Network equipment regulation and control method based on intelligent gateway

Abstract

The invention relates to the technical field of network equipment monitoring and regulation and control, in particular to a network equipment regulation and control method based on an intelligent gateway, which is used for solving the problems that the existing network equipment regulation and control method of the intelligent gateway mainly depends on preset rules and manual operation and lacks intelligence and adaptability, so that the detection accuracy of network equipment is not high, regulation and control are not timely, and the efficiency is high. And the requirements of modern network management cannot be met. The method relates to the following modules: a network equipment monitoring module, a network equipment analysis module, an analysis and judgment module, an equipment regulation and control module and a regulation and control effect monitoring module. According to the method, real-time monitoring, abnormity regulation and control and abnormity prompting of the network equipment are realized, potential problems of the network equipment can be found and solved in time through abnormity detection and intelligent regulation and control, occurrence and diffusion of faults are avoided, the operation stability and performance of the network equipment are improved, and the requirements of different network environments are met.

Description

Technical Field

[0001] The present invention relates to the technical field of network equipment monitoring and control, and in particular to a network equipment control method based on an intelligent gateway. Background Art

[0002] With the rapid development of the Internet, the Internet of Things, and cloud computing technologies, the scale of networks continues to expand, and the number and complexity of network devices have increased dramatically. However, the failure of network equipment may cause serious business interruptions and data loss, causing huge losses to enterprises and users. As an intelligent hub connecting network devices and cloud platforms, smart gateways have powerful capabilities for data collection, analysis, decision-making, and control, and can centrally manage, dynamically regulate, and automate network devices. However, the existing network device regulation methods of smart gateways mainly rely on preset rules and manual operations, lack intelligence and adaptability, resulting in low accuracy in network device detection and untimely regulation, which cannot meet the needs of modern network management.

[0003] Therefore, it is of great significance to develop a network equipment control method based on intelligent gateway.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a network device control method based on an intelligent gateway, which solves the problem that the existing network device control method of the intelligent gateway mainly relies on preset rules and manual operations, lacks intelligence and adaptability, resulting in low accuracy of network equipment detection and untimely control, and cannot meet the needs of modern network management.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The network device control method based on the intelligent gateway includes the following steps:

[0008] Step 1: The network device monitoring module obtains the network device monitoring parameters of the monitoring device i, wherein the network device monitoring parameters include temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX, and sends the network device monitoring parameters to the network device analysis module;

[0009] Step 2: The network device analysis module obtains the network device abnormality coefficient SYi according to the network device monitoring parameters, and sends the network device abnormality coefficient SYi to the analysis and judgment module;

[0010] The specific process of the network device analysis module obtaining the network device abnormality coefficient SYi is as follows:

[0011] Normalize the temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX, and quantify the values ​​of the temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX according to the preset network equipment analysis model to obtain the network equipment abnormality coefficient SYi;

[0012] The network equipment analysis model is as follows:

[0013]

[0014] Where:

[0015] η is the preset error adjustment factor, and η=0.952;

[0016] π is a mathematical constant;

[0017] ε1, ε2, ε3, ε4 and ε5 are respectively the preset weight factors corresponding to the set temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX, ε1, ε2, ε3, ε4 and ε5 satisfy ε2>ε1>ε4>ε3>ε5>1.269, and ε1=2.39, ε2=2.85, ε3=1.73, ε4=2.01, ε5=1.38;

[0018] Step 3: The analysis and judgment module generates a device control instruction according to the network device abnormality coefficient SYi, and sends the device control instruction to the device control module;

[0019] Step 4: After receiving the device control instruction, the device control module restarts the abnormal network device, generates a control effect monitoring instruction after the restart is completed, and sends the control effect monitoring instruction to the control effect monitoring module;

[0020] Step 5: After receiving the control effect monitoring instruction, the control effect monitoring module obtains the abnormal coefficient TQi before adjustment and the abnormal coefficient THi after adjustment of the abnormal network device, and obtains the adjustment coefficient TJi according to the abnormal coefficient TQi before adjustment and the abnormal coefficient THi after adjustment, and sends the adjustment coefficient TJi to the analysis and judgment module;

[0021] Step 6: The analysis and judgment module generates a control effect monitoring instruction according to the post-adjustment abnormal coefficient TH i and the adjustment coefficient TJ i, and sends the control effect monitoring instruction to the equipment control module; wherein the control effect monitoring instruction includes a poor control effect instruction, a poor control effect instruction, a good control effect instruction, and an excellent control effect instruction;

[0022] Step 7: The device control module controls abnormal network devices according to the control effect monitoring instructions.

