CN115801594A - Method, apparatus and medium for constructing digital twin model of power data communication network - Google Patents
Method, apparatus and medium for constructing digital twin model of power data communication network Download PDFInfo
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Abstract
The invention relates to a construction method of a digital twin model of a power data communication network, which comprises the following steps: s1, constructing a physical model and an analysis model of a power data communication network; s2, acquiring state data and operation data of the entity equipment and inputting the state data and the operation data into a physical model and/or an analysis model to obtain a digital twin model facing the power data communication network; the digital twin model includes: the physical model is used for displaying entity information and state information of the power data communication network and displaying safety state data; and the analysis model is used for analyzing the safety state of the entity equipment in the electric power data communication network to obtain the safety state data of the entity equipment. The digital twin model obtained by the construction method provided by the invention can analyze and display the network security state of the electric power data communication network in time, and is convenient for managers to rapidly master the network security state of the electric power data communication network based on the digital twin model, thereby timely adopting effective security protection means.
Description
Technical Field
The invention relates to the technical field of power data communication networks, in particular to a method for constructing a digital twin model of a power data communication network.
Background
The power data communication network is a special communication network for a power system, is a basis for power grid dispatching automation, power grid operation marketization and power grid management informatization, and is used for meeting the requirements of various communication services such as power grid automation, relay protection, computer networking, image transmission and the like in the power system. Because the power system is an important pillar for regional economic development, and the safety and stability of the power system and the power data communication network have important influence on the safety and stability, economic operation and social stability of local regions, the network safety management of the power data communication network is very important. The network security management mode of the existing power data communication network is usually carried out by combining intelligent data acquisition and processing with manual analysis and study, and due to the fact that account data and alarm information of the power data communication network are various and the retrieval levels are multiple, managers can judge the network security state of the power data communication network only through complex data calling and calculation when managing and maintaining, and further make proper management decisions, network attacks cannot be found and positioned in time, and timely and effective security protection means are adopted.
The digital twin technology is a technology which fully utilizes data such as a physical model, sensor updating, operation history and the like, integrates a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation reasoning process, and completes mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment.
Disclosure of Invention
Technical problem to be solved
In view of the above disadvantages and shortcomings of the prior art, the present invention provides a digital twin model of a power data communication network, which solves the technical problems that the prior art is complicated in data information to be processed by a network security management means of the power data communication network, and network attacks cannot be found and positioned in time.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
in a first aspect, an embodiment of the present invention provides a method for constructing a digital twin model of a power data communication network, where the method includes:
s1, constructing a physical model of the power data communication network based on static data of entity equipment acquired in advance; and, constructing an analytical model;
s2, acquiring state data and operation data of the entity equipment, inputting the state data and the operation data into a physical model and/or an analysis model, and driving the physical model to perform data interaction with the analysis model to obtain a digital twin model facing the power data communication network;
the static data is data which is obtained in advance and relates to entity information of entity equipment, and the state data and the operation data are obtained based on original real-time data which is obtained by a sensing module and relates to the entity equipment in the power data communication network;
the digital twinning model comprises:
the physical model is used for displaying entity information and state information of the power data communication network based on the static data and the state data; the safety state data is used for receiving and displaying the safety state data sent by the analysis model;
and the analysis model is used for analyzing the safety state of the entity equipment in the electric power data communication network based on the state data and the operation data to obtain the safety state data of the entity equipment and sending the safety state data to the physical model.
The digital twin model obtained by the construction method provided by the embodiment of the invention comprises a physical model and an analysis model, the analysis model analyzes according to the state data and the operation data to obtain the safety state data of the electric power data communication network, and the physical model displays the safety state data, so that a manager can conveniently and intuitively master the network safety state of the electric power data communication network based on the digital twin model, and an effective safety protection means can be timely adopted.
Optionally, the static data at least includes a geometric size, material data, a location, a manufacturer, a model, a unique identifier, and a device type of the entity device;
the state data at least comprises the working state of the entity equipment, the CPU utilization rate, the memory utilization rate, the packet loss rate, the bit error rate, the communication delay and the data throughput;
the operation data at least comprises the number of sessions of the entity equipment, the number of newly-built sessions per second, the service type, the load capacity of service data, the port occupancy rate, the data transmission rate, the process operation parameters, the source port request type, the destination port request type and the system log.
