CN114374709B - 5G video and Internet of things distribution network monitoring system and method based on edge cloud cooperation - Google Patents
5G video and Internet of things distribution network monitoring system and method based on edge cloud cooperation Download PDFInfo
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Abstract
The application is applicable to the technical field of power grid monitoring, and provides a 5G video and Internet of things distribution network monitoring system and method based on edge cloud cooperation, wherein the 5G video and Internet of things distribution network monitoring system based on edge cloud cooperation comprises: the video acquisition node is used for acquiring original power grid monitoring video data of the distribution network environment; the 5G edge gateway is used for carrying out data processing and 5G adaptation on the original power grid monitoring video data; the system comprises an Internet of things module, a network distribution module and a network management module, wherein the Internet of things module is used for acquiring original power grid state data of a distribution network environment and preprocessing the original power grid state data; the 5G base station is used for receiving the first power grid monitoring video data processed by the 5G edge gateway and the first power grid state data processed by the Internet of things module and sending the first power grid monitoring video data and the first power grid state data to the central cloud server; and the central cloud server is used for distributing the power grid monitoring video data and the power grid state data to each monitoring center control station. Comprehensive monitoring, safety early warning and positioning traceability of the power grid distribution network environment are realized.
Description
Technical Field
The application belongs to the technical field of power grid monitoring, and particularly relates to a 5G video and Internet of things distribution network monitoring system and method based on edge cloud cooperation.
Background
In recent years, there has been a growing need for an integrated monitoring system for integrating technologies such as video monitoring, microclimate, icing monitoring, overhead tilt monitoring, insulation monitoring, and wire galloping monitoring on power transmission and distribution lines. The video monitoring requirements mainly based on machine vision are most common, and the line inspection personnel inspection pressure, the empty window period and the inspection efficiency can be greatly reduced through a remote video monitoring technology, so that the maintenance efficiency, the intellectualization and the informatization level of the power system are improved.
The intelligent safety pre-warning and accurate positioning of the power grid integrated information network, the adaptive access of different video monitoring devices, the intelligent processing of machine vision, the ultrahigh-definition visual analysis and the efficient distribution of video multiple points are realized, and the intelligent safety pre-warning and accurate positioning of the power grid integrated information network becomes a key technical problem of power grid monitoring.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a distribution network monitoring method, a distribution network monitoring device and terminal equipment, so as to improve the monitoring efficiency of power grid distribution network monitoring and realize safety early warning and positioning traceability of power grid monitoring results.
The application is realized by the following technical scheme:
in a first aspect, an embodiment of the present application provides a monitoring system for a 5G video and internet of things based on edge cloud collaboration, which is characterized by comprising: the video acquisition node is used for acquiring original power grid monitoring video data of the distribution network environment; the 5G edge gateway is used for carrying out data processing and 5G adaptation on the original power grid monitoring video data; the internet of things module is used for collecting original power grid state data of the distribution network environment and preprocessing the original power grid state data; the 5G base station is used for receiving the first power grid monitoring video data processed by the 5G edge gateway and the first power grid state data processed by the Internet of things module and sending the first power grid monitoring video data and the first power grid state data to the central cloud server; and the central cloud server is used for distributing the power grid monitoring video data and the power grid state data to each monitoring center control station.
In the embodiment of the application, the multi-channel streaming media framework is built through the combination of the edge gateway, the Internet of things module, the 5G base station and the central cloud server, so that the comprehensive acquisition and high-speed transmission of the environmental data of the transmission and distribution network are realized. The method comprises the steps of realizing edge optimization strategies such as video stream identification, compression coding, protocol conversion and the like through an edge gateway, collecting multidimensional state data of a distribution network environment through an Internet of things module, and connecting a front-end edge gateway and a rear-end 5G edge gateway through a central cloud server to realize relay and distribution of streaming media content. The monitoring efficiency of the power grid distribution network monitoring is improved, and meanwhile, the safety early warning and the positioning traceability of the power grid monitoring result are realized.
