Network complexity evaluation method and apparatus, optimization method and apparatus, device, medium, and product
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2025-09-25
- Publication Date
- 2026-04-02
Smart Images
Figure CN2025123916_02042026_PF_FP_ABST
Abstract
Description
Network complexity evaluation, optimization method, device, medium and product
[0001] Cross-reference to Related Applications
[0002] The present disclosure claims priority to Chinese Patent Application No. 202411367151.0, filed on September 29, 2024 in China, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD
[0003] The present disclosure relates to the technical field of communication network, and particularly relates to a communication network complexity evaluation, optimization method, device, medium and product. BACKGROUND
[0004] With the rapid expansion and enhancement of service elements and service capabilities of communication networks, network complexity has become one of the main factors restricting the development and deployment of communication networks.
[0005] In the related art, the evaluation method of communication network complexity includes subjective qualitative evaluation method, single-point quantitative evaluation method and multi-point quantitative evaluation method, and only the network complexity of a local network is evaluated. However, the 5th Generation mobile communication technology (5G), the 6th Generation mobile communication technology (6G) and future communication networks are composed of multiple communication networks such as access networks, core networks and service networks, and the end-to-end network is not a simple combination of local networks, but a cross-domain coordination, scheduling and optimization to provide end-to-end network capabilities and communication services. The related art cannot generate a comprehensive evaluation of the end-to-end network complexity, does not comprehensively consider the influence of multiple factors on network complexity, does not propose a systematic network complexity evaluation method, and has limitations. SUMMARY
[0006] The purpose of the embodiments of the present disclosure is to provide a communication network complexity evaluation, optimization method, device, medium and product, which considers the cross-domain coordination, scheduling and optimization requirements of end-to-end networks, realizes the network complexity evaluation of end-to-end communication networks, and is beneficial to the optimization of communication networks.
[0007] To achieve the above-mentioned purpose, the embodiments of the present disclosure provide a communication network complexity evaluation method, which comprises:
[0008] Obtaining communication network complexity original information of each segmented network in an end-to-end communication network, wherein the communication network complexity original information comprises evaluation data of each preset first evaluation element, a weight, 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 traffic;
[0009] According to the communication network complexity original information, calculating network complexity of each segmented network respectively;
[0010] Determining a weight of each segmented network in the end-to-end communication network, denoted as a segmented network weight;
[0011] According to the network complexity of each segmented network and the segmented network weight, calculating network complexity of the end-to-end communication network, for optimizing a communication network.
[0012] As an improvement of the above scheme, the calculating network complexity of each segmented network according to the communication network complexity original information comprises:
[0013] According to a preset evaluation score interval, normalizing the evaluation data of each first evaluation element into an evaluation score;
[0014] According to the evaluation score of each first evaluation element and the weight, obtaining a total evaluation score by using a weighted summation method;
[0015] According to a preset reference value of communication traffic, normalizing the evaluation data of the second evaluation element into a reference communication traffic;
[0016] Calculating a ratio of the total evaluation score and the reference communication traffic, to obtain the network complexity of the segmented network.
[0017] As an improvement of the above scheme, the method further comprises:
[0018] Sending the network complexity of the segmented network and / or the network complexity of the end-to-end communication network to a user system.
[0019] As an improvement of the above scheme, the first evaluation element comprises at least one of the following: a number of network nodes, a number of connections, a number of functions, a number of services, a number of interfaces, a number of protocols, a number of processes, signaling load, and device power consumption;
[0020] The second evaluation element is an average number of registered users, an average number of concurrent sessions, or an average data forwarding volume.
[0021] As an improvement of the above scheme, the segmented network comprises an access network segment, a core network segment, and a service network segment.
[0022] The embodiments of the present disclosure further provide a communication network optimization method, which comprises:
[0023] receiving a communication network optimization demand sent by a user system;
[0024] generating a segmented optimization instruction according to the communication network optimization demand;
[0025] optimizing each segmented network in an end-to-end communication network respectively according to the segmented optimization instruction.
[0026] As an improvement of the above-mentioned solution, the communication network optimization demand is generated by the user system according to network complexity of the segmented network and / or network complexity of the end-to-end communication network; the network complexity is calculated by using the communication network complexity evaluation method in any one of the above-mentioned solutions.
[0027] As an improvement of the above-mentioned solution, the communication network optimization demand is a reduction ratio of network complexity of the end-to-end communication network.
[0028] Then, the generating of the segmented optimization instruction according to the communication network optimization demand comprises:
[0029] decomposing the reduction ratio of network complexity of the end-to-end communication network into a reduction ratio of network complexity of each segmented network according to the segmented network weight of each segmented network;
[0030] obtaining an optimization strategy for each segmented network according to the reduction ratio of network complexity of each segmented network;
[0031] generating the segmented optimization instruction according to the optimization strategy.
[0032] As an improvement of the above-mentioned solution, the communication network optimization demand is a reduction ratio of network complexity of each segmented network.
[0033] Then, the generating of the segmented optimization instruction according to the communication network optimization demand comprises:
[0034] obtaining an optimization strategy for each segmented network according to the reduction ratio of network complexity of each segmented network;
[0035] generating the segmented optimization instruction according to the optimization strategy.
