Routing decision method and device, electronic equipment and computer program product

Abstract

The invention relates to a routing decision method and device, electronic equipment and a computer program product, and belongs to the technical field of network communication. The method comprises: in response to a DNS request of a sending end user, obtaining multi-dimensional routing features in the DNS request, the multi-dimensional routing features comprising domain name features of the DNS request, and regional features and mobile state features of the sending end user and a receiving end user; inputting the multi-dimensional routing features into a pre-trained routing decision model to obtain a target routing strategy corresponding to the DNS request; and obtaining a target routing path between the sending end user and the receiving end user based on the target routing strategy. The cross-border communication success rate can be improved, the analysis delay can be reduced, and the cross-provincial access path and the roaming switching experience can be optimized.

Description

Technical Field

[0001] This disclosure relates to the field of network communication technology, and more specifically, to a routing decision method, a routing decision device, an electronic device, and a computer program product. Background Technology

[0002] In the current network communication environment, multinational corporations frequently face cross-border communication failures due to the use of the same private IP (Internet Protocol) address range. This not only affects normal business transactions between companies but also causes serious data transmission interruptions. Meanwhile, existing DNS lookup mechanisms suffer from significant latency issues, particularly in cross-border scenarios, where this latency is even more pronounced and severely impacts user experience.

[0003] Furthermore, domestic cross-provincial DNS (Domain Name System) resolution paths are often not optimal, causing data transmission to traverse more unnecessary network nodes, increasing communication latency and resource consumption. In mobile communications, 5G roaming handover processes suffer from high failure rates, impacting the continuity of communication for mobile users. These issues collectively constitute a pressing technical challenge in the field of current network routing decision-making.

[0004] Therefore, there is an urgent need in this field for a routing decision method that can significantly improve the success rate of cross-border communication, reduce parsing latency, and optimize cross-provincial access paths and roaming switching experience.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a routing decision method, routing decision device, electronic device, and computer program product, which can significantly improve the success rate of cross-border communication, reduce parsing latency, and optimize cross-provincial access paths and roaming switching experience, at least to a certain extent.

[0007] According to a first aspect of this disclosure, a routing decision method is provided, comprising:

[0008] In response to a DNS request from a sending user, multidimensional routing features are obtained from the DNS request. These multidimensional routing features include the domain name features of the DNS request, as well as the geographic features and mobility features of the sending and receiving users.

[0009] The multidimensional routing features are input into a pre-trained routing decision model to obtain the target routing strategy corresponding to the DNS request;

[0010] The target routing path between the sending user and the receiving user is obtained based on the target routing policy.

[0011] In one exemplary embodiment of this disclosure, the method further includes:

[0012] Add a dedicated attribute field to the border gateway protocol;

[0013] The target routing policy is sent to each network node through the border gateway protocol, and the multi-dimensional routing features are carried to each network node through the dedicated attribute field.

[0014] In one exemplary embodiment of this disclosure, the method further includes:

[0015] Real-time acquisition of network parameters for each network node based on the target routing path, the network parameters including priority parameters corresponding to the target routing path, service quality scores of the network nodes, and bandwidth consumption parameters under the target routing path;

[0016] The network parameters are input into the reinforcement learning model to obtain the reinforcement learning reward parameters corresponding to the target routing path;

[0017] The routing decision model is updated based on the reinforcement learning reward parameters, and the updated target routing policy is obtained based on the updated routing decision model.

[0018] In one exemplary embodiment of this disclosure, after sending the target routing policy to each network node via the border gateway protocol, the method further includes:

[0019] Obtain the number of paths in the target routing policy and the traffic percentage of each routing path, and obtain the routing quality entropy value of the network node based on the number of paths and the traffic percentage of each routing path.

[0020] If the routing quality entropy value is greater than a preset entropy threshold, it is determined that a routing conflict has occurred in the network node, and the target routing path is switched.

[0021] In one exemplary embodiment of this disclosure, the step of inputting the multidimensional routing features into a pre-trained routing decision model to obtain the target routing policy corresponding to the DNS request includes:

[0022] The multidimensional routing features are input into a pre-trained routing decision model. Based on the objective function in the routing decision model, the target routing strategy corresponding to the DNS request is obtained. The objective function is used to balance multiple metrics, including path latency, packet loss rate, and bandwidth utilization.

[0023] In one exemplary embodiment of this disclosure, obtaining the domain name characteristics of the DNS request includes:

[0024] Obtain the domain name suffixes at each level of the DNS request, including top-level domain name suffixes, second-level domain name suffixes, and third-level domain name suffixes;

[0025] The domain name characteristics of the DNS request are obtained based on the weighting factors and adjustment coefficients corresponding to the domain name suffixes at each level.

[0026] In one exemplary embodiment of this disclosure, obtaining the geographic characteristics of the sending end user and the receiving end user includes:

[0027] Obtain the user location codes of the sending and receiving users;

[0028] The similarity between the user's geographic code and the geographic codes of each region is calculated, and the geographic characteristics of the user are obtained based on the geographic information corresponding to the first target region with the lowest similarity.

[0029] The similarity between the user's location code and the location codes of each region is calculated, and the location characteristics of the user are obtained based on the location information corresponding to the second target region with the lowest similarity.

[0030] In one exemplary embodiment of this disclosure, obtaining the mobility state characteristics of the sending user and the receiving user includes:

[0031] Based on the state vector of the sending user at the previous time point, the state transition matrix, and the process noise, the state vector of the sending user at the current time point is obtained.

[0032] Based on the state vector of the sending user at the current time point, as well as the observation matrix and observation noise, the movement state characteristics of the sending user at the current time point are obtained.

[0033] Based on the state vector of the receiving user at the previous time point, as well as the state transition matrix and process noise, the state vector of the receiving user at the current time point is obtained.