Optionally, the device types in the state data include a security interaction device, an internal and external network isolation device, a router, and a switch, and the analysis model includes:
the state monitoring module is used for judging the safety risk score of the entity equipment based on the state data of the entity equipment, and activating the danger identification module if the safety risk score of certain entity equipment is higher than a preset value;
the danger identification module is used for identifying and outputting a safety risk type based on state data and operation data according to the equipment type of the entity equipment; the danger identification module includes:
the classification unit is used for extracting feature data corresponding to the equipment type from the state data and the operation data based on the equipment type corresponding to the entity equipment, and sending the feature data to a first identification unit, a second identification unit, a third identification unit or a fourth identification unit corresponding to the equipment type;
the first identification unit is used for identifying the equipment type as the safety risk type of the safety interaction device according to the characteristic data sent by the classification unit;
the second identification unit is used for identifying the equipment type as the safety risk type of the internal and external network isolation device according to the characteristic data sent by the classification unit;
the third identification unit is used for identifying the equipment type as the security risk type of the router according to the characteristic data sent by the classification unit;
the fourth identification unit is used for identifying the equipment type as the security risk type of the switch according to the characteristic data sent by the classification unit;
and the output unit is used for sending the safety risk type of the entity equipment as the safety state data of the entity equipment to a physical model based on the safety risk types output by the first identification unit, the second identification unit, the third identification unit and the fourth identification unit.
Optionally, the first recognition unit, the second recognition unit, the third recognition unit, and the fourth recognition unit are any one of a deep convolutional neural Network model with adaptive weight parameters, a Long Short-Term Memory (LSTM) Recurrent neural Network model, a Back Propagation (BP) neural Network model, an Elman neural Network model (also called a Simple Recurrent Network (SRN)), and a Bi-directional Long Short-Term Memory (Bi-directional Long Short-Term Memory) neural Network model.
Optionally, in the classifying unit, the extracting, based on the device type corresponding to the entity device, the feature data corresponding to the device type from the status data and the operation data includes:
if the equipment type is a safety interaction device, respectively extracting the CPU utilization rate, the memory utilization rate, the session number, the number of newly-built sessions per second, the service type, the service data load capacity, the port occupancy rate, the process operation parameters, the source port request type, the destination port request type and the system log from the state data and the operation data as the characteristic data of the equipment type;
if the equipment type is an internal and external network isolation device, extracting packet loss rate, bit error rate, communication delay, data throughput and data transmission rate from state data and operating data respectively as characteristic data of the equipment type;
if the device type is a router or a switch, respectively extracting packet loss rate, communication delay, data throughput, service type, service data load capacity, port occupancy, data transmission rate, process operation parameters, source port request type, destination port request type and system logs from the state data and the operation data as characteristic data of the device type.
Optionally, the digital twin model further comprises:
the data preprocessing module is used for acquiring original real-time state data and original real-time operation data of entity equipment based on a sensing module preset in the power data communication network, preprocessing the original real-time state data and the original real-time operation data, and then sending the acquired state data and operation data to the physical model and/or the analysis model;
wherein the preprocessing operation comprises one or more of a data cleansing operation, a data reduction operation, and a data transformation operation.
Preferably, the S1 includes:
s101, establishing a three-dimensional geometric model corresponding to the entity equipment by using 3DsMax software based on the geometric size in the static data of the entity equipment;
s102, defining the surface color, transparency, roughness and texture of the three-dimensional geometric model in 3DsMax software based on material data in static data of entity equipment, and rendering and optimizing the edge of the three-dimensional geometric model;
s103, giving the position, the manufacturer, the model, the unique identification code and the equipment type in the static data of the entity equipment to the three-dimensional geometric model as label information to obtain a physical entity three-dimensional geometric model of the entity equipment;
and S104, constructing an analysis model.
Preferably, in S2, the acquiring the status data and the operation data includes:
acquiring original real-time state data and original real-time operation data of entity equipment based on a sensing module preset in a power data communication network, and inputting the original real-time state data and the original real-time operation data into a preset data preprocessing module to obtain state data and operation data;
the data preprocessing module is used for receiving the original real-time state data and the original real-time operation data and carrying out preprocessing operation on the original real-time state data and the original real-time operation data to obtain state data and operation data; wherein the preprocessing operation comprises one or more of a data cleansing operation, a data reduction operation, and a data transformation operation.
