Monitoring method and device based on device fingerprint

Abstract

The invention discloses a monitoring method and device based on equipment fingerprints, and belongs to the technical field of equipment monitoring. The importance degree of equipment attributes is determined through an analytic hierarchy process-entropy method, the equipment is divided into a first level, a second level and a third level (Three) according to the importance degree of the equipment attributes, and different monitoring intervals are set for each level; equipment data acquisition: acquiring equipment information by utilizing an SNMP (Simple Network Management Protocol), Syslog, an intelligent probe or a heartbeat mechanism technology; equipment state analysis: according to the information fed back by the equipment, dividing the health state of the equipment into three classes (Three): a steady state, a risk working state and a dangerous working state, and realizing three-state monitoring of the equipment state; and dynamically adjusting the equipment monitoring period: adaptively adjusting the monitoring period according to the equipment importance level and the equipment health state.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment monitoring, and relates to a monitoring method and device based on equipment fingerprint. Background Art

[0002] Health status monitoring of power equipment is crucial to ensuring the safety and stability of the power grid. Traditional monitoring methods often use fixed monitoring cycles, which are difficult to adapt to real-time changes in equipment status. For different types of equipment or systems, the application of a unified monitoring frequency often ignores the specific attributes and operating characteristics of the equipment, which may not only lead to a waste of resources, but also affect the monitoring effect and damage the accuracy and real-time nature of the monitoring results. In addition, the setting of commonly used monitoring frequencies usually lacks scientific methodology and clear standards, and mostly relies on the experience and judgment of operation and maintenance personnel, which may cause the setting of monitoring frequencies to be too high or too low, and fail to achieve the best monitoring effect. Summary of the invention

[0003] The present invention aims to solve the problems in the prior art and provides a monitoring method and device based on device fingerprint.

[0004] A device based on device fingerprint, comprising:

[0005] The device classification module is used to classify devices.

[0006] The device data acquisition module is used to receive the heartbeat data sent regularly by the monitored device, and includes an encryption and decryption module, which is used to encrypt, decrypt, receive or send information.

[0007] The device status analysis module analyzes the health status of the device based on the device attribute information in the heartbeat data.

[0008] The cycle adjustment module dynamically adjusts the information sending cycle of the monitored object according to the equipment importance level and equipment status.

[0009] A monitoring method based on device fingerprint includes the following steps:

[0010] Step S1: Classification of equipment importance: The importance of equipment attributes is determined by using the hierarchical analysis-entropy method. The equipment is divided into level one, level two, and level three (Three) according to the importance of the equipment attributes, and different monitoring intervals are set for each level.

[0011] Step S2: Device data collection: Use SNMP, Sys log, smart probe or heartbeat mechanism technology to collect device information.

[0012] Step S3: Equipment status analysis: According to the information fed back by the equipment, the equipment health status is divided into three categories (Three): stable state, risky working state and dangerous working state, so as to realize three-state monitoring of the equipment status.

[0013] Step S4: Dynamically adjust the equipment monitoring period: adaptively adjust the monitoring period according to the equipment importance level and equipment health status.

[0014] The advantages of the present invention are:

[0015] The implemented monitoring method and device significantly enhance the effectiveness and efficiency of the monitoring system by implementing intelligent monitoring of the importance and operating conditions of power equipment.

[0016] First, by accurately assessing the importance of equipment and monitoring equipment status in real time, the system can perform more frequent and detailed monitoring of key equipment. This not only ensures the reliable operation of key equipment, but also effectively reduces the risk of equipment failure. At the same time, for equipment that is running stably, the system can reduce excessive monitoring, thereby optimizing resource allocation and significantly reducing operation and maintenance costs.

[0017] Secondly, the dynamic adjustment of monitoring frequency strategy adopted by the present invention greatly improves the accuracy and timeliness of data monitoring. Through intelligent scheduling of monitoring resources, the system can respond to changes in equipment status in a timely manner, ensuring that equipment failures and performance degradation can be quickly identified and effectively handled. This provides strong data support for equipment maintenance and fault prevention, and improves maintenance efficiency and prevention effects.

