Network complexity evaluation and optimization method, device, equipment, medium and product

Abstract

The invention discloses a communication network complexity evaluation and optimization method and device, equipment, a medium and a product. The method comprises the following steps: acquiring communication network complexity original information of each segmented network in an end-to-end communication network; the original information comprises evaluation data and weight of each preset first evaluation element and evaluation data of a preset second evaluation element; the first evaluation element is an evaluation element related to network complexity, and the second evaluation element is an evaluation element related to communication service volume; respectively calculating the network complexity of each segmented network according to the original information; and according to the segmented network weight and the network complexity of each segmented network in the end-to-end communication network, calculating the network complexity of the end-to-end communication network for optimizing the communication network. By adopting the method and the device, the cross-domain coordination, scheduling and optimization requirements of the end-to-end network are considered, the network complexity evaluation of the end-to-end communication network is realized, and the optimization of the communication network is facilitated.

Description

Technical Field

[0001] This invention relates to the field of communication network technology, and in particular to a method, apparatus, device, medium and product for evaluating and optimizing the complexity of communication networks. Background Technology

[0002] While the service elements and capabilities of communication networks are rapidly expanding and strengthening, network complexity has become one of the main factors restricting the development and deployment of communication networks.

[0003] Existing technologies for assessing communication network complexity include subjective qualitative assessment methods, single-point quantitative assessment methods, and multi-point quantitative assessment methods, all of which only evaluate network complexity at the local network level. However, 5G, 6G, and future communication networks consist of multiple communication networks, including access networks, core networks, and service networks. End-to-end networks are not simply combinations of local networks, but rather involve cross-domain coordination, scheduling, and optimization to provide end-to-end network capabilities and communication services. Existing technologies cannot generate a holistic evaluation of end-to-end network complexity, nor do they comprehensively consider the impact of multiple factors on network complexity, and they lack a systematic approach to assessing network complexity, thus exhibiting limitations. Summary of the Invention

[0004] The purpose of this invention is to provide a communication network complexity assessment, optimization method, apparatus, device, medium, and product that considers the cross-domain coordination, scheduling, and optimization requirements of end-to-end networks, realizes network complexity assessment of end-to-end communication networks, and is beneficial to communication network optimization.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for evaluating the complexity of a communication network, the method comprising:

[0006] Obtain the original communication network complexity information of each segment of the end-to-end communication network; wherein, the original communication network complexity information includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element; the first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume.

[0007] Based on the original information of the communication network complexity, calculate the network complexity of each segmented network;

[0008] Determine the weight of each segmented network in the end-to-end communication network, and denot it as the segmented network weight;

[0009] The network complexity of the end-to-end communication network is calculated based on the network complexity and weight of each segment network, and used to optimize the communication network.

[0010] As an improvement to the above scheme, the step of calculating the network complexity of each segmented network based on the original information of the communication network complexity includes:

[0011] Based on the preset evaluation score range, the evaluation data of each of the first evaluation elements is normalized into an evaluation score;

[0012] The total evaluation score is calculated using a weighted summation method based on the evaluation score and weight of each of the first evaluation elements.

[0013] Based on the preset benchmark value of communication traffic volume, the evaluation data of the second evaluation element is normalized to the benchmark communication traffic volume.

[0014] The network complexity of the segmented network is obtained by calculating the ratio of the total evaluation score to the baseline communication traffic volume.

[0015] As an improvement to the above solution, the method further includes:

[0016] The network complexity of the segmented network and / or the network complexity of the end-to-end communication network are sent to the user system.

[0017] As an improvement to the above scheme, the first evaluation element includes at least one of the following: number of network nodes, number of connections, number of functions, number of services, number of interfaces, number of protocols, number of processes, signaling load, and device power consumption.

[0018] The second evaluation factor is the average number of registered users, the average number of concurrent sessions, or the average data forwarding volume.

[0019] As an improvement to the above scheme, the segmented network includes an access network segment, a core network segment, and a service network segment.

[0020] This invention also provides a communication network optimization method, the method comprising:

[0021] Receive communication network optimization requests sent by user systems;

[0022] Based on the communication network optimization requirements, generate segmented optimization instructions;

[0023] According to the segmentation optimization instructions, each segment of the end-to-end communication network is optimized separately.

[0024] As an improvement to the above scheme, the communication network optimization requirements are generated by the user system based on the network complexity of the segmented network and / or the network complexity of the end-to-end communication network; the network complexity is calculated using the communication network complexity evaluation method described in any of the above.

[0025] As an improvement to the above scheme, the communication network optimization requirement is to reduce the network complexity of the end-to-end communication network by a certain percentage.

[0026] The step of generating segmented optimization instructions based on the communication network optimization requirements includes:

[0027] Based on the segment network weight of each segment network, the reduction ratio of the network complexity of the end-to-end communication network is decomposed into the reduction ratio of the network complexity of each segment network.

[0028] The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage.

[0029] Segmented optimization instructions are generated based on the optimization strategy.

