Method and system for guaranteeing endogenous service quality of communication and sensing integrated network, and electronic equipment

Abstract

The invention provides an endophytic service quality guarantee method and system for a communication and sensing integrated network and electronic equipment, and belongs to the technical field of communication, and the method comprises the steps: generating index data representing the service quality of a business according to business sensing information; performing service quality grade division on the business according to the index data to obtain a target service quality grade; according to the target service quality level, allocating network resources of the communication and sensing integrated network to obtain a resource allocation result; performing frequency domain separation or time domain separation processing on the communication signal and the sensing signal according to the resource allocation result to generate a collaborative optimization parameter; and iteratively adjusting a resource allocation result according to the collaborative optimization parameter, the real-time channel state information and the current position information of the user to obtain an updated resource scheduling instruction, and outputting the updated resource scheduling instruction. According to the invention, through dynamic resource scheduling, communication and perception cooperative interference optimization and an adaptive adjustment mechanism, the stability of service quality in a complex environment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, system and electronic equipment for ensuring the quality of endogenous services in a telepathic integrated network. Background Art

[0002] Synapses are a key trend in the future evolution of communications technology and a hallmark of 6G networks. 6G networks place extremely stringent QoS (Quality of Service) requirements, including immersive communications, ultra-low latency, highly reliable communications, and high-precision, real-time positioning and perception. In synapses scenarios, communication and perception services share network resources. However, existing QoS assurance mechanisms are often designed for a single communication scenario, employing static resource allocation strategies and fixed priority rules. These mechanisms struggle to dynamically adapt to the diverse needs of synapses and network environments.

[0003] Therefore, how to ensure the stability of service quality in complex environments has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The present invention provides a method, system, electronic device and storage medium for ensuring the quality of service in an integrated inter-sensory network, which are used to solve the defects in the prior art and achieve the stability of service quality in a complex environment.

[0005] The present invention provides a method for ensuring the quality of endogenous services in a synaesthesia-integrated network, comprising the following steps: Generate indicator data representing business service quality based on business perception information; Classify the service quality level of the business according to the indicator data to obtain the target service quality level; Allocating network resources of the synergy network according to the target service quality level to obtain a resource allocation result; Perform frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters; Iteratively adjusting the resource allocation result according to the collaborative optimization parameter, the real-time channel state information, and the current location information of the user to obtain an updated resource scheduling instruction; The updated resource scheduling instruction is output to the network side of the current networking for execution.

[0006] According to a method for ensuring intrinsic service quality in a telepathic network provided by the present invention, the service quality level of the service is divided into service levels according to the indicator data to obtain a target service quality level, including: Establishing a hierarchical threshold table for the indicator data, wherein the hierarchical threshold table sets multiple threshold intervals for each indicator including at least latency, packet loss rate, perception accuracy and coverage to correspond to different service quality levels; Comparing each indicator in the indicator data with the grading threshold table item by item to obtain a candidate service quality level to which each indicator belongs; The candidate service quality levels of various indicators are weighted and summed according to the preset weighting coefficients to obtain a comprehensive evaluation score; The comprehensive evaluation score is mapped to a predefined score range to determine the target service quality level, wherein the grading threshold table is dynamically updated based on historical network performance data and business scenarios.

[0007] According to a method for ensuring intrinsic service quality of a synaesthesia network provided by the present invention, allocating network resources of the synaesthesia network according to the target service quality level to obtain a resource allocation result includes: According to the target quality of service level, searching the preset weight mapping table for initial weights corresponding to spectrum resources, transmit power resources, and computing resources; Scaling each of the initial weights according to the current network load factor to obtain a scaled weight; Constructing a priority weight set based on all the scaled weights; Under the priority weight set, a joint resource allocation optimization model is constructed with the goal of maximizing the weighted sum of communication throughput utility, perception accuracy utility, and computation completion rate utility, and under the constraints that interference power, overall power consumption, and service latency meet preset thresholds; Lagrange multipliers are introduced into the joint resource allocation optimization model, and the original variables and the dual variables are updated synchronously by using a gradient iteration method until the overall utility difference between two adjacent iterations is less than a convergence threshold; The converged continuous spectrum allocation vector, transmit power allocation vector, and computing resource allocation vector are mapped to the discrete sub-bands, power steps, and computing instances available in the system according to the nearest neighbor strategy to form a spectrum sub-band allocation vector, a transmit power allocation vector, and a computing resource capacity allocation vector; The spectrum subband allocation vector, the transmit power allocation vector, and the computing resource capacity allocation vector are combined to obtain the resource allocation result.

[0008] According to a method for ensuring endogenous service quality in a synergetic network provided by the present invention, frequency domain separation or time domain separation processing is performed on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters, including: When the resource allocation result indicates that frequency domain separation is adopted, generating a first optimization parameter according to the resource allocation result; When the resource allocation result indicates that time domain separation is adopted, generating a second optimization parameter according to the resource allocation result; The collaborative optimization parameter is obtained according to the first optimization parameter and / or the second optimization parameter.

[0009] According to a method for ensuring endogenous service quality in a telepathic network provided by the present invention, the first optimization parameters include a subcarrier mapping matrix, an identifier of a protected subcarrier, and a power mask; when the resource allocation result indicates the use of frequency domain separation, generating the first optimization parameters according to the resource allocation result includes: When the resource allocation result indicates that frequency domain separation is adopted, constructing the subcarrier mapping matrix according to the resource allocation result; Isolating a first target subcarrier set allocated for the communication signal from a second target subcarrier set allocated for the perception signal in the frequency domain, inserting at least one guard subcarrier between the first target subcarrier set and the second target subcarrier set, and determining an identifier of the guard subcarrier; A corresponding power mask is allocated to each target subcarrier, so that each target subcarrier corresponds to a unique power mask.

[0010] According to a method for ensuring endogenous service quality in a telepathic network provided by the present invention, the second optimization parameter includes the start time and duration of the communication time slot, the start time and duration of the perception time slot, and the duration of the guard interval; when the resource allocation result indicates the use of frequency domain separation, generating the first optimization parameter according to the resource allocation result includes: When the resource allocation result indicates that time domain separation is adopted, determining the starting time and duration of the communication time slot and the starting time and duration of the sensing time slot according to the resource allocation result; A guard interval is inserted at the boundary between adjacent time slots, and the duration of the guard interval is determined.

[0011] According to a method for ensuring intrinsic service quality in a synergistic network provided by the present invention, iteratively adjusting the resource allocation result according to the collaborative optimization parameter, real-time channel state information, and current location information of the user to obtain an updated resource scheduling instruction includes: Predicting the user's predicted location information in the next frame based on the continuously acquired user's current location information; Calculating a predicted channel gain vector for each service based on the predicted position information and the real-time channel state information; Inputting the predicted channel gain vector and the collaborative optimization parameter into an incremental resource scheduling function, and iteratively revising the resource allocation result using a gradient descent-dual update method with the goal of minimizing resource reconfiguration overhead and maximizing service quality utility, until the utility difference between two adjacent iterations is less than a preset convergence threshold or the number of iterations reaches an upper limit, thereby obtaining a revised allocation result; The converged modified allocation results are encapsulated as updated resource scheduling instructions.

