IP address resource allocation method and system based on machine learning

Abstract

The invention discloses an IP address resource allocation method and system based on machine learning, and relates to the technical field of network resource allocation, and the method comprises the steps: receiving an IP address allocation request message sent by a to-be-allocated device; in response to the IP address allocation request message, acquiring an IP address field belonging to the gateway group from the IP address resource pool, dividing the IP address field belonging to the gateway group into a plurality of subdivision address intervals, and storing the plurality of subdivision address intervals after division into an address interval state set; predicting the IP address demand of the to-be-allocated device to obtain an IP address demand prediction result; inputting the resource occupation state of each subdivided address interval in the address interval state set and an IP address demand prediction result into the resource allocation model after training optimization is completed, and outputting an IP address allocation strategy; and allocating IP address resources to the to-be-allocated equipment according to the output IP address allocation strategy. According to the invention, dynamic and intelligent IP address allocation is realized.

Description

Technical Field

[0001] The present application relates to the technical field of network resource allocation, and in particular, to a method and system for allocating IP address resources based on machine learning. Background Art

[0002] IP address resources refer to digital labels used in the Internet Protocol to uniquely identify devices on the network. These resources are the basis of network communications, allowing data to be accurately sent to the correct destination. With the rapid development of Internet technology, the number of Internet users and terminal devices has increased dramatically, and the demand for IP address resources has also increased. In particular, IPv4 address resources have been gradually exhausted and it is difficult to meet future needs.

[0003] There are many problems with the existing IP address resource allocation methods, including: Low resource allocation efficiency: Traditional IP address allocation methods often use static allocation, which lacks flexibility and leads to low resource utilization. Lack of intelligent management: Current IP address management is mostly manual and lacks intelligent analysis capabilities.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a method and system for allocating IP address resources based on machine learning to solve the above-mentioned technical problems.

[0006] The present application provides an IP address resource allocation method based on machine learning, which is used for dynamic management and optimization of IP addresses in a network system, comprising: receiving an IP address allocation request message sent by a device to be allocated; wherein the IP address allocation request message carries an access point name; in response to the IP address allocation request message, obtaining an IP address segment belonging to a gateway group from an IP address resource pool, dividing the IP address segment belonging to the gateway group into a plurality of subdivided address intervals, and saving the divided plurality of subdivided address intervals to an address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; predicting the IP address demand of the device to be allocated, and obtaining an IP address demand prediction result; inputting the resource occupancy state of each subdivided address interval in the address interval state set and the IP address demand prediction result into a resource allocation model that has been trained and optimized, and outputting an IP address allocation strategy; wherein the resource segment The allocation model is constructed based on a machine learning algorithm; according to the output IP address allocation strategy, IP address resources are allocated to the device to be allocated; wherein the resource allocation model is trained and optimized based on the following steps: obtaining a historical gateway group and a historical IP address demand set; obtaining the IP address segment belonging to the historical gateway group from the IP address resource pool, and dividing the IP address segment belonging to the historical gateway group into a plurality of historical subdivided address intervals; according to the plurality of historical subdivided address intervals and the historical IP address demand set, using the Lagrange dual decomposition method to determine the demand-subdivided address interval association decision matrix; using the demand-subdivided address interval association decision matrix to construct and train a random forest model to adjust the model parameters of the random forest model; wherein the model parameters include the number of decision trees, the maximum depth of the decision tree, and the minimum number of samples required for splitting each node; the random forest model that has been trained and optimized is determined as the resource allocation model.

[0007] Furthermore, predicting the IP address demand of the device to be allocated to obtain the IP address demand prediction result includes: collecting historical IP address usage data of the allocated devices in the network system; establishing and training an optimized IP address demand prediction model based on the historical IP address usage data based on a long short-term memory neural network model; wherein the IP address demand prediction model includes an input gate layer, a forget gate layer, an update gate layer and an output gate layer; predicting the IP address demand of the device to be allocated based on the trained and optimized IP address demand prediction model to obtain the IP address demand prediction result.