[0030] As an improvement to the above scheme, the communication network optimization requirement is to reduce the network complexity of each segmented network by a certain percentage.

[0031] The step of generating segmented optimization instructions based on the communication network optimization requirements includes:

[0032] The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage.

[0033] Segmented optimization instructions are generated based on the optimization strategy.

[0034] As an improvement to the above solution, the method further includes:

[0035] The network complexity of the end-to-end communication network before and after optimization is compared to generate network optimization results.

[0036] The network optimization results are sent to the user system.

[0037] This invention also provides a communication network complexity evaluation device, the device comprising:

[0038] The raw information acquisition module is used to acquire the raw information of the communication network complexity of each segment network in the end-to-end communication network; wherein, the raw information of the communication network complexity includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element; the first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume.

[0039] The segmented network complexity evaluation module is used to calculate the network complexity of each segmented network based on the original information of the communication network complexity.

[0040] The segmented network weight determination module is used to determine the weight of each segmented network in the end-to-end communication network, denoted as the segmented network weight.

[0041] An end-to-end network complexity assessment module is used to calculate the network complexity of the end-to-end communication network based on the network complexity of each segment network and the weight of the segment network, in order to optimize the communication network.

[0042] This invention also provides a communication network optimization device, the device comprising:

[0043] The optimization request receiving module is used to receive communication network optimization requests sent by user systems.

[0044] The segmented optimization instruction generation module is used to generate segmented optimization instructions according to the communication network optimization requirements.

[0045] The communication network optimization module is used to optimize each segment of the end-to-end communication network according to the segment optimization instructions.

[0046] This invention also provides an electronic terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the communication network complexity evaluation method or the communication network optimization method as described in any of the above embodiments.

[0047] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the communication network complexity evaluation method or the communication network optimization method as described in any of the above embodiments.

[0048] This invention also provides a computer program product, which includes a computer program or computer instructions. When the computer program or computer instructions are executed by a processor, they implement the communication network complexity evaluation method or the communication network optimization method as described above.

[0049] Compared with existing technologies, the communication network complexity assessment and optimization method, apparatus, device, medium, and product disclosed in this invention decompose the end-to-end communication network into multiple segmented networks. Based on assessment factors affecting network complexity and communication traffic volume, the network complexity of each segmented network is analyzed and measured. Then, combining the weight of each segmented network within the entire end-to-end network, the overall network complexity of the end-to-end network is analyzed and measured. The embodiments of this invention consider the cross-domain coordination, scheduling, and optimization requirements of end-to-end networks, enabling the generation of a comprehensive evaluation of end-to-end network complexity, improving the accuracy of communication network complexity assessment, and effectively adapting to the diverse and differentiated requirements of network complexity assessment in 5G, 6G, and future communication networks. This allows for effective and accurate network complexity assessment, facilitating iterative optimization of communication networks. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a communication network complexity evaluation method provided in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the communication network complexity evaluation system in an embodiment of the present invention;

[0052] Figure 3 This is a flowchart illustrating a communication network optimization method provided in an embodiment of the present invention;

[0053] Figure 4 This is a flowchart illustrating a more preferred communication network optimization method in an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the structure of a communication network complexity evaluation device provided in an embodiment of the present invention;

[0055] Figure 6 This is a schematic diagram of the structure of a communication network optimization device provided in an embodiment of the present invention;

[0056] Figure 7 This is a schematic diagram of the structure of an electronic terminal device provided in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0059] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0060] See Figure 1 This is a flowchart illustrating a communication network complexity evaluation method provided by an embodiment of the present invention. The embodiment of the present invention provides a communication network complexity evaluation method, specifically including steps S11 to S14:

[0061] S11. Obtain the original communication network complexity information of each segment network in the end-to-end communication network; wherein, the original communication network complexity information includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element; the first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume.

[0062] S12. Calculate the network complexity of each segmented network based on the original information of the communication network complexity.

[0063] S13. Determine the weight of each segmented network in the end-to-end communication network, denoted as the segmented network weight;

[0064] S14. Calculate the network complexity of the end-to-end communication network based on the network complexity of each segmented network and the weight of the segmented network, in order to optimize the communication network.

[0065] See Figure 2This is a schematic diagram of the communication network complexity assessment system in an embodiment of the present invention. The communication network optimization method of this embodiment is applied to the communication network complexity assessment system. The communication network complexity assessment system includes at least a communication network, a communication network complexity assessment component, and a user system. The end-to-end communication network consists of multiple segmented networks, including access networks, core networks, and service networks, used to implement related communication services. The communication network complexity assessment component is used to calculate the network complexity of the communication network, including segmented network complexity assessment units for each segmented network, such as access network segment complexity assessment units, core network segment complexity assessment units, and service network segment complexity assessment units, as well as an end-to-end network complexity assessment unit. At the deployment level, the communication network complexity assessment component can be deployed independently or co-deployed with the core network. The communication network optimization system also includes an AI (Artificial Intelligence) / ML (Machine Learning) system to provide AI / ML capabilities to the communication network complexity assessment component, assisting in the network complexity analysis of segmented networks and end-to-end networks. The communication network optimization system also includes multiple connection terminals, which participate in the end-to-end communication service flow of terminal-access network-core network-service network, and support one or more of fixed access, mobile access, and satellite access.

