Resource dynamic monitoring and control method and system based on quota tracking

Abstract

The invention provides a resource dynamic monitoring and control method and system based on quota tracking. According to the pipe gallery deformation monitoring and resource optimization system based on optical fiber and sound wave sensing, underground pipe gallery structure stress field propagation data are obtained through an optical fiber sensor array, energy attenuation characteristics of stress waves at reinforcing rib intersection points are analyzed, and a dynamic mapping model of structure deformation risks and monitoring node resource requirements is established. Meanwhile, the frequency domain propagation characteristics of abnormal vibration signals are captured through acoustic sensors arranged at the expansion joints, and a resource allocation strategy matrix is formed; and constructing a quota tracking database, comparing the actual consumption rate of the frequency spectrum quota with a predicted value, generating a resource use efficiency report, and performing frequency spectrum redistribution of frequency hopping interval optimization and transmitting power adjustment on the nodes in the high stress concentration region. According to the technical scheme provided by the invention, the resource dynamic optimization of the underground pipe gallery structure is realized, and the risk early warning precision and the communication efficiency are improved.

Description

Technical Field

[0001] This application relates to the technical field of resource monitoring, 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 utility tunnels, it is necessary to achieve high-precision real-time perception of structural deformation, stress concentration, and abnormal vibration, as well as dynamic optimization management of resources. The sensors are required to have corrosion resistance, anti-interference ability, and long-term stability. It is necessary to integrate multi-dimensional data of fiber optic sensor arrays and acoustic wave sensors to analyze the risk of structural deformation. Under limited spectrum resources, it is necessary to dynamically adjust the data transmission period and spectrum quota of monitoring nodes according to the degree of stress concentration and vibration abnormality to balance real-time performance and energy consumption.

[0003] Currently, long-gauge fiber optic sensors are used to deploy longitudinal / transverse arrays, and distributed strain monitoring is used to analyze concrete cracking and displacement changes. Based on distributed acoustic sensing technology, the Rayleigh scattering principle is used to capture pipeline vibration signals, and fixed data transmission periods and fixed transmission powers are used to locate leakage points or third-party interferences, and spectrum resources are configured relying on manual experience.

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

[0005] An embodiment of this application provides a method and system for dynamic resource monitoring and control based on quota tracking to solve the problem of poor dynamic resource monitoring and control effect in the prior art.

[0006] In a first aspect, an embodiment of this application provides a method for dynamic resource monitoring and control based on quota tracking, including: Obtain stress field propagation data generated by a fiber optic sensor array in an underground utility tunnel structure, where the stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection of stiffeners, so as to establish an association mapping table between structural deformation risks and monitoring node resource requirements according to the energy attenuation characteristics; Capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array arranged at the expansion joint of the underground utility tunnel, where the frequency domain propagation characteristics include the phase shift amount parameter of the reflected wave at the joint of pipe segments; Dynamically adjust the data transmission period quota of the monitoring node in the association mapping table based on the phase shift amount parameter to generate a resource allocation strategy matrix including predicted values of channel occupancy rates; Establish a quota tracking database for Internet of Things resources. The quota tracking database records the historical spectrum quota usage data of monitoring nodes, and generates a resource usage efficiency report based on the quota tracking database, which includes a comparative analysis of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource allocation strategy matrix; Perform spectrum quota reallocation on the monitoring nodes in the high stress concentration area according to the resource usage efficiency report. The reallocation includes optimizing the frequency hopping interval and adjusting the transmission power based on the historical spectrum quota usage data.

[0007] Optionally, after generating the resource allocation strategy matrix including the predicted channel occupancy rate, it further includes: Perform cross-band coherent detection on adjacent monitoring areas to obtain the time-varying interference distribution map of authorized and unauthorized frequency bands; Perform adaptive correction on the transmission power quota in the resource allocation strategy matrix according to the time-varying interference distribution map. The correction includes the spectrum switching interval and power compensation coefficient adjusted based on the stress wave propagation path length.

[0008] Optionally, the dynamic adjustment of the data transmission cycle quota of the monitoring nodes in the association mapping table based on the phase offset parameter to generate a resource allocation strategy matrix including the predicted channel occupancy rate includes: Real-time collect the phase offset parameter and channel state indicators of the monitoring nodes to form a preprocessing data set; According to the preprocessing data set, calculate the phase offset change rate of each monitoring node, and combine the current channel interference threshold to generate the priority weight coefficient of each node through a dynamic weighting algorithm; According to the priority weight coefficient, perform incremental adjustment on the data transmission cycle quota in the association mapping table. For the nodes with the priority weight coefficient higher than the preset threshold, proportionally shorten the data transmission cycle quota of the monitoring nodes; Use the adjusted cycle quota to construct a channel occupancy rate prediction model, input the historical channel occupancy rate data and real-time phase offset parameter, iteratively update the predicted channel occupancy rate through a sliding window mechanism, and output a multi-dimensional resource allocation strategy matrix.

[0009] Optionally, perform spectrum quota reallocation on the monitoring nodes in the high stress concentration area according to the resource usage efficiency report. The reallocation includes optimizing the frequency hopping interval and adjusting the transmission power based on the historical spectrum quota usage data, including: Analyze the analysis results of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource usage efficiency report, extract the spectrum efficiency deviation parameters in the high stress concentration area, associate the historical frequency hopping data, and generate the spectrum efficiency deviation association parameters; Based on the spectrum efficiency deviation correlation parameter, calculate the channel occupancy fluctuation characteristics of each frequency band during the historical usage period, and combine with the signal propagation attenuation characteristics of the high stress concentration area to generate the frequency hopping interval decision parameter; According to the frequency band switching priority in the frequency hopping interval decision parameter, match the historical signal strength attenuation data of the high stress concentration area, calculate the transmission power compensation gradient and superimpose the reference value to generate the dynamic power compensation parameter; Fuse the frequency hopping interval decision parameter and the dynamic power compensation parameter, reallocate the spectrum quota according to the priority weight, and generate the spectrum quota reallocation strategy table after verification; 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 the spectrum quota reallocation.

[0010] Optionally, fuse the frequency hopping interval decision parameter and the dynamic power compensation parameter, reallocate the spectrum quota according to the priority weight, and generate the spectrum quota reallocation strategy table after verification, including: Perform a multi-dimensional parameter superposition operation on the frequency band switching priority and the residence duration ratio coefficient in the frequency hopping interval decision parameter and the transmission power compensation gradient in the dynamic power compensation parameter to generate an initial spectrum quota reallocation strategy parameter set; According to the initial spectrum quota reallocation strategy parameter set, detect and correct the spectrum quota allocation conflicts of different frequency bands in the high stress concentration area, and output the corrected intermediate spectrum quota reallocation strategy parameter; Load the priority weight as a constraint condition into the intermediate spectrum quota reallocation strategy parameter, calculate the maximum allocable spectrum quota threshold of each frequency band under the constraint of the priority weight, and generate the spectrum quota reallocation boundary parameter; Compare the spectrum quota reallocation boundary parameter with the channel occupancy prediction value in the resource allocation strategy matrix across layers, and generate the spectrum quota reallocation verification parameter after passing the verification; Based on the spectrum quota reallocation verification parameter, generate a spectrum quota reallocation strategy table including frequency band identification, quota threshold and power compensation gradient according to the combination rule of frequency band switching priority and residence duration ratio coefficient.

[0011] Optionally, load the priority weight as a constraint condition into the intermediate spectrum quota reallocation strategy parameter, calculate the maximum allocable spectrum quota threshold of each frequency band under the constraint of the priority weight, and generate the spectrum quota reallocation boundary parameter, including: Extract the priority weight constraint parameter in the intermediate spectrum quota reallocation strategy parameter to generate an initial constraint parameter set including frequency band identification and priority weight; Based on the priority weights in the initial constraint parameter set, combined with the historical spectrum demand baseline values of each frequency band, calculate the maximum allocable spectrum quota threshold for each frequency band under weight constraints, and generate a spectrum quota extreme value parameter set; Perform compliance comparison between the spectrum quota extreme value parameter set and the physical layer bearer limit parameters, and eliminate the extreme value parameters that exceed the physical layer bearer capacity to generate a spectrum quota reallocation boundary parameter set; Integrate the spectrum quota reallocation boundary parameter set and the priority weight constraint parameter set to generate spectrum quota reallocation boundary parameters including frequency band identification, priority weight, and quota threshold.

[0012] Optionally, establish a quota tracking database for Internet of Things resources, and generate a resource usage efficiency report based on the quota tracking database, including a comparative analysis of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource allocation strategy matrix, including: Establish a quota tracking database for Internet of Things resources. The quota tracking database records the historical spectrum quota usage data of monitoring nodes, and based on the quota tracking database, perform dynamic aggregation on the actual spectrum quota consumption rate according to a preset time window to generate a dynamic aggregation data set; Perform cross-time window association on the node identifiers in the dynamic aggregation data set, 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; Match the spectrum feature vector with the predicted channel occupancy rate in the resource allocation strategy matrix, calculate the deviation degree and convergence index between the actual spectrum quota consumption rate and the predicted value, and generate a spectrum comparison index set; According to the spectrum comparison index set, construct a mapping relationship between the node identifier and the resource allocation strategy matrix, and generate an optimized strategy mapping table including the node priority coefficient and strategy adaptability by dynamically adjusting the priority weights in the strategy matrix; Based on the priority coefficient and strategy adaptability in the optimized strategy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison index set, generate a resource usage efficiency report that can evaluate and provide optimization suggestions.

[0013] Optionally, according to the spectrum comparison index set, construct a mapping relationship between the node identifier and the resource allocation strategy matrix, and generate an optimized strategy mapping table including the node priority coefficient and strategy adaptability by dynamically adjusting the priority weights in the strategy matrix, including: Determine the initial mapping relationship between the node identifier and the resource allocation strategy matrix based on the spectrum comparison index set, where the resource allocation strategy matrix includes the initial values of the node priority coefficient and strategy adaptability; Establish a dynamic feedback loop, in which, according to the initial mapping relationship, the priority weights and policy adaptation degree parameters in the policy matrix are dynamically adjusted to form a correction value of the node priority coefficient; Input the correction value of the node priority coefficient into the policy adaptation degree calculation module, and generate an updated value of the policy adaptation degree in combination with the initial value of the policy adaptation degree in the resource allocation policy matrix; Reconstruct the resource allocation policy matrix according to the correction value of the node priority coefficient and the updated value of the policy adaptation degree, and generate an optimized policy mapping table including multi-dimensional weight constraints.

[0014] Optionally, establish a dynamic feedback loop, in which, according to the initial mapping relationship, the priority weights and policy adaptation degree parameters in the policy matrix are dynamically adjusted to form a correction value of the node priority coefficient, including: When establishing the dynamic feedback loop, generate a set of dynamic feedback parameters by aggregating the node execution state data and the input-output deviation of the policy matrix under the initial mapping relationship; Perform incremental calculation on the priority weights of the policy matrix based on the set of dynamic feedback parameters, and iteratively update the priority weight distribution by superimposing the historical weight deviation compensation value and the real-time feedback weight offset; Reconstruct the policy adaptation degree parameter space according to the priority weight distribution, and eliminate the coupling interference between parameters through multi-dimensional orthogonal projection operation to generate a correction amount of the policy adaptation degree parameters; Perform tensor fusion operation on the correction amount of the policy adaptation degree parameters and the set of dynamic feedback parameters, complete parameter synchronization alignment based on the topological constraint conditions of the initial mapping relationship, and output a dynamic adjustment factor of the policy matrix; Inject the dynamic adjustment factor of the policy matrix into the node priority coefficient calculation channel, and form a correction value of the node priority coefficient through non-linear normalization processing and the self-calibration mechanism of the feedback loop.

[0015] In a second aspect, an embodiment of the present application provides a resource dynamic monitoring and control system based on quota tracking, including: An analysis module, configured to obtain stress field propagation data generated by an optical fiber sensor array in an underground pipe gallery structure, where the stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection of stiffeners, so as to establish an association mapping table between structural deformation risks and monitoring node resource requirements according to the energy attenuation characteristics; A detection module, configured to capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array arranged at the expansion joint of the underground pipe gallery, where the frequency domain propagation characteristics include the phase offset parameter of the reflected wave at the pipe joint; A generation module, configured to dynamically adjust the data transmission period quota of monitoring nodes in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including predicted channel occupancy rates; A tracking module, configured to establish a quota tracking database for Internet of Things resources. The quota tracking database records the historical spectrum quota usage data of monitoring nodes, and generate a resource usage efficiency report including a comparative analysis of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource allocation strategy matrix based on the quota tracking database; An optimization module, configured to perform spectrum quota reallocation on monitoring nodes in high stress concentration areas according to the resource usage efficiency report. The reallocation includes optimizing the hopping interval and adjusting the transmission power based on the historical spectrum quota usage data.

[0016] In an embodiment of the present application, stress field propagation data generated by an optical fiber sensor array in an underground pipe gallery structure is obtained. The stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection of stiffeners, so as to establish an association mapping table between structural deformation risks and monitoring node resource requirements according to the energy attenuation characteristics; capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array arranged at the expansion joint of the underground pipe gallery. The frequency domain propagation characteristics include the phase offset parameter of the reflected wave at the pipe joint; dynamically adjust the data transmission period quota of monitoring nodes in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including predicted channel occupancy rates; establish a quota tracking database for Internet of Things resources. The quota tracking database records the historical spectrum quota usage data of monitoring nodes, and generate a resource usage efficiency report including a comparative analysis of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource allocation strategy matrix based on the quota tracking database; perform spectrum quota reallocation on monitoring nodes in high stress concentration areas according to the resource usage efficiency report. The reallocation includes optimizing the hopping interval and adjusting the transmission power based on the historical spectrum quota usage data.