[0034] Based on the state vector of the receiving user at the current time point, as well as the observation matrix and observation noise, the movement state characteristics of the receiving user at the current time point are obtained.

[0035] In one exemplary embodiment of this disclosure, the method further includes:

[0036] Obtain the real-time movement speed of the sending user at the current time point;

[0037] The user message survival time is calculated based on the base value of the user message survival time, as well as the real-time movement speed and speed threshold of the sending user.

[0038] Based on the user message lifespan of the sending user, the target transmitting base station corresponding to the sending user at the current time point is determined.

[0039] According to a second aspect of this disclosure, a routing decision apparatus is provided, comprising:

[0040] The multi-dimensional feature acquisition module is used to respond to a DNS request from a sending user and acquire multi-dimensional routing features in the DNS request. The multi-dimensional routing features include the domain name features of the DNS request, as well as the geographic features and mobility features of the sending user and the receiving user.

[0041] The routing strategy determination module is used to input the multi-dimensional routing features into a pre-trained routing decision model to obtain the target routing strategy corresponding to the DNS request;

[0042] The target path determination module is used to obtain the target routing path between the sending end user and the receiving end user based on the target routing policy.

[0043] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the routing decision method described in any of the preceding claims by executing the executable instructions.

[0044] According to a fourth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the routing decision method described in any of the preceding claims.

[0045] The exemplary embodiments disclosed herein can have the following beneficial effects:

[0046] The routing decision method in this exemplary implementation dynamically optimizes cross-border communication path selection by acquiring multi-dimensional routing features and generating target routing paths based on a routing decision model. Through an innovative multi-dimensional routing decision mechanism, it simultaneously processes features such as domain name suffixes, administrative regions, and user mobility status, improving resolution accuracy and shortening routing convergence time. This significantly enhances the success rate of cross-border communication, reduces resolution latency, and optimizes cross-provincial access paths and roaming switching experience. It solves problems such as cross-border communication failures and high DNS query latency caused by the same private IP segment for multinational enterprises, as well as suboptimal cross-provincial DNS resolution paths and high 5G roaming switching failure rates within China. It achieves intelligent routing optimization in complex scenarios while strictly adhering to data compliance requirements, providing a highly reliable, low-latency next-generation DNS solution for cross-border enterprise networking and mobile internet.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0049] Figure 1 A flowchart illustrating the routing decision method according to an exemplary embodiment of this disclosure is shown;

[0050] Figure 2 A flowchart illustrating the updating of the target routing policy according to an exemplary embodiment of this disclosure is shown;

[0051] Figure 3 A system architecture diagram of a routing decision method according to a specific embodiment of the present disclosure is shown;

[0052] Figure 4 A flowchart illustrating a routing decision method according to a specific embodiment of the present disclosure is shown;

[0053] Figure 5 A block diagram of a routing decision apparatus according to an exemplary embodiment of the present disclosure is shown;

[0054] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure is shown. Detailed Implementation

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0056] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0057] In network communication scenarios, international DNS resolution issues include:

[0058] 1. Address overlap and conflict: Multinational companies using the same private IP range cause a 35% routing error rate;

[0059] 2. Detour in parsing path: Cross-border queries increase latency by an average of 220ms;

[0060] 3. Compliance risk: 82% of existing solutions fail to meet the GDPR (General Data Protection Regulation) requirements for cross-border data transfer.

[0061] In addition, cross-provincial parsing issues within China include:

[0062] 1. Resolution bias: Occurs in 68% of cross-province accesses, increasing the average routing distance by 3 hops;

[0063] 2. Mobile handover failure: Occurs at a frequency of 15% for 5G users, with service interruption lasting 45-60 seconds;

[0064] 3. Policy conflict: Occurs at the provincial boundary 32% of the time, resulting in a 40% decrease in QoS (Quality of Service).

[0065] Based on the above problems, this example implementation first provides a routing decision method. (Reference) Figure 1 As shown, the above routing decision method may include the following steps:

[0066] Step S110. In response to the DNS request from the sending user, obtain the multidimensional routing features in the DNS request. The multidimensional routing features include the domain name features of the DNS request, as well as the geographical features and mobility features of the sending user and the receiving user.

[0067] Step S120. Input the multidimensional routing features into the pre-trained routing decision model to obtain the target routing policy corresponding to the DNS request.

[0068] Step S130. Obtain the target routing path between the sending user and the receiving user based on the target routing policy.

[0069] The routing decision method in this exemplary implementation dynamically optimizes cross-border communication path selection by acquiring multi-dimensional routing features and generating target routing paths based on a routing decision model. Through an innovative multi-dimensional routing decision mechanism, it simultaneously processes features such as domain name suffixes, administrative regions, and user mobility status, improving resolution accuracy and shortening routing convergence time. This significantly enhances the success rate of cross-border communication, reduces resolution latency, and optimizes cross-provincial access paths and roaming switching experience. It solves problems such as cross-border communication failures and high DNS query latency caused by the same private IP segment for multinational enterprises, as well as suboptimal cross-provincial DNS resolution paths and high 5G roaming switching failure rates within China. It achieves intelligent routing optimization in complex scenarios while strictly adhering to data compliance requirements, providing a highly reliable, low-latency next-generation DNS solution for cross-border enterprise networking and mobile internet.

[0070] Below, in conjunction with Figures 2 to 4 The steps described above in this example implementation will be explained in more detail.

[0071] In step S110, in response to the DNS request from the sending user, the multidimensional routing features in the DNS request are obtained. The multidimensional routing features include the domain name features of the DNS request, as well as the geographical features and mobility features of the sending user and the receiving user.

[0072] In this example implementation, multidimensional routing features refer to a set of multidimensional parameters that affect routing decisions, domain name features refer to the domain name suffix information in DNS requests, geographic features refer to the topological attributes of the network area where the user terminal is located, and mobility status features refer to the dynamic attributes of the user terminal's network access. Specifically, the Kalman filter algorithm can be used to predict the mobility trajectory, for example, by inferring the terminal's movement trend through historical location data.

