Implementation method and device of IP network acquisition and control plug-in based on Matrix command adapter

Abstract

The invention provides an IP network acquisition and control plug-in implementation method and device based on a Matrix command adapter, and relates to the technical field of network communication, and the method comprises the steps: analyzing and converting a control instruction of heterogeneous network equipment through the Matrix command adapter, and generating a standardized intermediate instruction set; deploying a resource virtualization pool in the IP network acquisition and control plug-in, and dynamically allocating network bandwidth, computing resources and equipment interfaces; and matching the middle instruction set with the execution capability of the target equipment in real time, and generating an executable equipment control instruction. According to the method, heterogeneous network equipment instructions can be flexibly analyzed and converted through the dynamic protocol adaptation layer, new equipment and new protocols can be quickly adapted through template matching and self-defined script extension, the compatibility and expansibility of network management are improved, and the resource virtualization pool is combined with the prediction model and the dynamic priority queue, so that the network management efficiency is improved. The resources are intelligently allocated according to the real-time load and historical data of the equipment, the resource utilization rate is improved, operation of core service equipment is guaranteed, and the overall performance of the network is improved.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a method and device for realizing an IP network acquisition and control plug-in based on a Matrix command adapter. Background Art

[0002] In today's complex network environment, there are a large number of heterogeneous network devices with different protocols and interfaces, which leads to many challenges in network management and control.

[0003] The traditional Matrix command adapter has limited dynamic adaptation capabilities and conventionally relies on static protocol template libraries, which makes it difficult to meet the needs of new devices and new protocols. Existing IP network acquisition and control plug-ins have poor cross-platform compatibility and rigid resource allocation, and cannot adapt to the dynamic changes of device loads in large-scale heterogeneous network scenarios. They lack intelligent scheduling mechanisms, have low command execution efficiency, and poor reliability. Therefore, there is an urgent need for an implementation method and device for an IP network acquisition and control plug-in based on a Matrix command adapter to solve these problems. Summary of the invention

[0004] The invention aims to provide an implementation method and device of an IP network acquisition and control plug-in based on a Matrix command adapter to solve the problems of difficult protocol adaptation, unreasonable resource allocation, low instruction execution efficiency and poor cross-platform compatibility in the prior art, to achieve unified management and efficient control of heterogeneous network devices, and to improve network performance and reliability.

[0005] In order to solve the above technical problems, the present invention provides a method and device for implementing an IP network acquisition and control plug-in based on a Matrix command adapter, comprising the following steps: Step 1: Build a dynamic protocol adaptation layer to parse and convert the control instructions of heterogeneous network devices through the Matrix command adapter to generate a standardized intermediate instruction set; Step 2: Deploy a resource virtualization pool in the IP network acquisition and control plug-in to dynamically allocate network bandwidth, computing resources, and device interfaces. The allocation strategies include: S21, predict future bandwidth demand through time series analysis model, combine the real-time load and historical peak data of the device to calculate the resource allocation weight of the device; S22, deploy resource virtualization pool, divide priority queues by device type, and realize flexible resource allocation; Step 3: Based on the intelligent scheduling algorithm, the intermediate instruction set is matched with the execution capability of the target device in real time to generate executable device control instructions, specifically: S31, calculating the expected execution time of the device to execute the instruction according to the computing capability, processing delay and current queue depth of the device; S32, splits complex instructions into atomic sub-instruction sets, distributes them to multiple devices for parallel execution through a consistent hashing algorithm, and ensures the atomicity and result consistency of the sub-instructions; Step 4: Synchronize device status through a distributed event bus, and dynamically optimize resource allocation and instruction execution path based on feedback results; Step 5: Use a lightweight containerized deployment architecture to support cross-platform operation of plug-ins on edge computing nodes and cloud platforms.

[0006] As a preferred technical solution, based on step one, a dynamic protocol adaptation layer is constructed, and the control instructions of heterogeneous network devices are parsed and converted through a Matrix command adapter to generate a standardized intermediate instruction set. The specific steps are as follows: S11, protocol parsing: when the Matrix command adapter receives the original device command, it extracts the command parameters and operation types in the original device command through the semantic parsing module; S12, template matching: setting a protocol template library, including protocol type templates of several types of devices; matching the protocol type of the target device from the protocol template library, and generating a standardized intermediate instruction; S13, exception handling: If the protocol type in the protocol template library cannot match the protocol type of the target device, it means that the template is missing, and a protocol extension signal is generated; the protocol extension signal calls the user-defined script through the protocol extension interface to generate a new protocol type template.

