Network equipment optimization control system based on big data analysis

Abstract

The invention discloses a network equipment optimization control system based on big data analysis, which relates to the technical field of network equipment optimization and comprises a control center in communication connection with an equipment data acquisition module, a service level generation module, a real-time data acquisition module and an optimization control module. The equipment data acquisition module is used for acquiring basic performance data of network equipment; the service level generation module is used for generating a service processing level of the network equipment according to the basic performance data; the real-time data acquisition module is used for acquiring a real-time operation data set of network equipment; and the optimization control module is used for generating a corresponding optimization scheme and a control strategy according to the real-time operation data set of the network equipment and the corresponding service processing level.

Description

Technical Field

[0001] The present invention relates to the technical field of network equipment optimization, and in particular to a network equipment optimization control system based on big data analysis. Background Art

[0002] In the modern network environment, with the increase in the number of network devices and the complexity of network load, traditional network equipment management and control methods have been unable to cope with efficient and dynamic network needs. Problems such as uneven network resource allocation, bandwidth waste, and frequent equipment failures have seriously affected network performance and operational efficiency. Especially during peak hours or burst traffic, network bottlenecks are prone to occur, affecting the overall service quality. With the continuous advancement of big data, artificial intelligence (AI), and the Internet of Things (IoT) technologies, this situation has been significantly improved and alleviated.

[0003] How to use big data to ensure efficient and stable operation of network equipment is an issue we need to consider at present. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a network equipment optimization control system based on big data analysis.

[0005] The object of the present invention can be achieved by the following technical solutions: A network equipment optimization control system based on big data analysis includes a control center, wherein the control center is communicatively connected with an equipment data acquisition module, a service level generation module, a real-time data acquisition module and an optimization control module;

[0006] The device data collection module is used to collect basic performance data of network devices;

[0007] The service level generation module is used to generate the service processing level of the network device according to the basic performance data;

[0008] The real-time data collection module is used to collect real-time operation data sets of network devices;

[0009] The optimization control module is used to generate corresponding optimization schemes and control strategies according to the real-time operation data set of the network device and its corresponding business processing level.

[0010] Furthermore, the process of collecting basic performance data of network devices includes:

[0011] The basic performance data includes bandwidth, CPU utilization, disk IOPS and number of TCP connections;

[0012] Set the collection period;

[0013] The basic performance data of network devices is obtained regularly according to the collection cycle.

[0014] Furthermore, the process of generating the service processing level of the network device according to the basic performance data includes:

[0015] The business processing levels include a primary business processing platform, a secondary business processing platform, and a tertiary business processing platform;

[0016] Step a1: Establish bandwidth coordinate system;

[0017] Step a2: acquiring basic performance data of a plurality of network devices in the same collection period, mapping the bandwidth in the basic performance data to a bandwidth coordinate system according to time, and generating a plurality of bandwidth data curves;

[0018] Step a3: obtaining a plurality of bandwidth data curves and mapping them to represent a numerical interval on the bandwidth coordinate axis in a bandwidth coordinate system, obtaining an aggregation interval according to the numerical interval; and generating a bandwidth performance value of the network device according to the aggregation interval;

[0019] According to the principle process of obtaining the bandwidth performance value through the basic performance data from step a1 to step a3, the CPU performance value, the disk performance value and the connection performance value are obtained according to the basic performance data;

[0020] Set bandwidth coefficient, CPU coefficient, disk coefficient and connection coefficient, and generate the periodic comprehensive value of network equipment according to bandwidth coefficient, CPU coefficient, disk coefficient, connection coefficient, bandwidth performance value, CPU performance value, disk performance value and connection performance value;

[0021] Set the first-level performance interval, the second-level performance interval and the third-level performance interval;

[0022] If the periodic comprehensive value of the network device ∈ the first-level performance interval, the network device is marked as a first-level business processing platform; if the periodic comprehensive value of the network device ∈ the second-level performance interval, the network device is marked as a second-level business processing platform; if the periodic comprehensive value of the network device ∈ the third-level performance interval, the network device is marked as a third-level business processing platform;

[0023] When new basic performance data is generated, a new periodic comprehensive value of the network device is regenerated based on the new basic performance data, and a new service processing level of the network device is obtained based on the new periodic comprehensive value, and the new service processing level covers the original service processing level of the network device.

