Intelligent traffic scheduling method and system based on application perception and storage medium

Abstract

The application discloses an application-aware intelligent traffic scheduling method and system and a storage medium, and relates to the technical fields of network communication, traffic scheduling and edge computing.The technical problem to be solved is to realize unified intelligent traffic scheduling in a heterogeneous multi-protocol backend service environment.The technical scheme is as follows: an access request carrying a business semantic label is received;real-time state information of a plurality of backend service nodes is acquired, including at least one of resource utilization, connection number and queue depth;comprehensive scheduling scores are calculated based on the real-time state information and the business semantic label, and a target node is determined;the access request is routed to the target node;when a completion confirmation is not received within a preset deadline, a no-confirmation fuse mechanism is triggered to retry or the target node is marked as sub-healthy for exclusion;physical connections and logical session mappings are maintained through multiplexing connection management, and in-transit concurrency is calculated;and a future resource state projection value is extrapolated based on a historical snapshot through predictive back pressure calculation.The main uses are as follows: gateway, load balancer, service mesh and database proxy.

Description

Technical Field This invention relates to the fields of network communication, traffic scheduling and edge computing technologies, and specifically to an application-aware intelligent traffic scheduling method, system and storage medium. Background Technology With the large-scale deployment of cloud computing, microservice architecture, IoT, and AI inference services, the protocol types, load characteristics, and business semantics of backend service nodes are becoming increasingly diverse. Traditional load balancing, API gateways, connection pools, circuit breakers, backpressure mechanisms, and other technologies have evolved, but they still have significant shortcomings when facing complex scenarios such as multi-protocol hybrid scheduling, fine-grained connection reuse, and predictive resource protection. Existing load balancers are unaware of business semantics. Publication CN122476063A (Chongqing Quanlianrong Technology, published in 2026) proposes a scheduling decision model based on multi-source decision text, but it merges historical behavior, real-time status, and application content into a black-box "decision text," failing to abstract business semantics as an independent scheduling input factor throughout the call chain. Publication CN202511469452 (published in 2026) proposes a multi-dimensional perception cloud network traffic intelligent diversion method based on physical, logical, and application layer association graphs and semantic matching rules, but its semantic matching is used for association graph mining rather than as an input factor for comprehensive scheduling scoring, and it does not disclose the combination with the no-acknowledgment circuit breaker closed loop. Cisco US11165707B2 (licensed in 2021) discloses an application-aware routing strategy based on business insights, but it is only applicable to SD-WAN scenarios and does not disclose a unified abstraction of comprehensive scheduling scoring and a no-ACK circuit breaker mechanism. Traditional circuit breakers require explicit ACK failure counting, which cannot handle silent timeouts. Michael Nygard proposed a three-state circuit breaker model in his book *Release It!* (published in 2007), and AWS Prescriptive Guidance also disclosed a similar three-state (Closed / Open / Half-Open) circuit breaker, both relying on explicit failure counting or error rate to trigger state switching. The gateway circuit breaker solutions disclosed in publications CN119420631A (Neusoft Group, published in 2025), CN119363606B (China Mobile, published in 2025), and CN120110894A (Focus Technology, published in 2025) are still based on traditional models using error rates or circuit breakers. In scenarios involving silent timeouts (i.e., no response or explicit failure acknowledgment received), these solutions must wait for the error rate to accumulate to a threshold before triggering state switching, resulting in high circuit breaker latency and inability to support protocols without ACK semantics. Existing multiplexed connection management does not provide per-connection in-transit concurrency control. The ProxySQL open-source project exposes the N:M front-end and back-end connection sharing mechanism, but its reuse control is based on session-level state compatibility judgment (disabling multiplexing in scenarios such as transaction activation, temporary tables, and user variables). Once a session is routed to the back-end connection, it is exclusive during the execution of that query, and it does not expose fine-grained control over "maintaining a concurrency semaphore for each physical connection in transit". HikariCP connection pool (ConcurrentBag mechanism) PoolEntry only holds connection references and does not hold in-transit slot semaphores; its semaphore is the pool-level limit on the total number of connections. Vibur DBCP is based on "queue + semaphore", but its semaphore is also the pool-level limit on the total number of connections, not a per-connection in-transit slot. The SETTINGS_MAX_CONCURRENT_STREAMS defined in HTTP / 2 protocol RFC 9113 is a static configuration negotiated at the protocol layer, bound to the HTTP / 2 protocol, and cannot be adaptively switched at runtime. Publication number CN114490846A (XCMG HanCloud, published in 2022) discloses a database connection resource allocation method based on semaphores, but its semaphore is also the pool-level total number of connections, and the per-connection in-transit concurrency control is not disclosed. The existing backpressure mechanism is a passive response and cannot be scheduled in advance based on trend prediction. Huang et al. disclosed the concept of predictive backpressure in their 2013 paper "When Backpressure Meets Predictive Scheduling" (PBP algorithm) published on arXiv:1309.1110. However, its prediction object is future arrival traffic, and it assumes that the server knows the arrival information of the next D_n time slots (lookahead window model), rather than extrapolating trends based on historical snapshots. Its aggregation strategy uses maxWeight optimization instead of scalar aggregation with the maximum value. Virtana's US12,340,249B2 (licensed in 2025) discloses a priority-aware backpressure mechanism, but it rearranges tasks based on current resource availability and does not disclose regression and maximum value aggregation for each of the three resource dimensions. Google's LLM-D project (released in 2026) discloses predictive latency scheduling based on XGBoost and sliding windows, but it is applied to LLM inference scenarios and uses a nonlinear regression model, without disclosing least-squares linear regression based on historical snapshots. The CNCF open-source project Prometheus's predict_linear function and the sitmo open-source library's RollingRegression disclose the algorithm implementation of sliding window linear regression, but they are only used for monitoring alarms or trend filtering, and do not involve back pressure aggregation, threshold conversion, and scheduling scoring closed loop. Existing protocol identification is only used for security detection and does not switch processing pipelines. FoxIO's JA4 series fingerprints (including JA4, JA4S, JA4T, etc.) disclosed in US2024 / 0396914A1 and Akamai's TLS fingerprints disclosed in US20190190950 have been widely used in security products such as Cloudflare, AWS WAF, and VirusTotal for bot detection, asset discovery, and threat hunting. However, these solutions do not change the subsequent processing logic (pipeline) after identifying the protocol; the identification result is only used as a basis for security judgment. The location-sensitive feature entropy fingerprint packet identification method disclosed in CN115913723B (Beijing Winuot, authorized in 2026) and the network protocol identification method based on multi-dimensional fusion of traffic features disclosed in CN122316782A (Fiberhome Communications, published in 2026) are also used for network security identification rather than dynamic switching of the protocol parsing pipeline for gateway routing. Existing AIMD admission control does not retain the detection window perception backend recovery. Google's EP4036729B1 (licensed in 2024) discloses a data center latency-critical RPC admission control based on admit probability. Its trigger condition is per-RPC per-SLO miss, decreasing admitprobability by a constant rate. It does not disclose a bidirectional AIMD (additive growth + multiplicative decay) strategy, nor does it disclose a design for "preserving a probe window when the upper bound is less than 1". arXiv:2604.17111 (HiveMind, published in 2026) discloses a combination of AIMD backpressure and circuit breaking in LLM agent concurrent scheduling, but it uses backpressure instead of probabilistic rejection and only has lower bound protection without an upper bound probe window design. EP-2456142-A1 discloses AIMD-based overload detection, but it controls the transmission rate instead of the rejection rate and also lacks a probe window design. None of the above solutions disclose "overall scheduling score below a threshold" as a system-level trigger condition, nor do they disclose the purpose of "protecting the availability of the scheduling system itself". The existing protocol fingerprint rules have a single source and do not support multi-source aggregation coverage. Publication No. CN202511918795 (published in 2026) discloses a method for protocol adaptive adaptation and dynamic rule chain execution for heterogeneous IoT devices. Its protocol feature vectors are dynamically generated through information entropy and feature vectorization. The rules originate from a single source, and the method does not disclose a rule management mechanism that aggregates three sources: "built-in rules + dependency injection container registration + service discovery mechanism declaration." It also does not disclose a conflict resolution strategy of "registering later for the same protocol identifier overwrites registering earlier," nor does it disclose a multi-protocol fingerprint conflict handling mechanism of "traversing and matching in descending priority." Publication No. CN202511454714 (published in 2025) discloses a cross-protocol identifier data intelligent parsing middleware that uses FPGA hardware-level protocol stack dynamic switching, which is fundamentally different from software pipeline switching in terms of implementation level. The existing route calculation relies on remote registry queries, resulting in excessive latency. Publication No. CN121357186A (China Tower, published in 2026) discloses a NAT gateway load balancing method based on service node status. Service nodes actively report CPU, memory, and GPU information to the gateway via gRPC channels, and the gateway-side service awareness module performs a comprehensive evaluation. The Spring Cloud Gateway open-source project uses a HashMap to store routing information via InMemoryRouteDefinitionRepository by default, and the Envoy xDS protocol distributes configurations to the data plane memory through the control plane. However, the above solutions either use remote registry queries, resulting in high routing decision latency, or use a control plane-data plane separation architecture, resulting in complex configuration synchronization. Neither of these solutions discloses a complete closed loop of "maintaining a node quota registry in memory, having backend service nodes actively report status information updates, and completing route calculations in memory with decision latency in the sub-millisecond range." The technical problem to be solved by the present invention In summary, the existing technologies have the following technical problems: (1) Load balancing is not aware of business semantics and cannot be uniformly scheduled across protocols; (2) Traditional circuit breaking requires explicit ACK failure counting and cannot cope with silent timeout scenarios; (3) Multiplexing connection management does not provide fine-grained control of per-connection in-transit concurrency; (4) Backpressure mechanism is passively responded to and cannot be scheduled in advance based on trend prediction; (5) Protocol identification is only used for security detection and does not switch processing pipelines; (6) AIMD admission control does not retain the detection window to perceive backend recovery; (7) Protocol fingerprint rules have a single source and do not support multi-source aggregation coverage; (8) Routing calculation relies on remote registry queries, resulting in excessive latency; (9) Continuous data stream forwarding scenarios lack (10) The edge computing scenario lacks the autonomous capability after connection interruption and the smooth migration mechanism after recovery; (11) The cross-protocol linkage isolation of host failure and the lazy recovery mechanism driven by TTL are missing in the multi-protocol gateway; (12) In the continuous data flow scenario, the competition of multiplexed connection leads to back pressure coupling, and there is a lack of alternative models for each flow to occupy long connection; (13) The candidate strategy of AI-driven scheduling may cause system instability due to model illusion, and there is a lack of strategy verification pre-mechanism; (14) The communication between the multi-protocol gateway and the back-end service node lacks the encrypted secure transmission mechanism with authentication label; (15) The flow orchestration of the session protocol lacks the coordination mechanism of pre-built mapping and asynchronous channel. Summary of the Invention (a) Technical issues The technical problems to be solved by this invention include: How to enable scheduling decisions to simultaneously perceive business semantic tags and backend real-time status in multi-protocol hybrid scheduling scenarios, and achieve unified intelligent scheduling that is protocol-independent, algorithm-independent, and language-independent; How to trigger circuit breaking and failover without waiting for explicit ACK failure confirmation in a silent timeout request scenario, thereby reducing circuit breaking latency and supporting arbitrary protocols; How to achieve fine-grained control over the concurrency of in-transit requests for each physical connection in multiplexed connection management, and support adaptive switching between serial and parallel protocols; How to predict the back pressure value of backend nodes in advance based on the trend prediction of historical state snapshots and participate in scheduling decisions to avoid the avalanche caused by the lag of passive back pressure; How to dynamically load the corresponding protocol parsing and processing logic after protocol fingerprinting, realize dynamic switching of the protocol parsing pipeline, and support multi-source rule aggregation and conflict resolution; How to protect the availability of the scheduling system itself through the AIMD probabilistic rejection mechanism in system overload scenarios, while retaining the detection window to sense backend recovery; How to maintain a node quota registry in memory, with backend service nodes actively reporting status information, to achieve sub-millisecond routing decisions; How to achieve end-to-end backpressure propagation in continuous data stream forwarding scenarios, so that slow backend data can be propagated to the data source, while avoiding a single surge in backpressure triggering a global backpressure deadlock; How to achieve edge autonomy after connection interruption and smooth migration after recovery in edge computing scenarios, avoiding frequent state switching caused by connection jitter; How to implement cross-protocol linkage isolation and TTL-driven lazy recovery for host failures in a multi-protocol gateway to avoid secondary failures caused by a large number of requests when the host has just recovered; How to provide an alternative connection model with dedicated long connections per stream in continuous data streaming scenarios to avoid backpressure coupling caused by multiplexing connection contention; How to simulate and verify candidate strategies in AI-driven scheduling scenarios to prevent abnormal strategies from causing system instability; How to achieve encrypted and secure transmission with authentication tags in the communication between a multi-protocol gateway and a backend service node, ensuring that tampered messages are discarded before decryption; How to achieve coordination between pre-built mapping and asynchronous channels in the flow orchestration of session-based protocols, avoid blocking the event loop, and support the backfilling of the identity of the first packet? (II) Technical Solution To solve the above-mentioned technical problems, the present invention provides the following technical solution: Technical Solution 1 (corresponding to claim 1): An application-aware intelligent traffic scheduling method, comprising: a receiving step, receiving an access request from a client, the access request carrying a business semantic tag and request data; a sensing step, acquiring real-time status information of multiple backend service nodes, the real-time status information including at least one of resource utilization, number of connections, and queue depth; a decision-making step, calculating a comprehensive scheduling score for each backend service node based on the real-time status information and the business semantic tag, and determining a target node from the multiple backend service nodes according to the comprehensive scheduling score; a routing step, routing the access request to the target node; and a circuit breaker step, when the access request does not receive a completion confirmation from the target node within a preset deadline, triggering a no-confirmation circuit breaker mechanism to retry the access request to other backend service nodes, or marking the target node as sub-healthy and excluding it from the candidate node set. Technical Solution 2 (corresponding to claim 2): An application-aware intelligent traffic scheduling system, comprising: a protocol awareness module configured to receive access requests from clients, identify the communication protocol type followed by the access request, and extract business semantic tags from the access request; a status awareness module configured to obtain real-time status information of multiple backend service nodes; a scheduling decision module configured to calculate a comprehensive scheduling score for each backend service node based on the real-time status information and the business semantic tags, and determine a target node based on the comprehensive scheduling score; a multiplexed connection management module configured to maintain physical connections with the multiple backend service nodes, map the client's logical session to the physical connection, and control the concurrency of in-transit requests on each physical connection; and a fault-tolerant execution module configured to trigger a no-acknowledgment circuit breaker mechanism when the access request does not receive a completion confirmation from the target node within a preset deadline. Technical Solution 3 (corresponding to claim 3): A multiplexing connection management method, comprising: a connection establishment step, establishing physical connections for each backend service node up to a preset maximum number; a session allocation step, in response to multiple received client requests, allocating a logical session identifier to each client request; a mapping step, mapping the multiple logical session identifiers to the physical connections, wherein each physical connection is shared by multiple logical session identifiers; a concurrency control step, setting a concurrency limit for in-transit requests on each physical connection, and when the number of in-transit requests on the physical connection reaches the concurrency limit, allocating new logical sessions to other idle physical connections, or waiting when all physical connections reach their limits; and a reuse step, releasing the in-transit slots of the physical connection when the logical session is completed, and returning the physical connection to the idle queue for reuse by subsequent logical sessions. Technical Solution 4 (corresponding to Claim 4): A predictive backpressure calculation method, comprising: a sampling step, acquiring multiple historical state snapshots of a backend service node within a sliding window, each snapshot containing a state value of at least one resource dimension; a trend prediction step, for each resource dimension, calculating a trend change rate based on the historical state snapshots, and calculating the projection value of the resource dimension at a future time according to the trend change rate and a preset prediction step size; a backpressure conversion step, comparing the projection value of each resource dimension with a preset threshold, and proportionally converting projection values ​​exceeding the threshold into backpressure values ​​for that resource dimension; an aggregation step, taking the maximum value among the backpressure values ​​of each resource dimension as the predictive backpressure value of the backend service node; and an application step, using the predictive backpressure value as an input factor for the comprehensive scheduling score, participating in the scheduling decision of the backend service node. Technical Solution 5 (corresponding to claim 5): A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as described in any one of claims 1, 3, and 4. Technical Solution Six (corresponding to Claim 26): Based on the predictive backpressure calculation method, a three-stage backpressure collaborative control step is added: First, water level monitoring: when the backlog in the forwarding channel's transmission queue exceeds the high water level threshold, it is marked as unwritable; when it falls below the low water level threshold, it becomes writable again. Second, read transmission: in response to the unwritable state, the data source end read switch is closed, causing the receive window to shrink and transmitting backpressure to the data source end. Third, independent rate limiting: a token bucket rate limiter is set for each data stream to discard overspeeding data packets. The first and second stages work together to achieve end-to-end backpressure transmission, while the third stage operates independently to avoid a single-channel surge triggering a global deadlock. Technical Solution 7 (corresponding to claim 