KR102467254B1 - Apparatus and method for predicting internet speed between network switch device and subscriber terminal - Google Patents

Abstract

An apparatus and method for collecting information on each network switch device, learning it in a machine learning engine, and predicting and outputting the Internet speed of a network switch device and a subscriber terminal through a machine learning engine receiving network switch device information requiring prediction is initiated The device includes a collection unit for collecting data of environmental elements of the network switch device from network equipment; a pre-processing unit that normalizes and pre-processes the collected data for each element; A learning unit that receives preprocessed data for each element, learns the influence relationship between the data for each element, and predicts and outputs the Internet speed for the data of the input environmental factors of the network switch device after the learning is completed. ; and an input unit for inputting data of environmental factors of the network switch device to the learning unit for prediction.

Description

Apparatus and method for predicting internet speed between network switch device and subscriber terminal}

The present invention is a technology for predicting internet speed, and after receiving and learning information of a network switch device, a machine learning engine outputting information of a network switch device for which prediction is required is used to transmit data between a network switch device and a subscriber terminal at the Internet speed output. It relates to an apparatus and method for predicting the speed of

When a service quality complaint is filed by a communication service subscriber, the service quality problem is determined by comparing the speed measured at the subscriber terminal (eg, SLA) with the speed measured at the L2 network switch device. At the request of the subscriber, an engineer visits the subscriber's house to measure the service speed of the subscription product, but if it is determined to be normal, time and money are wasted.

Here, service quality may be degraded due to problems with environmental factors of L2 equipment, and it is difficult to accurately solve which factor among various environmental factors causes problems in service quality.

In addition, service quality may be degraded due to a problem of the subscriber terminal, in which case the subscriber does not acknowledge that there is a problem in the subscriber terminal and transfers the responsibility to the communication service provider. When a service quality complaint is raised by a malicious customer, it is inconvenient for the telecommunication company engineer to repeatedly respond to the customer, such as visiting the subscriber's house several times. Therefore, rather than simply informing that a subscriber station problem is suspected because there is no quality degradation in the speed of the L2 equipment, it is required to provide a service quality measurement result that there is no problem with the L2 equipment to make the subscriber understand.

When determining the service quality, the Internet speed of the subscriber terminal may be evaluated as the service quality based on the speed of the L2 lower port. Internet speed of the subscriber station is mainly affected by the amount of transmission and reception traffic of the L2 that the subscriber station accommodates, but other environmental influences cannot be ignored. This is because of various environmental factors such as the number of subscribers connected to L2, the number of L2s accommodated in the upper Passive Optical Network (PON) connected to L2, the optical level between L2 and OLT, and the UTP cable distance between L2 and subscriber terminals. This is because it has a great influence on the quality of service. In addition, there are various environmental factors that affect service quality and their interests, but it is impossible to figure out what effect other environmental factors cause except for L2 traffic.

Korea Patent Publication 2014-118100 (2014.10.08)

The present invention is to solve the above problems, to learn the data of the environmental factors of the network switch device to the machine learning engine to predict the Internet speed, and input the network switch device data for which prediction is required to the machine learning engine It is an object of the present invention to provide a device and method for outputting an Internet speed predicted between a network switch device and a subscriber terminal from a machine learning engine.

In addition, the device assigns a class of a network switch device model based on an Internet speed output through prediction of a machine learning engine, and provides a data criterion of an environmental element corresponding to each model class.

According to an aspect, an apparatus for estimating an Internet speed between a network switch device and a subscriber station includes a collection unit that collects data of environmental factors of the switch device that affect the Internet speed from network devices; a pre-processing unit that normalizes and pre-processes the collected data for each element; Receiving preprocessed data for each element, learning the influence relationship between the data for each element, and predicting and outputting the Internet speed for the data of the environmental factors of the switch device input after the learning is completed learning department; and an input unit for inputting environmental factor data of the switch device to the learning unit for the prediction.

The collecting unit collects data of the switch device related to Service Level Agreement (SLA) data measured in the subscriber station.

The collecting unit collects data from the network devices including a Network Management Server (NMS) and a control server.

The collecting unit collects data of the environmental factor including at least one of uplink traffic, the number of subscribers, and the number of switch devices accommodating a passive optical network (PON).

