PLC remote monitoring control method and system based on industrial Internet of Things

Abstract

The invention relates to the technical field of PLC remote monitoring and control, in particular to a PLC remote monitoring and control method and system based on the industrial internet of things, and the method comprises the steps: evaluating a chemical raw material feeding process through chemical raw material attribute data and prepared product formula data; evaluating the chemical reaction process of the chemical raw materials in the reaction equipment through the data of the reaction equipment; according to the chemical raw material feeding process evaluation result and the chemical reaction process evaluation result of the chemical raw materials in the reaction equipment, the conveying speed of conveying the chemical raw materials into the reaction equipment is analyzed; and according to the conveying speed analysis result of the chemical raw materials, the raw material conveying equipment on the chemical product production line is subjected to self-adaptive control through the PLC equipment. The full reaction efficiency of chemical raw materials is improved, and meanwhile the production efficiency of a chemical product production line is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of PLC remote monitoring and control, and in particular to a PLC remote monitoring and control method and system based on industrial Internet of Things. Background Art

[0002] In recent years, with the development of economic globalization, more chemical companies have begun to actively promote intelligent and digital transformation. This is not just about combining traditional production processes with information technology, but about changing production methods through comprehensive information construction to achieve efficient, energy-saving and environmentally friendly production goals. In this process, the application of industrial Internet of Things has become one of the important technologies. By integrating sensors, data acquisition devices and cloud computing platforms in production facilities, enterprises can monitor various types of data in the production process in real time, such as the use of raw materials, the operating status of equipment and the parameters of the production environment. Using industrial Internet of Things technology, enterprises can achieve comprehensive visualization and real-time analysis of production operations. This real-time monitoring capability enables enterprises to quickly identify bottlenecks and risks in the production process and adjust production strategies in time to improve production efficiency. At the same time, with the continuous development of PLC technology, automated control systems have also been widely used in chemical production. In the chemical production process, through real-time monitoring and intelligent judgment of PLC based on industrial Internet of Things, enterprises can dynamically adjust production parameters in the chemical production process to achieve more accurate and efficient control.

[0003] However, when monitoring and controlling through PLC, most of the existing technologies only focus on identifying anomalies in the production process by analyzing the operating data of the equipment. Although this method can effectively detect equipment failures and performance degradation, its application limitations are obvious. When implementing PLC monitoring systems, many companies regard it more as a passive monitoring tool rather than an intelligent system that actively optimizes the production process. Therefore, existing technical solutions often do not fully utilize the potential of PLC and cannot achieve adaptive control based on real-time data. Specifically, current PLC systems are mainly operated by setting fixed operating parameters and control rules. This traditional control method may be effective in some cases, but it is insufficient in complex and dynamic chemical production environments. For example, in the chemical reaction process, the conveying speed of raw materials is crucial to the efficiency of the reaction and the quality of the product. However, the existing technology often fails to consider how to dynamically adjust this conveying speed so that the raw materials can undergo a full and effective chemical reaction in the reaction equipment. During the reaction process, if the conveying speed is too fast, the raw materials will not be able to fully react chemically, thereby affecting the uniformity of the reaction and the quality of the final product. If the conveying speed is too slow, the production efficiency of the chemical production line may be affected. However, the existing PLC system does not implement this method of adaptively controlling the conveying speed, resulting in unstable reaction effects, which ultimately affects the overall production efficiency and product quality.

[0004] In order to solve these problems, the present application designs a PLC remote monitoring and control method and system based on industrial Internet of Things. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a PLC remote monitoring control method and system based on the industrial Internet of Things. According to the analysis results of the conveying speed of chemical raw materials, the raw material conveying equipment on the chemical product production line is adaptively controlled by the PLC device. While improving the full reaction efficiency of chemical raw materials, the present invention ensures the production efficiency of the chemical product production line and saves raw material costs for chemical product production.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a PLC remote monitoring and control method based on industrial Internet of Things, comprising the following steps:

[0008] S1. Obtain chemical raw material property data, reaction equipment data and product preparation formula data from chemical product production lines;

[0009] S2. Importing chemical raw material attribute data and preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process;

[0010] S3, importing the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment;

[0011] S4. Analyze the conveying speed of the chemical raw materials into the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw materials in the reaction equipment;

[0012] S5. Based on the analysis results of the conveying speed of chemical raw materials, the raw material conveying equipment on the chemical product production line is adaptively controlled through PLC equipment.

[0013] In one implementation of the present invention, step S2 includes the following specific steps:

[0014] S21, extracting chemical raw material attribute data and preparation product formula data, wherein the chemical raw material attribute data includes solid raw material input data, liquid raw material input data and gas raw material input data, and the preparation product formula data includes chemical raw material input ratio data;

[0015] S22. Substituting the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process coefficient calculation formula to calculate the chemical raw material feeding process coefficient, the chemical raw material feeding process coefficient calculation formula is:

[0016] RG=as×rg S +ay×rg L +aq×rg G ;

[0017] In the formula, RG represents the feed process coefficient of chemical raw materials, as, ay, and aq represent the feed proportion of solid raw materials, liquid raw materials, and gas raw materials in the feed proportion data of chemical raw materials, respectively, and rg S , rg L , rg G They respectively represent the solid raw material feeding process coefficient, liquid raw material feeding process coefficient, and gas raw material feeding process coefficient.

