocation.

[0281] In an embodiment of the present application, the initial mapping relationship between the node identifier and the resource allocation strategy matrix is ​​determined based on the spectrum comparison index set, wherein the resource allocation strategy matrix includes the initial value of the node priority coefficient and the initial value of the strategy adaptability. First, a data mapping algorithm (such as hash mapping or graph neural network) is used to associate the spectrum comparison indicator set with the resource allocation strategy matrix to ensure the consistency and integrity of the data. Subsequently, initial values ​​are assigned to the node priority coefficient and the strategy adaptability through an initialization algorithm (such as random initialization or initialization based on historical data). Finally, normalization is performed to ensure that the initial values ​​are in the same dimension, and an initial mapping relationship is generated to provide a data basis for subsequent dynamic adjustments.

[0282] 802. Establish a dynamic feedback loop, in which priority weights and policy adaptation parameters in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a node priority coefficient correction value;

[0283] In step 802, the dynamic feedback loop refers to a closed-loop control system that monitors resource allocation effects in real time and dynamically adjusts strategies.

[0284] Priority weight refers to the priority weight of each node determined based on a set of spectrum comparison indicators, and is used to optimize resource allocation.

[0285] The strategy adaptation parameter refers to the quantitative parameter of the matching degree between the resource allocation strategy and the actual demand, which is used to optimize the strategy.

[0286] The node priority coefficient correction value refers to the node priority coefficient adjusted through dynamic feedback, reflecting the optimization result of resource allocation.

[0287] In an embodiment of the present application, a dynamic feedback loop is established to dynamically adjust the priority weights and policy fitness parameters in the policy matrix based on the initial mapping relationship to form a node priority coefficient correction value. First, a feedback control algorithm (such as PID control or reinforcement learning) is used to construct a dynamic feedback loop to monitor the resource allocation effect in real time. Subsequently, an optimization algorithm (such as gradient descent or genetic algorithm) is used to adjust the priority weights and policy fitness parameters to generate a node priority coefficient correction value. Finally, a convergence analysis is performed to ensure the stability of the correction value, providing input for subsequent policy fitness updates.

[0288] 803. Input the node priority coefficient correction value into the policy adaptability calculation module, and generate a policy adaptability update value in combination with the policy adaptability initial value in the resource allocation policy matrix;

[0289] In step 803, the policy adaptation calculation module refers to a module used to calculate the degree of matching between the resource allocation policy and actual demand.

[0290] The strategy adaptation update value refers to the update parameter generated based on the node priority coefficient correction value and the strategy adaptation initial value, which reflects the optimization effect of the strategy.

[0291] In this embodiment, the policy fitness calculation module inputs the node priority coefficient correction value into the policy fitness calculation module and combines it with the initial policy fitness value in the resource allocation policy matrix to generate an updated policy fitness value. First, a weighted fusion algorithm (such as weighted averaging or fuzzy analytic hierarchy process) is used to combine the correction value with the initial value to generate an updated value framework. Subsequently, normalization is performed to ensure that the updated values ​​are in the same dimension, forming the updated policy fitness value. Finally, a Monte Carlo simulation is performed to verify the rationality of the updated value to ensure that it meets resource allocation requirements.

[0292] 804. Reconstruct the resource allocation strategy matrix according to the node priority coefficient correction value and the strategy adaptation update value, and generate an optimization strategy mapping table including multi-dimensional weight constraints.

[0293] In step 804, the multi-dimensional weight constraint refers to a constraint condition that comprehensively considers multi-dimensional factors such as priority weight and policy adaptability, and is used to optimize resource allocation.

[0294] The optimization strategy mapping table refers to a resource allocation strategy mapping table containing multi-dimensional weight constraints, which is used to guide resource scheduling.

[0295] In an embodiment of the present application, a resource allocation policy matrix is ​​reconstructed based on the revised node priority coefficients and the updated policy fitness values ​​to generate an optimized policy mapping table containing multi-dimensional weight constraints. First, a matrix reconstruction algorithm (such as singular value decomposition or principal component analysis) is used to integrate the revised and updated values ​​to reconstruct the resource allocation policy matrix. Subsequently, a multi-objective optimization algorithm (such as NSGA-II or MOEA / D) is used to balance the multi-dimensional weight constraints and generate an optimized policy mapping table. Finally, a closed-loop feedback mechanism is used to verify the validity of the mapping table to ensure that it meets resource scheduling requirements.

