Intelligent management method and system for computing network resources

Abstract

The invention relates to the technical field of resource management, in particular to an intelligent management method and system for computing network resources, and the method comprises the following steps: monitoring data based on computing power node flow, sampling node flow one by one, calculating the flow difference of adjacent nodes, and analyzing the change rate by using a time sequence. According to the method, the space-time characteristic value is extracted by sampling the node flow and combining the time sequence, the multi-dimensional understanding of the node flow is enhanced, the processing accuracy is improved, the fine dependency weight is obtained by using the node dependency relationship matrix and the multi-head dependency matrix, the description of the relationship between the nodes is enhanced, and the time and space characteristics are fused in the flow association prediction. In the resource scheduling process, reasonable marking and priority allocation are conducted on future overrun nodes through recursive calculation and load evaluation, allocation is made to be more dynamic, optimal resource allocation is achieved through priority accumulation in core and edge node matching adjustment, and the system stability and the utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource management, and in particular to a method and system for intelligent management of computing network resources. Background Art

[0002] The field of resource management technology involves the effective allocation, scheduling and optimization of various resources in a computer system to improve the overall performance and resource utilization of the system. These resources can include processors, memory, network bandwidth, storage space, etc., and are widely used in various systems such as computer operating systems, cloud computing, and distributed computing environments. The goal of resource management is to ensure that system resources are fully utilized to meet the needs of various tasks, while optimizing performance, saving costs and avoiding resource waste. Through intelligent resource management methods, the system can achieve automatic adjustment in a complex and changing operating environment, thereby achieving efficient and flexible resource allocation.

[0003] Among them, the intelligent management method of computing and network resources refers to the technical means of intelligent scheduling and management between computing power and network resources, aiming to improve the overall service performance and efficiency of computing and network by optimizing the coordinated allocation of computing power and network resources. This method is often used in cloud computing, edge computing and other fields to achieve dynamic and intelligent management of various computing and communication resources, ensure the effective provision of computing and communication services in complex network environments, and meet the needs of different users and tasks.

[0004] The existing technology lacks in-depth extraction of the spatiotemporal characteristics of node traffic in resource management. Usually, only simple analysis is performed, which makes it difficult to fully describe the dynamic characteristics of traffic, resulting in insufficient accuracy in the prediction of node traffic associations, affecting the rationality of resource scheduling. The processing of node dependencies is relatively simple and cannot fully reflect multi-dimensional dependencies, resulting in inaccurate resource scheduling in scenarios with complex node associations, which may cause resource waste or uneven distribution. The load assessment in the existing technology is mainly based on the current state and lacks the ability to predict future loads, resulting in the system's untimely response and delayed scheduling when facing load fluctuations. The resource allocation between core and edge nodes lacks a flexible adjustment mechanism, resulting in unreasonable resource scheduling in complex environments, bottleneck node overloads or idle resources, and reducing the overall system operation efficiency and resource utilization. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for intelligent management of computing network resources.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for intelligent management of computing network resources, comprising the following steps: S1: Based on the traffic monitoring data of computing nodes, the node traffic is sampled one by one, the traffic difference between adjacent nodes is calculated, the change rate is analyzed by time series, the spatial feature value is combined with the time feature, and the spatiotemporal feature value of the traffic is extracted; S2: Based on the spatiotemporal characteristic values ​​of the traffic, a node dependency matrix is ​​selected, the node spatial position and traffic sequence are taken, weights are obtained through matrix operations, a multi-head dependency matrix is ​​established using the average value between heads, and the node dependency weight value is obtained; S3: Based on the node dependency weight value, the spatial and temporal features are integrated, the module is called to merge the feature values, the node features are accumulated, the weighted fusion matching node traffic relationship is performed, the association matrix is ​​established, and the node traffic association prediction value is obtained; S4: Based on the node traffic association prediction value, select the node traffic time period, recursively calculate the future multiple time periods, evaluate the load status by interval accumulation, set a threshold to mark the over-limit node, allocate resource weights according to priority, and obtain the node resource scheduling plan; S5: Based on the node resource scheduling plan, analyze the load status of multiple nodes, adjust the resource ratio of core and edge nodes, accumulate the core node priority using priority parameters, reallocate resources according to the ratio, and obtain dynamic resource allocation results.

[0007] The traffic spatiotemporal characteristic values ​​include traffic space characteristics, traffic time characteristics, and adjacent node traffic difference characteristics. The node dependency weight values ​​include node dependency weights, node spatial position weights, and traffic sequence weights. The node traffic association prediction values ​​include node association matrices, time feature fusion values, and traffic relationship matching values. The node resource scheduling plan includes over-limit node marking, resource weight allocation, and priority sorting scheme. The dynamic resource allocation results include core node resource allocation, edge node resource allocation, and priority parameter adjustment.