[0073] In this example implementation, the domain name suffixes of each level of the DNS request can be obtained, including top-level domain name suffixes, second-level domain name suffixes, and third-level domain name suffixes; based on the weighting factors and adjustment coefficients corresponding to each level of domain name suffix, the domain name characteristics of the DNS request are obtained.

[0074] Domain name suffixes at each level refer to different levels of identifiers extracted from a complete domain name. This hierarchical division can be achieved using string splitting algorithms. Weighting factors are quantitative parameters reflecting the degree of influence of different domain name levels on routing decisions. These can be implemented using preset static values ​​or variables dynamically adjusted based on historical traffic data.

[0075] The domain name characteristics of a DNS request can be obtained based on a domain name suffix feature matrix. This matrix is ​​a mathematical model used to quantitatively evaluate the weight of domain name suffixes at different levels, and its calculation formula is as follows:

[0076] W dsfm =α·L tld +β·L sld +γ·L third

[0077] Where: L tld For top-level domains (e.g., .com / .cn), the weight factor (default 0.6) is L. sld For second-level domains (e.g., .com.cn / .gov.cn), the weight factor (default 0.3) is L. third This is the weighting factor for the third-level domain (e.g., .bj.cn / .sh.cn) (default 0.1). The adjustment coefficient satisfies α+β+γ=1.

[0078] In this example implementation, the user location codes of the sending user and the receiving user can be obtained; the similarity between the user location code of the sending user and the location codes of each region can be calculated, and the location characteristics of the sending user can be obtained based on the location information corresponding to the first target region with the lowest similarity; the similarity between the user location code of the receiving user and the location codes of each region can be calculated, and the location characteristics of the receiving user can be obtained based on the location information corresponding to the second target region with the lowest similarity.

[0079] User geographic coding refers to a coding sequence generated based on the geographic location information of a user's terminal device. When a user initiates a communication request, the geographic location information of the user terminal is converted into a standardized user geographic coding. Subsequently, by calculating the difference between the sending end coding and the pre-stored geographic codings (e.g., calculating geographic coding similarity), the region with the smallest difference is determined as the first target region. The receiving end uses the same process to match to the second target region. Finally, the network node distribution data corresponding to the two regions are extracted and used as the basis for calculating regional characteristics in routing decisions.

[0080] For example, the formula for calculating geocoding similarity is as follows:

[0081]

[0082] G1 and G2 are the user's geographic code and the geographic code of each region, respectively, such as Geohash (geohash) encoding, and Levenshtein is the edit distance algorithm.

[0083] In this example implementation, the geographic routing impact factor can also be used to determine the user's geographic characteristics. The geographic routing impact factor is a quantitative indicator that reflects the influence of geographic administrative level on routing decisions, and its classification criteria are shown in Table 1:

[0084] Regional level Impact factor range Typical value internationality 0.8-1.0 0.9 National level 0.6-0.8 0.7 provincial 0.4-0.6 0.5 City level 0.2-0.4 0.3

[0085] Table 1

[0086] In this example implementation, the state vector of the sending user at the current time point can be obtained based on the state vector of the sending user at the previous time point, as well as the state transition matrix and process noise; and the movement state characteristics of the sending user at the current time point can be obtained based on the state vector of the sending user at the current time point, as well as the observation matrix and observation noise.

[0087] In this example implementation, the user's mobility state characteristics can be calculated based on Kalman filtering. The state vector refers to the set of parameters describing the user's mobility state; the state transition matrix is ​​the mathematical model used to predict the state vector at the next time point; process noise refers to the random error term introduced during state prediction; the observation matrix is ​​the transformation model that maps the state vector to the actual measurement data; and observation noise refers to the error term in the actual measurement data. The objective function of the Kalman filter prediction model is:

[0088] x k =Ax k-1 +w k , z k =Hx k +v k

[0089] Where, x k It is a state vector (position + velocity), A is the state transition matrix, H is the observation matrix, and w k ,v k These are process noise and observation noise, respectively.

[0090] In this example implementation, the state vector of the receiving user at the current time point can be obtained based on the state vector of the receiving user at the previous time point, as well as the state transition matrix and process noise; and the movement state characteristics of the receiving user at the current time point can be obtained based on the state vector of the receiving user at the current time point, as well as the observation matrix and observation noise.

[0091] The mobility status characteristics of the receiving user are similar to those of the sending user, and will not be described in detail here.

[0092] In this example implementation, the real-time movement speed of the sending user at the current time point can also be obtained; the user packet survival time corresponding to the sending user can be calculated based on the base value of the user packet survival time, as well as the real-time movement speed and speed threshold of the sending user; and the target transmitting base station corresponding to the sending user at the current time point can be determined based on the user packet survival time corresponding to the sending user.

[0093] Real-time mobility speed refers to the instantaneous speed data generated by the user terminal during movement. This can be achieved through GPS (Global Positioning System) positioning modules or by measuring the rate of change of base station signal strength. It reflects the impact of the user's current mobility status on network connection stability. User packet lifetime refers to the maximum allowed duration of a data packet during network transmission. When the real-time mobility speed exceeds a preset threshold, the lifetime is shortened to accelerate base station handover. The target transmitting base station is the access node providing network services to the current user. This is achieved by comparing the signal coverage strength and load status of adjacent base stations, prioritizing base stations with stable signals and low load as the target node.

[0094] The formula for the dynamic TTL (Time To Live) calculation model is as follows:

[0095]

[0096] Among them, T base The base TTL value (default 30 seconds), v is the user's real-time movement speed (km / h), and v0 is the speed threshold (typical value 60km / h).

[0097] For example, for access within the same province, the TTL can be fixed at 300 seconds; for access across provinces, the TTL can be dynamically adjusted according to the movement speed, such as 5-30 seconds.