[0018] In one implementation of the present invention, the process of obtaining the solid raw material feeding process coefficient is as follows:

[0019] S23, substituting the solid raw material feeding data into the solid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the solid raw material in the chemical raw materials, the solid raw material feeding process coefficient calculation formula is:

[0020]

[0021] Wherein, Ms represents the feed amount of solid raw material in the solid raw material feed data, ρs represents the density of solid raw material in the solid raw material feed data, Vf represents the capacity of the reaction equipment, and ks represents the porosity of solid raw material in the solid raw material feed data;

[0022] The process of obtaining the liquid raw material feeding process coefficient is as follows:

[0023] S24, substituting the liquid raw material feeding data into the liquid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the liquid raw material in the chemical raw material, the liquid raw material feeding process coefficient calculation formula is:

[0024]

[0025] In the formula, ML represents the feed amount of the liquid raw material in the liquid raw material feed data, PL represents the pressure of the liquid raw material in the liquid raw material feed data, ρL represents the density of the liquid raw material in the liquid raw material feed data, QL represents the flow rate of the liquid raw material in the liquid raw material feed data, and UL represents the dynamic viscosity of the liquid raw material in the liquid raw material feed data;

[0026] The process of obtaining the gas raw material feeding process coefficient is as follows:

[0027] S25. Substituting the gas raw material feeding data into the gas raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the gas raw material in the chemical raw material. The gas raw material feeding process coefficient calculation formula is:

[0028]

[0029] Wherein, R represents the Boltzmann constant, T represents the absolute temperature of the gas raw material in the gas raw material feeding data, MG represents the molar mass of the gas raw material in the gas raw material feeding data, PG represents the absolute pressure of the gas raw material in the gas raw material feeding data, and ρG represents the density of the gas raw material in the gas raw material feeding data.

[0030] In one implementation of the present invention, step S3 includes the following specific steps:

[0031] S31. Extracting reaction equipment data on a chemical product production line;

[0032] S32, substituting the reaction equipment data into the chemical raw material complete reaction degree coefficient calculation formula to calculate the chemical raw material complete reaction degree coefficient of the chemical raw material in the reaction equipment, the chemical raw material complete reaction degree coefficient calculation formula is:

[0033]

[0034] Wherein, FG represents the coefficient of complete reaction degree of chemical raw materials in the reaction equipment, W represents the target yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, Wa represents the actual yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, and Wc represents the weight of chemical reaction residues of chemical raw materials in the reaction equipment in the reaction equipment data.

[0035] In one implementation of the present invention, step S4 includes the following specific steps:

[0036] S41, obtaining process coefficient sample data for training a chemical raw material complete reaction degree coefficient prediction model, wherein the process coefficient sample data includes the calculated input process coefficients of multiple chemical raw materials for preparing products and the chemical raw material complete reaction degree coefficients of the chemical raw materials in the reaction equipment, dividing the process coefficient sample data for training the chemical raw material complete reaction degree coefficient prediction model into a process coefficient sample training set and a process coefficient sample test set, constructing a first regression network model, taking the input process coefficients of the chemical raw materials in the process coefficient sample training set as the input of the first regression network model, taking the chemical raw material complete reaction degree coefficients of the corresponding chemical raw materials in the reaction equipment in the process coefficient sample training set as the output of the first regression network model, training the first regression network model to obtain a first initial regression network model; evaluating the model effect of the first initial regression network model using an average error algorithm, and selecting the corresponding first initial regression network model with a value greater than or equal to a preset first evaluation value as the chemical raw material complete reaction degree coefficient prediction model;

[0037] S42, calculating the feed process coefficient of the chemical raw material of the product to be prepared, inputting the calculated feed process coefficient of the chemical raw material of the product to be prepared into the chemical raw material complete reaction degree coefficient prediction model, and outputting the predicted chemical raw material complete reaction degree coefficient of the product to be prepared.

[0038] In one implementation of the present invention, step S4 further includes the following specific steps:

[0039] S43, obtaining conveying speed sample data for training a chemical raw material conveying speed prediction model, wherein the conveying speed sample data includes a calculated chemical raw material complete reaction degree coefficient of a plurality of chemical raw materials for preparing products in a reaction device and a chemical raw material conveying speed data set, wherein the chemical raw material conveying speed data set includes a solid raw material conveying speed, a liquid raw material conveying speed, and a gas raw material conveying speed; dividing the conveying speed sample data for training a chemical raw material conveying speed prediction model into a conveying speed sample training set and a conveying speed sample test set, constructing a second regression network model, taking the chemical raw material complete reaction degree coefficient in the conveying speed sample training set as the input of the second regression network model, taking the corresponding chemical raw material conveying speed data set in the conveying speed sample training set as the output of the second regression network model, training the second regression network model to obtain a second initial regression network model; evaluating the model effect of the second initial regression network model using an average error algorithm, and selecting a corresponding second initial regression network model that is greater than or equal to a preset second evaluation value as the chemical raw material conveying speed prediction model;

[0040] S44. Obtain the chemical raw material complete reaction degree coefficient of the product to be prepared predicted by the chemical raw material complete reaction degree coefficient prediction model, input the predicted chemical raw material complete reaction degree coefficient of the product to be prepared into the chemical raw material delivery speed prediction model, and output the predicted chemical raw material delivery speed data set of the product to be prepared.