[0036] As an improvement of the above-mentioned solution, the method further comprises:
[0037] comparing network complexity of the end-to-end communication network before and after optimization to generate a network optimization result.
[0038] sending the network optimization result to the user system.
[0039] The embodiments of the present disclosure further provide a communication network complexity evaluation device, and the device comprises:
[0040] an original information acquisition module, configured to acquire communication network complexity original information of each segmented network in an end-to-end communication network; wherein the communication network complexity original information comprises evaluation data of each preset first evaluation element, a weight, 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 traffic;
[0041] a segmented network complexity evaluation module, configured to calculate network complexity of each segmented network according to the communication network complexity original information;
[0042] a segmented network weight determination module, configured to determine a weight of each segmented network in the end-to-end communication network, denoted as a segmented network weight;
[0043] an end-to-end network complexity evaluation module, configured to calculate network complexity of the end-to-end communication network according to the network complexity of each segmented network and the segmented network weight, and configured to optimize the communication network.
[0044] The embodiments of the present disclosure further provide a communication network optimization device, and the device comprises:
[0045] an optimization demand receiving module, configured to receive a communication network optimization demand sent by a user system;
[0046] a segmented optimization instruction generation module, configured to generate a segmented optimization instruction according to the communication network optimization demand;
[0047] a communication network optimization module, configured to optimize each segmented network in an end-to-end communication network respectively according to the segmented optimization instruction.
[0048] The embodiments of the present disclosure further provide an electronic terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the communication network complexity evaluation method or the communication network optimization method according to any one of the above embodiments when executing the computer program.
[0049] The embodiments of the present disclosure further provide a computer readable storage medium, which comprises a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the communication network complexity evaluation method or the communication network optimization method according to any one of the above when the computer program runs.
[0050] The embodiments of the present disclosure further provide a computer program product, which comprises a computer program or computer instructions, wherein the computer program or the computer instructions are executed by a processor to implement the communication network complexity evaluation method or the communication network optimization method according to any one of the above.
[0051] Compared with the related art, the communication network complexity evaluation and optimization method, device, equipment, medium and product disclosed by the present disclosure decompose an end-to-end communication network into a plurality of segmented networks, analyze and measure the network complexity of each segmented network according to evaluation elements affecting the network complexity and communication traffic, and then combine the weight of each segmented network in the entire end-to-end network to analyze and measure the network complexity of the end-to-end network. The embodiments of the present disclosure consider the cross-domain coordination, scheduling and optimization requirements of the end-to-end network, can generate a whole evaluation of the end-to-end network complexity, improve the accuracy of the communication network complexity evaluation, can effectively adapt to the diversified and differentiated requirements of the network complexity evaluation in the 5G, 6G and future communication networks, can effectively and accurately evaluate the network complexity, and is beneficial to the iterative optimization of the communication network. BRIEF DESCRIPTION OF DRAWINGS
[0052] FIG. 1 is a flow diagram of a communication network complexity evaluation method according to an embodiment of the present disclosure;
[0053] FIG. 2 is a structural diagram of a communication network complexity evaluation system according to an embodiment of the present disclosure;
[0054] FIG. 3 is a flow diagram of a communication network optimization method according to an embodiment of the present disclosure;
[0055] FIG. 4 is a flow diagram of a more preferred communication network optimization method according to an embodiment of the present disclosure;
[0056] FIG. 5 is a structural diagram of a communication network complexity evaluation device according to an embodiment of the present disclosure;
[0057] FIG. 6 is a structural diagram of a communication network optimization device according to an embodiment of the present disclosure;
[0058] FIG. 7 is a structural diagram of an electronic terminal device according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0059] With reference to the drawings that illustrate embodiments of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of the present disclosure.
[0060] In the description of the present application, it should be understood that the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.
[0061] The terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise stated, the meaning of "multiple" is two or more.
[0062] Referring to FIG. 1, it is a flowchart of a communication network complexity evaluation method provided by an embodiment of the present disclosure. The embodiment of the present disclosure provides a communication network complexity evaluation method, which specifically includes steps S11 to S14:
[0063] S11, obtaining communication network complexity original information of each segmented network in an end-to-end communication network; wherein the communication network complexity original information includes evaluation data of each preset first evaluation element, weight, 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 traffic;
[0064] S12, calculating the network complexity of each segmented network according to the communication network complexity original information;
[0065] S13, determining the weight of each segmented network in the end-to-end communication network, denoted as segmented network weight;
[0066] S14, calculating the network complexity of the end-to-end communication network according to the network complexity of each segmented network and the segmented network weight, for optimizing the communication network.