[0007] As a preferred technical solution, the future bandwidth demand is predicted through the time series analysis model, and the resource allocation weight of the device is calculated by combining the real-time load and historical peak data of the device, specifically: The autoregressive integrated moving average model is used to fit the historical bandwidth equipment to obtain the future bandwidth demand within the predicted set time period. ; Get the future bandwidth requirements of device i , Real-time CPU utilization , historical peak bandwidth , where i represents the device number, and the resource allocation weight is calculated using a weighted average method. The formula is: , where N represents the total number of devices and j represents the device number. represents the future bandwidth requirement of device j, represents the historical peak bandwidth of device j, It represents the sum of the future predicted bandwidth requirements of all devices from the 1st to the Nth in the network. Indicates the sum of the historical peak bandwidths of all devices. They represent the weight coefficients corresponding to future bandwidth demand, real-time CPU utilization, and historical peak bandwidth respectively.

[0008] As a preferred technical solution, the resource virtualization pool adopts SDN-based virtualization technology, divides priority queues according to device types, and realizes flexible allocation of resources, as follows: S221, monitor the status of physical resources in the network in real time through the SDN controller, including the usage of network bandwidth, computing resources and device interfaces, and abstract them into virtual resources; S222, classify the devices in the network and set device type groups, including core business devices and common business devices; assign corresponding priorities to each device type in the device type group, create three different priority queues, namely high, medium and low, and sort the priorities of the devices in the three different priority queues, namely high, medium and low, in the order of resource allocation weights; S223, through the real-time monitoring of resource usage and resource requests in each priority queue by the SDN controller, requests are processed starting from the high priority queue. When the high priority queue request is satisfied, requests from the medium and low priority queues are processed in turn. If resources are insufficient, resource requirements of high priority devices are processed first, and requests from low priority devices are delayed or partially processed. When resources are idle, they are promptly allocated to waiting requests.

[0009] As a preferred technical solution, complex instructions are split into atomic sub-instruction sets, which are distributed to multiple devices for parallel execution through a consistent hashing algorithm to ensure the atomicity and result consistency of the sub-instructions. The details are as follows: S321, instruction splitting: identify complex instructions and mark them as R, and split them into n sub-instructions R1, R2, ..., Rn according to their logical structure and data dependency, so that each sub-instruction is relatively independent and can be executed in parallel; Record the dependencies between sub-instructions to form a dependency graph G=(V,E), where is a set of sub-instructions, and E is a set of edges representing the dependency relationship of sub-instructions; S322, device selection and allocation: Use the consistent hashing algorithm to allocate sub-instructions to multiple devices for execution. Specifically, the consistent hashing algorithm is used to map the devices to a virtual hash ring. Each sub-instruction is calculated by a hash function to obtain a hash value. The hash value is used to search the nearest device node clockwise on the hash ring as the execution device.

[0010] S323, Atomicity Guarantee: A two-phase commit protocol is used to ensure the atomicity of sub-instructions.

[0011] As a preferred technical solution, the distributed event bus adopts Kafka message queue to support real-time synchronization and exception backtracking of device status data.

[0012] As a preferred technical solution, the device status is synchronized through a distributed event bus, and resource allocation and instruction execution path are dynamically optimized according to the feedback results, as follows: Obtain status data published by the device in real time, including CPU utilization, memory usage, and queue depth; Dynamic optimization of resource allocation: Get the target resource utilization F1 and actual resource utilization F2 of the device, subtract the target resource utilization from the actual resource utilization to get the target resource deviation value F; adjust the resource allocation weight of the device according to the target resource deviation value. The formula is: , represents the learning rate; Sort the priority queues according to the updated resource allocation weights and allocate resources to the devices with the highest priority. Instruction execution path optimization: Calculate the execution capability score Sd based on the device status and select the optimal device. The calculation formula is expressed as , where Hg represents the expected time for the device to execute instructions, H3 represents the current queue depth, Indicates the queue depth penalty coefficient; According to the Q-Learning path selection algorithm, reinforcement learning is used to dynamically optimize instruction allocation. The formula is expressed as ,in, They represent the current state s and the action a selected. Indicates reward, Represents the discount factor.

