CN113904995A - Power grid Ka high-flux satellite network flow limiting method and device - Google Patents
Power grid Ka high-flux satellite network flow limiting method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for limiting network flow of a power grid Ka high-flux satellite, which are based on a software architecture of a management platform and a Ka high-flux satellite integrated portable station, realize flow saving in the network transmission process, adopt a Ka high-flux satellite network management technology, monitor the flow uploaded by an intelligent portable antenna, control the flow information, the data type and the emergency degree of the monitored flow, set corresponding flow threshold values under different network environments, and realize optimal cost configuration.
Description
Technical Field
The invention relates to a software development technology of a network management system in the technical field of computers, and also relates to a communication technology related to geographical position information, in particular to a method and a device for limiting the network flow of a power grid Ka high-flux satellite, a readable storage medium and a computer control system.
Background
In the field of power systems, because of the wide distribution and partial remoteness of power service networks, and the fact that ground public network communication networks and private network communication networks cannot completely meet real-time communication requirements, a Ka high-throughput satellite communication network needs to be introduced for supplementary services. The ground public network communication network is mainly a 3G, 4G or 5G wireless communication network. The private network communication network mainly comprises power system carrier communication, 1.4G or 1.8G wireless private network communication and a traditional Ku frequency band satellite communication private network.
Although the 4G/5G wireless public network is better covered in urban areas, a coverage blind area or poor coverage phenomenon usually exists in application scenes such as mountains and mountains, sea-crossing power transmission lines and the like; the construction of a 1.4G or 1.8G wireless private network communication system is still in a primary stage, and the overall coverage rate is not good enough; the traditional Ku frequency band satellite communication system has the inherent characteristics of large equipment volume, high weight, large power consumption, poor portability and expensive communication bandwidth resources.
Therefore, the application of the communication network in the power system is limited no matter the ground public network communication network or the ground private network communication network. In a typical satellite network access scene, an intelligent portable antenna of a satellite network is connected with a plurality of devices through a router, data collected by the devices are reported to a management platform through the intelligent portable antenna, and data analysis and monitoring are carried out through the management platform. However, there are some disadvantages in the existing satellite network access scenario: the construction of the power station is complex in geographic condition, when a Ka high-flux satellite network is accessed for data transmission, the intelligent portable antenna feeds the joint debugging result back to the management platform, a large amount of data reported by a plurality of devices can be obtained, and a large amount of flow is consumed by a camera or a flow consumption APP; when the anti-external-damage camera and the sensor for assisting the production of the power system are installed on the iron tower, the data transmission of the anti-external-damage camera and the sensing system is difficult on the iron tower which is not covered by public and private networks, and a large amount of flow consumption is caused during video return; the existing unmanned aerial vehicle data cannot provide stable high bandwidth and low cost guarantee when being transmitted back to a command center, and the problem of traffic excess transmission exists when the unmanned aerial vehicle transmits back videos or images through a Ka high-flux satellite network.
Disclosure of Invention
In order to solve the problems in the background art, a method for limiting network flow of a power grid Ka high-flux satellite is provided, when a high-flux satellite integrated portable station reports data, flow uploaded by an intelligent portable antenna is monitored, flow control is carried out based on monitored flow information, data types and emergency degrees, and cost optimal configuration is achieved.
The invention relates to a method for limiting the network flow of a power grid Ka high-flux satellite, which comprises the following steps:
s1, acquiring the MAC address of the power equipment connected with the router in the intelligent portable antenna;
s2, setting a flow threshold value of data uploading in a first unit time length according to the network environment of the MAC address, monitoring through a router that if the flow of the data uploading of the MAC address in the first unit time length exceeds the threshold value, reducing sampling frequency and performing current-limiting uploading on the data of the power equipment corresponding to the MAC address;
s3, acquiring data uploaded by the power equipment through the intelligent portable antenna and integrating the data into a Ka high-flux satellite network management platform;
s4, monitoring the state information of the data uploaded by the electric power equipment through the management platform, and identifying the emergency degree of the state information;
and S5, setting a flow limiting strategy for step-by-step response according to the emergency degree of the state information.