[0067] Referring to FIG. 2, it is a structural schematic diagram of a communication network complexity evaluation system in the embodiment of the present disclosure. The communication network optimization method of the embodiment of the present disclosure is applied to the communication network complexity evaluation system. The communication network complexity evaluation system at least includes a communication network, a communication network complexity evaluation component and a user system. The end-to-end communication network is composed of multiple segmented networks, including an access network, a core network and a service network, etc., for realizing related communication services. The communication network complexity evaluation component is used to realize the calculation of the network complexity of the communication network, including segmented network complexity evaluation units for each segmented network, such as an access network segment complexity evaluation unit, a core network segment complexity evaluation unit and a service network segment complexity evaluation unit, and further including an end-to-end network complexity evaluation unit. At the deployment level, the communication network complexity evaluation component can be independently deployed or deployed together with the core network. The communication network optimization system further includes an artificial intelligence (AI) / machine learning (ML) system, which is used to provide AI / ML capabilities for the communication network complexity evaluation component to assist in the network complexity analysis of the segmented network and the end-to-end network. The communication network optimization system further includes a multi-connection terminal, which participates in the end-to-end communication service flow of the terminal-access network-core network-service network and supports one or more of fixed access, mobile access and satellite access.
[0068] In the embodiment of the present disclosure, the communication network complexity original information of each segmented network in the communication network is collected respectively, including the evaluation data of each preset first evaluation element, the weight and the evaluation data of the 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 traffic.
[0069] Each segmented network sends the communication network complexity original information to the communication network complexity evaluation component for network complexity evaluation. Each segmented network complexity evaluation unit in the communication network complexity evaluation component evaluates the network complexity of the segmented network according to the original information of the segmented network responsible for it, so as to obtain the network complexity Cs of each segmented network, such as the network complexity Cs1 of the access network segment, the network complexity Cs2 of the core network segment and the network complexity Cs3 of the service network segment, and sends the network complexity of each segmented network to the end-to-end network complexity evaluation component.
[0070] The end-to-end network complexity evaluation component determines the weight of each segmented network in the end-to-end network according to network complexity empirical data and / or customized requirements input by the user system, as the segmented network weight Ws, for example, the weights of the access network segment, the core network segment and the service network segment are Ws1, Ws2 and Ws3 respectively. Then, according to the network complexity of the segmented network and the segmented network weight, the network complexity of the end-to-end communication network is calculated as: C E = (C S1 * W S1 + C S2 * W S2 +... + C SN * W SN .
[0071] Wherein, N is the number of segmented networks.
[0072] By using the technical means of the embodiments of the present disclosure, the end-to-end communication network is divided into multiple segmented networks, the network complexity of each segmented network is analyzed and measured, and then the weight of each segmented network in the entire end-to-end network is combined to analyze and measure the network complexity of the end-to-end network. The embodiments of the present disclosure consider the cross-domain coordination, scheduling and optimization requirements of the end-to-end network, can generate a overall evaluation of the end-to-end network complexity, improve the accuracy of communication network complexity evaluation, can effectively adapt to the diversification and differentiation requirements of network complexity evaluation in 5G, 6G and future communication networks, can effectively and accurately evaluate the network complexity, and is conducive to realizing the iterative optimization of the communication network.
[0073] As a preferred embodiment, the embodiments of the present disclosure are implemented on the basis of the above-mentioned embodiments. In the embodiments of the present disclosure, the communication network complexity raw information of each segmented network is collected respectively, the raw information includes a plurality of first evaluation elements related to network complexity, a weight corresponding to each first evaluation element, evaluation data of each first evaluation element, and a second evaluation element related to average communication traffic and corresponding evaluation data. Each segmented network sends the collected data set as communication network complexity raw information to the communication network complexity evaluation component.
[0074] Specifically, the management function module in each segment network determines the network complexity quantitative evaluation elements of the segment network according to the network complexity empirical data and / or user customization requirements, denoted as first evaluation elements, which include one or more of the following elements: network node quantity, connection quantity, function quantity, service quantity, interface quantity, protocol quantity, process quantity, signaling load, and device power consumption, and assigns weights to the first evaluation elements, the sum of the weights of the first evaluation elements being 1. The management function module queries its network management data to obtain evaluation data of each first evaluation element.
[0075] For example, the management function of the core network 1 determines the network complexity quantitative evaluation elements of the core network according to user customization requirements, including node quantity A, connection quantity B, function quantity C, service quantity D, interface quantity E, protocol quantity F, process quantity G, signaling load H, and device power consumption I, and the weights of the elements are W 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 the core network 1 queries its network management data to obtain evaluation data of the quantitative evaluation elements, including: network node quantity A is 5, connection quantity B is 15, function quantity C is 30, service quantity D is 60, and device power consumption I is 3kW, etc.
[0076] Further, the management function module in each segment network queries the control function module of the segment network to obtain the average communication traffic evaluation elements of the segment network, denoted as second evaluation elements, which are average registered user number, average concurrent session number, or average data forwarding volume. The evaluation data of the second evaluation elements is obtained.
[0077] As an example, the management function of the core network 1 queries the control function of the core network 1, including the Unified Data Management (UDM) / Unified Subscription Management function (USM), the Session Management Function (SMF), the User Plane Function (UPF), and other core network elements, to obtain the evaluation factors and evaluation data of the average communication traffic volume, for example, querying the UDM / USM to obtain the average number of registered users of 50,000, querying the SMF to obtain the average number of concurrent sessions of 50,000, and querying the UPF to obtain the average data forwarding volume of 6 Gbps.
[0078] The communication network complexity evaluation component receives the communication network complexity raw information provided by each segmented network such as the access network, the core network, and the service network, respectively analyzes the network complexity data of each segmented network, and obtains the network complexity Cs of each segmented network.