[0041] In one implementation of the present invention, step S5 includes the following specific steps:

[0042] S51, obtaining the predicted solid raw material conveying speed, liquid raw material conveying speed, and gas raw material conveying speed in the chemical raw material conveying speed data set of the product to be prepared;

[0043] S52. Set the conveying speed of the solid raw material conveying equipment on the chemical product production line to the predicted solid raw material conveying speed through the PLC device, set the conveying speed of the liquid raw material conveying equipment on the chemical product production line to the predicted liquid raw material conveying speed, and set the conveying speed of the gas raw material conveying equipment on the chemical product production line to the predicted gas raw material conveying speed.

[0044] In a second aspect, an embodiment of the present invention further provides a PLC remote monitoring and control system based on industrial Internet of Things, comprising:

[0045] The data acquisition module is used to obtain chemical raw material attribute data and reaction equipment data as well as preparation product formula data from the chemical product production line;

[0046] The chemical raw material feeding process evaluation module is used to import the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process;

[0047] The chemical reaction process evaluation module is used to import the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment;

[0048] A conveying speed analysis module is used to analyze the conveying speed of the chemical raw materials to the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw materials in the reaction equipment;

[0049] PLC conveying speed remote control module, used to adaptively control the raw material conveying equipment on the chemical product production line through PLC equipment according to the analysis results of the conveying speed of chemical raw materials;

[0050] A control module is used to control the operation of the data acquisition module, the chemical raw material feeding process evaluation module, the chemical reaction process evaluation module, the conveying speed analysis module and the PLC conveying speed remote control module.

[0051] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a PLC remote monitoring and control method based on the industrial Internet of Things by calling the computer program stored in the memory.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a PLC remote monitoring and control method based on the industrial Internet of Things.

[0053] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0054] The present invention evaluates the chemical raw material feeding process through chemical raw material attribute data and product preparation formula data; evaluates the chemical reaction process of the chemical raw material in the reaction equipment through reaction equipment data; analyzes the conveying speed of the chemical raw material to the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical raw material in the reaction equipment; and performs adaptive control of the raw material conveying equipment on the chemical product production line through the PLC device according to the analysis results of the conveying speed of the chemical raw material. The present invention improves the full reaction efficiency of the chemical raw materials while ensuring the production efficiency of the chemical product production line, saving raw material costs for chemical product production. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0056] Figure 1 It is a schematic diagram of the overall process of a PLC remote monitoring and control method based on industrial Internet of Things of the present invention;

[0057] Figure 2 A workflow diagram of a regression network model in a PLC remote monitoring and control method based on industrial Internet of Things of the present invention;

[0058] Figure 3 It is a structural schematic diagram of a PLC remote monitoring and control system based on industrial Internet of Things of the present invention. DETAILED DESCRIPTION

[0059] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0060] Example 1

[0061] like Figure 1 As shown, this embodiment provides a PLC remote monitoring and control method based on industrial Internet of Things, which specifically includes the following steps:

[0062] S1. Obtain chemical raw material property data, reaction equipment data and product preparation formula data from chemical product production lines;

[0063] S2. Importing chemical raw material attribute data and preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process;

[0064] In this embodiment, step S2 includes the following specific steps:

[0065] S21, extracting chemical raw material attribute data and preparation product formula data, wherein the chemical raw material attribute data includes solid raw material input data, liquid raw material input data and gas raw material input data, and the preparation product formula data includes chemical raw material input ratio data;

[0066] S22. Substituting the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process coefficient calculation formula to calculate the chemical raw material feeding process coefficient, the chemical raw material feeding process coefficient calculation formula is:

[0067] RG=as×rg S+ay×rg L +aq×rg G ;

[0068] In the formula, RG represents the feed process coefficient of chemical raw materials, as, ay, and aq represent the feed proportion of solid raw materials, liquid raw materials, and gas raw materials in the feed proportion data of chemical raw materials, respectively, and rg S , rg L , rg G They respectively represent the solid raw material feeding process coefficient, liquid raw material feeding process coefficient, and gas raw material feeding process coefficient.

[0069] In this embodiment, the process of obtaining the solid raw material feeding process coefficient is as follows:

[0070] S23, substituting the solid raw material feeding data into the solid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the solid raw material in the chemical raw materials, the solid raw material feeding process coefficient calculation formula is:

[0071]

[0072] Wherein, Ms represents the feed amount of solid raw material in the solid raw material feed data, ρs represents the density of solid raw material in the solid raw material feed data, Vf represents the capacity of the reaction equipment, and ks represents the porosity of solid raw material in the solid raw material feed data;

[0073] The process of obtaining the liquid raw material feeding process coefficient is as follows:

[0074] S24, substituting the liquid raw material feeding data into the liquid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the liquid raw material in the chemical raw material, the liquid raw material feeding process coefficient calculation formula is:

[0075]

[0076] In the formula, ML represents the feed amount of the liquid raw material in the liquid raw material feed data, PL represents the pressure of the liquid raw material in the liquid raw material feed data, ρL represents the density of the liquid raw material in the liquid raw material feed data, QL represents the flow rate of the liquid raw material in the liquid raw material feed data, and UL represents the dynamic viscosity of the liquid raw material in the liquid raw material feed data;

[0077] The process of obtaining the gas raw material feeding process coefficient is as follows:

[0078] S25. Substituting the gas raw material feeding data into the gas raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the gas raw material in the chemical raw material. The gas raw material feeding process coefficient calculation formula is:

[0079]

[0080] Wherein, R represents the Boltzmann constant, T represents the absolute temperature of the gas raw material in the gas raw material feeding data, MG represents the molar mass of the gas raw material in the gas raw material feeding data, PG represents the absolute pressure of the gas raw material in the gas raw material feeding data, and ρG represents the density of the gas raw material in the gas raw material feeding data.