[0008] Furthermore, the update gate layer is used to update the cell state, and the old cell state Update to current cell state ; ;in, ; t represents the current time; represents the input weight of the update gate layer; represents the bias of the update gate layer; represents the state structure of the forget gate layer, represents the input vector of the IP address demand prediction model at the current moment; Represents the activation vector of the update gate layer.

[0009] Further, the method of determining the demand-segmented address interval association decision matrix using the Lagrange dual decomposition method according to the multiple historical segmented address intervals and the historical IP address demand set includes: defining the objective function as: ;in, and Both are indicator variables used to index elements in a collection; An index representing historical IP address requirements; Indicates the index of the historical subdivision address range; Represents the historical IP address requirement set The historical subdivision address interval degree of association; is a prediction function used to represent historical IP address demand With historical segmentation address range The possibility of association; define the constraints as ; Wherein, the constraint condition is used to represent each historical IP address requirement Most related to Historical segment address range ;

[0010] Constructing the Lagrangian function ; in, , is the Lagrange multiplier; is the optimal solution to be solved; find the first optimal Lagrange multiplier and the second Lagrange multiplier so that the dual function Maximize; determine the optimal solution according to the first optimal Lagrangian multiplier and the second Lagrangian multiplier; and construct the demand-subdivided address interval association decision matrix according to the optimal solution.

[0011] Furthermore, after allocating IP address resources to the device to be allocated according to the output IP address allocation policy, the method also includes: monitoring the actual usage of the IP address resources allocated to the device to be allocated and collecting real-time feedback data; wherein the real-time feedback data includes the usage rate of the allocated IP address resources, the time when the device is disconnected, the time when the device joins the network, and the real-time change data of the network traffic; based on the collected real-time feedback data, evaluating the IP address allocation policy; and using the evaluation results and the real-time feedback data to adjust the IP address allocation policy.

[0012] Furthermore, historical IP address usage data includes IP address allocation data, device type, network traffic patterns, time period activity, geographic location, and connection duration.

[0013] Furthermore, after allocating IP address resources to the device to be allocated according to the output IP address allocation policy, the method further includes: updating the resource occupancy status of each subdivided address interval in the address interval status set.

[0014] Furthermore, allocating IP address resources to the device to be allocated according to the output IP address allocation policy includes: obtaining the service quality requirements of the network system and the load balancing requirements of the network system; allocating IP address resources to the device to be allocated according to the output IP address allocation policy, the service quality requirements of the network system and the load balancing requirements of the network system.

[0015] The present application provides an IP address resource allocation system based on machine learning, which is used for dynamic management and optimization of IP addresses in a network system, and includes an allocation request message receiving module, an address interval division module, an IP address demand prediction module, an IP address allocation strategy determination module and an IP address resource allocation module; wherein the allocation request message receiving module is used to receive an IP address allocation request message sent by a device to be allocated; wherein the IP address allocation request message carries an access point name; the address interval division module is used to respond to the IP address allocation request message, obtain an IP address segment belonging to a gateway group from an IP address resource pool, divide the IP address segment belonging to the gateway group into multiple subdivided address intervals, and save the divided multiple subdivided address intervals to an address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; the IP address demand prediction module is used to predict the IP address demand of the device to be allocated and obtain an IP address demand prediction result; the IP address allocation strategy determination module is used to allocate resources of each subdivided address interval in the address interval state set; The occupancy status and the IP address demand prediction result are input into the resource allocation model that has been trained and optimized, and an IP address allocation strategy is output; wherein the resource allocation model is constructed based on a machine learning algorithm; the IP address resource allocation module is used to allocate IP address resources to the device to be allocated according to the output IP address allocation strategy; wherein the resource allocation model is trained and optimized based on the following steps: obtaining a historical gateway group and a historical IP address demand set; obtaining an IP address segment belonging to the historical gateway group from the IP address resource pool, and dividing the IP address segment belonging to the historical gateway group into a plurality of historical subdivided address intervals; according to the plurality of historical subdivided address intervals and the historical IP address demand set, using the Lagrange dual decomposition method to determine the demand-subdivided address interval association decision matrix; using the demand-subdivided address interval association decision matrix to construct and train a random forest model to adjust the model parameters of the random forest model; wherein the model parameters include the number of decision trees, the maximum depth of the decision tree, and the minimum number of samples required for each node splitting; the random forest model that has been trained and optimized is determined as the resource allocation model.