[0079] Preferably, step S12, i.e., calculating the network complexity of each segmented network according to the communication network complexity raw information, includes:
[0080] According to the preset evaluation score interval, the evaluation data of each first evaluation factor is normalized to an evaluation score;
[0081] According to the evaluation score and the weight of each first evaluation factor, a total evaluation score is calculated by using the weighted summation method;
[0082] According to the preset reference value of the communication traffic volume, the evaluation data of the second evaluation factor is normalized to a reference communication traffic volume;
[0083] The ratio of the total evaluation score and the reference communication traffic volume is calculated to obtain the network complexity of the segmented network.
[0084] Specifically, in the embodiments of the present disclosure, the communication network complexity evaluation component sets the evaluation score interval according to the network complexity empirical data and / or user customization requirements for each network complexity quantitative evaluation factor, converts the evaluation data of each network complexity quantitative evaluation factor into a normalized evaluation score. The communication network complexity evaluation component queries the management function of the access network / core network / service network to obtain the reference evaluation factor and its reference value of the average communication traffic volume of each segmented network, and converts the evaluation data of the average communication traffic volume into a normalized reference communication traffic volume.
[0085] As an example, the communication network complexity evaluation component evaluates the score as follows for the number of functions C of the core network: C≤5, the evaluation score is 0.2; C=(5,10], the evaluation score is 0.4; C=(10,20], the evaluation score is 0.5; C=(20,50], the evaluation score is 0.6; C=(50,100], the evaluation score is 0.7; C=(100,200], the evaluation score is 0.8; C=(200,500], the evaluation score is 0.9; C>500, the evaluation score is 1. Assuming that the number of functions C of the core network 1 is 30, the communication network complexity evaluation component evaluates the normalized evaluation score thereof as 0.6.
[0086] The communication network complexity evaluation component queries the management function of the core network to obtain the benchmark evaluation factor of the average communication traffic of the core network and the benchmark value thereof, and takes 1000 average registered users as 1 normalized communication traffic unit. The average number of registered users of the core network 1 is 50,000, and the communication network complexity evaluation component evaluates the normalized communication traffic thereof as 50.
[0087] Further, the communication network complexity evaluation component respectively measures the network complexity data of each segmented network based on the network complexity data analysis result of the segmented network, and the data measurement method is as follows:
[0088] For a specific segmented network (an access network segment or a core network segment or a service network segment), the first evaluation factors are A, B,..., and N, the weights of the first evaluation factors are W A , W B ,..., and W N , the normalized evaluation scores of the first evaluation factors are E A , E B ,..., and E N , and the normalized benchmark communication traffic is T, then the network complexity of the segmented network is: C S =(W A *E A +W B *E B +...+W N *E N ) / T.
[0089] It can be understood that when the segmented network is composed of multiple segmented sub-networks, that is, the communication network contains multiple sets of access networks / multiple sets of core networks / multiple sets of 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 the segmented sub-network belongs. If there are segmented sub-networks, the same method is used to first calculate the complexity of the segmented network and then calculate the end-to-end network complexity.
[0090] It should be noted that in the above network complexity evaluation process, the AI / ML system provides AI / ML capabilities for the communication network complexity evaluation component to assist in segment and end-to-end network complexity analysis, including but not limited to, setting an evaluation score interval for the quantitative evaluation elements of the network complexity of the segmented network, and quantitatively evaluating the weight of the segmented network in the end-to-end network. In the above process, the communication network complexity evaluation component uses the data analysis and inference capabilities of the AI / ML system.
[0091] By using the technical means of the embodiments of the present disclosure, the quantitative evaluation elements and their weights, evaluation data of the network complexity of each segmented network, and the evaluation elements and evaluation data of the average communication traffic are collected by each segmented network, and the network complexity of each segmented network is calculated by considering various service elements, which can effectively and accurately evaluate the complexity of diversified elements, can adapt to the diversified and differentiated requirements of network complexity evaluation in 5G, 6G and future communication networks, and can effectively and accurately evaluate the network complexity.
[0092] As a preferred embodiment, the embodiments of the present disclosure are further implemented on the basis of any of the above embodiments, and the method further comprises step S15:
[0093] S15, sending the network complexity of the segmented network and / or the network complexity of the end-to-end communication network to a user system.
[0094] In the embodiments of the present disclosure, the communication network complexity evaluation component sends the measurement results of the network complexity C S and / or the network complexity C E of the end-to-end communication network to the user system, so that the user system generates 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] Referring to FIG. 3, which is a flow diagram of a communication network optimization method provided by the embodiments of the present disclosure, the embodiments of the present disclosure also provide a communication network optimization method, which comprises steps S21 to S23:
[0096] S21, receiving the communication network optimization requirements sent by the user system;
[0097] S22, generating segmented optimization instructions according to the communication network optimization requirements;
[0098] S23, optimizing each segmented network in the end-to-end communication network respectively according to the segmented optimization instructions.
[0099] In the embodiments of the present disclosure, a user system generates a communication network optimization requirement and sends it to the communication network complexity evaluation component. The communication network complexity evaluation component generates segment optimization instructions for each segment network, such as an access network, a core network, and a service network, based on the communication network optimization requirement. The segment optimization instructions are sent to the segment networks, such as the access network, the core network, and the service network. The segment networks receive and execute the segment optimization instructions to optimize the network.