[0081] It should be noted that solids, liquids and gases exhibit different behaviors in the reaction process due to their different physical and chemical properties in chemical reaction equipment. In chemical production, solid raw materials are usually in granular form, and their fluidity is affected by particle size, shape, density and adhesion; liquid raw materials have relatively high fluidity, but their flow characteristics are seriously affected by factors such as viscosity, temperature and pressure; the behavior of gas raw materials is more affected by temperature, pressure and intermolecular interaction forces. Therefore, by accurately calculating the feeding process coefficients of solids, liquids and gases, chemical companies can optimize the flow and mixing characteristics of various raw materials under specific reaction conditions. Specifically, these feeding process coefficients not only include the physical property parameters of different raw materials, but also take into account the flow resistance and changes in the reaction environment that may be encountered in the actual production process. By calculating the feeding process coefficients of solids, liquids and gases, companies can clarify the optimal delivery speed of various raw materials under different reaction conditions. This optimization can not only improve the mixing efficiency of raw materials in the reactor, but also effectively control the reaction time between raw materials, thereby ensuring that chemical reactions can be carried out under ideal conditions. More importantly, in the reaction equipment, the mixing uniformity of the raw materials has an important impact on the smooth progress of the reaction and the quality of the final product. By adjusting the delivery speed of solids, liquids and gases in a targeted manner, their distribution in the reactor can be improved to avoid local concentrations that are too high or too low. Such uniform mixing can effectively improve the conversion rate and selectivity of the reaction, making the final product more in line with quality standards.

[0082] S3, importing the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment;

[0083] In this embodiment, step S3 includes the following specific steps:

[0084] S31. Extracting reaction equipment data on a chemical product production line;

[0085] S32, substituting the reaction equipment data into the chemical raw material complete reaction degree coefficient calculation formula to calculate the chemical raw material complete reaction degree coefficient of the chemical raw material in the reaction equipment, the chemical raw material complete reaction degree coefficient calculation formula is:

[0086]

[0087] Wherein, FG represents the coefficient of complete reaction degree of chemical raw materials in the reaction equipment, W represents the target yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, Wa represents the actual yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, and Wc represents the weight of chemical reaction residues of chemical raw materials in the reaction equipment in the reaction equipment data.

[0088] It should be noted that the calculated complete reaction coefficient of chemical raw materials is an important tool for enterprises to evaluate the efficiency of the reaction process. By comparing the target output with the actual output, the operating status of the reaction equipment can be clearly and intuitively understood. When the complete reaction coefficient is low, it usually means that the reaction efficiency is not high or the reaction conditions are not ideal, which leads to a series of potential problems. For example, the reaction is very likely not to be completed, resulting in the failure of the raw materials to be fully converted into the desired products. At this time, the company needs to carefully analyze the problems in the reaction process and explore the causes of low reaction efficiency so as to take corresponding adjustments and optimization measures in time to pursue higher production efficiency and economic benefits. In addition, the complete reaction coefficient provides a clear direction for the company's production process optimization. By deeply analyzing the reaction equipment data, including the actual output of the reaction products and the weight of the chemical reaction residues, the company can identify bottlenecks and problems in the production process. For example, if the weight of the chemical reaction residues is high, it just means that the raw materials are not completely reacted or there is an unsatisfactory situation in the reaction path. This provides an important opportunity for improvement for the company. Further investigation and experimentation can be used to optimize the reaction conditions and improve the reaction path, thereby increasing the utilization rate of raw materials and reducing the generation of waste.

[0089] S4. Analyze the conveying speed of the chemical raw materials into the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw materials in the reaction equipment;

[0090] In this embodiment, step S4 includes the following specific steps:

[0091] S41, obtaining process coefficient sample data for training a chemical raw material complete reaction degree coefficient prediction model, wherein the process coefficient sample data includes the calculated input process coefficients of multiple chemical raw materials for preparing products and the chemical raw material complete reaction degree coefficients of the chemical raw materials in the reaction equipment, dividing the process coefficient sample data for training the chemical raw material complete reaction degree coefficient prediction model into a process coefficient sample training set and a process coefficient sample test set, constructing a first regression network model, taking the input process coefficients of the chemical raw materials in the process coefficient sample training set as the input of the first regression network model, taking the chemical raw material complete reaction degree coefficients of the corresponding chemical raw materials in the reaction equipment in the process coefficient sample training set as the output of the first regression network model, training the first regression network model to obtain a first initial regression network model; evaluating the model effect of the first initial regression network model using an average error algorithm, and selecting the corresponding first initial regression network model with a value greater than or equal to a preset first evaluation value as the chemical raw material complete reaction degree coefficient prediction model;

[0092] S42, calculating the feed process coefficient of the chemical raw material of the product to be prepared, inputting the calculated feed process coefficient of the chemical raw material of the product to be prepared into the chemical raw material complete reaction degree coefficient prediction model, and outputting the predicted chemical raw material complete reaction degree coefficient of the product to be prepared.