[0012] According to a method for ensuring the intrinsic service quality of a synergistic network provided by the present invention, before generating indicator data representing the service quality of a business based on the business perception information, the method includes: Deploy network function components that can be instantiated on multiple nodes on the network side of the current network; Synchronize the network function components to the base station side and the terminal side and complete the instantiation; The service perception information is collected in real time on the network side, the base station side and the terminal side through synchronized network function components.

[0013] The present invention also provides a synaesthesia integrated network endogenous service quality assurance system, comprising the following modules: A processing module is used to generate indicator data representing the service quality of the business based on the business perception information; The processing module is further configured to classify the service quality level of the business according to the indicator data to obtain a target service quality level; The processing module is further configured to allocate network resources of the synergy network according to the target service quality level to obtain a resource allocation result; The processing module is further configured to perform frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters; The processing module is further configured to iteratively adjust the resource allocation result according to the collaborative optimization parameter, the real-time channel state information, and the current location information of the user to obtain an updated resource scheduling instruction; The execution module is used to output the updated resource scheduling instruction to the network side of the current networking for execution.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for ensuring the quality of service inherent in the synaesthesia network as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for ensuring the quality of service inherent in the synaesthesia network as described above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for ensuring the quality of service inherent in the synaesthesia network.

[0017] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: First, based on service perception information, indicator data is generated to characterize service quality. This enables real-time quantification and standardization of service operational status, ensuring comparable and computable inputs for subsequent scheduling processes. Based on this indicator data, services are further classified into service quality levels to obtain target service quality levels. This allows for the establishment of differentiated management strategies based on service importance and resource sensitivity, improving the targeted and refined nature of resource scheduling. After obtaining the target service quality level, spectrum, transmit power, and computing resources in the integrated network are jointly allocated according to a predefined level-resource weight mapping relationship to form a resource allocation result, thereby achieving optimal resource utilization and service level matching across the entire network. Based on this resource allocation result, frequency domain separation or time domain separation processing is further performed to generate collaborative optimization parameters. This ensures that communication signals and perception signals have clear resource boundaries at the physical layer, thereby reducing the risk of mutual interference between the two types of services when sharing resources and improving the overall operational stability and service parallelization capabilities of the network. Subsequently, the collaborative optimization parameters are combined with real-time channel status information and the user's current location information to iteratively adjust the resource allocation results. Through predictive channel optimization and dynamic resource fine-tuning, updated resource scheduling instructions are obtained, enabling the scheduling strategy to quickly respond to and dynamically adapt to environmental changes, significantly improving the continuity and adaptability of network services. Finally, the updated resource scheduling instructions are output to the network side of the current network for execution, ensuring that the optimized configuration policy can quickly take effect on network nodes, thus achieving end-to-end closed-loop control, ensuring the stability of service quality and the network's dynamic optimization capabilities in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is one of the flow charts of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0020] Figure 2 This is the second flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0021] Figure 3 This is the third flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0022] Figure 4 This is the fourth flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0023] Figure 5 This is the fifth flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0024] Figure 6 This is the sixth flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0025] Figure 7 This is the seventh flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0026] Figure 8 This is the eighth flow chart of the method for ensuring the quality of endogenous service in the integrated network provided by the present invention.

[0027] Figure 9 It is a structural diagram of the synaesthesia integrated network endogenous service quality assurance system provided by the present invention.

[0028] Figure 10 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field without making creative efforts based on the embodiments of the present invention are within the scope of protection of the present invention.

[0030] It should be noted that, in the description of the present invention, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. The orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0031] The terms "first," "second," and so forth, used herein are used to distinguish similar objects, not to describe a specific order or precedence. It should be understood that such terms are interchangeable where appropriate, allowing embodiments of the present invention to be implemented in an order other than that illustrated or described herein. Furthermore, the terms "first," "second," and so forth generally distinguish objects of a single type, and do not limit the number of objects. For example, the first object may be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the connected objects.

[0032] The following combination Figures 1-10 The present invention describes a method, system, electronic device and storage medium for ensuring the quality of endogenous services in a synaesthesia-integrated network.

[0033] Figure 1 This is one of the flow charts of the method for ensuring the quality of service of the integrated network provided by the present invention, such as Figure 1 As shown, including but not limited to the following steps: Step 101: Generate indicator data representing the service quality of the business based on the business perception information.

[0034] In a preferred embodiment of the present invention, in order to realize the service quality assurance mechanism in the integrated network, it is first necessary to obtain perception data that can accurately characterize the business operation status, and generate structured indicator data based on this for subsequent service quality level division and resource scheduling. The business perception information includes but is not limited to the original data of multiple dimensions such as the delay and packet loss rate of the communication business, and the perception accuracy and coverage of the perception business. These data are uniformly aggregated through the network function component and input into the indicator calculation module. The indicator calculation module generates indicator data for characterizing the service quality of the business based on various types of original perception information according to preset calculation formulas and normalization processing rules. The indicator data has a unified data structure and can support the multidimensional analysis required for subsequent service quality level division.

[0035] In one possible implementation, Figure 2 This is the second flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention. Figure 2 As shown, before step 101, the method further includes the following steps: Step 201: Deploy a network function component that can be instantiated on multiple nodes on the network side of the current network.

[0036] Step 202: Synchronize the network function components to the base station side and the terminal side and complete the instantiation.

[0037] Step 203: Service perception information is collected in real time on the network side, the base station side, and the terminal side through the synchronized network functional components.

[0038] In one possible implementation of the present invention, to support the deployment and operation of a service quality assurance mechanism in a telepresence network, step 201 is first performed to deploy a network function component (NFC) instantiated on multiple nodes on the network side of the current network. The NFC is designed to be highly virtualized and portable, capable of being integrated in a containerized or microservices-based manner within the core network control entity and supporting on-demand migration to other network nodes to meet subsequent real-time service perception and service assurance requirements.

[0039] After completing component deployment on the network side, the system proceeds to step 202, where it synchronizes the network function components to the base station and terminal sides via the interface protocol and configuration synchronization mechanism, and completes the instantiation operation respectively. This synchronization process not only includes the distribution of component function definitions, but also covers the configuration of perception parameters, data format conventions, and trigger condition settings, ensuring that the components maintain functional consistency and coordinated responsiveness in different node environments.