[0100] Preferably, the communication network optimization requirement is generated by the user system based on the network complexity of the segment network and / or the network complexity of the end-to-end communication network. The network complexity is calculated by using the communication network complexity evaluation method described in any of the above embodiments.
[0101] The communication network optimization requirement is a quantitative adjustment method for the network complexity, i.e., a reduction ratio of the network complexity of the segment network and / or the end-to-end network. The segment optimization instruction is a quantitative evaluation element for the network complexity, i.e., a quantitative adjustment method for the first evaluation element.
[0102] In one embodiment, the communication network optimization requirement is a reduction ratio of the network complexity of the end-to-end communication network.
[0103] Then, step S22, i.e., generating segment optimization instructions based on the communication network optimization requirement, includes:
[0104] According to 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 a reduction ratio of the network complexity of each segment network.
[0105] According to the reduction ratio of the network complexity of each segment network, an optimization strategy for each segment network is obtained.
[0106] The segment optimization instructions are generated according to the optimization strategy.
[0107] For 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 evaluation component decomposes the 10% reduction in the end-to-end network complexity of normalized communication traffic into an 8% reduction in the complexity of the access network, a 12% reduction in the complexity of the core network, and a 10% reduction in the complexity of the service network according to the segment network weight Ws of each segment network, such as the access network, the core network, and the service network.
[0108] Further, the communication network complexity evaluation component generates an optimization strategy for each of the segmented networks according to the weight of the first evaluation element of each segmented network, for example, reducing the access network complexity of the normalized communication traffic by 5% is decomposed into reducing the number of functions from 20 to 19, reducing the number of services from 40 to 38, and reducing the device power consumption by 5%; reducing the core network complexity of the normalized communication traffic by 10% is decomposed into reducing the number of functions from 40 to 36, reducing the number of services from 80 to 68, and reducing the device power consumption by 10%; reducing the service network complexity of the normalized communication traffic by 5% is decomposed into reducing the number of functions from 30 to 27, reducing the number of services from 60 to 57, and reducing the device power consumption by 5%.
[0109] It can be understood that the reduction in the number of functions is achieved by turning off optional network functions; the reduction in the number of services is achieved by turning off low-usage network services; and the reduction in device power consumption is achieved by soft and hardware downsizing, which is not specifically limited herein.
[0110] In another embodiment, the communication network optimization requirement is a reduction ratio of network complexity for each of the segmented networks.
[0111] Then, step S22, i.e., generating segmented optimization instructions according to the communication network optimization requirement, includes:
[0112] According to the reduction ratio of network complexity for each of the segmented networks, an optimization strategy for each of the segmented networks is obtained;
[0113] Segmented optimization instructions are generated according to the optimization strategy.
[0114] For example, the optimization requirement of the communication network is to reduce the access network complexity of the normalized communication traffic by 5%, the core network complexity by 10%, and the service network complexity by 5%. Further, the communication network complexity evaluation component decomposes the reduction of the access network complexity of the normalized communication traffic by 5% into reducing the number of functions from 20 to 19, reducing the number of services from 40 to 38, and reducing the device power consumption by 5% according to the weight of the first evaluation element of each segmented network; decomposes the reduction of the core network complexity of the normalized communication traffic by 10% into reducing the number of functions from 40 to 36, reducing the number of services from 80 to 68, and reducing the device power consumption by 10%; and decomposes the reduction of the service network complexity of the normalized communication traffic by 5% into reducing the number of functions from 30 to 27, reducing the number of services from 60 to 57, and reducing the device power consumption by 5%.
[0115] It should be noted that when the communication network complexity evaluation component cannot generate segmented optimization instructions based on the communication network optimization requirement, an error response is returned to the user system, the user system updates the communication network optimization requirement, and the communication network complexity evaluation component generates segmented optimization instructions again.
[0116] The communication network complexity evaluation component generates corresponding segmented optimization instructions, i.e., quantitative adjustment methods for the first evaluation elements, according to the optimization strategy, and sends the generated segmented optimization instructions to the corresponding segmented networks respectively. The access network / core network / service network and the like segmented networks respectively receive the segmented optimization instructions of the segmented networks, and execute the segmented optimization instructions.
[0117] It should be noted that when the access network / core network / service network cannot execute the segmented optimization instructions, an error response is returned to the communication network complexity evaluation component; and the communication network complexity evaluation component updates the segmented optimization instructions of the segmented network.
[0118] In the above steps, the AI / ML system provides AI / ML capabilities for the communication network complexity evaluation component, and assists in the generation of segmented optimization instructions of the access network / core network / service network, including but not limited to, quantitative evaluation of network complexity quantitative evaluation elements of the access network / core network / service network, and quantitative evaluation of decomposition methods from end-to-end network complexity to segmented network complexity. In the above process, the communication network complexity evaluation component uses the data analysis and inference capabilities of the AI / ML system.