[0296] Here's a specific example:

[0297] In a smart city scenario involving dynamic allocation of vehicle-to-vehicle communication resources, one city has designed a dynamic resource allocation strategy based on the 5.9 GHz frequency band. The system first constructs an initial resource allocation matrix based on metrics such as channel interference and signal strength, assigning priorities based on vehicle type, congestion area, and mission urgency. Subsequently, it uses reinforcement learning to dynamically adjust weights, automatically increasing the priority of emergency vehicles in accident areas and reducing the priority of non-emergency vehicles. Taking into account environmental factors such as weather, the system prioritizes V2I communications, such as reducing the priority of ordinary vehicles during heavy rain. Finally, an optimized mapping table is generated that incorporates safety, efficiency, and fairness considerations. This table is then distributed to the RSU via edge computing, enabling spectrum slice allocation (dedicated frequency bands for emergency vehicles, high bandwidth for public transport cycles, and remaining resources shared by ordinary vehicles). A pilot program in Pingshan, Shenzhen, demonstrated a 35% reduction in critical communication latency and a 22% improvement in spectrum utilization.

[0298] In summary, steps 801 to 804 achieve precise adjustment and optimization of the resource allocation strategy matrix through the collaborative optimization of dynamic feedback and multi-dimensional weight constraints, significantly improving the rationality of resource allocation and system stability. First, the initial mapping relationship between the node identifier and the resource allocation strategy matrix is ​​determined based on the spectrum comparison index set, including the initial value of the node priority coefficient and the initial value of the strategy adaptability; secondly, a dynamic feedback loop is established to dynamically adjust the priority weight and strategy adaptability parameters in the strategy matrix to form a node priority coefficient correction value; then, the correction value is input into the strategy adaptability calculation module to generate a strategy adaptability update value; finally, the resource allocation strategy matrix is ​​reconstructed based on the correction value and the updated value to generate an optimized strategy mapping table containing multi-dimensional weight constraints. This design significantly improves the adaptability and optimization effect of the resource allocation strategy through the collaborative processing of dynamic feedback and multi-dimensional optimization, providing reliable technical support for resource scheduling in complex environments.

[0299] In some embodiments, the establishment of a dynamic feedback loop in step 802, in which priority weights and policy adaptation parameters in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a node priority coefficient correction value, includes:

[0300] 901. When establishing a dynamic feedback loop, a dynamic feedback parameter set is generated by aggregating the node execution state data under the initial mapping relationship and the input and output deviation of the policy matrix;

[0301] In step 901, the node execution status data refers to the real-time operation indicators of the monitoring node (such as spectrum occupancy, packet loss rate, transmission delay) and historical execution logs (such as failure frequency, resource consumption trend), which are used to reflect the actual operation status of the node.

[0302] The input-output deviation of the strategy matrix refers to the quantitative difference between the expected output of the strategy matrix (such as channel allocation plan) and the actual execution result (such as spectrum utilization, interference level), which is calculated using the mean square error or absolute error algorithm.

[0303] The dynamic feedback parameter set refers to the parameter set generated by aggregating the deviation between the node execution status data and the policy matrix. It is used to guide the dynamic adjustment of the policy matrix and includes statistical features within the time window (such as mean, variance) and real-time anomaly marking.

[0304] In an embodiment of the present application, a dynamic feedback parameter set is generated by aggregating the node execution status data under the initial mapping relationship and the deviation between the input and output of the policy matrix. First, a data aggregation technology (such as MapReduce or Spark Streaming) is used to collect the node execution status data in real time to ensure the integrity and real-time performance of the data. Subsequently, the deviation between the input and output of the policy matrix is ​​quantified using a deviation calculation algorithm (such as mean square error or absolute error). Finally, the node execution status data and the deviation are fused to generate a dynamic feedback parameter set, which provides a data basis for subsequent priority weight adjustment.

[0305] 902. Incrementally calculate the priority weights of the policy matrix based on the dynamic feedback parameter set, and iteratively update the priority weight distribution by superimposing the historical weight deviation compensation value and the real-time feedback weight offset;

[0306] In step 902, the historical weight deviation compensation value refers to a weight compensation parameter calculated based on past policy adjustment records (such as resource allocation deviation correction history) to offset system inherent deviations (such as response delays caused by device aging).

