Intelligent Network Resource Optimization Method Based on Multi-Protocol Digitalization

Abstract

The present invention relates to the technical field of network resource optimization, and particularly to an intelligent network resource optimization method based on multi-protocol digitization. The method includes: forming a data set by unifying protocols; collecting data; determining the production collaboration period; determining temporary applications; determining the concerned applications and sorting them to form a list; forming an allocation plan; adjusting preset thresholds or weight groups; and outputting the allocation plan. By unifying protocols, the present invention collects multi-party data in real time; combines the data to determine the production collaboration period and accurately identifies the resource-intensive periods; subsequently, determines the temporary applications and the concerned applications and sorts them to obtain a list of concerned applications to ensure sufficient resources for the concerned applications. Finally, adjusts the parameters through simulated allocation to achieve dynamic optimization of resource allocation, improve the performance of critical services and resource utilization rate, and effectively solve the problems of low data transmission accuracy and lag in optimization and adjustment caused by mutual interference of multi-protocols and over-reliance on static rules.

Description

Technical Field

[0001] The present invention relates to the technical field of network resource optimization, and in particular to an intelligent network resource optimization method based on multi-protocol digitization. Background Art

[0002] In the scenario of an intelligent factory, a variety of business applications are deployed in the enterprise campus network. These applications usually run based on different communication protocols, such as Modbus, Profinet, ZigBee, etc., forming a typical multi-protocol network environment. Although it is possible to unify them into an IP-based protocol through a protocol conversion gateway to achieve data standardization, in the actual production process, the use of network resources by each business application is highly dynamic and different. Traditional resource management methods are difficult to achieve unified perception and collaborative optimization of multi-protocol services, and problems such as key service response delays and uneven bandwidth allocation are likely to occur. Therefore, there is an urgent need for an optimization method with protocol fusion recognition capabilities and intelligent resource scheduling mechanisms to achieve efficient collaboration and dynamic allocation of network resources in a complex multi-protocol environment.

[0003] The patent document with the publication number CN113810402A discloses a multi-protocol adaptation method for network resource scheduling. The method includes: providing a certain number of adaptation interfaces according to actual needs; determining the type of communication protocol adopted by the network resource device, and selecting an adaptation interface matching the network resource device according to the type of communication protocol; completing the protocol adaptation between the network resource device and other network resource devices through the selected adaptation interface; converting the multiple communication protocols of each network resource device into a northbound interface to provide a unified northbound interface for the resource scheduling application program.

[0004] It can be seen that the multi-protocol adaptation method for network resource scheduling has the following problems: a certain number of adaptation interfaces need to be provided according to actual needs. In a complex network environment, hundreds or thousands of adaptation interfaces need to be developed and managed, with a huge workload; each interface needs to be precisely designed to meet the requirements of specific protocols, and the compatibility and interoperability between interfaces need to be ensured, with a large design difficulty; additional delays will be introduced during the process of converting multiple protocols into a northbound interface. Especially in the case of processing complex protocols or large amounts of data, the delay problem will be more obvious, thus affecting the real-time performance and efficiency of the entire network resource scheduling; it is mainly aimed at the multi-protocol adaptation problem in the network resource scheduling scenario and is not suitable for some special application scenarios; it cannot cover all emerging protocols in a timely manner, and when facing new protocols, it is necessary to re-develop and adapt the interfaces, affecting the scalability and adaptability of the system. Summary of the Invention

[0005] To this end, the present invention provides an intelligent network resource optimization method based on multi - protocol digitization, which is used to overcome the problems of low data transmission accuracy and lag in optimization and adjustment in the prior art due to mutual interference of multiple protocols and over - reliance on static rules through multi - protocol digitization, multi - party data monitoring, and dynamic adjustment mechanisms.

[0006] To achieve the above object, the present invention provides an intelligent network resource optimization method based on multi - protocol digitization, including:

[0007] Unify the protocols used by each business application in the enterprise campus network into IP - based protocols through a protocol conversion gateway to form a business protocol dataset;

[0008] Real - time collect the task scheduling volume of the production system, the remaining time limit of each task, the usage frequency of each business application, the data transmission volume, the bandwidth utilization rate, and the packet loss rate in the business protocol dataset;

[0009] Determine that the business application is in the production collaboration period according to the task scheduling volume, all the remaining time limits, and a preset busy index threshold to form a collaboration determination result;

[0010] Based on the collaboration determination result, determine a number of temporary applications according to the usage frequency and the transmission volume;

[0011] Determine a number of concerned applications according to the bandwidth utilization rate and the packet loss rate of each temporary application, and sort each concerned application according to a preset weight combination to form a concerned application list;

[0012] Based on the concerned application list, perform network resource allocation to form an allocation plan, and record the instruction delay time that occurs during the allocation process for each concerned application to form a number of delay times;

[0013] Adjust the preset busy index threshold according to each delay time and all the remaining time limits to form an adjusted busy index threshold, or adjust the preset weight combination to form an adjusted weight combination;

[0014] Output the allocation plan re - determined based on the adjusted weight combination or the adjusted busy index threshold;

[0015] The preset weight combination includes a preset utilization rate weight and a preset packet loss rate weight.

[0016] Further, determining that the business application is in the production collaboration period according to the task scheduling volume, all the remaining time limits, and a preset busy index threshold to form a collaboration determination result includes:

[0017] Calculate the sum of the reciprocals of all the remaining time limits to form the total urgency;

[0018] Calculate the ratio of the total urgency and the task scheduling volume to form a busyness index;

[0019] When the busyness index is greater than the preset busyness index threshold, it is determined that the business application is in the production collaboration period, and a collaboration determination result is formed.

[0020] Further, based on the collaboration determination result, a number of temporary applications are determined according to the usage frequency and the transmission volume, including:

[0021] Calculate the standard deviation of the usage frequency of the business application within a preset temporary duration to form a frequency fluctuation value;

[0022] When the frequency fluctuation value is greater than the preset frequency fluctuation value threshold, a number of temporary applications are determined according to the transmission volume.

[0023] Further, a number of temporary applications are determined according to the transmission volume, including:

[0024] Calculate the standard deviation of the transmission volume of the business application within the preset temporary duration to form a transmission volume fluctuation value;

[0025] When the transmission volume fluctuation value is greater than the preset transmission volume fluctuation value threshold, the business application is determined to be a temporary application to determine a number of temporary applications.

[0026] Further, a number of concerned applications are determined according to the bandwidth utilization rate and the loss rate of each temporary application, including:

[0027] Calculate the standard deviation of the bandwidth utilization rate of the temporary application within a preset determination duration to form a utilization rate determination fluctuation value;

[0028] Calculate the standard deviation of the loss rate of the temporary application within the preset determination duration to form a loss rate determination fluctuation value;

[0029] Determine a number of concerned applications according to the utilization rate determination fluctuation value and the loss rate determination fluctuation value.

[0030] Further, a number of concerned applications are determined according to the utilization rate determination fluctuation value and the loss rate determination fluctuation value, including:

[0031] Draw a change curve of the utilization rate determination fluctuation value within the preset determination duration to form a utilization rate curve;

[0032] Draw a change curve of the loss rate determination fluctuation value within the preset determination duration to form a loss rate curve;

[0033] Calculate the cosine similarity of the utilization rate curve and the loss rate curve to form a change consistency;

[0034] When the degree of change consistency is greater than a preset consistency threshold, determine that the temporary application is a concerned application to identify a number of concerned applications.

