A method for quality of service assurance in a communication

Abstract

The application belongs to the technical field of communication, and provides a communication service quality guarantee method. By converting service description information into intention data and combining environment data to determine dynamic weight, the service quality guarantee is adapted to business appeal. By constructing a virtual pre-play environment, parallel performance evaluation and optimization of candidate guarantee strategies are realized, avoiding network resource occupation and business interference problems, and screening the optimal quality guarantee strategy. The resource requirements of the quality guarantee strategy and the network resource capacity are mapped into a resource optimization problem, and the dynamic weight is used as a priority constraint to solve the resource allocation scheme, improving the utilization efficiency of network resources and the adaptability of business transmission. By comparing the measured data, the intention realization degree is determined, the deviation root cause is identified when the threshold is not reached, and the database is retained, and the dynamic weight, strategy generation and resource allocation scheme solving are updated, improving the stability and adaptive ability of the service quality guarantee, adapting to complex and variable communication network environment and diversified business transmission demand.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method for ensuring the quality of communication services. Background Technology

[0002] Current communication network technologies are continuously developing towards higher speeds, lower latency, and more connections. At the same time, the requirements for communication service quality (QoS) of various services are constantly increasing, and QoS assurance technologies are also continuously iterating and evolving. Most existing QoS assurance solutions rely on service type classification, network status monitoring, and static resource scheduling rules to achieve basic control. They can maintain basic service transmission stability in conventional communication scenarios and have been widely used in various communication service scenarios such as mobile communication networks, enterprise private networks, and home broadband.

[0003] However, existing technologies have many inherent defects in actual deployment and operation. Existing technologies mostly execute assurance strategies directly based on business identifiers or fixed performance indicators, which cannot accurately match the core needs and differentiated constraints of the business, resulting in a disconnect between quality assurance strategies and actual business requirements. Moreover, strategy performance verification and optimization are mostly carried out directly in the actual communication network, which consumes existing network operating resources and is prone to interfering with existing network services. Resource scheduling based solely on fixed priorities or static constraints is insufficient in adapting resource allocation to actual business needs and real-time network conditions. To address these shortcomings of existing technologies, this application aims to solve the technical problem of improving the accuracy, efficiency, and continuous iteration capability of communication service quality assurance. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings of the prior art and provide a method for ensuring the quality of communication services. The method includes: acquiring service description information and environmental data of a user terminal, converting the service description information into intent data, and determining the dynamic weight of the intent data based on the environmental data. A virtual pre-simulation environment is constructed based on real-time network status, intent data, and dynamic weights. Candidate assurance strategies are generated based on intent data. The performance of the candidate assurance strategies is evaluated in parallel based on dynamic weights in the virtual pre-simulation environment, and a quality assurance strategy is selected. The resource requirements of the quality assurance strategy are mapped to the network resource capabilities as a resource optimization problem. The resource allocation scheme is solved with dynamic weights as priority constraints, and a service assurance channel is established for the corresponding business data flow. Acquire measured data of business data flow in the service assurance channel, compare the measured data with the intent data to determine the degree of intent realization of the quality assurance strategy, and when the degree of intent realization is less than the preset threshold, identify the root cause of the deviation and store it in the case library to synchronously update the dynamic weight, strategy generation and resource allocation scheme solution.

[0005] Optionally, the transformation of the intent data includes: synchronizing and marking the service description information and environmental data of the user terminal with timestamps, and semantically decomposing the service description information to extract the demand elements, adaptation elements and constraint elements of service transmission and quantify and encapsulate them into intent data; Determining the dynamic weights includes: dividing the environmental data into dimensions such as terminal mobility status, network channel interference, network load, and regional user density; pre-setting a perturbation threshold range for each dimension; and determining the perturbation coefficient based on the matching relationship between the dimension data and the perturbation threshold range. Assign basic weight values ​​to demand elements, adaptation elements, and constraint elements, and calibrate the basic weight values ​​according to the disturbance coefficient to determine the dynamic weight of the intent data.

[0006] Optionally, the construction of the virtual pre-rehearsal environment includes: Based on the link bandwidth and transmission delay in real-time network conditions, a simulation space is constructed by combining environmental data and divided into a transmission simulation domain, a resource simulation domain, and a channel simulation domain. The demand elements are mapped to the link bandwidth and latency thresholds in the transmission simulation domain, the adaptation elements are mapped to the resource fluctuation range in the resource simulation domain, and the constraint elements are mapped to the interference shielding thresholds in the channel simulation domain. The simulation domain's computing resource quota is configured according to dynamic weights, and the simulation domain's parameter update cycle is configured according to the disturbance coefficient. The configured simulation domains are then integrated to form a virtual pre-simulation environment for the corresponding business data flow.

[0007] Optionally, the candidate guarantee generation strategy includes: Based on the demand elements, adaptation elements, and constraint elements in the intent data, corresponding transmission guarantee strategies, resource scheduling strategies, and channel adaptation strategies are generated. The three types of strategies are combined and arranged according to dynamic weights to obtain multiple strategy combinations. Strategy combinations that do not match the simulation domain parameters are eliminated based on the perturbation coefficient to form candidate protection strategies.

[0008] Optionally, the selected quality assurance strategy includes: The candidate guarantee strategies were simulated in parallel using a virtual pre-simulation environment. The bandwidth delay compliance rate in the transmission simulation domain, the resource occupancy rate in the resource simulation domain, and the interference suppression rate in the channel simulation domain were recorded. The bandwidth latency compliance rate, resource utilization rate, and interference suppression rate are weighted according to dynamic weights to obtain the comprehensive evaluation value of each group of candidate guarantee strategies; Candidate assurance strategies with interference suppression rates less than the interference shielding threshold are eliminated. The remaining assurance strategies are sorted in descending order of their comprehensive evaluation values, and the candidate assurance strategy with the highest comprehensive evaluation value is selected as the quality assurance strategy.

[0009] Optionally, the mapping to a resource optimization problem includes: Extract the resource requirements of the quality assurance strategy, where the transmission assurance strategy corresponds to the link bandwidth requirement, the resource scheduling strategy corresponds to the resource occupancy requirement, and the channel adaptation strategy corresponds to the anti-interference resource requirement. Retrieve the resource fluctuation range of the resource simulation domain, and combine it with the dynamic weights corresponding to the adaptation elements to determine the demand threshold and priority ranking for each resource demand dimension. Extract the available resources of network resource capabilities, and combine the link bandwidth in the real-time network status with the network load and regional user density in the environmental data to determine the upper limit of resource supply for each resource demand dimension. By taking the demand threshold and priority ranking of resource demand as constraints, the upper limit of resource supply as boundary conditions, and minimizing the deviation between the actual allocation of resources and the demand threshold as the objective function, a resource optimization problem is formed.

[0010] Optionally, the solution resource allocation scheme includes: The solution order for each resource demand dimension is determined according to the dynamic weights corresponding to the demand elements, adaptation elements, and constraint elements. The upper limit of resource supply for each resource demand dimension is adjusted based on the disturbance coefficient to obtain a resource supply boundary that adapts to the current environmental disturbance. With the goal of minimizing the deviation, the resource optimization problem is iteratively calculated dimension by dimension according to the solution order to obtain the initial resource allocation value for each resource demand dimension; The initial resource allocation value is compared with the resource fluctuation range of the resource simulation domain. Initial resource allocation values ​​that exceed the resource fluctuation range are eliminated, and the remaining resource allocation values ​​are integrated to form a resource allocation scheme for the corresponding business data flow. At the same time, a service guarantee channel is established for the corresponding business data flow.

[0011] Optionally, the root causes of the identification bias include: Obtain measured data of business data flow in the service assurance channel, compare the measured data with the intent data, and obtain the measured deviation values ​​of different elements; The measured deviation values ​​of different elements are weighted and summed according to dynamic weights to obtain the total deviation value. The total deviation value is then normalized with the deviation threshold to obtain the degree of intention achievement. When the degree of achievement of the intention is less than the preset threshold, the source of deviation of different elements is determined by combining the current disturbance coefficient with the dimensional data in the environmental data, so as to identify the root cause of the deviation. The root causes of deviations, measured deviation values ​​of different elements, current disturbance coefficients, dimensional data in environmental data, and degree of intent fulfillment are encapsulated and stored in the case library.

[0012] Optionally, the updated dynamic weights include: The weighted calibration range is determined by the ratio of the measured deviation value of the corresponding element to the total deviation value. The weight calibration factor is calculated based on the difference between the degree of intention realization and the preset threshold. The weight calibration magnitude is then multiplied by the weight calibration factor to obtain the weight calibration amount of the corresponding element. The basic weight values ​​of the corresponding elements are corrected based on the weight calibration amount, and after recalibration in combination with the current disturbance coefficient, the updated dynamic weights are obtained.

[0013] Optionally, updating the policy generation includes: Based on the elements corresponding to the root causes of the deviation, locate the transmission guarantee strategy, resource scheduling strategy or channel adaptation strategy that needs to be updated. Based on the magnitude of the measured deviation value of the corresponding element, determine the parameter correction direction of the corresponding strategy, and determine the parameter correction magnitude in combination with the disturbance coefficient. Based on the updated dynamic weights, the corrected strategies are rearranged to obtain multiple strategy combinations. Strategy combinations that do not match the simulation domain parameters are then eliminated to form updated candidate protection strategies.

[0014] Compared with existing technologies, the beneficial effects of this application are as follows: by converting business description information into intent data and combining it with environmental data to determine dynamic weights, service quality assurance is made to fit the actual needs of the business; by constructing a virtual pre-simulation environment, parallel performance evaluation and optimization of candidate assurance strategies are achieved, avoiding the problems of existing network resource occupation and business interference, improving the efficiency of strategy evaluation and optimization, so as to screen the optimal quality assurance strategy.

[0015] The resource requirements of the quality assurance strategy are mapped to network resource capabilities as a resource optimization problem. The resource allocation scheme is solved with dynamic weights as priority constraints to improve the utilization efficiency of network resources and the adaptability of service transmission. The degree of intention realization is determined by comparing measured data. When the threshold is not reached, the root cause of the deviation is identified and stored in the database. The dynamic weights, strategy generation and resource allocation scheme solution are updated synchronously to build a closed-loop optimization system for communication service quality assurance. Based on deviation identification, the optimization scheme is continuously iterated to effectively improve the stability and adaptability of communication service quality assurance and adapt to complex communication network environments and diverse service transmission needs. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a method for ensuring the quality of communication services provided in this application embodiment; Figure 2 A logical flowchart for constructing a virtual pre-simulation environment provided in an embodiment of this application; Figure 3 This is a logical flowchart illustrating the resource allocation solution provided in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more pairs. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0018] refer to Figure 1 This application provides a method for ensuring the quality of communication services, which includes: S1. Obtain the service description information and environmental data of the user terminal, convert the service description information into intent data, and determine the dynamic weight of the intent data based on the environmental data.

[0019] Furthermore, the conversion intent data includes: synchronizing and marking the service description information and environmental data of the user terminal with timestamps, and semantically decomposing the service description information to extract the demand elements, adaptation elements and constraint elements of service transmission and quantify and encapsulate them into intent data; Specifically, determining the dynamic weights includes: dividing the environmental data into dimensions such as terminal mobility status, network channel interference, network load, and regional user density; pre-setting a perturbation threshold range for each dimension; and determining the perturbation coefficient based on the matching relationship between the dimension data and the perturbation threshold range. Assign basic weight values ​​to demand elements, adaptation elements, and constraint elements, and calibrate the basic weight values ​​according to the disturbance coefficient to determine the dynamic weight of the intent data.

[0020] The service description information and environmental data of the user terminal belong to different data sampling entities and transmission links, and the original acquisition timing has asynchronous deviation. If intent parsing is directly performed based on the time-misaligned data, the matching relationship between service requirements and network environment will be distorted, and intent data that fits the actual transmission scenario cannot be formed. The unified clock on the core network side is used as the reference clock source. This reference clock source follows the communication network clock synchronization protocol and assigns a timestamp identifier under the reference clock to each piece of service description information reported by the terminal side and each set of environmental data acquired by the network side. A preset time offset judgment threshold is set to verify the difference between the timestamps of the service description information and the corresponding environmental data, and invalid data with a difference exceeding the judgment threshold is eliminated to ensure that the service data and environmental data participating in intent parsing form a matching relationship in the same time dimension.