The components of the uplink traffic may include a daily average uplink traffic measurement value of the switch device; average traffic measurement value for a given period of time at the subscriber's premises; the number of times the switch device exceeds an uplink traffic threshold; the number of times the traffic threshold is exceeded between the switch device and the upper PON; distance between the switch device and the upper PON; a Cyclical Redundancy Check (CRC) value of an uplink port of the switch device; line distance between the ports in the subscriber premises in the switch device; and at least one element among elements of an uplink reception light level of the switch device.

The element of the number of subscribers includes the number of TV subscribers accommodated in the switch device; the number of Internet subscribers accommodated in the switch device; number of TV subscribers accommodated in the top PON; number of Internet subscribers accommodated in the top PON; the number of excess traffic subscribers using more than a predetermined daily capacity accommodated in the switch device; and at least one element among elements of the number of UHD set-top subscribers accommodated in the switch device.

The learning unit may include: learning an influence relationship in which the Internet speed slows down as the element data of uplink traffic increases; Learning an influence relationship in which the Internet speed slows down as the element data of the number of subscribers increases; and PON acceptance learning of an influence relation in which the Internet speed becomes slower as the element data of the number of switch devices increases.

a switch device model assigning unit that allocates model grades of the switch device according to the Internet speed output from the learning unit and calculates an average of data for each environmental factor of the model of the switch device in each grade; and a switch device model DB storing the calculated average for each environmental factor in each class.

For comparison with at least one higher level having faster internet speed than the level of the model of the switch device corresponding to the internet speed output from the learning unit and at least one lower level having slower internet speed than the level, each A switch device model comparison unit for providing data of each environmental factor for each grade is further included.

According to another aspect, a method for predicting an Internet speed between a network switch device and a subscriber station by a device includes collecting data of environmental factors of the switch device that affect the Internet speed from network devices; Normalizing and pre-processing the collected data for each element; receiving preprocessed data for each element and learning an influence relationship between the data for each element; After the learning is completed, receiving data of environmental factors of the switch device for prediction; and estimating and outputting an internet speed based on the learning with respect to the inputted data of the switch device.

According to one aspect of the present invention, when data of environmental factors of a new network switch device is input to the machine learning engine, the Internet speed predicted by the network switch device is output, so that the environmental factors of the new network switch device can be pre-designed. It is possible to measure the Internet speed of the subscriber terminal without visiting the subscriber's home, as the Internet speed is output from the machine learning engine by input of environmental factors of the existing network switch device.

In addition, according to another aspect of the present invention, in order to upgrade to a higher network switch device model or downgrade to a lower network switch device model in the network switch device model class corresponding to the Internet speed output from the machine learning engine, certain environmental factors can provide the need for improvement.

The following drawings attached to this specification illustrate preferred embodiments of the present invention, and together with the detailed description of the present invention serve to further understand the technical idea of the present invention, the present invention is the details described in such drawings should not be construed as limited to
1 is a schematic configuration diagram of a prediction device according to an embodiment of the present invention.
FIG. 2 is a block diagram of the collecting unit of FIG. 1 collecting environmental element data of a network switch device.
3 to 5 are examples of data processed by the prediction device of FIG. 1 .
6 is a configuration diagram in which L2 models assigned to Internet speeds are compared according to another embodiment of the present invention.
7 is an exemplary diagram of L2 model data compared by the L2 model comparison unit of FIG. 6 .
8 is a schematic flowchart of a prediction method according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, the terms or words used in this specification and claims should not be construed as being limited to ordinary or dictionary meanings, and the inventors use the concept of terms appropriately to describe their invention in the best way. It should be interpreted as a meaning and concept consistent with the technical idea of the present invention based on the principle that it can be defined. Therefore, the embodiments described in this specification and the configurations shown in the drawings are only one of the most preferred embodiments of the present invention and do not represent all of the technical spirit of the present invention. It should be understood that there may be equivalents and variations.

1 is a schematic configuration diagram of a prediction device 100 according to an embodiment of the present invention. FIG. 2 is a configuration diagram in which the collection unit 111 of FIG. 1 collects environmental element data of the network switch device. 3 to 5 are examples of data processed by the prediction device 100 of FIG. 1 . Hereinafter, it will be described with reference to FIGS. 1 to 5 .

Referring to FIG. 1 , the prediction device 100 according to an embodiment of the present invention includes a collection unit 111, a pre-processing unit 113, a learning unit 115, and an input unit 170.