[0066] In this embodiment of the invention, the original information of the communication network complexity of each segment network in the communication network is collected respectively. The original information of the communication network complexity includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element. The first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume.

[0067] Each segmented network sends its original communication network complexity information to the communication network complexity assessment component for network complexity evaluation. Each segmented network complexity assessment unit within the communication network complexity assessment component evaluates the network complexity of its respective segmented network based on its original information, thereby obtaining the network complexity Cs of each segmented network, such as Cs1 for the access network segment, Cs2 for the core network segment, and Cs3 for the service network segment. The network complexity of each segmented network is then sent to the end-to-end network complexity assessment component.

[0068] The end-to-end network complexity assessment component determines the weight of each network segment in the end-to-end network based on network complexity experience data and / or customized requirements input by the user system. This weight is designated as the network segment weight Ws. For example, the weights of the access network segment, core network segment, and service network segment are Ws1, Ws2, and Ws3, respectively. Then, based on the network complexity of the network segments and their weights, the network complexity of the end-to-end communication network is calculated as follows:

[0069] C E =(C S1 *W S1 +C S2 *W S2 +...+C SN *W SN );

[0070] Where N is the number of segmented networks.

[0071] This invention employs the technical means of decomposing an end-to-end communication network into multiple segmented networks. The network complexity of each segmented network is analyzed and measured, and then, combined with the weight of each segmented network within the entire end-to-end network, the overall network complexity of the end-to-end network is analyzed and measured. This invention considers the cross-domain coordination, scheduling, and optimization requirements of end-to-end networks, enabling the generation of a comprehensive evaluation of end-to-end network complexity. This improves the accuracy of communication network complexity assessment and effectively adapts to the diverse and differentiated requirements of network complexity assessment in 5G, 6G, and future communication networks. It allows for effective and accurate network complexity assessment, facilitating iterative optimization of communication networks.

[0072] As a preferred embodiment, the present invention is implemented based on the above embodiments. In this embodiment, each segmented network collects raw information on the communication network complexity of its own segmented network. The raw information includes several first evaluation elements regarding network complexity, the weight corresponding to each first evaluation element, evaluation data for each first evaluation element, and a second evaluation element regarding average communication traffic volume and its corresponding evaluation data. Each segmented network sends the collected data set as raw information on communication network complexity to the communication network complexity evaluation component.

[0073] Specifically, the management function modules in each segmented network determine the quantitative evaluation elements of network complexity for their respective segmented network based on network complexity experience data and / or user-customized requirements. These elements are denoted as the first evaluation elements. The first evaluation elements include one or more of the following: number of network nodes, number of connections, number of functions, number of services, number of interfaces, number of protocols, number of processes, signaling load, and device power consumption. Weights are assigned to each of these elements, and the sum of the weights of all first evaluation elements is 1. Furthermore, the management function modules query their network management data to obtain the evaluation data for each of the first evaluation elements.

[0074] As an example, the management functions of core network 1 determine the quantitative evaluation elements of network complexity based on user customization requirements, including the number of nodes (A), number of connections (B), number of functions (C), number of services (D), number of interfaces (E), number of protocols (F), number of processes (G), signaling load (H), and device power consumption (I); their weights are W and W respectively. A =0.1, W B =0.05, W C =0.2, W D =0.1, W E =0.05, W F =0.1, W G =0.2, W H =0.1, W I =0.1. The management function of core network 1 queries its network management data to obtain evaluation data of quantitative evaluation elements, including: number of network nodes A is 5, number of connections B is 15, number of functions C is 30, number of services D is 60, and device power consumption I is 3kW, etc.

[0075] Furthermore, the management function module in each segment network queries the control function module of its segment network to obtain the evaluation element of the average communication traffic volume of its segment network, denoted as the second evaluation element. The second evaluation element is the average number of registered users, the average number of concurrent sessions, or the average data forwarding volume. The evaluation data for the second evaluation element is then obtained.

[0076] As an example, the management functions and control functions of core network 1, including core network elements such as UDM (Unified Data Management) / USM (Unified Subscription Management function), SMF (Session Management Function), and UPF (User Plane Function), are queried to obtain the evaluation factors and data of average communication traffic. For example, querying UDM / USM yields an average of 50,000 registered users, querying SMF yields an average of 50,000 concurrent sessions, and querying UPF yields an average of 6Gbps data forwarding.

[0077] The communication network complexity assessment component receives raw communication network complexity information from various segmented networks such as the access network, core network, and service network, performs network complexity data analysis on each segmented network, and obtains the network complexity Cs of each segmented network.

[0078] Preferably, step S12, namely, calculating the network complexity of each segmented network based on the original information of the communication network complexity, includes:

[0079] Based on the preset evaluation score range, the evaluation data of each of the first evaluation elements is normalized into an evaluation score;

[0080] The total evaluation score is calculated using a weighted summation method based on the evaluation score and weight of each of the first evaluation elements.

