Big data driven dust-free drag chain antistatic optimization design method

Abstract

The invention provides an antistatic optimization design method for a dust-free drag chain driven by big data, and relates to the technical field of computer aided design, self-energy supply of a static dissipation system is realized by utilizing mechanical vibration energy of the drag chain, dependence on an external power supply is avoided, and independence and flexibility of the system are improved; based on closed-loop control of real-time monitoring data, dynamic adjustment of the anti-static performance is achieved, the device adapts to variable industrial environments, and the static dissipation efficiency is improved; energy collection, storage and consumption are optimized through the energy storage management unit, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and specifically to a method for optimizing the antistatic design of a dust-free towline driven by big data. Background Technique

[0002] The flexible antistatic housing of a dust-free towline is a special material for towlines, aiming to provide effective electrostatic protection for cables, pipelines and other sensitive equipment inside the towline, and ensure its normal and safe operation in a dust-free environment. A functional module with an electrostatic dissipation unit is integrated into the flexible antistatic housing of the dust-free towline, which is composed of conductive or semiconductive materials and is used to effectively conduct and neutralize static charges to ensure the safe operation of the towline in a clean environment; this unit is closely combined with the structure and materials of the towline to prevent static charge accumulation and at the same time not generate particulate pollution; traditional antistatic designs are usually based on fixed conductive materials or static coatings, and it is difficult to achieve a dynamic balance between static charge accumulation and dissipation. At the same time, this design fails to effectively utilize the mechanical vibration energy during the operation of the equipment, so an external power supply is required for support, reducing the independence and flexibility of the system.

[0003] "Energy Harvesting Based on Piezoelectric Effect" by Nanjing University of Aeronautics and Astronautics describes the technical means in the prior art for converting mechanical vibration energy into electrical energy through a piezoelectric energy harvesting module;

[0004] In the prior art, the publication number is CN107066757A, and the name is a method for optimizing the module spectrum in the modular design of products supported by big data. For the behavior of users selecting the parameter level values of product modules, user behavior variables are defined, user behavior data is statistically analyzed, and the user demand satisfaction degree for the parameter level values is calculated. An optimization design model for the module spectrum is established with the goal of maximizing the user demand satisfaction degree of the module spectrum and minimizing the production cost, and the bisection method is used to solve the optimization design model of the module spectrum. By analyzing the big data of the parameter level values selected by users for product modules, the optimization design of the product module spectrum is guided, which can overcome the deficiencies of traditional methods and has the characteristics of simplicity, reasonableness and easy implementation.

[0005] The following deficiencies exist in the prior art:

[0006] 1. The existing electrostatic dissipation unit of the dust-free towline depends on an external power supply and does not effectively utilize the mechanical vibration energy of the dust-free towline itself for conversion of the external power supply;

[0007] 2. Due to the change of the motion state of the dust-free towline itself and the environment it is in, the electrostatic dissipation unit does not make dynamic adjustments according to the actual electrostatic accumulation situation of the flexible antistatic housing, and it is difficult to adapt to the changing industrial environment;

[0008] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0009] An object of the present invention is to provide a big data-driven anti-static optimization design method for a dust-free drag chain to solve the problems raised in the above background art.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A big data-driven anti-static optimization design method for a dust-free drag chain, which is applied to the optimization design of a flexible anti-static outer shell on the dust-free drag chain. It is characterized in that a piezoelectric energy harvesting module and an electrostatic dissipation unit are provided on the flexible anti-static outer shell. The piezoelectric energy harvesting module is connected to the input end of the energy storage and management unit. The specific steps include:

[0012] Step S1: Convert the mechanical vibration energy generated during the movement of the dust-free drag chain into electrical energy through the piezoelectric energy harvesting module, and use the energy storage and management unit to intelligently regulate and store the converted electrical energy;

[0013] Step S2: Real-time collect the data of the electrostatic accumulation situation of the flexible anti-static outer shell and input it into the big data analysis platform. The big data analysis platform analyzes the data of the electrostatic accumulation situation during the current monitoring time period to generate an electrostatic accumulation evaluation coefficient, and the electrostatic accumulation evaluation coefficient is used to generate an intelligent regulation strategy for the energy storage and management unit;

[0014] Step S3: Receive the intelligent regulation strategy and dynamically adjust the conductivity of the flexible anti-static outer shell through the electrostatic dissipation unit to achieve electrostatic dissipation;

[0015] Step S4: Obtain the electrostatic accumulation evaluation coefficients calculated by the flexible anti-static outer shell in the past M monitoring time periods, and perform an association mapping on the output values of these electrostatic accumulation evaluation coefficients to obtain a mapping result set;

[0016] Divide the mapping result set into two subsets before and after in chronological order, and separately calculate the superposition values of several mapping results in each subset. Compare and analyze the superposition values of the two subsets with the corresponding preset thresholds respectively to obtain a strategy adjustment coefficient, which is used to provide a calibration and optimization rule for the intelligent regulation strategy of the next monitoring time period.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] Independent energy supply: Utilize the mechanical vibration energy of the drag chain itself to realize the self-power supply of the electrostatic dissipation system, avoid relying on external power supplies, and improve the independence and flexibility of the system;

[0019] Dynamic response ability: Based on closed-loop control of real-time monitoring data, it realizes dynamic adjustment of antistatic performance, adapts to changing industrial environments, and improves the efficiency of static electricity dissipation;

[0020] Efficient energy management: Optimizes the collection, storage, and consumption of energy through the energy storage management unit, improving the energy utilization efficiency. Description of the Drawings

[0021] Figure 1 It is a schematic diagram of the overall method flow of the present invention. Detailed Embodiments

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0023] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to indicate relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0024] Embodiment 1:

[0025] Please refer to Figure 1 , the present invention provides a technical solution:

[0026] A big data-driven antistatic optimization design method for a dust-free drag chain is applied to the optimization design of a flexible antistatic housing on the dust-free drag chain. A piezoelectric energy harvesting module and an electrostatic dissipation unit are provided on the flexible antistatic housing. The piezoelectric energy harvesting module is connected to the input end of the energy storage management unit. The specific steps include:

[0027] Step S1: Convert the mechanical vibration energy generated during the movement of the dust-free drag chain into electrical energy through the piezoelectric energy harvesting module, and use the energy storage management unit to intelligently regulate and store the converted electrical energy;

[0028] Further explanation: The piezoelectric energy harvesting module includes a piezoelectric element, a mechanical coupling structure, and a rectification and voltage stabilization circuit;

[0029] The piezoelectric element is in the form of a thin sheet or a laminated structure; PZT (lead zirconate titanate) is selected as the piezoelectric ceramic material for the piezoelectric element; the size and shape of the piezoelectric element are optimized according to the specific installation position; to adapt to the industrial environment, a protective coating or encapsulation is applied to the outer surface of the piezoelectric element to prevent wear and contamination from the environment.