[0024] Further, the process of obtaining the aggregation interval according to the numerical interval and generating the bandwidth performance value of the network device according to the aggregation interval includes:

[0025] Set high frequency threshold;

[0026] Obtaining values ​​in the value interval whose occurrence times are greater than or equal to the high-frequency threshold, and generating a clustering interval according to adjacent values ​​in the value interval;

[0027] Set the optimal bandwidth performance range and total bandwidth weight;

[0028] Obtaining the distance between each clustering interval and the optimal bandwidth performance interval, obtaining the maximum distance among the distances, recording the maximum distance as the edge distance, and generating the bandwidth weight of the clustering interval according to the distance between the clustering interval and the optimal bandwidth performance interval, the edge distance and the total bandwidth weight;

[0029] Set the total bandwidth performance value;

[0030] Obtaining the number of aggregation intervals, dividing the total bandwidth performance value according to the number, and generating a bandwidth performance mean value;

[0031] The bandwidth performance value of the aggregation interval is generated according to the bandwidth weight corresponding to the aggregation interval and the bandwidth performance mean.

[0032] Furthermore, the process of collecting real-time operation data sets of network devices includes:

[0033] The operation data includes packet loss rate, interface status, round-trip delay and throughput;

[0034] Set monitoring period;

[0035] The operation data of the network equipment is acquired in real time, and the operation data is periodically integrated into a real-time operation data set according to time according to a monitoring cycle.

[0036] Furthermore, the process of generating corresponding optimization schemes and control strategies according to the real-time operation data set of the network device and its corresponding service processing level includes:

[0037] Establish the device coordinate system;

[0038] Obtaining a geographical location of the network device, mapping the network device to a device coordinate system according to the geographical location, and generating a device node;

[0039] Setting up a cloud computing platform according to the distribution of network devices so that each network device is within the controllable range of the cloud computing platform; obtaining the geographical location of the cloud computing platform, mapping the cloud computing platform to the device coordinate system according to the geographical location, and generating a platform node;

[0040] According to the device node and the platform node, the distance between the network device and each cloud computing platform is obtained, and the minimum distance among the distances is obtained. The cloud computing platform corresponding to the minimum distance is recorded as the control platform of the network device, and the network device is the controlled device of the cloud computing platform corresponding to the control platform;

[0041] Generate a state index of the network device according to the packet loss rate and interface status in the real-time operation data set of the network device, and generate a corresponding control strategy according to the state index;

[0042] An operation index of the network device is generated according to the round-trip delay and throughput in the real-time operation data set of the network device, and a corresponding optimization plan is generated according to the operation index.

[0043] Furthermore, the process of generating a state index of the network device according to the packet loss rate and the interface state in the real-time operation data set of the network device, and generating a corresponding control strategy according to the state index includes:

[0044] The interface status includes the operating status of several interfaces;

[0045] The operating status includes normal and abnormal;

[0046] Obtain the number of interfaces whose operating status is normal, and record the number as a healthy interface value; obtain the number of interfaces whose operating status is abnormal, and record the number as an abnormal interface value;

[0047] Set the state coefficient;

[0048] Generate a status index based on the status coefficient, packet loss rate, healthy interface value, and abnormal interface value;

[0049] Obtain the status index corresponding to each operation data in the real-time operation data set;

[0050] Set status thresholds and health thresholds;

[0051] If the state index is greater than the state threshold, the operation data is healthy operation data; if the state index is less than or equal to the state threshold, the operation data is unhealthy operation data;

[0052] Acquire the number of healthy operation data, and record the number as a healthy operation value; acquire the number of unhealthy operation data, and record the number as an unhealthy operation value;

[0053] Generate the health rate of the real-time operation data set according to the healthy operation value and the unhealthy operation value;

[0054] If the health rate is greater than or equal to the health threshold, no action is required;

[0055] If the health rate is less than the health threshold, the service processing level of the network device is obtained;

[0056] If the network device is a first-level business processing platform, a first-level warning message is generated and sent to the user to inform the user that the network device is in an abnormal state, and the user takes corresponding measures;

[0057] If the network device is a secondary business processing platform or a tertiary business processing platform, the data in the network device is obtained, and a security check is performed on the data to determine whether the data is safe. If the data is safe, the data is copied, packaged and sent to the corresponding control platform of the network device for backup processing, and a secondary warning message is generated and sent to the user to inform the user that the data status of the network device is abnormal and has been safely backed up, and the user will perform corresponding processing; if the data is not safe, a tertiary warning message is generated and sent to the user to inform the user that the data status of the network device is abnormal and has not been backed up, and the user will perform corresponding processing.

