Active static eliminating control method for anti-static turnover box based on state observer

Abstract

The invention relates to the technical field of static electricity control, in particular to an anti-static turnover box active static electricity removing control method based on a state observer, which comprises the following steps: acquiring environment temperature and environment humidity of a turnover box at a plurality of current and historical sampling moments, electrostatic voltage on the surface of the turnover box and estimated electrostatic charge quantity in the turnover box; and outputting a control signal of the turnover box at the current moment by using an improved PID control algorithm, and adjusting the positive and negative ion generator based on the control signal to actively eliminate static electricity of the turnover box. The method solves the problems that an existing control algorithm is insufficient in stability and limited in adaptability under complex environment conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrostatic control. More particularly, the present application relates to a state observer-based active electrostatic elimination control method for anti-static turnover boxes. BACKGROUND

[0002] In the process of electronic component turnover, precision structural part transfer and flammable and explosive material storage, anti-static turnover boxes are applied to isolate external frictional charging and reduce the risk of charge accumulation. Especially in high-density integrated circuit packaging workshops, semiconductor manufacturing lines and automated logistics systems, turnover boxes frequently come into contact and friction with conveyor belts, mechanical arms and air flow during handling, stacking and conveying, which easily causes surface charge accumulation. When the ambient temperature is high, the humidity is low or the humidity fluctuates rapidly, the air conductivity decreases, the surface charge discharge path of the material is limited, and static electricity is more likely to be retained for a long period of time or even discharge breakdown, resulting in degradation of electronic component performance, chip failure or safety hazards such as spark discharge. Therefore, how to monitor the static state of the turnover box in real time and implement active electrostatic elimination control has become an important technical issue to ensure production safety and product quality.

[0003] In the prior art, the common electrostatic control method mainly relies on passive anti-static materials or fixed-power ion air blowers for continuous release. Such a scheme usually uses a proportional-integral-derivative control algorithm with fixed parameters, or only switches according to a single voltage threshold. When the surface static voltage exceeds the set threshold, the ion generator is started, and when it is below the threshold, it is turned off.

[0004] However, under actual complex working conditions, the internal charge accumulation process of the turnover box has hysteresis and concealment, and the surface voltage often cannot timely reflect the internal charge change trend, resulting in a lack of response capability of the control system when the risk has not yet appeared, and the control may be excessive or the response may be lagged when the risk has already formed. In addition, the traditional control algorithm mostly uses fixed proportional, integral and derivative parameters, and the gain setting depends on artificial experience, which is difficult to adaptively adjust when the environmental temperature and humidity change or the charge fluctuates sharply, and is prone to problems such as oscillation, overshoot or slow response, thereby causing the existing control algorithm to have insufficient stability and limited adaptability under complex environmental conditions. SUMMARY

[0005] To solve the problem of insufficient stability and limited adaptability of the existing control algorithm under complex environmental conditions in the background art, the present application provides the following solutions.

[0006] This invention provides an active static electricity removal control method for anti-static turnover boxes based on a state observer, comprising: acquiring the ambient temperature, ambient humidity, electrostatic voltage on the surface of the turnover box, and estimated electrostatic charge inside the turnover box at the current and multiple historical sampling times; outputting a control signal for the turnover box at the current time using an improved PID control algorithm, and adjusting the positive and negative ion generator based on the control signal to actively remove static electricity from the turnover box; wherein, the improved PID control algorithm includes proportional gain... Integral gain and differential gain parameter The proportional gain Integral gain It is positively correlated with the adjustment factor, and the differential gain parameter is also positively correlated with the differential gain parameter. The adjustment factor is inversely correlated; the adjustment factor is positively correlated with the charge threat index, which is positively correlated with the degree of environmental disturbance and the estimated amount of electrostatic charge, and inversely correlated with the electrostatic voltage.

