Intelligent monitoring method and system based on Internet of Things, and storage medium

Abstract

The invention is suitable for the technical field of monitoring of the Internet of Things, and particularly relates to an intelligent monitoring method and system based on the Internet of Things and a storage medium, and the method comprises the steps: S100, drawing a PID diagram of a chemical production system, configuring a corresponding relation between Internet of Things equipment and production equipment, determining the importance degree of the production equipment, and determining the importance degree of the production equipment based on a preset comparison table; traversing a weight value of each Internet of Things device, and sorting all the Internet of Things devices according to a sequence of the weight values from high to low to obtain a sequence list; and S200, creating leaf nodes in one-to-one correspondence with the production equipment, and synchronizing the weight values to the corresponding leaf nodes in the PID graph. According to the method, the reliability of important equipment can be greatly improved by determining the hierarchy, normal transmission of data of the fluctuation equipment can be guaranteed by connecting the fluctuation equipment to the temporary layer, the performance bottleneck caused by overload is avoided, and the stability of the Internet of Things equipment is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things monitoring technology, and in particular to an Internet of Things-based intelligent monitoring method, system and storage medium. Background Art

[0002] The IoT intelligent monitoring technology realizes real-time monitoring of multiple targets such as the environment and equipment by deploying a large number of intelligent sensors, and uses wireless communication technology to achieve rapid data transmission and sharing. For example, in the field of chemical production, by setting up remote level gauges, pressure gauges, etc. and establishing a central control system, the control room can monitor the production status in real time, handle operating fluctuations and emergency response.

[0003] The IoT monitoring in the prior art generally uses a single network architecture. When the operating conditions of the chemical production system fluctuate, it may cause network congestion, resulting in slow equipment response, data loss, and affecting system stability. Therefore, "how to build a multi-network architecture and give priority to processing fluctuating data" is the technical problem that the present invention needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent monitoring method, system and storage medium based on the Internet of Things to solve the problem of "how to build a multi-network architecture and give priority to processing fluctuating data" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: An intelligent monitoring method based on the Internet of Things, the method comprising: S100: Draw a PID diagram of the chemical production system, configure the corresponding relationship between the IoT devices and the production equipment, determine the importance of the production equipment, traverse the weight value of each production equipment based on a preset comparison table, and sort all the production equipment in descending order according to the weight value to obtain a sequence list; S200: Create leaf nodes corresponding to production equipment one by one, and synchronize the weight values ​​to the corresponding leaf nodes. In the PID graph, mark the location of the production equipment. Based on the location, divide the production equipment into several groups, create branch nodes, and mount the leaf nodes to the branch nodes. Configure the corresponding relationship between the branch nodes and the groups, calculate the sum of the weight values ​​in each branch node, configure the network requirements of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree. S300: Acquire network devices of a chemical production system and cluster them into several levels, the levels including: a core layer, an edge layer, and a temporary layer, and synchronize the levels into the weighted tree; S400: Based on the sorting results of the production equipment, the branch nodes are connected to the core layer or the edge layer in turn, a monitoring mechanism is embedded in the weighted tree, and when the fluctuation data of the production equipment is obtained, the fluctuation equipment is defined and connected to the temporary layer.

[0006] Furthermore, the S100 includes: Based on the weight values, the production equipment is divided into several levels; The monitoring frequency corresponding to each level is configured, and the set value is selected, the set value and the corresponding relationship are integrated, and the Internet of Things device corresponding to the fluctuation device is adjusted.

[0007] Furthermore, the S100 further includes: Construct a multi-factor weight model, where the factors include at least: health and energy consumption; The multi-factor weight model is used to offset the weight value.

[0008] Further, the S200 includes: Configuring a load threshold for each branch node and embedding a load balancing strategy into the weighted tree; The position of the edge device is located, and the network device is reconstructed using the edge device.

[0009] Furthermore, the S200 also includes Record the real-time reading of the IoT device and set the fluctuation range of the real-time reading; The nodes with potential dangers are identified by comparison, and a backup link is established between the nodes with potential dangers and the temporary layer.

[0010] Further, the S300 includes: Determine a backup device, mount the backup device to a temporary layer, and dynamically adjust the layer; The temporary layer is used to perform hot switching on the potential risk node.

[0011] Furthermore, the S400 includes: defining the data of the real-time reading that exceeds the fluctuation range as fluctuation data, and integrating a timestamp into the fluctuation data; With time as the horizontal axis and fluctuation data as the vertical axis, draw a trend graph and select abnormal fluctuations.