Based on the first aspect, in some embodiments, the internet of things module includes a sensor network module, a CPU and a 5GNB-IoT gateway module, the sensor network module includes at least four internet of things sensor nodes, and the internet of things sensor nodes are configured to collect power grid state data of the distribution network environment and transmit the power grid state data to the 5G NB-IoT gateway module; the 5GNB-IoT gateway module is used for packaging the power grid state data processed by the CPU data in a 5G protocol to obtain first power grid state data and transmitting the first power grid state data to the 5G base station; the internet of things module collects power grid state data through the sensor network module, and the CPU is used for completing analog-to-digital conversion, edge intelligent identification, multidimensional data fusion, power grid state prediction and local decision of the power grid state data, controlling the 5G NB-IoT gateway module to complete 5G protocol encapsulation and accessing the 5G base station.
Based on the first aspect, in some embodiments, the at least four internet of things sensor nodes comprise: the temperature sensor node is used for collecting environmental temperature data of the distribution network environment; the humidity sensor node is used for collecting environmental humidity and irrigation data of the distribution network environment; the smoke sensor node is used for collecting environmental burning points and methane data of the distribution network environment; and the power terminal sensor node is used for collecting power running state data of the distribution network environment.
Based on the first aspect, in some embodiments, the 5G edge gateway comprises at least one front end 5G edge gateway and at least two back end 5G edge gateways; the front-end 5G edge gateway is used for carrying out data processing and 5G adaptation on the original power grid monitoring video data and then accessing the data to a 5G base station; the back-end 5G edge gateway is used for applying for establishing connection to the central cloud server to receive the first power grid monitoring video data and the first power grid state data.
Based on the first aspect, in some embodiments, at least one front-end 5G edge gateway, a central cloud server, and at least two back-end 5G edge gateways perform GStreamer streaming media multi-channel networking; the central cloud server is a relay, control and distribution platform, and establishes a point-to-multipoint GStreamer streaming media multichannel pipeline framework with at least one front end 5G edge gateway and at least two rear end 5G edge gateways.
In a second aspect, an embodiment of the present application provides a method for monitoring a distribution network, including: the method comprises the steps that original power grid monitoring video data of a distribution network environment are collected through a video collection node, the original power grid monitoring video data are identified, compressed and coded, protocol conversion and 5G encapsulation are carried out through a front-end 5G edge gateway, first power grid monitoring video data are obtained, and the first power grid monitoring video data are sent to a 5G base station; acquiring original power grid state data through an Internet of things module, performing data processing on the original power grid state data, and transmitting the first power grid state data obtained after processing to a 5G base station; the method comprises the steps that first power grid monitoring video data and first power grid state data are sent to a central cloud server through a streaming media channel of a 5G base station; the method comprises the steps that protocol conversion and data processing are carried out on first power grid monitoring video data and first power grid state data sent by a central cloud server through a back-end 5G edge gateway, and second power grid state data after data processing and second power grid monitoring video data are overlapped and synthesized; and based on the second power grid state data and the second power grid monitoring video data which are overlapped and synthesized, carrying out interface display and safety management on the distribution network monitoring video data and the environment state data.
Based on the second aspect, in some embodiments, the identifying, compression encoding, protocol conversion and 5G packaging of the original grid monitoring video data by the front-end 5G edge gateway, to obtain the first grid monitoring video data includes: performing inter-frame motion recognition on the original power grid monitoring video data through a recognition module of the front-end 5G edge gateway; compression encoding is carried out on the original power grid monitoring video data through a compression encoding module of the front-end 5G edge gateway; the protocol conversion module of the front-end 5G edge gateway is used for carrying out protocol conversion on the identified and compression-coded original power grid monitoring video data; and 5G packaging the identified, compression coded and protocol converted original power grid monitoring video data through a 5G module of the front-end 5G edge gateway to obtain first power grid monitoring video data.
Based on the second aspect, in some embodiments, the performing, by the identification module of the front-end 5G edge gateway, the inter-frame motion identification on the original grid monitoring video data includes: calculating adjacent frame difference to obtain a speed vector z= (u, v) of each pixel point between frames, wherein (u, v) is an offset, and the offset is calculated by the difference value between the pixel point position coordinates (x, y) of the previous frame and the current frame position coordinates (x+u, y+v); v is the absolute value v=abs (u, V), V shresold is a preset threshold, when the velocity vectors of all pixels continuously change and the offset is consistent, calculating whether V is smaller than V shresold, if V is smaller than V shresold, judging that there is no moving object in the image; if V is more than or equal to V shresold, judging that a moving target exists in the image, taking all pixels of which V is more than or equal to V shresold as a speed vector formed by the moving target, and determining a pixel block matrix of the moving target; and carrying out video windowing on the moving target pixel block matrix, and continuously tracking the moving target pixel block matrix to realize inter-frame motion recognition.