[0035] Further, sort the concerned applications according to a preset weight combination to form a list of concerned applications, including:

[0036] Calculate the standard deviation of the bandwidth utilization rate of the concerned applications within a preset sorting duration to form a utilization rate sorting fluctuation value;

[0037] Calculate the standard deviation of the loss rate of the concerned applications within the preset sorting duration to form a loss rate sorting fluctuation value;

[0038] Normalize all the utilization rate sorting fluctuation values within the preset sorting duration to form a normalized utilization rate fluctuation value, and normalize all the loss rate sorting fluctuation values within the preset sorting duration to form a normalized loss rate fluctuation value;

[0039] Sort the concerned applications according to the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization rate weight, and the preset loss rate weight to form a list of concerned applications.

[0040] Further, sort the concerned applications according to the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization rate weight, and the preset loss rate weight to form a list of concerned applications, including:

[0041] Perform a weighted sum of the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization rate weight, and the preset loss rate weight to form a sorting index;

[0042] Sort each of the concerned applications from high to low according to the corresponding sorting index to form a list of concerned applications.

[0043] Further, adjust the preset busy index threshold according to each of the delay times and all the remaining time limits to form an adjusted busy index threshold, or adjust the preset weight combination to form an adjusted weight combination, including:

[0044] Mark the tasks with a remaining time limit less than a preset remaining time limit threshold, count the number of marks, and form the number of urgent tasks;

[0045] When the number of urgent tasks is greater than a preset task number threshold, calculate the standard deviation of the delay time within a preset adjustment duration to form a delay fluctuation value;

[0046] When the delay fluctuation value is greater than a preset delay fluctuation threshold, calculate the relative deviation between the number of emergency tasks and the preset task quantity threshold, and reduce the preset busy index threshold according to the relative deviation and a preset exponential adjustment coefficient to form an adjusted busy index threshold;

[0047] When the delay fluctuation value is less than or equal to the preset delay fluctuation threshold, adjust the preset weight combination according to the delay fluctuation value to form an adjusted weight combination.

[0048] Further, adjusting the preset weight combination according to the delay fluctuation value to form an adjusted weight combination includes:

[0049] Calculate the relative deviation between the delay fluctuation value and the preset delay fluctuation threshold to form a delay deviation;

[0050] Adjust the preset utilization rate weight according to the delay deviation and a preset utilization rate weight adjustment coefficient to form an adjusted utilization rate weight, calculate the difference between the total weight and the adjusted utilization rate weight to form an adjusted loss rate weight, so as to form an adjusted weight combination.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows: by protocol conversion, the service application protocols are unified to form a data set, providing a basis for optimization; key data such as task scheduling volume and remaining time limit are collected in real time to comprehensively reflect the network state; the production collaboration period is determined by combining the task scheduling volume and the remaining time limit to accurately identify the resource-intensive periods; temporary applications are determined according to the usage frequency and transmission volume, and then the applications are sorted according to the bandwidth utilization rate and loss rate to focus on the applications, ensuring that resources are preferentially allocated to important applications; based on the delay time and remaining time limit adjustment parameters of the simulated allocation, the dynamic optimization of resource allocation is realized; the performance of key services is effectively improved, the resource utilization efficiency is increased, the self-adaptability and flexibility of the network are enhanced, the efficient and stable operation of the enterprise campus network is guaranteed, the enterprise operation efficiency and competitiveness are improved, and the problems of low data transmission accuracy and lag in optimization and adjustment caused by mutual interference of multiple protocols and over-reliance on static rules are effectively solved.

[0052] Further, by calculating the sum of the reciprocals of all remaining time limits to form a total urgency, and then using the ratio of the total urgency to the task scheduling volume as the busy index, it is determined that the service application is in the production collaboration period when the busy index is greater than the preset threshold. The reciprocal of the remaining time limit reflects the urgency of the task, and the larger the reciprocal, the more urgent the task; the task scheduling volume reflects the total amount of current production tasks. The busy index formed by combining the total urgency and the task scheduling volume can comprehensively consider the urgency of the task and the total task volume, so as to more accurately judge whether the service application is in the production collaboration period, can effectively identify the resource-intensive periods, ensure the performance and resource utilization efficiency of key services, and improve the overall operation efficiency of the system.

[0053] Furthermore, by first calculating the standard deviation of the usage frequency of business applications within a preset temporary duration to obtain a frequency fluctuation value, and when this value exceeds a preset threshold, further determining a temporary application based on the transmission volume, it is possible to accurately identify applications with high resource requirements during the production collaboration period. The standard deviation of the usage frequency reflects the degree of dispersion of the usage frequency of business applications within the preset temporary duration. The larger the standard deviation, the greater the fluctuation of the usage frequency and the more obvious the temporary requirements of the business. When the frequency fluctuation value exceeds the preset threshold, it indicates that there is a significant abnormal fluctuation in the usage frequency of the business application. Using the usage frequency as a preliminary screening indicator can quickly locate applications with large changes in activity. These applications are more likely to be the applications of concern and require priority resource allocation. The step-by-step screening not only reduces the calculation amount and improves the system efficiency, but also avoids screening using the transmission volume first and missing applications with high usage frequencies but small transmission volumes. These applications may have high requirements for network resources during certain periods but are ignored because of the small transmission volume, thus realizing dynamic resource optimization and improving the system performance and user experience.

[0054] Furthermore, temporary applications are accurately identified through hierarchical screening. First, using the number fluctuation value as the preliminary screening condition, only when the number fluctuation value exceeds the preset threshold will the transmission volume fluctuation value be further calculated, which avoids unnecessary calculations for all business applications and improves the operating efficiency of the system. Second, calculating the standard deviation of the transmission volume to form the transmission volume fluctuation value can quantify the degree of change in the transmission volume of business applications within the preset temporary duration. The larger the standard deviation, the greater the fluctuation of the transmission volume and the more obvious the temporary requirements of the business. Finally, when the transmission volume fluctuation value exceeds the preset threshold, the business application is determined as a temporary application, which can effectively identify business applications with temporarily high resource requirements during a specific period, so as to reasonably allocate resources for these temporary applications, improve resource utilization efficiency, and ensure the performance of key services and the overall operating efficiency of the system.

[0055] Furthermore, by calculating the standard deviations of the bandwidth utilization rate and the loss rate to form a fluctuation value, it is possible to accurately quantify the performance stability of temporary applications within a preset determined duration. The utilization rate-determined fluctuation value reflects the change in the resource usage efficiency of the application, while the loss rate-determined fluctuation value reflects the change in the reliability of data transmission. When these two fluctuation values are used in combination, the performance of temporary applications can be comprehensively evaluated, and temporary applications with large uncertainties in both resource utilization efficiency and data transmission during a specific period can be effectively identified, and then determined as the applications of concern. This not only ensures the performance and priority resource guarantee of the applications of concern, but also improves the utilization efficiency of the overall network resources and enhances the stability and reliability of the system.