[0016] Based on the embodiment provided by the present application, an IP address allocation request message sent by a device to be allocated is received; wherein the IP address allocation request message carries an access point name; in response to the IP address allocation request message, an IP address segment belonging to a gateway group is obtained from an IP address resource pool, the IP address segment belonging to the gateway group is divided into multiple subdivided address intervals, and the multiple subdivided address intervals after division are saved in an address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; the IP address demand of the device to be allocated is predicted to obtain an IP address demand prediction result; the resource occupancy state of each subdivided address interval in the address interval state set and the IP address demand prediction result are input into a resource allocation model that has been trained and optimized, and an IP address allocation strategy is output; wherein the resource allocation model is constructed based on a machine learning algorithm; according to the output IP address allocation strategy, IP address resources are allocated to the device to be allocated. Thus, the use of machine learning algorithms to predict future IP address needs is realized, and dynamic and intelligent IP address allocation is realized. Through this method, the utilization rate of IP address resources can be improved, the dynamic changes of the network system can be adapted, and network management can be simplified, operating costs can be reduced, and greater convenience and economic benefits can be brought to users and operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 This is a flowchart of an optional machine learning-based IP address resource allocation method according to an embodiment of the present application.

[0019] Figure 2 This is a flowchart of another optional machine learning-based IP address resource allocation method according to an embodiment of the present application.

[0020] Figure 3 This is a structural diagram of an optional machine learning-based IP address resource allocation system according to an embodiment of the present application.

[0021] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0023] Alternatively, if Figure 1As shown, the present application provides an IP address resource allocation method based on machine learning, which is used for dynamic management and optimization of IP addresses in a network system, including: S101, receiving an IP address allocation request message sent by a device to be allocated; wherein the IP address allocation request message carries an access point name; S102, in response to the IP address allocation request message, obtaining an IP address segment belonging to a gateway group from an IP address resource pool, dividing the IP address segment belonging to the gateway group into multiple subdivided address intervals, and saving the divided multiple subdivided address intervals to an address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; S103, predicting the IP address demand of the device to be allocated, and obtaining an IP address demand prediction result; S104, inputting the resource occupancy status of each subdivided address interval in the address interval state set and the IP address demand prediction result into a resource allocation model that has been trained and optimized, and outputting an IP address allocation strategy; wherein the resource allocation model is constructed based on a machine learning algorithm; S105, allocating IP address resources to the device to be allocated according to the output IP address allocation strategy.

[0024] Among them, the resource allocation model is trained and optimized based on the following steps: obtaining a historical gateway group and a historical IP address demand set; obtaining an IP address segment belonging to the historical gateway group from an IP address resource pool, and dividing the IP address segment belonging to the historical gateway group into multiple historical subdivided address intervals; according to multiple historical subdivided address intervals and a historical IP address demand set, using the Lagrange dual decomposition method to determine the demand-segmented address interval association decision matrix; using the demand-segmented address interval association decision matrix to construct and train a random forest model to adjust the model parameters of the random forest model; wherein the model parameters include the number of decision trees, the maximum depth of the decision tree, and the minimum number of samples required for each node splitting; and determining the trained and optimized random forest model as the resource allocation model.

[0025] Based on the embodiment provided by the present application, an IP address allocation request message sent by a device to be allocated is received; wherein the IP address allocation request message carries an access point name; in response to the IP address allocation request message, an IP address segment belonging to a gateway group is obtained from an IP address resource pool, the IP address segment belonging to the gateway group is divided into multiple subdivided address intervals, and the multiple subdivided address intervals after division are saved in an address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; the IP address demand of the device to be allocated is predicted to obtain an IP address demand prediction result; the resource occupancy state of each subdivided address interval in the address interval state set and the IP address demand prediction result are input into a resource allocation model that has been trained and optimized, and an IP address allocation strategy is output; wherein the resource allocation model is constructed based on a machine learning algorithm; and according to the output IP address allocation strategy, IP address resources are allocated to the device to be allocated. Thus, the network traffic and usage patterns are analyzed by using a machine learning algorithm, future IP address demand is predicted, and dynamic and intelligent IP address allocation is realized. This method can improve the utilization rate of IP address resources and adapt to dynamic changes in the network. It can also simplify network management, reduce operating costs, and bring greater convenience and economic benefits to users and operators.