[0023] As a preferred implementation of the present invention, the specific process of the network device monitoring module acquiring the network device monitoring parameters including the temperature and humidity information WS is as follows:

[0024] Obtain all network devices connected to the intelligent gateway and mark them as monitoring devices i in sequence, where i=1, ..., n, n is a positive integer, i is the number of any monitoring device, and n is the total number of monitoring devices; the network devices include switches, routers, servers, and industrial control equipment;

[0025] Get the real-time temperature of monitoring device i and the preset standard temperature, get the difference between the two, and mark it as the temperature difference value WC, get the real-time humidity of monitoring device i and the preset standard humidity, get the difference between the two, and mark it as the humidity difference value SC, quantify the temperature difference value WC and the humidity difference value SC, extract the values ​​of the temperature difference value WC and the humidity difference value SC, and substitute them into the formula for calculation. The temperature and humidity information WS is obtained; wherein w1 and w2 are preset proportional coefficients corresponding to the set temperature difference value WC and humidity difference value SC, respectively, w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, and w1=0.68, w2=0.32.

[0026] As a preferred implementation of the present invention, the specific process of the network device monitoring module obtaining the network device monitoring parameters including the pressure flow information YL is as follows:

[0027] Get the real-time voltage of monitoring device i and the preset standard voltage, get the difference between the two, and mark it as the pressure difference value YC, get the real-time current of monitoring device i and the preset standard current, get the difference between the two, and mark it as the flow difference value LC, quantify the pressure difference value YC and the flow difference value LC, extract the values ​​of the pressure difference value YC and the flow difference value LC, and substitute them into the formula for calculation. The pressure-flow information YL is obtained; wherein, e is a mathematical constant, y1 and y2 are preset proportional coefficients corresponding to the set pressure difference value YC and flow difference value LC, respectively, y1 and y2 satisfy y1+y2=1, 0<y1<y2<1, and y1=0.41 and y2=0.59.

[0028] As a preferred implementation of the present invention, the specific process of the network device monitoring module obtaining the network device monitoring parameters including the network speed information WV is as follows:

[0029] The average network upload rate and the average network download rate of the monitoring device i per unit time are obtained, the product of the two is obtained, and the product is marked as the network speed information WV.

[0030] As a preferred implementation of the present invention, the specific process of the network device monitoring module acquiring the network device monitoring parameters including the flow information LL is as follows:

[0031] Obtain the real-time flow of monitoring device i and mark it as the real flow value SL, obtain the average flow of monitoring device i in the most recent preset time period and mark it as the average flow value JL, obtain the difference between the real flow value SL and the average flow value JL, and mark it as flow information LL.

[0032] As a preferred implementation of the present invention, the specific process of the network device monitoring module obtaining the network device monitoring parameters including the operation information YX is as follows:

[0033] Get the total number of operation times and total operation time of monitoring device i, and mark them as row value XC and row time value XS respectively, draw an ellipse with the values ​​of row value XC and row time value XS as the semi-major axis and semi-minor axis of the ellipse respectively, get the area of ​​the ellipse, and mark it as operation information YX.

[0034] As a preferred embodiment of the present invention, the specific process of the analysis and judgment module generating the device control instruction is as follows:

[0035] The network device abnormality coefficient SYi is compared with the preset network device abnormality threshold SYy, and the comparison result is as follows:

[0036] If the network device abnormality coefficient SYi≥network device abnormality threshold SYy, the monitoring device i corresponding to the network device abnormality coefficient SYi is marked as an abnormal network device, and a device control instruction is generated and sent to the device control module.

