Transport network digital twinning ability assessment method, device and system

Abstract

The invention discloses a method and a device for evaluating the digital twinning ability of a transport network, and solves the problem that the digital twinning ability of the transport network is difficult to scientifically evaluate. A transport network digital twinning capability assessment method comprises the following steps: obtaining digital twinning transport network data, and determining an application scene data set through a first index; first scoring data; and in response to a plurality of application scene data sets, performing statistical calculation on all the first scoring data to generate second scoring data. According to the method, the evaluation object, the evaluation scene and the evaluation dimension of the digital twinning ability of the transport network are defined, the digital twinning ability score calculation rule is given, and the digital twinning ability level in each scene is displayed while the comprehensive evaluation result is obtained.

Description

Technical Field

[0001] This application relates to the technical field of transmission networks, and in particular, to a method, device, and system for evaluating the digital twin capabilities of a transmission network. Background Art

[0002] The scale of the transmission network is constantly expanding, and the traditional manual operation and maintenance mode can no longer meet the flexible and efficient management and control requirements. The application value and driving force of introducing digital twin technology are becoming increasingly clear. With the continuous evolution of the management and control system towards intelligent management, control, and analysis capabilities, digital twin technology is a supplement and enhancement to the future intelligent operation and management of the transmission network, providing a means of real-time simulation and verification to implement operation and management operations that cannot be carried out in the existing network, thereby deeply promoting the digital transformation of the network.

[0003] Currently, the industrial application and standardization research of digital twins in the transmission network are in their infancy. With the gradual implementation of digital twin technology in the field of transmission networks, how to comprehensively and scientifically verify the digital twin capabilities of the transmission network to understand the current technological development level has become one of the problems faced. Currently, there is still a lack of a systematic ability evaluation system and method for digital twins in the transmission network to comprehensively evaluate the technical level and application capabilities of digital twins in the transmission network. Therefore, there is an urgent need for a systematic method for evaluating the digital twin capabilities of the transmission network to scientifically evaluate the application capabilities of digital twin technology in the professional field of the transmission network. Summary of the Invention

[0004] The embodiments of this application provide a method and device for evaluating the digital twin capabilities of a transmission network, which solve the problem of difficult integrated evaluation of the digital twin capabilities of the transmission network.

[0005] In a first aspect, the embodiments of this application provide a method for evaluating the digital twin capabilities of a transmission network, including the steps of: Obtain digital twin transmission network data, and determine an application scenario data set through a first index; Calculate first scoring data of the digital twin capabilities of the application scenario data set according to a preset rule or algorithm; In response to including multiple application scenario data sets, statistically calculate all the first scoring data to generate second scoring data.

[0006] Further, determining the first scoring data specifically includes the steps of: Determine multiple evaluation dimension data sets in the application scenario data set through a second index, and determine the index data and index thresholds of each evaluation dimension data set through a third index; Compare the index data with the index thresholds to determine the dimension scoring data of a single dimension; the dimension scoring data includes index level indication information; Statistically calculate multiple dimension scoring data to generate first scoring data.

[0007] In one embodiment, the application scenario dataset includes at least one of the following: Planning and construction dataset, operation dataset, maintenance dataset, and optimization phase dataset.

[0008] In one embodiment, determining the first scoring data further includes the steps of: In response to the instruction of the GUI to determine that the first scoring data does not conform to the actual situation, perform any one of the following: Obtain the new digital twin transmission network data; Modify the twin model to obtain the digital twin transmission network data of the new twin model; In response to the fact that the application scenario dataset includes multiple evaluation dimension datasets, re-obtain the index data and index thresholds of each evaluation dimension dataset.

[0009] In a second aspect, an embodiment of the present application further provides a transmission network digital twin capability evaluation device, which uses the transmission network digital twin capability evaluation method described in any one of the embodiments of the first aspect, and includes: An acquisition module, which acquires digital twin transmission network data through an input interface and determines an application scenario dataset through a first index; A scoring module, which receives the application scenario dataset and calculates the first scoring data of the digital twin capability according to a preset rule or algorithm; A calculation module, which statistically calculates the first scoring data of all the application scenario datasets to generate second scoring data and outputs it through an output interface.

[0010] Furthermore, it further includes: A human-computer interaction module, which is respectively connected to the scoring module and the acquisition module through a manual interface. The scoring module transmits the dimension scoring data or the first scoring data through the manual interface; the acquisition module receives the instruction to determine that the first scoring data does not conform to the actual situation transmitted by the human-computer interaction module through the manual interface.

