Online drying process real-time monitoring and analyzing system based on Internet of Things
By leveraging IoT technology, combined with data acquisition, fault monitoring models, and optimized control, the problem of equipment failure during the drying process was solved, enabling efficient and stable monitoring and analysis of the tobacco drying system, thereby improving production stability and efficiency.
Patent Information
- Application Number
- CN202511158627.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-19
- Publication Date
- 2025-11-21
AI Technical Summary
In process industries, the drying process is prone to equipment failure due to heat accumulation, which affects production stability. Existing technologies are difficult to achieve efficient and accurate fault monitoring and optimization control, resulting in economic losses.
An IoT-based online real-time monitoring and analysis system for the drying process is adopted. Through data acquisition, fault monitoring model construction, cascade control, and blockchain storage, efficient data acquisition, analysis, and storage are achieved. Combined with Bayesian networks and Smith predictive compensation control, the operation of the drying system is optimized.
It improves the efficiency and accuracy of fault monitoring in the drying process, reduces errors, enhances the stability and efficiency of the drying system, can cope with strong disturbances and large load changes, and avoids data tampering and loss.
Smart Images
Figure CN120993854A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of Internet of Things, in particular to an online drying process real-time monitoring and analysis system based on Internet of Things. BACKGROUND
[0002] Solid materials required for process industry production contain more or less moisture in their composition. In order to facilitate storage, processing and transportation, it is necessary to remove the moisture. There are various methods for removing moisture, such as adsorption dehumidification, heating dehumidification and mechanical dehumidification, etc. Among them, heating dehumidification vaporizes and removes the moisture in the solid materials through heating, which is called drying. Drying is widely used in chemical industry, food industry and medical industry, and plays an important role in food preservation. Since drying is an indispensable link, the most direct drying method is to remove free water from the product by heating, which is a process of transferring heat and mass. In this process, due to the accumulation of heat and different production processes of different drying equipment, faults are easily caused, some components cannot work normally, and in severe cases, the production process will collapse, causing incalculable economic losses. Therefore, it is necessary to design an online drying process real-time monitoring and analysis system based on Internet of Things with intelligent analysis management and high stability. SUMMARY
[0003] The present application aims to provide an online drying process real-time monitoring and analysis system based on Internet of Things to solve the problems raised in the background.
[0004] In order to solve the above technical problems, the present application provides the following technical solution: an online drying process real-time monitoring and analysis method based on Internet of Things, comprising: collecting and processing data information of tobacco production drying; building a fault monitoring model and managing data output; performing operation optimization design processing of tobacco drying; performing tobacco drying data transmission and storage control.
[0005] According to the above technical solution, the collecting and processing of data information of tobacco production drying comprises: collecting tobacco production re-drying process parameter information according to a set frequency, and performing corresponding data processing of screening, eliminating and interpolating missing values.
[0006] According to the above technical solution, the building of a fault monitoring model and the management of data output comprise: establishing a tobacco production drying re-drying equipment fault monitoring model by expressing the set fault signs as nodes through Bayesian network fault diagnosis; After the fault monitoring model is completed, the collected data is substituted into the model, and the posterior probability distribution of other tobacco production drying and redrying equipment data values is obtained according to the sensor temperature value variable, and the posterior probability distribution is output.
[0007] According to the technical scheme, the operation optimization design processing of the tobacco drying includes: The tobacco drying system is controlled and optimized by adopting cascade control, so that the system contains a set number of controllers, and the output of the inner loop controller is input as the set value of the outer loop controller; After the cascade control optimization processing is completed, the set point tracker is controlled and managed by adopting the square error integral criterion, so that the set performance data is reached; The process mathematical model controller constructed by adopting the Smith prediction compensation control is used to analyze and control the drying system.
[0008] According to the technical scheme, the tobacco drying data transmission and storage control includes: After receiving the collected and processed tobacco drying monitoring data information, the data information is transmitted to the management personnel, so that the management personnel can judge the running state of the tobacco drying system according to the data information; The received tobacco drying monitoring data information is backed up and stored in the blockchain node, so that the data information tampering and loss condition is effectively avoided.
[0009] According to the technical scheme, an online drying process real-time monitoring and analysis system based on the Internet of Things includes: A control construction module is used for tobacco drying data collection and model construction management; An analysis processing module is used for tobacco drying operation analysis processing; A receiving and storing module is used for data information receiving and storing management.
[0010] According to the technical scheme, the control construction module includes: A data preprocessing module is used for tobacco drying data collection and preprocessing; A model construction module is used for analysis model construction management; A data output module is used for monitoring data information output management.
[0011] According to the technical scheme, the analysis processing module includes: A cascade control module is used for cascade control of the tobacco drying system; A control management module is used for control management of the set point tracker; A compensation control module is used for mathematical model controller compensation control management.
[0012] According to the technical scheme, the receiving storage module comprises: The receiving transmission module is used for receiving and transmitting data information. The storage management module is used for performing blockchain storage management on the data information.