[0008] As a further solution of the present invention, the step of extracting the spatiotemporal characteristic value of the flow is specifically as follows: S111: Based on the computing power node traffic monitoring data, time series sampling is performed on multiple nodes, the traffic data of each node at each moment is recorded, and a node traffic time series matrix is ​​generated; S112: using the node traffic time series matrix, calculating the traffic difference between any adjacent nodes, extracting the traffic difference feature values ​​between the adjacent nodes from the difference, and obtaining a node traffic difference feature matrix; S113: The node traffic difference characteristic matrix is ​​combined with the change rate obtained through time series analysis, and a composite formula is used: ; Calculate the spatiotemporal value of traffic flow at each node at the differentiated moment, integrate the data of all time points, and extract the spatiotemporal characteristic value of traffic flow; in, represents the spatiotemporal characteristic value of traffic, Represents the total number of nodes, represents the flow value of node i at time t, Representative Node At the moment The flow value, that is, the flow at the previous moment, represents the time interval adjustment coefficient of node i, represents the spatial weight coefficient of node i, Represents the traffic adjustment variable of node i.

[0009] As a further solution of the present invention, the step of obtaining the node dependency weight value is specifically as follows: S211: selecting a node dependency matrix according to the spatiotemporal characteristic value of the traffic flow, adjusting and verifying the dependency matrix in combination with the spatial position of the node and the traffic sequence data, and generating an adjusted node dependency matrix; S212: Starting from the adjusted node dependency matrix, integrating the node spatial position data and the traffic sequence through matrix operations, calculating the preliminary weight values ​​of the mutual dependence between multiple nodes, and generating a preliminary dependency weight matrix between nodes; S213: Combine the preliminary dependency weight matrix between nodes, use the average traffic characteristics between nodes, and use the formula: ; Integrate the weights between multiple nodes and calculate the node dependency weight value; in, represents the dependency weight between node i and node j, represents the position parameters of nodes i and k, represents the flow coefficient of nodes k and j, represents the initial dependency weight of nodes i and j, represents the adjustment coefficient of node i, represents the adjustment coefficient of node j, Represents the total number of intermediate nodes used for averaging.

[0010] As a further solution of the present invention, the step of obtaining the node traffic association prediction value is specifically as follows: S311: Based on the node dependency weight value, calling the space and time feature matrix, combining the space and time features of multiple nodes according to the weights, integrating the spatial position and time feature values, and obtaining a fused node feature matrix; S312: Utilizing the fused node feature matrix, performing accumulation processing on the node features, calculating the weighted sum of multiple node features based on the node dependency weights, and performing node matching analysis on the accumulation results of the node features to generate a weighted fused node feature value matrix; S313: Matching the traffic characteristics between nodes is performed by combining the node feature value matrix after weighted fusion with the traffic relationship of multiple nodes, using the formula: ; Calculate and generate node traffic correlation prediction values; in, represents the traffic correlation prediction value between node i and node j, represents the spatial characteristic coefficient of nodes i and k, represents the time feature weight of nodes k and j, represents the initial traffic matching relationship between nodes i and j, represents the feature matching weight between nodes i and j, Represents the dependency adjustment coefficient of node i, which is used to characterize the characteristic correction of node i in traffic dependency. Represents the dependency adjustment coefficient of node j, which is used to characterize the characteristic correction of node j in traffic dependency. Represents the total number of features between nodes and is used to normalize the entire calculation result.

[0011] As a further solution of the present invention, the step of obtaining the node resource scheduling plan is specifically: S411: Based on the node traffic association prediction value, select the traffic characteristic value of each node in the future time period, perform multi-time period recursive calculation for each node, gradually accumulate the traffic characteristics in multiple time periods, and obtain the accumulated traffic value of the node in the future time period; S412: using the accumulated flow value of the node in the future time period, setting the threshold range of the node load, judging the load status of each node in turn, marking all the overload nodes exceeding the set threshold, and obtaining the node load overload mark; S413: Based on the node load overlimit mark, resource weights are allocated according to the node load priorities, and resource scheduling amounts for multiple nodes are calculated using the formula: ; Allocate node resource scheduling amounts and obtain node resource scheduling plans; in, represents the resource scheduling amount of node i and node j, Represents the number of nodes or types of load priority factors involved in resource scheduling calculations. represents the load priority of node i, represents the load impact factor between node p and node j, represents the resource demand relationship between nodes i and j, represents the initial resource matching value of node i and node j, represents the load adjustment coefficient of node i and node j.

[0012] As a further solution of the present invention, the step of obtaining the dynamic resource allocation result is specifically: S511: Based on the node resource scheduling plan, call the load status of multiple nodes, analyze the load data of core nodes and edge nodes, calculate the resource ratio of core nodes and edge nodes, perform preliminary ratio matching of core and edge node resources, and obtain the resource ratio result of core and edge nodes; S512: Calculate the priority parameter of the core node using the core and edge node resource allocation result, calculate the priority parameters of the multi-core nodes in a weighted accumulation manner, and obtain the priority accumulation value of the core node; S513: Based on the accumulated priority value of the core node, readjust the resource ratio of the core and edge nodes, and combine the load requirements of the core nodes and edge nodes, using the formula: ; Calculate and generate dynamic resource allocation results; in, represents the dynamic resource allocation value between node i and node j, Represents the total number of nodes, represents the resource allocation value between nodes i and k, represents the load demand value of nodes k and j, represents the load correlation parameter between nodes i and j, represents the priority adjustment coefficient of node k, represents the load adjustment parameter of nodes i and j.

