An industrial host network monitoring system

Abstract

The present invention relates to the technical field of industrial host computers, and discloses an industrial host network monitoring system, including an industrial host body, wherein the industrial host body includes a network terminal, a data server, a web server, a monitoring host and a display, wherein the network terminal includes an intelligent learning module, an operation database, a feature point extraction module, an analysis module and a monitoring module electrically connected, wherein the intelligent learning module is electrically connected to the operation database, the feature point extraction module and the analysis module, and wherein the feature point extraction module is electrically connected to the analysis module. During use, the present invention sets an intelligent learning module, so that in response to abnormal information generated during operation of industrial equipment that meets and is suitable for intelligent learning, operation instructions can be issued in a timely and intelligent manner, thereby realizing more intelligent management and control of the industrial equipment.

Description

Technical Field

[0001] The invention relates to the technical field of industrial host computers, and in particular to an industrial host computer network monitoring system. Background Art

[0002] The role of industrial host is mainly reflected in improving production efficiency and product quality, data storage and analysis, fault diagnosis and maintenance, and promoting the development of intelligent manufacturing, improving production efficiency and product quality, and realizing fine control of industrial production processes through high-performance computing and rapid response of industrial computers, thereby improving production efficiency and product quality. Industrial computers have large-capacity data storage capabilities and can store and analyze collected data. Through statistics and analysis of large amounts of data, valuable information and rules can be obtained to provide support for corporate decision-making and optimization. Industrial computers play an important role in fault diagnosis and maintenance of industrial equipment. Through status monitoring and diagnosis of various components and sensors, faults can be discovered and repaired in a timely manner, ensuring the high reliability and stability of industrial equipment, and playing an indispensable role in the development of modern industries such as intelligent manufacturing. With the integration of the Internet of Things, big data, and artificial intelligence technologies, the application scenarios of industrial computers will become more extensive, and continue to provide strong support for the intelligent transformation of all walks of life.

[0003] In the prior art, the industrial host network monitoring monitors all the devices currently connected to the network during use. Due to the large amount of data received and processed, the existing industrial host is not intelligent enough in the monitoring process, and such cumbersome interactive information needs to be processed by the operator.

[0004] After searching, it is found that Chinese patent documents disclose an industrial monitoring system based on a network terminal (publication number: CN106254829A). The present invention discloses an industrial monitoring system based on a network terminal, including a network terminal, a data server, a web server, a monitoring host and a display. The network terminal corresponds to the display one by one. The monitoring host is connected to the web server and the network terminal through a network. The network terminal includes a first network terminal and a second network terminal. The display includes a first display and a second display. The first display and the first network terminal and the camera form a video monitoring system; the second display and the second network terminal and the data acquisition module form a data monitoring system. The present invention utilizes a monitoring host, a network terminal and a matching display, and can run independently on the display, and receive video information or data information sent by the video monitoring system and the data monitoring system, and is monitored by a dedicated person, thereby improving the reliability of monitoring, without the need to purchase a computer host or server, reducing costs, and having high mobility, but still has the following defects:

[0005] Although the above-mentioned industrial monitoring system based on network terminals realizes the use of monitoring host, network terminal and supporting display, can run independently on the display, and receive video information or data information sent by video monitoring system and data monitoring system, and is monitored by dedicated personnel respectively, which improves the reliability of monitoring, does not require the purchase of computer host or server, reduces costs, and has high mobility, but it still has the problem that the industrial host network monitoring monitors all current network-connected devices during use. Due to the large amount of data received and processed, the existing industrial host is not intelligent enough in the monitoring process, and such cumbersome interactive information needs to be processed by the operator. Summary of the invention

[0006] The purpose of the present invention is to provide an industrial host network monitoring system, which solves the problem that in the prior art in the background technology, the industrial host network monitoring monitors all currently network-connected devices during use. Due to the large amount of data received and processed, the existing industrial host is not intelligent enough in the monitoring process, and such cumbersome interactive information needs to be processed by the operator.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] An industrial host network monitoring system includes an industrial host body, wherein the industrial host body includes a network terminal, a data server, a web server, a monitoring host and a display;

[0009] The network terminal includes an intelligent learning module, an operation database, a feature point extraction module, an analysis module and a monitoring module, wherein the intelligent learning module is electrically connected to the operation database, the feature point extraction module and the analysis module, and the feature point extraction module is electrically connected to the analysis module and the monitoring module;

[0010] The function of the intelligent learning module is to learn the abnormal instructions of the industrial equipment transmitted to the network terminal, and complete the learning of the corresponding abnormal instructions according to the operation instructions issued by the operator.