[0093] In this embodiment, step S4 also includes the following specific steps:

[0094] S43, obtaining conveying speed sample data for training a chemical raw material conveying speed prediction model, wherein the conveying speed sample data includes a calculated chemical raw material complete reaction degree coefficient of a plurality of chemical raw materials for preparing products in a reaction device and a chemical raw material conveying speed data set, wherein the chemical raw material conveying speed data set includes a solid raw material conveying speed, a liquid raw material conveying speed, and a gas raw material conveying speed; dividing the conveying speed sample data for training a chemical raw material conveying speed prediction model into a conveying speed sample training set and a conveying speed sample test set, constructing a second regression network model, taking the chemical raw material complete reaction degree coefficient in the conveying speed sample training set as the input of the second regression network model, taking the corresponding chemical raw material conveying speed data set in the conveying speed sample training set as the output of the second regression network model, training the second regression network model to obtain a second initial regression network model; evaluating the model effect of the second initial regression network model using an average error algorithm, and selecting a corresponding second initial regression network model that is greater than or equal to a preset second evaluation value as the chemical raw material conveying speed prediction model;

[0095] S44. Obtain the chemical raw material complete reaction degree coefficient of the product to be prepared predicted by the chemical raw material complete reaction degree coefficient prediction model, input the predicted chemical raw material complete reaction degree coefficient of the product to be prepared into the chemical raw material delivery speed prediction model, and output the predicted chemical raw material delivery speed data set of the product to be prepared.

[0096] It should be noted that by accurately predicting the complete reaction degree and delivery speed of chemical raw materials, enterprises can effectively realize the efficient scheduling and use of raw materials, thereby ensuring that the raw materials in the reactor can fully react. The optimization of this process will significantly reduce the reaction time and improve production efficiency. In addition, by applying the complete reaction degree coefficient predicted by the model, enterprises can accurately control the entire production process to ensure that the quality of the prepared products reaches a higher level in terms of consistency and stability. This precise control allows enterprises to reasonably adjust the delivery speed of various raw materials according to actual needs, thereby effectively reducing the fluctuations and errors that may occur in the production process. Through this process, enterprises can ensure that the quality of the final product always meets the established standards, further improving customer satisfaction and trust. At the same time, precise control of the feeding process and delivery speed of raw materials can also enable enterprises to effectively reduce raw material waste and energy consumption, thereby significantly reducing production costs. By implementing a scientific and reasonable production process, the waste of resources caused by overproduction can be avoided, and the production of unqualified products can be reduced. Such optimization measures not only bring direct economic benefits to enterprises, but also promote the sustainable use of resources.

[0097] S5. Based on the analysis results of the conveying speed of chemical raw materials, the raw material conveying equipment on the chemical product production line is adaptively controlled through PLC equipment.

[0098] In this embodiment, step S5 includes the following specific steps:

[0099] S51, obtaining the predicted solid raw material conveying speed, liquid raw material conveying speed, and gas raw material conveying speed in the chemical raw material conveying speed data set of the product to be prepared;

[0100] S52. Set the conveying speed of the solid raw material conveying equipment on the chemical product production line to the predicted solid raw material conveying speed through the PLC device, set the conveying speed of the liquid raw material conveying equipment on the chemical product production line to the predicted liquid raw material conveying speed, and set the conveying speed of the gas raw material conveying equipment on the chemical product production line to the predicted gas raw material conveying speed.

[0101] Example 2

[0102] like Figure 3 As shown, this embodiment provides a PLC remote monitoring and control system based on industrial Internet of Things, including:

[0103] The data acquisition module is used to obtain chemical raw material attribute data and reaction equipment data as well as preparation product formula data from the chemical product production line;

[0104] The chemical raw material feeding process evaluation module is used to import the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process;

[0105] The chemical reaction process evaluation module is used to import the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment;

[0106] A conveying speed analysis module is used to analyze the conveying speed of the chemical raw materials to the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw materials in the reaction equipment;

[0107] PLC conveying speed remote control module, used to adaptively control the raw material conveying equipment on the chemical product production line through PLC equipment according to the analysis results of the conveying speed of chemical raw materials;

[0108] The control module is used to control the operation of the data acquisition module, the chemical raw material feeding process evaluation module, the chemical reaction process evaluation module, the conveying speed analysis module and the PLC conveying speed remote control module.

[0109] In this embodiment, the chemical raw material feeding process evaluation module is used to import the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process; the evaluation of the chemical raw material feeding process includes the following specific steps:

[0110] Extracting chemical raw material attribute data and product preparation formula data, wherein the chemical raw material attribute data includes solid raw material input data, liquid raw material input data and gas raw material input data, and the product preparation formula data includes chemical raw material input ratio data;

[0111] Substitute the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process coefficient calculation formula to calculate the chemical raw material feeding process coefficient. The chemical raw material feeding process coefficient calculation formula is:

[0112] RG=as×rg S +ay×rg L +aq×rg G ;

[0113] In the formula, RG represents the feed process coefficient of chemical raw materials, as, ay, and aq represent the feed proportion of solid raw materials, liquid raw materials, and gas raw materials in the feed proportion data of chemical raw materials, respectively, and rg S , rg L , rg G They respectively represent the solid raw material feeding process coefficient, liquid raw material feeding process coefficient, and gas raw material feeding process coefficient.