[0119] As a preferred embodiment, the embodiments of the present disclosure are further implemented based on any of the above embodiments, and the method further includes steps S24 and S25:
[0120] S24, comparing the network complexity of the end-to-end communication network before and after optimization to generate a network optimization result;
[0121] S25, sending the network optimization result to the user system.
[0122] In the embodiments of the present disclosure, after the access network / core network / service network and the like segmented networks execute the segmented optimization instructions, the communication network complexity evaluation component collects communication network complexity original information again, analyzes and measures the network complexity of the segmented networks 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] By using the technical means of the embodiments of the present disclosure, the communication network complexity evaluation result can be directly or indirectly used for communication network optimization. Based on the communication network optimization requirements provided by the user system, the communication network complexity evaluation component generates segmented optimization instructions of the access network / core network / service network, and the access network / core network / service network executes the segmented optimization instructions of the segmented networks respectively. The communication network complexity evaluation result can be used to continuously and iteratively optimize the communication network, and the value of the communication network complexity evaluation result is effectively exerted.
[0124] Referring to FIG. 4, it is a flow diagram of a more preferred communication network optimization method in the embodiments of the present disclosure. The embodiments of the present disclosure take the segmented network as the access network, the core network and the service network as an example to explain the complete communication network optimization process, including steps 1-14:
[0125] Step 1, the management function of the access network / core network / service network determines the network complexity quantitative evaluation elements of the segmented network according to the network complexity empirical data and / or user customization requirements, and assigns weights to them.
[0126] Step 2, the management function of the access network / core network / service network obtains the evaluation data of each network complexity quantitative evaluation element of the segmented network by querying and processing the network management data stored by it; queries the control function of the segmented network to obtain the evaluation elements and evaluation data of the average communication traffic of the segmented network.
[0127] Step 3, the access network / core network / service network sends the set of network complexity quantitative evaluation elements / weights / evaluation data of the segmented network, evaluation elements and evaluation data of the average communication traffic as communication network complexity original information to the communication network complexity evaluation component.
[0128] Step 4, the communication network complexity evaluation component receives the communication network complexity original information provided by the access network / core network / service network, and respectively analyzes the network complexity data of each segmented network, including: for each network complexity quantitative evaluation element, respectively set the evaluation score interval according to the network complexity empirical data and / or user customization requirements, and convert the evaluation data of each network complexity quantitative evaluation element into a normalized evaluation score; query the management function of the access network / core network / service network to obtain the baseline evaluation elements and baseline values of the average communication traffic of each segmented network, and convert the evaluation data of the average communication traffic into a normalized communication traffic; the AI / ML system provides AI / ML capabilities for the communication network complexity evaluation component to assist the segmented network complexity data analysis.
[0129] Step 5, the communication network complexity evaluation component respectively 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 evaluation component performs network complexity data analysis on the end-to-end network based on the measurement results of the network complexity of the segmented network, including: evaluating the weight of each segmented network in the end-to-end network according to network complexity empirical data and / or user customization requirements; when the segmented network is composed of multiple segmented sub-networks, further evaluating the weight of the segmented sub-networks; the AI / ML system provides AI / ML capabilities for the communication network complexity evaluation component to assist in end-to-end network complexity data analysis.
[0131] Step 7, the communication network complexity evaluation component performs network complexity data measurement on the end-to-end network based on the data analysis results of the end-to-end network.
[0132] Step 8, the communication network complexity evaluation component sends the measurement results of the segmented and end-to-end network complexity to the user system.
[0133] Step 9, the user system generates communication network optimization requirements for adjusting the complexity of the segmented and / or end-to-end network based on the received measurement results of the complexity of the segmented and end-to-end network; 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 evaluation component.
[0135] Step 11, the communication network complexity evaluation component generates segmented optimization instructions for the access network / core network / service network based on the communication network optimization requirements, i.e., quantitative adjustment methods for quantitative evaluation elements of network complexity; the AI / ML system provides AI / ML capabilities for the communication network complexity evaluation component to assist in generating segmented optimization instructions for the access network / core network / service network.
[0136] Step 12, the communication network complexity evaluation component sends the segmented optimization instructions to the access network / core network / service network.
[0137] Step 13, the access network / core network / service network respectively receives the segmented optimization instructions for the segmented network and executes the segmented optimization instructions.
[0138] Step 14, after the access network / core network / service network executes the segmented optimization instructions, the communication network complexity evaluation component again collects original communication network complexity information, performs segmented 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] Referring to FIG. 5, it is a structural schematic diagram of a communication network complexity evaluation device provided by an embodiment of the present disclosure. The embodiment of the present disclosure also provides a communication network optimization device 30, which comprises:
[0140] The original information acquisition module 31 is configured to acquire communication network complexity original information of each segment network in the end-to-end communication network, wherein the communication network complexity original information comprises evaluation data of each preset first evaluation element, a weight, 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 traffic;
[0141] The segment network complexity evaluation module 32 is configured to calculate network complexity of each segment network according to the communication network complexity original information.
[0142] The segment network weight determination module 33 is configured to determine a weight of each segment network in the end-to-end communication network, denoted as a segment network weight.
[0143] The end-to-end network complexity evaluation module 34 is configured to calculate network complexity of the end-to-end communication network according to the network complexity of each segment network and the segment network weight, for optimizing the communication network.