[0056] Furthermore, by plotting the utilization rate curve and the loss rate curve and calculating their cosine similarity to form the change consistency, the performance fluctuation consistency of the temporary application can be accurately evaluated. The utilization rate fluctuation value reflects the change in resource utilization efficiency, and the loss rate fluctuation value reflects the change in data transmission quality. The change consistency between the two is quantified by cosine similarity. When the change consistency is greater than the preset threshold, it indicates that the bandwidth utilization rate and the packet loss rate of the temporary application show similar and synchronous fluctuation trends within the preset determined time period. This synchrony means that when the resource utilization efficiency of the application changes, its data transmission quality will also change accordingly. Specifically, if the bandwidth utilization rate of an application increases, it usually means that it is using more network resources to transmit data; if the packet loss rate also increases synchronously at this time, it indicates that while the application's occupancy of network resources increases, the network quality is also affected, resulting in an increase in the number of lost packets during data transmission; this means that it is sensitive to both network resources and network quality, making the application highly sensitive in terms of resource utilization and data transmission quality. Thus, it is determined as a concerned application, which can effectively identify the temporary applications with high requirements for resources and data transmission quality during a specific period, and then determine them as concerned applications to ensure that these applications obtain preferential resource allocation, improving resource utilization efficiency and system performance; while when the change consistency is less than or equal to the preset consistency threshold, it shows that the fluctuation trends of the bandwidth utilization rate and the packet loss rate of the temporary application are inconsistent, meaning that the bandwidth utilization rate may be increasing, but the packet loss rate remains stable or decreases. This indicates that although the application's occupancy of network resources increases, the network quality is not significantly affected and data transmission remains stable, suggesting that its impact on network resources may be relatively small or unstable. Therefore, it is not determined as a concerned application, avoiding excessive attention to applications with insignificant impact on the network during resource allocation, thereby improving the accuracy and efficiency of resource allocation, ensuring that network resources can be reasonably allocated to truly important applications, and enhancing the overall network performance and user experience.

[0057] Furthermore, by calculating the utilization rate ranking fluctuation value and the loss rate ranking fluctuation value of the concerned applications and performing normalization processing, the fluctuation conditions of each concerned application in terms of resource utilization efficiency and data transmission quality can be quantitatively evaluated. Normalization processing ensures the comparability between different indicators and eliminates the influence of dimension and order of magnitude differences. By combining the preset utilization rate weight and loss rate weight for weighted ranking, the importance of resource utilization efficiency and data transmission quality can be comprehensively considered, making the ranking result more in line with the actual business requirements, accurately identifying the concerned applications with high requirements for resources and data transmission quality, and ensuring that these applications are given priority consideration in resource allocation, thereby improving the rationality of resource allocation and the stability of system performance.

[0058] Furthermore, by means of weighted summation, two key indicators, namely the normalized utilization fluctuation value and the normalized loss rate fluctuation value, are comprehensively considered, and different weights are assigned to them to reflect the different importance of resource utilization efficiency and data transmission quality in actual services. The sorting index formed by weighted summation can more accurately measure the comprehensive performance of the applications of interest. Sorting the applications of interest from high to low according to the sorting index can ensure that those applications with high resource requirements and poor current performance are preferentially identified and sorted, thus forming a list of applications of interest that better meets the business needs and system performance requirements, not only improving the accuracy of identifying applications of interest, but also enhancing the rationality of resource allocation and the overall performance of the system.

[0059] Furthermore, by marking tasks with a remaining time limit less than a preset threshold and counting the number of urgent tasks, the number of urgent tasks in the current network can be intuitively reflected, and then it can be judged whether the network is in a high-load state. When the number of urgent tasks exceeds the threshold, the standard deviation of the delay time is further calculated to obtain the delay fluctuation value to evaluate the stability of the network delay. If the delay fluctuation value is large, it means that the network delay is unstable. At this time, reducing the preset busy index threshold can more strictly determine the production collaboration period to ensure that critical tasks obtain sufficient resources. When the delay fluctuation value is within the normal range, the weight combination is adjusted to optimize resource allocation and avoid resource waste. This makes the network resource allocation more flexible and accurate, can effectively cope with changes in the network state, improve the performance of critical applications and user experience, and at the same time ensure the stable operation of the overall network.

[0060] Furthermore, by calculating the relative deviation between the delay fluctuation value and the preset delay fluctuation threshold to obtain the delay deviation, the fluctuation of the network delay can be accurately quantified. The delay deviation reflects the degree of deviation of the current network delay from the ideal state. A larger delay deviation means that the network delay fluctuates greatly, which may affect the execution of critical tasks. Adjusting the utilization weight and the loss rate weight based on the delay deviation and the preset weight adjustment coefficient can dynamically optimize the resource allocation strategy. When the delay deviation is large, it means that the fluctuation of the network delay has a significant impact on the resource utilization rate. At this time, it is necessary to increase the utilization weight to pay more attention to the efficient use of resources, reduce the negative impact of delay fluctuation on network performance, can preferentially guarantee the resource requirements of critical tasks, reduce the delay of urgent tasks, and thus improve the overall performance and stability of the system, not only improving the flexibility and adaptability of resource allocation, but also enhancing the robustness of the system in the face of high delay fluctuations to ensure that the performance of critical services is preferentially guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flowchart of the intelligent network resource optimization method based on multi-protocol digitization in this embodiment;

[0062] Figure 2It is a determination logic diagram for determining that the business application is in the production collaboration period in this embodiment;

[0063] Figure 3 It is a determination logic diagram for determining the temporary application in this embodiment;

[0064] Figure 4 It is a determination logic diagram for determining the concerned application in this embodiment. Detailed implementation manners

[0065] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0067] Please refer to Figure 1 as shown, which is a flowchart of an intelligent network resource optimization method based on multi - protocol digitization in this embodiment;

[0068] This embodiment provides an intelligent network resource optimization method based on multi - protocol digitization, including:

[0069] Unify the protocols used by each business application in the enterprise campus network into IP - based protocols through a protocol conversion gateway to form a business protocol data set;

[0070] Real - time collect the task scheduling volume of the production system, the remaining time limit of each task, the usage frequency of each business application, the data transmission volume, the bandwidth utilization rate, and the packet loss rate in the business protocol data set;

[0071] Determine that the business application is in the production collaboration period according to the task scheduling volume, all the remaining time limits, and a preset busy index threshold to form a collaboration determination result;

[0072] Based on the collaboration determination result, determine a number of temporary applications according to the usage frequency and the transmission volume;

[0073] Determine a number of concerned applications according to the bandwidth utilization rate and the packet loss rate of each temporary application, and sort each concerned application according to a preset weight combination to form a concerned application list;

[0074] Based on the concerned application list, perform network resource allocation to form an allocation plan, and record the instruction delay time that occurs during the allocation process for each concerned application to form a number of delay times;

[0075] Adjust the preset busy index threshold according to each of the delay times and all of the remaining time limits to form an adjusted busy index threshold, or adjust the preset weight combination to form an adjusted weight combination;

[0076] Output the allocation scheme re-determined based on the adjusted weight combination or the adjusted busy index threshold;

[0077] The preset weight combination includes a preset utilization rate weight and a preset loss rate weight.

[0078] An enterprise campus network refers to a computer network within an enterprise used to connect various devices, systems, and applications. In an intelligent factory, it includes, but is not limited to, wired networks, wireless networks, industrial Ethernet, and the Internet of Things. Business applications refer to various software systems and application programs running in the enterprise campus network, used to support the daily operations and production activities of the enterprise. In an intelligent factory, common business applications include, but are not limited to: production management systems, industrial automation software, enterprise resource planning systems, quality management systems, supply chain management systems, and data analysis and visualization tools. A business protocol dataset refers to a set of standardized data collections formed after uniformly converting the protocols used by each business application in the enterprise campus network into IP-based protocols through a protocol conversion gateway.

[0079] In an enterprise campus network, different business applications may use a variety of different communication protocols, such as industrial fieldbus protocols, wireless communication protocols, etc. For unified management and optimization, these protocols are uniformly converted into IP-based protocols through a protocol conversion gateway to form a set of standardized data collections, namely the business protocol dataset. This process of uniformly converting protocols into IP-based protocols means converting the data packets of various non-IP protocols into data packets that conform to the IP protocol standard so that they can be seamlessly transmitted and processed in an IP-based network.