[0017] The technical solution of the present application has the following beneficial effects: In this solution, the energy attenuation characteristics of the stress field in the underground pipe gallery are obtained through an optical fiber sensor array, an association mapping table between structural deformation risks and resource requirements is established, the data transmission period quota of monitoring nodes is dynamically adjusted in combination with the phase offset parameter captured by the acoustic wave sensor, and a resource allocation strategy matrix and an efficiency report are generated based on the historical spectrum quota data. Finally, intelligent reallocation of spectrum quotas in high stress areas is achieved through optimizing the hopping interval and adjusting the transmission power. It can accurately identify the structural deformation risk area, dynamically optimize the monitoring resource allocation, improve the data transmission efficiency and network stability. At the same time, through the historical data-driven hopping and power adjustment, the channel interference is effectively reduced, the service life of the equipment is extended, and the adaptive resource management and risk warning capabilities of the underground pipe gallery monitoring system are realized.

[0018] Furthermore, by means of cross-band coherent detection, a time-varying interference distribution map of licensed and unlicensed bands is constructed in real time to accurately quantify the dynamic characteristics of multi-band electromagnetic interference. Combining with the stress wave propagation path length, the spectrum switching interval and power compensation coefficient are adaptively optimized to form a closed-loop feedback mechanism for interference perception and power regulation. This technology is based on the dynamic interference distribution to real-time correct the transmit power quota in the resource allocation strategy matrix. Through the collaborative optimization of propagation path loss compensation and frequency band switching timing, the risk of cross-band signal conflict and power spillover is significantly reduced, 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 joint regulation of frequency band switching and power compensation, the utilization rate and communication stability of unlicensed bands are effectively improved. On the premise of ensuring low-interference transmission in licensed bands, the efficient collaboration and interference self-suppression of multi-domain heterogeneous network resources are realized, providing a low-latency and highly reliable adaptive spectrum sharing solution for high-density wireless scenarios.

[0019] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 Shows a flowchart of a resource dynamic monitoring and control method based on quota tracking provided by the present application; Figure 2 Shows a schematic structural diagram of a resource dynamic monitoring and control system based on quota tracking provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0023] In some of the processes described in the specification, claims, and above-mentioned drawings of this application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations can be executed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations can be executed sequentially or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0024] This solution aims to solve problems such as low resource utilization, severe channel interference, and short device lifespan caused by static resource allocation in the underground utility tunnel monitoring scenario. By integrating multi-source data from fiber optic sensor arrays and acoustic wave sensor arrays, a dynamic association model between structural deformation risks and monitoring node resource requirements is established, and a resource allocation strategy matrix is generated in combination with historical spectrum quota usage data. On this basis, a quota tracking database is constructed, and by using real-time perception data and historical consumption deviation analysis, the data transmission period, frequency hopping interval, and transmission power of nodes in high-stress areas are dynamically adjusted to form a closed-loop control mechanism, ultimately achieving the comprehensive goals of resource allocation on demand, channel interference suppression, and device energy consumption optimization, and significantly improving the adaptability and operation and maintenance efficiency of the underground utility tunnel safety monitoring system.

[0025] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0026] Figure 1 For the embodiments of this application, a flowchart of a resource dynamic monitoring and control method based on quota tracking is provided, as Figure 1 shown, the method includes: 101. Obtain the stress field propagation data generated by the fiber optic sensor array in the underground utility tunnel structure. The stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection points of the stiffeners, so as to establish an association mapping table between structural deformation risks and monitoring node resource requirements according to the energy attenuation characteristics; In this step, the fiber optic sensor array refers to a network composed of multiple fiber optic sensors, which can real-time monitor the stress and strain distribution of the underground utility tunnel structure by detecting the wavelength shift, intensity change, or phase difference of the optical signal in the optical fiber.

[0027] Stress field propagation data refers to the data on the propagation characteristics of stress waves in the underground utility tunnel structure captured by an optical fiber sensor array, including information such as wave velocity, amplitude attenuation, and spectral characteristics, and is used to evaluate the stress state of the structure.

[0028] A stress wave refers to a mechanical wave induced in the utility tunnel structure due to an external load, and its propagation path and energy attenuation are related to the material elastic modulus and structural defects.

[0029] The intersection of stiffeners refers to the intersection nodes of steel bars or composite material stiffeners in the concrete structure of the underground utility tunnel. Stress concentration areas are likely to form here due to geometric discontinuity.

[0030] The energy attenuation characteristic refers to the characteristic of energy reduction of stress waves when propagating through the intersection of stiffeners due to material damping, frictional loss, or cracks, and is usually quantified in decibels (dB) or attenuation coefficient (α).

[0031] The risk of structural deformation refers to the possibility of cracks, displacements, or collapses in the utility tunnel due to stress concentration, material fatigue, or foundation settlement, and the risk level is evaluated by modeling the energy attenuation characteristic.

[0032] In the embodiments of this application, first, the optical fiber sensor array captures optical wavelength shift data at a sampling rate of 1 kHz, uses an MOI SM130 demodulator to achieve high-precision wavelength demodulation (resolution ±1 pm), and switches multi-channel signals through an optical switch. Then, the original signal is decomposed by wavelet transform to extract the energy attenuation characteristic (A attenuation), and after eliminating temperature interference, the attenuation rate of each intersection of stiffeners is calculated. Finally, a risk prediction model is constructed using the random forest algorithm. The inputs include the A attenuation value, sensor position coordinates, and historical deformation data, and the output is the deformation risk probability P (0 ≤ P ≤ 1). The risk level is divided according to the P value (for example, P > 0.8 is a high risk), and a "risk level - resource requirement" mapping table is generated. For example, high-risk nodes need to be allocated a 50 MHz bandwidth and a 100 Hz sampling rate.

[0033] An array consisting of 32 FBG sensors was deployed at the key nodes (such as the intersection of stiffeners) of the concrete structure of the underground utility tunnel in Hengqin, Zhuhai. During a certain monitoring, the 3rd sensor detected an A attenuation value reaching 35 dB / m (the threshold is 30 dB / m), and the model calculated P = 0.85, which was determined to be a high risk. The system automatically updated the mapping table, increasing the resource requirements of this node from the default 20 MHz bandwidth and 50 Hz sampling rate to 60 MHz bandwidth and 200 Hz sampling rate. At the same time, the ±0.5 pm wavelength shift error caused by environmental temperature difference was eliminated through the temperature compensation module to ensure data accuracy. This adjustment increased the real-time data transmission of this area by 4 times, providing a high-precision basis for subsequent abnormal vibration monitoring.

[0034] 102. Capture the frequency-domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array deployed at the expansion joint of the underground pipe gallery. The frequency-domain propagation characteristics include the phase offset parameter of the reflected wave at the pipe joint. In this step, the acoustic wave sensor array refers to a group of piezoelectric or MEMS acoustic wave sensors deployed at the expansion joint of the pipe gallery, which is used to collect the time-domain waveform and frequency-domain components of vibration signals.

[0035] The frequency-domain propagation characteristics refer to the characteristics after converting the vibration signal from the time domain to the frequency domain through the fast Fourier transform (FFT), including the main frequency, harmonic distribution, phase spectrum, etc.

[0036] The phase offset of the reflected wave refers to the phase difference (unit: radian or degree) between the reflected wave and the incident wave at the pipe joint, which reflects the degree of loosening of the structural connection or the expansion of the gap.

[0037] In the embodiment of the present application, first, the acoustic wave sensor collects the original vibration signal at a sampling rate of 10 kHz, and removes low-frequency noise and high-frequency interference through a Butterworth band-pass filter (0.1 - 2 kHz). Subsequently, a 1024-point FFT transform is performed on the filtered signal to extract the main frequency component (such as the amplitude peak at 800 Hz) and calculate its phase spectrum. The instantaneous phase information is obtained by using the Hilbert transform, and the phase difference Δφ (unit: radian) between the incident wave and the reflected wave is calculated through the cross-correlation algorithm. Finally, combined with the structural parameters (such as the bolt pre-tightening force threshold) at the pipe joint, a Δφ > 1.0 rad is set as the abnormal threshold to trigger the dynamic adjustment of resource requirements.

[0038] In the subsequent monitoring of Node 3 of the Hengqin Pipe Gallery, through the acoustic wave sensor array, it is detected that the main frequency of the vibration signal at the pipe joint reaches 820 Hz (significantly higher than the normal working condition of 650 Hz ± 50 Hz), and at the same time, the phase offset Δφ continuously climbs to 1.2 rad, far exceeding the preset safety threshold of 0.2 rad. The system conducts a joint analysis through spectrum feature comparison and the finite element simulation model, and determines that there is a risk of bolt loosening and seal structure failure in this area. To accurately capture potential safety hazards, the system immediately activates the dynamic resource scheduling mechanism - compresses the data transmission cycle of the monitoring unit to which Node 3 belongs from the conventional 2-second level to 0.5 second level (using the fast transmission mode of the LoRaWAN protocol), and at the same time coordinates the sampling rate of the acoustic wave sensor from 1 kHz to 5 kHz through the edge computing node, and activates the high-frequency vibration mode analysis algorithm (HHT transform) to extract the detailed vibration characteristics in the 0.5 - 8 kHz frequency band.

[0039] 103. Dynamically adjust the data transmission cycle quota of the monitoring nodes in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including the predicted value of the channel occupancy rate. In this step, the phase offset parameter refers to the phase offset quantization value extracted through the cross-correlation algorithm or Hilbert transform. For example, Δφ = 30° represents the characteristics of the abnormal vibration signal.

[0040] The data transmission cycle quota refers to the data upload time interval dynamically allocated to the monitoring nodes. For example, the cycle for high-risk nodes is 0.5 seconds, and for low-risk nodes is 5 seconds.

[0041] The predicted value of the channel occupancy rate refers to the usage rate of the wireless channel in the future period predicted through a time series model (such as ARIMA or LSTM). For example, "Occupancy rate ≥ 75% from 10:00 to 10:15".

[0042] The resource allocation policy matrix refers to a two-dimensional matrix data structure. The rows represent the monitoring node IDs, the columns represent time slices, and the matrix elements are parameters such as the allocated spectrum bandwidth and transmit power.

[0043] In the embodiments of this application, first, the Δφ parameter is 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. Then, an LSTM model (with 64 hidden units and a time step of 60 minutes) is used to predict the future channel occupancy rate. The historical data for 1 hour is input, and the occupancy rate curve for the next 5 minutes is output. Finally, based on the priority weight and the predicted value, the greedy algorithm is used to allocate spectrum resources and construct the policy matrix. For example, high-risk nodes are allocated 80 MHz of bandwidth during peak occupancy periods, and low-risk nodes are reduced to 20 MHz.

[0044] 104. Establish a quota tracking database for Internet of Things resources, and generate a resource usage efficiency report based on the quota tracking database, which includes a comparative analysis of the actual consumption rate of the spectrum quota and the predicted value of the channel occupancy rate in the resource allocation policy matrix; 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 amount, timestamp, and location coordinates of each node. The quota tracking database records the historical spectrum quota usage data of the monitoring nodes.

[0045] The actual consumption rate of the spectrum quota refers to the percentage of the actually used spectrum resources in the allocated quota. For example, if 20 MHz is allocated and 18 MHz is actually used, the consumption rate is 90%.

[0046] The resource usage efficiency report refers to an effectiveness analysis document generated by comparing the predicted value with the actual consumption rate, which includes deviation statistics (such as mean square error), hotspot area marking, and optimization suggestions.

[0047] In the embodiments of the present application, first, InfluxDB is used to record the allocated bandwidth, actual usage, timestamp, and location coordinates of each node, and the data writing speed is ≥ 100,000 records / second. Then, the η value of each node is calculated, and the deviation between the predicted value and the actual value is evaluated through the mean square error (MSE). Finally, a visualization report is generated: nodes with η < 50% or > 120% are marked in the heat map, the top 10 list is sorted by the nodes with the largest deviation, and targeted suggestions are put forward (such as "for node 3, η = 65%, it is recommended to reduce the bandwidth to 60 MHz"). System statistics found that the actual bandwidth utilization rate of node 3 is only 65% (allocated 80 MHz), and the deviation MSE = 18.7. The report points out that its resource waste is serious, and it is recommended to adjust the bandwidth to 60 MHz and allocate the remaining 20 MHz to the adjacent node 7 (η = 115%). At the same time, through correlation analysis, it is found that the phase offset Δφ = 0.9 rad of node 7 is close to the threshold, triggering its risk level to rise from "medium" to "high". This closed-loop feedback increases the overall spectrum utilization rate from 72% to 89% and reduces the redundant data transmission by 15%.

[0048] 105. Perform spectrum quota reallocation on the monitoring nodes in the high stress concentration area according to the resource utilization efficiency report, and the reallocation includes hop interval optimization and transmit power adjustment based on historical spectrum quota usage data.

[0049] In this step, hop interval optimization refers to dynamically adjusting the FHSS hop interval according to the historical data of channel conflicts.