[0013] As a preferred technical solution, the lightweight containerized architecture is implemented based on Docker, and the plug-in module is split and deployed through microservices, specifically: S51, Microservice Splitting: Split the plug-in into multiple independent microservices according to function, including Matrix command adaptation, resource scheduling, instruction execution and status monitoring microservices; S52, Image building: Create a Docker image for each microservice, configure the operating environment in the Dockerfile, install dependencies, and copy the code; S53, orchestration and deployment: Use Kubernetes to orchestrate containers and define Deployment and Service resources through YAML files; S54, set CPU and memory requests and limits for microservice containers in Kubernetes.

[0014] The present application provides an implementation device of an IP network acquisition and control plug-in based on a Matrix command adapter, which adopts the above-described method, including a dynamic protocol adaptation module, a resource virtualization pool management module, an intelligent scheduling engine, a distributed event bus module, and a containerized deployment module; The dynamic protocol adaptation module is used to parse and convert the control instructions of the heterogeneous network devices through the Matrix command adapter to generate a standardized intermediate instruction set; The resource virtualization pool management module is used to deploy the resource virtualization pool in the IP network acquisition and control plug-in and dynamically allocate network bandwidth, computing resources and device interfaces; The intelligent scheduling engine is based on the intelligent scheduling algorithm, which matches the intermediate instruction set with the execution capability of the target device in real time and generates executable device control instructions. The distributed event bus module is used to synchronize device states through the distributed event bus and dynamically optimize resource allocation and instruction execution paths based on feedback results; The containerized deployment module is used to adopt a lightweight containerized deployment architecture to support cross-platform operation of plug-ins on edge computing nodes and cloud platforms.

[0015] As a preferred technical solution, the intelligent scheduling engine includes a hardware acceleration unit; the hardware acceleration unit uses a pipeline architecture to process multiple device instructions in parallel, and the pipeline architecture includes lexical analysis, syntax analysis, semantic mapping, and protocol conversion stages.

[0016] Compared with the related art, the implementation method and device of an IP network acquisition and control plug-in based on a Matrix command adapter provided by the present invention have the following beneficial effects: The present invention can flexibly parse and convert heterogeneous network device instructions through a dynamic protocol adaptation layer, quickly adapt to new devices and new protocols through template matching and custom script expansion, improve the compatibility and scalability of network management, and intelligently allocate resources according to the real-time load and historical data of the equipment through a resource virtualization pool combined with a prediction model and a dynamic priority queue, thereby improving resource utilization, ensuring the operation of core business equipment, and enhancing the overall performance of the network.

[0017] The present invention collaborates with an intelligent scheduling engine and a hardware acceleration unit to evaluate parallel processing instructions based on device capabilities, split complex instructions and ensure atomicity and consistency, thereby significantly improving execution efficiency and reliability and shortening response time.

[0018] The lightweight containerized architecture of the present invention enables cross-platform operation of plug-ins on edge computing nodes and cloud platforms, isolates the operating environment, reduces deployment and maintenance costs, and improves system flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of a method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter provided by the present invention; Figure 2The present invention provides a block diagram of the principle of an implementation device of an IP network acquisition and control plug-in based on a Matrix command adapter. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "group", "class" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0022] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0023] Please refer to Figure 1-Figure 2 A method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter comprises the following steps: Step 1: Build a dynamic protocol adaptation layer to parse and convert the control instructions of heterogeneous network devices through the Matrix command adapter to generate a standardized intermediate instruction set; Step 2: Deploy a resource virtualization pool in the IP network acquisition and control plug-in to dynamically allocate network bandwidth, computing resources, and device interfaces. The allocation strategies include: S21, predict future bandwidth demand through time series analysis model, combine the real-time load and historical peak data of the device to calculate the resource allocation weight of the device; S22, deploy resource virtualization pool, divide priority queues by device type, and realize flexible resource allocation; Step 3: Based on the intelligent scheduling algorithm, the intermediate instruction set is matched with the execution capability of the target device in real time to generate executable device control instructions, specifically: S31, obtain the device's processing delay H1, computing power H2 and current queue depth H3, calculate the expected time Hg for device k to execute instruction m, identify the complexity O of instruction m, and the formula is expressed as ,in, It represents the queue depth weight coefficient, which is used to measure the impact of queue depth on instruction execution time and is determined through experiments or machine learning algorithms. The processing delay of the device represents the inherent delay time before the device starts processing after receiving an instruction. The computing power represents the computing performance of the device measured in millions of instructions per second (MIPS). The current queue depth represents the number of instructions currently to be processed by the device. The quantification of complexity is determined based on factors such as the amount of computing and data processing required by the instruction, and can be obtained through empirical values ​​or historical data statistics. It should be noted that the expected execution time is calculated by comprehensively considering the device's processing delay, computing power, current queue depth, and instruction complexity. This multi-factor evaluation method can more accurately reflect the actual time it takes for the device to execute instructions. The queue depth weight coefficient is determined through experiments or machine learning algorithm optimization, which ensures the accuracy and scientificity of the calculation results and provides a reliable basis for subsequent instruction allocation. S32, splits complex instructions into atomic sub-instruction sets, distributes them to multiple devices for parallel execution through a consistent hashing algorithm, and ensures the atomicity and result consistency of the sub-instructions; Step 4: Synchronize device status through a distributed event bus, and dynamically optimize resource allocation and instruction execution path based on feedback results; Step 5: Use a lightweight containerized deployment architecture to support cross-platform operation of plug-ins on edge computing nodes and cloud platforms.