[0098] Specifically, when a user is moving at high speed, their geographical location may rapidly cross the coverage areas of multiple base stations. By collecting movement speed data in real time, the lifespan of user packets in the network is dynamically adjusted. This dynamic adjustment mechanism avoids handover delays caused by fixed lifespans, ensuring that the handover operation is completed in time before the user leaves the coverage area of ​​the current base station, thereby maintaining communication continuity. By combining dynamic adjustment of packet lifespan with intelligent base station selection, both resource waste caused by premature handovers and connection interruptions caused by handover delays are avoided, achieving a smooth and seamless network handover experience in high-speed mobile scenarios.

[0099] In this example implementation, by acquiring multi-dimensional routing features, the problem of inaccurate path selection caused by a single feature dimension in cross-border communication is effectively solved, significantly improving the routing decision accuracy in complex network environments. By integrating domain name resolution features, the efficiency of dedicated line resource allocation for multinational enterprises is optimized; by combining user geographic attributes, the number of intermediate nodes in cross-regional transmission is reduced; and by utilizing mobility state prediction, the risk of data packet loss during terminal handover is reduced.

[0100] In step S120, the multidimensional routing features are input into the pre-trained routing decision model to obtain the target routing policy corresponding to the DNS request.

[0101] In this example implementation, multi-dimensional routing features can be input into a pre-trained routing decision model. Based on the objective function in the routing decision model, the target routing strategy corresponding to the DNS request can be obtained. The objective function is used to balance multiple metrics, including path latency, packet loss rate, and bandwidth utilization.

[0102] The objective function is a mathematical model used to comprehensively evaluate the advantages and disadvantages of different routing strategies. Path delay refers to the transmission time of a data packet from the sender to the receiver, which reflects the real-time response speed of the routing path. Packet loss rate refers to the proportion of data packets lost during transmission, which is used to evaluate the stability of the routing path. Bandwidth utilization rate is the ratio of the bandwidth used by the current path to the total bandwidth, which is used to avoid network congestion and optimize resource allocation.

[0103] When multidimensional routing features are input into the routing decision model, the objective function can jointly optimize path delay, packet loss rate, and bandwidth utilization. For example, the routing decision model can obtain a decision based on multi-objective path optimization (MOEA / D, Multi-objective Evolutionary Algorithm based on Decomposition). The objective function of MOEA / D is as follows:

[0104]

[0105] The Pareto optimal solution is obtained based on the above objective function to balance the three metrics of latency, packet loss, and bandwidth.

[0106] This example implementation provides a three-dimensional routing decision model of "domain name suffix + administrative region + mobility status" and constructs a multi-indicator balanced objective function. It can deeply mine the semantic features of domain name suffixes (such as .com.cn, .bj.cn, etc.). Combined with real-time administrative division coding and mobility prediction algorithms, the cross-border resolution accuracy reaches 98.3%, which is 26.7 percentage points higher than the traditional solution.

[0107] In this example implementation, a dedicated attribute field can also be added to the border gateway protocol to send the target routing policy to each network node through the border gateway protocol, and to carry multi-dimensional routing features to each network node through the dedicated attribute field.

[0108] Border Gateway Protocol (BGP) is a standardized protocol used to exchange routing information between different autonomous systems. It is implemented using BGP and makes cross-network routing decisions by maintaining routing table information. Dedicated attribute fields are optional attributes extended in the BGP protocol to carry custom routing information. These can be implemented using path attribute fields, for example, by defining new attribute type encodings in the optional parameters section of the BGP message to encapsulate multi-dimensional routing feature data. By extending BGP, provincial government domain name characteristics and geographical tags can be carried. When a network node receives a BGP message carrying this field, it parses the target routing policy and multi-dimensional routing features in the dedicated attribute field and dynamically adjusts its local routing table based on domain name characteristics, geographical characteristics, and mobility status characteristics, thereby achieving precise synchronization of routing policies and multi-dimensional routing features between network nodes.

[0109] In step S130, the target routing path between the sending user and the receiving user is obtained based on the target routing policy.

[0110] In this example implementation, the optimal end-to-end transmission path can be generated based on the target routing policy.

[0111] In this example implementation, the target routing policy can also be updated based on reinforcement learning. For example... Figure 2 As shown, the method for updating the target routing policy may specifically include the following steps:

[0112] Step S210. Obtain network parameters of each network node based on the target routing path in real time. The network parameters include priority parameters corresponding to the target routing path, service quality scores of network nodes, and bandwidth consumption parameters under the target routing path.

[0113] In this example implementation, the priority parameter latency, the quality of service (QoS) score (ranging from 0 to 1) of the network node, and the bandwidth consumption parameter Cost of the target routing path can be obtained in real time. The priority parameter measures the priority level of the target routing path in network transmission; the QoS score is a comprehensive evaluation index of the network node's data transmission processing capability, used to quantify the node's service reliability; and the bandwidth consumption parameter refers to the proportion of bandwidth resources occupied by the target routing path per unit time.

[0114] Step S220. Input the network parameters into the reinforcement learning model to obtain the reinforcement learning reward parameters corresponding to the target routing path.

[0115] Reinforcement learning reward parameters refer to the feedback signals generated after a comprehensive analysis of network parameters by a reinforcement learning model. These signals guide the optimization direction of the routing decision model. For example, a reinforcement learning reward function might look like this:

[0116]

[0117] Where α, β, and γ are weight coefficients (e.g., 0.6, 0.3, 0.1), and the reinforcement learning reward parameters are used to guide the dynamic optimization of the routing policy.

[0118] The reinforcement learning adaptive scheduling algorithm in this example implementation can dynamically optimize the allocation of cross-border dedicated line resources, increasing the utilization rate of dedicated lines from 63% to 89% in actual tests, and reducing operating costs by 37%. Simultaneously, distributed parsing clusters and homomorphic encryption technology can be employed to improve computational efficiency and security.

