A network instruction cooperative processing system and method
By utilizing the parallel processing mechanism of the network instruction collaborative processing system, the performance bottleneck problem in high-concurrency network instruction processing is solved, achieving efficient and low-latency instruction processing and improving system performance and resource utilization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING XINQIAO INFORMATION TECH CO LTD
- Filing Date
- 2025-11-07
- Publication Date
- 2026-07-21
AI Technical Summary
Existing network command processing systems suffer from performance bottlenecks under high concurrency, resulting in significant end-to-end latency and low resource utilization, failing to meet the high concurrency and low latency requirements of modern network communication.
A network command collaborative processing system is adopted, which forms a command processing pipeline by the collaborative work of the intelligent agent, the intelligent agent, the intelligent agent, and the intelligent agent to achieve parallel processing and resource optimization.
It improves the system's high-concurrency processing capability and resource utilization, reduces end-to-end latency, meets the low-latency requirements of modern network communication, and enhances system scalability.
Smart Images
Figure CN121078144B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of pipeline collaborative processing technology, and in particular to a network instruction collaborative processing system and method. Background Technology
[0002] Existing network command processing systems typically employ a sequential processing approach, handling commands in the order of command reception, parsing, verification, execution, and feedback. This approach suffers from severe performance bottlenecks when dealing with high-concurrency network commands. For example, the sequential processing leads to long overall processing times and significant end-to-end latency, failing to meet the high-concurrency, low-latency requirements of modern network communication. Furthermore, the lack of effective coordination mechanisms between processing stages in traditional methods results in low resource utilization, further limiting system performance improvements. Summary of the Invention
[0003] To address the aforementioned problems in the existing technology, this application provides a network instruction collaborative processing system and method.
[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a network instruction cooperative processing system, including: a parsing agent, a verifying agent, an executing agent, and a feedback agent; the agents are connected to each other through a communication interface and a data transmission protocol to form an instruction processing pipeline; A parsing agent is used to parse network instructions to obtain key information and organize the format of the key information to obtain formatted instruction information; the key information includes instruction type, operation object, instruction priority label, instruction load and operation parameters; A verification agent is used to verify the legality and validity of the format instruction information; An execution agent is used to invoke corresponding system resources and services according to the format instruction information when the verification is successful, execute the operation corresponding to the format instruction information, and monitor the execution status and result of the operation in real time during the execution of the operation corresponding to the format instruction information. Feedback agents are used to send the execution status and results obtained from monitoring, as well as the error messages generated when verification fails, back to the initiator of the network command.
[0005] Optionally, the parsing agent includes an instruction format recognition and decomposition module, a key information extraction module, a data standardization processing module, and a parsing result encapsulation and transmission module connected in sequence. The instruction format recognition and decomposition module is used to perform the following operations: Identify and parse the format type of network commands; Based on the format type of the network instruction obtained through identification and parsing, the structure type of the network instruction is determined using preset format rules or dynamic matching algorithms; Based on the aforementioned structure type, network instructions are broken down into multiple components; The key information extraction module is used to perform the following operations: Extract information from each component to obtain the key information; Determine whether the key information includes context data; When the key information contains context data, extract the information from the context data and verify its format validity; The data standardization processing module is used to perform the following operations: After marking or filling in the missing fields in the key information in different formats with default values, the key information in different formats is converted into a unified data structure; The parsing result encapsulation and transmission module is used to perform the following operations: The data structure is encapsulated into a structured data packet containing parsing status, instruction details, or error information to obtain the formatted instruction information.
[0006] Optionally, the verification agent includes an instruction validity verification module, a security and permission verification module, a resource and status verification module, and an error handling and feedback module connected in sequence. The instruction validity verification module is used to perform the following operations: Based on the format instruction information provided by the parsing agent, verify whether its format conforms to the protocol specification, and reject instructions with incorrect format; Check the type, value range, and completeness of required fields for the operation parameters in the format instruction information; Verify that the instructions in the format instruction information conform to the communication process and field constraints by comparing them with the rules of the network protocol or custom protocol. The security and permission verification module is used to perform the following operations: The authentication information in the format instruction information is parsed to verify the legitimacy of the sender's identity, and combined with the access control list or role permission model, it is determined whether the user has the permission to execute the instruction. By using a rule engine or machine learning model, malicious features in the format instruction information can be identified to prevent potential attacks. The encrypted instructions in the format instruction information are decrypted, and the format instruction information is verified by hash verification or digital signature to ensure that the instruction content has not been tampered with during transmission. The resource and status verification module is used to perform the following operations: Verify whether the target resource corresponding to the operation object exists, whether it is online, and whether the current load allows the execution of network commands; Based on the current state, verify whether the network commands conform to the operational logic; The error handling and feedback module is used to perform the following operations: The verification results, error types, and detailed reasons are encapsulated into a structured message and passed to the feedback agent or directly triggered in the exception handling process, while invalid network commands are rejected.