[0024] In this application, based on step one, a dynamic protocol adaptation layer is constructed, and the control instructions of heterogeneous network devices are parsed and converted through the Matrix command adapter to generate a standardized intermediate instruction set. The specific steps are as follows: S11, protocol parsing: when the Matrix command adapter receives the original device command (such as a CLI command), it extracts the command parameters and operation types in the original device command through the semantic parsing module; S12, template matching: set up a protocol template library, including protocol type templates for several types of devices; match the protocol type of the target device (such as Huawei vRP and Cisco IOS) from the protocol template library to generate standardized intermediate instructions; S13, exception handling: If the protocol type in the protocol template library cannot match the protocol type of the target device, it means that the template is missing, and a protocol extension signal is generated; the protocol extension signal calls the user-defined script through the protocol extension interface to generate a new protocol type template.

[0025] It should be noted that the Matrix command adapter uses semantic parsing technology to accurately extract parameters and operation types from heterogeneous device instructions, just like translation software recognizes keywords and grammatical structures in different languages; the protocol template library is like a multilingual dictionary, storing various device protocol type templates for easy and quick matching; when encountering new device protocols, the user-defined script extension function allows professionals to write code to create new templates, just like adding new words to the dictionary, so that the system can adapt to various new devices and new protocols; unlike the traditional method of relying on static protocol template libraries, this dynamic protocol adaptation layer has self-expansion capabilities, which greatly improves the compatibility and scalability of network management, and can connect new devices without a lot of code modifications, saving development costs and time.

[0026] In this application, the future bandwidth demand is predicted through the time series analysis model, and the resource allocation weight of the device is calculated by combining the real-time load and historical peak data of the device, specifically: The autoregressive integrated moving average model is used to fit the historical bandwidth equipment to obtain the future bandwidth demand within the predicted set time period. ; Get the future bandwidth requirements of device i , Real-time CPU utilization , historical peak bandwidth The historical peak bandwidth is the highest bandwidth usage reached by the device at a certain moment or within a set time period during its past operation. i represents the device number. The resource allocation weight is calculated using a weighted average method. The formula is: , where N represents the total number of devices and j represents the device number. represents the future bandwidth requirement of device j, represents the historical peak bandwidth of device j, It represents the sum of the future predicted bandwidth requirements of all devices from the 1st to the Nth in the network. Indicates the sum of the historical peak bandwidths of all devices. They represent the weight coefficients corresponding to future bandwidth demand, real-time CPU utilization, and historical peak bandwidth respectively; It should be further explained that the time series analysis model uses the autoregressive integrated moving average model to predict future bandwidth demand. This model fully considers the trend and fluctuation of historical bandwidth data. Compared with simple empirical estimation or fixed rule allocation, it can more accurately grasp future resource demand and calculate resource allocation weights based on the real-time load of the device and historical peak data. It provides an accurate portrait of the resource requirements of different devices, making resource allocation more in line with the actual situation.