[0058] Furthermore, the process of generating an operation index of the network device according to the round-trip delay and throughput in the real-time operation data set of the network device, and generating a corresponding optimization solution according to the operation index includes:

[0059] Set the delay factor and throughput factor;

[0060] Generate an operational index based on delay factor, throughput factor, round trip delay, and throughput;

[0061] Obtaining the operation index corresponding to each operation data in the real-time operation data set to generate an operation index set; obtaining a number of operation increase ratio values ​​according to the operation index set to generate an operation increase ratio value set;

[0062] Obtain the number of operation increase ratio values ​​greater than or equal to 0, and record the number as a normal operation value; obtain the number of operation increase ratio values ​​less than 0, and record the number as an abnormal operation value;

[0063] Set the operating floating threshold and effective range;

[0064] like No action is required.

[0065] like Then set the throughput requirement value and obtain the service processing level GR of the network device;

[0066] If the network device is a first-level service processing platform, first-level expansion information is generated and sent to the user, informing the user that the service volume of the network device is too much, and the user shall handle it accordingly;

[0067] If the network device is a secondary business processing platform or a tertiary business processing platform, then obtaining other device nodes within the effective range of the device node corresponding to the network device;

[0068] If no other device nodes exist, secondary expansion information is generated and sent to the user, informing the user that the traffic of the network device is too much and no other device nodes are found to assist, and the user shall handle the situation accordingly;

[0069] If there are other device nodes, mark the other device nodes as pre - auxiliary nodes; obtain the service processing levels of the pre - auxiliary nodes, record the pre - auxiliary nodes with the service processing level ≥ GR as pre - auxiliary high nodes, and record the pre - auxiliary nodes with the service processing level < GR as pre - auxiliary low nodes; obtain the throughput idle values of the pre - auxiliary nodes;

[0070] Obtain the network device corresponding to a pre - auxiliary high node that is the nearest and has a throughput idle value ≥ the throughput demand value, and mark the network device as the high - level auxiliary device of this network device;

[0071] If there is no high - level auxiliary device, obtain the network device corresponding to a pre - auxiliary low node that is the nearest and has a throughput idle value ≥ (2 × the throughput demand value), and mark the network device as the regular auxiliary device of this network device;

[0072] Assist this network device through the auxiliary device. If the network device has data backed up on the cloud computing platform, grant the auxiliary device the permission to access the data;

[0073] If there is no regular auxiliary device, generate secondary expansion information, send the secondary expansion information to the user, inform the user that the traffic of this network device is too large and no other device nodes are found for assistance, and let the user handle it accordingly.

[0074] Compared with the prior art, the beneficial effects of the present invention are:

[0075] 1. The present invention divides the service capabilities of network devices into levels by obtaining the performance data of network devices to generate service processing levels, making the management of network devices more intelligent and efficient. At the same time, the service processing levels of network devices will change based on the update of network device performance data, ensuring the timeliness of the service processing levels of network devices;

[0076] 2. The present invention obtains the operation data of network devices, judges the operation status of network devices according to the operation data. When the status is abnormal, the data of the network device is backed up to the cloud in a timely manner to ensure the security of device data. When the operation is busy, it is assisted by obtaining an auxiliary device to reduce the service pressure of the network device. At the same time, the access permission of the auxiliary device to the backup data is opened, further improving the operation efficiency of the network device. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0078] Such as Figure 1As shown, a network equipment optimization control system based on big data analysis includes a control center, and the control center is connected to a device data acquisition module, a service level generation module, a real-time data acquisition module and an optimization control module;

[0079] The device data collection module is used to collect basic performance data of network devices;

[0080] The service level generation module is used to generate the service processing level of the network device according to the basic performance data;

[0081] The real-time data collection module is used to collect real-time operation data sets of network devices;

