Service channel access method based on distributed micro-service

Abstract

The invention discloses a service channel access method based on distributed micro-services, and relates to the technical field of data transmission, and the access method comprises the steps of system initialization and configuration, client request processing, data processing and system operation, and network connection and optimization. Decoupling and cooperative work of each functional module are realized through a distributed architecture, and the flexibility, expandability and reliability of the system are improved; by constructing a multi-layer data controllable and easy-to-track exchange transmission mechanism, encryption transmission, parameter verification, auditing and plaintext-ciphertext conversion are carried out on data, and meanwhile, a data transmission level tracking technology is utilized, so that a data link is effectively monitored, and data security and compliance are guaranteed. On the premise of meeting compliance, convenient access of domestic users to overseas high-value data services (such as AI large models, academic resources and the like) is realized, and safe, reliable and efficient internet experience is provided for the users.

Description

[0001] The present invention relates to the technical field of data transmission, and in particular to a service channel access method based on distributed microservices. Background Art

[0002] With the rapid development of information technology, the demand for data interaction and information sharing is growing worldwide. In the field of cross-domain data management and cross-border circulation, how to achieve efficient and stable data transmission between different regions and fields while ensuring data security and compliance has become a key issue.

[0003] In the existing technology, virtual private network (VPN) technology and proxy technology are relatively common means to achieve cross-border data access. However, these traditional technologies have exposed many serious defects.

[0004] From a security perspective, a large number of low-cost VPN and proxy services have obvious shortcomings in security protection. They lack advanced encryption technology and perfect security mechanisms and are extremely vulnerable to malicious attacks by hackers. Once hacked, users' private data, such as personal identity information, browsing history, and business secrets, will be exposed to criminals without reservation, leading to serious data leakage incidents, causing great damage to users' privacy and rights. At the same time, such services are often accompanied by risks such as IP address leakage and DNS leakage, which makes it difficult to hide the user's real identity and network activity trajectory, and anonymity is completely destroyed, providing opportunities for illegal and criminal acts such as online fraud and malicious tracking.

[0005] In terms of network security performance, VPN and proxy technologies often have difficulty providing stable and efficient services. Due to the limitations of their connection methods and server resources, users often encounter a significant slowdown in network speed during use, which seriously affects the timeliness and smoothness of data transmission. Especially in scenarios with high concurrent data requests, such as large-scale data downloads, real-time video conferencing, etc., network freezes and even interruptions frequently occur, significantly affecting the user experience and work efficiency. Moreover, in order to maintain the operation of such services, users usually need to pay higher fees, which increases the cost of use.

[0006] From the perspective of legal compliance, the use of VPN or proxy technology to access overseas websites is not in line with national laws, regulations and regulatory requirements. Although some users try to circumvent network supervision by using such technologies, they cannot completely escape legal accountability in actual use. Once discovered, users will face serious legal consequences, including fines, legal proceedings, etc., which will bring huge legal risks and adversely affect the rule of law in society.

[0007] Moreover, the Internet environment is becoming increasingly complex. When users access overseas networks through unreliable VPN or proxy services, they can easily inadvertently trigger the download and execution of these malicious programs, causing device infection and data theft, making it more difficult to eliminate security risks.

[0008] In summary, the existing VPN and proxy technologies can no longer meet the growing demand for cross-border data control and circulation. There is an urgent need for an innovative, secure, reliable and compliant service channel access method to solve the above-mentioned problems and ensure the safe and efficient transmission of data between different regions and fields. Summary of the invention

[0009] The purpose of the present invention is to provide a service channel access method based on distributed microservices to provide a trustworthy green channel with higher access stability and faster access speed, thereby reducing access interruptions and delays caused by insecure networks.