[0027] In this application, based on step S22, the resource virtualization pool adopts SDN-based virtualization technology to divide priority queues according to device types to achieve flexible resource allocation, as follows: S221, monitor the status of physical resources in the network in real time through the SDN controller, including the usage of network bandwidth, computing resources and device interfaces, specifically link bandwidth occupancy, device CPU utilization, and the number of interfaces used, and abstract them into virtual resources; S222, classify the devices in the network, set device type groups according to the functions and importance of the devices in the network, including core business devices and common business devices; assign corresponding priorities to each device type in the device type group, create three different priority queues, namely high, medium and low, and sort the priorities of the devices in the three different priority queues, namely high, medium and low, according to the order of resource allocation weights; S223, through the resource usage and resource requests in each priority queue monitored in real time by the SDN controller, when a resource request enters the queue, the controller checks the high priority queue to determine whether there are sufficient resources in the virtual resource pool, specifically: Set the initial total bandwidth of the virtual resource pool, identify the currently allocated bandwidth, and subtract the current allocated bandwidth from the initial total bandwidth to obtain the current available bandwidth B; set the initial total computing resources, identify the currently allocated computing resources, and subtract the current allocated computing resources from the initial total computing resources to obtain the current available computing resources C; set the initial total number of device interfaces, identify the currently allocated number of interfaces, and subtract the current allocated number of interfaces from the initial total number of device interfaces to obtain the current available number of device interfaces I; If a resource request is received from any device, the resource requirement information corresponding to the device is obtained, including the required bandwidth Bn, the required computing resources Cn and the required number of device interfaces In; if the conditions are met at the same time , it means that there are enough resources in the virtual resource pool to satisfy the request in the high priority queue, and resources will be allocated to the request immediately; otherwise, if the conditions cannot be met at the same time , indicating that the resources required for the request are insufficient, the request is recorded and waits for resource release, marking the request as a waiting request; after satisfying the high-priority queue request, the requests of the medium-priority queue and the low-priority queue are processed in the same way; during the resource allocation process, if resources are idle, the SDN controller will select requests from all waiting requests in order of priority for resource allocation; It should be noted that physical resources are abstracted into virtual resources through SDN-based virtualization technology, which facilitates unified management and allocation. Priority queues are divided by device type to ensure that core business devices obtain resources first, so that important tasks can be processed first. When resources are tight, high-priority devices are given priority, and low-priority device requests are reasonably delayed or partially processed. When resources are idle, they are allocated in time, which effectively improves resource utilization and ensures the stable operation of key network services.

[0028] In this application, complex instructions are split into atomic sub-instruction sets, which are distributed to multiple devices for parallel execution through a consistent hashing algorithm to ensure the atomicity and result consistency of the sub-instructions, as follows: S321, instruction splitting: identify complex instructions and mark them as R, and split them into n sub-instructions R1, R2, ..., Rn according to their logical structure and data dependency, so that each sub-instruction is relatively independent and can be executed in parallel; Record the dependencies between sub-instructions to form a dependency graph G=(V,E), where is a set of sub-instructions, and E is a set of edges representing the dependency relationship of sub-instructions; S322, device selection and allocation: Use consistent hashing algorithm to allocate sub-instructions to multiple devices for execution. Specifically, the hash function is set to H(x). For any instruction Ir, r represents the instruction number, hr=H(Ir) is calculated, the first clockwise device node Ds is found on the hash ring, and the sub-instruction Ir is allocated to the device Ds for execution. S323, Atomicity Guarantee: A two-phase commit protocol is used to ensure the atomicity of the sub-command. Specifically, in the first phase, the coordinator sends a pre-execution request to all devices that execute the sub-command, and the devices execute the sub-command locally and feedback the execution result (success or failure); in the second phase, if all devices feedback that the execution is successful, the coordinator sends a commit request to all devices, and the devices formally submit the execution result; if any device feedbacks failure, the coordinator sends a rollback request to all devices, and the devices undo the executed operation to ensure the atomicity of the sub-command; It should be noted that splitting complex instructions into atomic sub-instruction sets and distributing them to multiple devices for parallel execution using a consistent hashing algorithm fully utilizes the computing resources of multiple devices in the network and greatly improves the efficiency of instruction execution. The two-phase commit protocol ensures the atomicity of sub-instructions and the consistency of results, and each stage is carried out strictly in accordance with the rules, ensuring the integrity and correctness of the data and avoiding data confusion caused by the failure of some instruction execution.