27): Based on the application-aware intelligent traffic scheduling system, an edge autonomy module is added to maintain four autonomous states of edge nodes: online controlled, online autonomous, offline autonomous, and disconnected. The state transitions between the four states based on the connection status with the central node and the heartbeat detection results. After the connection is restored, an inertial recovery mechanism is adopted, and the availability of the central node is gradually verified through half-open detection before the state transition is completed. Technical Solution 8 (corresponding to claim 28): Based on the application-aware intelligent traffic scheduling system, a host health registry module is added to aggregate cross-protocol fault detection results. When any protocol detects a backend host fault, it is marked as unhealthy and a TTL liveness time tag is set. Before the TTL expires, all node identifiers associated with the host are automatically excluded. After the TTL expires, the availability is gradually verified through half-open probing and the healthy state is restored. The unhealthy state is shared across protocols to achieve host-level cross-protocol linkage isolation. Technical Solution Nine (corresponding to Claim 29): Based on the application of the intelligent traffic scheduling method, for continuous data streams, the routing step adopts an exclusive long connection model: after determining the target node based on the comprehensive scheduling score, an independent physical long connection is established for each continuous data stream. This connection is exclusively used throughout its entire lifecycle and does not participate in the reuse of the idle queue, eliminating the acquisition and release competition operation. Each exclusive long connection maintains an independent back pressure controller to achieve independent control of single-path back pressure. Technical Solution 10 (corresponding to claim 30): Based on the predictive backpressure calculation method, an additional step is added to verify the scheduling strategy corresponding to the predictive backpressure value through digital twin simulation: a lightweight digital twin model of the backend service node is constructed in memory, the candidate scheduling strategy determined based on the predictive backpressure value is input into the digital twin model for simulation and deduction, Pareto front multi-objective optimization is used to determine the Pareto optimal strategy set, only the strategies that pass the simulation verification are issued for execution, and the strategies that fail the verification are rejected. Technical Solution Eleven (corresponding to Claim 31): Based on the application-aware intelligent traffic scheduling system, the communication between the protocol awareness module and the backend service node adopts encrypted secure transmission: each data packet contains a random initialization vector, encrypted payload and authentication tag. The authentication tag verification at the receiving end is performed before decryption. After successful verification, the payload is decrypted. If the verification fails, the data packet is discarded and decryption is avoided. Technical Solution Twelve (corresponding to Claim 32): Based on the application-aware intelligent traffic scheduling method, for session-type protocols, the decision-making steps and routing steps adopt signaling-driven session lifecycle orchestration: after determining the target node based on the comprehensive scheduling score, a flow establishment request is sent, a mapping relationship between session identifiers and node identifiers is pre-established and the response timeout is started, and after receiving the response, the data transmission channel is asynchronously opened, the data flow is migrated to the in-transmission state after access, and after the data flow ends, the data transmission channel and the signaling channel are closed in sequence and the mapping relationship is removed. (III) Beneficial Effects The present invention has the following advantages over the prior art: Protocol independence: Through the unified abstraction of "business semantic tags + comprehensive scheduling score", the scheduling decision is not bound to any specific communication protocol. It can cover a variety of protocol types such as Web protocol, message middleware protocol, database line protocol, industrial control protocol, and industry-specific protocol, and realize a protocol-independent unified scheduling framework. Algorithm independence: The comprehensive scheduling score serves as a unified abstraction covering all routing algorithms such as round-robin, weighted round-robin, least connections, weighted least connections, random, and consistent hashing. The scheduling decision module can flexibly select the algorithm based on business semantic tags and real-time status without modifying the system architecture. Language independence: The method and system of this invention do not depend on any specific programming language for implementation. Business semantic tags are passed in the call chain through a context passing mechanism, supporting multiple implementation methods such as value passing based on scope or passing based on thread-local variables. Fast fault isolation without ACK: The no-acknowledgment circuit breaker mechanism is triggered when the per-request preset deadline expires. It does not require waiting for explicit ACK failure confirmation or failure counter state switching. It can handle silent timeout scenarios, reduce circuit breaker latency, and support protocols without ACK semantics. Fine-grained control of per-connection in-transit concurrency: Set an upper limit on the in-transit request concurrency for each physical connection, and hold a per-connection in-transit slot semaphore through the lease object to achieve fine-grained control of a single physical connection carrying N in-transit logical sessions at the same time, which is different from the pool-level total connection limit of existing connection pools. Multiplexing reduces the number of physical connections: By using a two-layer limiting model with a maximum limit on the number of physical connections per host and a maximum limit on the concurrency of concurrency per connection in transit, a large number of logical sessions can be carried with a small number of physical connections, significantly reducing the physical connection overhead of backend service nodes. Predictive backpressure advance scheduling avoids avalanche: The projected value at future time is calculated based on the trend change rate of historical state snapshots and the prediction step size. The projected value is then converted into a backpressure value through a threshold and the maximum value of each dimension is taken as the predictive backpressure value of the node. This makes the scheduling decision forward-looking and avoids avalanche caused by passive backpressure lag. AIMD preserves the rapid recovery of the probe window: The probabilistic rejection protection mechanism adopts an additive growth and multiplicative decay strategy. The rejection rate is limited to a preset lower bound and an upper bound. The upper bound is less than 1 to preserve the probe window to perceive backend recovery, ensuring that the system will not reject 100% of requests under high load and always retains the probe capability. Protocol fingerprint dynamic pipeline switching: The protocol fingerprint recognition module matches the first packet byte characteristics of the access request with the pre-registered protocol fingerprint rule set. After recognition, it dynamically loads the corresponding protocol parsing processing logic to realize the dynamic switching of the protocol parsing pipeline, which is different from the existing technology that only uses security identification without switching the processing logic. Host-level cross-protocol linkage isolation: When any protocol detects a backend host failure, host-level linkage isolation is performed on all node identifiers associated with the host to achieve cross-protocol failure linkage and prevent other protocols from continuing to send requests to the failed host; at the same time, the unhealthy node sets implemented by multiple circuit breakers are aggregated and uniformly merged into the exclusion set of the candidate node set to participate in routing exclusion. Sub-millisecond decision-making for in-memory routing: A node quota registry is maintained in memory. Backend service nodes actively report status information to update the registry. The routing calculation of the scheduling decision module is completed in memory, and the decision latency reaches the sub-millisecond level, which is different from the high latency caused by relying on remote registry queries. Protocol fingerprint rules are aggregated from multiple sources and conflict resolution is achieved through aggregation of at least one of the following sources: built-in rules, rules registered by the dependency injection container, and rules declared by the service discovery mechanism. Rules registered later with the same protocol identifier override rules registered earlier. The rules are traversed and matched in descending order of priority, which solves the problem of unified management and conflict resolution of multi-source protocol fingerprint rules. Triple backpressure coordination for zero memory accumulation: Triple backpressure coordination control achieves end-to-end backpressure transmission through L1 water level monitoring and L2 reading and transmission linkage, and L3 independent rate limiting avoids single-path surges that trigger global deadlock, achieving zero memory accumulation in continuous data stream forwarding scenarios and avoiding memory overflow caused by slow backend. Smooth migration of edge autonomy: The four-state migration mechanism of the edge autonomous state machine enables edge nodes to autonomously schedule and maintain local service availability when the connection is interrupted. The lazy recovery and half-open detection mechanism avoids frequent state switching caused by connection jitter, and achieves smooth migration after the connection is restored. Cross-protocol linkage isolation and TTL lazy recovery: The host health registry aggregates cross-protocol fault detection results, so that host faults detected by any protocol take effect on all protocols. TTL liveness time tag-driven lazy recovery and half-open detection avoid secondary faults caused by a large number of requests when the host has just recovered. Exclusive long-lived connections avoid backpressure coupling: The exclusive long-lived connection flow forwarding model establishes an independent physical long-lived connection for each persistent data flow, without pooling contention, and the backpressure of a single path is independently controlled, avoiding the problem of global connection unavailability caused by the backpressure of a single path in the multiplexing model. Digital twin simulation verification prevents anomaly strategies: The digital twin simulation verification mechanism simulates and deduces candidate scheduling strategies in memory, determines the optimal strategy through Pareto front multi-objective optimization, and only the verified strategy is issued for execution to prevent system instability caused by AI model illusions or abnormal parameters. Encrypted secure transmission and anti-tampering: The encrypted secure transmission mechanism uses a random initialization vector to ensure that the same plaintext is encrypted to produce different ciphertexts. The authentication tag verification precedes decryption to ensure that tampered messages are discarded before decryption, thereby strengthening communication security. Signaling-driven session orchestration avoids blocking: Signaling-driven session lifecycle orchestration avoids additional queries when the flow establishment response arrives through pre-built mapping, asynchronous channels avoid blocking the event loop, a five-state machine ensures the legality of state transitions, and first packet identity backfilling supports scenarios where the terminal identifier is delayed in determination. Attached Figure Description Figure 1 The flowchart of the intelligent traffic scheduling method applied in this invention shows the execution sequence of the five-step closed loop of receiving, sensing, decision-making, routing, and circuit breaking. Figure 2The present invention applies a modular structure diagram of the intelligent traffic scheduling system, showing five major modules: protocol sensing module, status sensing module, scheduling decision module, multiplexing connection management module, and fault-tolerant execution module, as well as their connection relationships. Figure 3 The flowchart of the multiplexing connection management method of the present invention shows the execution order of the five steps: connection establishment, session allocation, mapping, concurrency control, and multiplexing. Figure 4 The flowchart of the predictive back pressure calculation method of this invention shows the execution sequence of five steps: sampling, trend prediction, back pressure conversion, aggregation, and application. Figure 5 The present invention provides a schematic diagram of protocol fingerprint recognition and pipeline switching, illustrating the process of first packet byte feature matching, three-source rule aggregation, priority descending traversal, and dynamic loading of protocol parsing and processing logic. Figure 6 The AIMD probabilistic rejection mechanism state transition diagram of this invention shows the state transition relationships of additive growth of rejection rate, multiplicative decay, capping of lower and upper bounds, and retention of the detection window when the upper bound is less than 1. Figure 7 This invention presents a schematic diagram of multiple sets of fuse aggregation and host-level linkage isolation, illustrating the process by which multiple fuses maintain their own sets of unhealthy nodes, are uniformly merged into a candidate node exclusion set, and achieve host-level cross-protocol linkage isolation. Figure 8 The schematic diagram of the triple back pressure coordinated control of this invention shows the coordinated control process of L1 water level monitoring, L2 reading and transmission linkage, and L3 independent token bucket speed limiting; Figure 9 The state transition diagram of the edge autonomous state machine of this invention shows the transition conditions and inertial recovery half-open detection process of the four states: online controlled, online autonomous, offline autonomous, and disconnected. Figure 10 This invention presents a schematic diagram of the host health registry TTL lazy recovery, illustrating the process of cross-protocol fault aggregation, TTL liveness time tagging, and half-open detection recovery. Figure 11 This invention presents a schematic diagram of a dedicated long-connection flow forwarding model, illustrating a structure where each flow has a dedicated long connection, no pooling contention, and independent backpressure control for each path. Figure 12The digital twin simulation verification flowchart of this invention shows the closed loop of candidate strategy → digital twin simulation → Pareto front multi-objective optimization → verification pass / verification failure rejection. Figure 13 The present invention provides a flowchart of the encrypted secure transmission data packet format and verification process, illustrating the packet format of random initialization vector + encrypted payload + authentication tag and the process of verification preceding decryption. Figure 14 The signaling-driven session lifecycle orchestration state transition diagram of this invention shows the coordination of a five-state machine (Requested → Responded → Transmitting → Closing → Closed) with pre-built mappings and asynchronous channels. Detailed Implementation The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described are only for explaining the present invention and are not intended to limit the scope of protection of the present invention. Example 1: Application of a Perception-Based Intelligent Traffic Scheduling Method This embodiment corresponds to claims 1 and 7 to 12, and describes in detail the five-step closed loop of receiving, sensing, decision-making, routing, and circuit breaking. like Figure 1 As shown, the method in this embodiment includes the following steps: Step S101 (Receiving Step): Receive an access request from the client, the access request carrying business semantic tags and request data. The business semantic tag is a context abstraction throughout the call chain, rather than a simple request parameter. The business semantic tag includes at least one of the following: request priority identifier, user level identifier, business type identifier, and protocol-level metadata. The "protocol-level metadata" enables the present invention to uniformly schedule across protocols. For example, the Content-Type field of the Hypertext Transfer Protocol, the service / method identifier of the gRPC protocol, and the topic level of the Message Queue Telemetry Transport Protocol can all be included as protocol-level metadata in the business semantic tag. The business semantic tag is passed in the call chain through a context passing mechanism, including value passing based on scope or passing based on thread-local variables. This invention emphasizes "business semantic tags" as an independent scheduling input factor, which is different from the "multi-source decision text" black box model in publication number CN122476063A—the latter merges historical behavior, real-time status, and application content into decision text, without abstracting semantic tags that can run through the call chain; it is also different from the "association graph + semantic matching rule" path mining method in publication number CN202511469452—the latter uses semantic matching for association graph mining rather than as a scheduling scoring input factor. Step S102 (Perception Step): Obtain real-time status information of multiple backend service nodes. The real-time status information includes at least one of resource utilization, connection count, and queue depth. Resource utilization includes CPU utilization, memory utilization, GPU memory utilization, and network bandwidth utilization. The GPU memory utilization is collected through memory bandwidth saturation, which is the ratio of the number of bytes read / written from the memory per unit time to the theoretical peak bandwidth of the memory. The real-time status information also includes the current request queue depth and maximum queue capacity of the backend service nodes. The remaining capacity ratio of the queue depth is the ratio of the difference between the current queue depth and the maximum queue capacity to the maximum queue capacity. The status acquisition method includes: receiving real-time status information actively reported by the backend service nodes; and / or reading real-time status information from a node quota registry maintained in memory. Step S103 (Decision Step): Based on the real-time status information and the business semantic tags, calculate the comprehensive scheduling score of each backend service node, and determine the target node from the multiple backend service nodes according to the comprehensive scheduling score. The comprehensive scheduling score is calculated by weighted fusion of the following factors: weighted sum of resource utilization, remaining capacity ratio of queue depth, predictive backpressure value, and node weight configuration; the weights of the weighted fusion are dynamically adjusted according to the business semantic tags, wherein the weight of the predictive backpressure value corresponding to requests with high request priority and user level is increased, and the weight of the remaining capacity ratio of queue depth corresponding to requests with latency-sensitive business type is increased. Among them, the "predictive backpressure value" as an input factor for the comprehensive scheduling score is a unique design of this invention, which makes the scheduling score forward-looking and different from the reactive scoring of the prior art. The comprehensive scheduling score is a unified abstraction covering all routing algorithms (round-robin, weighted round-robin, least connections, weighted least connections, random, consistent hashing, etc.), and the scheduling decision module can flexibly select the algorithm according to the business semantic tags. The routing calculation is completed in memory, and the decision latency is in the sub-millisecond range. Step S104 (Roading Step): The access request is routed to the target node. The access request is routed through the multiplexing connection management module, which maps the client's logical session to the physical connection (see Embodiment 3 for details). Step S105 (Circuit Breaking Step): When the access request does not receive completion confirmation from the target node within the preset deadline, the no-confirmation circuit breaking mechanism is triggered, and the access request is retried to other backend service nodes, or the target node is marked as sub-healthy and excluded from the candidate node set. The "unacknowledged circuit breaker mechanism" of this invention differs from the traditional three-state circuit breaker model. The three-state circuit breaker (Closed / Open / Half-Open) proposed by Michael Nygard in "Release It!" and similar schemes disclosed by AWS Prescriptive Guidance both trigger state switching based on error rate or failure count, requiring explicit ACK confirmation of request success or failure. In scenarios with silent timeouts (i.e., no response or explicit failure confirmation received), traditional three-state circuit breakers must wait for the error rate to accumulate to a threshold before triggering a state switch, resulting in high circuit breaker latency. The unacknowledged circuit breaker mechanism of this invention triggers upon the expiration of a pre-set per-request deadline, without waiting for explicit ACK confirmation or failure counter state switching. It can handle silent timeout scenarios, reduce circuit breaker latency, and support protocols without ACK semantics. The preset deadline is dynamically calculated based on the business semantic tags, specifically including: querying a preset basic deadline timetable based on the business type identifier in the business semantic tags to obtain the basic deadline; correcting the basic deadline based on the user level identifier in the business semantic tags, with the deadline for higher-level users shortened accordingly; further adjusting the corrected deadline based on the protocol-level metadata in the business semantic tags; and transmitting the dynamically calculated deadline in the call chain through a custom option field or request header field in the communication protocol packet header. For example, the preset deadline for high-priority transaction requests can be set to 500 milliseconds, and the preset deadline for low-priority batch processing requests can be set to 30 seconds, enabling differentiated timeout control for requests of different business types and priorities. Retrying the access request to other backend service nodes includes: adding the target node to an exclusion set and setting a liveness time tag for each node in the exclusion set, automatically removing the corresponding node from the exclusion set after the liveness time tag expires; re-determining a new target node from the remaining candidate nodes based on the comprehensive scheduling score (instead of simple sequential retries), wherein the comprehensive scheduling score applies a penalty factor to previously faulty nodes in the exclusion set to reduce their weight; the maximum preset number of retries is dynamically determined based on the request priority identifier in the business semantic tag, with higher priority requests allowed more retries. When the overall scheduling score of all backend service nodes is lower than a preset threshold, a probability denial protection mechanism is triggered to deny the access request with a preset probability, thereby protecting the availability of the scheduling system itself. The preset threshold is dynamically determined based on the historical statistical values ​​of the overall scheduling score. The probability denial protection mechanism adopts the AIMD strategy (see Example 5 for details). Compared with existing technologies: This invention also differs from the "Circuit Breaker Method Based on LSTM Prediction and Early Warning" disclosed in Publication No. CN202311656441.2 (Daolv Tourism, applied in 2023)—the latter uses an LSTM neural network to predict the request failure rate and trigger the circuit breaker accordingly, still belonging to the traditional circuit breaker category based on prediction error rate, requiring training datasets and online inference, and does not disclose a unified scheduling abstraction of "business semantic labels + comprehensive scheduling score". The no-acknowledgment circuit breaker mechanism of this invention is triggered when the per-request preset deadline expires, without the need for training datasets, error rate accumulation, or LSTM model inference. It can cope with silent timeout scenarios and support no-ACK semantic protocols. Moreover, the business semantic labels enable scheduling decisions to simultaneously perceive business semantics and backend real-time status, achieving protocol-independent unified scheduling. The technical path and effects are different. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 2: Application-Aware Intelligent Traffic Scheduling System This embodiment corresponds to claims 2 and 15 to 18, and describes the five-module system structure in detail. like Figure 2 As shown, the system in this embodiment includes: Protocol awareness module 201: Configured to receive access requests from clients, identify the communication protocol type followed by the access request, and extract business semantic tags from the access request. The protocol awareness module has dual responsibilities: protocol identification and business semantic tag extraction. Protocol identification is implemented through a protocol fingerprinting module (see Example 6 for details). Status Awareness Module 202: Configured to obtain real-time status information from multiple backend service nodes. The status awareness module can obtain status through two channels: proactive reporting from backend service nodes and / or direct reading from the memory registry. Scheduling decision module 203: configured to calculate the comprehensive scheduling score of each backend service node based on the real-time status information and the business semantic tag, and determine the target node according to the comprehensive scheduling score. As a preferred embodiment, the scheduling decision module is further configured to: filter the candidate node set according to protocol type based on the protocol identifier carried in the business semantic tag, retaining only backend service nodes that support the protocol type as candidates (this protocol filtering feature has been downgraded from the claims to the preferred embodiment in the specification). Multiplexing connection management module 204: Configured to maintain physical connections with the multiple backend service nodes, map the client's logical sessions to the physical connections, and control the concurrency of in-transit requests on each physical connection. The multiplexing connection management module maintains a set of physical connections for each backend service node, not exceeding a preset limit. Each physical connection maintains an in-transit request concurrency semaphore. The number of permits for the in-transit request concurrency semaphore is dynamically adjusted based on the comprehensive scheduling score of the backend service node; nodes with higher scores have a larger number of semaphore permits for their corresponding connections. When acquiring a connection, active connections are preferentially reused from the idle queue. The criterion for determining an active connection is a successful response record within the most recent preset time period. When there are no available connections and the limit has not been reached, a new connection is created. When the limit has been reached, waiting logical sessions are prioritized according to the request priority identifier in the business semantic tag and then wait. The multiplexing connection management module performs periodic health checks on the physical connections in the idle queue, closes inactive physical connections, and updates the connection count (see Example 3 for details). Fault-tolerant execution module 205: configured to trigger a circuit breaker mechanism if the access request does not receive a completion confirmation from the target node within a preset deadline. The five-module system architecture of this invention differs from the six-module architecture (protocol conversion + data processing + security management + flow control + device management) in publication number CN120378503A (China Tower) – the latter lacks an independent multiplexed connection management module and a fault-tolerant execution module based on unacknowledged circuit breaking. Although the ProxySQL open-source project discloses the concept of N:M multiplexed connection management, it does not include "in-transit request concurrency control" as an independent module feature. Protocol fingerprinting module (additional module): configured to match the characteristics of the first packet byte of the access request with a pre-registered set of protocol fingerprint rules, automatically identify the communication protocol type, and dynamically load the corresponding protocol parsing processing logic according to the identification result (see Example 6 for details). The protocol fingerprinting module of this invention differs from the JA4 series fingerprints disclosed in FoxIO US2024 / 0396914A1 and the TLS fingerprints disclosed in Akamai US20190190950—W1 / W2—used for security identification, asset discovery, and bot detection, which do not change subsequent processing logic after identification. This invention dynamically loads the protocol parsing plugin and switches the pipeline after identification, a unique feature of gateway routing scenarios. Protocol fingerprint rules are aggregated from three sources: the protocol fingerprint rule set is obtained by aggregating at least one of the following sources: built-in rules, rules registered through the dependency injection container, and rules declared through the service discovery mechanism; rules registered later with the same protocol identifier override rules registered earlier. This three-source aggregation differs from the single rule source in publication number CN202511918795—the latter's protocol feature vector is dynamically calculated using information entropy and feature vectorization, and it does not disclose a rule management mechanism of three-source aggregation + priority descending traversal; the latter's "rule chain" is a business rule orchestration, while this invention's "protocol parsing processing logic" is a protocol parsing pipeline, resulting in different technical effects. Priority-descending traversal matching: The protocol fingerprinting module traverses the fingerprint rule set in descending order of priority for matching, returning the first matching protocol fingerprint; when multiple protocol fingerprints match simultaneously, the one with higher priority takes precedence. For example, the HTTP / 2 fingerprint rule has a higher priority than HTTP / 1.1, so when the first packet matches, HTTP / 2 is returned first. Multiple circuit breaker aggregation and host-level cross-protocol linkage isolation: such as Figure 7 As shown, the fault-tolerant execution module is also configured to: aggregate the unhealthy node set implemented by multiple circuit breakers, and merge the unhealthy node set into the exclusion set of the candidate node set to participate in route exclusion; and / or, when any protocol detects a backend host failure, perform host-level linkage isolation on all node identifiers associated with the host. The specific implementation of multiple fuse aggregation is as follows: the first fuse (based on the deadline timeout) maintains the unhealthy node set {A,B}, the second fuse (based on the failure count) maintains the unhealthy node set {B,C}, and the third fuse (based on the active health check) maintains the unhealthy node set {D}. They are all merged into the exclusion set {A,B,C,D}, and the scheduling decision module excludes them from the candidate node set. The specific implementation of host-level cross-protocol linkage isolation is as follows: The backend host host1 runs three protocol nodes: a MySQL protocol node (port 3306), a Redis protocol node (port 6379), and an HTTP protocol node (port 8080). When the MySQL protocol detects a failure on host1 (such as a connection timeout), the MySQL node, Redis node, and HTTP node on host1 are all added to the exclusion set, achieving cross-protocol linkage isolation and preventing other protocols from continuing to send requests to the faulty host. The multi-fuse aggregation and host-level cross-protocol linkage isolation of this invention has not been disclosed within the search scope: the three-state fuse disclosed in Michael Nygard's "Release It!" and AWS Prescriptive Guidance is a single-set model; the multi-fuse / multi-circuit breaker disclosed in publication numbers CN202510825149, CN202310711235, and CN202511084759 are all power physical devices, which are fundamentally different from software fuse aggregation; the cloud computing node isolation disclosed in publication number CN105051692A (Microsoft) is node-level power outage / blocking of transmission and reception, not host-level cross-protocol linkage. Compared with existing technologies: This invention also differs from the Envoy outlier detection mechanism of the Istio service mesh—Envoy's outlier detection is configured separately by filter / protocol (HTTP router / TCP proxy / Redis proxy / Thrift proxy each detect separately), HTTP failures do not automatically isolate TCP / Redis / Thrift traffic to the same host, and the base_ejection_time increases with the number of evictions rather than a fixed TTL tag; successful_active_health_check_uneject_host cancels eviction upon successful proactive health check rather than gradually allowing probe verification. The aggregation of multiple software circuit breakers in this invention unifies the unhealthy node sets maintained by multiple independent circuit breakers (based on deadline timeout, failure count, and proactive health check) into a unified candidate node exclusion set. Furthermore, when any protocol detects a host failure, it performs host-level cross-protocol linkage isolation on all node identifiers associated with that host, ensuring that the scheduling decisions for all protocols are synchronously affected by the host failure. This cross-protocol aggregation and unified exclusion mechanism is not publicly available within the search scope. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 3: Multiplexing Connection Management Method This embodiment corresponds to claims 3 and 19 to 21, and describes in detail the three-layer combination of the upper limit of the number of physical connections per host, the upper limit of the concurrency of requests in transit per connection, and the semaphore held by the lease object. like Figure 3 As shown, the method in this embodiment includes: Step S301 (Connection Establishment Step): For each backend service node, establish no more than the preset maximum number of physical connections. This is the first layer of restriction—the per-host physical connection limit, which restricts the total number of physical connections to a single backend node and prevents connection overload. This concept is publicly disclosed in the maximumPoolSize of database connection pools such as HikariCP. Step S302 (Session Assignment Step): In response to multiple received client requests, assign a logical session identifier to each client request. Step S303 (Mapping Step): Map the multiple logical session identifiers to the physical connections, where each physical connection is shared by multiple logical session identifiers. This is the core of N:M multiplexing—a small number of physical connections carrying a large number of logical sessions. The ProxySQL open-source project has publicly disclosed this mapping concept. Step S304 (Concurrency Control Step): Set a concurrency limit for incoming requests on each physical connection. When the number of incoming requests on a physical connection reaches the concurrency limit, the new logical session will be assigned to another idle physical connection, or wait when all physical connections have reached their limits. This is the second layer of restriction—the per-connection concurrency limit for incoming requests, which limits the number of concurrent incoming requests on a single physical connection. The maximum concurrency of requests in transit is determined based on the communication protocol type carried by the physical connection: for serial protocols with a request-response order model (such as MySQL Line Protocol, Redis RESP Protocol, HTTP / 1.1), the maximum concurrency is 1; for parallel protocols that support multiplexing (such as HTTP / 2, gRPC), the maximum concurrency is an integer greater than 1. This is protocol-adaptive concurrency—serial protocol = 1, parallel protocol = N. The "adaptive" nature of this invention refers to dynamic switching at runtime based on protocol fingerprint recognition results, rather than static configuration (unlike Istio / Envoy, which statically configures concurrency according to each protocol). The per-connection concurrency control of this invention differs from ProxySQL's multiplexing mechanism. ProxySQL's multiplexing uses session-level state compatibility judgment—determining "whether this session can switch to another backend connection." Its disabling rules are related to session state (transaction activation, LOCK TABLES, GET_LOCK, temporary tables, PREPARE, user variables, SQL_LOG_BIN=0, etc.). Once a session is routed to a backend connection, it is exclusive during the execution of that query, not "one physical connection simultaneously carrying N in-transit logical sessions." The per-connection concurrency limit of this invention allows one physical connection to simultaneously carry N in-transit logical sessions (N determined by the protocol type), a fine-grained control that ProxySQL lacks. The two-layer limiting model of this invention (maximum number of physical connections per host + maximum concurrency of requests in transit per connection) has not been publicly found in the search scope: HikariCP only has the first layer (total number of pool-level connections), and HTTP / 2 RFC9113 only has the second layer (per-connection stream limit). The combination of the two is an engineering innovation. HTTP / 2's SETTINGS_MAX_CONCURRENT_STREAMS is a static configuration negotiated at the protocol layer and is bound to the HTTP / 2 protocol; this invention is an application-layer scheduling abstraction that covers all communication protocols. Step S305 (Reuse Step): When a logical session completes, the in-transit slot of the physical connection is released, and the physical connection is returned to the idle queue for reuse by subsequent logical sessions. Periodic health checks are performed on the physical connections in the idle queue, inactive physical connections are closed, and the connection count is updated. As a preferred embodiment, the idle queue employs a Last-In-First-Out (LIFO) strategy, prioritizing the reuse of most recently returned physical connections (keeping connections active and reducing idle timeouts). This LIFO strategy has been downgraded from the claims to a preferred embodiment in the specification; those skilled in the art can choose different reuse strategies such as LIFO or FIFO depending on the actual scenario. When all physical connections reach the concurrency limit, the logical sessions waiting to acquire connections adopt a lightweight polling waiting strategy, and sleep for a preset short duration between each polling. The short duration is adapted to the virtualization thread scheduling model (the short duration is preferably 1-10 milliseconds, or the short duration is less than the carrier thread scheduling time slice). The lease object implements the mapping: the mapping between logical sessions and physical connections is implemented through lease objects. The lease object holds a reference to the physical connection and an in-transit slot semaphore, and explicitly releases the in-transit slot when the logical session is completed. This is the third layer—the lease object acts as a unified resource management container for "connection reference + in-transit slot semaphore". The design of this invention, which involves the lease object holding a per-connection in-flight slot semaphore, differs from HikariCP's PoolEntry and Vibur DBCP's Semaphore. HikariCP's ConcurrentBag is based on a CopyOnWriteArrayList + CAS state machine; PoolEntry holds a connection reference but not an in-flight slot semaphore, and its semaphore is a pool-level limit on the total number of connections (maximumPoolSize), not a per-connection in-flight semaphore. Vibur DBCP is implemented using a Queue guarded by Semaphore, but its Semaphore also has a pool-level limit on the total number of connections, not a per-connection in-flight slot. c3p0 uses a dynamic proxy to create a PooledConnection proxy, but does not hold an in-flight slot semaphore within the proxy. The exact same ternary combination of "lease object holding a connection reference + in-flight slot semaphore + explicit release" found within the search scope is not present in any other disclosure. Compared with existing technologies: The three-layer combination of this invention (maximum number of per-host physical connections + maximum concurrency of per-connection in-transit requests + semaphore holding per-connection in-transit slots by the lease object) differs from the mechanism of "rented reader holding in-flight slot" in the open-source project mda2av / ioxide—the latter only holds in-flight slots on the reading end and does not form a three-layer combination of "maximum number of per-host connections + maximum concurrency of per-connection in-transit + semaphore holding by the lease object," and does not disclose a runtime dynamic switching mechanism for "protocol adaptive concurrency (serial = 1 / parallel = N)." The three-layer combination of this invention covers all communication protocols under protocol-independent scheduling abstraction. The maximum concurrency of the serial protocol is 1, and the maximum concurrency of the parallel protocol is an integer greater than 1. The runtime switching is dynamic based on the protocol fingerprint recognition result rather than static configuration, which differs from the Istio / Envoy scheme of static configuration according to each protocol. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 4: Predictive Back Pressure Calculation Method This embodiment corresponds to claims 4 and 22 to 25, and describes in detail the five steps of sampling, trend prediction, back pressure conversion, aggregation, and application. like Figure 4 As shown, the method in this embodiment includes: Step S401 (Sampling Step): Obtain multiple historical state snapshots of the backend service nodes within a sliding window. Each snapshot contains state values ​​for at least one resource dimension. Resource dimensions include at least one of the following: CPU utilization, queue depth ratio, and connection ratio. The queue depth ratio is the ratio of the current queue depth to the maximum queue capacity, and the connection ratio is the ratio of the current number of active connections to the maximum number of connections. These three resource dimensions provide complete coverage of "computational resource bottlenecks + buffer resource bottlenecks + connection resource bottlenecks," and the normalized form of the ratios allows for comparison of nodes with different capacities. Step S402 (Trend Prediction Step): For each resource dimension, calculate the trend change rate based on the historical state snapshot, and calculate the projection value of the resource dimension at a future time according to the trend change rate and the preset prediction step size. The rate of change of trend can be calculated using any of the following methods, and the present invention does not limit the specific algorithm: Implementation Method 1 (Optional, Least Squares Linear Regression): The regression slope is calculated using the sampling index as the independent variable and the resource dimension state value as the dependent variable. The projection value is the sum of the products of the latest state value, the regression slope, and the prediction step size (i.e., projection value = latest state value + regression slope × prediction step size). Least squares linear regression is a lightweight, low-overhead, O(1) sliding window update engineering design. Implementation Method Two (Optional, Exponentially Weighted Moving Average (EWMA): Historical state snapshots are weighted by a time decay factor. The trend change rate is the difference between the current snapshot and the previous EWMA estimate multiplied by a smoothing coefficient. The projected value is the sum of the latest state value, the trend change rate, and the prediction step size. Exponentially weighted moving averages are more sensitive to recent data, respond faster to trend changes, and require only a single state variable, resulting in extremely low computational overhead. Implementation Method 3 (Optional, Kalman Filtering): The resource dimension state values ​​are modeled as a state-space model, including state transition equations and observation equations. The trend rate of change of the resource dimension is estimated iteratively through prediction and update steps. The projected value is the state estimate for future times by the Kalman filter. Kalman filtering can fuse process noise and observation noise, providing optimal estimation in noisy data scenarios, and is suitable for backend nodes with large fluctuations in resource state. The three implementation methods described above can be used individually or in combination (e.g., using different algorithms for different resource dimensions). Any method that extrapolates trends based on historical snapshots to calculate future projection values ​​is within the scope of this invention. The prediction object of this invention is "future resource state" (self-state projection), which differs from the "forward-looking arrivals" predicted by Huang et al. (arXiv:1309.1110, PBP algorithm). The PBP algorithm uses a lookahead window model, assuming that the server knows the arrival information for the next D_n time slots, and does not extrapolate trends based on historical snapshots. This invention is based on trend extrapolation from historical snapshots and does not assume that the future is known. This invention differs from Virtana's priority-aware backpressure mechanism (US12,340,249B2)—the latter rearranges tasks based on current resource availability and does not disclose the regression and aggregation of the three-dimensional resource dimensions separately. This invention also differs from Google's LLM-D project's Predicted-Latency Based Scheduling—the latter uses XGBoost regression (non-linear, feature-engineered, requires online training) to predict TTFT (first token latency) and TPOT (twice-to-thumbnail latency), applicable to LLM inference scenarios; this invention can use lightweight linear prediction algorithms (linear, lightweight, low-overhead, and capable of O(1) sliding window updates) such as least squares linear regression, exponentially weighted moving average, or Kalman filtering to predict resource state projection values, applicable to general gateway scheduling. The present invention is different from the predict_linear function of Prometheus and the RollingRegression of the sitmo open-source library — the latter only provides the algorithm implementation of sliding window linear regression, which is directly used for monitoring alarms or trend filtering, and does not involve backpressure aggregation, threshold conversion and scheduling scoring closed loop. The present invention includes a complete engineering closed loop of "sampling → regression → threshold conversion → maximum value taking → four-level division → backfilling → scheduling scoring". Step S403 (backpressure conversion step): comparing the projection value of each resource dimension with a preset threshold, and converting the projection value exceeding the threshold into the backpressure value of the corresponding resource dimension in proportion. As a preferred embodiment, the conversion adopts three-segment threshold linear interpolation: when the projection value is less than or equal to the threshold, the backpressure value is zero (to avoid false alarms); when the projection value is greater than or equal to the physical upper limit, the backpressure value is the full value (to avoid distortion of linear extrapolation after saturation); when the projection value is between the threshold and the physical upper limit, the backpressure value is calculated by linear interpolation (smooth transition). The three-segment threshold conversion has not been clearly disclosed in the backpressure field, and is different from the single hard threshold mutation judgment of Publication No. CN110933097A (Meiwei Buyongdeng) and the Boolean filtering of Koordinator. Step S404 (aggregation step): taking the maximum value among the backpressure values of all resource dimensions as the predictive backpressure value of the backend service node. Taking the maximum value is an engineered security strategy to ensure that the most dangerous dimension is exposed first, which is different from the maxWeight optimization of the PBP algorithm (maximization of the product of queue difference and transmission rate). Step S405 (application step): using the predictive backpressure value as an input factor of the comprehensive scheduling score to participate in the scheduling decision of the backend service node. The predictive backpressure value is further divided into multiple levels, including four levels: normal, warning, critical, and rejection, and different levels trigger different scheduling strategies: no intervention at the normal level; deweighting the node at the warning level (reducing the node weight in the comprehensive scheduling score); limiting the flow of the node at the critical level (reducing the probability of routing to the node); the rejection level indicates that the node should be excluded from the candidate node set. The four-level backpressure level is different from the V1 / V2 tri-state circuit breaker — the latter is based on state machine switching of failure counts, the four levels of the present invention are level divisions based on predictive backpressure values, and the rejection level triggers node-level exclusion (not request-level rejection). The method further includes: backfilling the predictive backpressure value into the latest state snapshot of the backend service node, serving as the basis for subsequent scheduling decisions and historical tracing. Backfilling to the snapshot forms a complete engineering closed loop of "sampling → prediction → transformation → aggregation → backfilling → next round of sampling," providing observability for operations and maintenance. Unlike the LLM-D project's training sample write-back (model training closed loop) and Prometheus's prediction results directly triggering alarms (no backfilling concept), the backfilling in this invention is "prediction results backfilled into the state object" for use in the next round of decision-making. Compared with the new existing technologies: This invention is also different from the two new back pressure solutions disclosed in 2025-2026: (1) AI large model gateway reactive multidimensional back pressure solution - the latter is based on real-time reactive resource monitoring to dynamically adjust the back pressure threshold, does not extrapolate the trend based on historical state snapshots, and does not disclose the complete engineering closed loop of "three-dimensional resource dimensions respectively regression → take the maximum value scalar aggregation → three-stage threshold conversion → four-level back pressure level → backfilling closed loop"; this invention is based on historical snapshot trend extrapolation without assuming known future arrival traffic, so that the scheduling decision has foresight and avoids the lag of passive reactive back pressure. (2) LSTM prediction scheduling scheme based on MCP (Model Context Protocol) - The latter uses LSTM neural network to predict request load and schedule accordingly. It requires training dataset and online inference, and the prediction object is request load rather than backend resource status. This invention can use lightweight linear prediction algorithms such as least squares linear regression, exponential weighted moving average or Kalman filtering (linear, lightweight, low overhead, and can be updated by O(1) sliding window). The prediction object is the resource status projection value of the backend service node (self-state projection). It is applied to general gateway scheduling rather than specific AI inference scenarios. The technical path and effect are different. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 5: AIMD Probabilistic Rejection Protection Mechanism This embodiment corresponds to claims 13 and 14, and describes in detail the additive growth and multiplicative decay strategy. like Figure 6 As shown, the AIMD probability rejection protection mechanism in this embodiment includes: Triggering condition: When the overall scheduling score of all backend service nodes is lower than a preset threshold, a probability rejection protection mechanism is triggered to reject the access request with a preset probability, thereby protecting the availability of the scheduling system itself. The preset threshold is dynamically determined based on the historical statistical values ​​of the overall scheduling score. The triggering condition of this invention is a system-level score-driven trigger where "the overall scheduling score of all nodes is lower than a preset threshold," which differs from the per-RPC per-SLO miss trigger (single request level) of Google's EP4036729B1 and the connection reset / 502 error trigger of arXiv:2604.17111 (HiveMind). The purpose of this invention is to "protect the availability of the scheduling system itself," unlike EP4036729B1's "protection of backend RPC latency SLO." Additive growth: When forwarding fails, the rejection rate increases linearly by a preset step size. This preset step size is dynamically adjusted based on the variance of the comprehensive scheduling score of the backend service nodes. The larger the score variance, the larger the step size to accelerate the increase in the rejection rate. For example, the rejection rate starts from 0 and increases by 0.1 for each forwarding failure. Multiplicative decay: When the number of consecutive successful forwards reaches a preset window number, the rejection rate is multiplicatively decayed by a preset factor. This preset factor changes according to the gradient of the number of consecutive successful forwards; the more consecutive successful forwards, the smaller the decay factor becomes to accelerate recovery. For example, when there are 10 consecutive successful forwards, the rejection rate is multiplied by 0.5. Capped design: The rejection rate is limited between a preset lower bound and an upper bound. The upper bound is less than 1 to preserve the detection window for backend recovery, the upper bound is 0.9 to preserve 10% of the detection window, and the lower bound is 0 to allow full forwarding. The core differentiating feature of this invention is the "preservation of the probe window when the upper bound is less than 1," ensuring that the system will not reject 100% of requests even under high load, always retaining 10% (when the upper bound = 0.9) of the probe capability to detect backend recovery. This differs from: EP4036729B1 (Google) does not mention an upper / lower bound capping design for admit probability, and its admitprobability only decreases additively without multiplicative growth; arXiv:2604.17111 (HiveMind)'s AIMD Backpressure only has lower bound Cmin protection (c_t←max(Cmin, c_t·β)), without a probe window design with an upper bound less than 1; EP-2456142-A1's AIMD overload detection controls the transmission rate rather than the rejection rate, and has no probe window design. The AIMD of this invention is a bidirectional adjustment (additive growth + multiplicative decay), unlike the unidirectional adjustment of EP4036729B1 (which only additively reduces the admit probability). This invention controls the rejection rate (rejection probability), unlike EP-2456142-A1 which controls the transmission rate, arXiv:2604.17111 which controls backpressure, and vLLM gateway AIMD which controls the concurrency budget. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 6: Protocol fingerprint recognition and dynamic pipeline switching This embodiment corresponds to claim 15 and describes in detail the first packet byte feature matching, three-source aggregation, priority descending traversal, and dynamic pipeline switching. like Figure 5 As shown, the protocol fingerprint recognition and pipeline switching in this embodiment include: Rule aggregation steps: The protocol fingerprint rule set is obtained through aggregation from at least one of the following sources: built-in rules, rules registered through the dependency injection container, and rules declared through the service discovery mechanism. Rules registered later with the same protocol identifier override rules registered earlier. The specific implementation of three-source aggregation is as follows: - Built-in rules: Fingerprint rules for common protocols such as Hypertext Transfer Protocol version 1.1, Hypertext Transfer Protocol version 2, MySQL Line Protocol, and Redis RESP Protocol are built into the system; - Rules registered by the dependency injection container: Third-party plugins register fingerprint rules for industry-specific protocols such as JT / T 808, SL651, and HJ212 through the dependency injection container; - Rules declared by the service discovery mechanism: Fingerprint rules for industrial control protocols such as Modbus and OPC UA are declared by the dynamic service discovery mechanism. After aggregation from three sources, rules registered later with the same protocol identifier override rules registered earlier. For example, if the dependency injection container registers HTTP / 2 protocol fingerprint rule v2.0, it overrides the built-in HTTP / 2 fingerprint rule v1.0. The rule management mechanism of the present invention, which combines three-source aggregation with post-registration overriding first-registration, differs from the single rule source in publication number CN202511918795. The latter's protocol feature vector is dynamically calculated through information entropy and feature vectorization, and it does not disclose three-source aggregation or conflict resolution strategies. Matching steps: Based on the characteristics of the first packet byte of the access request, traverse the protocol fingerprint rule set in descending order of priority for matching, and return the first matching protocol fingerprint; when multiple protocol fingerprints match at the same time, the one with higher priority is given priority. The specific implementation of the priority descending traversal: HTTP / 2 fingerprint rules have higher priority than HTTP / 1.1, and HTTP / 2 is returned first when the first packet matches. Priority descending traversal solves the conflict problem of "multiple protocol fingerprint rules matching the first packet at the same time", and the short-circuit matching strategy returned by the first match improves matching efficiency. The priority descending traversal matching of this invention differs from the AI ​​classifier decision mechanism of publication number CN202511918795—the latter is based on AI classification of information entropy + feature vector, while this invention is rule matching; it also differs from the single fingerprint matching of FoxIO US2024 / 0396914A1 (JA4)—the latter has no priority traversal mechanism. Loading steps: Dynamically load the corresponding protocol parsing and processing logic (pipeline switching) based on the recognition results. The specific implementation of dynamic loading is as follows: After identifying it as the HTTP / 2 protocol, the HTTP / 2 protocol parsing plugin is dynamically loaded, including processing logic such as frame parsing, stream multiplexing, and header compression; after identifying it as the MySQL protocol, the MySQL protocol parsing plugin is dynamically loaded, including processing logic such as handshake packet parsing, query message parsing, and result set parsing. The "dynamic loading of protocol parsing processing logic after identification" in this invention differs from FoxIO's US2024 / 0396914A1 (JA4) and Akamai's US20190190950 (TLS fingerprint)—W1 / W2 are used for security identification, asset discovery, and bot detection. After identification, they do not change the subsequent processing logic, but only serve as a basis for security judgment. The dynamic loading of the protocol parsing plugin and switching of the pipeline after identification in this invention is a unique feature of gateway routing scenarios. This invention also differs from the cross-protocol identifier data intelligent parsing middleware published in CN202511454714—the latter uses FPGA hardware-level protocol stack dynamic switching, while this invention uses software pipeline switching, thus implementing different levels. The specific list of protocol types (as an example in the specification, claim 33 has been downgraded) includes Hypertext Transfer Protocol version 1.1, Hypertext Transfer Protocol version 2, Hypertext Transfer Protocol version 3, WebSocket protocol, gRPC protocol, Message Queuing Telemetry Transport Protocol, JT / T 808 protocol, MySQL Thread protocol, PostgreSQL Thread protocol, SQL Server TDS protocol, Oracle TNS protocol, MongoDB Thread protocol, Redis RESP protocol, AMQP protocol, Kafka protocol, Modbus protocol, CoAP protocol, OPC UA protocol, DNP3 protocol, IEC 60870-5-104 protocol, IEC 61850 MMS protocol, SIP protocol, BACnet protocol, LWM2M protocol, MQTT-SN protocol, ENIP protocol, SL651 protocol, HJ212 protocol, Dubbo protocol, etc. Those skilled in the art can perform similar adaptations to other protocols in the list based on the protocol fingerprinting method disclosed in this application. The above list of protocols can be categorized by purpose as follows: Web protocols (HTTP / 1.1, HTTP / 2, HTTP / 3, WebSocket, gRPC), database protocols (MySQL, PostgreSQL, SQL Server TDS, Oracle TNS, MongoDB, Redis RESP), message middleware protocols (MQTT, AMQP, Kafka, MQTT-SN), Internet of Things protocols (CoAP, LWM2M, JT / T 808, SL651, HJ212), industrial control protocols (Modbus, OPC UA, DNP3, IEC 60870-5-104, IEC 61850 MMS, ENIP, BACnet), signaling protocols (SIP), RPC protocols (Dubbo), etc. Compared with existing technologies: The protocol fingerprinting module of this invention also differs from the protocol fingerprinting scheme disclosed in Publication No. CN121442027A (published in 2026)—the latter, while disclosing the concept of protocol fingerprinting, does not disclose the complete feature chain of "first packet byte feature matching + dynamic loading of protocol parsing processing logic (pipeline switching) + three-source aggregation (built-in rules + dependency injection container registration + service discovery mechanism declaration) + registration after the same protocol identifier overwrites registration before the first registration + priority descending traversal + first match return + priority of higher among multiple matches". The three-source aggregation + registration after the first registration + priority descending traversal matching mechanism of this invention also differs from the "multi-source firstmatch" build system configuration loading mechanism in the scikit-build open-source project—the latter is used for Python build system configuration lookup, not gateway protocol fingerprinting scenarios, and does not disclose the gateway routing-specific feature of "dynamically loading protocol parsing plugins and switching pipelines after identification". The complete feature chain of this invention serves as the basis for the independent claim of Division D, and no single prior art has been found to be disclosed within the search scope. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 7: Virtualized Thread Scheduling Model and Memory Routing This embodiment corresponds to claim 12 (combining original claim 13 and original claim 35, corresponding to the memory routing calculation part), and describes a virtualization thread scheduling model as a preferred embodiment of the specification (original claim 34 has been downgraded and is not a separate item in the claims). Virtualized thread scheduling model (preferred implementation): The concurrent processing of the system adopts a virtualized thread scheduling model, which allows a small number of operating system threads to carry a large number of logical concurrent tasks. The logical concurrent tasks include at least one of request forwarding, state awareness, and scheduling decision. The term "virtualized thread" as used in this invention is a broad concept, including but not limited to lightweight thread implementations such as Java Virtual Threads (the M:N scheduling model disclosed in JEP 444), Go goroutines, Kotlin coroutines, Rust async tasks, and Python asyncio tasks. When a request forwarding task is blocked, the virtualized thread scheduling model automatically unloads the operating system thread and schedules other concurrent logical tasks, significantly improving concurrency processing capabilities compared to the thread blocking and waiting of traditional thread pool models. As a preferred implementation, this invention avoids the pinning problem of carrier threads caused by the use of synchronized blocks in the virtualized thread scheduling model, and uses mechanisms such as reentrant locks (ReentrantLock) to avoid pinning. State-aware tasks and scheduling decision tasks pass business semantic tags through scoped values, echoing the context passing mechanism. When all physical connections reach the concurrency limit, the logical sessions waiting to acquire connections adopt a lightweight polling waiting strategy, and sleep for a preset short duration between each poll. The short duration is adapted to the virtualization thread scheduling model (the short duration is preferably 1-10 milliseconds, or the short duration is less than the carrier thread scheduling time slice) to avoid blocking the carrier thread for a long time. Memory routing: The routing calculation of the scheduling decision module is completed in memory, with a decision latency in the sub-millisecond range. The real-time status information is maintained in a node quota registry in memory, which is updated by status information actively reported by backend service nodes. The specific implementation of the node quota registry: The node quota registry is a ConcurrentHashMap, where the key is the node identifier and the value is a node quota object. The node quota object includes at least one of the following: CPU quota limit, memory quota limit, connection quota limit, and queue depth quota limit. Backend service nodes proactively report their current resource usage via a heartbeat protocol, and the gateway updates the node quota registry. The "node quota registry" of this invention maintains the status of the "quota dimension" (CPU quota, memory quota, connection quota, queue depth quota), which is different from the "computing power dimension" status collection of publication number CN121357186A (China Tower) - the latter's service perception module collects computing power and network status for comprehensive evaluation and makes a weighted score based on computing power; this invention performs over-quota exclusion based on quota. When the real-time status information of a node exceeds the quota limit in its quota registry, the node is excluded from the candidate node set. The "backend service node proactive reporting" of this invention differs from the heartbeat reporting of service registry entries such as Eureka, Consul, and Nacos—the latter report "liveness status," while this invention reports "resource quota usage" (fine-grained status information such as resource utilization, number of connections, and queue depth). It also differs from the control plane configuration distribution of the Envoy xDS protocol—the latter involves memory configuration synchronization with a separate control plane and data plane—while this invention involves backend nodes directly reporting to the gateway data plane. The specific algorithm for route calculation is as follows: traverse the candidate node set, read the real-time status of each node from the quota registry, calculate the comprehensive scheduling score (including factors such as weighted sum of resource utilization, queue depth-to-remaining capacity ratio, predictive backpressure value, and node weight configuration), and select the node with the highest score as the target node. The algorithm has a time complexity of O(N), where N is the number of candidate nodes. Under typical scales, the route calculation decision latency is in the sub-millisecond range. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 8: Computer-readable storage medium This embodiment corresponds to claims 5 and 6, describing an implementation method for the storage medium and computer program product. The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as described in any one of claims 1, 3, and 4. The computer-readable storage medium includes, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or other tangible media capable of storing program instructions. Specific examples include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. According to the revised text of the Patent Examination Guidelines (Order No. 84) which came into effect on January 1, 2026, "computer-readable storage medium" has become a legitimate claim subject matter. Claim 1 of this invention relates to a complete technical closed loop of "receiving-sensing-decision-routing-circuit breaking," claim 3 relates to "physical connection reuse + concurrency control," and claim 4 relates to "resource dimension temporal regression + backpressure aggregation." The algorithm features are all closely related to the technical problems to be solved, and the data processed by the algorithm (real-time status information, resource dimension status values) are all data with definite technical meaning in the technical field. The execution of the algorithm can directly reflect the natural law process of solving technical problems such as traffic scheduling, connection management, and resource backpressure, and achieve technical effects such as protocol-independent intelligent scheduling, a small number of physical connections carrying a large number of logical sessions, and predictive backpressure to avoid lag. It constitutes the technical solution described in Article 2, Paragraph 2 of the Patent Law. When the storage medium of this embodiment is executed by the processor, it can achieve the following technical effects: (1) Protocol-independent intelligent scheduling avoids scheduling rigidity caused by protocol binding; (2) A small number of physical connections carry a large number of logical sessions, reducing connection overhead; (3) Predictive backpressure avoids the lag of passive backpressure; (4) No-ACK circuit breaking reduces fault isolation delay; (5) Routing decision delay is in the sub-millisecond level. As a reserved path for divisional applications, the present invention may also provide a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1, 3, and 4. The 2024 edition of the Patent Examination Guidelines has permitted "computer program product" as a claim title. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 9: Triple Back Pressure Coordinated Control This embodiment corresponds to claim 26 and describes in detail the triple back pressure collaborative control mechanism (L1 water level monitoring + L2 reading and transmission + L3 independent speed limiting). In continuous data stream forwarding scenarios (such as video streams and media streams), slow processing speed of the backend streaming media server can lead to backlog in the forwarding channel's sending queue. If backpressure is not applied in a timely manner, it can cause memory accumulation and eventually memory overflow. The triple backpressure collaborative control in this embodiment includes: First-level water level monitoring: When the backlog in the transmission queue of a forwarding channel exceeds the high water level threshold (e.g., 256KB), the forwarding channel is marked as unwritable; when the backlog in the transmission queue falls back to below the low water level threshold (e.g., 64KB), the forwarding channel is restored to a writable state. This dual-threshold design of high and low water levels avoids jitter caused by frequent switching. The second layer of read propagation: In response to the forwarding channel being marked as non-writable, the read switch on the data source side is turned off (autoRead=false), causing the TCP receive window on the data source side to shrink, and the backpressure is propagated along the data link to the data source side (terminal device); in response to the forwarding channel returning to a writable state, the read switch on the data source side is turned on again. The first and second layers work together to achieve end-to-end backpressure propagation: streaming media server slows down → forwarding queue swells → high watermark is triggered → data source side read is turned off → data source side TCP window shrinks → terminal stops sending packets, with zero memory accumulation throughout the process. The third layer of independent rate limiting: A token bucket rate limiter is set for each data stream. When the instantaneous rate of the data stream exceeds the rate limiting threshold (e.g., 2Mbps), the overspeeding data packets are discarded. The third layer operates independently and does not affect the autoRead linkage between the first and second layers, thus avoiding a sudden increase in the rate of a single data stream that could trigger a global backpressure deadlock. The triple backpressure coordinated control of this invention differs from existing technologies: Netty's high and low water level isWritable mechanism and autoRead switch are known technologies, as are Guava RateLimiter / Sentinel's token bucket rate limiting. However, in existing technologies, water level linkage and token bucket rate limiting are used independently. The combination of "triple coordination (L1+L2 linkage transmission + L3 independent rate limiting to prevent deadlock)" has not been disclosed within the search scope. The backpressure mechanism in publication number CN112398945B (Industrial and Commercial Bank of China) is only a normal / current limiting two-state mechanism and does not disclose triple coordinated control. Compared with the newly added prior art: The triple back pressure collaborative control of the present invention is also different from the following newly added prior art: (1) The "Dynamically Adjusted Commitment Information Rate (CIR) Based on Token Bucket Algorithm + Cache Queue Depth Evaluation" scheme disclosed in CN202310989947.9 (Beijing Aoxing, applied in 2023) is a combination of L1 (queue depth) + L3 (token bucket CIR adjustment). However, the CIR adjustment is "dynamically adjusting the rate limit threshold" rather than "independent rate limit to prevent deadlock". Moreover, there is no L2 end-to-end read transmission mechanism, so it is impossible to transmit the back pressure along the data link to the data source end to achieve zero memory accumulation. The L1+L2 linkage of the present invention realizes end-to-end back pressure transmission (streaming media server slow → forwarding queue rises → high water level trigger → autoRead off → TCP receive window shrinks → terminal stops sending packets). L3 works independently of L1 / L2 to avoid single-path surge triggering global deadlock. The "linkage transmission + independent deadlock prevention" layered combination has not been disclosed in the search scope. (2) The "Multi-level backpressure + user type differentiated traffic control strategy" disclosed in CN116800675A (Huawei, applied in 2022) - the backpressure level of this scheme is based on the refinement of the cache queue length threshold, and the backpressure cancellation is based on the queue length threshold rather than an independent token bucket, without an independent L3 rate limiting layer to prevent deadlock; the L3 token bucket rate limiter of this invention is independent of the autoRead linkage of L1 / L2, and each data stream is independently rate-limited and overspeed packets are discarded. (3) Apache Flink credit-based backpressure mechanism - the latter is an L1+L2 linkage reference but only within Flink, without an independent L3 rate limiting layer; the L2 of this invention is transmitted to the external data source end (the TCP receive window shrinks to stop the terminal from sending packets), and L3 is independent of L1 / L2 to avoid single-path surge triggering global connection unavailability. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 10: Edge Autonomous State Machine This embodiment corresponds to claim 27, and describes in detail the four-state transition and lazy recovery mechanism of the edge autonomous state machine. In edge computing scenarios, network connections between edge nodes and central nodes may be interrupted due to network jitter, device movement, or other reasons. In such cases, edge nodes need autonomous capabilities to maintain local service availability. The edge autonomous state machine in this embodiment includes: The four states are defined as follows: - Online Controlled State (ONLINE_CLOUD): The edge node is connected to the central node normally and is scheduled by the central scheduling decision module. - Online Autonomous State (ONLINE_AUTONOMY): The connection with the central node is interrupted, but local resources are sufficient. The edge node autonomously completes the scheduling decision based on the locally maintained state information. - Offline Autonomous State (OFFLINE_AUTONOMY): The connection with the central node is interrupted and local resources are insufficient. Degradation processing is performed (such as rejecting low-priority requests and caching requests to be replayed after recovery). - Disconnected State (DISCONNECTED): The connection is interrupted and local resources are exhausted. Service is stopped and waiting for recovery. State transition conditions: - Online controlled → Online autonomous: Heartbeat with the central node times out (e.g., no response after 3 consecutive heartbeats); - Online autonomous → Offline autonomous: Local resource utilization exceeds a preset threshold (e.g., CPU > 80% or memory > 90%); - Offline autonomous → Disconnected: Local resources are exhausted (e.g., memory > 95%); - Any state → Online controlled: Connection restored and verified by half-open probe. Lazy recovery mechanism: After connection recovery, the system does not immediately migrate to the online and controlled state. Instead, it gradually allows probe requests to verify the availability of the central node through a half-open probe mechanism. Specific implementation of half-open probe: After connection recovery, 10% of requests are initially allowed to the central node. If N consecutive probes (e.g., 5) are successful, the state migration to online and controlled is completed; if the probe fails, the current autonomous state is maintained. Lazy recovery avoids frequent state switching caused by connection jitter. The edge autonomous state machine of this invention differs from existing technologies: AWS Greengrass supports local operation but lacks a four-state state machine; KubeEdge's CloudHub / EdgeHub dual-channel architecture lacks a four-state migration mechanism; and CN105051692A (Microsoft)'s cloud node isolation is node-level power failure / blocking of transmission and reception, not an edge autonomous state machine. The combination of "four-state migration + lazy recovery + half-open probing" is not publicly available within the search scope. Compared with the newly added prior art: The edge autonomous state machine of the present invention is also different from the following newly added prior art: (1) The "satellite cluster autonomous method based on KubeEdge" disclosed in CN118426914B (Zhejiang Laboratory, authorized in 2024) - the application management component of this scheme retrieves metadata and executes autonomous strategy (local restart / migration restart binary choice) when monitoring the abnormal status of Pod. It does not have the four-state subdivision of "online controlled / online autonomous / offline autonomous / disconnected", and does not have the lazy recovery + half-open detection mechanism after connection recovery. The four states of the present invention are based on the two-dimensional migration of "connection state + resource availability". After the connection is restored, it does not immediately migrate to online controlled, but gradually releases the detection request through half-open detection to verify the availability of the central node before completing the state migration, avoiding frequent state switching caused by connection jitter. (2) The node autonomy mechanism of the OpenYurt open source project - the latter caches kubelet requests locally through YurtHub and configures the autonomy duration (supports 24h or 0 permanent). After the timeout, the Pod is directly evicted. There is no gradual state migration of "offline autonomy degradation → disconnection and stop" and no half-open probe and gradual verification mechanism. The present invention combines the gradual migration of "offline autonomy (resource insufficiency degradation) → disconnection (resource exhaustion and stop)" with the lazy recovery of "half-open probe and gradual release → verification success → online control" to solve the fine-grained governance needs of dynamic changes in edge resources. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 11: Host Health Registry TTL Lazy Recovery This embodiment corresponds to claim 28, and describes in detail the cross-protocol fault aggregation and TTL lazy recovery mechanism of the host health registry. In a multi-protocol gateway scenario, the same backend host may simultaneously support multiple protocol services (such as MySQL port 3306, Redis port 6379, and HTTP port 8080). When any protocol detects a host failure, cross-protocol linkage should be implemented to isolate all protocol nodes on that host. The host health registry in this embodiment includes: Cross-protocol fault aggregation: When any protocol detects a backend host failure (such as connection timeout or circuit breaker triggering without acknowledgment), it marks the host as unhealthy in the host health registry and sets a time-to-live (TTL, e.g., 30 seconds). Before the TTL expires, the host health registry automatically excludes all node identifiers associated with that host from the candidate node set, ensuring that all protocol scheduling decisions exclude that host. Specific implementation: When the MySQL protocol detects a fault in host1 (such as a connection timeout), the host health registry marks host1 as unhealthy with a TTL of 30 seconds. The MySQL node (port 3306), Redis node (port 6379), and HTTP node (port 8080) of host1 are all added to the exclusion set. The scheduling decisions of the Redis protocol and the HTTP protocol synchronously exclude host1, achieving cross-protocol linkage isolation. TTL lazy recovery: After the TTL expires, the host is not immediately restored to a healthy state. Instead, a small number of probe requests are gradually allowed through a half-open probe mechanism (e.g., allowing one request to host1 first) to verify the host's availability before restoring it to a healthy state. Half-open probes avoid the secondary failure caused by a large number of requests when the host has just recovered but is still unstable. The host health registry of this invention differs from existing technologies: Hystrix / Eureka's TTL health check is single-protocol and lacks cross-protocol aggregation; CN105051692A (Microsoft)'s cloud node isolation lacks TTL lazy recovery; and Spring CloudCircuit Breaker's half-open state is driven by a failure counter rather than a TTL tag. The combination of "cross-protocol failure aggregation + TTL liveness tag + lazy recovery + half-open detection" is not publicly available within the search scope. Compared with the newly added existing technologies: The host health registry of the present invention is also different from the following newly added existing technologies: (1) The "cluster host health detection method" disclosed in CN110912779A (Suzhou Inspur Intelligent Technology, applied in 2019) - the scheme configures health detection service + custom port, HAProxy detects the health status of backend servers, abnormal status is removed from the backend group, and synced status is re-added; however, the scheme is a single protocol (HAProxy / MariaDB) detection, without cross-protocol fault aggregation, without TTL live time tag driver, and without TTL expiration lazy recovery + half-open detection mechanism; the "host-level cross-protocol fault aggregation" of the present invention makes the host fault perceived by any protocol effective for all protocols (HTTP fault automatically isolates TCP / Redis / gRPC traffic to the same host), TTL tag driver + lazy recovery half-open detection avoids secondary faults caused by a large number of requests when the host has just recovered. (2) Envoy service mesh's Outlier Detection mechanism - Although the latter has a base_ejection_time (default 30s) eviction time + successful_active_health_check_uneject_host to cancel eviction only after successful active health check, it is configured separately according to filter / protocol (http router / tcp proxy / redis proxy / thrift proxy each detect separately). HTTP failures will not automatically isolate TCP / Redis / Thrift traffic to the same host, and there is no "host-level cross-protocol unified registry" aggregation mechanism; and the base_ejection_time eviction time increases with the number of evictions (base_ejection_time * ejection_count) is not a fixed TTL tag, and successful_active_health_check_uneject_host cancels eviction upon successful active health check rather than gradually allowing probe verification. (3) Consul service mesh TTL health check + multi-protocol check - the latter supports multiple health checks such as TCP / HTTP / Nagios / Sensu script / TTL, but each check is independent and not aggregated (HTTP check failure will not automatically mark TCP check failure), DeregisterCriticalServiceAfter timeout directly cancels the service without lazy recovery; the cross-protocol fault aggregation + TTL expiration lazy recovery + half-open detection combination of the present invention has not been disclosed in the search scope. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 12: Exclusive Long Connection Flow Forwarding Model This embodiment corresponds to claim 29, and describes in detail the exclusive long-connection flow forwarding model as a further limitation of the weight 1 routing step in the continuous data flow scenario. For request-response model data protocols (such as database line protocols and caching protocols), the multiplexing connection management model described in claim 3 carries a large number of logical sessions with a small number of physical connections. However, for persistent data streams (such as video streams, media streams, and file transfer streams), each data stream has a long duration and a continuous data volume, and using a multiplexing model will lead to connection contention and backpressure coupling. Based on the routing steps in claim 1, this embodiment adopts a dedicated long connection model for persistent data streams: after determining the target node from multiple backend service nodes based on comprehensive scheduling scores, an independent physical long connection is established for the persistent data stream. The dedicated long connection model in this embodiment includes: Dedicated long-lived connection per stream: An independent physical long-lived connection is established for each persistent data stream. This physical long-lived connection is exclusively used throughout the entire lifecycle of the data stream and is not reused in the idle queue. Each data stream corresponds to an independent physical long-lived connection, eliminating contention between acquire and release operations. Lifecycle management: An exclusive long-lived connection is established at the start of a data stream, and the connection is closed and resources are released when the data stream ends. If stream establishment fails, a failure result is returned without throwing an exception, preventing exception propagation to the event loop thread. Independent backpressure control for a single channel: Each dedicated long connection maintains independent statistics on the amount of data in transit and a backpressure controller (such as the triple backpressure controller described in Example 9), realizing independent backpressure control for a single data stream. When a data stream triggers backpressure, only that data stream is affected, without affecting the transmission of other data streams, thus avoiding the problem of global connection unavailability caused by single-channel backpressure in the multiplexing model. The exclusive long-connection model of this invention differs from the multiplexing model of claim 3: claim 3 is suitable for short-connection scenarios in a request-response model, carrying a large number of logical sessions with a small number of physical connections; the exclusive long-connection model of this embodiment is suitable for persistent data stream scenarios, with one connection per stream and no pooling contention. Both are alternative implementations of the same inventive concept in different scenarios. The exclusive long-connection model of this invention also differs from the traditional connection pool model—connection pools reuse connections based on acquire / release contention, while the exclusive long-connection model of this embodiment has no contention operation. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 13: Digital Twin Simulation Verification This embodiment corresponds to claim 30, and describes in detail the mechanism for digital twin simulation verification of the scheduling strategy corresponding to the predictive backpressure value (lightweight memory simulation + Pareto front multi-objective optimization + policy verification followed by deployment). In AI-driven scheduling or dynamic policy adjustment scenarios, candidate scheduling strategies determined based on weighted predictive backpressure values ​​may lead to system instability due to model illusions or parameter anomalies. This embodiment performs digital twin simulation verification on the scheduling strategy corresponding to the predictive backpressure value, including: Digital Twin Model Construction: A lightweight digital twin model of the backend service node is built in memory. This digital twin model reflects the resource capacity (number of CPU cores, memory capacity, and maximum number of connections) and processing capacity constraints (maximum throughput and maximum concurrency) of the backend service node. The digital twin model uses a memory-based data structure to run, achieving millisecond-level simulation latency and supporting real-time verification of scheduling strategies. Simulation and Derivation: Candidate scheduling strategies determined based on predictive backpressure values ​​are input into a digital twin model for simulation and derivation to predict resource status changes and load distribution after the execution of the candidate scheduling strategies. The simulation and derivation include: simulating request routing distribution, resource utilization changes, queue depth changes, and backpressure value changes under the candidate strategies. Pareto Front Multi-Objective Optimization: Scheduling decisions often face multiple conflicting optimization objectives (such as low latency versus high throughput, resource utilization balancing versus hotspot avoidance). Pareto Front multi-objective optimization determines a Pareto optimal policy set among these objectives—that is, a policy that is superior to any other policy across all objectives. The final policy is then selected from this Pareto optimal policy set according to predefined preferences. Policy validation followed by deployment: Only policies validated through digital twin simulation are deployed and executed. Policies that fail validation (such as predicting resource utilization exceeding physical limits, predicting queue overflow, or predicting backpressure reaching rejection levels) are rejected. This policy validation-based deployment forms a complete closed loop: "candidate policy → digital twin simulation → Pareto front → deployment upon successful validation / rejection upon failed validation," preventing abnormal policies from causing system instability. The digital twin simulation verification of this invention differs from existing technologies: CN111931345A (Monitoring Data Prediction Method) uses digital twins for anomaly prediction rather than strategy verification; the back pressure prediction control of the State Energy Group does not employ Pareto optimization; and the AWSFault Injection Simulator is for fault injection rather than strategy simulation verification. The closed-loop model of "lightweight memory simulation + Pareto front multi-objective optimization + strategy verification followed by deployment" was not publicly available within the search scope. Compared with the newly added existing technologies: The digital twin simulation verification of the present invention is a verification pre-step for the issuance of the scheduling strategy corresponding to the predictive back pressure value, which is also different from the following newly added existing technologies: (1) The "Smart Park Collaborative Control Dynamic Resource Scheduling Method" disclosed in Publication No. CN202510944537 (published in 2025) - This scheme constructs a smart park digital twin model + RCN prediction network to predict resource status + triggers global scheduling and generates Pareto solution set by combining optimization algorithm + judges whether there is a solution in Pareto solution set that does not cause resource conflict through digital twin model; however, this scheme is applied to the smart park physical resource scheduling scenario, uses a high-fidelity three-dimensional geometric model instead of "a lightweight digital twin model that reflects resource capacity and processing capability constraints in a memory-resident manner and achieves millisecond-level simulation latency", and its prediction object is physical resource status instead of "the scheduling strategy corresponding to the predictive back pressure value". This invention is limited to a specific application scenario of "as a verification pre-processing step for the scheduling strategy corresponding to the predictive backpressure value". Based on the predictive backpressure calculation of weight 4, the digital twin model reflects the resource capacity and processing capability constraints of the backend service node in a memory-resident manner and achieves millisecond-level simulation latency. The verification conditions clearly include at least one of "predicted resource utilization exceeding the physical limit, predicted queue overflow, and predicted backpressure reaching the rejection level", which constitutes a "simulation-execution gating mechanism" to prevent the scheduling strategy from causing system instability due to abnormality. (2) The "Dual Mind World Model Inspired NetworkDigital Twin for Access Scheduling" published by arXiv:2602.04566 (published in 2026) - the latter constructs a network digital twin as a virtual mirror of the physical network, models traffic, queuing, latency and constraints, and performs imagination-driven deduction for scheduling decisions; however, it is applied to network access scheduling scenarios other than backend service node load scheduling, and does not disclose the simulation-execution gating closed loop of "Pareto front multi-objective optimization + policy verification and subsequent issuance". (3) The "Real-time Scheduling Simulation Optimization Method for Intelligent Production Line Based on Digital Twin and Reinforcement Learning" published in the Journal of Mechanical Engineering in 2026 - the latter uses digital twin to construct a high-fidelity model of production factor geometry-function-state + improved Q-Learning agent + hierarchical execution control architecture to realize perception-decision-execution closed loop; however, it is applied to the non-software load scheduling scenario of manufacturing production line scheduling, uses a high-fidelity geometric model instead of a lightweight memory model, and uses reinforcement learning instead of Pareto front multi-objective optimization. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 14: Encrypted Secure Transmission This embodiment corresponds to claim 31 and describes in detail the encrypted secure transmission mechanism (random initialization vector + authentication tag + integrity verification before decryption). In communication between a multi-protocol gateway and a backend service node, the original messages of some protocols (such as industry-specific protocols and industrial control protocols) are transmitted in plaintext, posing a risk of eavesdropping and tampering. This embodiment addresses this by providing encrypted secure transmission, including: Packet format: Each packet contains a randomly generated initialization vector (IV), an encrypted payload, and an authentication tag. The length of the initialization vector matches the packet length of the encryption algorithm (e.g., 16 bytes), and the length of the authentication tag is determined by the encryption algorithm (e.g., 16 bytes). Encryption Process: The sending end uses a symmetric encryption algorithm (such as the Chinese national standard SM4-GCM, AES-GCM, or other encryption modes with authentication functions) to encrypt the payload. An initialization vector is randomly generated each time encryption is performed, ensuring that the same plaintext produces different ciphertexts, preventing replay attacks and pattern analysis. The authentication tag is calculated by the encryption algorithm based on the key, initialization vector, and payload, and is used to verify data integrity and the authenticity of its source. The key for the symmetric encryption algorithm is securely exchanged between the communicating parties through a key negotiation mechanism (such as a key exchange protocol based on asymmetric encryption). Decryption and verification process: The authentication tag verification at the receiving end is performed before decryption. The authentication tag is recalculated using the local key, the received initialization vector, and the encrypted payload. If it matches the received authentication tag, the verification is successful, and the plaintext data is obtained by decrypting the payload. If the verification fails, the data packet is discarded and the decryption operation is avoided to prevent tampered or forged messages from entering the decryption logic and causing decryption anomalies or processing errors. The encrypted secure transmission of this invention achieves communication security hardening through a triple mechanism: "random initialization vector + authentication tag + integrity verification before decryption, with verification failure preventing decryption triggering." The design of authentication tag verification before decryption and preventing decryption triggering upon verification failure ensures that tampered messages are discarded before decryption, avoiding processing errors caused by decryption anomalies. This mechanism is applicable to all communication between the protocol awareness module and backend service nodes, serving as a general mechanism for communication security hardening. Compared with the newly added prior art: The encrypted secure transmission of the present invention serves as a reinforcement mechanism for the communication security between the protocol awareness module and the backend service node. It is applied to the multi-protocol hybrid communication scenario between the gateway and the backend service node, covering multiple communication protocol ports carried on the same backend service node so that the communication of any protocol port is protected by encryption and authenticated. The present invention also differs from the following newly added prior art: (1) "AES-GCM Authenticated Encryption in SRTP" disclosed in IETF RFC 7714 (December 2015) - the latter defines the application of AES-GCM in the Secure Real-Time Transport Protocol (SRTP), applied to the media stream encryption scenario rather than "multi-protocol hybrid communication between the protocol awareness module and the backend service node"; the encrypted secure transmission of the present invention covers multiple communication protocol ports carried on the same backend service node (such as MySQL port 3306, Redis port 6379, HTTP port 8080), so that the communication of any protocol port is protected by encryption and authenticated. It is a gateway communication security reinforcement mechanism with unified encryption of multiple protocols. (2) "ShangMi (SM) Cipher Suites for TLS 1.3" disclosed in IETF RFC 8998 (March 2021) - the latter defines the AEAD_SM4_GCM cipher suite for the TLS 1.3 protocol and binds to a single TLS protocol; the encrypted secure transmission of the present invention does not bind to any specific protocol, and serves as a reinforcement mechanism for communication security between the protocol awareness module and the backend service node, covering multi-protocol mixed communication scenarios. (3) "SM4-GCM Algorithm and its Application in Network Security Protocol" disclosed in Publication No. CN111555859A (Hangzhou Yixin Technology, published in 2020) - the latter replaces the encryption algorithm in GCM with SM4 and applies it to network security protocols, but does not disclose the multi-protocol mixed communication scenario between the protocol awareness module and the backend service node and the multi-protocol unified encryption feature of "covering multiple communication protocol ports carried on the same backend service node"; the encrypted secure transmission of the present invention is linked with the Quan2 scheduling system, serving as a communication security reinforcement mechanism for the protocol awareness module, and works in conjunction with features such as comprehensive scheduling scoring and unacknowledged circuit breaking to form a complete application-aware intelligent traffic scheduling system. (4) The IETF GCM-SST draft (July 2026) explicitly requires that "if the tag verification fails, the plaintext P must not be output" - this invention further clarifies that "if the verification fails, the data packet should be discarded and the decryption operation should be avoided to prevent the tampered or forged message from entering the decryption logic and causing decryption abnormalities or processing errors", emphasizing the technical effect of "avoiding the decryption operation" to avoid decryption abnormalities. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. Example 15: Signaling-Driven Session Lifecycle Orchestration This embodiment corresponds to claim 32 and describes in detail the signaling-driven session lifecycle orchestration mechanism (target node determination based on comprehensive scheduling score + pre-built mapping + response timeout + asynchronous channel + five-state machine). For session-based protocols that require pre-established connections (such as in-vehicle video protocols and real-time streaming media protocols), flow establishment negotiation must be completed through a signaling channel before data transmission. Only after successful flow establishment can the data transmission channel be opened. Based on the decision-making and routing steps in claim 1, this embodiment employs signaling-driven session lifecycle orchestration for session-based protocols: after determining the target node from multiple backend service nodes based on a comprehensive scheduling score, a flow establishment request is sent to the target node. The signaling-driven session lifecycle orchestration in this embodiment includes: A five-state machine: Session states include five states: Requested, Answered, Streaming, Closing, and Closed. Valid transition paths are: Requested → Answered / Closed; Answered → Streaming / Closing / Closed; Streaming → Closing / Closed; Closing → Closed; Closed is the final state. State transitions are validated before entry to prevent unauthorized jumps. The flow establishment request issuance process involves: sending a flow establishment request to the target node and pre-establishing a mapping relationship between session identifiers and node identifiers (e.g., a three-way mapping of session ID ↔ terminal identifier ↔ mobile phone number), while simultaneously starting a response timeout (e.g., 10 seconds). This pre-established mapping allows the session to be immediately located based on the terminal identifier upon arrival of the flow establishment response, eliminating the need for additional lookups. Data packets received between the issuance of the flow establishment request and the receipt of the response are buffered or discarded and counted (pendingPackets), and processed or discarded once the data transmission channel is ready. Response processing steps: Upon receiving the flow establishment response from the target node, cancel the response timeout, asynchronously open the data transmission channel, and transition the session state from "requested" to "responded." Asynchronously opening the data transmission channel avoids blocking the event loop thread—flow establishment response processing and data transmission channel establishment proceed in parallel. If the response is not received within the timeout period, trigger the timeout callback to close the session and clean up the mapping relationship. Data transmission steps: After the target node receives the data stream, it transitions the session state from acknowledged to in transit. When the first data packet arrives, the session's true identity information is filled in based on the terminal identifier carried in the data packet (e.g., the terminal identifier is only determined when the first data packet arrives). Closure Procedure: After the data stream ends or times out, the data transmission channel and signaling channel are closed sequentially, and the mapping between session identifiers and node identifiers is removed to prevent mapping leakage. The closure process uses a two-step migration from CLOSING to CLOSED to ensure that data in transit is processed correctly during the closure process. The signaling-driven session lifecycle orchestration of this invention achieves flow establishment orchestration for session-based protocols through "pre-built mapping + response timeout + asynchronous channel + five-state machine + first packet identity backfilling". Pre-built mapping avoids additional queries when the flow establishment response arrives; asynchronous channel avoids blocking the event loop; five-state machine ensures the legality of state transitions; first packet identity backfilling supports scenarios where the terminal identifier is determined only when the first data packet arrives. Compared with the newly added existing technologies: The signaling-driven session lifecycle orchestration of the present invention realizes the legal transition of session state through a five-state machine, and is attached to the determination of target node based on comprehensive scheduling score of right 1. It is also different from the following newly added existing technologies: (1) The video communication protocol of road transport vehicle satellite positioning system disclosed in JT / T 1078-2016 standard (Ministry of Transport, 2016) - the latter adopts signaling channel (multiplexing JT / T808 The architecture is a dual-channel system consisting of a TCP link and a streaming media channel (independent TCP / UDP ports). The sequence is: 0x9101 Real-time audio / video transmission request → 0x1003 Terminal response → Establishment of RTP streaming media channel → Transmission → Closure. However, the target node selection is based on static configuration or simple routing, not "determining the target node based on comprehensive scheduling score". The session state is an implicit state machine instead of an explicit five-state state machine of "requested / responded / transmitting / closing / closed". Furthermore, the intermediate state processing mechanism of "buffering or discarding and counting the data packets received after the stream establishment request is issued and before the response is received" and the first packet identity backfilling mechanism of "backfilling the real identity information of the session according to the terminal identifier when the first data packet arrives" are not disclosed. (2) GB / T 28181-2016 standard (Ministry of Public Security, 2016) published the national standard for China's public security video surveillance network - the latter is based on the SIP protocol + RTP / RTCP dual-channel architecture, INVITE→100 Trying→180 Ringing→200 OK→ACK session establishment process; however, its target node selection is based on SIP routing rather than "determining the target node based on comprehensive scheduling score"; its session state transition follows the SIP transaction state machine (Calling / Proceeding / Completed / Terminated) rather than the five-state state machine of this invention; and it does not disclose the features of "pre-built session identifier and node identifier mapping relationship" and "asynchronous opening of data transmission channel to avoid blocking event loop thread". (3) The "Method of controlling SIP sessions with state charts" disclosed in US9294515 (authorized in 2016) controls SIP sessions through state charts, receiving SIP requests → initiating SIP sessions → transmitting events through state charts → state changes defined by state charts until the session is completed; however, its state chart is a SIP protocol state machine rather than the five-state state machine of the present invention, namely "requested → responded → in transit → closing → closed", and does not disclose the combined features of "pre-built mapping + asynchronous channel + intermediate state data packet buffer count + first packet identity backfilling".(4) RTSP protocol state machine (RFC 2326, 1998) - The latter is a four-state state machine of Init / Ready / Playing / Recording, with SETUP / PLAY / PAUSE / TEARDOWN commands driving state transitions; the present invention is a five-state state machine (adding an intermediate state of "closing" to ensure that data in transit during the closing process is processed correctly), and the target node is determined based on comprehensive scheduling score rather than RTSP URL routing. (5) The "method for interrupting user sessions" disclosed in CN115278937A (China Telecom, published in 2022) - The latter dynamically stores the session state through a session index table, and the session identifier includes the first / second / third state identifiers to represent the session establishment / release / in session state respectively; however, its state identifier is a three-state state machine, which is not the five-state state machine of the present invention, and does not disclose the features of "pre-built session identifier and node identifier mapping relationship" and "determining the target node based on comprehensive scheduling score". The combination of "five-state state machine + pre-built mapping + asynchronous channel + intermediate state data packet buffer count + first packet identity backfilling" of the present invention has not been disclosed within the search scope. Those skilled in the art can make several improvements and modifications based on the above embodiments, but all such improvements and modifications fall within the protection scope of the claims of this invention. The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An application-aware intelligent traffic scheduling method, characterized in that, include: The receiving step involves receiving an access request from a client, the access request carrying business semantic tags and request data; The sensing step involves obtaining real-time status information of multiple backend service nodes, including at least one of resource utilization, number of connections, and queue depth. The decision-making steps involve calculating the comprehensive scheduling score of each backend service node based on the real-time status information and the business semantic tags, and determining the target node from the multiple backend service nodes according to the comprehensive scheduling score. The routing step routes the access request to the target node; The circuit breaker step involves triggering a no-acknowledgment circuit breaker mechanism when the access request does not receive completion confirmation from the target node within a preset deadline. This mechanism either retryes the access request to other backend service nodes or marks the target node as being in a sub-healthy state and excludes it from the candidate node set.