Based on the second aspect, in some embodiments, collecting, by the sensor network module, raw grid state data includes: acquiring environmental temperature data of a distribution network environment through a temperature sensor node; acquiring environmental humidity and irrigation data of a distribution network environment through a humidity sensor node; collecting environmental fire points and methane data of a distribution network environment through a smoke sensor node; collecting power running state data of a distribution network environment through a power terminal sensor node; the power operation state data includes voltage data, current data, and over-current data.
Based on the second aspect, in some embodiments, the method includes collecting original power grid state data through the internet of things module, performing data processing on the original power grid state data, and sending first power grid state data obtained after processing to the 5G base station, including: the method comprises the steps of carrying out aggregation, analog-to-digital conversion, intelligent edge identification, multidimensional data fusion, power grid state prediction and local decision on original power grid state data through a CPU to obtain first power grid state data; performing CRC (cyclic redundancy check), identity recognition, security management and access control on the first power grid state data through a CPU (Central processing Unit), and forming first power grid state data in an MQTT data frame format; and the CPU controls the 5G NB-IoT gateway module to package the first power grid state data in the MQTT data frame format into a 5G data frame format, and sends the first power grid state data in the 5G data frame format to the 5G base station.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a 5G video and internet of things distribution network monitoring system based on edge cloud collaboration provided by an embodiment of the present application;
Fig. 2 is a schematic diagram of a front end edge gateway structure according to an embodiment of the present application;
Fig. 3 is a schematic flow chart of a method for monitoring a distribution network according to an embodiment of the present application;
fig. 4 is a schematic diagram of a central cloud service function structure of a distribution network monitoring method according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
With the development of 5G technology, the requirements of power grid information collection are completely met by the characteristics of large bandwidth, low time delay, wide connection and the like, ultra-high definition video information perceived by a camera is integrated into big data and a cloud computing platform through a designed perception type video acquisition and processing module with perception capability, secondary operation is carried out, identification and decision are carried out from a monitoring video stream, massive Internet of things monitoring data are combined, depth analysis and mining are carried out, and accordingly video monitoring and Internet of things monitoring information island are thoroughly integrated. The 5G bandwidth connection capability can achieve large bandwidth in Gbps level, low latency in ms level, large connections in the billion order. Based on the ultra-performance heterogeneous edge processing technology and the edge cloud distributed architecture, the characteristics of 5G large bandwidth, low time delay and large connection and the machine vision technology are combined to perform intelligent monitoring and the integrated analysis of the internet of things, a joint solution of 5G+ machine vision, 5G+ internet of things, AI+ intelligent transmission, AI+ energy Internet and edge cloud combination is created, so that the precision of remote monitoring or monitoring, the inspection efficiency and the network transmission efficiency are improved, the power failure and energy consumption cost are reduced, and the digital transformation of the intelligent power grid is accelerated.
According to the application, the high-performance 5G edge video gateway and the Internet of things gateway build an ultra-high definition monitoring video stream and a multi-dimensional Internet of things monitoring data transmission channel on the 5G network, and realize the convergence, fusion and distribution of monitoring/monitoring data based on the central cloud server, and the 5G edge video gateway is combined to perform flow optimization, network cooperation structure, heterogeneous data high-speed access and multi-dimensional data fusion, so that the intelligent level of 5G network transmission and the intelligent level of monitoring/monitoring data are improved, the efficient self-adaptive scheduling and distribution of the transmission network are achieved, the intelligent identification and depth mining of monitoring/monitoring data are realized, and the sensing dimension and the monitoring level of the intelligent power grid are comprehensively improved.
As shown in fig. 1, the above-mentioned 5G video and internet of things distribution network monitoring system based on edge cloud cooperation may include:
The video acquisition node is used for acquiring original power grid monitoring video data of the distribution network environment;
the 5G edge gateway is used for carrying out data processing and 5G adaptation on the original power grid monitoring video data;
The internet of things module is used for collecting original power grid state data of the distribution network environment and preprocessing the original power grid state data;
The 5G base station is used for receiving the first power grid monitoring video data processed by the 5G edge gateway and the first power grid state data processed by the Internet of things module and sending the first power grid monitoring video data and the first power grid state data to the central cloud server;
and the central cloud server is used for distributing the power grid monitoring video data and the power grid state data to each monitoring center control station.