[0007] Optionally, the execution agent includes, in sequence, an instruction classification and execution scheduling module, a resource allocation and collaborative execution module, a concurrency control and performance optimization module, and an execution status monitoring and error handling module; The instruction classification and execution scheduling module is used to perform the following operations: The corresponding execution engine or module is invoked according to the instruction type; Based on the priority label of the instruction or the business scenario, the execution order of network instructions is dynamically adjusted. The network instructions are broken down into process flows, the execution order of subtasks is arranged according to dependencies, and the execution progress is managed through a state machine; The resource allocation and collaborative execution module is used to perform the following operations: Based on the instruction load, corresponding computing resources are allocated, and for big data computing instructions, a distributed computing framework is invoked to achieve parallel processing. When calling multiple services, the target service address is obtained through the service discovery mechanism, and the call is initiated according to the protocol to integrate the results returned by multiple services; In response to device control commands, coordinate the coordinated operation of multiple hardware resources. The concurrency control and performance optimization module is used to perform the following operations: It employs an asynchronous, non-blocking mode to process I / O-intensive instructions and merges batch instructions. Use locking mechanisms or distributed locks to prevent concurrent modifications of the same resource by multiple network commands; The results of frequently accessed instructions are returned directly using a cache, while periodic network instructions are pre-calculated; frequently accessed instructions refer to those that have reached a set threshold number of accesses. The execution status monitoring and error handling module is used to perform the following operations: The metrics for each network command are recorded and visualized through a monitoring system; these metrics include execution progress, time consumption, and resource usage. For exception handling during execution, corresponding recovery strategies are adopted according to the exception type; exception handling is automatically retried according to a preset strategy; for fatal exceptions, execution is terminated and the error status is marked; the fatal exceptions include: insufficient permissions and permanent resource unavailability; Transactional instructions are processed through a distributed transaction protocol, and a global rollback is triggered when an exception occurs.
[0008] Optionally, the feedback agent includes, in sequence, an execution result encapsulation and formatting module, a status feedback and asynchronous notification module, an exception feedback and circuit breaker handling module, and a monitoring data collection and feedback module; The execution result encapsulation and formatting module is used to perform the following operations: Depending on the caller's requirements or instruction type, the execution result is encapsulated into different protocol formats; Send result notifications via message queue; Return device status using binary protocol; Distinguish encapsulated content according to the type of execution result; Sensitive information is anonymized, and returned data fields are filtered according to the caller's permissions; The status feedback and asynchronous notification module is used to perform the following operations: The progress of network command execution is pushed in real time through streaming feedback; It supports registering callback addresses or event handlers to proactively push results when the instruction execution is complete. Select the appropriate notification method based on the caller type; The anomaly feedback and circuit breaker handling module is used to perform the following operations: A unified error response model is used for anomaly feedback; When under high load or when some components fail, the circuit breaker mechanism is triggered, and the degradation result is returned to the caller along with a description of the degradation strategy to prevent a cascading failure. For retryable exceptions, retry suggestions are included in the feedback; for errors requiring manual intervention, customer service contact information or operation instructions are provided in the feedback; retryable exceptions include temporary network failures; temporary refers to failures that occurred less than a set time value. The monitoring data collection and feedback module is used to perform the following operations: Key metrics of instruction execution are collected and encapsulated as monitoring data; these key metrics include processing time, resource consumption, and throughput. For exceptions during execution, the complete call chain log is associated, and the feedback includes log query entry points or summary information; When an execution metric is detected to exceed a threshold, an early warning notification is sent to the operation and maintenance system, along with real-time performance data; the real-time performance data includes the current number of concurrent users and resource utilization.
[0009] Secondly, this application provides a network command cooperative processing method, including: The network instructions are parsed to obtain key information, and the format of the key information is organized to obtain formatted instruction information; the key information includes instruction type, operation object, instruction priority label, instruction payload, and operation parameters; The legality and validity of the format instruction information are verified; When the verification is successful, the corresponding system resources and services are invoked according to the format instruction information to execute the operation corresponding to the format instruction information; During the execution of the operation corresponding to the format instruction information, the execution status and result of the operation are monitored in real time, and the execution status and result are fed back to the initiating end of the network instruction; When verification fails, an error message is generated and sent back to the originating end of the network command.
[0010] Optionally, the network instructions are parsed to obtain key information, and the format of the key information is organized to obtain format instruction information, including: Identify and parse the format type of network commands; Based on the format type of the network instruction obtained through identification and parsing, the structure type of the network instruction is determined using preset format rules or dynamic matching algorithms; Based on the aforementioned structure type, the network instructions are broken down into multiple components, and information from each component is extracted to obtain the key information. After marking or filling in the missing fields in the key information in different formats with default values, the key information in different formats is converted into a unified data structure; The data structure is encapsulated into a structured data packet containing parsing status, instruction details, or error information to obtain the formatted instruction information.
[0011] Optionally, the format of network commands can include JSON, XML, binary protocols, and custom text formats.