[0011] Preferably, the monitoring module is used to implement network monitoring of the operating status of each industrial device in the industrial system, and to obtain parameter data of the industrial device that generates abnormal information in real time, and to perform corresponding operation processing on it;

[0012] The function of the analysis module is to analyze the abnormal information obtained during the real-time monitoring of industrial equipment in the industrial system by the monitoring module, and determine whether the current abnormal information satisfies the intelligent learning module to obtain operation instructions for the abnormal information of the industrial equipment based on the conclusion of intelligent learning.

[0013] Preferably, the monitoring module is used to implement network monitoring of the operating status of each industrial device in the industrial system, and to obtain parameter data of the industrial device that generates abnormal information in real time, and to perform corresponding operation processing on it;

[0014] The function of the analysis module is to analyze the abnormal information obtained during the real-time monitoring of industrial equipment in the industrial system by the monitoring module, and determine whether the current abnormal information satisfies the intelligent learning module to obtain operation instructions for the abnormal information of the industrial equipment based on the conclusion of intelligent learning.

[0015] Preferably, the analysis module determines whether the current abnormal information satisfies the intelligent learning module to obtain the operation instruction for the abnormal information of the industrial equipment according to the conclusion of the intelligent learning. The specific operation process is:

[0016] S1: When the network monitoring of the operating status of each industrial equipment in the industrial system is in progress, abnormal information is generated;

[0017] S2: receiving abnormal information;

[0018] S3: Obtain the name and serial number of the industrial equipment with current abnormal information;

[0019] S4: Submit the acquired name and serial number of the industrial equipment to the operation database;

[0020] S5: Complete data search by name and serial number of industrial equipment through the operation database;

[0021] S6: Determine, based on the acquired data of the industrial equipment, whether the intelligent learning module is satisfied with generating an operation instruction for the abnormal information of the industrial equipment.

[0022] Preferably, the function of the feature point extraction module is to extract feature points of the acquired abnormal information in the industrial system, and to complete feature construction and feature selection for the abnormal information.

[0023] Preferably, the specific steps of the feature construction are:

[0024] Step 1: Complete standardization processing on information parameters in the received abnormal information;

[0025] Step 2: Normalize the abnormal information after standardization;

[0026] Step 3: Complete data enhancement operations on the normalized information;

[0027] Step 4: Extract local features of abnormal information after enhancement operation;

[0028] Step 5: Use linear and nonlinear spatial embedding methods;

[0029] Step 6: Complete nonlinear expansion;

[0030] Step 7: Complete the functional discretization of the feature points that have completed nonlinear expansion.

[0031] Preferably, the specific process of feature selection is:

[0032] Process 1: First, receive the abnormal information feature points of the completed feature points;

[0033] Process 2: Add corresponding floating thresholds according to the floating parameter data in the acquired abnormal information feature points;

[0034] Process 3: Obtain the range of abnormal information feature points;

[0035] Process 4: Submit the feature point parameter data to the job database to complete the search;

[0036] Process 5: Screening the relevant parameter data obtained by searching with the obtained abnormal information feature point range interval;

[0037] Process 6: Obtain the best abnormal information feature selection parameter data.

[0038] Preferably, by obtaining the best abnormal information feature selection parameter data for the acquired abnormal information, the abnormal information feature selection parameter data is submitted to the job database to complete the search and obtain the operation instructions for the abnormal information of the industrial equipment, and at the same time, the operation instructions for the abnormal information of the industrial equipment are floated to obtain the final operation instructions for the abnormal information of the industrial equipment.

[0039] Preferably, the specific operation process of the network terminal is:

[0040] (1): First, the monitoring module is used to monitor the parameter data generated by the operation of each industrial equipment in the industrial system;

[0041] (2): All parameter data acquired by the monitoring module are stored based on the name and serial number of each industrial device in the industrial system;

[0042] (3) When the monitoring module detects abnormal signals from industrial equipment;

[0043] (4) Analyzing the abnormal signal according to the analysis module to determine whether it is suitable to issue an operation instruction through the intelligent learning module;

[0044] (5) If it is not suitable for the intelligent learning module to issue an operation instruction, the operation instruction is transmitted to the operation end, and the operation instruction issued by the operator is manually issued, and the operation instruction and abnormal information are recorded;

[0045] (6) If it is appropriate to issue an operation instruction through the intelligent learning module, the operation instruction for obtaining abnormal information of the industrial equipment is completed through the feature point extraction module;

[0046] (7) The intelligent learning module completes intelligent learning based on all abnormal information of the industrial equipment stored in the operation database, and obtains operation instructions for the abnormal information of the industrial equipment based on the conclusion of the intelligent learning.

