CN114051005A

CN114051005A - Network flow control method, electronic device and storage medium

Info

Publication number: CN114051005A
Application number: CN202210040214.6A
Authority: CN
Inventors: 夏全军; 姚宁坡; 李廷军
Original assignee: Sprixin Technology Co ltd
Current assignee: Sprixin Technology Co ltd
Priority date: 2022-01-14
Filing date: 2022-01-14
Publication date: 2022-02-15

Abstract

The invention provides a network flow control method, electronic equipment and a storage medium, wherein the method comprises the following steps: carrying out flow monitoring on a cloud platform in the new energy system to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field; determining each target service based on the current service condition of the cloud platform, and determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service; and carrying out network flow control on the cloud platform based on the control decision of the cloud platform. According to the method, the electronic equipment and the storage medium provided by the invention, the cloud platform is subjected to network flow monitoring, and intelligent decisions are made and executed in time according to the service importance degree of each target service and the flow monitoring data, so that the network utilization rate of the cloud platform can be maximized, the network utilization rate is improved, and the electric power data can accurately, completely and timely reach a new energy electric field.

Description

Network flow control method, electronic device and storage medium

Technical Field

The invention relates to the technical field of new energy electric fields, in particular to a network flow control method, electronic equipment and a storage medium.

Background

With the rapid development of new energy technology, clean green energy is widely applied, and particularly for pollution-free clean green energy such as wind power or photovoltaic energy, a corresponding new energy electric field is continuously connected to a power grid. However, due to the inherent fluctuation, intermittency and random mutation factors of wind energy and light energy, the incorporation of a new energy electric field with a large installed capacity into a power grid can adversely affect the quality of electric energy and the stability of a power system. Therefore, in order to ensure the quality of electric energy and the safety and stability of the whole power system, the power grid puts higher requirements on the new energy electric field to obtain power data such as power prediction data, power generation amount prediction data and power transaction data.

At present, electric power data required by the new energy electric field are obtained by processing of each corresponding machine room device and transmitted to the device to which the new energy electric field belongs through each machine room device, and the accuracy of the electric power data is related to whether the electric power data can accurately, completely and timely reach the new energy electric field or not besides the advantages and disadvantages of processing algorithms of each machine room device.

Therefore, how to ensure that the power data can accurately, completely and timely reach the new energy electric field is an important issue to be solved in the industry at present.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a network flow control method, electronic equipment and a storage medium, which are used for ensuring that power data can accurately, completely and timely reach a new energy electric field.

The invention provides a network flow control method, which comprises the following steps:

carrying out flow monitoring on a cloud platform in the new energy system to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field;

determining each target service based on the current service condition of the cloud platform, and determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service;

and carrying out network flow control on the cloud platform based on the control decision of the cloud platform.

According to the network flow control method provided by the invention, the determining of the control decision of the cloud platform based on the first flow monitoring data and the service importance degree of each target service comprises the following steps:

analyzing the service condition of the first traffic monitoring data to obtain service traffic data of each target service;

and determining a control decision of the cloud platform based on the first traffic monitoring data, the service importance degree of each target service and the service traffic data of each target service.

According to the network flow control method provided by the present invention, the determining a control decision of the cloud platform based on the first flow monitoring data, the service importance of each target service, and the service flow data of each target service includes:

determining a network control mode of the cloud platform based on the first traffic monitoring data;

determining a service control sequence of the cloud platform based on the service importance degree of each target service and the service flow data of each target service;

and determining a control decision of the cloud platform based on the network control mode and the service control sequence.

According to the network flow control method provided by the invention, the determining the network control mode of the cloud platform based on the first flow monitoring data comprises the following steps:

determining whether the cloud platform adjusts the number of network connections based on the first traffic monitoring data; and/or the presence of a gas in the gas,

determining whether the cloud platform closes the service based on the first traffic monitoring data; and/or the presence of a gas in the gas,

determining whether the cloud platform opens a service based on the first traffic monitoring data; and/or the presence of a gas in the gas,

determining whether the cloud platform adjusts a data transceiving frequency based on the first traffic monitoring data.

According to the network flow control method provided by the invention, the service importance degree of any target service is determined based on the following steps:

and carrying out weighted calculation to obtain the service importance degree of any target service based on the importance situation and/or the emergency degree and/or the influence range and/or the economic influence degree and/or the remaining time and/or the available thread number of any target service.

According to the network flow control method provided by the invention, the first flow monitoring data comprises at least one of network connection number, uplink rate, downlink rate, network bandwidth use condition, data sending frequency and data receiving frequency.

According to the network flow control method provided by the invention, the new energy system further comprises each electric power data uploading end, each electric power data uploading end is used for uploading each electric power data to the cloud platform, and the cloud platform is further used for receiving the electric power data uploaded by each electric power data uploading end;

each electric power data uploading end comprises: the system comprises a power prediction data uploading end, a power generation amount prediction data uploading end and an electric power transaction data uploading end;

the network flow control of any power data uploading end comprises the following steps:

carrying out flow monitoring on any one electric power data uploading end to obtain second flow monitoring data;

determining a control decision of any power data uploading end based on the second flow monitoring data;

and carrying out network flow control on any electric power data uploading end based on the control decision of any electric power data uploading end.

According to the network flow control method provided by the invention, the power prediction data uploading terminal is used for receiving meteorological data of a meteorological downloading terminal and carrying out power prediction based on the meteorological data of the meteorological downloading terminal to obtain power prediction data;

the power prediction data uploading end is further used for uploading the power prediction data to the cloud platform;

the network flow control of the weather download terminal comprises the following steps:

carrying out flow monitoring on the meteorological download terminal to obtain third flow monitoring data;

determining each meteorological data being downloaded by the meteorological download end, and determining a control decision of the meteorological download end based on the third flow monitoring data and the meteorological importance degree of each meteorological data;

and carrying out network flow control on the weather downloading end based on the control decision of the weather downloading end.

The present invention also provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the network traffic control method according to any one of the above methods when executing the program.

The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the network traffic control method as described in any of the above.

