Communication equipment resource dynamic allocation method and system based on cloud computing

Abstract

The invention belongs to the technical field of cloud computing and communication resource management, and discloses a communication equipment resource dynamic allocation method and system based on cloud computing, and the method comprises the steps: obtaining biological migration tide data, communication network load historical data and environmental parameter data, constructing a migration trajectory prediction model and a resource demand prediction model, and carrying out the prediction of a migration trajectory; generating an elastic resource allocation scheme; base station group division is carried out on a communication network coverage area, resource demand space-time analysis is carried out based on a migration trajectory prediction result, and a resource utilization rate index is calculated; generating a resource scheduling instruction set based on the resource elastic allocation scheme, performing priority ranking, optimizing resource allocation in combination with a resource utilization rate index, and generating a base station parameter configuration instruction; adjusting the resource configuration of the base station according to the configuration instruction, monitoring the operation state, evaluating the resource efficiency, performing feedback tuning and applying to the resource elastic allocation scheme; and the equipment operation and maintenance complexity and the manual intervention requirement are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing and communication resource management. More specifically, the present invention relates to a method and system for dynamically allocating communication device resources based on cloud computing. Background Art

[0002] With the continuous expansion of the global communication network coverage area, a problem that has been long ignored by the industry has gradually emerged: the waste of communication resources and the phenomenon of sudden congestion on the migration routes of seasonal migratory birds; research has found that on the migration routes of seasonal migratory birds, there will be a "communication resource tidal effect" in spring and autumn every year, that is, a large number of miniature tracking devices worn by birds simultaneously access the network, resulting in a 300% explosion in the resource demand of some remote base stations within 48 hours, while there is resource idleness in the adjacent areas during the same period; traditional resource allocation methods focus on the laws of human activities and completely ignore this special network load pattern caused by biological migration. The existing technologies cannot effectively predict this resource demand fluctuation with ultra-short cycle and high spatial heterogeneity, and also lack a targeted resource dynamic allocation mechanism, resulting in a large amount of bandwidth and computing resources being unable to meet the scientific research data transmission requirements at critical moments, and at the same time causing resource waste in adjacent areas.

[0003] In view of this, the present invention proposes a method and system for dynamically allocating communication device resources based on cloud computing to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A method for dynamically allocating communication device resources based on cloud computing, comprising:

[0005] Step S1: Obtain biological migration tidal data, historical communication network load data, and environmental parameter data; construct a migration trajectory prediction model according to the biological migration tidal data and environmental parameter data to obtain a migration trajectory prediction result; construct a resource demand prediction model according to the historical communication network load data and the migration trajectory prediction result to obtain resource load prediction data; generate a resource allocation strategy based on the resource load prediction data to obtain a resource flexible allocation plan;

[0006] Step S2: Divide the base station groups in the communication network coverage area to obtain base station group data; perform a spatio-temporal analysis of resource demand according to the base station group data and the migration trajectory prediction result to obtain a resource demand distribution map; calculate the resource utilization rate of the resource demand distribution map to obtain a resource utilization rate index;

[0007] Step S3: Generate scheduling instructions for the cloud computing resource center based on the resource elastic allocation plan to obtain a resource scheduling instruction set; perform priority sorting on the resource scheduling instruction set to obtain a hierarchical scheduling sequence; optimize resource allocation according to the hierarchical scheduling sequence and resource utilization metrics to obtain an optimal resource allocation strategy; generate base station configuration parameters based on the optimal resource allocation strategy to obtain base station parameter configuration instructions;

[0008] Step S4: Adjust the resource configuration of each regional base station according to the base station parameter configuration instructions to obtain real-time base station resource configuration data; monitor the operating status of the real-time base station resource configuration data to obtain resource operating status data; evaluate the resource utilization efficiency according to the resource operating status data to obtain a resource effectiveness evaluation result; perform feedback optimization based on the resource effectiveness evaluation result to obtain optimization parameters; apply the optimization parameters to the resource elastic allocation plan to achieve closed-loop optimized allocation of communication device resources.

[0009] Further, step S1 includes:

[0010] Step S11: Collect historical data on the migration of migratory bird populations and conduct spatio-temporal characteristic analysis to obtain a migration activity feature library; collect meteorological environment data and seasonal change data to obtain environmental impact factor data;

[0011] Step S12: Obtain communication network area division data; construct a network topology structure according to the communication network area division data to obtain network topology data; collect historical load data by collecting communication load time series data through a network monitoring system;

[0012] Step S13: Construct a biological migration trajectory prediction model based on the migration activity feature library and environmental impact factor data to obtain migration behavior pattern data; extract temporal sequence features from the migration behavior pattern data to obtain migration temporal sequence features;

[0013] Step S14: Perform spatial trajectory prediction according to the migration behavior pattern data and migration temporal sequence features to obtain a migration path probability map; divide the migration path probability map into time windows to obtain time-sharing migration density distribution data;

[0014] Step S15: Map the time-sharing migration density distribution data to the network topology data to obtain tidal load prediction data; construct a resource demand prediction model according to the historical load data and tidal load prediction data to obtain resource fluctuation prediction data;

[0015] Step S16: Identify peaks and analyze mutations in the resource fluctuation prediction data to obtain a resource demand mutation index;

[0016] Step S17: Conduct resource pool distribution analysis based on network topology data to obtain a resource pool capability map; perform schedulable resource assessment on the resource pool capability map to obtain a resource schedulable matrix;

[0017] Step S18: Generate a resource elastic allocation plan according to the resource demand mutation index and the resource schedulable matrix.

[0018] Furthermore, constructing a biological migration trajectory prediction model based on the migration activity feature library and environmental impact factor data to obtain migration behavior pattern data includes:

[0019] Classify the species migration behavior patterns in the migration activity feature library to obtain species migration type data;

[0020] Extract environmental trigger factors according to the environmental impact factor data to obtain a migration trigger index;

[0021] Construct a deep learning migration prediction network based on the species migration type data to obtain a migration prediction basic model;

[0022] Use the migration trigger index to optimize the model parameters of the migration prediction basic model to obtain an adaptive migration prediction model;

[0023] Generate migration behavior pattern data based on the adaptive migration prediction model.

[0024] Furthermore, step S2 includes:

[0025] Step S21: Conduct base station geographical distribution analysis according to the communication network area division data to obtain base station geographical location data; perform spatial clustering analysis on the base station geographical location data to obtain base station spatial group data;

[0026] Step S22: Calculate the communication coverage area according to the base station spatial group data to obtain coverage area data;

[0027] Step S23: Conduct regional overlap degree analysis based on the coverage area data and the migration path probability map to obtain a migration influence coefficient;

[0028] Step S24: Conduct resource capacity assessment on the base station spatial group data to obtain group resource capacity data;

[0029] Step S25: Generate a resource demand distribution map according to the migration influence coefficient and the group resource capacity data;

[0030] Step S26: Conduct time series analysis on the resource demand distribution map to obtain resource fluctuation cycle data; calculate the resource utilization rate according to the resource fluctuation cycle data to obtain a resource utilization rate index.

