An intelligent monitoring method and system based on the Internet of Things and a storage medium

Abstract

The application is suitable for the technical field of Internet of Things monitoring, and particularly relates to an intelligent monitoring method and system based on Internet of Things and a storage medium, the method comprising: S100: drawing a PID diagram of a chemical production system, configuring a correspondence relationship between Internet of Things devices and production devices, determining the importance of the production devices, traversing the weight values of each Internet of Things device based on a preset comparison table, and sorting all Internet of Things devices in descending order of the weight values to obtain a sequence list; S200: creating leaf nodes corresponding to the production devices one by one, and synchronizing the weight values to the corresponding leaf nodes in the PID diagram. The application can greatly improve the reliability of important devices by determining the level, and can guarantee the normal transmission of fluctuating device data by connecting the fluctuating devices to a temporary layer, avoid performance bottlenecks caused by overload, and greatly improve the stability of Internet of Things devices.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things monitoring, and in particular to an intelligent monitoring method and system based on the Internet of Things and a storage medium. BACKGROUND

[0002] The Internet of Things 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 realizes rapid transmission and sharing of data with the help of wireless communication technology; for example, in the field of chemical production, by setting up remote liquid level meters, pressure gauges, etc., and building a central control system, the production state can be monitored in real time in the control room to handle working condition fluctuations and emergency treatment.

[0003] The Internet of Things monitoring in the prior art generally uses a single network architecture, and when the working condition of the chemical production system fluctuates, it may cause network congestion, resulting in slow device response, data loss, and affecting system stability; therefore, how to construct a multi-network architecture and prioritize processing fluctuation data is a technical problem to be solved by the present application. SUMMARY

[0004] The purpose of the present application is to provide an intelligent monitoring method and system based on the Internet of Things and a storage medium to solve the problem of how to construct a multi-network architecture and prioritize processing fluctuation data as described in the background.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] An intelligent monitoring method based on the Internet of Things, the method comprising:

[0007] S100: Draw a PID diagram of a chemical production system, configure the correspondence between Internet of Things devices and production devices, determine the importance of production devices, based on a preset comparison table, traverse the weight value of each production device, and sort all production devices in order from high to low according to the weight value to obtain a sequence list;

[0008] S200: Create a leaf node corresponding to each production device, and synchronize the weight value to the corresponding leaf node, mark the location of the production device in the PID diagram, based on the location, divide the production device into several groups, create a branch node, and mount the leaf node to the branch node, configure the correspondence between the branch node and the group, count the total weight value in each branch node, configure the network demand of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree;

[0009] S300: Obtain network devices of the chemical production system and cluster them into several levels, the levels including: a core layer, an edge layer and a temporary layer, synchronize the levels to the weighted tree;

[0010] S400: based on the sorting result of the production equipment, sequentially connecting the branch node into the core layer or the edge layer, embedding a monitoring mechanism into the weighted tree, when the fluctuation data of the production equipment is obtained, defining a fluctuation device, and connecting the fluctuation device into a temporary layer.

[0011] Further, the S100 comprises:

[0012] Based on the weight value, the production equipment is divided into several levels;

[0013] The monitoring frequency corresponding to each level is configured, and a 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.

[0014] Further, the S100 further comprises:

[0015] A multi-factor weight model is constructed, wherein the factors at least include health degree and energy consumption;

[0016] The weight value is offset by using the multi-factor weight model.

[0017] Further, the S200 comprises:

[0018] The load threshold of each branch node is configured, and a load balancing strategy is embedded in the weighted tree;

[0019] The position of the edge device is located, and the network device is reconstructed by using the edge device.

[0020] Further, the S200 further comprises

[0021] The real-time reading of the Internet of Things device is recorded, and the fluctuation range of the real-time reading is set;

[0022] The hidden danger node is compared, and a standby link between the hidden danger node and the temporary layer is built.

[0023] Further, the S300 comprises:

[0024] The standby device is determined, the standby device is mounted into the temporary layer, and the level is dynamically adjusted;

[0025] The hidden danger node is switched by using the temporary layer.

[0026] Further, the S400 comprises:

[0027] Data whose real-time reading exceeds the fluctuation range is defined as fluctuation data, and a time stamp is integrated into the fluctuation data;

[0028] A trend chart is drawn with time as the horizontal coordinate and fluctuation data as the vertical coordinate, and abnormal fluctuations are selected.

