Distributed high-availability interface service and data interaction system

Abstract

The invention provides a distributed high-availability interface service and data interaction system. The distributed high-availability interface service and data interaction system comprises a unified access platform, a client application module and a distributed configuration center, client application modules are arranged on clients and servers of different operating systems and devices; performing corresponding protocol communication and data subscription on the transmission data through the client application module; receiving and protocol conversion are carried out on transmission data through the uniform access platform and the distributed access layer, and end-to-end secure transmission is carried out; and monitoring, parameter adjustment and anomaly detection are carried out on the unified access platform through the distributed configuration center.

Description

Technical Field

[0001] This invention relates to the field of data interaction technology, and more specifically, to a distributed, highly available interface service and data interaction system. Background Technology

[0002] In existing technologies, with the rapid development of IoT, mobile internet, and cloud computing, the demand for interface services and data interaction between various terminal devices and business systems has increased dramatically. Current systems generally face the following technical challenges: Poor compatibility with multiple systems and protocols: The lack of a unified management platform for heterogeneous terminal access with different operating systems, device types, and communication protocols (such as MQTT, TCP, WebSocket, etc.) leads to complex system integration and high development and maintenance costs. Insufficient stability under high concurrency scenarios: Traditional architectures struggle to support large-scale concurrent access and massive data processing; intelligent routing and load balancing mechanisms are prone to performance bottlenecks under high throughput, affecting service availability. Weak data security and privacy protection mechanisms: Data lacks end-to-end encryption protection during transmission and storage; access control granularity is coarse, making it vulnerable to unauthorized access, data leakage, and network attacks. Inadequate dynamic feedback and anomaly detection capabilities: The lack of real-time, intelligent status feedback and anomaly identification mechanisms makes it difficult to promptly detect and respond to potential system problems, affecting business continuity and system reliability. Summary of the Invention

[0003] In view of this, the present invention proposes a distributed, highly available interface service and data interaction system to solve the problems existing in the prior art.

[0004] To achieve the above objectives, this invention proposes a distributed, highly available interface service and data interaction system, comprising: The system comprises a unified access platform, client application modules, and a distributed configuration center. Client application modules are configured on clients and servers operating on different operating systems and devices. These modules communicate with transmitted data using corresponding protocols and subscribe to data. The unified access platform receives and converts transmitted data via a distributed access layer, ensuring secure end-to-end transmission. The distributed configuration center monitors, adjusts parameters, and detects anomalies in the unified access platform.

[0005] Optionally, the distributed access layer set in the unified access platform includes a distributed gateway cluster, and the gateway cluster is equipped with a protocol adapter for protocol conversion and unification of internal data formats.

[0006] Optionally, a data subscription interface module can be set up in the client application module to provide a standardized interface for subscription services. The data subscription interface model is deployed independently using a microservice architecture and has a RESTful API to support subscription services.

[0007] Optionally, during end-to-end secure transmission, the client application module encrypts the transmitted data, and the unified access platform verifies and transmits the encrypted transmitted data. The client application module also employs the principle of least privilege and authentication mechanism to restrict permissions and perform security authentication on the transmitted data, while the unified access platform has a WAF protection and authentication mechanism to perform deep inspection and protection authentication on the transmitted data.

[0008] Optionally, for the distributed configuration center, the gateway nodes, protocol adapters and routing policies in the unified access platform can be monitored and the link status can be set in real time.

[0009] Optionally, for the distributed configuration center, monitoring information from the unified access platform can be obtained through the distributed configuration center, and anomaly detection based on rules, statistics, and machine learning can be performed on the monitoring information at the protocol level, business level, and performance level.

[0010] Optionally, for the distributed configuration center, the status, business load, abnormal situations, and performance indicators of the unified access platform can be analyzed through the distributed configuration center, and the data processing and resource allocation strategies can be dynamically adjusted based on the analysis results.

[0011] Optionally, a multi-level risk warning system is also set up in the distributed configuration center to provide early warning of the data interaction process of the unified access platform.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a distributed, highly available interface service and data interaction system, which has the following advantages: Unified access and management of heterogeneous terminals has been achieved: By building a unified access platform that supports multiple protocols (such as MQTT, TCP, WebSocket, etc.) and providing standardized API interfaces and protocol adapter mechanisms, seamless integration of different operating systems, devices and protocols has been achieved, which has greatly reduced the complexity of system integration and access costs.