[0037] As a preferred embodiment of the present invention, the specific process of the control effect monitoring module obtaining the adjustment coefficient TJ i is as follows:

[0038] After receiving the control effect monitoring instruction, the network device abnormality coefficient SYi of the preset time before and after the restart of the abnormal network device is obtained, and they are marked as the abnormality coefficient before adjustment TQi and the abnormality coefficient after adjustment THi, respectively. The abnormality coefficient before adjustment TQi and the abnormality coefficient after adjustment THi are quantified, and the values ​​of the abnormality coefficient before adjustment TQi and the abnormality coefficient after adjustment THi are extracted, and substituted into the formula for calculation. According to the formula Get the adjustment coefficient TJ i;

[0039] The adjustment coefficient TJ i is sent to the analysis and judgment module.

[0040] As a preferred embodiment of the present invention, the specific process of the analysis and judgment module generating the control effect monitoring instruction is as follows:

[0041] The adjusted abnormal coefficient TH i is compared with the preset network device abnormal threshold SYy, and the adjustment coefficient TJ i is compared with the preset adjustment coefficient threshold TJy. The comparison results are as follows:

[0042] If the adjusted abnormal coefficient TH i≥network device abnormal threshold SYy and the adjustment coefficient TJ i<adjustment coefficient threshold TJy, a poor control effect instruction is generated and sent to the device control module;

[0043] If the adjusted abnormal coefficient TH i≥the network device abnormal threshold SYy and the adjustment coefficient TJ i≥the adjustment coefficient threshold TJy, a poor control effect instruction is generated and sent to the device control module;

[0044] If the adjusted abnormal coefficient TH i < the network device abnormal threshold SYy and the adjustment coefficient TJ i < the adjustment coefficient threshold TJy, a better control effect instruction is generated and sent to the device control module;

[0045] If the adjusted abnormal coefficient TH i<network device abnormal threshold SYy and the adjustment coefficient TJ i≥adjustment coefficient threshold TJy, an excellent control effect instruction is generated and sent to the device control module.

[0046] As a preferred implementation of the present invention, the specific process of the device control module controlling abnormal network devices is as follows:

[0047] After receiving the instruction of poor regulation effect, the abnormal network equipment is controlled and shut down;

[0048] After receiving the instruction of poor regulation effect, the indicator light on the abnormal network device lights up and displays red;

[0049] After receiving the instruction with better control effect, the indicator light on the abnormal network device lights up and displays yellow;

[0050] After receiving the instruction of good regulation effect, the abnormal network equipment is controlled to operate normally.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The method first collects data from network devices to obtain network device monitoring parameters, then uses data analysis technology to obtain network device anomaly coefficients based on network device monitoring parameters to comprehensively measure the degree of abnormality of network devices, and the larger the network device anomaly coefficient, the higher the degree of abnormality, and finally restarts the control, then continuously monitors the control effect, and provides classification prompts based on the control effect monitoring results, and finally performs further control;

[0053] This method realizes real-time monitoring, abnormal regulation and abnormal prompts of network equipment. Through abnormal detection and intelligent regulation, it can timely discover and solve potential problems of network equipment, avoid the occurrence and spread of faults, improve the operating stability and performance of network equipment, and adapt to the needs of different network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0055] Figure 1 The present invention is a block diagram of the principles of the network device control method based on the intelligent gateway.