[0026] Furthermore, if Figure 2 As shown, the IP address demand of the device to be allocated is predicted to obtain the IP address demand prediction result, including: S201, collecting the historical IP address usage data of the allocated devices in the network system; S202, based on the long short-term memory neural network model, establishing and training an optimized IP address demand prediction model according to the historical IP address usage data; wherein the IP address demand prediction model includes an input gate layer, a forget gate layer, an update gate layer and an output gate layer; S203, according to the trained and optimized IP address demand prediction model, the IP address demand of the device to be allocated is predicted to obtain the IP address demand prediction result.

[0027] Based on the embodiments provided in the present application, the long short-term memory neural network model can be used to capture long-term dependencies in time series data, thereby improving the prediction accuracy of IP address demand; by accurately predicting IP address demand, IP address resources can be more effectively planned and allocated, reducing resource waste; the collection and analysis of historical IP address usage data helps to understand network usage patterns, thereby conducting more scientific network planning; the automated IP address demand prediction model reduces manual intervention and improves the efficiency of network management; the LSTM model can adapt to the dynamic changes in network demand, so that the prediction model can timely reflect the latest network usage trends; by reducing unnecessary IP address allocation, network operating costs can be reduced; reasonable IP address allocation can avoid network congestion and improve the overall performance of the network; timely meeting the IP address requirements of devices can enhance the network experience of end users; the prediction results can provide decision support for network expansion and upgrades; the prediction model can be adjusted and expanded according to the growth of the network scale to adapt to changing needs; by predicting future IP address requirements, network administrators can flexibly adjust network configurations to adapt to different usage scenarios; the collected historical data and prediction results can be used for further analysis to promote data-driven network management.

[0028] Furthermore, the update gate layer is used to update the cell state, converting the old cell state Update to current cell state ; in, ; t represents the current time; Represents the input weight of the update gate layer; Indicates the bias of updating the gate layer; Represents the state structure of the forget gate layer, represents the input vector of the IP address demand prediction model at the current moment; Represents the activation vector for updating the gate layer.

[0029] Based on the embodiments provided in the present application, the update gate layer dynamically determines which information should be retained in the cell state and which information should be updated or forgotten, thereby enabling the model to capture dynamic changes in the data. The update gate layer helps the LSTM neural network model capture long-term dependencies, which is particularly important for sequence prediction tasks such as IP address demand prediction. By effectively updating the cell state, the model can more accurately predict future IP address demand. The model can adapt to changes in time series data and respond quickly to changes in network conditions. The calculation formula of the update gate layer ensures the smoothness and continuity of state transitions, which helps improve the prediction stability of the model. By reasonably updating the cell state, the model can avoid overfitting of training data and improve generalization ability. The working mechanism of the update gate layer can help researchers and network administrators understand how the model makes predictions based on historical data. The model can flexibly handle sequence data of different lengths and adapt to different prediction tasks. The update gate layer enables the model to update the cell state in real time and make real-time predictions. Even when there is noise in the input data, the update gate layer can help the model maintain stable prediction performance. The update gate layer helps to fuse feature information from different time steps into the cell state to improve the accuracy of prediction. By adjusting the parameters of the update gate layer, the model can be optimized to adapt to the prediction task. For complex time series data, the update gate layer can help the model recognize and understand complex patterns in the data.