[0029] Further, the system comprises:

[0030] The obtaining module is configured to draw a PID chart of the chemical production system, configure a correspondence between the Internet of Things devices and the production devices, determine the importance of the production devices, traverse a weight value of each production device based on a preset comparison table, sort all the production devices in descending order of the weight values, and obtain a sequence list.

[0031] The generating module is configured to create leaf nodes corresponding to the production devices one by one, synchronize the weight values to the corresponding leaf nodes, mark positions of the production devices in the PID chart, divide the production devices into a plurality of groups based on the positions, create branch nodes, mount the leaf nodes to the branch nodes, configure a correspondence between the branch nodes and the groups, count a total of the weight values in each branch node, configure network requirements of each branch node, integrate all the branch nodes and the leaf nodes, and generate a weighted tree.

[0032] The synchronizing module is configured to obtain network devices of the chemical production system and cluster them into a plurality of levels, the levels comprising a core layer, an edge layer, and a temporary layer, and synchronize the levels to the weighted tree.

[0033] The accessing module is configured to sequentially access the branch nodes to the core layer or the edge layer according to the sorting result of the production devices, embed a monitoring mechanism into the weighted tree, define a fluctuation device when fluctuation data of the production devices are obtained, and access the fluctuation device to the temporary layer.

[0034] Further, the obtaining module comprises:

[0035] The cutting unit is configured to cut the production devices into a plurality of levels according to the weight values.

[0036] The adjusting unit is configured to configure a monitoring frequency corresponding to each level, select a set value, integrate the set value and the correspondence, and adjust an Internet of Things device corresponding to the fluctuation device.

[0037] The constructing unit is configured to construct a multi-factor weight model, wherein the factors at least include health degree and energy consumption.

[0038] The offset unit is configured to offset the weight values by using the multi-factor weight model.

[0039] Further, a storage medium having stored thereon a computer program, which, when executed, implements the above-mentioned intelligent monitoring method based on Internet of Things.

[0040] Compared with the prior art, the present application has the following advantages:

[0041] By drawing the PID diagram, the mutual relationship and layout of each production equipment can be intuitively displayed, the production dynamics can be timely mastered, the deployment process of the Internet of Things equipment can be optimized by determining the importance of the production equipment, the network demand of the production equipment in each group can be determined and classified management can be performed by generating the weighted tree, the stability of the important production equipment is further improved, the normal transmission of the fluctuation equipment data can be preferentially ensured 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 in the chemical production system is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flowchart of the intelligent monitoring method based on Internet of Things provided by the embodiment of the present application is provided.

[0043] Figure 2 A first sub-flowchart of the intelligent monitoring method based on Internet of Things provided by the embodiment of the present application is provided.

[0044] Figure 3 A second sub-flowchart of the intelligent monitoring method based on Internet of Things provided by the embodiment of the present application is provided.

[0045] Figure 4 A third sub-flowchart of the intelligent monitoring method based on Internet of Things provided by the embodiment of the present application is provided.

[0046] Figure 5 A fourth sub-flowchart of the intelligent monitoring method based on Internet of Things provided by the embodiment of the present application is provided.

[0047] Figure 6 A composition diagram of the intelligent monitoring system based on Internet of Things provided by the embodiment of the present application is provided.

[0048] Figure 7 A composition diagram of the obtained module in the intelligent monitoring system based on Internet of Things provided by the embodiment of the present application is provided.

[0049] Figure 8 A composition diagram of the generated module in the intelligent monitoring system based on Internet of Things provided by the embodiment of the present application is provided.

[0050] Figure 9 A composition diagram of the synchronization module in the intelligent monitoring system based on Internet of Things provided by the embodiment of the present application is provided.

[0051] Figure 10The composition block diagram of the access module in the intelligent monitoring system based on the Internet of Things is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0052] In order to make the objects, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0053] In embodiment 1, Figure 1 The implementation flow of the intelligent monitoring method based on the Internet of Things provided by the embodiments of the present application is shown, and S100 is described in detail as follows:

[0054] S100: Draw the PID diagram of the chemical production system, configure the correspondence between the Internet of Things devices and the production devices, determine the importance of the production devices, traverse the weight value of each Internet of Things device based on the preset comparison table, and sort all Internet of Things devices in descending order of the weight value to obtain a sequence list.