[0144] Preferably, the segment network comprises an access network segment, a core network segment, and a service network segment.
[0145] By using the technical means of the embodiments of the present disclosure, the end-to-end communication network is decomposed into multiple segment networks, the network complexity of each segment network is analyzed and measured, and then the network complexity of the end-to-end network is analyzed and measured in combination with the weight of each segment network in the entire end-to-end network. The embodiments of the present disclosure consider the cross-domain coordination, scheduling, and optimization requirements of the end-to-end network, can generate a whole evaluation of the end-to-end network complexity, improve the precision of the communication network complexity evaluation, can effectively adapt to the diversified and differentiated requirements of the network complexity evaluation in the 5G, 6G, and future communication networks, can effectively and accurately evaluate the network complexity, and is beneficial to realizing the iterative optimization of the communication network.
[0146] Preferably, the segment network complexity evaluation module 32 is specifically configured to:
[0147] According to a preset evaluation score interval, the evaluation data of each first evaluation element is normalized into an evaluation score;
[0148] According to the evaluation score of each first evaluation element and the weight, a total evaluation score is calculated by using a weighted summation method;
[0149] According to a preset reference value of communication traffic, the evaluation data of the second evaluation element is normalized into a reference communication traffic.
[0150] a ratio of the total evaluation score and the benchmark communication traffic is calculated to obtain a network complexity of the segmented network.
[0151] Preferably, the first evaluation element includes at least one of the following: a number of network nodes, a number of connections, a number of functions, a number of services, a number of interfaces, a number of protocols, a number of processes, a signaling load, and a device power consumption; and the second evaluation element is an average number of registered users, an average number of concurrent sessions, or an average data forwarding volume.
[0152] By using the technical means of the embodiments of the present disclosure, the network complexity of each segmented network is calculated by considering various service elements, which can effectively and accurately evaluate the complexity of diversified elements, can adapt to the diversified and differentiated requirements of network complexity evaluation in 5G, 6G and future communication networks, and can effectively and accurately evaluate the network complexity.
[0153] As a preferred implementation, the apparatus 30 further includes:
[0154] The network complexity sending module is configured to send the network complexity of the segmented network and / or the network complexity of the end-to-end communication network to a user system.
[0155] Referring to FIG. 6, which is a structural schematic diagram of a communication network optimization apparatus provided by an embodiment of the present disclosure, the embodiment of the present disclosure further provides a communication network optimization apparatus 40, which includes:
[0156] The network optimization demand receiving module 41 is configured to receive a communication network optimization demand sent by a user system.
[0157] The segmented optimization instruction generating module 42 is configured to generate a segmented optimization instruction according to the communication network optimization demand.
[0158] The communication network optimization module 43 is configured to optimize each segmented network in the end-to-end communication network respectively according to the segmented optimization instruction.
[0159] In a preferred implementation, the communication network optimization demand is a reduction ratio of the network complexity of the end-to-end communication network.
[0160] The segmented optimization instruction generating module is specifically configured to:
[0161] According to the segmented network weight of each segmented network, the reduction ratio of the network complexity of the end-to-end communication network is decomposed into a reduction ratio of the network complexity of each segmented network.
[0162] According to the reduction ratio of the network complexity of each segmented network, an optimization strategy for each segmented network is obtained.
[0163] generating the segment optimization instruction according to the optimization strategy.
[0164] In another preferred embodiment, the communication network optimization requirement is a reduction ratio of network complexity of each segment network.
[0165] The segment optimization instruction generation module 42 is specifically configured to:
[0166] obtaining an optimization strategy for each segment network according to the reduction ratio of network complexity of each segment network;
[0167] generating the segment optimization instruction according to the optimization strategy.
[0168] As a preferred embodiment, the apparatus 40 further comprises:
[0169] a network optimization result generation module configured to compare the network complexity of the end-to-end communication network before optimization and the network complexity of the end-to-end communication network after optimization, and generate a network optimization result;
[0170] a network optimization result sending module configured to send the network optimization result to the user system.
[0171] By using the technical means of the embodiments of the present disclosure, the communication network complexity evaluation result can be directly or indirectly used for communication network optimization, and the communication network can be continuously iteratively optimized by using the communication network complexity evaluation result, so that the value of the communication network complexity evaluation result is effectively exerted.
[0172] It should be noted that the communication network optimization apparatus provided by the embodiments of the present disclosure is used to execute all process steps of the communication network optimization method of the above-mentioned embodiments, and the working principles and beneficial effects of the two are one-to-one corresponding, thus not being repeated.
[0173] Referring to FIG. 7, which is a structural schematic diagram of a communication network optimization device provided by the embodiments of the present disclosure, the embodiments of the present disclosure further provide a communication network optimization device 50, which comprises a processor 51, a memory 52, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the communication network optimization method as described in any one of the above-mentioned embodiments when executing the computer program.
[0174] The embodiments of the present disclosure further provide a computer readable storage medium, which comprises a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the communication network optimization method as described in any one of the above-mentioned embodiments when the computer program is running.
[0175] The embodiments of the present disclosure further provide a computer program product, which comprises a computer program or computer instructions, and the computer program or the computer instructions, when executed by a processor, implement the communication network optimization method according to any one of the above embodiments.