[0018] In addition, the system's intelligent monitoring method not only improves the overall effectiveness and efficiency of the monitoring system, but also optimizes the use of resources. Through differentiated management, the system can concentrate resources to monitor key equipment and avoid waste of resources, thereby improving the economic benefits of the entire monitoring system.

[0019] Finally, by dynamically adjusting the monitoring frequency and intelligent monitoring methods, the system significantly enhanced the stability of the power system, reduced the impact of equipment failures on the system, and improved the overall reliability and economic benefits of the system. Such an intelligent monitoring system not only ensures the normal operation of power equipment, but also provides a solid foundation for the long-term stability and efficient operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. As shown in the figure:

[0021] Figure 1 This is a schematic diagram of the device monitoring method of the present application.

[0022] Figure 2 This is a flow chart of the equipment monitoring method of the present application.

[0023] Figure 3 This is the structural system diagram of the hierarchical analysis method of this application.

[0024] Figure 4 This is the architecture diagram of the hierarchical analysis method of this application.

[0025] Figure 5 This is the architecture diagram of the hierarchical analysis method and entropy method of this application.

[0026] Figure 6 This is a schematic diagram of the process of device data collection for this application.

[0027] Figure 7 This is a schematic diagram of the structure of the equipment monitoring device of the present application.

[0028] Figure 8 Schematic diagram of the structure of the equipment monitoring device in the embodiment of the present application. DETAILED DESCRIPTION

[0029] 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.

[0030] Example 1: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown, a monitoring method and device based on device fingerprint are used for power equipment to solve the problems existing in the health status monitoring of traditional power equipment, especially the problem that the fixed monitoring cycle is difficult to adapt to the real-time status changes of the equipment.

[0031] A monitoring method based on device fingerprint (Adaptive TT (Three-Three)) contains the following steps:

[0032] Step S1: Classification of equipment importance: The importance of equipment attributes is determined by using the hierarchical analysis-entropy method. The equipment is divided into level one, level two, and level three according to the importance of the equipment attributes. Each level corresponds to a different monitoring interval, such as 16 minutes, 64 minutes, or 256 minutes.

[0033] Step S2: Equipment data collection: Use SNMP, Sys log, smart probe, heartbeat mechanism and other technologies to collect equipment information.

[0034] Step S3: Equipment status analysis: According to the information fed back by the equipment, the equipment health status is divided into three categories (Three): stable state, risky working state, and dangerous working state, so as to realize three-state monitoring of the equipment status.

[0035] Step S4: Dynamically adjust the equipment monitoring period: adaptively adjust the monitoring period according to the equipment importance level and equipment health status.

[0036] Among them, step S1 includes: using the hierarchical analysis method to determine the subjective weight of the equipment attributes, using the entropy method to determine the objective weight of the equipment attributes, and combining the subjective weight and the objective weight to divide the equipment into level one (T1), level two (T2), and level three (T3), and set different monitoring intervals for each level.

[0037] Among them, step S2 includes: acquiring device information by deploying intelligent probes, setting up Agents based on protocols such as SNMP, receiving heartbeat packets sent regularly based on protocols such as MQTT, etc. The monitoring information includes the operating status and performance parameters of the device.

[0038] Among them, step S3 includes: the equipment evaluates the equipment health status N according to the deviation between the collected data and the historical data, where the stable state is 0, the risky work is 1, and the dangerous work is 2.

[0039] The three-state monitoring based on the equipment status in step S3 includes the following steps:

[0040] To determine the "health status" of the device, the device regularly sends a heartbeat packet to the main station or background to feedback the current health status N of the device, where the stable state is 0, the risky work is 1, and the dangerous work is 2.

