Method and System for Allocating and Managing Post-Prime IP Addresses Based on Resource Primes

Abstract

The present invention discloses a method and system for allocating and managing post-prime IP addresses based on resource primes, including the following steps: S1. Select an initial composite prime pair, obtain composite primes, and use them as benchmark values to obtain an initial sequence, where represents the post-prime increment; S2. Apply the reverse prediction increment algorithm to obtain a reverse prediction result; S3. Adaptively adjust the post-prime increment to generate a post-prime sequence; S4. Establish a network topology map and map network regions and subnets into a graph model; S5. Dynamically adjust the composite prime pair for IP address allocation through a graphical scheduling algorithm; S6. Apply a dynamic IP address allocation mechanism in combination with the post-prime sequence, adjust the subnet segment size of each region, and continuously optimize the IP address allocation strategy. The present invention utilizes the generation of the post-prime sequence and the dynamic prediction algorithm to optimize the allocation and management of IP addresses, and has the advantages of flexibility and high resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the technical field of computer networks, and particularly to a method and system for allocating and managing post-prime IP addresses based on resource prime numbers. Background Art

[0002] With the rapid development of the Internet and the continuous progress of information technology, the number of global network devices has increased significantly, and the allocation and management of IP addresses have become a crucial task in network engineering. With the explosion of user numbers and the diversification of network devices, the deficiencies of traditional IP address allocation methods have gradually emerged. Especially in large-scale network environments, the management and allocation of IP addresses face many challenges. Most traditional IP address allocation methods rely on static allocation strategies, usually based on predefined rules or manual configuration, lacking flexibility and adaptability, and unable to cope with the rapid changes in network environments and demand fluctuations.

[0003] Currently, the allocation of IP addresses in a network is usually based on static rules or simple algorithms and cannot be optimized in real time according to dynamic change factors such as network load, bandwidth requirements, or node density. The traditional static IP address allocation method is based on a fixed address segment division method and cannot flexibly respond to changing network requirements, resulting in waste or insufficiency of IP address resources. In addition, as the network scale expands, the complexity of the network also increases continuously. Traditional methods are difficult to effectively handle the IP address allocation problem in large-scale networks, especially in the case of multiple subnets, multiple regions, and complex topologies. Network administrators need to manually adjust IP address allocation, which not only increases the complexity of management but also easily leads to uneven allocation, affecting network performance and the effective utilization of resources.

[0004] For network environments with large variations in bandwidth requirements and node density, existing IP address allocation methods also face limitations. Traditional methods often allocate IP addresses based on the initial settings of the network topology. However, as the network usage increases and devices change continuously, the bandwidth requirements and node density of the network will fluctuate greatly. Without an effective mechanism for dynamic adjustment, the allocation of IP addresses will not be able to adapt to these changes, resulting in network congestion or waste of IP address resources. The static IP allocation method in the prior art cannot respond to these dynamic changes in real time. Therefore, in the case of high bandwidth requirements and high node density, the performance and stability of the network are difficult to guarantee.

[0005] In addition, most existing IP address allocation management systems adopt a single allocation method, ignoring the mutual relationship between resources. For example, in traditional IP address allocation methods, the division of network regions and subnets is based on fixed rules, lacking adaptive adjustment according to factors such as bandwidth requirements, traffic prediction, and node density. This static allocation method not only fails to meet the rapidly changing network requirements but may also lead to excessive IP address allocation in some regions, resulting in waste of resources, while in other regions, network congestion may occur due to insufficient allocation. Therefore, the existing technology lacks a method capable of dynamically adjusting IP address allocation strategies based on real-time network status, making the traditional IP address allocation method unable to adapt to the complexity of modern networks and the need for efficient utilization of network resources.