[0176] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the processes of the above-mentioned embodiments can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.
[0177] The above is the preferred embodiment of the present disclosure. It should be pointed out that, for those skilled in the art, without departing from the principles of the present disclosure, a number of improvements and refinements can be made, which are also considered within the protection scope of the present disclosure.
Claims
1. A method for evaluating complexity of a communication network, the method comprising: obtaining complexity original information of each segment network in an end-to-end communication network, wherein the complexity original information comprises evaluation data of each preset first evaluation element, a weight, 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 traffic; calculating network complexity of each segment network according to the complexity original information; determining a weight of each segment network in the end-to-end communication network, denoted as segment network weight; and calculating network complexity of the end-to-end communication network according to the network complexity of each segment network and the segment network weight, for optimizing the communication network. The calculating network complexity of each segment network according to the complexity original information comprises: normalizing the evaluation data of each first evaluation element into evaluation scores according to a preset evaluation score interval; calculating a total evaluation score by using a weighted summation method according to the evaluation score of each first evaluation element and the weight; normalizing the evaluation data of the second evaluation element into a reference communication traffic according to a preset reference value of communication traffic; and calculating a ratio of the total evaluation score to the reference communication traffic to obtain the network complexity of the segment network. 3.The method of claim 1 or 2, further comprising: sending the network complexity of the segment network and / or the network complexity of the end-to-end communication network to a user system. The first evaluation element comprises 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 average number of registered users, average number of concurrent sessions, or average data forwarding volume.
2. The communication network complexity evaluation method of claim 1, wherein, The segment network comprises an access network segment, a core network segment, and a service network segment. 6.A method for optimizing a communication network, the method comprising: receiving a communication network optimization requirement sent by a user system; generating segment optimization instructions according to the communication network optimization requirement; and optimizing each segment network in an end-to-end communication network according to the segment optimization instructions. The communication network optimization requirement is generated by the user system according to network complexity of the segment network and / or network complexity of the end-to-end communication network; the network complexity is calculated by using the method for evaluating complexity of a communication network according to any one of claims 1 to 5. The communication network optimization requirement is a reduction ratio of the network complexity of the end-to-end communication network. The generating segment optimization instructions according to the communication network optimization requirement comprises: decomposing the reduction ratio of the network complexity of the end-to-end communication network into reduction ratios of network complexity of each segment network according to the segment network weight of each segment network. 4. The communication network complexity evaluation method of claim 1, wherein, 5. The communication network complexity evaluation method of claim 1, wherein, 7. The communication network optimization method of claim 6, wherein, 8. The communication network optimization method of claim 7, wherein, According to the reduction proportion of the network complexity of each of the segmented networks, an optimization strategy for each of the segmented networks is obtained; Segmented optimization instructions are generated according to the optimization strategy.
9. The communication network optimization method of claim 7, wherein, The communication network optimization requirement is the reduction proportion of the network complexity of each of the segmented networks; According to the communication network optimization requirement, segmented optimization instructions are generated, including: According to the reduction proportion of the network complexity of each of the segmented networks, an optimization strategy for each of the segmented networks is obtained; Segmented optimization instructions are generated according to the optimization strategy.
10. The communication network optimization method of claim 6, further comprising: Comparing the network complexity of the end-to-end communication network before and after optimization to generate a network optimization result; The network optimization result is sent to the user system.
11. A communication network complexity evaluation device, the device comprising: An original information acquisition module for acquiring communication network complexity original information of each segmented network in an end-to-end communication network; wherein the communication network complexity original information includes evaluation data of each preset first evaluation element, a weight, 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 traffic; A segmented network complexity evaluation module for calculating the network complexity of each of the segmented networks according to the communication network complexity original information; A segmented network weight determination module for determining the weight of each of the segmented networks in the end-to-end communication network, denoted as a segmented network weight; An end-to-end network complexity evaluation module for calculating the network complexity of the end-to-end communication network according to the network complexity of each of the segmented networks and the segmented network weight, for optimizing the communication network.
12. A communication network optimization device, the device comprising: An optimization requirement receiving module for receiving a communication network optimization requirement sent by a user system; A segmented optimization instruction generating module for generating segmented optimization instructions according to the communication network optimization requirement; A communication network optimization module for optimizing each segmented network in an end-to-end communication network according to the segmented optimization instructions.
13. An electronic terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor executing the computer program to implement the communication network complexity evaluation method of any one of claims 1 to 5, or the communication network optimization method of any one of claims 6 to 10.
14. A computer readable storage medium comprising a stored computer program, wherein, The computer readable storage medium is controlled to perform the communication network complexity evaluation method of any one of claims 1 to 5, or the communication network optimization method of any one of claims 6 to 10 when the computer program is running. The computer readable storage medium is controlled to perform the communication network complexity evaluation method of any one of claims 1 to 5, or the communication network optimization method of any one of claims 6 to 10 when the computer program is running.
15. A computer program product comprising computer programs or computer instructions which, when executed by a processor, implement the communication network complexity evaluation method according to any one of claims 1 to 5, or the communication network optimization method according to any one of claims 6 to 10.
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