Industrial equipment remote state monitoring management system based on Internet of Things

Abstract

The invention discloses an industrial equipment remote state monitoring management system based on Internet of Things, which relates to the technical field of industrial equipment monitoring and comprises an equipment modeling unit, a data processing unit, an anomaly analysis unit, a troubleshooting unit and a positioning display unit. According to the method, partition division is carried out based on the equipment operation model, the multiple monitoring nodes are preset, the real-time operation parameters are obtained through the sensor groups arranged at the monitoring nodes, the real-time operation parameters and the standard operation parameters are compared and analyzed to generate the operation fluctuation coefficient, the abnormal nodes are obtained, and then the abnormal influence area is delimited; function division is carried out according to operation results of industrial equipment involved in the abnormal distribution area graph, main influence nodes and secondary influence nodes are obtained based on operation relevance of the main working parts and the secondary working parts, and then troubleshooting results are obtained at the main influence nodes through a fault tree analysis method; the fault center can be quickly positioned when the industrial equipment has multiple faults, and the troubleshooting efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment monitoring, and in particular to an industrial equipment remote status monitoring and management system based on the Internet of Things. Background Art

[0002] Industrial equipment status monitoring and management methods are an important part of the development of the Industrial Internet. Through the Internet and Internet of Things technologies, real-time data interaction and information sharing between devices and between devices and systems are realized to achieve the digital transformation of the industry. The research and development of industrial equipment status monitoring and management methods aims to improve production efficiency and product quality by monitoring the operating status of equipment in real time, promptly discovering potential problems and taking preventive measures. The status monitoring of industrial equipment is mostly based on monitoring abnormal data during equipment operation. When monitoring the status of the equipment, it is first necessary to collect some parameters or data during the operation of the equipment, which requires sensors to obtain data. However, the existing technology only analyzes the data obtained by the sensor and alarms for abnormal faults; In large-scale production plants, multiple industrial equipment are required to form a continuous production line. The operating results of the industrial equipment affect each other. Therefore, once an abnormal operation is discovered, there may be multiple abnormal points. Tracking the primary abnormal point is the core work of troubleshooting. In addition, some faults in the working equipment may be caused by the influence of other components, while the other components themselves are not abnormal, making it difficult to troubleshoot. In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0003] The purpose of the present invention is to obtain real-time operating parameters through a sensor group set at a monitoring node, compare and analyze the real-time operating parameters with standard operating parameters to generate an operating fluctuation coefficient, delineate the main influencing nodes and the secondary influencing nodes, and then obtain fault troubleshooting results at the main influencing nodes through a fault tree analysis method. This can quickly locate the fault center when multiple faults occur in industrial equipment, thereby improving the efficiency of fault troubleshooting.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: an industrial equipment remote status monitoring and management system based on the Internet of Things, including an equipment modeling unit, a data processing unit, an abnormality analysis unit, a fault troubleshooting unit and a positioning display unit, wherein: The equipment modeling unit is used to obtain basic equipment data of industrial equipment to establish an equipment operation model, and then partition the equipment operation model and preset multiple monitoring nodes. The monitoring node distribution map is sent to the abnormality analysis unit and the fault troubleshooting unit for storage. At the same time, the standard operating parameters of the industrial equipment in a fault-free state are obtained and sent to the data processing unit; The data processing unit is used to obtain real-time operating parameters through a sensor group set at the monitoring node, compare and analyze the real-time operating parameters with standard operating parameters to generate an operating fluctuation coefficient, obtain a preset operating fluctuation judgment threshold to generate a data anomaly signal, obtain the monitoring node where the data anomaly signal is generated as an abnormal node, and send the position coordinates of the abnormal node to the abnormality analysis unit; The abnormality analysis unit obtains and processes the location coordinates of each abnormal node, demarcates the abnormal impact area on the monitoring node distribution map according to the preset monitoring range of the monitoring node, and obtains the abnormal distribution area map and sends it to the fault investigation unit; The fault troubleshooting unit is used to obtain and process the abnormal distribution area map, perform functional division according to the operation results of the industrial equipment involved in the abnormal distribution area map, obtain the main working parts and auxiliary working parts, and obtain the main influencing nodes and secondary influencing nodes based on the operation correlation of the main working parts and auxiliary working parts. Then, the fault troubleshooting results are obtained at the main influencing nodes through the fault tree analysis method and sent to the positioning display unit.