[0012] Furthermore, the system comprises: A module is obtained, which is used to draw a PID diagram of a chemical production system, configure the corresponding relationship between IoT devices and production equipment, determine the importance of production equipment, traverse the weight value of each production equipment based on a preset comparison table, and sort all production equipment in descending order according to the weight value to obtain a sequence list; A generation module is used to create leaf nodes corresponding to production equipment one by one, and synchronize the weight value to the corresponding leaf node, mark the location of the production equipment in the PID diagram, divide the production equipment into several groups based on the location, create branch nodes, and mount the leaf nodes to the branch nodes, configure the corresponding relationship between the branch nodes and the groups, calculate the sum of the weight values ​​in each branch node, configure the network requirements of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree; A synchronization module, used for acquiring network devices of the chemical production system and clustering them into several levels, the levels including: a core layer, an edge layer and a temporary layer, and synchronizing the levels into the weighted tree; The access module is used to connect the branch nodes to the core layer or edge layer in sequence according to the sorting results of the production equipment, embed a monitoring mechanism into the weighted tree, define the fluctuation equipment when the fluctuation data of the production equipment is obtained, and connect the fluctuation equipment to the temporary layer.

[0013] Furthermore, the obtaining module includes: A segmentation unit, used for segmenting the production equipment into a plurality of levels according to the weight value; An adjustment unit, configured to configure the monitoring frequency corresponding to each level, select a set value, integrate the set value and the corresponding relationship, and adjust the Internet of Things device corresponding to the fluctuation device; A construction unit is used to construct a multi-factor weight model, where the factors include at least: health and energy consumption; An offset unit is used to offset the weight value using the multi-factor weight model.

[0014] Furthermore, a storage medium stores a computer program, which implements the above-mentioned intelligent monitoring method based on the Internet of Things when executed.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By drawing a PID diagram, the relationship and layout of each production equipment can be intuitively displayed, and production dynamics can be grasped in a timely manner. By determining the importance of production equipment, the deployment process of IoT equipment can be optimized. By generating a weighted tree, the network requirements of production equipment in each group can be determined and classified management can be performed to further improve the stability of important production equipment. By connecting fluctuating equipment to the temporary layer, the normal transmission of fluctuating equipment data can be prioritized to avoid performance bottlenecks caused by overload, greatly improving the stability of IoT equipment in chemical production systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of an intelligent monitoring method based on the Internet of Things provided by an embodiment of the present invention; Figure 2 A first sub-flow diagram of the intelligent monitoring method based on the Internet of Things provided by an embodiment of the present invention; Figure 3 A second sub-flow diagram of the intelligent monitoring method based on the Internet of Things provided by an embodiment of the present invention; Figure 4 A third sub-flow diagram of the intelligent monitoring method based on the Internet of Things provided by an embodiment of the present invention; Figure 5 A fourth sub-flow chart of the intelligent monitoring method based on the Internet of Things provided by an embodiment of the present invention; Figure 6 A block diagram of the composition of an intelligent monitoring system based on the Internet of Things provided by an embodiment of the present invention; Figure 7 A block diagram of the components of a module in an intelligent monitoring system based on the Internet of Things provided by an embodiment of the present invention; Figure 8 A block diagram of the composition of a generation module in an intelligent monitoring system based on the Internet of Things provided by an embodiment of the present invention; Fig. 9 A block diagram of the composition of a synchronization module in an intelligent monitoring system based on the Internet of Things provided by an embodiment of the present invention; Fig.10 A block diagram of the composition of an access module in an intelligent monitoring system based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In Example 1, Figure 1The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S100 is described in detail below, as follows: S100: Draw a PID diagram of the chemical production system, configure the corresponding relationship between the IoT devices and the production equipment, determine the importance of the production equipment, traverse the weight value of each IoT device based on a preset comparison table, and sort all IoT devices in order from high to low according to the weight value to obtain a sequence list.

[0019] According to the location and relationship of each equipment, control valve and IoT device in the chemical production system, a PID diagram is drawn, and the IoT devices and production equipment are marked on the PID diagram; according to the process flow, the importance of each production equipment is determined. For example, the core equipment in the production system (such as reactors, main pumps, etc.) is more important, and the auxiliary equipment (such as storage tanks) is less important; at least one IoT device is deployed on each production equipment to monitor indicators such as pressure, temperature and liquid level of the production equipment; each IoT device corresponds to a weight value, and the correspondence between the IoT device and the weight value is stored in a comparison table, where the comparison table is pre-prepared by the management personnel of the production equipment; according to the weight value, all IoT devices are sorted, and the sorting result is defined as a sequential list. The sequential list can intuitively show the importance of the production equipment so that maintenance or adjustment measures can be taken first when resources are limited or failures occur; the importance is the degree of impact on the chemical production system when the indicator fluctuates.