[0082] The optimization control module is used to generate corresponding optimization schemes and control strategies according to the real-time operation data set of the network device and its corresponding service processing level;

[0083] It should be further explained that, in the specific implementation process, the process of collecting basic performance data of network equipment includes:

[0084] The basic performance data includes bandwidth, CPU utilization, disk IOPS and number of TCP connections;

[0085] Set the collection period;

[0086] Regularly obtain basic performance data of network devices according to the collection cycle;

[0087] It should be further explained that, in the specific implementation process, the process of generating the service processing level of the network device according to the basic performance data includes:

[0088] The business processing levels include a primary business processing platform, a secondary business processing platform, and a tertiary business processing platform;

[0089] Step a1: Establish bandwidth coordinate system;

[0090] Step a2: acquiring basic performance data of a plurality of network devices in the same collection period, mapping the bandwidth in the basic performance data to a bandwidth coordinate system according to time, and generating a plurality of bandwidth data curves;

[0091] Step a3: obtaining a plurality of bandwidth data curves and mapping them to represent a numerical interval on the bandwidth coordinate axis in a bandwidth coordinate system, obtaining an aggregation interval according to the numerical interval; and generating a bandwidth performance value of the network device according to the aggregation interval;

[0092] According to the principle process of obtaining the bandwidth performance value according to the basic performance data from step a1 to step a3, the CPU performance value, the disk performance value and the connection performance value are obtained according to the basic performance data;

[0093] Set bandwidth coefficient, CPU coefficient, disk coefficient and connection coefficient, and generate the periodic comprehensive value of network equipment according to bandwidth coefficient, CPU coefficient, disk coefficient, connection coefficient, bandwidth performance value, CPU performance value, disk performance value and connection performance value;

[0094] The cycle comprehensive value = bandwidth coefficient × bandwidth performance value + CPU coefficient × CPU performance value + disk coefficient × disk performance value + connection coefficient × connection performance value;

[0095] Set up first-level performance interval, second-level performance interval and third-level performance interval;

[0096] If the periodic comprehensive value of the network device ∈ the first-level performance interval, the network device is marked as a first-level business processing platform;

[0097] If the periodic comprehensive value of the network device ∈ the second-level performance interval, the network device is marked as a second-level service processing platform;

[0098] If the periodic comprehensive value of the network device ∈ the third-level performance interval, the network device is marked as a third-level business processing platform;

[0099] It should be noted that, when new basic performance data is generated, a new periodic comprehensive value of the network device is regenerated according to the new basic performance data, and a new service processing level of the network device is obtained according to the new periodic comprehensive value, and the new service processing level covers the original service processing level of the network device;

[0100] The process of obtaining the aggregation interval according to the numerical interval and generating the bandwidth performance value of the network device according to the aggregation interval includes:

[0101] Set high frequency threshold;

[0102] Obtaining values ​​in the value interval whose occurrence times are greater than or equal to the high-frequency threshold, and generating a clustering interval according to adjacent values ​​in the value interval;

[0103] Assume that the high-frequency threshold is 2, and there are values ​​num1, num2, num3, num4, and num5 on the coordinate axis, generate a value interval [num1, num4] and a value interval [num2, num5] based on the values, obtain the values ​​that appear frequently in the value interval, namely num2, num3, and num4, and generate a cluster interval [num2, num4] based on the adjacent values ​​in the values;

[0104] Set the optimal bandwidth performance range and total bandwidth weight;

[0105] Obtaining the distance between each clustering interval and the optimal bandwidth performance interval, obtaining the maximum distance among the distances, recording the maximum distance as the edge distance, and generating the bandwidth weight of the clustering interval according to the distance between the clustering interval and the optimal bandwidth performance interval, the edge distance and the total bandwidth weight;

[0106] Obtain the center coordinates of the clustering interval and the optimal bandwidth performance interval, record them as the clustering center coordinates and the optimal center coordinates respectively, and generate the distance between the clustering interval and the optimal bandwidth performance interval according to the clustering center coordinates and the optimal center coordinates;

[0107] Set the total bandwidth performance value;

[0108] Obtaining the number of aggregation intervals, dividing the total bandwidth performance value according to the number, and generating a bandwidth performance mean value;

