Big data driven anti-static optimization design method for dust-free drag chain

Abstract

The application provides a big data driven dust-free drag chain antistatic optimization design method, relates to the technical field of computer aided design, and realizes self-power supply of an electrostatic dissipation system by utilizing mechanical vibration energy of the drag chain itself, avoids dependence on an external power supply, and improves independence and flexibility of the system; based on closed loop control of real-time monitoring data, dynamic adjustment of antistatic performance is realized, the industrial environment is adapted, and electrostatic dissipation efficiency is improved; energy collection, storage and consumption are optimized by an energy storage management unit, and energy utilization efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design technology, specifically to a big data-driven antistatic optimization design method for cleanroom cable chains. Background Technology

[0002] The flexible antistatic housing of a cleanroom cable chain is a specialized material designed to provide effective electrostatic protection for cables, pipes, and other sensitive equipment within the chain, ensuring their safe and normal operation in a cleanroom environment. Integrated within this housing is a functional module with an electrostatic dissipation unit, composed of conductive or semi-conductive materials. This unit effectively conducts and neutralizes static charges, ensuring safe operation of the cable chain in a clean environment. It is tightly integrated with the chain's structure and materials to prevent static buildup without generating particulate contamination. Traditional antistatic designs are typically based on fixed conductive materials or static coatings, making it difficult to achieve a dynamic balance between static accumulation and dissipation. Furthermore, this design fails to effectively utilize the mechanical vibration energy generated during equipment operation, thus requiring an external power supply and reducing the system's independence and flexibility.

[0003] The paper "Energy Harvesting Based on Piezoelectric Effect" from Nanjing University of Aeronautics and Astronautics describes the existing technology of converting mechanical vibration energy into electrical energy through a piezoelectric energy harvesting module.

[0004] The existing technology, with publication number CN107066757A, entitled "A Module Pattern Optimization Design Method in Product Modular Design Supported by Big Data," defines user behavior variables, collects user behavior data, and calculates the user demand satisfaction for each parameter level. A module pattern optimization design model is established with the goal of maximizing user demand satisfaction and minimizing production costs. The model is solved using a bisection method. By analyzing big data on user-selected product module parameter levels, this method guides the optimization design of product module patterns, overcoming the shortcomings of traditional methods and featuring simplicity, rationality, and ease of implementation.

[0005] The existing technology has the following shortcomings:

[0006] 1. The existing cleanroom cable chain's own static dissipation unit relies on an external power source and does not effectively utilize the mechanical vibration energy of the cleanroom cable chain itself to convert external power sources.

[0007] 2. Due to the changing motion state of the cleanroom cable chain and the environment it is in, the static dissipation unit does not dynamically adjust according to the actual static accumulation of the flexible antistatic shell, making it difficult to adapt to the changing industrial environment.

[0008] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The purpose of this invention is to provide a big data-driven antistatic optimization design method for cleanroom cable chains to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] A big data-driven antistatic optimization design method for cleanroom cable chains is applied to the optimization design of flexible antistatic shells on cleanroom cable chains. Its key feature is that a piezoelectric energy harvesting module and an electrostatic dissipation unit are installed on the flexible antistatic shell. The piezoelectric energy harvesting module is connected to the input terminal of an energy storage management unit. Specific steps include:

[0012] Step S1: The mechanical vibration energy generated during the movement of the cleanroom cable chain is converted into electrical energy through the piezoelectric energy harvesting module, and the converted electrical energy is intelligently regulated and stored using the energy storage management unit.

[0013] Step S2: Collect static electricity accumulation data of the flexible antistatic shell in real time and input it into the big data analysis platform. The big data analysis platform analyzes the static electricity accumulation data within the current monitoring period to generate a static electricity accumulation evaluation coefficient. The static electricity accumulation evaluation coefficient is used to generate an intelligent control strategy for the energy storage management unit.

[0014] Step S3: Receive the intelligent control strategy and dynamically adjust the conductivity of the flexible antistatic shell through the static dissipation unit to achieve static dissipation;

[0015] Step S4: Obtain the static electricity accumulation evaluation coefficients calculated for the flexible antistatic shell over the past M monitoring time periods, and perform correlation mapping on the output values ​​of these static electricity accumulation evaluation coefficients to obtain a set of mapping results;

[0016] The mapping result set is divided into two subsets in chronological order, and the superposition value of several mapping results in each subset is calculated separately. The superposition value of the two subsets is compared and analyzed with the corresponding preset thresholds to obtain the strategy adjustment coefficient. This strategy adjustment coefficient is used to provide calibration and optimization rules for the intelligent control strategy of the next monitoring period.