[0007] The above technical solution achieves precise matching of ion release polarity and intensity with the current electrostatic state through intelligent adjustment of the positive and negative ion generators, thereby realizing instant neutralization of positive and negative charges, effectively shortening charge decay time, reducing potential discharge risks, and improving the stability and reliability of the system under environmental fluctuations, humidity changes, or abnormal temperature conditions. This enhances the overall stability, adaptability, and engineering practicality of active static electricity removal control.

[0008] Furthermore, the proportional gain for: , As the reference proportional gain, For the first The adjustment factor at each sampling time. For the natural constant An exponential function with base 0.

[0009] The above technical solution achieves nonlinear adaptive amplification of the control system by exponentially coupling the adjustment factor with the reference proportional gain, based on the dynamic changes in electrostatic risk. When the electrostatic risk or charge threat index increases, the adjustment factor increases accordingly, thereby multiplying the proportional effect and making the controller's response to errors more rapid and intense, accelerating the decay rate of charge on the surface and inside the turnover box. In contrast, during periods of low risk or stable environment, the proportional gain remains at a low level to avoid over-control that could lead to system oscillations or energy waste.

[0010] Furthermore, the differential gain for: , As the reference differential gain, For the first The adjustment factor at each sampling time. For the natural constant An exponential function with base 0.

[0011] The above technical solution achieves inverse exponential coupling between the adjustment factor and the reference differential gain, enabling the control system to automatically reduce the differential action when the electrostatic risk changes drastically or the charge threat index increases. This effectively suppresses control oscillations or noise amplification caused by over-response to high-frequency errors. When the risk is low or changes are gradual, the differential gain remains at a high level to make full use of the error change rate for predictive adjustment and respond in advance to potential electrostatic accumulation trends.

[0012] Furthermore, the integral gain for: , As the reference integral gain, For the first The adjustment factor at each sampling time. For the natural constant A logarithmic function with base 0.

[0013] The above technical solution couples the adjustment factor and the baseline integral gain in the form of a logarithmic function, enabling the integral action to gradually increase with changes in electrostatic risk. As the charge threat index and adjustment factor increase, the integral gain gradually increases, thereby strengthening the ability to correct long-term accumulated errors and helping to eliminate latent charge accumulation under sustained high-risk conditions. The gradual increase characteristic of the logarithmic mapping effectively suppresses control overshoot or system instability problems that may be caused by excessively rapid integral growth. In the low-risk or slowly changing phases, the increase in integral gain is limited, ensuring stable system operation without introducing over-adjustment.

[0014] Furthermore, the first Adjustment factor at each sampling time for: , For the natural constant Logarithmic function with base 0. , The first The, the Charge threat index at each sampling time.

[0015] The technical solution couples the instantaneous change amplitude of the charge threat index with the current threat level, and performs nonlinear mapping through a logarithmic function, so that the intensity and change trend of the electrostatic risk can be described at the same time. When the threat index rapidly rises or fluctuates sharply, the adjustment factor increases accordingly, thereby enhancing the response capability of the subsequent control algorithm to the risk change; and when the risk changes slowly or is at a low level, the adjustment factor naturally decreases, avoiding excessive control actions, thereby maintaining system stability. Through this dynamic adaptive adjustment, the control response can be rapidly enhanced in high-risk or sudden electrostatic accumulation scenarios, and stable operation can be maintained in low-risk or stable environmental conditions, significantly improving the forward-looking, sensitivity and overall robustness of the electrostatic control strategy, and providing a more reliable decision basis for active electrostatic elimination.

[0016] Further, the charge threat index at the m-th sampling moment is: , is the environmental disturbance degree at the m-th sampling moment in a historical setting time window with the m-th sampling moment as the latest point, , , are the estimated electrostatic charge and electrostatic voltage at the m-th sampling moment in a historical setting time window with the m-th sampling moment as the latest point, is a preset hyperparameter, is the total number of sampling moments in the historical setting time window.