[0307] Real-time feedback weight offset refers to a dynamic correction value calculated by the real-time difference between the current node state (such as sudden traffic surge) and the policy matrix output, for example, using reinforcement learning algorithms (such as Q-Learning) to generate short-term weight offsets.

[0308] Priority weight distribution refers to the quantitative result of the priority of each node in resource allocation. It is iteratively updated by superimposing historical compensation values ​​and real-time offsets to ensure that the weight changes dynamically with the environment.

[0309] In the embodiments of the present application, the priority weight distribution is iteratively updated by superimposing historical weight deviation compensation values ​​with real-time feedback weight offsets. First, an incremental calculation algorithm (such as gradient descent or Newton's method) is used to incrementally calculate the priority weights to ensure accurate weight adjustment. Subsequently, the priority weight distribution is iteratively updated by superimposing historical weight deviation compensation values ​​with real-time feedback weight offsets. Finally, convergence analysis is performed to ensure the stability of the weight distribution, providing input for subsequent reconstruction of the policy adaptation parameter space.

[0310] 903. Reconstruct the strategy adaptation parameter space according to the priority weight distribution, eliminate the coupling interference between parameters through multi-dimensional orthogonal projection operation, and generate the strategy adaptation parameter correction;

[0311] In step 903, inter-parameter coupling interference refers to nonlinear interference caused by mutual dependence of different strategy parameters (such as spectrum allocation weights and power control factors). For example, power boosting may aggravate spectrum occupancy conflicts.

[0312] Multi-dimensional orthogonal projection operation refers to mapping high-dimensional parameters to an orthogonal basis space through principal component analysis (PCA) or Gram-Schmidt orthogonalization algorithm to eliminate redundant correlations, such as separating the coupling effects of spectrum allocation parameters and power control parameters.

[0313] The policy adaptation parameter correction refers to the corrected parameter set, which reflects the optimization result of the matching between the policy matrix and the actual demand, such as the quantified adaptation value after reducing the parameter conflict through orthogonal projection.

[0314] In the embodiments of the present application, the strategy adaptation parameter space is reconstructed based on the priority weight distribution. Multidimensional orthogonal projection operations are used to eliminate coupling interference between parameters and generate strategy adaptation parameter corrections. First, a parameter space reconstruction algorithm (such as principal component analysis or singular value decomposition) is used to reconstruct the strategy adaptation parameter space to ensure the rationality of the parameter distribution. Subsequently, multidimensional orthogonal projection operations are used to eliminate coupling interference between parameters and generate strategy adaptation parameter corrections. Finally, Monte Carlo simulation is used to verify the rationality of the corrections to ensure that they meet resource allocation requirements.

[0315] 904. Perform a tensor fusion operation on the policy adaptation parameter correction amount and the dynamic feedback parameter set, complete parameter synchronization alignment based on the topological constraint conditions of the initial mapping relationship, and output a policy matrix dynamic adjustment factor;

[0316] In step 904, the tensor fusion operation refers to performing high-order tensor operations (such as tensor concatenation or modal product) on the strategy adaptation correction value and the dynamic feedback parameter set to retain multi-dimensional correlation features, such as the parameter correlation of the fusion time, space, and frequency band dimensions.

[0317] Topology constraints refer to network topology rules based on the initial mapping relationship (such as node communication distance limit and spectrum coverage range), which ensure that parameter alignment complies with physical layer constraints. For example, direct association of spectrum parameters of non-adjacent nodes is prohibited.

[0318] The policy matrix dynamic adjustment factor refers to a set of parameters that are fused and aligned, and is used to guide the global adjustment of the policy matrix, such as generating a frequency band switching priority matrix or a power compensation gradient table.

[0319] In an embodiment of the present application, by performing a tensor fusion operation on the policy adaptation parameter correction amount and the dynamic feedback parameter set, the parameter synchronization alignment is completed based on the topological constraints of the initial mapping relationship, and the policy matrix dynamic adjustment factor is output. First, a tensor fusion algorithm (such as tensor product or tensor decomposition) is used to fuse the policy adaptation parameter correction amount and the dynamic feedback parameter set to ensure data consistency. Subsequently, the parameter synchronization alignment is completed based on the topological constraints of the initial mapping relationship, and the policy matrix dynamic adjustment factor is output. Finally, the effectiveness of the adjustment factor is verified through a closed-loop feedback mechanism to ensure that it meets the resource scheduling requirements.