[0012] Optionally, the legality and validity of the format instruction information are verified, including: The format instruction information is subjected to format compliance checks, parameter validity verification, protocol specification matching, identity authentication and authorization, attack prevention and malicious behavior detection, data integrity and confidentiality verification, target resource availability checks, and context state consistency verification.
[0013] Optionally, the network instruction collaborative processing method is implemented using a pipelined collaborative processing mechanism.
[0014] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a network instruction cooperative processing system and method. By introducing multiple intelligent agents and adopting a parallel processing approach for efficient end-to-end instruction processing, it can support the high concurrency and high performance requirements of the instruction bus, reduce end-to-end instruction processing latency, and improve the overall system performance and resource utilization. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 A schematic diagram of the structure of a network instruction collaborative processing system provided in an embodiment of this application; Figure 2 This application provides a schematic diagram of the implementation process of a network command collaborative processing system according to an embodiment of the present application. Figure 3 A flowchart illustrating a network command collaborative processing method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0019] This application provides a network command cooperative processing system, such as Figure 1 As shown, the system includes: a parsing agent, a verifying agent, an executing agent, and a feedback agent.
[0020] To achieve efficient collaboration and data transmission among the intelligent agents, they are connected via specific communication interfaces and data transmission protocols, forming an instruction processing pipeline. This pipeline employs a pipelined collaborative processing mechanism. In this pipeline, each agent, after completing its own processing task, immediately passes the result to the next agent without waiting for the entire instruction processing flow to complete, thus achieving continuous and efficient instruction processing. Simultaneously, through reasonable task scheduling and resource allocation strategies, it is ensured that each agent can fully utilize system resources, avoiding resource idleness and contention, thereby further improving the system's concurrent processing capabilities and performance. The implementation process of the entire network instruction collaborative processing system relying on the instruction bus is as follows: Figure 2 As shown. Figure 2 In this context, the parsing agent cluster includes multiple parsing agents, the execution agent pool includes multiple execution agents, and the verification agent pool includes multiple verification agents.
[0021] The following describes the implementation method and effects of the network instruction collaborative processing system provided in this application, based on the function and structure of each intelligent agent.
[0022] 1. Analyzing the intelligent agent 1.1 Function: The parsing agent parses network instructions to obtain key information and organizes the format of this key information to obtain formatted instruction information. Key information includes instruction type, target object, instruction priority label, instruction payload, and operation parameters. Specifically, the parsing agent performs format parsing on the instructions, breaking them down into their constituent parts, extracting the key information, and then passing the parsed information to the subsequent verification agent.
[0023] 1.2 Structure: The parsing agent consists of a sequence of modules: instruction format recognition and decomposition module, key information extraction module, data standardization processing module, and parsing result encapsulation and transmission module.
[0024] The instruction format recognition and decomposition module is used to perform the following operations: (1) Multi-format compatibility: It supports the recognition and parsing of various network command formats (such as JSON, XML, binary protocol, custom text format, etc.) and determines the structure type of the command through preset format rules or dynamic matching algorithms.
[0025] (2) Structured decomposition: The original instruction data is decomposed into independent components according to the format rules. For example, JSON instructions are split into key-value pairs, arrays, nested objects, etc., or binary instructions are parsed into headers, parameter segments, data segments, etc. according to byte offsets.
[0026] The key information extraction module is used to perform the following operations: (1) Basic information extraction: Extract core elements from the disassembled instructions, including: instruction type (such as read, write, delete, query and other operation instructions), target address / object (such as the IP of the device being operated, database table name, API interface path, etc.), operation parameters (such as numerical, string, Boolean and other parameter values), instruction identifier (such as unique ID, version number, serial number, etc.).
[0027] (2) Context information parsing: If the instruction contains context data (such as authentication token, session ID, timestamp, etc.), the parsing agent needs to extract and verify its format validity to provide a basis for the subsequent verification stage.
[0028] The data standardization module is used to perform the following operations: (1) Unified Format Conversion: Converting instruction information in different formats into a unified data structure (such as an object, structure, or standard message format) within the system to eliminate the impact of format differences on subsequent processing. For example, converting XML instructions into JSON objects, or generating corresponding field mapping tables for binary instructions. (2) Missing value handling: Mark or fill in the missing fields that may exist in the instructions with default values (in accordance with the protocol specifications) to ensure that the data passed to subsequent agents is complete and usable.
[0029] The parsing result encapsulation and transmission module is used to perform the following operations: (1) Structured encapsulation: The extracted and standardized instruction information is encapsulated into a structured data packet containing the parsing status (success / failure), instruction details, and error information (if any).
[0030] (2) Efficient transmission mechanism: The encapsulated parsing results are transmitted to the verification agent in real time through high-speed data channels (such as memory queues and message middleware) to ensure the continuity of pipeline processing.
[0031] 2. Verify the intelligent agent 2.1 Function: Verify the legality and validity of the format instruction information by the verification agent.