[0055] According to the positions and mutual relationships of the devices, control valves and Internet of Things devices in the chemical production system, the PID diagram is drawn, and the Internet of Things devices and the production devices are marked in the PID diagram; according to the process flow, the importance of each production device is determined, for example, the core devices (such as reactors, main pumps, etc.) in the production system have a higher importance, and the auxiliary devices (such as storage tanks) have a lower importance; at least one Internet of Things device is deployed on each production device to monitor the pressure, temperature and liquid level of the production device; each Internet of Things device corresponds to a weight value, and the correspondence between the Internet of Things devices and the weight values is stored in a comparison table, wherein the comparison table is prepared in advance by the management personnel of the production device; according to the weight value, all Internet of Things devices are sorted, and the sorting result is defined as a sequence list, which can intuitively show the importance of the production device, so that maintenance or adjustment measures can be taken in priority when resources are limited or faults occur; wherein the importance is also the influence degree on the chemical production system when the index fluctuates.

[0056] For example, in the methanol section, there are generally configured synthesis towers, cooling tanks and rectification towers, etc., and each tower is also matched with corresponding tower kettle pumps, separation pumps, compressors and storage tanks, etc. production devices, wherein at least one Internet of Things device is deployed on each production device to monitor the pressure, temperature and liquid level of the production device.

[0057] In the embodiment, in order to guarantee the stability and continuity of the production equipment monitoring, the Internet of Things equipment transmits data through multiple wired networks, and switches, routers and adapters and the like are needed in the process, a plurality of communication links are built by integrating the above-mentioned devices, so as to transmit the real-time readings in the Internet of Things equipment; in addition, the level can be understood as different communication links.

[0058] S200: create a leaf node corresponding to each production equipment, synchronize the weight value to the corresponding leaf node, mark the position of the production equipment in the PID graph, cut the Internet of Things equipment into a plurality of groups based on the position and the corresponding relationship, create a branch node, mount the leaf node to the branch node, count the sum of the weight values in each branch node, configure the network demand of each branch node, integrate all the branch nodes and leaf nodes, and generate a weighted tree.

[0059] For each production equipment, a leaf node is created, and the weight value corresponding to each production equipment is uploaded to the corresponding leaf node. According to the position and the corresponding relationship of the production equipment, the Internet of Things equipment is cut into a plurality of groups, and a branch node is created for each group. According to the grouping relationship, the leaf node is mounted to the corresponding branch node, and the sum of the weight values of the leaf nodes under each branch node is calculated. The network demand of each branch node is determined, which includes bandwidth demand, delay requirement and redundancy demand, etc. All the leaf nodes and branch nodes are integrated to generate a weighted tree.

[0060] The leaf node and the branch node are basic structures for storing data, and are only used to refer to the production equipment and the group in order to simplify the data processing process, similar to a placeholder. The weighted tree can intuitively show the network demand of the Internet of Things equipment in each group.

[0061] S300: obtain the network equipment of the chemical production system, and cluster into a plurality of levels, the levels including a core layer, an edge layer and a temporary layer, synchronize the levels to the weighted tree.

[0062] Determine the network equipment in the chemical production system, and divide the network equipment into a plurality of levels according to the performance of the network equipment, including a core layer, an edge layer and a temporary layer. The network equipment corresponding to the core layer and the 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 limited processing capacity.

[0063] S400: based on the sorting result of the Internet of Things equipment, sequentially connect the branch node to the core layer or the edge layer, embed a monitoring mechanism into the weighted tree, and when the fluctuation data of the production equipment is obtained, define a fluctuation equipment and connect the fluctuation equipment to the temporary layer.

[0064] According to the correspondence between the production equipment and the Internet of Things equipment, in the order of the weight values from large to small, all the Internet of Things equipment are sequentially connected to the core layer and the edge layer, when the production equipment fluctuation is monitored by the monitoring mechanism, the corresponding Internet of Things equipment is defined as the fluctuation equipment, and the fluctuation equipment is connected to the temporary layer, the data in the fluctuation equipment is transmitted by using the network equipment corresponding to the temporary layer, so as to avoid performance bottleneck; wherein the monitoring mechanism is: a specific monitoring method for production equipment, such as monitoring by DCS central control system or field instrument.