[0114] In this embodiment, the process of obtaining the solid raw material feeding process coefficient is as follows:

[0115] Substitute the solid raw material feeding data into the solid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the solid raw material in the chemical raw materials. The solid raw material feeding process coefficient calculation formula is:

[0116]

[0117] Wherein, Ms represents the feed amount of solid raw material in the solid raw material feed data, ρs represents the density of solid raw material in the solid raw material feed data, Vf represents the capacity of the reaction equipment, and ks represents the porosity of solid raw material in the solid raw material feed data;

[0118] The process of obtaining the liquid raw material feeding process coefficient is as follows:

[0119] Substitute the liquid raw material feeding data into the liquid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the liquid raw material in the chemical raw material. The liquid raw material feeding process coefficient calculation formula is:

[0120]

[0121] In the formula, ML represents the feed amount of the liquid raw material in the liquid raw material feed data, PL represents the pressure of the liquid raw material in the liquid raw material feed data, ρL represents the density of the liquid raw material in the liquid raw material feed data, QL represents the flow rate of the liquid raw material in the liquid raw material feed data, and UL represents the dynamic viscosity of the liquid raw material in the liquid raw material feed data;

[0122] The process of obtaining the gas raw material feeding process coefficient is as follows:

[0123] Substitute the gas raw material feeding data into the gas raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the gas raw material in the chemical raw material. The gas raw material feeding process coefficient calculation formula is:

[0124]

[0125] Wherein, R represents the Boltzmann constant, T represents the absolute temperature of the gas raw material in the gas raw material feeding data, MG represents the molar mass of the gas raw material in the gas raw material feeding data, PG represents the absolute pressure of the gas raw material in the gas raw material feeding data, and ρG represents the density of the gas raw material in the gas raw material feeding data.

[0126] In this embodiment, the chemical reaction process evaluation module is used to import the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment; the evaluation of the chemical reaction process of the chemical raw material in the reaction equipment includes the following specific steps:

[0127] Extract reaction equipment data on chemical product production lines;

[0128] Substitute the reaction equipment data into the chemical raw material complete reaction degree coefficient calculation formula to calculate the chemical raw material complete reaction degree coefficient of the chemical raw material in the reaction equipment. The chemical raw material complete reaction degree coefficient calculation formula is:

[0129]

[0130] Wherein, FG represents the coefficient of complete reaction degree of chemical raw materials in the reaction equipment, W represents the target yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, Wa represents the actual yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, and Wc represents the weight of chemical reaction residues of chemical raw materials in the reaction equipment in the reaction equipment data.

[0131] In this embodiment, the conveying speed analysis module is used to analyze the conveying speed of the chemical raw material to the reaction device according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw material in the reaction device;

[0132] In this embodiment, analyzing the delivery rate of the chemical raw material to the reaction device includes the following specific steps:

[0133] like Figure 2As shown, process coefficient sample data for training a chemical raw material complete reaction degree coefficient prediction model is obtained, wherein the process coefficient sample data includes the calculated input process coefficients of multiple chemical raw materials for preparing products and the chemical raw material complete reaction degree coefficients of the chemical raw materials in the reaction equipment, the process coefficient sample data for training the chemical raw material complete reaction degree coefficient prediction model is divided into a process coefficient sample training set and a process coefficient sample test set, a first regression network model is constructed, the input process coefficients of the chemical raw materials in the process coefficient sample training set are used as inputs of the first regression network model, the chemical raw material complete reaction degree coefficients of the corresponding chemical raw materials in the reaction equipment in the process coefficient sample training set are used as outputs of the first regression network model, the first regression network model is trained to obtain a first initial regression network model; the first initial regression network model is evaluated for model effect by using an average error algorithm, and the corresponding first initial regression network model having a value greater than or equal to a preset first evaluation value is selected as the chemical raw material complete reaction degree coefficient prediction model;

[0134] Calculate the feed process coefficient of the chemical raw material of the product to be prepared, input the calculated feed process coefficient of the chemical raw material of the product to be prepared into the chemical raw material complete reaction degree coefficient prediction model, and output the predicted chemical raw material complete reaction degree coefficient of the product to be prepared.

[0135] In this embodiment, analyzing the delivery speed of the chemical raw material to the reaction device also includes the following specific steps:

[0136] like Figure 2 As shown, the conveying speed sample data for training the chemical raw material conveying speed prediction model is obtained, and the conveying speed sample data includes the calculated chemical raw material complete reaction degree coefficients of multiple chemical raw materials for preparing products in the reaction equipment and the chemical raw material conveying speed data set, wherein the chemical raw material conveying speed data set includes the solid raw material conveying speed, the liquid raw material conveying speed and the gas raw material conveying speed; the conveying speed sample data for training the chemical raw material conveying speed prediction model is divided into a conveying speed sample training set and a conveying speed sample test set, a second regression network model is constructed, the chemical raw material complete reaction degree coefficients in the conveying speed sample training set are used as the input of the second regression network model, the corresponding chemical raw material conveying speed data set in the conveying speed sample training set is used as the output of the second regression network model, and the second regression network model is trained to obtain a second initial regression network model; the second initial regression network model is evaluated by using an average error algorithm to evaluate the model effect, and the corresponding second initial regression network model greater than or equal to the preset second evaluation value is selected as the chemical raw material conveying speed prediction model;

[0137] Obtain the chemical raw material complete reaction degree coefficient of the product to be prepared predicted by the chemical raw material complete reaction degree coefficient prediction model, input the predicted chemical raw material complete reaction degree coefficient of the product to be prepared into the chemical raw material conveying speed prediction model, and output the predicted chemical raw material conveying speed data set of the product to be prepared.