[0081] Based on the preset benchmark value of communication traffic volume, the evaluation data of the second evaluation element is normalized to the benchmark communication traffic volume.

[0082] The network complexity of the segmented network is obtained by calculating the ratio of the total evaluation score to the baseline communication traffic volume.

[0083] Specifically, in this embodiment of the invention, the communication network complexity assessment component sets assessment score ranges for each quantitative assessment element of network complexity based on empirical network complexity data and / or user-customized requirements, and converts the assessment data of each quantitative assessment element of network complexity into a normalized assessment score. The communication network complexity assessment component queries the management functions of the access network / core network / service network to obtain the benchmark assessment elements and their benchmark values ​​for the average communication traffic volume of each segment network, and converts the assessment data of the average communication traffic volume into a normalized benchmark communication traffic volume.

[0084] As an example, the communication network complexity assessment component evaluates the number of functions C in the core network as follows: C≤5, score is 0.2; C=(5,10], score is 0.4; C=(10,20], score is 0.5; C=(20,50], score is 0.6; C=(50,100], score is 0.7; C=(100,200], score is 0.8; C=(200,500], score is 0.9; C>500, score is 1. Assuming the number of functions C in core network 1 is 30, the normalized score given by the communication network complexity assessment component is 0.6.

[0085] The communication network complexity assessment component queries the core network's management functions to obtain the benchmark assessment elements and their benchmark values ​​for the core network's average communication traffic volume, using an average of 1000 registered users as one normalized unit of communication traffic volume. Core network 1 has an average of 50,000 registered users, and the communication network complexity assessment component evaluates its normalized communication traffic volume to be 50.

[0086] Furthermore, based on the network complexity data analysis results of the segmented network, the communication network complexity assessment component performs network complexity data measurement on each segmented network separately. The data measurement method is as follows:

[0087] For a specific segmented network (access network segment, core network segment, or service network segment), the first evaluation element is A, B, ..., N, and the weight of the first evaluation element is W. A W B , ..., W N The normalized evaluation score of the first evaluation element is E. A E B , ..., E N If the normalized baseline communication traffic is T, then the network complexity of the segmented network is:

[0088] C S =(W A *E A +W B *E B +...+W N *E N ) / T.

[0089] Understandably, when a segmented network consists of multiple segmented sub-networks, meaning the communication network includes multiple access networks / core networks / service networks, the weights of the segmented sub-networks are further evaluated, and the sum of the weights of each segmented sub-network is the weight of the segmented network to which it belongs. If there are segmented sub-networks, the same method is used: first calculate the segmented network complexity, and then calculate the end-to-end network complexity.

[0090] It should be noted that in the aforementioned network complexity assessment process, the AI / ML system provides AI / ML capabilities to the communication network complexity assessment component, assisting in segmented and end-to-end network complexity analysis. This includes, but is not limited to, setting evaluation score ranges for quantitative evaluation elements of network complexity in segmented networks and quantifying the weights of segmented networks in end-to-end networks. In this process, the communication network complexity assessment component utilizes the data analysis and reasoning capabilities of the AI / ML system.

[0091] By employing the technical means of this invention, each segmented network collects quantitative evaluation elements and their weights and evaluation data regarding network complexity, as well as evaluation elements and evaluation data regarding average communication traffic volume. It considers multiple service elements to calculate the network complexity of each segmented network, enabling effective and accurate evaluation of the complexity of diverse elements. This approach adapts to the diverse and differentiated requirements for network complexity evaluation in 5G, 6G, and future communication networks, ensuring effective and accurate network complexity assessment.

[0092] As a preferred embodiment, the present invention is further implemented based on any of the above embodiments, and the method further includes step S15:

[0093] S15. Send the network complexity of the segmented network and / or the network complexity of the end-to-end communication network to the user system.

[0094] In this embodiment of the invention, the communication network complexity evaluation component will segment the network C. S The network complexity C of and / or end-to-end communication networks E The measurement results are sent to the user system so that the user system can generate communication network optimization requirements based on the measurement results of the network complexity of the segmented network and / or the end-to-end communication network.

[0095] See Figure 3 This is a flowchart illustrating a communication network optimization method provided in an embodiment of the present invention. The embodiment of the present invention also provides a communication network optimization method, the method comprising steps S21 to S23:

[0096] S21. Receive the communication network optimization request sent by the user system;

[0097] S22. Generate segmentation optimization instructions based on the communication network optimization requirements;

[0098] S23. Optimize each segment of the end-to-end communication network according to the segment optimization instructions.

[0099] In this embodiment of the invention, the user system generates communication network optimization requirements and sends them to the communication network complexity assessment component. Based on the communication network optimization requirements, the communication network complexity assessment component generates segmented optimization instructions for each segment of the network, such as the access network, core network, and service network, and sends them to the segmented networks. The segmented networks receive and execute the segmented optimization instructions to optimize the network.