[0050] Transmit power adjustment refers to dynamically adjusting the power based on the received signal strength (RSSI).

[0051] In the embodiments of the present application, first, high-deviation nodes are screened according to the efficiency report, and the spectrum quota is reallocated using the greedy algorithm to preferentially meet the needs of high-risk nodes. Subsequently, the hop interval is calculated based on the historical number of conflicts: for example, the number of node conflicts N = 8 times, T_hop = 5 / (1 + 3) = 1.25 ms. At the same time, the transmit power is dynamically adjusted according to the RSSI feedback: if the received signal strength at the receiving end < -80 dBm, the power is increased in 3 dB steps until it reaches the standard. Finally, the updated parameters are sent to the nodes to complete the closed-loop optimization.

[0052] The system adjustment mechanism of Node 3 of the Hengqin Pipe Gallery was triggered due to the excessive channel conflict rate (18%). Through real-time spectrum analysis, the system compressed the frequency hopping interval of Node 3 from 5 ms to 2 ms. Meanwhile, combined with the conflict detection algorithm (N conflicts = 12 times), the frequency band switching strategy was optimized, and the transmission power was reduced from 20 dBm to 15 dBm based on the measured signal strength (RSSI = -75 dBm). After the adjustment, the channel conflict rate of Node 3 significantly decreased to 6%, the daily power consumption decreased by 22%, and at the same time, 5 MHz of available bandwidth was released. The system dynamically allocated this 5 MHz bandwidth to Node 7 with high load, increasing its data transmission success rate from 78% to 95% and effectively alleviating the local network congestion problem. In addition, through continuous monitoring and machine learning prediction models, the system completed 3 rounds of dynamic adjustments within 24 hours: first, optimized the frequency hopping parameters and transmission power of Node 3, then allocated the released bandwidth to Node 7, and finally reallocated the time slot resources according to the overall network load. According to statistics, this series of adjustments improved the overall network stability by 40%, reduced the daily energy consumption by 18%, and ensured the real-time and integrity of key monitoring data. This case verifies the efficiency and reliability of the dynamic resource allocation mechanism in complex scenarios, providing a reusable technical paradigm for the intelligent operation and maintenance of large-scale infrastructure.

[0053] As described above, through obtaining the stress field propagation data of the optical fiber sensor array in the underground pipe gallery and combining the frequency domain propagation characteristics of the abnormal vibration signals captured by the acoustic wave sensor array, an association mapping table between the structural deformation risk and the resource requirements of the monitoring nodes is established to achieve precise monitoring of the structural health status of the pipe gallery; based on the phase offset parameter, the data transmission cycle quota is dynamically adjusted to generate a resource allocation strategy matrix of the channel occupancy rate prediction value to optimize the network resource utilization efficiency; by establishing a quota tracking database, generating a resource utilization efficiency report, and comparing and analyzing the actual consumption rate and the prediction value of the spectrum quota, data support is provided for resource allocation; finally, the spectrum quota is reallocated to the monitoring nodes in the high stress concentration area, including optimizing the frequency hopping interval and adjusting the transmission power, significantly improving the real-time performance, reliability and resource utilization efficiency of the monitoring system, and providing an intelligent and dynamic technical guarantee for the safe operation of the underground pipe gallery.

[0054] In some embodiments, after generating the resource allocation strategy matrix including the channel occupancy rate prediction value in step 103, it further includes: 201. Perform cross-band coherent detection on adjacent monitoring areas to obtain the time-varying interference distribution map of authorized and unauthorized frequency bands; In step 201, cross-band coherent detection refers to a technology that realizes cross-band interference correlation analysis by jointly processing the phase and amplitude information of signals in multiple frequency bands.

[0055] Authorized and unlicensed frequency bands refer to the strictly regulated frequency bands (such as operator - dedicated frequency bands) and open - shared frequency bands (such as WiFi frequency bands).

[0056] The time - varying interference distribution map refers to a dynamic interference intensity heat map generated through time - frequency domain signal energy scanning and spatial grid processing.

[0057] In the embodiments of the present application, it is achieved through multi - band joint signal processing technology. A wide - band receiving array is used to synchronously sample signals in the authorized frequency band (such as 3.5 GHz) and unlicensed frequency band (such as 5.8 GHz) of adjacent monitoring areas. Based on an adaptive filtering algorithm (such as LMS or RLS filtering), the signals in the target frequency band are separated, and the time - frequency characteristics of the interference signals are extracted using the coherent integration technology. The cross - band interference phase difference is calculated through the cross - correlation function, and the spatial distribution of the interference source is located by combining the frequency - domain energy detection algorithm (such as Welch periodogram estimation). The key parameters include the sampling rate (determined by the frequency - band bandwidth), the coherent integration time (dynamically optimized based on the time - varying characteristics of the interference), and the dynamic threshold value (jointly calibrated by historical interference data and real - time signal - to - noise ratio). Finally, through multi - dimensional data fusion (including the frequency domain, time domain, and spatial domain), a time - varying interference distribution map updated in milliseconds is generated. This map stores the interference intensity, time delay, and coherence index of each frequency band in the spatial grid unit in matrix form.

[0058] 202. Perform adaptive correction on the transmit power quota in the resource allocation strategy matrix according to the time - varying interference distribution map. The correction includes the spectrum switching interval and power compensation coefficient adjusted based on the stress wave propagation path length.

[0059] 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.

[0060] The transmit power quota refers to the maximum power upper limit value allowed for a base station or terminal to transmit on a specific frequency band.

[0061] The stress wave propagation path length refers to an equivalent propagation distance calculation model after the electromagnetic wave diffracts and reflects in a complex environment.

[0062] The spectrum switching interval refers to the time slot reserved for frequency - band switching to avoid interference, and its duration is jointly determined by the device switching delay and the interference fluctuation frequency.

[0063] The power compensation coefficient refers to a power scaling factor dynamically adjusted according to the path loss and interference intensity, used to maintain the target signal - to - noise ratio.

[0064] In the embodiments of the present application, based on a dynamic resource allocation algorithm (such as reinforcement learning Q-learning or a convex optimization model), a time-varying interference map is input into the resource allocation policy matrix. For the correction of the transmit power quota, first, the path loss compensation coefficient is calculated in combination with the stress wave propagation path length (modeled by ray tracing or geometric diffraction theory), where the path length is solved in real time from the geometric relationship between the base station and the user equipment and the obstacle distribution (through LiDAR point cloud or digital map). The spectrum switching interval is optimized through a Markov decision process, and its parameters are jointly calibrated by the device switching delay (hardware index) and the interference fluctuation frequency (extracted from the map). The power compensation coefficient is dynamically adjusted through a backpropagation neural network, with the input including the interference intensity, path loss exponent, and user QoS requirements, and the output being the power adjustment step size and direction. Finally, the corrected parameters are mapped to the policy matrix through nonlinear programming to form an optimal power allocation scheme that takes into account both spectral efficiency and interference suppression, and is embedded in the base station controller to achieve closed-loop control. Further, after modifying the resource allocation policy matrix, step 104 is continued to be executed.

[0065] The following is a specific example: In the industrial Internet of Things scenario, a smart factory deploys a hybrid network of 5G private network and WiFi 6 unlicensed band to achieve cross-band coordination for real-time control of AGV unmanned vehicles and AR remote inspections. Millisecond-level signals are collected through a distributed broadband receiving array (covering the 3.5 GHz licensed band and the 5.9 GHz unlicensed band), and an improved LMS adaptive filtering algorithm is used to separate the target frequency band, and a spatial interference energy matrix is constructed based on the weighted Welch periodogram estimation. The ray tracing model is combined to simulate the propagation path of electromagnetic waves in an environment with dense metal devices, and the cross-band phase offset is calculated through the cross-correlation function. Finally, a three-dimensional time-varying map (update period 10 ms) including interference intensity, delay spread, and coherence bandwidth is generated, which can identify the 5.9 GHz periodic pulse interference caused by the high-frequency movement of the robotic arm. The resource allocation policy matrix adopts a two-layer reinforcement learning framework: first, based on the path loss exponent in the interference map (the 12 dB attenuation of the 3.5 GHz signal by the metal shelf calculated by the stress wave diffraction model), the spectrum switching interval of the AGV scheduling channel is dynamically adjusted to 80 μs (originally 200 μs) to avoid time slot conflicts with the WiFi video stream; secondly, the power compensation coefficient is calculated through a deep Q network (DQN), and the transmit power is increased by 6 dB in the edge area to offset the multipath fading effect, while the power in the central area is constrained to decrease by 4 dB to prevent intermodulation interference. The actual measurement shows that this solution reduces the AGV control command delay from 15 ms to 5 ms, the AR video stream stuttering rate drops by 73%, and the unlicensed band channel utilization rate increases from 58% to 82%.

[0066] In summary, steps 201 to 202 capture the dynamic interference distribution characteristics of authorized and unauthorized frequency bands through cross-band coherent detection, adaptively optimize the spectrum switching interval in combination with the stress wave propagation path length, and dynamically correct the power compensation coefficient based on the channel attenuation model to form a closed-loop feedback mechanism for interference perception and resource regulation. This technology can accurately quantify the matching relationship between interference intensity and power quota, and significantly improve the allocation efficiency of spectrum resources in a time-varying interference environment by dynamically adjusting the transmit power threshold and frequency band switching strategy, suppress the disorderly power competition in the unauthorized frequency band, reduce the risk of cross-band signal conflicts, and at the same time enhance the robustness and adaptability of the network topology in complex electromagnetic scenarios. Its core advantage lies in establishing a collaborative framework for multi-dimensional interference suppression and power compensation, optimizing the system energy efficiency while ensuring communication reliability, extending the device battery life, and achieving electromagnetic environment self-healing and resource dynamic balance through real-time closed-loop regulation, providing a low-latency and high-stability spectrum sharing solution for heterogeneous networks.

[0067] In some embodiments, dynamically adjusting the data transmission cycle quota of the monitoring nodes in the association mapping table based on the phase offset parameter in step 104 and generating a resource allocation strategy matrix including predicted values of channel occupancy rates includes: 301. Real-time collect the phase offset parameter and channel state indicators of the monitoring nodes to form a preprocessed data set; In step 301, the phase offset parameter refers to the phase change amount of the signal received by the monitoring node relative to the transmitted signal, which is used to characterize the channel propagation characteristics.

[0068] The channel state indicators refer to a set of parameters reflecting the communication link quality, including signal-to-noise ratio, bit error rate, and spectrum occupancy rate.

[0069] The preprocessed data set refers to the original monitoring data set that has been processed by noise suppression, feature extraction, and format standardization.

[0070] In the embodiments of the present application, by deploying distributed sensor nodes and software-defined radio (SDR) modules, the phase offset (such as ±15° phase jitter caused by Doppler frequency shift) and channel state indicators (such as signal-to-noise ratio, bit error rate) of the monitoring nodes in the UAV cluster are collected in real time. The Kalman filter algorithm is used to suppress the noise of the original data, and key features (such as interference pulse width, spectrum occupancy rate) 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 100 ms sampling interval under a 20 MHz bandwidth). Finally, the edge computing node performs spatio-temporal alignment and format standardization on heterogeneous data (such as Beidou B1C band and 5G NR band signals) to generate a preprocessed data set including the mean value, variance of the phase offset, and channel state heat map.

[0071] 302. Calculate the change rate of the phase offset of each monitoring node based on the preprocessed data set, and combine it with the current channel interference threshold to generate the priority weight coefficient of each node through a dynamic weighting algorithm; In step 302, the change rate of the phase offset refers to the change rate of the phase offset per unit time, which is used to evaluate the dynamic characteristics of the channel.

[0072] The channel interference threshold refers to the critical value for determining whether the channel is interfered, which is dynamically calibrated by training a model with historical data.

[0073] The priority weight coefficient refers to the resource allocation priority value dynamically calculated based on the node phase change rate and interference intensity.

[0074] In the embodiment of the present application, based on the preprocessed data set, first calculate the change rate of the phase offset (such as Δφ / Δt, unit: degrees / second) by the sliding window statistical method, and combine it with the real-time channel interference threshold (dynamically calibrated by the support vector machine model trained with historical data). A dynamic weighting algorithm is constructed using the deep reinforcement learning (DRL) framework: taking the phase change rate, interference intensity, and node geographical location as inputs, and iteratively generating the weight coefficient through the Q-learning strategy. For example, in an urban canyon scenario, the weight coefficient of a node with a phase mutation (such as a change rate exceeding 30 degrees / second within 10 ms) caused by building reflections is increased to 1.8 times the reference value. Finally, the priority weight matrix of each node is output through normalization, and the weight value range is limited between 0.5 and 2.0, which is used for subsequent resource scheduling decisions.

[0075] 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 the preset threshold, proportionally shorten the data transmission cycle quota of the monitoring nodes; In step 303, the association mapping table refers to a data structure that records the corresponding relationship between nodes and resource allocation strategies.

[0076] The data transmission cycle quota refers to the length of the data transmission time window allocated to a node, which is used to control resource occupancy.

[0077] In the embodiments of the present application, incremental optimization is performed on the data transmission cycle quotas in the association mapping table according to the priority weight coefficients. A mixed-integer linear programming (MILP) model is adopted, and the constraint conditions include the total bandwidth upper limit (such as 100 MHz) and the node minimum service level agreement (SLA). When the node weight coefficient exceeds a 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 200 ms to 120 ms. At the same time, a reverse protection mechanism is introduced to extend the cycle of low-weight nodes (such as 0.6) to 300 ms to avoid resource starvation. The adjustment process is synchronized to all nodes through a distributed consistency algorithm (such as the Raft protocol) to ensure the consistency of the network-wide resource allocation strategy.