[0138] In this embodiment, the PLC conveying speed remote control module is used to adaptively control the raw material conveying equipment on the chemical product production line through the PLC device according to the conveying speed analysis results of the chemical raw materials; it includes the following specific steps:

[0139] Obtaining the predicted solid raw material conveying speed, liquid raw material conveying speed, and gas raw material conveying speed in the chemical raw material conveying speed data set of the product to be prepared;

[0140] Through the PLC device, the conveying speed of the solid raw material conveying equipment on the chemical product production line is set to the predicted solid raw material conveying speed, the conveying speed of the liquid raw material conveying equipment on the chemical product production line is set to the predicted liquid raw material conveying speed, and the conveying speed of the gas raw material conveying equipment on the chemical product production line is set to the predicted gas raw material conveying speed.

[0141] The above-mentioned parameters and steps for each unit module to realize the corresponding functions in a PLC remote monitoring and control system based on industrial Internet of Things of the present invention can refer to the parameters and steps in the embodiment of a PLC remote monitoring and control method based on industrial Internet of Things above, and will not be repeated here.

[0142] Example 3

[0143] An electronic device according to an embodiment of the present invention comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a PLC remote monitoring and control method based on the industrial Internet of Things by calling the computer program stored in the memory. It should be noted that all computer programs of a PLC remote monitoring and control method based on the industrial Internet of Things are implemented in C language, wherein the data acquisition module, the chemical raw material feeding process evaluation module, the chemical reaction process evaluation module, the conveying speed analysis module, the PLC conveying speed remote control module and the control module are all controlled by a remote server.

[0144] Example 4

[0145] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0146] When the computer program runs on a computer device, the computer device executes the above-mentioned PLC remote monitoring and control method based on industrial Internet of Things.

[0147] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0148] The system and medium provided in the embodiments of the present invention correspond one-to-one to the method, and therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0149] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0153] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0154] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0155] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0156] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A PLC remote monitoring and control method based on industrial Internet of Things, characterized in that: The steps include: S1. Obtain chemical raw material property data, reaction equipment data and product preparation formula data from chemical product production lines; S2. Importing chemical raw material attribute data and preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process; S3, importing the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment; S4. Analyze the conveying speed of the chemical raw materials into the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw materials in the reaction equipment; S5. Based on the analysis results of the conveying speed of chemical raw materials, the raw material conveying equipment on the chemical product production line is adaptively controlled through PLC equipment.

2. A PLC remote monitoring and control method based on industrial Internet of Things according to claim 1, characterized in that: The step S2 comprises the following specific steps: S21, extracting chemical raw material attribute data and preparation product formula data, wherein the chemical raw material attribute data includes solid raw material input data, liquid raw material input data and gas raw material input data, and the preparation product formula data includes chemical raw material input ratio data; S22. Substituting the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process coefficient calculation formula to calculate the chemical raw material feeding process coefficient, the chemical raw material feeding process coefficient calculation formula is: RG=as×rg S +ay×rg L +aq×rg G ; In the formula, RG represents the feed process coefficient of chemical raw materials, as, ay, and aq represent the feed proportion of solid raw materials, liquid raw materials, and gas raw materials in the feed proportion data of chemical raw materials, respectively, and rg S , rg L , rg G They respectively represent the solid raw material feeding process coefficient, liquid raw material feeding process coefficient, and gas raw material feeding process coefficient.

3. A PLC remote monitoring and control method based on industrial Internet of Things according to claim 2, characterized in that: The process of obtaining the solid raw material feeding process coefficient is as follows: S23, substituting the solid raw material feeding data into the solid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the solid raw material in the chemical raw materials, the solid raw material feeding process coefficient calculation formula is: Wherein, Ms represents the feed amount of solid raw material in the solid raw material feed data, ρs represents the density of solid raw material in the solid raw material feed data, Vf represents the capacity of the reaction equipment, and ks represents the porosity of solid raw material in the solid raw material feed data; The process of obtaining the liquid raw material feeding process coefficient is as follows: S24, substituting the liquid raw material feeding data into the liquid raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the liquid raw material in the chemical raw material, the liquid raw material feeding process coefficient calculation formula is: In the formula, ML represents the feed amount of the liquid raw material in the liquid raw material feed data, PL represents the pressure of the liquid raw material in the liquid raw material feed data, ρL represents the density of the liquid raw material in the liquid raw material feed data, QL represents the flow rate of the liquid raw material in the liquid raw material feed data, and UL represents the dynamic viscosity of the liquid raw material in the liquid raw material feed data; The process of obtaining the gas raw material feeding process coefficient is as follows: S25. Substituting the gas raw material feeding data into the gas raw material feeding process coefficient calculation formula to calculate the feeding process coefficient of the gas raw material in the chemical raw material. The gas raw material feeding process coefficient calculation formula is: Wherein, R represents the Boltzmann constant, T represents the absolute temperature of the gas raw material in the gas raw material feeding data, MG represents the molar mass of the gas raw material in the gas raw material feeding data, PG represents the absolute pressure of the gas raw material in the gas raw material feeding data, and ρG represents the density of the gas raw material in the gas raw material feeding data.