2. An application-aware intelligent traffic scheduling system, characterized in that, include: The protocol awareness module is configured to receive access requests from clients, identify the communication protocol type followed by the access request, and extract business semantic tags from the access request. The status awareness module is configured to obtain real-time status information of multiple backend service nodes; The scheduling decision module is configured to calculate the comprehensive scheduling score of each backend service node based on the real-time status information and the business semantic tags, and determine the target node based on the comprehensive scheduling score. The multiplexing connection management module is configured to maintain physical connections with the multiple backend service nodes, map the client's logical sessions to the physical connections, and control the concurrency of in-transit requests on each physical connection. The fault-tolerant execution module is configured to trigger a circuit breaker mechanism if the access request does not receive a completion confirmation from the target node within a preset deadline.

3. A multiplexing connection management method, characterized in that, include: The connection establishment process involves establishing no more than a preset maximum number of physical connections for each backend service node. The session allocation step, in response to multiple received client requests, assigns a logical session identifier to each client request; The mapping step maps the plurality of logical session identifiers to the physical connection, wherein each physical connection is shared by the plurality of logical session identifiers; The concurrency control steps set a concurrency limit for each physical connection. When the number of in-transit requests on the physical connection reaches the concurrency limit, the new logical session will be assigned to other idle physical connections, or the connection will wait when all physical connections have reached their limits. The reuse step involves releasing the in-transit slot of the physical connection when the logical session is completed, and returning the physical connection to the idle queue for reuse by subsequent logical sessions.