[0005] Furthermore, the positioning display unit is used to obtain an abnormal distribution area map and a fault troubleshooting result. The fault troubleshooting result includes the fault location coordinates and the degree of fault impact. The fault node is marked with identifier A at the fault location coordinates, and the affected node is marked with identifier B at the coordinates of other abnormal nodes to obtain a fault distribution map and perform a visual display.

[0006] Furthermore, the specific process of generating the running fluctuation coefficient is as follows: S101: The sensor group is specifically a multi-type sensor configured based on the data types of transmission components and interference-affected components involved in the operation of the industrial equipment. Real-time operating parameters are acquired through the multi-type sensors. The real-time operating parameters include data A1, data A2, data A3, ..., data An, where n is the number of the multi-type sensors. S102. Obtain standard operating parameters of the industrial equipment in a fault-free state, where the standard operating parameters correspond to the real-time operating parameters, specifically data B1, data B2, data B3, ..., data Bn, where n is the number of data types used for state assessment; S103. Calculate the operating fluctuation coefficient Uk according to the following formula: , where i = 1, 2, 3, …, n, n is the number of data types for status assessment, that is, the number of multi-type sensors. The operation fluctuation coefficient is used to reflect the degree of abnormal state during the operation of industrial equipment. The larger the operation fluctuation coefficient, the higher the degree of abnormal state during the operation of industrial equipment. Conversely, the smaller the operation fluctuation coefficient, the lower the degree of abnormal state during the operation of industrial equipment.

[0007] Furthermore, the specific process of obtaining the location coordinates of abnormal nodes is as follows: S201: Obtain a preset operation fluctuation judgment threshold. If the operation fluctuation coefficient Uk is greater than or equal to the operation fluctuation judgment threshold, generate a data anomaly signal, and obtain a corresponding monitoring node based on the source of the data anomaly signal. S202. Establish a three-dimensional coordinate system based on the base plane of the equipment operation model, mark all monitoring nodes in the three-dimensional coordinate system to obtain a monitoring node distribution map, and simultaneously obtain all monitoring nodes that generate data abnormality signals, mark them as abnormal nodes, and then obtain the position coordinates of the abnormal nodes.

[0008] Furthermore, with the location coordinates of the monitoring node as the center, the monitoring range of the monitoring node is preset to R, then the effective monitoring area S of the monitoring node is: S=πR 2 , and then obtain the position coordinates of each abnormal node, and use a circular image with an area of S to cover the monitoring node distribution map at the abnormal node to represent the abnormal impact area to obtain the abnormal distribution area map.

[0009] Furthermore, the specific process of obtaining the main influencing nodes and the secondary influencing nodes is as follows: S301. Obtain an abnormal distribution area map, record the working components involved in the abnormal distribution area map, and mark all working components in the abnormal impact area as abnormal components to be analyzed; S302. Obtain the operating principle and operating result of each working component of the industrial equipment from the network database, perform functional division based on the operating result of the abnormal component to be analyzed, and classify the working component whose operating result is a direct production object as a primary working component, and classify the working component whose operating result is an indirect production object as a secondary working component; The main working components include: power system: such as electric motors, engines, pumps, etc., used to drive equipment or provide power; Transmission system: such as gears, bearings, chains, etc., responsible for transmitting power to other components; Control systems: such as PLCs, sensors, actuators, etc., used to control and monitor equipment operations; Executive components: such as working heads, cutting tools, nozzles, etc., which directly contact the processed or produced materials and perform the core work of the equipment; Structural frame: provides support and stability for the equipment to ensure normal operation of the equipment; The main working components include: cooling system: such as radiator, water pump, fan, etc., used to maintain normal equipment temperature; Lubrication system: such as lubricating oil pumps, oil pipes, etc., to reduce friction and extend the service life of major components; Electrical systems: such as wires, power supplies, fuses, etc., which provide power but are not directly involved in the core operation of the equipment; Protective components: such as protective covers and protective nets, which play a role in safety protection; Auxiliary tools: such as hydraulic pumps, pneumatic systems, etc., are used to provide additional operating capabilities but are not the core drive system of the equipment.

