A quantitative dosing system based on on-line thickness measurement technology

Abstract

This invention provides a quantitative feeding system based on online thickness measurement technology. The system includes hardware components: an online thickness gauge, a data processing unit, an environmental monitoring device, and a feeding control device. The online thickness gauge is a non-contact laser thickness gauge used to measure the thickness of the supplied material in real time. The data processing unit includes an embedded microcontroller for receiving and processing thickness data, and performing data filtering and thickness correction. The environmental monitoring device includes a temperature and humidity sensor and a light intensity detector. This quantitative feeding system, through the integration of the non-contact laser thickness gauge, environmental monitoring device, and embedded microcontroller, enables real-time monitoring of material thickness and environmental parameters. Furthermore, by using data filtering and thickness correction, it eliminates the influence of environmental noise and improves measurement accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of online thickness measurement technology, specifically a quantitative feeding system based on online thickness measurement technology. BACKGROUND

[0002] Basic structure and principle: The system is based on online thickness measurement technology, which controls the feeding amount by real-time monitoring of material thickness, thereby achieving accurate feeding of materials. The core structure of the system includes an online thickness gauge, a data processing unit and a feeding control device. The online thickness gauge continuously measures the thickness of the material and transmits the data to the data processing unit. The data processing unit calculates the required material thickness according to the preset feeding standard and feeds the result back to the feeding control device, thereby dynamically adjusting the feeding rate to achieve uniform feeding.

[0003] The system may be disturbed by environmental conditions in actual application, for example, temperature and humidity changes will affect the accuracy of thickness measurement, resulting in reduced feeding accuracy. The durability and stability of the system may have potential problems, which need to be further optimized and improved to meet the needs of industrial applications. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a quantitative feeding system based on online thickness measurement technology, which solves the problem of being disturbed by environmental conditions; the durability and stability of the system may have potential problems.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a quantitative feeding system based on online thickness measurement technology, comprising:

[0006] Hardware part: online thickness gauge, data processing unit, environmental monitoring device and feeding control device;

[0007] The online thickness gauge is a non-contact laser thickness gauge for real-time measurement of the thickness of the feeding material;

[0008] The data processing unit includes an embedded microcontroller for receiving and processing thickness data and performing data filtering and thickness correction;

[0009] The environmental monitoring device includes a temperature and humidity sensor and a light intensity detector for collecting environmental data and real-time monitoring of temperature, humidity and light intensity changes;

[0010] The feeding control device adopts a closed-loop control system, which dynamically adjusts the feeding rate through a PID control algorithm to ensure feeding accuracy;

[0011] Software part: software modules for processing thickness data and environmental data, the software modules include data correction module, environmental correction model, control algorithm module and self-learning module;

[0012] The data correction module performs acoustic filtering on the thickness gauge data;

[0013] The environmental correction model adjusts the thickness data based on environmental changes. The formula for this correction model is:

[0014] T ′ =T×(1+α×E)

[0015] Where T is the original thickness value, T ′ The corrected thickness value is E, where E is the rate of change of environmental factors and α is the environmental impact coefficient.

[0016] The control algorithm module includes a PID control algorithm, and the material feeding adjustment formula is as follows:

[0017] F = K × (T) ′ -T0)

[0018] Where F is the material feeding adjustment value, T0 is the target thickness value, and K is the control gain coefficient.

[0019] Preferably, the non-contact laser thickness gauge is equipped with a self-calibration function to adapt to changes in the light source caused by long-term use, thereby improving thickness measurement accuracy and system durability. The environmental monitoring device has a sensor for collecting wind speed to detect thickness errors caused by airflow, and incorporates the wind speed variable V into the environmental correction model, the formula of which is:

[0020] T ′ =T×(1+α×E+β×V)

[0021] Where β is the wind speed influence coefficient.

[0022] Preferably, the embedded microcontroller of the data processing unit includes a multi-channel parallel data processing module for processing real-time interaction between thickness data and environmental data, thereby improving data processing speed and ensuring real-time system response. In the PID control algorithm of the feeding control device, the gain parameter K is automatically adjusted according to fluctuations in environmental conditions to adapt to drastic environmental changes and ensure the stability of the feeding rate. Its dynamic control formula is:

[0023] K = K0 × (1 + γ × ΔE)

[0024] Where K0 is the initial control gain, ΔE is the dynamic rate of change of environmental factors, and γ is the gain adjustment coefficient.