[0047] Preferably, the operation database is electrically connected to the writing module, and the writing module is electrically connected to the monitoring module and the backup database;

[0048] The writing module is used to write all parameter data of industrial equipment in the industrial system into the operation database and the backup database according to the set number of writing thresholds and interval writing thresholds;

[0049] The function of the backup database is to receive and store all parameter data of industrial equipment in the industrial system from the write module, and at the same time, according to the calibration threshold set by the operator, when the calibration threshold is reached, the parameter data in the operating database and the backup database are calibrated.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0051] 1. The present invention sets up an intelligent learning module during use, so that in response to abnormal information generated during operation of industrial equipment that meets and is suitable for intelligent learning, operation instructions can be issued in a timely and intelligent manner, thereby achieving more intelligent management and control of industrial equipment.

[0052] 2. During use, the present invention realizes accurate and scalable matching of the generated abnormal information with corresponding past abnormal information through the set feature point extraction module, and according to the data difference between the past abnormal information and the current abnormal information, the parameters of the operation instructions issued by the intelligent learning module are floated, so that more abnormal information can be processed, and the issued operation instructions can be more accurate and effective.

[0053] 3. During use, the present invention adopts an operation database and a backup database. In this way, according to the calibration threshold set by the operator, when the calibration threshold is reached, the parameter data in the operation database and the backup database are calibrated to achieve data integrity in the industrial system. At the same time, all parameter data of the industrial equipment in the industrial system are written through the set number of write thresholds and interval write thresholds, which can reduce the usage load of the operation database and the backup database. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the system structure of the industrial host body in the present invention;

[0055] Figure 2 A schematic diagram of the system structure of the network terminal in the present invention;

[0056] Figure 3 A schematic diagram of a specific operation flow of an operation instruction for obtaining abnormal information of the industrial equipment in the present invention;

[0057] Figure 4 It is a schematic diagram of specific steps of characteristic construction in the present invention;

[0058] Figure 5 A schematic diagram of a specific process of feature selection in the present invention;

[0059] Figure 6 It is a schematic diagram of the specific operation flow of the network terminal in the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0061] Example 1

[0062] See also Figure 1-6 , an industrial host network monitoring system, including an industrial host body, the industrial host body including a network terminal, a data server, a web server, a monitoring host and a display;

[0063] The network terminal includes an intelligent learning module, an operation database, a feature point extraction module, an analysis module and a monitoring module. The intelligent learning module is electrically connected to the operation database, the feature point extraction module and the analysis module. The feature point extraction module is electrically connected to the analysis module and the monitoring module.

[0064] The function of the intelligent learning module is to learn the abnormal instructions of the industrial equipment transmitted to the network terminal, and complete the learning of the corresponding abnormal instructions according to the operation instructions issued by the operator.

[0065] The function of the data server is to verify the security of the data generated by the industrial devices in the industrial system.

[0066] The role of a web server is to respond to requests from clients and provide corresponding services, including but not limited to the World Wide Web, email, and file transfer.

[0067] The function of the monitoring host is to monitor the safety of industrial equipment in the industrial system.

[0068] The function of the display is to display parameter data such as the operation of industrial equipment in the industrial system.

[0069] The intelligent learning module learns the abnormal instructions of the industrial equipment monitored by the monitoring module, completes the intelligent learning based on all the abnormal information of the industrial equipment stored in the operation database, and obtains the operation instructions for the abnormal information of the industrial equipment based on the conclusion of the intelligent learning.

[0070] The intelligent learning module issues operation instructions based on the abnormal instructions of the industrial equipment monitored by the monitoring module, which must meet the intelligent operation number threshold and the intelligent operation time threshold. The intelligent operation number threshold is that the same abnormal instruction generated by the industrial equipment is greater than the set intelligent operation number threshold.