[0040] After component instantiation is complete, step 203 is executed, where synchronized network functional components collect service perception information in real time from the network, base station, and terminal sides. This information includes, but is not limited to, latency and packet loss during communication services, as well as perception accuracy and coverage during perception services. During the collection process, each instantiated component obtains key operational status parameters in real time through a data exchange interface with the local service processing flow. Based on the deployment strategy, the component can choose to perform local pre-processing or directly upload the information to the core analysis unit to complete the aggregation and standardization of the perception information.

[0041] By executing steps 201 to 203 in sequence, the system realizes the distributed deployment and collaborative collection of service quality assurance functions in a multi-level network structure, so that the integrated network can continuously obtain high-quality perception data support when facing dynamic business loads and complex scene changes, and provide complete and real-time basic data guarantee for subsequent indicator generation, service level division and resource optimization scheduling.

[0042] Step 102: Classify the service quality level of the service according to the indicator data to obtain the target service quality level.

[0043] In one possible embodiment of the present invention, to achieve refined resource guarantees for different service types in the telepresence network, it is necessary to classify the services into service quality levels based on the collected indicator data, thereby providing a classification basis for subsequent resource allocation. Therefore, step 102 is executed to classify the services into service quality levels based on the indicator data to obtain a target service quality level.

[0044] In one possible implementation, Figure 3 This is the third flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention. Figure 3 As shown, step 102 specifically includes the following steps: Step 301: Establish a hierarchical threshold table for indicator data. The hierarchical threshold table sets multiple threshold intervals for each indicator including at least latency, packet loss rate, perception accuracy and coverage to correspond to different service quality levels.

[0045] Step 302: Compare each indicator in the indicator data with the grading threshold table item by item to obtain the candidate service quality level to which each indicator belongs.

[0046] Step 303: Perform weighted summation on the candidate service quality levels of various indicators according to preset weighting coefficients to obtain a comprehensive evaluation score.

[0047] Step 304: Map the comprehensive evaluation score to a predefined score range to determine the target service quality level, wherein the grading threshold table is dynamically updated based on historical network performance data and business scenarios.

[0048] In one possible implementation of the present invention, to accurately categorize service quality levels, a measurable and adaptable evaluation system must be established based on the collected indicator data. Therefore, during step 102, the process is further refined into steps 301 to 304, gradually mapping and generating the target service quality levels from the original indicators.

[0049] First, in step 301, the system establishes a grading threshold table for the indicator data. The grading threshold table constitutes a set of dynamically maintainable hierarchical rules, which is used to map multiple service quality indicators corresponding to different services to specific grade labels. To ensure the comprehensiveness and scalability of the evaluation, the grading threshold table covers at least four basic indicators: latency, packet loss rate, perception accuracy and coverage, and sets multiple grading intervals for each indicator, each interval corresponding to a different service quality level. For example, latency can be divided into a low-latency level of milliseconds, a medium-latency level and a non-real-time level, and so on. The grading threshold table not only comes from a pre-set standard template, but can also be dynamically adjusted according to historical network performance data and current business scenarios to maintain adaptability to environmental changes.

[0050] In step 302, the system enters the generated indicator data item by item into the aforementioned grading threshold table. By matching the indicator values ​​with the interval rules under the corresponding indicator items, the system determines which level interval each indicator falls into, thereby obtaining the candidate service quality level corresponding to each indicator. This step provides preliminary evaluation results for the level determination, forming an intermediate mapping set of multiple indicators and multiple levels.

[0051] To avoid uneven weighting or bias among the various indicators in the evaluation, the system introduces a weighted summation mechanism in step 303. The system weights and superimposes each candidate QoS level based on preset weighting coefficients to generate a unified comprehensive evaluation score. Weighting coefficients can be predefined based on the service type. For example, in high-definition video transmission, bandwidth and latency are more important, while in perception applications, perception accuracy and coverage are more important. This creates an evaluation weight structure that can be adjusted as needed.

[0052] Finally, in step 304, the system matches the comprehensive evaluation score with a predefined score range and determines the final target service quality level based on the score range. This mapping process is completed rapidly through a table lookup and forms an integrated linkage mechanism with the grading threshold table, achieving a balance between accuracy and efficiency in grading different services. Notably, the dynamic update capability of the grading threshold table enables the grading process to adapt to the long-term evolution of network operating conditions and continuous changes in service models, thus possessing high adaptability and practical deployment value.

[0053] Step 103: Allocate network resources of the interawareness network according to the target service quality level to obtain a resource allocation result.

[0054] In one possible implementation of the present invention, to achieve differentiated guarantees and optimized resource utilization for multiple types of services, it is necessary to finely allocate network resources for the inter-sensory network based on the target quality of service levels obtained in the previous steps, thereby generating a resource allocation result that meets the requirements of multi-service collaboration. Therefore, when executing step 103, the system comprehensively considers the service level, the current network load status, and multi-dimensional resource supply conditions to construct a joint allocation strategy for global resource configuration.

[0055] In one possible implementation, Figure 4 This is the fourth flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention. Figure 4 As shown, step 103 specifically includes the following steps: Step 401: According to the target service quality level, the initial weights corresponding to the spectrum resources, the transmission power resources and the computing resources are retrieved from a preset weight mapping table.

[0056] Step 402: scaling each initial weight according to the current network load factor to obtain scaled weights.

[0057] Step 403: Construct a priority weight set based on all scaled weights.

[0058] Step 404: Under the priority weight set, with the goal of maximizing the weighted sum of communication throughput utility, perception accuracy utility, and computation completion rate utility, and under the constraints that interference power, overall power consumption, and service delay meet preset thresholds, a joint resource allocation optimization model is constructed.

[0059] Step 405: Introduce Lagrange multipliers into the joint resource allocation optimization model, and use gradient iteration to synchronously update the original variables and the dual variables until the overall utility difference between two adjacent iterations is less than the convergence threshold.

[0060] Step 406: Map the converged continuous spectrum allocation vector, transmit power allocation vector, and computing resource allocation vector to the discrete sub-bands, power steps, and computing power instances available in the system according to the nearest neighbor strategy to form a spectrum sub-band allocation vector, a transmit power allocation vector, and a computing resource capacity allocation vector.

[0061] Step 407: Combine the spectrum sub-band allocation vector, the transmit power allocation vector, and the computational resource capacity allocation vector to obtain a resource allocation result.

[0062] In one possible implementation of the present invention, in order to achieve differentiated protection for services with different service quality levels and make the resource allocation results more consistent with the complex resource requirements in the synergetic network, the system further refines step 103 into steps 401 to 407 to implement a refined allocation strategy from service level to resource results.

[0063] In step 401, the system first retrieves the initial weights for the spectrum resources, transmit power resources, and computing resources corresponding to the target service quality level determined in the previous step from a preset weight mapping table. The weight mapping table is a multi-dimensional resource-level correspondence table, pre-built in the system's scheduling configuration center. It maps different service quality levels to the priority allocation ratios of various network resources. The purpose of this step is to ensure that resource allocation reflects the importance of service levels at the initial stage, providing a differentiated basis for subsequent optimized scheduling.