The invention is based on the software architecture of the management platform and the Ka high-flux satellite integrated portable station, realizes the flow saving in the network transmission process, adopts the Ka high-flux satellite network management technology, monitors the flow uploaded by the intelligent portable antenna, controls the flow of the monitored flow information, data types and emergency degree, sets the corresponding flow threshold value under different network environments, and realizes the optimal cost configuration.
The Ka high-flux satellite is a novel communication satellite adopting a Ka frequency band. The Ka frequency band has the characteristics of wide usable bandwidth, less interference and small equipment volume. Therefore, the Ka band broadband satellite communication system can provide a new means for new services such as high-speed satellite communication, gigabit broadband digital transmission, High Definition Television (HDTV), satellite news collection (SNG), VSAT service, direct-to-home (DTH) service, and personal satellite communication.
Specifically, the intelligent portable antenna is connected with a plurality of electric power devices through a router in a satellite network, reports data acquired by the electric power devices to a management platform, and performs data analysis and monitoring through the management platform.
Further, the network environment comprises a 4G network and a 5G network, and when the power equipment is in an application scene common to the 4G network and the 5G network, the power equipment is preferentially switched to the 5G network for data reporting; when the 4G network environment is switched, synchronously switching the flow threshold of data uploading in the first unit time length; the traffic threshold is set to 1.5 times in a 4G network environment in a 5G network environment.
Further, the data uploaded by the power equipment comprise images, videos and sensing data, the management platform judges state information of the monitored content according to the images, the videos and the sensing data, and the state information comprises artificially damaged content and naturally damaged content.
Further, the management platform monitors state information of the data uploaded by the electric power equipment, and the step of identifying the emergency degree of the state information comprises the following steps: acquiring influence factors of historical natural damage contents, including wind, rain, thunder, fire and flood, through data uploaded by the power equipment;
establishing a neural network model, inputting the quantized values of the influencing factors into an input layer m0 of the neural network, wherein the middle layer of the neural network is m1, m2 and … … mK < -1 >, the output layer is mK, and the output vector is response grade and comprises primary response, secondary response and tertiary response; k-th layer output Y of the network(k)Comprises the following steps:
net(k)＝W(k)Y(k-1)+b(k)
wherein, b(k)Is a bias vector of the k-th layer, w(k)Is a weight matrix of the k-th layer, Y(k-1)For the k-1 th layer output, f(k)Is the k-th layer activation function, W is the weight matrix, i is the intermediate parameter of the vector dimension, k is the number of layers, j is the intermediate parameter of the vector dimension, T represents the meaning of the vector transposition, mkIs the number of nodes of the k-th layer, Wi,j (k)Is the weight matrix factor of the ith input vector to the jth output vector of the kth layer,
Yj (k-1)is the j-th dimension of the output vector of layer k-1, bi (k)Is the i dimension, net, of the bias vector of the k layeri (k)The ith dimension, net, of the input to the k-th (hidden) layer neuron(k)Each element in the set of elements represents a weighted sum of the input layer vector and the bias vector;
obtaining node input values and node output values of each layer in the neural network model according to layer-by-layer calculation; reversely deriving layer by layer through a network updating algorithm of error back propagation, and updating the weight of the neural network model to obtain the neural network model of the state information response level; and carrying out current limiting control of corresponding response levels on the real-time Ka high-flux satellite network flow according to the trained neural network.
Further, the step of setting a step-by-step responsive flow restriction policy according to the urgency of the status information includes: for the first-level response, when the equipment is jointly tuned, the upper limit flow of one-time joint tuning is 1G, in a video return network, the upper limit flow of a single camera per month is 5G, the upper limit flow of a single sensor per month is 0.2G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 2G;
for secondary response, when the equipment is jointly tuned, the upper limit flow of primary joint tuning is 2G, in a video return network, the upper limit flow of a single camera per month is 7G, the upper limit flow of a single sensor per month is 0.3G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 3G;
for three-level response, when the equipment is jointly tuned, the upper limit flow of one-time joint tuning is 3G, in a video return network, the upper limit flow of a single camera per month is 9G, the upper limit flow of a single sensor per month is 0.4G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 4G.