[0078] 304. Using the adjusted cycle quotas, construct a channel occupancy rate prediction model. Input historical channel occupancy rate data and real-time phase offset parameters, and iteratively update the channel occupancy rate prediction values through a sliding window mechanism, and output a multi-dimensional resource allocation strategy matrix.

[0079] In step 304, the channel occupancy rate prediction model refers to a mathematical model for predicting future channel usage based on historical data and real-time parameters.

[0080] The sliding window mechanism refers to a data processing method for dynamically updating prediction values through a fixed-time window.

[0081] The multi-dimensional resource allocation strategy matrix refers to a decision matrix containing multi-dimensional resource scheduling schemes such as frequency bands, power, and time slots.

[0082] In the embodiments of the present application, using the adjusted cycle quotas, construct a channel occupancy rate prediction model based on a long short-term memory network (LSTM). The input data includes historical occupancy rates (granularity of 5 minutes) and real-time phase offset parameters (frequency domain features extracted by wavelet transform). The prediction values are iteratively updated through a sliding window mechanism (window length of 60 seconds, step size of 10 seconds), and the attention mechanism is used to weight the data at key time points. The model outputs a multi-dimensional resource allocation strategy matrix, including a frequency band switching sequence (such as the time delay of switching from 5.8 GHz to 2.4 GHz < 50 ms), a power adjustment gradient (such as a step size of ±3 dB), and a routing priority mapping table. The final strategy is distributed to each node through a federated learning framework, achieving a 22% increase in global spectral efficiency and a 35% reduction in end-to-end delay.

[0083] The following is a specific example: In the scenario of vehicle-road coordination in a smart city, a hybrid communication system supporting the 5.9GHz C-V2X dedicated frequency band and the 4.9GHz urban emergency private network is deployed on a certain main road, and cross-network coordination for the priority passage of emergency vehicles and real-time linkage of traffic signals needs to be achieved. For step 301, the phase offset (such as the ±8° phase mutation caused by a vehicle's sudden braking) and channel state indicators (including the 12dB signal-to-noise ratio fluctuation caused by the interference of high-voltage transmission lines in the 4.9GHz frequency band) are collected in real time by roadside units (RSUs) and on-vehicle terminals. The improved Kalman filtering algorithm is used to eliminate road environment noise, and a preprocessing dataset containing timestamps, geographical coordinates, and spectrum occupancy heatmaps is generated through multi-source data alignment. Based on the phase offset change rate (such as the 30 degrees / second instantaneous change generated when an ambulance accelerates) and the dynamic interference threshold (predicted and calibrated through a Gaussian process regression model), the double Q-network reinforcement learning algorithm is used to generate node priority weights - the weight coefficient of the ambulance is increased to 2.2 times, and that of ordinary vehicles is reduced to 0.7 times. In step 303, the association mapping table is reconstructed according to the weight coefficient, the data transmission cycle of the ambulance is compressed from 100ms to 45ms, and at the same time, the cycle of non-emergency vehicles is extended to 180ms, and the total bandwidth utilization rate is ensured not to exceed 85% through a mixed integer programming model. An LSTM-TCN fusion prediction model is constructed based on the adjusted cycle quota. The historical 24-hour channel occupancy rate and real-time phase parameters (multi-scale features are extracted through wavelet packet transform) are input, and the prediction value is iteratively updated through a 30-second sliding window. The output policy matrix guides the RSU to dynamically allocate the 5.9GHz frequency band for the signal control instruction of the ambulance (time delay < 20ms), and the 4.9GHz frequency band is used for the traffic light status broadcast. It is measured that the passing efficiency of the ambulance is increased by 55%, the multi-band conflict rate is decreased by 68%, and the channel prediction error rate is maintained below 7.2% in rainy weather.

[0084] In summary, steps 301 to 304 construct a dynamic preprocessing data set by collecting node phase offsets and channel state metrics in real time. Through the correlation analysis of the phase offset change rate and the channel interference threshold, a dynamic weighting algorithm is used to generate node priority weight coefficients, realizing accurate quantitative mapping of multi-dimensional channel states. Based on the weight coefficients, an adaptive incremental adjustment of the data transmission cycle quota is performed to form a dynamic resource scheduling mechanism centered around high-priority nodes. This technology iteratively updates the channel occupancy prediction model through a sliding window mechanism, fuses historical data with real-time parameters, and establishes a multi-dimensional resource allocation strategy matrix, which can effectively suppress channel congestion and signal conflicts, and improve the spatio-temporal consistency of spectrum resource allocation. Its core value lies in constructing a closed-loop optimization system of "data collection - weight calculation - quota adjustment - model prediction". Through the synergistic effect of dynamic priority scheduling and channel state prediction, the elastic response ability of the network topology in a complex interference environment is significantly enhanced. While ensuring the transmission timeliness of key nodes, load balancing and interference suppression of the channel resources of the entire network are achieved, providing a low-latency and highly reliable adaptive resource management solution for heterogeneous communication systems.

[0085] In some embodiments, in step 105, the reallocation of the spectrum quota for the monitoring nodes in the high stress concentration area according to the resource usage efficiency report includes optimizing the hopping interval and adjusting the transmission power based on the historical spectrum quota usage data, including: 401. Analyze the analysis results of the actual consumption rate of the spectrum quota and the predicted value of the channel occupancy rate in the resource usage efficiency report, extract the spectrum efficiency deviation parameter in the high stress concentration area, correlate the historical hopping data, and generate the spectrum efficiency deviation correlation parameter; In step 401, the actual consumption rate of the spectrum quota refers to the proportion of the spectrum resources actually used by the monitoring node within a specific time period, reflecting the resource utilization efficiency.

[0086] The predicted value of the channel occupancy rate refers to the estimated value of the future channel occupancy rate generated based on historical data and a prediction model (such as ARIMA or LSTM).

[0087] The spectrum efficiency deviation parameter refers to a quantitative index of the difference between the actual consumption rate and the predicted value, used to evaluate the spectrum usage efficiency.

[0088] The historical hopping data refers to the historical records of the monitoring node switching between different frequency bands, including information such as switching time and frequency band occupancy rate.

[0089] The spectrum efficiency deviation correlation parameter refers to a comprehensive parameter generated by combining the spectrum efficiency deviation parameter and the historical hopping data, used to guide subsequent decisions.

[0090] In the embodiments of the present application, by analyzing the analysis results of the actual consumption rate of spectrum quota and the predicted value of channel occupancy rate in the resource utilization efficiency report, the spectrum efficiency deviation parameters in the high stress concentration area are extracted, and the spectrum efficiency deviation correlation parameters are generated by associating with historical frequency hopping data. First, a time series analysis method (such as ARIMA or LSTM) is used to compare the actual consumption rate of spectrum quota with the predicted value, and the deviation rate between the two is calculated as a quantization index of the spectrum efficiency deviation parameters. Subsequently, by associating 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 the spectrum efficiency deviation and the frequency hopping behavior. Finally, through a weighted fusion algorithm (such as the entropy weight method or the analytic hierarchy process), the spectrum efficiency deviation parameters are combined with the historical frequency hopping data to generate the spectrum efficiency deviation correlation parameters, providing data support for subsequent frequency hopping decisions.

[0091] 402. Based on the spectrum efficiency deviation correlation parameters, 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 frequency hopping interval decision parameters; In step 402, the channel occupancy fluctuation characteristics refer to the occupancy rate fluctuation of each frequency band in the historical usage period, reflecting the stability of the frequency band.

[0092] The signal propagation attenuation characteristics refer to the attenuation degree of the signal in different frequency bands in the high stress concentration area, which affects the frequency band selection.

[0093] The frequency hopping interval decision parameters refer to the parameters generated based on the channel occupancy fluctuation characteristics and the signal propagation attenuation characteristics, and are used to optimize the frequency band switching strategy.

[0094] In the embodiments of the present application, by calculating the channel occupancy fluctuation characteristics of each frequency band in the historical usage period, and combining the signal propagation attenuation characteristics of the high stress concentration area, the frequency hopping interval decision parameters are generated. First, the wavelet transform or Fourier transform is used to analyze the occupancy rate fluctuation characteristics of each frequency band, extract its frequency and amplitude information, and quantify the stability of the frequency band. Subsequently, combined with the signal propagation attenuation characteristics of the high stress concentration area (such as the path loss model or the empirical attenuation formula), the attenuation degree of different frequency bands is calculated. Finally, through a multi-objective optimization algorithm (such as NSGA-II), the stability of the frequency band and the signal attenuation characteristics are balanced to generate the frequency hopping interval decision parameters and optimize the frequency band switching strategy.

[0095] 403. According to the frequency band switching priority in the frequency hopping interval decision parameters, match the historical signal strength attenuation data of the high stress concentration area, calculate the transmission power compensation gradient and superimpose the reference value to generate dynamic power compensation parameters; 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 high-stability frequency band is preferentially selected.

[0096] The historical signal strength attenuation data refers to the historical record of the signal strength attenuation of the signal in different frequency bands within the high stress concentration area.

[0097] The transmit power compensation gradient refers to the power adjustment amplitude calculated according to the signal strength attenuation data, which is used to compensate for signal attenuation.

[0098] The dynamic power compensation parameter refers to the power compensation parameter after superimposing the reference value, which is used to dynamically adjust the transmit power.

[0099] In the embodiment of the present application, by matching the historical signal strength attenuation data of the high stress concentration area, the transmit power compensation gradient is calculated and the reference value is superimposed to generate the dynamic power compensation parameter. First, based on the frequency band switching priority, the target frequency band is determined, and its historical signal strength attenuation data (such as received signal strength indication RSSI or signal-to-noise ratio SNR) is extracted. Subsequently, the gradient descent method or the least squares method is used to calculate the transmit power compensation gradient to ensure that the signal strength meets the coverage requirements. Finally, the compensation gradient is superimposed on the reference transmit power value to generate the dynamic power compensation parameter, which is used to adjust the transmit power in real time.

[0100] 404. Integrate the frequency hopping interval decision parameter and the dynamic power compensation parameter, reallocate the spectrum quota according to the priority weight, and generate the spectrum quota reallocation strategy table after verification; In step 404, the priority weight refers to the priority of each frequency band determined according to the spectrum efficiency deviation correlation parameter and the frequency hopping interval decision parameter.

[0101] The spectrum quota reallocation strategy table refers to the strategy table generated after integrating the frequency hopping interval decision parameter and the dynamic power compensation parameter, which is used to guide resource allocation.

[0102] In the embodiment of the present application, by integrating the frequency hopping interval decision parameter and the dynamic power compensation parameter, the spectrum quota is reallocated according to the priority weight, and the spectrum quota reallocation strategy table is generated after verification. First, a weighted fusion algorithm (such as weighted average or fuzzy analytic hierarchy process) is used to combine the frequency hopping interval decision parameter and the dynamic power compensation parameter to generate the comprehensive priority weight. Subsequently, the spectrum quota of each frequency band is reallocated according to the priority weight to ensure that the high-priority frequency band obtains more resources. Finally, the reallocation strategy is verified through Monte Carlo simulation or cross-validation to generate the spectrum quota reallocation strategy table, which provides a basis for resource scheduling.

[0103] ​​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 perform spectrum quota reallocation.

[0104] In step 405, the resource scheduling module refers to the functional module in the monitoring node responsible for spectrum resource allocation and scheduling.

[0105] The quota tracking database refers to the database that records the spectrum quota usage data and is used for real-time update and historical analysis.

[0106] Spectrum quota reallocation refers to adjusting the spectrum resource allocation of the monitoring node according to the reallocation strategy table to optimize the resource utilization efficiency.

[0107] In the embodiment of the present application, by recording the spectrum usage data after reallocation in real time and updating it to the quota tracking database, perform spectrum quota reallocation. First, load the reallocation strategy table into the resource scheduling module, and adjust the frequency band switching and transmission power in real time through a dynamic scheduling algorithm (such as a greedy algorithm or a genetic algorithm). Subsequently, record the spectrum usage data after reallocation (such as occupancy rate, signal strength, etc.) and update it to the quota tracking database for subsequent analysis and optimization. Finally, through a closed-loop feedback mechanism, ensure the real-time performance and effectiveness of the reallocation strategy, and improve the system resource utilization efficiency and signal transmission stability.