[0030] The mechanical coupling structure is a spring or a flexible bracket; the mechanical coupling structure is directly attached to the high-vibration part of the flexible antistatic housing of the dust-free tow chain, so that vibration energy can be continuously obtained during the movement of the dust-free tow chain; the coupling structure is firmly attached to the high-vibration part of the flexible antistatic housing through high-quality bolts or industrial special bonding technology; in the design, the elastic characteristics of the coupling structure are used to maximize the energy acquisition efficiency of the piezoelectric element and keep consistent with the frequency response of the dust-free tow chain.

[0031] The piezoelectric element is attached to the high-vibration part of the flexible antistatic housing through the mechanical coupling structure;

[0032] The following explanations are required for the high-vibration part:

[0033] Multiple measurement points are set on the flexible antistatic housing of the dust-free tow chain. When the dust-free tow chain is at different moving speeds and environmental indicators, the vibration data of these measurement points are recorded. The vibration data includes vibration frequency and vibration acceleration;

[0034] The multiple measurement points include the following positions A, B, and C;

[0035] Position A is the middle part of the flexible antistatic housing;

[0036] Position B is the connection or corner part of the flexible antistatic housing;

[0037] Position C is the area of the electric drive corresponding to the flexible antistatic housing;

[0038] The collected vibration data is analyzed to extract key features. The key features include the maximum vibration acceleration or the maximum vibration frequency;

[0039] Taking the maximum vibration acceleration as the key feature, the vibration threshold of the key feature is set as V th , and the value of the vibration threshold V th is selected within the range of 70% to 85% of the maximum vibration acceleration;

[0040] V th = 0.75×A max

[0041] where, A max is the maximum vibration acceleration recorded by the flexible antistatic housing under the moving speed and environmental indicators in the current monitoring period;

[0042] Compare the vibration data of each measurement point with the set vibration threshold V th ;

[0043] Take the measurement points greater than the vibration threshold V th as high-vibration parts;

[0044] The energy storage management unit includes a supercapacitor and an intelligent energy management chip;

[0045] The supercapacitor selects an electrochemical double-layer capacitor (EDLC), and its high energy density, fast charge and discharge characteristics are suitable for the electrical energy storage of mechanical vibration acquisition in this embodiment.

[0046] After the electrical energy collected by the piezoelectric element is output, it is converted into direct current through a rectification and voltage stabilization circuit and directly supplied to the supercapacitor;

[0047] The supercapacitor stores a large amount of electrical energy through the electrochemical double layer formed on the electrode surface and can charge and discharge quickly in a short time.

[0048] The rectification and voltage stabilization circuit uses a full-bridge rectification circuit and a voltage regulator.

[0049] The full-bridge rectification circuit converts the alternating current generated by the piezoelectric element into direct current, and then uses a voltage regulator to stabilize the output voltage within the acceptable range of the supercapacitor to avoid damage to the energy storage components caused by high voltage;

[0050] Connect the supercapacitor to an intelligent energy management chip (such as ADP5050, BQ24650, etc.), and the intelligent energy management chip is responsible for dynamically monitoring the energy state and determining the priority of electrical energy storage and release.

[0051] Connect the power input terminal of the intelligent energy management chip to the rectified DC power supply, and its output terminal is connected to the power supply load and the supercapacitor to form a closed-loop system.

[0052] According to the drag chain static electricity demand curve, the intelligent management chip uses big data analysis to adjust the electrical energy release strategy to ensure the static electricity stability of the drag chain in various working environments.

[0053] The working principle of the piezoelectric energy harvesting module is as follows:

[0054] Piezoelectric effect: During the movement of the dust-free drag chain, the mechanical vibration of the flexible antistatic housing is transmitted to the piezoelectric element through the mechanical coupling structure, causing it to deform; due to the piezoelectric effect, this physical deformation will generate charges at both ends of the piezoelectric element, forming an electric current;

[0055] Electric energy conversion: Along with the frequent vibration of the dust-free drag chain movement, the piezoelectric element continuously generates voltage; the rectification and voltage stabilization circuit converts this alternating current into stable direct current, which is directly supplied to the supercapacitor for storage.

[0056] Step S2: Real-time collect the data of the static electricity accumulation situation of the flexible antistatic housing and input it into the big data analysis platform. The big data analysis platform analyzes the data of the static electricity accumulation situation during the current monitoring time period to generate a static electricity accumulation evaluation coefficient, which is used to generate an intelligent regulation strategy for the energy storage management unit;

[0057] Further explanation: The data of the static electricity accumulation situation includes: the static charge amount index, voltage index and current index during the current monitoring time period; and the hysteresis influence coefficient used to describe the hysteresis characteristics of the static electricity accumulation;

[0058] The hysteresis influence coefficient is evaluated by analyzing the hysteresis influence degree of the dust-free drag chain on the charge release speed and charge accumulation speed under different movement speeds and environmental indexes;

[0059] Define the calculation formula of the hysteresis influence coefficient η as follows:

[0060]

[0061] Where, R d is the charge release speed of the flexible antistatic housing during the current monitoring time period (unit: Coulombs / s); Coulombs / s is the unit of the charge flow rate, indicating the amount of charge passing through a certain cross-section per second;