[0011] In one embodiment, the determining module is further configured to determine multiple evaluation dimension datasets in the application scenario dataset through a second index, and determine the index data and index thresholds of each evaluation dimension dataset through a third index. The scoring module is further configured to compare the index data and the index thresholds to determine the dimension scoring data of a single dimension. The calculation module is further configured to statistically calculate the multiple dimension scoring data to generate the first scoring data.

[0012] In a third aspect, an embodiment of the present application further provides a digital twin capability evaluation system for a transport network, which includes the digital twin capability evaluation device described in any one of the embodiments of the second aspect, and further includes a management and control system and a digital twin system. The digital twin capability evaluation device of the transport network is respectively connected to the management and control system, the digital twin system, and the transport network through interfaces and transmits data.

[0013] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method described in any one of the embodiments of the first aspect.

[0014] In a fifth aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any one of the embodiments of the first aspect.

[0015] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: The present application clarifies the evaluation object, evaluation scenario, and evaluation dimension of the digital twin capability of the transport network, gives the calculation rules for the digital twin capability score, and analyzes the index weights of each scenario and each evaluation dimension under each scenario. First, evaluate the score values of different dimensions in a specific scenario of a single evaluation object. After completing the digital twin capability evaluation of all scenarios of a single evaluation object, calculate the overall digital twin capability score of the evaluation object. The above scoring method is comprehensive and objective. On this basis, it focuses on solving the implementation of the general method for evaluating the digital twin capability in the existing transport network, and shows the digital twin capability level under each scenario while obtaining the comprehensive evaluation result. The method is scientific and reasonable, and is of significance for the exploration and development of the digital twin of the transport network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 It is a schematic diagram of the overall architecture of the digital twin of the transport network in the prior art; Figure 2 It is a schematic diagram of the framework of the digital twin capability evaluation system of the transport network in the prior art; Figure 3-1 It is a flowchart of a method for evaluating the digital twin capability of a transport network provided by an embodiment of the present application; Figure 3-2 It is a flowchart of a method for evaluating the digital twin capability of a transport network with multi-dimensional scoring provided by an embodiment of the present application; Figure 4Schematic diagram of the digital twin capability evaluation system provided by the embodiments of the present application; Figure 5 Deployment schematic diagram of the digital twin capability evaluation system for the transport network provided by the embodiments of the present application; Figure 6 Schematic diagram of the structure of an electronic device provided by the embodiments of the present application. Detailed implementation manners

[0017] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] The following details the technical solutions provided by the embodiments of the present application in conjunction with the drawings.

[0019] The digital twin architecture of the transport network is as Figure 1 shown and can be divided into a network application layer, a network twin layer, and a physical network layer, with the three layers interacting with each other. Combining with the transport network management and control system, the overall architecture of the transport network digital twin is jointly constructed.

[0020] The combination of the digital twin and the transport network management and control system effectively enhances the network service simulation and emulation capabilities throughout the life cycle of the transport network, provides rich proactive operation means, and helps operators transform towards a proactive operation mode of network services.

[0021] Based on the overall architecture of the transport network digital twin, the twin capabilities are driven by the application services of typical scenarios in the application layer, and the twin capabilities are classified around six elements: model, data, service, interaction, management, and the twin as a whole. Therefore, a digital twin capability evaluation system for the transport network is modularly constructed according to three dimensions: network intelligent operation scenarios, capability categories, and capability evaluation indicators, as Figure 2 shown. According to actual application requirements, specific typical application scenarios, capability categories, and capability evaluation indicators can be flexibly selected, and certain flexibility and scalability are required.

[0022] Figure 3-1 The embodiments of the present application provide a flowchart of a method for evaluating the digital twin capabilities of a transport network, including steps: 310 to 330.

[0023] Step 310, obtain digital twin transport network data, and determine the application scenario dataset through the first index; Obtain the evaluation intent signal for the digital twin capabilities of the transport network, obtain the digital twin transport network data, and determine the evaluation object of the intent signal; the evaluation object includes the digital twin network hierarchical dataset. The digital twin network hierarchical dataset (L0~L3) includes professional domains such as OTN (L0 / L1) and SPN (L1 / L2 / L3).

[0024] For example, obtain the evaluation object and scope of the digital twin capabilities of the transport network: The evaluation object includes the selected digital twin network hierarchy (L0~L3), including professional domains such as OTN (L0 / L1) and SPN (L1 / L2 / L3).