[0064] Then, in step 402, the system obtains the network load factor fed back by the current network operation status. This load factor can be calculated in real time by the scheduling controller monitoring dynamic parameters such as system bandwidth occupancy, computing node utilization, and terminal connection density. On this basis, the system scales the initial weight obtained in step 401. The scaling method can be a linear function, an exponentially decreasing function, or an adaptive function based on historical load trends. The specific function can be pre-set according to business needs. Through this scaling process, the compression of high-priority services can be avoided when resources are tight, while limiting the excessive occupancy of low-level services, thereby improving the global rationality and dynamic response capabilities of resource allocation.

[0065] Next, in step 403, the system combines the scaled resource weights into a unified priority weight set, which serves as the input parameter for the objective function of the resource optimization model. This priority weight set not only reflects the varying levels of dependency of different service levels on resource types, but also incorporates the current network load adjustment results, making resource allocation decisions more timely and adaptable to specific scenarios.

[0066] Then in step 404, the system constructs a joint resource allocation optimization model based on the above-mentioned priority weight set. The objective function of the optimization model is set to maximize the weighted sum of communication throughput utility, perception accuracy utility, and calculation completion rate utility under the resource constraints of the entire network. The utility function can be designed as a logarithmic, linear, or saturation function according to different resource types to reflect the marginal improvement characteristics of resources on business performance. At the same time, several constraints are also introduced into the optimization model: including but not limited to the interference power upper limit of spectrum usage, the total transmission power not exceeding the system maximum power consumption threshold, and the business service delay not exceeding the task completion time limit. This joint optimization model takes into account both performance maximization and resource availability, and is the core technical foundation for achieving efficient scheduling.

[0067] To solve the aforementioned nonlinear constrained optimization problem, in step 405, the system employs a dual optimization method based on Lagrange multipliers. This method constructs a Lagrange function and introduces the corresponding dual variables, alternating between the original and dual variables using gradient descent. During each iteration, the system calculates the total utility function value and all constraint residuals for the current resource allocation result and determines whether convergence conditions are met. For example, the difference in the objective function between two consecutive iterations is below a preset threshold ε, or the maximum number of iterations has been reached. This optimization method ensures stable model convergence while maintaining low computational complexity, making it suitable for real-time allocation calculations in network environments.

[0068] In step 406, the system discretizes the spectrum allocation vector, transmit power allocation vector, and computing resource allocation vector obtained through convergence in the above steps. Because resources available in real networks are typically in fixed granularity units (e.g., 20 MHz spectrum subbands, 100 mW power steps, 1 CPU core compute instance), the system uses a nearest-neighbor mapping strategy to map continuous allocations to the nearest available resource unit, preventing configuration errors from causing execution bias. Ultimately, the system generates the spectrum subband allocation vector, transmit power allocation vector, and computing resource capacity allocation vector, ensuring that the scheduling results can be directly used for system command issuance and execution.

[0069] Finally, in step 407, the system structurally combines the three discretized resource vectors to form the final resource allocation result. This result clearly indicates the spectrum allocation subband number, allocated transmit power, and number of allocated computing resource units for each service. It is stored and distributed in a unified data structure to drive the scheduling and channel configuration of communication and perception signals.

[0070] By completely executing steps 401 to 407, the present invention not only realizes the refined allocation of resources driven by business levels, but also maximizes the service quality of high-priority businesses while adapting to changes in network status in real time, providing effective technical support for building a unified scheduling and perception coordination mechanism in the integrated network.

[0071] Step 104: Perform frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters.

[0072] In one possible implementation of the present invention, to further reduce the mutual interference between communication signals and perception signals in a synergistic network and improve the collaborative efficiency of the two types of services in resource coexistence scenarios, it is necessary to further optimize the signal processing method based on the resource allocation results after completing resource allocation. Therefore, in step 104, based on the aforementioned resource allocation results, the system performs separation processing of communication signals and perception signals and generates collaborative optimization parameters for subsequent scheduling and modulation.

[0073] In one possible implementation, Figure 5 This is the fifth flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention. Figure 5 As shown, step 104 specifically includes the following steps: Step 501: When the resource allocation result indicates that frequency domain separation is adopted, a first optimization parameter is generated according to the resource allocation result.

[0074] In one possible implementation of the present invention, to ensure effective isolation of communication signals and perception signals at the physical layer after frequency domain resource allocation and to avoid performance degradation caused by spectrum cross-interference, it is necessary to perform frequency domain separation configuration based on the resource allocation result. Therefore, in step 501, when the resource allocation result indicates that the current network scenario should adopt frequency domain separation, the system generates a first optimization parameter based on the resource allocation result to guide the physical implementation of subcarrier allocation and transmission control.

[0075] In a possible implementation, the first optimization parameter includes a subcarrier mapping matrix, an identifier of a protection subcarrier, and a power mask; Figure 6 This is the sixth flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention, such as Figure 6 As shown, step 501 specifically includes the following steps: Step 601: When the resource allocation result indicates that frequency domain separation is adopted, a subcarrier mapping matrix is ​​constructed according to the resource allocation result.

[0076] Step 602: Isolate the first target subcarrier set allocated for the communication signal and the second target subcarrier set allocated for the perception signal in the frequency domain, insert at least one protection subcarrier between the first target subcarrier set and the second target subcarrier set, and determine the identifier of the protection subcarrier.

[0077] Step 603: Allocate a corresponding power mask to each target subcarrier, so that each target subcarrier corresponds to a unique power mask.

[0078] In a possible implementation of the present invention, in order to achieve effective isolation of communication signals and perception signals in the frequency domain and ensure that the physical layer configuration of spectrum resources is compatible with the service type, the system further refines the execution of step 501 and completes steps 601 to 603 in sequence to generate a complete first optimization parameter, on the premise that the resource allocation result clearly adopts the frequency domain separation strategy.

[0079] First, in step 601, the system constructs a subcarrier mapping matrix based on the resource allocation result. The resource allocation result clearly indicates the spectrum subband range allocated to the communication service and the perception service respectively, and the system divides the spectrum into several discrete subcarrier units accordingly. Then the system extracts the first target subcarrier set used by the communication service and the second target subcarrier set used by the perception service based on the spectrum subband allocation vector. In the process of constructing the subcarrier mapping matrix, the system arranges all physical subcarriers in ascending order based on the frequency domain order, marks each subcarrier with its allocation, and uses Boolean values ​​or type labels to identify whether it corresponds to a communication signal, a perception signal or an unused state, thereby forming a complete subcarrier mapping matrix structure. This structure will serve as the core configuration basis in subsequent transmission waveform modulation and resource scheduling.

