Digital twin network capability maturity assessment method, device and storage medium

Abstract

The application provides a digital twin network capability maturity evaluation method, device and storage medium, and relates to the technical field of communication.The method of the application collects twin network capability information and physical network capability information corresponding to each operation scene of the digital twin network, obtains scores and weight values corresponding to the twin network capability information and the physical network capability information respectively, thereby combining the characteristics of the telecommunication network and business development, and obtaining the maturity of each operation scene according to the obtained scores and weight values.After obtaining the maturity of all operation scenes, the digital twin network capability maturity level of the telecommunication network is obtained according to the operation scene coverage rate and the maturity of each operation scene, so as to determine the targeted improvement development direction for the telecommunication network operator according to the digital twin network capability maturity level and level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a digital twin network capability maturity evaluation method, device and storage medium. BACKGROUND

[0002] Digital twin is a simulation process integrating multi-discipline, multi-physical quantity, multi-scale and multi-probability, which fully utilizes physical model, sensor update and operation history data to complete mapping in virtual space, thereby reflecting the whole life cycle process of the corresponding entity equipment. Digital twin is a concept beyond reality, which can be regarded as a digital mapping system of one or more important and interdependent equipment systems.

[0003] The existing digital twin system maturity evaluation method includes a reference model for the whole industry, or a digital twin system maturity evaluation method for each specific industry, such as a digital economy development maturity evaluation method. However, the existing digital twin system maturity evaluation method is not directly applicable to the operator digital twin network capability maturity evaluation, and is not related to the actual situation of the development of the telecommunication operator network and business. It is also not applicable to the maturity evaluation of the telecommunication operator digital twin network, and cannot further analyze and determine the maturity level and level of the operator digital twin network capability through quantitative evaluation.

[0004] The existing technology still lacks the ability to combine the development characteristics of the telecommunication network and business to construct an operator digital twin network capability maturity model, thereby further analyzing and determining the maturity level and level of the operator digital twin network capability through quantitative evaluation. SUMMARY

[0005] The present application provides a digital twin network capability maturity evaluation method, device and storage medium to solve the problem that the existing digital twin capability maturity model cannot combine the development characteristics of the telecommunication network and business, and still lacks the ability to further analyze and determine the maturity level and level of the operator digital twin network capability through quantitative evaluation.

[0006] In a first aspect, the present application provides a digital twin network capability maturity evaluation method, comprising:

[0007] Collecting index information corresponding to each operation scene of the digital twin network, wherein the index information includes operation scene coverage rate corresponding to the network intelligent operation scene dimension, twin network capability information corresponding to the twin network capability dimension and physical network capability information corresponding to the physical network capability dimension under each operation scene;

[0008] obtain a score corresponding to the twin network capability information and the physical network capability information under each operation scenario respectively, and obtain the maturity of each operation scenario according to the weight value corresponding to the twin network capability information and the physical network capability information respectively and the score corresponding to the twin network capability information and the physical network capability information respectively;

[0009] obtain the maturity level of the digital twin network capability according to the operation scenario coverage and the maturity of each operation scenario.

[0010] In a possible design, the obtaining of the maturity level of the digital twin network capability according to the operation scenario coverage and the maturity of each operation scenario includes:

[0011] obtain a scenario average maturity according to the maturity of each operation scenario;

[0012] obtain a coverage maturity according to the operation scenario coverage and the scenario coverage weight value;

[0013] obtain a total index of the maturity of the digital twin network capability according to the sum of the scenario average maturity and the coverage maturity;

[0014] obtain the maturity level of the digital twin network capability according to the total index of the maturity of the digital twin network capability.

[0015] In a possible design, the obtaining of the index information corresponding to each operation scenario according to the index system quantitative standard includes:

[0016] generate an information collection template according to the index system quantitative standard, where the information collection template includes a plurality of information filling items, and each information filling item is used to fill in an index;

[0017] fill the running information of the operation scenario into the data filling information item according to the information filling item in the information collection template, to obtain the index information corresponding to each operation scenario.

[0018] In a possible design, the obtaining of the score corresponding to the twin network capability information under each operation scenario includes:

[0019] According to the capability size indicated by each sub-indicator in the twin network capability information, a maturity level corresponding to each sub-indicator is obtained; the twin network capability information includes at least one sub-indicator in resource model and management information, network perception information, simulation information, intelligent decision information, and intelligent management and control information, wherein the resource model and management information is used to indicate virtual mirror reproduction capability of the digital twin network, the network perception information is used to indicate real-time state perception capability of the digital twin network, the simulation information is used to indicate high-fidelity simulation capability of the digital twin network, the intelligent decision information is used to indicate cognitive decision capability of the digital twin network, and the intelligent management and control information is used to indicate decision task intelligent execution capability of the digital twin network.

[0020] According to the score corresponding to the maturity level of each sub-indicator and the weight value corresponding to each sub-indicator, weighted summation is performed to obtain a score corresponding to the twin network capability information under each operation scenario.

[0021] In a possible design, the obtaining of the score corresponding to the physical network capability information under each operation scenario includes:

[0022] According to the capability size indicated by each sub-indicator in the physical network capability information, a maturity level corresponding to each sub-indicator is obtained; the physical network capability information includes at least one of network resource management information, network capability opening information, whole-network capability orchestration information, or intelligent security protection information; wherein the network resource management information is used to indicate resource whole-life cycle management and auditing capability of the digital twin network, the network capability opening information is used to indicate decoupling opening interface capability of the digital twin network, the whole-network capability orchestration information is used to indicate forming executable scheme capability of the digital twin network, and the intelligent security protection information is used to indicate intelligent security protection capability of the digital twin network.

[0023] According to the score corresponding to the maturity level of each sub-indicator and the weight value corresponding to each sub-indicator, weighted summation is performed to obtain a score corresponding to the physical network capability information under each operation scenario.