[0062] R a is the charge accumulation speed of the flexible antistatic housing during the current monitoring time period (unit: Coulombs / s);

[0063] S(v, ΔA, ΔT) is a comprehensive correction factor related to the average movement speed v, contact area change ΔA, and temperature change ΔT of the dust-free drag chain during the current monitoring time period; it is used to describe the influence of these factors on the hysteresis behavior;

[0064] The comprehensive correction factor S(v, ΔA, ΔT) is fitted in the following two forms according to the experimental situation:

[0065] When the comprehensive correction factor S(v, ΔA, ΔT) is a linear model:

[0066] S(v, ΔA, ΔT) = 1 - α1·v + α2·ΔA - α3·ΔT

[0067] α1 is the correlation coefficient related to the average motion speed, representing the direct influence of the average motion speed v on the lag; α2 is the correlation coefficient related to the change in contact area, representing the influence of the increase in contact area on the lag; α3 is the correlation coefficient related to the temperature change, representing the influence of the increase in temperature on the lag.

[0068] When the comprehensive correction factor S(v,ΔA,ΔT) is a non-linear model:

[0069]

[0070] β1 is the influence coefficient of the square effect of the increase in the average motion speed on the lag;

[0071] β2 is the square effect coefficient of the change in contact area; β3 is the square effect coefficient of the temperature change;

[0072] During the current monitoring time period, the change in contact area ΔA represents the change in the area of contact between the flexible antistatic housing and external components; specifically, during the reciprocating motion of the dust-free drag chain, the motion of the flexible antistatic housing will drive the change in the area of contact with external components, and this change in area is the change in contact area ΔA;

[0073] During the current monitoring time period, the temperature change ΔT represents the temperature change generated by friction at the contact surface between the flexible antistatic housing and external components during the reciprocating motion of the dust-free drag chain;

[0074] When the average motion speed v is in the high-speed range, R d <R a At this time, η<1 is obtained, indicating that the charge release speed lags behind the charge accumulation speed, and the actual static charge accumulation data will continue to increase;

[0075] It should be noted that there is an upper limit value for the average motion speed v of the dust-free drag chain in actual applications. Therefore, based on this upper limit value, the friction contact time between the corresponding contact surfaces of the flexible antistatic housing is sufficient to make R d <R a ;

[0076] R d <R a Indicates a high level of static charge accumulation, and the actual static charge accumulation data will continue to increase, reflecting insufficient lag control ability of the system; it is necessary to increase release measures or optimize material properties to reduce accumulation;

[0077] High-speed range: When the average motion speed v is larger, the lag influence coefficient η is smaller, and the charge accumulation speed R a is greater than the charge release rate R d , and the lag effect is significant; at this time, the combined action of the rapid change in contact area and the increase in temperature further exacerbates the lag phenomenon, resulting in a decrease in the lag influence coefficient η.

[0078] When the average motion speed v is in the intermediate speed range and R d = R a , η = S(v, ΔA, ΔT) is obtained, indicating that the charge release rate is synchronized with the charge accumulation rate, and the electrostatic accumulation situation is to maintain the charge balance state;

[0079] Intermediate speed range: The lag influence coefficient η shows a non-linear change, indicating that there is a complex correlation between the charge release rate R d and the charge accumulation rate R a ; The dynamic change of the contact area and the influence of temperature show a significant lag adjustment effect in this intermediate speed range.

[0080] When the average motion speed v is in the low speed range and R d > R a , η > 1 is obtained, indicating that the charge accumulation rate lags behind the charge release rate, and the electrostatic accumulation level is low;

[0081] Low speed range: The slower average motion speed v of the dust-free drag chain means that the actions of contact and separation between the components of the drag chain or between the drag chain and other objects are reduced, resulting in a lower frequency of triboelectrification. The friction frequency is positively correlated with the charge accumulation rate. Therefore, the smaller the speed, the lower the charge accumulation rate;

[0082] When the average motion speed v approaches 0 more and more, the charge accumulation rate R a is smaller, indicating that the lag influence coefficient η is larger and the charge release rate mechanism dominates; At this time, the influence of the change of the contact area and the change of temperature on the lag is small, but due to the small charge accumulation rate R a , the electrostatic accumulation is mainly controlled by the charge release rate R d ;

[0083] Define the correlation calculation formula of the charge accumulation rate R a as follows:

[0084] R a = k a ·v·μ·(1 + E ext )

[0085] Among them, k a is the normalization adjustment factor of the charge accumulation rate; v is the average motion speed of the dust-free drag chain in the current monitoring time period; μ is the surface friction coefficient of the flexible antistatic housing; The larger the surface friction coefficient and the average motion speed, the more opportunities for friction interaction are increased, resulting in more charge accumulation, which is a direct proportional relationship;

[0086] E ext is the influence factor of the external environmental electric field intensity; The presence of the external electric field causes more charges to accumulate on the material surface;

[0087] Define the charge release rate R d The associated calculation formula is as follows:

[0088]

[0089] Where: k d is the normalization adjustment factor of the charge release speed;

[0090] In this embodiment, k a = 1; k d = 1;

[0091] τ is the dielectric relaxation time, defined as describing the response speed of the flexible antistatic housing to changes in the external electrostatic environment;

[0092] is the dielectric constant of the flexible antistatic housing; dh is the average thickness of the flexible antistatic housing; DE bace is the base conductivity of the flexible antistatic housing;

[0093] The following experimental data table will show the charge release speed R of the dust-free drag chain under different average movement speeds and environmental indicators d , the charge accumulation speed R a and its corresponding hysteresis effect coefficient η.

[0094] Table 1 Study on the hysteresis effect coefficient η:

[0095]

[0096] The data analysis in Table 1 is as follows:

[0097] Low speed (0.5 m / s): Under this condition, the charge release speed R d is greater than the charge accumulation speed R a , resulting in a hysteresis effect coefficient η of 1.5×S(v,ΔA,ΔT), indicating good charge release effect and low charge accumulation level.