[0031] Furthermore, step S3 includes:

[0032] Step S31: Quantify the computing resource requirements according to the resource elastic allocation plan to obtain computing resource allocation data; quantify the storage resource requirements for the resource elastic allocation plan to obtain storage resource allocation data; quantify the bandwidth resource requirements for the resource elastic allocation plan to obtain bandwidth resource allocation data;

[0033] Step S32: Generate a resource scheduling instruction set based on the computing resource allocation data, storage resource allocation data, and bandwidth resource allocation data;

[0034] Step S33: Evaluate the timeliness of the resource scheduling instruction set according to the time-sharing migration density distribution data to obtain instruction timeliness level data;

[0035] Step S34: Sort the resource scheduling instruction set according to the instruction timeliness level data to obtain a hierarchical scheduling sequence;

[0036] Step S35: Perform cloud resource scheduling simulation according to the hierarchical scheduling sequence and resource utilization rate indicators to obtain a resource allocation prediction result; detect resource competition conflicts for the resource allocation prediction result to obtain a conflict resolution strategy;

[0037] Step S36: Generate an optimal resource allocation strategy based on the resource allocation prediction result and the conflict resolution strategy; generate a base station parameter configuration instruction according to the optimal resource allocation strategy.

[0038] Further, step S32 includes:

[0039] Step S321: Convert the computing resource allocation data for virtual machine resource configuration to obtain virtual machine configuration instructions;

[0040] Step S322: Map the storage resource allocation data to storage units to obtain storage allocation instructions;

[0041] Step S323: Plan network channels for the bandwidth resource allocation data to obtain bandwidth allocation instructions;

[0042] Step S324: Standardize the instruction formats of the virtual machine configuration instructions, storage allocation instructions, and bandwidth allocation instructions to obtain standardized scheduling instructions;

[0043] Step S325: Integrate the standardized scheduling instructions to obtain a resource scheduling instruction set.

[0044] Further, step S34 includes:

[0045] Step S341: Construct an instruction priority evaluation model according to the instruction timeliness level data to obtain a priority evaluation function;

[0046] Step S342: Apply a priority evaluation function to the resource scheduling instruction set to obtain instruction priority values;

[0047] Step S343: Sort the resource scheduling instructions according to the instruction priority values to obtain priority sequence data;

[0048] Step S344: Analyze the resource dependency relationships of the priority sequence data to obtain a dependency constraint matrix; adjust the priority sequence data according to the dependency constraint matrix to obtain a hierarchical scheduling sequence.

[0049] Further, step S4 includes:

[0050] Step S41: Parse and convert the base station parameter configuration instructions to obtain a base station configuration parameter set; update the parameters of each base station according to the base station configuration parameter set to obtain base station parameter update data;

[0051] Step S42: Verify the configuration effectiveness of the base station parameter update data to obtain a configuration verification result;

[0052] Step S43: Monitor the base station resource performance according to the configuration verification result to obtain resource performance data; collect the resource usage status of the resource performance data to obtain resource operation status data;

[0053] Step S44: Calculate energy efficiency indicators according to the resource operation status data to obtain energy efficiency evaluation data; evaluate the quality of service of the resource operation status data to obtain quality of service indicators;

[0054] Step S45: Conduct a comprehensive resource efficiency evaluation based on the energy efficiency evaluation data and the quality of service indicators to obtain a resource efficiency evaluation result;

[0055] Step S46: Analyze the optimization space of the resource efficiency evaluation result to obtain optimization direction data; generate a parameter tuning strategy according to the optimization direction data to obtain tuning parameters; feedback the tuning parameters to the resource elastic allocation scheme to achieve closed-loop optimization of resource configuration.

[0056] Further, step S45 includes:

[0057] Step S451: Standardize the multi-dimensional indicators of the energy efficiency evaluation data to obtain standardized energy efficiency indicators;

[0058] Step S452: Map the quality of service indicators to user experience to obtain experience satisfaction data;

[0059] Step S453: Construct a resource efficiency evaluation model, and perform weighted fusion on the standardized energy efficiency indicators and the experience satisfaction data to obtain a comprehensive efficiency index;

[0060] Step S454: Generate a resource efficiency evaluation result based on the comprehensive efficiency index.

[0061] A communication device resource dynamic allocation system based on cloud computing, comprising:

[0062] Migration elasticity prediction module: Obtain biological migration tide data, communication network load historical data, and environmental parameter data; construct a migration trajectory prediction model based on the biological migration tide data and environmental parameter data to obtain a migration trajectory prediction result; construct a resource demand prediction model based on the communication network load historical data and the migration trajectory prediction result to obtain resource load prediction data; generate a resource allocation strategy based on the resource load prediction data to obtain a resource elastic allocation plan;

[0063] Spatio-temporal demand analysis module: Divide the base station groups in the communication network coverage area to obtain base station group data; perform spatio-temporal analysis of resource demand based on the base station group data and the migration trajectory prediction result to obtain a resource demand distribution map; calculate the resource utilization rate of the resource demand distribution map to obtain a resource utilization rate index;

[0064] Dynamic scheduling optimization module: Generate scheduling instructions for the cloud computing resource center based on the resource elastic allocation plan to obtain a resource scheduling instruction set; perform priority sorting on the resource scheduling instruction set to obtain a hierarchical scheduling sequence; perform resource allocation optimization based on the hierarchical scheduling sequence and the resource utilization rate index to obtain an optimal resource allocation strategy; generate base station configuration parameters based on the optimal resource allocation strategy to obtain base station parameter configuration instructions;

[0065] Feedback tuning module: Adjust the resource configuration of each regional base station according to the base station parameter configuration instructions to obtain real-time base station resource configuration data; monitor the operation status of the real-time base station resource configuration data to obtain resource operation status data; evaluate the resource utilization efficiency according to the resource operation status data to obtain a resource efficiency evaluation result; perform feedback tuning based on the resource efficiency evaluation result to obtain tuning parameters; apply the tuning parameters to the resource elastic allocation plan to achieve closed-loop optimized allocation of communication device resources.

[0066] The technical effects and advantages of a communication device resource dynamic allocation method and system based on cloud computing according to the present invention:

[0067] The present invention constructs a migration trajectory prediction model by obtaining biological migration tide data and environmental parameter data, which can accurately predict the spatio-temporal distribution characteristics of the communication network load, provide a scientific basis for resource allocation, and improve the forward-looking and accuracy of resource allocation; by dividing the base station groups in the communication network coverage area, it is possible to conduct differential analysis according to the resource demand characteristics of different regions, obtain a more refined resource demand distribution map, provide spatial accuracy for resource allocation, and improve resource utilization efficiency; by sorting the resource scheduling instruction sets by priority, it is possible to reasonably allocate limited resources in the case of resource competition, solve the resource scheduling conflict problem, ensure the communication service quality of key areas, and at the same time, the base station parameter configuration instructions generated based on the optimal resource allocation strategy can accurately guide the base stations in each region to adjust resource configuration and improve the accuracy and execution efficiency of the configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of a method for dynamically allocating communication device resources based on cloud computing according to the present invention;

[0069] Figure 2 It is a schematic diagram of a system for dynamically allocating communication device resources based on cloud computing according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Embodiment 1;

[0072] Please refer to Figure 1 As shown, a method for dynamically allocating communication device resources based on cloud computing in this embodiment includes:

[0073] Step S1: Obtain biological migration tide data, historical communication network load data, and environmental parameter data; construct a migration trajectory prediction model according to the biological migration tide data and environmental parameter data to obtain a migration trajectory prediction result; construct a resource demand prediction model according to the historical communication network load data and the migration trajectory prediction result to obtain resource load prediction data; generate a resource allocation strategy based on the resource load prediction data to obtain a resource flexible allocation plan;

[0074] Step S2: Divide the base station groups in the communication network coverage area to obtain base station group data; perform spatio-temporal analysis of resource requirements based on the base station group data and the migration trajectory prediction results to obtain a resource requirement distribution map; calculate the resource utilization rate for the resource requirement distribution map to obtain a resource utilization rate index;

[0075] Step S3: Generate scheduling instructions for the cloud computing resource center based on the resource elastic allocation plan to obtain a resource scheduling instruction set; perform priority sorting on the resource scheduling instruction set to obtain a hierarchical scheduling sequence; optimize resource allocation according to the hierarchical scheduling sequence and the resource utilization rate index to obtain an optimal resource allocation strategy; generate base station configuration parameters based on the optimal resource allocation strategy to obtain base station parameter configuration instructions;

[0076] Step S4: Adjust the resource configuration of each regional base station according to the base station parameter configuration instructions to obtain real-time base station resource configuration data; monitor the operating status of the real-time base station resource configuration data to obtain resource operating status data; evaluate the resource utilization efficiency based on the resource operating status data to obtain a resource effectiveness evaluation result; perform feedback optimization based on the resource effectiveness evaluation result to obtain optimization parameters; apply the optimization parameters to the resource elastic allocation plan to achieve closed-loop optimization allocation of communication device resources.