[0013] The system's high-concurrency processing capabilities and elastic scalability have been improved: a distributed gateway cluster and intelligent load balancing strategy are adopted, combined with dynamic resource scheduling and elastic scaling mechanisms, to ensure stable operation and efficient resource utilization in high-concurrency and high-traffic scenarios.

[0014] Enhanced data security and privacy protection capabilities: Through end-to-end encrypted transmission, least privilege access control, WAF protection, and multi-factor authentication mechanisms, data security is achieved throughout the entire process of transmission, storage, and access, effectively preventing data leakage and unauthorized access.

[0015] Intelligent status feedback and anomaly management have been achieved: a real-time status feedback mechanism and an AI-driven anomaly detection model have been introduced, which can dynamically identify and respond to system anomalies, support automatic policy adjustment and risk warning, and significantly improve system reliability and business continuity. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings: Figure 1 This is a schematic diagram of the system architecture in an embodiment of the present invention. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] This invention designs a distributed, highly available interface service and data interaction system, such as... Figure 1 As shown. In terms of service capacity, it needs to meet the requirements of large-scale, massive access to various types of terminals, supporting access to a large number of different types of mobile devices and public service applications of different types, levels, and scenarios; it should make existing platforms easier to access and operate, significantly lowering the threshold for public service application access. It should reduce the complexity of application access implementation and shorten access time. Through interface services, it should achieve unified access across multiple terminals, protocols, and platforms. The interface service should support high-concurrency processing, elastic resource expansion, and provide full-process visualized access management, providing high-quality, unified, and standardized data sources for upper-layer data analysis. It should provide efficient and flexible data access capabilities for third-party systems, achieving seamless integration across multiple systems, devices, and protocols. Key requirements include: Multi-access capability and interface versatility: The system needs to provide a unified multi-access management platform that supports seamless adaptation to different operating systems and devices. It should be configurable to support multiple protocols (such as MQTT, TCP, WebSocket, etc.) to ensure rapid access and data acquisition for third-party systems. Simultaneously, the platform should employ intelligent routing and load balancing strategies to guarantee stable operation under high concurrency and high throughput.

[0019] Real-time data subscription and dynamic activation: The system must provide efficient data subscription functionality, allowing third-party applications to activate data services in real time through custom configurations. The platform should possess dynamic resource adjustment capabilities to ensure flexible response to different data sources and business scenarios, meeting the needs of rapidly changing business requirements.

[0020] The data interaction function is used for secure protection and feedback management of data interaction, ensuring stable and secure data collection, and providing third-party systems with secure, efficient, and flexible data access capabilities. Key requirements include: Data security and privacy protection: All data interactions with third-party systems must be protected by end-to-end encryption. The system should implement automated data security controls, eliminating the need for third parties to expose databases or sensitive information. Simultaneously, WAF protection, a principle of least privilege, and strict authentication mechanisms should be employed to ensure the security of data transmission and storage, preventing data leaks and unauthorized access.

[0021] Dynamic status feedback and request management: The system should return a success, failure, or exception status in real time when a third party initiates a data request, along with detailed error codes and explanations. Through AI-powered data insights and anomaly detection mechanisms, the system can automatically identify potential problems in requests and quickly optimize and adjust during data interaction, ensuring business continuity and risk warning capabilities.

[0022] To achieve the above objectives, this invention designs a distributed, highly available interface service and data interaction system. The system's key features include: a unified access platform and multi-protocol support to ensure seamless integration across multiple systems and devices; real-time subscription and dynamic resource adjustment to meet the flexibility and response speed requirements of different business scenarios; data security and privacy protection through end-to-end encryption, least privilege, and strict authentication mechanisms; and request status feedback and anomaly detection to provide immediate feedback and automatic optimization, ensuring business continuity.

[0023] The aforementioned distributed, highly available interface service and data interaction system includes: The specific functions of the interface service are as follows: 1. Multi-access Management and Protocol Adaptation: A unified access platform is set up to support protocols such as MQTT, TCP, and WebSocket. Seamless adaptation to multiple operating systems and devices is achieved through configuration methods on the unified access platform. Simultaneously, intelligent routing and load balancing mechanisms are implemented to ensure stability under high concurrency and high throughput scenarios.