[0098] Medium-low speed (1.0 m / s): The gap between the charge release speed R d and the charge accumulation speed R a decreases, causing η to be 1.125×S(v,ΔA,ΔT), and the system begins to show an accumulation phenomenon. C / s is the abbreviation of Coulombs / s;

[0099] Medium speed (1.5 m / s): At this speed, the charge release speed is equal to the charge accumulation speed. Therefore, the hysteresis effect coefficient η is S(v,ΔA,ΔT), achieving a dynamic balance between electrostatic accumulation and charge release.

[0100] Medium-high speed (2.0 m / s): The charge accumulation speed is higher than the charge release speed, resulting in the hysteresis effect coefficient η dropping to 0.8×S(v,ΔA,ΔT), indicating that the charge release lags behind the charge accumulation.

[0101] High speed (2.5 m / s): The gap between the charge accumulation speed and the charge release speed further expands, and the hysteresis effect coefficient η is further reduced to 0.78×S(v,ΔA,ΔT).

[0102] Further explanation: Define the electrostatic accumulation evaluation coefficient as EAC, and the expression of EAC is as follows:

[0103]

[0104] Among them, the function f(Q , U , I , η) is defined as:

[0105]

[0106] EAC is used to quantify the electrostatic accumulation status of the flexible antistatic housing during the current monitoring period, and its value range is (0,1); the output is used to guide the intelligent regulation strategy of the energy storage management unit;

[0107] Q is the static charge quantity index obtained after consistent dimensionless processing, representing the static charge level measured on the flexible antistatic housing during the current monitoring period; the static charge quantity index is measured by the electrostatic voltage method or the capacitance method; specifically, a non-contact electrometer is used for measurement; since the static charge decreases, the voltage under the unit capacitance decreases; the static charge quantity passing through per unit time decreases, resulting in a decrease in current; therefore, the static charge quantity index has a direct proportional relationship with both the voltage index and the current index;

[0108] U is the voltage index obtained after consistent dimensionless processing, representing the voltage level measured during the current monitoring period;

[0109] I is the current index obtained after consistent dimensionless processing, representing the current intensity level measured during the current monitoring period; η is the hysteresis effect coefficient; e is the natural constant;

[0110] The output values of Q, U, and I are all within the range of (0,1), and the closer the output value is to 1, the higher the numerical level of the corresponding parameter;

[0111] b1, b2, b3, and b4 are the weight coefficients of the corresponding parameters, and the values of b1, b2, b3, and b4 are all within the range of (0,1), and b1 + b2 + b3 + b4 = 1;

[0112] f(Q,U,I,η) increases, e-f(Q,U,I,η) Decrease, The value increases, indicating that the electrostatic accumulation of the flexible antistatic housing shows an increasing trend during the current monitoring period;

[0113] The intelligent regulation strategy of the energy storage management unit specifically includes:

[0114] Set the adjustment threshold range of the electrostatic accumulation evaluation coefficient EAC as [q1, q2], and set 0.23 ≤ q1 < q2 ≤ 0.73; the specific values of q1 and q2 will be determined by the expert group using the fuzzy analytic hierarchy process (FAHP) to reasonably reflect the assessment and control of the electrostatic accumulation risk;

[0115] When EAC is greater than q2, it indicates a high level of electrostatic accumulation, and it is necessary to increase the conductivity of the flexible antistatic housing to enhance electrostatic dissipation, and specifically generate the first strategy data for increasing conductivity;

[0116] The first strategy data includes that the flexible antistatic housing needs to reach a high target conductivity range;

[0117] Record the high target conductivity range as [DE bace + μ1, DE bace + μ2], μ1 < μ2; DE bace is the basic conductivity of the flexible antistatic housing; the basic conductivity refers to the inherent conductive performance of the flexible antistatic housing itself without any external adjustment or regulation, and μ1 and μ2 are the first fluctuation factor and the second fluctuation factor respectively;

[0118] When EAC is less than q1, it indicates a low level of electrostatic accumulation; it is necessary to reduce the conductivity of the flexible antistatic housing to reduce electrostatic dissipation, and specifically generate the second strategy data for reducing conductivity;

[0119] The second strategy data includes that the flexible antistatic housing needs to reach a low target conductivity range;

[0120] Record the low target conductivity range as [DE bace + μ3, DE bace + μ4], μ3 < μ4; and DE bace + μ1 > DE bace + μ4; μ3 and μ4 are the third fluctuation factor and the fourth fluctuation factor respectively;

[0121] μ1, μ2, μ3 and μ4 are determined by the expert group based on experimental data using the fuzzy analytic hierarchy process (FAHP);

[0122] When the value of EAC is within the interval [q1, q2], it indicates a medium level of electrostatic accumulation; there is no need to adjust the conductivity of the flexible antistatic housing.

[0123] Step S3: Receive the intelligent regulation strategy and dynamically adjust the conductivity of the flexible antistatic housing through the electrostatic dissipation unit to achieve electrostatic dissipation;

[0124] Further explanation: The electrostatic dissipation unit includes an adjustable conductive polymer material, a low-power drive circuit, and a grounding system;

[0125] The drive circuit includes a signal generation unit, which is a PWM controller or an operational amplifier circuit, used to generate pulse signals with specific frequencies and amplitudes; this signal will act on the conductivity regulation of the adjustable conductive polymer material to ensure that the applied voltage is within the range of no more than 10V to avoid damage to the material;

[0126] Set the working mode of the drive circuit as follows to achieve low power consumption:

[0127] Active regulation mode: When it is necessary to increase or decrease the conductivity, that is, for the first strategy data or the second strategy data, the drive circuit is in an efficient working state, providing the required pulse signals to regulate the conductivity of the conductive polymer material;

[0128] Standby monitoring mode: When the EAC value is within the interval [q1, q2] and there is no need to adjust the conductivity, the drive circuit automatically switches to the low-power standby state, only maintaining the basic monitoring function;

[0129] Explanation of the grounding system:

[0130] Set multiple grounding paths inside the flexible antistatic housing; these grounding paths are evenly distributed in various parts of the housing, specifically set in the left, middle, and right parts of the flexible antistatic housing to ensure that when static electricity accumulates, there are multiple grounding paths to effectively conduct the static electricity;

[0131] The grounding system integrates adjustable resistance elements to monitor and adjust the grounding resistance in real time;

[0132] Use a resistance sensor to continuously monitor the grounding resistance to ensure that it remains within the allowable range (less than 10Ω). The monitoring data is fed back to the control system through the MCU;

[0133] Based on the real-time monitoring data, the MCU can automatically adjust the resistance of the grounding channel during the dynamic regulation of the electrostatic dissipation unit; when the grounding resistance increases, the MCU can instruct the drive circuit to adjust the current, and actively sweep the static charges in the grounding path through the change of the current;

[0134] To increase the conductivity of the flexible antistatic housing, the specific logic includes:

[0135] The microcontroller MCU of the static electricity dissipation unit generates a high conductivity control signal for the tunable conductive polymer material according to the received first policy data; in this embodiment, the high conductivity control signal is a low voltage pulse signal with a voltage not exceeding 10V.

[0136] The low-power driving circuit is used to transmit the high conductivity control signal to the control adjustment terminal of the tunable conductive polymer material. By increasing the ion migration or electron transfer mechanism, the ion migration or electron transfer mechanism inside the tunable conductive polymer material is effectively activated, resulting in a reversible electrochemical response in the molecular structure of the polymer, so as to increase the conductivity of the tunable conductive polymer material to the high target conductivity range; in this embodiment, the high target conductivity range is set to be greater than 0.01 S / m, and the response time does not exceed 100 ms.

[0137] Reduce the conductivity of the flexible antistatic housing. The specific logic includes:

[0138] The microcontroller MCU of the static electricity dissipation unit generates a low conductivity control signal for the tunable conductive polymer material according to the second policy data.

[0139] The low-power driving circuit is used to transmit the low conductivity control signal to the control adjustment terminal of the tunable conductive polymer material. By reducing the ion migration or electron transfer in the tunable conductive polymer material, the conductivity of the tunable conductive polymer material is reduced to the low target conductivity range. In this embodiment, the low target conductivity range is set to be less than 0.001 S / m, and the response time does not exceed 100 ms.

[0140] Ion migration is: when ions move within the polymer, they can carry charges and affect the overall conductivity of the material.

[0141] Electron transfer is: the migration of electrons along the polymer chain is another conduction mechanism; the transfer of electrons in conductive polymers can be changed by adjusting the doping level, molecular structure or external conditions. External conditions include electric field control.

[0142] By applying or changing the electric field strength to manage the movement of ions and electrons, the conductivity of the material is thus adjusted.

[0143] It should be noted that: the tunable conductive polymer material is polyaniline or polythiophene or polypyrrole material.

[0144] For polyaniline (PANI):

[0145] Characteristics: Polyaniline is an electrochemically sensitive conductive polymer, and its conductivity can be changed by an external electric field to adjust the doping degree (such as acid doping or dedoping).

[0146] For polythiophenes, in this embodiment, it is composed of a composite of poly(3,4-ethylenedioxythiophene) (PEDOT) and polystyrenesulfonic acid (PSS);

[0147] Characteristic: Based on the stimulation of an electric field, the electron mobility and doping rate of the PEDOT material can be adjusted, thereby changing the conductivity of the material.

[0148] For polypyrrole (PPy):

[0149] Characteristic: By applying an external electric field or electrochemical means to regulate electron transfer and ion migration, thereby changing its conductivity.

[0150] The control adjustment terminal of the tunable conductive polymer material is an interface or contact point on the tunable conductive polymer material. Through this point, a control signal can be input to adjust the conductivity of the material. This port is an electrode, contact point, or other input interface of the tunable conductive polymer material for receiving an electrical signal from the drive circuit;

[0151] The electrode is a conductive part used to provide electrical connection, connected to the drive circuit to transmit signals.

[0152] The contact point or connector is an interface used to facilitate connection and disconnection, connected to the drive circuit through a wire or contact.

[0153] Integrated circuit port: If the tunable conductive polymer material is partially integrated into the circuit, the control adjustment terminal is a pin or pad of an integrated circuit.

[0154] Step S4: Obtain the electrostatic accumulation evaluation coefficients calculated for the flexible antistatic housing in the past M monitoring time periods, and perform an association mapping on the output values of these electrostatic accumulation evaluation coefficients to obtain a mapping result set;

[0155] Divide the mapping result set into two subsets, front and back, in chronological order, and separately calculate the superposition values of several mapping results within each subset. Compare and analyze the superposition values of the two subsets with the corresponding preset thresholds respectively to obtain a strategy adjustment coefficient, which is used to provide a calibration and optimization rule for the intelligent control strategy in the next monitoring time period.

[0156] Further explanation: Perform the following association mapping on the output values of the electrostatic accumulation evaluation coefficients for M monitoring time periods:

[0157]

[0158] Among them, i ∈ {1, 2, …, M}, and M < 10; if is the representation of "if";

[0159] Define the mapping result set as PF i ∈ {PF1, PF2, …, PFM}; PF i is the associated mapping value corresponding to the electrostatic accumulation evaluation coefficient in the i-th monitoring time period;

[0160] The two subsets before and after the initial setting are respectively

[0161] Calculate the pre-subset The superposition value QZ1 and the preset threshold YS1 are respectively

[0162] Calculate the post-subset The superposition value QZ2 and the preset threshold YS2 are respectively Denote rounding up;

[0163] Among them, the selection rules for YS1 and YS2 are: initially set PF i ∈{PF1, PF2, …, PF M}, the value of PF i is always 1; that is, it means that the EAC value corresponding to each mapping result is within the interval [q1, q2], and all are in the case of medium electrostatic accumulation level;

[0164] Define the calculation formula of the strategy adjustment coefficient as follows:

[0165]