[0108] The following is a specific example: In the optimization scenario of urban rail transit wireless communication systems, for the spectrum resource allocation requirements in high-stress concentration areas (such as tunnels and underground stations), the following complete implementation example can be constructed: First, based on the wireless communication network resource utilization efficiency report of a certain subway line, analyze the results of the actual consumption rate of spectrum quotas and the predicted values of channel occupancy rates, extract the spectrum efficiency deviation parameters in the tunnel area, and correlate with historical frequency hopping data. Generate the spectrum efficiency deviation correlation parameter α = 0.78 through Pearson correlation coefficient analysis. Subsequently, based on α = 0.78, calculate the channel occupancy fluctuation characteristics of each frequency band during the historical usage period, and combine with the signal propagation attenuation characteristics in the tunnel area. Use wavelet transform and multi-objective optimization algorithms to generate frequency hopping interval decision parameters and optimize the frequency band switching strategy. Then, according to the frequency band switching priorities in the frequency hopping interval decision parameters, match the historical signal strength attenuation data in the tunnel area, calculate the transmission power compensation gradient through the gradient descent method, and superimpose the reference value to generate the dynamic power compensation parameter β = 1.2. Then, fuse the frequency hopping interval decision parameters and the dynamic power compensation parameters, reallocate the spectrum quotas according to the priority weights, and verify through Monte Carlo simulation to generate the spectrum quota reallocation strategy table. Finally, load the 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 the spectrum quota reallocation, significantly improving the signal coverage quality and communication stability in the tunnel area. This solution realizes resource optimization and signal transmission stability improvement in high-stress concentration areas through the collaborative processing of spectrum efficiency deviation analysis, frequency hopping decision optimization, power compensation adjustment, and quota reallocation execution, providing reliable technical support for the optimization of wireless communication systems in complex environments.

[0109] In summary, steps 401 to 405 achieve resource optimization and signal transmission stability improvement of the monitoring nodes in high-stress concentration areas through spectrum quota reallocation technology. First, based on the analysis results of the actual consumption rate of spectrum quotas and the predicted values of channel occupancy rates, extract the spectrum efficiency deviation parameters and correlate with historical frequency hopping data to generate the spectrum efficiency deviation correlation parameter, quantifying the matching degree between spectrum usage efficiency and channel occupancy rate; secondly, combine the channel occupancy fluctuation characteristics and signal propagation attenuation characteristics to generate the frequency hopping interval decision parameters and optimize the frequency band switching strategy; subsequently, calculate the transmission power compensation gradient according to the signal strength attenuation data to generate the dynamic power compensation parameter to ensure that the signal coverage range and strength meet the requirements; finally, fuse the frequency hopping interval and power compensation parameters, reallocate the spectrum quotas according to the priority weights, 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.

[0110] In some embodiments, in step 404, the hopping interval decision parameter and the dynamic power compensation parameter are fused, and the spectrum quota is redistributed according to the priority weight, and after verification, a spectrum quota redistribution strategy table is generated, including: 501. Perform a multi-dimensional parameter superposition operation on the frequency band switching priority and the residence duration ratio coefficient in the hopping interval decision parameter and the transmission power compensation gradient in the dynamic power compensation parameter to generate an initial set of spectrum quota redistribution strategy parameters; In step 501, the frequency band switching priority refers to the switching order of each frequency band determined according to the hopping interval decision parameter, and the high-stability frequency band is preferentially selected.

[0111] The residence duration ratio coefficient refers to the proportion of the residence time of the monitoring node in a specific frequency band, reflecting the frequency of use of the frequency band.

[0112] The transmission power compensation gradient refers to the power adjustment amplitude calculated according to the signal strength attenuation data, which is used to compensate for signal attenuation.

[0113] The multi-dimensional parameter superposition operation refers to an algorithm (such as weighted average or analytic hierarchy process) that performs weighted fusion on the frequency band switching priority, the residence duration ratio coefficient, and the transmission power compensation gradient.

[0114] The initial set of spectrum quota redistribution strategy parameters refers to the initial set of strategy parameters generated after fusing multi-dimensional parameters, which is used for subsequent optimization.

[0115] In the embodiments of the present application, the frequency band switching priority and the residence duration ratio coefficient in the 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 an initial set of spectrum quota redistribution strategy parameters. First, the analytic hierarchy process (AHP) or the entropy weight method is used to determine the weights of the frequency band switching priority and the residence duration ratio coefficient, and weighted fusion is performed in combination with the transmission power compensation gradient to generate the initial strategy parameters. Subsequently, normalization processing is performed to ensure that all parameters are in the same dimension, forming a high-quality data set for subsequent analysis. Finally, a multi-objective optimization algorithm (such as NSGA-II) is used to balance the frequency band stability and the power compensation requirements to generate an initial set of spectrum quota redistribution strategy parameters, providing data support for subsequent conflict detection and correction.

[0116] 502. According to the initial set of spectrum quota redistribution strategy parameters, detect and correct the spectrum quota allocation conflicts in different frequency bands in the high stress concentration area, and output the corrected intermediate spectrum quota redistribution strategy parameters.

[0117] In step 502, the spectrum quota allocation conflict refers to the resource overlap or competition phenomenon that occurs during the resource allocation process of different frequency bands.

[0118] The correction strategy refers to adjusting the spectrum quota allocation through a conflict detection algorithm (such as the maximum matching or Hungarian algorithm) to eliminate conflicts.

[0119] The intermediate strategy parameters for spectrum quota reallocation refer to the set of strategy parameters after correcting conflicts, which are used for subsequent constraint optimization.

[0120] In the embodiments of this application, based on the initial set of strategy parameters for spectrum quota reallocation, this step detects and corrects the spectrum quota allocation conflicts in different frequency bands within the high stress concentration area, and outputs the corrected intermediate strategy parameters. First, a conflict detection algorithm (such as the maximum matching or Hungarian algorithm) is used to identify spectrum quota allocation conflicts and quantify the degree of conflict. Subsequently, conflicts are eliminated through correction strategies (such as resource reallocation or priority adjustment) to optimize the spectrum quota allocation. Finally, the corrected strategy parameters are verified through Monte Carlo simulation to ensure their rationality and effectiveness, generating the intermediate strategy parameters for spectrum quota reallocation, providing a reliable input for subsequent constraint optimization.

[0121] 503. Load the priority weight as a constraint condition into the intermediate strategy parameters for spectrum quota reallocation, calculate the maximum allocable spectrum quota threshold for each frequency band under the constraint of the priority weight, and generate the boundary parameters for spectrum quota reallocation; In step 503, the priority weight refers to the priority of each frequency band determined according to the spectrum efficiency deviation correlation parameter and the frequency hopping interval decision parameter.

[0122] The maximum allocable spectrum quota threshold refers to the maximum amount of spectrum resources that can be allocated to each frequency band under the constraint of the priority weight.

[0123] The boundary parameters for spectrum quota reallocation refer to the boundary parameters generated based on the priority weight and the maximum allocable threshold, which are used to guide resource allocation.

[0124] In the embodiments of this application, the priority weight is loaded as a constraint condition into the intermediate strategy parameters for spectrum quota reallocation, calculate the maximum allocable spectrum quota threshold for each frequency band under the constraint of the priority weight, and generate the boundary parameters for spectrum quota reallocation. First, the Lagrange multiplier method or the gradient descent method is used to construct an objective function, and optimization is solved by combining the priority weight and the spectrum quota demand. Subsequently, the parameter set is iteratively updated to gradually approach the global optimal solution, generating the maximum allocable spectrum quota threshold for each frequency band. Finally, the output is restricted to a preset interval through the boundary condition projection method (such as the Simplex projection algorithm) to generate the boundary parameters for spectrum quota reallocation, providing a basis for subsequent cross-layer parameter comparison.

[0125] 504. Perform cross-layer parameter comparison between the boundary parameters for spectrum quota reallocation and the predicted channel occupancy rate values in the resource allocation strategy matrix, and generate the verification parameters for spectrum quota reallocation after passing the verification. In step 504, the predicted channel occupancy rate refers to the estimated future channel occupancy rate generated based on historical data and a prediction model (such as ARIMA or LSTM).

[0126] Cross-layer parameter comparison refers to an algorithm (such as KL divergence or Euclidean distance) that compares and validates the spectrum quota reallocation boundary parameters with the predicted channel occupancy rate.

[0127] The spectrum quota reallocation verification parameter refers to the policy parameter after comparison and verification to ensure the rationality and effectiveness of resource allocation.

[0128] In the embodiments of this application, through cross-layer parameter comparison and verification with the predicted channel occupancy rate in the resource allocation strategy matrix, spectrum quota reallocation verification parameters are generated. First, the KL divergence or Euclidean distance is used to calculate the difference between the boundary parameters and the predicted channel occupancy rate to quantify the verification result. Subsequently, the verification parameters are iteratively optimized through cross-validation or Monte Carlo simulation to ensure their accuracy and stability. Finally, spectrum quota reallocation verification parameters are generated, providing reliable data support for the generation of the policy table.

[0129] 505. Based on the spectrum quota reallocation verification parameters, according to the combination rule of the frequency band switching priority and the residence duration ratio coefficient, generate a spectrum quota reallocation policy table including the frequency band identifier, the quota threshold, and the power compensation gradient.

[0130] In step 505, the frequency band identifier refers to the unique number or name used to identify different frequency bands.

[0131] The quota threshold refers to the maximum allocable spectrum resource amount for each frequency band in the reallocation strategy.

[0132] The power compensation gradient refers to the power adjustment amplitude calculated based on the signal strength attenuation data, used to compensate for signal attenuation.

[0133] The spectrum quota reallocation policy table refers to the policy table including the frequency band identifier, the quota threshold, and the power compensation gradient, used to guide resource scheduling.

[0134] In the embodiments of this application, based on the spectrum quota reallocation verification parameters, according to the combination rule of the frequency band switching priority and the residence duration ratio coefficient, generate a spectrum quota reallocation policy table including the frequency band identifier, the quota threshold, and the power compensation gradient. 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 policy table framework. Subsequently, through normalization processing, ensure that each parameter is in the same dimension to form a policy table that can be used for resource scheduling. Finally, through a closed-loop feedback mechanism, verify the effectiveness of the policy table, and generate a spectrum quota reallocation policy table including the frequency band identifier, the quota threshold, and the power compensation gradient, providing a reliable basis for resource scheduling.

[0135] The following is a specific example: In the scenario of wireless communication resource scheduling in an industrial Internet of Things intelligent factory, an automobile manufacturing plant deployed a dynamic spectrum reallocation system to solve the spectrum resource conflicts among AGV carriers, robotic arms, and environmental sensors. In the specific implementation: the system performed multi-dimensional superposition on the hopping priority of the AGV (band switching weight 0.8), the residence time ratio of the robotic arm control signal (0.65), and the power compensation gradient of the laser positioning device (+5 dBm) to generate initial policy parameters; when detecting the spectrum quota overlap conflict between the welding robot (2.4 GHz band) and the AGV navigation system (5.8 GHz band), the power gradient of the welding robot was dynamically reduced to +2 dBm through the channel quality index (CQI); based on the real-time priority of the AGV transportation path (the weight of emergency material distribution was increased to 0.9), the maximum allocable threshold of each band was calculated (such as the 5.8 GHz band was limited by 40%); after cross-layer verification, a spectrum quota policy table including dedicated band identification (AGV dedicated 5.8 GHz / 20 MHz), dynamic power compensation (laser positioning +3 dBm), and residence time (robotic arm control 600 ms) was generated, reducing the communication interruption rate of the production line by 58% and increasing the equipment collaboration efficiency by 33%.

[0136] In summary, steps 501 to 505 achieved the spectrum quota reallocation in the high stress concentration area through multi-dimensional parameter fusion and constraint optimization technology, significantly improving the resource utilization efficiency and signal transmission stability. First, a multi-dimensional parameter superposition operation was performed on the band switching priority and residence time ratio coefficient in the hopping interval decision parameters and the transmit power compensation gradient in the dynamic power compensation parameters to generate a set of initial policy parameters for spectrum quota reallocation; second, the spectrum quota allocation conflicts in different bands within the high stress concentration area were detected and corrected, and the corrected intermediate policy parameters were output; then, the priority weight was loaded as a constraint condition into the intermediate policy parameters, the maximum allocable spectrum quota threshold of each band was calculated, and boundary parameters were generated; next, the boundary parameters were compared with the predicted channel occupancy values in the resource allocation policy matrix for cross-layer parameter comparison to generate verification parameters; finally, according to the combination rule of the band switching priority and residence time ratio coefficient, a spectrum quota reallocation policy table including band identification, quota threshold, and power compensation gradient was generated. This design significantly improved the rationality and adaptability of spectrum quota allocation through multi-dimensional fusion and constraint optimization, providing a reliable solution for resource scheduling in complex environments.

[0137] In some embodiments, in step 603, loading the priority weight as a constraint condition into the intermediate policy parameters of the spectrum quota reallocation, calculating the maximum allocable spectrum quota threshold of each band under the constraint of the priority weight, and generating the boundary parameters of the spectrum quota reallocation includes: 601. Extract the priority weight constraint parameters in the intermediate policy parameters of spectrum quota reallocation, and generate an initial constraint parameter set containing frequency band identifiers and priority weights; In step 601, the priority weight constraint parameter refers to the priority weight of each frequency band determined based on the spectrum efficiency deviation correlation parameter and the frequency hopping interval decision parameter.

[0138] The frequency band identifier refers to the unique number or name used to identify different frequency bands.

[0139] The initial constraint parameter set refers to the parameter set containing frequency band identifiers and priority weights, which is used for subsequent calculation of the maximum allocable threshold.

[0140] In the embodiment of the present application, by extracting the priority weight constraint parameters in the intermediate policy parameters of spectrum quota reallocation, an initial constraint parameter set containing frequency band identifiers and priority weights is generated. First, data parsing techniques (such as regular expressions or data mining algorithms) are used to extract the priority weight constraint parameters from the intermediate policy parameters to ensure the integrity and accuracy of the data. Subsequently, an initial constraint parameter set is constructed through the mapping relationship between the frequency band identifier and the priority weight. Finally, through normalization processing, it is ensured 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 allocable threshold.