[0056] Figure 2 The present invention is a flow chart of a network device control method based on an intelligent gateway. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1:

[0059] See also Figure 1 As shown, this embodiment is a network device control method based on an intelligent gateway, involving the following modules: a network device monitoring module, a network device analysis module, an analysis and judgment module, a device control module, and a control effect monitoring module;

[0060] The network device monitoring module is used to obtain the network device monitoring parameters of the monitoring device i and send the network device monitoring parameters to the network device analysis module; wherein the network device monitoring parameters include temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX;

[0061] The network device analysis module is used to obtain the network device abnormality coefficient SYi according to the network device monitoring parameters, and send the network device abnormality coefficient SYi to the analysis and judgment module;

[0062] The analysis and judgment module is used to generate a device control instruction according to the abnormal coefficient SYi of the network device, and send the device control instruction to the device control module; it is also used to generate a control effect monitoring instruction according to the abnormal coefficient TH i after adjustment and the adjustment coefficient TJ i, and send the control effect monitoring instruction to the device control module; wherein the control effect monitoring instruction includes a poor control effect instruction, a poor control effect instruction, a good control effect instruction and an excellent control effect instruction;

[0063] The device control module is used to restart the abnormal network device after receiving the device control instruction, generate a control effect monitoring instruction after the restart is completed, and send the control effect monitoring instruction to the control effect monitoring module; and is also used to control the abnormal network device according to the control effect monitoring instruction;

[0064] Among them, the control effect monitoring module is used to obtain the pre-adjustment abnormality coefficient TQi and the post-adjustment abnormality coefficient TH i of the abnormal network equipment after receiving the control effect monitoring instruction, and obtain the adjustment coefficient TJ i according to the pre-adjustment abnormality coefficient TQi and the post-adjustment abnormality coefficient TH i, and send the adjustment coefficient TJ i to the analysis and judgment module.

[0065] Embodiment 2:

[0066] See also Figure 2 As shown, this embodiment is a network device control method based on an intelligent gateway, comprising the following steps:

[0067] Step 1: The network device monitoring module obtains the network device monitoring parameters of the monitoring device i, wherein the network device monitoring parameters include temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX, and sends the network device monitoring parameters to the network device analysis module;

[0068] Step 2: The network device analysis module obtains the network device abnormality coefficient SYi according to the network device monitoring parameters, and sends the network device abnormality coefficient SYi to the analysis and judgment module;

[0069] Step 3: The analysis and judgment module generates a device control instruction according to the network device abnormality coefficient SYi, and sends the device control instruction to the device control module;

[0070] Step 4: After receiving the device control instruction, the device control module restarts the abnormal network device, generates a control effect monitoring instruction after the restart is completed, and sends the control effect monitoring instruction to the control effect monitoring module;

[0071] Step 5: After receiving the control effect monitoring instruction, the control effect monitoring module obtains the abnormal coefficient TQi before adjustment and the abnormal coefficient THi after adjustment of the abnormal network device, and obtains the adjustment coefficient TJi according to the abnormal coefficient TQi before adjustment and the abnormal coefficient THi after adjustment, and sends the adjustment coefficient TJi to the analysis and judgment module;

[0072] Step 6: The analysis and judgment module generates a control effect monitoring instruction according to the post-adjustment abnormal coefficient TH i and the adjustment coefficient TJ i, and sends the control effect monitoring instruction to the equipment control module; wherein the control effect monitoring instruction includes a poor control effect instruction, a poor control effect instruction, a good control effect instruction, and an excellent control effect instruction;

[0073] Step 7: The device control module controls abnormal network devices according to the control effect monitoring instructions.

[0074] Embodiment 3:

[0075] Based on any of the above embodiments, Embodiment 3 of the present invention is a network device monitoring module. The function of the network device monitoring module is to obtain network device monitoring parameters, wherein the network device monitoring parameters include temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX. The specific process is as follows:

[0076] The network device monitoring module obtains all network devices connected to the intelligent gateway and marks them as monitoring devices i in sequence, where i=1, ..., n, n is a positive integer, i is the number of any monitoring device, and n is the total number of monitoring devices; the network devices include switches, routers, servers, and industrial control devices;

[0077] The network equipment monitoring module obtains the real-time temperature of the monitoring device i and the preset standard temperature, obtains the difference between the two, and marks it as the temperature difference value WC, obtains the real-time humidity of the monitoring device i and the preset standard humidity, obtains the difference between the two, and marks it as the humidity difference value SC, quantifies the temperature difference value WC and the humidity difference value SC, extracts the values ​​of the temperature difference value WC and the humidity difference value SC, and substitutes them into the formula for calculation. The temperature and humidity information WS is obtained; wherein w1 and w2 are respectively the preset proportional coefficients corresponding to the set temperature difference value WC and the humidity difference value SC, w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, and w1=0.68 and w2=0.32;