[0024] The unified access platform features a unified access gateway and standardized API interface specifications. Supporting multiple protocols such as RESTful API, WebSocket, MQTT, and CoAP, it enables seamless access from different terminals. At the interface layer, a protocol adapter mechanism is introduced to uniformly format and standardize access data, ensuring smooth transmission and parsing of data generated by different types of terminals and operating systems. This approach effectively enables cross-platform data interaction and enhances the system's adaptability in heterogeneous terminal environments.

[0025] Specifically, the unified access platform design employs a layered distributed architecture to achieve unified access and management for multiple protocols and terminals. A distributed access layer, centered on a unified access gateway cluster, is designed with a stateless architecture. Each gateway node can independently handle access requests, and dynamic discovery and collaboration between nodes are achieved through a distributed service registry (such as Nacos or Consul). The gateway exposes standardized RESTful API interfaces and has a built-in multi-protocol conversion engine, supporting real-time parsing and conversion of various communication protocols such as MQTT, TCP, WebSocket, and CoAP through pluggable protocol adapters. All protocol data is converted into a unified internal data format (such as a standardized structure based on Protobuf or JSON Schema) at the access layer, enabling seamless integration and subsequent processing of heterogeneous data.

[0026] After the unified access platform converts the data to the aforementioned unified internal format, flexible adaptation is achieved through a configuration-driven strategy for cross-platform and device compatibility. For different operating systems (such as iOS, Android, HarmonyOS, Windows, etc.) and devices, a lightweight, customizable client application module (client SDK) is set up in the relevant operating system of the corresponding device. This module encapsulates basic capabilities such as protocol communication, security authentication, and data serialization. The encapsulated protocol communication, security authentication, and data serialization are then used to collect device fingerprints (including device model, operating system version, network status, etc.) to generate unique identifiers for subsequent access control, routing optimization, and abnormal behavior detection. The client SDK is then deployed to transmit the corresponding data to the unified access platform, which performs data conversion or receives unified internal data transmitted through the unified access platform from different operating systems or devices, achieving seamless adaptation.

[0027] Within the gateway cluster, dynamic load balancing algorithms, such as minimum connection count, response time weighting, or consistent hashing, are used to distribute requests to the optimal nodes. For long-connection protocols, such as WebSocket and TCP, a distributed session management scheme stores connection state information in an external cache cluster, enabling stateless connection migration and fault recovery. Simultaneously, based on real-time monitoring metrics such as node CPU utilization, network throughput, and request latency, an elastic scaling controller, such as Kubernetes HPA, automatically adjusts the number of gateway and adapter instances to dynamically respond to traffic fluctuations.

[0028] At the protocol adaptation and data processing level, a scalable protocol adapter framework was designed. Each protocol corresponds to an independent adapter service, employing an event-driven architecture to handle data parsing, format conversion, and exception recovery. The adapter achieves high-concurrency connection management through an asynchronous non-blocking model (such as based on Netty or Reactor frameworks) and supports dynamically loading protocol parsing rules, allowing for the addition or updating of protocol support without restarting the service. All access data, after being cleaned, verified, and standardized, is uniformly published to a distributed message queue (such as Kafka or Pulsar), decoupling the access layer from business processing and providing data buffering and order guarantee capabilities.

[0029] The unified access platform's configuration and management are achieved through a unified control plane. Configuration information for all gateway nodes, protocol adapters, and routing policies is stored in a distributed configuration center, supporting real-time push and dynamic activation. Operations personnel can monitor the entire access status through a visual console, including real-time connection counts, protocol distribution, node health, and anomaly alarms. Furthermore, the system integrates distributed tracing tools (such as SkyWalking) to generate a full-link tracing identifier for each access request, enabling end-to-end performance monitoring and problem localization for cross-protocol and cross-node call links. Through this layered, decoupled, and flexible technical design, the system ensures high availability and high performance while supporting large-scale unified access to complex heterogeneous terminal environments.

[0030] 2. Real-time Subscription and Dynamic Resource Adjustment: A data subscription interface module is set up in the interface service to provide an efficient data subscription interface and support third-party customized data service activation. It features dynamic resource adjustment capabilities, automatically allocating bandwidth and computing resources based on data volume and business needs.