[0017] The technical solution couples the environmental disturbance degree, the estimated electrostatic charge, and the corresponding electrostatic voltage in the historical time window, so that the charge threat index can reflect the absolute amount of charge accumulation, environmental triggering effect, and risk change trend at the same time. In the case of high charge accumulation or enhanced environmental disturbance, the charge threat index naturally increases, thereby identifying potential electrostatic risks in advance, even if the surface voltage has not reached the alarm threshold, the internal high-risk state can be reflected; when the risk is low or the environmental conditions are stable, the index remains at a low level, avoiding excessive intervention. Through the cumulative calculation of the historical window, not only the electrostatic state at a single moment is considered, but also the trend characteristics of long-term charge accumulation and environmental fluctuations are reflected, realizing the unified description of short-time sudden increase and long-term accumulation risks.

[0018] Further, the environmental disturbance degree at the m-th sampling moment is: , is the environmental temperature at the m-th sampling moment, ,​​​​​​​​ is the environment humidity at the first sampling moment, is the first super parameter. is the first super parameter.

[0019] The technical scheme couples the environment temperature, humidity and instantaneous change amplitude of humidity to model, so that the environment disturbance degree can reflect the steady-state influence and dynamic fluctuation characteristics of the environment on the static electricity accumulation ability. In the condition of low humidity or rapid humidity drop, the environment disturbance degree is naturally amplified, thereby embodying the risk aggravation of the environment on the limited charge discharge and easy static electricity accumulation. When the humidity is relatively high or changes smoothly, the environment disturbance degree is correspondingly reduced to avoid excessive response. By combining the temperature level with the humidity and its change amplitude, the sensitivity to the abnormal change of the environment can be enhanced, so that the system can identify the potential static electricity risk in advance when the humidity drops suddenly or the temperature and humidity are abnormally combined, while the running stability in the stable environment stage is maintained, thereby providing more accurate, forward-looking and reliable data support for subsequent static electricity risk assessment and active static electricity removal control.

[0020] Further, based on the control signal, the positive and negative ion generator is adjusted, including: if the control signal is positive, the positive and negative ion generator generates negative ions; if the control signal is negative, the positive and negative ion generator generates positive ions.

[0021] Further, the temperature and humidity sensor is used to obtain the environment temperature and humidity, the static electricity meter is used to obtain the static electricity voltage on the surface of the turnover box, and the state observer is used to obtain the estimated static electricity charge in the turnover box.

[0022] Further, it also includes denoising and standardization processing of the environment temperature, humidity, static electricity voltage and estimated static electricity charge.

[0023] The beneficial effects of the present application are: The present application realizes the nonlinear adaptive adjustment of control gain by combining the improved PID control algorithm and the adaptive adjustment factor, so that the system can automatically adjust the size and direction of proportional, integral and differential action according to the charge threat index and the environment disturbance intensity, enhance the immediate response, long-term cumulative correction and fluctuation prediction ability of the error. At the same time, through the intelligent adjustment of the positive and negative ion generator, the ion release polarity and intensity are accurately matched with the static electricity state, so that the positive and negative charges are quickly neutralized, the charge decay speed is significantly accelerated, the potential discharge risk is reduced, and the system can run stably and have high response ability in the case of environment fluctuation or rapid change of temperature and humidity. Overall, the adaptability, stability and reliability of the active static electricity removal of the turnover box are improved, the timely intervention of sudden static electricity accumulation, the continuous control of long-term accumulated risk, and the safety and engineering practicability of static electricity protection under complex working conditions are comprehensively improved. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flow chart illustrating a state observer based anti-static tote active static control method according to an embodiment of the present application; Figure 2 is a PID gain parameter over time effect diagram of the state observer based anti-static tote active static control method according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] State observer based anti-static tote active static control method embodiment.

[0026] As Figure 1 shown, the flow chart of the state observer based anti-static tote active static control method of the embodiment of the present application includes the following steps: S1: Obtain the environmental temperature, environmental humidity, static voltage on the surface of the tote, and estimated static charge amount inside the tote at the current and historical multiple sampling time points.