In a second aspect, the present invention also provides an electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method of constructing a digital twin model of a power data communication network according to the first aspect.
In a third aspect, the present invention also provides a computer storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the method for constructing a digital twin model of a power data communication network according to the first aspect.
(III) advantageous effects
The embodiment of the invention provides a method for constructing a digital twin model of a power data communication network, wherein the constructed digital twin model comprises a physical model and an analysis model, the analysis model analyzes according to state data and operation data to obtain safety state data of the power data communication network, and the physical model displays the safety state data, so that a manager can conveniently and intuitively master the network safety state of the power data communication network based on the digital twin model, and an effective safety protection means can be timely adopted.
In the analysis model provided by the embodiment of the invention, aiming at the actual situation that the attack means of the network attack on different entity devices are different, different neural network models are respectively adopted for identification based on different device types, so that the identification precision of the digital twin model on the safety risk type can be improved, and more reliable reference information is provided for managers to make decisions.
Drawings
Fig. 1 is a schematic flow chart of a method for constructing a digital twin model of a power data communication network provided in an embodiment;
FIG. 2 is a block diagram of a digital twin model of a power data communications network provided in an embodiment;
FIG. 3 is a block diagram of a digital twin model of another power data communications network provided in an embodiment;
a block schematic diagram of the analytical model provided in the embodiment of fig. 4.
Detailed Description
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example one
As shown in fig. 1, the present embodiment provides a method of constructing a digital twin model of a power data communication network.
The power data communication network includes a physical device, the physical device including: the system comprises a safety interaction device, an internal and external network isolation device, routers and switches, wherein a plurality of routers and a plurality of switches are in communication connection to form an internal network, the internal network is in communication connection with the external network through the internal and external network isolation device, and a plurality of safety interaction devices are in communication connection with the internal network; the internal and external network isolation device is used for isolating and forwarding service data between the internal network and the external network, and the safety interaction device is used for communicating with the external network through the internal network based on interaction data with a user. Specifically, the secure interaction device may be a computer or other terminal equipment, and security measures such as communication encryption and data desensitization are usually set on the secure interaction device.
The construction method comprises the following steps:
s1, constructing a physical model of a power data communication network based on static data of entity equipment acquired in advance; and, constructing an analytical model;
s2, state data and operation data of the entity equipment are obtained, the state data and the operation data are input into the physical model and/or the analysis model, the physical model and the analysis model are driven to carry out data interaction, and a digital twin model facing the power data communication network is obtained.
The static data is data which is obtained in advance and relates to entity information of entity equipment, and the state data and the operation data are obtained based on original real-time data which is obtained by a sensing module and relates to the entity equipment in the power data communication network;
as shown in fig. 2, the digital twin model includes:
the physical model is used for displaying entity information and state information of the power data communication network based on the static data and the state data; the safety state data is used for receiving and displaying the safety state data sent by the analysis model;
and the analysis model is used for analyzing the safety state of the entity equipment in the power data communication network based on the state data and the operation data to obtain the safety state data of the entity equipment and sending the safety state data to the physical model.
Specifically, the static data at least includes a geometric size, material data, a position, a manufacturer, a model, a unique identifier, and a device type of the physical device. Specifically, the unique identification code is used for uniquely marking the identity of the entity equipment in the power data communication network, and each unique identification code is unique in the current power data communication network; the position can be longitude and latitude Information of the entity equipment or GIS (Geographic Information Systems) position Information; the device types are classified according to the specific role or role of the entity device in the power data communication network, and specifically include a security interaction device, an internal and external network isolation device, a router and a switch.
The state data at least comprises the working state of the entity equipment, the CPU utilization rate, the memory utilization rate, the packet loss rate, the bit error rate, the communication delay and the data throughput. The working state comprises running and stopping running.
The operation data at least comprises the number of sessions of the entity equipment, the number of newly-built sessions per second, the service type, the load capacity of service data, the port occupancy rate, the data transmission rate, the process operation parameters, the source port request type, the destination port request type and the system log.
It should be noted that, the sensing module is also sometimes referred to as a data acquisition module, and in the power data communication network, it is usually a software module, and is embedded in a software system of a physical device or executed based on an application layer of the software system.