[0006] Therefore, how to provide a method and system for allocating and managing post-prime IP addresses based on resource primes is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] An object of the present invention is to propose a method and system for allocating and managing post-prime IP addresses based on resource primes. The present invention makes full use of the generation of post-prime sequences and the reverse prediction increment algorithm, combines network topology and dynamic traffic prediction, and optimizes IP address allocation management. By adaptively adjusting the post-prime increment, the subnet segment size is flexibly adjusted according to changes in bandwidth requirements and node density, ensuring the efficient utilization of IP address resources. This method has high adaptability, flexibility, and real-time performance, can effectively cope with dynamic changes in complex network environments, improve network performance and stability, significantly reduce resource waste, and the management process is automated, greatly simplifying the operation and maintenance work.

[0008] The method for allocating and managing post-prime IP addresses based on resource primes according to an embodiment of the present invention includes the following steps:

[0009] S1. Select an initial composite prime pair , and through obtain the composite prime , and use it as a reference value to obtain the initial sequence as , where represents the post-prime increment;

[0010] S2. Based on historical network traffic, bandwidth requirements, and node density, apply the reverse prediction increment algorithm, extract rules through retrospective analysis, and obtain the reverse prediction result;

[0011] S3. Adaptively adjust the post-prime increment according to the reverse prediction result to generate a post-prime sequence;

[0012] S4. Establish a network topology map, map the network area and subnets into a graph model, where the node attributes include bandwidth requirements, traffic prediction, and node density, and the edges represent the connection relationships between nodes;

[0013] S5. Through a graphical scheduling algorithm, traverse the nodes and edges in the network topology map, and dynamically adjust the composite prime number pairs for IP address allocation according to the topological structure of the nodes;

[0014] S6. Combine the post-prime number sequence and apply a dynamic IP address allocation mechanism to allocate IP address blocks to different regions, and adjust the size of the subnet segments in each region according to the bandwidth requirements and node density, continuously optimizing the IP address allocation strategy.

[0015] Optionally, the specific steps of S2 include:

[0016] S21. Obtain historical network traffic data, bandwidth requirement data, and node density data, and construct a standardized data set , where represents the th data point, represents the total number of historical data points;

[0017] S22. Process the standardized data set, use a regression model to fit the historical data, and further extract the non-linear trend in the data through high-order polynomial regression. The regression model is expressed as:

[0018] ;

[0019] Among them, represents the output of the regression model, , and represent the regression coefficients, represents the error term of the regression model;

[0020] S23. Combine the output of the regression model, analyze the historical data through backtracking, and use the reverse prediction increment algorithm to predict the post-prime number increment, obtaining the reverse prediction result:

[0021] ;

[0022] Among them, represents the predicted post-prime number increment at time , , and represent the regression coefficients, represents the data point at time , represents the constant term.

[0023] Optionally, S3 specifically includes:

[0024] S31. According to the reverse prediction result, when the bandwidth demand prediction shows that a bandwidth surge will occur in the future, the reverse prediction increment algorithm automatically adjusts the calculation rule of the post-prime increment, so that the post-prime increment value increases, thereby expanding the range of the sub-network segment; if the bandwidth demand is lower than the set threshold, the reverse prediction increment algorithm adjusts the calculation rule of the post-prime increment, so that the post-prime increment value decreases;

[0025] S32. Combining the node density data, optimize the calculation of the post-prime increment. The adjustment formula for the weighted influence of the node density is as follows:

[0026] ;

[0027] Where, represents the post-prime increment at the adjusted time , and represent constants, represents the node density at time , and represent the exponential weight coefficients;

[0028] S33. Generate a post-prime sequence based on the adjusted post-prime increment , where represents the th post-prime, represents the sequence length:

[0029] ;

[0030] Where, represents the composite prime number, represents the post-prime increment at the adjusted time .

[0031] Optionally, S4 specifically includes:

[0032] S41. Obtain the geographical location, bandwidth demand, traffic prediction and node density data of the network area, and map each network area and sub-network into a graph model, where the nodes of the graph represent the network area or sub-network;

[0033] S42. According to the geographical information and connection relationship of the network area, organize all nodes according to the corresponding topological structure to construct a network topology graph;

[0034] S43. Map the bandwidth demand, traffic prediction and node density corresponding to each node as the basic attributes of the node, and add edge connection attributes to each connected node.