[0025] Preferably, the control algorithm module has a self-learning function. By analyzing historical environmental changes and material supply adjustment data, it automatically optimizes the environmental impact coefficient α and wind speed impact coefficient β in the environmental correction model, making the system more adaptable in long-term operation. The update frequency of the environmental correction model is adaptively adjusted according to the rate of environmental change to reduce unnecessary correction frequency and improve the system's operational stability and durability.

[0026] Preferably, the system further includes an anomaly detection module, which can identify equipment malfunctions or extreme environmental changes, issue an alarm in abnormal situations, and automatically reduce the feeding rate to protect the equipment from damage. The anomaly detection formula is:

[0027] A = θ × (|T′ - T0|)

[0028] When A exceeds the preset threshold, an alarm is issued, and θ is the fault detection coefficient.

[0029] Preferably, the anomaly detection module has a self-diagnostic function, which can diagnose and record based on the equipment's historical operating data, generate data for analyzing the equipment's durability, and provide support for system maintenance. The thickness correction model dynamically corrects the thickness data based on the rate of change of environmental factors monitored in real time, so as to ensure that the feeding accuracy meets the set tolerance range.

[0030] Preferably, the feeding control device includes a feeding rate limiter, which can automatically reduce the feeding rate when abnormal fluctuations are detected to avoid instability in the feeding system. The control formula is:

[0031] F limit =max(F,F min )

[0032] Where F limit F is the adjusted value for the restricted material supply. min This is the minimum allowable value for the feeding rate.

[0033] Preferably, the embedded microcontroller is connected to a remote monitoring system via a local area network, and can transmit data to external devices in real time for remote status monitoring, parameter adjustment, and system maintenance. The remote monitoring system has data analysis capabilities, can identify the impact of environmental changes on feeding accuracy, and provide optimization suggestions to the operator. The system has a power failure emergency mode, which automatically reduces the feeding rate and records the current data status when the power fluctuates, so as to achieve seamless connection and data recovery after the system is restored.

[0034] This invention provides a quantitative feeding system based on online thickness measurement technology. It has the following advantages:

[0035] By integrating a non-contact laser thickness gauge, environmental monitoring device, and embedded microcontroller, the system can monitor material thickness and environmental parameters in real time. Through data filtering and thickness correction, it eliminates the influence of environmental noise, improving measurement accuracy. Furthermore, the system's environmental correction model and dynamic feeding adjustment function ensure precise feeding control under varying environmental conditions such as temperature, humidity, and wind speed, enabling the system to adapt to complex environmental conditions and avoiding inaccurate feeding caused by environmental interference. By combining a PID control algorithm and a self-learning module, the system achieves high precision in feeding rate control, further enhancing system stability and feeding accuracy.

[0036] This system also boasts significant advantages in durability and adaptability. The laser thickness gauge, equipped with self-calibration, maintains stable accuracy during long-term operation. The adaptive update frequency of the environmental correction model reduces unnecessary correction operations, lowering system wear and tear. The system's anomaly detection module features fault warning and self-diagnostic capabilities. In the event of extreme conditions or equipment failure, the system can promptly issue an alarm and automatically reduce the feeding rate to prevent equipment damage and ensure system continuity. Through remote monitoring and data analysis, the system can perform remote status monitoring and parameter optimization. The power failure emergency mode allows the system to quickly recover after an unexpected power outage. These design features ensure the system remains stable and durable under various operating conditions, effectively improving operational efficiency and extending its service life. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the internal structure of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, this embodiment of the invention provides a quantitative feeding system based on online thickness measurement technology, including hardware components: an online thickness gauge, a data processing unit, an environmental monitoring device, and a feeding control device. The non-contact laser thickness gauge is equipped with a self-calibration function to adapt to changes in the light source caused by long-term use, thereby improving thickness measurement accuracy and system durability. The environmental monitoring device has a sensor for collecting wind speed to detect thickness errors caused by airflow, and incorporates the wind speed variable V into the environmental correction model, the formula of which is:

[0040] T ′ =T×(1+α×E+β×V)

[0041] Where β is the wind speed influence coefficient, the embedded microcontroller of the data processing unit includes a multi-channel parallel data processing module for real-time interaction of thickness data and environmental data to improve data processing speed and ensure the real-time response of the system. In the PID control algorithm of the feeding control device, the gain parameter K is automatically adjusted according to the fluctuation of environmental conditions to adapt to drastic environmental changes and ensure the stability of the feeding rate. Its dynamic control formula is:

[0042] K = K0 × (1 + γ × ΔE)

[0043] Where K0 is the initial control gain, ΔE is the dynamic rate of change of environmental factors, and γ is the gain adjustment.

[0044] The online thickness gauge is a non-contact laser thickness gauge used to measure the thickness of the supplied material in real time.

[0045] The data processing unit includes an embedded microcontroller for receiving and processing thickness data, and performing data filtering and thickness correction.

[0046] The environmental monitoring device includes temperature and humidity sensors and light intensity detectors, which are used to collect environmental data and monitor changes in temperature, humidity and light intensity in real time.

[0047] The feeding control device adopts a closed-loop control system, which dynamically adjusts the feeding rate through a PID control algorithm to ensure feeding accuracy. The control algorithm module has a self-learning function, which automatically optimizes the environmental impact coefficient and wind speed impact coefficient in the environmental correction model by analyzing historical environmental changes and feeding adjustment data, so that the system has higher adaptability in long-term operation. The update frequency of the environmental correction model is adaptively adjusted according to the rate of environmental change to reduce unnecessary correction frequency and improve the system's operational stability and durability.

[0048] Software Components: This section includes software modules for processing thickness and environmental data. These modules include a data correction module, an environmental correction model, a control algorithm module, and a self-learning module. The system also includes an anomaly detection module, which can identify equipment malfunctions or extreme environmental changes. In abnormal situations, it issues an alarm and automatically reduces the feeding rate to protect the equipment from damage. The anomaly detection formula is:

[0049] A = θ × (|T′ - T0|)

[0050] When A exceeds a preset threshold, an alarm is issued. θ is the fault detection coefficient. The anomaly detection module has a self-diagnostic function, capable of diagnosing and recording based on historical equipment operating data, generating data for analyzing equipment durability and providing support for system maintenance. The thickness correction model dynamically corrects the thickness data based on the real-time monitored rate of change of environmental factors to ensure that the feeding accuracy meets the set tolerance range. The feeding control device includes a feeding rate limiter, which automatically reduces the feeding rate when abnormal fluctuations are detected to prevent instability in the feeding system. The control formula is:

[0051] F limit =max(F,F min )

[0052] Where F limit F is the adjusted value for the restricted material supply. min The embedded microcontroller is connected to the remote monitoring system via a local area network to set the minimum allowable feeding rate. It can transmit data to external devices in real time for remote status monitoring, parameter adjustment, and system maintenance. The remote monitoring system has data analysis capabilities, which can identify the impact of environmental changes on feeding accuracy and provide optimization suggestions to the operator. The system has a power failure emergency mode, which automatically reduces the feeding rate and records the current data status when the power fluctuates, so as to achieve seamless connection and data recovery after the system is restored.

[0053] The data correction module performs acoustic filtering on the thickness gauge data;

[0054] The environmental correction model adjusts the thickness data based on environmental changes. The formula for this correction model is:

[0055] T ′ =T×(1+α×E)

[0056] Where T is the original thickness value, T ′ The corrected thickness value is E, the rate of change of environmental factors is E, and the environmental impact coefficient is α.