According to the network flow control method, the electronic equipment and the storage medium, the cloud platform in the new energy system is subjected to flow monitoring to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field; then, each target service is determined based on the current service condition of the cloud platform, a control decision of the cloud platform is determined based on the first flow monitoring data and the service importance degree of each target service, network flow control is carried out on the cloud platform based on the control decision of the cloud platform, based on the control decision, network flow monitoring is carried out on the cloud platform, intelligent decision is timely made and executed according to the service importance degree of each target service and the flow monitoring data, the network utilization rate of the cloud platform can be maximized, the network utilization rate is improved, and therefore power data can accurately, completely and timely reach a new energy electric field.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a network traffic control method according to the present invention;

fig. 2 is a second schematic flow chart of the network traffic control method according to the present invention;

fig. 3 is a third schematic flow chart of a network traffic control method according to the present invention;

fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In recent years, with the rapid development of new energy technology, clean green energy is widely applied, and particularly for pollution-free clean green energy such as wind power or photovoltaic energy, a corresponding new energy electric field is continuously connected to a power grid and is more widely applied. However, due to the inherent fluctuation, intermittency and random mutation factors of wind energy and light energy, the incorporation of a new energy electric field with a large installed capacity into a power grid can adversely affect the quality of electric energy and the stability of a power system. Therefore, in order to ensure the electric energy quality and the safety and stability of the whole power system, the electric network increasingly makes a strict examination on a new energy electric field (new energy station), and the examination range is increasingly wide.

At present, electric power data required by the new energy electric field are obtained by processing of each corresponding machine room device and transmitted to the device to which the new energy electric field belongs through each machine room device, and the accuracy of the electric power data is related to whether the electric power data can accurately, completely and timely reach the new energy electric field or not besides the advantages and disadvantages of processing algorithms of each machine room device. Specifically, the prior art has the following problems:

(1) because each machine room can purchase a certain bandwidth in consideration of the economic efficiency and the service needs of the machine rooms, the uplink and downlink bandwidths of one machine room can be problematic, and the problem of the machine room can affect another machine room interacting with the machine room.

(2) Due to the special service, a large amount of data may need to be uploaded in a short time, and the main server and the standby server are both simultaneously uploaded, so that the bandwidth is easily caused to reach the upper limit in a certain time period, and further the network packet loss rate is serious, and therefore, the electric power data cannot completely reach the new energy electric field.

(3) Due to the requirement of the national standard on new energy assessment, power data such as power prediction data, generated energy prediction data and power transaction data need to be uploaded in a short time and completed, and therefore the power data need to reach a new energy electric field in time.

(4) Due to interaction among services such as power prediction, power generation amount prediction, generation of electric power transaction data and the like, once a network has a problem, all the services are affected, and therefore the electric power data need to accurately, completely and timely reach a new energy electric field.

(5) Due to the economic consideration, the probability of problems of each service needs to be reduced under the limited bandwidth, and therefore, the electric power data needs to accurately, completely and timely reach a new energy electric field under the limited bandwidth.

In summary, how to ensure that the power data can accurately, completely and timely reach the new energy electric field is an important issue to be solved in the industry at present.

Based on the above problems, the present invention provides a network traffic control method. Fig. 1 is a schematic flow diagram of a network traffic control method provided by the present invention, as shown in fig. 1, the method includes:

and 110, carrying out flow monitoring on a cloud platform in the new energy system to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field.

Here, the new energy system is a new energy intelligent service platform, which is used for generating electric power data required by the new energy electric field and transmitting the electric power data to the new energy electric field. The new energy system comprises a plurality of devices, the devices are located in different hosting rooms, and the different rooms need to be communicated with each other through a network, for example, an Ethernet.

The new energy system includes but is not limited to: the system comprises a cloud platform, a power prediction data uploading end, a power generation amount prediction data uploading end, an electric power transaction data uploading end, a meteorological downloading end, a station downloading end and the like.

Here, the cloud platform (cloud server) is configured to receive the uploaded power data, store the power data, and send the power data to the new energy farm. The cloud platform may be an ari cloud, an tengcong cloud, or the like, which is not limited in the embodiments of the present invention.

In an embodiment, the cloud platform includes a primary server and a standby server, that is, the cloud platform is divided into the primary server and the standby server. Further, the cloud platform comprises a main server and two standby servers. For example, two different rooms distributed in beijing in airy cloud and one room distributed in shanghai are adopted as the cloud platform.

It can be understood that the power data required by the new energy electric field are provided with services by the cloud platform, that is, the power data are received, stored and issued by the cloud platform, so that the stability of each service of the new energy electric field can be ensured, and the particularity and the overall economy of each service are considered.

It should be noted that the power prediction data uploading end, the power generation amount prediction data uploading end, the electric power transaction data uploading end and the weather downloading end are located in different hosting rooms. The station downloading end is located in each new energy electric field, namely the station downloading end is deployed in each new energy electric field.

In addition, it should be noted that the weather downloading end is used for downloading weather data and transmitting the weather data to the power prediction data uploading end; the power prediction data uploading terminal is used for carrying out power prediction according to the meteorological data to obtain power prediction data and uploading the power prediction data to the cloud platform; the power generation amount prediction data uploading end is used for downloading power prediction data in the cloud platform, performing power generation amount calculation based on the power prediction data to obtain power generation amount prediction data, and uploading the power generation amount prediction data to the cloud platform; the electric power transaction data uploading end is used for downloading power prediction data in the cloud platform, generating electric power transaction data based on the power prediction data and uploading the electric power transaction data to the cloud platform; the station downloading end is used for downloading power data such as power prediction data, generated energy prediction data and power transaction data from the cloud platform to the new energy electric field.

Here, the first flow monitoring data includes, but is not limited to: the number of network connections (the number of thread connections), the uplink rate (uplink traffic per unit time), the downlink rate (downlink traffic per unit time), the network bandwidth usage, the data transmission frequency (data transmission rate), the data reception frequency (data reception rate), and so on. The network connection number comprises an uplink network connection number and a downlink network connection number.

Specifically, network connection condition monitoring is carried out on the cloud platform to obtain the number of network connections; monitoring the uplink condition of the cloud platform to obtain an uplink rate; monitoring the downlink condition of the cloud platform to obtain a downlink rate; monitoring the bandwidth use condition of the cloud platform to obtain the network bandwidth use condition; monitoring data sending conditions of the cloud platform to obtain data sending frequency; and monitoring the data receiving condition of the cloud platform to obtain the data receiving frequency.

Here, the new energy farm (new energy station) may be a wind power farm, a photovoltaic farm, or the like. Because wind energy and light energy are influenced by weather conditions, the accuracy of power prediction data of a wind power electric field or a photovoltaic electric field is influenced by meteorological data, and based on the power prediction data, a corresponding power prediction uploading end needs to perform power prediction according to the meteorological data to obtain power prediction data.