The digital twin model obtained by the construction method provided by the embodiment of the invention comprises a physical model and an analysis model, the analysis model analyzes according to the state data and the operation data to obtain the safety state data of the electric power data communication network, and the physical model displays the safety state data, so that a manager can conveniently and quickly master the network safety state of the electric power data communication network based on the digital twin model, and an effective safety protection means can be timely adopted.
Example two
For better understanding of the first embodiment, the present embodiment is described in detail with reference to the specific structure of the digital twin model.
As shown in fig. 3, the digital twin model of the present embodiment includes a data preprocessing module, a physical model and an analysis model, and each module is described in detail below.
And the data preprocessing module is used for acquiring original real-time state data and original real-time operation data of the entity equipment based on a preset sensing module in the power data communication network, preprocessing the original real-time state data and the original real-time operation data, and then sending the acquired state data and operation data to the physical model and/or the analysis model. Wherein the preprocessing operation comprises one or more of a data cleansing operation, a data reduction operation, and a data transformation operation. The preprocessing operation is used to collate, deduplicate, or unify data formats of the acquired raw data so as to convert the raw data into data that can be directly circulated or used in the digital twin model.
The physical model is used for displaying entity information and state information of the power data communication network based on the static data and the state data; and the safety state data is used for receiving and displaying the safety state data sent by the analysis model. The entity information is displayed based on static data of the entity equipment to reflect the real physical appearance of the entity equipment, and the state information is displayed based on state data of the entity equipment to reflect the current state of the entity equipment. Preferably, in order to make the display screen of the physical device clearer and cleaner, 2 to 3 more critical items in the status data may be displayed by default based on the device type of the physical device, and the rest of the operation data may be displayed when the manager refers to the device. For example, for the internal and external network isolation device, the working state and data throughput of the entity device are used as default display state data; and regarding the safety interaction device, the working state, the CPU utilization rate and the memory utilization rate are used as default display state data.
And the analysis model is used for analyzing the safety state of the entity equipment in the electric power data communication network based on the state data and the operation data to obtain the safety state data of the entity equipment and sending the safety state data to the physical model.
Specifically, as shown in fig. 4, the analysis model includes a state monitoring module and a risk identification module:
and the state monitoring module is used for judging the safety risk score of the entity equipment based on the state data of the entity equipment, and activating the danger identification module if the safety risk score of certain entity equipment is higher than a preset value. Specifically, the safety risk score may be obtained by weighted summation of scores corresponding to numerical values of each item of data in the state data, or may be obtained based on other prior art means. For example, for the CPU utilization item of the secure interactive device, the score is 10 points when the CPU utilization reaches 100%, and when the CPU utilization decreases, the score of the item is proportionally decreased to obtain the score of the CPU utilization item; similarly, the scores of other status data items of the security interaction device are set in an analog manner, and then the security interaction devices are directly added, or the security risk scores of the security interaction device are obtained by weighted addition according to the importance degree of the status data items. The preset value is set according to a specific calculation method of the safety risk score, and preferably, the preset value is 60% -80% of the safety risk score.
And the danger identification module is used for identifying and outputting the safety risk type based on the state data and the operation data according to the equipment type of the entity equipment. The security risk type corresponds to a common network attack means, and specifically includes: tamper message attacks, fake message attacks, denial of service attacks, traffic analysis attacks, eavesdropping attacks, and the like.
The danger identification module comprises a classification unit, a first identification unit, a second identification unit, a third identification unit, a fourth identification unit and an output unit:
and the classification unit is used for extracting characteristic data corresponding to the equipment type from the state data and the operation data based on the equipment type corresponding to the entity equipment, and sending the characteristic data to the first identification unit, the second identification unit, the third identification unit or the fourth identification unit corresponding to the equipment type.
And the first identification unit is used for identifying the equipment type as the safety risk type of the safety interaction device according to the characteristic data sent by the classification unit.
And the second identification unit is used for identifying the equipment type as the safety risk type of the internal and external network isolation device according to the characteristic data sent by the classification unit.
And the third identification unit is used for identifying the equipment type as the security risk type of the router according to the characteristic data sent by the classification unit.
And the fourth identification unit is used for identifying the equipment type as the security risk type of the switch according to the characteristic data sent by the classification unit.