[0021] This eliminates the problem of data timing misalignment, ensures the timeliness and matching accuracy of the intent parsing data source, avoids distortion of business requirement parsing due to timing deviation, and provides a data foundation for subsequent intent data extraction.

[0022] Business description information is usually presented in unstructured forms such as natural language and business protocol instructions. Its content contains redundant information and core business requirements, which cannot be applied to subsequent stages. Based on a preset business semantic parsing rule base, semantic decomposition operation is performed. This rule base has built-in communication business feature word segmentation logic and business element classification and matching logic. By performing word segmentation, feature matching and classification labeling on business description information, the unstructured business description information is decomposed into three types of elements: requirement elements, adaptation elements and constraint elements.

[0023] For high-definition video calls, the service description information requires smooth, uninterrupted transmission, compatibility with medium-sized network resources, and avoidance of strong channel interference scenarios. Among these, the requirement elements represent the core performance guarantee indicators of the service transmission, such as the smooth transmission performance requirements of high-definition video calls; the compatibility elements represent the service's requirements for the range of network resources it can adapt to, such as the service's requirements for adapting to medium-sized network resources; and the constraint elements represent the environmental limitations that the service transmission must meet, such as the limitation requirements for avoiding strong network channel interference. After accurately extracting the core elements, invalid and redundant content in the semantics is removed.

[0024] This transforms unstructured business description information into structured core business elements, clarifies the core objectives of business quality assurance, resource adaptation boundaries, and transmission limitations, solves the technical problem of the inability to quantify and apply unstructured business requirements, and provides a core content carrier for the quantification and encapsulation of intent data.

[0025] The core elements extracted through semantic decomposition are in textual form, which cannot adapt to the parameter calling requirements of virtual pre-rehearsal environment and resource allocation. Among them, the requirement element is mapped to the quantitative indicators of transmission performance such as transmission bandwidth and transmission latency, the adaptation element is mapped to the quantitative indicators of resource adaptation such as resource occupancy rate and resource fluctuation range, and the constraint element is mapped to the quantitative indicators of transmission constraints such as channel interference intensity and signal threshold. The three types of quantitative indicators are integrated and encapsulated into a single data unit according to the preset data structure to complete the transformation of intent data.

[0026] The core elements extracted above are quantified and mapped. The demand elements are mapped to performance indicators such as transmission bandwidth of not less than 4Mbps and transmission latency of not more than 50ms. The adaptation elements are mapped to resource indicators such as network resource utilization rate of 30% to 60%. The constraint elements are mapped to constraint indicators such as channel interference intensity of not more than -85dBm. After being encapsulated according to a fixed data structure, the intent data of the service is formed.

[0027] This transforms abstract business requirements into technical parameters that the system can recognize, improving the efficiency and accuracy of subsequent processes in calling business requests and providing a data foundation for dynamic weight calibration and simulation parameter mapping.

[0028] Environmental data contains scattered parameters, and the impact of different parameters on the stability of service transmission varies significantly. Without a unified standard for categorizing dimensions and a basis for judging the degree of disturbance, it is impossible to accurately quantify the degree of environmental impact on service transmission. This paper divides environmental data into terminal mobility status, network channel interference, network load, and regional user density according to the dimensions of impact on service transmission. Based on the communication network operating parameters and service transmission adaptation characteristics, three levels of disturbance threshold ranges—low disturbance, medium disturbance, and high disturbance—are preset for each dimension. These threshold ranges are independent of each other and cover the numerical range of the corresponding dimension, forming an environmental disturbance judgment system.

[0029] Taking high-definition video call services as an example, preset disturbance threshold ranges are set for four dimensions. In the terminal mobility dimension, a moving speed of no more than 5km / h is the low disturbance range, 5km / h to 20km / h is the medium disturbance range, and above 20km / h is the high disturbance range. In the network channel interference dimension, an interference intensity of no more than -90dBm is the low disturbance range, -90dBm to -80dBm is the medium disturbance range, and above -80dBm is the high disturbance range. In the network load dimension, a resource utilization rate of no more than 50% is the low disturbance range, 50% to 80% is the medium disturbance range, and above 80% is the high disturbance range. In the regional user density dimension, a single cell with no more than 20 users is the low disturbance range, 20 to 50 users is the medium disturbance range, and above 50 users is the high disturbance range.

[0030] This will enable the standardization and organization of environmental data, establish quantitative standards for judging environmental disturbances, improve the accuracy and consistency of environmental impact assessments, and provide an objective basis for the calculation of disturbance coefficients.

[0031] Different levels of environmental disturbance have varying degrees of impact on service transmission. The acquired dimensional data are substituted into the corresponding preset disturbance threshold ranges to match the disturbance level of each dimensional data. Based on the correspondence between disturbance level and disturbance coefficient, fixed disturbance coefficients are assigned to different disturbance levels. Low disturbance level corresponds to a smaller disturbance coefficient, medium disturbance level corresponds to a medium-sized disturbance coefficient, and high disturbance level corresponds to a larger disturbance coefficient. This coefficient is only used as an adjustment parameter for weight calibration and has no actual physical meaning.

[0032] For example, the real-time acquired environmental data includes a terminal moving speed of 12 km / h, network channel interference intensity of -85 dBm, network load rate of 68%, and regional user density of 38 people / cell. All four dimensions of data match the medium disturbance range of the corresponding dimension, and the disturbance coefficient assigned to this environmental state is 1.2. If the environmental data matches the low disturbance range, the disturbance coefficient is 0.8, and if it matches the high disturbance range, the disturbance coefficient is 1.5.

[0033] This transforms abstract environmental disturbance levels into quantitative adjustment parameters, enabling the quantification of the impact of the environment on service transmission, avoiding subjective assessments of environmental impact, and providing an objective basis for the dynamic calibration of basic weight values.

[0034] The inherent importance of demand elements, adaptation elements, and constraint elements to business quality assurance varies. Without an initial weight benchmark, dynamic weight calibration will have no reference, which may lead to a biased and distorted priority in subsequent assurance strategies and resource allocation. Based on the core assurance requirements of the business type, initial basic weight values ​​are assigned to demand elements, adaptation elements, and constraint elements, and the sum of the basic weight values ​​of the three types of elements is a fixed value. This basic weight value represents the default importance of each type of element and does not change with the real-time environment.

[0035] For example, high-definition video call services prioritize smooth transmission, so a base weight value of 0.5 is assigned to the demand element, a base weight value of 0.3 is assigned to the adaptation element, and a base weight value of 0.2 is assigned to the constraint element. The sum of the base weight values ​​of the three types of elements is 1, thus clarifying the default priority order of the business elements.

[0036] This establishes an initial importance ranking of core business elements, provides a fixed benchmark for dynamic weight calibration, ensures the stability and rationality of weight calculation, and avoids subsequent execution deviations caused by unfounded initial weights.

[0037] Basic weight values ​​can only represent the inherent importance of business elements and cannot adapt to real-time environmental disturbances. If fixed weights are used for subsequent processing, the business assurance plan will not be well adapted to the actual network environment. The basic weight values ​​of various elements are multiplied by the disturbance coefficient to complete the initial calibration. The basic weight values ​​after the initial calibration are normalized to ensure that the sum of the element weight values ​​after calibration remains fixed, eliminating the problem of numerical overflow caused by multiplying the disturbance coefficients, and forming dynamic weights that change with real-time environmental disturbances.

[0038] For example, the basic weight values ​​of the elements are 0.5 for demand elements, 0.3 for adaptation elements, and 0.2 for constraint elements. The disturbance coefficient corresponding to the real-time environment is 1.2. After preliminary calibration, the weights are 0.6 for demand elements, 0.36 for adaptation elements, and 0.24 for constraint elements. After normalization, the dynamic weights are 0.48 for demand elements, 0.29 for adaptation elements, and 0.23 for constraint elements.

[0039] This enables adaptive adjustment of business element weights, dynamically optimizing business assurance priorities based on real-time network environment disturbances, improving the environmental adaptability of subsequent virtual pre-simulation environment configuration, assurance strategy generation, and resource allocation scheme solving, and enhancing the dynamic response capability of communication service quality assurance.

[0040] S2. Construct a virtual pre-simulation environment based on real-time network status, intent data, and dynamic weights. Generate candidate assurance strategies based on intent data. Evaluate the performance of candidate assurance strategies in parallel based on dynamic weights in the virtual pre-simulation environment and select the quality assurance strategy.

[0041] like Figure 2 As shown, constructing a virtual rehearsal environment includes: Based on the link bandwidth and transmission delay in real-time network conditions, a simulation space is constructed by combining environmental data and divided into a transmission simulation domain, a resource simulation domain, and a channel simulation domain. The demand elements are mapped to the link bandwidth and latency thresholds in the transmission simulation domain, the adaptation elements are mapped to the resource fluctuation range in the resource simulation domain, and the constraint elements are mapped to the interference shielding thresholds in the channel simulation domain. The simulation domain's computing resource quota is configured according to dynamic weights, and the simulation domain's parameter update cycle is configured according to the disturbance coefficient. The configured simulation domains are then integrated to form a virtual pre-simulation environment for the corresponding business data flow.

[0042] Verification and optimization of communication service quality assurance strategies, if directly implemented in actual communication networks, would consume existing network resources, interfere with on-network service transmission, and prevent parallel comparative testing of multiple strategies. Furthermore, the transmission quality of service data streams is independently and collaboratively affected by three core factors: transmission performance, resource scheduling, and channel interference. This study extracts real-time network bandwidth and transmission latency as the underlying network parameters for the simulation space, and retrieves environmental data as environmental constraints. Based on the digital simulation architecture of the communication network, a low-level simulation framework is built, divided into three simulation domains: the transmission simulation domain simulates changes in the transmission performance indicators of service data streams, the resource simulation domain simulates the allocation and occupancy of network resources, and the channel simulation domain simulates interference fluctuations and signal transmission status of wireless channels. A unified data interaction interface and timing synchronization mechanism are set up in each simulation domain to ensure the consistency and real-time nature of parameter transmission between domains.

[0043] For example, the real-time network status has a link bandwidth of 100Mbps, a transmission latency of 20ms, and environmental data corresponding to a terminal moving speed of 12km / h, a network channel interference intensity of -85dBm, a network load rate of 68%, and a regional user density of 38 people / cell. Based on the above parameters, a simulation space is built, which is divided into a transmission simulation domain, a resource simulation domain, and a channel simulation domain. Each simulation domain independently loads the simulation driver module of the corresponding dimension.

[0044] This allows for the construction of a non-real-network simulation platform that closely matches the actual network and environment, avoiding the risks of resource consumption and service interference in real-network testing. The domain-based settings enable modular management of simulation parameters, improving the accuracy of multi-dimensional indicator simulation and preventing distortion of simulation results caused by mutual interference between different types of simulation parameters.

[0045] Intent data represents business-level demand indicators that cannot be identified and executed by the simulation domain. These demand elements, characterizing core business transmission performance, are mapped to link bandwidth and transmission delay thresholds in the transmission simulation domain, serving as the criteria for performance compliance during simulation. Adaptability elements, representing the scope of business resource adaptation, are mapped to resource fluctuation ranges in the resource simulation domain, constraining the reasonable range of resource allocation and occupancy during simulation. Constraint elements, representing business transmission interference limitations, are mapped to interference shielding thresholds in the channel simulation domain, serving as the tolerable upper limit for channel interference during simulation. All thresholds directly utilize the quantitative indicators of the intent data, ensuring the uniqueness and accuracy of the mapping.

[0046] Using the intent quantification indicators of high-definition video call services, the transmission bandwidth corresponding to the demand element is no less than 4Mbps and the transmission latency is no more than 50ms. These indicators are directly mapped to the link bandwidth threshold and transmission latency threshold in the transmission simulation domain. The network resource occupancy rate corresponding to the adaptation element is 30%-60%, which is directly mapped to the resource fluctuation range in the resource simulation domain. The channel interference intensity corresponding to the constraint element is no higher than -85dBm, which is mapped to the interference shielding threshold in the channel simulation domain. This completes the full mapping of the three types of elements to the corresponding simulation domain thresholds.

[0047] This transforms abstract business intents into quantifiable and executable threshold parameters that can be identified in the simulation space, enabling seamless integration between business requirements and simulation logic. It ensures that the simulation process strictly aligns with business assurance requirements, thus avoiding the problem of mismatch between simulation results and actual business needs from the outset.