Here, the power data required for the new energy farm may include, but is not limited to: power forecast data, power generation forecast data, power trading data, and the like.

Step 120, determining each target service based on the current service condition of the cloud platform, and determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service.

The cloud platform can simultaneously carry out various services, and based on the services, each target service required to be executed currently is determined based on the current service condition of the cloud platform. The target services may include, but are not limited to: power prediction business, power generation capacity prediction business, power transaction data generation business and the like.

Here, the service importance level of each target service is used to indicate the importance level of each target service, the service importance level may be represented by a numerical value, and a larger numerical value indicates that the corresponding target service is more important. On this basis, data transmission of the more important target service is preferentially ensured.

Specifically, whether network flow control is performed on the cloud platform is determined based on the first flow monitoring data, and if the network flow control is required to be performed on the cloud platform, a network control mode of the cloud platform is determined based on the first flow monitoring data; or, determining a network control mode of the cloud platform directly based on the first traffic monitoring data; then, based on the service importance degree of each target service, determining whether the network flow corresponding to each target service is adjusted, and determining the service control sequence of each target service, thereby preferentially ensuring the data transmission of the important target service; and finally, determining a control decision of the cloud platform based on a network control mode and a service control sequence.

The network control mode of the cloud platform includes but is not limited to one or more of the following: whether to adjust the number of network connections, whether to close the service, whether to open the service, whether to adjust the data transceiving frequency, and the like.

For example, whether the cloud platform adjusts the network connection number (thread number) is determined based on the first traffic monitoring data, and/or whether the cloud platform closes the service is determined, and/or whether the cloud platform opens the service is determined, and/or whether the cloud platform adjusts the data transceiving frequency is determined, and then, based on the service importance degree of each target service, the target service requiring network connection number adjustment is determined, and/or the target service requiring closing is determined, and/or the service requiring opening is determined, and/or the target service requiring data transmission frequency adjustment is determined, and/or the target service requiring data reception frequency adjustment is determined.

And step 130, performing network flow control on the cloud platform based on the control decision of the cloud platform.

Here, the control decision of the cloud platform includes a network control mode for performing network flow control on the cloud platform, a target service corresponding to each network control mode, and a service control sequence of each target service to be controlled.

According to the network flow control method provided by the embodiment of the invention, the cloud platform in the new energy system is subjected to flow monitoring to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field; then, each target service is determined based on the current service condition of the cloud platform, a control decision of the cloud platform is determined based on the first flow monitoring data and the service importance degree of each target service, network flow control is carried out on the cloud platform based on the control decision of the cloud platform, based on the control decision, network flow monitoring is carried out on the cloud platform, intelligent decision is timely made and executed according to the service importance degree of each target service and the flow monitoring data, the network utilization rate of the cloud platform can be maximized, the network utilization rate is improved, and therefore power data can accurately, completely and timely reach a new energy electric field.

Based on the foregoing embodiment, fig. 2 is a second schematic flow chart of the network traffic control method provided in the present invention, as shown in fig. 2, in the method, in step 120, determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service includes:

step 121, analyzing the service condition of the first traffic monitoring data to obtain the service traffic data of each target service.

Here, the traffic data includes an uplink rate and/or a downlink rate and/or a number of network connections (number of threads) and/or a network bandwidth usage and/or a data transmission frequency and/or a data reception frequency.

In addition, after the service traffic data of each target service is obtained, the percentage of the uplink rate of each target service in the uplink rate of the first traffic monitoring data and/or the percentage of the downlink rate of each target service in the downlink rate of the first traffic monitoring data may be obtained. Namely, the overall uplink rate of the cloud platform occupied by each target service is analyzed, and/or the overall downlink rate of the cloud platform occupied by each target service is analyzed.

And step 122, determining a control decision of the cloud platform based on the first traffic monitoring data, the service importance degree of each target service and the service traffic data of each target service.

Here, the service traffic data of each target service is used to indicate a data transmission rate of each target service, and further, the data transmission rate is compared with a preset transmission rate threshold, based on the comparison result, whether the network traffic corresponding to each target service is adjusted or not is determined, and a service control sequence of each target service is determined. That is, the uplink rate of any target service is compared with a preset uplink rate threshold, and/or the downlink rate of any target service is compared with a preset downlink rate threshold. The preset uplink rate threshold is smaller than the maximum uplink rate of the cloud platform, and the preset downlink rate threshold is smaller than the minimum downlink rate of the cloud platform; the specific values of the preset uplink rate threshold and the preset downlink rate threshold are set according to actual requirements, which is not limited in the present invention.

Specifically, whether network flow control is performed on the cloud platform is determined based on the first flow monitoring data, and if the network flow control is required to be performed on the cloud platform, a network control mode of the cloud platform is determined based on the first flow monitoring data; or, determining a network control mode of the cloud platform directly based on the first traffic monitoring data; then, based on the service importance degree of each target service and the service flow data of each target service, determining whether the network flow corresponding to each target service is adjusted, and determining the service control sequence of each target service, thereby preferentially ensuring the data transmission of the important target service and ensuring the safety of the data transmission; and finally, determining a control decision of the cloud platform based on a network control mode and a service control sequence.

The network flow control method provided by the embodiment of the invention analyzes the service condition of the first flow monitoring data to obtain the service flow data of each target service, wherein the service flow data comprises an uplink rate and/or a downlink rate; and determining a control decision of the cloud platform based on the first flow monitoring data, the service importance degree of each target service and the service flow data of each target service. By the mode, intelligent decision making and execution are timely carried out according to the service importance degree of each target service and the first flow monitoring data, so that the network utilization rate of the cloud platform can be maximized, and the network utilization rate is improved, so that the electric power data can accurately, completely and timely reach a new energy electric field; meanwhile, an intelligent decision is made based on the service flow data of each target service, so that the electric power data can be transmitted accurately and completely, and the electric power data can reach a new energy electric field accurately, completely and timely.

Based on the foregoing embodiment, fig. 3 is a third schematic flow chart of a network traffic control method provided by the present invention, as shown in fig. 3, in the method, the step 122 includes:

step 1221, determining a network control mode of the cloud platform based on the first traffic monitoring data.

Here, the network control manner of the cloud platform includes, but is not limited to, one or more of the following: whether to adjust the number of network connections, whether to close the service, whether to open the service, whether to adjust the data transceiving frequency, and the like.