The video acquisition node comprises a 4K ultra-high definition monitoring camera module, and the 4K ultra-high definition monitoring camera module is connected with a front end 5G edge gateway through a CSI interface or a USB port and is used for acquiring power grid audio and video monitoring data.
As shown in fig. 2, the 5G edge gateway includes a video data processing module and a 5G module. The video data processing module comprises an identification module, a compression coding module and a protocol conversion module and is used for identifying, compression coding and protocol conversion of original power grid monitoring video data. The 5G module is used for sending the first power grid monitoring video data processed by the video data processing module to the 5G base station.
Optionally, the video data processing module includes a main control chip, such as JETSON XAVIER NX GPU processor, for processing the received grid monitoring video data.
Optionally, the 5G edge gateway may further include: AI processor, power, operating system, nvme solid state disk, video processing accelerator, CSI/USB/HDMI etc..
The 5G edge gateways include at least one front end 5G edge gateway and at least two back end 5G edge gateways. The front-end 5G edge gateway is used for carrying out data processing and 5G adaptation on the original power grid monitoring video data and then accessing the data to the 5G base station. The back-end 5G edge gateway is used for applying for establishing connection to the central cloud server to receive the first power grid monitoring video data and the first power grid state data.
And at least one front-end 5G edge gateway, a central cloud server and at least two back-end 5G edge gateways perform GStreamer stream media multi-channel networking. The central cloud server is a relay, control and distribution platform, and establishes a point-to-multipoint GStreamer streaming media multichannel pipeline framework with at least one front end 5G edge gateway and at least two rear end 5G edge gateways.
The internet of things module includes sensor network module, CPU and 5G NB-IoT gateway module, and the sensor network module includes four at least thing networking sensor nodes, and four at least thing networking sensor nodes include: the temperature sensor node is used for collecting environmental temperature data of the distribution network environment; the humidity sensor node is used for collecting environmental humidity and irrigation data of the distribution network environment; the smoke sensor node is used for collecting environmental burning points and methane data of the distribution network environment; and the power terminal sensor node is used for collecting power running state data of the distribution network environment. The internet of things sensor node is used for collecting power grid state data of the distribution network environment and transmitting the power grid state data to the 5G NB-IoT gateway module.
The CPU is a main control chip, such as an STM32 processor, and is connected with the sensor network module through an I/O port, and the CPU is used for performing edge optimization processing on the received power grid state data acquired by the sensor network module.
The 5G NB-IoT gateway module is connected with the main control chip through the GPIO, and is used for carrying out 5G protocol encapsulation on the power grid state data processed by the CPU data to obtain first power grid state data, and transmitting the first power grid state data to the 5G base station.
The internet of things module collects power grid state data through the sensor network module, and the CPU is used for completing analog-to-digital conversion, edge intelligent identification, multidimensional data fusion, power grid state prediction and local decision of the power grid state data, controlling the 5G NB-IoT gateway module to complete 5G protocol encapsulation and accessing the 5G base station.
The system forms a data transmission channel through the 5G edge gateway, the Internet of things module, the central cloud server and the 5G public network, localized real-time intelligent diagnosis is realized based on a computing platform of the 5G edge gateway, intelligent operation and information interaction are realized, system constitution, response time and transmission bandwidth pressure are simplified, and terminal intelligent level and response time are improved. Cloud management and multipoint content distribution are carried out by utilizing a central cloud server, safety control and safety isolation of monitoring data are realized, a network and a data structure are automatically reconstructed through edge cloud cooperation and flexibility, the monitoring safety, reliability and operation efficiency of a power distribution system are improved, and intelligent efficient health management work of a distribution network is comprehensively supported.
Fig. 3 is a schematic flowchart of a method for monitoring a distribution network according to an embodiment of the present application, and the method for monitoring a distribution network is described in detail as follows:
step 101: the method comprises the steps of collecting original power grid monitoring video data of a distribution network environment through a video collecting node, identifying, compressing and encoding, converting protocol and packaging the original power grid monitoring video data through a front-end 5G edge gateway to obtain first power grid monitoring video data, and sending the first power grid monitoring video data to a 5G base station.