The prediction device 100 is a computer terminal composed of a memory and a processor. Each component 111 to 170 may be loaded into the memory of the prediction device 100 in the form of a program and executed through a processor. For example, each component 111 to 170 is produced as a program for predicting Internet speed based on a machine learning engine, and then the program is executed by the processor of the predicting device 100 to obtain information on the predicted Internet speed. It can be displayed on the screen of the prediction device 100.

The collecting unit 111 collects data of environmental factors of the network switch device that affect internet speed from network devices. The internet speed is a communication speed between a network switch device and a subscriber station, and corresponds to the speed of the network switch device or the speed of the subscriber station. Environmental factors of the network switch device determine the internet speed. That is, the service quality of the Internet speed is determined according to which environmental factors the network switch device is built with.

The network switch device of the present invention corresponds to a switch device located in each network layer. Hereinafter, it is assumed that the network switch device is an L2 network layer switch device corresponding to a switch device for accommodating subscribers providing Internet services to Internet terminals of subscribers. to explain.

Referring to FIG. 2 , a collection unit 111 collects data of L2 environmental factors from the network equipment including a Network Management Server (NMS) 211 and a control server 213 . NMS 211 holds management data of network services involved in uplink and downlink communication of L2. The control server 213 holds control data of network equipment including SLA measurement data of the subscriber station 250 .

Here, the collection unit 111 collects service level agreement (SLA) data measured by the subscriber terminal 250 and related environmental factor data of the L2 from the control server 213 . For example, in order to measure service quality, the subscriber terminal 250 accesses a specific URL provided by a communication company for SLA measurement. At this time, the communication company measures the quality of the speed of the subscriber terminal 250 through a specific URL, and at the same time measures the environmental factor data of the L2. That is, the telecommunications company holds big data involved in the SLA measurement process of each subscriber terminal 250 through the URL, and the collection unit 111 collects data on environmental factors of L2 using the big data. can

Referring to FIG. 3 , the collection unit 111 collects data of the environmental factors including at least one of an uplink traffic factor, a subscriber number factor, and a PON accommodating L2 number factor.

In the case of the uplink traffic element, it may have a plurality of element items related to L2 traffic. For example, there is an L2 daily average uplink traffic measurement value, an average traffic measurement value for a predetermined time in a subscriber's home, and the like.

In the case of the number of subscribers element, a number of element items related to L2 and the number of subscribers of PONs connected to a higher level of L2 may be included. For example, there are the number of TV subscribers accommodated in L2, the number of Internet subscribers accommodated in L2, and the like.

In the case of the factor of the number of L2s accommodated in the PON, the number of lower L2s accommodated in the upper PON affects the Internet speed of the L2.

Referring to FIG. 4, a table of the environmental factors including SS 250, L2, and their speeds collected through SLA measurement is shown. The table is completed through the collection of the collection unit 111, and the "speed" of the table can classify the L2 model into "high", "medium", and "low" grades according to the Internet speed. For reference, the first L2 where the speed "955.0" is recorded is rated "high".

The preprocessing unit 113 performs preprocessing such as normalizing processing on the collected data of each element item of environmental factors as shown in FIG. 3 .

The learning unit 115 receives data for each element preprocessed by the preprocessing unit 113, and learns an influence relationship between the data for each element that can output the Internet speed of L2. The machine learning engine learns to predict the internet speed recorded in each L2 as nth order of learning proceeds for n (n>=2) pieces of L2 data.

The influence relationship learned by the learning unit 115 is shown in FIG. 3 . The learning unit 115 learns an effect relationship in which the Internet speed slows down as the traffic data increases in the uplink traffic elements. The learning unit 115 learns an influence relationship in which the Internet speed slows down as the number of subscribers increases in the element of the number of subscribers. In addition, the learning unit 115 learns an effect relationship in which the Internet speed becomes slower as the number of L2 increases in the elements of the number of L2s accepted by the PON. Through training, the machine learning engine learns the influence relationship between input data (environmental factor data in L2) and output data (predicted internet speed) to approximate real L2 data.

Referring to FIG. 5 , after learning of the table information of FIG. 4 as environmental factors of L2 through the learning unit 115 is completed, a graph of 3D environmental factors for each L2 is shown. For convenience of description, a graph of three environmental factors, i.e., uplink traffic, the number of tv subscribers, and the number of PON accommodating L2s, is shown.