[0048] Simulation domains correspond to different business needs, and their importance and simulation priority vary. If computing resources are allocated equally, it will lead to insufficient resources in the core simulation domain and waste of resources in the non-core simulation domain. Different levels of environmental disturbance will result in different fluctuation frequencies of network and environmental parameters. Fixed parameter update cycles cannot adapt to dynamically changing simulation scenarios. By using dynamic weights as the basis for resource allocation, more simulation computing resources such as CPU and memory are allocated to simulation domains with higher weights to ensure the computing efficiency and response speed of the core simulation domains.

[0049] The larger the disturbance coefficient, the higher the degree of environmental disturbance and the shorter the parameter update cycle, ensuring the real-time performance of the simulation parameters. The smaller the disturbance coefficient, the longer the parameter update cycle, avoiding redundant calculations that cause resource consumption. After completing the configuration of simulation domain resources and cycles, the runtime sequence of the simulation domain is calibrated, the parameter transmission link between domains is established, and all configuration parameters and runtime logic are integrated to form a virtual pre-simulation environment for business data flow.

[0050] For example, the dynamic weights of demand elements, adaptation elements, and constraint elements are 0.48, 0.29, and 0.23, respectively. 48% of the simulation computing resources are allocated to the transmission simulation domain, 29% to the resource simulation domain, and 23% to the channel simulation domain. The current disturbance coefficient is 1.2, representing a medium-level environmental disturbance. The parameter update cycle for the simulation domain is configured to 500ms. For a low-disturbance scenario with a disturbance coefficient of 0.8, the cycle is set to 1000ms. For a high-disturbance scenario with a disturbance coefficient of 1.5, the cycle is set to 200ms. After configuration, the simulation domains are integrated to form a virtual pre-simulation environment for the high-definition video call service data stream.

[0051] This enables on-demand optimized allocation of simulation computing resources, improves overall simulation efficiency, adapts parameter update cycles to environmental disturbances, balances simulation real-time performance and resource utilization, and creates a virtual pre-simulation environment that can accurately support subsequent strategy simulation evaluation, determining the authenticity and reliability of strategy test results.

[0052] Furthermore, the candidate guarantee strategies include: Based on the demand elements, adaptation elements, and constraint elements in the intent data, corresponding transmission guarantee strategies, resource scheduling strategies, and channel adaptation strategies are generated. The three types of strategies are combined and arranged according to dynamic weights to obtain multiple strategy combinations. Strategy combinations that do not match the simulation domain parameters are eliminated based on the perturbation coefficient to form candidate protection strategies.

[0053] Different elements of intent data correspond to the core performance requirements, resource adaptation boundaries, and anti-interference constraints of service transmission. The guarantee objectives and technical implementation paths of these elements are fundamentally different. If a single guarantee strategy is adopted, it is impossible to simultaneously meet the requirements of the three types of elements, and it is also impossible to achieve flexible combination and optimization of subsequent strategies. Therefore, we establish a corresponding generation rule between intent elements and basic guarantee strategies. Based on the preset strategy template library and combined with the quantitative indicators of intent data, we generate transmission guarantee strategies, resource scheduling strategies, and channel adaptation strategies respectively.

[0054] The transmission assurance strategy is generated based on the transmission performance requirements of the demand elements. Its core revolves around the link bandwidth and latency thresholds in the transmission simulation domain, configuring link selection and transmission protocol optimization techniques to ensure transmission performance meets standards. The resource scheduling strategy is generated based on the resource adaptation requirements of the adaptation elements. Its core revolves around the resource fluctuation range in the resource simulation domain, configuring resource allocation algorithms and dynamic resource adjustment logic techniques to ensure resource usage remains within a reasonable range. The channel adaptation strategy is generated based on the anti-interference requirements of the constraint elements. Its core revolves around the interference shielding threshold in the channel simulation domain, configuring channel selection, interference suppression, and signal enhancement techniques to ensure channel interference does not exceed the tolerance range and to ensure accurate matching between the strategy and service requirements.

[0055] Using the aforementioned high-definition video call scenario, the intent data corresponds to a link bandwidth ≥ 4Mbps and a transmission latency ≤ 50ms. Based on these indicators, a transmission guarantee strategy is generated, specifically including selecting a low-latency transmission link and enabling the TCP protocol optimization mechanism to ensure that the transmission latency is controlled within the threshold range and the link bandwidth is stably not lower than 4Mbps. The adaptation element corresponds to a resource utilization rate of 30%-60%. Based on this indicator, a resource scheduling strategy is generated, specifically including adopting a dynamic resource allocation algorithm to adjust the resource utilization in real time according to the service transmission requirements, ensuring that the resource utilization rate is maintained within a preset floating range. The constraint element corresponds to a channel interference intensity ≤ -85dBm. Based on this indicator, a channel adaptation strategy is generated, specifically including selecting a channel frequency band with an interference intensity lower than -85dBm and enabling an adaptive interference suppression algorithm to avoid the impact of strong interference on service transmission.

[0056] This enables a precise binding between business needs and protection strategies. Each basic protection strategy is designed to address the needs of a specific type of intent element, avoiding the problem that a single strategy cannot cover multi-dimensional needs. At the same time, it provides standardized basic modules for subsequent strategy combinations, ensuring the flexibility and adaptability of strategy combinations.

[0057] A single basic assurance strategy can only meet the needs of one type of intent element and cannot achieve comprehensive business quality assurance. The priority of different strategy combinations should be consistent with the importance of business elements. Different levels of environmental disturbance will lead to differences in simulation domain parameters. Some strategy combinations are incompatible with the current simulation domain parameters. If they are directly entered into subsequent simulation evaluation, it will cause waste of resources. Dynamic weights are used as the priority basis for strategy combinations. The basic assurance strategy with the highest weight is used as the core strategy, and the other two types of strategies are used as auxiliary strategies. Multiple sets of strategy combinations are arranged in a way that the core strategy is fixed and the auxiliary strategies are combined.

[0058] Retrieve the disturbance coefficients and parameters of each simulation domain in the virtual pre-simulation environment, compare the parameter requirements of the strategy combination with the corresponding simulation domain parameters. If the parameter requirements of the strategy combination exceed the reasonable range of the simulation domain parameters, and the deviation cannot be compensated by adjusting the environmental disturbance corresponding to the disturbance coefficient, then it is determined to be mismatched and eliminated; retain all strategy combinations that adapt to the simulation domain parameters and environmental disturbance states to form candidate protection strategies.

[0059] Using the aforementioned business scenario, the dynamic weights have the following proportions: demand element 0.48, adaptation element 0.29, and constraint element 0.23. Therefore, the transmission guarantee strategy is taken as the core strategy, and it is combined with resource scheduling strategy and channel adaptation strategy with different parameter configurations to form a total of 6 strategy combinations. The current disturbance coefficient is 1.2, corresponding to the simulation domain parameters of 100Mbps bandwidth and 20ms latency in the transmission simulation domain, a fluctuation range of 30%-60% in the resource simulation domain, and an interference shielding threshold of -85dBm in the channel simulation domain. The 6 strategy combinations are compared, and one resource scheduling strategy exceeds the 30%-60% resource fluctuation range, and one channel adaptation strategy cannot meet the interference shielding threshold of -85dBm. Moreover, these two deviations cannot be compensated for by environmental adjustments at the medium disturbance level. The mismatched combinations are eliminated, and the remaining 4 strategy combinations are used as candidate guarantee strategies.

[0060] This achieves comprehensive coverage of business needs, ensures priority protection of core needs by dynamically weighting the strategies, eliminates mismatched combinations to prevent invalid strategies from entering the subsequent simulation stage, and ensures that all candidate protection strategies are adapted to the current network environment and simulation scenario, thereby improving the efficiency and accuracy of subsequent strategy evaluation.

[0061] Specifically, the selected quality assurance strategies include: The candidate guarantee strategies were simulated in parallel using a virtual pre-simulation environment. The bandwidth delay compliance rate in the transmission simulation domain, the resource occupancy rate in the resource simulation domain, and the interference suppression rate in the channel simulation domain were recorded. The bandwidth latency compliance rate, resource utilization rate, and interference suppression rate are weighted according to dynamic weights to obtain the comprehensive evaluation value of each group of candidate guarantee strategies; Candidate assurance strategies with interference suppression rates less than the interference shielding threshold are eliminated. The remaining assurance strategies are sorted in descending order of their comprehensive evaluation values, and the candidate assurance strategy with the highest comprehensive evaluation value is selected as the quality assurance strategy.

[0062] The actual adaptability and performance of candidate protection strategies need to be determined through simulation verification. If a serial simulation method is used, the strategy evaluation cycle will be greatly extended and simulation computing resources will be wasted. The basic protection strategies correspond to simulation domains, and their performance needs to be quantitatively characterized by the core indicators of each simulation domain. Recording only the indicators of a single domain cannot comprehensively evaluate the overall protection effect of the strategy. The candidate protection strategies are loaded into the simulation threads in the virtual pre-simulation environment, and all threads are started synchronously and run in parallel to ensure that the simulation environment, runtime and parameter configuration of each strategy combination are consistent, and to avoid evaluation deviations caused by differences in simulation conditions.

[0063] During the simulation, the bandwidth latency compliance rate of the transmission simulation domain, the resource occupancy rate of the resource simulation domain, and the interference suppression rate of the channel simulation domain are acquired and recorded in real time through the virtual pre-simulation environment. The bandwidth latency compliance rate represents the performance implementation degree of the transmission guarantee strategy, the resource occupancy rate represents the suitability of the resource scheduling strategy, and the interference suppression rate represents the anti-interference effect of the channel adaptation strategy. The acquisition cycle is consistent with the parameter update cycle of the virtual pre-simulation environment to ensure the real-time performance and completeness of the indicator data.

[0064] For example, by calling the parallel simulation function of the virtual pre-simulation environment, the aforementioned four sets of strategies are loaded into four simulation threads, and the simulation is started synchronously. The simulation runtime is set to 10 minutes, and the parameter acquisition cycle is consistent with the parameter update cycle of the virtual pre-simulation environment, i.e., 500ms. The core indicators recorded during the simulation are as follows: the first set of strategies corresponds to a bandwidth latency compliance rate of 98%, a resource utilization rate of 45%, and an interference suppression rate of -88dBm; the second set of strategies corresponds to a bandwidth latency compliance rate of 95%, a resource utilization rate of 52%, and an interference suppression rate of -83dBm; the third set of strategies corresponds to a bandwidth latency compliance rate of 99%, a resource utilization rate of 48%, and an interference suppression rate of -86dBm; and the fourth set of strategies corresponds to a bandwidth latency compliance rate of 92%, a resource utilization rate of 38%, and an interference suppression rate of -87dBm.

[0065] This shortens the evaluation cycle of multiple strategy combinations, improves the overall efficiency of strategy evaluation, enables a comprehensive and quantitative representation of the performance of candidate safeguard strategies, avoids strategy selection bias caused by single indicator evaluation, provides an accurate and complete data foundation for subsequent processing, and ensures the scientific and rational nature of subsequent strategy selection.

[0066] The core performance indicators of the three simulation domains correspond to different service assurance dimensions. If a simple summation or average calculation method is used to evaluate the performance of the strategy, the priority assurance requirements of the core elements will be ignored, resulting in the selected strategy failing to meet the service requirements. The weight ratios of the demand element, adaptation element, and constraint element in the dynamic weights are respectively allocated to the bandwidth delay compliance rate of the transmission simulation domain, the resource utilization rate of the resource simulation domain, and the interference suppression rate of the channel simulation domain. Among them, the bandwidth delay compliance rate and resource utilization rate are calculated using the original percentage values, while the interference suppression rate is calculated using the normalized value, that is, the value of the interference intensity is converted into the corresponding percentage. The lower the interference intensity, the higher the normalized percentage.

