CN114007149A - Monitoring method, device and system of power system, storage medium and processor - Google Patents
Monitoring method, device and system of power system, storage medium and processor Download PDFInfo
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Abstract
The invention discloses a monitoring method, a monitoring device, a monitoring system, a storage medium and a processor of a power system. Wherein, the method comprises the following steps: acquiring a plurality of groups of power system data, wherein the plurality of groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data; uploading a plurality of groups of power system data to a cloud server in real time; and inputting a plurality of groups of power data into a power system monitoring model, and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on a plurality of groups of sample data of the target power system. The invention solves the technical problem that the monitoring of the running state of the power system is not intelligent.
Description
Technical Field
The invention relates to the field of power protection, in particular to a monitoring method, a monitoring device, a monitoring system, a monitoring storage medium and a monitoring processor of a power system.
Background
The power supply of outdoor large-scale activities ensures that sensitive users on site have more types and huge number, such as lamplight, sound, screens and the like. The characteristics of part of sensitive loads are special, which may cause the problems of voltage fluctuation, current harmonic and other electric energy quality, and the operation characteristics of the on-site guarantee equipment also have a direct relation with the success or failure of the guarantee. Therefore, higher requirements are put on the guarantee work of the power system, and the operation characteristics of equipment and loads need to be monitored as soon as possible when a power quality event occurs in the guarantee process. However, the power system that outdoor large-scale activities depend on has large data volume, wide distribution range and many aspects needing monitoring, and the requirement of high-speed response to the power quality event in the power supply guarantee work cannot be met even if the analysis work is carried out on the mass data manually.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a monitoring method, a monitoring device, a monitoring system, a storage medium and a monitoring processor of an electric power system, and at least solves the technical problem that monitoring of the running state of the electric power system is not intelligent.
According to an aspect of an embodiment of the present invention, there is provided a monitoring method of an electric power system, including: acquiring a plurality of sets of power system data, wherein the plurality of sets of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data; uploading the multiple groups of power system data to a cloud server in real time; and inputting the multiple groups of power data into a power system monitoring model, and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on multiple groups of sample data of the target power system.
Optionally, uploading the multiple sets of power system data to a cloud server in real time, including: deploying an edge processor on a data acquisition side of the target power system, wherein the edge processor is used for performing edge calculation; pre-processing the plurality of sets of power system data using the edge processor; and uploading the preprocessed multiple groups of electric power system data to the cloud server in real time.
Optionally, deploying an edge processor on a data acquisition side of the target power system includes: deploying a plurality of the edge processors on a plurality of data acquisition sides of the power system, respectively, wherein the data acquisition device comprises at least one of: a sensor, a power protection terminal device; and accessing a plurality of edge processors and the cloud server into a distributed network, wherein the cloud server and any one edge processor are one node in the distributed network.
Optionally, uploading the multiple sets of power system data to a cloud server in real time, further comprising: dividing a plurality of groups of power system data into first data, second data and third data according to the type of equipment generating the plurality of groups of power system data; uploading the first data to the cloud server in real time by using a URLLC service in a 5G communication technology; uploading the second data to the cloud server by adopting a block chain encryption communication technology; and uploading the third data to the cloud server by adopting an HPLC electronic carrier communication technology.
Optionally, uploading the second data to the cloud server by using a block chain encryption communication technology, including: establishing connection between target equipment and an Etherhouse Geth node, wherein the target equipment is equipment for generating the second data; acquiring equipment information of the second equipment; sending the equipment information to the Geth node, triggering an intelligent contract of the Geth node, and performing node consensus authentication of the equipment information; under the condition that the equipment information passes authentication, sending the second data to the Geth node; and the Geth node uploads the second data to the cloud server.
Optionally, after outputting the operating state of the target power system, the method further includes: judging whether the running state of the target power system is normal or not; and when the running state of the target power system is abnormal, carrying out alarm prompt.