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

[0018] Self-sufficient energy supply: The electrostatic dissipation system is self-powered by utilizing the mechanical vibration energy of the cable chain itself, avoiding dependence on external power sources and enhancing the system's independence and flexibility.

[0019] Dynamic response capability: Closed-loop control based on real-time monitoring data enables dynamic adjustment of antistatic performance, adapting to changing industrial environments and improving static dissipation efficiency.

[0020] High-efficiency energy management: The energy storage management unit optimizes the collection, storage and consumption of energy, thereby improving energy utilization efficiency. Attached Figure Description

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

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

[0023] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0024] Example 1:

[0025] Please see Figure 1 The present invention provides a technical solution:

[0026] A big data-driven antistatic optimization design method for cleanroom cable chains is applied to the optimization design of flexible antistatic shells on cleanroom cable chains. The flexible antistatic shells are equipped with piezoelectric energy harvesting modules and static dissipation units. The piezoelectric energy harvesting module is connected to the input of an energy storage management unit. Specific steps include:

[0027] Step S1: The mechanical vibration energy generated during the movement of the cleanroom cable chain is converted into electrical energy through the piezoelectric energy harvesting module, and the converted electrical energy is intelligently regulated and stored using the energy storage management unit.

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

[0029] The piezoelectric element is a thin sheet or a multilayer structure; PZT (lead zirconate titanate) is selected as the piezoelectric ceramic material; the size and shape of the piezoelectric element are optimized according to the specific installation location; to adapt to the industrial environment, the piezoelectric element is coated or encapsulated to prevent wear and contamination from the environment.

[0030] The mechanical coupling structure is a spring or a flexible support; the mechanical coupling structure is directly attached to the high-vibration parts of the flexible antistatic shell on the cleanroom cable chain, so that the cleanroom cable chain can continuously acquire vibration energy during movement; the coupling structure is firmly attached to the high-vibration parts of the flexible antistatic shell by high-quality bolts or industrial-grade adhesive technology; in the design, the elastic characteristics of the coupling structure are designed to maximize the energy acquisition efficiency of the piezoelectric element and keep in line with the frequency response of the cleanroom cable chain.

[0031] The piezoelectric element is attached to the high-vibration part of a flexible antistatic shell via a mechanical coupling structure.

[0032] The following explanation is required for areas experiencing high vibration:

[0033] Multiple measurement points are set on the flexible antistatic shell of the cleanroom cable chain. Vibration data of these measurement points are recorded under different movement speeds and environmental conditions. The vibration data includes vibration frequency and vibration acceleration.

[0034] Multiple measurement points include the following locations: A, B, and C;

[0035] Location A is the middle of the flexible antistatic shell;

[0036] Location B is the joint or corner of the flexible antistatic shell;

[0037] Location C is the area of ​​the electric actuator corresponding to the flexible antistatic housing;

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

[0039] The maximum vibration acceleration is used as the key feature, and the vibration threshold of the key feature is set to V. th The vibration threshold V was measured within the range of 70% to 85% of the maximum vibration acceleration. th The selection of numerical values;

[0040] V th =0.75×A max

[0041] Among them, A max It is the maximum vibration acceleration recorded by the flexible antistatic shell under the motion speed and environmental conditions during the current monitoring period;

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

[0043] Will be greater than the vibration threshold V th The measurement points are designated as high-vibration areas;

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

[0045] The supercapacitor selected is an electrochemical double-layer capacitor (EDLC), which is suitable for the electrical energy storage of mechanical vibration data in this embodiment due to its high energy density and fast charging and discharging characteristics.

[0046] After the electrical energy collected by the piezoelectric element is output, it is converted into DC power through a rectification and voltage regulation circuit, and then directly supplies power to the supercapacitor.

[0047] Supercapacitors store a large amount of electrical energy through the electrochemical double layer formed on the electrode surface, enabling them to charge and discharge rapidly in a short period of time.

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

[0049] The full-bridge rectifier circuit converts the AC power generated by the piezoelectric element into DC power, and then uses a voltage regulator to stabilize the output voltage within the acceptable range of the supercapacitor, so as to avoid damage to the energy storage components caused by high voltage.