Specifically, the step 1221 includes:

Here, whether to adjust the number of network connections includes whether to adjust the number of uplink network connections or the number of downlink network connections. Whether to adjust the data transceiving frequency includes whether to adjust the data transmitting frequency and the data receiving frequency.

In a specific embodiment, comparing the uplink rate in the first traffic monitoring data with a preset uplink increase connection threshold, and if the uplink rate in the first traffic monitoring data is less than the preset uplink increase connection threshold, determining that the cloud platform needs to increase the number of uplink network connections; and comparing the downlink rate in the first traffic monitoring data with a preset downlink increasing connection threshold, and determining that the cloud platform needs to increase the number of downlink network connections if the downlink rate in the first traffic monitoring data is less than the preset downlink increasing connection threshold. The preset uplink increasing connection threshold is smaller than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be the purchased uplink network bandwidth, the preset downlink increasing connection threshold is smaller than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be the purchased downlink network bandwidth, and the preset uplink increasing connection threshold and the preset downlink increasing connection threshold may be set according to actual needs.

In another specific embodiment, the uplink rate in the first traffic monitoring data is compared with a preset uplink reduction connection threshold, and if the uplink rate in the first traffic monitoring data is greater than the preset uplink reduction connection threshold, it is determined that the cloud platform needs to reduce the number of uplink network connections; and comparing the downlink rate in the first traffic monitoring data with a preset downlink reduction connection threshold, and determining that the cloud platform needs to reduce the number of downlink network connections if the downlink rate in the first traffic monitoring data is greater than the preset downlink reduction connection threshold. The preset uplink reduced connection threshold is smaller than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be the purchased uplink network bandwidth, the preset downlink reduced connection threshold is smaller than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be the purchased downlink network bandwidth, and the preset uplink reduced connection threshold and the preset downlink reduced connection threshold may be set according to actual needs.

In another specific embodiment, the uplink rate in the first traffic monitoring data is compared with a preset uplink reduced sending frequency threshold, and if the uplink rate in the first traffic monitoring data is greater than the preset uplink reduced sending frequency threshold, it is determined that the cloud platform needs to reduce the data sending frequency; and comparing the downlink rate in the first traffic monitoring data with a preset downlink reduction receiving frequency threshold, and if the downlink rate in the first traffic monitoring data is greater than the preset downlink reduction receiving frequency threshold, determining that the cloud platform needs to reduce the data receiving frequency. The preset uplink reduced sending frequency threshold is smaller than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be the purchased uplink network bandwidth, the preset downlink reduced receiving frequency threshold is smaller than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be the purchased downlink network bandwidth, and the preset uplink reduced sending frequency threshold and the preset downlink reduced receiving frequency threshold may be set according to actual needs.

In another specific embodiment, the uplink rate in the first traffic monitoring data is compared with a preset uplink increasing sending frequency threshold, and if the uplink rate in the first traffic monitoring data is smaller than the preset uplink increasing sending frequency threshold, it is determined that the cloud platform needs to increase the data sending frequency; and comparing the downlink rate in the first traffic monitoring data with a preset downlink increasing receiving frequency threshold, and determining that the cloud platform needs to increase the data receiving frequency if the downlink rate in the first traffic monitoring data is less than the preset downlink increasing receiving frequency threshold. The preset uplink increasing sending frequency threshold is smaller than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be the purchased uplink network bandwidth, the preset downlink increasing receiving frequency threshold is smaller than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be the purchased downlink network bandwidth, and the preset uplink increasing sending frequency threshold and the preset downlink increasing receiving frequency threshold may be set according to actual needs.

In another specific embodiment, the uplink rate in the first traffic monitoring data is compared with a preset uplink closing service threshold, and if the uplink rate in the first traffic monitoring data is greater than the preset uplink closing service threshold, it is determined that the cloud platform needs to close the uplink service; and comparing the downlink rate in the first traffic monitoring data with a preset downlink closing service threshold, and determining that the cloud platform needs to close the downlink service if the downlink rate in the first traffic monitoring data is greater than the preset downlink closing service threshold. The preset uplink close service threshold is smaller than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be the purchased uplink network bandwidth, the preset downlink close service threshold is smaller than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be the purchased downlink network bandwidth, and the preset uplink close service threshold and the preset downlink close service threshold may be set according to actual needs, which is not specifically limited in the embodiment of the present invention.

In another specific embodiment, the uplink rate in the first traffic monitoring data is compared with a preset uplink open service threshold, and if the uplink rate in the first traffic monitoring data is smaller than the preset uplink open service threshold, it is determined that the cloud platform needs to open the uplink service; and comparing the downlink rate in the first traffic monitoring data with a preset downlink open service threshold, and determining that the cloud platform needs to open the downlink service if the downlink rate in the first traffic monitoring data is less than the preset downlink open service threshold. The preset uplink opening service threshold is smaller than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be the purchased uplink network bandwidth, the preset downlink opening service threshold is smaller than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be the purchased downlink network bandwidth, and the preset uplink opening service threshold and the preset downlink opening service threshold may be set according to actual needs, which is not specifically limited in the embodiment of the present invention.

In another embodiment, the network connection number in the first traffic monitoring data is compared with a preset increased network connection number threshold, and if the network connection number in the first traffic monitoring data is smaller than the preset increased network connection number threshold, it is determined that the cloud platform needs to increase the network connection number; and comparing the network connection number in the first traffic monitoring data with a preset reduced network connection number threshold, and if the network connection number in the first traffic monitoring data is greater than the preset reduced network connection number threshold, determining that the cloud platform needs to reduce the network connection number. The preset increased network connection number threshold value is smaller than the preset decreased network connection number threshold value, and the preset increased network connection number threshold value and the preset decreased network connection number threshold value may be set according to an actual situation, which is not specifically limited in the embodiment of the present invention.

In another specific embodiment, the network bandwidth usage in the first traffic monitoring data is compared with a preset increase threshold, and if the network bandwidth usage in the first traffic monitoring data is smaller than the preset increase threshold, it is determined that the cloud platform needs to increase the number of network connections and/or needs to open a service and/or needs to increase the data transceiving frequency; comparing the network bandwidth use condition in the first traffic monitoring data with a preset reduction threshold, and if the network bandwidth use condition in the first traffic monitoring data is greater than the preset reduction threshold, determining that the cloud platform needs to reduce the number of network connections and/or needs to close services and/or needs to reduce data transceiving frequency. The preset increase threshold is smaller than the preset decrease threshold, both the preset increase threshold and the preset decrease threshold are smaller than the maximum network bandwidth of the cloud platform, and the preset increase threshold and the preset decrease threshold may be set according to an actual situation, which is not specifically limited in the embodiment of the present invention.