[0029] In this application, the distributed event bus adopts the Kafka message queue to support real-time synchronization of device status data and exception backtracking. It should be noted that the use of the Kafka message queue through the distributed event bus has the characteristics of high throughput and low latency, and can quickly synchronize device status data to ensure timely transmission of device status information. The exception backtracking function helps to quickly locate and solve faults when problems occur, thereby improving the stability and reliability of the system.

[0030] In this application, the device status is synchronized through a distributed event bus, and resource allocation and instruction execution paths are dynamically optimized based on feedback results, as follows: Obtain status data published by the device in real time, including CPU utilization, memory usage, and queue depth; Dynamic optimization of resource allocation: Get the target resource utilization F1 and actual resource utilization F2 of the device, subtract the target resource utilization from the actual resource utilization to get the target resource deviation value F; adjust the resource allocation weight of the device according to the target resource deviation value. The formula is: , represents the learning rate; Sort the priority queues according to the updated resource allocation weights and allocate resources to the devices with the highest priority. Instruction execution path optimization: Calculate the execution capability score Sd based on the device status and select the optimal device. The calculation formula is expressed as , where Hg represents the expected time for the device to execute instructions, H3 represents the current queue depth, Indicates the queue depth penalty coefficient; According to the Q-Learning path selection algorithm, reinforcement learning is used to dynamically optimize instruction allocation. The formula is expressed as ,in, They represent the current state s and the action a selected. Indicates reward, represents the discount factor; It should be noted that by dynamically optimizing resource allocation and instruction execution paths based on device status feedback, the system can adapt to changes in the network environment in real time. By adjusting resource allocation weights and selecting the best device to execute instructions, the system performance is continuously improved to ensure that the network is always in an efficient operating state.

[0031] In this application, the lightweight containerized architecture is implemented based on Docker, and the plug-in module is split and deployed through microservices, specifically: S51, Microservice Splitting: Split the plug-in into multiple independent microservices according to function, including Matrix command adaptation, resource scheduling, instruction execution and status monitoring microservices; S52, Image building: Create a Docker image for each microservice, configure the operating environment in the Dockerfile, install dependencies, and copy the code; S53, orchestration and deployment: Use Kubernetes to orchestrate containers and define Deployment and Service resources through YAML files; S54, set CPU and memory requests and limits for microservice containers in Kubernetes; It should be noted that the lightweight containerized deployment architecture based on Docker enables cross-platform operation of plug-ins on edge computing nodes and cloud platforms. This means that enterprises can flexibly choose the deployment environment according to their business needs, meet real-time requirements in edge computing scenarios, use powerful computing resources on cloud platforms, and adapt to different network infrastructures; The plug-in modules are split and deployed through microservices, and complex functions are split into multiple independent microservices. Each microservice focuses on one function, which reduces the system coupling and facilitates development, maintenance and expansion. Kubernetes orchestration containers define Deployment and Service resources through YAML files to achieve automatic deployment, management and expansion of containers, improving the flexibility and maintainability of the system.

[0032] As an implementation device of an IP network acquisition and control plug-in based on a Matrix command adapter provided in this application, the method described above is adopted, including a dynamic protocol adaptation module, a resource virtualization pool management module, an intelligent scheduling engine, a distributed event bus module and a containerized deployment module; The dynamic protocol adaptation module is used to parse and convert the control instructions of heterogeneous network devices through the Matrix command adapter to generate a standardized intermediate instruction set; The resource virtualization pool management module is used to deploy the resource virtualization pool in the IP network acquisition and control plug-in and dynamically allocate network bandwidth, computing resources and device interfaces; The intelligent scheduling engine is based on the intelligent scheduling algorithm, which matches the intermediate instruction set with the execution capability of the target device in real time and generates executable device control instructions. The distributed event bus module is used to synchronize device status through the distributed event bus and dynamically optimize resource allocation and instruction execution path based on feedback results; The containerized deployment module is used to adopt a lightweight containerized deployment architecture and supports cross-platform operation of plug-ins on edge computing nodes and cloud platforms.

[0033] In the present application, the intelligent scheduling engine includes a hardware acceleration unit; the hardware acceleration unit uses a pipeline architecture to process multi-device instructions in parallel, and the pipeline architecture includes lexical analysis, grammatical analysis, semantic mapping, and protocol conversion stages.