[0080] Next, in step 602, in order to further reduce the adjacent channel interference that may exist at the frequency domain boundary, the system analyzes the frequency domain boundary between the first target subcarrier set and the second target subcarrier set, and inserts at least one idle subcarrier between the two sets as a protection subcarrier. The protection subcarrier does not carry communication or perception signals, exists only as an interference barrier, and has a configuration feature of zero power or extremely low power. The system automatically selects the insertion position based on the frequency domain proximity, and records the physical position identifier of the protection subcarrier as the identification information of the protection subcarrier. By inserting the protection subcarrier, the intermodulation interference or sidelobe leakage caused by insufficient spectrum spacing can be effectively weakened, and the waveform independence and system robustness when communication and perception run in parallel can be improved.

[0081] Then, in step 603, the system further sets a corresponding power mask for each allocated target subcarrier. The power mask sets the maximum allowable transmit power based on the subcarrier's allocation purpose, service level, and expected transmission conditions. For example, for communication subcarriers in the edge band and close to the protection subcarrier, the system can configure a lower power cap to avoid leakage to the perception subcarrier; while for subcarriers in the core communication band, a higher power mask can be set to enhance signal strength. The power mask corresponds to the subcarrier number in a vector form, and the whole constitutes the key input of the system in the physical layer modulation and transmission control.

[0082] By executing steps 601 to 603 in sequence, the system completes the mapping and transformation process from spectrum resource allocation results to frequency domain modulation configuration parameters, which not only realizes the physical isolation of spectrum at the service level, but also enhances the anti-interference capability between frequency domain signals through the joint mechanism of protecting subcarriers and power masks, and provides a complete and executable first set of optimization parameters for the construction of collaborative optimization parameters, thus laying a solid foundation for the collaborative operation of communication and perception services in the integrated network.

[0083] Step 502: When the resource allocation result indicates that time domain separation is adopted, a second optimization parameter is generated according to the resource allocation result.

[0084] In one possible implementation of the present invention, to further avoid time overlap between communication signals and sensing signals at the physical channel level, which could lead to signal interference, scheduling conflicts, or system performance degradation, when the resource allocation results indicate the use of a time-domain separation strategy, the system must accurately divide the communication time slots and sensing time slots on the time axis and set the necessary guard intervals, thereby generating second optimization parameters for link scheduling and frame structure configuration. Therefore, in step 502, the system generates the second optimization parameters based on the resource allocation results, providing a time-domain structural foundation for subsequent coordinated scheduling and fused waveform modulation.

[0085] In a possible implementation, the second optimization parameter includes the start time and duration of the communication time slot, the start time and duration of the sensing time slot, and the duration of the guard interval; Figure 7 This is the seventh flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention. Figure 7 As shown, step 502 includes the following steps: Step 701: When the resource allocation result indicates that time domain separation is adopted, the starting time and duration of the communication time slot and the starting time and duration of the perception time slot are determined according to the resource allocation result.

[0086] Step 702: insert a guard interval at the boundary between adjacent time slots and determine the duration of the guard interval.

[0087] In one possible implementation of the present invention, when the resource allocation results indicate that the time-domain separation strategy should be adopted for the current scheduling scenario, the system needs to further clarify the time configuration details of the communication time slot and the sensing time slot to ensure that the scheduling boundaries of different types of services on the physical layer channel resources are clear and do not interfere with each other. Therefore, in step 502, to construct the second optimization parameters, the system further performs steps 701 and 702 to complete the time slot boundary setting and the insertion of the protection mechanism.

[0088] Specifically, in step 701, the system first determines the start time and duration of the communication time slot and the perception time slot respectively based on the scheduling strategy of the communication service and perception service provided in the resource allocation result, combined with the transmission cycle, delay tolerance, service frame structure requirements and service priority of each type of service. When calculating the start time of the communication time slot, the system prioritizes ensuring that it covers the time required for the scheduling control channel, uplink data transmission interval and feedback mechanism in the frame structure, ensuring that the communication service has a complete physical layer transmission window. For the configuration of the perception time slot, the system considers the minimum continuous time required for the transmission, propagation, target echo reception and post-processing of the sensing signal, and on this basis plans its start time and duration to ensure that the perception process is completely closed within the time slot.

[0089] After completing the basic timeslot configuration, the system proceeds to step 702. To further avoid scheduling overlap or signal interference caused by time overlap or unclear boundaries between communication and sensing services, the system inserts a guard interval at the boundary connecting the communication timeslot and the sensing timeslot. The guard interval is a reserved period of time without service scheduling. It can be set to a fixed duration (such as 10μs or 20μs) or dynamically determined based on the current service density, the degree of frequency domain configuration conflict, and switching overhead. The guard interval not only provides a buffer for modulation reconfiguration at the hardware layer, but also reduces energy leakage and interference overlap during frequent switching, improving the stability of scheduling and the consistency of waveform connection. After determining the guard interval, the system uses it as a key component of the second optimization parameter and submits it, along with the start time and duration, to the scheduling management unit for resource distribution and configuration.

[0090] By executing steps 701 to 702, the system achieves precise temporal division between communication and sensing slots based on resource allocation results, and establishes an effective conflict buffering mechanism at slot boundaries by introducing guard intervals. This configuration significantly reduces the probability of interference from synaesthesia services on shared physical channels, enhancing the adaptability of the frame structure and the robustness of system operation.

[0091] Step 503: Obtain collaborative optimization parameters according to the first optimization parameter and / or the second optimization parameter.

[0092] Specifically, when the resource allocation result indicates that the frequency domain separation method is adopted, the system has completed the generation of the first optimization parameters in the previous step, including the subcarrier mapping matrix, the identification of the protection subcarrier and the power mask; if the resource allocation result indicates that the time domain separation method is adopted, the generated second optimization parameters include the starting time of the communication time slot and the perception time slot, the duration and the duration of the protection interval. In this step, the system first determines whether a single separation strategy or a mixed separation strategy is adopted in the current scheduling scenario. If it is the former, the first optimization parameter or the second optimization parameter is directly selected as the collaborative optimization parameter; if it is the latter, that is, part of the spectrum is used for frequency domain separation and the other part uses time domain multiplexing, then the system jointly encapsulates the two types of optimization parameters and constructs the collaborative optimization parameters based on a unified data structure.

[0093] During this encapsulation process, the system maps the frequency-domain subcarrier configuration to the time-domain slot configuration, ensuring scheduling consistency across the resource boundaries of each service flow in both the frequency and time domains. Furthermore, the frequency-domain indices of all guard subcarriers and the time-domain positions of all guard intervals are encoded as a protection zone information segment. The power mask and slot continuity control fields are appended to the collaborative optimization parameters to form a complete, standardized, and parseable data structure.

[0094] Step 105: Iteratively adjust the resource allocation result according to the collaborative optimization parameters, the real-time channel state information, and the user's current location information to obtain an updated resource scheduling instruction.