[0031] Specifically, a data subscription interface module is set up in the client SDK. This module serves as the standardized entry point for the unified access platform to provide subscription services. The data subscription interface module is deployed independently using a microservice architecture, exposing a complete set of RESTful APIs to support the data subscription needs of third-party systems. The interface design provides detailed interface documentation and SDK support for easy integration by third parties. Internally, the subscription interface module adopts a layered processing architecture. The access layer is responsible for request verification and protocol conversion, converting requests of different formats into an internally unified format; the business layer handles subscription logic, including parameter validation, rule compilation, and resource verification; the execution layer is responsible for the distribution and execution of subscription rules. The module has a built-in rule engine that supports various condition expression syntaxes, including SQL-based filtering conditions, JSONPath-based data extraction, and time-window-based sampling rules. The rule engine compiles user-configured subscription conditions into efficient execution plans and optimizes them into matching logic suitable for streaming data processing. The subscription interface module is deeply integrated with other components of the unified access platform. When a third party creates a subscription, it first collaborates with the unified access platform to distribute the subscription rules to the corresponding gateway nodes, establishing a data flow transmission path from the data source to the subscriber.

[0032] The client SDK's built-in lightweight subscription interface module's proxy component serves as an extension of the unified access platform's subscription functionality on the terminal side. Upon SDK startup, it automatically synchronizes the device-related subscription configuration from the platform and establishes a local subscription rule cache. The subscription proxy employs an incremental synchronization mechanism, periodically querying the platform for subscription configuration changes and synchronizing only the changed portions, reducing network traffic. The local subscription cache uses encrypted storage to ensure the security of the subscription configuration. The SDK supports hot updates of subscription rules; when subscription configurations change, they take effect without restarting the application, ensuring service continuity. The subscription proxy maintains version information for subscription rules, ensuring consistency between the local cache and the platform configuration.

[0033] When device sensors or applications generate data, the subscription agent first performs local rule matching to determine if the data meets the subscribed conditions. The matching process uses optimized algorithms, indexing common condition patterns to accelerate execution, even on resource-constrained devices. For simple threshold conditions and enumeration matching rules, the SDK performs the judgment locally; only matching data enters the subsequent processing flow. For complex conditions or rules requiring cross-device association, the SDK records relevant data characteristics, and the platform performs the final matching judgment. This hierarchical matching strategy ensures rule accuracy while minimizing unnecessary data uploads, saving network bandwidth and device power. The SDK subscription agent also implements local data preprocessing functionality, performing preliminary processing on the data according to the subscription configuration. For example, for data subscribed to aggregate statistics, the SDK can perform sliding window calculations locally before uploading the statistical results; for data subscribed to a specific format, the SDK performs format conversion locally; for data subscribed to compressed transmission, the SDK performs local compression before uploading. The preprocessing function adopts a plug-in design, with different processing logics encapsulated as independent plug-ins, dynamically loaded and executed according to the subscription configuration. The SDK also supports local data caching. When the network is unavailable, matching data is cached in local storage and uploaded in batches once the network is restored. A serial number mechanism ensures that data is not lost or duplicated. Cache management employs an intelligent strategy, automatically managing cached data based on data priority, validity period, and storage space.

[0034] During the subscription creation phase, third parties submit applications through the subscription interface. After parameter verification and resource evaluation, the platform compiles the subscription rules into an execution plan. Simultaneously, the platform distributes the subscription configuration to the SDKs of relevant devices. Upon receiving the configuration, the SDK establishes a rule cache locally and returns confirmation. Once the platform confirms that all relevant devices are ready, it officially activates the subscription and begins data delivery. The subscription activation process employs a two-phase commit protocol to ensure consistency between the platform and the terminal status. After activation, the platform continuously monitors the subscription execution status, including metrics such as data matching volume, delivery success rate, and end-to-end latency, and periodically synchronizes status information with the SDK through a control channel.

[0035] During the subscription operation phase, the unified access platform and SDK maintain real-time collaboration. The unified access platform dynamically adjusts resource allocation based on the subscription's data traffic. When a surge in data volume for a subscription is detected, corresponding processing resources are automatically increased; when data volume decreases, idle resources are gradually released. Resource adjustments adopt a gradual strategy to avoid frequent fluctuations affecting service stability. Simultaneously, the platform allocates differentiated resource guarantees to subscriptions of different priorities based on the subscription's SLA requirements, with high-priority subscriptions enjoying resource reservations and priority scheduling. The SDK adjusts its local behavior according to the resource policies issued by the platform. When the platform notifies of bandwidth limitations, the SDK automatically enables measures such as data compression, reducing sampling frequency, and aggregating small data packets; when platform resources are sufficient, the SDK reverts to high-quality transmission mode. The SDK periodically reports local resource usage to the platform, including CPU, memory, storage, and power status, and the platform optimizes its global resource allocation strategy based on the aggregated data.