[0071] The intelligent operation time threshold is the time when the industrial equipment can generate autonomous operation instructions, which is less than the set intelligent operation time threshold. If it is greater than the intelligent operation time threshold, no operation instructions for the abnormal information of the industrial equipment will be issued, and it is necessary to re-complete intelligent learning according to the corresponding industrial equipment operation instructions stored in the job database. The intelligent operation time threshold must meet the requirements of the most recent operation instruction for the abnormal information of the industrial equipment obtained according to the conclusion of the intelligent learning without any abnormality, and the farthest operation instruction for the abnormal information of the industrial equipment obtained according to the conclusion of the intelligent learning without any abnormality that meets the intelligent operation number threshold, all of which are within the intelligent operation time threshold.

[0072] The function of the data server is to store the parameter data generated by the corresponding industrial equipment in the industrial system based on the name and serial number of each industrial equipment in the industrial system. The parameter data generated in the industrial system includes the operation data of the industrial equipment, the abnormal information record of the industrial equipment and the operation instructions matching the abnormal information of the industrial equipment.

[0073] The monitoring module is used to realize network monitoring of the operation status of each industrial equipment in the industrial system, and to obtain parameter data of the industrial equipment that generates abnormal information in real time, and to perform corresponding operation processing on it so that the industrial equipment can complete the recovery operation. The corresponding operation processing is that the operator operates the abnormal information and obtains the operation instructions for the abnormal information of the industrial equipment according to the conclusion of the intelligent learning through the intelligent learning module;

[0074] The role of the analysis module is to analyze the current abnormal information after obtaining the abnormal information during the real-time monitoring of industrial equipment in the industrial system by the monitoring module, and determine whether the current abnormal information satisfies the intelligent learning module to obtain the operation instructions for the abnormal information of the industrial equipment based on the conclusion of intelligent learning.

[0075] The function of the feature point extraction module is to extract the feature points of the abnormal information in the acquired industrial system, and to complete feature construction and feature selection for the abnormal information.

[0076] Both feature construction and feature selection are to find relevant data for variables or attributes in the abnormal information, and represent them as being very domain-specific, while associating relevant data, and associating relevant data is the data enhancement operation.

[0077] The specific process of feature association related data is as follows:

[0078] (1) Convert the raw data in the abnormal information into standardized features;

[0079] (2) Supplement features through feature construction methods;

[0080] (3) Integrate feature construction into feature modeling.

[0081] In feature association related data, internal representations of similar structured features are computed, for example, by such units in an artificial neural network.

[0082] The operation database is electrically connected to the writing module, and the writing module is electrically connected to the monitoring module and the backup database;

[0083] The function of the write module is to write all parameter data of industrial equipment in the industrial system into the operation database and the backup database according to the set number of write thresholds and interval write thresholds;

[0084] The write-in threshold is that when the parameter data of the receiving monitoring module in the write module reaches the set write-in threshold, all parameter data are written into the backup database and the job database, and the cycle continues;

[0085] The function of the interval write threshold is to write the parameter data of the receiving monitoring module in the write module to the backup database and the job database when the set interval time threshold is triggered, and reset the interval write threshold after the write threshold is triggered.

[0086] The function of the backup database is to receive and write to the module to complete the storage of all parameter data of industrial equipment in the industrial system, and according to the calibration threshold set by the operator, when the calibration threshold is reached, the parameter data in the operating database and the backup database are calibrated.

[0087] Example 2

[0088] See also Figure 1 , Figure 2 and Figure 3 , an industrial host network monitoring system, including an analysis module, the analysis module determines whether the current abnormal information satisfies the intelligent learning module to obtain the operation instructions for the abnormal information of the industrial equipment according to the conclusion of the intelligent learning. The specific operation process is:

[0089] S1: When the network monitoring of the operating status of each industrial equipment in the industrial system is in progress, abnormal information is generated;

[0090] S2: receiving abnormal information;

[0091] S3: Obtain the name and serial number of the industrial equipment with current abnormal information;

[0092] S4: Submit the acquired name and serial number of the industrial equipment to the operation database;

[0093] S5: Complete data search by name and serial number of industrial equipment through the operation database;

[0094] S6: Determine, based on the acquired data of the industrial equipment, whether the intelligent learning module is satisfied with generating an operation instruction for the abnormal information of the industrial equipment.