[0035] Optionally, S5 specifically includes:

[0036] S51. Obtain a network topology graph. In the graph, take the bandwidth requirement, traffic prediction, and node density of each node as the attributes of the node, where the edges represent the connection relationships between the nodes, and record the initial composite prime number pairs of each node , to obtain composite primes as the preliminary post-prime distribution values;

[0037] S52. Define a scheduling weight function for each node:

[0038] ;

[0039] wherein, represents the scheduling weight of node , represents the bandwidth requirement of node , represents the traffic prediction of node , represents the node density of node , represents the maximum value of the bandwidth requirement, represents the maximum value of the traffic prediction, represents the maximum value of the node density, , , and represent the weight adjustment coefficients;

[0040] S53. Through a graphical scheduling algorithm, when traversing each node in the topology graph, dynamically adjust the composite prime number pairs of each node according to the scheduling weight of the node and the connection strength between the nodes, to obtain the adjusted composite primes:

[0041] ;

[0042] wherein, represents the adjusted composite prime, represents the adjustment coefficient related to the connection weight of node , represents the connection strength between node and node , represents the total number of nodes, represents the scheduling weight of node ;

[0043] S54. According to the scheduled composite prime number pairs , assign IP addresses to nodes to generate corresponding IP address segments. If the bandwidth requirements, traffic predictions, and node densities of nodes exceed the set threshold range, automatically increase the composite prime number pairs to expand the IP address range;

[0044] S55. When the network topology or node attributes change, re-evaluate the bandwidth requirements, traffic predictions, and node densities of each node, update the composite prime number pairs, and adjust the IP address allocation scheme in real time.

[0045] Optionally, the S6 specifically includes:

[0046] S61. Obtain the bandwidth requirements, node densities, and current post-prime number sequences of each area in the network topology diagram, and determine the initial subnet segment size of the area according to the bandwidth requirements and node densities of each area:

[0047] ;

[0048] Among them, represents the initial subnet segment size of the th area, represents the th area's bandwidth requirement, represents the th area's node density, represents the length of the post-prime number sequence of the th area;

[0049] S62. According to the historical network traffic and traffic prediction changes, predict the future bandwidth requirement change trend of each area through regression analysis method to obtain the bandwidth requirement prediction value, and adjust the subnet segment size of each area according to the bandwidth requirement prediction value:

[0050] ;

[0051] Among them, represents the adjusted subnet segment size of the th area, represents the adjustment coefficient of the bandwidth requirement change, represents the th area's bandwidth requirement prediction value at time ;

[0052] S63. Use the dynamic IP address allocation mechanism to allocate IP address blocks for each area in the network topology diagram in combination with the adjusted subnet segment size;

[0053] S64. When the network topology changes or the node attributes change, re-evaluate the bandwidth requirements and node densities of each area, and adjust the subnet segment size of the area to continuously optimize the IP address allocation strategy.

[0054] The post-prime IP address allocation and management system based on resource primes according to an embodiment of the present invention includes the following modules:

[0055] An initial composite prime selection module, configured to select an initial composite prime pair and calculate a composite prime as a reference value;

[0056] A historical data analysis and prediction module, configured to analyze historical network traffic, bandwidth requirements, and node density data, and predict future bandwidth requirements;

[0057] A post-prime increment adjustment module, which dynamically adjusts the post-prime increment according to the prediction result to optimize the post-prime sequence;

[0058] A network topology construction module, configured to construct a network topology diagram and map network regions and subnets into a graph model;

[0059] A graphical scheduling module, configured to dynamically adjust the composite prime pair of each node according to the network topology structure to implement IP address allocation;

[0060] A dynamic IP address allocation module, configured to allocate IP addresses to different regions according to the adjusted subnet segment size and continuously optimize the IP address allocation strategy.