[0109] Generate a bandwidth performance value of the aggregation interval according to the bandwidth weight corresponding to the aggregation interval and the bandwidth performance mean;

[0110] The bandwidth performance value=bandwidth weight×bandwidth performance mean;

[0111] It should be further explained that, in the specific implementation process, the process of collecting the real-time operation data set of the network device includes:

[0112] The operation data includes packet loss rate, interface status, round-trip delay and throughput;

[0113] Set monitoring period;

[0114] Acquire the operation data of the network equipment in real time, and regularly integrate the operation data into a real-time operation data set according to time according to the monitoring cycle;

[0115] The operation data set = {operation data 1 (packet loss rate 1, interface status 1, round trip delay 1, throughput 1), operation data 2 (packet loss rate 2, interface status 2, round trip delay 2, throughput 2

[0116] ), ..., operation data n (Packet Loss Rate n , interface status n , round trip delay n , throughput n )}, where n is a positive integer;

[0117] It should be further explained that, in the specific implementation process, the process of generating corresponding optimization schemes and control strategies according to the real-time operation data set of the network device and its corresponding service processing level includes:

[0118] Establish the device coordinate system;

[0119] Obtaining the geographical location of the network device, mapping the network device to the device coordinate system according to the geographical location, and generating a device node; that is, the network device and the device node are in a corresponding relationship;

[0120] Set up the cloud computing platform according to the distribution of network devices, so that each network device is within the controllable range of the cloud computing platform;

[0121] Obtaining the geographical location of the cloud computing platform, mapping the cloud computing platform to the device coordinate system according to the geographical location, and generating a platform node; that is, the cloud computing platform and the platform node are in a corresponding relationship;

[0122] According to the device node and the platform node, the distance between the network device and each cloud computing platform is obtained, and the minimum distance among the distances is obtained. The cloud computing platform corresponding to the minimum distance is recorded as the control platform of the network device, and the network device is the controlled device of the cloud computing platform corresponding to the control platform;

[0123] Generate a state index of the network device according to the packet loss rate and interface status in the real-time operation data set of the network device, and generate a corresponding control strategy according to the state index;

[0124] Generate an operation index of the network device according to the round-trip delay and throughput in the real-time operation data set of the network device, and generate a corresponding optimization plan according to the operation index;

[0125] The process of generating a state index of a network device according to the packet loss rate and the interface state in the real-time operation data set of the network device and generating a corresponding control strategy according to the state index includes:

[0126] The interface status includes the operating status of several interfaces;

[0127] The operating status includes normal and abnormal;

[0128] Obtain the number of interfaces in normal operation status, and record the number as a healthy interface value;

[0129] Obtaining the number of interfaces whose operating status is abnormal, and recording the number as an abnormal interface value;

[0130] Set the state coefficient;

[0131] Generate a status index based on the status coefficient, packet loss rate, healthy interface value, and abnormal interface value;

[0132]

[0133] Obtain the status index corresponding to each operation data in the real-time operation data set;

[0134] Set status thresholds and health thresholds;

[0135] If the state index is greater than the state threshold, the operation data is healthy operation data;

[0136] If the state index is less than or equal to the state threshold, the operation data is unhealthy operation data;

[0137] Acquire the number of healthy operation data, and record the number as a healthy operation value;

[0138] Acquire the number of unhealthy operation data, and record the number as an unhealthy operation value;

[0139] Generate the health rate of the real-time operation data set according to the healthy operation value and the unhealthy operation value;

[0140]

[0141] If the health rate is greater than or equal to the health threshold, no action is required;

[0142] If the health rate is less than the health threshold, the business processing level of the network device is obtained; if the network device is a first-level business processing platform, a first-level warning message is generated and sent to the user to inform the user that the network device is in an abnormal state and the user performs corresponding processing; if the network device is a second-level business processing platform or a third-level business processing platform, the data in the network device is obtained, and a security check is performed on the data to determine whether the data is safe. If the data is safe, the data is copied and packaged and sent to the corresponding control platform of the network device for backup processing, and a second-level warning message is generated and sent to the user to inform the user that the data state of the network device is abnormal and has been safely backed up, and the user performs corresponding processing; if the data is not safe, a third-level warning message is generated and sent to the user to inform the user that the data state of the network device is abnormal and has not been backed up, and the user performs corresponding processing;