[0013] Compared with the prior art, the present application has the beneficial effects that: the present application, by setting the control construction module, the analysis processing module and the receiving storage module, makes the fault monitoring of the tobacco production drying and redrying equipment more efficient and accurate, effectively reduces the generation of errors, improves the tobacco drying efficiency, adopts level control to optimize the control of the tobacco drying system, when there is strong disturbance and large load change in the controlled process, which is not easy to control, can effectively improve the control performance, reduce the interference, make the operation of the tobacco drying system more stable. BRIEF DESCRIPTION OF DRAWINGS
[0014] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, which together with the embodiments of the present application, are used to explain the present application, and do not constitute a limitation on the present application. In the drawings: Figure 1 is a flowchart of the online drying process real-time monitoring and analysis method provided by the first embodiment of the present application; Figure 2 is a module composition diagram of the online drying process real-time monitoring and analysis system provided by the second embodiment of the present application. DETAILED DESCRIPTION
[0015] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0016] Embodiment one: Figure 1 The flowchart of the online drying process real-time monitoring and analysis method provided by the first embodiment of the present application, the present embodiment can be applied to the tobacco drying system, the method can be executed by the online drying process real-time monitoring and analysis system based on the Internet of Things provided by the present application, the system is composed of multiple software and hardware modules, as shown in Figure 1 The method specifically comprises the following steps: S101, collecting and processing the data information of the tobacco production drying; Exemplarily, in the embodiment of the present application, the tobacco production redrying section process parameter information is collected according to the set frequency, and data processing of corresponding screening, elimination and missing value interpolation is performed; in this step, the collected parameter data includes: the set temperature in the cut tobacco cylinder, the tobacco cut moisture content when leaving the machine, the tobacco cut moisture content when being packed, the tobacco cut volume when being packed, the dust remover valve opening and the pressure in the cut tobacco cylinder, and since the collected data has the characteristics of invalidity, missing and abnormality, the incomplete and inconsistent dirty data collected needs to be screened, eliminated and missing value interpolated, wherein the missing value is filled by using the "median interpolation", so that the data filled by the median value can better fit the nearby data, the error is small, and since there are drum type cut tobacco machines, belts and other equipment in the tobacco sheet redrying section, the collected data contains noise, the data to be processed is further divided into a cluster according to the attribute, the values falling outside the cluster are regarded as noise or isolated points, and these abnormal data are directly deleted, so that the accuracy and efficiency of subsequent analysis and processing are effectively improved.
[0017] S102, fault monitoring model construction and data output management are performed; Exemplarily, in the embodiment of the present application, the set fault signs are expressed as nodes by the Bayesian network fault diagnosis, and the tobacco production drying redrying equipment fault monitoring model is established; since there are many elements in the drying process, the most common element is various sensors, which run through the entire drying process, and with the increase of the material in the drying process, the change of temperature causes the change of various sensor values, the devices containing sensors are connected in sequence, and the change of the sensor at the previous stage will affect the sensor at the next stage, that is, the measurement result at the next stage does not necessarily truly reflect the actually collected data, the sensor may be affected by external interference, resulting in measurement error, it may also be that the sensor itself is faulty, and it may also be that the measurement error is caused by misoperation, and the fault position can be located according to the sensor error, therefore, through this step, the fault signs are expressed as nodes by the Bayesian network fault diagnosis, and the tobacco production drying redrying equipment fault monitoring model is established, when abnormal signs appear, the subsequent fault reason and the possibility of fault occurrence are inferred according to the causal relationship and probability distribution between the nodes, so that the fault monitoring of the tobacco production drying redrying equipment is more efficient and accurate, the error is effectively reduced, and the tobacco drying efficiency is improved.
[0018] After the fault monitoring model is constructed, the collected data is substituted into the model, the posterior probability distribution of other tobacco production drying redrying equipment data values is obtained according to the sensor temperature value variable, and the output processing is performed.
[0019] S103, tobacco drying operation optimization design processing is performed; Exemplarily, in the embodiment of the present application, the cascade control is adopted to optimize the control of the tobacco drying system, so that the system comprises a set number of controllers, and the output of an inner loop controller is input as a set value of an outer loop controller; since the cascade control has good anti-interference ability and is easy to operate, it is used in processes with large time delay, strong interference and large load variation, so that the control performance can be effectively improved, the interference can be reduced, and the operation of the tobacco drying system is more stable when strong disturbance and large load variation exist in the controlled process.
[0020] After the optimization of the cascade control is completed, the set point tracker is controlled and managed by the integral of squared error criterion to achieve the set performance data; since overshoot often occurs in the double closed loop control, which easily causes serious oscillation of the system, the set point tracker is controlled and managed by the integral of squared error criterion through this processing, so that the overshoot condition can be eliminated or reduced, and the system operation is more smooth and stable.
[0021] The process mathematical model controller constructed by the Smith prediction compensation control is used to analyze and control manage the drying system; since the process mathematical model controller constructed based on the Smith prediction compensation control has the advantages of simple structure, no need for accurate mathematical model, easy online tuning, etc., it is suitable for processes with large time delay and strong interference, and can effectively improve the anti-interference ability and tracking performance, so that the analysis of the stability of the drying system can be simplified, the calculation can be reduced, the efficiency of the overall design of the control system can be improved, the dynamic response speed is adjustable, the robust stability is good, and the influence of unmeasurable disturbance can be effectively eliminated.