[0067] The comprehensive evaluation value is calculated as follows: bandwidth latency compliance rate × dynamic weight of demand elements + resource utilization rate × dynamic weight of adaptation elements + normalized value of interference suppression rate × dynamic weight of constraint elements. For example, the dynamic weights are 0.48 for demand elements, 0.29 for adaptation elements, and 0.23 for constraint elements. The core indicators of the four candidate strategies are weighted and calculated. First, the interference suppression rate is normalized. The interference shielding threshold is -85dBm. The normalized value is set to 100% when the interference suppression rate is ≤-85dBm, and decreases by 5% for every 1dBm above the threshold. Based on this, the normalized values ​​of the interference suppression rates for the four strategies are calculated to be 100%, 85%, 95%, and 90%. Substituting these values, the comprehensive evaluation value for the first group is obtained as: 98% × 0.48 + 45% × 0.29 + 100% × 0.23 = 0.4704 + 0.1305 + 0.23 = 0.8309; Group 2 comprehensive evaluation value = 95% × 0.48 + 52% × 0.29 + 85% × 0.23 = 0.456 + 0.1508 + 0.1955 = 0.8023; Group 3 comprehensive evaluation value = 99% × 0.48 + 48% × 0.29 + 95% × 0.23 = 0.4752 + 0.1392 + 0.2185 = 0.8329; Group 4 comprehensive evaluation value = 92% × 0.48 + 38% × 0.29 + 90% × 0.23 = 0.4416 + 0.1102 + 0.207 = 0.7588.

[0068] This scientific integration of performance indicators from different dimensions highlights the priority of ensuring core business needs, avoids evaluation bias caused by single indicators or average calculations, and ensures that the comprehensive evaluation value can accurately reflect the overall guarantee capability of each candidate strategy, providing an objective and unified evaluation standard for subsequent strategy selection.

[0069] The constraint elements correspond to the anti-interference requirements, which are the bottom line conditions for service transmission. If the interference suppression rate of the candidate guarantee strategy does not reach the interference shielding threshold, the service transmission quality will be substandard, and such strategies have no practical application value. Compare the interference suppression rate of each group of candidate guarantee strategies with the interference shielding threshold in the channel simulation domain. If the interference suppression rate is greater than the interference shielding threshold, that is, the interference intensity exceeds the tolerable range, it is judged as unqualified and eliminated. For the remaining candidate guarantee strategies after eliminating unqualified ones, sort them in descending order of comprehensive evaluation value. If there are guarantee strategies with the same comprehensive evaluation value, the strategy with the higher bandwidth delay compliance rate is given priority. Select the candidate guarantee strategy with the highest comprehensive evaluation value at the top of the ranking as the quality guarantee strategy for the current service data stream, and record the core parameters and comprehensive evaluation value of the strategy to provide a basis for subsequent solution solving.

[0070] Using the aforementioned business scenario, the interference shielding threshold in the channel simulation domain is -85dBm. The interference suppression rates of the four candidate strategies are compared. The interference suppression rate of the second strategy is -83dBm, which is greater than the interference shielding threshold, and is therefore deemed unqualified and eliminated. The remaining three qualified strategies (groups 1, 3, and 4) are sorted in descending order of their comprehensive evaluation values. The sorting result is: group 3 (0.8329) > group 1 (0.8309) > group 4 (0.7588). Therefore, the third candidate guarantee strategy is selected as the quality assurance strategy for the video call service data stream, with a corresponding bandwidth latency compliance rate of 99%, resource utilization rate of 48%, and interference suppression rate of -86dBm.

[0071] This avoids invalid strategies from entering the subsequent resource allocation process, ensuring that the selected quality assurance strategy is the optimal solution for the current scenario, fully meeting the core needs of the business and the requirements of environmental adaptation, and guaranteeing the quality of business transmission. The selected quality assurance strategy serves as the input for the resource optimization problem mapping, and its core parameters determine the demand dimensions and priorities of subsequent resource allocation, which is the core basis for subsequent processing.

[0072] S3. Map the resource requirements of the quality assurance strategy to the network resource capabilities as a resource optimization problem, solve the resource allocation scheme with dynamic weights as priority constraints, and establish a service assurance channel for the corresponding business data flow.

[0073] Furthermore, this can be mapped to resource optimization problems including: Extract the resource requirements of the quality assurance strategy, where the transmission assurance strategy corresponds to the link bandwidth requirement, the resource scheduling strategy corresponds to the resource occupancy requirement, and the channel adaptation strategy corresponds to the anti-interference resource requirement. Retrieve the resource fluctuation range of the resource simulation domain, and combine it with the dynamic weights corresponding to the adaptation elements to determine the demand threshold and priority ranking for each resource demand dimension. Extract the available resources of network resource capabilities, and combine the link bandwidth in the real-time network status with the network load and regional user density in the environmental data to determine the upper limit of resource supply for each resource demand dimension. By taking the demand threshold and priority ranking of resource demand as constraints, the upper limit of resource supply as boundary conditions, and minimizing the deviation between the actual allocation of resources and the demand threshold as the objective function, a resource optimization problem is formed.

[0074] Quality assurance strategies serve as the optimal guarantee for current business data flows. However, the strategies themselves only specify the guarantee methods and performance goals, without directly reflecting the specific resource requirements. If resource requirements cannot be accurately extracted, subsequent resource allocation will lack a clear basis, leading to a disconnect between resource allocation and strategy requirements, and failing to achieve business quality assurance goals. Furthermore, combinations of quality assurance strategies correspond to different resource support dimensions.

[0075] The quality assurance strategies are broken down, and the resource requirements corresponding to the basic strategies are extracted separately. The transmission assurance strategy corresponds to the link bandwidth requirement, the core of which is the minimum link bandwidth requirement to support the performance of service transmission. When extracting, the link bandwidth threshold in the transmission simulation domain and the transmission optimization parameters in the strategy are combined. The resource scheduling strategy corresponds to the resource occupancy requirement, the core of which is the scale of network resource occupancy required during the execution of the strategy. When extracting, the resource fluctuation range in the resource simulation domain and the resource scheduling logic in the strategy are combined. The channel adaptation strategy corresponds to the anti-interference resource requirement, the core of which is the resources required to achieve interference suppression, such as anti-interference channel resources and signal enhancement resources. When extracting, the interference shielding threshold in the channel simulation domain and the anti-interference technology in the strategy are combined.

[0076] For example, the quality assurance strategy is candidate strategy group 3. After decomposing this strategy, we extract various resource requirements. The transmission assurance strategy corresponds to the link bandwidth requirement. Combining the link bandwidth threshold of ≥4Mbps in the transmission simulation domain and the low-latency transmission optimization requirement in the strategy, the extracted link bandwidth requirement is ≥4Mbps to ensure transmission latency ≤50ms. The resource scheduling strategy corresponds to the resource occupancy requirement. Combining the resource fluctuation range of 30%-60% in the resource simulation domain and the dynamic resource allocation logic in the strategy, the extracted resource occupancy requirement is 45%-50% to match the simulation performance of 48% resource occupancy in the strategy. The channel adaptation strategy corresponds to the anti-interference resource requirement. Combining the interference shielding threshold of ≤-85dBm in the channel simulation domain and the adaptive interference suppression algorithm in the strategy, the extracted anti-interference resource requirement is 1 dedicated anti-interference channel and signal enhancement resource power ≥20dBm.

[0077] This enables the transformation of quality assurance strategies from assurance measures to resource requirements, clarifies the specific goals and dimensions of resource allocation, avoids allocation deviations caused by ambiguous resource requirements, and ensures that the resource requirements of each basic strategy are covered through classification and extraction, providing accurate input data for subsequent processing.

[0078] The extracted resource requirements are only a basic range. If they are used directly for resource allocation, it will lead to a lack of clear judgment criteria for resource allocation and will fail to take into account the priority of core business needs. At the same time, the resource requirements need to be adapted to the resource fluctuation range of the resource simulation domain to avoid the requirements exceeding the reasonable range and making allocation infeasible. The resource fluctuation range of the aforementioned resource simulation domain is retrieved, and the extracted resource requirements are compared with the resource fluctuation range. Combined with the dynamic weights corresponding to the adaptation elements, the requirement threshold of each resource requirement dimension is determined.

[0079] For link bandwidth requirements, the extracted basic requirements are used as a benchmark, and the upper and lower fluctuation thresholds are determined based on the bandwidth fluctuation range of the resource fluctuation range. For resource occupancy requirements, the extracted requirement range is used as the core threshold, conforming to the constraints of the resource fluctuation range. For anti-interference resource requirements, the requirement threshold is determined by combining the interference shielding threshold and the anti-interference resource configuration range in the resource fluctuation range. Then, dynamic weights are used as the priority ranking basis, with the requirement element having the highest weight and its resource requirement having the highest priority; the adaptation element has the next highest weight and priority; and the constraint element has the lowest weight and priority, forming a priority ranking of resource requirements to ensure that the resource supply corresponding to the core requirements is given priority during the resource allocation process.

[0080] For example, the resource simulation domain has a resource fluctuation range of 30%-60%, a link bandwidth fluctuation range of ±10% of the real-time bandwidth (real-time link bandwidth 100Mbps, i.e., 90-110Mbps), and an anti-interference resource fluctuation range of 1-2 dedicated channels and power 18-22dBm. The dynamic weight corresponding to the adaptation element is 0.29. Based on the extracted resource requirements, the demand thresholds for each resource demand dimension are determined. The link bandwidth demand threshold is 4-6Mbps, with a basic requirement of 4Mbps, which is determined based on the bandwidth fluctuation range. The resource occupancy demand threshold is 45%-50%, consistent with the extracted demand range and fitting the resource fluctuation range. The anti-interference resource demand threshold is 1 dedicated channel and power 20-22dBm, determined based on the anti-interference resource fluctuation range. Based on the dynamic weights, the priority order is determined as link bandwidth demand > resource occupancy demand > anti-interference resource demand.

[0081] This ensures that resource requirements align with the reasonable range of the resource simulation domain, preventing allocation failures caused by requirements exceeding resource capacity; prioritization clarifies the order of resource allocation, ensuring that resources corresponding to core business needs are prioritized, and providing a basis for constructing constraints for subsequent resource optimization problems.

[0082] The amount of available network resources is limited, and resource allocation cannot exceed the actual carrying capacity of the network. If only the resource demand is specified without determining the supply limit, it will lead to an infeasible solution to the resource optimization problem, or the allocation result will exceed the network resource capacity. Extract the available resource quantity of the network resource capacity, including the total amount of available link bandwidth, available computing resources, and available anti-interference resources. Retrieve the link bandwidth parameters of the real-time network status, and combine them with the real-time occupancy of the link bandwidth to determine the allocable margin of link bandwidth resources.

[0083] Then, the network load and regional user density of the environmental data are retrieved. The higher the network load and the greater the regional user density, the less the resource allocation margin is. The available resource quantity is then adjusted to determine the upper limit of resource supply for each resource demand dimension. The upper limit of supply is not greater than the available resource quantity. Combined with the resource fluctuation range of the resource simulation domain, it is ensured that the upper limit of supply is within a reasonable range and does not exceed the resource carrying capacity.

[0084] For example, the available network resource capacity is 80Mbps of available link bandwidth (minus 20Mbps occupied by other on-network services), the upper limit of available resource utilization is 60%, and the available anti-interference resources are 2 dedicated channels with a power upper limit of 25dBm; in the real-time network status, the link bandwidth is 100Mbps, the current link utilization rate is 20%, and the allocable link bandwidth is 80Mbps; in the environmental data, the network load rate is 68%, and the regional user density is 38 people / cell, which is a medium disturbance scenario. In order to correct the upper limit of various resource supply, the upper limit of link bandwidth demand supply is determined to be 80Mbps, and the upper limit of resource utilization demand supply is determined to be 60%, in order to fit the upper limit of the floating range of the resource simulation domain, and combined with network load correction to avoid excessive resource utilization. The upper limit of anti-interference resource demand supply is determined to be 2 dedicated channels with a power of 25dBm (not exceeding the available anti-interference resources).

[0085] This clarifies the maximum boundary of resource allocation, prevents the solution of resource optimization problems from exceeding network resource capacity, and adjusts the supply limit by combining real-time network status and environmental data to ensure that it fits the current network operation scenario, improves the feasibility and adaptability of resource allocation, and provides a basis for constructing boundary conditions for resource optimization problems.

[0086] The aforementioned resource demand thresholds, priority rankings, and resource supply limits cannot be directly used to solve resource allocation schemes. The core elements of constructing the resource optimization problem are: using resource demand thresholds and priority rankings as constraints to ensure that the demand thresholds of each dimension are met during resource allocation, and following the priority ranking to prioritize high-priority resource demands; and using the resource supply limit as a boundary condition to ensure that the amount of resource allocation does not exceed the network resource carrying capacity and conforms to the resource fluctuation range of the resource simulation domain.