[0057] The control algorithm module includes a PID control algorithm, and the material feeding adjustment formula is as follows:

[0058] F = K × (T′ - T0)

[0059] Where F is the material feeding adjustment value, T0 is the target thickness value, and K is the control gain coefficient.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A quantitative feeding system based on online thickness measurement technology, characterized in that, include: Hardware components: online thickness gauge, data processing unit, environmental monitoring device, and material supply control device; The online thickness gauge is a non-contact laser thickness gauge used to measure the thickness of the supplied material in real time; The data processing unit includes an embedded microcontroller for receiving and processing thickness data, and performing data filtering and thickness correction. The environmental monitoring device includes a temperature and humidity sensor and a light intensity detector, used to collect environmental data and monitor changes in temperature, humidity and light intensity in real time. The feeding control device adopts a closed-loop control system, which dynamically adjusts the feeding rate through a PID control algorithm to ensure feeding accuracy. Software component: Software modules for processing thickness data and environmental data, including a data correction module, an environmental correction model, a control algorithm module, and a self-learning module; The data correction module performs acoustic filtering on the thickness gauge data; The environmental correction model adjusts the thickness data based on environmental changes. The formula for this correction model is: ； in, This is the original thickness value. This is the corrected thickness value. For the rate of change of environmental factors, This is the environmental impact coefficient; The control algorithm module includes a PID control algorithm, and the material feeding adjustment formula is as follows: ； in, For material supply adjustment value, For the target thickness value, To control the gain coefficient; The non-contact laser thickness gauge is equipped with a self-calibration function to adapt to changes in the light source caused by long-term use, thereby improving thickness measurement accuracy and system durability. The environmental monitoring device has a wind speed sensor to detect thickness errors caused by airflow and to record wind speed variables. Incorporating the environmental correction model, the formula is: ； in, This refers to the wind speed influence coefficient. The embedded microcontroller of the data processing unit includes a multi-channel parallel data processing module for real-time interaction of thickness data and environmental data, thereby improving data processing speed and ensuring real-time system response. The gain parameter in the PID control algorithm of the feeding control device... It automatically adjusts according to fluctuations in environmental conditions to adapt to drastic environmental changes and ensure the stability of the material feeding rate. Its dynamic control formula is: ； in, This is the initial control gain. The dynamic rate of change of environmental factors. This is a gain adjustment system.

2. The quantitative feeding system based on online thickness measurement technology according to claim 1, characterized in that: The control algorithm module is equipped with a self-learning function, which automatically optimizes the environmental impact coefficient in the environmental correction model by analyzing historical environmental changes and material supply adjustment data. Wind speed influence coefficient This makes the system more adaptable in long-term operation. The update frequency of the environmental correction model is adaptively adjusted according to the rate of environmental change to reduce unnecessary correction frequency and improve the system's operational stability and durability.

3. The quantitative feeding system based on online thickness measurement technology according to claim 1, characterized in that: The system also includes an anomaly detection module, which can identify equipment malfunctions or extreme environmental changes. In abnormal situations, it issues an alarm and automatically reduces the feeding rate to protect the equipment from damage. The anomaly detection formula is: ； Among them, when An alarm will be issued when the threshold is exceeded. This is the fault detection coefficient.

4. The quantitative feeding system based on online thickness measurement technology according to claim 3, characterized in that: The anomaly detection module has a self-diagnostic function, which can diagnose and record based on the equipment's historical operating data, generate data for analyzing the equipment's durability, and provide support for system maintenance. The thickness correction dynamically corrects the thickness data based on the real-time monitored rate of change of environmental factors to ensure that the feeding accuracy meets the set tolerance range.

5. A quantitative feeding system based on online thickness measurement technology according to claim 1, characterized in that: The feeding control device includes a feeding rate limiter, which can automatically reduce the feeding rate when abnormal fluctuations are detected to prevent instability in the feeding system. The control formula is: ； in This is the adjusted value for the material supply after the restriction. This is the minimum allowable value for the feeding rate.

6. The quantitative feeding system based on online thickness measurement technology according to claim 1, characterized in that: The embedded microcontroller is connected to the remote monitoring system via a local area network and can transmit data to external devices in real time for remote status monitoring, parameter adjustment, and system maintenance. The remote monitoring system has data analysis capabilities, which can identify the impact of environmental changes on feeding accuracy and provide optimization suggestions to the operator. The system has a power failure emergency mode, which automatically reduces the feeding rate and records the current data status when the power fluctuates, so as to achieve seamless connection and data recovery after the system is restored.

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