[0166] Among them, TZ is the strategy adjustment coefficient;

[0167] When , TZ > 0; it means that the ratio of the superposition value QZ1 of the pre-subset to the preset threshold YS1 is greater than the ratio of the superposition value QZ2 of the post-subset to the preset threshold YS2; furthermore, it represents that the electrostatic accumulation level is in a decreasing state;

[0168] The calibration and optimization rules of the intelligent control strategy are: in the next monitoring time period, for the high target conductivity range [DE bace + μ1, DE bace + μ2] of the first strategy data, implement the following adjustments:

[0169]

[0170] The explanation of the adjustment formula is as follows:

[0171] When , TZ > 0; since the electrostatic accumulation level is in a decreasing state; therefore, it is necessary to implement a reduction rule for the high target conductivity range [DE bace + μ1, DE bace + μ2] of the first strategy data, through and The setting effectively reduces the output values of μ1 and μ2, thereby reducing the high target conductivity range; while The setting is to smooth the values to avoid a large impact of excessive values on the adjustment result; the purpose of this adjustment is to reduce the sensitivity of the high target conductivity range [DE bace +μ1,DE bace +μ2], so as to avoid being unable to effectively identify the high target conductivity range [DE bace +μ1,DE bace +μ2] when the electrostatic accumulation level is in a decreasing state;

[0172] Furthermore, when , TZ = 0; it means that the ratio of the superimposed value QZ1 of the front subset to the preset threshold YS1 is equal to the ratio of the superimposed value QZ2 of the back subset to the preset threshold YS2; furthermore, it represents that the electrostatic accumulation level is in a stable state;

[0173] When , TZ < 0; it means that the ratio of the superimposed value QZ1 of the front subset to the preset threshold YS1 is less than the ratio of the superimposed value QZ2 of the back subset to the preset threshold YS2; furthermore, it represents that the electrostatic accumulation level is in an increasing state;

[0174] The calibration and optimization rule of the intelligent control strategy is: in the next monitoring time period, the following adjustments are implemented for the low target conductivity range [DE bace +μ3,DE bace +μ4] of the second strategy data:

[0175]

[0176] The description of the adjustment formula is as follows:

[0177] When , TZ < 0; since the electrostatic accumulation level is in an increasing state; therefore, an increase rule needs to be implemented for the low target conductivity range [DE bace +μ3,DE bace +μ4] of the second strategy data. Through and 's settings, the output values of μ3 and μ4 are effectively increased, thereby increasing the low target conductivity range; the purpose of this adjustment is to increase the sensitivity of the low target conductivity range [DE bace +μ3,DE bace +μ4], so as to avoid being unable to effectively identify the low target conductivity range [DE bace +μ3,DE bace +μ4] when the electrostatic accumulation level is in an increasing state;

[0178] In the next monitoring period, receive the adjusted intelligent control strategy and dynamically adjust the conductivity of the flexible antistatic housing through the electrostatic dissipation unit to achieve electrostatic dissipation.

[0179] Embodiment 2:

[0180] This embodiment is used to verify the innovation and advantages of the "intelligent control strategy" described in the present invention compared with the traditional strategy in terms of electrostatic accumulation control, and a comparative experiment is designed. Three different models of flexible antistatic housing samples are selected for the experiment, which are respectively labeled as "Model A", "Model B" and "Model C". The basic conductivity DE of each model bace is set to 0.3 S / m, 0.4 S / m and 0.5 S / m to cover the requirements in different application scenarios.

[0181] The test steps are as follows:

[0182] 1) Set the threshold range of the electrostatic accumulation evaluation coefficient EAC:

[0183] Through the expert group using the fuzzy analytic hierarchy process (FAHP), determine that the adjustment threshold range of the electrostatic accumulation evaluation coefficient is [q1, q2], where q1 = 0.3 and q2 = 0.6. This range reasonably reflects the assessment and control of the electrostatic accumulation risk.

[0184] 2) Define the target conductivity range:

[0185] High target conductivity range: [DE bace + μ1, DE bace + μ2], where μ1 = 0.05 and μ2 = 0.1.

[0186] Low target conductivity range: [DE bace + μ3, DE bace + μ4], where μ3 = -0.05 and μ4 = -0.02.

[0187] 3) Obtain the electrostatic accumulation evaluation coefficient EAC:

[0188] During the experiment, evaluate the electrostatic accumulation situation of each flexible antistatic housing in the past M = 6 monitoring periods, and record the EAC value corresponding to each period; the recorded EAC values are 0.45, 0.50, 0.55, etc.;

[0189] 4) Association mapping and strategy adjustment:

[0190] According to the following formula:

[0191]

[0192] Map each EAC value to the corresponding PFi Values are obtained to get the mapping result set PF i ∈ {PF1, PF2, …, PF M}.

[0193] The mapping result set is divided into two subsets, a front subset and a rear subset, in chronological order:

[0194] Front subset {PF1, PF2, PF3}, rear subset {PF4, PF5, PF6};

[0195] Calculate the superposition value and the preset threshold:

[0196]

[0197] Calculation of the strategy adjustment coefficient:

[0198]

[0199] According to the value of TZ, adjust the high target or low target conductivity range:

[0200] If TZ > 0, adjust the high target conductivity range; to reduce the sensitivity of the high target conductivity range [DE bace + μ1, DE bace + μ2];

[0201] If TZ < 0, adjust the low target conductivity range to increase the sensitivity of the low target conductivity range [DE bace + μ3, DE bace + μ4];

[0202] If TZ = 0, keep the current conductivity range unchanged.

[0203] Compare the conductivity adjustment results and the electrostatic dissipation efficiency under the implementation of the calibration optimization rule and without the calibration optimization rule.