[0087] The objective function is to minimize the deviation between the actual allocation of each resource demand dimension and the demand threshold, that is, to ensure that the resource allocation is as close as possible to the demand threshold and reduce the allocation deviation. The three core elements are integrated and mapped to form a resource optimization problem. The solution direction is to find the resource allocation of each resource demand dimension that minimizes the objective function under the premise of satisfying the constraints and boundary conditions.

[0088] For example, the constraints are: link bandwidth requirement threshold of 4-6 Mbps, resource utilization requirement threshold of 45%-50%, and anti-interference resource requirement threshold of 1 dedicated channel + power of 20-22 dBm. Based on the priority ranking, the boundary conditions are determined as: link bandwidth supply limit of 80 Mbps, resource utilization supply limit of 60%, and anti-interference resource supply limit of 2 dedicated channels + power of 25 dBm. The objective function is to minimize the sum of the deviations of the actual allocated link bandwidth from 4-6 Mbps, the deviations of the actual allocated resource utilization from 45%-50%, and the deviations of the actual allocated anti-interference resources from 1 dedicated channel + 20-22 dBm. This forms a resource optimization problem.

[0089] This transforms the dispersed resource demand and supply parameters into a mathematical optimization problem, providing a logical framework for solving subsequent resource allocation schemes. It ensures that the solution to the resource allocation scheme has clear objectives and constraints, while achieving precise matching of resource demand and supply, thus providing a prerequisite guarantee for the feasibility of the resource allocation scheme.

[0090] like Figure 3 As shown, solving for the resource allocation scheme includes: The solution order for each resource demand dimension is determined according to the dynamic weights corresponding to the demand elements, adaptation elements, and constraint elements. The upper limit of resource supply for each resource demand dimension is adjusted based on the disturbance coefficient to obtain a resource supply boundary that adapts to the current environmental disturbance. With the goal of minimizing the deviation, the resource optimization problem is iteratively calculated dimension by dimension according to the solution order to obtain the initial resource allocation value for each resource demand dimension; The initial resource allocation value is compared with the resource fluctuation range of the resource simulation domain. Initial resource allocation values ​​that exceed the resource fluctuation range are eliminated, and the remaining resource allocation values ​​are integrated to form a resource allocation scheme for the corresponding business data flow. At the same time, a service guarantee channel is established for the corresponding business data flow.

[0091] Resource optimization problems involve resource requirements across dimensions such as link bandwidth, resource occupancy, and anti-interference resources. Each dimension corresponds to different business elements. If solutions are applied out of order, high-priority resource requirements may not be met, hindering optimal resource allocation. Therefore, a dynamic weighting relationship is established between resource requirement dimensions and their corresponding dynamic weights. Requirement elements have the highest dynamic weights, and their associated link bandwidth requirements are the first priority. Adaptation elements have the next highest dynamic weights, and their associated resource occupancy requirements are the second priority. Constraint elements have the lowest dynamic weights, and their associated anti-interference resource requirements are the third priority. This ensures that high-priority resource requirements are met first and avoids parameter conflicts during the solution process for different dimensions.

[0092] For example, the dynamic weights are 0.48 for demand elements, 0.29 for adaptation elements, and 0.23 for constraint elements. Based on this, the solution order for the resource demand dimension is determined as link bandwidth demand → resource occupancy demand → anti-interference resource demand. This order runs through the entire iterative calculation process to ensure that link bandwidth resources are allocated first.

[0093] This clarifies the logical sequence of solving resource optimization problems, precisely binds resource allocation priorities with the importance of business elements, avoids disordered solving leading to insufficient supply of core resources, ensures that resource allocation always revolves around core business needs, and provides clear logical guidance for subsequent iterative calculations.

[0094] The resource supply ceiling is a static parameter based on real-time network status and environmental data, which does not fully consider the dynamic impact of environmental disturbances on resource supply capacity. The disturbance coefficient represents the degree of current environmental disturbance. The greater the disturbance, the worse the stability of network resource supply, and the actual supplyable resources will fluctuate accordingly. If the static supply ceiling is directly used for the solution, the solution result will exceed the actual supply range and cannot be implemented.

[0095] The disturbance coefficient is positively correlated with the adjustment range of the supply ceiling. That is, the larger the disturbance coefficient, the larger the adjustment range, and the resource supply ceiling is appropriately lowered to adapt to the fluctuations in resource supply caused by environmental disturbances. The smaller the disturbance coefficient, the smaller the adjustment range, and the original supply ceiling is maintained to make full use of network resources. During the adjustment process, the adjustment range is adjusted in combination with the characteristics of the resource demand dimension to ensure that the adjusted resource supply boundary conforms to the current environmental disturbance state and the actual carrying capacity of network resources, and remains compatible with the resource fluctuation range of the resource simulation domain.

[0096] For example, if the current disturbance coefficient is 1.2 (medium-level environmental disturbance), the upper limit of resource supply is 80Mbps link bandwidth, 60% resource utilization, and 2 dedicated channels + 25dBm power for anti-interference. After correction based on the disturbance coefficient, the correction range is set to 10% in the medium disturbance scenario. The resulting resource supply boundary is 80Mbps link bandwidth × (1-10%) = 72Mbps, 60% × (1-10%) = 54% resource utilization, and 1 dedicated channel + 25dBm × (1-10%) = 22.5dBm power for anti-interference (rounded to 22dBm). The corrected supply boundary is adapted to medium-level environmental disturbance, ensuring the stability of resource supply.

[0097] This enables dynamic adaptation of the resource supply boundary, incorporates environmental disturbances into the resource allocation solution, avoids infeasibility of the solution due to static supply limits, improves the adaptability of the resource allocation scheme to the current environment, and ensures that the resource supply boundary is always within the network resource carrying capacity.

[0098] Calculating the allocation values ​​for all dimensions at once can easily lead to conflicting allocation results, making it impossible to simultaneously satisfy the objective function, constraints, and boundary conditions. To minimize the deviation, the resource optimization problem is iteratively calculated dimension by dimension in the order of solution. That is, the goal is to minimize the absolute deviation between the initial allocation value of a single resource demand dimension and the center value of the corresponding demand threshold interval. A fixed step size is used for approximation adjustment, and after each dimension calculation is completed, the initial allocation value is used as a fixed constraint condition and substituted into the subsequent process.

[0099] For the first priority of link bandwidth demand, the median value of its demand threshold interval is extracted as the starting value for iteration. It is then double-checked against the demand threshold and the corrected resource supply boundary. If the check passes, the median value of the interval is used as the initial allocation value. If the check fails, the value is adjusted step by step with a fixed step size of 0.1 Mbps, and then successively approaches the value interval that simultaneously satisfies both types of constraints. Each adjustment is followed by a constraint check until a compliant value with the smallest deviation from the center value of the demand threshold interval is obtained.

[0100] For the resource occupancy demand of the second order, the numerical range of this dimension is defined by combining the initial allocation value of the link bandwidth as a fixed constraint. The center value of the interval corresponding to the demand threshold is extracted as the iteration starting value. Successive approximation iterations are performed with a fixed adjustment step size of 0.5%. The corresponding initial allocation value is calculated iteratively, and it is ensured that the resource occupancy does not exceed the supply boundary and is adapted to the link bandwidth allocation until the deviation is minimized.

[0101] For the third-order anti-interference resource requirement, combined with the initial allocation values ​​of the first two dimensions as fixed constraints, the numerical range of the anti-interference resources is limited. The center value of the interval of the requirement threshold for this dimension is used as the starting value for iteration. Successive approximation iterations are carried out with a fixed adjustment step size of 0.1dBm to verify whether the value meets the requirement threshold, resource supply boundary and multi-dimensional resource coordination constraints, and to determine the initial allocation value of the anti-interference resource requirement. During the iterative calculation process, the allocation value is checked at each step to see if it meets the constraints and boundary conditions. If it does not meet the constraints, it is iterated and adjusted again until the initial resource allocation value that meets the requirements is obtained.

[0102] Iterative calculations were performed in the order of link bandwidth → resource occupancy → anti-interference resources. The link bandwidth requirement threshold was 4-6 Mbps, the supply boundary was 72 Mbps, and the initial allocation value was 5 Mbps. The resource occupancy requirement threshold was 45%-50%, the supply boundary was 54%, and the initial allocation value was 48% based on the link bandwidth allocation value of 5 Mbps. The anti-interference resource requirement threshold was 1 dedicated channel + 20-22 dBm, the supply boundary was 1 channel + 22 dBm, and the initial allocation value was 1 dedicated channel + 21 dBm based on the allocation values ​​of the first two dimensions. Thus, the initial resource allocation values ​​for the three dimensions were obtained.

[0103] This avoids parameter conflicts in multi-dimensional resource allocation, ensures that the initial allocation value of each resource demand dimension meets the demand threshold, supply boundary and objective function, achieves precise matching between resource allocation and business needs, gradually approaches the optimal solution of the resource optimization problem, and provides basic data for the integration of subsequent resource allocation schemes.

[0104] The initial resource allocation values ​​only satisfy the constraints of demand thresholds and supply boundaries, without verifying their compatibility with the resource fluctuation range of the resource simulation domain. If the initial allocation value exceeds this fluctuation range, it will lead to a mismatch between resource allocation and the simulation environment, making it impossible to coordinate with the simulation logic of the virtual pre-simulation environment and affecting the business quality assurance effect. The initial resource allocation value of each resource demand dimension is compared with the resource fluctuation range of the corresponding resource simulation domain. If the initial allocation value exceeds the fluctuation range, it is judged as unqualified and removed. The demand threshold and supply boundary of the dimension are re-verified to ensure that the remaining allocation values ​​are all within the resource fluctuation range.

[0105] The remaining qualified initial resource allocation values ​​are integrated and sorted according to the priority of resource demand dimension to form a resource allocation scheme, clarifying the allocation parameters of each dimension. Based on the integrated resource allocation scheme, a service guarantee channel is established for the current business data flow, and the resource allocation parameters are bound to the channel to ensure that the channel can accurately schedule the allocated resources and realize the implementation of the resource allocation scheme. During the channel establishment process, it is necessary to keep synchronized with the simulation domain parameters of the virtual pre-simulation environment to ensure that the channel operation is consistent with the simulation logic.

[0106] For example, the resource fluctuation range in the resource simulation domain is 90-110Mbps link bandwidth (real-time bandwidth ±10%), 30%-60% resource utilization, and 1-2 dedicated channels +18-22dBm for anti-interference resources. The initial allocation values ​​of the three dimensions (5Mbps link bandwidth, 48% resource utilization, 1 dedicated channel +21dBm for anti-interference resources) are compared with the resource fluctuation range, and all are within the range, so no elimination is needed. The qualified allocation values ​​are integrated to form a resource allocation scheme, with 5Mbps link bandwidth allocated, 48% resource utilization allocated, and 1 dedicated channel +21dBm allocated for anti-interference resources. A service guarantee channel is established for the business data flow.

[0107] This avoids deviations in protection effectiveness caused by the allocation scheme being out of sync with the simulation environment. The resource allocation scheme is complete and feasible, and the allocation parameters in various dimensions provide a basis for resource scheduling. The service guarantee channel enables the implementation of the scheme, ensuring that the allocated resources can be applied to the business data flow and providing hardware support for business quality assurance.

[0108] S4. Obtain the measured data of the business data flow in the service assurance channel, compare the measured data with the intent data to determine the degree of intent realization of the quality assurance strategy, and when the degree of intent realization is less than the preset threshold, identify the root cause of the deviation and store it in the case library to synchronously update the dynamic weight, strategy generation and resource allocation scheme solution.

[0109] Furthermore, identifying the root causes of the deviation includes: Obtain measured data of business data flow in the service assurance channel, compare the measured data with the intent data, and obtain the measured deviation values ​​of different elements; The measured deviation values ​​of different elements are weighted and summed according to dynamic weights to obtain the total deviation value. The total deviation value is then normalized with the deviation threshold to obtain the degree of intention achievement. When the degree of achievement of the intention is less than the preset threshold, the source of deviation of different elements is determined by combining the current disturbance coefficient with the dimensional data in the environmental data, so as to identify the root cause of the deviation. The root causes of deviations, measured deviation values ​​of different elements, current disturbance coefficients, dimensional data in environmental data, and degree of intent fulfillment are encapsulated and stored in the case library.