[0041] The monitoring frequency is adaptively adjusted based on the importance of the equipment and the equipment status. For equipment in a high abnormal state, the system will shorten the monitoring cycle in order to closely track the operating status of the equipment and improve the accuracy and timeliness of fault prediction and detection. On the contrary, when the equipment is in a low abnormal state, the system will extend the monitoring cycle to reduce monitoring costs and effectively utilize monitoring resources. This dynamic adjustment mechanism ensures that monitoring activities are both efficient and economical, and maximizes the operational stability and safety of the system.

[0042] Among them, step S4 includes: when the health status of the equipment is a stable state, it is recorded as health status 0, and the original monitoring cycle is maintained; when the health status of the equipment is a risky state, it is recorded as health status 1, and the monitoring cycle is shortened; when the health status of the equipment is a dangerous state, it is recorded as health status 2, and a higher monitoring frequency is set to closely monitor the equipment parameters.

[0043] According to the formula t = 2 N ×w determines the actual monitoring interval time t of the equipment, where is the monitoring interval w of the equipment importance, and N is the health status of the equipment.

[0044] Example 2: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown, a device based on device fingerprint includes:

[0045] The device classification module is used to classify devices.

[0046] The device data acquisition module is used to receive the heartbeat data sent regularly by the monitored device, and includes an encryption and decryption module, which is used to encrypt, decrypt, receive or send information.

[0047] The device status analysis module analyzes the health status of the device based on the device attribute information in the heartbeat data.

[0048] The cycle adjustment module dynamically adjusts the information sending cycle of the monitored object according to the equipment importance level and equipment status.

[0049] Example 3: Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown, a monitoring method based on device fingerprint (adaptive TT (Three-Three)) contains the following steps:

[0050] Step S1: Classification of equipment importance levels includes: constructing a key equipment parameter system based on the analytic hierarchy process and the entropy method, and then determining the equipment level based on the score.

[0051] The Analytical Hierarchy Process (AHP) is a decision-making method that combines qualitative and quantitative methods. Its basic steps are the establishment of a hierarchical structure, the construction of a judgment matrix, the determination of the weight vector of the judgment matrix, the consistency test of the judgment matrix, the total ranking of the hierarchy and the consistency test.

[0052] The levels are divided into three categories:

[0053] ①The highest level (target level): is the predetermined goal or result of analyzing the problem.

[0054] ② Middle layer (structural layer): The intermediate links involved in achieving the goal, which can be composed of several levels.

[0055] ③The bottom layer (indicator layer): various indicators and measures to achieve the goals.

[0056] Figure 4 The specific steps of the hierarchical analysis method in the present invention include:

[0057] Step S201: Establish a hierarchical structure system for equipment evaluation: Figure 3 As shown in the figure, the target layer is the importance of equipment, the criterion layer is the equipment value, operating income, monitoring cost, and performance status, and the solution layer is the equipment cost, maintenance cost, operating intensity, work efficiency, downtime loss, communication cost, embedded cost, reliable life, failure rate, and repair rate.

[0058] Step S202, expert scoring: Expert scoring is performed based on various indicators. Quantitative scoring is performed on equipment cost, maintenance cost, reliable life, failure rate and repair rate, as shown in Table 1; qualitative scoring is performed on operation intensity, work efficiency, downtime loss, communication overhead and embedded overhead. The evaluation levels are excellent, good, medium, poor and poor, as shown in Table 1, with scores of 5, 4, 3, 2 and 1 respectively.

[0059] Table 1:

[0060]

[0061] Step S203, construct a judgment matrix: the specific criteria are shown in Table 2.

[0062] Table 2:

[0063]

[0064] The weight vector AB is obtained as [0.375 0.170 0.303 0.152], B1-C is [0.7500.250], B2-C is [0.169 0.461 0.370], B3-C is [0.333 0.667], and B4-C is [0.548 0.241 0.211].

[0065] Step S204: Matrix consistency check: CI = (λ max -n) / (n-1) and CR=CI / RI are used to calculate the consistency index CI and the consistency ratio CR.