[0320] 905. Inject the strategy matrix dynamic adjustment factor into the node priority coefficient calculation channel, and form a node priority coefficient correction value through nonlinear normalization processing and feedback loop self-calibration mechanism.

[0321] In step 905, nonlinear normalization processing refers to mapping the adjustment factor to the [0, 1] interval through Sigmoid function or Min-Max scaling to avoid interference from extreme values, such as limiting the power compensation gradient to a range that the device can withstand.

[0322] The feedback loop self-calibration mechanism refers to the dynamic correction of normalization parameters based on historical error feedback, such as using a PID controller to adjust the weight coefficient to ensure system stability.

[0323] The node priority coefficient correction value refers to the final output priority quantification result, which reflects the dynamically adjusted resource allocation weight. For example, the priority coefficient of the emergency communication node is increased from 0.6 to 0.9.

[0324] In an embodiment of the present application, a dynamic adjustment factor of the policy matrix is ​​injected into the node priority coefficient calculation channel, and a node priority coefficient correction value is formed through nonlinear normalization processing and a feedback loop self-calibration mechanism. First, a nonlinear normalization processing algorithm (such as a Sigmoid function or a Tanh function) is used to normalize the adjustment factor to ensure that the parameters are in the same dimension. Subsequently, a node priority coefficient correction value is formed through a feedback loop self-calibration mechanism (such as PID control or reinforcement learning). Finally, the rationality of the correction value is ensured through data verification, providing a reliable basis for resource scheduling.

[0325] Here's a specific example:

[0326] In the real-time scheduling optimization scenario of smart microgrids, this dynamic feedback mechanism can be used to construct an adaptive energy allocation model. Taking an integrated photovoltaic, storage, and charging system in an industrial park as an example, the system uses an edge IoT agent to collect real-time node execution status data, including PV panel output fluctuations, energy storage SOC values, and sudden changes in charging pile load. This data is then combined with day-ahead forecast deviations to construct an initial policy matrix. When PV output plummets by 30% at noon, the incremental calculation module dynamically increases the energy storage system's priority weight to 0.82 by combining the historical energy storage over-discharge compensation coefficient (0.15) with the real-time load urgency offset (+0.3). Orthogonal projection is also used to eliminate the parameter coupling interference caused by temperature on inverter efficiency. After topological constraint alignment, the modified parameter tensor generates an adjustment factor consisting of a time window shift factor (Δt = 120s) and a power correction gradient (ΔP = 15kW / step). Ultimately, a priority sequence is established for charging pile power limiting (40% load reduction) and non-critical load shedding in feeder-level scheduling, restoring power stability within 5 minutes. This example demonstrates the multi-dimensional collaborative optimization capability of the dynamic feedback mechanism in response to fluctuations in renewable energy.

[0327] In summary, steps 901 to 905 achieve precise adjustment and optimization of the resource allocation strategy matrix through the collaborative processing of dynamic feedback and multi-dimensional parameter optimization, significantly improving the rationality of resource allocation and system stability. First, the deviation between the node execution state data and the strategy matrix is ​​aggregated to generate a set of dynamic feedback parameters; second, the priority weights are incrementally calculated based on the feedback parameters, and the weight distribution is iteratively updated; then, the strategy adaptation parameter space is reconstructed to eliminate the coupling interference between parameters and generate corrections; then, the corrections are tensor-fused with the feedback parameters to complete parameter synchronization alignment and output a dynamic adjustment factor; finally, the adjustment factor is injected into the node priority coefficient calculation channel, and the correction value is formed through normalization and self-calibration mechanism. This design significantly improves the adaptability and optimization effect of the resource allocation strategy through the collaborative processing of dynamic feedback and multi-dimensional optimization, providing reliable technical support for resource scheduling in complex environments.