[0025] For example, the transport network scope in the embodiments of this application covers the L0-L3 layers of the transport network / bearer network. For example: optical networks such as L0-L1 OTN / ROADM / WDM, and packet transport networks such as L2-L3 SPN / PTN.

[0026] It is also necessary to determine whether the evaluation object is cross-domain, including single-domain or multi-domain scenario datasets.

[0027] The cross-domain of the transport network can be divided by different manufacturers, different regions, or different technology domains.

[0028] The first index is the application scenario index. Through the application scenario index, the application scenario dataset of the digital twin transport network data can be searched. As shown in Table 1 specifically.

[0029] The application scenario dataset can be the network configuration information of the transport network, including the digital twin application scenario dataset of the evaluation object in the entire life cycle of the transport network.

[0030] In one of the embodiments, as shown in Table 1, the application scenario dataset includes at least one of the following: Planning and construction dataset, operation dataset, maintenance dataset, and optimization phase dataset.

[0031] What is represented in the ability domain in Table 1 is the application scenario of the digital twin capabilities in the entire life cycle of the transport network.

[0032] For example, selecting one or more network configuration information as the digital twin application scenario dataset in the entire life cycle of the transport network may include datasets for planning and construction scenarios, operation scenarios, maintenance scenarios, and optimization phase scenarios.

[0033] Step 320: Calculate the first scoring data of the digital twin capabilities of the application scenario dataset according to a preset rule or algorithm; Calculate the score of the digital twin ability in the application scenario dataset according to the preset rules or algorithms, that is, the first score; The preset rules, for example, in step 320, determine the first score data, such as Figure 3-2 , specifically including the steps: Step 320-1: Determine multiple evaluation dimension datasets in the application scenario dataset through the second index, and determine the index data and index thresholds of each evaluation dimension dataset through the third index; The second index is the index of the evaluation index data table, which is used to classify a single application scenario dataset according to different evaluation dimensions to obtain multiple evaluation dimension datasets.

[0034] As shown in Table 1, the ability category (number of indicators) refers to different evaluation dimensions of a single application scenario.

[0035] Table 1 Transmission Network Digital Twin Ability Evaluation System

[0036] Calculate the digital twin ability level in a specific scenario of the transmission network according to the module evaluation: Determine the digital twin ability evaluation dimension, and the evaluation dimension is divided into six dimensions: model, data, service, interaction, management, and twin body overall.

[0037] For example, different evaluation dimensions can be considered as different network optimization processes for the network configuration information as the application scenario dataset. The network optimization process refers to determining the digital twin ability evaluation calculation rules for the transmission network.

[0038] For example, determine the digital twin ability evaluation dimension dataset, and the evaluation dimension dataset includes being divided into six dimensions: model ability, data ability, service ability, interaction ability, management ability, and twin body ability overall.

[0039] The third index includes Tables 2-7, and evaluates the ability of different ability evaluation dimension datasets according to the evaluation index grading.

[0040] The evaluation indicators of the model ability are shown in Table 2.

[0041] Table 2 Model Ability Evaluation Index Framework

[0042] The evaluation indicators of the data ability are shown in Table 3.

[0043] Table 3 Data Ability Evaluation Index Framework

[0044] The evaluation indicators of the service ability are shown in Table 4.

[0045] Table 4 Service capability evaluation indicator framework

[0046] The evaluation indicators of interactive ability are shown in Table 5.

[0047] Table 5 Interaction capability evaluation index framework

[0048] The evaluation indicators of management ability are shown in Table 6.

[0049] Table 6 Management capability evaluation indicator framework

[0050] The evaluation indicators of the overall capability of the twins are shown in Table 7.

[0051] Table 7 Twin capability evaluation index framework

[0052] Determine the evaluation method used for each dimension and determine the scoring rules or algorithms.

[0053] According to the evaluation indicators and definitions of each dimension, scores are given according to the evaluation dimensions, and six scoring dimensions are calculated: , , , , and .

[0054] The third index is the index of the evaluation index. The evaluation index data table is determined through the second index. The index data and index threshold in a single evaluation dimension data set are searched in the evaluation index data table through the third index. The index data and index threshold are compared as rules or algorithms.

[0055] The scoring of each dimension is averaged or weighted averaged to calculate the scenario score of the classic scenario.

[0056] The six dimensions above are scored using the average method or weighted average method to score the digital twin capabilities of each typical scenario, and the calculation results are , as the first score.