[0066] Where: max is the maximum eigenvalue, n is the order of the judgment matrix, and RI is the average random consistency index related to the matrix order. If the calculated CR is less than 0.1, we generally believe that the judgment matrix has satisfactory consistency; on the contrary, if it is greater than 0.1, it means that there may be contradictions before and after the comparison or judgment process, and the judgment matrix needs to be adjusted and modified. Repeat the above steps for the processed matrix again until CR is less than 0.1.

[0067] The entropy method is an objective method for determining weights, which only depends on the discreteness of the source data. In the theory of information entropy, the smaller the entropy value, the greater the uncertainty of the source and the less information it contains. The larger the entropy value, the smaller the uncertainty and the more information it contains. Based on this feature, the information entropy theory is applied to the judgment of the discreteness between indicators. The greater the discreteness of an indicator, the greater the influence of the indicator on the system, that is, the greater the weight.

[0068] The following specific steps are also included:

[0069] Step ①: Calculate the weight of the index value of the ith solution under the jth index

[0070]

[0071] Step ②: Calculate the entropy value of the jth indicator Wherein, k=1 / ln(n)>0;

[0072] Step ③: Calculate the entropy weight of the jth indicator

[0073] Step ④: Determine the comprehensive weight of the indicators

[0074] Among them, r ij is the quantitative evaluation value of the jth indicator of the i-th evaluation object, i = 1, 2, L, n, j = 1, 2, L, m, m is the number of indicators, n is the number of samples, α j is the weight of the subjective assignment method.

[0075] As shown in Table 3.

[0076] Table 3:

[0077]

[0078] like Figure 5 As shown in the figure, the subjective weight of equipment attributes is determined by the analytic hierarchy process, and the objective weight of equipment attributes is determined by the entropy method. The total score of the equipment is calculated by combining the subjective weight and the objective weight. The equipment is divided into level one, level two, and level three, and a different monitoring interval w is set for each level.

[0079] In this embodiment, the preset equipment scores are 0-1 points for level 1, 1-2.5 points for level 2, and 2.5 points or more for level 3, and the preset monitoring intervals are 2048 minutes, 512 minutes, and 64 minutes. Equipment A has a score of 1.687, the equipment level is level 2, and the monitoring interval w is 512 minutes; equipment B has a score of 0.863, the equipment level is level 1, and the monitoring interval w is 2048 minutes; equipment C has a score of 2.538, the equipment level is level 3, and the monitoring interval w is 64 minutes.

[0080] Step S2, equipment data collection, also includes the following steps:

[0081] By collecting and analyzing the characteristic parameters of the device, a unique fingerprint of each device is generated. These fingerprint data include static and dynamic information of the device. Static information includes basic device information, operating system information, chip information and communication information, etc. Dynamic information includes CPU utilization, memory utilization, disk utilization, etc. For example, the collected information can include operating system version, operating system release information, port information, IP address, CPU utilization, memory utilization and disk utilization;

[0082] A heartbeat mechanism is used to monitor the device status. A suitable heartbeat mechanism is selected according to system requirements and device characteristics, such as Ping, TCP, UDP, HTTP, MQTT or a custom protocol. The device then periodically reports heartbeat information to the server. The heartbeat packet contains identification information, timestamp, and custom device behavior information.

[0083] like Figure 6As shown in the figure, the device and server interact using the MQTT protocol. MQTT (Message Queuing Telemetry Transport) is a lightweight, open standard communication protocol that is particularly suitable for communication between IoT devices. The device periodically pushes its own attribute information to the MQTT server. The MQTT information is encrypted using a symmetric key. After the MQTT server pushes the information to the platform backend server, the platform backend server receives the information and decrypts it using the symmetric key.

[0084] Step S3, device status analysis, also includes the following steps: Determining the "health status" of the device is the field of status monitoring. After receiving the heartbeat packet, the server analyzes its device status and feeds back the health status N, where the stable state is 0, the risky work is 1, and the dangerous work is 2.

[0085] The collected data is divided into string type and numeric type.

