Digital twinborn model construction method and system of power communication network, equipment and medium

Abstract

The invention relates to the technical field of digital twinning, and discloses a digital twinning model construction method and system for an electric power communication network, and the method comprises the steps: S1, constructing a digital twinning power grid of the electric power communication network, the digital twinning power grid comprises an infrastructure layer, a platform layer and an application layer which are connected in parallel, and the platform layer comprises a twinning model layer; s2, constructing a random forest model based on a random forest algorithm; s3, importing a prediction result in the power communication network into a random forest model to generate an accuracy prediction model of the power communication network; and S4, importing the accuracy prediction model into a twinborn model layer to complete construction of a digital twinborn model. The system comprises an infrastructure layer, a platform layer and an application layer. The invention further discloses electronic equipment and a computer readable storage medium. The method is used for promoting full-field intelligent digital analysis and data mining of the power communication network.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a method and system, equipment, and medium for constructing a digital twin model of an electric power communication network, which are used to promote intelligent digital analysis and data mining in all fields of the electric power communication network. Background Art

[0002] The electric power communication network is an important infrastructure to support the construction of a strong smart grid, ensure grid security and realize the modernization of corporate management.

[0003] With the continuous development of the economy, the scale of the entire power communication network is getting larger and larger. The understanding of the entire power communication network is particularly important, and the need to establish a digital and intelligent perception model is more urgent.

[0004] Digital twin technology is a new technology that integrates artificial intelligence, 5G technology, and big data analysis. It is also an integrated cross-science, multi-physics, and multi-dimensional simulation process. As a high-tech technology, digital twin technology provides a new way of thinking for the correct understanding of the power communication network, reflecting the real physical space into the virtual space, so that the real-time status of the entire power communication network can be perceived. At present, this technology has been applied to aerospace technology, unmanned driving technology, intelligent manufacturing, industrial automation, equipment fault diagnosis and evaluation, medical treatment and many other aspects.

[0005] Therefore, the present invention provides a method, system, device, and medium for constructing a digital twin model of an electric power communication network, which is used to promote intelligent digital analysis and data mining in all fields of the electric power communication network. Summary of the invention

[0006] The present invention provides a method, system, device, and medium for constructing a digital twin model of an electric power communication network, which are used to promote intelligent digital analysis and data mining in all fields of the electric power communication network.

[0007] The present invention is implemented by the following technical solution: A method for constructing a digital twin model of a power communication network comprises the following steps:

[0008] Step S1, constructing a digital twin power grid of the power communication network, wherein the digital twin power grid includes an infrastructure layer, a platform layer, and an application layer connected in parallel, and the platform layer includes a twin model layer;

[0009] Step S2, constructing a random forest model based on the random forest algorithm;

[0010] Step S3, importing the prediction results in the power communication network into the random forest model to generate an accuracy prediction model for the power communication network;

[0011] Step S4, importing the accuracy prediction model into the twin model layer to complete the construction of the digital twin model.

[0012] In order to better implement the present invention, further, step S1 includes:

[0013] The infrastructure layer includes a terminal side, a network side, and a cloud side connected in parallel;

[0014] The platform layer includes a management and control layer, a data aggregation layer, and a twin model layer connected in parallel;

[0015] The application layer includes a device layer, a power grid layer, a business layer and an operation management layer connected in parallel.

[0016] In order to better implement the present invention, further, the terminal side includes a measurement perception device, the network side includes a network connection device, and the cloud side includes an intelligent computing device;

[0017] The management and control layer includes an IoT device platform and a power grid resource control platform;

[0018] The twin model layer includes an accuracy prediction model;

[0019] The power grid layer includes a backbone communication network and a terminal communication access network, and the backbone communication network includes a transmission network, a service network and a support network.

[0020] In order to better implement the present invention, further, step S2 includes:

[0021] The random forest model constructed based on the random forest algorithm is expressed as:

[0022]

[0023] Among them, H(x) is the constructed random forest model, k is the number of decision trees, and h i is the completed i-th decision tree, Y is the predicted result, and I(x) is the indicative function.

[0024] In order to better implement the present invention, further, step S3 includes:

[0025] Get the prediction result of the i-th point in the power communication network and the actual result P(i), the predicted result The actual result P(i) is imported into the random forest model to generate an accuracy prediction model of the power communication network, and is expressed as:

[0026]

[0027] in, is the prediction result of the i-th point in the power communication network, P(i) is the actual measurement result at the i-th moment in the power communication network, P is the actual measurement result at the previous moment (i.e., point i-1) in the power communication network, i is the prediction point number, r is the accuracy of the prediction of the i-th point, and n is the length of the sampling point.

[0028] In order to better implement the present invention, further, the average prediction accuracy rv of the power communication network is calculated according to the accuracy r of the prediction at the i-th point, and is expressed as:

[0029] n is the length of the sampling points.