[0020] For example, in the methanol section, synthesis towers, cooling tanks and distillation towers are generally configured. Each tower is also equipped with corresponding production equipment such as tower bottom pumps, separation pumps, compressors and storage tanks. At least one IoT device is deployed on each production equipment to monitor indicators such as pressure, temperature and liquid level of the production equipment.

[0021] In this embodiment, in order to ensure the stability and continuity of production equipment monitoring, the IoT devices transmit data through multiple wired networks, which require devices such as switches, routers, and adapters. By integrating the above devices, multiple communication links are built to transmit real-time readings in the IoT devices; in addition, the levels can be understood as different communication links.

[0022] S200: Create leaf nodes corresponding to production equipment one by one, and synchronize the weight values ​​to the corresponding leaf nodes. In the PID diagram, mark the locations of the production equipment. Based on the locations and the corresponding relationships, divide the IoT devices into several groups, create branch nodes, and mount the leaf nodes to the branch nodes. Count the sum of the weight values ​​in each branch node, configure the network requirements for each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree.

[0023] Create a leaf node for each production device, and upload the weight value corresponding to each production device to the corresponding leaf node. According to the location and corresponding relationship of the production equipment, the IoT devices are divided into multiple groups, and a branch node is created for each group. According to the group relationship, the leaf nodes are mounted to the corresponding branch nodes, and the sum of the weight values ​​of the leaf nodes under each branch node is calculated. The network requirements of each branch node are determined, where the network requirements include: bandwidth requirements, delay requirements, and redundancy requirements, etc. All leaf nodes and branch nodes are integrated to generate a weighted tree.

[0024] The leaf nodes and branch nodes are the basic structures for storing data, which are only used to refer to production equipment and groups in order to simplify the data processing process, similar to placeholders; the weighted tree can intuitively display the network requirements of IoT devices in each group.

[0025] S300: Acquire network devices of a chemical production system and cluster them into several levels, including a core layer, an edge layer, and a temporary layer, and synchronize the levels into the weighted tree.

[0026] Identify the network equipment in the chemical production system, and divide the network equipment into multiple levels according to their performance, including the core layer, edge layer, and temporary layer; the network equipment corresponding to the core layer and edge layer has a large data throughput and high stability, while the network equipment corresponding to the temporary layer has a relatively low data throughput and relatively limited processing capacity.

[0027] S400: Based on the sorting results of the IoT devices, the branch nodes are connected to the core layer or the edge layer in turn, a monitoring mechanism is embedded in the weighted tree, and when the fluctuation data of the production equipment is obtained, the fluctuation equipment is defined and connected to the temporary layer.

[0028] According to the correspondence between production equipment and IoT devices, all IoT devices are connected to the core layer and edge layer in descending order of weight value. When the monitoring mechanism is used to detect fluctuations in production equipment, the corresponding IoT device is defined as a fluctuating device, and the fluctuating device is connected to the temporary layer. The network equipment corresponding to the temporary layer is used to transmit the data in the fluctuating device to avoid performance bottlenecks. The monitoring mechanism is: a specific monitoring method for production equipment, such as monitoring through a DCS central control system or field instruments.

[0029] In Example 2, Figure 2 The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S100 is described in detail below, as follows: S101: Based on the weight values, the production equipment is divided into several levels.

[0030] S102: configuring the monitoring frequency corresponding to each level, selecting a set value, integrating the set value and the corresponding relationship, and adjusting the Internet of Things device corresponding to the fluctuation device.

[0031] The production equipment is divided into multiple levels, each level corresponds to a monitoring frequency, and the IoT devices are adjusted according to this monitoring frequency so that the IoT devices can collect data on the production equipment according to this monitoring frequency.

[0032] In Example 3, Figure 2 The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S100 is further described in detail below, as follows: S103: Construct a multi-factor weight model, where the factors include at least: health and energy consumption.

[0033] The purpose of configuring weight values ​​for IoT devices is to prioritize monitoring of important indicators in important equipment. However, in addition to the importance of production equipment, it is also necessary to prioritize monitoring of production equipment with lower health or higher energy consumption. In other words, determine the factors that affect the monitoring frequency of IoT devices, which include at least health and energy consumption.