Specifically, the first identification unit, the second identification unit, the third identification unit and the fourth identification unit are any one of a deep convolution neural network model, an LSTM recurrent neural network model, a BP neural network model, an Elman neural network model and a BILSTM neural network model with adaptive weight parameters. The embodiment aims to emphasize that different neural network models are used for identifying the security risk aiming at different equipment types, so that in practical application, the neural network model with higher identification precision is selected in a targeted manner according to common network attack means of entity equipment of different equipment types, and the identification precision of the security risk type is improved. Preferably, the first recognition unit is a BILSTM neural network model, the second recognition unit is an Elman neural network model, and the third recognition unit and the fourth recognition unit are BP neural network models. In addition, after the initial building of the neural network model is completed, a training set is required to be used for training to obtain adaptive weight parameters, the training process is realized by a conventional technical means, and details are not repeated here.
And the output unit is used for sending the safety risk type of the entity equipment as the safety state data of the entity equipment to a physical model based on the safety risk types output by the first identification unit, the second identification unit, the third identification unit and the fourth identification unit.
As a preferred implementation of this embodiment, in the classifying unit, the extracting, based on the device type corresponding to the physical device, the feature data corresponding to the device type from the status data and the operation data includes:
if the equipment type is a safety interaction device, respectively extracting the CPU utilization rate, the memory utilization rate, the session number, the number of newly-built sessions per second, the service type, the service data load capacity, the port occupancy rate, the process operation parameters, the source port request type, the destination port request type and the system log from the state data and the operation data as the characteristic data of the equipment type;
if the equipment type is an internal and external network isolation device, extracting packet loss rate, bit error rate, communication delay, data throughput and data transmission rate from state data and operating data respectively as characteristic data of the equipment type;
if the device type is a router or a switch, extracting packet loss rate, communication delay, data throughput, service type, service data load capacity, port occupancy rate, data transmission rate, process operation parameters, source port request type, destination port request type and system log from the state data and the operation data respectively as characteristic data of the device type.
Based on the device type corresponding to the entity device, the operation of extracting the characteristic data corresponding to the device type from the state data and the operation data can enable the characteristic data input into the corresponding neural network model to be more representative, thereby eliminating interference data and further improving the identification precision of the corresponding neural network model.
The digital twin model provided by this embodiment monitors the operating state of each entity device in the power data communication network based on the state monitoring module, and activates the risk identification module to predict the security risk type of the entity device when the security risk score of the entity device reaches a preset value, which can quickly warn the network attack means affecting the operating state of the entity device. Compared with the method for directly identifying the network attack means in real time by using the neural network model in the prior art, the digital twin model provided by the invention determines whether to activate the danger identification module or not by using the method for calculating the safety risk score, so that the calculation power of the digital twin model can be effectively saved, and the method has better real-time property. In addition, the danger identification module selects a neural network model with higher corresponding identification precision for each identification unit according to common network attack means of entity equipment with different equipment types, extracts characteristic data corresponding to the equipment types from state data and operation data through the classification unit, and inputs the characteristic data into the corresponding identification unit, so that the identification precision of the analysis model on the safety risk types is further improved, and more reliable reference information is provided for managers to make decisions
EXAMPLE III
As shown in fig. 4, this embodiment further provides a method for constructing a digital twin model of a power data communication network, which is used to construct the digital twin model in the first or second embodiment, and the method includes:
s1, constructing a physical model of the power data communication network based on static data of entity equipment acquired in advance; and constructing an analysis model.
Specifically, the S1 includes the following substeps:
s101, establishing a three-dimensional geometric model corresponding to the entity equipment by using 3DsMax software based on the geometric size in the static data of the entity equipment;
s102, defining the surface color, transparency, roughness and texture of the three-dimensional geometric model in 3DsMax software based on material data in static data of entity equipment, and rendering and optimizing the edge of the three-dimensional geometric model;
s103, giving the position, the manufacturer, the model, the unique identification code and the equipment type in the static data of the entity equipment to the three-dimensional geometric model as label information to obtain the physical entity three-dimensional geometric model of the entity equipment.
And S104, constructing an analysis model based on the physical model, wherein the analysis model is the analysis model described in the second embodiment.
And S2, acquiring state data and operation data of the entity equipment, inputting the state data and the operation data into a physical model and/or an analysis model, and driving the physical model to perform data interaction with the analysis model to obtain a digital twin model facing the power data communication network.