4. A PLC remote monitoring and control method based on industrial Internet of Things according to claim 3, characterized in that: The step S3 comprises the following specific steps: S31. Extracting reaction equipment data on a chemical product production line; S32, substituting the reaction equipment data into the chemical raw material complete reaction degree coefficient calculation formula to calculate the chemical raw material complete reaction degree coefficient of the chemical raw material in the reaction equipment, the chemical raw material complete reaction degree coefficient calculation formula is: Wherein, FG represents the coefficient of complete reaction degree of chemical raw materials in the reaction equipment, W represents the target yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, Wa represents the actual yield of chemical reaction products of chemical raw materials in the reaction equipment in the reaction equipment data, and Wc represents the weight of chemical reaction residues of chemical raw materials in the reaction equipment in the reaction equipment data.

5. A PLC remote monitoring and control method based on industrial Internet of Things according to claim 4, characterized in that: The step S4 comprises the following specific steps: S41, obtaining process coefficient sample data for training a chemical raw material complete reaction degree coefficient prediction model, wherein the process coefficient sample data includes the calculated input process coefficients of multiple chemical raw materials for preparing products and the chemical raw material complete reaction degree coefficients of the chemical raw materials in the reaction equipment, dividing the process coefficient sample data for training the chemical raw material complete reaction degree coefficient prediction model into a process coefficient sample training set and a process coefficient sample test set, constructing a first regression network model, taking the input process coefficients of the chemical raw materials in the process coefficient sample training set as the input of the first regression network model, taking the chemical raw material complete reaction degree coefficients of the corresponding chemical raw materials in the reaction equipment in the process coefficient sample training set as the output of the first regression network model, training the first regression network model to obtain a first initial regression network model; evaluating the model effect of the first initial regression network model using an average error algorithm, and selecting the corresponding first initial regression network model with a value greater than or equal to a preset first evaluation value as the chemical raw material complete reaction degree coefficient prediction model; S42, calculating the feed process coefficient of the chemical raw material of the product to be prepared, inputting the calculated feed process coefficient of the chemical raw material of the product to be prepared into the chemical raw material complete reaction degree coefficient prediction model, and outputting the predicted chemical raw material complete reaction degree coefficient of the product to be prepared.

6. A PLC remote monitoring and control method based on industrial Internet of Things according to claim 5, characterized in that: The step S4 also includes the following specific steps: S43, obtaining conveying speed sample data for training a chemical raw material conveying speed prediction model, wherein the conveying speed sample data includes a calculated chemical raw material complete reaction degree coefficient of a plurality of chemical raw materials for preparing products in a reaction device and a chemical raw material conveying speed data set, wherein the chemical raw material conveying speed data set includes a solid raw material conveying speed, a liquid raw material conveying speed, and a gas raw material conveying speed; dividing the conveying speed sample data for training a chemical raw material conveying speed prediction model into a conveying speed sample training set and a conveying speed sample test set, constructing a second regression network model, taking the chemical raw material complete reaction degree coefficient in the conveying speed sample training set as the input of the second regression network model, taking the corresponding chemical raw material conveying speed data set in the conveying speed sample training set as the output of the second regression network model, training the second regression network model to obtain a second initial regression network model; evaluating the model effect of the second initial regression network model using an average error algorithm, and selecting a corresponding second initial regression network model that is greater than or equal to a preset second evaluation value as the chemical raw material conveying speed prediction model; S44. Obtain the chemical raw material complete reaction degree coefficient of the product to be prepared predicted by the chemical raw material complete reaction degree coefficient prediction model, input the predicted chemical raw material complete reaction degree coefficient of the product to be prepared into the chemical raw material delivery speed prediction model, and output the predicted chemical raw material delivery speed data set of the product to be prepared.

7. A PLC remote monitoring and control method based on industrial Internet of Things according to claim 6, characterized in that: The step S5 comprises the following specific steps: S51, obtaining the predicted solid raw material conveying speed, liquid raw material conveying speed, and gas raw material conveying speed in the chemical raw material conveying speed data set of the product to be prepared; S52. Set the conveying speed of the solid raw material conveying equipment on the chemical product production line to the predicted solid raw material conveying speed through the PLC device, set the conveying speed of the liquid raw material conveying equipment on the chemical product production line to the predicted liquid raw material conveying speed, and set the conveying speed of the gas raw material conveying equipment on the chemical product production line to the predicted gas raw material conveying speed.

8. A PLC remote monitoring and control system based on industrial Internet of Things, which is implemented based on a PLC remote monitoring and control method based on industrial Internet of Things as claimed in any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to obtain chemical raw material attribute data and reaction equipment data as well as product preparation formula data from the chemical product production line; The chemical raw material feeding process evaluation module is used to import the chemical raw material attribute data and the preparation product formula data into the chemical raw material feeding process evaluation model to evaluate the chemical raw material feeding process; The chemical reaction process evaluation module is used to import the reaction equipment data into the chemical raw material reaction process evaluation model to evaluate the chemical reaction process of the chemical raw material in the reaction equipment; A conveying speed analysis module is used to analyze the conveying speed of the chemical raw materials to the reaction equipment according to the evaluation results of the chemical raw material feeding process and the evaluation results of the chemical reaction process of the chemical raw materials in the reaction equipment; PLC conveying speed remote control module, used to adaptively control the raw material conveying equipment on the chemical product production line through PLC equipment according to the analysis results of the conveying speed of chemical raw materials; A control module is used to control the operation of the data acquisition module, the chemical raw material feeding process evaluation module, the chemical reaction process evaluation module, the conveying speed analysis module and the PLC conveying speed remote control module.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a PLC remote monitoring and control method based on industrial Internet of Things as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes a PLC remote monitoring and control method based on industrial Internet of Things as described in any one of claims 1 to 7.

Images