[0141] 602. Based on the priority weights in the initial constraint parameter set, combined with the historical spectrum demand baseline values of each frequency band, calculate the maximum allocable spectrum quota threshold of each frequency band under weight constraints, and generate a spectrum quota extreme value parameter set; In step 602, the historical spectrum demand baseline value refers to the average value of the spectrum demand of each frequency band during the historical usage period, which reflects the usage of frequency band resources.

[0142] The maximum allocable spectrum quota threshold refers to the maximum amount of spectrum resources that can be allocated to each frequency band under priority weight constraints.

[0143] The spectrum quota extreme value parameter set refers to the extreme value parameter set generated based on the priority weight and the historical demand baseline value, which is used to guide resource allocation.

[0144] In the embodiments of the present application, by combining the historical spectrum demand baseline values of each frequency band, the maximum allocable spectrum quota threshold of each frequency band under weight constraints is calculated, and a set of spectrum quota extreme value parameters 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, an objective function is constructed by the Lagrange multiplier method or the gradient descent method, and optimization is solved by combining the priority weight and the historical demand baseline value. Finally, a set of spectrum quota extreme value parameters is generated to provide data support for subsequent compliance comparison. This step ensures the rationality of resource allocation through the collaborative optimization of weight constraints and historical demands.

[0145] 603. Perform compliance comparison on the set of spectrum quota extreme value parameters and the physical layer bearing limit parameters, and eliminate the extreme value parameters that exceed the physical layer bearing capacity to generate a set of spectrum quota reallocation boundary parameters; In step 603, the physical layer bearing limit parameters refer to the maximum bearing capacity parameters of physical layer hardware devices (such as antennas, filters), which limit the upper limit of spectrum resource allocation. In step 603, the physical layer bearing limit parameters refer to the maximum bearing capacity parameters of physical layer hardware devices (such as antennas, filters), which limit the upper limit of spectrum resource allocation.

[0146] Compliance comparison refers to an algorithm (such as threshold filtering or KL divergence) for comparing and validating the set of spectrum quota extreme value parameters and the physical layer bearing limit parameters.

[0147] The set of spectrum quota reallocation boundary parameters refers to the set of boundary parameters generated after eliminating the extreme value parameters that exceed the physical layer bearing capacity, ensuring the feasibility of resource allocation.

[0148] In the embodiments of the present application, by performing compliance comparison on the set of spectrum quota extreme value parameters and the physical layer bearing limit parameters, the extreme value parameters that exceed the physical layer bearing capacity are eliminated to generate a set of spectrum quota reallocation boundary parameters. First, a threshold filtering algorithm or KL divergence is used to calculate the difference between the extreme value parameters and the physical layer bearing limit parameters to quantify the compliance result. Subsequently, the extreme value parameters that exceed the bearing capacity are eliminated to generate a set of boundary parameters. Finally, the rationality of the set of boundary parameters is verified through Monte Carlo simulation to ensure that it meets the bearing capacity of the physical layer hardware devices. This step ensures the feasibility of resource allocation through compliance comparison and parameter optimization.

[0149] 604. Integrate the set of spectrum quota reallocation boundary parameters and the set of priority weight constraint parameters to generate spectrum quota reallocation boundary parameters including frequency band identification, priority weight, and quota threshold.

[0150] In step 604, the set of priority weight constraint parameters refers to the set of parameters including frequency band identification and priority weight, which is used to guide resource allocation.

[0151] The spectrum quota reallocation boundary parameters refer to the parameters generated after integrating the boundary parameter set and the priority weight constraint set, including frequency band identification, priority weight, and quota threshold, which are used to guide resource scheduling.

[0152] In the embodiments of this application, by integrating the spectrum quota reallocation boundary parameter set and the priority weight constraint parameter set, spectrum quota reallocation boundary parameters including frequency band identification, priority weight, and quota threshold are generated. 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, through normalization processing, it is ensured that each parameter is in the same dimension, forming boundary parameters that can be used for resource scheduling. Finally, through a closed-loop feedback mechanism, the effectiveness of the boundary parameters is verified, and spectrum quota reallocation boundary parameters including frequency band identification, priority weight, and quota threshold are generated, providing a reliable basis for resource scheduling. This step ensures the accuracy and stability of resource allocation through parameter integration and verification.

[0153] The following is a specific example: In the scenario of optimizing the smart city Internet of Things communication system, for the spectrum resource allocation requirements in high-density device areas (such as intelligent transportation hubs, industrial parks), the following complete embodiment can be constructed: First, based on the resource utilization efficiency report of a certain smart city Internet of Things communication network, the priority weight constraint parameters in the intermediate strategy parameters of spectrum quota reallocation are extracted to generate an initial constraint parameter set including frequency band identification and priority weight. Subsequently, based on the priority weight in the initial constraint parameter set, combined with the historical spectrum demand baseline value of each frequency band (such as the historical demand average value of 50 MHz for the NB-IoT frequency band), the maximum allocable spectrum quota threshold of each frequency band under the weight constraint is calculated through the gradient descent method (such as the maximum quota of 60 MHz for the NB-IoT frequency band), generating a spectrum quota extreme value parameter set. Then, the spectrum quota extreme value parameter set is compared with the physical layer bearing limit parameters, and the extreme value parameters exceeding the physical layer bearing capacity are removed through KL divergence calculation, generating a spectrum quota reallocation boundary parameter set. Finally, 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 identification, priority weight, and quota threshold, significantly improving the device connection quality and communication stability in the intelligent transportation hub area. This solution realizes the optimization of spectrum quota reallocation in high-density device areas through the collaborative optimization of priority weight constraints, historical demand baseline value analysis, and physical layer bearing limits, providing reliable technical support for the optimization of the Internet of Things communication system in complex environments.

[0154] In summary, steps 601 to 604 achieve the precise generation of the spectrum quota reallocation boundary parameters through the collaborative optimization of priority weight constraints and physical layer bearer limitations. First, extract the priority weight constraint parameters in the intermediate strategy parameters of spectrum quota reallocation to generate an initial constraint set containing frequency band identifiers and priorities. Second, based on the priority weights in the initial constraint set and combined with the historical spectrum demand baseline values of each frequency band, calculate the maximum allocable thresholds of each frequency band under the weight constraints to generate a set of spectrum quota extreme value parameters. Subsequently, perform a compliance comparison between the set of extreme value parameters and the physical layer bearer limitation parameters, and eliminate the extreme value parameters that exceed the physical layer bearer capacity to generate a set of spectrum quota reallocation boundary parameters. Finally, integrate the set of boundary parameters and the set of priority weight constraints to generate spectrum quota reallocation boundary parameters containing 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.

[0155] In some embodiments, the quota tracking database for Internet of Things resources established in step 104 generates a resource utilization efficiency report based on the quota tracking database, including a comparative analysis of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource allocation strategy matrix, including: 701. Establish a quota tracking database for Internet of Things resources. The quota tracking database records the historical spectrum quota usage data of monitoring nodes, and based on the quota tracking database, perform dynamic aggregation on the actual spectrum quota consumption rate according to a preset time window to generate a dynamic aggregation data set; In step 701, the quota tracking database refers to a database that records the historical spectrum quota usage data of monitoring nodes and is used to store and analyze resource usage.

[0156] The preset time window refers to a time period for dividing historical data (such as every 10 minutes as a window) to facilitate dynamic aggregation analysis.

[0157] The actual spectrum quota consumption rate refers to the proportion of the spectrum resources actually used by a monitoring node within a specific time window.

[0158] The dynamic aggregation data set refers to a data set generated by aggregating the actual spectrum quota consumption rate according to a time window and is used for subsequent feature extraction and analysis.

[0159] In the embodiments of the present application, by establishing a quota tracking database for Internet of Things resources, recording the historical spectrum quota usage data of monitoring nodes, and performing dynamic aggregation on the actual spectrum quota consumption rate according to a preset time window, a dynamic aggregation data set is generated. First, a quota tracking database is constructed using distributed database technology (such as HBase or Cassandra) to record the spectrum quota usage data of each node in real time. Subsequently, the historical data is dynamically aggregated through time window division (such as a 10-minute window for each), and the actual spectrum quota consumption rate within each window is calculated. Finally, through data cleaning and normalization processing, a dynamic aggregation data set is generated to provide a data basis for subsequent feature extraction and analysis.

[0160] 702. Cross-time-window associate the node identifiers in the dynamic aggregation data set, 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; In step 702, the node identifier refers to the unique number or name used to identify different monitoring nodes.

[0161] The temporal fluctuation characteristics refer to the change trend and periodic characteristics of the spectrum consumption rate in the time dimension, reflecting the dynamic changes in resource usage.

[0162] The spatial distribution characteristics refer to the distribution characteristics of monitoring nodes in the spatial dimension, reflecting the resource usage correlation between nodes.

[0163] The node spectrum fluctuation index refers to an index that quantifies the temporal fluctuation characteristics of the spectrum consumption rate, reflecting the stability of resource usage.

[0164] The spatial correlation degree refers to an index that quantifies the spatial distribution characteristics of the spectrum consumption rate between nodes, reflecting the regional characteristics of resource usage.

[0165] The spectrum feature vector refers to a feature vector including the node spectrum fluctuation index and spatial correlation degree, which is used for subsequent matching and analysis.

[0166] In the embodiments of the present application, by cross-time-window associating the node identifiers, the temporal fluctuation characteristics and spatial distribution characteristics of the spectrum consumption rate are extracted, and a spectrum feature vector including the node spectrum fluctuation index and spatial correlation degree is generated. First, a time series analysis method (such as ARIMA or LSTM) is used to extract the temporal fluctuation characteristics of the spectrum consumption rate and quantify its change trend 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 degree. Finally, the temporal fluctuation characteristics and spatial distribution characteristics are fused to generate a spectrum feature vector to provide feature data for subsequent matching and analysis.

[0167] 703. Match the spectral feature vector with the predicted channel occupancy rate in the resource allocation policy matrix, calculate the deviation degree and convergence index between the actual consumption rate of the spectrum quota and the predicted value, and generate a set of spectrum comparison indicators. In step 703, the resource allocation policy matrix refers to a matrix containing the predicted channel occupancy rate, which is used to guide the allocation of spectrum resources.

[0168] The deviation degree refers to a quantitative index of the difference between the actual consumption rate of the spectrum quota and the predicted value, reflecting the accuracy of resource allocation.

[0169] The convergence index refers to the degree of convergence of the spectrum consumption rate over time, reflecting the stability of resource allocation.

[0170] The set of spectrum comparison indicators refers to a parameter set containing the deviation degree and the convergence index, which is used for subsequent policy optimization.

[0171] In the embodiment of the present application, by matching the spectral feature vector with the predicted channel occupancy rate in the resource allocation policy matrix, calculating the deviation degree and convergence index between the actual consumption rate of the spectrum quota and the predicted value, a set of spectrum comparison indicators is generated. First, a similarity calculation algorithm (such as cosine similarity or Euclidean distance) is used to match the spectral feature vector with the predicted channel occupancy rate to quantify the deviation degree between the two. Subsequently, the convergence index of the spectrum consumption rate is calculated through convergence analysis (such as moving window mean or gradient descent method). Finally, the deviation degree and the convergence index are integrated to generate a set of spectrum comparison indicators, providing a quantitative basis for subsequent policy optimization.

[0172] 704. According to the set of spectrum comparison indicators, construct a mapping relationship between the node identifier and the resource allocation policy matrix, and generate an optimized policy mapping table containing the node priority coefficient and policy fitness by dynamically adjusting the priority weights in the policy matrix. 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.

[0173] The priority weight refers to the priority weight of each node determined according to the set of spectrum comparison indicators, which is used to optimize resource allocation.

[0174] The node priority coefficient refers to a parameter that quantifies the node priority, reflecting the importance of resource allocation.

[0175] The policy fitness refers to the degree of matching between the resource allocation policy and the actual demand, reflecting the effectiveness of the policy.

[0176] The optimized policy mapping table refers to a parameter table containing the node priority coefficient and policy fitness, which is used to guide resource scheduling.

[0177] In the embodiments of the present application, by constructing a mapping relationship between node identifiers and a resource allocation policy matrix, the priority weights in the policy matrix are dynamically adjusted to generate an optimized policy mapping table including node priority coefficients and policy adaptation degrees. First, a mapping algorithm (such as hash mapping or graph neural network) is used to construct the mapping relationship between node identifiers and the policy matrix to ensure data correlation. Subsequently, the priority weights in the policy matrix are optimized through a dynamic adjustment algorithm (such as reinforcement learning or genetic algorithm) to generate node priority coefficients and policy adaptation degrees. Finally, the optimized parameters are integrated to generate an optimized policy mapping table, providing a policy basis for subsequent resource scheduling.

[0178] 705. Based on the priority coefficients and policy adaptation degrees in the optimized policy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison index set, a resource usage efficiency report capable of evaluating and providing optimization suggestions is generated.

[0179] In step 705, the deviation threshold refers to the allowable upper limit of the deviation between the actual consumption rate of the spectrum quota and the predicted value, which is used to evaluate the resource usage efficiency.

[0180] The convergence factor refers to the quantization parameter of the spectrum consumption rate convergence index, which is used to evaluate the stability of resource allocation.

[0181] The resource usage efficiency report refers to a report including evaluation results and optimization suggestions, which is used to guide resource optimization and decision-making.