[0095] In a possible implementation of the present invention, although the preliminary resource allocation has been completed based on the service quality level in the previous step, and the collaborative optimization parameters have been generated according to the separation method, due to the complex factors such as the rapid change of real-time channel status, dynamic migration of user locations and sudden changes in business needs faced by the synergistic network during operation, static resource configuration is often difficult to continuously meet the current service quality assurance goals. Therefore, in order to enhance the dynamic adaptability of the system, it is necessary to introduce an iterative adjustment mechanism based on collaborative optimization parameters, real-time channel status information and user current location information to further optimize the resource scheduling strategy. To this end, in step 105, the system performs iterative adjustment of the resource allocation results and outputs the updated resource scheduling instructions.

[0096] In one possible implementation, Figure 8 This is the eighth flow chart of the method for ensuring the quality of service of the integrated network provided by the present invention. Figure 8 As shown, step 105 specifically includes the following steps: Step 801: predicting the user's predicted location information in the next frame based on the continuously acquired user's current location information.

[0097] Step 802: Calculate the predicted channel gain vector of each service based on the predicted position information and the real-time channel state information.

[0098] Step 803: The predicted channel gain vector and the collaborative optimization parameters are input into the incremental resource scheduling function. With the goal of minimizing the resource reconfiguration overhead and maximizing the service quality utility, the resource allocation result is iteratively corrected using the gradient descent-dual update method until the utility difference between two adjacent iterations is less than the preset convergence threshold or the number of iterations reaches the upper limit, thereby obtaining the corrected allocation result.

[0099] Step 804: Encapsulate the converged modified allocation result into an updated resource scheduling instruction.

[0100] In one possible implementation of the present invention, considering that the spatial location and channel status of users in a telepresence network can fluctuate dramatically over short periods of time, relying solely on static resource allocation results makes it difficult to meet the service quality assurance requirements for business continuity. Therefore, the system introduces an iterative adjustment mechanism. Based on the generated collaborative optimization parameters, it further combines the predicted location and predicted channel conditions to dynamically modify the resource allocation strategy. To implement this mechanism, the system refines steps 801 to 804 in step 105 to complete resource scheduling optimization based on user status prediction and channel estimation.

[0101] First, in step 801, the system collects the user's current location information in real time from the terminal or base station and continuously updates it over time, forming a position sequence within a time window. Based on this sequence, the system constructs a user trajectory prediction model using the Kalman filter algorithm, using the position and velocity state of the previous frame to estimate the user's predicted position in the next frame. This predicted position serves as the spatial parameter input for the next round of scheduling optimization, helping to anticipate potential channel change trends that the user may face.

[0102] Next, in step 802, the system jointly models the predicted location information with the currently collected real-time channel state information. Combining propagation environment characteristics with the wireless channel model, the system calculates a predicted channel gain vector for each service flow in the next scheduling period. This channel gain vector specifically describes the link quality indicators of each service under the current location prediction, such as channel bandwidth response, delay spread, and signal-to-noise ratio. This vector is used to assess the degree to which the current resource configuration supports the actual transmission performance of the service.

[0103] In step 803, the system inputs the above-mentioned channel gain vector and the previously generated collaborative optimization parameters into the incremental resource scheduling function. This function comprehensively considers the channel quality changes, service level constraints and resource reconfiguration costs to construct an optimization objective function to minimize resource migration overhead and maximize network utility. After constructing the optimization model, the system adopts gradient descent and dual variable iterative update strategy to solve it. In each round of iteration, the resource allocation variables and dual weight parameters are adjusted respectively, and gradient feedback updates are performed according to the system performance indicators. Convergence conditions are set, including the change in resource configuration utility in two consecutive rounds is less than the preset threshold or the maximum number of iterations is reached, to ensure that the solution process converges stably within a limited time.

[0104] In step 804, after the resource scheduling function converges, the system obtains a revised resource allocation result for the current prediction environment. This result significantly optimizes channel adaptability, user location matching, and scheduling stability compared to the original resource configuration. The system encapsulates this revised result into an updated resource scheduling instruction, which contains the revised spectrum subband allocation information, transmit power configuration, calculated resource mapping relationship, and scheduling effective time. This resource scheduling instruction is then distributed to each functional node in the network via a control channel to achieve real-time resource reallocation and link updates.

[0105] By executing steps 801 to 804, the system implements a high-frequency iterative correction mechanism for resource allocation results based on prediction information, effectively improving the dynamic adaptability, resource utilization efficiency and business continuity guarantee capabilities of the scheduling process, and providing key real-time optimization capability support for the parallel scheduling of multiple services in the integrated network.

[0106] Step 106: Output the updated resource scheduling instruction to the network side of the current networking for execution.

[0107] Specifically, in step 804, the system generated a fully packaged resource scheduling instruction. This instruction includes the updated spectrum subband allocation vector, transmit power allocation vector, and computational resource capacity allocation vector, along with information such as the scheduling policy's effective timestamp, the corresponding user identifier, and the channel control field. This resource scheduling instruction is transmitted as a structured data packet over the scheduling control channel, ensuring that network nodes can accurately identify the scheduling content and its applicable users.

[0108] During the output process, the system synchronously sends the instruction to multiple key execution entities in the network, including the core network scheduler, base station link control unit, and access point resource management module. Each entity updates local scheduling parameters based on the instruction content, reconfiguring the subcarrier mapping table, transmit power table, and computing resource scheduling table to achieve real-time control of the modulator, power amplifier, and computing platform. Furthermore, for terminals or edge nodes involved in perception services, the system also forwards the instruction to the perception processing link, ensuring that the transmission and reception links of the perception signal are strictly isolated and coordinated with the communication task in terms of time and frequency resources.

[0109] The scheduling instruction output process is coordinated with a unified network clock and scheduling cycle, so that the effectiveness of the scheduling strategy is precisely aligned with the business cycle, thereby ensuring the timing consistency of communication and perception task scheduling and the spatial accuracy of resource allocation.

[0110] By executing step 106, the system completes a closed loop from resource allocation strategy calculation to physical layer execution configuration, enabling updated resource scheduling instructions to take effect quickly across the integrated network. This not only improves the system's responsiveness to network status changes but also enhances the controllability and effectiveness of service scheduling, providing strong support for achieving stable and efficient service quality assurance in the integrated network.

[0111] Reference Figure 9 , Figure 9 This is a schematic diagram of the structure of the inter-sensory network endogenous service quality assurance system provided by the present invention, which includes: A processing module is used to generate indicator data representing the service quality of the business based on the business perception information; The processing module is also used to classify the service quality level of the business according to the indicator data to obtain the target service quality level; The processing module is further used to allocate network resources of the synaesthesia network according to the target service quality level and obtain a resource allocation result; The processing module is further configured to perform frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters; The processing module is further used to iteratively adjust the resource allocation result according to the collaborative optimization parameters, the real-time channel state information and the current location information of the user to obtain an updated resource scheduling instruction; The execution module is used to output the updated resource scheduling instructions to the network side of the current networking for execution.