[0086] For string data, such as operating system version, operating system release information, port information, IP address, etc., collect data X i Compare with the original database device attribute information, if they are the same, m i is 0, otherwise it is 1, and the string data state quantity M1 is calculated:

[0087]

[0088] For numerical data, such as CPU utilization, memory utilization, disk utilization, etc., the data collected by monitoring is X i The reference value of this monitoring quantity is D i , if X i >D i , then m i is 1, otherwise it is 0, and the numerical data state quantity M2 is calculated:

[0089]

[0090] The total state quantity M = M1 + M2, where m i For each monitoring component value, i represents each type of monitoring information, n1 represents the total number of string-type monitoring information, and n2 represents the total number of numerical-type monitoring information, and thresholds d1 and d2 are set.

[0091] If M<d1, the equipment is judged to be in a stable state;

[0092] If d1≤M<d2, the equipment is judged to be in a risky state;

[0093] If M≥d2, the equipment is judged to be in a dangerous state.

[0094] Taking the monitoring device operating system version, operating system release information, port information, IP address, CPU utilization, memory utilization, and disk utilization as an example, the reference values ​​of the numerical monitoring quantities CPU utilization, memory utilization, and disk utilization are set to 0.92, 0.9, and 0.8, and the thresholds d1 and d2 are 1 and 3 respectively. When the collected character data and the original database attributes are the same, M1=0, and the collected numerical data are 0.70, 0.80, and 0.82, m1=0, m2=0, m3=1, and M2=1, then M=1, M≥d1=1, which is a risk state.

[0095] Among them, when the health status of the equipment is "dangerous operation", the back-end server generates an alarm message and displays an early warning on the visual interface.

[0096] Step S4 dynamically adjusts the equipment monitoring period, and also includes the following steps:

[0097] According to the formula t = 2 N ×w determines the actual monitoring interval t of the device, where the device importance monitoring interval is w, N is the device health status, and the background server initiates a request to the MQTT server, and the MQTT server pushes the information to the device.

[0098] Example 4: Figure 2 As shown, a monitoring method based on device fingerprint includes the following steps:

[0099] Step 1: Use the analytic hierarchy process and entropy method to determine the importance of equipment into three levels: T1, T2, and T3.

[0100] Step 2: Determine the initial monitoring period W according to the equipment importance level.

[0101] Step 3: Collect device behavior characteristic parameters.

[0102] Step 4: Evaluate the equipment health status based on the monitoring parameters and determine the health level N.

[0103] Step 5: If the health status is stable, N=0, go to step 8.

[0104] Step 6: If the health status is a risky state, N=1, execute step 8.

[0105] Step 7: If the health status is dangerous, N=2, go to step 8.

[0106] Step 8: Set monitoring period t=2 N ×w.

[0107] Example 5: Figure 8As shown, a device based on device fingerprint in the embodiment includes: a device importance classification module, a receiving module, a state analysis module, an encryption and decryption module and a period adjustment module.

[0108] The equipment importance classification module is used to classify equipment.

[0109] The receiving module is used to receive the heartbeat data sent regularly by the monitored device.

[0110] The status analysis module analyzes the health status of the device based on the device attribute information in the heartbeat data.

[0111] The encryption and decryption module is used to encrypt and decrypt the received or sent MQTT information.

[0112] The cycle adjustment module dynamically adjusts the information sending cycle of the monitored object according to the equipment importance level and equipment status.

[0113] Among them, the encryption and decryption module can use the national secret algorithm SM4 for symmetric encryption and decryption.

[0114] Among them, the cycle adjustment module comprehensively considers the equipment importance level and equipment status:

[0115] When the health status of the device is stable, it is recorded as health status 0 and the original monitoring cycle is maintained.

[0116] When the health status of the device is a risky state, it is recorded as health status 1, and a monitoring cycle adjustment request is sent to the MQTT server to increase the monitoring frequency. The MQTT server pushes the information to the monitored object.

[0117] When the health status of the device is in a dangerous state, it is recorded as health status 2, and a monitoring period adjustment request is sent to the MQTT server to set a higher monitoring frequency to closely monitor the device parameters.