[0182] In the embodiments of the present application, based on the priority coefficients and policy adaptation degrees in the optimized policy mapping table, combined with the deviation threshold and convergence factor of the spectrum comparison index set, a resource usage efficiency report capable of evaluating and providing optimization suggestions is generated. First, a report generation framework (such as JasperReports or Tableau) is used to construct a report template, integrating the priority coefficients, policy adaptation degrees, and spectrum comparison indexes. Subsequently, the resource usage efficiency is evaluated through the deviation threshold and convergence factor to generate optimization suggestions (such as adjusting the spectrum quota or optimizing the priority weights). Finally, the evaluation results and optimization suggestions are integrated through data visualization technology to generate a resource usage efficiency report, providing clear technical support for decision-makers.

[0183] The following is a specific example. In the scenario of Internet of Things (IoT) spectrum resource management for the intelligent transportation system in a smart city, to alleviate the communication congestion problem during the morning and evening rush hours, a certain city's traffic management bureau has deployed a traffic signal networking control system based on LoRaWAN. The system installs monitoring nodes at 500 intersections in the central urban area. Each node needs to transmit the traffic signal status, vehicle flow video stream, and environmental sensor data in real time. By establishing a quota tracking database, the system records the historical spectrum usage data of each node with a 15-minute time window. For example, it is found that the average consumption rate of the node at the Jiefang South Road intersection is as high as 98% during the morning rush hour (7:00 - 9:00), while it is only 35% during the off-peak period. Through dynamic aggregation analysis across a 72-hour window, the system identifies that the spectrum consumption rate of the nodes around the hospital shows a pulsed fluctuation characteristic (such as sudden data transmission when an ambulance has the right of way), and the spatial correlation degree of the nodes in the commercial area is as high as 0.83, manifested as the synchronous increase in spectrum occupancy of adjacent nodes during promotional activities. When matching these feature vectors with the preset "holiday traffic plan" strategy matrix, it is found that the actual consumption rate of the Jiefang South Road node deviates from the predicted value by 42%, and the convergence index is lower than 0.6, triggering the dynamic adjustment mechanism. The system increases the priority weight of 10 nodes around the top three hospitals by 300% and marks them as the red warning level in the strategy mapping table. At the same time, it configures an elastic bandwidth pool for the commercial area nodes and shunts non-real-time data through the NB-IoT channel. The final generated resource report shows that after optimization, the peak value of the spectrum utilization rate in the core area during the morning rush hour drops by 28%, and the communication delay is shortened from 850 ms to 210 ms. It is also recommended to deploy edge computing gateways at the nodes across the viaduct to achieve local data processing, which is expected to further reduce the spectrum load by 15%. Through spatio-temporal feature mining and dynamic strategy adaptation, this embodiment realizes the precise scheduling of urban-level IoT resources.

[0184] In summary, steps 701 to 705 achieve the precise evaluation and optimization of the usage efficiency of IoT spectrum resources through dynamic aggregation and feature analysis technologies, significantly improving the rationality of resource allocation and the stability of the system. First, a quota tracking database is established, and dynamic aggregation is performed on the actual consumption rate of spectrum quotas according to a preset time window to generate a dynamic aggregation data set. Secondly, the temporal fluctuation characteristics and spatial distribution characteristics of the spectrum consumption rate are extracted to generate spectrum feature vectors. Subsequently, the feature vectors are matched with the predicted values of the channel occupancy rate, and the deviation degree and convergence index are calculated to generate a set of spectrum comparison indicators. Then, the mapping relationship between the node identifier and the resource allocation strategy matrix is constructed, and the priority weight is dynamically adjusted to generate an optimized strategy mapping table. Finally, based on the priority coefficient and strategy adaptation degree, combined with the deviation threshold and convergence factor, a resource usage efficiency report is generated. Through the collaborative processing of multi-dimensional data analysis and strategy optimization, this design significantly improves the evaluation accuracy and optimization effect of the spectrum resource usage efficiency, providing reliable technical support for IoT resource scheduling in complex environments.

[0185] In some embodiments, in step 704, according to the set of spectrum comparison metrics, a mapping relationship between node identifiers and resource allocation strategy matrices is constructed, and by dynamically adjusting the priority weights in the strategy matrix, an optimized strategy mapping table including node priority coefficients and strategy fitness degrees is generated, including: 801. Determine an initial mapping relationship between node identifiers and resource allocation strategy matrices based on the set of spectrum comparison metrics, where the resource allocation strategy matrix includes initial values of node priority coefficients and initial values of strategy fitness degrees; In step 801, the set of spectrum comparison metrics refers to a parameter set including deviation degree and convergence metrics, which is used to guide resource allocation strategies.

[0186] A node identifier refers to a unique number or name used to identify different monitoring nodes.

[0187] A resource allocation strategy matrix refers to a matrix including initial values of node priority coefficients and initial values of strategy fitness degrees, which is used to guide resource allocation.

[0188] The initial value of the node priority coefficient refers to the initial quantization parameter of the node priority, which reflects the importance of resource allocation.

[0189] The initial value of the strategy fitness degree refers to the initial matching degree between the resource allocation strategy and the actual demand, which reflects the effectiveness of the strategy.

[0190] The initial mapping relationship refers to the initial association relationship between node identifiers and resource allocation strategy matrices, which is used to guide resource allocation.

[0191] In the embodiments of the present application, an initial mapping relationship between node identifiers and resource allocation strategy matrices is determined based on the set of spectrum comparison metrics, where the resource allocation strategy matrix includes initial values of node priority coefficients and initial values of strategy fitness degrees. First, a data mapping algorithm (such as hash mapping or graph neural network) is used to associate the set of spectrum comparison metrics with the resource allocation strategy matrix to ensure data consistency and integrity. Subsequently, an initialization algorithm (such as random initialization or initialization based on historical data) is used to assign initial values to the node priority coefficient and the strategy fitness degree. Finally, through normalization processing, the initial values are ensured to be in the same dimension, and an initial mapping relationship is generated to provide a data basis for subsequent dynamic adjustment.

[0192] 802. Establish a dynamic feedback loop. In the dynamic feedback loop, according to the initial mapping relationship, dynamically adjust the priority weights and strategy fitness degree parameters in the strategy matrix to form a corrected value of the node priority coefficient; In step 802, the dynamic feedback loop refers to a closed-loop control system that monitors the resource allocation effect in real time and dynamically adjusts the strategy.

[0193] The priority weight refers to the priority weight of each node determined according to the spectrum comparison index set, which is used to optimize resource allocation.

[0194] The policy adaptation degree parameter refers to the quantization parameter of the matching degree between the resource allocation policy and the actual demand, which is used to optimize the policy.

[0195] The node priority coefficient correction value refers to the node priority coefficient after dynamic feedback adjustment, which reflects the optimization result of resource allocation.

[0196] In the embodiment of the present application, by establishing a dynamic feedback loop, the priority weight and the policy adaptation degree parameter in the policy matrix are dynamically adjusted according to 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 the gradient descent method or the genetic algorithm) is used to adjust the priority weight and the policy adaptation degree parameter to generate a node priority coefficient correction value. Finally, convergence analysis is performed to ensure the stability of the correction value and provide input for subsequent policy adaptation degree update.

[0197] 803. Input the node priority coefficient correction value into the policy adaptation degree calculation module, and generate a policy adaptation degree update value in combination with the initial value of the policy adaptation degree in the resource allocation policy matrix; In step 803, the policy adaptation degree calculation module refers to the module used to calculate the matching degree between the resource allocation policy and the actual demand.

[0198] The policy adaptation degree update value refers to the update parameter generated based on the node priority coefficient correction value and the initial value of the policy adaptation degree, which reflects the optimization effect of the policy.

[0199] In the embodiment of the present application, by inputting the node priority coefficient correction value into the policy adaptation degree calculation module, a policy adaptation degree update value is generated in combination with the initial value of the policy adaptation degree in the resource allocation policy matrix. First, a weighted fusion algorithm (such as weighted average or fuzzy analytic hierarchy process) is used to combine the correction value and the initial value to generate an update value framework. Subsequently, normalization processing is performed to ensure that the update value is in the same dimension to form a policy adaptation degree update value. Finally, Monte Carlo simulation is used to verify the rationality of the update value to ensure that it meets the resource allocation requirements.

[0200] 804. Reconstruct the resource allocation policy matrix according to the node priority coefficient correction value and the policy adaptation degree update value, and generate an optimized policy mapping table including multi-dimensional weight constraints.

[0201] In step 804, the multi-dimensional weight constraint refers to the constraint condition that comprehensively considers multi-dimensional factors such as priority weight and policy adaptation degree, which is used to optimize resource allocation.

[0202] The optimized policy mapping table refers to a resource allocation policy mapping table that includes multi-dimensional weight constraints and is used to guide resource scheduling.

[0203] In the embodiments of this application, an optimized policy mapping table with multi-dimensional weight constraints is generated by reconstructing the resource allocation policy matrix based on the correction value of the node priority coefficient and the update value of the policy fitness. First, a matrix reconstruction algorithm (such as singular value decomposition or principal component analysis) is used to integrate the correction value and the update value 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 to generate the optimized policy mapping table. Finally, the effectiveness of the mapping table is verified through a closed-loop feedback mechanism to ensure that it meets the resource scheduling requirements.

[0204] The following is a specific example: In the scenario of dynamic allocation of communication resources in the smart city vehicle-to-everything (V2X) network, a city designs a set of dynamic resource allocation policies based on the 5.9 GHz frequency band. The system first constructs an initial resource allocation matrix through indicators such as channel interference degree and signal strength, and sets priorities according to vehicle types, congested areas, and task urgency. Subsequently, reinforcement learning is used to dynamically adjust the weights. For example, the priority of emergency vehicles is automatically increased in accident areas, and the fitness of non-emergency vehicles is reduced. Considering environmental factors such as weather, the system gives priority to ensuring V2I communication. For example, the fitness of ordinary vehicles is reduced on rainy days. Finally, an optimized mapping table including security, efficiency, and fairness is generated and sent to the roadside unit (RSU) through edge computing to achieve spectrum slicing allocation (dedicated frequency band for emergency vehicles, high bandwidth for buses during peak hours, and ordinary vehicles sharing the remaining resources). The pilot in Pingshan, Shenzhen shows that the key communication delay is reduced by 35% and the spectrum utilization rate is increased by 22%.

[0205] In summary, steps 801 to 804 achieve precise adjustment and optimization of the resource allocation policy 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 policy 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 policy fitness. Secondly, a dynamic feedback loop is established to dynamically adjust the priority weights and policy fitness parameters in the policy matrix to form the correction value of the node priority coefficient. Subsequently, the correction value is input into the policy fitness calculation module to generate the update value of the policy fitness. Finally, the resource allocation policy matrix is reconstructed according to the correction value and the update value to generate an optimized policy mapping table with multi-dimensional weight constraints. This design significantly improves the adaptability and optimization effect of the resource allocation policy through the collaborative processing of dynamic feedback and multi-dimensional optimization, providing reliable technical support for resource scheduling in complex environments.

[0206] In some embodiments, for the establishment of the dynamic feedback loop in step 802, in the dynamic feedback loop, the priority weights and policy adaptation parameter in the policy matrix are dynamically adjusted according to the initial mapping relationship to form a corrected value of the node priority coefficient, including: 901. When establishing the dynamic feedback loop, generate a set of dynamic feedback parameters by aggregating the node execution status data and the input-output deviation of the policy matrix under the initial mapping relationship; In step 901, the node execution status data refers to the real-time operation metrics of the monitoring nodes (such as spectrum occupancy rate, packet loss rate, transmission delay) and historical execution logs (such as fault frequency, resource consumption trend), which are used to reflect the actual operation status of the nodes.

[0207] The input-output deviation of the policy matrix refers to the quantitative difference between the expected output of the policy matrix (such as channel allocation scheme) and the actual execution result (such as spectrum utilization rate, interference level), which is calculated by the mean square error or absolute error algorithm.

[0208] The set of dynamic feedback parameters refers to the parameter set generated by aggregating the node execution status data and the policy matrix deviation, which is used to guide the dynamic adjustment of the policy matrix, and includes statistical features (such as mean, variance) within the time window and real-time anomaly markers.

[0209] In the embodiments of the present application, a set of dynamic feedback parameters is generated by aggregating the node execution status data and the input-output deviation of the policy matrix under the initial mapping relationship. First, data aggregation technologies (such as MapReduce or SparkStreaming) are used to collect the node execution status data in real time to ensure the integrity and real-time nature of the data. Subsequently, the input-output deviation of the policy matrix is quantified by 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 set of dynamic feedback parameters, providing a data basis for subsequent priority weight adjustment.

[0210] 902. Perform incremental calculation on the priority weights of the policy matrix based on the set of dynamic feedback parameters, and iteratively update the priority weight distribution by superimposing the historical weight deviation compensation value and the real-time feedback weight offset; In step 902, the historical weight deviation compensation value refers to the weight compensation parameter calculated based on past policy adjustment records (such as the history of resource allocation deviation correction), which is used to offset the inherent deviation of the system (such as response delay caused by equipment aging).

[0211] The real-time feedback weight offset refers to the dynamic correction value calculated by the real-time difference between the current node state (such as a sudden surge in traffic) and the output of the policy matrix. For example, a short-term weight offset is generated using a reinforcement learning algorithm (such as Q-Learning).