[0078] The network equipment monitoring module obtains the real-time voltage of the monitoring device i and the preset standard voltage, obtains the difference between the two, and marks it as the voltage difference value YC, obtains the real-time current of the monitoring device i and the preset standard current, obtains the difference between the two, and marks it as the flow difference value LC, quantifies the voltage difference value YC and the flow difference value LC, extracts the values ​​of the voltage difference value YC and the flow difference value LC, and substitutes them into the formula for calculation. According to the formula Obtain pressure-flow information YL; wherein e is a mathematical constant, y1 and y2 are preset proportional coefficients corresponding to the set pressure difference value YC and flow difference value LC, respectively, y1 and y2 satisfy y1+y2=1, 0<y1<y2<1, and y1=0.41, y2=0.59;

[0079] The network device monitoring module obtains the average network upload rate and the average network download rate of the monitoring device i per unit time, obtains the product of the two, and marks it as network speed information WV;

[0080] The network device monitoring module obtains the real-time flow of the monitoring device i and marks it as the real flow value SL, obtains the average flow of the monitoring device i in the recent preset time period and marks it as the average flow value JL, obtains the difference between the real flow value SL and the average flow value JL, and marks it as the flow information LL;

[0081] The network device monitoring module obtains the total number of operation times and the total operation time of the monitoring device i, and marks them as the row value XC and the row time value XS respectively, draws an ellipse with the values ​​of the row value XC and the row time value XS as the semi-major axis and the semi-minor axis of the ellipse respectively, obtains the area of ​​the ellipse, and marks it as the operation information YX;

[0082] The network device monitoring module sends the temperature and humidity information WS, the pressure and flow information YL, the network speed information WV, the flow information LL and the operation information YX to the network device analysis module.

[0083] Embodiment 4:

[0084] Based on any of the above embodiments, Embodiment 4 of the present invention is a network device analysis module. The function of the network device analysis module is to obtain the network device abnormality coefficient SYi. The specific process is as follows:

[0085] The network equipment analysis module normalizes the temperature and humidity information WS, the pressure and flow information YL, the network speed information WV, the flow information LL and the operation information YX, and quantifies the values ​​of the temperature and humidity information WS, the pressure and flow information YL, the network speed information WV, the flow information LL and the operation information YX according to the preset network equipment analysis model to obtain the network equipment abnormality coefficient SYi;

[0086] The network equipment analysis model is as follows:

[0087]

[0088] Where:

[0089] η is the preset error adjustment factor, and η=0.952;

[0090] π is a mathematical constant;

[0091] ε1, ε2, ε3, ε4 and ε5 are respectively the preset weight factors corresponding to the set temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX, ε1, ε2, ε3, ε4 and ε5 satisfy ε2>ε1>ε4>ε3>ε5>1.269, and ε1=2.39, ε2=2.85, ε3=1.73, ε4=2.01, ε5=1.38;

[0092] The network equipment analysis module sends the network equipment abnormality coefficient SYi to the analysis and judgment module.

[0093] Embodiment 5:

[0094] Based on any of the above embodiments, Embodiment 5 of the present invention is an analysis and judgment module, which has two functions:

[0095] One of them is to generate device control instructions. The specific process is as follows:

[0096] The analysis and judgment module compares the network device abnormality coefficient SYi with the preset network device abnormality threshold SYy, and the comparison results are as follows:

[0097] If the network device abnormality coefficient SYi ≥ the network device abnormality threshold SYy, the monitoring device i corresponding to the network device abnormality coefficient SYi is marked as an abnormal network device, and a device control instruction is generated and sent to the device control module;

[0098] The second function is to generate control effect monitoring instructions, where the control effect monitoring instructions include poor control effect instructions, poor control effect instructions, good control effect instructions and excellent control effect instructions. The specific process is as follows:

[0099] The analysis and judgment module compares the adjusted abnormal coefficient TH i with the preset network device abnormal threshold SYy, and compares the adjustment coefficient TJ i with the preset adjustment coefficient threshold TJy. The comparison results are as follows:

[0100] If the adjusted abnormal coefficient TH i≥network device abnormal threshold SYy and the adjustment coefficient TJ i<adjustment coefficient threshold TJy, a poor control effect instruction is generated and sent to the device control module;

[0101] If the adjusted abnormal coefficient TH i≥the network device abnormal threshold SYy and the adjustment coefficient TJ i≥the adjustment coefficient threshold TJy, a poor control effect instruction is generated and sent to the device control module;

[0102] If the adjusted abnormal coefficient TH i < the network device abnormal threshold SYy and the adjustment coefficient TJ i < the adjustment coefficient threshold TJy, a better control effect instruction is generated and sent to the device control module;

[0103] If the adjusted abnormal coefficient TH i<network device abnormal threshold SYy and the adjustment coefficient TJ i≥adjustment coefficient threshold TJy, an excellent control effect instruction is generated and sent to the device control module.

[0104] Embodiment 6:

[0105] Based on any of the above embodiments, Embodiment 6 of the present invention is a device control module, and the device control module has two functions:

[0106] One of them is to obtain the generation control effect monitoring instructions. The specific process is as follows:

[0107] After receiving the device control instruction, the device control module restarts the abnormal network device, generates a control effect monitoring instruction after the restart is completed, and sends the control effect monitoring instruction to the control effect monitoring module;

[0108] The second function is to regulate abnormal network devices. The specific process is as follows:

[0109] After receiving the instruction of poor control effect, the device control module controls the abnormal network device to shut down;

[0110] After receiving the instruction of poor control effect, the device control module controls the display light on the abnormal network device to light up and display red;

[0111] After receiving the instruction of better control effect, the device control module controls the display light on the abnormal network device to light up and display yellow;

[0112] After receiving the instruction of good control effect, the device control module controls the abnormal network equipment to operate normally.

[0113] Embodiment 7:

[0114] Based on any of the above embodiments, Embodiment 7 of the present invention is a control effect monitoring module. The function of the control effect monitoring module is to obtain the adjustment coefficient TJ i. The specific process is as follows:

[0115] After receiving the control effect monitoring instruction, the control effect monitoring module obtains the network device abnormality coefficient SYi of the preset time before and after the abnormal network device is restarted, and marks them as the abnormality coefficient before adjustment TQi and the abnormality coefficient after adjustment THi, quantizes the abnormality coefficient before adjustment TQi and the abnormality coefficient after adjustment THi, extracts the values ​​of the abnormality coefficient before adjustment TQi and the abnormality coefficient after adjustment THi, and substitutes them into the formula for calculation. According to the formula Get the adjustment coefficient TJ i;

[0116] The control effect monitoring module sends the adjustment coefficient TJ i to the analysis and judgment module.

[0117] Based on the above embodiments 1-7, the working principle of the present invention is as follows:

[0118] A network device control method based on an intelligent gateway, wherein the network device monitoring parameters of the monitoring device are obtained through a network device monitoring module, wherein the network device monitoring parameters include temperature and humidity information, pressure flow information, network speed information, flow information and operation information; a network device abnormality coefficient is obtained according to the network device monitoring parameters through a network device analysis module; a device control instruction is generated according to the network device abnormality coefficient through an analysis and judgment module; after receiving the device control instruction through the device control module, the abnormal network device is restarted; after the restart is completed, a control effect monitoring instruction is generated; after receiving the control effect monitoring instruction through the control effect monitoring module, the abnormal coefficient before and after the abnormal coefficient of the abnormal network device are obtained, and the adjustment coefficient is obtained according to the abnormal coefficient before and after the abnormal coefficient; a control effect monitoring instruction is generated according to the abnormal coefficient after and after the abnormal coefficient through the analysis and judgment module; wherein the control effect monitoring instruction includes a poor control effect instruction The method comprises the following steps: firstly, the method collects data from the network equipment and obtains the monitoring parameters of the network equipment, and then uses the data analysis technology to obtain the abnormal coefficient of the network equipment according to the monitoring parameters of the network equipment, which can comprehensively measure the abnormal degree of the network equipment, and the larger the abnormal coefficient of the network equipment, the higher the abnormal degree, and finally restarts the control, and then continuously monitors the control effect, and classifies and prompts according to the control effect monitoring results, and finally performs further control; the method realizes real-time monitoring, abnormal control and abnormal prompt of network equipment, and through abnormal detection and intelligent control, it can timely discover and solve potential problems of network equipment, avoid the occurrence and spread of faults, improve the operation stability and performance of network equipment, and adapt to the needs of different network environments.