[0065] In embodiment 2, Figure 2 The implementation process of the intelligent monitoring method based on the Internet of Things is shown, and S100 is described in detail as follows:

[0066] S101: The production equipment is divided into several levels based on the weight values.

[0067] S102: Configure the monitoring frequency corresponding to each level, select a set value, integrate the set value and the correspondence, and adjust the Internet of Things equipment corresponding to the fluctuation equipment.

[0068] The production equipment is divided into multiple levels, each level corresponds to a monitoring frequency, and the Internet of Things equipment is adjusted according to the monitoring frequency, so that the Internet of Things equipment collects data of the production equipment according to the monitoring frequency.

[0069] In embodiment 3, Figure 2 The implementation process of the intelligent monitoring method based on the Internet of Things is shown, and S100 is described in detail as follows:

[0070] S103: A multi-factor weight model is constructed, wherein the factors at least include: health degree and energy consumption.

[0071] The weight value of the Internet of Things equipment is configured to monitor important indicators in important equipment preferentially, but in addition to the importance of production equipment, the production equipment with low health degree or large energy consumption also needs to be monitored preferentially; in other words, the influencing factors affecting the monitoring frequency of the Internet of Things equipment are determined, wherein the influencing factors at least include health degree and energy consumption.

[0072] S104: The weight value is offset by using the multi-factor weight model.

[0073] The original weight value is input into the multi-factor weight model, the original weight value is offset by collecting the influencing factors, the corrected weight value is obtained, and the Internet of Things equipment is sorted by using the corrected weight value, wherein the multi-factor weight model can be obtained from existing data.

[0074] In embodiment 4, Figure 3 The implementation process of the intelligent monitoring method based on the Internet of Things is shown, and S200 is described in detail as follows:

[0075] S201: Configure the load threshold of each branch node, and embed the load balancing strategy in the weighted tree.

[0076] The memory usage, storage usage, sending frequency and throughput of the Internet of Things device in each branch node are determined, and the load threshold is determined, which is calculated by the maximum data transmission capacity of the network device corresponding to the Internet of Things device; the load balancing strategy is embedded, wherein the load balancing strategy is: when the load of the network device corresponding to the Internet of Things device approaches or exceeds the load threshold, part of the task is transferred to the node with lower load.

[0077] S202: Locate the position of the edge device, and reconstruct the network device by using the edge device.

[0078] In order to improve the data processing efficiency of the Internet of Things device, the edge device can also be deployed in the chemical production system to reduce the delay and relieve the network bandwidth pressure; after introducing the edge device, the working mode of the network device is adjusted.

[0079] In embodiment 5, Figure 3 The implementation process of the intelligent monitoring method based on the Internet of Things is shown, and S200 is described in detail as follows:

[0080] S203: Record the real-time readings of the Internet of Things device, and set the fluctuation range of the real-time readings.

[0081] The real-time readings of each Internet of Things device are recorded, wherein the real-time readings correspond to the indicators of the production device, and the fluctuation range of each real-time reading is determined according to production planning, process requirements and historical data analysis.

[0082] S204: Compare the hidden danger node, and build a backup link between the hidden danger node and the temporary layer.

[0083] The production device with real-time readings exceeding the fluctuation range is defined as a hidden danger node, and the transmission data of the Internet of Things device in the hidden danger node is transferred to the backup link to ensure the stability and continuity of data transmission.

[0084] In embodiment 6, Figure 4 The implementation process of the intelligent monitoring method based on the Internet of Things is shown, and S300 is described in detail as follows:

[0085] S301: Determine the standby device, mount the standby device in the temporary layer, and dynamically adjust the layer.

[0086] Define the standby device from the network device, start the standby device when a communication link fails, and transfer the load in the temporary layer to the standby device.

[0087] S302: Hot switching of the hidden node using the temporary layer.

[0088] When the hidden node fails, the data in the hidden node is hot switched using the temporary layer and the standby device.

[0089] In embodiment 7, Figure 5 The implementation flow of the intelligent monitoring method based on the Internet of Things is shown, and S400 is described in detail as follows:

[0090] S401: Define the data whose real-time reading exceeds the fluctuation range as fluctuation data, and integrate a time stamp into the fluctuation data.

[0091] When the real-time reading of a certain Internet of Things device exceeds the fluctuation range, the real-time reading is determined as fluctuation data, a time stamp is inserted into the fluctuation data, and the generation time of the fluctuation data is recorded.