[0100] Preferably, the communication network optimization requirements are generated by the user system based on the network complexity of the segmented network and / or the network complexity of the end-to-end communication network; the network complexity is calculated using the communication network complexity evaluation method described in any of the above embodiments.

[0101] The communication network optimization requirement refers to a quantitative adjustment method for network complexity, specifically the reduction ratio of network complexity in segmented networks and / or end-to-end networks. The segmented optimization instruction refers to a quantitative assessment element of network complexity, i.e., a quantitative adjustment method for the first assessment element.

[0102] In one implementation, the communication network optimization requirement is a reduction in the network complexity of the end-to-end communication network.

[0103] Step S22, namely generating segmented optimization instructions based on the communication network optimization requirements, includes:

[0104] Based on the segment network weight of each segment network, the reduction ratio of the network complexity of the end-to-end communication network is decomposed into the reduction ratio of the network complexity of each segment network.

[0105] The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage.

[0106] Segmented optimization instructions are generated based on the optimization strategy.

[0107] As an example, the communication network optimization requirement is a 10% reduction in the end-to-end network complexity of normalized communication traffic. The communication network complexity assessment component decomposes the 10% reduction in end-to-end network complexity of normalized communication traffic into an 8% reduction in access network complexity, a 12% reduction in core network complexity, and a 10% reduction in service network complexity, based on the segment network weights Ws of each segment network, such as the access network / core network / service network.

[0108] Furthermore, the communication network complexity assessment component generates an optimization strategy for each segmented network based on the weights of the first assessment elements of each segmented network. For example, reducing the access network complexity of normalized communication traffic by 5% is decomposed into reducing the number of functions from 20 to 19, the number of services from 40 to 38, and reducing equipment power consumption by 5%; reducing the core network complexity of normalized communication traffic by 10% is decomposed into reducing the number of functions from 40 to 36, the number of services from 80 to 68, and reducing equipment power consumption by 10%; and reducing the service network complexity of normalized communication traffic by 5% is decomposed into reducing the number of functions from 30 to 27, the number of services from 60 to 57, and reducing equipment power consumption by 5%.

[0109] Understandably, the reduction in the number of functions is achieved by disabling optional network functions; the reduction in the number of services is achieved by disabling low-utilization network services; and the reduction in device power consumption is achieved by scaling down hardware and software, which are not specifically limited here.

[0110] In another implementation, the communication network optimization requirement is a reduction in the network complexity of each segmented network.

[0111] Step S22, namely generating segmented optimization instructions based on the communication network optimization requirements, includes:

[0112] The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage.

[0113] Segmented optimization instructions are generated based on the optimization strategy.

[0114] As an example, the optimization requirements for the communication network are a 5% reduction in access network complexity, a 10% reduction in core network complexity, and a 5% reduction in service network complexity for normalized communication traffic. Further, the communication network complexity assessment component, based on the weights of the first assessment elements for each segmented network, decomposes the 5% reduction in access network complexity for normalized communication traffic into a reduction of the number of functions from 20 to 19, the number of services from 40 to 38, and a 5% reduction in device power consumption; decomposes the 10% reduction in core network complexity for normalized communication traffic into a reduction of the number of functions from 40 to 36, the number of services from 80 to 68, and a 10% reduction in device power consumption; and decomposes the 5% reduction in service network complexity for normalized communication traffic into a reduction of the number of functions from 30 to 27, the number of services from 60 to 57, and a 5% reduction in device power consumption.

[0115] It should be noted that when the communication network complexity assessment component cannot generate segmented optimization instructions based on the communication network optimization requirements, it returns an error response to the user system. The user system then updates the communication network optimization requirements, and the communication network complexity assessment component regenerates the segmented optimization instructions.

[0116] The communication network complexity assessment component generates corresponding segmented optimization instructions based on the optimization strategy, which are quantitative adjustment methods for the first assessment element, and sends the generated segmented optimization instructions to the corresponding segmented networks. Access network / core network / service network, etc., each receive and execute the segmented optimization instructions from their respective segmented network.

[0117] It should be noted that when the access network / core network / service network cannot execute the segmentation optimization instructions, an error response is returned to the communication network complexity assessment component; the communication network complexity assessment component updates the segmentation optimization instructions for that segmented network.

[0118] In the above steps, the AI / ML system provides AI / ML capabilities to the communication network complexity assessment component, assisting in the generation of segmented optimization instructions for the access network / core network / service network. This includes, but is not limited to, quantifying the adjustment methods for quantitative assessment elements of network complexity in the access network / core network / service network, and quantifying the decomposition methods from end-to-end network complexity to segmented network complexity. Throughout this process, the communication network complexity assessment component utilizes the data analysis and reasoning capabilities of the AI / ML system.

[0119] As a preferred embodiment, the present invention is further implemented based on any of the above embodiments, and the method further includes steps S24 and S25:

[0120] S24. Compare the network complexity of the end-to-end communication network before and after optimization, and generate network optimization results;

[0121] S25. Send the network optimization results to the user system.