[0030] Furthermore, according to the multiple historical subdivided address intervals and the historical IP address demand set, the demand-subdivided address interval association decision matrix is ​​determined using the Lagrange dual decomposition method, including: defining the objective function as: ;in, and Both are indicator variables used to index elements in a collection; An index representing historical IP address requirements; Indicates the index of the historical subdivision address range; Represents the historical IP address demand set With historical segmentation address range degree of association; is a prediction function used to represent historical IP address demand With historical segmentation address range The possibility of association; define the constraints as ; Among them, the constraint is used to represent each historical IP address requirement Most related to Historical segment address range ; Construct Lagrangian function ; in, , is the Lagrange multiplier; is the optimal solution to be solved; find the first optimal Lagrange multiplier and the second Lagrange multiplier so that the dual function Maximize; determine the optimal solution based on the first optimal Lagrangian multiplier and the second Lagrangian multiplier; and construct a demand-segmented address interval association decision matrix based on the optimal solution.

[0031] Based on the embodiments provided in the present application, an integrated learning method is used to construct multiple decision trees, and the stability and accuracy of the prediction are improved through a voting mechanism; random forests can process a large amount of data and features, reduce the risk of overfitting, and improve the generalization ability of the model; optimizing model parameters can improve the performance of the model and make it better adapt to specific data sets and application scenarios; the association decision matrix provides a clear guide for resource allocation, making allocation decisions more scientific and reasonable; this process can ensure that the optimal solution for resource allocation is found under the premise of satisfying the constraints; the optimal solution provides an optimal strategy for resource allocation under all constraints.

[0032] Furthermore, after allocating IP address resources to the device to be allocated according to the output IP address allocation strategy, the method also includes: monitoring the actual usage of the IP address resources allocated to the device to be allocated and collecting real-time feedback data; wherein the real-time feedback data includes the usage rate of the allocated IP address resources, the time when the device is disconnected, the time when the device joins the network, and the real-time change data of the network traffic; based on the collected real-time feedback data, evaluating the IP address allocation strategy; and using the evaluation results and the real-time feedback data to adjust the IP address allocation strategy.

[0033] Furthermore, historical IP address usage data includes IP address allocation data, device type, network traffic patterns, time period activity, geographic location, and connection duration.

[0034] Furthermore, after allocating IP address resources to the device to be allocated according to the output IP address allocation policy, the method further includes: updating the resource occupancy status of each subdivided address interval in the address interval status set.

[0035] Furthermore, the IP address allocation strategy output by the resource allocation model is combined with the adjustment result of the network administrator to optimize the IP address allocation strategy.

[0036] Furthermore, according to the output IP address allocation strategy, IP address resources are allocated to the device to be allocated, including: obtaining the service quality requirements of the network system and the load balancing requirements of the network system; according to the output IP address allocation strategy, the service quality requirements of the network system and the load balancing requirements of the network system, IP address resources are allocated to the device to be allocated.

[0037] Alternatively, if Figure 3 As shown, the present application provides an IP address resource allocation system based on machine learning, which is used for dynamic management and optimization of IP addresses in a network system, including an allocation request message receiving module 301, an address interval division module 302, an IP address demand prediction module 303, an IP address allocation strategy determination module 304 and an IP address resource allocation module 305; wherein the allocation request message receiving module 301 is used to receive an IP address allocation request message sent by a device to be allocated; wherein the IP address allocation request message carries an access point name; the address interval division module 302 is used to respond to the IP address allocation request message, obtain an IP address segment belonging to a gateway group from an IP address resource pool, and divide the IP address segment belonging to the gateway group into multiple Subdividing the address interval, and saving the multiple subdivided address intervals after division into the address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; an IP address demand prediction module 303, used to predict the IP address demand of the device to be allocated, and obtain the IP address demand prediction result; an IP address allocation strategy determination module 304, used to input the resource occupancy status of each subdivided address interval in the address interval state set and the IP address demand prediction result into the resource allocation model that has been trained and optimized, and output the IP address allocation strategy; wherein the resource allocation model is constructed based on a machine learning algorithm; an IP address resource allocation module 305, used to allocate IP address resources to the device to be allocated according to the output IP address allocation strategy.