[0024] In a possible design, the twin network capability information and the physical network capability information both correspond to a first weight value.

[0025] The scenario coverage weight value corresponds to a second weight value, and the second weight value is less than the first weight value.

[0026] In a possible design, the weight value corresponding to each sub-indicator is equal.

[0027] In a second aspect, the present application provides a digital twin network capability maturity evaluation device, including:

[0028] an acquisition module, configured to collect index information corresponding to each operation scenario of a digital twin network, wherein the index information comprises an operation scenario coverage rate corresponding to a network intelligent operation scenario dimension, twin network capability information corresponding to the twin network capability dimension under each operation scenario, and physical network capability information corresponding to the physical network capability dimension;

[0029] a first processing module, configured to acquire a score corresponding to the twin network capability information and the physical network capability information under each operation scenario respectively, and acquire a maturity of each operation scenario according to a weight value corresponding to the twin network capability information and the physical network capability information respectively and the score corresponding to the twin network capability information and the physical network capability information respectively;

[0030] a second processing module, configured to acquire a digital twin network capability maturity level for a telecommunication network according to the operation scenario coverage rate and the maturity of each operation scenario.

[0031] In a third aspect, the present application provides an electronic device, comprising a processor and a memory connected to the processor in communication;

[0032] the memory stores computer execution instructions;

[0033] the processor executes the computer execution instructions stored in the memory to implement the digital twin network capability maturity evaluation method.

[0034] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the digital twin network capability maturity evaluation method.

[0035] The present application provides a digital twin network capability maturity evaluation method, device and storage medium. The twin network capability information and the physical network capability information corresponding to each operation scenario of a digital twin network are collected, the scores and the weight values corresponding to the twin network capability information and the physical network capability information respectively are acquired, and the telecommunication network and the business development characteristics are combined. The maturity of each operation scenario is acquired according to the acquired scores and weight values. After the maturity of all operation scenarios is acquired, the digital twin network capability maturity level for a telecommunication network is acquired according to the operation scenario coverage rate and the maturity of each operation scenario, so as to determine the targeted improvement development direction for a telecommunication network operator according to the digital twin network capability maturity level and the level. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0037] Figure 1 Flowchart of the method provided by the embodiment of the present application Figure One ;

[0038] Figure 2 Flowchart of the method provided by the embodiment of the present application Figure Two ;

[0039] Figure 3 Index system quantization standard diagram provided by the embodiment of the present application;

[0040] Figure 4 Operation scene requirement diagram provided by the embodiment of the present application;

[0041] Figure 5 Structural schematic diagram of the digital twin network capability maturity assessment device provided by the embodiment of the present application;

[0042] Figure 6 Structural schematic diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0043] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present application, as detailed in the appended claims, but not all implementations. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0044] Firstly, the related concepts or terms involved in the present application are explained:

[0045] Digital twin is a comprehensive use of perception, calculation, modeling and other information technologies to describe, predict and simulate physical entities, and then realize mapping in virtual space, and reflect the whole life cycle process of the entity equipment in the corresponding physical space.

[0046] Digital twin plays an important role in realizing the digital transformation and intelligent upgrading of industry, and has important significance for accelerating the development of digital economy and promoting the integration of digital economy and real economy. In recent years, the research on digital twin theory has begun to be applied in manufacturing, agriculture, aerospace, transportation, medical care, and power industries.

[0047] Compared with the practical development of digital twin in other fields, the application of digital twin technology in the field of telecommunications network is still in the exploratory stage. The positioning, current situation and development path of the digital twin network of each telecommunications network operator are not clear. There is a lack of a clear methodology to evaluate the relative level of the specific practice of the telecommunications network and its ideal state, i.e. a lack of a digital twin maturity model for the telecommunications network field.

[0048] The existing digital twin system maturity evaluation method has not formed a general criterion and system to guide the construction of digital twin maturity models in various fields. Although the existing technology proposes a digital twin system maturity evaluation method for the entire industry, which has certain reference and guidance, or a digital twin system maturity evaluation method for specific industries such as digital economy development maturity assessment method, the existing technology cannot be directly used to evaluate the relative level of the specific practice of the telecommunications network and its ideal state, which is irrelevant to the actual situation of the development of the telecommunications network and business of the telecommunications operator, i.e. it cannot be applied to the digital twin maturity model for the telecommunications network field. Therefore, a digital twin network capability maturity evaluation method for the telecommunications network is needed to determine the digital twin network capability maturity level and level of the telecommunications network operator through quantitative evaluation and analysis, so as to form a more targeted improvement direction.

[0049] The present application provides a digital twin network capability maturity evaluation method, which collects the twin network capability information and physical network capability information corresponding to each operation scenario of the digital twin network, obtains the scores and weight values corresponding to the twin network capability information and physical network capability information respectively, and combines the development characteristics of the telecommunications network and business, and obtains the maturity of each operation scenario according to the obtained scores and weight values. After obtaining the maturity of all operation scenarios, the digital twin network capability maturity level for the telecommunications network is obtained according to the operation scenario coverage rate and the maturity of each operation scenario, so as to determine the targeted improvement and development direction for the telecommunications network operator according to the digital twin network capability maturity level and level.