[0010] To achieve the above object, the present invention provides the following technical solution: a service channel access method based on distributed microservices, comprising the following steps:

[0011] Step S1. System initialization and configuration;

[0012] Step S2. Client request processing;

[0013] Step S3. Data processing and system operation;

[0014] Step S4. Network connection and optimization;

[0015] The system initialization and configuration in step S1 includes the following steps:

[0016] Step S101. Distributed cache system configuration;

[0017] Step S102. Distributed database deployment and configuration;

[0018] Step S103. Build a message conversion component;

[0019] Step S104: Building a distributed service architecture;

[0020] The client request processing in step S2 includes the following steps:

[0021] Step S201. The client initiates a request;

[0022] Step S202. Message processing and preliminary application;

[0023] The data processing and system operation in step S3 includes the following steps:

[0024] Step S301. Stream computing and data transfer;

[0025] Step S302: Distributed cache strategy execution;

[0026] Step S303. Distributed database operation;

[0027] Step S304. Message conversion and module interaction;

[0028] The network connection and optimization in step S4 includes the following steps:

[0029] Step S401. Data channel and proxy selection;

[0030] Step S402: Multiplexing and high concurrency processing;

[0031] Step S403. Trinity layout and performance tracking;

[0032] Step S404: Real-time content alarm processing.

[0033] The process of configuring the distributed cache system in step S101 is as follows:

[0034] For the Redis distributed cache system, carefully set the multi-node distributed storage parameters based on system performance estimates and business needs;

[0035] The multi-node distributed storage parameters include the directory structure and storage strategy of multi-point storage of files, the rules and quantity of data sharding, and the triggering conditions and data recovery mechanism of the disaster recovery function.

[0036] The process of distributed database deployment and configuration in step S102 is as follows:

[0037] Install and configure the distributed database MariaDB, set the node relationship and data synchronization strategy of master-slave replication according to data storage requirements and business logic, determine the sharding key and sharding rules of the sharding mechanism, and establish the database table structure and index;

[0038] The database table structure and index are suitable for storing historical records, review and release logs, and customer usage records.

[0039] The process of building the message conversion component in step S103 is as follows:

[0040] Build message conversion components, configure the type and parameters of message queues according to the communication requirements and business processes between modules in the system, set the topics and subscriber rules for message publishing / subscription, and establish the communication protocol and data format for task publishing and execution status feedback;

[0041] The type includes any one of Kafka and RabbitMQ, and the parameters are queue length and message persistence mode.

[0042] The process of building the distributed service architecture in step S104 is as follows:

[0043] According to the concept of microservice architecture, different AI large model functional modules are split and developed independently, and the Spring Cloud framework is used for service governance and configuration management;

[0044] Configure an independent operating environment and resources for each microservice module, configure routing rules and load balancing strategies through the gateway, and establish communication connections and data transmission formats between each microservice and the gateway;

[0045] The communication connection and data transmission mode is HTTP / HTTPS.

[0046] The process of the client initiating a request in step S201 is as follows:

[0047] Based on business needs, the client uses the http / https protocol to initiate a service request to the system gateway. The request contains parameters and information for connecting to specific overseas service resources.

[0048] The parameters and information include the requested service type, related data and keywords.

[0049] The message processing and preliminary application in step S202 includes the following steps:

[0050] After receiving the request, the system gateway performs parameter verification and data transmission processing on the incoming message in the form of ciphertext-plaintext-ciphertext.

[0051] The process of streaming computing and data transfer in step S301 is as follows:

[0052] During the data processing process, real-time data is generated, and the streaming computing engine captures and processes these high-throughput real-time data in real time;

[0053] The process of executing the distributed cache strategy in step S302 is as follows:

[0054] The distributed cache system manages the configuration and metadata of frequently accessed AI models and real-time token count results based on the cache strategy. When there is a data query request, it first searches for matching data in the cache. If the cache hits, the cached data is directly returned to reduce database query pressure and response time. If the cache misses, the data is obtained from the distributed database and stored in the cache according to the cache strategy. At the same time, cache-related information is updated. During the cache processing, data traffic is monitored in real time. According to the formula Calculate the data flow Q. When Q exceeds the preset threshold, trigger the cache expansion mechanism to allocate additional cache resources from the distributed database to expand the cache capacity C, ensuring the stability and efficiency of the cache system.