[0077] Preferably, step S1 includes the following steps:

[0078] Step S11: Collect historical data on the migration of migratory bird populations and perform spatio-temporal characteristic analysis to obtain a migration activity feature library; collect meteorological environment data and seasonal change data to obtain environmental impact factor data;

[0079] Step S12: Obtain communication network area division data; construct a network topology structure based on the communication network area division data to obtain network topology data; collect historical load data by collecting communication load time series data through a network monitoring system;

[0080] Step S13: Construct a biological migration trajectory prediction model based on the migration activity feature library and the environmental impact factor data to obtain migration behavior pattern data; extract temporal sequence features from the migration behavior pattern data to obtain migration temporal sequence features;

[0081] Step S14: Perform spatial trajectory prediction based on the migration behavior pattern data and the migration temporal sequence features to obtain a migration path probability map; perform time window division on the migration path probability map to obtain time-sharing migration density distribution data;

[0082] Step S15: Map the time-sharing migration density distribution data to the network topology data to obtain tidal load prediction data; construct a resource requirement prediction model based on the historical load data and the tidal load prediction data to obtain resource fluctuation prediction data;

[0083] Step S16: Perform peak identification and mutation analysis on the resource fluctuation prediction data to obtain the resource demand mutation index;

[0084] Step S17: Conduct resource pool distribution analysis based on the network topology data to obtain the resource pool capacity map; perform schedulable resource evaluation on the resource pool capacity map to obtain the resource schedulable matrix;

[0085] Step S18: Generate a resource elastic allocation plan according to the resource demand mutation index and the resource schedulable matrix.

[0086] Specifically, collect the migration history data of migratory bird populations and conduct spatio-temporal characteristic analysis to obtain a migration activity feature library. The collection methods include satellite tracking data, banding data, observation site records, and radio telemetry data, covering the main migration seasons in the past 5 years. Conduct spatio-temporal clustering analysis on the collected migration data, and use the density clustering algorithm DBSCAN to identify migration routes and key stopover points. The clustering process can be expressed as: Cen = DBSCAN(Point, ε, MinPts), where Point represents the set of migration trajectory points, ε represents the clustering radius parameter (usually set to 5 to 10 kilometers, indicating the maximum distance for determining the same cluster), MinPts represents the density threshold (usually set to 5 to 10 points, indicating the minimum number of points required to form a cluster), and Cen is the set of clustering results. Through clustering analysis, identify the high-frequency channels, key stopover areas, and migration density change patterns in the migration routes, and construct a migration activity feature library. At the same time, collect meteorological environment data and seasonal change data, including seasonal indicators such as temperature curves, precipitation, wind direction and speed, air pressure changes, and vegetation cover indices, to form an environmental impact factor dataset. Obtain communication network area division data, including network deployment information such as base station location coordinates, coverage range, frequency configuration, and antenna direction. Construct a network topology structure according to the communication network area division data, which is represented by a weighted directed graph G(V, E, W), where V represents the set of network nodes (including base stations, controllers, gateways, and core network devices, etc.), E represents the connection relationship between nodes (including physical links and logical links), and W represents the connection weight matrix (including indicators such as bandwidth capacity, link delay, and transmission distance). The network topology structure model comprehensively describes the physical and logical connection relationships of the communication network, providing a spatial reference for subsequent resource allocation. Collect communication load time series data through a network monitoring system, including indicators such as the number of user connections, data traffic, and resource utilization rate of each network node at different time points. Conduct time series analysis on the historical load data to identify daily load fluctuation patterns, periodic change rules, and abnormal load events, providing basic data for subsequent load prediction. Build a biological migration trajectory prediction model based on the migration activity feature library and environmental impact factor data. Adopt a deep learning method that combines long short-term memory network (LSTM) with an attention mechanism to capture the temporal dependence relationship of migration behavior and the influence weight of environmental factors. The model input includes historical migration data X 1 and environmental factor data X 2 , and the output is the predicted migration behavior Yact. The prediction process can be expressed as: Yact = LSTM(X 1 ,X 2; θ), where θ represents the set of model parameters, including network weights and bias terms. The LSTM network is particularly suitable for processing time series data with long-term dependencies and can effectively capture the seasonal patterns and environmental response characteristics in the migratory behavior of migratory birds. Through this model, data on the migratory behavior patterns of different populations are generated, including detailed information such as the predicted migration time window, path selection probability, flight speed and direction, and duration of stay. Temporal feature extraction is performed on the migratory behavior pattern data, and the wavelet transform method is used to analyze the periodicity, suddenness, and change trend characteristics of migratory activities. Through multi-scale wavelet analysis, the change characteristics of migratory activities at different time scales are extracted, including daily, weekly, and seasonal changes, to obtain the migratory temporal features. Spatial trajectory prediction is carried out based on the migratory behavior pattern data and migratory temporal features. The particle filter algorithm is used to simulate the population migration trajectory, and this algorithm can handle the non-linear dynamic characteristics and random uncertainties in the migration process. The particle filter process can be expressed as: pS(x t |z:t)≈∑w i ×δ(x t -xi t ), where x t is the state vector at time t (including position, speed, direction, etc.), z:t is the observation sequence at time t, w i is the weight of the i-th particle (representing the probability that this particle represents the true state), xi t$S_i(t)$ is the state of the $i$-th particle at time $t$, and $\delta$ is the Dirac function (used for discrete probability representation). Through the particle filtering algorithm, a large number of possible migration paths are sampled and evaluated to generate a migration path probability map, which represents the probability distribution of different regions being passed by the migratory bird population at different time points. The migration path probability map is divided into time windows, and the prediction time range (usually the migration season) is divided into multiple time windows, each window is usually 1 to 3 days. Calculate the migration density distribution $MD(r, Tim)$ in each time window, where $r$ represents the spatial region (usually divided into grid cells), and $Tim$ represents the time window. The migration density distribution reflects the expected number of migratory birds per unit area per unit time and is a key indicator for quantifying the intensity of migration activities. Through time window division, time-sharing migration density distribution data is obtained, which accurately describes the spatio-temporal evolution process and dynamic change characteristics of migration activities. Map the time-sharing migration density distribution data to the network topology data to establish a mapping relationship between migration activities and communication load. The mapping function can be expressed as: $L′(v,t)=L(v,t)+α×D(r(v),t)$, where $L′(v,t)$ represents the predicted load of node $v$ at time $t$, $L(v,t)$ represents the baseline load (expected load under normal conditions), $r(v)$ represents the region covered by node $v$, $D(r(v),t)$ represents the migration density of this region at time $t$, and $α$ is the mapping coefficient (the impact factor of migration activities on communication load calibrated according to historical data). Through this mapping relationship, the additional communication requirements caused by migratory bird migration are quantified to generate tidal load prediction data. A resource demand prediction model is constructed based on historical load data and tidal load prediction data, and the random forest regression method is used to predict the resource demand of network nodes. The model can be expressed as: $R(v,t)=RF(L′(v,t),F(v))$, where $R(v,t)$ represents the predicted value of the resource demand of node $v$ at time $t$ (including computing resources, storage resources, and network resources), $RF$ represents the random forest model, $L′(v,t)$ represents the predicted load, and $F(v)$ represents the feature vector of node $v$ (including processing power, hardware configuration, etc.). The random forest model consists of multiple decision trees, each tree is trained using a random subset of features, and the final prediction result is obtained by taking the average of the outputs of all trees, which has good generalization ability and robustness. Through this model, resource fluctuation prediction data is generated to describe the change trend and fluctuation law of the resource demand of each network node in the future time period. Peak identification and mutation analysis are performed on the resource fluctuation prediction data, and the Z-score-based anomaly detection method is used to identify the mutation points of resource demand. Cluster analysis is performed on the identified mutation points to calculate the resource demand mutation index:

[0087] I(v, t) = Z(v, t) × D(r(v), t) × w(v), where w(v) represents the importance weight of node v (determined according to the position and function of the node in the network). The resource demand mutation index comprehensively considers three factors: the amplitude of resource demand change, the impact of migration density, and the importance of nodes, providing a quantitative indicator for resource allocation priority. The higher the mutation index, the more urgently the node needs to be allocated resources at this time point to prevent the decline of service quality. Based on the network topology data, the resource pool distribution is analyzed, and the network resources are abstracted as distributed resource pools. Each resource pool contains three types of resources: computing resources, storage resources, and network resources. The resource pool capacity can be represented as a matrix Cap, where Cap[j, k] represents the capacity of the k-th type of resource in resource pool j (k = 1 represents computing resources, k = 2 represents storage resources, and k = 3 represents network resources). By analyzing the connection relationship and scheduling delay between resource pools, a resource pool capacity map is generated to describe the resource distribution status and resource scheduling ability of the entire network. The scheduling delay matrix between resource pools is Delay, where Delay[j, m] represents the time delay required to schedule resources from resource pool j to resource pool m. The schedulable resource evaluation is performed on the resource pool capacity map, and the available resource amount of each resource pool at different time points is calculated as Aus(j, t, k) = Cap[j, k] - U(j, t, k), where U(j, t, k) represents the usage amount of the k-th type of resource in resource pool j at time t. According to the resource availability and scheduling constraints (based on the scheduling delay matrix, the scheduling waiting time is greater than the time delay), a resource schedulable matrix Max is generated, where Max[j, m, t] represents the maximum schedulable resource amount from resource pool j to resource pool m at time t, and the calculation formula is: Max[j, m, t] = min(Aus(j, t, k), Bac(m, t)), where Bac(m, t) represents the maximum resource reception capacity of resource pool m at time t. The resource schedulable matrix comprehensively describes the feasibility and constraint conditions of resource scheduling in the network, providing a basis for generating a resource allocation plan. A resource elastic allocation plan is generated according to the resource demand mutation index and the resource schedulable matrix. A multi-objective optimization algorithm is adopted, and the objective function includes minimizing the resource allocation cost, minimizing the risk of service quality decline, and maximizing the resource utilization rate. The optimization problem can be expressed as: minf() = w 1 × CB(Sch) + w 2 × Qua(Sch) - w 3 × Use(Sch), where Sch represents the resource allocation plan (including allocation time, source node, target node, and resource amount), CB(Sch) represents the allocation cost function (proportional to the scheduling distance and resource amount), Qua(Sch) represents the service quality risk function (proportional to the degree of unmet demand), Use(Sch) represents the resource utilization rate function (proportional to the overall resource utilization efficiency), w 1, w 2 and w 3 are weight coefficients (determined according to service priorities). Limited by the resource schedulable matrix and network constraints, the multi-objective optimization problem is solved through mixed integer programming or genetic algorithm to generate a resource flexible allocation plan, including an allocation schedule (specifying when to trigger resource allocation), a resource migration path (specifying which resource pool to allocate from to which resource pool), and an allocation strategy (specifying the specific allocation ratios of various types of resources), so as to realize the dynamic optimization configuration of communication resources and effectively cope with the tidal changes in communication load caused by the migration of migratory birds.

[0088] Preferably, step S2 includes the following steps:

[0089] Step S21: Analyze the geographical distribution of base stations based on the communication network area division data to obtain base station geographical location data; perform spatial clustering analysis on the base station geographical location data to obtain base station spatial group data;

[0090] Step S22: Calculate the communication coverage area according to the base station spatial group data to obtain coverage area data;

[0091] Step S23: Perform regional overlap analysis based on the coverage area data and the migration path probability map to obtain a migration influence coefficient;

[0092] Step S24: Evaluate the resource capabilities of the base station spatial group data to obtain group resource capability data;

[0093] Step S25: Generate a resource demand distribution map according to the migration influence coefficient and the group resource capability data;

[0094] Step S26: Perform time series analysis on the resource demand distribution map to obtain resource fluctuation period data; calculate the resource utilization rate according to the resource fluctuation period data to obtain a resource utilization rate index.

[0095] Specifically, for the analysis of the geographical distribution of base stations based on the data of communication network area division, first, collect the geographical coordinate data of all base stations in the network area, including longitude, latitude, and altitude. For each base station, its geographical location is represented by a vector, and the geographical location data of all base stations in the entire communication network is organized into a set. In addition, collect technical parameters such as the coverage radius, transmission power, and antenna direction of each base station to form a set of base station technical parameters. Based on the geographical location data and technical parameters of the base stations, analyze the spatial relationships between the base stations, including distance, signal coverage overlap degree, etc., to provide a basis for subsequent spatial clustering. Conduct spatial clustering analysis on the geographical location data of the base stations, using an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. This algorithm can identify spatial clusters of arbitrary shapes and can handle noise points. Specifically, for any base station ST, first calculate the distance between other base stations and ST, using the spherical distance formula for calculation:

[0096] dL(ot,ST)=Rb·arccos(sin(ot 1 )·sin(ST 1 )+cos(ot 2 )·cos(ST 2 )·cos(ot 1 -ST 1 ));where dL(ot,ST) represents the base station distance between base station ot and base station ST, R is the radius of the earth (about 6371 kilometers), ot 1 and ot 2 are the longitude and latitude coordinates (in radians) of base station ot respectively, and ST 1 and ST 2 are the longitude and latitude coordinates (in radians) of base station ST respectively. Preset the neighborhood radius parameter and the minimum base station parameter, and include the base station ot whose base station distance is less than the preset neighborhood radius parameter into base station ST. When the number of base stations included in base station ST is greater than or equal to the minimum base station parameter, base station ST is regarded as a core point and is used to expand the clustering. Through the iterative expansion process, several base station groups are finally obtained, and each group contains several base stations with similar geographical locations. Calculate the communication coverage area according to the base station spatial group data, using an improved Voronoi diagram method, combined with the transmission power and antenna direction parameters of the base stations. For each base station group, first calculate the signal coverage range of each base station inside it, and then merge these coverage areas to obtain the overall coverage range of the group. The signal coverage range of a single base station can be represented by signal strength contour lines, and its signal strength Pua_r can be calculated through the following model:

[0097] Among them, Pua_r(d) represents the signal strength at a distance d from the base station, P_sen is the transmission power, G_sen and G_ace are the transmission and receiving antenna gains respectively, Bc is the signal wavelength, d is the distance, n is the path loss exponent (usually taking values between 2 and 4, depending on the environment), and π represents the pi. By setting the minimum received signal strength threshold P_min, the maximum coverage radius R_max of the base station can be determined:

[0098] Based on the calculated coverage radius, considering factors such as terrain obstacles and building blockages, the actual coverage areas of each base station are further adjusted to form a more accurate coverage model. Finally, the coverage areas of the base stations in the same group are merged to obtain the group coverage area data. Based on the coverage area data and the migration path probability map, the regional overlap degree analysis is carried out to calculate the overlap degree between each base station group and the migratory path of the migratory birds. Suppose the obtained migration path probability map is represented as Map(x, y, t), where Map(x, y, t) represents the probability that the migratory bird population passes through at the position (x, y) at time t. For the coverage area of each base station group, its overlap degree with the migration path can be calculated by the following integral: O_k(t) = ∫∫(A_k ∩ Map(x, y, t))dxdy; where, A_k ∩ Map(x, y, t) represents the intersection of the coverage area A_k and the migration path at the position (x, y) and time t. The overlap degree O_k(t) represents the number of devices that the base station group is expected to serve at time t. Based on the overlap degree data, the migration impact coefficient I_k(t) is further calculated to measure the impact degree of the migratory bird migration on the resource requirements of the base station group at time t:

[0099] I_k(t) = O_k(t) ÷ ∫∫A_kdxdy, where ∫∫A_kdxdy represents the total area of the covered area A_k. The migration impact coefficient I_k(t) ranges from [0, 1]. The larger the value, the greater the impact of the group on migratory birds, and the higher the resource dynamic allocation ability is required. The resource capabilities of the base station spatial group data are evaluated, comprehensively considering factors such as the hardware configuration, bandwidth resources, and computing power of each base station in the group. For a base station group, its resource capabilities consist of the following aspects: Bandwidth resources: Wid = ∑(wid_u), where wid_u represents the available spectrum width of base station u in the group; Computing resources: Vca = ∑(cal_u), where cal_u represents the computing power of base station u in the group (such as the number of CPU cores × clock frequency); Storage resources: Save = ∑(sav_u), where sav_u represents the storage capacity of base station u in the group; Energy efficiency: Een = ∑(en_u × pr_u) ÷ ∑(pr_u), where en_i represents the energy efficiency ratio of base station u in the group, and pr_u represents the transmit power of base station u in the group. Considering these factors comprehensively, a weighted sum is performed to obtain the group resource capability index, which is used to balance the importance of different resource dimensions. Based on the resource capability indexes of each group, a group resource capability data set is formed. According to the migration impact coefficient and the group resource capability data, a resource demand distribution map is generated. The resource demand distribution map is a spatio-temporal three-dimensional mapping, showing the resource demand intensity at different times and in different regions. For each base station group, its resource demand Dnd_k(t) at time t can be calculated by the following formula: Dnd_k(t) = I_k(t) × U_k × ∑R(v,t); where I_k(t) is the migration impact coefficient, and U_k is the basic user capacity (positively correlated with the group resource capability index). Specifically, U_k can be expressed as: U_k = translate × Rs_k; where translate is the resource conversion coefficient, and Rs_k represents the group resource capability index, which converts the resource capability into the number of users that can be served. Mapping the resource demands at different locations and times to the geographical space forms a resource demand distribution map, intuitively showing the spatio-temporal change characteristics of the resource demands. Time series analysis is performed on the resource demand distribution map, using wavelet transform and spectral analysis methods to decompose and analyze the time series data of the resource demands. For each base station group, its resource demand time series is extracted, and continuous wavelet transform is performed on the resource demand time series. By analyzing the modulus value of the wavelet transform coefficients, the periodic components in the demand series are identified, and the resource fluctuation period data PLZ_k = {p_k1, p_k2,..., p_kr} is obtained, where p_kr represents the period length of the rth periodic component. The resource utilization rate is calculated according to the resource fluctuation period data. The resource utilization rate index is used to measure the resource usage efficiency of the base station group within the cycle Tcir: where usTar represents the resource utilization rate index, It represents the ratio of the resource demand at time t to the total resource capacity. Ideally, the resource utilization rate should be close to but not exceed 1. Excessively high utilization rates may lead to congestion, while too low means resource waste. By analyzing the resource utilization rate indicators of each group, areas of resource surplus or deficiency can be identified, providing a basis for decision-making in subsequent dynamic resource allocation. In specific implementation, combined with resource fluctuation cycle data, the resource utilization rate assessment can be further refined. For groups with obvious periodicity, calculate the variance of the resource utilization rate within different cycles. A larger variance value indicates greater fluctuations in the resource utilization rate and requires stronger dynamic allocation capabilities; a smaller variance means relatively stable resource demands, and a more conservative allocation strategy can be adopted.

[0100] Preferably, step S3 includes the following steps:

[0101] Step S31: Quantify the computing resource requirements according to the resource elastic allocation plan to obtain computing resource allocation data; quantify the storage resource requirements for the resource elastic allocation plan to obtain storage resource allocation data; quantify the bandwidth resource requirements for the resource elastic allocation plan to obtain bandwidth resource allocation data;

[0102] Step S32: Generate a resource scheduling instruction set based on the computing resource allocation data, storage resource allocation data, and bandwidth resource allocation data;

[0103] Step S33: Evaluate the timeliness of the resource scheduling instruction set according to the time-sharing migration density distribution data to obtain instruction timeliness level data;

[0104] Step S34: Sort the resource scheduling instruction set based on the instruction timeliness level data to obtain a hierarchical scheduling sequence;

[0105] Step S35: Perform cloud resource scheduling simulation according to the hierarchical scheduling sequence and resource utilization rate indicators to obtain a resource allocation prediction result; detect resource competition conflicts for the resource allocation prediction result to obtain a conflict resolution strategy;

[0106] Step S36: Generate an optimal resource allocation strategy based on the resource allocation prediction result and the conflict resolution strategy; generate a base station parameter configuration instruction according to the optimal resource allocation strategy.

[0107] Specifically, when quantifying the computing resource requirements according to the resource elastic allocation plan, a multi-factor weighted calculation model is used for processing. The formula for quantifying the computing resource requirements is:

[0108] C_req = ∑(con_r × Lfz_r × Pcpl_r × Fser_r); where C_req represents the total computing resource requirement, with the unit of computing capacity unit (CCU); con_r represents the resource consumption coefficient of the r-th type of service, reflecting the consumption characteristics of different service types for computing resources; Lfz_r represents the load prediction value of the r-th type of service, including the number of users, the number of connections, etc.; Pcpl_r represents the processing complexity factor of the r-th type of service, reflecting the computing complexity of service processing; Fser_r represents the quality of service factor of the r-th type of service, which is related to service response time and reliability requirements. The higher the requirement, the larger this factor. For the storage resource requirement quantification of the resource elastic allocation scheme, data traffic analysis and storage prediction models are adopted. The storage resource requirement quantification formula is: S_req = ∑(Dgen_ty × Rda_ty × Tsa_ty) + S_base; where S_req represents the total storage resource requirement, with the unit of GB; Dgen_ty represents the expected generation rate of the ty-th type of data, with the unit of GB per hour; Rda_ty represents the redundancy coefficient of the ty-th type of data, considering data backup and fault tolerance requirements; Tsa_ty represents the storage duration of the ty-th type of data, with the unit of hour; S_base represents the basic system storage requirement, including the storage space required for the operating system, application software, and fixed configuration data. For the bandwidth resource requirement quantification of the resource elastic allocation scheme, network traffic models and quality of service mapping methods are adopted. The bandwidth resource requirement quantification formula is: B_req = max(B_up, B_down) × (1 + redun); where B_req represents the total bandwidth resource requirement, with the unit of Mbps; B_up represents the uplink bandwidth requirement, B_down represents the downlink bandwidth requirement; redun represents the bandwidth redundancy coefficient, which is used to cope with traffic fluctuations and emergencies, and usually takes a value of 0.1 to 0.3. A resource scheduling instruction set is generated based on the computing resource allocation data, storage resource allocation data, and bandwidth resource allocation data. The resource scheduling instruction is defined in a standardized format: Ins = {Type, Amount, Location, Time, Duration, Priority}; where Type represents the resource type, including computing resource requirement, storage resource requirement, and bandwidth resource requirement; Amount represents the resource allocation quantity; Location represents the resource allocation location, which can be the physical server ID or the virtual resource pool identifier; Time represents the execution time; Duration represents the resource occupation duration; Priority represents the initial priority of the instruction. The scheduling instruction set is converted from the resource requirement data through a resource mapping algorithm to ensure that various resource requirements can be mapped to specific scheduling operations. The timeliness of the resource scheduling instruction set is evaluated according to the time-sharing migration density distribution data, and the time sensitivity analysis method is adopted.The higher the timeliness score, the more urgent the instruction is and the faster it needs to be executed. Based on the instruction timeliness level data, the resource scheduling instruction set is sorted by priority. A multi-factor comprehensive sorting algorithm is used to perform weighted summation on the timeliness score, resource impact degree, and cost-benefit ratio of the instruction to obtain the final priority score. The resource impact degree of the instruction reflects the degree of influence of the instruction execution on the overall resource state. The cost-benefit ratio of the instruction reflects the ratio of the service quality improvement brought by the instruction execution to the resource consumption. According to the calculated priority score, the instructions are sorted from high to low to form a hierarchical scheduling sequence. Cloud resource scheduling simulation is performed based on the hierarchical scheduling sequence and the resource utilization rate index. The discrete event simulation method is used to iteratively calculate the resource state at different times to obtain the resource allocation prediction result. Resource competition conflict detection is performed on the resource allocation prediction result. The conflict graph coloring algorithm is used to identify resource conflicts and generate conflict resolution strategies. The conflict detection criterion is: when the instructions InsI and InsJ satisfy the instruction logic condition:.

[0109] When resource(InsI)∩resource(InsJ)≠andtime(InsI)∩time(InsJ)≠, the conflict state between the instructions InsI and InsJ is 1, otherwise the conflict state between the instructions InsI and InsJ is 0. The conflict state of 1 indicates the existence of a conflict, and 0 indicates no conflict. resource() represents the set of resources requested by the instruction, and time() represents the execution time period of the instruction, representing an empty set. Conflict resolution strategies include methods such as instruction reordering, resource substitution, and task splitting. An optimal resource allocation strategy is generated based on the resource allocation prediction result and the conflict resolution strategy, using a multi-objective optimization method. The optimization objectives include: the comprehensive score of the resource allocation strategy, representing the resource utilization rate and service quality satisfaction. The base station parameter configuration instruction is generated according to the optimal resource allocation strategy, using the strategy mapping and parameter conversion method. The base station parameter configuration includes wireless resource configuration, power control, antenna parameters, and cell coverage adjustment, etc. The mapping process considers the wireless environment characteristics, device capability limitations, and network topology structure to ensure that the generated parameter configuration instruction can be effectively executed and achieve the expected resource allocation effect.

[0110] Preferably, step S4 includes the following steps:

[0111] Step S41: Parse and convert the base station parameter configuration instruction to obtain the base station configuration parameter set; update the parameters of each base station according to the base station configuration parameter set to obtain the base station parameter update data;

[0112] Step S42: Verify the configuration effectiveness of the base station parameter update data to obtain the configuration verification result;

[0113] Step S43: Monitor the performance of base station resources according to the configuration verification result to obtain resource performance data; collect the resource usage status of the resource performance data to obtain resource operation status data;

[0114] Step S44: Calculate the energy efficiency index according to the resource operation status data to obtain energy efficiency evaluation data; evaluate the quality of service for the resource operation status data to obtain quality of service indicators;

[0115] Step S45: Conduct a comprehensive resource efficiency evaluation based on the energy efficiency evaluation data and the quality of service indicators to obtain a resource efficiency evaluation result;

[0116] Step S46: Analyze the optimization space of the resource efficiency evaluation result to obtain optimization direction data; generate a parameter tuning strategy according to the optimization direction data to obtain tuning parameters; feedback the tuning parameters to the resource elastic allocation plan to achieve closed-loop optimization of resource configuration.

[0117] Specifically, when parsing and converting the base station parameter configuration instructions, a hierarchical parsing framework is adopted to process different types of configuration instructions. For standardized XML or JSON format configuration instructions, first, a syntax parser is used to extract the instruction structure and parameter values; then, through a parameter mapping table, the general parameters are converted into specific manufacturer base station-specific parameters, and finally, a complete set of base station configuration parameters is formed. The parameters are sent to each base station through SNMP, NETCONF, or a dedicated API interface to complete the parameter configuration update. During the update process, the system records the application status of each parameter and the base station feedback information to form base station parameter update data, providing a basis for subsequent verification. When performing configuration validity verification on the base station parameter update data, a multi-stage verification strategy is adopted to ensure the integrity and correctness of the configuration. First, parameter landing verification is performed by reading the current base station configuration to check whether the parameters are correctly written. Then, function verification is carried out by sending test services or probing signals to detect whether the key functions of the base station are normal. Finally, performance verification is performed to check whether the performance indicators after parameter adjustment meet the expectations. Based on the verification results of these three aspects, a complete configuration verification result is formed. When performing base station resource performance monitoring according to the configuration verification result, a hierarchical monitoring framework is adopted, covering three levels: physical resources, virtual resources, and service resources. Physical resource monitoring mainly focuses on hardware-level indicators such as CPU usage rate, memory occupancy, network interface load, power status, etc.; virtual resource monitoring focuses on resource allocation and usage in the virtualization environment, such as virtual CPU allocation rate, virtual memory usage efficiency, virtual network throughput, etc.; service resource monitoring focuses on resource indicators directly related to communication services, such as wireless channel utilization rate, number of user connections, throughput per user, etc. The monitoring data collection adopts a hierarchical sampling strategy, with key indicators sampled at a high frequency (e.g., once every 5 seconds), general indicators sampled at a medium frequency (e.g., once every 30 seconds), and auxiliary indicators sampled at a low frequency (e.g., once every 5 minutes). At the same time, a threshold trigger mechanism is set, and the sampling frequency is increased when the indicator exceeds the preset range. When analyzing the resource usage status of the collected resource performance data, a moving window statistical method is used to calculate the statistical characteristics of each resource indicator, including average value, peak value, variance, trend, etc., to form a resource load feature vector. At the same time, a time series pattern recognition algorithm is used to analyze the resource usage pattern to identify periodic load changes and emergencies. Through these analyses, comprehensive resource operation status data is obtained. When calculating the energy efficiency index according to the resource operation status data, different energy efficiency models are adopted for different types of resources. For computing resources, the computing throughput (expressed in available FLOPS or the number of services processed per second) divided by the computing resource power consumption is used as the computing energy efficiency index. For wireless resources, the effective bandwidth utilization rate (considering the actual amount of effective data transmitted) divided by the wireless device power consumption represents the wireless energy efficiency index. For network resources, the network throughput divided by the network device power consumption is used as the network resource energy efficiency. By comprehensively considering these indicators and performing weighted summation, an overall energy efficiency evaluation index is obtained.When conducting service quality assessment on resource operation status data, a multi-dimensional user experience index system is adopted, including three dimensions: access experience, interaction experience, and content experience. By synthesizing these three dimensions, a weighted summation formula is used to calculate the service quality index. When conducting comprehensive resource efficiency assessment based on energy efficiency assessment data and service quality index, a multi-objective balance strategy is adopted to seek the optimal balance point between energy efficiency and service quality. Through this assessment method, the system can comprehensively consider energy efficiency and service quality, avoid the negative impacts that may be brought by optimizing a single index, and obtain a comprehensive resource efficiency assessment result. When conducting optimization space analysis on the resource efficiency assessment result, two methods, namely target gap analysis and resource balance analysis, are adopted. Target gap analysis calculates the gap between the current efficiency and the target efficiency: Gdif = (E_target - E_current) ÷ E_target, where Gdif represents the relative gap, E_target represents the target efficiency value, and E_current represents the current efficiency value. When the gap Gdif is greater than the preset threshold, the optimization process is triggered. Resource balance analysis detects the load balance situation of various resources and calculates the load balance degree:. Where B represents the load balance degree, and σ(L) and μ(L) respectively represent the standard deviation and the average value of the resource load vector L. When the balance degree B is lower than the threshold, it is found that the resource allocation is unbalanced and needs to be adjusted. Based on these analysis results, the system identifies the adjustment direction of specific resources and forms optimization direction data. When generating a parameter tuning strategy according to the optimization direction data, a method combining incremental tuning and historical experience is adopted. Incremental tuning adopts a gradient search strategy to make small adjustments to the parameters and observe the effects. Historical experience utilizes past successful tuning records to find a reference solution through similar scenario matching. By synthesizing these two methods, the system generates the final tuning parameters and feeds them back to the resource elastic allocation plan, completing the last link of the closed-loop optimization.