[0013] A computing network resource intelligent management system, the computing network resource intelligent management system is used to execute the above-mentioned computing network resource intelligent management method, the system comprises: The traffic feature extraction module samples the traffic of multiple nodes one by one based on the traffic monitoring data of computing nodes, calculates the traffic difference between nodes, analyzes the change rate in combination with the time series, extracts the spatial and temporal features, and obtains the spatiotemporal feature values ​​of the traffic; The node dependency weight calculation module selects the node dependency matrix based on the spatiotemporal characteristic value of the traffic flow, calculates the weight by multiplying and accumulating the spatial position and the traffic sequence, and establishes a multi-head dependency matrix by taking the average value between heads to obtain the node dependency weight value; The association prediction matrix establishment module is based on the node dependency weight value, integrates the spatial and temporal features, calls the module to accumulate node features, weights and fuses the traffic relationship between matching nodes, establishes the association matrix, and obtains the node traffic association prediction value; The dynamic resource scheduling module recursively calculates the traffic in multiple time periods in the future based on the node traffic association prediction value, cumulatively evaluates the load status, marks the overloaded nodes, reallocates resource weights according to priority, and obtains dynamic resource allocation results.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by sampling node traffic and extracting spatiotemporal eigenvalues ​​in combination with time series, the multi-dimensional understanding of node traffic is enhanced and the processing accuracy is improved. The node dependency matrix and the multi-head dependency matrix are used to obtain refined dependency weights to enhance the description of the relationship between nodes. The time and space characteristics are integrated in the traffic correlation prediction to ensure the accuracy of the prediction. In resource scheduling, recursive calculation and load evaluation are used to reasonably mark and prioritize future over-limit nodes, making the allocation more dynamic. The core and edge node ratio adjustment achieves optimal resource allocation through priority accumulation, improving system stability and utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flow chart of the steps of extracting spatiotemporal characteristic values ​​of flow in the present invention; Figure 3 A flowchart of the steps for obtaining the node dependency weight value of the present invention; Figure 4 A flowchart of the steps for obtaining the node flow correlation prediction value of the present invention; Figure 5 A flowchart of the steps for obtaining a node resource scheduling plan of the present invention; Figure 6 This is a flow chart of the steps for obtaining the dynamic resource allocation results of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] Example 1: Please refer to Figure 1 The present invention provides a technical solution: a method for intelligent management of computing network resources, comprising the following steps: S1: Based on the traffic monitoring data of computing nodes, the node traffic is sampled one by one, the traffic difference between adjacent nodes is calculated, the change rate is analyzed by time series, the spatial feature value is combined with the time feature, and the spatiotemporal feature value of the traffic is extracted; S2: Based on the spatiotemporal characteristics of traffic, the node dependency matrix is ​​selected, the node spatial position and traffic sequence are taken, the weight is obtained through matrix operation, and the multi-head dependency matrix is ​​established using the average value between heads to obtain the node dependency weight value; S3: Based on the node dependency weight value, the spatial and temporal features are integrated, the module is called to merge the feature values, the node features are accumulated, the weighted fusion matches the traffic relationship between nodes, the association matrix is ​​established, and the node traffic association prediction value is obtained; S4: Based on the node traffic correlation prediction value, select the node traffic time period, recursively calculate the future multiple time periods, evaluate the load status by interval accumulation, set the threshold to mark the over-limit node, allocate resource weights according to priority, and obtain the node resource scheduling plan; S5: Based on the node resource scheduling plan, analyze the load status of multiple nodes, adjust the resource ratio of core and edge nodes, use the priority parameters to accumulate the core node priority, reallocate resources according to the ratio, and obtain dynamic resource allocation results.

[0019] The spatiotemporal characteristic values ​​of traffic include traffic spatial characteristics, traffic time characteristics, and traffic difference characteristics of adjacent nodes. The node dependency weight values ​​include the dependency weights between nodes, the spatial position weights of nodes, and the traffic sequence weights. The node traffic association prediction values ​​include the node association matrix, the time feature fusion value, and the traffic relationship matching value. The node resource scheduling plan includes overlimit node marking, resource weight allocation, and priority sorting scheme. The dynamic resource allocation results include core node resource allocation, edge node resource allocation, and priority parameter adjustment.