[0022] S104, tobacco drying data transmission and storage control is performed; Exemplarily, in the embodiment of the present application, after receiving the data information of the tobacco drying monitoring collected and processed, the data information is transmitted to the management personnel, so that the management personnel can judge the running state of the tobacco drying system according to the data information; The received data information of the tobacco drying monitoring is backed up and stored in the blockchain node, so that the data information tampering and loss condition can be effectively avoided.
[0023] Embodiment two: the embodiment two of the present application provides an online drying process real-time monitoring and analysis system based on the Internet of Things, Figure 2 For the module structure diagram of the online drying process real-time monitoring and analysis system based on the Internet of Things provided in the embodiment two, as Figure 2 shown, the system comprises: A control construction module is used to collect data and construct a model for tobacco drying. An analysis processing module is configured to perform operation analysis processing of tobacco drying. A receiving storage module is configured to perform receiving and storage management of data information.
[0024] In some embodiments of the present application, the control construction module comprises: A data preprocessing module is configured to perform tobacco drying data collection and preprocessing. A model construction module is configured to perform analysis model construction management. A data output module is configured to perform output management of monitoring data information.
[0025] In some embodiments of the present application, the analysis processing module comprises: A cascade control module is configured to perform cascade control of the tobacco drying system. A control management module is configured to perform control management of the set point tracker. A compensation control module is configured to perform compensation control management of the mathematical model controller.
[0026] In some embodiments of the present application, the receiving storage module comprises: A receiving transmission module is configured to perform data information receiving and transmission. A storage management module is configured to perform blockchain storage management of data information.
[0027] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms “include”, “contain” or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.
[0028] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and does not limit the present application, although the above embodiments of the present application are described in detail, for those skilled in the art, the technical solutions recorded in the above embodiments can be modified, or some technical features can be replaced. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. An online drying process real-time monitoring and analysis method based on Internet of Things, characterized in that: The application relates to a tobacco drying data information acquisition and processing system. Fault monitoring model construction and data output management are performed. Tobacco drying operation optimization design processing is performed. Tobacco drying data transmission and storage control is performed. The tobacco drying data information acquisition and processing system comprises the following steps:
2. The online monitoring and analysis method of the IoT based drying process in real time as claimed in claim 1 wherein: According to a set frequency, tobacco production redrying section process parameter information is acquired, and corresponding data processing, such as screening, elimination and missing value interpolation, is performed. The fault monitoring model construction and data output management comprise the following steps:
3. The online monitoring and analysis method of the IoT based drying process as claimed in claim 1, wherein: Through Bayesian network fault diagnosis, the set fault signs are expressed as nodes, and a tobacco production drying redrying equipment fault monitoring model is established. After the fault monitoring model is constructed, the acquired data is substituted into the model, the posterior probability distribution of other tobacco production drying redrying equipment data values is obtained according to the sensor temperature value variable, and output processing is performed. The tobacco drying operation optimization design processing comprises the following steps:
4. The online monitoring and analysis method of the IoT based drying process as claimed in claim 1, wherein: Through cascade control, the tobacco drying system is optimized and controlled, the system contains a set number of controllers, and the output of an inner ring controller is used as the set value input of an outer ring controller. After the cascade control optimization processing is completed, a square error integral criterion is used to control and manage the set point tracker, so that the set performance data is reached. A process mathematical model controller constructed by Smith prediction compensation control is used to analyze and control the drying system. The tobacco drying data transmission and storage control comprises the following steps:
5. The online monitoring and analysis method of the IoT based drying process as claimed in claim 1, wherein: After the acquired tobacco drying monitoring data information is received, the data information is transmitted to the management personnel, so that the running state of the tobacco drying system can be judged according to the data information. The received tobacco drying monitoring data information is backed up and stored in a blockchain node, so that data information tampering and loss are effectively avoided. The application comprises the following:
6. An online drying process real-time monitoring and analysis system based on Internet of Things, characterized in that: A control construction module is used for tobacco drying data acquisition and model construction management. An analysis processing module is used for tobacco drying operation analysis processing. A receiving and storing module is used for data information receiving and storing management. The control construction module comprises the following:
7. The online IoT based drying process real time monitoring and analysis system as claimed in claim 6 comprises of: A data preprocessing module is used for tobacco drying data acquisition and preprocessing. A model construction module is used for analysis model construction management. A data output module is used for monitoring data information output management. The analysis processing module comprises the following:
8. The online IoT based drying process real time monitoring and analysis system as claimed in claim 6 comprises of: A cascade control module is used for cascade control of the tobacco drying system. A control management module is used for control management of the set point tracker. A compensation control module is used for mathematical model controller compensation control management. The receiving and storing module comprises the following:
9. The online IoT based drying process real time monitoring and analysis system as claimed in claim 6 comprises of: A receiving and transmission module is used for data information receiving and transmission. A storage management module is used for blockchain storage management of the data information.