[0080] In an enterprise campus network, different business applications may use a variety of different communication protocols, such as industrial fieldbus protocols, wireless communication protocols, etc. For unified management and optimization, these protocols are uniformly converted into IP-based protocols through a protocol conversion gateway. The role of the protocol conversion gateway is to identify and convert the data packets of different protocols so that they can be seamlessly transmitted in the network. The converted data is integrated into a unified business protocol dataset, which contains the communication data of all business applications and provides a basis for subsequent network resource optimization.

[0081] The key metrics in the real-time collection of business protocol data are the basis for achieving intelligent optimization. These metrics include the task scheduling volume of the production system, i.e., the number of tasks to be processed, which reflects the busyness of the system and can be obtained through the production management system; the remaining time limit of each task, i.e., the difference between the task deadline and the current time, which reflects the urgency of the task and is also calculated through the production management system; the usage frequency of business applications, i.e., the number of times the application is started or used per unit time, which reflects the activity of the application and is statistically counted through the application management system; the data transmission volume, i.e., the amount of data transmitted by the application per unit time, which reflects the bandwidth requirement of the application and is monitored through the network traffic monitoring system; the bandwidth utilization rate, i.e., the proportion of network bandwidth occupied by the application, which reflects the occupancy of network resources by the application and is calculated through the network performance monitoring tool; and the packet loss rate, i.e., the proportion of packets lost in network transmission, which reflects the network quality and is monitored through the network performance monitoring tool. The real-time collection of these data provides an accurate basis for subsequent resource optimization, helping to improve the utilization efficiency of network resources and pay attention to the performance of applications.

[0082] In the optimization of enterprise campus network resources, the allocation plan refers to the specific strategies and plans for reasonably allocating network resources according to the application priorities and resource requirements in the list of applications to be concerned. After receiving the list of applications to be concerned re-determined based on the adjusted weight combination or the adjusted busy index threshold, first, the real-time data of all applications to be concerned are summarized, including the bandwidth utilization rate, packet loss rate, instruction delay time, etc. of the application. According to parameters such as the bandwidth utilization rate, packet loss rate, and instruction delay time of the application, combined with the preset threshold, the performance level of the application is classified. Using the preset evaluation model, comprehensively considering the business importance of the application, the network topology location, and the potential impact on the overall system performance, the risk level of each application to be concerned is evaluated. Next, the system uses the preset resource allocation algorithm (based on the optimization model or intelligent scheduling algorithm) to conduct multi-dimensional analysis on various applications to be concerned, including the business importance of the application, the network topology location, the load situation, and its potential impact on the overall system performance; according to the analysis results, multiple resource allocation plans are simulated, such as bandwidth adjustment, processing capacity adjustment, and storage resource optimization. Finally, integrating the analysis results and scheduling suggestions, a comprehensive network resource allocation plan including application status, performance causes, risk assessment, emergency response measures, and resource optimization planning is generated, providing a scientific basis for enterprise campus network management to achieve rapid performance optimization, timely adjustment of resource allocation, ensure the stable operation of the overall network, and business continuity.

[0083] For example, during a certain production collaboration period, the system selected the following applications for attention: Application A: High bandwidth utilization rate, low packet loss rate, and short instruction latency, determined to be a high-performance application. Application B: Medium bandwidth utilization rate, high packet loss rate, and long instruction latency, determined to be a medium-risk application. Application C: Low bandwidth utilization rate, low packet loss rate, and long instruction latency, determined to be a low-risk application.

[0084] The network resource optimization measures obtained by integrating the analysis results are as follows: (1) Application A: Bandwidth adjustment: Maintain the current bandwidth allocation to ensure high-performance operation; Processing capacity adjustment: Maintain the current processing capacity allocation to ensure that critical services are not affected; Storage resource optimization: Dynamically adjust storage resources according to business requirements. (2) Application B: Bandwidth adjustment: Appropriately increase the bandwidth allocation to reduce the packet loss rate; Processing capacity adjustment: Appropriately increase the processing capacity allocation to reduce the instruction latency; Storage resource optimization: Dynamically adjust storage resources according to business requirements. (3) Application C: Continuous monitoring: Continuously monitor the instruction latency of Application C to observe whether it returns to normal; Preventive maintenance: Arrange regular maintenance to check the network connection of Application C to prevent it from deteriorating further. Finally, a comprehensive network resource allocation plan is generated that includes application status, performance causes, risk assessment, emergency response measures, and resource optimization planning.

[0085] The preset utilization weight refers to the relative importance of the resource utilization rate fluctuation value when comprehensively evaluating the applications under attention. It depends on the sensitivity of the business application to the resource utilization rate, the fluctuation of historical data, and the priority of the system for resource allocation. It is usually set between 0.3 and 0.7. In this embodiment, it is set to 0.6, which can ensure that the resource utilization rate fluctuation occupies an important position in the comprehensive evaluation, while not ignoring the role of other factors, thereby improving the accuracy of identifying the applications under attention.

[0086] The preset loss rate weight refers to the relative importance of the data loss rate fluctuation value when comprehensively evaluating the applications under attention. It depends on the sensitivity of the business application to the data loss rate, the fluctuation of historical data, and the priority of the system for data transmission quality. It is usually set between 0.3 and 0.7. In this embodiment, it is set to 0.4, which can ensure that the data loss rate fluctuation occupies an important position in the comprehensive evaluation, while not ignoring the role of other factors, thereby improving the accuracy of identifying the applications under attention.

[0087] The protocols used by various business applications are uniformly converted into IP-based protocols through a protocol conversion gateway to form a business protocol dataset. Then, a variety of key metrics in the business protocol dataset are collected in real time, including the task scheduling volume of the production system, the remaining time limit of each task, the usage frequency of business applications, the data transmission volume, the bandwidth utilization rate, and the packet loss rate. Based on the collected data, combined with the task scheduling volume, all the remaining time limits, and a preset busy index threshold, it is determined whether the business application is in the production collaboration period. After determining the production collaboration period, a number of temporary applications are determined according to the usage frequency and transmission volume, and then, based on the bandwidth utilization rate and loss rate of these temporary applications, a number of concerned applications are further determined, and the concerned applications are sorted according to a preset weight combination to form a list of concerned applications. Subsequently, a network resource allocation simulation is performed on the concerned applications in the list of concerned applications to form a simulated allocation plan, and the instruction delay time that occurs to each concerned application during the simulation process is recorded. Finally, according to these delay times and all the remaining time limits, the preset busy index threshold or weight combination is adjusted to optimize the resource allocation strategy, and the list of concerned applications is re-determined based on the adjusted parameters, and network resources are allocated to the concerned applications, so as to improve the utilization efficiency of network resources and the performance of concerned applications.

[0088] Unify the business application protocols through protocol conversion to form a dataset, providing a basis for optimization; collect key data such as task scheduling volume and remaining time limit in real time to comprehensively reflect the network status; determine the production collaboration period by combining the task scheduling volume and the remaining time limit to accurately identify the resource-intensive periods; determine temporary applications according to the usage frequency and transmission volume, and then sort the concerned applications according to the bandwidth utilization rate and loss rate to ensure that resources are preferentially allocated to important applications; adjust the parameters based on the delay time of the simulated allocation and the remaining time limit to achieve dynamic optimization of resource allocation; effectively improve the performance of key services, improve the utilization efficiency of resources, enhance the self-adaptability and flexibility of the network, ensure the efficient and stable operation of the enterprise campus network, improve the enterprise operation efficiency and competitiveness, and effectively solve the problems of low data transmission accuracy and lag in optimization and adjustment caused by mutual interference of multiple protocols and over-reliance on static rules.