Here, if the speed standard for classifying the level is set by the administrator (e.g., 900M or more "high" grade, 700~900M "medium" grade, 700M or less "low" grade), the Internet speed of each L2 device is determined by the corresponding L2 Models can be ranked. Each class forms a specific position in the 3D graph. Actually, in FIG. 3, a k-dimensional graph is formed according to the number of items of k (k>=2) environmental factors used for learning, and an L2 model for each class forms a position in the space of the k-dimensional graph. The learning unit 115 learns the influence relationship between the environmental factors to output the actual Internet speed of L2 with respect to the environmental factors of the L2 data input in the learning process, and the machine learning data of the learned influence relationship is k Determine the location of L2 in the dimensional graph. For reference, when data of 15 environmental factors in FIG. 3 is learned, a 15-dimensional graph is formed.

After learning is completed in the learning unit 115, the input unit 170 receives L2 environmental factor data for prediction, and inputs the input L2 data to the learning unit 115. Then, the machine learning engine of the learning unit 115 predicts and outputs the Internet speed for the input L2 data.

6 is a configuration diagram in which L2 models assigned to Internet speeds are compared according to another embodiment of the present invention.

Another embodiment of the present invention is configured to further include an L2 model allocation unit 651, an L2 model DB 653 and an L2 model comparison unit 655.

The L2 model allocator 651 allocates a grade according to the grade standard selected by the manager as shown in FIG. 4 according to the L2 internet speed output from the learning part 115, and assigns a grade according to the environmental factor of the L2 model for each grade. Calculate numerical information such as mean and standard deviation of data. The L2 model allocation unit 651 stores the calculated information in the L2 model DB 653.

Here, the reason why the L2 model allocator 651 assigns grades to the prediction result of the Internet speed output from the learning unit 115 and calculates the numerical information for each grade is the average and standard deviation among the L2 models. This is to compare and refer to the design of L2 equipment.

Referring to FIG. 7 , the L2 model comparison unit 655 is configured for each environmental factor of each model class from the L2 model DB 653 for n levels of the L2 model corresponding to the Internet speed output from the learning unit 115. See calculated numerical information. When the numerical information is referred to, the L2 model comparator 655 selects an n+1 level in which the Internet speed is higher by 1 level than the n level of the corresponding L2, and an n- level in which the Internet speed is lowered by 1 level than the n level. Create a table of 1 rating and comparison information, and provide the table in which data of each environmental factor is compared for each rating of n-1, n and n+1. Then, through the table, the administrator can design the capacity and environment of the L2 equipment. For example, when a new L2 device is installed, the L2 capacity and environment design suitable for the appropriate Internet speed can be designed while the manager changes L2 environmental factor data suitable for the installation purpose and region by referring to the table.

In addition, when an environmental factor of L2 in actual service is input, the Internet speed predicted by the learning unit 115 corresponds to the service speed of L2 and the speed of the subscriber terminal 250. That is, the network operation is normal when the actual measured speed of L2, the internet speed predicted by the learning unit 115, and the actual internet speed of the subscriber terminal 250 match within the error range. Then, in order to measure the SLA of the subscriber station 250, it is possible to predict (measure) the speed of the subscriber station 250 corresponding to the subscriber's home speed with the output data of the learning unit 115 without visiting the home.

Here, through the L2 model comparator 655, the actual L2 data and the average data of the grades assigned to L2 are compared for each item of the environmental factor, and in the case of an item that does not match, L2 may be considered abnormal or inspected. Furthermore, when only the SLA speed of the subscriber station 250 is out of the error range while the predicted Internet speed and the actual measured speed of L2 are within the error range, a reliable diagnosis of a problem of the subscriber station 250 can be made. This is because the predicted Internet speed of the learning unit 115 as a value close to the actual measured speed of L2 guarantees reliability of the Internet speed predicted by the learning unit 115. If the actual measured speed of L2 does not match the Internet speed predicted by the learning unit 115 out of the error range, an abnormality of L2 may be diagnosed.

8 is a schematic flowchart of a prediction method according to an embodiment of the present invention.

The prediction device 100 collects data of environmental factors of L2 that affect internet speed from network equipment (S810).