[0050] Connect the supercapacitor to a smart energy management chip (such as ADP5050, BQ24650, etc.), and the smart energy management chip will be responsible for dynamically monitoring the energy status and determining the priority of energy storage and release.

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

[0052] Based on the static electricity demand curve of the cable chain, the intelligent management chip uses big data analysis to adjust the power release strategy, ensuring the static electricity stability of the cable 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 cleanroom cable chain, the mechanical vibration of the flexible antistatic shell 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] Power conversion: With the frequent vibration of the cleanroom cable chain, the piezoelectric element continuously generates voltage; the rectifier and voltage regulator circuit converts this alternating current into stable direct current, which is directly supplied to the supercapacitor for storage.

[0056] Step S2: Collect static electricity accumulation data of the flexible antistatic shell in real time and input it into the big data analysis platform. The big data analysis platform analyzes the static electricity accumulation data within the current monitoring period to generate a static electricity accumulation evaluation coefficient. The static electricity accumulation evaluation coefficient is used to generate an intelligent control strategy for the energy storage management unit.

[0057] Further explanation: The data on static electricity accumulation includes: static charge quantity, voltage, and current parameters during the current monitoring period; and the hysteresis coefficient, which describes the hysteresis characteristics of static electricity accumulation.

[0058] The hysteresis coefficient is used to evaluate the degree of hysteresis effect of the charge release rate and charge accumulation rate on the cleanroom cable chain under different movement speeds and environmental indicators.

[0059] The formula for calculating the lag effect coefficient η is defined as follows:

[0060]

[0061] Among them, R d It is the charge release rate of the flexible antistatic shell during the current monitoring period (unit: Coulombs / s); Coulombs / s is the unit of charge flow rate, which represents the amount of charge passing through a certain cross section per second;

[0062] R a It is the charge accumulation rate of the flexible antistatic shell during the current monitoring 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 cleanroom cable conveyor during the current monitoring period; it is used to describe the impact of these factors on hysteresis behavior.

[0064] The comprehensive correction factor S(v,ΔA,ΔT) is fitted to the following two forms based on experimental conditions:

[0065] When the overall 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 of average motion speed, representing the direct effect of average motion speed v on hysteresis; α2 is the correlation coefficient of contact area change, representing the effect of increased contact area on hysteresis; α3 is the correlation coefficient of temperature change, representing the effect of increased temperature on hysteresis.

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

[0069]

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

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

[0072] During the current monitoring period, the change in contact area ΔA represents the change in the area of ​​the flexible antistatic shell in contact with external components. Specifically, during the reciprocating motion of the cleanroom cable chain, the movement of the flexible antistatic shell will cause a 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 period, the temperature change ΔT represents the temperature change caused by friction at the contact surface between the flexible antistatic shell and external components during the reciprocating motion of the dust-free cable chain.

[0074] When the average velocity v is in the high-speed range, R d <R a When η < 1, it means that the charge release rate lags behind the charge accumulation rate, and the actual electrostatic accumulation data will continue to increase.

[0075] It should be noted that the average speed v of the cleanroom cable chain has an upper limit in practical applications. Therefore, based on this upper limit, the frictional contact time between the contact surfaces corresponding to the flexible antistatic housing is sufficient to allow R to... d <R a ;

[0076] R d <R a This indicates a high level of static electricity accumulation, and the actual static electricity accumulation data will continue to increase, reflecting insufficient hysteresis control capability of the system; it is necessary to add release measures or optimize material properties to reduce accumulation.

[0077] High-speed range: When the average velocity v is greater, the hysteresis coefficient η is smaller, and the charge accumulation rate R... a Greater than the charge release rate R d The hysteresis effect is significant; at this time, the rapid change in contact area and the increase in temperature work together to further aggravate the hysteresis phenomenon, which reduces the hysteresis effect coefficient η.

[0078] When the average velocity v is in the middle velocity range, and R d =R a When η = S(v,ΔA,ΔT), we get η = S(v,ΔA,ΔT), which means that the charge release rate is synchronized with the charge accumulation rate, and the electrostatic accumulation is in a state of maintaining charge balance.

[0079] In the intermediate speed range: the hysteresis coefficient η exhibits a nonlinear change, indicating that the charge release rate R d and charge accumulation rate R a There are complex relationships between them; the dynamic changes in contact area and the influence of temperature show a significant hysteresis adjustment effect in this intermediate speed range.