[0089] Please continue to refer to Figure 2 as shown, which is the determination logic diagram for determining that the business application is in the production collaboration period in this embodiment;

[0090] Determine that the business application is in the production collaboration period according to the task scheduling volume, all the remaining time limits, and the preset busy index threshold to form a collaboration determination result, including:

[0091] Calculate the sum of the reciprocals of all the remaining time limits to form the total urgency;

[0092] Calculate the ratio of the total urgency to the task scheduling volume to form the busy index;

[0093] When the busy index is greater than the preset busy index threshold, it is determined that the business application is in the production collaboration period, and a collaboration determination result is formed.

[0094] The preset busy index threshold is a key parameter for judging whether the business application is in the production collaboration period. It depends on the characteristics of the business application, historical data, resource usage in the business collaboration period, and the performance requirements of the system. It is usually set between 80% and 90%. In this embodiment, it is set to 85%, which can effectively identify resource-intensive periods, ensure the performance and resource utilization efficiency of critical services, and thus improve the overall operation efficiency of the system.

[0095] By calculating the reciprocals of all remaining time limits and summing them, the total urgency is obtained. Then, the total urgency is divided by the task scheduling volume to calculate the busy index. Finally, when the busy index is greater than the preset busy index threshold, it is determined that the business application is in the production collaboration period, and a collaboration determination result is formed.

[0096] By calculating the reciprocals of all remaining time limits and summing them to form the total urgency, and then using the ratio of the total urgency to the task scheduling volume as the busy index. When the busy index is greater than the preset threshold, it is determined that the business application is in the production collaboration period. The reciprocal of the remaining time limit reflects the urgency of the task, and the larger the reciprocal, the more urgent the task; the task scheduling volume reflects the total amount of current production tasks. The busy index formed by combining the total urgency and the task scheduling volume can comprehensively consider the urgency of the tasks and the total task volume, so as to more accurately judge whether the business application is in the production collaboration period, effectively identify resource-intensive periods, ensure the performance and resource utilization efficiency of critical services, and improve the overall operation efficiency of the system.

[0097] Specifically, based on the collaboration determination result, a number of temporary applications are determined according to the usage frequency and the transmission volume, including:

[0098] Calculate the standard deviation of the usage frequency of the business application within a preset temporary duration to form a frequency fluctuation value;

[0099] When the frequency fluctuation value is greater than the preset frequency fluctuation value threshold, a number of temporary applications are determined according to the transmission volume.

[0100] The preset temporary duration refers to the time range used to evaluate temporary applications. It depends on the characteristics of the business application, the fluctuation of historical data, and the sensitivity of the system to fluctuations in usage frequency and transmission volume. It is usually set between 1 hour and 4 hours. In this embodiment, it is set to 2 hours, which can effectively balance the computational complexity and the representativeness of the data, ensure that the fluctuations of the business application are captured within a sufficient time range, and will not mask short-term abnormal fluctuations due to too long a time, thus improving the accuracy of identifying temporary applications.

[0101] The preset frequency fluctuation value threshold is a key parameter for judging whether the usage frequency fluctuation of a business application is abnormal. It depends on the historical usage frequency data of the business application, the stability of business requirements, and the system's tolerance for abnormal fluctuations. It is usually set between 1.5 times and 3 times the standard deviation of historical data. In this embodiment, it is set to 2 times the standard deviation of historical data, which can avoid misjudgment due to normal fluctuations and ensure timely detection of abnormal fluctuations, thereby reasonably allocating resources and improving the system's response efficiency.

[0102] The selection range of historical data depends on the specific situation of the business, usually between the past 10 days and 2 months. In this embodiment, it is set to the past 1 month to ensure that the threshold can reflect the usage frequency fluctuations of the business application in different time periods.

[0103] By calculating the standard deviation of the usage frequency of the business application within a preset temporary duration, the frequency fluctuation value is obtained; when the frequency fluctuation value is greater than the preset frequency fluctuation value threshold, the temporary application is determined based on the transmission volume.

[0104] By first calculating the standard deviation of the usage frequency of the business application within a preset temporary duration to obtain the frequency fluctuation value, and when this value exceeds the preset threshold, further determining the temporary application based on the transmission volume, it is possible to accurately identify the applications with high resource requirements during the production collaboration period. The standard deviation of the usage frequency reflects the degree of dispersion of the usage frequency of the business application within the preset temporary duration. The larger the standard deviation, the greater the fluctuation of the usage frequency and the more obvious the temporary requirements of the business. When the frequency fluctuation value exceeds the preset threshold, it indicates that there is a significant abnormal fluctuation in the usage frequency of the business application. Using the usage frequency as a preliminary screening indicator can quickly locate the applications with large changes in activity, and these applications are more likely to be the applications of concern and require priority resource allocation. The step-by-step screening not only reduces the calculation amount and improves the system efficiency, but also avoids screening using the transmission volume first and missing those applications with high usage frequency but small transmission volume. These applications may have high requirements for network resources during certain periods but are ignored because of the small transmission volume, thus realizing dynamic resource optimization and improving the system performance and user experience.

[0105] Please continue to refer to Figure 3 as shown, which is the decision logic diagram for determining the temporary application in this embodiment;

[0106] Determine a number of temporary applications according to the transmission volume, including:

[0107] Calculate the standard deviation of the transmission volume of the business application within the preset temporary duration to form a transmission volume fluctuation value;

[0108] When the transmission volume fluctuation value is greater than the preset transmission volume fluctuation value threshold, determine that the business application is a temporary application to determine a number of temporary applications.

[0109] The preset transmission volume fluctuation value threshold is a key parameter for judging whether the transmission volume fluctuation of a service application is abnormal. It depends on the historical transmission volume data of the service application, the stability of the service requirements, and the system's tolerance for abnormal fluctuations. It is usually set between 1.5 times and 3 times the standard deviation of the historical data. In this embodiment, it is set to 2 times the standard deviation of the historical data, which can effectively identify temporary applications with large transmission volume fluctuations, avoid misjudgment due to normal fluctuations, and at the same time ensure the timely discovery of abnormal fluctuations, so as to reasonably allocate resources and improve the system's response efficiency.

[0110] The selection range of historical data depends on the specific situation of the service, usually between the past 10 days and 2 months. In this embodiment, it is set to the past 1 month to ensure that the threshold can reflect the data transmission volume fluctuations of the service application in different time periods.

[0111] When the number of fluctuations value exceeds the preset number of fluctuations value threshold, calculate the standard deviation of the transmission volume of the service application within the preset temporary duration, so as to form the transmission volume fluctuation value; subsequently, if the transmission volume fluctuation value is greater than the preset transmission volume fluctuation value threshold, then determine the service application as a temporary application, thereby determining a number of temporary applications.

[0112] Precisely identify temporary applications through a hierarchical screening method. First, use the number of fluctuations value as the preliminary screening condition. Only when the number of fluctuations value exceeds the preset threshold will the transmission volume fluctuation value be further calculated, which avoids unnecessary calculations for all service applications and improves the system's operation efficiency. Secondly, calculating the standard deviation of the transmission volume to form the transmission volume fluctuation value can quantify the degree of change in the transmission volume of the service application within the preset temporary duration. The larger the standard deviation, the greater the transmission volume fluctuation and the more obvious the temporary demand of the service. Finally, when the transmission volume fluctuation value exceeds the preset threshold, determine the service application as a temporary application, which can effectively identify service applications with temporary high resource demands during a specific period, so as to reasonably allocate resources for these temporary applications, improve resource utilization efficiency, and ensure the performance of key services and the overall operation efficiency of the system.