Specifically, the acquiring the state data and the operation data of the entity device includes:
acquiring original real-time state data and original real-time operation data of entity equipment based on a sensing module preset in a power data communication network, and inputting the original real-time state data and the original real-time operation data into a preset data preprocessing module to obtain state data and operation data; the data preprocessing module is used for receiving the original real-time state data and the original real-time operation data, and preprocessing the original real-time state data and the original real-time operation data to obtain state data and operation data.
Example four
The present embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the method for constructing the digital twin model of the electric power data communication network according to the first to third embodiments are implemented.
In addition, the present embodiment also provides a computer storage medium, wherein a computer program is stored on the computer readable storage medium, and when being executed by a processor, the computer program realizes the steps of the method for constructing the digital twin model of the electric power data communication network according to the first to third embodiments.
Since the system/apparatus described in the above embodiment of the present invention is a system/apparatus used for implementing the method of the above embodiment of the present invention, based on the method described in the above embodiment of the present invention, a person skilled in the art can understand the specific structure and variation of the system/apparatus, and therefore the detailed description is omitted here. All systems/devices adopted by the methods of the above embodiments of the present invention are within the intended scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third and the like are for convenience only and do not denote any order. These words are to be understood as part of the name of the component.
Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all such variations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.
Claims (10)
1. A method for constructing a digital twin model of a power data communication network, the method comprising:
s1, constructing a physical model of the power data communication network based on static data of entity equipment acquired in advance; and, constructing an analytical model;
s2, acquiring state data and operation data of the entity equipment, inputting the state data and the operation data into a physical model and/or an analysis model, and driving the physical model to perform data interaction with the analysis model to obtain a digital twin model facing the power data communication network;
the static data is data which is obtained in advance and relates to entity information of entity equipment, and the state data and the operation data are obtained based on original real-time data which is obtained by a sensing module and relates to the entity equipment in the power data communication network;
the digital twinning model comprises:
the physical model is used for displaying entity information and state information of the power data communication network based on the static data and the state data; the safety state data is used for receiving and displaying the safety state data sent by the analysis model;
and the analysis model is used for analyzing the safety state of the entity equipment in the electric power data communication network based on the state data and the operation data to obtain the safety state data of the entity equipment and sending the safety state data to the physical model.
2. The construction method according to claim 1,
the static data at least comprises the geometric dimension, material data, position, manufacturer, model, unique identification code and equipment type of the entity equipment;
the state data at least comprises the working state of the entity equipment, the CPU utilization rate, the memory utilization rate, the packet loss rate, the bit error rate, the communication delay and the data throughput;
the operation data at least comprises the conversation quantity of the entity equipment, the quantity of the newly established conversation per second, the service type, the service data load capacity, the port occupancy rate, the data transmission rate, the process operation parameter, the source port request type, the destination port request type and the system log.
3. The building method according to claim 2, wherein the device types in the state data comprise a security interaction device, an intranet and extranet isolation device, a router and a switch, and the analysis model comprises:
the state monitoring module is used for judging the safety risk score of the entity equipment based on the state data of the entity equipment, and activating the danger identification module if the safety risk score of certain entity equipment is higher than a preset value;
the danger identification module is used for identifying and outputting a safety risk type based on state data and operation data according to the equipment type of the entity equipment; the danger identification module includes:
the classification unit is used for extracting feature data corresponding to the equipment type from the state data and the operation data based on the equipment type corresponding to the entity equipment, and sending the feature data to a first identification unit, a second identification unit, a third identification unit or a fourth identification unit corresponding to the equipment type;
the first identification unit is used for identifying the equipment type as the safety risk type of the safety interaction device according to the characteristic data sent by the classification unit;
the second identification unit is used for identifying the equipment type as the safety risk type of the internal and external network isolation device according to the characteristic data sent by the classification unit;
the third identification unit is used for identifying the equipment type as the security risk type of the router according to the characteristic data sent by the classification unit;
the fourth identification unit is used for identifying the equipment type as the security risk type of the switch according to the characteristic data sent by the classification unit;
and the output unit is used for sending the safety risk type of the entity equipment as the safety state data of the entity equipment to a physical model based on the safety risk types output by the first identification unit, the second identification unit, the third identification unit and the fourth identification unit.