[0027] In a preferred embodiment, in order to realize fine and dynamic monitoring of the static state of the tote, the environmental temperature and humidity data are obtained in real time by a high-precision digital temperature and humidity sensor. At the same time, a high-input-impedance non-contact electrometer is arranged at the key area on the surface of the tote for real-time acquisition of the static voltage value on the surface of the tote, avoiding disturbance to the original electric field distribution caused by contact measurement, thereby improving the authenticity and repeatability of the voltage acquisition data.

[0028] At the same time, in view of the difficulty in directly measuring the static charge amount inside the tote, the state observer is further utilized to obtain the estimated static charge amount inside the tote.

[0029] Further, in order to improve the data quality and subsequent fusion analysis accuracy, the environmental temperature, environmental humidity, static voltage, and estimated static charge amount are subjected to multi-stage denoising and standardization processing. First, in the denoising stage, the original acquisition signal is subjected to smoothing processing by using an adaptive sliding time window filtering method. In the standardization processing stage, in order to eliminate the influence of the dimensional difference between different physical quantities on the comprehensive evaluation, a dynamic time window statistical method is used to compress the interval of each type of data. By calculating the data mean value within a set time range, the original data is converted into a dimensionless relative change amount, and the statistical interval is updated in rolling manner with time, so that the standardization result can adapt to seasonal changes and working condition fluctuations of the environment.

[0030] S2: Output the control signal of the tote at the current time point by using the improved PID control algorithm.

[0031] As Figure 2The figure shows the effect of the PID gain parameter changing over time in the active static removal control method for anti-static turnover boxes based on a state observer according to an embodiment of the present invention.

[0032] In a preferred embodiment, the improved PID control algorithm includes proportional gain. Integral gain and differential gain parameter The proportional gain for: , As the reference proportional gain, For the first The adjustment factor at each sampling time. For the natural constant An exponential function with base 0.5. The differential gain. for: , As the reference differential gain, For the first The adjustment factor at each sampling time. For the natural constant An exponential function with base 0.5. The integral gain. for: , As the reference integral gain, For the first The adjustment factor at each sampling time. For the natural constant A logarithmic function with base 0.

[0033] By incorporating a risk adjustment factor into the gain regulation process of the proportional, integral, and derivative stages, the control parameters can be nonlinearly and adaptively adjusted according to changes in risk status. Specifically, an exponential amplification mechanism is adopted for the proportional stage, so that when the adjustment factor increases and the risk change intensifies, the proportional action rapidly strengthens, thereby improving the system's immediate response to errors and accelerating the suppression of electrostatic risks. At the same time, an inverse exponential adjustment method is adopted for the derivative stage, so that the derivative gain is appropriately reduced during periods of severe risk fluctuations, avoiding control oscillations or amplified noise due to excessive sensitivity to the rate of change, and thus suppressing high-frequency disturbances in the system. Finally, a logarithmic gradual increase mechanism is introduced into the integral stage, so that the integral action gradually increases with the degree of risk, but the growth rate is limited. This not only eliminates long-term accumulated errors but also prevents overshoot or control lag due to integral saturation during high-risk stages.

[0034] No. Adjustment factor at each sampling time for: , For the natural constant Logarithmic function with base 0. , are respectively the charge threat indexes at the first , the first sampling moments.

[0035] By introducing the difference value of the charge threat indexes at adjacent moments, the dynamic fluctuation degree of the risk state can be reflected. When the charge threat index appears rapid rise or severe shock, the difference value significantly increases, thereby enhancing the sensitivity of the adjustment factor. At the same time, multiplying the change amplitude by the current threat level makes the fluctuation in the high-risk background be further amplified, while the slight fluctuation in the low-risk state is naturally inhibited, thereby embodying the adaptive adjustment idea of high risk high sensitivity and low risk low response. Further compression processing by the natural logarithm function avoids infinite amplification of the value when the risk grows rapidly, improves the stability and controllability of the overall model, and at the same time retains the response ability to early abnormal changes.

[0036] the charge threat index at the first sampling moment is: , is the environmental disturbance degree at the first sampling moment in the historical setting time window with the first sampling moment as the latest point, , are respectively the estimated electrostatic charge and electrostatic voltage at the first sampling moment in the historical setting time window with the first sampling moment as the latest point, is a preset hyperparameter, is the total number of sampling moments in the historical setting time window.