[0020] See also Figure 2 ,The specific steps for extracting the spatiotemporal characteristics of traffic are: S111: Based on the computing power node traffic monitoring data, time series sampling is performed on multiple nodes, the traffic data of each node at each moment is recorded, and a node traffic time series matrix is ​​generated; Based on the flow monitoring of computing power nodes, the flow data of each node at each moment is recorded during the time series sampling process. These data sets constitute the node flow timing matrix. This process ensures the continuity and comparability of the flow of each node, thereby allowing the flow changes to be accurately measured in subsequent analysis. The node flow timing matrix is ​​created by capturing the flow data every second through the measuring equipment and transmitting it to the centralized processing system through the data interface for integration and synchronization, ensuring the real-time update and accuracy of the data, and obtaining the node flow timing matrix.

[0021] S112: using the node traffic time series matrix, calculating the traffic difference between any adjacent nodes, extracting the traffic difference feature value between the adjacent nodes from the difference, and obtaining the node traffic difference feature matrix; Based on the obtained node traffic time series matrix, the traffic difference characteristic value is obtained by calculating the traffic difference between any adjacent nodes. This process not only involves simple numerical subtraction operations, but also uses advanced data processing technology to identify and analyze nodes with abnormal traffic changes. This data processing process includes smoothing of time series data, outlier detection and trend analysis, thereby ensuring the accuracy and reliability of the traffic difference characteristic value and obtaining the node traffic difference characteristic matrix.

[0022] S113: The node traffic difference feature matrix is ​​combined with the change rate obtained through time series analysis, and a composite formula is used: ; Calculate the spatiotemporal value of traffic flow at each node at the differentiated moment, integrate the data of all time points, and extract the spatiotemporal characteristic value of traffic flow; in, represents the spatiotemporal characteristic value of traffic, Represents the total number of nodes, represents the flow value of node i at time t, Representative Node At the moment The flow value, that is, the flow at the previous moment, represents the time interval adjustment coefficient of node i, represents the spatial weight coefficient of node i, Represents the traffic adjustment variable of node i.

[0023] formula: ; The benefit of the formula is that it not only takes into account the combination of time and space factors, but also introduces square and square root operations, enhances the sensitivity to outliers, and adjusts the coefficients Increased adaptability to different node characteristics.

[0024] Detailed explanation of the formula and the process of formula calculation and derivation: Set the actual data as follows: , first calculate the square of the time difference: ; Calculate the square root of the denominator multiplied by the weight:

[0025] Calculated : ; The result shows that for node 1, the traffic increase has a higher spatiotemporal characteristic value between time t1 and t2, which indicates that the traffic has increased significantly during this time period. For the step result, it means that node 1 needs to be monitored closely at this time point.

[0026] See also Figure 3 , the specific steps for obtaining the node dependent weight value are: S211: selecting a node dependency matrix according to the spatiotemporal characteristic values ​​of the traffic flow, adjusting and verifying the dependency matrix in combination with the spatial position of the node and the traffic sequence data, and generating an adjusted node dependency matrix; Based on the selection of spatiotemporal characteristic values ​​of traffic and the adjustment of the node dependency matrix, the dependency between nodes can be effectively adjusted and confirmed by integrating the spatial position of the node and the traffic sequence data. This process first involves the extraction of the spatial position data of the node, which comes from the actual geographical location information of the node deployment, and the traffic sequence data comes from the previous traffic monitoring records. After these data are processed through time series analysis, a time series diagram of the traffic pattern is generated for each node. Then, these data are input into the dependency matrix, and the mutual influence between the nodes is analyzed through matrix operations to determine the relationship strength and degree of dependence between the nodes, thereby generating an adjusted node dependency matrix. This matrix can provide a scientific basis for further optimization and management of the network.

[0027] S212: Starting from the adjusted node dependency matrix, integrating the node spatial position data and the flow sequence through matrix operations, calculating the preliminary weight values ​​of the mutual dependence between multiple nodes, and generating a preliminary dependency weight matrix between nodes; Starting from the adjusted node dependency matrix, the node spatial location data and traffic sequence are integrated through matrix operations. In this step, the integration of node spatial location data is mainly based on the spatial analysis technology of the geographic information system to determine the physical distance and connectivity between nodes. The traffic sequence data is obtained through previous data analysis. The key lies in how to use these data to calculate the mutual dependence weights between nodes. This not only involves simple data integration, but also requires the application of complex matrix operation formulas. Through these formulas, the dependency weights between nodes can be calculated. This weight matrix helps to further understand the interaction dynamics of nodes in the network and provide data support for the optimization and adjustment of the network.

[0028] S213: Combined with the preliminary dependency weight matrix between nodes, using the average traffic characteristics between nodes, the formula is adopted: ; Integrate the weights between multiple nodes and calculate the node dependency weight value; in, represents the dependency weight between node i and node j, represents the position parameters of nodes i and k, represents the flow coefficient of nodes k and j, represents the initial dependency weight of nodes i and j, represents the adjustment coefficient of node i, represents the adjustment coefficient of node j, Represents the total number of intermediate nodes used for averaging.

[0029] formula: ; The benefit of the formula is that by introducing the product of the position between nodes and the flow coefficient, weighted averaging and then normalizing by adding the square root of the node adjustment coefficient, the accuracy of the calculation and the rationality of the node weight distribution are increased.