[0034] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0035] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter, characterized in that: The following steps are involved: Step 1: Build a dynamic protocol adaptation layer to parse and convert the control instructions of heterogeneous network devices through the Matrix command adapter to generate a standardized intermediate instruction set; Step 2: Deploy a resource virtualization pool in the IP network acquisition and control plug-in to dynamically allocate network bandwidth, computing resources, and device interfaces. The allocation strategies include: S21, predict future bandwidth demand through time series analysis model, combine the real-time load and historical peak data of the device to calculate the resource allocation weight of the device; S22, deploy resource virtualization pool, divide priority queues by device type, and realize flexible resource allocation; Step 3: Based on the intelligent scheduling algorithm, the intermediate instruction set is matched with the execution capability of the target device in real time to generate executable device control instructions, specifically: S31, calculating the expected execution time of the device to execute the instruction according to the computing capability, processing delay and current queue depth of the device; S32, splits complex instructions into atomic sub-instruction sets, distributes them to multiple devices for parallel execution through a consistent hashing algorithm, and ensures the atomicity and result consistency of the sub-instructions; Step 4: Synchronize device status through a distributed event bus, and dynamically optimize resource allocation and instruction execution path based on feedback results; Step 5: Use a lightweight containerized deployment architecture to support cross-platform operation of plug-ins on edge computing nodes and cloud platforms.

2. According to claim 1, a method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter is characterized in that: Based on step 1, a dynamic protocol adaptation layer is constructed, and the control instructions of heterogeneous network devices are parsed and converted through the Matrix command adapter to generate a standardized intermediate instruction set. The specific steps are as follows: S11, protocol parsing: when the Matrix command adapter receives the original device command, it extracts the command parameters and operation types in the original device command through the semantic parsing module; S12, template matching: setting a protocol template library, including protocol type templates of several types of devices; matching the protocol type of the target device from the protocol template library, and generating a standardized intermediate instruction; S13, exception handling: If the protocol type in the protocol template library cannot match the protocol type of the target device, it means that the template is missing, and a protocol extension signal is generated; the protocol extension signal calls the user-defined script through the protocol extension interface to generate a new protocol type template.

3. According to claim 1, a method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter is characterized in that: The future bandwidth demand is predicted through the time series analysis model. The resource allocation weight of the device is calculated by combining the real-time load and historical peak data of the device. Specifically, it is: The autoregressive integrated moving average model is used to fit the historical bandwidth equipment to obtain the future bandwidth demand within the predicted set time period. ; Get the future bandwidth requirements of device i , Real-time CPU utilization , historical peak bandwidth , where i represents the device number, and the resource allocation weight is calculated using a weighted average method. The formula is: , where N represents the total number of devices and j represents the device number. represents the future bandwidth requirement of device j, represents the historical peak bandwidth of device j, It represents the sum of the future predicted bandwidth requirements of all devices from the 1st to the Nth in the network. Indicates the sum of the historical peak bandwidths of all devices. They represent the weight coefficients corresponding to future bandwidth demand, real-time CPU utilization, and historical peak bandwidth respectively.

4. According to the method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter according to claim 1, it is characterized in that: Based on step S22, the resource virtualization pool adopts SDN-based virtualization technology to divide priority queues according to device types to achieve flexible allocation of resources, as follows: S221, monitor the status of physical resources in the network in real time through the SDN controller, including the usage of network bandwidth, computing resources and device interfaces, and abstract them into virtual resources; S222, classifying the devices in the network and setting device type groups, including core business devices and common business devices; And assign a corresponding priority to each device type in the device type group, create three different priority queues, namely high, medium and low, and sort the device priorities in the three different priority queues according to the order of resource allocation weight; S223, through the real-time monitoring of resource usage and resource requests in each priority queue by the SDN controller, requests are processed starting from the high priority queue. When the high priority queue request is satisfied, requests from the medium and low priority queues are processed in turn. If resources are insufficient, resource requirements of high priority devices are processed first, and requests from low priority devices are delayed or partially processed. When resources are idle, they are promptly allocated to waiting requests.