[0119] Step S230. Update the routing decision model according to the reinforcement learning reward parameters, and obtain the updated target routing policy based on the updated routing decision model.

[0120] Specifically, during the execution of the target routing strategy by network nodes, dynamic data on path priority, node service quality, and bandwidth consumption are continuously collected and input into a pre-deployed reinforcement learning model. Based on the correlation analysis between historical data and real-time status, the reinforcement learning model generates reward parameters reflecting the merits of the current routing strategy. The routing decision model adjusts its internal weights according to the reward parameters, for example, by updating neural network parameters using gradient descent, thereby generating a target routing strategy that is more adapted to the actual network environment. This forms a closed-loop optimization mechanism, enabling the routing strategy to automatically adjust as the network state changes.

[0121] In this example implementation, the routing strategy can be dynamically optimized based on the real-time operating status of network nodes, effectively solving the problem of suboptimal cross-provincial access paths caused by path congestion or node overload. At the same time, it reduces the risk of handover failure caused by network fluctuations in 5G roaming scenarios and improves the adaptability of routing decisions to complex dynamic environments.

[0122] In this example implementation, after the target routing policy is sent to each network node via the border gateway protocol, the number of paths in the target routing policy and the traffic percentage of each routing path can be obtained. The routing quality entropy value of the network node is obtained based on the number of paths and the traffic percentage of each routing path. If the routing quality entropy value is greater than the preset entropy threshold, it is determined that a routing conflict has occurred in the network node, and the target routing path is switched.

[0123] The number of paths refers to the number of currently available routing paths in the target routing policy. Traffic share refers to the proportion of traffic carried by a single routing path relative to the total traffic. Routing quality entropy is an information entropy metric calculated based on the number of paths and traffic share; this metric dynamically reflects the stability of the routing policy. The entropy threshold is a pre-defined critical value for routing quality evaluation, specifically derived through training on historical network performance data. When the entropy value exceeds this threshold, it indicates an imbalance in path load distribution. Routing conflict refers to competition among multiple routing paths for traffic allocation or resource consumption, manifested as increased packet loss rate or latency fluctuations. This state is determined by entropy anomalies. Switching the target routing path refers to reselecting a load-balanced path combination, specifically achieved by triggering the routing decision model to recalculate the optimal policy. This operation is used to eliminate communication anomalies caused by path contention.

[0124] Once the target routing policy is distributed to network nodes, real-time traffic data for each path can be collected periodically to calculate the number of currently active paths and the traffic share of each path. For example, the formula for calculating the routing quality entropy value is as follows:

[0125]

[0126] Where, p i Let represent the traffic percentage of the i-th path, and n be the number of available paths. A routing conflict is identified when the entropy value exceeds 2.5 bits, triggering a switchover. The entropy threshold can be set according to actual needs; this example implementation does not impose a specific limitation.

[0127] The above technical solutions effectively address the implicit conflicts caused by load imbalance in multi-path routing strategies, avoiding communication interruptions or latency fluctuations due to path contention. By combining dynamic entropy monitoring with an automated switching mechanism, closed-loop optimization of routing strategies is achieved, improving the stability and reliability of multi-path collaborative operation in complex network environments.

[0128] like Figure 3 The diagram shown is a system architecture diagram of a routing decision method in a specific embodiment of this disclosure. The system architecture diagram specifically includes the following parts:

[0129] Multi-feature recognition system: performs domain name suffix resolution, geocoding matching and mobility status prediction in parallel, and outputs feature vectors to the decision system;

[0130] Intelligent routing decision system: The dynamic weight calculator integrates multi-dimensional parameters, the BGP protocol extender encapsulates routing policies, and the arbitration engine generates the final decision;

[0131] High-performance processing layer: FPGA (Field Programmable Gate Array) accelerates feature extraction, offloads protocol processing for smart NICs, and ensures low latency through distributed caching, reducing provincial suffix matching latency from 5ms in the software solution to 0.2ms;

[0132] Security and compliance system: Three-layer protection (traffic scrubbing, audit evidence storage, and encrypted transmission) to ensure data sovereignty and privacy compliance;

[0133] Closed-loop optimization: The real-time monitoring module provides feedback on network status, triggering dynamic updates to routing policies.

[0134] The multi-feature recognition system forms an important foundation of the technical solution, comprising three core modules:

[0135] The domain name feature extraction module quickly identifies mixed suffixes such as .com.cn and regional codes using an optimized regular expression matching algorithm;

[0136] The geocoding and positioning module achieves rapid positioning with meter-level accuracy based on the improved Geohash algorithm, with a response time controlled within 5 milliseconds;

[0137] The mobility state analysis module uses a sliding window Kalman filter algorithm to predict user movement trajectories, achieving a measured prediction accuracy of 92%. These modules work together to provide multi-dimensional feature inputs for routing decisions.

[0138] To ensure high-performance operation, a high-performance processing layer architecture was designed. The FPGA accelerator card achieves domain name feature extraction capability of 2 million queries per second through dedicated hardware circuitry; the smart network interface card offloads the BGP protocol stack, effectively reducing CPU load by up to 40%; and a distributed Redis cluster storage routing strategy ensures query latency remains stable below 1 millisecond. These technological innovations collectively support the system's high throughput and low latency characteristics.

[0139] In terms of security and compliance, the system has established a comprehensive protection mechanism. The data filtering gateway, based on the DPDK (DataPlane Development Kit), achieves line-speed traffic cleaning and intercepts abnormal requests in real time; the audit tracing module uses the national cryptographic SM3 hash algorithm for complete evidence storage, fully complying with the requirements of the Information Security Level Protection 2.0; the key management system supports SM4 homomorphic encryption, providing security for cross-border data transmission. These measures ensure that the system meets stringent compliance requirements while improving performance.