[0036] The subscription change and termination phases also employ edge-cloud collaboration. When a third party modifies subscription conditions, the platform first conducts an impact assessment to determine the scope of the change's impact on resources and devices. Then, it issues configuration change instructions to the SDKs of the relevant devices. The SDKs update their local rule caches and perform a rolling restart, ensuring that ongoing data processing is not affected during rule switching. Once the platform confirms that all devices are ready, it switches to the new subscription rules and clears the resources occupied by the old rules. When a subscription terminates, the platform first stops data delivery, then notifies the SDK to clear local caches and temporary data, and finally releases all occupied resources and updates billing information. The entire change and termination process maintains transactional consistency, ensuring no data loss or duplicate billing occurs. All lifecycle operations have complete audit logs, supporting operation traceability and accountability.

[0037] The dynamic resource adjustment system makes intelligent decisions based on real-time monitoring data and predictive models. The platform establishes a multi-layered monitoring system, monitoring server CPU, memory, disk I / O, and network bandwidth usage at the infrastructure layer; monitoring metrics such as connection count, request rate, processing latency, and queue depth at the service layer; and monitoring business metrics such as data traffic, matching rate, and delivery success rate at the subscription layer. Monitoring data is collected at a rate of seconds and aggregated into a time-series database for real-time analysis and historical storage. Based on the monitoring data, the system runs anomaly detection algorithms to identify resource bottlenecks and performance anomalies, automatically triggering resource adjustment decisions.

[0038] Resource decision-making employs a combination of rule engines and machine learning. The rule engine handles explicit resource adjustment scenarios, such as automatically scaling up compute instances when CPU utilization consistently exceeds 80%, increasing bandwidth quotas when network bandwidth utilization exceeds 90%, and optimizing task scheduling strategies when data processing latency exceeds SLA requirements. The machine learning model handles complex non-linear relationships, training a resource demand prediction model using historical data to anticipate future resource needs and implement preventative resource adjustments. The prediction model comprehensively considers various influencing factors such as business cycles, holiday effects, promotional activities, and weather conditions to improve prediction accuracy. Resource decision-making also considers cost constraints, selecting the most cost-effective resource allocation scheme while meeting service quality requirements, achieving a balance between cost and performance.

[0039] Resource adjustment employs a tiered strategy. The first tier involves parameter adjustment, optimizing resource usage by modifying service configuration parameters such as thread pool size, cache capacity, and connection timeout. This adjustment takes effect immediately and has a limited impact. The second tier involves instance scaling, adjusting processing capacity by increasing or decreasing the number of service instances, using a rolling update approach to ensure uninterrupted service. The third tier involves architectural adjustments, addressing persistent resource bottlenecks by considering architectural optimizations such as data sharding, read / write separation, and tiered caching. A complete rollback mechanism is in place for resource adjustment, automatically reverting to the previous state in case of anomalies. The effectiveness of adjustments is continuously monitored and evaluated, and the adjustment strategy is optimized based on actual results, forming a self-optimizing closed-loop system. All resource adjustment operations are meticulously recorded, including the reasons for the adjustment, the basis for the decision, the execution process, and the effect evaluation, supporting post-event analysis and strategy optimization.

[0040] The data interaction functions are as follows: 1. Data Security and Privacy Protection: End-to-end encryption ensures data security during transmission and storage. Employing the principle of least privilege, WAF protection, and strict authentication mechanisms prevents unauthorized access and data leakage. Third parties do not need direct access to the database or sensitive information, achieving secure isolation.

[0041] Specifically, an end-to-end encryption system is implemented during data transmission to ensure data security from client-side generation to server-side storage. A corresponding client SDK is configured on the server side. The client SDK performs local encryption immediately after data acquisition, using a hybrid encryption scheme combining asymmetric and symmetric encryption. The client SDK generates a random symmetric session key for encrypting transmitted data; then, it uses the server's public key to encrypt this session key, forming a complete data packet. This design combines the efficiency of symmetric encryption with the secure distribution advantages of asymmetric encryption. The encryption algorithm uses the SM series algorithm certified by the State Cryptography Administration or the internationally recognized AES-256-GCM algorithm to ensure that the encryption strength meets national security standards. The encryption process is completed in a trusted execution environment on the client side, preventing the key from being maliciously read from memory.