[0032] 2.2 Structure: The verification agent consists of a command validity verification module, a security and permission verification module, a resource and status verification module, and an error handling and feedback module, which are connected in sequence.
[0033] The instruction validity verification module is used to perform the following operations: (1) Format compliance check: Based on the structured instructions provided by the parsing agent, verify whether their format conforms to the protocol specifications (such as JSON field integrity, binary instruction checksum, HTTP request header format, etc.), and reject instructions with incorrect formats. Example: If the instruction is an API call, verify whether the URL path, request method (GET / POST, etc.) and parameter format conform to the interface definition.
[0034] (2) Parameter validity verification: Check the type (e.g., numeric, string, array), value range (e.g., age must be a positive integer, IP address format must be correct), and completeness of required fields of the command parameters to avoid execution exceptions due to incorrect parameters. Example: Verify whether the page number parameter in the database query command is a non-negative integer to prevent negative page numbers from causing program errors.
[0035] (3) Protocol specification matching: Compare the instructions with the rules of network protocols (such as TCP / IP, MQTT, CoAP) or custom protocols to verify whether the instructions conform to the communication process (such as handshake protocol, message order) and field constraints (such as length limit, version number compatibility).
[0036] The security and permission verification module is used to perform the following operations: (1) Identity Authentication and Authorization: Parse the authentication information (such as JWT tokens, API keys, digital certificates) in the command to verify the legitimacy of the sender's identity, and combine it with the Access Control List (ACL) or Role-Based Access Control (RBAC) model to determine whether the user has the permission to execute the command. Example: Verify whether the device certificate in the IoT device command is valid, and whether the device has the permission to operate the specified sensor.
[0037] (2) Attack prevention and malicious behavior detection: Through rule engines or machine learning models, identify malicious features in commands (such as SQL injection statements, cross-site scripting (XSS) attack parameters, and high-frequency repeated requests) to prevent potential attack behaviors. Example: When SQL injection keywords such as "DROP TABLE" are detected in the command parameters, the command is directly rejected and a security alert is triggered.
[0038] (3) Data integrity and confidentiality verification: Decrypt the encrypted instructions (if any) and verify whether the data has been tampered with during transmission by hash verification (such as MD5, SHA-256) or digital signature to ensure the authenticity of the instructions.
[0039] The resource and status verification module is used to perform the following operations: (1) Target resource availability check: Verify that the target resource (such as server, database, hardware device) operated on by the command exists, is online, and whether the current load allows the operation to be performed, so as to avoid invalid commands occupying system resources. Example: Before writing to the database, check whether the target table exists and whether the database connection is available.
[0040] (2) Context state consistency verification: Combine the current system state (such as session state, transaction progress) to verify whether the instruction conforms to the operation logic. For example, transactional instructions need to ensure that the current session is in a transaction-open state to avoid data inconsistency caused by cross-session operations.
[0041] The error handling and feedback module is used to perform the following operations: (1) Encapsulation of verification results: The verification results (pass / fail), error types (such as insufficient permissions, incorrect parameters, security threats) and detailed reasons are encapsulated into structured messages and passed to the feedback agent or directly triggered to trigger the exception handling process.
[0042] (2) Fast failure mechanism: Directly reject obviously invalid instructions (such as uncertified or seriously formatted instructions) to avoid entering the execution stage and reduce system resource consumption.
[0043] 3. Executing the intelligent agent 3.1 Function: The execution agent, based on the received instruction information, invokes the corresponding system resources and services to execute the operation corresponding to the instruction. Specifically, upon successful verification, the execution agent invokes the corresponding system resources and services according to the formatted instruction information, executes the operation corresponding to the formatted instruction information, and monitors the execution status and result of the operation in real time during the execution of the operation corresponding to the formatted instruction information.
[0044] 3.2 Structure: The execution agent includes, in sequence, an instruction classification and execution scheduling module, a resource allocation and collaborative execution module, a concurrency control and performance optimization module, and an execution status monitoring and error handling module.
[0045] The instruction classification and execution scheduling module is used to perform the following operations: (1) Multi-type instruction adaptation: The corresponding execution engine or module is called according to the instruction type (such as data read / write, calculation task, device control, API call, etc.). For example, database operation instructions: call the SQL execution engine or ORM framework to complete operations such as query, insert, and update. Device control instructions: send control signals to hardware devices through the driver interface (such as motor start / stop, sensor data acquisition).
[0046] (2) Execution priority management: Based on the priority tags of instructions (such as urgent tasks, real-time requests, and ordinary business) or business scenarios (such as payment instructions taking precedence over query instructions), the execution order is dynamically adjusted to ensure that critical tasks are processed first.
[0047] (3) Execution process orchestration: Decompose complex instructions (such as transaction operations across multiple services), arrange the execution order of subtasks according to dependencies (such as querying first and then updating), and manage the execution progress through a state machine.