[0055] The process of the distributed database operation in step S303 is as follows:

[0056] Distributed databases are responsible for storing key data.

[0057] The key data include historical records, review and release logs and customer usage records;

[0058] The process of message conversion and module interaction in step S304 is as follows:

[0059] When a task is released, the task information is published to the corresponding service module through the message queue. The service module processes the task after receiving it and feeds back the execution status to the message conversion component through the message queue. In the review and release process, the status change information is promptly notified to the relevant modules through the message queue. Each module performs asynchronous processing and interaction based on the event-driven architecture.

[0060] The process of data channel and proxy selection in step S401 is as follows:

[0061] During data transmission, the system uses intelligent algorithms to select the appropriate data channel and the optimal proxy channel based on the parameters and specific conditions in the customer message;

[0062] First, a list of qualified data channels is screened out based on parameters such as service type, data volume, and security requirements in the message. Then, for the proxy channel, the evaluation value S is calculated based on the delay D, bandwidth B, and packet loss rate L through the formula S=a×D+b×B+c×L, where a, b, and c are the weight coefficients corresponding to the delay, bandwidth, and packet loss rate, respectively, and a+b+c=1. The optimal proxy channel is determined based on the size of S.

[0063] The process of multiplexing and high concurrency processing in step S402 is as follows:

[0064] The system adopts multiplexed connections and high-concurrency processing mechanisms. It schedules network traffic through the IP layer at the network layer and uses virtual thread technology to create a large number of virtual threads. Each TCP connection corresponds to a virtual thread. When there is a network request, the request is assigned to the corresponding virtual thread for processing.

[0065] The process of the three-in-one layout and performance tracking in step S403 is as follows:

[0066] The system strengthens the three-in-one layout of the basic layer, data layer, and application layer. The distributed tracing technology is used to track the transmission process of requests between services at all levels. After the request enters the system, a unique tracing ID is generated for each request, and the tracing information is recorded in each service module and network node through which the request passes. When performance problems and system bottlenecks occur, developers can query the transmission path of the entire request and detailed information of each link through the tracing ID to quickly locate the link where the problem is. At the same time, static compilation technology is used to compile the program into a native executable file within the Quarkus framework. During the compilation process, the program code structure is optimized, memory usage is reduced, and startup time is shortened, thereby improving the system's production efficiency and performance.

[0067] The process of the content real-time alarm processing in step S404 is as follows:

[0068] During the continuous operation of the system, the content real-time alarm function monitors key performance indicators in real time, collects system operation data regularly, and compares it with the preset threshold. When the indicator exceeds the threshold, the developer is notified through a unified user interface. The R&D personnel take prompt measures based on the alarm information to ensure the stable operation of the system.

[0069] The notification method is either email notification or SMS notification.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The present invention provides a service channel access method based on distributed microservices. The present invention innovatively implements the service channel access method based on distributed microservices, realizes the decoupling and collaborative work of various functional modules through a distributed architecture, and improves the flexibility, scalability and reliability of the system.

[0072] The present invention constructs a multi-layer data controllable and easy-to-trace data exchange and transmission mechanism to encrypt transmission, verify parameters, audit, and convert plaintext into ciphertext. At the same time, it uses data transmission layer tracking technology to effectively monitor data links and ensure data security and compliance.

[0073] The present invention provides a cross-border, secure and green data channel. On the premise of meeting legal compliance requirements, it enables domestic users to conveniently access high-value overseas data services (such as various advanced AI large models, academic resources, etc.), providing users with a safe, reliable and efficient Internet experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A technical schematic diagram of a service channel access method based on distributed microservices provided in an embodiment of the present invention;

[0075] Figure 2A system structure diagram of a service channel access method based on distributed microservices provided in an embodiment of the present invention;

[0076] Figure 3 A technical schematic diagram of another service channel access method based on distributed microservices provided in an embodiment of the present invention;

[0077] Figure 4 A data flow diagram of a service channel based on distributed microservices provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] See also Figures 1 to 4 The present invention provides a service channel access method based on distributed microservices, comprising the following steps:

[0080] Step S1. System initialization and configuration;

[0081] Step S2. Client request processing;

[0082] Step S3. Data processing and system operation;

[0083] Step S4. Network connection and optimization;

[0084] The system initialization and configuration in step S1 includes the following steps:

[0085] Step S101. Distributed cache system configuration: for the Redis distributed cache system, carefully set the multi-node distributed storage parameters according to the system performance estimate and business needs, including the directory structure and storage strategy of multi-point file storage, the rules and number of data shards, and the triggering conditions and data recovery mechanism of the disaster recovery function, etc., to ensure that the cache system runs stably and the data is safe and reliable in high concurrency and large data volume scenarios;

[0086] Step S102. Distributed database deployment and configuration: Install and configure the distributed databases Cassandra and MariaDB, set the node relationship and data synchronization strategy of master-slave replication according to data storage requirements and business logic, determine the sharding key and sharding rules of the sharding mechanism, and establish database table structures and indexes suitable for storing historical records, review and release logs, customer usage records and other types of data to ensure efficient data storage, query and consistency maintenance;

[0087] Step S103. Build a message conversion component: Build a message conversion component, configure the type of message queue (such as Kafka, RabbitMQ, etc.) and parameters (such as queue length, message persistence method, etc.) according to the communication requirements and business processes between modules in the system, set the topic and subscriber rules for message publishing / subscription, establish the communication protocol and data format for task publishing and execution status feedback, and ensure smooth and efficient communication between modules;

[0088] Step S104. Distributed service architecture construction: According to the concept of microservice architecture, different AI large model functional modules are split and independently developed, and the Spring Cloud framework is used for service governance and configuration management. Configure an independent operating environment and resources for each microservice module, configure routing rules and load balancing strategies through the gateway, establish HTTP communication connections and data transmission formats between each microservice and the gateway, and realize the deployment and initialization of distributed services.

[0089] The client request in step S2 includes the following steps:

[0090] Step S201. The client initiates a request: Based on business needs, the client uses the http / https protocol to initiate a service request to the system gateway. The request contains parameters and information for connecting to specific overseas service resources, such as the requested service type (OpenAI model call, Google Scholar search, etc.), relevant data or keywords, etc.

[0091] Step S202. Message processing and preliminary application: After receiving the request, the system performs parameter verification and data transfer processing in the form of ciphertext-plaintext-ciphertext on the incoming message. First, the ciphertext is decrypted, the legitimacy and integrity of the parameters in the message are extracted and verified, and then the plaintext data is integrated and classified according to the transparent and consistent topological function. According to the requested service type, the corresponding artificial intelligence model is called for preliminary data processing and application, such as text analysis, image recognition, etc., to generate preliminary processing results or further data request instructions.

[0092] The data processing and system operation process in step S3 includes the following steps:

[0093] Step S301. Streaming computing and data flow: During the data processing process, when real-time data is generated (such as token data when a user requests a large model to generate text or images), the streaming computing engine captures and processes these high-throughput real-time data in real time. The token count data is processed according to the predefined calculation logic to generate real-time analysis results, such as token usage frequency statistics, user behavior analysis, etc. The processing results are then passed to the distributed cache system for caching on the one hand, for subsequent fast query and statistical analysis, and on the other hand, are passed to the distributed database for persistent storage to ensure long-term data preservation and traceability;

[0094] Step S302. Distributed cache strategy execution: The distributed cache system performs cache management for the configuration and metadata of frequently accessed AI large models and real-time token counting results according to the cache strategy. When there is a data query request, the matching data is first searched in the cache. If the cache hits, the cached data is directly returned to reduce the database query pressure and response time; if the cache misses, the data is obtained from the distributed database and stored in the cache according to the cache strategy, and the cache-related information (such as cache time, access frequency, etc.) is updated at the same time. During the cache processing, the data flow is monitored in real time, and according to the formula Calculate the data flow Q. When Q exceeds the preset threshold, trigger the cache expansion mechanism to allocate additional cache resources from the distributed database to expand the cache capacity C, ensuring the stability and efficiency of the cache system.