[0113] Specifically, determine a number of concerned applications according to the bandwidth utilization rate and the loss rate of each of the temporary applications, including:

[0114] Calculate the standard deviation of the bandwidth utilization rate of the temporary application within the preset determination duration to form a utilization rate determination fluctuation value;

[0115] Calculate the standard deviation of the loss rate of the temporary application within the preset determination duration to form a loss rate determination fluctuation value;

[0116] Determine a number of concerned applications according to the utilization rate determination fluctuation value and the loss rate determination fluctuation value.

[0117] The preset determination duration is a key parameter for analyzing the utilization rate and loss rate fluctuations of temporary applications. It depends on the characteristics of business applications, the fluctuations of historical data, and the sensitivity of the system to performance fluctuations. It is usually set between 1 hour and 4 hours. In this embodiment, it is set to 2 hours, which can not only cover enough data points to reflect the performance fluctuations of business applications, but also not cover up short-term abnormal fluctuations due to too long time, thus improving the accuracy of identifying applications of concern.

[0118] Within the preset determination duration, calculate the standard deviation of the bandwidth utilization rate of the temporary application to obtain the utilization rate fluctuation value; then, calculate the standard deviation of the loss rate of the temporary application within the same duration to form the loss rate fluctuation value; finally, determine a number of applications of concern based on these two fluctuation values.

[0119] By calculating the standard deviation of the bandwidth utilization rate and the loss rate to form the fluctuation value, the performance stability of the temporary application within the preset determination duration can be accurately quantified. The utilization rate determination fluctuation value reflects the change in the resource usage efficiency of the application, while the loss rate determination fluctuation value reflects the change in the reliability of data transmission. When these two fluctuation values are used in combination, the performance of the temporary application can be comprehensively evaluated, and temporary applications with large uncertainties in both resource utilization efficiency and data transmission during a specific period can be effectively identified, and thus determined as applications of concern. This not only ensures the priority guarantee of the performance and resources of the applications of concern, but also improves the utilization efficiency of the overall network resources and enhances the stability and reliability of the system.

[0120] Please continue to refer to Figure 4 as shown, which is the decision logic diagram for determining applications of concern in this embodiment;

[0121] Determine a number of applications of concern according to the utilization rate determination fluctuation value and the loss rate determination fluctuation value, including:

[0122] Draw the change curve of the utilization rate determination fluctuation value within the preset determination duration to form the utilization rate curve;

[0123] Draw the change curve of the loss rate determination fluctuation value within the preset determination duration to form the loss rate curve;

[0124] Calculate the cosine similarity of the utilization rate curve and the loss rate curve to form the change consistency;

[0125] When the change consistency is greater than the preset consistency threshold, determine that the temporary application is an application of concern to determine a number of applications of concern.

[0126] The preset consistency threshold is a key parameter for determining whether the change trends of the utilization rate fluctuation value and the loss rate fluctuation value of the temporary application are consistent. It depends on the historical data fluctuation of the business application, the system's tolerance for fluctuation consistency, and the accuracy requirement for identifying the application of interest. It is usually set between 0.7 and 0.9. In this embodiment, it is set to 0.8, which can effectively identify the temporary applications with highly consistent change trends of the utilization rate and the loss rate, avoid misjudgment caused by inconsistent change trends, and ensure the timely discovery of the application of interest, so as to reasonably allocate resources and improve the response efficiency of the system.

[0127] By plotting the change curve of the fluctuation value determined by the utilization rate and the change curve of the fluctuation value determined by the loss rate, the utilization rate curve and the loss rate curve are obtained; then, the cosine similarity of these two curves is calculated to obtain the change consistency; finally, when the change consistency is greater than the preset consistency threshold, the temporary application is determined as the application of interest, thereby obtaining a number of applications of interest.

[0128] By plotting the utilization rate curve and the loss rate curve and calculating their cosine similarity to form the change consistency, the performance fluctuation consistency of the temporary application can be accurately evaluated. The utilization rate fluctuation value reflects the change in resource utilization efficiency, and the loss rate fluctuation value reflects the change in data transmission quality. The change consistency between the two is quantified by cosine similarity. When the change consistency is greater than the preset threshold, it indicates that the bandwidth utilization rate and the packet loss rate of the temporary application show similar and synchronous fluctuation trends within the preset determined duration. This synchrony means that when the resource utilization efficiency of the application changes, its data transmission quality will also change accordingly. Specifically, if the bandwidth utilization rate of an application increases, it usually means that it is using more network resources to transmit data; if the packet loss rate also increases synchronously at this time, it indicates that while the application's occupancy of network resources increases, the network quality is also affected, resulting in an increase in the number of lost packets during data transmission; this means that it is sensitive to both network resources and network quality, making the application highly sensitive in terms of resource utilization and data transmission quality. Thus, it is determined as a concerned application, which can effectively identify temporary applications with high requirements for resources and data transmission quality during a specific period, and then determine them as concerned applications to ensure that these applications obtain priority resource allocation, improving resource utilization efficiency and system performance; while when the change consistency is less than or equal to the preset consistency threshold, it indicates that the bandwidth utilization rate and the packet loss rate of the temporary application have inconsistent fluctuation trends, meaning that the bandwidth utilization rate may be increasing, but the packet loss rate remains stable or decreases. This shows that although the application's occupancy of network resources increases, the network quality is not significantly affected and data transmission remains stable, indicating that its impact on network resources may be relatively small or unstable. Therefore, it is not determined as a concerned application, avoiding excessive attention to applications with insignificant impact on the network during resource allocation, thereby improving the accuracy and efficiency of resource allocation, ensuring that network resources can be reasonably allocated to truly important applications, and enhancing the overall network performance and user experience.

[0129] Specifically, according to the preset weight combination, each concerned application is sorted to form a list of concerned applications, including:

[0130] Calculate the standard deviation of the bandwidth utilization rate of the concerned application within the preset sorting duration to form the utilization rate sorting fluctuation value;

[0131] Calculate the standard deviation of the loss rate of the concerned application within the preset sorting duration to form the loss rate sorting fluctuation value;

[0132] Normalize all the utilization rate sorting fluctuation values within the preset sorting duration to form the utilization rate normalized fluctuation value, and normalize all the loss rate sorting fluctuation values within the preset sorting duration to form the loss rate normalized fluctuation value;

[0133] Sort each application of interest according to the normalized utilization fluctuation value, the normalized loss rate fluctuation value, the preset utilization weight, and the preset loss rate weight to form a list of applications of interest.

[0134] The preset sorting duration is the time range used to calculate the standard deviation of the utilization rate and the standard deviation of the loss rate of the applications of interest, which depends on the characteristics of the business applications, the fluctuations of historical data, and the sensitivity of the system to performance fluctuations. It is usually set between 1 hour and 12 hours. In this embodiment, it is set to 6 hours, which can effectively balance the computational complexity and the representativeness of the data, ensure that the performance fluctuations of the applications of interest are captured within a sufficient time range, and thus improve the accuracy of identifying the applications of interest.

[0135] Calculate the standard deviation of the utilization rate of the applications of interest to form a utilization rate sorting fluctuation value; then, calculate the standard deviation of the loss rate of the applications of interest within the same duration to form a loss rate sorting fluctuation value. Then, normalize the utilization rate sorting fluctuation value and the loss rate sorting fluctuation value to form a normalized utilization rate fluctuation value and a normalized loss rate fluctuation value. Based on the preset weight combination, that is, the preset utilization weight and the preset loss rate weight, perform a weighted calculation on the normalized utilization rate fluctuation value and the normalized loss rate fluctuation value, sort each application of interest according to the calculation result, and finally form a list of applications of interest.