[0048] The resource allocation and collaborative execution module is used to perform the following operations: (1) Dynamic scheduling of computing resources: Allocate corresponding computing resources according to the instruction load (such as CPU intensive, IO intensive), for example, using thread pools / process pools to manage execution threads to avoid resource exhaustion under high concurrency. For big data computing instructions, call distributed computing frameworks (such as Spark, Flink) to achieve parallel processing.
[0049] (2) Cross-module / service collaboration: When an instruction needs to call multiple services (such as cross-service calls in a microservice architecture), the target service address is obtained through the service discovery mechanism, and the call is initiated according to the protocol (such as gRPC, RESTful) to integrate the results returned by multiple services.
[0050] (3) Hardware resource collaborative control: Coordinate the operation of multiple hardware resources (such as sensors, actuators, and communication modules) in response to equipment control commands. For example, in industrial automation scenarios, simultaneously control the movement of the robotic arm and the acquisition of sensor data.
[0051] The concurrency control and performance optimization module is used to perform the following operations: (1) High-concurrency execution strategy: Asynchronous non-blocking mode (such as Node.js and Java NIO based on event loop) is used to handle IO-intensive instructions and reduce thread blocking. Batch instructions (such as batch writing to the database) are merged to reduce the number of IO operations and improve throughput.
[0052] (2) Execution conflict resolution: Avoid concurrent modifications of the same resource by multiple instructions through locking mechanisms (such as optimistic locking, pessimistic locking) or distributed locks (such as Redis locks). For example, prevent overselling in inventory deduction scenarios.
[0053] (3) Caching and pre-computation optimization: For frequently accessed command results (such as query operations), use caching (such as Redis) to return them directly, reducing redundant calculations. Pre-compute periodic commands (such as timed statistics) to improve real-time response speed.
[0054] The execution status monitoring and error handling module is used to perform the following operations: (1) Real-time status tracking: Record the execution progress (such as pending execution, in execution, completed), time consumption, resource usage and other indicators of each instruction, and display them visually through the monitoring system to facilitate fault location.
[0055] (2) Exception Capture and Recovery: For exceptions during execution (such as database connection timeout, device communication failure, and computational overflow), corresponding recovery strategies are adopted according to the exception type. Retryable exceptions (such as temporary network fluctuations): Automatic retry according to preset strategies (such as exponential backoff). Fatal exceptions (such as insufficient permissions or permanent resource unavailability): Terminate execution and mark the error status.
[0056] (3) Transaction consistency guarantee: For transactional instructions (such as cross-database transfers), the atomicity of multi-step operations is ensured through distributed transaction protocols (such as 2PC, TCC). When an exception occurs, a global rollback is triggered to avoid data inconsistency.
[0057] 4. Feedback agent 4.1 Function: The feedback agent generates corresponding feedback messages based on the information content and sends these messages back to the initiating end of the instruction. Specifically, the feedback agent feeds back the monitored execution status and results, as well as error messages generated when verification fails, to the initiating end of the network instruction.
[0058] 4.2 Structure: It includes the execution result encapsulation and formatting module, the status feedback and asynchronous notification module, the exception feedback and circuit breaker handling module, and the monitoring data collection and feedback module, which are connected in sequence.
[0059] The execution result encapsulation and formatting module is used to perform the following operations: (1) Multi-protocol response adaptation: Based on the caller's requirements or instruction type, the execution result is encapsulated into different protocol formats (such as HTTP, TCP, message queue protocols). For example, Web service requests: return an HTTP response in JSON / XML format (200 OK / 201 Created). Asynchronous tasks: send result notifications via message queues (such as Kafka, RabbitMQ). Device control: return device status in binary protocols (such as Modbus, CANopen).
[0060] (2) Result classification processing: The encapsulated content is differentiated according to the result type (success / partial success / failure). Successful results include data payload, operation time, request ID and other metadata. Failed results include error code (e.g. 400 BadRequest), error description and suggested solution (e.g. "Parameter format is incorrect, please check XXX field").
[0061] (3) Data desensitization and permission filtering: Sensitive information (such as user privacy data, system internal parameters) is desensitized (e.g., masked, encrypted), and the returned data fields are filtered according to the caller's permissions (e.g., ordinary users only receive basic information, while administrators can obtain the full data).
[0062] The status feedback and asynchronous notification module is used to perform the following operations: (1) Real-time status synchronization: For long-running tasks (such as big data calculation and file transfer), the execution progress is pushed in real time through streaming feedback (such as WebSocket and SSE) (such as "60% completed" and "Currently processing the 1000th data item") to improve the user experience.
[0063] (2) Asynchronous result callback: Supports registering callback addresses or event handlers, and actively pushes the results when the instruction is completed (such as setting a callback URL in the API gateway, and automatically notifying the client after the task is completed), avoiding polling and waiting by the caller.
[0064] (3) Multi-terminal notification adaptation: Select the appropriate notification method according to the type of caller (such as front-end application, third-party system, hardware device). Front-end application (pushing JSON data through WebSocket), third-party system (calling its provided Webhook interface), hardware device (returning status code through physical interfaces such as serial port and Bluetooth).