[0080] When the average speed v is in the low-speed range, and R d >R a When η > 1, it indicates that the charge accumulation rate lags behind the charge release rate, and the electrostatic accumulation level is low.

[0081] In the low-speed range: a slower average speed (v) of the cleanroom cable chain means less contact and separation between the chain's components or other objects, resulting in a lower frequency of triboelectric charging. The triboelectric frequency is positively correlated with the charge accumulation rate; therefore, the lower the speed, the lower the charge accumulation rate.

[0082] As the average velocity v approaches 0, the charge accumulation velocity R... a The smaller the value, the larger the hysteresis coefficient η, indicating that the charge release rate mechanism dominates. At this point, changes in contact area and temperature have a smaller impact on the hysteresis, but the charge accumulation rate R... a Small, static electricity accumulation is mainly affected by the charge release rate R d control;

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

[0084] R a =k a ·v·μ·(1+E ext )

[0085] Where, k a is the normalized adjustment factor for charge accumulation rate; v is the average movement speed of the cleanroom cable chain during the current monitoring period; μ is the surface friction coefficient of the flexible antistatic shell; the larger the surface friction coefficient and the average movement speed, the greater the chance of frictional interaction, resulting in more charge accumulation, which is directly proportional.

[0086] E ext It is an influencing factor of the intensity of the external environmental electric field; the presence of the external electric field leads to the accumulation of more charge on the material surface;

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

[0088]

[0089] Where: k d It is a normalization adjustment factor for the charge release rate;

[0090] This embodiment k a =1; k d =1;

[0091] τ is the dielectric relaxation time, defined as follows: Describe the response speed of the flexible antistatic enclosure when subjected to changes in the external electrostatic environment;

[0092] d is the dielectric constant of the flexible antistatic shell; dh is the average thickness of the flexible antistatic shell; DE bace It is the basic conductivity of the flexible antistatic shell;

[0093] The following experimental data table will show the charge release rate R of the cleanroom cable conveyor under different average movement speeds and environmental parameters. d Charge accumulation rate R a And its corresponding lag effect coefficient η.

[0094] Table 1. Study on the lag 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 rate R d Greater than the charge accumulation rate R a This results in a hysteresis coefficient η of 1.5×S(v,ΔA,ΔT), indicating that the charge release effect is good and the charge accumulation level is low.

[0098] Medium to low speed (1.0 m / s): Charge release rate R d With charge accumulation rate R a As the gap between them decreases, η becomes 1.125 × S(v,ΔA,ΔT), and the system begins to exhibit accumulation. C / s is an abbreviation for Coulombs / s;

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

[0100] At medium to high speeds (2.0 m / s): the charge accumulation rate is higher than the charge release rate, causing the hysteresis coefficient η to drop to 0.8 × S(v, ΔA, ΔT), which shows that charge release lags behind charge accumulation.

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

[0102] Further explanation: The electrostatic accumulation evaluation coefficient is defined as EAC, and the expression for 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 static electricity accumulation status of the flexible antistatic shell during the current monitoring period, with a value range of (0,1); the output is used to guide the intelligent control strategy of the energy storage management unit.

[0107] Q is the static charge level obtained after uniform dimensionless processing, representing the static charge level of the flexible antistatic shell measured during the current monitoring period. The static charge level is measured by the electrostatic voltage method or capacitance method. Specifically, a non-contact electrometer is used for measurement. Since the decrease in static charge leads to the decrease in voltage per unit capacitance, and the decrease in the amount of static charge passing through per unit time leads to the decrease in current, the static charge level is directly proportional to both the voltage and current levels.

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

[0109] I is the current index obtained after uniform dimensionless processing, representing the current intensity level measured during the current monitoring period; η is the hysteresis 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 interval (0,1), and b1+b2+b3+b4=1;

[0112] As f(Q,U,I,η) increases, e-f(Q,U,I,η) Decrease An increase in the value indicates that the static electricity accumulation of the flexible antistatic shell is increasing during the current monitoring period;

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

[0114] The adjustment threshold range for the electrostatic accumulation evaluation coefficient (EAC) is set to [q1, q2], and 0.23 ≤ q1 < q2 ≤ 0.73 is set. The specific values ​​of q1 and q2 will be determined by the expert group using the fuzzy hierarchical analysis method (FAHP) to reasonably reflect the assessment and control of electrostatic accumulation risk.