[0038] Among them, the resource allocation model is trained and optimized based on the following steps: obtaining a historical gateway group and a historical IP address demand set; obtaining an IP address segment belonging to the historical gateway group from an IP address resource pool, and dividing the IP address segment belonging to the historical gateway group into multiple historical subdivided address intervals; according to multiple historical subdivided address intervals and a historical IP address demand set, using the Lagrange dual decomposition method to determine the demand-segmented address interval association decision matrix; using the demand-segmented address interval association decision matrix to construct and train a random forest model to adjust the model parameters of the random forest model; wherein the model parameters include the number of decision trees, the maximum depth of the decision tree, and the minimum number of samples required for each node splitting; and determining the trained and optimized random forest model as the resource allocation model.

[0039] Based on the embodiment provided by the present application, according to the allocation request message receiving module, the address interval division module, the IP address demand prediction module, the IP address allocation strategy determination module and the IP address resource allocation module, the use of machine learning algorithms to predict future IP address needs is realized, and dynamic and intelligent IP address allocation is realized. Through this method, the utilization rate of IP address resources can be improved, the dynamic changes of the network system can be adapted, and network management can be simplified, operating costs can be reduced, and greater convenience and economic benefits can be brought to users and operators.

[0040] It should be noted that in the present application, the embodiments implemented on the IP address resource allocation system side based on machine learning can be referenced with the embodiments implemented on the IP address resource allocation method side based on machine learning, and the present application will not go into details one by one.

[0041] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A machine learning-based IP address resource allocation method for dynamic management and optimization of IP addresses in a network system, characterized in that: include: Receiving an IP address allocation request message sent by a device to be allocated; wherein the IP address allocation request message carries an access point name; In response to the IP address allocation request message, obtain an IP address segment belonging to a gateway group from an IP address resource pool, divide the IP address segment belonging to the gateway group into a plurality of subdivided address intervals, and save the divided plurality of subdivided address intervals to an address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; Predicting the IP address demand of the device to be allocated to obtain an IP address demand prediction result; Input the resource occupancy status of each subdivided address interval in the address interval status set and the IP address demand prediction result into the trained and optimized resource allocation model, and output the IP address allocation strategy; wherein the resource allocation model is constructed based on a machine learning algorithm; Allocate IP address resources to the device to be allocated according to the output IP address allocation policy; The resource allocation model is trained and optimized based on the following steps: Get the historical gateway group and historical IP address demand set; Acquire the IP address segment belonging to the historical gateway group from the IP address resource pool, and divide the IP address segment belonging to the historical gateway group into a plurality of historical subdivided address intervals; Determine a demand-segmented address interval association decision matrix using Lagrange dual decomposition method according to the multiple historical segmented address intervals and the historical IP address demand set; Using the demand-segmented address interval association decision matrix, a random forest model is constructed and trained to adjust model parameters of the random forest model; wherein the model parameters include the number of decision trees, the maximum depth of the decision trees, and the minimum number of samples required for each node to split; The random forest model that has been trained and optimized is determined as the resource allocation model.

2. The method for allocating IP address resources based on machine learning according to claim 1, characterized in that: The predicting of the IP address demand of the device to be allocated to obtain the IP address demand prediction result includes: Collect historical IP address usage data of allocated devices in the network system; Based on the long short-term memory neural network model, an IP address demand prediction model is established and trained to optimize the IP address demand prediction model according to the historical IP address usage data; wherein the IP address demand prediction model includes an input gate layer, a forget gate layer, an update gate layer and an output gate layer; According to the trained and optimized IP address demand prediction model, the IP address demand of the device to be allocated is predicted to obtain the IP address demand prediction result.

3. The method for allocating IP address resources based on machine learning according to claim 2, characterized in that: The update gate layer is used to update the cell state and convert the old cell state Update to current cell state ; ； in, ; t represents the current time; represents the input weight of the update gate layer; represents the bias of the update gate layer; represents the state structure of the forget gate layer, represents the input vector of the IP address demand prediction model at the current moment; Represents the activation vector of the update gate layer.