[0057] Step 320-2: Compare the indicator data and the indicator threshold to determine dimension scoring data of a single dimension; the dimension scoring data includes indicator level indication information; Compare the indicator data and indicator threshold of a single dimension to obtain the dimension score data of a single dimension.

[0058] For example, the comparison result of the index data of the single dimension with the index threshold is that the dimension score of this dimension is unqualified and qualified.

[0059] Step 320-3: Statistically calculate the score data of multiple dimensions to generate the first score data.

[0060] The average method or weighted average method is used for the first scores of all dimensions to generate the evaluation data of the evaluation object.

[0061] The dimension score data contains index level indication information. For example, the index levels are A level, B level, and C level. When calculating using the weighted average method, the weights can be assigned according to the index. The higher the importance of the index, the higher the weight: Level A is an important index; Level B is a reference index and serves as a characterization parameter for each ability category. Quantitative scoring is not required initially; Level C is a general index and can be selected and determined according to actual evaluation requirements.

[0062] Furthermore, in step 320, determining the first score data further includes the steps: Step 320-4: Judge the instruction that the dimension score data or the first score data does not conform to the actual situation Judging that the dimension score data or the first score data does not conform to the actual situation includes any of the following situations: Before obtaining the first score data, manually judge whether the score data of each dimension meets the requirements.

[0063] For example, judge whether the index level of the dimension score data of each dimension conforms to the actual situation, After obtaining the first score data, manually judge whether the first score data meets the requirements.

[0064] The real transmission network data of the transmission network is calculated according to the same rules or algorithms and then compared with the score data of the digital twin transmission network data. If the difference is greater than the set threshold, it is considered not to conform to the actual situation.

[0065] Step 320-5: In response to the instruction that the dimension score data or the first score data does not conform to the actual situation judged by the GUI, execute any of the following: Obtain new digital twin transmission network data; Modify the twin model and obtain the digital twin transmission network data of the new twin model; In response to the application scenario dataset containing multiple evaluation dimension datasets, re-obtain the index data and index thresholds of each evaluation dimension dataset.

[0066] For example, if the judgment does not conform to the actual situation, it can be done by re - adopting a new set of data sets, or retraining and inferring the twin model, or obtaining new ability evaluation data for the six modules and then performing subsequent statistical scoring.

[0067] It should be noted that since the data of the digital twin transmission network is time - varying, changing the time range, type range, etc. of the data can also re - perform the twin calculation.

[0068] It should also be noted that modifying the twin model is not necessary. It is possible to only re - obtain the data of the digital twin transmission network and obtain new results through the original twin model, and then re - evaluate the new results.

[0069] It should also be noted that when modifying the twin model, it is considered that the updated and modified twin model is a new model. Therefore, all evaluation dimensions need to be re - evaluated.

[0070] For example, in the performance optimization scenario, it is generally implemented using a performance optimization twin model. Generally, the parameters of the twin model do not need to be modified during evaluation. Only when the twin model itself is adjusted and upgraded, will the twin model be updated and modified. The updated and modified model needs to have the capabilities of all dimensions re - evaluated.

[0071] Step 330: In response to including multiple application scenario data sets, perform statistical calculations on all the first scoring data to generate second scoring data.

[0072] Comprehensive digital twin ability evaluation under multiple application scenarios for the same evaluation object: According to the twin body evaluation object, use the average method or weighted average method, etc. to perform digital twin comprehensive ability scoring on multiple application scenarios, and calculate , that is, the second scoring data.

[0073] As Figure 5 shown, input the model, data, service, interaction, management, and twin body overall data into six scoring modules. Within a single module, score the specific indicators of each module, average or weight - average the scores of each indicator, and output the digital twin ability scores of the corresponding six modules 、 、 、 、 、 。

[0074] Determine the GUI instruction according to the prediction accuracy, and re - evaluate the score of the dimension or calculate the scenario score.

[0075] Manually judge whether the evaluation results of the digital twin capabilities of the six scoring modules conform to the actual situation. If they do not conform, re-score. If they conform, output the digital twin capability score in a specific scenario by using the average or weighted average method. 。

[0076] The digital twin capability scores of the same evaluation object in multiple specific scenarios Output the digital twin capability score of the transport network of a specific evaluation object by using the average or weighted average method 。

[0077] The transport network digital twin architecture of the embodiments of the present application and related concepts defined for the digital twin capability evaluation, such as evaluation object, evaluation scenario, evaluation dimension, etc. The scoring rules for the digital twin capabilities of the transport network and the allocation rules for the weights of each dimension; the digital twin capability evaluation method based on the weighted average method according to the evaluation dimension within a single scenario of a specific evaluation object; the specific object digital twin capability evaluation method for averaging or weighting the digital twin capabilities of all scenarios of a specific evaluation object; the present application is applicable to the capability evaluation of digital twins in the fields of transport networks or bearer networks, including optical networks such as OTN / ROADM / WDM, and packet transport networks such as SPN / PTN. The digital twin capability evaluation of networks in other fields also has reference value.