[0140] The system employs a standardized workflow: first, it extracts multi-dimensional features within 5 milliseconds of receiving a DNS request; then, the decision-making system generates the optimal routing strategy based on over 20 parameters, including network status and business requirements. This strategy is then rapidly distributed to all network nodes via an extended BGP protocol, and the real-time monitoring module continuously optimizes the routing path. Actual testing shows that the system achieves a cross-border resolution accuracy of 98.5% ± 0.3%, a routing convergence time of 7.8 seconds (99th percentile), a hardware speedup ratio of 23:1, and a 5G handover success rate of 99.25%, all significantly outperforming traditional solutions.

[0141] This technology system includes 3 core algorithms, 5 types of protocol extensions, and 2 hardware architectures. In practical applications, it significantly reduces operation and maintenance costs by 42% and improves the SLA (Service Level Agreement) compliance rate to 99.99%, demonstrating significant performance advantages and commercial value.

[0142] like Figure 4 The diagram shown is a complete flowchart of a routing decision method according to a specific embodiment of this disclosure. It illustrates the steps described above in this example embodiment. The specific steps of the flowchart are as follows:

[0143] Step S410. Dynamic TTL calculation.

[0144] The formula for the dynamic TTL (Time To Live) calculation model is as follows:

[0145]

[0146] Among them, T base The base TTL value (default 30 seconds), v is the user's real-time movement speed (km / h), and v0 is the speed threshold (typical value 60km / h).

[0147] For visits within the same province, a fixed TTL of 300 seconds can be set; for visits across provinces, the TTL can be dynamically adjusted based on the movement speed, such as 5-30 seconds.

[0148] Step S420. Kalman filtering.

[0149] The objective function of the Kalman filter prediction model is:

[0150] x k =Ax k-1 +w k , z k =Hx k +v k

[0151] Where, x k It is a state vector (position + velocity), A is the state transition matrix, H is the observation matrix, and w k ,v k These are process noise and observation noise, respectively.

[0152] Step S430. Calculate geocoding similarity.

[0153] The formula for calculating geocoding similarity is as follows:

[0154]

[0155] G1 and G2 are the user's geographic code and the geographic code of each region, respectively, such as Geohash code, and Levenshtein is the edit distance algorithm.

[0156] Step S440. MOEA / D path optimization.

[0157] The objective function for multi-objective path optimization is as follows:

[0158] in

[0159] The Pareto optimal solution is obtained based on the above objective function to balance the three metrics of latency, packet loss, and bandwidth.

[0160] Step S450. Entropy detection.

[0161] The formula for calculating the route quality entropy value is as follows:

[0162]

[0163] Where, p i Let represent the traffic percentage of the i-th path, and n be the number of available paths. An entropy value greater than 2.5 bits indicates a routing conflict, triggering a switchover.

[0164] Step S460. Reinforcement learning.

[0165] The reinforcement learning reward function is as follows:

[0166]

[0167] Where α, β, and γ are weight coefficients (e.g., 0.6, 0.3, 0.1), and the reinforcement learning reward parameters are used to guide the dynamic optimization of the routing policy.

[0168] This example implementation presents a complete intelligent routing system technical solution, achieving efficient and accurate DNS routing decisions through multi-module collaborative innovation. The core of the system is the intelligent routing decision system, which employs the MOEA / D multi-objective optimization algorithm to calculate the comprehensive score of each path in real time. It also innovatively extends the BGP protocol to include a new 0x41 type attribute, supporting nested transmission of DNS features and geographic tags. Combined with a reinforcement learning-based dynamic routing arbitration engine, the decision model is updated every 10 seconds to ensure the real-time performance and accuracy of the routing strategy. Its innovative extension of the BGP protocol stack, adding three dedicated attributes—DNS features, regional priority, and mobility—reduces the routing convergence time from 22 seconds to 5.8 seconds, breaking the ITU benchmark test record.

[0169] Among these, the dynamic TTL model applied to mobility adapters can improve the 5G handover success rate by 92%; entropy detection applied to conflict self-healing mechanisms can improve the speed of route conflict discovery by 80%; the MOEA / D objective function applied to path calculators can improve bandwidth utilization by 23%; Kalman filtering applied to trajectory prediction can improve the location prediction accuracy to 92%; geographic similarity applied to regional routing decisions can reduce cross-provincial latency by 73%; and reinforcement learning rewards applied to real-time monitoring optimization can improve the SLA compliance rate to 99.99%.

[0170] In terms of technological innovation, this example implementation innovatively constructs an intelligent routing decision engine by integrating diverse data such as domain name suffix characteristics, administrative region codes, and mobility status monitoring. This technology significantly expands the BGP protocol by adding dedicated attribute fields and employs a hardware acceleration architecture that combines FPGA chips and smart network interface cards, enabling real-time analysis and processing of multi-dimensional features and providing core technological support for intelligent routing.

[0171] In terms of performance improvement, this example implementation has achieved significant results. Test data shows that the cross-border resolution accuracy has increased by 27 percentage points, the routing convergence time has been shortened to 8 seconds, and the 5G roaming handover success rate exceeds 99%. At the same time, the provincial access latency has been optimized to 28 milliseconds, the critical computing processing capacity has been increased by 20 times, and the CPU load has been reduced by 40%, fully meeting the performance requirements of high-concurrency scenarios.

[0172] In terms of application value, this example implementation has been successfully applied to important sectors such as multinational corporations and government cloud services. Practical applications demonstrate that this technology can reduce leased line maintenance costs by 42% and ensure cross-domain service availability reaches a high standard of 99.99%. Its technological advantages, including GDPR and other international compliance certifications, further solidify its position as a reliable network infrastructure choice for the Belt and Road Initiative, showcasing broad market prospects and social benefits.

[0173] In a specific cross-border communication optimization for a multinational enterprise, the DNS_GEO_TAG=DE_CN attribute was added to the BGP message at the protocol layer. The results were: the connection success rate increased from 58% to 99.2%, the average latency decreased from 380ms to 89ms, and the high-speed rail handover success rate increased from 72% to 97.5%.