[0092] S402: Draw a change trend graph with time as the horizontal coordinate and fluctuation data as the vertical coordinate, and select abnormal fluctuations.

[0093] Draw a change trend graph with time as the horizontal coordinate and fluctuation data as the vertical coordinate; insert a threshold line into the change trend graph, and when the fluctuation data exceeds the threshold line, it means that the corresponding production device has abnormal fluctuations.

[0094] A storage medium is disclosed in the present application, which stores a computer program and can execute all steps of S100-S400.

[0095] Figure 6 The composition structure block diagram of the intelligent monitoring system based on the Internet of Things is shown, and the intelligent monitoring system based on the Internet of Things 1 comprises:

[0096] The obtaining module 11 is used for drawing a PID graph of the chemical production system, configuring the correspondence between the Internet of Things device and the production device, determining the importance of the production device, traversing the weight value of each Internet of Things device based on the preset comparison table, and sorting all Internet of Things devices in descending order of the weight value to obtain a sequence list.

[0097] The generating module 12 is used for creating leaf nodes corresponding to production devices one by one, synchronizing the weight values into the corresponding leaf nodes, marking the positions of the production devices in the PID graph, cutting the Internet of Things devices into a plurality of groups based on the positions and the corresponding relationship, creating branch nodes, mounting the leaf nodes into the branch nodes, counting the sum of the weight values in each branch node, configuring the network demand of each branch node, integrating all the branch nodes and the leaf nodes, and generating a weighted tree;

[0098] The synchronizing module 13 is used for acquiring network devices of a chemical production system and clustering into a plurality of levels, the levels including a core layer, an edge layer and a temporary layer, and synchronizing the levels into the weighted tree.

[0099] The accessing module 14 is used for sequentially accessing the branch nodes into the core layer or the edge layer based on the sorting result of the Internet of Things devices, embedding a monitoring mechanism into the weighted tree, defining a fluctuation device when fluctuation data of the production device is acquired, and accessing the fluctuation device into the temporary layer.

[0100] Figure 7 The component structure block diagram of the intelligent monitoring system based on the Internet of Things is shown, and the obtaining module 11 includes:

[0101] The cutting unit 111 is used for cutting the production devices into a plurality of levels according to the weight values.

[0102] The adjusting unit 112 is used for 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.

[0103] The constructing unit 113 is used for constructing a multi-factor weight model, wherein the factors at least include health degree and energy consumption.

[0104] The offset unit 114 is used for offsetting the weight values by using the multi-factor weight model.

[0105] Figure 8 The component structure block diagram of the intelligent monitoring system based on the Internet of Things is shown, and the generating module 12 includes:

[0106] The embedding unit 121 is used for configuring the load threshold of each branch node and embedding a load balancing strategy into the weighted tree.

[0107] The reconstructing unit 122 is used for positioning the position of an edge device and reconstructing the network device by using the edge device.

[0108] The setting unit 123 is configured to record real-time readings of the Internet of Things device and set a fluctuation range of the real-time readings.

[0109] The building unit 124 is configured to compare hidden danger nodes and build a backup link between the hidden danger nodes and a temporary layer.

[0110] Figure 9 The component structure block diagram of the intelligent monitoring system based on the Internet of Things is shown, and the synchronization module 13 comprises:

[0111] The adjusting unit 131 is configured to determine a backup device, mount the backup device into a temporary layer, and dynamically adjust the layer.

[0112] The switching unit 132 is configured to use the temporary layer to perform hot switching on the hidden danger nodes.

[0113] Figure 10 The component structure block diagram of the intelligent monitoring system based on the Internet of Things is shown, and the access module 14 comprises:

[0114] The integrating unit 141 is configured to define data, whose real-time readings exceed the fluctuation range, as fluctuation data, and integrate a time stamp into the fluctuation data.

[0115] The defining unit 142 is configured to take time as an abscissa, take fluctuation data as an ordinate, draw a change trend graph, and select abnormal fluctuation.

[0116] The obtaining module 11 is mainly configured to complete step S100, the generating module 12 is mainly configured to complete step S200, the synchronization module 13 is mainly configured to complete step S300, and the access module 14 is mainly configured to complete step S400.

[0117] The cutting unit 111 is mainly configured to complete step S101, the adjusting unit 112 is mainly configured to complete step S102, the building unit 113 is mainly configured to complete step S103, and the offset unit 114 is mainly configured to complete step S104.