[0078] Figure 4 The structural diagram of a transport network digital twin capability evaluation device provided by the embodiments of the present application uses the transport network digital twin capability evaluation method described in any one of the first aspects, and includes: An acquisition module 410, which acquires digital twin transport network data through an input interface and determines an application scenario data set through a first index; A scoring module 420, which receives the application scenario data set and calculates the first scoring data of the digital twin capabilities according to a preset rule or algorithm; A calculation module 430, which statistically calculates the first scoring data of all the application scenario data sets to generate second scoring data and outputs it through an output interface.

[0079] Further, the acquisition module further includes a first acquisition unit, which acquires digital twin transport network data through an output interface and determines an application scenario data set through a first index; The scoring module further includes a first scoring unit, which receives the application scenario data set and calculates the first scoring data of the digital twin capabilities according to a preset rule or algorithm; The calculation module further includes a first calculation unit, which statistically calculates the first scoring data of all the application scenario data sets to generate second scoring data and outputs it through an output interface.

[0080] Further, it further includes: A human-computer interaction module 440, which is connected to the scoring module and the acquisition module respectively through a manual interface; The scoring module transmits the dimension scoring data or the first scoring data through the manual interface; The acquisition module receives, through the manual interface, an instruction indicating that the first scoring data does not conform to the actual situation transmitted by the human-computer interaction module.

[0081] In one embodiment, the determination module further includes a second determination unit for determining multiple evaluation dimension data sets in the application scenario data set through a second index.

[0082] It further includes a third determination unit for determining the index data and index thresholds of each evaluation dimension data set through a third index.

[0083] The scoring module further includes a second scoring unit for comparing the index data and the index thresholds to determine the dimension scoring data of a single dimension.

[0084] The calculation module further includes a second calculation unit for statistically calculating multiple dimension scoring data to generate first scoring data.

[0085] Based on the above implementation process of the transmission network digital twin capability evaluation method, a digital twin capability evaluation system equivalent to the transmission network management and control system and the digital twin system (which can also be deployed in a fused manner with the management and control system) is designed. The deployment location of this digital twin capability evaluation system is as Figure 5 shown.

[0086] Figure 5 This is a structural diagram of a transmission network digital twin capability evaluation system provided by an embodiment of the present application, which includes the transmission network digital twin capability evaluation device described in any embodiment of the second aspect, and also includes a management and control system 510 and a digital twin system 520.

[0087] According to the layering of the transmission network system, the management and control system is at the network management and control layer, connecting the subsystems or APPs of the service layer. At the same time, it connects the digital twin system in the twin layer and the transmission network A in the network element layer.

[0088] The transmission network digital twin capability evaluation device is respectively connected to the management and control system, the digital twin system, and the transmission network through interfaces.

[0089] For example, the transmission network digital twin capability evaluation device is connected to the digital twin system through a TEWI interface, connected to the management and control system through an NBI interface, and connected to the transmission network A through an SBI interface.

[0090] The digital twin capability evaluation system is connected to each layer of the transmission network and the digital twin system through interfaces.

[0091] The above embodiments are used to implement any situation of the digital twin capability evaluation system in the specification.

[0092] It should be noted that the execution subject of each step of the method provided in the embodiments of the present application can be the same device, or the method can also be executed by different devices. For example, the execution subject of steps 310 and 320 can be device 1, and the execution subject of step 330 can be device 2; or, the execution subject of step 310 can be device 1, and the execution subjects of steps 320 and 330 can be device 2; and so on.

[0093] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] Therefore, the present application also proposes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method described in any one of the embodiments of the present application.

[0095] Furthermore, the present application also proposes an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any one of the embodiments of the present application.