In some embodiments, the implementation of step 101 may include steps 1011 through 1012.
Step 1011: and acquiring an original power grid monitoring video through a 4K ultra-high definition monitoring camera module in the video acquisition node.
The data stream mode of the original power grid monitoring video data is a 4K low-delay ultra-high definition original code mode, the frame rate of the original power grid monitoring video data is 15-60 frames/second, and the original power grid monitoring video data format comprises a YUV/YUV2 format.
Optionally, the method for monitoring a distribution network may further include: the front-end 5G edge gateway receives a video acquisition request issued by a central cloud server, wherein the video acquisition request comprises a frame rate requirement of a video to be acquired. The front-end 5G edge gateway controls the video acquisition node to acquire original power grid monitoring video data meeting the frame rate requirement.
Step 1012: and identifying, compression encoding, protocol conversion and 5G encapsulation are carried out on the original power grid monitoring video data through a front-end 5G edge gateway, so that first power grid monitoring video data is obtained.
In some embodiments, the implementation of step 1012 may include steps 10121 through 10124.
Step 10121: and carrying out inter-frame motion recognition on the original power grid monitoring video data through a recognition module of the front-end 5G edge gateway.
And calculating the difference of adjacent frames to obtain a speed vector z= (u, v) of each pixel point between frames, wherein (u, v) is an offset, and the offset is calculated by the difference value between the pixel point position coordinates (x, y) of the previous frame and the current frame position coordinates (x+u, y+v).
V is the absolute value V=abs (u, V), V shresold is a preset threshold, the whole image area is continuously changed, whether V is smaller than V shresold is calculated, if V is smaller than V shresold, the speed vectors of all pixels are continuously changed, the offset is consistent, and no moving object is judged in the image; if V is more than or equal to V shresold, a moving object exists in the image, the relative motion of the object and the background exists in the image, the moving object exists in the image is judged, all pixels with V more than or equal to V shresold are used as velocity vectors formed by the moving object, and a moving object pixel block matrix is determined.
And carrying out video windowing on the moving target pixel block matrix, and continuously tracking the moving target pixel block matrix to realize inter-frame motion recognition.
Step 10122: and carrying out compression coding on the original power grid monitoring video data by a compression coding module of the front-end 5G edge gateway.
Each frame of image is divided into macro blocks, H.264/H.265 protocol compression coding is carried out on each macro block, high-speed real-time hardware compression is realized by utilizing GPU (graphics processing Unit) for macro block processing, and macro block framing is integrated to form a video code stream.
Step 10123: and carrying out protocol conversion on the identified and compression-coded original power grid monitoring video data through a protocol conversion module of the front-end 5G edge gateway.
Specifically, a MAC address-geographic information white list database of each front-end 5G edge gateway node is established, the MAC address of each node corresponds to a physical address, namely a geographic information number, in the data transmission process, a source MAC ID is used as a frame header overhead to participate in transmission, in the data correctness and security verification, the source ID is used as an identification and admission white list number, and if monitoring data alarm is abnormal, source tracing and early warning can be carried out through the source ID.
And packaging the monitoring video data into frames based on the source ID, wherein the specific structure of the node frame comprises the source ID and the destination ID of the front-end 5G edge gateway node, the source IP and the destination IP, the verification information, the monitoring video coding data and the like, and packaging the data into an RTMP protocol push flow format, namely a flv data format. The source ID is MAC address information of each node and is used for mapping geographic positions, white list admission and accurate safety verification, and the packaged first power grid monitoring video data can be used for realizing source tracing, admission and safety verification of the data.
Step 10124: and 5G packaging the identified, compression coded and protocol converted original power grid monitoring video data through a 5G module of the front-end 5G edge gateway to obtain first power grid monitoring video data.
And the 5G module packages the original power grid monitoring video data in the RTMP streaming media format into a 5G signal frame structure to obtain first power grid monitoring video data, and completes the streaming of the video stream to the central cloud server through the GStreamer pipeline frame.
Step 102: the method comprises the steps of collecting original power grid state data through an Internet of things module, carrying out data processing on the original power grid state data, and sending the first power grid state data obtained after processing to a 5G base station.