[0030] Detailed explanation of the formula and the process of formula calculation and derivation: Assume there are 3 nodes, the position parameters between nodes is the inverse of the distance, assuming , node flow coefficient Assuming that it is the flow difference, assuming , initial dependency weight Based on the previous traffic analysis, it is assumed that , node adjustment coefficient and Assume that : ; The result shows that the dependency weight value between node 1 and node 2 is -1.132, indicating that under the given parameter settings, node 1 has a low dependence on node 2, and it may be necessary to re-evaluate the traffic configuration or physical connectivity between nodes to improve the overall efficiency of the network.

[0031] See also Figure 4 ,The specific steps for obtaining the node traffic association prediction value are: S311: Based on the node dependency weight value, the spatial and temporal feature matrix is ​​called, the spatial and temporal features of multiple nodes are combined according to the weights, and the spatial position and temporal feature values ​​are integrated to obtain a fused node feature matrix; Based on the comprehensive data of node dependency weight values ​​and spatial-temporal characteristics, an in-depth analysis of node feature integration was initiated, which involves the precise measurement of spatial position and the synchronous processing of time data. By aggregating the feature data of multiple nodes, the system can provide a comprehensive view showing the performance of each node under different temporal and spatial conditions. In this process, the data of each node is accumulated and adjusted according to its weight, ensuring the integrity and representativeness of the data, forming a matrix containing the comprehensive characteristics of all nodes, which provides basic data for subsequent analysis.

[0032] S312: Utilizing the fused node feature matrix, performing accumulation processing on the node features, calculating the weighted sum of multiple node features based on the node dependency weights, and performing node matching analysis on the accumulated results of the node features to generate a weighted fused node feature value matrix; Using the comprehensive feature matrix, the interdependence between nodes is further analyzed through weighted calculation. In this step, the system first matches the characteristics of each node with the characteristics of other nodes, analyzes the correlation between them, strengthens the relationship between nodes through matrix operations, and performs weighted processing on the characteristics between each pair of nodes to ensure that the characteristics of each node not only represent its own state, but also reflect the interaction with other nodes. This weighted processing helps to accurately identify potential connections between nodes and lays the foundation for building more advanced network models.

[0033] S313: Matching the traffic characteristics between nodes is performed by combining the node feature value matrix after weighted fusion and the traffic relationship of multiple nodes, using the formula: ; Calculate and generate node traffic correlation prediction values; in, represents the traffic correlation prediction value between node i and node j, represents the spatial characteristic coefficient of nodes i and k, represents the time feature weight of nodes k and j, represents the initial traffic matching relationship between nodes i and j, represents the feature matching weight between nodes i and j, Represents the dependency adjustment coefficient of node i, which is used to characterize the characteristic correction of node i in traffic dependency. Represents the dependency adjustment coefficient of node j, which is used to characterize the characteristic correction of node j in traffic dependency. Represents the total number of features between nodes and is used to normalize the entire calculation result.

[0034] formula: ; The benefit of the formula is that it allows the precise calculation of traffic correlation prediction values ​​between nodes through specific node characteristics and time series data combined with the dependency weights between nodes, which improves the accuracy of the prediction and the efficiency of network traffic management.

[0035] Detailed explanation of the formula and the process of formula calculation and derivation: Consider a simplified network with three nodes and the spatial characteristic coefficient of each node The time feature weights are 1.2, 0.8 and 1.5 respectively. They are 0.5, 1.1 and 0.7 respectively, and the initial flow matching relationship are 1.0, 1.3 and 0.9, feature matching weights 0.9 and 1.1, node-dependent adjustment coefficient They are 1.0 and 1.2 respectively. The specific calculation process is as follows: ; The result shows that the traffic correlation prediction value between node 1 and node 2 is 0.645, which indicates that the two nodes have a moderate degree of correlation after considering spatial and temporal factors. This prediction result will help network administrators adjust resource allocation and traffic management strategies to optimize network performance.

[0036] See also Figure 5 , the specific steps for obtaining the node resource scheduling plan are: S411: based on the node traffic association prediction value, select the traffic characteristic value of each node in the future time period, perform multi-time period recursive calculation for each node, gradually accumulate the traffic characteristics in multiple time periods, and obtain the accumulated traffic value of the node in the future time period; When making node resource scheduling plans based on node traffic correlation prediction values, we must first evaluate the traffic characteristic values ​​of the nodes in the future time period. This step requires the use of time series analysis methods to predict possible traffic changes at each node. By performing regression analysis on past traffic data, we can obtain the patterns and trends of traffic changes, predict future traffic, and then allocate weights based on the operational importance and traffic carrying capacity of each node to ensure that important nodes can obtain the necessary resource support first when traffic surges, thereby avoiding service interruptions due to insufficient resources. This analysis process not only needs to consider the data of a single node, but also the mutual influence between nodes in the entire network, and use complex network analysis and optimization algorithms to ensure that resources are reasonably allocated in the network.