[0050] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0051] Example One

[0052] Figure 1 Method flow diagram provided for the embodiments of the present application Figure One . As shown in Figure 1 , the method comprises:

[0053] S101, collecting index information of the digital twin network in each operation scenario, wherein the index information comprises operation scenario coverage corresponding to the network intelligent operation scenario dimension, twin network capability information corresponding to the twin network capability dimension and physical network capability information corresponding to the physical network capability dimension under each operation scenario;

[0054] Wherein, the digital twin network framework facing the telecommunication network includes network intelligent operation scenario, twin network and physical network. Based on the network intelligent operation scenario, the twin network aims to build a virtual twin body precisely mapped with the physical network and interacted in real time through the digitization of all elements such as users, services, network elements, etc. of the whole network, and to provide the technical verification, dynamic optimization capability needed for simulation, cognition and decision-making. The physical network is composed of all physical entities and virtual networks constituting an end-to-end network, and provides formatted data, capabilities and control interfaces to the digital twin network to receive and execute control commands;

[0055] Specifically, the information collection template generated according to the quantitative standard of the constructed index system collects the index information of each operation scenario. The more comprehensive the operation scenario range covered by the digital twin network, the more significant the effect of the intelligent operation of the telecommunication network. Therefore, the corresponding operation scenario coverage needs to be obtained according to the technical capability development stage of the telecommunication operator and the operation scenario range covered by the digital twin network. The more mature the technical capability of the telecommunication operator, the more operation scenario range covered by the digital twin network, that is, the operation scenario coverage is used to indicate the proportion of the number of scenarios covered by the digital twin network. It is also necessary to collect the index information of the digital twin network in each operation scenario. For the twin network capability and the physical network capability of each operation scenario, a plurality of sub-capability information corresponding to each capability is obtained. The twin network capability information corresponding to the twin network capability includes a plurality of corresponding sub-capability information, and the physical network capability information corresponding to the physical network capability includes a plurality of corresponding sub-capability information.

[0056] S102, acquire the respective scores of the twin network capability information and the physical network capability information corresponding to each operation scenario, and acquire the maturity of each operation scenario according to the respective weight values and the respective scores of the twin network capability information and the physical network capability information;

[0057] Specifically, after acquiring a plurality of sub-capability information corresponding to each of the twin network capability and the physical network capability for each operation scenario, a corresponding grade of each sub-capability information is acquired according to the index system quantitative standard, wherein the index system quantitative standard is provided with a corresponding relationship between each sub-capability information grade and score, and a corresponding relationship between each sub-capability information and weight value, and the maturity of each operation scenario is acquired according to the corresponding weight value and the corresponding score of each sub-capability information.

[0058] S103, acquire the digital twin network capability maturity level according to the operation scenario coverage rate and the maturity of each operation scenario;

[0059] Specifically, after acquiring the maturity of each operation scenario according to the corresponding weight value and the corresponding score of each sub-capability information, the total index of the digital twin network capability maturity is calculated by combining the number of scenarios covered by the digital twin network, the operation scenario coverage rate, and the maturity of each operation scenario, and a corresponding grade of the total index of the digital twin network capability maturity is acquired according to the index system quantitative standard, that is, the digital twin network capability maturity level, wherein the index system quantitative standard is provided with a corresponding relationship between the total index of the digital twin network capability maturity and the digital twin network capability maturity level.

[0060] The present application provides a digital twin network capability maturity evaluation method, which acquires the respective scores and weight values corresponding to the twin network capability information and the physical network capability information corresponding to each operation scenario of the digital twin network, thereby combining the characteristics of the telecommunication network and the business development, and acquiring the maturity of each operation scenario according to the acquired scores and weight values, and after acquiring the maturity of all operation scenarios, acquiring the digital twin network capability maturity level for the telecommunication network according to the operation scenario coverage rate and the maturity of each operation scenario, so as to determine the targeted improvement and development direction for the telecommunication network operator according to the digital twin network capability maturity level and the level.

[0061] The digital twin network capability maturity evaluation method of the present application will be described in detail below by using a specific embodiment.

[0062] Example Two

[0063] Figure 2A method flow diagram provided for an embodiment of the present application Figure Two , Figure 3 A quantitative standard diagram for the index system, Figure 4 An operation scene requirement diagram. In combination with the diagrams shown in Figure 2 , Figure 3 and Figure 4 , the method comprises:

[0064] S201, generating an information collection template according to the index system standard;

[0065] As shown in Figure 3 , the index system standard comprises a network intelligent operation scene dimension, a twin network capability dimension, and a physical network capability dimension, the twin network capability dimension comprises resource models and management, network perception, simulation simulation, intelligent decision-making, and intelligent management and control, the physical network capability dimension comprises network resource management, network capability opening, network-wide capability arrangement, or intelligent security protection, and the information collection template comprises a plurality of information filling items, each information filling item being used to fill in an index;

[0066] Specifically, since the twin network capability dimension and the physical network capability dimension both comprise a plurality of sub-indices corresponding to the respective dimensions, the information collection template generates a plurality of information filling items corresponding to the twin network capability dimension and the physical network capability dimension, respectively, so that a plurality of sub-capability indices corresponding to the respective dimensions in the operation scene are respectively filled into the corresponding information filling items, and the information filling items related to the network intelligent operation scene dimension are used to write in operation scene coverage;

[0067] Further, as shown in Figure 4 , the operation scenes in the network intelligent operation scene dimension comprise service experience, network planning, network construction, network maintenance, and network optimization, wherein each operation scene comprises a plurality of sub-operation scenes corresponding to the respective operation scenes, the service experience comprises customer perception evaluation, network element health evaluation, service health evaluation, and intention translation, the network planning comprises template design, deployment strategy design, network planning demand analysis and prediction, network planning and planning simulation, the network construction comprises service opening simulation, network deployment simulation, engineering debugging simulation, and engineering optimization simulation, the network maintenance comprises network fault and hidden danger identification, problem positioning and processing, network change evaluation and implementation verification, software version upgrade, and hardware spare parts replacement, and the network optimization comprises problem definition positioning, problem identification prediction, scheme decision-making, and scheme implementation effect verification.

[0068] S202, filling running information of the operation scene into the data filling information item according to the information filling item in the information collection template, to obtain twin network capability information and physical network capability information corresponding to each operation scene;

[0069] The running information related to the twin network capability in the operation scenario is filled into a plurality of information filling items corresponding to the twin network capability dimension to obtain twin network capability information, and the running information related to the physical network capability in the operation scenario is filled into a plurality of information filling items corresponding to the physical network capability dimension to obtain physical network capability information.