[0061] The beneficial effects of the present invention are:

[0062] First of all, by introducing an IP address allocation and management method based on generating post-primes from resource primes, the present invention overcomes the staticness and inefficiency of traditional IP address allocation methods. Traditional IP address allocation methods are usually based on fixed rules or manual configuration and cannot dynamically respond to changes in bandwidth requirements and node density in the network environment. However, through the dynamic adjustment mechanism of the post-prime sequence in the present invention, combined with multiple factors such as historical network traffic, bandwidth requirements, and node density, the post-prime increment can be automatically adjusted according to real-time network demands, thereby achieving more accurate and flexible IP address allocation. This method significantly improves the utilization efficiency of IP addresses and avoids the possible waste or shortage of resources in traditional methods.

[0063] Secondly, through the construction of a network topology diagram and a graphical scheduling algorithm, the present invention further enhances the intelligence and efficiency of IP address allocation. In the prior art, IP address allocation is often based on simple rules or predetermined address segments, and this method cannot flexibly cope with complex network topologies and dynamic changing demands. However, in the present invention, by establishing a network topology diagram, mapping network regions and subnets into a graph model, and dynamically adjusting the composite prime pair of each node according to node attributes such as bandwidth requirements, traffic prediction, and node density. This enables IP address allocation to be adjusted in real time according to the network topology structure, thereby more effectively optimizing resource allocation and enhancing the stability and performance of the network.

[0064] In addition, through the reverse prediction increment algorithm and the adaptive adjustment mechanism, the present invention enables the IP address allocation to be continuously optimized. Traditional IP address allocation methods lack real-time feedback and dynamic adjustment mechanisms and cannot cope with changes in network load. The present invention combines the retrospective analysis of historical data, uses the reverse prediction algorithm to predict the post-prime increment, and dynamically adjusts the subnet segment size according to the prediction results. Whether it is a sharp increase in bandwidth demand or an increase in node density, the system can respond in real time and adjust the allocation strategy. This feedback mechanism not only improves the accuracy of IP address allocation but also reduces the need for manual intervention and improves the automation level of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0066] Figure 1 is the overall flowchart of the post-prime IP address allocation management method based on resource prime numbers proposed by the present invention;

[0067] Figure 2 is the structural schematic diagram of the post-prime IP address allocation management system based on resource prime numbers proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] The present invention will now be further described in detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, and therefore only showing the components related to the present invention.

[0069] Refer to Figure 1 , the post-prime IP address allocation management method based on resource prime numbers includes the following steps:

[0070] S1. Select an initial composite prime pair , and through obtain the composite prime , and use it as a reference value to obtain the initial sequence as , where represents the post-prime increment;

[0071] S2. Based on historical network traffic, bandwidth demand, and node density, apply the reverse prediction increment algorithm, extract the rules through retrospective analysis, and obtain the reverse prediction result;

[0072] S3. Adaptively adjust the post-prime increment according to the reverse prediction result to generate a post-prime sequence;

[0073] S4. Establish a network topology map, map the network area and subnets into a graph model, where the node attributes include bandwidth requirements, traffic prediction, and node density, and the edges represent the connection relationships between nodes;

[0074] S5. Through a graphical scheduling algorithm, traverse the nodes and edges in the network topology map, and dynamically adjust the composite prime pairs for IP address allocation according to the topological structure of the nodes;

[0075] S6. Combine the post-prime number sequence to apply a dynamic IP address allocation mechanism, allocate IP address blocks to different regions, and adjust the size of the subnet segments in each region according to bandwidth requirements and node density, continuously optimizing the IP address allocation strategy.

[0076] In this embodiment, the S2 specifically includes:

[0077] S21. Obtain historical network traffic data, bandwidth requirement data, and node density data, and construct a standardized data set , where represents the th data point, represents the total number of historical data points;

[0078] S22. Process the standardized data set, use a regression model to fit the historical data, and further extract the non-linear trend in the data through high-order polynomial regression. The regression model is expressed as:

[0079] ;

[0080] Among them, represents the output of the regression model, , and represent regression coefficients, represents the error term of the regression model;

[0081] S23. Combine the output of the regression model, analyze the historical data through backtracking, and use the reverse prediction increment algorithm to predict the post-prime number increment, obtaining the reverse prediction result:

[0082] ;

[0083] Among them, represents the predicted post-prime number increment at time , , and represent regression coefficients, represents the data point at time , represents the constant term.