According to another aspect of the embodiments of the present invention, there is also provided a monitoring device for an electric power system, including: the acquisition module is used for acquiring multiple groups of power system data, wherein the multiple groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data; the uploading module is used for uploading the multiple groups of power system data to a cloud server in real time; and the output module is used for inputting the multiple groups of power data into a power system monitoring model and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on multiple groups of sample data of the target power system.
According to another aspect of the embodiments of the present invention, there is also provided a monitoring system of an electric power system, including: the system comprises a data acquisition device, an edge calculation device, a data transmission device and a cloud server, wherein the data acquisition device is used for acquiring multiple groups of power system data, the multiple groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data; the data transmission device is used for uploading the multiple groups of power system data to the cloud server in real time by adopting URLLC service in the 5G communication technology; and the cloud server is used for inputting the multiple groups of power data into a power system monitoring model and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on multiple groups of sample data of the target power system.
Optionally, the system further includes: the edge computing device is deployed beside the data acquisition device and is used for preprocessing the multiple groups of power system data.
According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above power system monitoring methods.
According to a further aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes the power system monitoring method described in any one of the above.
In the embodiment of the invention, a mode of acquiring multiple groups of power system data generated by a target power system is adopted, the multiple groups of power system data are uploaded to a cloud server in real time, a power system monitoring model obtained through deep learning is input, and the running state of the target power system is output, so that the purpose of monitoring the running state of the target power system in real time is achieved, the technical effect of intelligently monitoring the running state of the power system is realized, and the technical problem that the running state of the power system is not intelligently monitored is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a schematic flow chart of a monitoring method for an electric power system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a communication scheme of a monitoring system according to an alternative embodiment of the present invention;
fig. 3 is a block diagram of a monitoring device of an electric power system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, partial terms or terms appearing in the description of the embodiments of the present application are applied to the following explanations:
URLLC service, i.e., Ultra-reliable & Low-latency communication service (URLLC), a service supported by the 5G new radio standard.
The Etherhouse Geth node is used for establishing communication with other clients through the Geth node in order to communicate with a blockchain client, and functions of signing, broadcasting transaction, intelligent contract interaction and the like are realized.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for monitoring a power system, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a schematic flow chart of a monitoring method for a power system according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, acquiring a plurality of groups of power system data, wherein the plurality of groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data. Alternatively, the target power system may be a power system for supporting outdoor large-scale activities, including lights, sounds, screens, and power supply networks for supplying power thereto, for example. The sensor data may be deployed near the target power system for monitoring data of specific characteristics of the target power system, for example, may be used for monitoring load access conditions, component temperatures, communication states, and the like in the target power system. The power protection terminal equipment is professional monitoring equipment for guaranteeing the power supply stability of a target power system.
And step S104, uploading the multiple groups of electric power system data to a cloud server in real time. Because the amount of data generated by a large-scale outdoor electric power system is huge, data processing is difficult to perform locally, and meanwhile, due to the limitation of the environment scene of the activity, large-scale reliable computing equipment or servers cannot be deployed nearby locally, the embodiment provides that the data is uploaded to the cloud server in real time, the technical problem can be solved, and the data of the electric power system can be properly stored.
And S106, inputting a plurality of groups of power data into a power system monitoring model, and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on a plurality of groups of sample data of the target power system.
It should be noted that the power system monitoring model may be trained in advance and stored in the cloud server, and may be directly invoked when needed. Because the data volume of the target power system is huge, mining and analyzing the data of the power system in a manual or automatic mode is time-consuming and labor-consuming, and the accuracy cannot be guaranteed. Therefore, the present embodiment proposes that the power system monitoring model obtained based on deep learning neural network model training is used to process the power system data, and the model outputs the operating state of the power system. Alternatively, the model may be trained from sample data of the target power system. The sample data can be a plurality of groups of sample data collected on site after a power system is built on a power supply site of outdoor large-scale activities, and the sample data comprises artificial marks for identifying the operating state of the power system in each group of sample data. In addition, the sample data can also be sample data of other power systems similar to the target power system, and the sample data of the similar power systems can be used, so that more ready-made sample data can be provided for model training, and the model training efficiency is improved.