[0034] S104: Utilize the multi-factor weight model to shift the weight value.

[0035] The original weight value is input into the multi-factor weight model. By collecting the influencing factors, the original weight value is offset to obtain the corrected weight value, and the corrected weight value is used to sort the IoT devices. The multi-factor weight model can be obtained from the existing data.

[0036] In Example 4, Figure 3 The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S200 is described in detail below, as follows: S201: Configure the load threshold of each branch node, and embed the load balancing strategy into the weighted tree.

[0037] Determine the memory usage, storage usage, transmission frequency, throughput and other data of the IoT device in each branch node, and determine the load threshold, which is calculated by the maximum data transmission capacity of the network device corresponding to the IoT device; embed a load balancing strategy, where the load balancing strategy is: when the load of the network device corresponding to a certain IoT device is close to or exceeds the load threshold, some tasks will be transferred to nodes with lower load.

[0038] S202: Locate the position of the edge device, and use the edge device to reconstruct the network device.

[0039] In order to improve the data processing efficiency of IoT devices, edge devices can also be deployed in chemical production systems to reduce latency and alleviate network bandwidth pressure. After the edge devices are introduced, the working mode of the network devices can be adjusted.

[0040] In Example 5, Figure 3 The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S200 is further described in detail below, as follows: S203: Record the real-time reading of the IoT device and set a fluctuation range of the real-time reading.

[0041] Record the real-time readings of each IoT device, where the real-time readings correspond to the indicators of the production equipment. Determine the fluctuation range of each real-time reading based on production planning, process requirements, and historical data analysis.

[0042] S204: Identify potential risk nodes by comparison, and build a backup link between the potential risk nodes and the temporary layer.

[0043] Production equipment whose real-time readings are outside the fluctuation range is defined as a potential danger node, and the transmission data of the IoT devices in the potential danger node is transferred to the backup link to ensure the stability and continuity of data transmission.

[0044] In Example 6, Figure 4 The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S300 is described in detail below, as follows: S301: Determine a backup device, mount the backup device to a temporary layer, and dynamically adjust the layer.

[0045] It is defined as a backup device from the network equipment. When a communication link fails, the backup device is started and the load in the temporary layer is transferred to the backup device.

[0046] S302: Using the temporary layer, hot-switch the node with potential danger.

[0047] When a potential node fails, the data in the potential node is hot-swapped using a temporary layer and backup equipment.

[0048] In Example 7, Figure 5 The implementation process of the intelligent monitoring method based on the Internet of Things provided by the embodiment of the present invention is shown, and S400 is described in detail below, as follows: S401: defining the data of the real-time reading that exceeds the fluctuation range as fluctuation data, and integrating a timestamp into the fluctuation data.

[0049] When the real-time reading of a certain IoT device exceeds the fluctuation range, the real-time reading is determined as fluctuation data, and a timestamp is inserted into the fluctuation data to record the generation time of the fluctuation data.

[0050] S402: With time as the horizontal axis and fluctuation data as the vertical axis, a change trend graph is drawn, and abnormal fluctuations are selected.

[0051] With time as the horizontal axis and fluctuation data as the vertical axis, a change trend graph is drawn; a threshold line is inserted into the change trend graph. When the fluctuation data exceeds the threshold line, it means that the corresponding production equipment has experienced abnormal fluctuations.

[0052] The present application discloses a storage medium on which a computer program is stored, and which can execute all steps of S100 to S400.

[0053] Figure 6 The structure diagram of the intelligent monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown. The intelligent monitoring system based on the Internet of Things 1 includes: Obtaining module 11, which is used to draw a PID diagram of a chemical production system, configure the corresponding relationship between IoT devices and production equipment, determine the importance of production equipment, traverse the weight value of each IoT device based on a preset comparison table, and sort all IoT devices in descending order according to the weight value to obtain a sequence list; A generation module 12 is used to create leaf nodes corresponding to production equipment one by one, and synchronize the weight value to the corresponding leaf node, mark the location of the production equipment in the PID diagram, divide the IoT device into several groups based on the location and the corresponding relationship, create branch nodes, and mount the leaf nodes to the branch nodes, calculate the sum of the weight values ​​in each branch node, configure the network requirements of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree; A synchronization module 13 is used to obtain network devices of the chemical production system and cluster them into several levels, including a core layer, an edge layer and a temporary layer, and synchronize the levels into the weighted tree; The access module 14 is used to connect the branch nodes to the core layer or edge layer in sequence based on the sorting results of the Internet of Things devices, embed a monitoring mechanism into the weighted tree, define the fluctuation device when the fluctuation data of the production equipment is obtained, and connect the fluctuation device to the temporary layer.

