Quantitative feeding system based on online thickness measurement technology

Abstract

The invention provides a quantitative feeding system based on an online thickness measurement technology. The quantitative feeding system based on the on-line thickness measurement technology comprises a hardware part, a control part and a feeding part, wherein the hardware part comprises an on-line thickness gauge, a data processing unit, an environment monitoring device and a feeding control device; the on-line thickness gauge is a non-contact laser thickness gauge and is used for measuring the thickness of a feeding material in real time; the data processing unit comprises an embedded microcontroller and is used for receiving and processing thickness data and executing data filtering and thickness correction, and the environment monitoring device comprises a temperature and humidity sensor and a light intensity detector. According to the quantitative feeding system based on the on-line thickness measurement technology, through integration of the non-contact laser thickness gauge, the environment monitoring device and the embedded microcontroller, the system can monitor the material thickness and environment parameters in real time, and through the modes of data filtering, thickness correction and the like, the influence of environment noise is eliminated, and the measurement precision is improved.

Description

Technical Field

[0001] The invention relates to the technical field of online thickness measurement technology, and in particular to a quantitative feeding system based on the online thickness measurement technology. Background Art

[0002] Basic structure and principle: The system is based on online thickness measurement technology. It controls the feeding amount by real-time monitoring of material thickness, thereby achieving accurate material supply. 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 a uniform feeding effect.

[0003] The system may be affected by environmental conditions in actual applications. For example, changes in temperature and humidity can affect the accuracy of thickness measurement and reduce feeding accuracy. The durability and stability of the system may be hidden dangers and need to be further optimized and improved to meet the needs of industrial applications. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a quantitative feeding system based on online thickness measurement technology, which solves the problem of being disturbed by environmental conditions and the problem that there may be hidden dangers in the durability and stability of the system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a quantitative feeding system based on online thickness measurement technology, comprising:

[0006] Hardware: 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, which is used to measure the thickness of the feed material in real time;

[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, which are used to collect environmental data and monitor changes in temperature, humidity and light intensity in real time;

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

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

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

[0013] The environmental correction model corrects the thickness data according to environmental changes. The correction model formula is:

[0014] T ′ =T×(1+α×E)

[0015] Where T is the original thickness value, T ′ is the corrected thickness value, 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 feed adjustment formula is:

[0017] F=K×(T ′ -T0)

[0018] Among them, F is the feed 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 the changes in light source caused by long-term use, improve the thickness measurement accuracy and system durability, and the environmental monitoring device is equipped with a sensor for collecting wind speed to detect the thickness error caused by air flow, and incorporate the wind speed variable V into the environmental correction model, the formula of which is:

[0020] T ′ =T×(1+α×E+β×V)

[0021] Among them, β is the wind speed influence coefficient.

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

[0023] K=K0×(1+γ×ΔE)

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

[0025] Preferably, the control algorithm module is provided with a self-learning function, which automatically optimizes the environmental impact coefficient α and the wind speed impact coefficient β in the environmental correction model by analyzing historical environmental changes and feed 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 operating stability and durability of the system.

[0026] Preferably, the system further includes an abnormality detection module, which can identify equipment failures or extreme environmental changes, issue an alarm in abnormal situations and automatically reduce the feed rate to protect the equipment from damage. The abnormality 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 abnormality detection module has a self-diagnosis function, can diagnose and record according to the equipment operation history data, generate data for analyzing the durability of the equipment, and provide support for system maintenance. The thickness correction model dynamically corrects the thickness data according to the real-time monitored rate of change of environmental factors 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 of the feeding system. The control formula is:

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

[0032] where F limit is the feed adjustment value after limitation, F min is the minimum allowable value of the feed 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 a data analysis function, which can identify the impact of environmental changes on feeding accuracy and provide optimization suggestions to operators. 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] The present invention provides a quantitative feeding system based on online thickness measurement technology. It has the following beneficial effects:

[0035] Through the integration of non-contact laser thickness gauge, environmental monitoring device and embedded microcontroller, the system can monitor material thickness and environmental parameters in real time, and eliminate the influence of environmental noise and improve measurement accuracy through data filtering and thickness correction. In addition, the system's environmental correction model and dynamic feeding adjustment function ensure accurate feeding control under changing environmental conditions such as temperature, humidity, wind speed, etc., so that the system can adapt to complex environmental conditions and avoid inaccurate feeding caused by environmental interference. By combining PID control algorithm and self-learning module, the system can achieve high precision in feeding rate control, further improving the stability and feeding accuracy of the system.

[0036] The system also has significant advantages in durability and adaptability. The laser thickness gauge equipped with self-calibration function can maintain stable accuracy in long-term operation, and the adaptive update frequency of the environmental correction model reduces unnecessary correction operations and reduces system wear. The system's abnormal detection module has fault warning and self-diagnosis functions. When extreme conditions or equipment failures occur, the system can promptly alarm and automatically reduce the feed rate to prevent equipment damage and ensure system continuity. Through remote monitoring and data analysis functions, the system can perform remote status monitoring and parameter optimization, and the power failure emergency mode enables the system to recover quickly after an unexpected power outage. The above design enables the system to remain stable and durable under different working conditions, effectively improving operating efficiency and service life. . BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the internal structure of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0040] T ′ =T×(1+α×E+β×V)

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

[0042] K=K0×(1+γ×ΔE)

[0043] Among them, K0 is the initial control gain, ΔE is the dynamic change rate 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 feed 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 a temperature and humidity sensor and a light intensity detector, 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 and dynamically adjusts the feeding rate through a PID control algorithm to ensure feeding accuracy. The control algorithm module is equipped with a self-learning function. By analyzing historical environmental changes and feeding adjustment data, it automatically optimizes the environmental impact coefficient and wind speed impact coefficient 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 changes to reduce unnecessary correction frequency and improve the system's operating stability and durability.