Through the steps, the mode of acquiring multiple groups of power system data generated by the target power system is adopted, the multiple groups of power system data are uploaded to the cloud server in real time and input to the power system monitoring model obtained through deep learning, the running state of the target power system is output, and the purpose of monitoring the running state of the target power system in real time is achieved, so that the technical effect of intelligently monitoring the running state of the power system is achieved, and the technical problem that monitoring of the running state of the power system is not intelligent is solved.
As an optional embodiment, the method for uploading the multiple sets of power system data to the cloud server in real time may be as follows: deploying an edge processor on a data acquisition side of the target power system, wherein the edge processor is used for performing edge calculation; preprocessing multiple groups of power system data by using an edge processor; and uploading the preprocessed multiple groups of power system data to a cloud server in real time.
As an optional embodiment, an edge processor is deployed on a data acquisition side of a target power system, and a plurality of edge processors may be deployed on a plurality of data acquisition sides of the power system, respectively, where the data acquisition device includes at least one of: a sensor, a power protection terminal device; and then, accessing the plurality of edge processors and the cloud server into a distributed network, wherein the cloud server and any one edge processor are one node in the distributed network. By adopting a network layout mode of a distributed network, the stability of the power monitoring system can be improved, and the influence on the overall monitoring effect when a single node breaks down is avoided.
As an optional embodiment, the multiple sets of power system data are uploaded to the cloud server in real time, and the following method may also be adopted: dividing the plurality of sets of power system data into first data, second data and third data according to the type of equipment generating the plurality of sets of power system data; uploading the first data to a cloud server in real time by using a URLLC service in a 5G communication technology; uploading the second data to a cloud server by adopting a block chain encryption communication technology; and uploading the third data to a cloud server by adopting an HPLC (high performance liquid chromatography) electronic carrier communication technology.
For the electric power guarantee scene, various electric power equipment sources can appear in the electric power guarantee site, different equipment can belong to different individuals or units, the types of the equipment are also various, and different requirements can not be well met sometimes by adopting a uniform electric power data transmission technology. The execution scene of the power guarantee may be indoor, square or field, the power equipment needing to be guaranteed in different scenes is necessarily different, and the data processing requirements of different power equipment are also necessarily different. For example, the electric device may include, but is not limited to, a broadcasting vehicle, a lighting system, an electric device in a confidential place, and the like, and in a scene such as an indoor or urban square, the electric device may access electric power through an urban power supply system, but in a field, because there is no mature power pipeline in the environment, the electric device needs to adopt another electric power access method, and the difference also necessarily affects the data transmission method that can be selected for the electric device.
Optionally, the technical problem can be solved by classifying data generated by the power equipment in the power system according to scenes and requirements and uploading different types of data to the cloud server in a data transmission mode meeting requirements. Specifically, the type of the power system data may include first data, second data, and third data, where the first data is data with the highest timeliness requirement level and needs to be transmitted in the fastest manner; the second data is data with the highest confidentiality requirement level and needs to be transmitted in a safest mode; the third data is relatively common data, the requirements on timeliness and confidentiality are not so high, and the third data can be transmitted in a more economical mode.
Further, the first data may include, but is not limited to, live broadcast signals or data of power equipment that is very important in power guarantee activities, and must be uploaded at high speed in real time to ensure that the server can complete data processing as soon as possible; the second data may include, but is not limited to, data related to privacy of the field power device user, or data of a special object, for example, a power system arrangement of a certain place involves national or military secrets, the security requirement on the power system data is high, data uploading cannot be performed at ordinary times, but data uploading from the field to the cloud server is required due to the requirement of power guarantee, and such data may be divided into the second data; the third data may be data generated by a power device accessing the city power line.