[0042] The client SDK and the unified access platform establish a complete key management lifecycle. During initial connection, a secure key exchange protocol is used to negotiate the session key, supporting ECDH key exchange based on elliptic curve cryptography. The session key has an expiration date, and a new key is automatically negotiated before expiration to prevent the risk of prolonged use of the same key. The key negotiation process involves two-way authentication: the client verifies the server's certificate, and the server verifies the client's device identity. Key materials are immediately removed from memory after use to prevent leakage of residual information. For scenarios where mobile devices may be lost, the SDK supports remote key revocation, allowing the platform to immediately revoke the encryption capabilities of lost devices and prevent the decryption of historical data. The entire encryption system supports forward secrecy, ensuring that the security of historical sessions is not affected even if private keys are leaked long-term.

[0043] The principle of least privilege is implemented through a fine-grained access control matrix. For each client SDK with different permissions, an independent permission configuration file is created for each third-party application, each user, and each device, clearly defining the scope of accessible data and operational permissions. Permission configuration adopts an attribute-based access control model, comprehensively considering multiple dimensions such as subject attributes (user role, department, security level), resource attributes (data category, sensitivity level, business scope), environmental attributes (access time, geographical location, device type), and operational attributes (read, write, delete, share). The access decision engine evaluates access requests in real time, granting access only when all conditions are met. Permission granularity is refined to the data field level, allowing control over whether a specific user can access specific fields in a specific table, or even a specific range of field values.

[0044] In the WAF protection and authentication mechanism, the Web Application Firewall is deployed at the outermost layer of the unified access platform, performing deep inspection and protection on all data transmitted via APIs. The WAF is configured with multi-layered protection rules: the basic layer implements general attack protection, including the identification and blocking of common web attacks such as SQL injection, cross-site scripting, path traversal, and command injection; the business layer implements semantic analysis to detect abnormal request patterns that do not conform to business logic; and the behavioral layer implements intelligent threat detection, identifying new attacks and zero-day exploits based on machine learning models. The WAF rule base is updated in real time and linked with the threat intelligence platform to promptly obtain the latest attack characteristics and protection strategies. The protection strategy adopts a layered response mechanism, implementing request shaping and delayed response for probing attacks, and immediately blocking confirmed attacks and recording source information.

[0045] The authentication mechanism implements a multi-factor authentication system. Basic authentication uses a secure token mechanism. The client SDK obtains an access token through device certificates and user credentials. The token uses the JWT standard format and includes declaration information such as the issuer, validity period, and scope of permissions. Token signing uses an asymmetric algorithm to prevent forgery and tampering. Tokens have reasonable validity periods: short-term tokens are used for regular API access, while long-term tokens are only used in specific scenarios such as backend batch processing. For high-risk operations such as password modification, access to sensitive data, and management functions, second-factor authentication is mandatory, supporting multiple verification methods such as SMS verification codes, time-based dynamic tokens, and biometric recognition. Intelligent response is implemented for authentication failures. When consecutive failed attempts are detected, protection mechanisms are automatically triggered, such as increasing the difficulty of the verification code, temporarily locking the account, and notifying the security administrator.

[0046] Device authentication and binding mechanisms ensure access security. Each client SDK generates a unique device fingerprint upon initial deployment, integrating information from multiple dimensions such as hardware characteristics, software environment, and network attributes. The device fingerprint is bound to the device certificate, and the platform maintains a database of legitimate device characteristics. Upon each connection, the SDK submits the device certificate; the platform verifies the certificate's validity and compares it with device characteristics to prevent the certificate from being copied and used on unauthorized devices. For mobile devices, the SDK detects the device's security status, such as whether it is jailbroken, has malware, or is in debug mode. If security risks are detected, sensitive operations are restricted or connections are denied. Device management supports remote control; security administrators can revoke access permissions for suspicious devices and clear sensitive cached data at any time. Device connection status is monitored in real time; abnormal connection behaviors, such as frequent IP changes or access from uncommon geographical locations, trigger security alerts.