[0122] In this embodiment of the invention, after the segmented networks such as the access network / core network / service network execute the segmentation optimization instruction, the communication network complexity assessment component again collects the original information of communication network complexity, analyzes and measures the network complexity of the segmented network and the end-to-end network, compares the network complexity before and after optimization, calculates the communication network optimization effect, and sends it to the user system.

[0123] Using the technical means of this invention, the communication network complexity assessment results can be directly or indirectly used for communication network optimization. The communication network complexity assessment component generates segmented optimization instructions for the access network / core network / service network based on the communication network optimization requirements provided by the user system. The access network / core network / service network execute the segmented optimization instructions of their respective segments, which can utilize the communication network complexity assessment results to continuously iterate and optimize the communication network, effectively leveraging the value of the communication network complexity assessment results.

[0124] See Figure 4 This is a flowchart illustrating a more preferred communication network optimization method in this embodiment of the invention. Taking a segmented network as an example, comprising the access network, core network, and service network, this embodiment explains the complete communication network optimization process, including steps 1 to 14:

[0125] Step 1: The management functions of the access network / core network / service network determine the quantitative assessment elements of network complexity for this segment network based on network complexity experience data and / or user customization requirements, and assign weights to them.

[0126] Step 2: The management functions of the access network / core network / service network query and process the network management data stored therein to obtain the evaluation data of each quantitative evaluation element of the network complexity of this segment network; and query the control functions of this segment network to obtain the evaluation elements and evaluation data of the average communication traffic volume of this segment network.

[0127] Step 3: The access network / core network / service network sends the set of quantitative evaluation elements / weights / evaluation data of network complexity of this segment network, evaluation elements and evaluation data of average communication traffic volume as the original information of communication network complexity to the communication network complexity evaluation component.

[0128] Step 4: The communication network complexity assessment component receives the raw communication network complexity information provided by the access network / core network / service network, and performs network complexity data analysis on each segment of the network. This includes: setting evaluation score ranges for each quantitative evaluation element of network complexity based on network complexity experience data and / or user-customized requirements, and converting the evaluation data of each quantitative evaluation element of network complexity into normalized evaluation scores; querying the management functions of the access network / core network / service network to obtain the benchmark evaluation elements and their benchmark values ​​for the average communication traffic volume of each segment of the network, and converting the evaluation data of the average communication traffic volume into normalized communication traffic volume; the AI / ML system provides AI / ML capabilities to the communication network complexity assessment component to assist in the segmented network complexity data analysis.

[0129] Step 5: The communication network complexity assessment component measures the network complexity of each segmented network based on the network complexity data analysis results of the segmented network.

[0130] Step 6: The communication network complexity assessment component performs network complexity data analysis on the end-to-end network based on the network complexity measurement results of the segmented network. This includes: evaluating the weight of each segmented network in the end-to-end network based on empirical network complexity data and / or user-customized requirements; further evaluating the weight of the segmented sub-networks when the segmented network consists of multiple segmented sub-networks; and providing AI / ML capabilities to the communication network complexity assessment component to assist in the end-to-end network complexity data analysis.

[0131] Step 7: The communication network complexity assessment component measures the network complexity of the end-to-end network based on the data analysis results of the end-to-end network.

[0132] Step 8: The communication network complexity assessment component sends the segmented and end-to-end network complexity measurement results to the user system.

[0133] Step 9: Based on the received segmentation and end-to-end network complexity measurement results, the user system generates communication network optimization requirements for adjusting segmentation and / or end-to-end network complexity; the communication network optimization requirements are quantitative adjustment methods for network complexity.

[0134] Step 10: The user system sends the communication network optimization requirements to the communication network complexity assessment component.

[0135] Step 11: Based on the communication network optimization requirements, the communication network complexity assessment component generates segmented optimization instructions for the access network / core network / service network, which is a quantitative adjustment method for the quantitative assessment elements of network complexity; the AI / ML system provides AI / ML capabilities to the communication network complexity assessment component to assist in the generation of segmented optimization instructions for the access network / core network / service network.

[0136] Step 12: The communication network complexity assessment component sends segmentation optimization instructions to the access network / core network / service network.

[0137] Step 13: The access network / core network / service network respectively receive the segmentation optimization instructions from their respective segmented networks and execute the segmentation optimization instructions.

[0138] Step 14: After the access network / core network / service network executes the segmentation optimization command, the communication network complexity assessment component once again collects the original information of communication network complexity, performs segmentation and end-to-end network complexity analysis and measurement, compares the network complexity before and after optimization, calculates the communication network optimization effect, and sends it to the user system.

[0139] See Figure 5This is a schematic diagram of a communication network complexity evaluation device provided in an embodiment of the present invention. The present invention also provides a communication network optimization device 30, which includes:

[0140] The raw information acquisition module 31 is used to acquire the raw information of the communication network complexity of each segment network in the end-to-end communication network; wherein, the raw information of the communication network complexity includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element; the first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume.