[0037] The environmental disturbance degree, the estimated electrostatic charge and the corresponding electrostatic voltage in the historical time window are weighted, coupled and energyized to form a comprehensive representation of the electrostatic risk. Specifically, by introducing weights to the environmental disturbance degree at each moment in history, the charge accumulation formed under adverse environmental conditions such as high temperature and low humidity or severe humidity fluctuations is given a higher influence coefficient, thereby embodying the amplification effect of the environment on the electrostatic runaway risk. At the same time, the estimated charge is processed in the form of square, which produces a nonlinear enhancement effect on the overall index when the charge level is high, thereby strengthening the sensitivity to high charge state and avoiding underestimating the risk in the early stage. Then, by constructing a suppression relationship with the corresponding voltage value, the risk assessment tends to be stable when the voltage is high and has certain external characteristics, while in the case where the voltage has not yet risen significantly but the internal charge has accumulated a lot, the charge threat index can still maintain a high level, thereby making up for the lag caused by simply relying on voltage judgment.

[0038] the environmental disturbance degree at the i-th sampling moment is: , is the i-th sampling moment, environmental temperature at the i-th sampling moment, , is the i-th sampling moment, environmental humidity at the i-th sampling moment, is the first hyperparameter.

[0039] By coupling the modeling of environmental temperature, environmental humidity and the instantaneous change amplitude of environmental humidity, the comprehensive influence mechanism of the environment on the accumulation and discharge ability of static electricity is described. Specifically, by constructing a ratio relationship between the current temperature and the current humidity, the environmental disturbance degree is naturally amplified when the humidity is low, thereby reflecting the physical characteristics that charges are not easy to discharge and static electricity is more likely to accumulate in low humidity environments. At the same time, an adjustment factor is introduced in the humidity term to avoid numerical abnormalities when the humidity is close to zero or extremely low, improving stability and engineering applicability. Further, the influence of the change amplitude of humidity between adjacent moments is superimposed, so that when the environmental humidity fluctuates rapidly, the environmental disturbance degree is also enhanced, thereby representing the impact effect of environmental mutation on the charge balance state of the material surface.

[0040] S3: adjusting the positive and negative ion generator based on the control signal to actively remove static electricity from the turnover box.

[0041] In a preferred embodiment, the positive and negative ion generator is adjusted based on the control signal to actively remove static electricity from the turnover box, which is the key execution mechanism to realize the closed-loop management of static risk.

[0042] Specifically, the control system determines the polarity and intensity of the current charge on the surface and inside of the turnover box in real time according to the control signal output by the risk assessment and adaptive adjustment algorithm, and dynamically switches the output polarity of the ion generator so that it releases air ions opposite to the current dominant charge polarity, thereby accelerating the charge decay process through charge neutralization principle; when the control signal reflects a positive charging trend, the ion generator is driven to generate ions of opposite polarity for neutralization, and when the control signal reflects a negative charging trend, the output is switched to the other polarity to ensure that the static electricity direction always matches the actual charging state, avoiding the phenomenon of charge superposition due to incorrect polarity judgment. At the same time, the amplitude of the control signal is also used to adjust the ion output intensity, so that the ion release amount and the static electricity risk level form a dynamic corresponding relationship, maintaining low power operation at low risk stage to reduce energy consumption and equipment burden, and automatically increasing ion output intensity when risk increases or shows rapid growth trend, thereby shortening the duration of high risk and improving the elimination efficiency. Through this control method combining polarity adaptation and intensity grading adjustment, the passive quantitative release is transformed into active intelligent neutralization, which not only significantly improves the static electricity elimination speed and stability, but also enhances the response ability to sudden charge accumulation, improves the safety, reliability and engineering practical value of the overall operation.