[0010] S303. Mark the main working components in the abnormal distribution area diagram as main influencing nodes, and mark the secondary working components that have operation relevance with the main working components as secondary influencing nodes. The operation relevance is other working components that have a collaborative relationship with the operation results of the working component.

[0011] Furthermore, the specific process of obtaining the troubleshooting results is as follows: S401. Acquire real-time operating parameters of an abnormal node of a main working component, compare the real-time operating parameters with standard operating parameters to obtain abnormal data performance, determine a top event based on the abnormal data performance, and determine an analysis scope as a boundary condition based on the fault impact range of the top event; Top event selection: The top event is the failure of the core function of the equipment (such as "production line shutdown"); Boundary conditions: such as eliminating human operating errors and focusing on equipment failures; S402, Fault Tree Construction and Logical Decomposition: Starting from the top event, list all direct causes (i.e., intermediate events), and then decompose to the bottom event. Analyze the process using logic gates, as follows: AND gate: All input events occur simultaneously, resulting in the top event; For example, “battery fire = BMS failure ∧ battery cell short circuit ∧ cooling system failure”; OR gate: Any input event can lead to the top event; For example, “Robot arm stuck = harmonic reducer wear ∨ servo motor overload”; S403. Derive minimum cut sets from the top event layer by layer downward or from the bottom events layer by layer upward, and then identify the minimum cut set, that is, the minimum combination of bottom events that leads to the top event. The number of minimum cut sets reflects the system risk, and each cut set represents a failure mode. For example, the minimum cut set of abnormal opening of subway doors is {door controller failure} or {mechanical lock failure}.

[0012] S404: Obtain bottom event failure probability data λi, and calculate the top event failure probability Ptop according to the following formula: , where t is the number of failures; S405: Calculate the intersection of minimum cut sets. If multiple cut sets contain the same base event, the event is a candidate for the fault center, and the fault troubleshooting result can be obtained.