[0054] Figure 7The structure diagram of the intelligent monitoring system based on the Internet of Things provided by an embodiment of the present invention is shown, and the obtaining module 11 includes: A segmentation unit 111, configured to segment the production equipment into a plurality of levels according to the weight value; The adjustment unit 112 is used to configure the monitoring frequency corresponding to each level, select a set value, integrate the set value and the corresponding relationship, and adjust the Internet of Things device corresponding to the fluctuation device; A construction unit 113 is used to construct a multi-factor weight model, wherein the factors include at least: health and energy consumption; The offset unit 114 is used to offset the weight value using the multi-factor weight model.

[0055] Figure 8 The structure diagram of the composition of the intelligent monitoring system based on the Internet of Things provided by the embodiment of the present invention is shown, and the generation module 12 includes: The embedding unit 121 is used to configure the load threshold of each branch node and embed the load balancing strategy into the weighted tree; A reconstruction unit 122, configured to locate the position of the edge device and to reconstruct the network device using the edge device; A setting unit 123 is used to record the real-time reading of the IoT device and set the fluctuation range of the real-time reading; The building unit 124 is used to identify the potential risk nodes and build a backup link between the potential risk nodes and the temporary layer.

[0056] Fig. 9 The structure diagram of the intelligent monitoring system based on the Internet of Things provided by the embodiment of the present invention is shown, and the synchronization module 13 includes: The adjustment unit 131 is used to determine a backup device, mount the backup device to a temporary layer, and dynamically adjust the layer; The switching unit 132 is used to perform hot switching on the potential danger node by using the temporary layer.

[0057] Fig.10 The structure diagram of the intelligent monitoring system based on the Internet of Things provided by the embodiment of the present invention is shown, and the access module 14 includes: An integration unit 141 is used to define the data of the real-time reading that exceeds the fluctuation range as fluctuation data, and integrate a timestamp into the fluctuation data; The definition unit 142 is used to draw a change trend graph with time as the horizontal axis and the fluctuation data as the vertical axis, and select abnormal fluctuations.

[0058] The obtaining module 11 is mainly used to complete step S100, the generating module 12 is mainly used to complete step S200, the synchronizing module 13 is mainly used to complete step S300, and the accessing module 14 is mainly used to complete step S400; The segmentation unit 111 is mainly used to complete step S101, the adjustment unit 112 is mainly used to complete step S102, the construction unit 113 is mainly used to complete step S103, and the offset unit 114 is mainly used to complete step S104; The embedding unit 121 is mainly used to complete step S201, the reconstruction unit 122 is mainly used to complete step S202, the setting unit 123 is mainly used to complete step S203, and the building unit 124 is mainly used to complete step S204; The adjustment unit 131 is mainly used to complete step S301, and the switching unit 132 is mainly used to complete step S302; The integration unit 141 is mainly used to complete step S401, and the definition unit 142 is mainly used to complete step S402.

[0059] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0061] The functions that can be achieved by the intelligent monitoring method based on the Internet of Things are all completed by a computer device, and the computer device includes one or more processors and one or more memories, and at least one program code is stored in the one or more memories. The program code is loaded and executed by the one or more processors to achieve the functions of the intelligent monitoring method based on the Internet of Things.

[0062] The processor takes out instructions from the memory one by one, analyzes the instructions, and then completes the corresponding operations according to the instruction requirements, generating a series of control commands, so that the various parts of the computer can automatically, continuously and coordinately move to become an organic whole, realize the input of programs, the input of data, and the calculation and output of results. The arithmetic operations or logical operations generated in this process are all completed by the operator; the memory includes a read-only memory, which is used to store computer programs, and a protection device is provided outside the memory.