Here, as described above with reference to FIG. 2 , data of environmental factors that determine the Internet speed of L2 are collected from the network equipment including the NMS 211 and the control server 213 . Data of the L2 related to the SLA data measured by the subscriber terminal 250 may be collected through the control server 213 . In addition, as described above with reference to FIG. 3, data of the environmental factor includes at least one or more of uplink traffic, the number of subscribers, and the number of PON accommodating L2 factors. Each piece of data can contain unique items. For example, in the case of the uplink traffic element, items such as an L2 daily average uplink traffic measurement value, an average traffic measurement value for a predetermined time in the subscriber's home, and the number of times the L2 uplink traffic threshold is exceeded are included.

When the data of L2 is collected, the prediction device 100 normalizes the collected data and pre-processes them (S811).

When the pre-processing is completed, the prediction device 100 receives the pre-processed data for each element and learns the influence relationship between the data for each element (S830). The influence relationship is learned in the machine learning engine so that the collected internet speed of L2 is output for the input data of environmental factors. The learning includes learning of an influence relationship in which the Internet speed slows down as the element data of uplink traffic increases, learning of an influence relationship in which the Internet speed slows down as the element data of the number of subscribers increases, and element data of the number of PON acceptance L2 At least one or more of the learning of the influence relation in which the Internet speed slows down as it increases.

After the learning is completed, the prediction device 100 receives data of environmental factors of L2 for which prediction of Internet speed is required (S850).

When the data is input, the prediction device 100 predicts and outputs the Internet speed of L2 based on the learning in step S830 (S851). The administrator checks the Internet speed of L2 displayed on the screen.

Here, the prediction device 100 may assign a grade of a model corresponding to the predicted Internet speed to the corresponding L2 (S853). The prediction apparatus 100 calculates an average of data for each environmental factor of the L2 model in the class assigned to L2, and stores the calculated average in the L2 model DB 653 for each environmental factor in each class. The reason for allocating the grades is to compare the high speed level with the low speed level for L2, and the manager checks the comparison information to design the L2 model level and level change.

When the model grade is assigned, the prediction apparatus 100 outputs comparison information of the upper grade and the lower grade for the model grade of L2 on the screen as shown in FIG. 7 (S855). After confirming the comparison information, the manager changes the design of L2 environmental factors within the same grade or changes the design of L2 environmental factors to change them to L2 of other grades.

Although the present invention has been described with limited examples and drawings, the present invention is not limited thereto, and the technical idea of the present invention and claims to be described below are made by those skilled in the art to which the present invention belongs. Of course, various modifications and variations are possible within the equivalent range of the scope.

100: prediction terminal 111: collection unit
113: pre-processing unit 115: learning unit
170: input unit

Claims (18)