[0328] Figure 2 The present invention provides a schematic diagram of a resource dynamic monitoring and control system based on quota tracking, as shown in FIG. Figure 2 As shown, the system includes:

[0329] Analysis module 21 is used to obtain stress field propagation data generated by the optical fiber sensor array in the underground pipeline corridor structure, wherein the stress field propagation data includes the energy attenuation characteristics of the stress wave at the intersection of the reinforcement ribs, and to establish a correlation mapping table between the structural deformation risk and the monitoring node resource requirements based on the energy attenuation characteristics;

[0330] Detection module 22, configured to capture the frequency domain propagation characteristics of abnormal vibration signals using an acoustic wave sensor array deployed at the expansion joints of the underground pipe gallery, wherein the frequency domain propagation characteristics include a phase offset parameter of the reflected wave at the pipe joint connection;

[0331] A generating module 23 is configured to dynamically adjust the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including a channel occupancy prediction value;

[0332] A tracking module 24 is configured to establish a quota tracking database for IoT resources, wherein the quota tracking database records historical spectrum quota usage data of monitoring nodes, and generates a resource utilization efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix;

[0333] The optimization module 25 is configured to perform spectrum quota reallocation on monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, wherein the reallocation includes frequency hopping interval optimization and transmission power adjustment based on historical spectrum quota usage data.

[0334] Figure 2 The resource dynamic monitoring and control system based on quota tracking can be executed Figure 1 The implementation principle and technical effects of the method for a resource dynamic monitoring and control system based on quota tracking described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the resource dynamic monitoring and control system based on quota tracking in the above embodiment has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0335] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A resource dynamic monitoring and control method based on quota tracking, characterized in that: include: Obtaining stress field propagation data generated by an optical fiber sensor array in an underground pipe gallery structure, the stress field propagation data including the energy attenuation characteristics of stress waves at the intersection of reinforcement bars, and establishing a correlation mapping table between structural deformation risk and monitoring node resource requirements based on the energy attenuation characteristics; The frequency domain propagation characteristics of abnormal vibration signals are captured by an acoustic wave sensor array arranged at the expansion joints of the underground pipe gallery. The frequency domain propagation characteristics include the phase offset parameters of the reflected waves at the pipe joints. Dynamically adjusting the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter, and generating a resource allocation strategy matrix including a channel occupancy prediction value; Establishing a quota tracking database for IoT resources, the quota tracking database records historical spectrum quota usage data of monitoring nodes, and generating a resource utilization efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix; Performing spectrum quota reallocation for monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, wherein the reallocation includes frequency hopping interval optimization and transmit power adjustment based on historical spectrum quota usage data; The dynamically adjusting the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter to generate a resource allocation strategy matrix including a channel occupancy prediction value includes: Real-time collection of phase offset parameters and channel status indicators of monitoring nodes to form a pre-processing data set; Calculating the phase offset change rate of each monitoring node based on the preprocessed data set, and generating a priority weight coefficient for each node using a dynamic weighting algorithm in combination with the current channel interference threshold; According to the priority weight coefficient, the data transmission cycle quota in the association mapping table is incrementally adjusted, and the data transmission cycle quota of the monitoring node is proportionally shortened for nodes with a priority weight coefficient higher than a preset threshold; The channel occupancy prediction model is constructed using the adjusted periodic quota. The historical channel occupancy data and real-time phase offset parameters are input. The channel occupancy prediction value is iteratively updated through the sliding window mechanism, and a multi-dimensional resource allocation strategy matrix is ​​output.

2. The method according to claim 1, characterized in that After generating the resource allocation strategy matrix including the channel occupancy prediction value, the method further includes: Perform cross-band coherent detection on adjacent monitoring areas to obtain time-varying interference distribution maps of authorized and unauthorized frequency bands; Adaptively modifying the transmit power quota in the resource allocation strategy matrix according to the time-varying interference distribution map, wherein the modification includes adjusting a spectrum switching interval and a power compensation coefficient based on the stress wave propagation path length.

3. The method according to claim 1, characterized in that According to the resource efficiency report, high stress The monitoring nodes in the centralized area perform spectrum quota reallocation, which includes frequency hopping interval optimization and transmit power adjustment based on historical spectrum quota usage data, including: parsing the analysis results of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource utilization efficiency report, extracting the spectrum efficiency deviation parameter of the high stress concentration area, correlating it with historical frequency hopping data, and generating a spectrum efficiency deviation correlation parameter; Based on the spectrum efficiency deviation correlation parameter, the channel occupancy fluctuation characteristics of each frequency band in the historical usage period are calculated, and the frequency hopping interval decision parameter is generated by combining the signal propagation attenuation characteristics of the high stress concentration area; According to the frequency switching priority in the frequency hopping interval decision parameter, historical signal strength attenuation data of the high stress concentration area is matched, the transmission power compensation gradient is calculated and superimposed with the reference value to generate a dynamic power compensation parameter; The frequency hopping interval decision parameter and the dynamic power compensation parameter are integrated to reallocate the spectrum quota according to the priority weight, and generate a spectrum quota reallocation strategy table after verification; The spectrum quota reallocation strategy table is loaded into the resource scheduling module of the monitoring node, the spectrum usage data after reallocation is recorded in real time and updated to the quota tracking database, and the spectrum quota reallocation is executed.