[0070] Specifically, the running information is filled into a plurality of information filling items generated according to the twin network capability dimension, and the obtained twin network capability information includes at least one sub-index in resource model and management information, network perception information, simulation information, intelligent decision information and intelligent management and control information. The running information is filled into a plurality of information filling items generated according to the physical network capability dimension, and the obtained physical network capability information includes at least one sub-index in network resource management information, network capability opening information, whole network capability arrangement information or intelligent security protection information.

[0071] S203, according to the capability size indicated by each sub-index in the twin network capability information, obtaining the maturity level corresponding to each sub-index;

[0072] The resource model and management information are used to indicate the virtual mirror reproduction capability of the digital twin network, the network perception information is used to indicate the real-time state perception capability of the digital twin network, the simulation information is used to indicate the high-fidelity simulation capability of the digital twin network, the intelligent decision information is used to indicate the cognitive decision capability of the digital twin network, and the intelligent management and control information is used to indicate the decision task intelligent execution capability of the digital twin network.

[0073] Specifically, after obtaining the sub-index corresponding to each operation scenario respectively twin network capability information and physical network capability information, according to the corresponding relationship between each sub-index and the respective maturity level in the index system standard, the maturity level corresponding to each sub-index is obtained, wherein the corresponding relationship between each sub-index and the respective maturity level is established according to the capability size of each sub-index from low to high, and five levels are established, such as the maturity level corresponding to the sub-index with the lowest capability is L1, and the maturity level corresponding to the sub-index with the highest capability is L5.

[0074] Further, for the resource model and management information, when the data model of the twin network can only describe the coding, name, manufacturer, shape and other data of a single physical network element, it is considered that the virtual mirror reproduction capability of the digital twin network is the lowest, and the corresponding maturity level is L1.

[0075] When the twin network can construct a digital embryo model of the physical network element, topology and scene function in a single network and single domain, the corresponding maturity level is L2.

[0076] When the twin network can build a digital mapping model of physical network elements, topology, scene function and natural environment in a single network cross-domain, it can realize model parameter adjustment of physical network part elements, and confirm the corresponding maturity level as L3;

[0077] When the twin network can build a hyper-realistic mapping model of all network physical entities and environment, the model parameters can be updated in real time according to the physical network running state, and the corresponding maturity level is confirmed as L4;

[0078] When the twin network can build an intelligent twin model of all elements and life cycle of physical network, and the intelligent twin model can be optimized according to the feedback of physical network, the corresponding maturity level is confirmed as L5;

[0079] Further, for network perception information, when the twin network can mainly collect structured data and confirm the island type data ownership, the corresponding maturity level is confirmed as L1;

[0080] When the twin network can realize network dynamic visualization based on single-domain multi-source heterogeneous high-precision data, the corresponding maturity level is confirmed as L2;

[0081] When the twin network can realize cross-domain multi-source heterogeneous real-time network data perception ability, the corresponding maturity level is confirmed as L3;

[0082] When the twin network can use external data sources to support advanced data analysis and artificial intelligence driven network hypersensitive perception of entity network, the corresponding maturity level is confirmed as L4;

[0083] When the twin network can realize network perception prediction based on digital twin network and data exchange sharing with partners, the corresponding maturity level is confirmed as L5;

[0084] Further, for simulation information, when the twin network can statically describe and visually simulate and display physical network elements, the corresponding maturity level is confirmed as L1;

[0085] When the twin network can simulate offline for physical network, and preview the trend state simulation, the corresponding maturity level is confirmed as L2;

[0086] When the twin network can simulate online for physical network, and evaluate the difference between simulation results and physical indicators, the corresponding maturity level is confirmed as L3;

[0087] When the twin network can simulate real-time for physical network, and can monitor the state of physical network elements in real time, the corresponding maturity level is confirmed as L4;

[0088] When the twin network can simulate the physical network and control the physical network in real time based on the simulation results, the corresponding maturity level is L5;

[0089] Further, for intelligent decision information, when the twin network can operate based on expert experience, and the decision is mainly made by artificial, the corresponding maturity level is L1;

[0090] When the twin network can assist cognitive decision automation based on manually configured static rules, the corresponding maturity level is L2;

[0091] When the twin network can assist cognitive decision intelligence based on dynamically generated knowledge by external injection or AI inference, and combined with pre-defined rules for screening, the corresponding maturity level is L3;

[0092] When the twin network can realize intelligentization of the whole process of cognitive decision based on dynamically generated knowledge by external injection or AI inference, the corresponding maturity level is L4;

[0093] When the twin network can realize full-scene intelligent closed loop of the whole network facing multi-service, multi-field and full life cycle based on knowledge self-learning and self-evolution, the corresponding maturity level is L5;

[0094] Further, for intelligent control information, when there is no interaction between the physical network and the digital twin network, and manual operation is required, the corresponding maturity level is L1;

[0095] When there is one-way dynamic interaction between the physical network and the digital twin network, and the instruction execution is completed according to the artificial pre-configured rules, the corresponding maturity level is L2;

[0096] When there is two-way dynamic interaction between the physical network and the digital twin network, and the instruction automatic execution is completed based on the cognitive decision results, the corresponding maturity level is L3;

[0097] When there is two-way real-time interaction between the physical network and the digital twin network, and the intelligent scheduling execution is realized according to the decision scheme, the corresponding maturity level is L4;

[0098] When there is two-way intelligent closed loop between the physical network and the digital twin network, the physical network can be controlled in real time, and the digital twin network can be optimized intelligently based on the feedback of the physical network, the corresponding maturity level is L5.