Further, the step of reducing the sampling frequency to perform current-limiting uploading on the data of the power equipment corresponding to the MAC address includes: when the flow rate of data uploading of the MAC address in the first unit time length exceeds a set threshold value, the sampling frequency of data transmission of the power equipment is reduced according to the upper limit flow rate, and the sampling frequency comprises the steps of reducing the single sampling time length, reducing the fineness of single sampling data, reducing the image definition and the accurate bit number of sensor data.
The invention also provides a device for limiting the flow of the power grid Ka high-flux satellite network, which comprises:
means for obtaining a MAC address of a power device connected to a router in a smart portable antenna;
the device is used for setting a flow threshold value of data uploading in first unit time according to the network environment of the MAC address, monitoring that if the flow of the data uploading of the MAC address in the first unit time exceeds the threshold value through a router, reducing sampling frequency and carrying out current-limiting uploading on the data of the electric power equipment corresponding to the MAC address;
the device is used for acquiring data uploaded by the power equipment through the intelligent portable antenna and integrating the data into a Ka high-flux satellite network management platform;
the management platform is used for monitoring state information of data uploaded by the electric power equipment and identifying the emergency degree of the state information;
and the device is used for setting a flow limiting strategy for step-by-step response according to the emergency degree of the state information.
Further, the present invention provides a readable storage medium having a control program stored thereon, characterized in that: when executed by a processor, the control program implements the grid Ka high-throughput satellite network flow limiting method as described in any one of the above.
Further, the present invention provides a computer control system, including a storage, a processor, and a control program stored in the storage and executable by the processor, wherein: when the processor executes the control program, the method for limiting the network flow of the power grid Ka high-flux satellite is realized.
In order that the invention may be more clearly understood, specific embodiments thereof will be described hereinafter with reference to the accompanying drawings.
Drawings
Fig. 1 is a flow chart of a method for limiting the network flow of a power grid Ka high-throughput satellite according to an embodiment of the present invention.
Fig. 2 is a high throughput satellite network networking architecture diagram according to an embodiment of the invention.
FIG. 3 is a diagram of a management platform software architecture according to an embodiment of the present invention.
Fig. 4 is a flow chart of the Ka high-throughput satellite integrated portable station traffic management according to the embodiment of the present invention.
Detailed Description
Please refer to fig. 1, which is a flowchart illustrating a method for restricting a network traffic of a power grid Ka high-throughput satellite according to an embodiment of the present invention.
The invention relates to a method for limiting the network flow of a power grid Ka high-flux satellite, which comprises the following steps:
s1, acquiring the MAC address of the power equipment connected with the router in the intelligent portable antenna;
s2, setting a flow threshold value of data uploading in a first unit time length according to the network environment of the MAC address, monitoring through a router that if the flow of the data uploading of the MAC address in the first unit time length exceeds the threshold value, reducing sampling frequency and performing current-limiting uploading on the data of the power equipment corresponding to the MAC address;
s3, acquiring data uploaded by the power equipment through the intelligent portable antenna and integrating the data into a Ka high-flux satellite network management platform;
s4, monitoring the state information of the data uploaded by the electric power equipment through the management platform, and identifying the emergency degree of the state information;
and S5, setting a flow limiting strategy for step-by-step response according to the emergency degree of the state information.
The invention is based on the software architecture of the management platform and the Ka high-flux satellite integrated portable station, realizes the flow saving in the network transmission process, adopts the Ka high-flux satellite network management technology, monitors the flow uploaded by the intelligent portable antenna, controls the flow of the monitored flow information, data types and emergency degree, sets the corresponding flow threshold value under different network environments, and realizes the optimal cost configuration.
Fig. 2 is a high throughput satellite network networking architecture diagram according to an embodiment of the invention.
The management platform of the embodiment is responsible for centralized management and control of network access, equipment and user service equipment of a plurality of Ka high-throughput satellite integrated portable stations. Each different user needs to configure an independent server to deploy an independent information center. The Ka high-flux satellite integrated portable station is connected with a command and scheduling information center of a power grid emergency command center to complete the functions of front satellite network access and command communication. The Ka high-flux satellite communication network provides services such as high-flux satellite communication, safe access, channel management and the like for the high-flux satellite integrated portable station in the practical application scene of the power system; the management platform realizes a satellite network management system and can remotely monitor, manage and maintain network elements in a network. The management platform and the Ka high-flux satellite integrated portable station are connected through a high-flux satellite communication network, and fig. 2 is a networking architecture under a newly-built power station application scene.