4. The method according to claim 3, characterized in that The frequency hopping interval decision parameter and the dynamic power compensation parameter are integrated to reallocate spectrum quotas according to priority weights. After verification, a spectrum quota reallocation strategy table is generated, including: Performing a multi-dimensional parameter superposition operation on the frequency band switching priority and the dwell time proportional coefficient in the frequency hopping interval decision parameter and the transmit power compensation gradient in the dynamic power compensation parameter to generate an initial strategy parameter set for spectrum quota reallocation; Detecting and correcting spectrum quota allocation conflicts in different frequency bands in a high stress concentration area based on the spectrum quota reallocation initial strategy parameter set, and outputting corrected spectrum quota reallocation intermediate strategy parameters; Loading the priority weight as a constraint condition into the spectrum quota reallocation intermediate strategy parameter, calculating the maximum allocatable spectrum quota threshold of each frequency band under the priority weight constraint, and generating the spectrum quota reallocation boundary parameter; Perform a cross-layer parameter comparison between the spectrum quota reallocation boundary parameter and the channel occupancy prediction value in the resource allocation strategy matrix. After verification, generate the spectrum quota reallocation verification parameter. Based on the spectrum quota reallocation verification parameters, a spectrum quota reallocation strategy table including frequency band identification, quota threshold and power compensation gradient is generated according to the combination rule of frequency band switching priority and dwell time ratio coefficient.

5. The method according to claim 4, characterized in that Load the priority weights as constraints to the Describe the intermediate strategy parameters for spectrum quota reallocation, calculate the maximum allocatable spectrum quota threshold for each frequency band under the priority weight constraint, and generate the spectrum quota reallocation boundary parameters, including: Extracting priority weight constraint parameters from spectrum quota reallocation intermediate strategy parameters to generate an initial constraint parameter set including frequency band identifiers and priority weights; Based on the priority weights in the initial constraint parameter set and in combination with the historical spectrum demand baseline values ​​of each frequency band, the maximum allocatable spectrum quota threshold of each frequency band under the weight constraint is calculated to generate a spectrum quota extreme value parameter set; Comparing the spectrum quota extreme value parameter set with the physical layer load limit parameters for compliance, eliminating extreme value parameters that exceed the physical layer load capacity, and generating a spectrum quota reallocation boundary parameter set; The spectrum quota reallocation boundary parameter set and the priority weight constraint parameter set are integrated to generate spectrum quota reallocation boundary parameters including frequency band identifier, priority weight and quota threshold.

6. The method according to claim 1, characterized in that Establishing a quota tracking database for IoT resources, and generating a resource utilization efficiency report based on the quota tracking database, including a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix, including: Establishing a quota tracking database for IoT resources, wherein the quota tracking database records historical spectrum quota usage data of monitoring nodes, and dynamically aggregating the actual spectrum quota consumption rate according to a preset time window based on the quota tracking database to generate a dynamic aggregated data set; Correlate the node identifiers in the dynamic aggregation dataset across time windows, extract the temporal fluctuation characteristics and spatial distribution characteristics of the spectrum consumption rate, and generate a spectrum feature vector containing the node spectrum fluctuation index and spatial correlation degree; Match the spectrum feature vector with the channel occupancy rate prediction value in the resource allocation strategy matrix, calculate the deviation and convergence index between the actual spectrum quota consumption rate and the prediction value, and generate a spectrum comparison index set; Based on the spectrum comparison index set, a mapping relationship between node identification and resource allocation strategy matrix is ​​constructed. By dynamically adjusting the priority weights in the strategy matrix, an optimized strategy mapping table including node priority coefficients and strategy adaptability is generated. Based on the priority coefficient and policy adaptability in the optimization strategy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison indicator set, a resource utilization efficiency report that can be used to evaluate and provide optimization suggestions is generated.