[0095] Step S303. Distributed database operation: The distributed database is responsible for storing various key data, including historical records, review and release logs, customer usage records, etc. In the process of data storage, the master-slave replication and sharding mechanism is strictly followed to ensure data consistency and availability. When there is a data query or transaction processing request, data query and operation are performed on the corresponding sharding node or master-slave node according to the type of request and data distribution, and the results are returned. At the same time, cross-node data query and transaction processing are supported to ensure the correctness and integrity of data operations in the system in a distributed environment.

[0096] Step S304. Message conversion and module interaction: The message conversion component plays a bridging and coordinating role in the system operation process. When a task is released (such as data processing tasks, review tasks, etc.), the task information is released to the corresponding service module through the message queue. The service module processes the task after receiving it and feeds back the execution status to the message conversion component through the message queue. In the review and release process, the status change information is notified to the relevant modules in a timely manner through the message queue. Each module performs asynchronous processing and interaction based on the event-driven architecture to improve the flexibility and concurrent processing capabilities of the system.

[0097] The network connection and optimization process in step S4 includes the following steps:

[0098] Step S401. Data channel and proxy selection: During the data transmission process, the system uses intelligent algorithms to select appropriate data channels and optimal proxy channels according to the parameters and specific conditions in the client message. First, a list of qualified data channels is screened out according to the service type, data volume, security requirements and other parameters in the message. Then, for the proxy channel, according to the delay D, bandwidth B and packet loss rate L, the formula S = a × D + b × B + c × L is used, where a, b, c are the weight coefficients corresponding to the delay, bandwidth and packet loss rate, and a + b + c = 1, and the evaluation value S is calculated. The proxy channels are sorted according to the size of S, and the optimal proxy channel is selected. The proxy channel is selected from the proxy pool according to random or specific rules (such as polling, weighted polling, etc.). The proxy pool dynamically updates the proxy server list during operation (such as regularly detecting the availability of the proxy server, removing unavailable proxies and adding new available proxies), detecting the availability of the proxy (by sending heartbeat packets or simulating requests, etc.) and performing load balancing (distributing requests according to the current load of the proxy server) to ensure the high availability and stability of the proxy;

[0099] Step S402. Multiplexing and high-concurrency processing: The system adopts multiplexed connections and high-concurrency processing mechanisms to schedule network traffic through the IP layer at the network layer. For multiplexed API Keys, set their weights (such as setting different API Key weights according to the priorities of different services or users), enable or disable (such as dynamically enable or disable certain API Keys according to business time periods or security status) and other parameters according to business needs and security policies. Support automatic disabling of invalid API Keys (by regularly checking the validity of API Keys, such as verifying with the authentication server) and automatic rotation (according to the set rotation rules, such as rotating API Keys at a fixed time or by the number of requests). At the same time, using virtual thread technology, a large number of virtual threads are created, and each TCP connection corresponds to a virtual thread. When there is a network request, the request is assigned to the corresponding virtual thread for processing, reducing IO blocking, improving the system's concurrent processing capabilities, and ensuring that the system runs stably and efficiently in high-concurrency network request scenarios;

[0100] Step S403. Trinity layout and performance tracking: The system strengthens the trinity layout of the basic layer, data layer, and application layer, and tracks the transmission process of requests between services at each level through distributed tracing technology (using distributed tracing tools such as Zipkin and Jaeger). After the request enters the system, a unique tracing ID is generated for each request, and relevant tracing information (request time, processing time, data transmission status, etc.) is recorded in each service module and network node through which the request passes. When performance problems or system bottlenecks occur, developers can use the tracing ID to query the transmission path of the entire request and detailed information of each link, so as to quickly locate the link where the problem lies. At the same time, using static compilation technology, the program is compiled into a native executable file within the Quarkus framework. During the compilation process, the program code structure is optimized, memory usage is reduced (such as by removing useless code, optimizing data structure storage, etc.), and startup time is shortened (such as initializing some resources in advance, optimizing class loading mechanisms, etc.), thereby improving the system's production efficiency and performance;