The intelligent portable antenna is connected with the plurality of electric power equipment through the router in the satellite network, reports the data acquired by the electric power equipment to the management platform, and performs data analysis and monitoring through the management platform. In the management platform, all equipment is managed through a unified interface, and differences of different equipment operation interfaces are invisible to a user; the management platform equipment system of each industry has specificity, such as a public security industry integrated geographic information, Beidou positioning, cluster scheduling, testimony verification and administrative office system, an electric power industry integrated unmanned aerial vehicle, an individual soldier graph transmission and video conference system, and a shipping industry integrated communication-in-motion antenna, a video monitoring and broadband internet access system; the management platform supports remote operation, integrates a geographic information platform and a video platform, and has the advantages of adjustable images, portrayal tracks and accurate vehicle and ship positioning. Fig. 3 is a diagram of the management platform software architecture according to an embodiment of the present invention.
The embodiment monitors the traffic uploading quantity of the electric power equipment connected with the router through the router arranged in the intelligent portable antenna. And when the quantity of the uploaded traffic in a specified time exceeds a preset traffic threshold, prohibiting the traffic uploading of the equipment (identifying the equipment through the MAC address), or prohibiting the traffic uploading in the specified time, or sampling and uploading according to a specified sampling frequency. Wherein the unit of the uploading quantity of the traffic is bytes.
After the intelligent portable antenna uploads the data to the management platform, the management platform monitors and analyzes the uploaded data, including image, video and sensing data information, determines state information of a scene of a data source, determines a data reporting strategy according to the emergency degree of the state information, and adopts different sampling frequencies to perform sampling reporting or prohibits the reporting within a specified time length. The emergency degree of the state information can be used for monitoring the response level of unknown state information in real time by inputting natural influence factors through training a neural network model, and the upper limit flow is adjusted according to different response levels.
Fig. 4 is a flow chart of the Ka high-throughput satellite integrated portable station traffic management according to the embodiment of the present invention.
Step1.1 monitoring the flow uploading quantity of the power equipment connected with the intelligent portable antenna through a router on the intelligent portable antenna;
step1.2 judging whether the current flow uploading quantity exceeds a flow threshold value set under the network environment, if not, continuing monitoring, and if so, carrying out the next step;
step1.3 analyzing the state information of the application scene of the power equipment, inputting the acquired field image into a neural network, identifying the emergency degree of the field by the neural network, judging whether the response level of the flow limit corresponds to the current flow threshold value, if not, adjusting the flow threshold value according to the response level, and if so, carrying out the next step;
step1.4 judging whether the current network environment is switched, if not, adjusting the flow threshold value according to the response level, and if so, limiting the flow according to the strategy corresponding to the switched network environment.
In the embodiment, the flow control of the power equipment access network with different MAC addresses is carried out through the high-throughput satellite integrated portable station, the information center of the satellite network management platform is responsible for analyzing the emergency degree of the current scene according to the uploaded data and issuing a network control strategy according to the emergency degree, and the router in the intelligent portable antenna carries out the network flow control according to the network control strategy.
The network environment comprises a 4G network and a 5G network, and when the power equipment is in an application scene common to the 4G network and the 5G network, the power equipment is preferentially switched to the 5G network for data reporting; when the 4G network environment is switched, synchronously switching the flow threshold of data uploading in the first unit time length; the traffic threshold is set to 1.5 times in a 4G network environment in a 5G network environment. And when the intelligent portable antenna is switched to different networks for data reporting, determining different network current limiting strategies according to the currently used network scene.
The data uploaded by the power equipment comprise images, videos and sensing data, the management platform judges state information of monitoring contents according to the images, the videos and the sensing data, and the state information comprises artificial destruction contents and natural destruction contents.