[0118] Three-state monitoring based on equipment status:

[0119] To determine the "health status" of the device, the device regularly sends a heartbeat packet to the main station or background to feedback the current health status N of the device, where the stable state is 0, the risky work is 1, and the dangerous work is 2.

[0120] The monitoring frequency is adaptively adjusted based on the importance of the equipment and the equipment status. For equipment in a high abnormal state, the system will shorten the monitoring cycle in order to closely track the operating status of the equipment and improve the accuracy and timeliness of fault prediction and detection. On the contrary, when the equipment is in a low abnormal state, the system will extend the monitoring cycle to reduce monitoring costs and effectively utilize monitoring resources. This dynamic adjustment mechanism ensures that monitoring activities are both efficient and economical, and maximizes the operational stability and safety of the system.

[0121] The above embodiments are provided only for the purpose of describing the present invention, and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims. Various equivalent substitutions and modifications made without departing from the spirit and principles of the present invention should all be included within the scope of the present invention.

Claims

1. A monitoring method based on device fingerprint, characterized in that: Contains the following steps: Step S1: Classification of equipment importance: The importance of equipment attributes is determined by using the hierarchical analysis-entropy method. The equipment is divided into level one, level two, and level three according to the importance of the equipment attributes. Different monitoring intervals are set for each level. Step S2: Device data collection: Use SNMP, Syslog, smart probe or heartbeat mechanism technology to collect device information; Step S3: Equipment status analysis: According to the information fed back by the equipment, the equipment health status is divided into three categories (Three): stable state, risky working state and dangerous working state, so as to realize the three-state monitoring of the equipment status; Step S4: Dynamically adjust the equipment monitoring period: adaptively adjust the monitoring period according to the equipment importance level and equipment health status.

2. According to claim 1, a monitoring method based on device fingerprint is characterized in that: Step S2, equipment data collection, also includes the following steps: By collecting and analyzing the characteristic parameters of the device, a unique fingerprint of each device is generated. The fingerprint data includes static and dynamic information of the device. Static information includes basic device information, operating system information, chip information and communication information. Dynamic information includes CPU utilization, memory utilization and disk utilization. The collected information can include operating system version, operating system release information, port information, IP address, CPU utilization, memory utilization and disk utilization. A heartbeat mechanism is used to monitor the device status. A suitable heartbeat mechanism is selected according to system requirements and device characteristics, such as Ping, TCP, UDP, HTTP, MQTT or a custom protocol. The device then periodically reports heartbeat information to the server. The heartbeat packet contains identification information, timestamp, and custom device behavior information.

3. The monitoring method based on device fingerprint according to claim 1, characterized in that: Step S3, device status analysis, also includes the following steps: after receiving the heartbeat packet, the server analyzes its device status and feeds back a health status N, where a stable state is 0, a risky operation is 1, and a dangerous operation is 2. The collected data is divided into string type and numeric type. For string data, operating system version, operating system release information, port information, IP address, collection data X i Compare with the original database device attribute information, if they are the same, m i is 0, otherwise it is 1, and the string data state quantity M1 is calculated: For numerical data, such as CPU utilization, memory utilization, and disk utilization, the data collected by monitoring is X i The reference value of this monitoring quantity is D i , if X i >D i , then m i is 1, otherwise it is 0, and the numerical data state quantity M2 is calculated: The total state quantity M = M1 + M2, where m i For each monitoring component value, i represents each type of monitoring information, n1 represents the total number of string type monitoring information, n2 represents the total number of numerical type monitoring information, and sets the thresholds d1 and d2. If M<d1, the equipment is judged to be in a stable state; If d1≤M<d2, the equipment is judged to be in a risky state; If M≥d2, the equipment is judged to be in a dangerous state. Monitor seven types of device information: device operating system version, operating system release information, port information, IP address, CPU utilization, memory utilization, and disk utilization. Set the reference values ​​of the numerical monitoring quantities CPU utilization, memory utilization, and disk utilization to 0.92, 0.9, and 0.8, and the thresholds d1 and d2 to 1 and 3, respectively. When the collected character data and the original database attributes are the same, M1=0, and when the collected numerical data are 0.70, 0.80, and 0.82, m1=0, m2=0, m3=1, and M2=1, then M=1, M≥d1=1, which is a risk state. Among them, when the health status of the equipment is "dangerous operation", the back-end server generates an alarm message and displays an early warning on the visual interface.