In another specific embodiment, the data sending frequency in the first traffic monitoring data is compared with a preset increased data sending frequency threshold, and if the data sending frequency in the first traffic monitoring data is smaller than the preset increased data sending frequency threshold, it is determined that the cloud platform needs to increase the data sending frequency; and comparing the data sending frequency in the first flow monitoring data with a preset reduced data sending frequency threshold, and if the data sending frequency in the first flow monitoring data is greater than the preset reduced data sending frequency threshold, determining that the cloud platform needs to reduce the data sending frequency. The preset increased data sending frequency threshold is smaller than the preset decreased data sending frequency threshold, and the preset increased data sending frequency threshold and the preset decreased data sending frequency threshold may be set according to actual conditions, which is not specifically limited in the embodiment of the present invention.

In another embodiment, the data receiving frequency in the first traffic monitoring data is compared with a preset increased data receiving frequency threshold, and if the data receiving frequency in the first traffic monitoring data is smaller than the preset increased data receiving frequency threshold, it is determined that the cloud platform needs to increase the data receiving frequency; and comparing the data receiving frequency in the first flow monitoring data with a preset reduced data receiving frequency threshold, and if the data receiving frequency in the first flow monitoring data is greater than the preset reduced data receiving frequency threshold, determining that the cloud platform needs to reduce the data receiving frequency. The preset increased data receiving frequency threshold is smaller than the preset decreased data receiving frequency threshold, and the preset increased data receiving frequency threshold and the preset decreased data receiving frequency threshold may be set according to an actual situation, which is not specifically limited in the embodiment of the present invention.

Of course, the network control mode of the cloud platform may also be determined in other modes, which is not described herein any more.

Step 1222, determining a service control sequence of the cloud platform based on the service importance of each target service and the service traffic data of each target service.

And 1223, determining a control decision of the cloud platform based on the network control mode and the service control sequence.

Specifically, based on the service importance of each target service and the service traffic data of each target service, it is determined whether each target service needs to perform network traffic adjustment (i.e., it is determined whether the network control manner needs to be executed), and the service control order of each target service is determined, and further based on the two determination results, the service control order of the cloud platform is determined. For example, the service control sequence of each target service is service 3, service 2, service 4, and service 1, and service 2 does not need to perform network traffic adjustment, and the service control sequence of the cloud platform is service 3, service 4, and service 1.

Here, the service importance level of any target service is calculated by weighting based on the importance situation and/or the urgency level and/or the influence range and/or the economic influence level and/or the remaining time and/or the number of available threads (the number of available network connections) of any target service.

The importance of each target service may be preset, for example, the importance is obtained by assigning according to the service importance. The urgency level of each target service may be preset, for example, the urgency level may be obtained by assigning a value according to the service urgency level. The influence range of each target service may be preset, for example, the influence range is obtained by assigning values according to the service influence range. The economic impact degree of each target service can be preset, for example, the economic impact degree is obtained by assigning according to the economic impact degree of the service. The remaining time of each target service is obtained by performing a difference operation between the deadline of the corresponding target service and the current time, that is, the remaining time = deadline-current time, and the deadline may be set in advance according to an actual situation. The available thread number of each target service is obtained by performing difference operation based on the maximum thread number of the corresponding target service and the current thread number, that is, the available thread number = the maximum thread number — the current thread number, and the maximum thread number may be set in advance according to actual conditions.

At this time, the traffic importance of any target traffic = weight 1 × importance + weight 2 × urgency + weight 3 × influence range + weight 4 × economic influence + weight 5 × remaining time + weight 6 × available number of threads.

Then, the service control sequence of each target service is determined based on the following steps:

if the number of network connections needs to be increased, the service with larger service importance degree is preferentially increased;

if the number of network connections needs to be reduced, the service with smaller service importance degree is reduced preferentially;

if the service needs to be closed, the service with smaller service importance degree is closed preferentially;

if the service needs to be opened, the service with larger service importance degree is opened preferentially;

if the data sending frequency needs to be increased, the service with larger service importance degree is preferentially increased;

if the data sending frequency needs to be reduced, the service with smaller service importance degree is reduced preferentially;

if the data receiving frequency needs to be increased, the service with larger service importance degree is preferentially increased;

if the data reception frequency needs to be reduced, the traffic priority is reduced as the traffic importance degree is smaller.

In a specific embodiment, the uplink rate of a target service is compared with a preset uplink rate peak value, and if the uplink rate of the target service is greater than the preset uplink rate peak value, it is determined that the target service does not need to increase the number of uplink network connections and/or does not need to increase the data transmission frequency; and comparing the uplink rate of a target service with a preset uplink rate valley value, and if the uplink rate of the target service is less than the preset uplink rate valley value, determining that the target service does not need to reduce the number of uplink network connections and/or does not need to reduce the data transmission frequency. The preset uplink rate peak value is greater than the preset uplink rate valley value, both the preset uplink rate peak value and the preset uplink rate valley value are less than the maximum uplink rate of the cloud platform, the maximum uplink rate of the cloud platform may be a purchased uplink network bandwidth, and the preset uplink rate peak value and the preset uplink rate valley value may be set according to the uplink network bandwidth, which is not specifically limited in the embodiment of the present invention.

In another embodiment, the downlink rate of a target service is compared with a preset downlink rate peak value, and if the downlink rate of the target service is greater than the preset downlink rate peak value, it is determined that the target service does not need to increase the number of downlink network connections and/or does not need to increase the data receiving frequency; and comparing the downlink rate of a target service with a preset downlink rate valley value, and if the downlink rate of the target service is less than the preset downlink rate valley value, determining that the target service does not need to reduce the number of downlink network connections and/or does not need to reduce the data receiving frequency. The preset downlink rate peak value is greater than the preset downlink rate valley value, and both the preset downlink rate peak value and the preset downlink rate valley value are less than the maximum downlink rate of the cloud platform, the maximum downlink rate of the cloud platform may be a purchased downlink network bandwidth, and the preset downlink rate peak value and the preset downlink rate valley value may be set according to the downlink network bandwidth, which is not specifically limited in the embodiment of the present invention.