[0119] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0120] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0121] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. A network device control method based on an intelligent gateway, characterized in that: The following steps are involved: Step 1: The network device monitoring module obtains the network device monitoring parameters of the monitoring device i, wherein the network device monitoring parameters include temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX, and sends the network device monitoring parameters to the network device analysis module; Step 2: The network device analysis module obtains the network device abnormality coefficient SYi according to the network device monitoring parameters, and sends the network device abnormality coefficient SYi to the analysis and judgment module; The specific process of the network device analysis module obtaining the network device abnormality coefficient SYi is as follows: The values ​​of the temperature and humidity information WS, the pressure and flow information YL, the network speed information WV, the flow information LL and the operation information YX are quantified according to the preset network equipment analysis model to obtain the network equipment abnormality coefficient SYi; The network equipment analysis model is as follows: Where: η is the preset error adjustment factor; π is a mathematical constant; ε1, ε2, ε3, ε4 and ε5 are respectively the preset weight factors corresponding to the set temperature and humidity information WS, pressure and flow information YL, network speed information WV, flow information LL and operation information YX; Step 3: The analysis and judgment module generates a device control instruction according to the network device abnormality coefficient SYi, and sends the device control instruction to the device control module; Step 4: After receiving the device control instruction, the device control module restarts the abnormal network device, generates a control effect monitoring instruction after the restart is completed, and sends the control effect monitoring instruction to the control effect monitoring module; Step 5: After receiving the control effect monitoring instruction, the control effect monitoring module obtains the abnormal coefficient TQi before adjustment and the abnormal coefficient THi after adjustment of the abnormal network device, and obtains the adjustment coefficient TJi according to the abnormal coefficient TQi before adjustment and the abnormal coefficient THi after adjustment, and sends the adjustment coefficient TJi to the analysis and judgment module; Step 6: The analysis and judgment module generates a control effect monitoring instruction according to the abnormal coefficient THi after adjustment and the adjustment coefficient TJi, and sends the control effect monitoring instruction to the equipment control module; wherein the control effect monitoring instruction includes a poor control effect instruction, a poor control effect instruction, a good control effect instruction and an excellent control effect instruction; Step 7: The device control module controls abnormal network devices according to the control effect monitoring instructions.

2. The network device control method based on the intelligent gateway according to claim 1 is characterized in that: The specific process of the network device monitoring module obtaining the network device monitoring parameters including the temperature and humidity information WS is as follows: Obtain all network devices connected to the intelligent gateway, and mark them as monitoring devices i in sequence, where i=1, ..., n, n is a positive integer, i is the number of any monitoring device, and n is the total number of monitoring devices; Get the real-time temperature of monitoring device i and the preset standard temperature, get the difference between the two, and mark it as the temperature difference value WC, get the real-time humidity of monitoring device i and the preset standard humidity, get the difference between the two, and mark it as the humidity difference value SC, quantify the temperature difference value WC and the humidity difference SC, and calculate the humidity difference value according to the formula The temperature and humidity information WS is obtained.

3. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the network device monitoring module obtaining the network device monitoring parameters including the pressure flow information YL is as follows: Get the real-time voltage of monitoring device i and the preset standard voltage, get the difference between the two, and mark it as the voltage difference value YC, get the real-time current of monitoring device i and the preset standard current, get the difference between the two, and mark it as the flow difference value LC, quantify the voltage difference value YC and the flow difference value LC, and use the formula The pressure-flow information YL is obtained; wherein, e is a mathematical constant, and y1 and y2 are preset proportional coefficients corresponding to the set pressure difference value YC and flow difference value LC, respectively.

4. The network device control method based on the intelligent gateway according to claim 1 is characterized in that: The specific process of the network device monitoring module obtaining the network device monitoring parameters including the network speed information WV is as follows: The average network upload rate and the average network download rate of the monitoring device i per unit time are obtained, the product of the two is obtained, and the product is marked as the network speed information WV.

5. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the network device monitoring module obtaining the network device monitoring parameters including the flow information LL is as follows: Obtain the real-time flow of monitoring device i and mark it as the real flow value SL, obtain the average flow of monitoring device i in the most recent preset time period and mark it as the average flow value JL, obtain the difference between the real flow value SL and the average flow value JL, and mark it as flow information LL.

6. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the network device monitoring module obtaining the network device monitoring parameters including the operation information YX is as follows: Get the total number of operation times and total operation time of monitoring device i, and mark them as row value XC and row time value XS respectively, draw an ellipse with the values ​​of row value XC and row time value XS as the semi-major axis and semi-minor axis of the ellipse respectively, get the area of ​​the ellipse, and mark it as operation information YX.

7. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the analysis and judgment module generating the device control instruction is as follows: The network device abnormality coefficient SYi is compared with the preset network device abnormality threshold SYy, and the comparison result is as follows: If the network device abnormality coefficient SYi≥network device abnormality threshold SYy, the monitoring device i corresponding to the network device abnormality coefficient SYi is marked as an abnormal network device, and a device control instruction is generated and sent to the device control module.

8. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the control effect monitoring module obtaining the adjustment coefficient TJi is as follows: After receiving the control effect monitoring instruction, the network device abnormality coefficient SYi of the preset time before and after the abnormal network device is restarted is obtained, and they are marked as the abnormal coefficient before adjustment TQi and the abnormal coefficient after adjustment THi, respectively. The abnormal coefficient before adjustment TQi and the abnormal coefficient after adjustment THi are quantified according to the formula Get the adjustment coefficient TJi; The adjustment coefficient TJi is sent to the analysis and judgment module.

9. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the analysis and judgment module generating the control effect monitoring instruction is as follows: The adjusted abnormal coefficient THi is compared with the preset network device abnormal threshold SYy, and the adjustment coefficient TJi is compared with the preset adjustment coefficient threshold TJy. The comparison results are as follows: If the adjusted abnormal coefficient THi≥network device abnormal threshold SYy and the adjustment coefficient TJi<adjustment coefficient threshold TJy, a poor control effect instruction is generated and sent to the device control module; If the adjusted abnormal coefficient THi≥network device abnormal threshold SYy and the adjustment coefficient TJi≥adjustment coefficient threshold TJy, a poor control effect instruction is generated and sent to the device control module; If the adjusted abnormal coefficient THi is less than the network device abnormal threshold SYy and the adjustment coefficient TJi is less than the adjustment coefficient threshold TJy, a better control effect instruction is generated and sent to the device control module; If the adjusted abnormal coefficient THi is less than the network device abnormal threshold SYy and the adjustment coefficient TJi is greater than or equal to the adjustment coefficient threshold TJy, an excellent control effect instruction is generated and sent to the device control module.

10. The network device control method based on intelligent gateway according to claim 1 is characterized in that: The specific process of the device control module controlling abnormal network devices is as follows: After receiving the instruction of poor regulation effect, the abnormal network equipment is controlled and shut down; After receiving the instruction of poor regulation effect, the indicator light on the abnormal network device lights up and displays red; After receiving the instruction with better control effect, the indicator light on the abnormal network device lights up and displays yellow; After receiving the instruction of good regulation effect, the abnormal network equipment is controlled to operate normally.

Images