[0143] The process of generating an operation index of a network device according to the round-trip delay and throughput in the real-time operation data set of the network device and generating a corresponding optimization solution according to the operation index includes:

[0144] Set the delay factor and throughput factor;

[0145] Generate an operational index based on delay factor, throughput factor, round trip delay, and throughput;

[0146] The operation index = delay coefficient × round-trip delay + throughput coefficient × throughput;

[0147] Obtain the operation indices corresponding to each operation data in the real-time operation dataset, and generate an operation index set; the operation index set = {operation index 1, operation index 2, ……, operation index n}, where n is a positive integer;

[0148] Obtain several operation increment ratios according to the operation index set, and generate an operation increment ratio set;

[0149] where i is a positive integer;

[0150] The operation increment ratio set = {operation increment ratio 2 / 1 , operation increment ratio 3 / 2 , ……, operation increment ratio n / n-1}, where n is a positive integer;

[0151] Obtain the number of operation increment ratios greater than or equal to 0, and record the number as the normal operation value;

[0152] Obtain the number of operation increment ratios less than 0, and record the number as the abnormal operation value;

[0153] Set the operation floating threshold and the effective range;

[0154] If then no operation is required;

[0155] If then set the throughput demand value, and obtain the service processing level GR of the network device;

[0156] If the network device is a first-level service processing platform, generate first-level expansion information, and send the first-level expansion information to the user to inform the user that the traffic volume of this network device is too large, and let the user perform corresponding processing;

[0157] If the network device is a second-level service processing platform or a third-level service processing platform, obtain other device nodes within the effective range of the device node corresponding to this network device;

[0158] If there are no other device nodes, generate second-level expansion information, and send the second-level expansion information to the user to inform the user that the traffic volume of this network device is too large and no other device nodes are found for assistance, and let the user perform corresponding processing;

[0159] If there are other device nodes, mark the other device nodes as pre-assist nodes; obtain the service processing level of the pre-assist nodes, and record the pre-assist nodes with the service processing level ≥ GR as pre-assist high nodes, and record the pre-assist nodes with the service processing level < GR as pre-assist low nodes;

[0160] Obtain the throughput idle value of the pre-assist nodes;

[0161] The throughput idle value is the difference between the maximum throughput and the minimum throughput in the latest real-time operation data set of the network device;

[0162] Obtain a network device corresponding to a pre-assistive high node that is closest and has a throughput idle value ≥ a throughput demand value, and mark the network device as a high-level auxiliary device of the network device;

[0163] If there is no advanced auxiliary device, obtain a network device corresponding to a pre-auxiliary low node with the nearest distance and a throughput idle value ≥ (2×throughput requirement value), and mark the network device as a regular auxiliary device of the network device;

[0164] The network device is assisted by an auxiliary device. If the network device has data backed up on the cloud computing platform, the auxiliary device is given permission to access the data;

[0165] If there is no conventional auxiliary device, the secondary expansion information is generated and sent to the user, informing the user that the traffic of the network device is too much and no other device node is found for assistance, and the user shall handle it accordingly;

[0166] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A network equipment optimization control system based on big data analysis, including a control center, characterized in that: The control center is communicatively connected with a device data acquisition module, a service level generation module, a real-time data acquisition module and an optimization control module; The device data collection module is used to collect basic performance data of network devices; The service level generation module is used to generate the service processing level of the network device according to the basic performance data; The real-time data collection module is used to collect real-time operation data sets of network devices; The optimization control module is used to generate corresponding optimization schemes and control strategies according to the real-time operation data set of the network device and its corresponding business processing level.

2. The network equipment optimization control system based on big data analysis according to claim 1 is characterized in that: The process of collecting basic performance data of network devices includes: The basic performance data includes bandwidth, CPU utilization, disk IOPS and number of TCP connections; Set the collection period; The basic performance data of network devices is obtained regularly according to the collection cycle.