In another embodiment, the network connection number of a target service is compared with a preset network connection number peak value, and if the network connection number of the target service is greater than the preset network connection number peak value, the target service is determined not to need to increase the network connection number; and comparing the network connection number of a target service with the preset network connection number valley value, and if the network connection number of the target service is smaller than the preset network connection number valley value, determining that the network connection number of the target service does not need to be reduced. The preset network connection number peak value is greater than the preset network connection number valley value, both the preset network connection number peak value and the preset network connection number valley value are less than the maximum network connection number of the cloud platform, and the preset network connection number peak value and the preset network connection number valley value can be set according to actual needs.

In another specific embodiment, the network bandwidth usage of a target service is compared with a preset peak value of the network bandwidth usage, and if the network bandwidth usage of the target service is greater than the preset peak value of the network bandwidth usage, it is determined that the target service does not need to increase the number of network connections and/or does not need to increase the data transmission frequency and/or does not need to increase the data reception frequency; comparing the network bandwidth use condition of a target service with a preset network bandwidth use condition valley value, and if the network bandwidth use condition of the target service is smaller than the preset network bandwidth use condition valley value, determining that the target service does not need to reduce the number of network connections and/or does not need to reduce the data transmission frequency and/or does not need to reduce the data receiving frequency. The peak value of the preset network bandwidth usage is greater than the valley value of the preset network bandwidth usage, both the peak value of the preset network bandwidth usage and the valley value of the preset network bandwidth usage are less than the maximum network bandwidth of the cloud platform, and the peak value of the preset network bandwidth usage and the valley value of the preset network bandwidth usage can be set as required.

In another embodiment, the data transmission frequency of a target service is compared with the preset data transmission frequency peak value, and if the data transmission frequency of the target service is greater than the preset data transmission frequency peak value, it is determined that the target service does not need to increase the data transmission frequency; and comparing the data transmission frequency of a target service with a preset data transmission frequency valley value, and if the data transmission frequency of the target service is less than the preset data transmission frequency valley value, determining that the data transmission frequency of the target service does not need to be reduced. The preset data sending frequency peak value is greater than the preset data sending frequency valley value, both the preset data sending frequency peak value and the preset data sending frequency valley value are less than the maximum data sending frequency of the cloud platform, and the preset data sending frequency peak value and the preset data sending frequency valley value can be set according to actual needs.

In another embodiment, the data receiving frequency of a target service is compared with the preset data receiving frequency peak value, and if the data receiving frequency of the target service is greater than the preset data receiving frequency peak value, it is determined that the data receiving frequency of the target service does not need to be increased; and comparing the data receiving frequency of a target service with a preset data receiving frequency valley value, and if the data receiving frequency of the target service is less than the preset data receiving frequency valley value, determining that the data receiving frequency of the target service does not need to be reduced. The preset data receiving frequency peak value is greater than the preset data receiving frequency valley value, both the preset data receiving frequency peak value and the preset data receiving frequency valley value are less than the maximum data receiving frequency of the cloud platform, and the preset data receiving frequency peak value and the preset data receiving frequency valley value can be set according to actual needs.

In another specific embodiment, after obtaining the service traffic data of each target service, the percentage of the uplink rate of each target service in the uplink rate of the first traffic monitoring data may be obtained, and/or the percentage of the downlink rate of each target service in the downlink rate of the first traffic monitoring data may be obtained.

Based on this, comparing the uplink rate ratio of a target service with the preset uplink rate ratio peak value, and if the uplink rate ratio of the target service is greater than the preset uplink rate ratio peak value, determining that the target service does not need to increase the number of uplink network connections and/or does not need to increase the data transmission frequency; comparing the uplink rate ratio of a target service with a preset uplink rate ratio valley value, and if the uplink rate ratio of the target service is smaller than the preset uplink rate ratio valley value, determining that the target service does not need to reduce the number of uplink network connections and/or does not need to reduce the data transmission frequency. The preset uplink rate ratio peak value is greater than the preset uplink rate ratio valley value, both the preset uplink rate ratio peak value and the preset uplink rate ratio valley value are less than the maximum uplink rate ratio of the cloud platform, and the preset uplink rate ratio peak value and the preset uplink rate ratio valley value may be set according to actual needs.

Comparing the downlink rate ratio of a target service with a preset downlink rate ratio peak value, and if the downlink rate ratio of the target service is greater than the preset downlink rate ratio peak value, determining that the target service does not need to increase the number of downlink network connections and/or does not need to increase the data receiving frequency; and comparing the downlink rate ratio of a target service with a preset downlink rate ratio valley value, and if the downlink rate ratio of the target service is smaller than the preset downlink rate ratio valley value, determining that the target service does not need to reduce the number of downlink network connections and/or does not need to reduce the data receiving frequency. The preset downlink rate ratio peak value is greater than the preset downlink rate ratio valley value, both the preset downlink rate ratio peak value and the preset downlink rate ratio valley value are less than the maximum downlink rate ratio of the cloud platform, and the preset downlink rate ratio peak value and the preset downlink rate ratio valley value may be set according to actual needs.

Of course, it may also be determined whether each target service needs to perform network traffic adjustment in other manners, which is not described herein any more.

The network flow control method provided by the embodiment of the invention determines a network control mode of a cloud platform based on first flow monitoring data; determining a service control sequence of the cloud platform based on the service importance degree of each target service and the service flow data of each target service; and determining a control decision of the cloud platform based on a network control mode and a service control sequence. By the method, an intelligent decision is made according to the first flow monitoring data to obtain a network control mode of the cloud platform, and the control decision of the cloud platform can be determined more accurately, so that the electric power data can accurately, completely and timely reach a new energy electric field; meanwhile, an intelligent decision is made according to the service importance degree of each target service and the service flow data of each target service to obtain the service control sequence of the cloud platform, and the control decision of the cloud platform can be determined more accurately, so that the electric power data can accurately, completely and timely reach a new energy electric field.