The degree of urgency may determine the impact of different factors on the degree of urgency based on the type of scene being monitored. In the electric power external damage prevention monitoring image, if an external damage image is monitored, the emergency degree of a scene is determined according to the emergency degree of the monitored image content. The emergency degree is determined according to an actual scene, when the power equipment is monitored, the monitored image content comprises artificial damage content and natural damage content, the neural network can train the behavior characteristics of artificial damage, identify the possibility that different behaviors are corresponding to possible damage behaviors, also can train the natural damage characteristics, and identify the emergency degree corresponding to the current image content according to the historical data training result.
In the embodiment, the natural damage content is used as an input vector of a neural network, and the influence factors of the historical natural damage content, including wind, rain, thunder, fire and flood, are acquired through data uploaded by the power equipment;
establishing a neural network model, inputting the quantized values of the influencing factors into an input layer m0 of the neural network, wherein the middle layer of the neural network is m1, m2 and … … mK < -1 >, the output layer is mK, and the output vector is response grade and comprises primary response, secondary response and tertiary response; k-th layer output Y of the network(k)Comprises the following steps:
net(k)＝W(k)Y(k-1)+b(k)
wherein, b(k)Is a bias vector of the k-th layer, w(k)Is a weight matrix of the k-th layer, Y(k-1)For the k-1 th layer output, f(k)Is the k-th layer activation function, W is the weight matrix, i is the intermediate parameter of the vector dimension, k is the number of layers, j is the intermediate parameter of the vector dimension, T represents the meaning of the vector transposition, mkIs the number of nodes of the k-th layer, Wi,j (k)Is the ith input vector of the k layerThe weight matrix factor of the jth output vector,
Yj (k-1)is the j-th dimension of the output vector of layer k-1, bi (k)Is the i dimension, net, of the bias vector of the k layeri (k)The ith dimension, net, of the input to the k-th (hidden) layer neuron(k)Each element in the set of elements represents a weighted sum of the input layer vector and the bias vector;
obtaining node input values and node output values of each layer in the neural network model according to layer-by-layer calculation; reversely deriving layer by layer through a network updating algorithm of error back propagation, and updating the weight of the neural network model to obtain the neural network model of the state information response level; and carrying out current limiting control of corresponding response levels on the real-time Ka high-flux satellite network flow according to the trained neural network.
In this embodiment, quantized values of natural influencing factors are input, wind, rain, thunder, fire and flood are divided into three types, namely high, medium and low, and the quantized values correspond to 3, 2 and 1 respectively; in other alternative embodiments, the image captured at the scene may be input into a neural network, and the network may identify the emergency level of the scene. Through the differentiated processing of the events with different emergency degrees, the content transmission amount of the non-emergency events is reduced, and the transmission efficiency is improved. The traffic transmission strategy of this embodiment tears n-level responses to correspond one-to-one to n-level urgency. Different levels of urgency correspond to different traffic allocations.
For the first-level response, when the equipment is jointly tuned, the upper limit flow of one-time joint tuning is 1G, in a video return network, the upper limit flow of a single camera per month is 5G, the upper limit flow of a single sensor per month is 0.2G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 2G;
for secondary response, when the equipment is jointly tuned, the upper limit flow of primary joint tuning is 2G, in a video return network, the upper limit flow of a single camera per month is 7G, the upper limit flow of a single sensor per month is 0.3G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 3G;
for three-level response, when the equipment is jointly tuned, the upper limit flow of one-time joint tuning is 3G, in a video return network, the upper limit flow of a single camera per month is 9G, the upper limit flow of a single sensor per month is 0.4G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 4G.
When the flow rate of data uploading of the MAC address in the first unit time length exceeds a set threshold value, the sampling frequency of data transmission of the power equipment is reduced according to the upper limit flow rate, and the sampling frequency comprises the steps of reducing the single sampling time length, reducing the fineness of single sampling data, reducing the image definition and the accurate bit number of sensor data. And adjusting the acquisition frequency according to the distributed flow. When the emergency degree is increased, more flow is distributed, and more comprehensive data is collected.
In this embodiment, for the case of switching network scenes, the flows before and after switching may be counted separately, or total flow may be counted according to a predetermined time period, and whether to trigger flow limitation is determined according to the total flow within the predetermined time period. When the flow quantity is sampled and screened, the sampling frequency can be dynamically updated according to the field information.