[0084] In this embodiment, step S3 specifically includes:

[0085] S31. According to the reverse prediction result, when the bandwidth demand prediction shows that a bandwidth surge will occur in the future, the reverse prediction increment algorithm automatically adjusts the calculation rule of the post-prime increment, so that the value of the post-prime increment increases, thereby expanding the range of the sub-network segment; if the bandwidth demand is lower than the set threshold, the reverse prediction increment algorithm adjusts the calculation rule of the post-prime increment, so that the value of the post-prime increment decreases;

[0086] S32. Combine the node density data to optimize the calculation of the post-prime increment. The adjustment formula for the weighted influence of the node density is as follows:

[0087] ;

[0088] Where, represents the post-prime increment at the adjusted time , and represent constants, represents the node density at time , and represent the exponential weight coefficients;

[0089] S33. Generate a post-prime sequence based on the adjusted post-prime increment , where represents the th post-prime number, represents the sequence length:

[0090] ;

[0091] Where, represents the composite prime number, represents the post-prime increment at the adjusted time .

[0092] In this embodiment, step S4 specifically includes:

[0093] S41. Obtain the geographical location, bandwidth demand, traffic prediction, and node density data of the network area, and map each network area and sub-network into a graph model, where the nodes of the graph represent the network area or sub-network;

[0094] S42. According to the geographical information and connection relationship of the network area, organize all the nodes according to the corresponding topological structure to construct a network topology graph;

[0095] S43. Map the bandwidth demand, traffic prediction, and node density corresponding to each node as the basic attributes of the node, and add edge connection attributes to each connected node.

[0096] In this embodiment, S5 specifically includes:

[0097] S51. Obtain a network topology graph. In the graph, take the bandwidth requirement, traffic prediction, and node density of each node as the attributes of the node, where the edge represents the connection relationship between nodes, and record the initial composite prime number pair of each node , and obtain the composite prime number as the preliminary post-prime number allocation value;

[0098] S52. Define a scheduling weight function for each node:

[0099] ;

[0100] Among them, represents the scheduling weight of node , represents the bandwidth requirement of node , represents the traffic prediction of node , represents the node density of node , represents the maximum value of the bandwidth requirement, represents the maximum value of the traffic prediction, represents the maximum value of the node density, , , and represent the weight adjustment coefficients;

[0101] S53. Through the graphical scheduling algorithm, when traversing each node in the topology graph, dynamically adjust the composite prime number pair of each node according to the scheduling weight of the node and the connection strength between nodes, and obtain the adjusted composite prime number:

[0102] ;

[0103] Among them, represents the adjusted composite prime number, represents the adjustment coefficient related to the connection weight of node , represents the connection strength between node and node , represents the total number of nodes, represents the scheduling weight of node ;

[0104] S54. According to the scheduled composite prime number pair , assign IP addresses to the nodes to generate corresponding IP address segments. If the bandwidth requirements, traffic predictions, and node densities of the nodes exceed the set threshold range, automatically increase the twin prime pairs to expand the IP address range;

[0105] S55. When the network topology or node attributes change, re-evaluate the bandwidth requirements, traffic predictions, and node densities of each node, update the twin prime pairs, and adjust the IP address allocation scheme in real time.

[0106] In this embodiment, the S6 specifically includes:

[0107] S61. Obtain the bandwidth requirements, node densities, and the current post-prime number sequence of each area in the network topology graph, and determine the initial subnet segment size of the area according to the bandwidth requirements and node densities of each area:

[0108] ;

[0109] Among them, represents the initial subnet segment size of the th area, represents the bandwidth requirement of the th area, represents the node density of the th area, represents the length of the post-prime number sequence of the th area;

[0110] S62. According to the historical network traffic and traffic prediction changes, predict the future bandwidth requirement change trend of each area through regression analysis to obtain the bandwidth requirement prediction value, and adjust the subnet segment size of each area according to the bandwidth requirement prediction value:

[0111] ;

[0112] Among them, represents the adjusted subnet segment size of the th area, represents the adjustment coefficient of the bandwidth requirement change, represents the th area's bandwidth requirement prediction value at time ;

[0113] S63. Use the dynamic IP address allocation mechanism to allocate IP address blocks for each area in the network topology graph in combination with the adjusted subnet segment size;

[0114] S64. When the network topology changes or the node attributes change, re-evaluate the bandwidth requirements and node densities of each area, and adjust the subnet segment size of the area to continuously optimize the IP address allocation strategy.