An apparatus for predicting Internet speed between a network switch device and a subscriber terminal,
a collection unit that collects data of environmental factors of the switch device that affect the Internet speed from network devices;
a pre-processing unit that normalizes and pre-processes the collected data for each element;
Receive preprocessed data for each element, learn the influence relationship between the data for each element, and after the learning is completed, between the network switch device and the subscriber terminal for the input data of the environmental factor of the switch device. a learning unit that predicts and outputs the Internet speed of; and
An input unit configured to input data of environmental factors of the switch device to the learning unit for the prediction;
The data of the environmental factor includes the number of subscribers connected to the network switch device and the number of network switch devices accommodated in an upper Passive Optical Network (PON) connected to the network switch device.
Device. In claim 1,
The collection unit,
The device characterized in that for collecting data of the switch device related to SLA (Service Level Agreement) data measured in the subscriber station. In claim 1,
The collection unit,
A device characterized in that for collecting data from the network equipment including a Network Management Server (NMS) and a control server. According to claim 1,
The collection unit,
and collecting data of said environmental factor further comprising uplink traffic. According to claim 4,
The components of the uplink traffic are:
a daily average uplink traffic measurement value of the switch device;
average traffic measurement value for a given period of time at the subscriber's premises;
the number of times the switch device exceeds an uplink traffic threshold;
the number of times the traffic threshold is exceeded between the switch device and the upper PON;
distance between the switch device and the upper PON;
a Cyclical Redundancy Check (CRC) value of an uplink port of the switch device;
line distance between the ports in the subscriber premises in the switch device; and
Uplink receive light level of the switch device
A device characterized in that it comprises at least one element from among the elements of. According to claim 4,
The element of the number of subscribers is,
the number of TV subscribers accommodated in the switch device;
the number of Internet subscribers accommodated in the switch device;
number of TV subscribers accommodated in the top PON;
the number of Internet subscribers accommodated in the top PON;
the number of excess traffic subscribers using more than a predetermined daily capacity accommodated in the switch device; and
Number of UHD set-top subscribers accommodated in the switch device
A device characterized in that it comprises at least one element from among the elements of. According to claim 1,
The learning unit,
Learning an influence relationship in which the Internet speed slows down as the element data of uplink traffic increases;
Learning an influence relationship in which the Internet speed slows down as the element data of the number of subscribers increases; and
PON Acceptance Learning of the influence relationship in which the Internet speed slows down as the element data of the number of switch devices increases.
Device characterized in that for performing at least one or more of the learning. According to claim 1,
a switch device model assigning unit that allocates model grades of the switch device according to the Internet speed output from the learning unit and calculates an average of data for each environmental factor of the model of the switch device in each grade; and
A switch device model DB that stores the calculated average for each environmental factor in each class
Device characterized in that it further comprises. According to claim 1,
For comparison with at least one higher level having faster internet speed than the level of the model of the switch device corresponding to the internet speed output from the learning unit and at least one lower level having slower internet speed than the level, each Apparatus further comprising a switch device model comparison unit providing data of each environmental factor for each grade. A method for predicting the Internet speed between a network switch device and a subscriber terminal by a device,
collecting data of environmental factors of the switch device that affect the internet speed from network devices;
Normalizing and pre-processing the collected data for each element;
receiving preprocessed data for each element and learning an influence relationship between the data for each element;
After the learning is completed, receiving data of environmental factors of the switch device for prediction; and
Based on the learning of the inputted data of the switch device, predicting and outputting the Internet speed between the network switch device and the subscriber terminal,
The data of the environmental factor includes the number of subscribers connected to the network switch device and the number of network switch devices accommodated in an upper Passive Optical Network (PON) connected to the network switch device.
Way. In claim 10,
The collecting step is
and collecting data of the switch device related to Service Level Agreement (SLA) data measured in the subscriber station. In claim 10,
The collecting step is
A method characterized in that the step of collecting from the network equipment including a network management server (NMS) and a control server. According to claim 10,
The collecting step is
and collecting data of the environmental factor further including uplink traffic. According to claim 13,
The components of the uplink traffic are:
a daily average uplink traffic measurement value of the switch device;
average traffic measurement value for a given period of time at the subscriber's premises;
the number of times the switch device exceeds an uplink traffic threshold;
the number of times a traffic threshold is exceeded between the switch device and a higher Passive Optical Network (PON);
distance between the switch device and the upper PON;
a Cyclical Redundancy Check (CRC) value of an uplink port of the switch device;
line distance between the ports in the subscriber premises in the switch device; and
Uplink receive light level of the switch device
A method comprising at least one element among the elements of. According to claim 10,
The element of the number of subscribers is,
the number of TV subscribers accommodated in the switch device;
the number of Internet subscribers accommodated in the switch device;
number of TV subscribers accommodated in the top PON;
the number of Internet subscribers accommodated in the top PON;
the number of excess traffic subscribers using more than a predetermined daily capacity accommodated in the switch device; and
Number of UHD set-top subscribers accommodated in the switch device
A method comprising at least one element among the elements of. According to claim 10,
The learning step is
Learning an influence relationship in which the Internet speed slows down as the element data of uplink traffic increases;
Learning an influence relationship in which the Internet speed slows down as the element data of the number of subscribers increases; and
PON Acceptance Learning of the influence relationship in which the Internet speed slows down as the element data of the number of switch devices increases.
Method characterized in that the step of performing at least one or more of the learning. According to claim 10,
After the outputting step,
According to the output internet speed, the model class of the switch device is assigned, the average of data for each environmental factor of the model of the switch device is calculated in each class, and the calculated average is converted into a DB for each environmental factor in each class. The method further comprising the step of storing in. According to claim 10,
After the outputting step,
Regarding the grade of the model of the switch device corresponding to the output Internet speed, for comparison with at least one higher grade having faster Internet speed than the grade and at least one lower grade having slower Internet speed, each grade The method of claim 1 , further comprising providing environmental factor data.

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