[0065] The exception feedback and circuit breaker handling module is used to perform the following operations: (1) Standardized error encapsulation: Adopt a unified error response model (such as RFC 7807 ProblemDetails), which includes error type (such as business error, system error), error code (such as “ERR_001_PARAM_MISSING”), and context information (such as the instruction ID and parameter value where the error occurred), so that the caller can quickly locate the problem.
[0066] (2) Circuit breaking and degradation feedback: When the system is under high load or some components fail, the feedback agent triggers the circuit breaking mechanism, returns the degradation result to the caller (such as "Service is busy, please try again later"), and attaches a degradation strategy description (such as "Only basic query function is currently supported") to prevent the avalanche effect.
[0067] (3) Retry and Compensation Notification: For retryable anomalies (such as temporary network failures), the feedback should include a retry suggestion (such as "It is recommended to retry in 3 seconds, and the number of retries should not exceed 3"). For errors that require manual intervention (such as insufficient permissions), the feedback should provide customer service contact information or operation instructions.
[0068] The monitoring data collection and feedback module is used to perform the following operations: (1) Summary of execution metrics: Collect key metrics of instruction execution (such as processing time, resource consumption, and throughput), encapsulate them into monitoring data (such as Metrics in Prometheus format) for the monitoring system to analyze. For example, record the RT (response time), CPU utilization, and memory usage of each instruction, count the number of successful / failed instructions per unit time, and generate throughput reports.
[0069] (2) Exception Logs and Tracing: For exceptions during execution, associate them with complete call chain logs (e.g., generate Trace IDs through OpenTelemetry), and the feedback includes log query entry or summary information (e.g., "The error occurred in module A, call chain ID: XXX"), which is convenient for operation and maintenance personnel to trace.
[0070] (3) Performance warning feedback: When the execution indicators are detected to exceed the threshold (such as RT exceeding 500ms, error rate exceeding 5%), the system will actively send a warning notification (such as email, SMS) to the operation and maintenance system, along with real-time performance data (such as current concurrency, resource utilization).
[0071] Based on the above description, the network command cooperative processing system provided in this application has the following advantages: 1. This application improves processing efficiency and reduces end-to-end latency by decoupling the instruction processing flow and employing parallel processing by different intelligent agents. Specifically, this application decomposes the instruction processing flow into multiple stages, which are then processed in parallel by different intelligent agents. This breaks the limitations of traditional sequential processing, significantly shortens the processing time of a single instruction, and improves the system's efficiency in handling high-concurrency network instructions. The parallel operation of the verification agent and the execution agent reduces the waiting time of instructions in the system, effectively lowering end-to-end latency and meeting the low-latency requirements of modern network communication.
[0072] 2. Through a pipelined collaborative processing mechanism and a reasonable task scheduling strategy, this application enables each intelligent agent to make full use of system resources, avoids resource idleness and waste, and improves the overall resource utilization rate of the system.
[0073] 3. Enhanced system scalability: Each processing stage is handled by an independent agent, facilitating system functional expansion and optimization. When new processing functions need to be added or existing processing logic needs to be improved, only the corresponding agent needs to be modified or replaced, without affecting the overall system architecture or the normal operation of other agents.
[0074] Based on the same inventive concept, this application also provides a network instruction cooperative processing method for implementing the network instruction cooperative processing system described above. The solution provided by this method is similar to the implementation scheme described in the above system; therefore, the specific limitations in one or more network instruction cooperative processing method embodiments provided below can be found in the limitations of the network instruction cooperative processing method described above, and will not be repeated here.
[0075] In one exemplary embodiment, such as Figure 3 As shown, a network instruction cooperative processing method implemented using a pipelined cooperative processing mechanism is provided, including: Step 100: Parse the network commands to obtain key information, and organize the format of the key information to obtain formatted command information. Key information includes command type, operation object, command priority label, command payload, and operation parameters.
[0076] Step 101: Verify the legality and validity of the format instruction information. This verification mainly includes: checking format compliance, validating parameters, matching protocol specifications, authentication and authorization, preventing attacks and detecting malicious behavior, verifying data integrity and confidentiality, checking target resource availability, and verifying context state consistency.
[0077] Step 102: When the verification is successful, the corresponding system resources and services are invoked according to the format instruction information, and the operation corresponding to the format instruction information is executed.
[0078] Step 103: During the execution of the operation corresponding to the format instruction information, monitor the execution status and result of the operation in real time, and feed back the execution status and result to the initiating end of the network instruction.
[0079] Step 104: When the verification fails, generate an error message and send the error message back to the initiator of the network command.
[0080] As an optional implementation method, the implementation process of step 100 includes: Identify and parse the format types of network commands. Network command format types include JSON, XML, binary protocols, and custom text formats.
[0081] Based on the format type of the network instructions obtained through identification and parsing, the structure type of the network instructions is determined using preset format rules or dynamic matching algorithms.
[0082] Based on the structure type, network instructions are broken down into multiple components, and information from each component is extracted to obtain key information.