Specifically, the first power grid state data is obtained by carrying out aggregation, analog-to-digital conversion, intelligent edge identification, multi-dimensional data fusion, power grid state prediction and local decision on the original power grid state data through a CPU. And carrying out CRC (cyclic redundancy check), identity recognition, security management and access control on the first power grid state data through the CPU to form the first power grid state data in the MQTT data frame format. And the CPU controls the 5G NB-IoT gateway module to package the first power grid state data in the MQTT data frame format into a 5G data frame format, and sends the first power grid state data in the 5G data frame format to the 5G base station.
Optionally, the method for monitoring a distribution network may further include: and the Internet of things module is used for self-adaptively collecting power grid state data, and intelligent identification and early warning are carried out.
Specifically, the sensor network module adaptively collects power grid state data, and the CPU gathers the original power grid state data, performs analog-to-digital conversion, intelligent edge identification, multidimensional data fusion and power grid state prediction. And obtaining power grid state early warning results of combustion, water filling, biogas, power failure and the like, and carrying out local early warning on the power grid state.
Step 103: and sending the first power grid monitoring video data and the first power grid state data to the central cloud server through a streaming media channel of the base station.
And the front-end 5G edge gateway sends the first power grid monitoring video data to the central cloud server in a 5G signal frame format through a streaming media channel of the base station, and pushes the first power grid monitoring video data to the central cloud server. And the central cloud server stores the first power grid monitoring video and distributes the first power grid monitoring video to each back-end 5G edge gateway.
As shown in fig. 4, the central cloud server of the 5G video and internet of things distribution network monitoring system based on the above-mentioned edge cloud cooperation includes the following functional modules:
(1) And the video stream distribution service module is used for controlling the stream pulling application of the back-end 5G edge gateway through the central cloud server, establishing a video stream replication point and distributing the video stream to the admitted back-end 5G edge gateway.
(2) And the security management and control module manages the whitelist to control the pull flow application admission of the back-end 5G edge gateway through the central cloud server, and performs virtual security isolation on the multi-channel streaming media channels of the point-to-multipoint.
(3) And the GStreamer streaming media pipeline relay module receives and controls the front end 5G edge gateway to upload the monitoring video stream through the central cloud server, and distributes the monitoring video stream to the multipoint rear end 5G edge gateway, thereby realizing the functions of a relay and a distribution platform in a media network channel supported by the energy Internet.
Step 104: and carrying out protocol conversion and data processing on the first power grid monitoring video data and the first power grid state data sent by the central cloud server through the back-end 5G edge gateway, and superposing and synthesizing the processed power grid state data and the power grid monitoring video data.
In some embodiments, the implementation of step 104 may include steps 1041 to 1046.
Step 1041: and at least two rear-end 5G edge gateways are respectively connected with a 5G base station to complete network access, and a point-to-multipoint streaming media GStreamer streaming media multichannel pipeline and an Internet of things multichannel special pipeline are established by a central cloud server and at least two rear-end 5G edge gateways.
Step 1042: the back-end 5G edge gateway receives the first power grid state data and the first power grid monitoring video data from the central cloud server to the 5G base station in a 5G package mode, and finishes the conversion from the 5G protocol to the RTMP protocol to the power grid monitoring video data and the conversion from the 5G protocol to the MQTT protocol to the power grid state data.
Step 1043: and under the GStreamer pipeline framework, the rear end 5G edge gateway demodulates the power grid monitoring video data of the RTMP protocol through the data processing module to obtain H.264/H.265 video compression data.
Step 1044: the data processing module builds a high-performance H.264/H.265 decoding server, performs high-degree parallelization processing, realizes high-speed real-time hardware decompression, and obtains second power grid monitoring video data.
Step 1045: the data processing module subscribes/downloads the power grid state data of the MQTT protocol in real time, and divides the power grid state data into multi-dimensional sensing network monitoring data and intelligent early warning data to obtain second power grid state data.
Step 1046: and superposing the power grid state data and the intelligent early warning data with the decompressed original code video stream in real time in a video windowing mode to complete visual composition of the video stream data.
Step 105: and based on the second power grid state data and the second power grid monitoring video data which are overlapped and synthesized, carrying out interface display and safety management on the distribution network monitoring video data and the environment state data.
And the monitoring center receives the second power grid state data and the second power grid monitoring video data which are overlapped and synthesized, and performs interface display, early warning and tracing.