[0204] Record the conductivity changes and the electrostatic dissipation efficiency of each model under different strategies, and demonstrate the advantages of the strategy of the present invention through data analysis; the experimental results are shown in Table 1:

[0205] Table 1 Comparative study on the calibration optimization rules of the intelligent control strategy:

[0206]

[0207] Data analysis:

[0208] As can be seen from Table 1, the intelligent control strategy with calibration optimization rules significantly improves the electrostatic dissipation efficiency in all models of flexible antistatic enclosures. The specific improvement ratios are 6.25% for Model A, 6.02% for Model B, and 4.65% for Model C, and the average improvement ratio reaches 5.64%. In contrast, the traditional strategy without calibration optimization rules shows no improvement in electrostatic dissipation efficiency, remaining at 80%, 83%, and 86%.

[0209] From the above data, it can be concluded that the "intelligent control strategy" in the present invention effectively improves the electrostatic dissipation efficiency of flexible antistatic enclosures through calibration optimization rules, demonstrating significant innovation and technical advantages. The average improvement ratio reaches 5.64%, fully proving the effectiveness of calibration optimization rules in electrostatic accumulation control.

[0210] Supplementary explanations are as follows:

[0211] Electrostatic accumulation evaluation coefficient EAC: The electrostatic accumulation evaluation coefficient of each model in the experiment to ensure consistent experimental conditions.

[0212] Base conductivity (S / m): The initial conductivity of each model of flexible antistatic enclosure.

[0213] Adjusted conductivity (S / m): The change in conductivity after the intelligent control strategy with or without calibration optimization rules.

[0214] Electrostatic dissipation efficiency (%): The adjusted electrostatic dissipation efficiency, and the experimental results show that the group with calibration optimization performs better.

[0215] Improvement in dissipation efficiency (%): The percentage increase in efficiency compared to the group without calibration optimization.

[0216] Average improvement ratio (%): The average improvement ratio of the dissipation efficiency of all models, reaching 5.64% overall.

[0217] Through the above description of the implementation manner, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disc of a computer, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0218] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus or device and execute the instructions), or used in combination with these instruction execution systems, apparatus or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transport a program for use by or in combination with an instruction execution system, apparatus or device.

[0219] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0220] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A big data-driven antistatic optimization design method for a dust-free drag chain, which is applied to the optimization design of a flexible antistatic outer shell on a dust-free drag chain, is characterized in that, A piezoelectric energy harvesting module and an electrostatic dissipation unit are provided on a flexible antistatic housing. The piezoelectric energy harvesting module is connected to the input end of the energy storage and management unit. The specific steps include: Step S1: Convert the mechanical vibration energy generated during the movement of the dust-free towline into electrical energy through the piezoelectric energy harvesting module, and use the energy storage and management unit to intelligently regulate and store the converted electrical energy; Step S2: Collect the data of the electrostatic accumulation situation of the flexible antistatic housing in real time and input it into the big data analysis platform. The big data analysis platform analyzes the data of the electrostatic accumulation situation during the current monitoring time period to generate an electrostatic accumulation evaluation coefficient, and the electrostatic accumulation evaluation coefficient is used to generate an intelligent regulation strategy for the energy storage and management unit; Step S3: Receive the intelligent regulation strategy and dynamically adjust the conductivity of the flexible antistatic housing through the electrostatic dissipation unit to achieve electrostatic dissipation; Step S4: Obtain the electrostatic accumulation evaluation coefficients calculated by the flexible antistatic housing in the past M monitoring time periods, and perform correlation mapping on the output values of these electrostatic accumulation evaluation coefficients to obtain a mapping result set; Divide the mapping result set into two subsets, the front and the back, in chronological order, and separately calculate the superimposed values of several mapping results in each subset. Compare and analyze the superimposed values of the two subsets with the corresponding preset thresholds to obtain a strategy adjustment coefficient, which is used to provide a calibration and optimization rule for the intelligent regulation strategy in the next monitoring time period.

2. The big data-driven dust-free towline antistatic optimization design method according to claim 1, wherein: The piezoelectric energy harvesting module includes a piezoelectric element, a mechanical coupling structure, and a rectification and voltage stabilization circuit; The piezoelectric element is a thin sheet or a laminated structure; the mechanical coupling structure is a spring or a flexible bracket; The piezoelectric element is attached to the high-vibration part of the flexible antistatic housing through the mechanical coupling structure; The energy storage and management unit includes a supercapacitor and an intelligent energy management chip; the rectification and voltage stabilization circuit uses a full-bridge rectification circuit and a voltage regulator.

3. The big data-driven antistatic optimization design method for a dust-free drag chain according to claim 2, wherein: The data of the electrostatic accumulation situation includes the static charge quantity index, voltage index, and current index during the current monitoring time period; and the hysteresis influence coefficient used to describe the hysteresis characteristics of electrostatic accumulation; The hysteresis influence coefficient is evaluated by analyzing the hysteresis influence degree of the dust-free towline on the charge release speed and charge accumulation speed under different movement speeds and environmental indicators; Define the calculation formula of the hysteresis influence coefficient η as follows: wherein, R d is the charge release rate of the flexible antistatic housing during the current monitoring period; R a is the charge accumulation rate of the flexible antistatic housing during the current monitoring period; S(v,ΔA,ΔT) is a comprehensive correction factor related to the average movement speed v, contact area change ΔA, and temperature change ΔT of the dust-free towline during the current monitoring time period; When the average motion speed v is in the high-speed range, R d <R a at this time, η < 1 is obtained, indicating that the charge release speed lags behind the charge accumulation speed, and the actual electrostatic accumulation data will continue to increase; When the average motion speed v is in the intermediate speed range and R d = R a at this time, η = S(v, ΔA, ΔT) is obtained, indicating that the charge release speed is synchronized with the charge accumulation speed, and the electrostatic accumulation situation is to maintain the charge balance state; When the average motion speed v is in the low-speed range and R d > R a , η > 1 is obtained, indicating that the charge accumulation speed lags behind the charge release speed and the electrostatic accumulation level is low.