[0037] S412: using the accumulated traffic value of the node in the future time period, setting the threshold range of the node load, judging the load status of each node in turn, marking all the overload nodes exceeding the set threshold, and obtaining the node load overload mark; In the calculation of the accumulated traffic value of the node in the future time period, the real-time traffic data of the node is accumulated, and the node is judged whether it is overloaded according to the set threshold, and the overloaded node is marked. This requires collecting the traffic data of each node at different time points and analyzing the data through mathematical modeling methods. For example, the previous traffic data is smoothed using moving average or exponential smoothing to reduce the impact of outliers and ensure the accuracy and reliability of the traffic data. Then, by comparing the total traffic in different time periods with the threshold, the nodes at risk are identified to provide a basis for subsequent resource scheduling. This step requires not only data processing technology, but also an in-depth understanding of the characteristics of network operation and the dependencies between nodes.

[0038] S413: Based on the node load overlimit mark, resource weights are allocated according to the node load priority, and the resource scheduling amount of multiple nodes is calculated using the formula: ; Allocate node resource scheduling amounts and obtain node resource scheduling plans; in, represents the resource scheduling amount of node i and node j, Represents the number of nodes or types of load priority factors involved in resource scheduling calculations. represents the load priority of node i, represents the load impact factor between node p and node j, represents the resource demand relationship between nodes i and j, represents the initial resource matching value of node i and node j, represents the load adjustment coefficient of node i and node j.

[0039] formula: ; The benefit of the formula is that it allows the system to dynamically adjust resource allocation when facing different node demands by comprehensively considering the load differences between nodes, resource demand relationships, and load adjustment coefficients, thereby enhancing the system's responsiveness to traffic fluctuations and resource utilization efficiency.

[0040] Detailed explanation of the formula and the process of formula calculation and derivation: Set the load value between node i and node j to units, represents the load influence factor, Units represent resource demand relationships, Units represent the initial resource matching, is the load adjustment factor. Substitute into the formula: ; ; ; ; ; The result shows that the resource scheduling amount between node i and node j is 147.35 units, which reflects how the resources between nodes are calculated and allocated under the given node priority, load impact factor and resource demand relationship, thereby supporting key support for nodes with high traffic.

[0041] See also Figure 6 , the specific steps for obtaining the dynamic resource allocation results are: S511: Based on the node resource scheduling plan, call the load status of multiple nodes, analyze the load data of core nodes and edge nodes, calculate the resource ratio of core nodes and edge nodes, perform preliminary ratio matching of core and edge node resources, and obtain the resource ratio result of core and edge nodes; Based on a detailed analysis of the node resource scheduling plan and in accordance with the specific resource allocation strategy, we first call the real-time load data of each node. This data comes from the system's monitoring module and is updated in real time to ensure the freshness and accuracy of the data. We then analyze the load status of the core nodes and edge nodes. Next, based on the preset resource allocation model and combined with the operating efficiency and response time data of each node, we calculate a preliminary resource allocation result. This calculation process takes into account the mutual influence and dependency between nodes and uses a load balancing algorithm to improve the overall operating efficiency of the system.

[0042] S512: Calculate the priority parameter of the core node using the resource allocation result of the core and edge nodes, and calculate the priority parameters of the multi-core nodes in a weighted accumulation manner to obtain the priority accumulation value of the core node; By refining the resource allocation results of core and edge nodes, the priority of each core node is quantified, and a dynamic priority algorithm is used. This algorithm dynamically adjusts the priority parameters according to the node's task urgency, previous data processing efficiency and its position in the network to ensure that critical tasks can be processed first. The accumulated core node priority is expressed as a numerical indicator, which will directly affect the node's resource allocation decision.

[0043] S513: Based on the accumulated priority value of the core node, readjust the resource ratio of the core and edge nodes, and combine the load requirements of the core nodes and edge nodes, using the formula: ; Calculate and generate dynamic resource allocation results; in, represents the dynamic resource allocation value between node i and node j, Represents the total number of nodes, represents the resource allocation value between nodes i and k, represents the load demand value of nodes k and j, represents the load correlation parameter between nodes i and j, represents the priority adjustment coefficient of node k, represents the load adjustment parameter of nodes i and j.

[0044] formula: ; The benefit of the formula is that it takes into account the differences and priority adjustments between nodes, optimizing the overall network performance by balancing the resource allocation among nodes.

[0045] Detailed explanation of the formula and the process of formula calculation and derivation: First, set the resource ratio of node i and node k to , the load demand of node k and node j is , the load correlation parameter between nodes i and j is , the priority adjustment coefficient of node k is , the load adjustment parameters of nodes i and j are ; Assumptions: , , , , ,calculate: ; The result shows that the dynamic resource allocation value between node i and node j is 2, indicating that the two nodes are relatively balanced in resource allocation and can effectively support task processing and data exchange between nodes.