Based on any of the above embodiments, in the method, the service importance of any target service is determined based on the following steps:

Here, the importance of each target service may be preset, for example, an importance is assigned according to the service importance. The urgency level of each target service may be preset, for example, the urgency level may be obtained by assigning a value according to the service urgency level. The influence range of each target service may be preset, for example, the influence range is obtained by assigning values according to the service influence range. The economic impact degree of each target service can be preset, for example, the economic impact degree is obtained by assigning according to the economic impact degree of the service. The remaining time of each target service is obtained by performing a difference operation between the deadline of the corresponding target service and the current time, that is, the remaining time = deadline-current time, and the deadline may be set in advance according to an actual situation. The available thread number of each target service is obtained by performing difference operation based on the maximum thread number of the corresponding target service and the current thread number, that is, the available thread number = the maximum thread number — the current thread number, and the maximum thread number may be set in advance according to actual conditions.

The network flow control method provided by the embodiment of the invention performs weighting calculation to obtain the service importance degree of any target service based on the importance condition and/or the emergency degree and/or the influence range and/or the economic influence degree and/or the remaining time and/or the available thread number of any target service. Based on the method, support is provided for determining the service importance degree of each target service, so that follow-up intelligent decisions can be made and executed in time according to the service importance degree and the flow monitoring data of each target service, the network utilization rate of the cloud platform can be maximized, the network utilization rate is improved, and therefore the electric power data can accurately, completely and timely reach a new energy electric field.

Based on any one of the embodiments, in the method, the new energy system further includes each power data uploading end, the each power data uploading end is used for uploading each power data to the cloud platform, and the cloud platform is further used for receiving the power data uploaded by each power data uploading end;

Here, each power data uploading end includes, but is not limited to, one or more of the following: the device comprises a power prediction data uploading end, a power generation amount prediction data uploading end, an electric power transaction data uploading end and the like.

Here, each power data includes, but is not limited to, one or more of the following: power forecast data, power generation forecast data, power trading data, and the like. Each of the power data may be embodied in a file, that is, the power data is stored in the file.

Specifically, the power prediction data uploading terminal is used for carrying out power prediction to obtain power prediction data and uploading the power prediction data to the cloud platform; the power generation amount prediction data uploading end is used for downloading power prediction data in the cloud platform, performing power generation amount calculation based on the power prediction data to obtain power generation amount prediction data, and uploading the power generation amount prediction data to the cloud platform; the electric power transaction data uploading end is used for downloading power prediction data in the cloud platform, generating electric power transaction data based on the power prediction data, and uploading the electric power transaction data to the cloud platform.

In one embodiment, the power prediction data uploading end uploads the power prediction data through the FTP. Furthermore, the power prediction data uploading end is divided into a main server and a standby server, so that normal generation and uploading of the power prediction data are ensured. The power prediction data may be embodied in a file, that is, the power prediction data is stored in the file.

In one embodiment, the electric power generation amount prediction data uploading end uploads the electric power generation amount prediction data through the FTP. Furthermore, the uploading end of the power generation amount prediction data is divided into a main server and a standby server, so that the normal generation and uploading of the power generation amount prediction data are ensured. The power generation amount prediction data may be embodied as a file, that is, the power generation amount prediction data is stored in the file.

In one embodiment, the electric power transaction data uploading terminal uploads the electric power transaction data through the FTP. Furthermore, the electric power transaction data uploading end is divided into a main server and a standby server, so that normal generation and uploading of the electric power transaction data are ensured. The power transaction data may be embodied in a file, that is, the power transaction data is stored in the file.

Here, the second traffic monitoring data includes, but is not limited to: the number of network connections (the number of thread connections), the uplink rate (uplink traffic per unit time), the downlink rate (downlink traffic per unit time), the network bandwidth usage, the data transmission frequency (data transmission rate), the data reception frequency (data reception rate), and so on. The network connection number comprises an uplink network connection number and a downlink network connection number.

Specifically, whether network flow control is performed on the electric power data uploading end is determined based on the second flow monitoring data, and if the network flow control is required to be performed on the electric power data uploading end, a network control mode of the electric power data uploading end is determined based on the second flow monitoring data; or, a network control mode of the electric power data uploading end is determined directly based on the second flow monitoring data, and finally, a control decision of the cloud platform is determined based on the network control mode.

The network control mode of the power data uploading end includes but is not limited to one or more of the following: whether to adjust the number of network connections, whether to adjust the data transceiving frequency, etc.

Here, the determination of the network control manner of the electric power data uploading end is substantially the same as the determination of the network control manner of the cloud platform, and a more specific execution process may be obtained with reference to the above embodiments, which is not described in detail herein.

According to the network flow control method provided by the embodiment of the invention, the network flow monitoring is carried out on each electric power data uploading end, and an intelligent decision is made and executed in time according to the flow monitoring data, so that the network utilization rate of each electric power data uploading end can be maximized, the network utilization rate is improved, and the electric power data can accurately, completely and timely reach a new energy electric field.

Based on any one of the embodiments, in the method, the power prediction data uploading terminal is used for receiving meteorological data of a meteorological downloading terminal and performing power prediction based on the meteorological data of the meteorological downloading terminal to obtain power prediction data;

Here, the weather downloading terminal is used for downloading weather data and transmitting the weather data to the power prediction data uploading terminal. Specifically, the weather downloading terminal downloads various weather data from the weather server, and the importance degree of each weather data is different.

In a specific embodiment, the weather downloading terminal downloads various weather data from the weather server through the FTP. Furthermore, the weather server is divided into a main weather server and a standby weather server, so that normal downloading of weather data is ensured. Namely, if the main weather server fails to download the weather data, the main weather server automatically transfers to the standby weather server to download the weather data. In addition, each new energy electric field needs to correspond to several kinds of meteorological data, and the meteorological data is considered to be successful as long as the meteorological data is downloaded successfully from one meteorological server.

The meteorological data can be embodied by meteorological files, namely the meteorological data is stored in the meteorological files.

Here, the third flow monitoring data includes, but is not limited to: the number of network connections (the number of thread connections), the uplink rate (uplink traffic per unit time), the downlink rate (downlink traffic per unit time), the network bandwidth usage, the data transmission frequency (data transmission rate), the data reception frequency (data reception rate), and so on. The network connection number comprises an uplink network connection number and a downlink network connection number.

Here, the weather importance level of each weather data is used to indicate the importance level of each weather data, the weather importance level may be represented by a numerical value, and a larger numerical value indicates that the corresponding weather data is more important. On this basis, the more important data transmission of the meteorological data is preferentially ensured.