7. The method according to claim 6, characterized in that Based on the spectrum comparison index set, a mapping relationship between node identification and resource allocation strategy matrix is ​​constructed. By dynamically adjusting the priority weights in the strategy matrix, an optimized strategy mapping table containing node priority coefficients and strategy adaptability is generated, including: Determining an initial mapping relationship between a node identifier and a resource allocation strategy matrix based on a spectrum comparison indicator set, wherein the resource allocation strategy matrix includes an initial value of a node priority coefficient and an initial value of a strategy adaptability; Establishing a dynamic feedback loop in which priority weights and policy adaptation parameters in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a node priority coefficient correction value; Inputting the node priority coefficient correction value into the strategy fitness calculation module, and generating a strategy fitness update value in combination with the strategy fitness initial value in the resource allocation strategy matrix; The resource allocation strategy matrix is ​​reconstructed according to the node priority coefficient correction value and the strategy adaptation degree update value, and an optimization strategy mapping table including multi-dimensional weight constraints is generated.

8. The method according to claim 7, characterized in that Establishing a dynamic feedback loop in which priority weights and policy adaptation parameters in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a node priority coefficient correction value, including: When establishing a dynamic feedback loop, a dynamic feedback parameter set is generated by aggregating the node execution state data under the initial mapping relationship and the input and output deviation of the strategy matrix; Incrementally calculating the priority weights of the policy matrix based on the dynamic feedback parameter set, and iteratively updating the priority weight distribution by superimposing the historical weight deviation compensation value and the real-time feedback weight offset; Reconstructing the strategy adaptation parameter space according to the priority weight distribution, eliminating the coupling interference between parameters through multi-dimensional orthogonal projection operation, and generating the strategy adaptation parameter correction; Performing a tensor fusion operation on the policy adaptation parameter correction amount and the dynamic feedback parameter set, completing parameter synchronization alignment based on the topological constraints of the initial mapping relationship, and outputting a dynamic adjustment factor of the policy matrix; The strategy matrix dynamic adjustment factor is injected into the node priority coefficient calculation channel, and the node priority coefficient correction value is formed through nonlinear normalization processing and feedback loop self-calibration mechanism.

9. A resource dynamic monitoring and control method based on quota tracking, characterized in that: include: An analysis module is used to obtain stress field propagation data generated by an optical fiber sensor array in the underground pipeline corridor structure. The stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection of reinforcement bars, and to establish a correlation mapping table between structural deformation risks and monitoring node resource requirements based on the energy attenuation characteristics; A detection module is used to capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array deployed at the expansion joints of the underground pipe gallery. The frequency domain propagation characteristics include the phase offset parameters of the reflected waves at the pipe joints. A generating module, configured to dynamically adjust the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including a channel occupancy prediction value; A tracking module is used to establish a quota tracking database for IoT resources, the quota tracking database records the historical spectrum quota usage data of the monitoring node, and generates a resource utilization efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the channel occupancy rate prediction value in the resource allocation strategy matrix; an optimization module, configured to perform spectrum quota reallocation for monitoring nodes in high stress concentration areas according to the resource utilization efficiency report, wherein the reallocation includes frequency hopping interval optimization and transmit power adjustment based on historical spectrum quota usage data; The dynamically adjusting the data transmission cycle quota of the monitoring node in the association mapping table based on the phase offset parameter to generate a resource allocation strategy matrix including a channel occupancy prediction value includes: Real-time collection of phase offset parameters and channel status indicators of monitoring nodes to form a pre-processing data set; Calculating the phase offset change rate of each monitoring node based on the preprocessed data set, and generating a priority weight coefficient for each node using a dynamic weighting algorithm in combination with the current channel interference threshold; According to the priority weight coefficient, the data transmission cycle quota in the association mapping table is incrementally adjusted, and the data transmission cycle quota of the monitoring node is proportionally shortened for nodes with a priority weight coefficient higher than a preset threshold; The channel occupancy prediction model is constructed using the adjusted periodic quota. The historical channel occupancy data and real-time phase offset parameters are input. The channel occupancy prediction value is iteratively updated through the sliding window mechanism, and a multi-dimensional resource allocation strategy matrix is ​​output.

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