[0110] After the service assurance channel is established and operational, the actual implementation effect of the resource allocation scheme needs to be verified through measured data. Simulation data alone cannot reflect the deviation in real business transmission. Measured data of business data streams during transmission is continuously acquired through the service assurance channel. The acquired content corresponds to the quantitative indicators of the intent data, including the actual bandwidth of the transmission link, the actual transmission delay, the actual resource occupancy rate, and the actual channel interference intensity. The sampling period is consistent with the parameter update period of the virtual pre-simulation environment to ensure the real-time and continuous nature of the data. After sampling, the measured data of each type of element is compared with the corresponding intent threshold to calculate the measured deviation value of different elements. A positive deviation value indicates that the measured data exceeds the upper limit of the intent threshold, a negative value indicates that it is below the lower limit of the threshold, and a zero value indicates no deviation. All deviation values ​​are quantified and recorded.

[0111] For example, the quantitative indicators of intent data are link bandwidth ≥ 4Mbps, transmission delay ≤ 50ms, resource utilization rate 30%-60%, and channel interference intensity ≤ -85dBm; the measured data obtained through the service guarantee channel are actual link bandwidth 3.8Mbps, actual transmission delay 52ms, actual resource utilization rate 51%, and actual channel interference intensity -83dBm; the measured deviation of link bandwidth is calculated as 3.8-4=-0.2Mbps, the measured deviation of transmission delay is calculated as 52-50=2ms; the deviation of adaptation element is calculated as 51-48=3%, of which 48% in the resource allocation scheme is used as the benchmark to fit the intent threshold range; the measured deviation of constraint element is calculated as -83-(-85)=2dBm.

[0112] This enables precise quantification of business transmission deviations, clarifies the specific dimensions and values ​​of the deviations, avoids the problem of vague deviations being untraceable, provides basic data for subsequent root cause identification, and ensures the appropriate analysis of deviations.

[0113] The measured deviation values ​​of each element can only reflect the deviation in a single dimension and cannot reflect the overall deviation of the service transmission. Judging the service quality based solely on the deviation of a single element can easily lead to a one-sided judgment and fail to fully assess the actual effect of the resource allocation scheme. Different elements have different levels of importance to service quality, and their deviations have different weights. The measured deviation value of each element is multiplied by the dynamic weight of the corresponding element, and all weighted deviation values ​​are summed to obtain the total deviation value. The deviation value is then standardized to ensure that the dimensions of the deviation values ​​in different dimensions are consistent. A fixed deviation threshold is preset, which is based on the service type and is the maximum tolerable overall deviation value of the service. The total deviation value and the deviation threshold are normalized and calculated. The degree of achievement = (1 - total deviation value / deviation threshold). The calculation result ranges from 0 to 1. The closer the value is to 1, the higher the degree of fit between the actual transmission effect and the service requirements. Conversely, the smaller the value is, the greater the deviation.

[0114] For example, the dynamic weights are 0.48 for the demand element, 0.29 for the adaptation element, and 0.23 for the constraint element. After standardization, the measured deviation values ​​of each element are 0.1 for the demand element, 0.06 for the adaptation element, and 0.08 for the constraint element. The total deviation value is 0.1×0.48+0.06×0.29+0.08×0.23=0.048+0.0174+0.0184=0.0838. The deviation threshold is 0.1, which is the maximum overall deviation that the video call service can tolerate. The degree of intent fulfillment is (1-0.0838 / 0.1)=0.162, or 16.2%, to reflect the degree of fit between the current service transmission effect and the intent requirements.

[0115] This enables the transformation from single-dimensional deviation to overall deviation, highlighting the impact weight of deviations in core elements, avoiding one-sided judgments, and providing a trigger standard for deviation root cause identification based on the degree of intent realization. This ensures that root cause identification is only initiated when the degree of deviation exceeds the tolerable range, avoiding invalid root cause analysis and improving deviation processing efficiency.

[0116] When the degree of intention fulfillment is less than the preset threshold, it indicates that the service transmission deviation has exceeded the tolerable range. If the root cause cannot be identified, subsequent updates will lack direction and will be unable to correct the deviation and improve service quality. The deviation of each element is associated with the four dimensions of environmental data and the disturbance coefficient. The deviation of the demand element is mainly related to the terminal mobility status and network channel interference dimension; the deviation of the adaptation element is mainly related to the network load and regional user density dimension; and the deviation of the constraint element is mainly related to the network channel interference dimension.

[0117] The system retrieves real-time data for each dimension of the current disturbance coefficient and environmental data. By combining the magnitude and direction of the deviation values ​​of each element, the source of the deviation is determined. If the disturbance coefficient is high and a certain dimension of the environmental data exceeds the preset disturbance threshold range, and the corresponding element has a large deviation value, then the abnormality of that environmental dimension data is determined to be the root cause of the deviation. If the disturbance coefficient is normal and the environmental data is normal, then the system combines the resource allocation plan and protection strategy to determine whether the root cause is due to a deviation in strategy execution or resource allocation. During the identification process, the system clarifies the correspondence between the root cause and the deviation element to ensure that the root cause can accurately explain the cause of the deviation.

[0118] For example, if the intent fulfillment rate is 16.2%, and the preset threshold is 80%, which is the minimum acceptable fit for the business, the intent fulfillment rate is less than the preset threshold, and root cause identification is initiated. The current disturbance coefficient is 1.2, which is a medium-level environmental disturbance. The environmental data dimensions are: terminal moving speed 18km / h, network channel interference intensity -83dBm, network load rate 68%, and regional user density 38 people / cell. Combining the deviation values ​​of each element, the deviations of both the demand element and the constraint element are relatively large. Moreover, the network channel interference intensity exceeds the threshold of -85dBm, and the terminal moving speed is close to the upper limit of medium disturbance 20km / h. Therefore, the root cause of the deviation is determined to be that the network channel interference intensity exceeds the interference shielding threshold, and the excessively high terminal moving speed leads to a decrease in link stability, thus causing the deviation between the demand element and the constraint element. The adaptation element deviation is small, and there is no corresponding environmental anomaly, so it is determined to be a non-primary root cause.

[0119] By linking environmental data, disturbance coefficients, and deviation values ​​of various elements, the root cause of deviations can be accurately located, the source of deviations can be identified, and root cause misjudgment or omission can be avoided. This provides a clear basis for targeted updates of subsequent dynamic weights, safeguard strategies, and resource allocation schemes, ensuring that subsequent updates can resolve deviation issues.

[0120] The root causes of deviations and related data serve as a reference for subsequent business quality assurance optimization. Without encapsulation and storage, it becomes impossible to quickly retrieve historical data for root cause localization and strategy optimization when encountering similar deviations, hindering continuous iteration and upgrading of the assurance solution. The encapsulation scope includes the root causes of deviations, measured deviation values ​​of each element, current disturbance coefficients, dimensional data in environmental data, and intent achievement. During the encapsulation process, the relationships between each data point are clearly defined, and the type, timestamp, and corresponding resource allocation scheme of the corresponding business data stream are labeled to ensure data traceability. After encapsulation, the data is stored in a case library, which uses a categorized storage logic to classify and archive data according to the type of root cause of deviation and business type, facilitating subsequent retrieval and reuse.

[0121] For example, the root cause of the deviation, the measured deviation value, the current disturbance coefficient of 1.2, environmental data (terminal moving speed of 18km / h, network channel interference of -83dBm, network load of 68%, regional user density of 38 people / cell), and the intention realization degree of 16.2% are encapsulated; the corresponding video call service is labeled, the timestamp is the service transmission period, and the associated resource allocation scheme is marked; and the root cause is stored in the case library according to the type of channel interference and terminal movement.

[0122] This enables the standardized retention and categorized archiving of deviation-related data, providing historical reference for rapid root cause identification and strategy optimization for similar deviations in the future, promoting the continuous iteration of business quality assurance solutions, and improving the closed-loop logic of technical solutions to ensure the traceability and reusability of deviation handling.

[0123] Specifically, updating the dynamic weights includes: The weighted calibration range is determined by the ratio of the measured deviation value of the corresponding element to the total deviation value. The weight calibration factor is calculated based on the difference between the degree of intention realization and the preset threshold. The weight calibration magnitude is then multiplied by the weight calibration factor to obtain the weight calibration amount of the corresponding element. The basic weight values ​​of the corresponding elements are corrected based on the weight calibration amount, and after recalibration in combination with the current disturbance coefficient, the updated dynamic weights are obtained.

[0124] Dynamic weight updates must focus on the elements corresponding to the root causes of deviations. If the calibration range is not determined in conjunction with the deviation ratio, it will lead to errors in weight adjustment and affect the core priority of business assurance. It is necessary to clarify the element type corresponding to the root cause of deviation and extract the measured deviation value of this type of element. Calculate the ratio of the measured deviation value of this element to the total deviation value. This ratio is the weight calibration range. The calibration range ranges from 0 to 1. The larger the ratio, the larger the calibration range, indicating that the weight of this element needs to be adjusted more strongly. The measured deviation value of elements that are not root causes of deviations accounts for a very low percentage. Their weight calibration range is set to 0 and no adjustment is required to ensure the targeted nature of weight adjustment.

[0125] For example, the root causes of the above deviations are excessive network channel interference and excessive terminal movement speed. The corresponding elements are demand elements and constraint elements, and the adaptation element is the element corresponding to the non-deviation root cause. The measured deviation values ​​are 0.1 for demand elements and 0.08 for constraint elements, with a total deviation value of 0.0838. The weighted calibration range is calculated, where the calibration range for demand elements = 0.1 ÷ 0.0838 ≈ 1.193, and when it exceeds 1, it is taken as 1 to avoid over-calibration. The calibration range for constraint elements = 0.08 ÷ 0.0838 ≈ 0.955, and the calibration range for adaptation elements = 0, which is the element corresponding to the non-deviation root cause.

[0126] This enables targeted quantification of the weight calibration magnitude, precisely binding the weight adjustment intensity with the degree of deviation impact, ensuring that the weight adjustment focuses on core deviation elements, avoiding ineffective adjustments to non-core elements, and providing a scientific basis for subsequent calibration quantity calculations, thus ensuring the rationality and pertinence of the weight adjustment.

[0127] The weight calibration range only reflects the proportion of the impact of the deviation factor and does not consider the severity of the deviation. If the difference between the degree of intention achievement and the preset threshold is large, even if the calibration range is the same, the weight adjustment intensity needs to be increased to ensure that the adjusted weight can effectively avoid the deviation. If the difference is small, the adjustment intensity should be appropriately reduced to avoid excessive adjustment that causes excessive weight fluctuations and affects the stability of business assurance. The difference between the preset threshold and the degree of intention achievement is calculated to intuitively reflect the severity of the deviation. The larger the difference, the more severe the deviation, and the larger the calibration factor.

[0128] The difference is standardized by dividing it by a preset threshold to obtain the weight calibration factor. The calibration factor ranges from 0 to 1. Standardization ensures that the calibration factor and calibration amplitude have the same dimensions, avoiding calculation errors. The weight calibration amplitude is multiplied by the weight calibration factor to obtain the weight calibration amount of the corresponding element. A positive calibration amount indicates that the weight of the element needs to be increased, while a negative value indicates that the weight needs to be decreased.

[0129] Using the aforementioned business scenario, the preset threshold is 80% (0.8), and the intention realization rate is 16.2% (0.162). The difference between the intention realization rate and the preset threshold is calculated as 0.8 - 0.162 = 0.638. After standardization, the weight calibration factor is obtained as 0.638 ÷ 0.8 ≈ 0.798. Combining the determined weight calibration range, the weight calibration amount is calculated, where the calibration amount for demand elements is 1 × 0.798 ≈ 0.798, with the calibration range set to 1. The calibration amount for constraint elements is 0.955 × 0.798 ≈ 0.762, and the calibration amount for adaptation elements is 0 × 0.798 = 0.

[0130] This ensures that the weight calibration amount not only matches the proportion of the deviation's impact but also adapts to the severity of the deviation, avoiding under-calibration or over-calibration, improving the accuracy of weight adjustment, and providing an adjustment basis for basic weight correction.