Specifically, whether network flow control is carried out on the weather download terminal is determined based on the third flow monitoring data, and if the network flow control is required to be carried out on the weather download terminal, the network control mode of the weather download terminal is determined based on the third flow monitoring data; or, directly determining a network control mode of the weather download terminal based on the third flow monitoring data; then, determining whether the network flow corresponding to each meteorological data is adjusted or not and determining the meteorological control sequence of each meteorological data based on the meteorological importance degree of each meteorological data, thereby preferentially ensuring the data transmission of the important meteorological data; and finally, determining a control decision of the weather downloading end based on the network control mode and the weather control sequence.

The network control mode of the weather downloading terminal includes but is not limited to one or more of the following: whether to adjust the number of network connections, whether to adjust the data transceiving frequency, etc.

Here, the determination of the control decision of the weather download end is substantially the same as the determination of the control decision of the cloud platform, and a more specific execution process can be obtained with reference to the above embodiments, which is not described in detail herein.

According to the network flow control method provided by the embodiment of the invention, the network flow monitoring is carried out on the meteorological download terminal, and an intelligent decision is made and executed in time according to the flow monitoring data, so that the network utilization rate of the meteorological download terminal can be maximized, and the network utilization rate is improved, thereby ensuring that the meteorological data can accurately, completely and timely reach the power prediction upload terminal, and further ensuring that the electric power data can accurately, completely and timely reach a new energy electric field.

Based on any of the above embodiments, in the method, the power prediction data uploading end performs power prediction based on the following steps:

and if the meteorological download end can not download the meteorological data, the power prediction data uploading end carries out power prediction based on the alternate meteorological data to obtain the power prediction data.

Here, the replacement weather data may be weather data that was recently successfully downloaded, for example, weather data of the previous day.

Specifically, when the weather downloading end does not successfully download the weather data, the power prediction data uploading end uses the alternate weather data to perform power prediction to obtain power prediction data, so that power generation amount prediction data is obtained by performing power generation amount prediction subsequently, and the power trading decision is made to obtain power trading data.

According to the network flow control method provided by the embodiment of the invention, the power prediction data uploading end carries out power prediction based on the alternate meteorological data to obtain the power prediction data, so that when the meteorological data downloading end has problems, the power prediction can still be carried out, the generated energy prediction and the electric power transaction decision can be carried out, and the electric power data can be ensured to accurately, completely and timely reach a new energy electric field.

Based on any of the above embodiments, in the method, the power prediction data uploading end uploads data based on the following steps:

and if the power prediction data uploading end cannot generate the power prediction data, the power prediction data uploading end uploads the replacement power prediction data to the cloud platform.

Here, the substitute power prediction data may be power prediction data obtained by the latest power prediction, for example, power prediction data of the previous day.

Specifically, if the power prediction data uploading end does not generate the power prediction data, the substitute power prediction data is used for modifying and uploading, and the subsequent power generation prediction and power transaction decision can be calculated by using the substitute power prediction data.

According to the network flow control method provided by the embodiment of the invention, the power prediction data uploading end uploads the replacement power prediction data, so that when the power prediction of the power prediction data uploading end has a problem, the power prediction data can still be uploaded, the generated energy prediction and the electric power transaction decision can be carried out, and further, the electric power data can accurately, completely and timely reach a new energy electric field.

Based on any one of the above embodiments, in the method, each electric power data uploading end includes a main server and a standby server, and the cloud platform includes a main server and a standby server.

In a specific embodiment, the power prediction data uploading end is divided into a main server and a standby server, so that normal generation and uploading of the power prediction data are ensured.

In a specific embodiment, the uploading end of the electric energy generation amount prediction data is divided into a main server and a standby server, so that the normal generation and uploading of the electric energy generation amount prediction data are ensured.

In a specific embodiment, the electric power transaction data uploading end is divided into a main server and a standby server, so that normal generation and uploading of electric power transaction data are ensured.

In one embodiment, the cloud platform includes a primary server and two standby servers. For example, two different machine rooms distributed in beijing in airy cloud and one machine room distributed in shanghai are adopted as a cloud platform, so that normal uploading and issuing of electric power data are ensured.

According to the network flow control method provided by the embodiment of the invention, each electric power data uploading end and the cloud platform comprise the main server and the standby server, so that when a certain server goes wrong, the electric power data can still be transmitted, the safety of electric power data transmission is improved, and the electric power data can accurately, completely and timely reach a new energy electric field.

Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a network traffic control method comprising: carrying out flow monitoring on a cloud platform in the new energy system to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field; determining each target service based on the current service condition of the cloud platform, and determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service; and carrying out network flow control on the cloud platform based on the control decision of the cloud platform.

In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, a computer is capable of executing the network traffic control method provided by the above methods, and the method includes: carrying out flow monitoring on a cloud platform in the new energy system to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field; determining each target service based on the current service condition of the cloud platform, and determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service; and carrying out network flow control on the cloud platform based on the control decision of the cloud platform.

In yet another aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the network traffic control method provided by the above methods, the method including: carrying out flow monitoring on a cloud platform in the new energy system to obtain first flow monitoring data, wherein the cloud platform is used for storing electric power data required by the new energy electric field; determining each target service based on the current service condition of the cloud platform, and determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance degree of each target service; and carrying out network flow control on the cloud platform based on the control decision of the cloud platform.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling network traffic, comprising:

2. The method according to claim 1, wherein the determining a control decision of the cloud platform based on the first traffic monitoring data and the service importance of each target service includes:

3. The method according to claim 2, wherein the determining a control decision of the cloud platform based on the first traffic monitoring data, the service importance of each target service, and the service traffic data of each target service includes:

4. The method according to claim 3, wherein the determining the network control mode of the cloud platform based on the first traffic monitoring data includes:

5. The method of claim 1, wherein the service importance of any target service is determined based on the following steps:

6. The method according to any one of claims 1 to 5, wherein the first traffic monitoring data comprises at least one of a number of network connections, an uplink rate, a downlink rate, a network bandwidth usage, a data transmission frequency, and a data reception frequency.

7. The network flow control method according to claim 1, wherein the new energy system further includes each power data uploading terminal, the each power data uploading terminal is configured to upload each power data to the cloud platform, and the cloud platform is further configured to receive the power data uploaded by the each power data uploading terminal;

8. The network flow control method according to claim 7, wherein the power prediction data uploading terminal is configured to receive meteorological data of a meteorological downloading terminal, and perform power prediction based on the meteorological data of the meteorological downloading terminal to obtain power prediction data;

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the network traffic control method according to any of claims 1 to 8.

10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the network traffic control method according to any of claims 1 to 8.