[0030] The present invention also provides a digital twin model construction system for a power communication network, comprising an infrastructure layer, a platform layer and an application layer, wherein:

[0031] The infrastructure layer includes the terminal side, network side and cloud side connected in parallel to ensure the operation of the power information communication network;

[0032] The platform layer includes the management and control layer, data aggregation layer, and twin model layer connected in parallel, which are used for data processing and core operation;

[0033] The application layer, including the device layer, power grid layer, business layer and operation management layer connected in parallel, is used to apply the digital twin model.

[0034] In order to better implement the present invention, further, the terminal side includes a measurement perception device, the network side includes a network connection device, and the cloud side includes an intelligent computing device, which are respectively used for measurement perception, network connection and intelligent computing;

[0035] The management and control layer includes an IoT device platform and a power grid resource control platform, which are used to uniformly control and manage the basic equipment and power grid resources;

[0036] The twin model layer includes an accuracy prediction model for predicting the accuracy of power grid data;

[0037] The power grid layer includes a backbone communication network and a terminal communication access network. The backbone communication network includes a transmission network, a service network and a support network, and is used to perform power grid dispatching, operation risk analysis and full life cycle management through the backbone communication network and the terminal communication access network.

[0038] The present invention also provides an electronic device, which includes a processor and a memory; the processor includes the digital twin model construction system of the power communication network recorded in the above-mentioned second aspect.

[0039] The present invention also provides a computer-readable storage medium, which includes instructions; when the instructions are executed on the electronic device described in the third aspect, the electronic device executes the method described in the first aspect.

[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0041] (1) The present invention provides a method, system, device, and medium for constructing a digital twin model of a power communication network, which can improve the detection accuracy and precision of the power communication network;

[0042] (2) The present invention provides a method, system, device, and medium for constructing a digital twin model of an electric power communication network, which is used to promote intelligent digital analysis and data mining in all fields of the electric power communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention is further described in conjunction with the following drawings and embodiments, and all the concepts and innovations of the present invention should be regarded as the disclosed contents and the protection scope of the present invention.

[0044] Figure 1 A method for constructing a digital twin model of a power communication network and a method flow chart in a system, device, and medium provided in an embodiment of the present application;

[0045] Figure 2 A structural diagram of a method for constructing a digital twin model of a power communication network and a system, equipment, and medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Embodiment 1:

[0049] The power communication network is an important infrastructure to support the construction of a strong and intelligent power grid, ensure the safety of the power grid and realize the modernization of corporate management. The power communication network consists of a backbone communication network and a terminal communication access network. The backbone communication network covers 35kV and above power grid plants and stations and various production and office sites, and the terminal communication access network covers 10kV (or 20kV / 6kV) and 0.4kV power grid-related sites.

[0050] The backbone communication network is divided into transmission network, business network and support network according to its function.

[0051] The transmission network should be based on optical communication technology, supplemented by carrier, microwave and satellite communication technology. Power line carrier communication is a communication technology unique to the power grid and one of the effective transmission methods for relay protection signals in the power system. The frequency, tower, station site and computer room of the microwave communication system are valuable resources of the power company. Transmission network level The transmission network is divided into three levels: inter-provincial, provincial and prefecture-level. The inter-provincial backbone transmission network mainly covers company headquarters, branches, provincial companies, state-controlled direct-dispatching power plants and substations (converter stations), etc. The provincial backbone transmission network mainly covers provincial companies, prefecture-level companies, provincial-controlled direct-dispatching power plants and substations, etc. The prefecture-level backbone transmission network mainly covers prefecture-level companies, county companies, prefecture (county)-controlled direct-dispatching power plants and substations, power supply stations (business offices), etc.

[0052] The business network is carried on the transmission network, and mainly includes data communication network, dispatching switching network, administrative switching network and video and telephone conferencing system.

[0053] The supporting network mainly includes synchronization network, network management system, communication power supply and emergency communication.

[0054] When the existing power communication network is in operation, an autoregressive model is usually used to periodically predict the services of each communication node based on the operation data. However, due to the low accuracy of the collected data, the prediction results are not very accurate.

[0055] This embodiment, based on digital twin technology and random forest algorithm, proposes a method for constructing a digital twin model of an electric power communication network, corrects the problem of low accuracy of collected data, and promotes intelligent digital analysis and data mining in all fields of the electric power communication network. Constructing a digital twin model of an electric power communication network is a set of complex technologies and application systems for new digital smart grids. The integration of multiple types of technologies, the integration of multi-source data, and the connection of various platforms are key elements in the construction of digital twin power grids, and its core is a high-precision, multi-coupled digital twin power grid model. Through data fusion processing, superposition system modeling and dynamic modeling, a digital twin model is generated, providing a powerful means to promote intelligent digital analysis and data mining in all fields of the power grid.