[0043] The scheme of the present application introduces an improved PID control algorithm and an adaptive adjustment factor, so that the system can dynamically adjust the proportional, integral and derivative gains according to the charge threat index and environmental disturbance, so that the controller can quickly enhance the response ability at high risk stage, and maintain stable output at low risk or stable stage to avoid excessive adjustment or oscillation. At the same time, the output polarity and intensity of the positive and negative ion generators are intelligently adjusted through the control signal to realize the immediate neutralization of the positive and negative charges on the turnover box, thereby accelerating the charge decay speed, reducing the potential discharge risk, and improving the stability and reliability of the system under temperature and humidity fluctuations, environmental disturbances or sudden charge accumulation. Further, the collected data is denoised and standardized to eliminate the influence of measurement noise on control decision, ensuring the accuracy and repeatability of the adjustment action. Overall, the closed-loop intelligent control of the turnover box static electricity management is realized, which significantly improves the response speed, accuracy and safety of active static electricity elimination, and enhances the robustness and engineering practicability of the system under complex working conditions.

[0044] In the description of the present specification, the meaning of "a plurality of", "several" is at least two, for example, two, three or more, etc., unless otherwise explicitly specified.

[0045] While the specification has illustrated and described various embodiments of the application, it will be clear to those of ordinary skill in the art that various changes, modifications, and substitutions can be made thereto without departing from the spirit and scope of the application. It is understood that in the process of practicing the application, various alternatives, modifications, and equivalents can be employed.

Claims

1. A method for active static electricity removal control of anti-static turnover boxes based on a state observer, characterized in that, include: The ambient temperature, ambient humidity, electrostatic voltage on the surface of the turnover box, and estimated electrostatic charge inside the turnover box are obtained at the current and multiple historical sampling times. An improved PID control algorithm is used to output a control signal for the turnover box at the current moment, and the positive and negative ion generators are adjusted based on the control signal to actively remove static electricity from the turnover box. Among them, the improved PID control algorithm includes proportional gain. Integral gain and differential gain parameter The proportional gain Integral gain It is positively correlated with the adjustment factor, and the differential gain parameter is also positively correlated with the differential gain parameter. The regulating factors are inversely correlated; The adjustment factor is positively correlated with the charge threat index, which is positively correlated with the degree of environmental disturbance and the estimated amount of electrostatic charge, and negatively correlated with electrostatic voltage.

2. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, The proportional gain for: , As the reference proportional gain, For the first The adjustment factor at each sampling time. For the natural constant An exponential function with base 0.

3. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, The differential gain for: , As the reference differential gain, For the first The adjustment factor at each sampling time. For the natural constant An exponential function with base 0.

4. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, The integral gain for: , As the reference integral gain, For the first The adjustment factor at each sampling time. For the natural constant A logarithmic function with base 0.

5. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, No. Adjustment factor at each sampling time for: , For the natural constant Logarithmic function with base 0. , The first The, the Charge threat index at each sampling time.

6. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, No. Charge threat index at each sampling time for: , For the first Within the historical time window where the sampling time is the latest point, the [number]th sampling moment... The degree of environmental disturbance at each sampling time , Each of the following is the first Within the historical time window where the sampling time is the latest point, the [number]th sampling moment... Estimated electrostatic charge and electrostatic voltage at each sampling time. To preset hyperparameters, The total number of sampling moments within the set historical time window.

7. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, No. Environmental disturbance level at each sampling time for: , For the first Ambient temperature at each sampling time , For the first Ambient humidity at each sampling time This is the first hyperparameter.

8. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, Adjusting the positive and negative ion generator based on the control signal includes: if the control signal is positive, controlling the positive and negative ion generator to generate negative ions; if the control signal is negative, controlling the positive and negative ion generator to generate positive ions.

9. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, Ambient temperature and humidity are obtained using temperature and humidity sensors, electrostatic voltage on the surface of the turnover box is obtained using an electrometer, and estimated electrostatic charge inside the turnover box is obtained using a state observer.

10. The active static electricity removal control method for anti-static turnover boxes based on a state observer according to claim 1, characterized in that, It also includes noise reduction and standardization of ambient temperature, ambient humidity, electrostatic voltage, and estimated electrostatic charge.

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