Specifically, a hierarchical early warning model is established according to the second power grid state data and the second power grid monitoring video data after superposition and synthesis, and early warning evaluation grades and processing suggestions of abnormal monitoring data are preset. When abnormal monitoring data are found, scene tracking is carried out, and tracing and alarming of power grid faults or abnormal conditions are completed through the MAC ID address-geographic information database.
The invention is based on a cloud distribution-based 5G ultra-high definition distribution network monitoring technology, solves the problems of light-weight video monitoring and Internet of things environment monitoring of typical scenes of transmission and transformation and distribution networks under the condition of a 5G communication network, and utilizes a 5G edge gateway, a central cloud server and a 5G network to build a video network streaming media frame through intelligent optimization processing, central distribution and transmission of monitoring data and video data, thereby realizing portable ultra-high definition grid edge cloud collaborative distribution network environment monitoring, and further enhancing the real-time, wide coverage, adaptability and intellectualization of full scene monitoring of a power grid.
The embodiment of the application also provides edge computer equipment, and the method for monitoring the 5G video and the Internet of things distribution network based on the edge cloud cooperation can be realized by the computer equipment.
The computer equipment uses NVUDIA Jetson Xavier NX as a base plate to build an edge computing GPU, carries MICRO HDMI, 24pin multifunctional pins, 4 USB3.0 interfaces, an audio interface, a fan interface, a gigabit network port, a 5V power interface and an M.2NVME PCIE hard disk, and is provided with a wifi module.
Specifically, the edge computing GPU adopts 384-core NVIDIA Volta TM GPU with 48Tensor Cores, the highest frequency can reach 1100MHz, and 16 paths of H.264 encoded 1080P video stream decoding can be performed. The CPU adopts 6-core NVIDIA CARMELV8.2 64-bit CPU, maximum frequency 2-core@1900MHz,4/6-core@1400MHz. The deep learning accelerator employs two NVDLA acceleration engines.
Wherein the memory 8GB 128-bit LPDDR4x@1866MHz, and the reading speed is 59.7GB/s.
The bus includes hardware, software, or both, coupling components of the computer device to each other. The bus includes, but is not limited to, at least one of: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), local Bus (Local Bus). Wherein I2C is used for the connection between the microcontroller and the peripheral, starts the bus to transfer data, and generates a clock to open the transfer.
In addition, in combination with the method for monitoring the 5G video and the internet of things distribution network based on the edge cloud cooperation in the above embodiment, the embodiment of the application can be realized by providing a computer readable storage medium. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement the method for monitoring the 5G video and the Internet of things distribution network based on the edge cloud cooperation in any one of the embodiments.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the method of the various embodiments of the present application.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In several embodiments provided by the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and are merely a logical functional division, and there may be other manners of dividing the apparatus in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (5)
1. A distribution network monitoring method, comprising:
The method comprises the steps that original power grid monitoring video data of a distribution network environment are collected through a video collection node, the original power grid monitoring video data are identified, compressed and coded, protocol converted and 5G packaged through a front-end 5G edge gateway, first power grid monitoring video data are obtained, and the first power grid monitoring video data are sent to a 5G base station;
Acquiring original power grid state data through an Internet of things module, performing data processing on the original power grid state data, and transmitting the first power grid state data obtained after processing to a 5G base station;
the first power grid monitoring video data and the first power grid state data are sent to a central cloud server through a streaming media channel of a 5G base station;
The protocol conversion and the data processing are carried out on the first power grid monitoring video data and the first power grid state data sent by the central cloud server through a back-end 5G edge gateway, and the second power grid state data after the data processing and the second power grid monitoring video data are overlapped and synthesized;
Based on the second power grid state data and the second power grid monitoring video data which are overlapped and synthesized, carrying out interface display and safety management on the distribution network monitoring video data and the environment state data;
the protocol conversion and the data processing are performed on the first power grid monitoring video data and the first power grid state data sent by the central cloud server through the back-end 5G edge gateway, and the second power grid state data after the data processing and the second power grid monitoring video data are overlapped and synthesized, including:
the at least two back-end 5G edge gateways are respectively connected with the 5G base station to complete network access, and the central cloud server and the at least two back-end 5G edge gateways establish a point-to-multipoint streaming media GStreamer streaming media multichannel pipeline;
receiving first power grid state data and first power grid monitoring video data from the central cloud server to the 5G base station in a 5G package mode through the rear-end 5G edge gateway, and completing conversion from a 5G protocol to an RTMP protocol to the power grid monitoring video data and conversion from the 5G protocol to an MQTT protocol to the power grid state data;
under the GSstreamer streaming media multichannel pipeline framework, the rear end 5G edge gateway demodulates the power grid monitoring video data of the RTMP protocol through a data processing module to obtain H.264/H.265 video compression data;
The data processing module builds a high-performance H.264/H.265 decoding server, performs parallelization processing, realizes real-time hardware decompression, and obtains second power grid monitoring video data;
The data processing module subscribes or downloads the power grid state data of the MQTT protocol in real time, and divides the power grid state data into multi-dimensional sensing network monitoring data and intelligent early warning data to obtain second power grid state data;
And the back-end 5G edge gateway overlaps the power grid state data and the intelligent early warning data with the decompressed original code video stream in real time in a video windowing mode to complete visual combination of video stream data.