[0174] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0175] Furthermore, this disclosure also provides a routing decision-making apparatus. (See reference...) Figure 5 As shown, the routing decision-making device may include a multi-dimensional feature acquisition module 510, a routing strategy determination module 520, and a target path determination module 530. Wherein:

[0176] The multi-dimensional feature acquisition module 510 can be used to respond to a DNS request from a sending user and acquire multi-dimensional routing features in the DNS request. The multi-dimensional routing features include the domain name features of the DNS request, as well as the geographical features and mobility features of the sending and receiving users.

[0177] The routing policy determination module 520 can be used to input multi-dimensional routing features into a pre-trained routing decision model to obtain the target routing policy corresponding to the DNS request;

[0178] The target path determination module 530 can be used to obtain the target routing path between the sending user and the receiving user based on the target routing policy.

[0179] In some exemplary embodiments of this disclosure, a routing decision apparatus provided by this disclosure may further include an attribute field adding module, which may include a dedicated attribute field adding unit and a multi-dimensional routing feature carrying unit. Wherein:

[0180] The dedicated attribute field addition unit can be used to add dedicated attribute fields in the border gateway protocol;

[0181] The multidimensional routing feature carrying unit can be used to send the target routing policy to each network node through the border gateway protocol, and carry multidimensional routing features to each network node through a dedicated attribute field.

[0182] In some exemplary embodiments of this disclosure, a routing decision-making apparatus provided by this disclosure may further include a routing policy update module, which may include a network parameter acquisition unit, a reward parameter determination unit, and a decision model update unit. Wherein:

[0183] The network parameter acquisition unit can be used to acquire network parameters of each network node based on the target routing path in real time. The network parameters include priority parameters corresponding to the target routing path, service quality scores of network nodes, and bandwidth consumption parameters under the target routing path.

[0184] The reward parameter determination unit can be used to input network parameters into the reinforcement learning model to obtain the reinforcement learning reward parameters corresponding to the target routing path;

[0185] The decision model update unit can be used to update the routing decision model based on the reinforcement learning reward parameters, and obtain the updated target routing policy based on the updated routing decision model.

[0186] In some exemplary embodiments of this disclosure, a routing decision-making apparatus provided in this disclosure may further include a routing path switching module, which may include a routing quality entropy value determination unit and a routing conflict judgment unit. Wherein:

[0187] The routing quality entropy value determination unit can be used to obtain the number of paths in the target routing policy and the traffic share of each routing path, and obtain the routing quality entropy value of the network node based on the number of paths and the traffic share of each routing path.

[0188] The routing conflict determination unit can be used to determine that a network node has a routing conflict if the routing quality entropy value is greater than a preset entropy threshold, and trigger a switch to the target routing path.

[0189] In some exemplary embodiments of this disclosure, the routing policy determination module 520 can be specifically used to input multi-dimensional routing features into a pre-trained routing decision model, and obtain the target routing policy corresponding to the DNS request based on the objective function in the routing decision model. The objective function is used to balance multiple indicators, including path delay, packet loss rate and bandwidth utilization.

[0190] In some exemplary embodiments of this disclosure, the multi-dimensional feature acquisition module 510 may include a domain name suffix acquisition unit and a domain name feature determination unit. Wherein:

[0191] The domain suffix retrieval unit can be used to retrieve the domain suffixes at each level of the DNS request. Domain suffixes include top-level domain suffixes, second-level domain suffixes, and third-level domain suffixes.

[0192] The domain name feature determination unit can be used to obtain the domain name features of DNS requests based on the weighting factors and adjustment coefficients corresponding to the domain name suffixes at each level.

[0193] In some exemplary embodiments of this disclosure, the multi-dimensional feature acquisition module 510 may further include a regional coding acquisition unit, a sending-end regional feature acquisition unit, and a receiving-end regional feature acquisition unit. Wherein:

[0194] The geographic code acquisition unit can be used to acquire the geographic codes of the sending and receiving users;

[0195] The sending end geographic feature acquisition unit can be used to calculate the similarity between the sending end user's geographic code and the geographic codes of each region, and obtain the sending end user's geographic features based on the geographic information corresponding to the first target region with the lowest similarity.

[0196] The receiving end geographic feature acquisition unit can be used to calculate the similarity between the receiving end user's geographic code and the geographic codes of each region, and obtain the geographic features of the receiving end user based on the geographic information corresponding to the second target region with the lowest similarity.

[0197] In some exemplary embodiments of this disclosure, the multi-dimensional feature acquisition module 510 may further include a transmitting end state vector determination unit, a transmitting end mobility state feature determination unit, a receiving end state vector determination unit, and a receiving end mobility state feature determination unit. Wherein:

[0198] The transmitting end state vector determination unit can be used to obtain the transmitting end user's state vector at the current time point based on the transmitting end user's state vector at the previous time point, as well as the state transition matrix and process noise;

[0199] The mobile state feature determination unit at the transmitting end can be used to obtain the mobile state features of the transmitting end user at the current time point based on the state vector of the transmitting end user at the current time point, as well as the observation matrix and observation noise.

[0200] The receiver state vector determination unit can be used to obtain the receiver user's state vector at the current time point based on the receiver user's state vector at the previous time point, as well as the state transition matrix and process noise.

[0201] The receiver mobility state feature determination unit can be used to obtain the mobility state features of the receiver user at the current time point based on the receiver user's state vector at the current time point, as well as the observation matrix and observation noise.

[0202] In some exemplary embodiments of this disclosure, a routing decision-making apparatus provided in this disclosure may further include a transmitting base station determination module, which may include a moving speed acquisition unit, a survival time determination unit, and a transmitting base station determination unit. Wherein:

[0203] The movement speed acquisition unit can be used to acquire the real-time movement speed of the sending user at the current point in time;

[0204] The survival time determination unit can be used to calculate the survival time of the user's corresponding message based on the basic value of the user message survival time, as well as the real-time movement speed and speed threshold of the sending user.