3. The network equipment optimization control system based on big data analysis according to claim 2 is characterized in that: The process of generating the service processing level of network equipment based on basic performance data includes: The business processing levels include a primary business processing platform, a secondary business processing platform, and a tertiary business processing platform; Step a1: Establish bandwidth coordinate system; Step a2: acquiring basic performance data of a plurality of network devices in the same collection period, mapping the bandwidth in the basic performance data to a bandwidth coordinate system according to time, and generating a plurality of bandwidth data curves; Step a3: obtaining a plurality of bandwidth data curves and mapping them to represent a numerical interval on the bandwidth coordinate axis in a bandwidth coordinate system, obtaining an aggregation interval according to the numerical interval; and generating a bandwidth performance value of the network device according to the aggregation interval; According to the principle process of obtaining the bandwidth performance value through the basic performance data from step a1 to step a3, the CPU performance value, the disk performance value and the connection performance value are obtained according to the basic performance data; Set bandwidth coefficient, CPU coefficient, disk coefficient and connection coefficient, and generate the periodic comprehensive value of network equipment according to bandwidth coefficient, CPU coefficient, disk coefficient, connection coefficient, bandwidth performance value, CPU performance value, disk performance value and connection performance value; Set up first-level performance interval, second-level performance interval and third-level performance interval; If the periodic comprehensive value of the network device ∈ the first-level performance interval, the network device is marked as a first-level business processing platform; if the periodic comprehensive value of the network device ∈ the second-level performance interval, the network device is marked as a second-level business processing platform; if the periodic comprehensive value of the network device ∈ the third-level performance interval, the network device is marked as a third-level business processing platform; When new basic performance data is generated, a new periodic comprehensive value of the network device is regenerated based on the new basic performance data, and a new service processing level of the network device is obtained based on the new periodic comprehensive value, and the new service processing level covers the original service processing level of the network device.

4. The network equipment optimization control system based on big data analysis according to claim 3 is characterized in that: The process of obtaining the aggregation interval according to the numerical interval and generating the bandwidth performance value of the network device according to the aggregation interval includes: Set high frequency threshold; Obtaining values ​​in the value interval whose occurrence times are greater than or equal to the high-frequency threshold, and generating a clustering interval according to adjacent values ​​in the value interval; Set the optimal bandwidth performance range and total bandwidth weight; Obtaining the distance between each clustering interval and the optimal bandwidth performance interval, obtaining the maximum distance among the distances, recording the maximum distance as the edge distance, and generating the bandwidth weight of the clustering interval according to the distance between the clustering interval and the optimal bandwidth performance interval, the edge distance and the total bandwidth weight; Set the total bandwidth performance value; Obtaining the number of aggregation intervals, dividing the total bandwidth performance value according to the number, and generating a bandwidth performance mean value; The bandwidth performance value of the aggregation interval is generated according to the bandwidth weight corresponding to the aggregation interval and the bandwidth performance mean.

5. The network equipment optimization control system based on big data analysis according to claim 4 is characterized in that: The process of collecting real-time operation data sets of network devices includes: The operation data includes packet loss rate, interface status, round-trip delay and throughput; Set monitoring period; The operation data of the network equipment is acquired in real time, and the operation data is periodically integrated into a real-time operation data set according to time according to a monitoring cycle.

6. The network equipment optimization control system based on big data analysis according to claim 5 is characterized in that: The process of generating corresponding optimization schemes and control strategies based on the real-time operation data set of network devices and their corresponding service processing levels includes: Establish the device coordinate system; Obtaining a geographical location of the network device, mapping the network device to a device coordinate system according to the geographical location, and generating a device node; Setting up a cloud computing platform according to the distribution of network devices so that each network device is within the controllable range of the cloud computing platform; obtaining the geographical location of the cloud computing platform, mapping the cloud computing platform to the device coordinate system according to the geographical location, and generating a platform node; According to the device node and the platform node, the distance between the network device and each cloud computing platform is obtained, and the minimum distance among the distances is obtained. The cloud computing platform corresponding to the minimum distance is recorded as the control platform of the network device, and the network device is the controlled device of the cloud computing platform corresponding to the control platform; Generate a state index of the network device according to the packet loss rate and interface status in the real-time operation data set of the network device, and generate a corresponding control strategy according to the state index; An operation index of the network device is generated according to the round-trip delay and throughput in the real-time operation data set of the network device, and a corresponding optimization plan is generated according to the operation index.