[0118] The embedding unit 121 is mainly configured to complete step S201, the reconstructing unit 122 is mainly configured to complete step S202, the setting unit 123 is mainly configured to complete step S203, and the building unit 124 is mainly configured to complete step S204.

[0119] The adjusting unit 131 is mainly configured to complete step S301, and the switching unit 132 is mainly configured to complete step S302.

[0120] The integrating unit 141 is mainly configured to complete step S401, and the defining unit 142 is mainly configured to complete step S402.

[0121] The technical features of the above-described embodiments can be combined in any manner, and for brevity, not all possible combinations of the technical features in the above-described embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered within the scope of the present disclosure.

[0122] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that, for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.

[0123] The functions that can be achieved by the intelligent monitoring method based on the Internet of Things are completed by a computer device, the computer device includes one or more processors and one or more memories, 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 realize the functions of the intelligent monitoring method based on the Internet of Things.

[0124] The processor fetches instructions from the memory one by one, analyzes the instructions, and then completes the corresponding operation according to the instruction requirements to generate a series of control commands, so that the parts of the computer automatically, continuously and coordinately act as an organic whole, realize the input of the program, the input of the data and the operation and output the results, the arithmetic operation or logical operation generated in this process is completed by the operation unit; the memory includes a read-only memory, the read-only memory is used to store a computer program, and the memory is externally provided with a protection device.

[0125] For example, the computer program can be divided into one or more modules, one or more modules are stored in the memory and executed by the processor to complete the present application. One or more modules can be a series of computer program instruction segments capable of completing a specific function, which is used to describe the execution process of the computer program in the terminal device.

[0126] Those skilled in the art can understand that the description of the service device above is only an example and does not constitute a limitation on the terminal device, and can include more or less components than the above description, or combine certain components, or different components, for example, can include input / output devices, network access devices, buses, etc.

[0127] The above-described only the preferred embodiments of the present application, and does not limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application, etc., should be included in the protection scope of the present application.

Claims

1. An Internet of Things-based intelligent monitoring method, characterized in that, The method comprises: S100: draw a PID diagram of a chemical production system, configure a correspondence relationship between Internet of Things devices and production devices, determine the importance of the production devices, traverse the weight value of each Internet of Things device based on a preset comparison table, sort all Internet of Things devices in descending order of the weight value to obtain a sequence list, wherein each Internet of Things device corresponds to a weight value, and the mapping relationship between the Internet of Things device and the weight value is stored in the comparison table, and the comparison table is prepared in advance by a production device manager; S200: create leaf nodes corresponding to the production devices one by one, synchronize the weight value to the corresponding leaf nodes, mark the positions of the production devices in the PID diagram, divide the Internet of Things devices into several groups based on the positions and the correspondence relationship, create branch nodes, and mount the leaf nodes into the branch nodes, count the sum of the weight values in each branch node, configure the network demand of each branch node, integrate all branch nodes and leaf nodes, and generate a weighted tree; S300: obtain network devices of the chemical production system and cluster them into several levels, wherein the levels comprise a core layer, an edge layer, and a temporary layer, and synchronize the levels to the weighted tree; S400: based on the sequence list, sequentially connect the branch nodes to the core layer or the edge layer, embed a monitoring mechanism into the weighted tree, when fluctuation data of the production devices are obtained, define corresponding Internet of Things devices as fluctuation devices via the correspondence relationship, and connect the fluctuation devices to the temporary layer. 2.The IoT-based smart monitoring method according to claim 1, wherein, The S200 comprises: configure a load threshold of each branch node, and embed a load balancing strategy into the weighted tree; locate the position of an edge device, and reconstruct the network devices by using the edge device. 3.The IoT-based smart monitoring method according to claim 1, wherein, The S200 further comprises: record real-time readings of the Internet of Things devices, and set a fluctuation range of the real-time readings; compare hidden danger nodes, and build a backup link between the hidden danger nodes and the temporary layer. 4.The IoT-based smart monitoring method according to claim 3, wherein, The S400 comprises: define data whose real-time readings exceed the fluctuation range as fluctuation data, and integrate a time stamp into the fluctuation data; draw a change trend diagram with time as the horizontal coordinate and fluctuation data as the vertical coordinate, and select abnormal fluctuations.

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