[0056] like Figure 1As shown, the present invention first constructs a digital twin power grid for mapping the power communication network; then, a random forest model is constructed based on the random forest algorithm; then, the prediction results in the power communication network are imported into the random forest model to generate an accuracy prediction model of the power communication network; finally, the accuracy prediction model is imported into the twin model layer to complete the construction of the digital twin model. The digital twin power grid includes an infrastructure layer, a platform layer, and an application layer connected in parallel, and the twin model layer is arranged in the platform layer.

[0057] Embodiment 2:

[0058] This embodiment is further optimized on the basis of embodiment 1, and a random forest model is constructed based on the random forest algorithm and is expressed as follows:

[0059]

[0060] Among them, H(x) is the constructed random forest model, k is the number of decision trees, and h i is the completed i-th decision tree, Y is the predicted result, and I(x) is the indicative function.

[0061] The other parts of this embodiment are the same as those of Embodiment 1, and thus will not be described in detail.

[0062] Embodiment 3:

[0063] This embodiment is further optimized on the basis of the above-mentioned embodiment 1 or embodiment 2. Figure 2 As shown in the figure, the digital twin power grid is mainly composed of three levels: infrastructure layer, platform layer and application layer.

[0064] The infrastructure layer mainly includes measurement and perception devices on the terminal side, network connection devices on the network side, and intelligent computing devices on the cloud side.

[0065] The platform layer mainly includes the management and control layer, the data aggregation layer, and the twin model layer. The management and control layer includes the IoT device platform and the power grid resource control platform, which are used to uniformly control and manage the basic equipment and the resources on the power grid side. The data aggregation layer is to aggregate all the data. The twin model layer is composed of an accuracy prediction model. Its function is to combine with the data aggregation layer to establish a digital twin model corresponding to the entity, which is the core of the entire platform layer.

[0066] The application layer can be divided into the equipment layer, the power grid layer, the business layer, and the operation management layer according to its function and different scenarios. The equipment layer is mainly used for the management and evaluation of equipment status, fault diagnosis, and full life cycle management, and is the foundation of the entire application layer. The power grid layer is the power communication network, including the backbone communication network and the terminal communication access network. It is mainly used for power grid dispatching, operation risk analysis, and full life cycle management, and is the core of the application layer. The business layer mainly supports the business development around infrastructure, production, dispatching, marketing, training and other departments, and is the key support for the application layer. The main function of the operation management layer is to optimize the company's asset management, operational risk management, and investment decision-making. It is the top-level application of the application layer.

[0067] The other parts of this embodiment are the same as those of the above-mentioned embodiment 1 or 2, and thus will not be described in detail.

[0068] Embodiment 4:

[0069] This embodiment further optimizes any one of the above embodiments 1-3 to obtain the prediction result of the i-th point in the power communication network. and the actual result P(i), the predicted result The actual result P(i) is imported into the random forest model to generate the accuracy prediction model of the power communication network, and is expressed as:

[0070]

[0071] in, is the prediction result of the i-th point in the power communication network, P(i) is the actual measurement result of the i-th moment in the power communication network, P is the actual measurement result of the previous moment (i.e., point i-1) in the power communication network, i is the prediction point number, and r is the prediction accuracy.

[0072] The other parts of this embodiment are the same as any one of the above embodiments 1-3, so they are not repeated here.

[0073] Embodiment 5:

[0074] This embodiment is further optimized on the basis of any one of the above embodiments 1-4, and the average prediction accuracy rv of the power communication network is calculated according to the accuracy r of the prediction at the i-th point, and is expressed as:

[0075] n is the length of the sampling points.

[0076] The other parts of this embodiment are the same as any one of the above embodiments 1-4, so they are not repeated here.

[0077] Embodiment 6:

[0078] The present invention also provides a digital twin model construction system for a power communication network matching the method, comprising an infrastructure layer, a platform layer and an application layer, wherein:

[0079] The infrastructure layer includes the terminal side, network side and cloud side connected in parallel to ensure the operation of the power information communication network;

[0080] The platform layer includes the management and control layer, data aggregation layer, and twin model layer connected in parallel, which are used for data processing and core operation;

[0081] The application layer, including the device layer, power grid layer, business layer and operation management layer connected in parallel, is used to apply the digital twin model.

[0082] The device side includes measurement and perception equipment, the network side includes network connection equipment, and the cloud side includes intelligent computing equipment, which are used for measurement and perception, network connection, and intelligent computing respectively;

[0083] The management and control layer includes the IoT device platform and the power grid resource control platform, which are used to uniformly control and manage the basic equipment and grid resources.