[0047] The client SDK of the third-party system is securely separated from the core data environment through a multi-layered isolation architecture. At the network layer, a physically isolated DMZ zone is deployed. The third-party client SDK access service is deployed in the DMZ, while the core data processing service is deployed in an intranet security zone. The two are connected through a strictly controlled one-way access policy. At the data layer, a logically isolated data access layer is implemented. Third-party query requests are converted into secure database queries through a data proxy service. The proxy service implements complete SQL injection protection, result set filtering, and data masking. Third parties can only access data through predefined stored procedures and views and cannot directly manipulate the underlying table structure. Query results are dynamically masked, with masking rules determined based on the visitor's identity. For example, ordinary users see partially masked sensitive information, while authorized users see the complete information.

[0048] 2. Dynamic Status Feedback and Anomaly Management: Real-time return of request status, including success, failure, and exceptions, with detailed error codes and explanations. AI data insights and anomaly detection mechanisms automatically identify potential problems and optimize the data interaction process. The system can automatically adjust processing strategies to ensure business continuity and risk warning capabilities.

[0049] Within the unified access platform, all API requests generate status markers at each key processing stage, including stages such as reception confirmation, protocol parsing, authentication, business processing, and result return. Each status change is communicated synchronously or asynchronously to the distributed configuration center and the client SDK sending the information via the unified access platform: for short-lived operations, a synchronous response is used, directly including success or failure result codes in the HTTP response body; for time-consuming asynchronous processing, the request ID and status query interface are returned immediately, with the final result subsequently pushed via Webhook callbacks or message queues. The status feedback content is designed using a structured standard, including machine-readable code and human-readable explanations. The error code system uses a hierarchical classification coding system; for complex errors, the feedback information also includes the associated business context, the sequence of failed operation steps, and related data identifiers, facilitating problem localization. The feedback content supports multi-language localization, returning error explanations in the corresponding language based on the language preference in the request header.

[0050] The unified access platform transmits real-time monitoring information to the distributed configuration center, enabling real-time monitoring and analysis of the entire data interaction chain. The unified access platform's anomaly detection covers multiple dimensions: at the protocol level, it monitors abnormal request frequency, message format, and connection patterns; at the business level, it monitors illegal parameter combinations, access sequences violating business rules, and abnormal data operation patterns; and at the performance level, it monitors indicators such as abnormal response time, abnormal resource consumption, and sudden increases in error rate. The detection algorithm employs a multi-model fusion strategy: rule-based systems quickly identify known anomaly patterns, statistical models discover behaviors deviating from historical baselines, and machine learning models identify complex nonlinear anomaly patterns.

[0051] The distributed configuration center acquires relevant data from the unified access platform and employs an AI data insight engine to deeply analyze massive amounts of interaction data, establishing a dynamic profile of normal business behavior. The engine uses unsupervised learning algorithms to automatically identify potential patterns and relationships in the data, such as typical access patterns of specific user groups during specific time periods, call dependencies between different API interfaces, and the time distribution characteristics of data operations. Based on these insights, fine-grained behavioral baselines are established, with independent baseline models for each user, device, and API endpoint. Real-time traffic is compared with the baseline to calculate anomaly scores; alarms are triggered when scores exceed thresholds. The model continuously learns online, automatically adjusting the baseline as business develops and user behavior changes to adapt to business evolution. Root cause analysis is also performed, combining topology awareness and causal inference techniques. When an anomaly is detected, the system automatically constructs an impact graph to analyze the related services and components that may be affected. Based on service dependency graphs and traffic tracing data, the source propagation path of the anomaly is located. Causal analysis algorithms identify key factors leading to the anomaly, distinguishing between symptoms and root causes, such as determining whether increased response latency is due to excessive database load, network congestion, or code defects. Root cause analysis results are presented visually, showing the timeline of the anomaly, its scope of impact, trends in relevant metrics, and possible hypotheses about the root causes. The analysis process incorporates domain knowledge and combines business logic to understand the business implications of the anomaly, distinguishing between technical and business anomalies (such as normal traffic growth caused by promotional activities).

[0052] The distributed configuration center's policy decision engine continuously evaluates multi-dimensional inputs, including the current status of the unified access platform, business load, anomalies, and performance metrics, dynamically adjusting data processing and resource allocation strategies. The policy library predefines multiple processing modes: performance optimization configuration in normal mode, core function assurance configuration in degraded mode, and maximum availability configuration in emergency mode. The engine automatically selects the appropriate mode based on the severity of the anomaly and business priority, generating specific parameter adjustment plans. Policy adjustments are implemented gradually, first verifying effectiveness on a small scale, and then rolling out nationwide only after confirming safety and effectiveness.