[0099] S204, according to the score corresponding to the maturity level of each sub-indicator and the weight value corresponding to each sub-indicator, weighted sum is performed to obtain the score corresponding to the twin network capability information in each operation scene;

[0100] Specifically, after obtaining the maturity level corresponding to each sub-indicator according to the correspondence between each sub-indicator and the respective maturity level in the index systematization standard, the product of the score corresponding to each sub-indicator of the twin network capability information in each operation scenario and the weight value is obtained according to the correspondence between each sub-indicator and the score and the correspondence between each sub-indicator and the weight value in the index systematization standard, and the sum of the product of the score corresponding to each sub-indicator and the weight value is obtained, so as to obtain the score corresponding to the twin network capability information in each operation scenario.

[0101] Further, the maturity levels are sequentially corresponding to the first preset score such as 20, the second preset score such as 40, the third preset score such as 60, the fourth preset score such as 80 and the fifth preset score such as 100 from small to large, so that each sub-indicator in the twin network capability information obtains the corresponding preset score according to the respective maturity level, and the corresponding preset score is taken as the score of each sub-indicator in the twin network capability information.

[0102] Further, according to the correspondence between each sub-indicator of the twin network capability information and the weight value in the index systematization standard, the twin network capability information corresponds to the first weight value such as 40%, and each sub-indicator of the twin network capability information is evenly divided according to the first weight value to obtain the weight value corresponding to each sub-indicator such as 8%.

[0103] S205, obtaining the maturity level corresponding to each sub-indicator according to the capability size indicated by each sub-indicator in the physical network capability information;

[0104] The sub-indicators of the physical network capability information include at least one of network resource management information, network capability opening information, whole network capability arrangement information or intelligent security protection information, wherein the network resource management information is used to indicate the resource whole life cycle management and audit capability of the digital twin network, the network capability opening information is used to indicate the decoupling opening interface capability of the digital twin network, the whole network capability arrangement information is used to indicate the forming executable scheme capability of the digital twin network, and the intelligent security protection information is used to indicate the intelligent security protection capability of the digital twin network.

[0105] Specifically, after obtaining the sub-indicators corresponding to each operation scenario physical network capability information, the maturity level corresponding to each sub-indicator is obtained according to the correspondence between each sub-indicator and the respective maturity level in the index systematization standard, wherein the correspondence between each sub-indicator and the respective maturity level is established according to the capability size of each sub-indicator from low to high, and five levels are established, such as the maturity level corresponding to the sub-indicator with the lowest capability is L1, and the maturity level corresponding to the sub-indicator with the highest capability is L5.

[0106] Further, for network resource management information, the physical network can only manage and manually audit part of the active resources online, and the corresponding maturity level is L1;

[0107] The physical network can visually manage and automatically audit all active resources in the network, and manage part of the passive resources online, and the corresponding maturity level is L2;

[0108] The physical network can visually manage all passive resources in the network, and automatically audit part of the passive resources, and the corresponding maturity level is L3;

[0109] The physical network can visually manage and automatically audit the full life cycle of all passive resources in the network, and the corresponding maturity level is L4;

[0110] The physical network can visually manage and intelligently audit the full life cycle of all resources in the network, and the corresponding maturity level is L5;

[0111] Further, for network capability opening information, the physical network can only access and call point-to-point capabilities between business platforms in a single network and a single domain as needed, and the corresponding maturity level is L1;

[0112] The physical network can build a capability opening platform and realize access of part of the network capabilities, and the corresponding maturity level is L2;

[0113] The physical network can build a unified capability opening platform for the entire network, standardize access of all network capabilities, and realize capability sharing and reuse, and the corresponding maturity level is L3;

[0114] The physical network can realize connection with other business systems through the network capability opening platform, and the network capabilities can be productized, and the corresponding maturity level is L4;

[0115] The physical network can pull all network capabilities according to business scenarios, realize supply, demand, management, and operation of the full life cycle of the network capabilities, and the corresponding maturity level is L5;

[0116] Further, for network capability orchestration information, the physical network can only be orchestrated in a single network and a single domain, and can statically orchestrate network services based on manual methods as needed, and the corresponding maturity level is L1;

[0117] The physical network can realize local semi-automatic orchestration according to preset orchestration rules in an end-to-end orchestration scheme in a single network and across domains, and the corresponding maturity level is L2;

[0118] The physical network can confirm that the corresponding maturity level is L3 when it can realize local automatic arrangement according to the cognitive analysis result for the end-to-end arrangement scheme of the main service across the network;

[0119] The physical network can confirm that the corresponding maturity level is L4 when it can realize the end-to-end arrangement scheme of the whole network and realize the whole process automatic arrangement according to the strategy formed by the decision;

[0120] The physical network can confirm that the corresponding maturity level is L5 when it can realize real-time intelligent arrangement of network services according to the service intention of the whole network;

[0121] Further, for intelligent security protection information, the physical network mainly uses traditional static and passive security protection, and the corresponding maturity level is L1;

[0122] The physical network starts to pay attention to intelligent application security protection and assesses the business security compliance, and the corresponding maturity level is L2;

[0123] The physical network strengthens the security of AI algorithm and data security protection capability, and the corresponding maturity level is L3;

[0124] The physical network realizes dynamic and adaptive active security defense capability, and the corresponding maturity level is L4;

[0125] The physical network introduces and strengthens the counterattack capability against intelligent malicious attacks, and the corresponding maturity level is L5.

[0126] S206, according to the score corresponding to the maturity level of each sub-indicator and the weight value corresponding to each sub-indicator, weighted summation is performed to obtain the score corresponding to the physical network capability information in each operation scene;

[0127] Specifically, according to the corresponding relationship between the level of each sub-indicator in the index system standard and the score, and the corresponding relationship between each sub-indicator and the weight value, the product of the score corresponding to each sub-indicator of the physical network capability information in each operation scene and the weight value is obtained, and the product of the score corresponding to each sub-indicator and the weight value is summed, thereby obtaining the score corresponding to the physical network capability information in each operation scene;

[0128] Further, the corresponding relationship between the level of each sub-indicator in the index system standard and the score is sequentially corresponding to the first preset score, the second preset score, the third preset score, the fourth preset score and the fifth preset score from small to large, so that each sub-indicator in the physical network capability information obtains the corresponding preset score according to the respective maturity level, and the corresponding preset score is respectively taken as the score of each sub-indicator in the physical network capability information;

[0129] Further, for the corresponding relationship between each sub-index of the physical network capability information in the index system standard and the weight value, the physical network capability information corresponds to a first weight value such as 40%, and each sub-index of the physical network capability information is evenly divided according to the first weight value level to obtain a corresponding weight value such as 10% of each sub-index.