[0048] Software part: software module for processing thickness data and environmental data. The software module includes data correction module, environmental correction model, control algorithm module and self-learning module. The system also includes an abnormality detection module. The abnormality detection module can identify equipment failure or extreme environmental changes, issue an alarm and automatically reduce the feed rate in abnormal situations to protect the equipment from damage. The abnormality detection formula is:

[0049] A=θ×(|T′-T0|)

[0050] Among them, when A exceeds the preset threshold, an alarm is issued, θ is the fault detection coefficient, the abnormal detection module has a self-diagnosis function, can diagnose and record according to the equipment operation history data, generate data for analyzing the durability of the equipment, and provide support for system maintenance. The thickness correction model dynamically corrects the thickness data according to the real-time monitored environmental factor change rate to ensure that the feeding accuracy meets the set tolerance range. The feeding control device includes a feeding rate limiter, which can automatically reduce the feeding rate when abnormal fluctuations are detected to avoid instability of the feeding system. The control formula is:

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

[0052] where F limit is the feed adjustment value after limitation, F min 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 operators. 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 corrects the thickness data according to environmental changes. The correction model formula is:

[0055] T ′ =T×(1+α×E)

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

[0057] The control algorithm module includes a PID control algorithm, and the feed adjustment formula is:

[0058] F=K×(T′-T0)

[0059] Among them, F is the feed adjustment value, T0 is the target thickness value, and K is the control gain coefficient.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention 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: online thickness gauge, data processing unit, environmental monitoring device and feeding control device; The online thickness gauge is a non-contact laser thickness gauge, which is used to measure the thickness of the feed 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, which are 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, and dynamically adjusts the feeding rate through a PID control algorithm to ensure feeding accuracy; Software part: a software module for processing thickness data and environmental data, the software module includes 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 corrects the thickness data according to environmental changes. The correction model formula is: T′=T×(1+α×E) Wherein, T is the original thickness value, T′ is the corrected thickness value, E is the environmental factor change rate, and α is the environmental impact coefficient; The control algorithm module includes a PID control algorithm, and the feed adjustment formula is: F=K×(T′-T0) Among them, F is the feed adjustment value, T0 is the target thickness value, and K is the control gain coefficient.

2. The quantitative feeding system based on online thickness measurement technology according to claim 1 is characterized in that: The non-contact laser thickness gauge is equipped with a self-calibration function to adapt to the changes in light source caused by long-term use, improve the thickness measurement accuracy and system durability. The environmental monitoring device is equipped with a sensor for collecting wind speed to detect the thickness error caused by air flow, and incorporate the wind speed variable V into the environmental correction model, the formula of which is: T′=T×(1+α×E+β×V) Among them, β is the wind speed influence coefficient.

3. The quantitative feeding system based on online thickness measurement technology according to claim 1 is characterized in that: The embedded microcontroller of the data processing unit includes a multi-channel data parallel processing module for processing the real-time interaction of thickness data and environmental data to improve the data processing speed and ensure the real-time response of the system. The gain parameter K in the PID control algorithm of the feeding control device is automatically adjusted according to the fluctuation of environmental conditions to adapt to the drastic changes in the environment and ensure the stability of the feeding rate. The dynamic control formula is: K=K0×(1+γ×ΔE) Among them, K0 is the initial control gain, ΔE is the dynamic change rate of environmental factors, and γ is the gain adjustment coefficient.

4. The quantitative feeding system based on online thickness measurement technology according to claim 1 is characterized in that: The control algorithm module is provided with a self-learning function. By analyzing historical environmental changes and feed adjustment data, it automatically optimizes the environmental impact coefficient α and wind speed impact coefficient β in the environmental correction model, 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 environmental change rate to reduce unnecessary correction frequency and improve the system's operating stability and durability.

5. The quantitative feeding system based on online thickness measurement technology according to claim 1 is characterized in that: The system also includes an abnormality detection module, which can identify equipment failures or extreme environmental changes, issue an alarm and automatically reduce the feed rate in abnormal situations to protect the equipment from damage. The abnormality detection formula is: A=θ×(|T′-T0|) When A exceeds the preset threshold, an alarm is issued, and θ is the fault detection coefficient.

6. The quantitative feeding system based on online thickness measurement technology according to claim 1 is characterized in that: The abnormality detection module has a self-diagnosis function, can diagnose and record according to the historical data of equipment operation, generate data for analyzing the durability of equipment, and provide support for system maintenance. The thickness correction model dynamically corrects the thickness data according to the change rate of environmental factors monitored in real time to ensure that the feeding accuracy meets the set tolerance range.

7. The quantitative feeding system based on online thickness measurement technology according to claim 1 is 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 avoid instability of the feeding system. The control formula is: F limit =max(F,F min ) where F limit is the feed adjustment value after limitation, F min is the minimum allowable value of the feed rate.

8. The quantitative feeding system based on online thickness measurement technology according to claim 1 is 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 a data analysis function that can identify the impact of environmental changes on feeding accuracy and provide optimization suggestions to operators. The system has a power failure emergency mode that automatically reduces the feeding rate and records the current data status when the power fluctuates to achieve seamless connection and data recovery after the system is restored.

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