[0131] The base weights are the initial benchmark. Correcting the base weights only through weight calibration does not take into account the impact of current environmental disturbances on the importance of elements. This will lead to insufficient adaptability of the updated dynamic weights to the environment. The importance of each element will change depending on the degree of environmental disturbance. Based on the base weight value, the base weights are initially corrected by combining the weight calibration of the corresponding elements. Elements corresponding to the root causes of deviation need to have their weights increased by adding the weight calibration to the base weight value. Elements corresponding to non-root causes of deviation have a calibration of 0, keeping the base weights unchanged.

[0132] After initial correction, the weight values ​​are normalized to ensure that the sum of the element weights is 1. The normalized initial corrected weights are multiplied by the current disturbance coefficient to complete the environmental adaptation calibration. Since the disturbance coefficient represents the degree of environmental disturbance, multiplication makes the weights adapt to the impact of the current environment on each element, resulting in the updated dynamic weights.

[0133] For example, combining the weight calibration values, the initial corrected weights for the demand elements are: 0.5 + 0.798 = 1.298; the initial corrected weights for the adaptability elements are: 0.3 + 0 = 0.3; and the initial corrected weights for the constraint elements are: 0.2 + 0.762 = 0.962. After normalizing the initial corrected weights, the sum is 1.298 + 0.3 + 0.962 = 2.56. The normalized weights are: demand element 1.298 ÷ ​​2.56 ≈ 0.507; adaptability element 0.3 ÷ 2.56. The current perturbation coefficient is 1.2. After recalibrating based on the perturbation coefficient, the updated dynamic weights are: demand element 0.507×1.2≈0.608, adaptation element 0.117×1.2≈0.140, and constraint element 0.375×1.2≈0.450. After normalization, the updated dynamic weights are: demand element 0.467, adaptation element 0.108, and constraint element 0.425.

[0134] This addresses the issue of weight adaptation corresponding to the root causes of deviations. By combining perturbation coefficient calibration, the weights are adapted to the current environment, ensuring that the updated dynamic weights can avoid deviations and conform to the environmental perturbation state. This improves the adaptability and rationality of the dynamic weights, providing a priority basis for subsequent strategy updates and resource allocation scheme optimization.

[0135] Specifically, the update strategy generation includes: Based on the elements corresponding to the root causes of the deviation, locate the transmission guarantee strategy, resource scheduling strategy or channel adaptation strategy that needs to be updated. Based on the magnitude of the measured deviation value of the corresponding element, determine the parameter correction direction of the corresponding strategy, and determine the parameter correction magnitude in combination with the disturbance coefficient. Based on the updated dynamic weights, the corrected strategies are rearranged to obtain multiple strategy combinations. Strategy combinations that do not match the simulation domain parameters are then eliminated to form updated candidate protection strategies.

[0136] The original protection strategy parameter configuration was based on the initial dynamic weights and environmental conditions, which did not adapt to the current deviation root causes and corresponding deviation situations and environmental disturbances. Blindly updating all strategies would waste resources and fail to resolve core deviations. It is crucial to clarify the protection strategy types associated with the elements corresponding to the deviation root causes. For example, if the deviation root cause corresponds to a demand element, such as excessive terminal movement speed leading to decreased link stability or excessive transmission latency, the transmission protection strategy needs to be updated. For deviation root causes corresponding to constraint elements, such as excessive network channel interference, the channel adaptation strategy needs to be updated. For deviation root causes corresponding to adaptation elements, the resource scheduling strategy needs to be updated. The strategies corresponding to elements not related to deviation root causes do not need to be updated to avoid ineffective adjustments.

[0137] Based on the measured deviation value of the corresponding element of the deviation root cause, the direction of parameter correction for the corresponding strategy is determined. If the measured deviation value is positive, that is, the measured data exceeds the upper limit of the intention threshold, the direction of strategy parameter correction is to strengthen the corresponding protection capability, such as enhancing anti-interference parameters. If the measured deviation value is negative, that is, the measured data is below the lower limit of the intention threshold, the direction of correction is to improve the corresponding performance parameters, such as increasing the link bandwidth configuration. The larger the absolute value of the measured deviation value, the more targeted the correction direction is.

[0138] The parameter correction range is determined by combining the current disturbance coefficient. That is, the larger the disturbance coefficient, the more severe the environmental disturbance, and the larger the policy parameter correction range, to ensure that the corrected policy can adapt to the current environmental disturbance state. The smaller the disturbance coefficient, the smaller the correction range, to avoid over-correction that leads to an imbalance between the policy and the environment. The correction range is quantified by combining the measured deviation value with the disturbance coefficient to fit the simulation domain parameters of the virtual pre-simulation environment.

[0139] For example, the root cause of the above deviation is that the network channel interference intensity exceeds -85dBm and the terminal's movement speed is too high. The corresponding strategies that need to be updated are the channel adaptation strategy and the transmission guarantee strategy, while the resource scheduling strategy does not need to be updated. Among the measured deviation values, the constraint element deviation value is 2dBm (positive value, exceeding the threshold), the demand element deviation value is -0.2Mbps (negative value, below the threshold), and the transmission delay deviation value is 2ms (positive value, exceeding the threshold). Therefore, the direction of correction for the transmission guarantee strategy is to increase the link bandwidth configuration and optimize the transmission protocol to reduce latency; the direction of correction for the channel adaptation strategy is to strengthen anti-interference capabilities and reduce the actual channel interference intensity.

[0140] For example, the correction range is set to = absolute value of measured deviation × disturbance coefficient × 0.1 to ensure a reasonable range. Based on the calculated correction range, in the transmission guarantee strategy, the link bandwidth correction range = 0.2 × 1.2 × 0.1 = 0.02 Mbps, and the transmission delay correction range = 2 × 1.2 × 0.1 = 0.24 ms; in the channel adaptation strategy, the interference suppression correction range = 2 × 1.2 × 0.1 = 0.2 dBm. After completing the strategy parameter correction, the original transmission guarantee strategy's link bandwidth configuration of 4 Mbps is improved to 4.02 Mbps; the original transmission delay control parameter of ≤50 ms is optimized to ≤49.76 ms; and the original channel adaptation strategy's interference suppression target of ≤-85 dBm is strengthened to ≤-85.2 dBm, thus completing the parameter correction for the strategy that needs updating.

[0141] This avoids wasting resources due to ineffective strategy adjustments, ensures that strategy updates focus on core deviation issues, ensures that strategy corrections can specifically address deviations, and ensures that the corrected strategy can adapt to the current environmental disturbance state, avoiding insufficient or excessive corrections, and improving the adaptability of the strategy to the environment and deviation requirements.

[0142] The revised single-strategy approach cannot fully guarantee business quality. There is a mismatch between the revised strategy parameters and the simulation domain parameters of the virtual pre-simulation environment. If the revised strategy is directly included in the candidate strategy, it will lead to invalid simulation evaluation. Based on the updated dynamic weights, the priority logic of the strategy combination is determined. The element with the highest weight corresponds to the revised strategy as the core strategy, and the other two types of strategies are used as auxiliary strategies. Multiple strategy combinations are arranged in a way that the core strategy is fixed and the auxiliary strategies are flexibly combined. This ensures that the combination logic is consistent with the priority of each element after the update, highlighting the priority of core deviation correction.

[0143] The core parameters of the transmission simulation domain, resource simulation domain, and channel simulation domain in the virtual pre-simulation environment are retrieved. The parameter requirements of the strategy combination are compared one by one with the corresponding simulation domain parameters. If the parameter requirements of the strategy combination exceed the reasonable range of the simulation domain parameters, and the deviation cannot be compensated by adjusting the environment corresponding to the current disturbance coefficient, it is judged as an unmatched combination and is eliminated. If the strategy combination parameters are compatible with the simulation domain parameters and meet the indicators of the intent data, the combination is retained. All compatible strategy combinations are organized to form updated candidate guarantee strategies, and the correction focus and the root cause of the adaptation deviation for each strategy combination are marked.

[0144] For example, the updated dynamic weights are 0.467 for demand elements, 0.108 for adaptation elements, and 0.425 for constraint elements. The revised transmission guarantee strategy is the core strategy, which is associated with the demand elements and has the highest weight. The revised channel adaptation strategy and the unupdated resource scheduling strategy are auxiliary strategies, forming a total of 4 strategy combinations: revised transmission guarantee strategy, revised channel adaptation strategy and original resource scheduling strategy; revised transmission guarantee strategy, revised channel adaptation strategy and fine-tuned resource scheduling strategy with a fine-tuning range of 0.5%; revised transmission guarantee strategy, original channel adaptation strategy and original resource scheduling strategy; revised transmission guarantee strategy, revised channel adaptation strategy and over-adjusted resource scheduling strategy, with resource occupancy exceeding the simulation domain range of 30%-60%.

[0145] The parameters of the current simulation domain, including a transmission simulation domain bandwidth of 90-110Mbps and a latency of ≤50ms; a resource simulation domain occupancy rate of 30%-60%; and a channel simulation domain interference shielding threshold of ≤-85.2dBm, were retrieved. Four strategy combinations were compared. In the third strategy combination, the original channel adaptation strategy was not fully corrected, and the interference suppression target remained ≤-85dBm, which did not match the current channel simulation domain interference shielding threshold of ≤-85.2dBm and could not be compensated for by environmental adjustments with a disturbance coefficient of 1.2; therefore, it was eliminated. In the fourth strategy combination, the resource scheduling strategy was over-adjusted, and the resource occupancy rate exceeded the simulation domain range of 30%-60%; therefore, it was eliminated. The parameters of the first and second strategy combinations were both compatible with the simulation domain parameters and were retained, forming the updated candidate protection strategies.

[0146] This enables comprehensive coverage of multi-dimensional deviation correction and business assurance, ensuring the priority of core deviation correction strategies; preventing invalid strategies from entering subsequent simulation evaluation stages, reducing resource waste, ensuring that updated candidate assurance strategies are all adapted to the current simulation environment and environmental disturbance state, and improving the efficiency of subsequent strategy evaluation.

[0147] Specifically, solving the updated resource allocation scheme includes: Based on the elements corresponding to the root causes of the deviation, locate the resource demand dimension that needs to be updated, retrieve the updated dynamic weights, readjust the priority ranking of the corresponding resource demand dimension, and modify the resource supply ceiling of the corresponding resource demand dimension in combination with the current disturbance coefficient. Based on the updated candidate guarantee strategy, the demand thresholds for the corresponding resource demand dimension are extracted, the constraints of the resource optimization problem are updated, and the resource optimization problem is recalculated iteratively to obtain the initial resource allocation values ​​for the corresponding resource demand dimension. The updated initial resource allocation values ​​are compared with the resource fluctuation range and the actual carrying capacity of the service guarantee channel, and the remaining resource allocation values ​​are then integrated to form the updated resource allocation scheme.

[0148] The initial resource allocation scheme is built based on business scenarios without deviations and initial dynamic weights. However, the current business transmission has a clear root cause of deviation, and the adaptability of the corresponding resource demand dimensions has failed. If the original resource priority and supply limit are used, the resource allocation will continue to deviate from the business correction needs. The business elements corresponding to the root cause of the deviation are bound to the three types of resource demand dimensions to accurately lock the resource dimensions that only need to be updated, while the non-deviation related dimensions retain their original configuration to reduce ineffective adjustments.

[0149] The updated dynamic weights are retrieved, and the priorities of the corresponding resource demand dimensions are rearranged according to the weight percentage. The higher the weight percentage of a dimension, the higher the priority of resource allocation, ensuring that the resource demand corresponding to the core deviation correction is met first. Based on the quantitative correction rules of the disturbance coefficient and the upper limit of resource supply, the upper limit of the supply of locked resource dimensions is dynamically adjusted. The disturbance coefficient and the correction magnitude are positively correlated. The higher the degree of environmental disturbance, the greater the correction magnitude of the upper limit of resource supply. The corrected upper limit of supply must simultaneously meet the constraints of the actual available network resources and the parameters of the simulation domain, and must not exceed the physical carrying capacity limit.

[0150] For example, the root cause of the deviation is the demand element and the constraint element. The resource demand dimensions that need to be updated are link bandwidth demand and anti-interference resource demand. The resource occupancy demand does not need to be updated because it is not related to the deviation. After the update, the dynamic weights are demand element 0.467, adaptation element 0.108, and constraint element 0.425. The priority of the resource demand dimensions is readjusted to link bandwidth demand > anti-interference resource demand > resource occupancy demand. The current disturbance coefficient is 1.2, corresponding to a medium level of environmental disturbance. The supply limit correction coefficient is set to 0.9. The original resource supply limit was link bandwidth 72Mbps and anti-interference resource 22dBm. After the correction, the resource supply boundary is link bandwidth 64.8Mbps and anti-interference resource 19.8dBm. The resource occupancy supply limit remains unchanged at 54%.