For the three data, the following different data transmission modes can be adopted: uploading the first data to the cloud server in real time by using a URLLC service in a 5G communication technology; uploading the second data to the cloud server by adopting a block chain encryption communication technology; and uploading the third data to the cloud server by adopting an HPLC electronic carrier communication technology. The URLLC is an ultra-high reliable low-delay communication service in the 5G standard, and can ensure the timeliness of data transmission to the maximum extent. The block chain encryption communication technology can ensure the reliability of data transmission and avoid the leakage of sensitive information. The HPLC is a short for broadband power line carrier technology, has the characteristics of large bandwidth and high transmission rate, and can be suitable for data transmission of power equipment accessed to urban power lines.
Optionally, the data other than the three data may be transmitted by using a 4G communication technology, and the data is uploaded to the cloud server. The 4G communication technology is more mature and lower in cost, the 5G communication technology, the block chain encryption communication technology and the HPLC communication technology have the advantages, and operation and maintenance personnel can flexibly select the combination of the communication technologies according to the specific situation of power supply activities. Fig. 2 is a schematic view of a communication mode of the monitoring system according to an optional embodiment of the present invention, and as shown in fig. 2, uploading data of the monitoring terminal to the cloud server may also select a 4G and 5G hybrid network, and select a suitable data transmission path for data of multiple types of monitoring terminals in combination with a block chain encryption communication technology and an HPLC technology. For example, for a sensor with a small generated data amount, data can be uploaded to a cloud server in a 4G communication mode, and for a professional power protection device which is more precise and can generate mass data, data is uploaded to the cloud server in a 5G communication mode for subsequent analysis.
As an optional embodiment, the second data is uploaded to the cloud server by using a block chain encryption communication technology, which may be as follows: establishing connection between target equipment and an Ether house Geth node, wherein the target equipment is equipment for generating second data; acquiring equipment information of second equipment; sending the equipment information to a Geth node, triggering an intelligent contract of the Geth node, and performing node consensus authentication on the equipment information; under the condition that the equipment information passes the authentication, second data are sent to a Geth node; and the Geth node uploads the second data to the cloud server. In the above optional embodiment, the device information of the target device is authenticated through the intelligent contract of the Geth node, so that the identity of the target device is verified, and on this basis, the data of the target device is encrypted and uploaded through the block chain technology, so that the security of data transmission of the target device is ensured.
As an alternative embodiment, after the operation state of the target power system is output, whether the operation state of the target power system is normal may be further determined; and when the running state of the target power system is abnormal, giving an alarm.
Example 2
According to an embodiment of the present invention, there is also provided a monitoring device of an electric power system for implementing the monitoring method of the electric power system, and fig. 3 is a block diagram of a structure of the monitoring device of the electric power system according to the embodiment of the present invention, and as shown in the drawing, the monitoring device 30 of the electric power system includes: the acquisition module 32, the upload module 34, and the output module 36 are described below with respect to the monitoring device 30 of the power system.
An obtaining module 32, configured to obtain multiple sets of power system data, where the multiple sets of power system data are data generated by a target power system, and the power system data includes at least one of: sensor data, power conservation terminal equipment data;
an uploading module 34, connected to the acquiring module 32, for uploading multiple sets of power system data to a cloud server in real time;
and the output module 36 is connected to the uploading module 34, and is configured to input multiple sets of power data into the power system monitoring model, and output the operating state of the target power system, where the power system monitoring model is a neural network model obtained by performing deep learning on multiple sets of sample data of the target power system.
It should be noted here that the acquiring module 32, the uploading module 34 and the outputting module 36 correspond to steps S102 to S106 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1.
Example 3
An embodiment of the present invention may provide a computer device, and optionally, in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.
The memory may be configured to store software programs and modules, such as program instructions/modules corresponding to the monitoring method and apparatus for the power system in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, so as to implement the above-mentioned monitoring method for the power system. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a plurality of groups of power system data, wherein the plurality of groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data; uploading a plurality of groups of power system data to a cloud server in real time; and inputting a plurality of groups of power data into a power system monitoring model, and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on a plurality of groups of sample data of the target power system.