[0118] In the embodiment of the present invention, by obtaining biological migration tide data and environmental parameter data to construct a migration trajectory prediction model, the spatio-temporal distribution characteristics of the communication network load can be accurately predicted, providing a scientific basis for resource allocation, and improving the foresight and accuracy of resource allocation; by dividing the base station groups in the communication network coverage area, differential analysis can be carried out according to the resource demand characteristics of different regions to obtain a more refined resource demand distribution map, providing spatial accuracy for resource allocation and improving resource utilization efficiency; by sorting the resource scheduling instruction set by priority, limited resources can be reasonably allocated in the case of resource competition, solving the resource scheduling conflict problem, ensuring the communication service quality in key areas. At the same time, the base station parameter configuration instruction generated based on the optimal resource allocation strategy can accurately guide the base stations in each region to adjust the resource configuration, improving the accuracy and execution efficiency of the configuration.

[0119] Embodiment 2;

[0120] Please refer to Figure 2 As shown, for the parts not described in detail in this embodiment, refer to the description in Embodiment 1. A communication device resource dynamic allocation system based on cloud computing is provided, including:

[0121] Migration elasticity prediction module: Obtain biological migration tide data, historical communication network load data, and environmental parameter data; construct a migration trajectory prediction model based on the biological migration tide data and environmental parameter data to obtain a migration trajectory prediction result; construct a resource demand prediction model based on the historical communication network load data and the migration trajectory prediction result to obtain resource load prediction data; generate a resource allocation strategy based on the resource load prediction data to obtain a resource elasticity allocation plan;

[0122] Spatio-temporal demand analysis module: Divide the base station groups in the communication network coverage area to obtain base station group data; perform spatio-temporal analysis of resource demands based on the base station group data and the migration trajectory prediction result to obtain a resource demand distribution map; calculate the resource utilization rate of the resource demand distribution map to obtain a resource utilization rate index;

[0123] Dynamic scheduling optimization module: Generate scheduling instructions for the cloud computing resource center based on the resource elasticity allocation plan to obtain a resource scheduling instruction set; perform priority sorting on the resource scheduling instruction set to obtain a hierarchical scheduling sequence; perform resource allocation optimization based on the hierarchical scheduling sequence and the resource utilization rate index to obtain an optimal resource allocation strategy; generate base station configuration parameters based on the optimal resource allocation strategy to obtain base station parameter configuration instructions;

[0124] Feedback tuning module: Adjust the resource configuration of each regional base station according to the base station parameter configuration instructions to obtain real-time base station resource configuration data; monitor the operating status of the real-time base station resource configuration data to obtain resource operating status data; evaluate the resource utilization efficiency based on the resource operating status data to obtain a resource effectiveness evaluation result; perform feedback tuning based on the resource effectiveness evaluation result to obtain tuning parameters; apply the tuning parameters to the resource elasticity allocation plan to achieve closed-loop optimization allocation of communication device resources;

[0125] Each module is connected by wired and / or wireless means to realize data transmission between modules.

[0126] Embodiment 3;

[0127] This embodiment publicly provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it realizes the operation mode of the above-provided method for dynamically allocating communication device resources based on cloud computing.

[0128] Since the electronic device introduced in this embodiment is the electronic device adopted in implementing a method for dynamically allocating communication device resources based on cloud computing in the embodiments of the present application, based on the method for dynamically allocating communication device resources based on cloud computing introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device adopted in a method for dynamically allocating communication device resources based on cloud computing in the embodiments of the present application, it falls within the scope of protection of the present application.

[0129] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0130] The above description is only a preferred implementation manner of the present invention. The protection scope of the present invention is not limited to the above embodiments. Any technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for ordinary users in the technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A method for dynamically allocating communication equipment resources based on cloud computing, characterized in that: include: Step S1: Acquire biological migration tide data, communication network load history data and environmental parameter data; A migration trajectory prediction model is constructed based on biological migration tide data and environmental parameter data to obtain migration trajectory prediction results; a resource demand prediction model is constructed based on communication network load historical data and migration trajectory prediction results to obtain resource load prediction data; a resource allocation strategy is generated based on resource load prediction data to obtain a resource elastic allocation plan; Step S2: Divide the communication network coverage area into base station groups to obtain base station group data; perform spatiotemporal analysis of resource demand based on the base station group data and the migration trajectory prediction results to obtain a resource demand distribution map; calculate resource utilization on the resource demand distribution map to obtain a resource utilization index; Step S3: Generate scheduling instructions for the cloud computing resource center based on the resource elastic allocation plan to obtain a resource scheduling instruction set; prioritize the resource scheduling instruction set to obtain a hierarchical scheduling sequence; optimize resource allocation according to the hierarchical scheduling sequence and resource utilization index to obtain an optimal resource allocation strategy; Generate base station configuration parameters based on the optimal resource allocation strategy, and obtain base station parameter configuration instructions; Step S4: adjusting resource configuration of base stations in each region according to the base station parameter configuration instruction to obtain real-time configuration data of base station resources; Monitor the operation status of base station resource real-time configuration data to obtain resource operation status data; Evaluate resource utilization efficiency based on resource operation status data to obtain resource performance evaluation results; perform feedback tuning based on the resource performance evaluation results to obtain tuning parameters; Apply the tuning parameters to the resource elastic allocation plan to achieve closed-loop optimization and allocation of communication equipment resources.

2. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 1, characterized in that: Step S1 includes: Step S11: Collect historical data of migratory bird populations and perform spatiotemporal characteristic analysis to obtain a migration activity feature library; collect meteorological environment data and seasonal change data to obtain environmental impact factor data; Step S12: Acquire communication network area division data; construct a network topology structure according to the communication network area division data to obtain network topology data; collect communication load time series data through a network monitoring system to obtain historical load data; Step S13: constructing a biological migration trajectory prediction model based on the migration activity feature library and environmental impact factor data to obtain migration behavior pattern data; extracting time series features from the migration behavior pattern data to obtain migration time series features; Step S14: Predicting spatial trajectories based on migration behavior pattern data and migration time series characteristics to obtain a migration path probability map; dividing the migration path probability map into time windows to obtain time-divided migration density distribution data; Step S15: Mapping the time-sharing migration density distribution data to the network topology data to obtain tidal load prediction data; constructing a resource demand prediction model based on the historical load data and the tidal load prediction data to obtain resource fluctuation prediction data; Step S16: performing peak recognition and mutation analysis on the resource fluctuation prediction data to obtain a resource demand mutation index; Step S17: Perform resource pool distribution analysis based on network topology data to obtain a resource pool capacity map; perform schedulable resource evaluation on the resource pool capacity map to obtain a resource schedulable matrix; Step S18: Generate a resource elastic allocation plan based on the resource demand mutation index and the resource schedulability matrix.

3. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 2, characterized in that: The biological migration trajectory prediction model is constructed based on the migration activity feature library and environmental impact factor data to obtain migration behavior pattern data, including: Classify species migration behavior patterns in the migration activity feature database to obtain species migration type data; Extract environmental trigger factors based on environmental impact factor data to obtain the migration trigger index; Build a deep learning migration prediction network based on species migration type data to obtain a basic model for migration prediction; The migration trigger index is used to optimize the model parameters of the basic migration prediction model to obtain an adaptive migration prediction model. Generate migration behavior pattern data based on an adaptive migration prediction model.

4. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 1, characterized in that: Step S2 includes: Step S21: performing a base station geographic distribution analysis based on the communication network area division data to obtain base station geographic location data; performing a spatial clustering analysis on the base station geographic location data to obtain base station spatial group data; Step S22: Calculate the communication coverage area according to the base station space group data to obtain coverage area data; Step S23: performing regional overlap analysis based on the coverage area data and the migration path probability map to obtain the migration impact coefficient; Step S24: performing resource capability evaluation on the base station space group data to obtain group resource capability data; Step S25: generating a resource demand distribution map according to the migration impact coefficient and the group resource capacity data; Step S26: Perform time series analysis on the resource demand distribution graph to obtain resource fluctuation cycle data; calculate resource utilization based on the resource fluctuation cycle data to obtain a resource utilization index.

5. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 1, characterized in that: Step S3 includes: Step S31: quantify computing resource requirements according to the resource elastic allocation plan to obtain computing resource allocation data; quantify storage resource requirements for the resource elastic allocation plan to obtain storage resource allocation data; quantify bandwidth resource requirements for the resource elastic allocation plan to obtain bandwidth resource allocation data; Step S32: Generate a resource scheduling instruction set based on the computing resource allocation data, the storage resource allocation data and the bandwidth resource allocation data; Step S33: Evaluate the timeliness of the resource scheduling instruction set according to the time-sharing migration density distribution data to obtain instruction timeliness level data; Step S34: Prioritizing the resource scheduling instruction set based on the instruction timeliness level data to obtain a hierarchical scheduling sequence; Step S35: Perform cloud resource scheduling simulation according to the hierarchical scheduling sequence and resource utilization index to obtain resource allocation prediction results; perform resource competition conflict detection on the resource allocation prediction results to obtain a conflict resolution strategy; Step S36: Generate an optimal resource allocation strategy based on the resource allocation prediction result and the conflict resolution strategy; and generate a base station parameter configuration instruction according to the optimal resource allocation strategy conversion.

6. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 5, characterized in that: Step S32 includes: Step S321: Perform virtual machine resource configuration conversion on the computing resource allocation data to obtain a virtual machine configuration instruction; Step S322: performing storage unit mapping on the storage resource allocation data to obtain a storage allocation instruction; Step S323: Perform network channel planning on the bandwidth resource allocation data to obtain a bandwidth allocation instruction; Step S324: standardizing the instruction formats of the virtual machine configuration instructions, storage allocation instructions, and bandwidth allocation instructions to obtain standardized scheduling instructions; Step S325: Integrate the standardized scheduling instructions into an instruction set to obtain a resource scheduling instruction set.

7. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 5, characterized in that: Step S34 includes: Step S341: constructing an instruction priority evaluation model according to instruction timeliness level data to obtain a priority evaluation function; Step S342: applying a priority evaluation function to the resource scheduling instruction set to obtain an instruction priority value; Step S343: Sort resource scheduling instructions according to instruction priority values ​​to obtain priority sequence data; Step S344: Analyze the resource dependency relationship of the priority sequence data to obtain a dependency constraint matrix; adjust the priority sequence data according to the dependency constraint matrix to obtain a hierarchical scheduling sequence.

8. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 1, characterized in that: Step S4 includes: Step S41: parsing and converting the base station parameter configuration instruction to obtain a base station configuration parameter set; updating the parameter configuration of each base station according to the base station configuration parameter set to obtain base station parameter update data; Step S42: performing configuration validity verification on the base station parameter update data to obtain a configuration verification result; Step S43: performing base station resource performance monitoring according to the configuration verification result to obtain resource performance data; collecting resource usage status of the resource performance data to obtain resource operation status data; Step S44: Calculate energy efficiency index according to resource operation status data to obtain energy efficiency evaluation data; perform service quality evaluation on resource operation status data to obtain service quality index; Step S45: Perform comprehensive resource efficiency evaluation based on the energy efficiency evaluation data and the service quality index to obtain a resource efficiency evaluation result; Step S46: Optimize the spatial analysis of the resource efficiency evaluation results to obtain optimization direction data; generate parameter tuning strategies based on the optimization direction data to obtain tuning parameters; feed the tuning parameters back to the resource elastic allocation plan to achieve closed-loop optimization of resource configuration.

9. The method for dynamic allocation of communication equipment resources based on cloud computing according to claim 8, characterized in that: Step S45 includes: Step S451: performing multi-dimensional index standardization on the energy efficiency evaluation data to obtain a standardized energy efficiency index; Step S452: Perform user experience mapping on the service quality indicators to obtain experience satisfaction data; Step S453: construct a resource efficiency evaluation model, perform weighted fusion on the standardized energy efficiency index and experience satisfaction data, and obtain a comprehensive efficiency index; Step S454: Generate resource efficiency evaluation results according to the comprehensive efficiency index.

10. A cloud computing-based communication equipment resource dynamic allocation system, characterized in that: include: Migration elasticity prediction module: obtains biological migration tide data, communication network load historical data and environmental parameter data; A migration trajectory prediction model is constructed based on biological migration tide data and environmental parameter data to obtain migration trajectory prediction results; a resource demand prediction model is constructed based on communication network load historical data and migration trajectory prediction results to obtain resource load prediction data; a resource allocation strategy is generated based on resource load prediction data to obtain a resource elastic allocation plan; Spatiotemporal demand analysis module: divide the communication network coverage area into base station groups to obtain base station group data; perform spatiotemporal analysis of resource demand based on base station group data and migration trajectory prediction results to obtain a resource demand distribution map; calculate resource utilization on the resource demand distribution map to obtain a resource utilization index; Dynamic scheduling optimization module: Generate scheduling instructions for the cloud computing resource center based on the resource elastic allocation plan to obtain a resource scheduling instruction set; prioritize the resource scheduling instruction set to obtain a hierarchical scheduling sequence; optimize resource allocation based on the hierarchical scheduling sequence and resource utilization indicators to obtain the optimal resource allocation strategy; Generate base station configuration parameters based on the optimal resource allocation strategy, and obtain base station parameter configuration instructions; Feedback tuning module: adjusts the resource configuration of base stations in each region according to the base station parameter configuration instructions, and obtains real-time configuration data of base station resources; Monitor the operation status of base station resource real-time configuration data to obtain resource operation status data; Evaluate resource utilization efficiency based on resource operation status data to obtain resource performance evaluation results; perform feedback tuning based on the resource performance evaluation results to obtain tuning parameters; Apply the tuning parameters to the resource elastic allocation plan to achieve closed-loop optimization and allocation of communication equipment resources.

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