[0136] By calculating the utilization rate sorting fluctuation value and the loss rate sorting fluctuation value of the applications of interest and performing normalization processing, it is possible to quantitatively evaluate the fluctuations of each application of interest in terms of resource utilization efficiency and data transmission quality. The normalization processing ensures the comparability between different indicators and eliminates the influence of dimensional and order-of-magnitude differences. Combining the preset utilization weight and loss rate weight for weighted sorting can comprehensively consider the importance of resource utilization efficiency and data transmission quality, make the sorting result more in line with the actual business needs, accurately identify the applications of interest with high requirements for resources and data transmission quality, ensure that these applications are given priority in resource allocation, and thus improve the rationality of resource allocation and the stability of system performance.

[0137] Specifically, sorting each application of interest according to the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization weight, and the preset loss rate weight to form a list of applications of interest includes:

[0138] Perform a weighted sum on the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization weight, and the preset loss rate weight to form a sorting index;

[0139] Sort each of the applications of interest from high to low according to the corresponding sorting index to form a list of applications of interest.

[0140] By multiplying the normalized utilization fluctuation value by the preset utilization weight, multiplying the normalized loss rate fluctuation value by the preset loss rate weight, and then adding these two products together, a sorting index is formed. Then, according to the sorting index corresponding to each application of interest, a sorting is performed from high to low, and finally a list of applications of interest is formed.

[0141] By means of weighted summation, two key indicators, namely the normalized utilization fluctuation value and the normalized loss rate fluctuation value, are comprehensively considered, and different weights are assigned to them to reflect the different importance of resource utilization efficiency and data transmission quality in actual business. The sorting index formed by weighted summation can more accurately measure the comprehensive performance of the applications of interest. Sorting the applications of interest from high to low according to the sorting index can ensure that those applications with high resource requirements and poor current performance are preferentially identified and sorted, thus forming a list of applications of interest that better meets the business needs and system performance requirements, not only improving the accuracy of identifying applications of interest, but also enhancing the rationality of resource allocation and the overall performance of the system.

[0142] Specifically, adjusting the preset busy index threshold according to each of the delay times and all of the remaining time limits to form an adjusted busy index threshold, or adjusting the preset weight combination to form an adjusted weight combination, includes:

[0143] Mark the tasks whose remaining time limit is less than the preset remaining time limit threshold, count the number of marked times, and form the number of urgent tasks;

[0144] When the number of urgent tasks is greater than the preset task number threshold, calculate the standard deviation of the delay time within the preset adjustment duration to form a delay fluctuation value;

[0145] When the delay fluctuation value is greater than the preset delay fluctuation threshold, calculate the relative deviation between the number of urgent tasks and the preset task number threshold, and reduce the preset busy index threshold according to the relative deviation and the preset index adjustment coefficient to form an adjusted busy index threshold;

[0146] When the delay fluctuation value is less than or equal to the preset delay fluctuation threshold, adjust the preset weight combination according to the delay fluctuation value to form an adjusted weight combination.

[0147] The preset remaining time limit threshold refers to a time threshold used to judge whether a task is urgent in task scheduling, which depends on the urgency of the task, the statistical results of historical data, and the performance requirements of the system, and is usually set between 1 hour and 12 hours. In this embodiment, it is set to 6 hours, which can effectively identify urgent tasks and ensure that these tasks can be preferentially processed, thereby improving the response efficiency of the system.

[0148] The preset delay fluctuation threshold refers to the maximum allowable value of the standard deviation of the delay time within a certain period, which depends on the network stability and the sensitivity of the service to delay. It is usually set between 2 and 3 times the standard deviation of historical data. In this embodiment, it is set to 2.5 times the standard deviation of historical data, which can effectively identify the situations with large delay fluctuations, so as to adjust the resource allocation in a timely manner and improve the network performance.

[0149] The preset task quantity threshold refers to the maximum allowable number of urgent tasks within a certain period, which depends on the system processing capacity and the urgency of the service. It is usually set between 10 and 50. In this embodiment, it is set to 30, which can effectively identify the situations where the system is in a high-load state, so as to adjust the resource allocation in a timely manner and ensure the stable operation of the system.

[0150] The preset exponential adjustment coefficient refers to the coefficient used to calculate the adjustment amount when adjusting the preset busy index threshold, which depends on the system adjustment strategy and service requirements. It is usually set between 0.5 and 0.8. In this embodiment, it is set to 0.7, which can effectively adjust the preset busy index threshold to ensure that the system can adjust the resource allocation in a timely manner under high-load conditions and improve the adaptability and stability of the system.

[0151] By marking the tasks with the remaining time limit less than the preset remaining time limit threshold and counting the number of marked times to form the number of urgent tasks. If the number of urgent tasks is greater than the preset task quantity threshold, further calculate the standard deviation of the delay time within the preset adjustment duration to form the delay fluctuation value. When the delay fluctuation value is greater than the preset delay fluctuation threshold, calculate the relative deviation between the number of urgent tasks and the preset task quantity threshold, and reduce the preset busy index threshold according to this relative deviation and the preset exponential adjustment coefficient, so as to form the adjusted busy index threshold. When the delay fluctuation value is less than or equal to the preset delay fluctuation threshold, adjust the preset weight combination according to the delay fluctuation value to form the adjusted weight combination.

[0152] By marking the tasks with the remaining time limit less than the preset threshold and counting the number of urgent tasks, it can intuitively reflect the number of urgent tasks in the current network, and then judge whether the network is in a high-load state. When the number of urgent tasks exceeds the threshold, further calculate the standard deviation of the delay time to obtain the delay fluctuation value to evaluate the stability of the network delay. If the delay fluctuation value is large, it means that the network delay is unstable. At this time, reducing the preset busy index threshold can more strictly determine the production collaboration period to ensure that key tasks obtain sufficient resources. When the delay fluctuation value is within the normal range, adjust the weight combination to optimize the resource allocation and avoid resource waste. Make the network resource allocation more flexible and accurate, be able to effectively respond to the changes in the network state, improve the performance of key applications and the user experience, and at the same time ensure the stable operation of the overall network.

[0153] Specifically, adjusting the preset weight combination according to the delay fluctuation value to form an adjusted weight combination includes:

[0154] Calculating the relative deviation between the delay fluctuation value and the preset delay fluctuation threshold to form a delay deviation;

[0155] Adjusting the preset utilization rate weight according to the delay deviation and the preset utilization rate weight adjustment coefficient to form an adjusted utilization rate weight, calculating the difference between the total weight and the adjusted utilization rate weight to form an adjusted loss rate weight, so as to form an adjusted weight combination, and the delay deviation and the adjusted utilization rate weight are positively correlated.

[0156] The preset utilization rate weight adjustment coefficient is a coefficient that determines the amplitude of the utilization rate weight adjustment, depending on the adjustment strategy of the system, business requirements, and the fluctuation of historical data, and is usually set between 0.01 and 0.1. In this embodiment, it is 0.05, which can effectively balance the sensitivity and stability of the weight adjustment. A smaller adjustment coefficient can avoid excessive weight adjustment leading to system instability, and at the same time ensure that the system can flexibly adjust the weight according to the delay deviation, improving the rationality of resource allocation and the overall performance of the system.

[0157] The total weight refers to the sum of the adjusted utilization rate weight and the adjusted loss rate weight, and the value is 1.

[0158] When the delay fluctuation value is less than or equal to the preset delay fluctuation threshold, but the number of urgent tasks is greater than the preset task number threshold, calculate the relative deviation between the delay fluctuation value and the preset delay fluctuation threshold to obtain a delay deviation. According to the delay deviation and the preset weight adjustment coefficient, adjust the preset utilization rate weight, and calculate the difference between the total weight and the adjusted utilization rate weight to obtain the adjusted loss rate weight, and finally form an adjusted weight combination.