[0130] S207, obtaining the maturity of each operation scene according to the weight value and the corresponding score of the twin network capability information and the physical network capability information respectively;

[0131] Specifically, after obtaining the corresponding scores of the twin network capability information and the physical network capability information in each operation scene respectively, the sum of the corresponding scores of the twin network capability information and the physical network capability information in each operation scene is taken as the maturity of a single operation scene, that is, the maturity of each operation scene, which is represented by the following formula:

[0132]

[0133] Wherein, M 单场景 is the maturity of a single operation scene, X i is the corresponding score of the sub-index, W i is the corresponding weight of the sub-index, and i is the number of sub-indices.

[0134] S208, obtaining the average maturity of the scene according to the maturity of each operation scene;

[0135] Specifically, after obtaining the maturity of each operation scene according to the weight value and the corresponding score of the twin network capability information and the physical network capability information respectively, the sum of the maturity of all operation scenes is obtained, and the quotient of the sum of the maturity of all operation scenes and the number of operation scenes is taken as the average maturity of the scene, which is represented by the following formula:

[0136]

[0137] Wherein, Z is the average maturity of the scene, M 单场景 is the maturity of a single operation scene, and S is the number of operation scenes.

[0138] S209, obtaining the coverage maturity according to the operation scene coverage rate and the scene coverage weight value;

[0139] Specifically, after obtaining the average maturity of the scene according to the maturity of each operation scene, the product of the operation scene coverage rate and the scene coverage weight value is obtained, and the product of the operation scene coverage rate and the scene coverage weight value is taken as the coverage maturity.

[0140] Further, the operation scene coverage rate is used to indicate the number of operation scenes involved in the target project, including business processes, network professional coverage and implementation functions, in order to avoid missing all operation scenes and thus missing index information. After obtaining the operation scene coverage rate through the information filling item related to the network intelligent operation scene dimension in the information collection template, the corresponding score is obtained according to the operation scene coverage rate in the index system standard, and the total value of the operation scene coverage rate is divided into five different index ranges in order from small to large, such as [0, 20%], (20%, 40%], (40%, 60%], (60%, 80%] and (80%, 100%]. The five different index ranges are in order from small to large, and the first preset score, the second preset score, the third preset score, the fourth preset score and the fifth preset score are corresponded, so that when the operation scene coverage rate falls into one of the five different index ranges, the corresponding preset score is obtained, and the obtained preset score is taken as the corresponding score. According to the corresponding relationship with the scene coverage weight value, the corresponding weight value is obtained, so that the scene coverage weight value corresponds to the second weight value such as 20%, and the second weight value is less than the first weight value.

[0141] S210, obtaining the digital twin network capability maturity total index according to the sum of the scene average maturity and the coverage maturity;

[0142] Specifically, after taking the product of the operation scene coverage rate and the scene coverage weight value as the coverage maturity, the sum of the coverage maturity and the scene average maturity is obtained, and the obtained sum is taken as the digital twin network capability maturity total index, which is represented by the following formula:

[0143] M 总指数 =X 场景覆盖率 *W 场景覆盖率 +Z;

[0144] Wherein, M 总指数 is the digital twin network capability maturity total index, X 场景覆盖率 is the operation scene coverage rate, W 场景覆盖率 is the scene coverage weight value, and Z is the scene average maturity.

[0145] S211, obtaining the digital twin network capability maturity level according to the digital twin network capability maturity total index;

[0146] Wherein, the digital twin network capability maturity level includes basic starting level, planning exploration level, stable development level, pioneer innovation level and future leading level in order from small to large according to capability;

[0147] Specifically, after obtaining the digital twin network capability maturity total index, the corresponding digital twin network capability maturity level is obtained according to the corresponding relationship between the digital twin network capability maturity total index and the digital twin network capability maturity level in the index system standard. The corresponding relationship between the digital twin network capability maturity total index and the digital twin network capability maturity level in the index system standard divides the total value of the network capability maturity total index into five different index ranges, such as [0, 20%], (20%, 40%), (40%, 60%), (60%, 80%) and (80%, 100%) in order from small to large. The five different index ranges are in turn corresponding to the basic starting level, the planning exploration level, the stable development level, the pioneer innovation level and the future leading level, so that when the network capability maturity total index falls into one of the five different index ranges, the corresponding digital twin network capability maturity level is obtained. After obtaining the corresponding digital twin network capability maturity level, the user can confirm the characteristics indicated by the higher digital twin network capability maturity level according to the current digital twin network capability maturity level and make targeted technical improvements, thereby providing a targeted improvement development direction for the development of the telecommunication network operator.

[0148] Further, the characteristics of the basic starting level are used to indicate that the digital twin network describes and visually simulates the static information of the physical network element, and the physical space and the digital space have no dynamic connection, mainly relying on manual experience to support operation and control of the physical network.

[0149] The characteristics of the planning exploration level are used to indicate that the digital twin network can establish a one-way information flow mechanism from the physical network to the twin network, and through the simulation preview of the trend state of the physical network, the static state of the physical network space elements can be reflected, supporting automatic operation in specific scenarios.

[0150] The characteristics of the stable development level are used to indicate that the digital twin network establishes a two-way information interaction mechanism between the physical network and the twin network, and the twin network can remotely control the physical network, and the change state of the physical network can also be reflected in the twin network in real time, supporting automatic operation in some scenarios.