[0101] Step S404. Real-time content alarm processing: During the continuous operation of the system, the real-time content alarm function monitors key performance indicators (such as response time, error rate, etc.) in real time. The system operation data is collected regularly (such as the response time of each service module, the number of data processing errors, etc. collected at regular intervals) and compared with the preset threshold. When the indicator exceeds the threshold, the developer is notified through a unified user interface (such as the system management console), and the notification method may include email notification, SMS notification, etc. The developer takes prompt measures based on the alarm information, such as checking the logs of related service modules, adjusting system configuration parameters, fixing program vulnerabilities, etc., to ensure the stable operation of the system.

[0102] Those skilled in the art will appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. However, whether to perform in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed system modules and methods can be implemented in other ways. For example, the embodiments of the computer system described above are only schematic, and the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be skipped and not executed. Another point is that the mutual coupling or communication connection shown can be through some interfaces or communication connections, including networks, computing methods and logic.

[0104] In addition, each functional module in each embodiment of the present invention may be integrated into one system module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0105] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, storage server, optical disk and other media that can store program software.

[0106] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific implementation method. All technical features recorded in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0107] It should be noted that the above examples are only specific implementation methods of the present invention. Obviously, the present invention is not limited to the above implementation methods, and there are many similar variations. All variations directly derived or associated from the contents disclosed by the technicians in this field should fall within the protection scope of the present invention.

[0108] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A service channel access method based on distributed microservices, characterized by the following steps: Step S1. System initialization and configuration; Step S2. Client request processing; Step S3. Data processing and system operation; Step S4. Network connection and optimization; The system initialization and configuration in step S1 includes the following steps: Step S101. Distributed cache system configuration; Step S102. Distributed database deployment and configuration; Step S103. Build a message conversion component; Step S104: Building a distributed service architecture; The client request processing in step S2 includes the following steps: Step S201. The client initiates a request; Step S202. Message processing and preliminary application; The data processing and system operation in step S3 includes the following steps: Step S301. Stream computing and data transfer; Step S302: Distributed cache strategy execution; Step S303. Distributed database operation; Step S304. Message conversion and module interaction; The network connection and optimization in step S4 includes the following steps: Step S401. Data channel and proxy selection; Step S402: Multiplexing and high concurrency processing; Step S403. Trinity layout and performance tracking; Step S404: Real-time content alarm processing.

2. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of configuring the distributed cache system in step S101 is as follows: For the Redis distributed cache system, carefully set the multi-node distributed storage parameters based on system performance estimates and business needs; The multi-node distributed storage parameters include the directory structure and storage strategy of multi-point storage of files, the rules and number of data shards, and the triggering conditions and data recovery mechanism of the disaster recovery function.

3. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of distributed database deployment and configuration in step S102 is as follows: Install and configure the distributed databases Cassandra and MariaDB, set the node relationship and data synchronization strategy of master-slave replication according to data storage requirements and business logic, determine the sharding key and sharding rules of the sharding mechanism, and establish the database table structure and index; The database table structure and index are suitable for storing historical records, review and release logs, and customer usage records.

4. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of building the message conversion component in step S103 is as follows: Build message conversion components, configure the type and parameters of message queues according to the communication requirements and business processes between modules in the system, set the topics and subscriber rules for message publishing / subscription, and establish the communication protocol and data format for task publishing and execution status feedback; The type includes any one of Kafka and RabbitMQ, and the parameters are queue length and message persistence mode.

5. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of building the distributed service architecture in step S104 is as follows: According to the concept of microservice architecture, different AI large model functional modules are split and developed independently, and the SpringCloud framework is used for service governance and configuration management; Configure an independent operating environment and resources for each microservice module, configure routing rules and load balancing strategies through the gateway, and establish communication connections and data transmission formats between each microservice and the gateway; The communication connection and data transmission mode is HTTP / HTTPS.

6. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of the client initiating a request in step S201 is as follows: Based on business needs, the client uses the http / https protocol to initiate a service request to the system gateway. The request contains parameters and information for connecting to specific overseas service resources. The parameters and information include the requested service type, related data and keywords.

7. The service channel access method based on distributed microservices according to claim 1, characterized in that: The message processing and preliminary application in step S202 includes the following steps: After receiving the request, the system gateway performs parameter verification and data transmission processing on the incoming message in the form of ciphertext-plaintext-ciphertext.

8. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of streaming computing and data transfer in step S301 is as follows: During the data processing process, real-time data is generated, and the streaming computing engine captures and processes these high-throughput real-time data in real time; The process of executing the distributed cache strategy in step S302 is as follows: The distributed cache system manages cache data such as the configuration and metadata of frequently accessed AI models and real-time token count results based on the cache strategy. When there is a data query request, the cache is first searched for matching data. If the cache hits, the cached data is directly returned, reducing database query pressure and response time; If the cache misses, the data is retrieved from the distributed database and stored in the cache according to the cache strategy, and the cache is updated at the same time. During the cache processing, the data flow is monitored in real time, and the formula Calculate the data flow Q. When Q exceeds the preset threshold, trigger the cache expansion mechanism to allocate additional cache resources from the distributed database to expand the cache capacity C, ensuring the stability and efficiency of the cache system. The process of the distributed database operation in step S303 is as follows: Distributed databases are responsible for storing key data. The key data include historical records, review and release logs and customer usage records; The process of message conversion and module interaction in step S304 is as follows: When a task is released, the task information is published to the corresponding service module through the message queue. The service module processes the task after receiving it and feeds back the execution status to the message conversion component through the message queue. In the review and release process, the status change information is promptly notified to the relevant modules through the message queue. Each module performs asynchronous processing and interaction based on the event-driven architecture.

9. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of data channel and proxy selection in step S401 is as follows: During data transmission, the system uses intelligent algorithms to select appropriate data channels and proxy channels based on the parameters and specific conditions in the customer message; First, a list of qualified data channels is screened out according to parameters such as service type, data volume, and security requirements in the message. Then, for the proxy channel, the delay D, bandwidth B, and packet loss rate L are used through the formula S=a×D+b×B+c×L, where a, b, and c are the weight coefficients corresponding to the delay, bandwidth, and packet loss rate, respectively, and a+b+c=1. Calculate the evaluation value S based on real-time data, and sort by the size of S to obtain the optimal proxy channel; The process of multiplexing and high concurrency processing in step S402 is as follows: The system uses multiplexing connections and high-concurrency processing mechanisms. It schedules network traffic through the IP layer at the network layer and uses virtual thread technology to create a trusted thread pool, which contains a large number of virtual threads. Each TCP connection corresponds to a virtual thread. When there is a network request, the request is assigned to the corresponding virtual thread for processing.

10. The service channel access method based on distributed microservices according to claim 1, characterized in that: The process of the three-in-one layout and performance tracking in step S403 is as follows: The system strengthens the three-in-one layout of the basic layer, data layer, and application layer. The distributed tracing technology is used to track the transmission process of requests between services at all levels. After the request enters the system, a unique tracing ID is generated for each request, and the tracing information is recorded in each service module and network node through which the request passes. When performance problems and system bottlenecks occur, developers can query the transmission path of the entire request and detailed information of each link through the tracing ID to quickly locate the link where the problem is. At the same time, static compilation technology is used to compile the program into a native executable file within the Quarkus framework. During the compilation process, the program code structure is optimized, memory usage is reduced, and startup time is shortened, thereby improving the system's production efficiency and performance. The process of the content real-time alarm processing in step S404 is as follows: During the continuous operation of the system, the content real-time alarm function monitors key performance indicators in real time, collects system operation data regularly, and compares it with the preset threshold. When the indicator exceeds the threshold, the operation and maintenance personnel are notified through a unified user interface. The operation and maintenance personnel take prompt measures based on the alarm information to ensure the stable operation of the system. The notification method is either email notification or SMS notification.

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