[0159] By calculating the relative deviation between the delay fluctuation value and the preset delay fluctuation threshold to obtain a delay deviation, the fluctuation of the network delay can be accurately quantified. The delay deviation reflects the degree of deviation of the current network delay from the ideal state. A larger delay deviation means a larger fluctuation of the network delay, which may affect the execution of key tasks. Based on the delay deviation and the preset weight adjustment coefficient, adjusting the utilization rate weight and the loss rate weight can dynamically optimize the resource allocation strategy. When the delay deviation is large, it means that the fluctuation of the network delay has a significant impact on the resource utilization rate. At this time, it is necessary to increase the utilization rate weight to pay more attention to the efficient use of resources, reduce the negative impact of the delay fluctuation on the network performance, and can give priority to ensuring the resource requirements of key tasks, reducing the delay of urgent tasks, thereby improving the overall performance and stability of the system, not only improving the flexibility and adaptability of resource allocation, but also enhancing the robustness of the system in the face of high delay fluctuations, ensuring that the performance of key services is prioritized.

[0160] So far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. An intelligent network resource optimization method based on multi - protocol digitization, characterized in that, Including: Unify the protocols used by various business applications in the enterprise campus network into IP-based protocols through a protocol conversion gateway to form a business protocol dataset; Real-time collect the task scheduling volume of the production system, the remaining time limit of each task, the usage frequency of each business application, the data transmission volume, the bandwidth utilization rate, and the packet loss rate in the business protocol dataset; Determine that the business application is in the production collaboration period based on the task scheduling volume, all the remaining time limits, and a preset busy index threshold to form a collaboration determination result; Based on the collaboration determination result, determine several temporary applications according to the usage frequency and the transmission volume; Determine several applications of concern according to the bandwidth utilization rate and the packet loss rate of each temporary application, and sort each application of concern according to a preset weight combination to form a list of applications of concern; Perform network resource allocation based on the list of applications of concern to form an allocation plan, and record the instruction delay time that occurs during the allocation process for each application of concern to form several delay times; Adjust the preset busy index threshold according to each delay time and all the remaining time limits to form an adjusted busy index threshold, or adjust the preset weight combination to form an adjusted weight combination; Output the allocation plan re-determined based on the adjusted weight combination or the adjusted busy index threshold; The preset weight combination includes a preset utilization rate weight and a preset packet loss rate weight.

2. The intelligent network resource optimization method based on multi - protocol digitization according to claim 1, wherein Determine that the business application is in the production collaboration period based on the task scheduling volume, all the remaining time limits, and a preset busy index threshold to form a collaboration determination result, including: Calculate the sum of the reciprocals of all the remaining time limits to form the total urgency; Calculate the ratio of the total urgency to the task scheduling volume to form a busy index; When the busy index is greater than the preset busy index threshold, determine that the business application is in the production collaboration period to form a collaboration determination result.

3. The intelligent network resource optimization method based on multi-protocol digitization according to claim 2, characterized in that Based on the collaboration determination result, determine several temporary applications according to the usage frequency and the transmission volume, including: Calculate the standard deviation of the usage frequency of the business application within a preset temporary duration to form a frequency fluctuation value; When the frequency fluctuation value is greater than a preset frequency fluctuation value threshold, determine several temporary applications according to the transmission volume.

4. The intelligent network resource optimization method based on multi - protocol digitization according to claim 3, characterized in that Determine several temporary applications according to the transmission volume, including: Calculate the standard deviation of the transmission volume of the business application within the preset temporary duration to form a transmission volume fluctuation value; When the transmission volume fluctuation value is greater than a preset transmission volume fluctuation value threshold, determine that the business application is a temporary application to determine several temporary applications.

5. The intelligent network resource optimization method based on multi-protocol digitization according to claim 4, characterized in that Determine several applications of concern according to the bandwidth utilization rate and the packet loss rate of each temporary application, including: Calculate the standard deviation of the bandwidth utilization rate of the temporary application within a preset determination duration to form a utilization rate determination fluctuation value; Calculate the standard deviation of the packet loss rate of the temporary application within the preset determination duration to form a packet loss rate determination fluctuation value; Determine several applications of concern according to the utilization rate determination fluctuation value and the packet loss rate determination fluctuation value.

6. The intelligent network resource optimization method based on multi - protocol digitization according to claim 5, characterized in that, Determine several applications of concern according to the utilization rate determination fluctuation value and the packet loss rate determination fluctuation value, including: Plot the change curve of the determined utilization rate fluctuation value within the preset determined time period to form a utilization rate curve; Plot the change curve of the determined loss rate fluctuation value within the preset determined time period to form a loss rate curve; Calculate the cosine similarity between the utilization rate curve and the loss rate curve to form a change consistency; When the change consistency is greater than the preset consistency threshold, determine that the temporary application is a concerned application to determine a number of concerned applications.

7. The intelligent network resource optimization method based on multi - protocol digitization according to claim 6, characterized in that, Sort the concerned applications according to the preset weight combination to form a list of concerned applications, including: Calculate the standard deviation of the bandwidth utilization rate of the concerned applications within the preset sorting time period to form a utilization rate sorting fluctuation value; Calculate the standard deviation of the loss rate of the concerned applications within the preset sorting time period to form a loss rate sorting fluctuation value; Normalize all the utilization rate sorting fluctuation values within the preset sorting time period to form a normalized utilization rate fluctuation value, and normalize all the loss rate sorting fluctuation values within the preset sorting time period to form a normalized loss rate fluctuation value; Sort the concerned applications according to the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization rate weight, and the preset loss rate weight to form a list of concerned applications.

8. The intelligent network resource optimization method based on multi-protocol digitization according to claim 7, characterized in that Sort the concerned applications according to the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization rate weight, and the preset loss rate weight to form a list of concerned applications, including: Perform a weighted sum on the normalized utilization rate fluctuation value, the normalized loss rate fluctuation value, the preset utilization rate weight, and the preset loss rate weight to form a sorting index; Sort each of the concerned applications from high to low according to the corresponding sorting index to form a list of concerned applications.

9. The intelligent network resource optimization method based on multi-protocol digitization according to claim 8, characterized in that Adjust the preset busy index threshold according to each of the delay times and all the remaining time limits to form an adjusted busy index threshold, or adjust the preset weight combination to form an adjusted weight combination, including: Mark the tasks with the remaining time limit less than the preset remaining time limit threshold, count the number of marked times to form the number of urgent tasks; When the number of urgent tasks is greater than the preset task number threshold, calculate the standard deviation of the delay time within the preset adjustment time period to form a delay fluctuation value; When the delay fluctuation value is greater than the preset delay fluctuation threshold, calculate the relative deviation between the number of urgent tasks and the preset task number threshold, and reduce the preset busy index threshold according to the relative deviation and the preset index adjustment coefficient to form an adjusted busy index threshold; When the delay fluctuation value is less than or equal to the preset delay fluctuation threshold, adjust the preset weight combination according to the delay fluctuation value to form an adjusted weight combination.

10. The intelligent network resource optimization method based on multi-protocol digitization according to claim 9, wherein Adjust the preset weight combination according to the delay fluctuation value to form an adjusted weight combination, including: Calculate the relative deviation between the delay fluctuation value and the preset delay fluctuation threshold to form a delay deviation; Adjust the preset utilization rate weight according to the delay deviation and the preset utilization rate weight adjustment coefficient to form an adjusted utilization rate weight, calculate the difference between the total weight and the adjusted utilization rate weight to form an adjusted loss rate weight, so as to form an adjusted weight combination.

Images