In the embodiment, the Ka high-throughput satellite network traffic limitation is performed through monitoring strategies of three layers: based on network environment restrictions, based on traffic threshold restrictions, and based on urgency restrictions. Different flow thresholds are preset under different network environments, once the flow thresholds are exceeded, state information of the application scene is monitored, the emergency degree and the corresponding response level are identified, the flow thresholds are correspondingly adjusted, meanwhile, the independent flow transmission quantity of each power device is monitored, and if the preset flow is exceeded, a data flow-limiting uploading strategy of a set sampling frequency is carried out on the power device according to the mac address.
Compared with the prior art, the software architecture based on the management platform and the Ka high-flux satellite integrated portable station realize the flow saving in the network transmission process, adopt the Ka high-flux satellite network management technology, monitor the flow uploaded by the intelligent portable antenna, control the flow of the monitored flow information, data types and emergency degree, set the corresponding flow threshold value under different network environments, and realize the optimal cost configuration. Different application scenarios, with different flow control policies: when the flow of the router in the first unit time length exceeds the set flow threshold corresponding to the network scene, starting a current limiting measure, recording the MAC address of the power equipment exceeding the flow threshold, and closing the flow transmission of the power equipment corresponding to the MAC address in the preset time length. When the power equipment reports data to the management platform through the intelligent portable antenna, the management platform identifies and analyzes the reported data, identifies the emergency degree of a scene corresponding to the data (pictures and videos), inputs the quantitative value of a natural destruction factor through neural network training, outputs the emergency degree of the application scene, obtains a corresponding response level, and adjusts the upper limit of the flow corresponding to the MAC address. And determining a reported data sampling screening mechanism according to the adjusted flow threshold, for example, sampling and reporting according to a specified sampling frequency, and increasing the sampling frequency or increasing the data volume uploaded at a single time, such as increasing the image definition and increasing the single monitoring time, if the upper limit flow is increased under the condition that the data volume uploaded at a single time is not changed.
The present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are included in the scope of the claims and the equivalent technology of the present invention if they do not depart from the spirit and scope of the present invention.
Claims (10)
1. A power grid Ka high-flux satellite network flow limiting method comprises the following steps:
acquiring an MAC address of power equipment connected with a router in an intelligent portable antenna;
setting a flow threshold value of data uploading in a first unit time length according to a network environment where the MAC address is located, monitoring through a router that if the flow of the data uploading of the MAC address in the first unit time length exceeds the threshold value, reducing sampling frequency and performing current-limiting uploading on data of the electric power equipment corresponding to the MAC address;
acquiring data uploaded by the power equipment through an intelligent portable antenna and integrating the data into a Ka high-flux satellite network management platform;
monitoring state information of data uploaded by the electric power equipment through the management platform, and identifying the emergency degree of the state information;
and setting a flow limiting strategy for step-by-step response according to the emergency degree of the state information.
2. The method for limiting the flux of the Ka high-flux satellite network of the power grid according to claim 1, wherein the method comprises the following steps: the intelligent portable antenna is connected with the plurality of electric power equipment through the router in the satellite network, reports the data acquired by the electric power equipment to the management platform, and performs data analysis and monitoring through the management platform.
3. The method for limiting the flux of the Ka high-flux satellite network of the power grid according to claim 1, wherein the method comprises the following steps: the network environment comprises a 4G network and a 5G network, and when the power equipment is in an application scene common to the 4G network and the 5G network, the power equipment is preferentially switched to the 5G network for data reporting; when the 4G network environment is switched, synchronously switching the flow threshold of data uploading in the first unit time length; the traffic threshold is set to 1.5 times in a 4G network environment in a 5G network environment.
4. The grid Ka high-throughput satellite network flow limiting method as claimed in claim 1, wherein the data uploaded by the power equipment comprises images, videos and sensing data, the management platform determines status information of the monitored content according to the images, videos and sensing data, and the status information comprises artificially damaged content and naturally damaged content.