4. The method for allocating IP address resources based on machine learning according to claim 1, characterized in that: The method of determining a demand-segmented address interval association determination matrix using the Lagrange dual decomposition method according to the multiple historical segmented address intervals and the historical IP address demand set includes: The objective function is defined as: ; in, and Both are indicator variables used to index elements in a collection; An index representing historical IP address requirements; Indicates the index of the historical subdivision address range; Represents the historical IP address requirement set The historical subdivision address interval degree of association; is a prediction function used to represent historical IP address demand With historical segmentation address range Possibility of association; Define the constraints as ; Wherein, the constraint condition is used to represent each historical IP address requirement Most related to Historical segment address range ; Constructing the Lagrangian function ; ； in, , is the Lagrange multiplier; is the optimal solution to be solved; Find the first optimal Lagrange multiplier and the second Lagrange multiplier so that the dual function maximize; Determining the optimal solution according to the first optimal Lagrangian multiplier and the second Lagrangian multiplier; According to the optimal solution, the demand-subdivided address interval association determination matrix is ​​constructed.

5. The method for allocating IP address resources based on machine learning according to claim 1, characterized in that: After allocating IP address resources to the device to be allocated according to the output IP address allocation policy, the method further includes: Monitor the actual usage of the IP address resources allocated to the device to be allocated and collect real-time feedback data; wherein the real-time feedback data includes the usage rate of the allocated IP address resources, the time when the device is disconnected, the time when the device joins the network, and the real-time change data of the network traffic; Evaluate the IP address allocation strategy based on the collected real-time feedback data; The evaluation results and the real-time feedback data are used to adjust the IP address allocation strategy.

6. The method for allocating IP address resources based on machine learning according to claim 2, characterized in that: Historical IP address usage data includes IP address allocation data, device type, network traffic patterns, time period activity, geographic location, and connection duration.

7. The method for allocating IP address resources based on machine learning according to claim 1, characterized in that: After allocating IP address resources to the device to be allocated according to the output IP address allocation policy, the method further includes: The resource occupation status of each subdivided address interval in the address interval status set is updated.

8. The method for allocating IP address resources based on machine learning according to claim 1, characterized in that: The allocating IP address resources to the device to be allocated according to the output IP address allocation policy includes: Obtaining the quality of service requirements of the network system and the load balancing requirements of the network system; According to the output IP address allocation strategy, the service quality requirement of the network system and the load balancing requirement of the network system, IP address resources are allocated to the device to be allocated.

9. A machine learning-based IP address resource allocation system, the system implementing the method as claimed in claim 1, the system is used for dynamic management and optimization of IP addresses in a network system, characterized in that: It includes an allocation request message receiving module, an address interval division module, an IP address demand prediction module, an IP address allocation strategy determination module and an IP address resource allocation module; wherein, The allocation request message receiving module is used to receive an IP address allocation request message sent by a device to be allocated; wherein the IP address allocation request message carries an access point name; The address interval division module is used to obtain the IP address segment belonging to the gateway group from the IP address resource pool in response to the IP address allocation request message, divide the IP address segment belonging to the gateway group into multiple subdivided address intervals, and save the divided multiple subdivided address intervals to the address interval state set; wherein the gateway group is the group to which the access point name carried in the IP address allocation request message belongs; The IP address demand prediction module is used to predict the IP address demand of the device to be allocated and obtain an IP address demand prediction result; The IP address allocation strategy determination module is used to input the resource occupancy status of each subdivided address interval in the address interval status set and the IP address demand prediction result into the resource allocation model that has been trained and optimized, and output the IP address allocation strategy; wherein the resource allocation model is constructed based on a machine learning algorithm; The IP address resource allocation module is used to allocate IP address resources to the device to be allocated according to the output IP address allocation policy; The resource allocation model is trained and optimized based on the following steps: Get the historical gateway group and historical IP address demand set; Acquire the IP address segment belonging to the historical gateway group from the IP address resource pool, and divide the IP address segment belonging to the historical gateway group into a plurality of historical subdivided address intervals; Determine a demand-segmented address interval association decision matrix using Lagrange dual decomposition method according to the multiple historical segmented address intervals and the historical IP address demand set; Using the demand-segmented address interval association decision matrix, a random forest model is constructed and trained to adjust model parameters of the random forest model; wherein the model parameters include the number of decision trees, the maximum depth of the decision trees, and the minimum number of samples required for each node to split; The random forest model that has been trained and optimized is determined as the resource allocation model.

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