[0141] The segmented network complexity evaluation module 32 is used to calculate the network complexity of each segmented network based on the original information of the communication network complexity.

[0142] Segmented network weight determination module 33 is used to determine the weight of each segmented network in the end-to-end communication network, denoted as segmented network weight;

[0143] The end-to-end network complexity evaluation module 34 is used to calculate the network complexity of the end-to-end communication network based on the network complexity of each segment network and the weight of the segment network, so as to optimize the communication network.

[0144] Preferably, the segmented network includes an access network segment, a core network segment, and a service network segment.

[0145] This invention employs the technical means of decomposing an end-to-end communication network into multiple segmented networks. The network complexity of each segmented network is analyzed and measured, and then, combined with the weight of each segmented network within the entire end-to-end network, the overall network complexity of the end-to-end network is analyzed and measured. This invention considers the cross-domain coordination, scheduling, and optimization requirements of end-to-end networks, enabling the generation of a comprehensive evaluation of end-to-end network complexity. This improves the accuracy of communication network complexity assessment and effectively adapts to the diverse and differentiated requirements of network complexity assessment in 5G, 6G, and future communication networks. It allows for effective and accurate network complexity assessment, facilitating iterative optimization of communication networks.

[0146] Preferably, the segmented network complexity evaluation module 32 is specifically used for:

[0147] Based on the preset evaluation score range, the evaluation data of each of the first evaluation elements is normalized into an evaluation score;

[0148] The total evaluation score is calculated using a weighted summation method based on the evaluation score and weight of each of the first evaluation elements.

[0149] Based on the preset benchmark value of communication traffic volume, the evaluation data of the second evaluation element is normalized to the benchmark communication traffic volume.

[0150] The network complexity of the segmented network is obtained by calculating the ratio of the total evaluation score to the baseline communication traffic volume.

[0151] Preferably, the first evaluation element includes at least one of the following: number of network nodes, number of connections, number of functions, number of services, number of interfaces, number of protocols, number of processes, signaling load, and device power consumption; the second evaluation element is the average number of registered users, the average number of concurrent sessions, or the average data forwarding volume.

[0152] By employing the technical means of this invention, multiple service elements are considered to calculate the network complexity of each segmented network, enabling effective and accurate assessment of the complexity of diverse elements. This approach adapts to the diverse and differentiated requirements for network complexity assessment in 5G, 6G, and future communication networks, and allows for effective and accurate network complexity assessment.

[0153] In a preferred embodiment, the device 30 further includes:

[0154] A network complexity sending module is used to send the network complexity of the segmented network and / or the network complexity of the end-to-end communication network to the user system.

[0155] See Figure 6 This is a schematic diagram of a communication network optimization device provided in an embodiment of the present invention. The present invention also provides a communication network optimization device 40, which includes:

[0156] The network optimization request receiving module 41 is used to receive the communication network optimization request sent by the user system;

[0157] The segmented optimization instruction generation module 42 is used to generate segmented optimization instructions according to the communication network optimization requirements.

[0158] The communication network optimization module 43 is used to optimize each segment of the end-to-end communication network according to the segment optimization instructions 42.

[0159] In a preferred embodiment, the communication network optimization requirement is a reduction in the network complexity of the end-to-end communication network.

[0160] The segmented optimization instruction generation module is specifically used for:

[0161] Based on the segment network weight of each segment network, the reduction ratio of the network complexity of the end-to-end communication network is decomposed into the reduction ratio of the network complexity of each segment network.

[0162] The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage.

[0163] Segmented optimization instructions are generated based on the optimization strategy.

[0164] In another preferred embodiment, the communication network optimization requirement is a reduction ratio of the network complexity of each segment network.

[0165] The segmented optimization instruction generation module 42 is specifically used for:

[0166] The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage.

[0167] Segmented optimization instructions are generated based on the optimization strategy.

[0168] In a preferred embodiment, the device 40 further includes:

[0169] The network optimization result generation module is used to compare the network complexity of the end-to-end communication network before and after optimization, and generate network optimization results.

[0170] The network optimization result sending module is used to send the network optimization results to the user system.

[0171] By employing the technical means of the embodiments of the present invention, the communication network complexity assessment results can be directly or indirectly used for communication network optimization, enabling continuous iterative optimization of the communication network using the communication network complexity assessment results, and effectively leveraging the value of the communication network complexity assessment results.

[0172] It should be noted that the communication network optimization device provided in this embodiment of the invention is used to execute all the process steps of the communication network optimization method in the above embodiment. The working principle and beneficial effect of the two are one-to-one, so they will not be described again.

[0173] See Figure 7 This is a schematic diagram of the structure of a communication network optimization device provided in an embodiment of the present invention. The present invention also provides a communication network optimization device 50, including a processor 51, a memory 52, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the communication network optimization method as described in any of the above embodiments.

[0174] This invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the communication network optimization method as described in any of the above embodiments.

[0175] This invention also provides a computer program product, which includes a computer program or computer instructions. When the computer program or computer instructions are executed by a processor, they implement the communication network optimization method as described in any of the above embodiments.