[0046] A computing network resource intelligent management system, which is used to execute the above-mentioned computing network resource intelligent management method, and the system includes: The traffic feature extraction module samples the traffic of multiple nodes one by one based on the traffic monitoring data of computing nodes, calculates the traffic difference between nodes, analyzes the change rate in combination with the time series, extracts the spatial and temporal features, and obtains the spatiotemporal feature values ​​of the traffic; The node dependency weight calculation module selects the node dependency matrix based on the spatiotemporal characteristic values ​​of the traffic flow, calculates the weight by multiplying and accumulating the spatial position and the traffic sequence, and establishes a multi-head dependency matrix by taking the average value between heads to obtain the node dependency weight value; The association prediction matrix building module is based on the node dependency weight value, integrates the spatial and temporal features, calls the module to accumulate node features, weightedly fuses and matches the traffic relationship between nodes, builds the association matrix, and obtains the node traffic association prediction value; The dynamic resource scheduling module recursively calculates the traffic in multiple time periods in the future based on the node traffic correlation prediction value, cumulatively evaluates the load status, marks the overloaded nodes, reallocates resource weights according to priority, and obtains the dynamic resource allocation results.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for intelligent management of computing network resources, characterized in that: The following steps are involved: Based on the traffic monitoring data of computing nodes, the node traffic is sampled one by one, the traffic difference between adjacent nodes is calculated, the change rate is analyzed by time series, the spatial feature value is combined with the time feature, and the spatiotemporal feature value of the traffic is extracted; Based on the spatiotemporal characteristic values ​​of the traffic, a node dependency matrix is ​​selected, the node spatial position and traffic sequence are taken, weights are obtained through matrix operations, a multi-head dependency matrix is ​​established using the average value between heads, and the node dependency weight value is obtained; Based on the node dependency weight value, the spatial and temporal features are integrated, the module is called to merge the feature values, the node features are accumulated, the weighted fusion matching node traffic relationship is performed, the association matrix is ​​established, and the node traffic association prediction value is obtained; Based on the node traffic correlation prediction value, select the node traffic time period, recursively calculate the future multiple time periods, evaluate the load status by interval accumulation, set the threshold to mark the over-limit node, allocate resource weights according to priority, and obtain the node resource scheduling plan; Based on the node resource scheduling plan, the load status of multiple nodes is analyzed, the resource ratio of core and edge nodes is adjusted, the priority parameters are used to accumulate the core node priorities, and the resources are reallocated according to the ratio to obtain the dynamic resource allocation result.

2. The method for intelligent management of computing network resources according to claim 1, characterized in that: The traffic spatiotemporal characteristic values ​​include traffic space characteristics, traffic time characteristics, and adjacent node traffic difference characteristics. The node dependency weight values ​​include node dependency weights, node spatial position weights, and traffic sequence weights. The node traffic association prediction values ​​include node association matrices, time feature fusion values, and traffic relationship matching values. The node resource scheduling plan includes over-limit node marking, resource weight allocation, and priority sorting scheme. The dynamic resource allocation results include core node resource allocation, edge node resource allocation, and priority parameter adjustment.

3. The method for intelligent management of computing network resources according to claim 2, characterized in that: The steps of extracting the spatiotemporal characteristic values ​​of traffic flow are specifically as follows: Based on the computing power node traffic monitoring data, time series sampling is performed on multiple nodes, the traffic data of each node at each moment is recorded, and the node traffic time series matrix is ​​generated; Using the node traffic time series matrix, the traffic difference between any adjacent nodes is calculated, and the traffic difference characteristic values ​​between the adjacent nodes are extracted from the difference to obtain the node traffic difference characteristic matrix; The node traffic difference characteristic matrix is ​​combined with the change rate obtained through time series analysis, and a composite formula is used: ； Calculate the spatiotemporal value of traffic flow at each node at the differentiated moment, integrate the data of all time points, and extract the spatiotemporal characteristic value of traffic flow; in, represents the spatiotemporal characteristic value of traffic, Represents the total number of nodes, represents the flow value of node i at time t, Representative Node At the moment The flow value, that is, the flow at the previous moment, represents the time interval adjustment coefficient of node i, represents the spatial weight coefficient of node i, Represents the traffic adjustment variable of node i.

4. The method for intelligent management of computing network resources according to claim 3, characterized in that: The steps for obtaining the node dependency weight value are specifically as follows: According to the spatiotemporal characteristic values ​​of the traffic flow, a node dependency matrix is ​​selected, and the dependency matrix is ​​adjusted and verified in combination with the spatial position of the node and the traffic sequence data to generate an adjusted node dependency matrix; Starting from the adjusted node dependency matrix, integrating the node spatial position data and the flow sequence through matrix operations, calculating the preliminary weight values ​​of the mutual dependence between multiple nodes, and generating a preliminary dependency weight matrix between nodes; Combined with the preliminary dependency weight matrix between nodes, using the average traffic characteristics between nodes, the formula is adopted: ； Integrate the weights between multiple nodes and calculate the node dependency weight value; in, represents the dependency weight between node i and node j, represents the position parameters of nodes i and k, represents the flow coefficient of nodes k and j, represents the initial dependency weight of nodes i and j, represents the adjustment coefficient of node i, represents the adjustment coefficient of node j, Represents the total number of intermediate nodes used for averaging.