[0063] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0064] Those skilled in the art will appreciate that the description of the service device is merely an example and does not constitute a limitation on the terminal device, which may include more or fewer components than described above, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring method based on the Internet of Things, characterized in that: The method comprises: S100: Draw a PID diagram of the chemical production system, configure the corresponding relationship between the IoT devices and the production equipment, determine the importance of the production equipment, traverse the weight value of each IoT device based on a preset comparison table, and sort all IoT devices in descending order according to the weight value to obtain a sequence list; S200: Create leaf nodes corresponding to production equipment one by one, and synchronize the weight values ​​to the corresponding leaf nodes. In the PID graph, mark the location of the production equipment. Based on the location and the corresponding relationship, divide the IoT devices into several groups, create branch nodes, and mount the leaf nodes to the branch nodes. Count the sum of the weight values ​​in each branch node, configure the network requirements of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree. S300: Acquire network devices of a chemical production system and cluster them into several levels, the levels including: a core layer, an edge layer, and a temporary layer, and synchronize the levels into the weighted tree; S400: Based on the sorting results of the IoT devices, the branch nodes are connected to the core layer or the edge layer in turn, a monitoring mechanism is embedded in the weighted tree, and when the fluctuation data of the production equipment is obtained, the fluctuation equipment is defined and connected to the temporary layer.

2. The intelligent monitoring method based on the Internet of Things according to claim 1 is characterized in that: The S100 includes: Based on the weight values, the production equipment is divided into several levels; The monitoring frequency corresponding to each level is configured, and the set value is selected, the set value and the corresponding relationship are integrated, and the Internet of Things device corresponding to the fluctuation device is adjusted.

3. The intelligent monitoring method based on the Internet of Things according to claim 2 is characterized in that: The S100 further includes: Construct a multi-factor weight model, where the factors include at least: health and energy consumption; The multi-factor weight model is used to offset the weight value.

4. The intelligent monitoring method based on the Internet of Things according to claim 1 is characterized in that: The S200 includes: Configuring a load threshold for each branch node and embedding a load balancing strategy into the weighted tree; The position of the edge device is located, and the network device is reconstructed using the edge device.

5. The intelligent monitoring method based on the Internet of Things according to claim 1 is characterized in that: The S200 also includes Record the real-time reading of the IoT device and set the fluctuation range of the real-time reading; The nodes with potential dangers are identified by comparison, and a backup link is established between the nodes with potential dangers and the temporary layer.

6. The intelligent monitoring method based on the Internet of Things according to claim 5 is characterized in that: The S300 includes: Determine a backup device, mount the backup device to a temporary layer, and dynamically adjust the layer; The temporary layer is used to perform hot switching on the potential risk node.

7. The intelligent monitoring method based on the Internet of Things according to claim 5 is characterized in that: The S400 includes: defining the data of the real-time reading that exceeds the fluctuation range as fluctuation data, and integrating a timestamp into the fluctuation data; With time as the horizontal axis and fluctuation data as the vertical axis, draw a trend graph and select abnormal fluctuations.

8. An intelligent monitoring system based on the Internet of Things, characterized in that: The system comprises: A module is obtained, which is used to draw a PID diagram of a chemical production system, configure the corresponding relationship between IoT devices and production equipment, determine the importance of production equipment, traverse the weight value of each production equipment based on a preset comparison table, and sort all production equipment in descending order according to the weight value to obtain a sequence list; A generation module is used to create leaf nodes corresponding to production equipment one by one, and synchronize the weight value to the corresponding leaf node, mark the location of the production equipment in the PID diagram, divide the production equipment into several groups based on the location, create branch nodes, and mount the leaf nodes to the branch nodes, configure the corresponding relationship between the branch nodes and the groups, calculate the sum of the weight values ​​in each branch node, configure the network requirements of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree; A synchronization module, used for acquiring network devices of the chemical production system and clustering them into several levels, the levels including: a core layer, an edge layer and a temporary layer, and synchronizing the levels into the weighted tree; The access module is used to connect the branch nodes to the core layer or edge layer in sequence according to the sorting results of the production equipment, embed a monitoring mechanism into the weighted tree, define the fluctuation equipment when the fluctuation data of the production equipment is obtained, and connect the fluctuation equipment to the temporary layer.

9. The intelligent monitoring system based on the Internet of Things according to claim 8, characterized in that: The obtaining module comprises: A segmentation unit, used for segmenting the production equipment into a plurality of levels according to the weight value; An adjustment unit, configured to configure the monitoring frequency corresponding to each level, select a set value, integrate the set value and the corresponding relationship, and adjust the Internet of Things device corresponding to the fluctuation device; A construction unit is used to construct a multi-factor weight model, where the factors include at least: health and energy consumption; An offset unit is used to offset the weight value using the multi-factor weight model.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the intelligent monitoring method based on the Internet of Things as described in any one of claims 1 to 7 is implemented.

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