[0053] Dynamic optimization of the data interaction process covers the entire link. During the request reception phase, configurations such as connection pool size, request queue length, and timeout parameters are automatically adjusted based on the current load. During the protocol processing phase, validation logic is automatically enhanced or the frequency is temporarily limited for protocol types with frequent anomalies. During the business processing phase, defensive checks are added or simplified versions are downgraded for business logic paths with high anomalies. During the data storage phase, caching, indexing, and sharding strategies are dynamically adjusted based on access patterns. Optimization decisions are based on real-time monitoring data and predictive models; for example, resources are reserved in advance when an upcoming business peak is predicted, and the protection of relevant components is strengthened in advance when specific abnormal patterns are detected.

[0054] Fault tolerance and degradation strategies ensure business continuity. The system defines clear degradation levels: Level 1 degradation only shuts down non-core functions, ensuring the main business processes; Level 2 degradation simplifies core functions, sacrificing some performance or features to maintain service; Level 3 degradation only provides read-only or cached data services; the final level, the emergency mode, provides the most basic service availability. Degradation decisions are triggered automatically, based on system health scores and business impact assessments. Fault tolerance mechanisms include various technologies such as request retries, failover, and data compensation. The retry strategy is intelligent, determining whether to retry, as well as the retry interval and number of times, based on the error type; failover automatically identifies healthy backend instances and switches traffic; data compensation restores data consistency by compensating for failed asynchronous operations. All degradation and fault tolerance operations have complete audit logs for easy post-event analysis and optimization.

[0055] The distributed configuration center employs a multi-tiered risk warning system, categorizing risks into four levels: Informational, Warning, Severe, and Emergency. Warnings are triggered based on multi-indicator aggregation analysis, including anomaly detection results, performance trend predictions, resource usage predictions, and security threat intelligence. The warning content is structured, containing key information such as risk type, scope of impact, urgency level, recommended measures, and expected timeline.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed, highly available interface service and data interaction system, characterized in that, include: A unified access platform, client application modules, and a distributed configuration center; client application modules are configured on clients and servers operating on different operating systems and devices; The client application module communicates with the transmitted data using the corresponding protocol and subscribes to the data. The unified access platform receives and converts transmitted data through a distributed access layer, and performs end-to-end secure transmission; the distributed configuration center monitors the unified access platform, adjusts parameters, and detects anomalies.

2. The system according to claim 1, characterized in that, The distributed access layer set up in the unified access platform includes a distributed gateway cluster, and the gateway cluster is equipped with a protocol adapter for protocol conversion and unification of internal data format.

3. The system according to claim 1, characterized in that, A data subscription interface module is set up in the client application module. The data subscription interface module provides a standardized interface for subscription services. The data subscription interface model is deployed independently using a microservice architecture and is equipped with a RESTful API to support subscription services.

4. The system according to claim 1, characterized in that, In end-to-end secure transmission, the client application module encrypts the transmitted data, and the unified access platform verifies and transmits the encrypted transmitted data. The client application module also adopts the principle of least privilege and authentication mechanism to restrict permissions and perform security authentication on the transmitted data, while the unified access platform is equipped with a WAF protection and authentication mechanism to perform deep inspection and protection authentication on the transmitted data.

5. The system according to claim 1, characterized in that, For the distributed configuration center, the gateway nodes, protocol adapters and routing policies in the unified access platform are monitored in real time and the link status is set.

6. The system according to claim 1, characterized in that, For the distributed configuration center, monitoring information from the unified access platform is obtained through the distributed configuration center, and anomaly detection based on rules, statistics, and machine learning is performed on the monitoring information at the protocol, business, and performance levels.

7. The system according to claim 1, characterized in that, For the distributed configuration center, the status, business load, abnormal situations, and performance indicators of the unified access platform are analyzed through the distributed configuration center, and the data processing and resource allocation strategies are dynamically adjusted based on the analysis results.

8. The system according to claim 1, characterized in that, The distributed configuration center also includes a multi-level risk warning system for unified early warning of data interaction processes on the access platform.

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