5. The grid Ka high-throughput satellite network flow limiting method of claim 4, wherein the management platform monitors state information of the data uploaded by the electric power equipment, and the step of identifying the urgency of the state information comprises: acquiring influence factors of historical natural damage contents, including wind, rain, thunder, fire and flood, through data uploaded by the power equipment;
establishing a neural network model, inputting the quantized values of the influencing factors into an input layer m0 of the neural network, wherein the middle layer of the neural network is m1, m2 and … … mK < -1 >, the output layer is mK, and the output vector is response grade and comprises primary response, secondary response and tertiary response; k-th layer output Y of the network(k)Comprises the following steps:
net(k)＝W(k)Y(k-1)+b(k)
wherein, b(k)Is a bias vector of the k-th layer, w(k)Is a weight matrix of the k-th layer, Y(k-1)For the k-1 th layer output, f(k)Is the k-th layer activation function, W is the weight matrix, i is the intermediate parameter of the vector dimension, k is the number of layers, j is the intermediate parameter of the vector dimension, T represents the meaning of the vector transposition, mkIs the number of nodes of the k-th layer, Wi,j (k)Is the weight matrix factor of the ith input vector to the jth output vector of the kth layer,
Yj (k-1)is the j-th dimension of the output vector of layer k-1, bi (k)Is the i dimension, net, of the bias vector of the k layeri (k)The ith dimension, net, of the input to the k-th (hidden) layer neuron(k)Each element in the set of elements represents a weighted sum of the input layer vector and the bias vector;
obtaining node input values and node output values of each layer in the neural network model according to layer-by-layer calculation; reversely deriving layer by layer through a network updating algorithm of error back propagation, and updating the weight of the neural network model to obtain the neural network model of the state information response level; and carrying out current limiting control of corresponding response levels on the real-time Ka high-flux satellite network flow according to the trained neural network.
6. The method as claimed in claim 1, wherein the step of setting a step-by-step responsive flow limitation strategy according to the urgency of the status information comprises: for the first-level response, when the equipment is jointly tuned, the upper limit flow of one-time joint tuning is 1G, in a video return network, the upper limit flow of a single camera per month is 5G, the upper limit flow of a single sensor per month is 0.2G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 2G;
for secondary response, when the equipment is jointly tuned, the upper limit flow of primary joint tuning is 2G, in a video return network, the upper limit flow of a single camera per month is 7G, the upper limit flow of a single sensor per month is 0.3G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 3G;
for three-level response, when the equipment is jointly tuned, the upper limit flow of one-time joint tuning is 3G, in a video return network, the upper limit flow of a single camera per month is 9G, the upper limit flow of a single sensor per month is 0.4G, and when the unmanned aerial vehicle carries out video return, the upper limit flow of a single camera is 4G.
7. The method as claimed in claim 1, wherein the step of reducing the sampling frequency to perform current-limiting uploading on the data of the power device corresponding to the MAC address comprises: when the flow rate of data uploading of the MAC address in the first unit time length exceeds a set threshold value, the sampling frequency of data transmission of the power equipment is reduced according to the upper limit flow rate, and the sampling frequency comprises the steps of reducing the single sampling time length, reducing the fineness of single sampling data, reducing the image definition and the accurate bit number of sensor data.
8. A power grid Ka high-flux satellite network flow limiting device comprises:
means for obtaining a MAC address of a power device connected to a router in a smart portable antenna;
the device is used for setting a flow threshold value of data uploading in first unit time according to the network environment of the MAC address, monitoring that if the flow of the data uploading of the MAC address in the first unit time exceeds the threshold value through a router, reducing sampling frequency and carrying out current-limiting uploading on the data of the electric power equipment corresponding to the MAC address;
the device is used for acquiring data uploaded by the power equipment through the intelligent portable antenna and integrating the data into a Ka high-flux satellite network management platform;
the management platform is used for monitoring state information of data uploaded by the electric power equipment and identifying the emergency degree of the state information;
and the device is used for setting a flow limiting strategy for step-by-step response according to the emergency degree of the state information.
9. A readable storage medium having a control program stored thereon, characterized in that: the control program is executed by a processor to realize the power grid Ka high-throughput satellite network flow limiting method according to any one of claims 1 to 7.
10. A computer control system comprising a memory, a processor, and a control program stored in said memory and executable by said processor, characterized in that: the processor executes the control program to realize the power grid Ka high-throughput satellite network flow limiting method according to any one of claims 1 to 7.
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