[0084] The twin model layer includes an accuracy prediction model, which is used to predict the accuracy of power grid data;

[0085] The power grid layer includes the backbone communication network and the terminal communication access network. The backbone communication network includes the transmission network, business network and support network, which are used for power grid dispatching, operation risk analysis and full life cycle management through the backbone communication network and the terminal communication access network.

[0086] The present invention also provides an electronic device, which includes a processor and a memory; the processor includes the digital twin model construction system of the power communication network recorded above.

[0087] Embodiment 7:

[0088] The present invention also provides a computer-readable storage medium, which includes instructions; when the instructions are executed on the electronic device described in the above embodiment, the electronic device executes the method described in the above embodiment. Optionally, the computer-readable storage medium may be a memory.

[0089] The processor involved in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0090] The memory involved in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0091] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0093] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0095] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one device or distributed on multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0096] In addition, each functional module in each embodiment of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device.

[0097] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or may contain one or more servers, data centers and other data storage devices that can be integrated with a medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0098] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for constructing a digital twin model of a power communication network, characterized in that: The following steps are involved: Step S1, constructing a digital twin power grid of the power communication network; the digital twin power grid includes an infrastructure layer, a platform layer, and an application layer connected in parallel, and the platform layer includes a twin model layer; Step S2, constructing a random forest model based on the random forest algorithm; Step S3, importing the prediction results in the power communication network into the random forest model to generate an accuracy prediction model for the power communication network; Step S4, importing the accuracy prediction model into the twin model layer to complete the construction of the digital twin model.

2. The method for constructing a digital twin model of a power communication network according to claim 1, characterized in that: When constructing the digital twin power grid of the power communication network in step S1, the end side, network side, and cloud side connected in parallel are set at the infrastructure layer, the management and control layer, data aggregation layer, and twin model layer connected in parallel are set at the platform layer, and the device layer, power grid layer, business layer, and operation management layer connected in parallel are set at the application layer.

3. The method for constructing a digital twin model of a power communication network according to claim 2, characterized in that: The terminal side includes a measurement sensing device, the network side includes a network connection device, and the cloud side includes an intelligent computing device; The management and control layer includes an IoT device platform and a power grid resource control platform; The twin model layer includes an accuracy prediction model; The power grid layer includes a backbone communication network and a terminal communication access network, and the backbone communication network includes a transmission network, a service network and a support network.

4. The method for constructing a digital twin model of a power communication network according to claim 1, characterized in that: The random forest model constructed based on the random forest algorithm in step S2 is expressed as: Among them, H(x) is the constructed random forest model, k is the number of decision trees, and h i is the completed i-th decision tree, Y is the predicted result, and I(x) is the indicative function.

5. The method for constructing a digital twin model of a power communication network according to claim 1, characterized in that: The step S3 comprises: Get the prediction result of the i-th point in the power communication network and the actual result P(i), the predicted result The actual result P(i) is imported into the random forest model to generate an accuracy prediction model of the power communication network, and is expressed as: in, is the prediction result of the i-th point in the power communication network, P(i) is the actual measurement result at the i-th moment in the power communication network, P is the actual measurement result at the previous moment (i.e., point i-1) in the power communication network, i is the prediction point number, r is the accuracy of the prediction of the i-th point, and n is the length of the sampling point.

6. The method for constructing a digital twin model of a power communication network according to claim 5, characterized in that: The step S3 further comprises: The average prediction accuracy rv of the power communication network is calculated according to the prediction accuracy r of the i-th point, and is expressed as: n is the length of the sampling points.

7. A digital twin model construction system for a power communication network, characterized in that: It includes infrastructure layer, platform layer and application layer, among which: The infrastructure layer includes the terminal side, network side and cloud side connected in parallel to ensure the operation of the power information communication network; The platform layer includes the management and control layer, data aggregation layer, and twin model layer connected in parallel, which are used for data processing and core operation; The application layer, including the device layer, power grid layer, business layer and operation management layer connected in parallel, is used to apply the digital twin model.

8. The digital twin model construction system of the power communication network according to claim 7, characterized in that: The terminal side includes a measurement perception device, the network side includes a network connection device, and the cloud side includes an intelligent computing device, which are respectively used for measurement perception, network connection and intelligent computing; The management and control layer includes an IoT device platform and a power grid resource control platform, which are used to uniformly control and manage the basic equipment and power grid resources; The twin model layer includes an accuracy prediction model for predicting the accuracy of power grid data; The power grid layer includes a backbone communication network and a terminal communication access network. The backbone communication network includes a transmission network, a service network and a support network, and is used to perform power grid dispatching, operation risk analysis and full life cycle management through the backbone communication network and the terminal communication access network.

9. An electronic device, characterized in that: It comprises a processor and a memory; the processor comprises the digital twin model construction system of the electric power communication network as described in claim 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes instructions; when the instructions are executed on the electronic device as claimed in claim 9, the electronic device executes the method as claimed in any one of claims 1-6.

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