[0083] After marking or filling in missing fields in key information of different formats with default values, the key information of different formats is converted into a unified data structure.
[0084] The data structure is encapsulated into a structured data packet containing parsing status, instruction details, or error information to obtain formatted instruction information.
[0085] In summary, this application mainly relates to a pipelined collaborative processing technology, aiming to provide a high-concurrency, high-performance instruction bus technology solution. Specifically, this application decouples the instruction processing process (parsing, verification, execution, and feedback), introduces multiple intelligent agents to perform efficient end-to-end instruction processing in parallel, in order to support the high-concurrency, high-performance requirements of the instruction bus, reduce end-to-end instruction processing latency, and thus improve the overall system performance and resource utilization.
[0086] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data for network instruction collaborative processing. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a network instruction collaborative processing method.
[0087] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0088] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0089] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0090] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0093] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0094] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0095] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A network command collaborative processing system, characterized in that, include: The system consists of a parsing agent, a verifying agent, an executing agent, and a feedback agent; the agents are connected through communication interfaces and data transmission protocols to form an instruction processing pipeline. In the instruction processing pipeline, each agent immediately passes the processing result to the next agent after completing its own processing task; the verification agent and the execution agent work in parallel. A parsing agent is used to parse network instructions to obtain key information and organize the format of the key information to obtain formatted instruction information; the key information includes instruction type, operation object, instruction priority label, instruction load and operation parameters; A verification agent is used to verify the legality and validity of the format instruction information; An execution agent is used to invoke corresponding system resources and services according to the format instruction information when the verification is successful, execute the operation corresponding to the format instruction information, and monitor the execution status and result of the operation in real time during the execution of the operation corresponding to the format instruction information. Feedback agent is used to send the execution status and results obtained from monitoring, as well as the error information generated when the verification fails, back to the initiator of the network command; The verification agent includes, in sequence, an instruction validity verification module, a security and permission verification module, a resource and status verification module, and an error handling and feedback module; The instruction validity verification module is used to perform the following operations: Based on the format instruction information provided by the parsing agent, verify whether its format conforms to the protocol specification, and reject instructions with incorrect format; Check the type, value range, and completeness of required fields for the operation parameters in the format instruction information; Verify that the instructions in the format instruction information conform to the communication process and field constraints by comparing them with the rules of the network protocol or custom protocol. The security and permission verification module is used to perform the following operations: The authentication information in the format instruction information is parsed to verify the legitimacy of the sender's identity, and combined with the access control list or role permission model, it is determined whether the user has the permission to execute the instruction. By using a rule engine or machine learning model, malicious features in the format instruction information can be identified to prevent potential attacks. The encrypted instructions in the format instruction information are decrypted, and the format instruction information is verified by hash verification or digital signature to ensure that the instruction content has not been tampered with during transmission. The resource and status verification module is used to perform the following operations: Verify whether the target resource corresponding to the operation object exists, whether it is online, and whether the current load allows the execution of network commands; Based on the current state, verify whether the network commands conform to the operational logic; The error handling and feedback module is used to perform the following operations: The verification results, error types, and detailed reasons are encapsulated into a structured message and passed to the feedback agent or directly triggered in the exception handling process, while invalid network commands are rejected.
2. The network command collaborative processing system according to claim 1, characterized in that, The parsing agent includes, in sequence, an instruction format recognition and decomposition module, a key information extraction module, a data standardization processing module, and a parsing result encapsulation and transmission module. The instruction format recognition and decomposition module is used to perform the following operations: Identify and parse the format type of network commands; Based on the format type of the network instruction obtained through identification and parsing, the structure type of the network instruction is determined using preset format rules or dynamic matching algorithms; Based on the aforementioned structure type, network instructions are broken down into multiple components; The key information extraction module is used to perform the following operations: Extract information from each component to obtain the key information; Determine whether the key information includes context data; When the key information contains context data, extract the information from the context data and verify its format validity; The data standardization processing module is used to perform the following operations: After marking or filling in the missing fields in the key information in different formats with default values, the key information in different formats is converted into a unified data structure; The parsing result encapsulation and transmission module is used to perform the following operations: The data structure is encapsulated into a structured data packet containing parsing status, instruction details, or error information to obtain the formatted instruction information.