[0115] Reference Figure 2 , a post-prime IP address allocation and management system generated based on resource primes, includes the following modules:

[0116] Initial composite prime selection module, used to select initial composite prime pairs and calculate composite primes as reference values;

[0117] Historical data analysis and prediction module, used to analyze historical network traffic, bandwidth requirements, and node density data, and predict future bandwidth requirements;

[0118] Post-prime increment adjustment module, dynamically adjusts the post-prime increment according to the prediction results to optimize the post-prime sequence;

[0119] Network topology construction module, used to construct a network topology diagram and map network regions and subnets into a graph model;

[0120] Graphical scheduling module, used to dynamically adjust the composite prime pairs of each node according to the network topology structure to achieve IP address allocation;

[0121] Dynamic IP address allocation module, used to allocate IP addresses to different regions according to the adjusted subnet segment size and continuously optimize the IP address allocation strategy.

[0122] Example 1:

[0123] To verify the feasibility of the present invention in implementation, the present invention is applied to the IP address allocation and management of the data center of a large Internet company. The company has multiple network regions, including office networks, customer service areas, development and testing areas, and storage server areas, etc. There are significant differences in the network requirements and node densities of each region. Traditional IP address allocation methods cannot effectively adapt to these dynamically changing requirements, resulting in waste and uneven distribution of network resources. Especially in the case of a sharp increase in network bandwidth requirements, traditional methods usually cannot quickly adjust the IP address allocation, resulting in insufficient IP addresses in some regions while there are too many idle IP addresses in other regions, thus affecting the overall network performance. To solve this problem, the company decides to adopt the dynamic IP address allocation and management method based on resource primes proposed by the present invention.

[0124] In this embodiment, the system of the present invention first dynamically adjusts the size of the subnetwork segments based on data such as the bandwidth requirements, node density, and traffic prediction of each area. For example, the bandwidth requirements of the office network and the customer service area are relatively high, and the system increases the size of the subnetwork segments in these areas according to the bandwidth requirements; while the storage server area has relatively low bandwidth requirements, so the subnetwork segments in this area are appropriately reduced. Through this dynamic adjustment strategy, the present invention can effectively allocate IP address resources according to real-time data, avoiding the waste of resources in the traditional static allocation method. At the same time, the system can also reasonably allocate IP addresses according to the network topology and the connection relationship between nodes, ensuring that the resources in each area are reasonably configured.

[0125] To further verify the effectiveness of the present invention, we conducted a three-month test during which multiple key data were collected, including information such as bandwidth requirements, node density, and traffic changes. The data collection showed that the average bandwidth requirement of the office network during the daytime working hours was 60 Gbps, and the peak could reach 100 Gbps; the average bandwidth requirement of the customer service area was 40 Gbps, and the peak was 70 Gbps; the bandwidth requirement of the storage server area was relatively low, with an average of 20 Gbps and a peak of 50 Gbps; and the bandwidth requirement of the development and testing area was 30 Gbps, with a peak of 60 Gbps. In terms of the density of network nodes, the node density in the office network area was relatively high, about 1000 nodes / km², while the node density in the storage server area was relatively low, about 200 nodes / km².

[0126] During the test, we compared the performance of the traditional static IP address allocation method and the dynamic IP address allocation method of the present invention in multiple key indicators. After applying the present invention, the utilization rate of network resources has been significantly improved, and the utilization rate of IP address resources has increased from the original 68% to 93%. Especially in areas with large fluctuations in bandwidth requirements, the system can flexibly adjust IP address allocation, reducing the waste of idle IP addresses. In terms of network performance, after applying the present invention, the overall network latency has been reduced by 20%, and the occurrence frequency of network congestion has also been reduced by 50%. Especially when the bandwidth requirement surges, the traditional method takes more than 10 minutes to adjust the IP address, while the present invention can complete the adjustment within 2 minutes, greatly improving the response speed of the system.