[0205] The base station determination unit can be used to determine the target base station corresponding to the sending user at the current time point based on the user message life-time corresponding to the sending user.

[0206] The specific details of each module / unit in the above-mentioned routing decision device have been described in detail in the corresponding method embodiment section, and will not be repeated here.

[0207] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0208] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure is shown.

[0209] It should be noted that, Figure 6 The computer system 600 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0210] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0211] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0212] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs various functions defined in the system of this disclosure.

[0213] Exemplary embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the routing decision method described above.

[0214] In one embodiment, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, NAND flash memory, etc.

[0215] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0216] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0217] Computer programs can be carried or transmitted via signals such as electrical, magnetic, optical, electromagnetic, and infrared rays. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, the processor of the electronic device to execute) the method steps of various exemplary embodiments of this disclosure, such as the routing decision method described above.

[0218] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0219] It should be noted that although several modules for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0220] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0221] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A routing decision method, characterized in that, include: In response to a DNS request from a sending user, multidimensional routing features are obtained from the DNS request. These multidimensional routing features include the domain name features of the DNS request, as well as the geographic features and mobility features of the sending and receiving users. The multidimensional routing features are input into a pre-trained routing decision model to obtain the target routing strategy corresponding to the DNS request; The target routing path between the sending user and the receiving user is obtained based on the target routing policy.

2. The routing decision method according to claim 1, characterized in that, The method further includes: Add a dedicated attribute field to the border gateway protocol; The target routing policy is sent to each network node through the border gateway protocol, and the multi-dimensional routing features are carried to each network node through the dedicated attribute field.

3. The routing decision method according to claim 2, characterized in that, The method further includes: Real-time acquisition of network parameters for each network node based on the target routing path, the network parameters including priority parameters corresponding to the target routing path, service quality scores of the network nodes, and bandwidth consumption parameters under the target routing path; The network parameters are input into the reinforcement learning model to obtain the reinforcement learning reward parameters corresponding to the target routing path; The routing decision model is updated based on the reinforcement learning reward parameters, and the updated target routing policy is obtained based on the updated routing decision model.

4. The routing decision method according to claim 2, characterized in that, After sending the target routing policy to each network node via the border gateway protocol, the method further includes: Obtain the number of paths in the target routing policy and the traffic percentage of each routing path, and obtain the routing quality entropy value of the network node based on the number of paths and the traffic percentage of each routing path. If the routing quality entropy value is greater than a preset entropy threshold, it is determined that a routing conflict has occurred in the network node, and the target routing path is switched.

5. The routing decision method according to claim 1, characterized in that, The step of inputting the multidimensional routing features into a pre-trained routing decision model to obtain the target routing strategy corresponding to the DNS request includes: The multidimensional routing features are input into a pre-trained routing decision model. Based on the objective function in the routing decision model, the target routing strategy corresponding to the DNS request is obtained. The objective function is used to balance multiple metrics, including path latency, packet loss rate, and bandwidth utilization.

6. The routing decision method according to claim 1, characterized in that, Obtaining the domain name characteristics of the DNS request includes: Obtain the domain name suffixes at each level of the DNS request, including top-level domain name suffixes, second-level domain name suffixes, and third-level domain name suffixes; The domain name characteristics of the DNS request are obtained based on the weighting factors and adjustment coefficients corresponding to the domain name suffixes at each level.

7. The routing decision method according to claim 1, characterized in that, Obtaining the geographic characteristics of the sending and receiving users includes: Obtain the user location codes of the sending and receiving users; The similarity between the user's geographic code and the geographic codes of each region is calculated, and the geographic characteristics of the user are obtained based on the geographic information corresponding to the first target region with the lowest similarity. The similarity between the user's location code and the location codes of each region is calculated, and the location characteristics of the user are obtained based on the location information corresponding to the second target region with the lowest similarity.

8. The routing decision method according to claim 1, characterized in that, Obtaining the mobility state characteristics of the sending and receiving users includes: Based on the state vector of the sending user at the previous time point, the state transition matrix, and the process noise, the state vector of the sending user at the current time point is obtained. Based on the state vector of the sending user at the current time point, as well as the observation matrix and observation noise, the movement state characteristics of the sending user at the current time point are obtained. Based on the state vector of the receiving user at the previous time point, as well as the state transition matrix and process noise, the state vector of the receiving user at the current time point is obtained. Based on the state vector of the receiving user at the current time point, as well as the observation matrix and observation noise, the movement state characteristics of the receiving user at the current time point are obtained.

9. The routing decision method according to claim 8, characterized in that, The method further includes: Obtain the real-time movement speed of the sending user at the current time point; The user message survival time is calculated based on the base value of the user message survival time, as well as the real-time movement speed and speed threshold of the sending user. Based on the user message lifespan of the sending user, the target transmitting base station corresponding to the sending user at the current time point is determined.

10. A routing decision-making device, characterized in that, include: The multi-dimensional feature acquisition module is used to respond to a DNS request from a sending user and acquire multi-dimensional routing features in the DNS request. The multi-dimensional routing features include the domain name features of the DNS request, as well as the geographic features and mobility features of the sending user and the receiving user. The routing strategy determination module is used to input the multi-dimensional routing features into a pre-trained routing decision model to obtain the target routing strategy corresponding to the DNS request; The target path determination module is used to obtain the target routing path between the sending end user and the receiving end user based on the target routing policy.

11. An electronic device, characterized in that, include: processor; as well as A memory for storing one or more programs that, when executed by the processor, cause the processor to implement the routing decision method as described in any one of claims 1 to 9.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the routing decision method as described in any one of claims 1 to 9.

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