[0176] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0177] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the complexity of a communication network, characterized in that, The method includes: Obtain the original communication network complexity information of each segment of the end-to-end communication network; wherein, the original communication network complexity information includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element; the first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume. Based on the original information of the communication network complexity, calculate the network complexity of each segmented network; Determine the weight of each segmented network in the end-to-end communication network, and denot it as the segmented network weight; The network complexity of the end-to-end communication network is calculated based on the network complexity and weight of each segment network, and used to optimize the communication network.

2. The communication network complexity evaluation method as described in claim 1, characterized in that, The step of calculating the network complexity of each segmented network based on the original information of the communication network complexity includes: Based on the preset evaluation score range, the evaluation data of each of the first evaluation elements is normalized into an evaluation score; The total evaluation score is calculated using a weighted summation method based on the evaluation score and weight of each of the first evaluation elements. Based on the preset benchmark value of communication traffic volume, the evaluation data of the second evaluation element is normalized to the benchmark communication traffic volume. The network complexity of the segmented network is obtained by calculating the ratio of the total evaluation score to the baseline communication traffic volume.

3. The communication network complexity evaluation method as described in claim 1 or 2, characterized in that, The method further includes: The network complexity of the segmented network and / or the network complexity of the end-to-end communication network are sent to the user system.

4. The communication network complexity evaluation method as described in claim 1, characterized in that, The first evaluation element includes at least one of the following: number of network nodes, number of connections, number of functions, number of services, number of interfaces, number of protocols, number of processes, signaling load, and device power consumption; The second evaluation factor is the average number of registered users, the average number of concurrent sessions, or the average data forwarding volume.

5. The communication network complexity evaluation method as described in claim 1, characterized in that, The segmented network includes access network segment, core network segment, and service network segment.

6. A communication network optimization method, characterized in that, The method includes: Receive communication network optimization requests sent by user systems; Based on the communication network optimization requirements, generate segmented optimization instructions; According to the segmentation optimization instructions, each segment of the end-to-end communication network is optimized separately.

7. The communication network optimization method as described in claim 6, characterized in that, The communication network optimization requirements are generated by the user system based on the network complexity of the segmented network and / or the network complexity of the end-to-end communication network; the network complexity is calculated using the communication network complexity evaluation method described in any one of claims 1 to 5.

8. The communication network optimization method as described in claim 7, characterized in that, The communication network optimization requirement is the reduction ratio of the network complexity of the end-to-end communication network; The step of generating segmented optimization instructions based on the communication network optimization requirements includes: Based on the segment network weight of each segment network, the reduction ratio of the network complexity of the end-to-end communication network is decomposed into the reduction ratio of the network complexity of each segment network. The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage. Segmented optimization instructions are generated based on the optimization strategy.

9. The communication network optimization method as described in claim 7, characterized in that, The communication network optimization requirement is the reduction ratio of the network complexity of each segmented network. The step of generating segmented optimization instructions based on the communication network optimization requirements includes: The optimization strategy for each segmented network is obtained by reducing the network complexity of each segmented network by a certain percentage. Segmented optimization instructions are generated based on the optimization strategy.

10. The communication network optimization method as described in claim 6, characterized in that, The method further includes: The network complexity of the end-to-end communication network before and after optimization is compared to generate network optimization results. The network optimization results are sent to the user system.

11. A communication network complexity evaluation device, characterized in that, The device includes: The raw information acquisition module is used to acquire the raw information of the communication network complexity of each segment network in the end-to-end communication network; wherein, the raw information of the communication network complexity includes the evaluation data and weight of each preset first evaluation element and the evaluation data of each preset second evaluation element; the first evaluation element is an evaluation element about network complexity, and the second evaluation element is an evaluation element about communication traffic volume. The segmented network complexity evaluation module is used to calculate the network complexity of each segmented network based on the original information of the communication network complexity. The segmented network weight determination module is used to determine the weight of each segmented network in the end-to-end communication network, denoted as the segmented network weight. An end-to-end network complexity assessment module is used to calculate the network complexity of the end-to-end communication network based on the network complexity of each segment network and the weight of the segment network, in order to optimize the communication network.

12. A communication network optimization device, characterized in that, The device includes: The optimization request receiving module is used to receive communication network optimization requests sent by user systems. The segmented optimization instruction generation module is used to generate segmented optimization instructions according to the communication network optimization requirements. The communication network optimization module is used to optimize each segment of the end-to-end communication network according to the segment optimization instructions.

13. An electronic terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the communication network complexity evaluation method as described in any one of claims 1 to 5, or the communication network optimization method as described in any one of claims 6 to 10.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the communication network complexity assessment method as described in any one of claims 1 to 5, or the communication network optimization method as described in any one of claims 6 to 10.

15. A computer program product, characterized in that, The computer program product includes a computer program or computer instructions, which, when executed by a processor, implement the communication network complexity evaluation method as described in any one of claims 1 to 5, or the communication network optimization method as described in any one of claims 6 to 10.

Images