5. The method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter according to claim 1, characterized in that: Complex instructions are split into atomic sub-instruction sets, which are distributed to multiple devices for parallel execution through a consistent hashing algorithm to ensure the atomicity and result consistency of the sub-instructions. The details are as follows: S321, instruction splitting: identify complex instructions and mark them as R, and split them into n sub-instructions R1, R2, ..., Rn according to their logical structure and data dependency, so that each sub-instruction is relatively independent and can be executed in parallel; Record the dependencies between sub-instructions to form a dependency graph G=(V,E), where is a set of sub-instructions, and E is a set of edges representing the dependency relationship of sub-instructions; S322, device selection and allocation: Use consistent hashing algorithm to allocate sub-instructions to multiple devices for execution. Specifically, the consistent hashing algorithm is used to map the devices to a virtual hash ring. Each sub-instruction is calculated by a hash function to obtain a hash value. The hash value is used to find the nearest device node clockwise on the hash ring as the execution device. S323, Atomicity Guarantee: A two-phase commit protocol is used to ensure the atomicity of sub-instructions.

6. The method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter according to claim 1, characterized in that: The distributed event bus uses Kafka message queues to support real-time synchronization of device status data and exception backtracking.

7. The method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter according to claim 1, characterized in that: The device status is synchronized through the distributed event bus, and resource allocation and instruction execution path are dynamically optimized based on the feedback results, as follows: Obtain status data published by the device in real time, including CPU utilization, memory usage, and queue depth; Dynamic optimization of resource allocation: Get the target resource utilization F1 and actual resource utilization F2 of the device, subtract the target resource utilization from the actual resource utilization to get the target resource deviation value F; adjust the resource allocation weight of the device according to the target resource deviation value. The formula is: , represents the learning rate; Sort the priority queues according to the updated resource allocation weights and allocate resources to the devices with the highest priority. Instruction execution path optimization: Calculate the execution capability score Sd based on the device status and select the optimal device. The calculation formula is expressed as , where Hg represents the expected time for the device to execute instructions, H3 represents the current queue depth, Indicates the queue depth penalty coefficient; According to the Q-Learning path selection algorithm, reinforcement learning is used to dynamically optimize instruction allocation. The formula is expressed as ,in, They represent the current state s and the action a selected. Indicates reward, Represents the discount factor.

8. The method for implementing an IP network acquisition and control plug-in based on a Matrix command adapter according to claim 1, characterized in that: The lightweight containerized architecture is implemented based on Docker, and the plug-in module is split and deployed through microservices, specifically: S51, Microservice Splitting: Split the plug-in into multiple independent microservices according to function, including Matrix command adaptation, resource scheduling, instruction execution and status monitoring microservices; S52, Image building: Create a Docker image for each microservice, configure the operating environment in the Dockerfile, install dependencies, and copy the code; S53, orchestration and deployment: Use Kubernetes to orchestrate containers and define Deployment and Service resources through YAML files; S54, set CPU and memory requests and limits for microservice containers in Kubernetes.

9. A device for implementing an IP network acquisition and control plug-in based on a Matrix command adapter, using the method described in any one of claims 1 to 8, characterized in that: It includes dynamic protocol adaptation module, resource virtualization pool management module, intelligent scheduling engine, distributed event bus module and containerized deployment module; The dynamic protocol adaptation module is used to parse and convert the control instructions of the heterogeneous network devices through the Matrix command adapter to generate a standardized intermediate instruction set; The resource virtualization pool management module is used to deploy the resource virtualization pool in the IP network acquisition and control plug-in and dynamically allocate network bandwidth, computing resources and device interfaces; The intelligent scheduling engine is based on the intelligent scheduling algorithm, which matches the intermediate instruction set with the execution capability of the target device in real time and generates executable device control instructions. The distributed event bus module is used to synchronize device states through the distributed event bus and dynamically optimize resource allocation and instruction execution paths based on feedback results; The containerized deployment module is used to adopt a lightweight containerized deployment architecture to support cross-platform operation of plug-ins on edge computing nodes and cloud platforms.

10. The device for implementing an IP network acquisition and control plug-in based on a Matrix command adapter according to claim 9, characterized in that: The intelligent scheduling engine includes a hardware acceleration unit; the hardware acceleration unit uses a pipeline architecture to process multi-device instructions in parallel, and the pipeline architecture includes lexical analysis, grammatical analysis, semantic mapping, and protocol conversion stages.

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