[0013] Furthermore, the mark A is represented by a red ☆, and the mark B is represented by a green △, and the faults are graded according to their impact, including level 1, level 2, and level 3, where: The first level of fault impact corresponds to the use of small-sized markers A and B; The fault impact level 2 corresponds to the use of medium-sized markers A and B; The third level of fault impact corresponds to the use of large-size labels A and B.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This IoT-based industrial equipment remote status monitoring and management system is partitioned and delineated based on the equipment operation model and multiple monitoring nodes are preset. Real-time operating parameters are obtained through a sensor group set at the monitoring node. The real-time operating parameters are compared and analyzed with standard operating parameters to generate an operation fluctuation coefficient. Abnormal nodes are obtained and then abnormal impact areas are delineated. Functional division is performed according to the operation results of the industrial equipment involved in the abnormal distribution area diagram. Based on the operation correlation of the main working parts and the auxiliary working parts, the main impact nodes and the secondary impact nodes are obtained. Then, the fault tree analysis method is used to obtain the fault troubleshooting results at the main impact nodes. When multiple faults occur in industrial equipment, the fault center can be quickly located, thereby improving the troubleshooting efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Shown is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0017] Example: like Figure 1 As shown, the remote status monitoring and management system for industrial equipment based on the Internet of Things includes an equipment modeling unit, a data processing unit, an abnormality analysis unit, a fault troubleshooting unit, and a positioning display unit, wherein: The equipment modeling unit is used to obtain basic equipment data of industrial equipment to establish an equipment operation model, and then partition the equipment operation model and preset multiple monitoring nodes. The monitoring node distribution map is sent to the abnormality analysis unit and the fault troubleshooting unit for storage. At the same time, the standard operating parameters of the industrial equipment in a fault-free state are obtained and sent to the data processing unit; The data processing unit is used to obtain real-time operating parameters through a sensor group set at the monitoring node, compare and analyze the real-time operating parameters with standard operating parameters to generate an operating fluctuation coefficient, obtain a preset operating fluctuation judgment threshold to generate a data anomaly signal, obtain the monitoring node where the data anomaly signal is generated as an abnormal node, and send the position coordinates of the abnormal node to the abnormality analysis unit; The specific process of generating the running fluctuation coefficient is as follows: S101. The sensor group is specifically a multi-type sensor set according to the data types of transmission components and interference-affected components involved in the operation of the industrial equipment. Real-time operating parameters are obtained through the multi-type sensors. The real-time operating parameters include data A1, data A2, data A3, ..., data An, where n is the number of the multi-type sensors. S102. Obtain standard operating parameters of the industrial equipment in a fault-free state. The standard operating parameters correspond to the real-time operating parameters, specifically data B1, data B2, data B3, ..., data Bn, where n is the number of data types used for state assessment. S103. Calculate the operating fluctuation coefficient Uk according to the following formula: , where i = 1, 2, 3, …, n, n is the number of data types for status assessment, that is, the number of multi-type sensors. The operation fluctuation coefficient is used to reflect the degree of abnormal state during the operation of industrial equipment. The larger the operation fluctuation coefficient, the higher the degree of abnormal state during the operation of industrial equipment. Conversely, the smaller the operation fluctuation coefficient, the lower the degree of abnormal state during the operation of industrial equipment.

[0018] The specific process of obtaining the location coordinates of abnormal nodes is as follows: S201: Obtain a preset operation fluctuation judgment threshold. If the operation fluctuation coefficient Uk is greater than or equal to the operation fluctuation judgment threshold, generate a data anomaly signal, and obtain a corresponding monitoring node based on the source of the data anomaly signal. S202. Establish a three-dimensional coordinate system based on the base plane of the equipment operation model, mark all monitoring nodes in the three-dimensional coordinate system to obtain a monitoring node distribution map, and simultaneously obtain all monitoring nodes that generate data abnormality signals, mark them as abnormal nodes, and then obtain the position coordinates of the abnormal nodes.

[0019] The abnormality analysis unit obtains and processes the location coordinates of each abnormal node, demarcates the abnormal impact area on the monitoring node distribution map according to the preset monitoring range of the monitoring node, and obtains the abnormal distribution area map and sends it to the fault investigation unit; Taking the location coordinates of the monitoring node as the center, the preset monitoring range of the monitoring node is R, then the effective monitoring area of the monitoring node is S: S=πR2, and then the location coordinates of each abnormal node are obtained. A circular image with an area of S is used to cover the monitoring node distribution map at the abnormal node to represent the abnormal impact area to obtain the abnormal distribution area map.