4. The big data-driven antistatic optimization design method for dust-free drag chains according to claim 3, wherein: Define the electrostatic accumulation evaluation coefficient as EAC, and the expression of EAC is as follows: Among them, the function f(Q , U , I , η) is defined as: EAC is used to quantify the electrostatic accumulation status of the flexible antistatic housing during the current monitoring time period, and the value range is (0,1); Q is the static charge quantity index obtained after consistent dimensionless processing, representing the static charge level measured by the flexible antistatic housing during the current monitoring time period; U is the voltage index obtained after consistent dimensionless processing, representing the voltage level measured during the current monitoring time period; I is the current index obtained after consistent dimensionless processing, representing the current intensity level measured during the current monitoring time period; η is the hysteresis influence coefficient; e is the natural constant; The output values of Q, U, and I are all within the range of (0, 1). The closer the output value is to 1, the higher the numerical level of the corresponding parameter; b1, b2, b3, and b4 are the weight coefficients of the corresponding parameters, and the values of b1, b2, b3, and b4 are all within the range of (0, 1), and b1 + b2 + b3 + b4 = 1; The intelligent regulation strategy of the energy storage management unit specifically includes: Set the adjustment threshold range of the electrostatic accumulation evaluation coefficient EAC as [q1, q2], and set 0.23 ≤ q1 < q2 ≤ 0.73; When EAC is greater than q2, it indicates a high level of electrostatic accumulation, and it is necessary to increase the conductivity of the flexible antistatic housing to enhance electrostatic dissipation, and specifically generate the first strategy data for increasing conductivity; The first strategy data includes that the flexible antistatic housing needs to reach a high target conductivity range; The high target conductivity range is denoted as [DE bace +μ1, DE bace +μ2], where μ1 < μ2; DE bace is the base conductivity of the flexible antistatic housing; μ1 and μ2 are the first and second fluctuation factors respectively; When EAC is less than q1, it indicates a low level of electrostatic accumulation; it is necessary to reduce the conductivity of the flexible antistatic housing to reduce electrostatic dissipation, and specifically generate the second strategy data for reducing conductivity; The second strategy data includes that the flexible antistatic housing needs to reach a low target conductivity range; Denote the low target conductivity range as [DE bace + μ3, DE bace + μ4], where μ3 < μ4; and DE bace + μ1 > DE bace + μ4; μ3 and μ4 are the third fluctuation factor and the fourth fluctuation factor respectively; When the value of EAC is within the range of [q1, q2], it indicates a medium level of electrostatic accumulation; there is no need to adjust the conductivity of the flexible antistatic housing.

5. The big data-driven antistatic optimization design method for a dust-free drag chain according to claim 4, characterized in that: The electrostatic dissipation unit includes an adjustable conductive polymer material, a low-power drive circuit, and a grounding system; To increase the conductivity of the flexible antistatic housing, the specific logic includes: The microcontroller MCU of the electrostatic dissipation unit generates a high-conductivity control signal for the adjustable conductive polymer material according to the received first strategy data; Use the low-power drive circuit to transmit the high-conductivity control signal to the control adjustment terminal of the adjustable conductive polymer material to increase the conductivity of the adjustable conductive polymer material to the high target conductivity range; To reduce the conductivity of the flexible antistatic housing, the specific logic includes: The microcontroller MCU of the electrostatic dissipation unit generates a low-conductivity control signal for the adjustable conductive polymer material according to the second strategy data; Use the low-power drive circuit to transmit the low-conductivity control signal to the control adjustment terminal of the adjustable conductive polymer material to reduce the conductivity of the adjustable conductive polymer material to the low target conductivity range.

6. The big data-driven antistatic optimization design method for dust-free drag chains according to claim 5, characterized in that: Perform the following correlation mapping on the output values of the electrostatic accumulation evaluation coefficient for M monitoring time periods: where i ∈ {1, 2, …, M}, and M < 10; if is the representation of "if"; Define the mapping result set as PF i ∈{PF1, PF2, …, PF M}; PF i is the associated mapping value corresponding to the electrostatic accumulation evaluation coefficient in the i-th monitoring time period; Before and after the initial setting, the two subsets are respectively and Pre-calculation subset The superimposed value QZ1 and the preset threshold value YS1 are respectively Calculated subset The superimposed value QZ2 and the preset threshold value YS2 are respectively Indicates rounding up to the nearest integer; Among them, the selection rules for YS1 and YS2 are: initially set PF i ∈{PF1, PF2, …, PF M}, the value of PF i is always 1; that is, it means that the EAC values corresponding to each mapping result are all within the interval [q1, q2], and they are all in the case of medium electrostatic accumulation level; Define the calculation formula of the strategy adjustment coefficient as follows: where TZ is the strategy adjustment coefficient.

7. The method for optimizing the antistatic design of a dust-free drag chain driven by big data according to claim 6, characterized in that: When is true, TZ > 0; it means that the ratio of the superposition value QZ1 of the front subset to the preset threshold value YS1 is greater than the ratio of the superposition value QZ2 of the rear subset to the preset threshold value YS2; furthermore, it represents that the electrostatic accumulation level is in a decreasing state; The calibration and optimization rule for the intelligent control strategy is as follows: in the next monitoring time period, for the high target conductivity range [DE bace + μ1, DE bace + μ2] of the first strategy data, the following adjustments are implemented: When is true, TZ = 0; it means that the ratio of the superposition value QZ1 of the front subset to the preset threshold value YS1 is equal to the ratio of the superposition value QZ2 of the rear subset to the preset threshold value YS2; Furthermore, it represents that the electrostatic accumulation level is maintained in a stable state; When is true, TZ < 0; it means that the ratio of the superposition value QZ1 of the front subset to the preset threshold value YS1 is less than the ratio of the superposition value QZ2 of the rear subset to the preset threshold value YS2; furthermore, it represents that the electrostatic accumulation level is in an increasing state; The calibration and optimization rule for the intelligent control strategy is as follows: In the next monitoring time period, for the low target conductivity range [DE bace +μ3, DE bace +μ4] of the second strategy data, the following adjustments are implemented: In the next monitoring time period, receive the adjusted intelligent regulation strategy and dynamically adjust the conductivity of the flexible antistatic housing through the electrostatic dissipation unit to achieve electrostatic dissipation.

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