[0127] By implementing the present invention, the company has successfully solved the problems of staticity and resource waste in IP address allocation, improved resource utilization rate, reduced network latency, and can quickly respond in the case of a sharp increase in bandwidth demand. In addition, the automated adjustment function of the system reduces the need for manual intervention and greatly improves management efficiency. These results indicate that the present invention is not only innovative in theory, but also can bring significant performance improvement in practical applications, providing an efficient and flexible IP address management solution for similar enterprises.

[0128] As described above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent replacements or changes, should be covered by the protection scope of the present invention.

Claims

1. A method for allocating and managing post-prime IP addresses based on resource primes, characterized in that, It includes the following steps: S1. Select an initial pair of composite prime numbers , and through obtain a composite prime number , and use it as a reference value to obtain an initial sequence , where represents the increment of the subsequent prime number; S2. Based on historical network traffic, bandwidth requirements, and node density, apply the reverse prediction increment algorithm, extract patterns through retrospective analysis, and obtain the reverse prediction result; S3. Adaptively adjust the post-prime increment according to the reverse prediction result to generate a post-prime sequence; S4. Establish a network topology graph, map the network area and subnets into a graph model, where the node attributes include bandwidth requirements, traffic prediction, and node density, and the edges represent the connection relationships between nodes; S5. Through the graphical scheduling algorithm, traverse the nodes and edges in the network topology graph, and dynamically adjust the composite prime number pairs for IP address allocation according to the topological structure of the nodes; S6. Combine the post-prime number sequence and apply the dynamic IP address allocation mechanism to allocate IP address blocks to different regions, and adjust the subnet segment size of each region according to the bandwidth requirements and node density, continuously optimizing the IP address allocation strategy; The specific content of S6 includes: S61. Obtain the bandwidth requirements, node density, and the current post-prime number sequence of each region in the network topology graph, and determine the initial subnet segment size of the region according to the bandwidth requirements and node density of each region: ； Among them, represents the initial subnet segment size of the th area, represents the bandwidth requirement of the th area, represents the node density of the th area, represents the length of the post-prime number sequence of the th area; S62. According to the changes in historical network traffic and traffic prediction, predict the future bandwidth requirement change trend of each region through regression analysis to obtain the bandwidth requirement prediction value, and adjust the subnet segment size of each region according to the bandwidth requirement prediction value: ； Among them, represents the size of the adjusted sub-network segment of the th area, represents the adjustment coefficient of the change in bandwidth demand, represents the th area at time predicted value of bandwidth demand; S63. Use the dynamic IP address allocation mechanism to allocate IP address blocks to each region in the network topology graph in combination with the adjusted subnet segment size; S64. When the network topology changes or the node attributes change, re-evaluate the bandwidth requirements and node density of each region, and adjust the subnet segment size of the region to continuously optimize the IP address allocation strategy.

2. The method for allocating and managing post-prime IP addresses based on resource primes according to claim 1, wherein The specific content of S2 includes: S21. Obtain historical network traffic data, bandwidth demand data, and node density data, and construct a standardized data set , where represents the th data point, represents the total number of historical data points; S22. Process the standardized data set, fit the historical data using a regression model, and further extract the non-linear trend in the data through high-order polynomial regression. The regression model is expressed as: ； Among them, represents the output of the regression model, , and represent the regression coefficients, represents the error term of the regression model; S23. Combine the output of the regression model, retrospectively analyze the historical data, and use the reverse prediction increment algorithm to predict the post-prime number increment to obtain the reverse prediction result: ； Among them, represents the predicted post-prime increment at time , , and represent regression coefficients, represents the data point at time , represents the constant term.