7. The network equipment optimization control system based on big data analysis according to claim 6 is characterized in that: The process of generating a state index of a network device according to the packet loss rate and the interface state in the real-time operation data set of the network device and generating a corresponding control strategy according to the state index includes: The interface status includes the operating status of several interfaces; The operating status includes normal and abnormal; Obtain the number of interfaces whose operating status is normal, and record the number as a healthy interface value; obtain the number of interfaces whose operating status is abnormal, and record the number as an abnormal interface value; Set the state coefficient; Generate a status index based on the status coefficient, packet loss rate, healthy interface value, and abnormal interface value; Obtain the status index corresponding to each operation data in the real-time operation data set; Set status thresholds and health thresholds; If the state index is greater than the state threshold, the operation data is healthy operation data; if the state index is less than or equal to the state threshold, the operation data is unhealthy operation data; Acquire the number of healthy operation data, and record the number as a healthy operation value; acquire the number of unhealthy operation data, and record the number as an unhealthy operation value; Generate the health rate of the real-time operation data set according to the healthy operation value and the unhealthy operation value; If the health rate is greater than or equal to the health threshold, no operation is required; If the health rate is less than the health threshold, obtain the service processing level of the network device; If the network device is a first-level service processing platform, generate a first-level warning message, send the first-level warning message to the user, inform the user that the status of the network device is abnormal, and let the user perform corresponding processing; If the network device is a second-level service processing platform or a third-level service processing platform, obtain the data in the network device, perform a security check on the data, and determine whether the data is secure. If the data is secure, copy and package the data and send it to the corresponding control platform of the network device for backup processing, and generate a second-level warning message, send the second-level warning message to the user, inform the user that the data status of the network device is abnormal and has been securely backed up, and let the user perform corresponding processing; If the data is not secure, generate a third-level warning message, send the third-level warning message to the user, inform the user that the data status of the network device is abnormal and there is no backup, and let the user perform corresponding processing.

8. The network equipment optimization control system based on big data analysis according to claim 7 is characterized in that: The process of generating the operation index of the network device based on the round-trip delay and throughput in the real-time operation data set of the network device and generating the corresponding optimization plan includes: Set the delay coefficient and throughput coefficient; Generate the operation index according to the delay coefficient, throughput coefficient, round-trip delay and throughput; Obtain the operation index corresponding to each operation data in the real-time operation data set to generate an operation index set; obtain several operation increase ratios according to the operation index set to generate an operation increase ratio set; Obtain the number of operation increase ratios greater than or equal to 0, and record the number as the normal operation value; obtain the number of operation increase ratios less than 0, and record the number as the abnormal operation value; Set the operation floating threshold and effective range; like No action is required. like Then set the throughput requirement value and obtain the service processing level GR of the network device; If the network device is a first-level service processing platform, generate a first-level expansion information, send the first-level expansion information to the user, inform the user that the business volume of the network device is too large, and let the user perform corresponding processing; If the network device is a second-level service processing platform or a third-level service processing platform, obtain other device nodes within the effective range of the device node corresponding to the network device; If there are no other device nodes, generate a second-level expansion information, send the second-level expansion information to the user, inform the user that the business volume of the network device is too large and no other device nodes are found for assistance, and let the user perform corresponding processing; If there are other device nodes, mark the other device nodes as pre-assist nodes; obtain the service processing level of the pre-assist nodes, record the pre-assist nodes with the service processing level ≥ GR as pre-assist high nodes, and record the pre-assist nodes with the service processing level < GR as pre-assist low nodes; obtain the throughput idle value of the pre-assist nodes; Obtain the network device corresponding to a pre-assist high node that is the closest and has a throughput idle value ≥ the throughput demand value, and mark the network device as the high-level assist device of the network device; If there is no high-level assist device, obtain the network device corresponding to a pre-assist low node that is the closest and has a throughput idle value ≥ (2 × throughput demand value), and mark the network device as the regular assist device of the network device; The network device is assisted by an auxiliary device. If the network device has data backed up on the cloud computing platform, the auxiliary device is given permission to access the data; If there is no conventional auxiliary device, secondary expansion information is generated and sent to the user, informing the user that the network device has too much traffic and no other device nodes are found for assistance, and the user takes corresponding action.

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