[0020] The specific process of obtaining the main influencing nodes and the secondary influencing nodes is as follows: S301. Obtain an abnormal distribution area map, record the working components involved in the abnormal distribution area map, and mark all working components in the abnormal impact area as abnormal components to be analyzed; S302. Obtain the operating principle and operating result of each working component of the industrial equipment from the network database, perform functional division based on the operating result of the abnormal component to be analyzed, and classify the working component whose operating result is a direct production object as a primary working component, and classify the working component whose operating result is an indirect production object as a secondary working component; The main working components include: power system: such as electric motors, engines, pumps, etc., used to drive equipment or provide power; Transmission system: such as gears, bearings, chains, etc., responsible for transmitting power to other components; Control systems: such as PLCs, sensors, actuators, etc., used to control and monitor equipment operations; Executive components: such as working heads, cutting tools, nozzles, etc., which directly contact the processed or produced materials and perform the core work of the equipment; Structural frame: provides support and stability for the equipment to ensure normal operation of the equipment; The main working components include: cooling system: such as radiator, water pump, fan, etc., used to maintain normal equipment temperature; Lubrication system: such as lubricating oil pumps, oil pipes, etc., to reduce friction and extend the service life of major components; Electrical systems: such as wires, power supplies, fuses, etc., which provide power but are not directly involved in the core operation of the equipment; Protective components: such as protective covers and protective nets, which play a role in safety protection; Auxiliary tools: such as hydraulic pumps, pneumatic systems, etc., are used to provide additional operating capabilities but are not the core drive system of the equipment.

[0021] S303. Mark the main working components in the abnormal distribution area diagram as main influencing nodes, and mark the secondary working components that have operation relevance with the main working components as secondary influencing nodes. The operation relevance refers to other working components that have a collaborative relationship with the operation results of the working component.

[0022] The fault troubleshooting unit is used to obtain and process the abnormal distribution area map, perform functional division according to the operation results of the industrial equipment involved in the abnormal distribution area map, obtain the main working parts and auxiliary working parts, and obtain the main influencing nodes and secondary influencing nodes based on the operation correlation of the main working parts and auxiliary working parts. Then, the fault troubleshooting results are obtained at the main influencing nodes through the fault tree analysis method and sent to the positioning display unit.

[0023] The specific process of obtaining troubleshooting results is as follows: S401. Acquire real-time operating parameters of an abnormal node of a main working component, compare the real-time operating parameters with standard operating parameters to obtain abnormal data performance, determine a top event based on the abnormal data performance, and determine an analysis scope as a boundary condition based on the fault impact range of the top event; Top event selection: The top event is the failure of the core function of the equipment (such as "production line shutdown"); Boundary conditions: such as eliminating human operating errors and focusing on equipment failures; S402, Fault Tree Construction and Logical Decomposition: Starting from the top event, list all direct causes (i.e., intermediate events), and then decompose to the bottom event. Analyze the process using logic gates, as follows: AND gate: All input events occur simultaneously, resulting in the top event; For example, “battery fire = BMS failure ∧ battery cell short circuit ∧ cooling system failure”; OR gate: Any input event can lead to the top event; For example, “Robot arm stuck = harmonic reducer wear ∨ servo motor overload”; S403. Derive minimum cut sets from the top event layer by layer downward or from the bottom events layer by layer upward, and then identify the minimum cut set, that is, the minimum combination of bottom events that leads to the top event. The number of minimum cut sets reflects the system risk, and each cut set represents a failure mode. For example, the minimum cut set of abnormal opening of subway doors is {door controller failure} or {mechanical lock failure}.

[0024] S404: Obtain bottom event failure probability data λi, and calculate the top event failure probability Ptop according to the following formula: , where t is the number of failures; S405: Calculate the intersection of minimum cut sets. If multiple cut sets contain the same base event, the event is a candidate for the fault center, and the fault troubleshooting result can be obtained.

[0025] The positioning display unit is used to obtain the abnormal distribution area map and fault troubleshooting results. The fault troubleshooting results include the fault location coordinates and the degree of fault impact. The fault node is marked with identifier A at the fault location coordinates, and the affected nodes are marked with identifier B at the coordinates of other abnormal nodes to obtain the fault distribution map and perform visual display.

[0026] Marker A is represented by a red ☆, and mark B is represented by a green △. The faults are graded according to their impact, including level 1, level 2, and level 3. The first level of fault impact corresponds to the use of small-sized markers A and B; The fault impact level 2 corresponds to the use of medium-sized markers A and B; The third level of fault impact corresponds to the use of large-size labels A and B.