[0112] In a possible implementation, the processing module is further configured to: Establishing a hierarchical threshold table for indicator data, wherein the hierarchical threshold table sets multiple threshold intervals for each indicator including at least latency, packet loss rate, perception accuracy and coverage to correspond to different service quality levels; Compare each indicator in the indicator data with the grading threshold table item by item to obtain the candidate service quality level to which each indicator belongs; The candidate service quality levels of various indicators are weighted and summed according to the preset weighting coefficients to obtain a comprehensive evaluation score; The comprehensive evaluation score is mapped to a predefined score range to determine the target service quality level, where the grading threshold table is dynamically updated based on historical network performance data and business scenarios.

[0113] In a possible implementation, the processing module is further configured to: According to the target service quality level, the initial weights corresponding to the spectrum resources, transmit power resources and computing resources are retrieved from the preset weight mapping table; Scale each initial weight proportionally according to the current network load factor to obtain the scaled weight; Construct a priority weight set based on all scaled weights; Under the priority weight set, a joint resource allocation optimization model is constructed with the goal of maximizing the weighted sum of communication throughput utility, perception accuracy utility, and computation completion rate utility, and under the constraints that interference power, overall power consumption, and service latency meet preset thresholds. Lagrange multipliers are introduced into the joint resource allocation optimization model, and the original and dual variables are updated synchronously using gradient iteration until the overall utility difference between two consecutive iterations is less than the convergence threshold. The converged continuous spectrum allocation vector, transmit power allocation vector, and computing resource allocation vector are mapped to the discrete sub-bands, power steps, and computing instances available in the system according to the nearest neighbor strategy to form a spectrum sub-band allocation vector, a transmit power allocation vector, and a computing resource capacity allocation vector; The spectrum subband allocation vector, the transmit power allocation vector, and the computational resource capacity allocation vector are combined to obtain a resource allocation result.

[0114] In a possible implementation, the processing module is further configured to: When the resource allocation result indicates that frequency domain separation is adopted, generating a first optimization parameter according to the resource allocation result; When the resource allocation result indicates that time domain separation is adopted, generating a second optimization parameter according to the resource allocation result; A collaborative optimization parameter is obtained according to the first optimization parameter and / or the second optimization parameter.

[0115] In a possible implementation, the processing module is further configured to: When the resource allocation result indicates that frequency domain separation is adopted, constructing a subcarrier mapping matrix according to the resource allocation result; Isolating a first target subcarrier set allocated for the communication signal from a second target subcarrier set allocated for the perception signal in the frequency domain, inserting at least one guard subcarrier between the first target subcarrier set and the second target subcarrier set, and determining an identifier of the guard subcarrier; A corresponding power mask is allocated to each target subcarrier, so that each target subcarrier corresponds to a unique power mask.

[0116] In a possible implementation, the processing module is further configured to: When the resource allocation result indicates that time domain separation is adopted, determining the starting time and duration of the communication time slot and the starting time and duration of the sensing time slot according to the resource allocation result; A guard interval is inserted at the boundary between adjacent time slots, and the duration of the guard interval is determined.

[0117] In a possible implementation, the processing module is further configured to: Predicting the user's predicted location information in the next frame based on the continuously acquired user's current location information; Calculate the predicted channel gain vector for each service based on the predicted location information and real-time channel state information; The predicted channel gain vector and the collaborative optimization parameters are input into the incremental resource scheduling function. With the goal of minimizing resource reconfiguration overhead and maximizing service quality utility, the resource allocation result is iteratively revised using a gradient descent-dual update method until the utility difference between two consecutive iterations is less than a preset convergence threshold or the number of iterations reaches an upper limit, resulting in a revised allocation result. The converged modified allocation results are encapsulated as updated resource scheduling instructions.

[0118] In a possible implementation, the processing module is further configured to: Deploy network function components that can be instantiated on multiple nodes on the network side of the current network; Synchronize network function components to the base station side and the terminal side and complete instantiation; Through synchronized network functional components, service perception information is collected in real time on the network side, base station side, and terminal side.

[0119] It should be noted that the synaesthesia network intrinsic service quality assurance system provided by the present invention can execute the synaesthesia network intrinsic service quality assurance method of any of the above embodiments during specific operation, which will not be described in detail in this embodiment.

[0120] Figure 10 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 10 As shown, the electronic device may include: a processor 1010 (processor), a communications interface 1020 (Communications Interface), a memory 1030 (memory), and a communication bus 1040, wherein the processor 1010, the communications interface 1020, and the memory 1030 communicate with each other via the communication bus 1040. The processor 1010 may call logic instructions in the memory 1030 to execute a method for ensuring intrinsic service quality in a telepathic network. The method includes: generating indicator data representing service quality based on service perception information; classifying services into service quality levels based on the indicator data to obtain target service quality levels; allocating network resources of the telepathic network based on the target service quality levels to obtain resource allocation results; performing frequency domain separation or time domain separation processing on communication signals and perception signals based on the resource allocation results to generate collaborative optimization parameters; iteratively adjusting the resource allocation results based on the collaborative optimization parameters, real-time channel state information, and the user's current location information to obtain updated resource scheduling instructions; and outputting the updated resource scheduling instructions to the network side of the current network for execution.

[0121] Furthermore, the logic instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0122] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the endogenous service quality assurance method of the synergetic network provided by the above-mentioned embodiments, the method including: generating indicator data representing the service quality of the service based on the service perception information; dividing the service into service quality levels based on the indicator data to obtain the target service quality level; allocating network resources of the synergetic network based on the target service quality level to obtain a resource allocation result; performing frequency domain separation or time domain separation processing on the communication signal and the perception signal based on the resource allocation result to generate collaborative optimization parameters; iteratively adjusting the resource allocation result based on the collaborative optimization parameters, real-time channel status information and the user's current location information to obtain an updated resource scheduling instruction; outputting the updated resource scheduling instruction to the network side of the current networking for execution.

[0123] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for ensuring the intrinsic service quality of the synergetic network provided in the above-mentioned embodiments, the method comprising: generating indicator data representing the service quality of the service based on service perception information; dividing the service into service quality levels based on the indicator data to obtain a target service quality level; allocating network resources of the synergetic network based on the target service quality level to obtain a resource allocation result; performing frequency domain separation or time domain separation processing on the communication signal and the perception signal based on the resource allocation result to generate collaborative optimization parameters; iteratively adjusting the resource allocation result based on the collaborative optimization parameters, real-time channel status information and the user's current location information to obtain an updated resource scheduling instruction; and outputting the updated resource scheduling instruction to the network side of the current networking for execution.