Optionally, the processor may further execute the program code of the following steps: upload multiunit electric power system data to high in the clouds server in real time, include: deploying an edge processor on a data acquisition side of the target power system, wherein the edge processor is used for performing edge calculation; preprocessing multiple groups of power system data by using an edge processor; and uploading the preprocessed multiple groups of power system data to a cloud server in real time.
Optionally, the processor may further execute the program code of the following steps: deploying an edge processor at a data acquisition side of a target power system, comprising: deploying a plurality of edge processors on a plurality of data acquisition sides of the power system, respectively, wherein the data acquisition device comprises at least one of: a sensor, a power protection terminal device; the method comprises the steps of accessing a plurality of edge processors and a cloud server into a distributed network, wherein the cloud server and any one edge processor are one node in the distributed network.
Optionally, the processor may further execute the program code of the following steps: upload multiunit electric power system data to high in the clouds server in real time, still include: dividing the plurality of sets of power system data into first data, second data and third data according to the type of equipment generating the plurality of sets of power system data; uploading the first data to a cloud server in real time by using a URLLC service in a 5G communication technology; uploading the second data to a cloud server by adopting a block chain encryption communication technology; and uploading the third data to a cloud server by adopting an HPLC (high performance liquid chromatography) electronic carrier communication technology.
Optionally, the processor may further execute the program code of the following steps: uploading second data to a cloud server by adopting a block chain encryption communication technology, and the method comprises the following steps: establishing connection between target equipment and an Ether house Geth node, wherein the target equipment is equipment for generating second data; acquiring equipment information of second equipment; sending the equipment information to a Geth node, triggering an intelligent contract of the Geth node, and performing node consensus authentication on the equipment information; under the condition that the equipment information passes the authentication, second data are sent to a Geth node; and the Geth node uploads the second data to the cloud server.
Optionally, the processor may further execute the program code of the following steps: after the operation state of the target power system is output, the method further comprises the following steps: judging whether the running state of the target power system is normal or not; and when the running state of the target power system is abnormal, giving an alarm.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
Example 4
Embodiments of the present invention also provide a computer-readable storage medium. Alternatively, in this embodiment, the computer-readable storage medium may be used to store the program code executed by the monitoring method of the power system provided in embodiment 1.
Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network, or in any one of a group of mobile terminals.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: acquiring a plurality of groups of power system data, wherein the plurality of groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data; uploading a plurality of groups of power system data to a cloud server in real time; and inputting a plurality of groups of power data into a power system monitoring model, and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on a plurality of groups of sample data of the target power system.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: upload multiunit electric power system data to high in the clouds server in real time, include: deploying an edge processor on a data acquisition side of the target power system, wherein the edge processor is used for performing edge calculation; preprocessing multiple groups of power system data by using an edge processor; and uploading the preprocessed multiple groups of power system data to a cloud server in real time.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: deploying an edge processor at a data acquisition side of a target power system, comprising: deploying a plurality of edge processors on a plurality of data acquisition sides of the power system, respectively, wherein the data acquisition device comprises at least one of: a sensor, a power protection terminal device; the method comprises the steps of accessing a plurality of edge processors and a cloud server into a distributed network, wherein the cloud server and any one edge processor are one node in the distributed network.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: upload multiunit electric power system data to high in the clouds server in real time, still include: dividing the plurality of sets of power system data into first data, second data and third data according to the type of equipment generating the plurality of sets of power system data; uploading the first data to a cloud server in real time by using a URLLC service in a 5G communication technology; uploading the second data to a cloud server by adopting a block chain encryption communication technology; and uploading the third data to a cloud server by adopting an HPLC (high performance liquid chromatography) electronic carrier communication technology.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: uploading second data to a cloud server by adopting a block chain encryption communication technology, and the method comprises the following steps: establishing connection between target equipment and an Ether house Geth node, wherein the target equipment is equipment for generating second data; acquiring equipment information of second equipment; sending the equipment information to a Geth node, triggering an intelligent contract of the Geth node, and performing node consensus authentication on the equipment information; under the condition that the equipment information passes the authentication, second data are sent to a Geth node; and the Geth node uploads the second data to the cloud server.
Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: after the operation state of the target power system is output, the method further comprises the following steps: judging whether the running state of the target power system is normal or not; and when the running state of the target power system is abnormal, giving an alarm.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (10)
1. A method of monitoring a power system, comprising:
acquiring a plurality of sets of power system data, wherein the plurality of sets of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data;
uploading the multiple groups of power system data to a cloud server in real time;
and inputting the multiple groups of power data into a power system monitoring model, and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on multiple groups of sample data of the target power system.
2. The method of claim 1, wherein uploading the plurality of sets of power system data to a cloud server in real-time comprises:
deploying an edge processor on a data acquisition side of the target power system, wherein the edge processor is used for performing edge calculation;
pre-processing the plurality of sets of power system data using the edge processor;
and uploading the preprocessed multiple groups of electric power system data to the cloud server in real time.
3. The method of claim 2, wherein deploying an edge processor on a data collection side of the target power system comprises:
deploying a plurality of the edge processors on a plurality of data acquisition sides of the power system, respectively, wherein the data acquisition device comprises at least one of: a sensor, a power protection terminal device;
and accessing a plurality of edge processors and the cloud server into a distributed network, wherein the cloud server and any one edge processor are one node in the distributed network.
4. The method of claim 1, wherein uploading the plurality of sets of power system data to a cloud server in real-time further comprises:
dividing a plurality of groups of power system data into first data, second data and third data according to the type of equipment generating the plurality of groups of power system data;
uploading the first data to the cloud server in real time by using a URLLC service in a 5G communication technology;
uploading the second data to the cloud server by adopting a block chain encryption communication technology;
and uploading the third data to the cloud server by adopting an HPLC electronic carrier communication technology.
5. The method of claim 4, wherein uploading the second data to the cloud server using a block chain encryption communication technique comprises:
establishing connection between target equipment and an Etherhouse Geth node, wherein the target equipment is equipment for generating the second data;
acquiring equipment information of the second equipment;
sending the equipment information to the Geth node, triggering an intelligent contract of the Geth node, and performing node consensus authentication of the equipment information;
under the condition that the equipment information passes authentication, sending the second data to the Geth node;
and the Geth node uploads the second data to the cloud server.
6. The method of claim 1, wherein after outputting the operating state of the target power system, further comprising:
judging whether the running state of the target power system is normal or not;
and when the running state of the target power system is abnormal, carrying out alarm prompt.
7. A monitoring device for an electrical power system, comprising:
the acquisition module is used for acquiring multiple groups of power system data, wherein the multiple groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data;
the uploading module is used for uploading the multiple groups of power system data to a cloud server in real time;
and the output module is used for inputting the multiple groups of power data into a power system monitoring model and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on multiple groups of sample data of the target power system.
8. A monitoring system for an electrical power system, comprising: a data acquisition device, an edge calculation device, a data transmission device and a cloud server, wherein,
the data acquisition device is used for acquiring multiple groups of power system data, wherein the multiple groups of power system data are data generated by a target power system, and the power system data comprise at least one of the following data: sensor data, power conservation terminal equipment data;
the data transmission device is used for uploading the multiple groups of power system data to the cloud server in real time by using a URLLC service in a 5G communication technology;
and the cloud server is used for inputting the multiple groups of power data into a power system monitoring model and outputting the running state of the target power system, wherein the power system monitoring model is a neural network model obtained by deep learning based on multiple groups of sample data of the target power system.
9. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the power system monitoring method according to any one of claims 1 to 6.
10. A processor configured to run a program, wherein the program is configured to perform the power system monitoring method according to any one of claims 1 to 6 when the program is run.
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