[0027] The present invention performs partitioning and presets multiple monitoring nodes based on the equipment operation model, obtains real-time operation parameters through a sensor group set at the monitoring node, compares and analyzes the real-time operation parameters with the standard operation parameters to generate an operation fluctuation coefficient, obtains abnormal nodes and then delineates the abnormal impact area, performs functional division according to the operation results of the industrial equipment involved in the abnormal distribution area diagram, obtains the main impact nodes and the secondary impact nodes based on the operation correlation of the main working parts and the auxiliary working parts, and then obtains the fault tree analysis method at the main impact node to obtain the fault troubleshooting result, which can quickly locate the fault center when multiple faults occur in the industrial equipment, thereby improving the fault troubleshooting efficiency.

[0028] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0029] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by those skilled in the art according to actual conditions. The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The remote status monitoring and management system for industrial equipment based on the Internet of Things is characterized by: It includes equipment modeling unit, data processing unit, abnormality analysis unit, fault troubleshooting unit and positioning display unit, among which: The equipment modeling unit is used to obtain basic equipment data of industrial equipment to establish an equipment operation model, and then partition the equipment operation model and preset multiple monitoring nodes. The monitoring node distribution map is sent to the abnormality analysis unit and the fault troubleshooting unit for storage. At the same time, the standard operating parameters of the industrial equipment in a fault-free state are obtained and sent to the data processing unit; The data processing unit is used to obtain real-time operating parameters through a sensor group set at the monitoring node, compare and analyze the real-time operating parameters with standard operating parameters to generate an operating fluctuation coefficient, obtain a preset operating fluctuation judgment threshold to generate a data anomaly signal, obtain the monitoring node where the data anomaly signal is generated as an abnormal node, and send the position coordinates of the abnormal node to the abnormality analysis unit; The abnormality analysis unit obtains and processes the location coordinates of each abnormal node, demarcates the abnormal impact area on the monitoring node distribution map according to the preset monitoring range of the monitoring node, and obtains the abnormal distribution area map and sends it to the fault investigation unit; The fault troubleshooting unit is used to obtain and process the abnormal distribution area map, perform functional division according to the operation results of the industrial equipment involved in the abnormal distribution area map, obtain the main working parts and auxiliary working parts, and obtain the main influencing nodes and secondary influencing nodes based on the operation correlation of the main working parts and auxiliary working parts. Then, the fault troubleshooting results are obtained at the main influencing nodes through the fault tree analysis method and sent to the positioning display unit.

2. The remote status monitoring and management system for industrial equipment based on the Internet of Things according to claim 1 is characterized in that: The positioning display unit is used to obtain an abnormal distribution area map and a fault troubleshooting result. The fault troubleshooting result includes the fault location coordinates and the degree of fault impact. The fault node is marked with identifier A at the fault location coordinates, and the affected nodes are marked with identifier B at the coordinates of other abnormal nodes to obtain a fault distribution map and perform a visual display.

3. The remote status monitoring and management system for industrial equipment based on the Internet of Things according to claim 1 is characterized in that: The specific process of generating the running fluctuation coefficient is as follows: S101: The sensor group is specifically a multi-type sensor configured based on the data types of transmission components and interference-affected components involved in the operation of the industrial equipment. Real-time operating parameters are acquired through the multi-type sensors. The real-time operating parameters include data A1, data A2, data A3, ..., data An, where n is the number of the multi-type sensors. S102. Obtain standard operating parameters of the industrial equipment in a fault-free state, where the standard operating parameters correspond to the real-time operating parameters, specifically data B1, data B2, data B3, ..., data Bn, where n is the number of data types used for state assessment; S103. Calculate the operating fluctuation coefficient Uk according to the following formula: , where i = 1, 2, 3, …, n, n is the number of data types for status assessment, that is, the number of multi-type sensors, and the operation fluctuation coefficient is used to reflect the degree of abnormal status during the operation of industrial equipment.