3. The method for allocating and managing post-prime IP addresses based on resource primes according to claim 2, wherein The specific content of S3 includes: S31. According to the reverse prediction result, when the bandwidth requirement prediction shows that a bandwidth surge will occur in the future, the reverse prediction increment algorithm automatically adjusts the calculation rule of the post-prime number increment to increase the post-prime number increment value, thereby expanding the range of the subnet segment; if the bandwidth requirement is lower than the set threshold, the reverse prediction increment algorithm adjusts the calculation rule of the post-prime number increment to reduce the post-prime number increment value; S32. Combine the node density data to optimize the calculation of the post-prime number increment. The adjustment formula for the weighted influence of node density is as follows: ； Among them, represents the post-prime increment at the adjusted time , and represents a constant, represents the time node density of, and represents the exponential weight coefficient; S33. Generate a post-prime number sequence based on the adjusted post-prime increment , where represents the th post-prime number, represents the sequence length: ； Among them, represents a composite prime number, represents the adjusted time and the subsequent prime number increment.

4. The method for allocating and managing post-prime IP addresses generated based on resource primes according to claim 1, wherein The specific content of S4 includes: S41. Obtain the geographical location, bandwidth requirements, traffic prediction, and node density data of the network area, and map each network area and subnet into a graph model, where the nodes of the graph represent the network area or subnet; S42. According to the geographical information and connection relationships of the network area, organize all nodes according to the corresponding topological structure to construct a network topology graph; S43. Map the bandwidth requirements, traffic predictions, and node densities corresponding to each node as the basic attributes of the node, and add edge connection attributes to each connected node.

5. The method for allocating and managing post-prime IP addresses generated based on resource primes according to claim 1, characterized in that, The specific steps of S5 are as follows: S51. Obtain a network topology diagram. In the diagram, take the bandwidth requirement, traffic prediction, and node density of each node as the attributes of the node, where the edges represent the connection relationships between the nodes, and record the initial composite prime number pairs of each node , and obtain a composite prime number as the preliminary post-prime number allocation value; S52. Define a scheduling weight function for each node: ； Among them, represents the scheduling weight of node . represents the bandwidth requirement of node . represents the traffic prediction of node . represents the node density of node . represents the maximum value of the bandwidth requirement, represents the maximum value of the traffic prediction, represents the maximum value of the node density, , , and represent the weight adjustment coefficient; S53. When traversing each node in the topology graph through the graphical scheduling algorithm, according to the scheduling weight of the node and the connection strength between nodes, dynamically adjust the composite prime number pairs of each node to obtain the adjusted composite prime numbers: ； Among them, represents the adjusted composite prime number, represents a node and is the adjustment coefficient related to the connection weight, represents a node and a node and represents the connection strength therebetween, represents the total number of nodes, represents a node and is the scheduling weight; S54. According to the scheduled composite prime number pairs , assign IP addresses to the nodes to generate corresponding IP address segments. If the bandwidth requirements, traffic predictions, and node densities of the nodes exceed the set threshold range, automatically increase the composite prime number pairs to expand the IP address range; S55. When the network topology or node attributes change, re-evaluate the bandwidth requirements, traffic predictions, and node densities of each node, update the composite prime number pairs, and adjust the IP address allocation scheme in real time.

6. A post-prime IP address allocation and management system based on resource primes, which executes the method for generating and allocating post-prime IP addresses based on resource primes according to any one of claims 1 to 5, characterized in that It includes the following modules: Initial composite prime number selection module, which is used to select the initial composite prime number pairs and calculate the composite prime numbers as the reference values; Historical data analysis and prediction module, which is used to analyze the historical network traffic, bandwidth requirements, and node density data, and predict the future bandwidth requirements; Post-prime number increment adjustment module, which dynamically adjusts the post-prime number increment according to the prediction results to optimize the post-prime number sequence; Network topology construction module, which is used to construct a network topology graph and map the network area and subnets into a graph model; Graphical scheduling module, which is used to dynamically adjust the composite prime number pairs of each node according to the network topology structure to achieve IP address allocation; Dynamic IP address allocation module, which is used to allocate IP addresses to different regions according to the adjusted subnet segment size and continuously optimize the IP address allocation strategy.

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