[0095] Example 3

[0096] See also Figure 1 , Figure 2 , Figures 4 to 5 , an industrial host network monitoring system, including a feature point extraction module, and the specific steps of feature construction are:

[0097] Step 1: Complete standardization processing on information parameters in the received abnormal information;

[0098] Step 2: Normalize the abnormal information after standardization;

[0099] Step 3: Complete data enhancement operations on the normalized information;

[0100] Step 4: Extract local features of abnormal information after enhancement operation;

[0101] Step 5: Use linear and nonlinear spatial embedding methods;

[0102] Step 6: Complete nonlinear expansion;

[0103] Step 7: Complete the functional discretization of the feature points that have completed nonlinear expansion.

[0104] The specific process of feature selection is:

[0105] Process 1: First, receive the abnormal information feature points of the completed feature points;

[0106] Process 2: Add corresponding floating thresholds according to the floating parameter data in the acquired abnormal information feature points;

[0107] Process 3: Obtain the range of abnormal information feature points;

[0108] Process 4: Submit the feature point parameter data to the job database to complete the search;

[0109] Process 5: Screening the relevant parameter data obtained by searching with the obtained abnormal information feature point range interval;

[0110] Process 6: Obtain the best abnormal information feature selection parameter data.

[0111] By obtaining the best abnormal information feature selection parameter data for the acquired abnormal information, and submitting the abnormal information feature selection parameter data to the operation database, the operation instructions for the abnormal information of the industrial equipment are searched and obtained. At the same time, the operation instructions for the abnormal information of the industrial equipment are floated to obtain the final operation instructions for the abnormal information of the industrial equipment.

[0112] The operation instructions for the abnormal information of the industrial equipment are subjected to floating processing, and the floating threshold of the floating processing is the direct difference of the corresponding information feature points, and the floating ratio is obtained through the difference.

[0113] Example 4

[0114] See also Figure 1 , Figure 2 and Figure 6 , an industrial host network monitoring system, including a network terminal, the specific operation process of the network terminal is:

[0115] (1): First, the monitoring module is used to monitor the parameter data generated by the operation of each industrial equipment in the industrial system;

[0116] (2): All parameter data acquired by the monitoring module are stored based on the name and serial number of each industrial device in the industrial system;

[0117] (3) When the monitoring module detects abnormal signals from industrial equipment;

[0118] (4) Analyzing the abnormal signal according to the analysis module to determine whether it is suitable to issue an operation instruction through the intelligent learning module;

[0119] (5) If it is not suitable for the intelligent learning module to issue an operation instruction, the operation instruction is transmitted to the operation end, and the operation instruction issued by the operator is manually issued, and the operation instruction and abnormal information are recorded;

[0120] (6) If it is appropriate to issue an operation instruction through the intelligent learning module, the operation instruction for obtaining abnormal information of the industrial equipment is completed through the feature point extraction module;

[0121] (7) The intelligent learning module completes intelligent learning based on all abnormal information of the industrial equipment stored in the operation database, and obtains operation instructions for the abnormal information of the industrial equipment based on the conclusion of the intelligent learning.

[0122] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit thereof, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial host network monitoring system, comprising an industrial host body, characterized in that: The industrial host body includes a network terminal, a data server, a web server, a monitoring host and a display; The network terminal includes an intelligent learning module, an operation database, a feature point extraction module, an analysis module and a monitoring module, wherein the intelligent learning module is electrically connected to the operation database, the feature point extraction module and the analysis module, and the feature point extraction module is electrically connected to the analysis module and the monitoring module; The function of the intelligent learning module is to learn the abnormal instructions of the industrial equipment transmitted to the network terminal, and complete the learning of the corresponding abnormal instructions according to the operation instructions issued by the operator; The function of the feature point extraction module is to extract the feature points of the abnormal information in the acquired industrial system, and to complete feature construction and feature selection for the abnormal information; The specific steps of the feature construction are: Step 1: Complete standardization processing on information parameters in the received abnormal information; Step 2: Normalize the abnormal information after standardization; Step 3: Complete data enhancement operations on the normalized information; Step 4: Extract local features of abnormal information after enhancement operation; Step 5: Use linear and nonlinear spatial embedding methods; Step 6: Complete nonlinear expansion; Step 7: Complete the functional discretization of the feature points that have completed nonlinear expansion.