[0124] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. That is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0125] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods of each embodiment or certain portions of the embodiments.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for ensuring the quality of endogenous services in a synaesthesia-integrated network, characterized in that: include: Generate indicator data representing business service quality based on business perception information; Classify the service quality level of the business according to the indicator data to obtain the target service quality level; Allocating network resources of the synergy network according to the target service quality level to obtain a resource allocation result; Perform frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters; Iteratively adjusting the resource allocation result according to the collaborative optimization parameter, the real-time channel state information, and the current location information of the user to obtain an updated resource scheduling instruction; The updated resource scheduling instruction is output to the network side of the current networking for execution.

2. The method for ensuring the quality of endogenous service in a synaesthesia-integrated network according to claim 1, characterized in that: The categorizing of the service quality levels of the services according to the indicator data to obtain the target service quality levels includes: Establishing a hierarchical threshold table for the indicator data, wherein the hierarchical threshold table sets multiple threshold intervals for each indicator including at least latency, packet loss rate, perception accuracy and coverage to correspond to different service quality levels; Comparing each indicator in the indicator data with the grading threshold table item by item to obtain a candidate service quality level to which each indicator belongs; The candidate service quality levels of various indicators are weighted and summed according to the preset weighting coefficients to obtain a comprehensive evaluation score; The comprehensive evaluation score is mapped to a predefined score range to determine the target service quality level, wherein the grading threshold table is dynamically updated based on historical network performance data and business scenarios.

3. The method for ensuring the quality of service of the integrated network according to claim 1, characterized in that: Allocating network resources of the intersensory network according to the target service quality level to obtain a resource allocation result includes: According to the target quality of service level, searching the preset weight mapping table for initial weights corresponding to spectrum resources, transmit power resources, and computing resources; Scaling each of the initial weights according to the current network load factor to obtain a scaled weight; Constructing a priority weight set based on all the scaled weights; Under the priority weight set, a joint resource allocation optimization model is constructed with the goal of maximizing the weighted sum of communication throughput utility, perception accuracy utility, and computation completion rate utility, and under the constraints that interference power, overall power consumption, and service latency meet preset thresholds; Lagrange multipliers are introduced into the joint resource allocation optimization model, and the original variables and the dual variables are updated synchronously by using a gradient iteration method until the overall utility difference between two adjacent iterations is less than a convergence threshold; The converged continuous spectrum allocation vector, transmit power allocation vector, and computing resource allocation vector are mapped to the discrete sub-bands, power steps, and computing instances available in the system according to the nearest neighbor strategy to form a spectrum sub-band allocation vector, a transmit power allocation vector, and a computing resource capacity allocation vector; The spectrum subband allocation vector, the transmit power allocation vector, and the computing resource capacity allocation vector are combined to obtain the resource allocation result.

4. The method for ensuring the quality of service of the integrated network according to claim 1, characterized in that: The performing frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters includes: When the resource allocation result indicates that frequency domain separation is adopted, generating a first optimization parameter according to the resource allocation result; When the resource allocation result indicates that time domain separation is adopted, generating a second optimization parameter according to the resource allocation result; The collaborative optimization parameter is obtained according to the first optimization parameter and / or the second optimization parameter.

5. The method for ensuring the quality of endogenous service in a synaesthesia-integrated network according to claim 4 is characterized in that: The first optimization parameters include a subcarrier mapping matrix, an identifier of a protection subcarrier, and a power mask; When the resource allocation result indicates that frequency domain separation is adopted, generating a first optimization parameter according to the resource allocation result includes: When the resource allocation result indicates that frequency domain separation is adopted, constructing the subcarrier mapping matrix according to the resource allocation result; Isolating a first target subcarrier set allocated for the communication signal from a second target subcarrier set allocated for the perception signal in the frequency domain, inserting at least one guard subcarrier between the first target subcarrier set and the second target subcarrier set, and determining an identifier of the guard subcarrier; A corresponding power mask is allocated to each target subcarrier, so that each target subcarrier corresponds to a unique power mask.

6. The method for ensuring the quality of endogenous service in a synaesthesia-integrated network according to claim 4, characterized in that: The second optimization parameters include the starting time and duration of the communication time slot, the starting time and duration of the sensing time slot, and the duration of the guard interval; when the resource allocation result indicates the use of frequency domain separation, generating the first optimization parameters according to the resource allocation result includes: When the resource allocation result indicates that time domain separation is adopted, determining the starting time and duration of the communication time slot and the starting time and duration of the sensing time slot according to the resource allocation result; A guard interval is inserted at the boundary between adjacent time slots, and the duration of the guard interval is determined.

7. The method for ensuring the quality of endogenous service in a synaesthesia-integrated network according to claim 1, characterized in that: The iteratively adjusting the resource allocation result according to the collaborative optimization parameter, the real-time channel state information, and the current location information of the user to obtain an updated resource scheduling instruction includes: Predicting the user's predicted location information in the next frame based on the continuously acquired user's current location information; Calculating a predicted channel gain vector for each service based on the predicted position information and the real-time channel state information; Inputting the predicted channel gain vector and the collaborative optimization parameter into an incremental resource scheduling function, and iteratively revising the resource allocation result using a gradient descent-dual update method with the goal of minimizing resource reconfiguration overhead and maximizing service quality utility, until the utility difference between two adjacent iterations is less than a preset convergence threshold or the number of iterations reaches an upper limit, thereby obtaining a revised allocation result; The converged modified allocation results are encapsulated as updated resource scheduling instructions.

8. The method for ensuring the quality of endogenous service in a synaesthesia-integrated network according to claim 1, characterized in that: Before generating indicator data representing the service quality of the business based on the business perception information, the following steps are included: Deploy network function components that can be instantiated on multiple nodes on the network side of the current network; Synchronize the network function components to the base station side and the terminal side and complete the instantiation; The service perception information is collected in real time on the network side, the base station side and the terminal side through synchronized network function components.

9. A synaesthesia-integrated network endogenous service quality assurance system, characterized in that: include: A processing module is used to generate indicator data representing the service quality of the business based on the business perception information; The processing module is further configured to classify the service quality level of the business according to the indicator data to obtain a target service quality level; The processing module is further configured to allocate network resources of the synergy network according to the target service quality level to obtain a resource allocation result; The processing module is further configured to perform frequency domain separation or time domain separation processing on the communication signal and the perception signal according to the resource allocation result to generate collaborative optimization parameters; The processing module is further configured to iteratively adjust the resource allocation result according to the collaborative optimization parameter, the real-time channel state information, and the current location information of the user to obtain an updated resource scheduling instruction; The execution module is used to output the updated resource scheduling instruction to the network side of the current networking for execution.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for ensuring the quality of service inherent in the synaesthesia network as described in any one of claims 1 to 8 is implemented.

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