3. The network command collaborative processing system according to claim 1, characterized in that, The execution agent includes, in sequence, an instruction classification and execution scheduling module, a resource allocation and collaborative execution module, a concurrency control and performance optimization module, and an execution status monitoring and error handling module; The instruction classification and execution scheduling module is used to perform the following operations: The corresponding execution engine or module is invoked according to the instruction type; Based on the priority label of the instruction or the business scenario, the execution order of network instructions is dynamically adjusted. The network instructions are broken down into process flows, the execution order of subtasks is arranged according to dependencies, and the execution progress is managed through a state machine; The resource allocation and collaborative execution module is used to perform the following operations: Based on the instruction load, corresponding computing resources are allocated, and for big data computing instructions, a distributed computing framework is invoked to achieve parallel processing. When calling multiple services, the target service address is obtained through the service discovery mechanism, and the call is initiated according to the protocol to integrate the results returned by multiple services; In response to device control commands, coordinate the coordinated operation of multiple hardware resources. The concurrency control and performance optimization module is used to perform the following operations: It employs an asynchronous, non-blocking mode to process I / O-intensive instructions and merges batch instructions. Use locking mechanisms or distributed locks to prevent concurrent modifications of the same resource by multiple network commands; The results of frequently accessed instructions are returned directly using a cache, while periodic network instructions are pre-calculated; frequently accessed instructions refer to those that have reached a set threshold number of accesses. The execution status monitoring and error handling module is used to perform the following operations: The metrics for each network command are recorded and visualized through a monitoring system; these metrics include execution progress, time consumption, and resource usage. For exception handling during execution, corresponding recovery strategies are adopted according to the exception type; Automatically retry exception handling according to preset strategies; For fatal exceptions, terminate execution and mark the error status; The fatal exceptions include: insufficient permissions and permanent unavailability of resources; Transactional instructions are processed through a distributed transaction protocol, and a global rollback is triggered when an exception occurs.
4. The network command collaborative processing system according to claim 1, characterized in that, The feedback agent includes, in sequence, an execution result encapsulation and formatting module, a status feedback and asynchronous notification module, an exception feedback and circuit breaker handling module, and a monitoring data collection and feedback module. The execution result encapsulation and formatting module is used to perform the following operations: Depending on the caller's requirements or instruction type, the execution result is encapsulated into different protocol formats; Send result notifications via message queue; Return device status using binary protocol; Distinguish encapsulated content according to the type of execution result; Sensitive information is anonymized, and returned data fields are filtered according to the caller's permissions; The status feedback and asynchronous notification module is used to perform the following operations: The progress of network command execution is pushed in real time through streaming feedback; It supports registering callback addresses or event handlers to proactively push results when the instruction execution is complete. Select the appropriate notification method based on the caller type; The anomaly feedback and circuit breaker handling module is used to perform the following operations: A unified error response model is used for anomaly feedback; When under high load or when some components fail, the circuit breaker mechanism is triggered, and the degradation result is returned to the caller along with a description of the degradation strategy to prevent a cascading failure. For retryable exceptions, retry suggestions are included in the feedback; for errors requiring manual intervention, customer service contact information or operation instructions are provided in the feedback; retryable exceptions include temporary network failures; temporary refers to failures that occurred less than a set time value. The monitoring data collection and feedback module is used to perform the following operations: Key metrics of instruction execution are collected and encapsulated as monitoring data; these key metrics include processing time, resource consumption, and throughput. For exceptions during execution, the complete call chain log is associated, and the feedback includes log query entry points or summary information; When an execution metric is detected to exceed a threshold, an early warning notification is sent to the operation and maintenance system, along with real-time performance data; the real-time performance data includes the current number of concurrent users and resource utilization.
5. A network command collaborative processing method, characterized in that, Applied to the network instruction cooperative processing system as described in any one of claims 1-4; the method includes: The network instructions are parsed to obtain key information, and the format of the key information is organized to obtain formatted instruction information; the key information includes instruction type, operation object, instruction priority label, instruction payload, and operation parameters; The legality and validity of the format instruction information are verified; When the verification is successful, the corresponding system resources and services are invoked according to the format instruction information to execute the operation corresponding to the format instruction information; During the execution of the operation corresponding to the format instruction information, the execution status and result of the operation are monitored in real time, and the execution status and result are fed back to the initiating end of the network instruction; When verification fails, an error message is generated and sent back to the originating end of the network command.
6. The network command collaborative processing method according to claim 5, characterized in that, The network instructions are parsed to obtain key information, and the format of the key information is organized to obtain format instruction information, including: Identify and parse the format type of network commands; Based on the format type of the network instruction obtained through identification and parsing, the structure type of the network instruction is determined using preset format rules or dynamic matching algorithms; Based on the aforementioned structure type, the network instructions are broken down into multiple components, and information from each component is extracted to obtain the key information. After marking or filling in the missing fields in the key information in different formats with default values, the key information in different formats is converted into a unified data structure; The data structure is encapsulated into a structured data packet containing parsing status, instruction details, or error information to obtain the formatted instruction information.
7. The network command collaborative processing method according to claim 6, characterized in that, Network commands can be formatted in JSON, XML, binary protocols, and custom text formats.
8. The network command collaborative processing method according to claim 5, characterized in that, Verifying the legality and validity of the format instruction information includes: The format instruction information is subjected to format compliance checks, parameter validity verification, protocol specification matching, identity authentication and authorization, attack prevention and malicious behavior detection, data integrity and confidentiality verification, target resource availability checks, and context state consistency verification.
9. The network command collaborative processing method according to claim 5, characterized in that, The network command collaborative processing method is implemented using a pipeline collaborative processing mechanism.