4. The remote status monitoring and management system for industrial equipment based on the Internet of Things according to claim 1 is characterized in that: The specific process of obtaining the location coordinates of abnormal nodes is as follows: S201: Obtain a preset operation fluctuation judgment threshold. If the operation fluctuation coefficient Uk is greater than or equal to the operation fluctuation judgment threshold, generate a data anomaly signal, and obtain a corresponding monitoring node based on the source of the generated data anomaly signal. S202. Establish a three-dimensional coordinate system based on the base plane of the equipment operation model, mark all monitoring nodes in the three-dimensional coordinate system to obtain a monitoring node distribution map, and simultaneously obtain all monitoring nodes that generate data abnormality signals, mark them as abnormal nodes, and then obtain the location coordinates of the abnormal nodes.

5. The remote status monitoring and management system for industrial equipment based on the Internet of Things according to claim 1 is characterized in that: Taking the location coordinates of the monitoring node as the center, the preset monitoring range of the monitoring node is R, then the effective monitoring area S of the monitoring node is: S=πR 2 , and then obtain the position coordinates of each abnormal node, and use a circular image with an area of S to cover the monitoring node distribution map at the abnormal node to represent the abnormal impact area to obtain the abnormal distribution area map.

6. The remote status monitoring and management system for industrial equipment based on the Internet of Things according to claim 1 is characterized in that: The specific process of obtaining the main influencing nodes and the secondary influencing nodes is as follows: S301. Obtain an abnormal distribution area map, record the working components involved in the abnormal distribution area map, and mark all working components in the abnormal impact area as abnormal components to be analyzed; S302. Obtain the operating principle and operating result of each working component of the industrial equipment from the network database, and perform functional division based on the operating result of the abnormal component to be analyzed. Working components whose operating results are direct production targets are classified as primary working components, and working components whose operating results are indirect production targets are classified as secondary working components. S303. Mark the main working components in the abnormal distribution area diagram as main influencing nodes, and mark the secondary working components that have operation relevance with the main working components as secondary influencing nodes. The operation relevance is other working components that have a collaborative relationship with the operation results of the working component.

7. The remote status monitoring and management system for industrial equipment based on the Internet of Things according to claim 1 is characterized in that: The specific process of obtaining troubleshooting results is as follows: S401. Acquire real-time operating parameters of an abnormal node of a main working component, compare the real-time operating parameters with standard operating parameters to obtain abnormal data performance, determine a top event based on the abnormal data performance, and determine an analysis scope as a boundary condition based on the fault impact range of the top event; S402, Fault Tree Construction and Logical Decomposition: Starting from the top event, list all direct causes (i.e., intermediate events), and then decompose to the bottom event. Analyze the process using logic gates, as follows: AND gate: All input events occur simultaneously resulting in the top event; OR gate: Any input event can lead to the top event; S403. Derive minimum cut sets from the top event layer by layer downward or from the bottom events layer by layer upward, and then identify the minimum cut set, that is, the minimum combination of bottom events that leads to the top event. The number of minimum cut sets reflects the system risk, and each cut set represents a failure mode. S404: Obtain bottom event failure probability data λi, and calculate the top event failure probability Ptop according to the following formula: , where t is the number of failures; S405: Calculate the intersection of minimum cut sets. If multiple cut sets contain the same base event, the event is a candidate for the fault center, and the fault troubleshooting result can be obtained.

8. The remote state monitoring and management system for industrial equipment based on the Internet of Things according to claim 2 is characterized in that: The symbol A is represented by a red ☆, and the symbol B is represented by a green △. The fault impact is graded according to the degree of the fault, including level 1 fault impact, level 2 fault impact, and level 3 fault impact, where: The first level of fault impact corresponds to the use of small-sized markers A and B; The fault impact level 2 corresponds to the use of medium-sized markers A and B; The third level of fault impact corresponds to the use of large-size labels A and B.

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