2. The distribution network monitoring method according to claim 1, wherein the identifying, compression encoding, protocol conversion and 5G packaging the original grid monitoring video data through the front-end 5G edge gateway to obtain first grid monitoring video data includes:
performing inter-frame motion recognition on the original power grid monitoring video data through a recognition module of a front-end 5G edge gateway;
the original power grid monitoring video data is compressed and encoded through a compression and encoding module of a front-end 5G edge gateway;
The protocol conversion module of the front-end 5G edge gateway is used for carrying out protocol conversion on the identified and compression-coded original power grid monitoring video data;
and 5G packaging the identified, compression coded and protocol converted original power grid monitoring video data through a 5G module of the front-end 5G edge gateway to obtain first power grid monitoring video data.
3. The distribution network monitoring method as claimed in claim 2, wherein the identifying module for identifying the inter-frame motion of the original power grid monitoring video data by using the front-end 5G edge gateway comprises:
Calculating adjacent frame difference to obtain a speed vector z= (u, v) of each pixel point between frames, wherein (u, v) is an offset, and the offset is calculated by the difference value between the pixel point position coordinates (x, y) of the previous frame and the current frame position coordinates (x+u, y+v);
V is the absolute value v=abs (u, V), V shresold is a preset threshold, when the velocity vectors of all pixels continuously change and the offset is consistent, calculating whether V is smaller than V shresold, if V is smaller than V shresold, judging that there is no moving object in the image; if V is more than or equal to V shresold, judging that a moving target exists in the image, taking all pixels of which V is more than or equal to V shresold as a speed vector formed by the moving target, and determining a pixel block matrix of the moving target;
and carrying out video windowing on the moving target pixel block matrix, and continuously tracking the moving target pixel block matrix to realize inter-frame motion recognition.
4. The distribution network monitoring method according to claim 1, wherein the collecting, by the sensor network module, the raw power grid status data includes:
Acquiring environmental temperature data of a distribution network environment through a temperature sensor node;
acquiring environmental humidity and irrigation data of a distribution network environment through a humidity sensor node;
collecting environmental fire points and methane data of a distribution network environment through a smoke sensor node;
Collecting power running state data of a distribution network environment through a power terminal sensor node; the power operation state data includes voltage data, current data, and over-current data.
5. The method for monitoring a distribution network according to claim 1, wherein the collecting, by the internet of things module, original power grid state data, performing data processing on the original power grid state data, and sending the first power grid state data obtained after the processing to a 5G base station, includes:
The method comprises the steps of carrying out aggregation, analog-to-digital conversion, intelligent edge identification, multidimensional data fusion, power grid state prediction and local decision on original power grid state data through a CPU to obtain first power grid state data;
performing CRC (cyclic redundancy check), identity recognition, security management and access control on the first power grid state data through a CPU (Central processing Unit), and forming the first power grid state data in an MQTT data frame format;
and controlling a 5G NB-IoT gateway module through a CPU to encapsulate the first power grid state data in the MQTT data frame format into a 5G data frame format, and sending the first power grid state data in the 5G data frame format to a 5G base station.
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| CN115665590B (en) * | 2022-10-21 | 2023-06-20 | 北京中电飞华通信有限公司 | Internet of things data acquisition system and method based on eSIM card and 5G communication |
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