[0151] The characteristics of the pioneer innovation level are used to indicate that the digital twin network can establish a two-way real-time control mechanism between the physical network and the twin network, and the twin network can not only monitor the element state of the physical space, but also realize intelligent control, prediction and optimization of the physical network based on cognitive decision, supporting intelligent operation in the whole process of some scenarios.

[0152] The future leading level feature is used to indicate that the digital twin network can be self-learned and self-evolved based on knowledge, and support autonomous reconfiguration of the twin network according to changes of the physical network and demand scenarios, and realize full-scene intelligent closed loop of the whole network facing multiple services, multiple fields and full life cycle.

[0153] The present application provides a digital twin network capability maturity evaluation method, which collects twin network capability information and physical network capability information of the digital twin network in each operation scenario, obtains scores and weight values corresponding to the twin network capability information and the physical network capability information respectively, thereby combining the characteristics of the telecommunication network and service development, and obtaining the maturity of each operation scenario according to the obtained scores and weight values, and obtaining the digital twin network capability maturity level of the telecommunication network according to the operation scenario coverage rate and the maturity of each operation scenario after obtaining the maturity of all operation scenarios, so as to determine the targeted improvement and development direction for the telecommunication network operator according to the digital twin network capability maturity level and level.

[0154] The embodiment of the present application can divide the function modules of the electronic device or the host device according to the above-mentioned method examples, for example, each function module can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be realized in the form of hardware or in the form of a software function module. It should be noted that the division of the modules in the embodiment of the present application is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0155] Figure 5 A structure diagram of a digital twin network capability maturity evaluation device provided by the embodiment of the present application is shown in FIG. 5. Figure 5 As shown in the figure, the device 500 includes:

[0156] The acquisition module 501 is configured to collect index information corresponding to each operation scenario of the digital twin network, wherein the index information includes an operation scenario coverage rate corresponding to a network intelligent operation scenario dimension, twin network capability information corresponding to a twin network capability dimension and physical network capability information corresponding to a physical network capability dimension under each operation scenario.

[0157] The first processing module 502 is configured to obtain scores corresponding to the twin network capability information and the physical network capability information under each operation scenario respectively, and obtain the maturity of each operation scenario according to the weight values and the corresponding scores of the twin network capability information and the physical network capability information.

[0158] The second processing module 503 is configured to acquire the digital twin network capability maturity level according to the operation scenario coverage rate and the maturity of each operation scenario.

[0159] Further, the second processing module 503 is specifically configured to acquire a scenario average maturity according to the maturity of each operation scenario.

[0160] According to the operation scenario coverage rate and the scenario coverage weight value, a coverage maturity is acquired.

[0161] According to the sum of the scenario average maturity and the coverage maturity, the digital twin network capability maturity total index is acquired.

[0162] According to the digital twin network capability maturity total index, the digital twin network capability maturity level is acquired.

[0163] Further, the acquisition module 501 is specifically configured to generate an information collection template according to an index system standard, wherein the information collection template includes a plurality of information filling items, each information filling item is used to fill in an index, and the operation information of each operation scenario is filled into the data filling information item according to the information filling item in the information collection template, so as to obtain the index information corresponding to each operation scenario.

[0164] Further, the first processing module 502 is specifically configured to acquire a maturity level corresponding to each sub-index according to the capability size indicated by each sub-index in the twin network capability information; the twin network capability information includes at least one sub-index in resource model and management information, network perception information, simulation simulation information, intelligent decision-making information and intelligent management and control information, wherein the resource model and management information are used to indicate the virtual mirror reproduction capability of the digital twin network, the network perception information is used to indicate the real-time state perception capability of the digital twin network, the simulation simulation information is used to indicate the high-fidelity simulation capability of the digital twin network, the intelligent decision-making information is used to indicate the cognitive decision-making capability of the digital twin network, and the intelligent management and control information is used to indicate the decision-making task intelligent execution capability of the digital twin network.

[0165] According to the score corresponding to the maturity level of each sub-index and the weight value corresponding to each sub-index, weighted summation is performed to obtain a score corresponding to the twin network capability information under each operation scenario.

[0166] Further, the first processing module 502 is specifically configured to obtain a maturity level corresponding to each sub-indicator according to a capability size indicated by the physical network capability information of each sub-indicator; the physical network capability information includes at least one of network resource management information, network capability opening information, whole-network capability arrangement information, or intelligent security protection information; the network resource management information is used to indicate resource full life cycle management and auditing capability of a digital twin network, the network capability opening information is used to indicate decoupling opening interface capability of the digital twin network, the whole-network capability arrangement information is used to indicate forming executable scheme capability of the digital twin network, and the intelligent security protection information is used to indicate intelligent security protection capability of the digital twin network.

[0167] According to the score corresponding to the maturity level of each sub-indicator and the weight value corresponding to each sub-indicator, weighted summation is performed to obtain a score corresponding to the physical network capability information under each operation scenario.

[0168] Further, the second processing module 503 is further configured to correspond to a first weight value for the twin network capability information and the physical network capability information, and correspond to a second weight value for the scenario coverage weight value, the second weight value being less than the first weight value.

[0169] Further, the first processing module 502 is specifically configured to equalize the weight value corresponding to each sub-indicator.

[0170] Figure 6 A structural schematic diagram of an electronic device provided by an embodiment of the present application is provided. As shown in the structural schematic diagram of the electronic device 600, the electronic device 600 includes at least one processor 601 and a memory 602. The electronic device 600 further includes a communication component 603. The processor 601, the memory 602, and the communication component 603 are connected through a bus 604. Figure 6

[0171] In the specific implementation process, the at least one processor 601 executes computer execution instructions stored in the memory 602, so that the at least one processor 601 performs the digital twin network capability maturity evaluation method as executed by the electronic device side.