[0151] This enables targeted optimization of resource allocation logic, avoids computational redundancy and configuration disorder caused by global adjustments, ensures resource supply for core deviations, improves the adaptability of resource allocation to the current environment, and lays a compliant foundation for resolving subsequent resource optimization problems from the underlying constraint level.

[0152] The constraints of the initial resource optimization problem are constructed based on the uncorrected strategy parameters and business requirements, which can no longer match the updated candidate guarantee strategies. If the original constraints are directly used for calculation, the solution results will be incompatible with the current strategy execution requirements. The updated candidate guarantee strategies are traversed, and the execution parameters of the corresponding resource requirement dimension in each strategy are extracted. These parameters are integrated into a resource requirement threshold to replace the constraint threshold of the original resource optimization problem. The newly determined priority ranking and the corrected resource supply boundary are substituted into the resource optimization problem to complete the full update of the constraints and boundary conditions, while keeping the original objective function unchanged.

[0153] The calculation is carried out using a dimensional iterative algorithm according to the updated priority order. In each round of calculation, the current dimension allocation value is checked to see if it meets the updated constraints and boundary requirements. If an out-of-bounds error occurs, the previous dimension allocation parameters are adjusted backtracked until all dimensions obtain the required initial resource allocation values. The iterative process must be synchronized with the parameter update cycle of the virtual pre-simulation environment to ensure the consistency of the calculation logic.

[0154] For example, resource requirement thresholds are extracted from the updated candidate guarantee strategy. The link bandwidth requirement threshold is 4.02-6 Mbps, the anti-interference resource requirement threshold is one dedicated channel with a power of 20-21 dBm, and the resource occupancy requirement threshold remains at 45%-50%. These thresholds and the revised supply boundary are substituted into the resource optimization problem to complete the constraint update. The initial resource allocation values ​​are obtained by iteratively calculating each dimension according to the priority of link bandwidth requirement, anti-interference resource requirement, and resource occupancy requirement, with a link bandwidth of 5 Mbps, an anti-interference resource of one dedicated channel with a power of 20.5 dBm, and a resource occupancy of 48%.

[0155] This enables dynamic adaptation and updating of resource optimization problems, eliminates parameter conflicts between strategies and resource allocation, and ensures the synergistic rationality of resource allocation values ​​through iterative calculations in each dimension. It also ensures that the initial allocation values ​​accurately meet the dual needs of deviation correction and strategy execution, providing basic data for the integration of subsequent resource allocation schemes.

[0156] The initial resource allocation values ​​only satisfy mathematical constraints and do not verify their compatibility with the inherent parameters of the resource simulation domain and the physical carrying capacity of the service guarantee channel. If the allocation values ​​are directly adopted to form the scheme, the allocation parameters may exceed the floating range of the simulation domain or the carrying capacity limit of the channel. The initial resource allocation values ​​of each dimension are compared with the corresponding range one by one to determine whether the allocation value is within the range. At the same time, the actual carrying capacity parameters of the service guarantee channel are retrieved. Any allocation value that is determined to be out of bounds in either round of comparison is removed. For the missing dimension parameters after removal, backtracking and fine-tuning are performed in combination with priority and supply boundary. All qualified resource allocation values ​​are integrated according to the resource demand dimension to form an updated resource allocation scheme including resource configuration parameters, and the corresponding deviation root causes, updated dynamic weights and candidate guarantee strategy identifiers are marked.

[0157] For example, the resource fluctuation range is 90-110Mbps link bandwidth, 30%-60% resource utilization, 1-2 dedicated channels with power of 18-22dBm for anti-interference resources, and the actual carrying capacity of the service guarantee channel is a maximum link bandwidth allocation of 65Mbps and a maximum carrying capacity of 2 dedicated channels for anti-interference resources; the initial resource allocation values ​​are 5Mbps link bandwidth, 20.5dBm anti-interference resources, and 48% resource utilization, all of which are within the compliance range after double comparison; the updated resource allocation scheme is formed by integration, and the corresponding video call service, the root cause of the deviation is excessive channel interference and excessive terminal movement speed, and the associated updated candidate guarantee strategy are marked.

[0158] This process eliminates unexecutable abnormal parameters, ensuring that the resource allocation scheme simultaneously adapts to both simulation domain logic constraints and physical channel carrying capacity. This enables the scheme to possess resource configuration logic and traceability, which is then used for resource scheduling execution of service assurance channels. The updated resource allocation scheme will be applied to real-time resource scheduling of business data streams, providing a resource configuration benchmark for the next round of business transmission testing and root cause identification of deviations. This will promote the formation of a continuous iterative optimization closed loop in the communication service quality assurance system, improving the stability and quality compliance rate of business transmission.

Claims

1. A method for ensuring the quality of communication services, characterized in that, include: Obtain service description information and environmental data from user terminals, transform the service description information into intent data, and determine the dynamic weight of the intent data based on the environmental data; A virtual pre-simulation environment is constructed based on real-time network status, intent data, and dynamic weights. Candidate assurance strategies are generated based on intent data. The performance of the candidate assurance strategies is evaluated in parallel based on dynamic weights in the virtual pre-simulation environment, and a quality assurance strategy is selected. The resource requirements of the quality assurance strategy are mapped to the network resource capabilities as a resource optimization problem. The resource allocation scheme is solved with dynamic weights as priority constraints, and a service assurance channel is established for the corresponding business data flow. Acquire measured data of business data flow in the service assurance channel, compare the measured data with the intent data to determine the degree of intent realization of the quality assurance strategy, and when the degree of intent realization is less than the preset threshold, identify the root cause of the deviation and store it in the case library to synchronously update the dynamic weight, strategy generation and resource allocation scheme solution.

2. The communication service quality assurance method as described in claim 1, characterized in that, The transformation of the intent data includes: synchronizing and marking the service description information and environmental data of the user terminal with timestamps, and semantically decomposing the service description information to extract the demand elements, adaptation elements and constraint elements of service transmission and quantify and encapsulate them into intent data; Determining the dynamic weights includes: dividing the environmental data into dimensions such as terminal mobility status, network channel interference, network load, and regional user density; pre-setting a perturbation threshold range for each dimension; and determining the perturbation coefficient based on the matching relationship between the dimension data and the perturbation threshold range. Assign basic weight values ​​to demand elements, adaptation elements, and constraint elements, and calibrate the basic weight values ​​according to the disturbance coefficient to determine the dynamic weight of the intent data.

3. The communication service quality assurance method as described in claim 2, characterized in that, The construction of the virtual pre-simulation environment includes: Based on the link bandwidth and transmission delay in real-time network conditions, a simulation space is constructed by combining environmental data and divided into a transmission simulation domain, a resource simulation domain, and a channel simulation domain. The demand elements are mapped to the link bandwidth and latency thresholds in the transmission simulation domain, the adaptation elements are mapped to the resource fluctuation range in the resource simulation domain, and the constraint elements are mapped to the interference shielding thresholds in the channel simulation domain. The simulation domain's computing resource quota is configured according to dynamic weights, and the simulation domain's parameter update cycle is configured according to the disturbance coefficient. The configured simulation domains are then integrated to form a virtual pre-simulation environment for the corresponding business data flow.

4. The communication service quality assurance method as described in claim 3, characterized in that, The candidate generation guarantee strategy includes: Based on the demand elements, adaptation elements, and constraint elements in the intent data, corresponding transmission guarantee strategies, resource scheduling strategies, and channel adaptation strategies are generated. The three types of strategies are combined and arranged according to dynamic weights to obtain multiple strategy combinations. Strategy combinations that do not match the simulation domain parameters are eliminated based on the perturbation coefficient to form candidate protection strategies.

5. A method for ensuring quality of service in communication as described in claim 4, characterized in that, The selected quality assurance strategy includes: The candidate guarantee strategies were simulated in parallel using a virtual pre-simulation environment. The bandwidth delay compliance rate in the transmission simulation domain, the resource occupancy rate in the resource simulation domain, and the interference suppression rate in the channel simulation domain were recorded. The bandwidth latency compliance rate, resource utilization rate, and interference suppression rate are weighted according to dynamic weights to obtain the comprehensive evaluation value of each group of candidate guarantee strategies; Candidate assurance strategies with interference suppression rates less than the interference shielding threshold are eliminated. The remaining assurance strategies are sorted in descending order of their comprehensive evaluation values, and the candidate assurance strategy with the highest comprehensive evaluation value is selected as the quality assurance strategy.

6. A method for ensuring quality of service in communication as described in claim 5, characterized in that, The mapping as a resource optimization problem includes: Extract the resource requirements of the quality assurance strategy, where the transmission assurance strategy corresponds to the link bandwidth requirement, the resource scheduling strategy corresponds to the resource occupancy requirement, and the channel adaptation strategy corresponds to the anti-interference resource requirement. Retrieve the resource fluctuation range of the resource simulation domain, and combine it with the dynamic weights corresponding to the adaptation elements to determine the demand threshold and priority ranking for each resource demand dimension. Extract the available resources of network resource capabilities, and combine the link bandwidth in the real-time network status with the network load and regional user density in the environmental data to determine the upper limit of resource supply for each resource demand dimension. By taking the demand threshold and priority ranking of resource demand as constraints, the upper limit of resource supply as boundary conditions, and minimizing the deviation between the actual allocation of resources and the demand threshold as the objective function, a resource optimization problem is formed.

7. A method for ensuring quality of service in communication as described in claim 6, characterized in that, The solution resource allocation scheme includes: The solution order for each resource demand dimension is determined according to the dynamic weights corresponding to the demand elements, adaptation elements, and constraint elements. The upper limit of resource supply for each resource demand dimension is adjusted based on the disturbance coefficient to obtain a resource supply boundary that adapts to the current environmental disturbance. With the goal of minimizing the deviation, the resource optimization problem is iteratively calculated dimension by dimension according to the solution order to obtain the initial resource allocation value for each resource demand dimension; The initial resource allocation value is compared with the resource fluctuation range of the resource simulation domain. Initial resource allocation values ​​that exceed the resource fluctuation range are eliminated, and the remaining resource allocation values ​​are integrated to form a resource allocation scheme for the corresponding business data flow. At the same time, a service guarantee channel is established for the corresponding business data flow.

8. A method for ensuring quality of service in communication as described in claim 7, characterized in that, The root causes of the identification bias include: Obtain measured data of business data flow in the service assurance channel, compare the measured data with the intent data, and obtain the measured deviation values ​​of different elements; The measured deviation values ​​of different elements are weighted and summed according to dynamic weights to obtain the total deviation value. The total deviation value is then normalized with the deviation threshold to obtain the degree of intention achievement. When the degree of achievement of the intention is less than the preset threshold, the source of deviation of different elements is determined by combining the current disturbance coefficient with the dimensional data in the environmental data, so as to identify the root cause of the deviation. The root causes of deviations, measured deviation values ​​of different elements, current disturbance coefficients, dimensional data in environmental data, and degree of intent fulfillment are encapsulated and stored in the case library.

9. A method for ensuring quality of service in communication as described in claim 8, characterized in that, The updated dynamic weights include: The weighted calibration range is determined by the ratio of the measured deviation value of the corresponding element to the total deviation value. The weight calibration factor is calculated based on the difference between the degree of intention realization and the preset threshold. The weight calibration magnitude is then multiplied by the weight calibration factor to obtain the weight calibration amount of the corresponding element. The basic weight values ​​of the corresponding elements are corrected based on the weight calibration amount, and after recalibration in combination with the current disturbance coefficient, the updated dynamic weights are obtained.

10. A method for ensuring quality of service in communication as described in claim 9, characterized in that, Updating the policy generation includes: Based on the elements corresponding to the root causes of the deviation, locate the transmission guarantee strategy, resource scheduling strategy or channel adaptation strategy that needs to be updated. Based on the magnitude of the measured deviation value of the corresponding element, determine the parameter correction direction of the corresponding strategy, and determine the parameter correction magnitude in combination with the disturbance coefficient. Based on the updated dynamic weights, the corrected strategies are rearranged to obtain multiple strategy combinations. Strategy combinations that do not match the simulation domain parameters are then eliminated to form updated candidate protection strategies.

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