2. The industrial host network monitoring system according to claim 1, characterized in that: The intelligent learning module learns the abnormal instructions of the industrial equipment monitored by the monitoring module, completes the intelligent learning based on all the abnormal information of the industrial equipment stored in the operation database, and obtains the operation instructions for the abnormal information of the industrial equipment according to the conclusion of the intelligent learning; The function of the data server is to store the parameter data generated by the corresponding industrial equipment in the industrial system based on the name and serial number of each industrial equipment in the industrial system. The parameter data generated in the industrial system includes the operation data of the industrial equipment, the abnormal information record of the industrial equipment and the operation instructions matching the abnormal information of the industrial equipment.

3. The industrial host network monitoring system according to claim 1, characterized in that: The monitoring module is used to realize network monitoring of the operating status of each industrial device in the industrial system, and to obtain parameter data of the industrial device that generates abnormal information in real time, and to perform corresponding operations on it; The function of the analysis module is to analyze the abnormal information obtained during the real-time monitoring of industrial equipment in the industrial system by the monitoring module, and determine whether the current abnormal information satisfies the intelligent learning module to obtain operation instructions for the abnormal information of the industrial equipment based on the conclusion of intelligent learning.

4. The industrial host network monitoring system according to claim 3 is characterized in that: The analysis module determines whether the current abnormal information satisfies the intelligent learning module to obtain the operation instructions for the abnormal information of the industrial equipment according to the conclusion of the intelligent learning. The specific operation process is: S1: When the network monitoring of the operating status of each industrial equipment in the industrial system is in progress, abnormal information is generated; S2: receiving abnormal information; S3: Obtain the name and serial number of the industrial equipment with current abnormal information; S4: Submit the acquired name and serial number of the industrial equipment to the operation database; S5: Complete data search by name and serial number of industrial equipment through the operation database; S6: Determine, based on the acquired data of the industrial equipment, whether the intelligent learning module is satisfied with generating an operation instruction for the abnormal information of the industrial equipment.

5. The industrial host network monitoring system according to claim 1, characterized in that: The specific process of feature selection is as follows: Process 1: First, receive the abnormal information feature points of the completed feature points; Process 2: Add corresponding floating thresholds according to the floating parameter data in the acquired abnormal information feature points; Process 3: Obtain the range of abnormal information feature points; Process 4: Submit the feature point parameter data to the job database to complete the search; Process 5: Screening the relevant parameter data obtained by searching with the obtained abnormal information feature point range interval; Process 6: Obtain the best abnormal information feature selection parameter data.

6. The industrial host network monitoring system according to claim 5, characterized in that: By obtaining the best abnormal information feature selection parameter data for the acquired abnormal information, the abnormal information feature selection parameter data is submitted to the operation database to complete the search and obtain the operation instructions for the abnormal information of the industrial equipment. At the same time, the operation instructions for the abnormal information of the industrial equipment are floated to obtain the final operation instructions for the abnormal information of the industrial equipment.

7. The industrial host network monitoring system according to claim 1, characterized in that: The specific operation process of the network terminal is as follows: (1): First, the monitoring module is used to monitor the parameter data generated by the operation of each industrial equipment in the industrial system; (2): All parameter data acquired by the monitoring module are stored based on the name and serial number of each industrial equipment in the industrial system; (3) When the monitoring module detects abnormal signals from industrial equipment; (4) The analysis module analyzes the abnormal signal to determine whether it is suitable for issuing operation instructions through the intelligent learning module; (5) If it is not suitable for the intelligent learning module to issue an operation instruction, the operation instruction is transmitted to the operation end, and the operator manually issues the operation instruction, and the operation instruction and abnormal information issued by the operator are recorded; (6) If it is appropriate to issue an operation instruction through the intelligent learning module, the operation instruction for obtaining abnormal information of the industrial equipment is completed through the feature point extraction module; (7) The intelligent learning module completes intelligent learning based on all abnormal information of the industrial equipment stored in the operation database, and obtains operation instructions for the abnormal information of the industrial equipment based on the conclusion of the intelligent learning.

8. The industrial host network monitoring system according to claim 1, characterized in that: The operation database is electrically connected to the writing module, and the writing module is electrically connected to the monitoring module and the backup database; The writing module is used to write all parameter data of industrial equipment in the industrial system into the operation database and the backup database according to the set number of writing thresholds and interval writing thresholds; The function of the backup database is to receive and store all parameter data of industrial equipment in the industrial system from the write module, and at the same time, according to the calibration threshold set by the operator, when the calibration threshold is reached, the parameter data in the operating database and the backup database are calibrated.

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