[0172] The specific implementation process of the processor 601 can refer to the above-mentioned method embodiments, which have similar implementation principles and technical effects, and will not be described here again in this embodiment.

[0173] ​In the above embodiments, it should be understood that the processor can be a central processing unit (CPU) and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the disclosed method can be directly embodied as hardware processor execution or combined with hardware and software modules in the processor for execution.

[0174] The memory can include a high-speed RAM memory and can also include a non-volatile storage NVM, such as at least one disk memory.

[0175] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0176] The functions realized by the electronic device and the master device described above are introduced for the scheme provided by the embodiments of the present application. It can be understood that the electronic device or the master device includes the hardware structure and / or software modules corresponding to the execution of each function in order to realize the above functions. The units and algorithm steps of each example described in combination with the embodiments disclosed in the embodiments of the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software driven hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present application.

[0177] The present application also provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, when the processor executes the computer execution instructions, the method for evaluating the digital twin network capability maturity is realized.

[0178] The computer readable storage medium described above can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0179] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium, and can write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a host device.

[0180] The present application also provides a computer program product, which comprises a computer program stored in a readable storage medium, and at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the electronic device to perform the scheme provided by any of the above embodiments.

[0181] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the foregoing storage medium includes ROM, RAM, magnetic disk or optical disk and various storage medium that can store program codes.

[0182] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A digital twin network capability maturity assessment method, applied to intelligent network operation, characterized in that, The method includes: Collect indicator information of the digital twin network in each operation scenario, wherein the indicator information includes the operation scenario coverage rate corresponding to the network intelligent operation scenario dimension, the twin network capability information corresponding to the twin network capability dimension in each operation scenario, and the physical network capability information corresponding to the physical network capability dimension. Obtain the scores corresponding to the twin network capability information and the physical network capability information in each operational scenario, and obtain the maturity of each operational scenario based on the weight values ​​and scores corresponding to the twin network capability information and the physical network capability information. The maturity level of the digital twin network capability is obtained based on the coverage of the operational scenarios and the maturity of each operational scenario.

2. The method according to claim 1, characterized in that, The step of obtaining the digital twin network capability maturity level based on the coverage of the operational scenarios and the maturity of each operational scenario includes: Based on the maturity of each operational scenario, obtain the average maturity of the scenarios; Based on the operational scenario coverage rate and scenario coverage weight value, the coverage maturity is obtained; The total capability maturity index of the digital twin network is obtained by summing the average maturity of the scenario and the coverage maturity. The digital twin network capability maturity level is obtained based on the overall digital twin network capability maturity index.

3. The method according to claim 1, characterized in that, Based on the quantitative standards of the indicator system, collect indicator information corresponding to each operational scenario, including: Based on the standardized indicator system, an information collection template is generated, wherein the information collection template includes multiple information fields, each of which is used to fill in one indicator. According to the information filling items in the information collection template, the operation information of the operation scenario is filled into the information filling items to obtain the indicator information corresponding to each operation scenario.

4. The method according to claim 1, characterized in that, The step of obtaining the score corresponding to the twin network capability information in each operational scenario includes: Based on the capability level indicated by each sub-indicator in the digital twin network capability information, the maturity level corresponding to each sub-indicator is obtained; the digital twin network capability information includes at least one sub-indicator from resource model and management information, network perception information, simulation information, intelligent decision-making information, and intelligent control information, wherein the resource model and management information is used to indicate the virtual mirror reproduction capability of the digital twin network, the network perception information is used to indicate the real-time state perception capability of the digital twin network, the simulation information is used to indicate the high-fidelity simulation capability of the digital twin network, the intelligent decision-making information is used to indicate the cognitive decision-making capability of the digital twin network, and the intelligent control information is used to indicate the intelligent execution capability of the decision-making task of the digital twin network; The scores corresponding to the maturity levels of each sub-indicator and the weight values ​​of each sub-indicator are weighted and summed to obtain the scores corresponding to the twin network capability information in each operational scenario.

5. The method according to claim 1, characterized in that, The process of obtaining the score corresponding to the physical network capability information in each operational scenario includes: Based on the capability level indicated by each sub-indicator in the physical network capability information, the maturity level corresponding to each sub-indicator is obtained; the physical network capability information includes at least one of network resource management information, network capability openness information, network-wide capability orchestration information, or intelligent security protection information; wherein, the network resource management information is used to indicate the resource lifecycle management and auditing capabilities of the digital twin network, the network capability openness information is used to indicate the decoupling open interface capabilities of the digital twin network, the network-wide capability orchestration information is used to indicate the ability of the digital twin network to form executable solutions, and the intelligent security protection information is used to indicate the intelligent security protection capabilities of the digital twin network; The scores corresponding to the maturity levels of each sub-indicator and the weight values ​​of each sub-indicator are weighted and summed to obtain the score corresponding to the physical network capability information in each operational scenario.

6. The method according to claim 2, characterized in that, Both the twin network capability information and the physical network capability information correspond to the first weight value; The scene coverage weight value corresponds to a second weight value, and the second weight value is less than the first weight value.

7. The method according to claim 4 or 5, characterized in that, Each sub-indicator has an equal weight value.

8. A digital twin network capability maturity assessment device, characterized in that, Also includes: The acquisition module is used to collect indicator information of the digital twin network in each operation scenario. The indicator information includes the operation scenario coverage rate corresponding to the network intelligent operation scenario dimension, the twin network capability information corresponding to the twin network capability dimension in each operation scenario, and the physical network capability information corresponding to the physical network capability dimension. The first processing module is used to obtain the scores corresponding to the twin network capability information and the physical network capability information in each operation scenario, and to obtain the maturity of each operation scenario based on the weight values ​​and scores corresponding to the twin network capability information and the physical network capability information. The second processing module is used to obtain the capability maturity level of the digital twin network based on the coverage of the operational scenarios and the maturity of each operational scenario.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

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