[0096] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the process inFigure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 the steps of the functions specified in one block or multiple blocks

[0099] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory. The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media

[0100] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The shown electronic device 600 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application. It includes: one or more processors 620; a storage device 610 for storing one or more programs. When the one or more programs are run by the one or more processors 620, the one or more processors 620 implement the method for evaluating the digital twin ability of a transport network provided by the embodiments of the present application, including the steps of obtaining an evaluation object of the digital twin ability of the transport network; the evaluation object includes the digital twin network layer determining the digital twin classic scenarios of the evaluation object in the entire life cycle of the transport network, and calculating the scores of each evaluation dimension in the classic scenario according to a preset rule or algorithm using the average method or the weighted average method for the scores of each dimension to calculate the scenario score of the classic scenario

[0101] The electronic device 600 further includes an input device 630 and an output device 640; the processor 620, the storage device 610, the input device 630, and the output device 640 in the electronic device can be connected through a bus or other means. In the figure, the connection through the bus 650 is taken as an example

[0102] The storage device 610, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and module units, such as the program instructions corresponding to the digital twin capability evaluation method for the transmission network in the embodiments of the present application. The storage device 610 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the terminal, etc. In addition, the storage device 610 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the storage device 610 can further include a memory remotely set relative to the processor 620, and these remote memories can be connected through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] The input device 630 can be used to receive input digital, character information, or voice information, and generate key signal inputs related to the user settings and function controls of the electronic device. The output device 640 can include electronic devices such as a display screen and a speaker.

[0104] It should also be noted that the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, commodity, or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity, or device including the said element.

[0105] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for evaluating the capability of a digital twin of a transmission network, characterized in that: Includes steps: Acquire digital twin transmission network data and determine the application scenario data set through the first index; Calculate first scoring data of the digital twin capability of the application scenario data set according to a preset rule or algorithm; In response to including a plurality of application scenario data sets, all of the first scoring data are statistically calculated to generate second scoring data.

2. The method for evaluating the capability of a digital twin of a transmission network according to claim 1, characterized in that: Determining the first scoring data specifically includes the following steps: Determine multiple evaluation dimension data sets in the application scenario data set through the second index, and determine the indicator data and indicator threshold of each evaluation dimension data set through the third index; Comparing the indicator data with the indicator threshold, determining dimension scoring data of a single dimension; the dimension scoring data includes indicator level indication information; Statistical calculations are performed on the scoring data of multiple dimensions to generate first scoring data.

3. The method for evaluating the capability of a digital twin of a transmission network according to claim 1, characterized in that: The application scenario dataset includes at least one of the following: Planning and construction data sets, operation data sets, maintenance data sets and optimization phase data sets.

4. The method for evaluating the capability of a digital twin of a transmission network according to claim 1, characterized in that: Determining the first scoring data further includes the steps of: In response to the GUI determining that the dimension scoring data or the first scoring data does not conform to the actual instruction, any one of the following is performed: Acquire new digital twin transmission network data; Modifying the twin model to obtain the digital twin transmission network data of the new twin model; In response to the application scenario dataset containing multiple evaluation dimension datasets, the indicator data and indicator threshold of each evaluation dimension dataset are re-acquired.

5. A transmission network digital twin capability assessment device, using the transmission network digital twin capability assessment method according to any one of claims 1 to 4, characterized in that: Include: An acquisition module acquires digital twin transmission network data through an input interface and determines an application scenario data set through a first index; A scoring module receives an application scenario data set and calculates first scoring data of the digital twin capability according to a preset rule or algorithm; The calculation module performs statistical calculations on the first scoring data of all the application scenario data sets to generate second scoring data, and outputs the second scoring data through the output interface.

6. The transmission network digital twin capability assessment device according to claim 5, characterized in that: Also includes: A human-computer interaction module, wherein the human-computer interaction module is connected to the scoring module and the acquisition module respectively through a manual interface, and the scoring module transmits the dimension scoring data or the first scoring data through the manual interface; the acquisition module receives the instruction transmitted by the human-computer interaction module through the manual interface to determine that the first scoring data does not conform to the actual situation.

7. The transmission network digital twin capability assessment device according to claim 5, characterized in that: The determination module is further used to determine multiple evaluation dimension data sets in the application scenario data set through the second index, and determine the indicator data and indicator threshold of each evaluation dimension data set through the third index; The scoring module is also used to compare the indicator data and the indicator threshold to determine the dimension scoring data of a single dimension; The calculation module is also used to perform statistical calculations on the scoring data of multiple dimensions to generate first scoring data.

8. A transmission network digital twin capability assessment system, characterized in that: A transmission network digital twin capability assessment device comprising any one of claims 5 to 7, further comprising a management and control system and a digital twin system; The transmission network digital twin capability assessment device is connected to the management and control system, the digital twin system and the transmission network through interfaces respectively.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

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