4. A predictive back pressure calculation method, characterized in that, Trend extrapolation based on historical state snapshots without assuming known future arrival traffic includes: The sampling step involves obtaining multiple historical state snapshots of the backend service nodes within a sliding window, with each snapshot containing a state value for at least one resource dimension. The trend prediction step involves calculating the trend change rate for each resource dimension based on the historical state snapshot, and calculating the projection value of that resource dimension at a future time based on the trend change rate and a preset prediction step size. The back pressure conversion step compares the projection value of each resource dimension with a preset threshold, and converts the projection value that exceeds the threshold into the back pressure value of that resource dimension proportionally. In the aggregation step, the maximum value among the backpressure values ​​of each resource dimension is taken as the predictive backpressure value of the backend service node, so that the most dangerous dimension is exposed first. The application steps involve using the predictive backpressure value as an input factor for the comprehensive scheduling score, and participating in the scheduling decision of the backend service nodes.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1, 3, and 4.

6. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1, 3, and 4.

7. The method according to claim 1, characterized in that, The resource utilization includes CPU utilization, memory utilization, GPU memory utilization, and network bandwidth utilization; wherein the GPU memory utilization is collected through memory bandwidth saturation, which is the ratio of the number of bytes read / written from the memory per unit time to the theoretical peak bandwidth of the memory; the real-time status information also includes the current request queue depth and maximum queue capacity of the backend service node, wherein the remaining capacity ratio of the queue depth is the ratio of the difference between the current queue depth and the maximum queue capacity to the maximum queue capacity.

8. The method according to claim 1, characterized in that, The comprehensive scheduling score is calculated by weighted fusion of the following factors: the weighted sum of resource utilization, the remaining capacity ratio of queue depth, the predictive backpressure value, and the node weight configuration; the weight of the weighted fusion is dynamically adjusted according to the business semantic tags, wherein the predictive backpressure value corresponding to requests with high request priority and user level tags has an increased weight, and the remaining capacity ratio of queue depth corresponding to requests with delay-sensitive business type tags has an increased weight.

9. The method according to claim 1, characterized in that, The business semantic tag includes at least one of the following: request priority identifier, user level identifier, business type identifier, and protocol-level metadata; the business semantic tag is passed in the call chain through a context passing mechanism, which includes value passing based on scope or passing based on thread-local variables.

10. The method according to claim 1, characterized in that, The preset deadline is dynamically calculated based on the business semantic tag, specifically including: querying a preset basic deadline schedule according to the business type identifier in the business semantic tag to obtain the basic deadline; correcting the basic deadline according to the user level identifier in the business semantic tag, with the deadline for higher-level users shortened accordingly; further adjusting the corrected deadline according to the protocol-level metadata in the business semantic tag; and transmitting the dynamically calculated deadline in the call chain through the custom option field or request header field of the communication protocol packet header.

11. The method according to claim 1, characterized in that, The step of retrying the access request to other backend service nodes includes: adding the target node to an exclusion set and setting a liveness time tag for each node in the exclusion set, automatically removing the corresponding node from the exclusion set after the liveness time tag expires; re-determining a new target node from the remaining candidate nodes based on the comprehensive scheduling score, wherein the comprehensive scheduling score applies a penalty factor to previously faulty nodes in the exclusion set for downgrading; and dynamically determining the maximum preset number of retries based on the request priority identifier in the business semantic tag, allowing more retries for higher priority requests.

12. The method according to claim 1, characterized in that, The step of obtaining real-time status information of multiple backend service nodes includes: receiving the real-time status information actively reported by the backend service nodes; and / or reading the real-time status information from a node quota registry maintained in memory; the routing calculation of the scheduling decision module is completed in memory, and the node quota registry is updated by the status information actively reported by the backend service nodes.

13. The method according to claim 1, characterized in that, The method further includes: when the overall scheduling score of all backend service nodes is lower than a preset threshold, a probability rejection protection mechanism is triggered to reject the access request with a preset probability in order to protect the availability of the scheduling system itself.

14. The method according to claim 13, characterized in that, The probabilistic rejection protection mechanism employs an additive growth and multiplicative decay strategy: When forwarding fails, the rejection rate increases linearly by a preset step size. The preset step size is dynamically adjusted based on the comprehensive scheduling score variance of the backend service nodes. The larger the score variance, the larger the step size to accelerate the increase in the rejection rate. When the number of consecutive successful forwards reaches a preset window number, the rejection rate is multiplicatively reduced by a preset factor. The preset factor changes according to the gradient of the number of consecutive successful forwards. The more consecutive successful forwards there are, the smaller the reduction factor becomes to accelerate recovery. The rejection rate is limited between a preset lower bound and an upper bound. The upper bound is less than 1 to preserve the detection window for backend recovery, the upper bound is 0.9 to preserve 10% of the detection window, and the lower bound is 0 to allow full forwarding.

15. The system according to claim 2, characterized in that, The system also includes a protocol fingerprint recognition module, configured as follows: Based on the characteristics of the first packet byte of the access request, it is matched with the pre-registered protocol fingerprint rule set to automatically identify the communication protocol type, and the corresponding protocol parsing and processing logic is dynamically loaded according to the identification result; The protocol fingerprint rule set is obtained by aggregating from at least one of the following sources: built-in rules, rules registered through a dependency injection container, and rules declared through a service discovery mechanism; rules registered later with the same protocol identifier override rules registered earlier. The protocol fingerprint recognition module traverses the fingerprint rule set in descending order of priority for matching and returns the first matching protocol fingerprint; when multiple protocol fingerprints match at the same time, the one with higher priority is given priority.

16. The system according to claim 2, characterized in that, The multiplexing connection management module is further configured to: maintain a set of physical connections for each backend service node that does not exceed a preset limit; maintain an in-transit request concurrency semaphore for each physical connection; dynamically adjust the number of permits for the in-transit request concurrency semaphore based on the comprehensive scheduling score of the backend service node, with nodes having higher scores having a larger number of permits for their corresponding connections; when acquiring a connection, prioritize reusing active connections from the idle queue; the criteria for determining an active connection is that it has a successful response record within the most recent preset time period; create a new connection when there are no available connections and the limit has not been reached; when the limit has been reached, prioritize the waiting logical sessions according to the request priority identifier in the business semantic tag and then wait.

17. The system according to claim 2 or 16, characterized in that, The multiplexing connection management module is also configured to perform periodic health checks on physical connections in the idle queue, close inactive physical connections, and update the connection count.

18. The system according to claim 2, characterized in that, The fault-tolerant execution module is further configured to: aggregate multiple software circuit breakers to implement their respective sets of unhealthy nodes, and merge the sets of unhealthy nodes of the multiple software circuit breakers into the exclusion set of the candidate node set to participate in routing exclusion. The multiple software circuit breakers include at least two of the following: circuit breakers based on deadline timeout, circuit breakers based on failure count, and circuit breakers based on active health checks; and / or, when any protocol detects a backend host failure, perform host-level cross-protocol linkage isolation on all node identifiers associated with the host, so that the scheduling decisions of all protocols are synchronously effective due to the host failure.

19. The method according to claim 3, characterized in that, The upper limit of concurrency for in-transit requests is determined based on the type of communication protocol carried by the physical connection: for serial protocols with a request-response order model, the upper limit of concurrency is 1; For parallel protocols that support multiplexing, the upper limit of concurrency is an integer greater than 1.

20. The method according to claim 3, characterized in that, When all physical connections reach the concurrency limit, logical sessions waiting to acquire connections adopt a lightweight polling waiting strategy, and sleep for a preset short duration between each polling. The short duration is adapted to the virtualization thread scheduling model.

21. The method according to claim 3, characterized in that, The mapping between the logical session and the physical connection is implemented through a lease object, which holds a reference to the physical connection and an in-transit slot semaphore, and explicitly releases the in-transit slot when the logical session is completed.

22. The method according to claim 4, characterized in that, The resource dimensions include at least one of the following: CPU utilization, queue depth ratio, and connection ratio; the state value of the queue depth ratio is the ratio of the current queue depth to the maximum queue capacity, and the state value of the connection ratio is the ratio of the current number of active connections to the maximum number of connections.

23. The method according to claim 4, characterized in that, The step of converting projection values ​​exceeding the threshold into back pressure values ​​proportionally includes: when the projection value is less than or equal to the threshold, the back pressure value is zero; when the projection value is greater than or equal to the physical upper limit, the back pressure value is full; when the projection value is between the threshold and the physical upper limit, the back pressure value is calculated by linear interpolation.

24. The method according to claim 4, characterized in that, The predictive backpressure value is also divided into multiple levels, including four levels: normal, warning, severe, and rejection. Different levels trigger different scheduling strategies, and the rejection level indicates that the node should be excluded from the candidate node set.

25. The method according to claim 4, characterized in that, The method further includes: backfilling the predictive backpressure value into the latest state snapshot of the backend service node as input for the next round of sampling and as a basis for historical tracing, forming a complete engineering closed loop of "sampling → prediction → transformation → aggregation → backfilling → next round of sampling".

26. The method according to claim 4, characterized in that, The method also includes a triple back pressure coordinated control step: The first level of water level monitoring involves marking the forwarding channel as unwritable when the backlog in the sending queue of the forwarding channel exceeds the high water level threshold; and restoring the forwarding channel to a writable state when the backlog in the sending queue falls back below the low water level threshold. The second read transmission occurs in response to the forwarding channel being marked as unwritable. The read switch at the data source end is turned off, causing the receiving window at the data source end to shrink and the back pressure to be transmitted to the data source end along the data link. In response to the forwarding channel being restored to a writable state, the read switch at the data source end is reactivated; The third layer of independent rate limiting sets a token bucket rate limiter for each data stream. When the instantaneous rate of the data stream exceeds the rate limiting threshold, the overspeeding data packet is discarded. The first and second layers work together to achieve end-to-end back pressure, while the third layer works independently to avoid triggering a global back pressure deadlock due to a sudden increase in the rate of a single data stream.

27. The system according to claim 2, characterized in that, The system also includes an edge autonomy module, configured as follows: Maintain the autonomous state of the edge nodes, which includes online controlled state, online autonomous state, offline autonomous state, and disconnected state; Based on the connection status with the central node and the heartbeat detection results, migration is performed between the four autonomous states; When the connection with the central node is normal, it is in an online controlled state and is uniformly scheduled by the scheduling decision module. When the connection with the central node is interrupted and local resources are sufficient, the edge node migrates to an online autonomous state, where the edge node autonomously makes scheduling decisions based on the locally maintained state information. When the connection with the central node is interrupted and local resources are insufficient, the system migrates to an offline autonomous state and performs a degradation process. When the connection is interrupted and local resources are exhausted, the system will migrate to the disconnected state. The edge autonomous module adopts a lazy recovery mechanism. After the connection is restored, it does not immediately migrate to the online controlled state. Instead, it gradually releases probe requests through a half-open probe mechanism to verify the availability of the central node. Only after the verification is successful will the state migration be completed.

28. The system according to claim 2, characterized in that, The system also includes a host health registry module, configured as follows: Aggregate fault detection results across protocols. When any protocol detects a fault in a backend host, mark the host as unhealthy in the host health registry and set a liveness time tag. Before the survival time tag expires, the host health registry automatically excludes all node identifiers associated with the host from the candidate node set; A lazy recovery mechanism is adopted. After the liveness time tag expires, the host is not immediately restored to a healthy state. Instead, a small number of probe requests are gradually allowed through a half-open probe mechanism to verify the availability of the host before it is restored to a healthy state. Unhealthy states in the host health registry are shared across protocols, so that host failures detected by any protocol take effect on all protocols, achieving host-level cross-protocol linkage and isolation.

29. The method according to claim 1, characterized in that, For persistent data streams, the routing steps employ an exclusive long-lived connection model: After determining the target node from the multiple backend service nodes based on the comprehensive scheduling score, an independent physical long connection is established for the continuous data stream. The physical long connection is exclusively used throughout the entire lifecycle of the data stream and does not participate in the reuse of the idle queue. The physical long connection eliminates contention for acquisition and release, and closes the physical long connection and releases resources when the data flow ends; Each dedicated long connection maintains independent in-transit data volume statistics and back pressure controller, realizing independent back pressure control of a single data stream, so that the back pressure of a single stream does not affect the transmission of other data streams.

30. The method according to claim 4, characterized in that, As a prerequisite for verifying the issuance of the scheduling strategy corresponding to the predicted backpressure value, the method further includes a digital twin simulation verification step: A lightweight digital twin model of the backend service node is constructed in memory. The digital twin model reflects the resource capacity and processing capability constraints of the backend service node in a memory-resident manner, achieving millisecond-level simulation latency to support real-time verification of scheduling strategies. The candidate scheduling strategy determined based on the predicted backpressure value is input into the digital twin model for simulation and deduction to predict the resource status changes and load distribution after the candidate scheduling strategy is executed; Pareto front multi-objective optimization is adopted to determine the Pareto optimal policy set among multiple conflicting optimization objectives, and the final policy is selected from the Pareto optimal policy set according to preset preferences. Only strategies verified through digital twin simulation are issued for execution, and strategies that fail verification are rejected to prevent scheduling strategy anomalies from causing system instability; wherein the verification includes at least one of predicting that resource utilization exceeds the physical limit, predicting queue overflow, and predicting that back pressure reaches the rejection level.

31. The system according to claim 2, characterized in that, As a security reinforcement mechanism for communication between the protocol awareness module and the backend service node, the communication adopts encrypted secure transmission, which is applied to multi-protocol hybrid communication scenarios between the gateway and the backend service node: Each data packet contains a randomly generated initialization vector, an encrypted payload, and an authentication tag; The initial vector is used to ensure that the same plaintext is encrypted to produce different ciphertexts, and the authentication tag is used to verify the integrity of the data and the authenticity of the source; The payload is encrypted using a symmetric encryption algorithm, and the key of the symmetric encryption algorithm is securely exchanged between the two communicating parties through a key negotiation mechanism; The authentication tag verification at the receiving end is performed before decryption. If the verification is successful, the payload is decrypted to obtain the plaintext data; if the verification fails, the data packet is discarded and the decryption operation is avoided to prevent tampered or forged messages from entering the decryption logic and causing decryption abnormalities or processing errors. The encrypted secure transmission covers multiple communication protocol ports carried on the same backend service node, so that communication on any protocol port is protected by encryption and authenticated.

32. The method according to claim 1, characterized in that, For session-based protocols that require pre-established connections, the decision-making and routing steps employ signaling-driven session lifecycle orchestration, which utilizes a five-state machine to implement legitimate session state transitions. In the flow establishment request issuance step, after determining the target node from the multiple backend service nodes based on the comprehensive scheduling score, a flow establishment request is sent to the target node, and a mapping relationship between session identifier and node identifier is pre-established, while the response timeout timer is started. The response processing steps are as follows: after receiving the flow establishment response from the target node, cancel the response timeout, asynchronously open the data transmission channel to avoid blocking the event loop thread, and transfer the session state from the requested state to the responded state. If a response is not received within the timeout period, a timeout callback is triggered to close the session and clear the mapping relationship. In the data transmission process, after the target node receives the data stream, the session state is transitioned from the acknowledged state to the in-transmission state. When the first data packet arrives, the real identity information of the session is filled in based on the terminal identifier carried in the data packet, supporting scenarios where the terminal identifier is determined only when the first data packet arrives; In the shutdown step, after the data stream ends or times out, the data transmission channel and signaling channel are closed sequentially, and the mapping relationship between the session identifier and the node identifier is removed to prevent mapping leakage. The five-state machine includes five states: requested, responded, in transit, closing, and closed. Before state transition, the validity is verified to prevent illegal jumps. Data packets received after the flow establishment request is issued and before the response is received are cached or discarded and counted, and then processed or discarded after the data transmission channel is ready.

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