4. The monitoring method based on device fingerprint according to claim 1, characterized in that: Step S4 dynamically adjusts the equipment monitoring period, and also includes the following steps: according to the formula t=2 N ×w determines the actual monitoring interval t of the device, where the device importance monitoring interval is w, N is the device health status, and the background server initiates a request to the MQTT server, and the MQTT server pushes the information to the device.

5. The monitoring method based on device fingerprint according to claim 1, characterized in that: Step S1 also includes the following steps: using the hierarchical analysis method to determine the subjective weight of the equipment attributes, using the entropy method to determine the objective weight of the equipment attributes, and combining the subjective weight and the objective weight to divide the equipment into level one (T1), level two (T2), and level three (T3), and set different monitoring intervals for each level.

6. The monitoring method based on device fingerprint according to claim 1, characterized in that: Step S2 also includes the following steps: acquiring device information by deploying intelligent probes, setting up Agents based on the SNMP protocol, and receiving heartbeat packets sent regularly based on the MQTT protocol. The monitoring information includes the operating status and performance parameters of the device.

7. The monitoring method based on device fingerprint according to claim 1, characterized in that: The three-state monitoring based on the equipment status in step S3 includes the following steps: Determine the "health status" of the device. The device regularly sends a heartbeat packet to the main station or background to feedback the current health status N of the device. The stable state is 0, the risky work is 1, and the dangerous work is 2. The monitoring frequency is adaptively adjusted based on the importance of the equipment and the equipment status. For equipment in a high abnormal state, the system will shorten the monitoring cycle in order to closely track the operating status of the equipment and improve the accuracy and timeliness of fault prediction and detection. On the contrary, when the equipment is in a low abnormal state, the system will extend the monitoring cycle to reduce monitoring costs and effectively utilize monitoring resources. This dynamic adjustment mechanism ensures that monitoring activities are both efficient and economical, and maximizes the operational stability and safety of the system.

8. The monitoring method based on device fingerprint according to claim 1, characterized in that: Step S4 also includes the following steps: when the health status of the equipment is stable, it is recorded as health status 0 and the original monitoring cycle is maintained; when the health status of the equipment is risky, it is recorded as health status 1 and the monitoring cycle is shortened; when the health status of the equipment is dangerous, it is recorded as health status 2 and a higher monitoring frequency is set to pay close attention to the equipment parameters. According to the formula t = 2 N ×w determines the actual monitoring interval time t of the equipment, where is the monitoring interval w of the equipment importance, and N is the health status of the equipment.

9. A monitoring device based on device fingerprint, characterized in that: include: Equipment classification module, used to classify equipment; The device data acquisition module is used to receive the heartbeat data sent regularly by the monitored device, including an encryption and decryption module, which is used to encrypt, decrypt, receive or send information; The device status analysis module analyzes the health status of the device based on the device attribute information in the heartbeat data; The cycle adjustment module adjusts the information sending cycle of the monitored object according to the equipment importance level and equipment status dynamics.

10. The monitoring device based on device fingerprint according to claim 9, characterized in that: The encryption and decryption module uses the national secret algorithm SM4 for symmetric encryption and decryption; Comprehensive consideration of periodic adjustment based on equipment importance level and equipment status: When the health status of the device is stable, it is recorded as health status 0 and the original monitoring cycle is maintained. When the health status of the device is at risk, it is recorded as health status 1, and a monitoring cycle adjustment request is sent to the MQTT server to increase the monitoring frequency. The MQTT server pushes the information to the monitored object. When the health status of the device is in a dangerous state, it is recorded as health status 2, and a monitoring period adjustment request is sent to the MQTT server to set a higher monitoring frequency to closely monitor the device parameters.

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