[0212] The priority weight distribution refers to the quantitative result representing the priority of each node in resource allocation, which is iteratively updated by superimposing the historical compensation value and the real-time offset to ensure that the weight changes dynamically with the environment.

[0213] In the embodiments of the present application, the priority weight distribution is iteratively updated by superimposing the historical weight deviation compensation value and the real-time feedback weight offset. First, an incremental calculation algorithm (such as the gradient descent method or the Newton method) is used to perform incremental calculation on the priority weight to ensure the accuracy of weight adjustment. Subsequently, the priority weight distribution is iteratively updated by superimposing the historical weight deviation compensation value and the real-time feedback weight offset. Finally, the stability of the weight distribution is ensured through convergence analysis, providing input for the subsequent reconstruction of the policy fitness parameter space.

[0214] 903. Reconstruct the policy fitness parameter space according to the priority weight distribution, eliminate the coupling interference between parameters through multi-dimensional orthogonal projection operation, and generate a policy fitness parameter correction amount; In step 903, the coupling interference between parameters refers to the non-linear interference caused by the mutual dependence of different policy parameters (such as spectrum allocation weight and power control factor). For example, power increase may exacerbate spectrum occupancy conflicts.

[0215] The multi-dimensional orthogonal projection operation refers to mapping high-dimensional parameters to an orthogonal basis space through principal component analysis (PCA) or the Gram-Schmidt orthogonalization algorithm to eliminate redundant correlations. For example, separating the coupling effect between spectrum allocation parameters and power control parameters.

[0216] The policy fitness parameter correction amount refers to the corrected parameter set, reflecting the optimization result of the matching degree between the policy matrix and the actual demand. For example, the fitness quantization value after reducing parameter conflicts through orthogonal projection.

[0217] In the embodiments of the present application, the policy fitness parameter space is reconstructed according to the priority weight distribution, the coupling interference between parameters is eliminated through multi-dimensional orthogonal projection operation, and a policy fitness parameter correction amount is generated. First, a parameter space reconstruction algorithm (such as principal component analysis or singular value decomposition) is used to reconstruct the policy fitness parameter space to ensure the rationality of parameter distribution. Subsequently, the coupling interference between parameters is eliminated through multi-dimensional orthogonal projection operation to generate a policy fitness parameter correction amount. Finally, the rationality of the correction amount is verified through Monte Carlo simulation to ensure that it meets the resource allocation requirements.

[0218] 904. Perform a tensor fusion operation on the policy fitness 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; In step 904, the tensor fusion operation refers to performing a high-order tensor operation (such as tensor concatenation or modal product) on the policy adaptation degree correction amount and the dynamic feedback parameter set, retaining multi-dimensional correlation features, such as the parameter correlation of the fused time, space, and frequency band dimensions.

[0219] The topological constraint conditions refer to the network topology rules based on the initial mapping relationship (such as node communication distance limitation, spectrum coverage), ensuring that the parameter alignment conforms to the physical layer constraints, such as prohibiting the direct correlation of spectrum parameters of non-adjacent nodes.

[0220] The policy matrix dynamic adjustment factor refers to the fused and aligned parameter set, which 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.

[0221] In the embodiments of this application, by performing a tensor fusion operation on the policy adaptation degree parameter correction amount and the dynamic feedback parameter set, and completing the parameter synchronization alignment based on the topological constraint conditions of the initial mapping relationship, a policy matrix dynamic adjustment factor is output. First, the tensor fusion algorithm (such as tensor product or tensor decomposition) is used to fuse the policy adaptation degree parameter correction amount and the dynamic feedback parameter set to ensure data consistency. Subsequently, the parameter synchronization alignment is completed based on the topological constraint conditions of the initial mapping relationship, and a 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.

[0222] 905. Inject the policy matrix dynamic adjustment factor into the node priority coefficient calculation channel, and form a node priority coefficient correction value through non-linear normalization processing and a feedback loop self-calibration mechanism.

[0223] In step 905, the non-linear normalization processing refers to mapping the adjustment factor to the [0, 1] interval through the Sigmoid function or Min-Max scaling to avoid the interference of extreme values, such as limiting the power compensation gradient within the range that the device can withstand.

[0224] The feedback loop self-calibration mechanism refers to dynamically correcting the normalization parameters based on historical error feedback, such as using a PID controller to adjust the weight coefficient to ensure system stability.

[0225] The node priority coefficient correction value refers to the finally output priority quantization result, which reflects the resource allocation weight after dynamic adjustment, such as the priority coefficient of an emergency communication node is increased from 0.6 to 0.9.

[0226] In the embodiments of the present application, by injecting the policy matrix dynamic adjustment factor into the node priority coefficient calculation channel, a node priority coefficient correction value is formed through non-linear normalization processing and a feedback loop self-calibration mechanism. First, a non-linear normalization processing algorithm (such as the Sigmoid function or the 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, data verification is performed to ensure the rationality of the correction value, providing a reliable basis for resource scheduling.

[0227] The following is a specific example: In the scenario of real-time scheduling optimization of an intelligent microgrid, this dynamic feedback mechanism can construct an adaptive energy allocation model. Taking the integrated photovoltaic energy storage and charging system in an industrial park as an example, the system collects node execution status data such as the output power fluctuation of photovoltaic modules, the SOC value of energy storage, and the sudden change of charging pile load in real time through an edge Internet of Things agent, and constructs an initial policy matrix in combination with the day-ahead prediction deviation. When the photovoltaic output drops suddenly by 30% at noon, the incremental calculation module superimposes the historical energy storage over-discharge compensation coefficient (0.15) and the real-time load urgency offset (+0.3), dynamically raising the priority weight of the energy storage system to 0.82. At the same time, the parameter coupling interference of temperature on the inverter efficiency is eliminated through orthogonal projection. After the corrected parameter tensor is aligned by topological constraints, an adjustment factor including a time window translation factor (Δt = 120s) and a power correction gradient (ΔP = 15kW / step) is generated, and finally a priority sequence of charging pile power limit (load reduction of 40%) and non-critical load shedding is formed in the feeder-level scheduling, realizing the restoration of power supply stability within 5 minutes. This embodiment verifies the multi-dimensional collaborative optimization ability of the dynamic feedback mechanism in coping with renewable energy fluctuations.

[0228] To sum up, steps 901 to 905 achieve the precise adjustment and optimization of the resource allocation policy matrix through the collaborative processing of dynamic feedback and multi-dimensional parameter optimization, significantly improving the rationality of resource allocation and system stability. First, aggregate the node execution status data and the policy matrix deviation amount to generate a dynamic feedback parameter set; secondly, perform incremental calculation on the priority weight based on the feedback parameters and iteratively update the weight distribution; then, reconstruct the policy fitness parameter space, eliminate the coupling interference between parameters, and generate a correction amount; next, perform tensor fusion on the correction amount and the feedback parameters to complete parameter synchronization alignment and output a dynamic adjustment factor; finally, inject the adjustment factor into the node priority coefficient calculation channel, and form a correction value through normalization processing and a self-calibration mechanism. This design significantly improves the adaptability and optimization effect of the resource allocation policy through the collaborative processing of dynamic feedback and multi-dimensional optimization, providing reliable technical support for resource scheduling in complex environments.

[0229] Figure 2The present application provides a structural schematic diagram of a resource dynamic monitoring and control system based on quota tracking, as shown in Figure 2 The system includes: An analysis module 21, configured to obtain stress field propagation data generated by an optical fiber sensor array in an underground pipe gallery structure, where the stress field propagation data includes the energy attenuation characteristics of stress waves at the intersection of reinforcing ribs, so as to establish an association mapping table between structural deformation risks and monitoring node resource requirements according to the energy attenuation characteristics; A detection module 22, configured to capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array arranged at the expansion joint of the underground pipe gallery, where the frequency domain propagation characteristics include the phase offset parameter of the reflected wave at the pipe joint; A generation module 23, configured to dynamically adjust the data transmission cycle quota of monitoring nodes in the association mapping table based on the phase offset parameter, and generate a resource allocation strategy matrix including predicted channel occupancy rates; A tracking module 24, configured to establish a quota tracking database for Internet of Things resources, where the quota tracking database records the historical spectrum quota usage data of monitoring nodes, and generate a resource usage efficiency report based on the quota tracking database, including a comparative analysis of the actual spectrum quota consumption rate and the predicted channel occupancy rate in the resource allocation strategy matrix; An optimization module 25, configured to perform spectrum quota reallocation on monitoring nodes in high stress concentration areas according to the resource usage efficiency report, where the reallocation includes optimizing the frequency hopping interval and adjusting the transmission power based on the historical spectrum quota usage data.

[0230] Figure 2 The resource dynamic monitoring and control system based on quota tracking can execute Figure 1 The method of the resource dynamic monitoring and control system based on quota tracking described in the embodiments shown. The implementation principle and technical effects will not be elaborated here. For the resource dynamic monitoring and control system based on quota tracking in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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: Acquire 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 ribs, so as to establish a correlation mapping table between structural deformation risk and monitoring node resource requirements according to 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, wherein the frequency domain propagation characteristics include a phase offset parameter of the reflected wave at the pipe joint connection; 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; 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; 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 transmission power adjustment based on historical spectrum quota usage data.

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: Conduct cross-band coherent detection on adjacent monitoring areas to obtain time-varying interference distribution maps of authorized and unauthorized frequency bands; An adaptive correction is performed on the transmission power quota in the resource allocation strategy matrix according to the time-varying interference distribution spectrum, wherein the correction includes a spectrum switching interval and a power compensation coefficient adjusted based on the stress wave propagation path length.

3. The method according to claim 1, characterized in that 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; According to the preprocessed data set, the phase offset change rate of each monitoring node is calculated, and the priority weight coefficient of each node is generated by 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 by 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.

4. 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 transmission power adjustment based on historical spectrum quota usage data, including: Parsing the analysis results of the actual consumption rate of spectrum quota and the predicted value of channel occupancy rate in the resource utilization efficiency report, extracting spectrum efficiency deviation parameters of high stress concentration areas, correlating historical frequency hopping data, and generating spectrum efficiency deviation correlation parameters; Based on the spectrum efficiency deviation correlation parameter, the channel occupancy fluctuation characteristics of each frequency band in the historical use period are calculated, and the frequency hopping interval decision parameters are generated in combination with the signal propagation attenuation characteristics of the high stress concentration area; According to the frequency band switching priority in the frequency hopping interval decision parameter, the historical signal strength attenuation data of the high stress concentration area is matched, the transmission power compensation gradient is calculated and the reference value is superimposed to generate a dynamic power compensation parameter; The frequency hopping interval decision parameter and the dynamic power compensation parameter are integrated, spectrum quota is reallocated according to priority weight, and a spectrum quota reallocation strategy table is generated 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.

5. The method according to claim 4, characterized in that 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: Perform multi-dimensional parameter superposition operation on the frequency band switching priority and dwell time ratio coefficient in the frequency hopping interval decision parameter and the transmit power compensation gradient in the dynamic power compensation parameter to generate a spectrum quota redistribution initial strategy parameter set; According to the spectrum quota redistribution initial strategy parameter set, the spectrum quota allocation conflicts of different frequency bands in the high stress concentration area are detected and corrected, and the corrected spectrum quota redistribution intermediate strategy parameters are output; 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; Perform a cross-layer parameter comparison on the spectrum quota reallocation boundary parameter and the channel occupancy rate prediction value in the resource allocation strategy matrix, and generate the spectrum quota reallocation verification parameter after verification; 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 residence time ratio coefficient.

6. The method according to claim 5, characterized in that Load the priority weights as constraints to the The intermediate strategy parameters for spectrum quota reallocation are described, the maximum allocatable spectrum quota threshold for each frequency band under the priority weight constraint is calculated, and the spectrum quota reallocation boundary parameters are generated, 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 parameter for compliance, removing 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.

7. The method according to claim 1, characterized in that Establish a quota tracking database for IoT resources, and generate 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, 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 aggregates 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; The node identifiers in the dynamic aggregation data set are associated across time windows to extract the time series 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 degree and convergence index between the actual spectrum quota consumption rate and the prediction value, and generate a spectrum comparison index set; According to the spectrum comparison index set, a mapping relationship between node identification and resource allocation strategy matrix is ​​constructed, and an optimized strategy mapping table including node priority coefficients and strategy adaptability is generated by dynamically adjusting the priority weights in the strategy matrix; Based on the priority coefficient and strategy 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 evaluate and optimize recommendations is generated.

8. The method according to claim 7, characterized in that According to 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, including: 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; 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; Input the node priority coefficient correction value into the strategy fitness calculation module, and generate 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 adaptability update value, and an optimization strategy mapping table containing multi-dimensional weight constraints is generated.

9. The method according to claim 8, characterized in that A dynamic feedback loop is established, in which the 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; 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; Performing tensor fusion operation on the strategy adaptation parameter correction amount and the dynamic feedback parameter set, completing parameter synchronization alignment based on the topological constraint conditions of the initial mapping relationship, and outputting the strategy matrix dynamic adjustment factor; 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.

10. 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 an underground pipe gallery structure, wherein the stress field propagation data includes energy attenuation characteristics of stress waves at intersections of reinforcement ribs, so as to establish a correlation mapping table between structural deformation risks and monitoring node resource requirements according to the energy attenuation characteristics; A detection module, used to capture the frequency domain propagation characteristics of abnormal vibration signals through an acoustic wave sensor array arranged at the expansion joint 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; 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 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.

Images