4. The construction method according to claim 3, wherein the first recognition unit, the second recognition unit, the third recognition unit and the fourth recognition unit are any one of a deep convolutional neural network model, an LSTM recurrent neural network model, a BP neural network model, an Elman neural network model and a BILSTM neural network model with adaptive weight parameters.
5. The building method according to claim 3, wherein in the classifying unit, the extracting, based on the device type corresponding to the entity device, feature data corresponding to the device type from the status data and the operation data includes:
if the equipment type is a safety interaction device, respectively extracting the CPU utilization rate, the memory utilization rate, the session number, the newly-built session number per second, the service type, the service data load capacity, the port occupancy rate, the process operation parameters, the source port request type, the destination port request type and the system log from the state data and the operation data as characteristic data of the equipment type;
if the equipment type is an internal and external network isolation device, extracting packet loss rate, bit error rate, communication delay, data throughput and data transmission rate from state data and operating data respectively as characteristic data of the equipment type;
if the device type is a router or a switch, extracting packet loss rate, communication delay, data throughput, service type, service data load capacity, port occupancy rate, data transmission rate, process operation parameters, source port request type, destination port request type and system log from the state data and the operation data respectively as characteristic data of the device type.
6. The construction method according to claim 1, wherein the digital twin model further comprises:
the data preprocessing module is used for acquiring original real-time state data and original real-time operation data of entity equipment based on a sensing module preset in the power data communication network, preprocessing the original real-time state data and the original real-time operation data and then sending the acquired state data and operation data to the physical model and/or the analysis model;
wherein the preprocessing operation comprises one or more of a data cleansing operation, a data reduction operation, and a data transformation operation.
7. The method according to any one of claims 1 to 6, wherein S1 includes:
s101, establishing a three-dimensional geometric model corresponding to the entity equipment based on the geometric dimension in the static data of the entity equipment;
s102, defining the surface color, transparency, roughness and texture of the three-dimensional geometric model based on material data in static data of entity equipment, and performing rendering optimization on the edge of the three-dimensional geometric model;
s103, giving the position, the manufacturer, the model, the unique identification code and the equipment type in the static data of the entity equipment to the three-dimensional geometric model as label information to obtain a physical entity three-dimensional geometric model of the entity equipment;
and S104, constructing an analysis model.
8. The construction method according to any one of claims 1 to 6, wherein in S2, the acquiring the status data and the operation data of the entity device comprises:
acquiring original real-time state data and original real-time operation data of entity equipment based on a sensing module preset in a power data communication network, and inputting the original real-time state data and the original real-time operation data into a preset data preprocessing module to obtain state data and operation data;
the data preprocessing module is used for receiving the original real-time state data and the original real-time operation data and carrying out preprocessing operation on the original real-time state data and the original real-time operation data to obtain state data and operation data; wherein the preprocessing operation comprises one or more of a data cleansing operation, a data reduction operation, and a data transformation operation.
9. An electronic device, comprising: memory, processor and computer program stored on said memory and executable on said processor, said computer program, when executed by said processor, implementing the steps of the method of constructing a digital twin model of a power data communication network according to any one of claims 1 to 8.
10. A computer storage medium, characterized in that a computer program is stored on the computer readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method of constructing a digital twin model of an electric power data communication network according to any one of the claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117391310A (en) * | 2023-12-04 | 2024-01-12 | 南京瀚元科技有限公司 | Power grid equipment operation state prediction and optimization method based on digital twin technology |
| CN117478394A (en) * | 2023-11-07 | 2024-01-30 | 广州达悦信息科技有限公司 | Network security analysis method and system based on digital twinning |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117478394A (en) * | 2023-11-07 | 2024-01-30 | 广州达悦信息科技有限公司 | Network security analysis method and system based on digital twinning |
| CN117478394B (en) * | 2023-11-07 | 2024-05-17 | 广州达悦信息科技有限公司 | Network security analysis method, system, computer equipment and computer readable storage medium based on digital twin |
| CN117391310A (en) * | 2023-12-04 | 2024-01-12 | 南京瀚元科技有限公司 | Power grid equipment operation state prediction and optimization method based on digital twin technology |
| CN117391310B (en) * | 2023-12-04 | 2024-03-08 | 南京瀚元科技有限公司 | Power grid equipment operation state prediction and optimization method based on digital twin technology |
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