5. The method for intelligent management of computing network resources according to claim 4, characterized in that: The steps for obtaining the node traffic correlation prediction value are specifically as follows: Based on the node dependency weight value, the spatial and temporal feature matrix is ​​called, the spatial and temporal features of multiple nodes are combined according to the weights, and the spatial position and temporal feature values ​​are integrated to obtain a fused node feature matrix; Utilizing the fused node feature matrix, performing accumulation processing on the node features, calculating the weighted sum of multiple node features based on the node dependency weights, and performing node matching analysis on the accumulation results of the node features to generate a weighted fused node feature value matrix; Through the node feature value matrix after weighted fusion, combined with the traffic relationship of multiple nodes, the traffic characteristics between nodes are matched and processed, using the formula: ； Calculate and generate node traffic correlation prediction values; in, represents the traffic correlation prediction value between node i and node j, represents the spatial characteristic coefficient of nodes i and k, represents the time feature weight of nodes k and j, represents the initial traffic matching relationship between nodes i and j, represents the feature matching weight between nodes i and j, Represents the dependency adjustment coefficient of node i, which is used to characterize the characteristic correction of node i in traffic dependency. Represents the dependency adjustment coefficient of node j, which is used to characterize the characteristic correction of node j in traffic dependency. Represents the total number of features between nodes and is used to normalize the entire calculation result.

6. The method for intelligent management of computing network resources according to claim 5, characterized in that: The steps for obtaining the node resource scheduling plan are specifically as follows: Based on the node traffic association prediction value, the traffic characteristic value of each node in the future time period is selected, and multi-time period recursive calculation is performed for each node, and the traffic characteristics in multiple time periods are gradually accumulated to obtain the accumulated traffic value of the node in the future time period; The accumulated flow value of the node in the future time period is used to set the threshold range of the node load, and the load status of each node is judged in turn, and all nodes exceeding the set threshold are marked to obtain the node load overlimit mark; Based on the node load overlimit mark, resource weights are allocated according to the node load priority, and the resource scheduling amount of multiple nodes is calculated using the formula: ； Allocate node resource scheduling amounts and obtain node resource scheduling plans; in, represents the resource scheduling amount of node i and node j, Represents the number of nodes or types of load priority factors involved in resource scheduling calculations. represents the load priority of node i, represents the load impact factor between node p and node j, represents the resource demand relationship between nodes i and j, represents the initial resource matching value of node i and node j, represents the load adjustment coefficient of node i and node j.

7. The method for intelligent management of computing network resources according to claim 6, characterized in that: The steps for obtaining the dynamic resource allocation result are specifically as follows: Based on the node resource scheduling plan, the load status of multiple nodes is called, the load data of core nodes and edge nodes are analyzed, and the resource ratio of core nodes and edge nodes is calculated at the same time, and the resources of core nodes and edge nodes are preliminarily allocated to obtain the resource ratio result of core nodes and edge nodes; Calculate the priority parameters of the core nodes using the resource allocation result of the core and edge nodes, and calculate the priority parameters of the multi-core nodes in a weighted accumulation manner to obtain the priority accumulation value of the core nodes; Based on the accumulated priority value of the core node, the resource ratio of the core and edge nodes is readjusted, and the load requirements of the core nodes and edge nodes are combined to adopt the formula: ； Calculate and generate dynamic resource allocation results; in, represents the dynamic resource allocation value between node i and node j, Represents the total number of nodes, represents the resource allocation value between nodes i and k, represents the load demand value of nodes k and j, represents the load correlation parameter between nodes i and j, represents the priority adjustment coefficient of node k, represents the load adjustment parameter of nodes i and j.

8. An intelligent management system for computing network resources, characterized in that: According to any one of claims 1 to 7, the computing network resource intelligent management method, the system comprising: The traffic feature extraction module samples the traffic of multiple nodes one by one based on the traffic monitoring data of computing nodes, calculates the traffic difference between nodes, analyzes the change rate in combination with the time series, extracts the spatial and temporal features, and obtains the spatiotemporal feature values ​​of the traffic; The node dependency weight calculation module selects the node dependency matrix based on the spatiotemporal characteristic value of the traffic flow, calculates the weight by multiplying and accumulating the spatial position and the traffic sequence, and establishes a multi-head dependency matrix by taking the average value between heads to obtain the node dependency weight value; The association prediction matrix establishment module integrates the spatial and temporal features based on the node dependency weight value, calls the module to accumulate node features, performs weighted fusion matching on the traffic relationship between nodes, establishes the association matrix, and obtains the node traffic association prediction value; The dynamic resource scheduling module recursively calculates the traffic in multiple time periods in the future based on the node traffic association prediction value, cumulatively evaluates the load status, marks the overloaded nodes, reallocates resource weights according to priority, and obtains dynamic resource allocation results.

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