[0115] When EAC is greater than q2, it indicates a high level of static electricity accumulation, requiring an increase in the conductivity of the flexible antistatic shell to enhance static electricity dissipation. Specifically, the first strategy data for increasing conductivity is generated.

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

[0117] The range of high target conductivity is denoted as [DE]. bace +μ1,DE bace +μ2],μ1<μ2;DE bace It is the basic conductivity of the flexible antistatic shell; the basic conductivity refers to the inherent conductivity of the flexible antistatic shell itself without any external adjustment or control, 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 static electricity accumulation; the conductivity of the flexible antistatic shell needs to be reduced to decrease static electricity dissipation, and specific data for a second strategy to reduce conductivity is generated.

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

[0120] The range of low target conductivity is denoted as [DE]. bace +μ3,DE bace +μ4],μ3<μ4;and DE bace +μ1>DE bace +μ4; μ3 and μ4 are the third and fourth volatility factors, respectively;

[0121] μ1, μ2, μ3 and μ4 were determined by an expert group using fuzzy hierarchical analysis (FAHP) based on experimental data;

[0122] When the EAC value is within the range [q1, q2], it indicates a moderate level of static electricity accumulation; there is no need to adjust the conductivity of the flexible antistatic shell.

[0123] Step S3: Receive the intelligent control strategy and dynamically adjust the conductivity of the flexible antistatic shell through the static dissipation unit to achieve static 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 operational amplifier circuit, used to generate pulse signals of a specific frequency and amplitude. This signal will act on the conductivity adjustment of the adjustable conductive polymer material to ensure that the applied voltage does not exceed 10V, so as to avoid damage to the material.

[0126] The operating mode of the drive circuit is set as follows to achieve low power consumption:

[0127] Active adjustment mode: When it is necessary to increase or decrease the conductivity, i.e., when the first strategy data or the second strategy data is available, the drive circuit is in a high-efficiency working state, providing the required pulse signal to adjust the conductivity of the conductive polymer material;

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

[0129] Explanation of the grounding system:

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

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

[0132] A resistance sensor is used to continuously monitor the grounding resistance, ensuring it remains within the allowable range (less than 10Ω). The monitoring data is fed back to the control system via the MCU.

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

[0134] The specific logic for improving the conductivity of the flexible antistatic shell includes:

[0135] The microcontroller (MCU) of the electrostatic dissipation unit generates a high conductivity control signal for the tunable conductive polymer material based on the received first strategy data; in this embodiment, the high conductivity control signal is a low voltage pulse signal of no more than 10V.

[0136] A low-power driving circuit is used to transmit a high conductivity control signal to the control adjustment terminal of the tunable conductive polymer material. By increasing the ion migration or electron transport mechanism, the ion migration or electron transport mechanism inside the tunable conductive polymer material is effectively activated, causing a reversible electrochemical response in the polymer's molecular structure, thereby increasing the conductivity of the tunable conductive polymer material to a 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 is no more than 100 ms.

[0137] The specific logic for reducing the conductivity of the flexible antistatic shell includes:

[0138] The microcontroller (MCU) of the electrostatic dissipation unit generates a low conductivity control signal for the tunable conductive polymer material based on the second strategy data.

[0139] A low-power driving circuit is used to transmit a low conductivity control signal to the control adjustment terminal of the tunable conductive polymer material. By reducing ion migration or electron transfer in the tunable conductive polymer material, the conductivity of the tunable conductive polymer material is reduced to a 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 is no more than 100 ms.

[0140] Ion migration refers to the movement of ions within a polymer, where they can carry electrical charges and affect the overall conductivity of the material.

[0141] Electron transport is another mechanism of electrical conductivity: the migration of electrons along polymer chains. Electron transport in conductive polymers can be altered by adjusting doping levels, molecular structure, or external conditions. External conditions include electric field control.

[0142] The electrical conductivity of a material can be modulated by applying or changing the strength of an electric field to manage the movement of ions and electrons.

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

[0144] For polyaniline (PANI):

[0145] Characteristics: Polyaniline is an electrochemically sensitive conductive polymer whose conductivity can be altered by an applied electric field (such as acid doping or dedoping).

[0146] For polythiophenes, this embodiment uses a composite of poly(3,4-ethylenedioxythiophene) (PEDOT) and polystyrene sulfonic acid (PSS);

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

[0148] For polypyrrole (PPy):

[0149] Characteristics: Its conductivity can be altered by regulating electron transfer and ion migration through an external electric field or electrochemical means.

[0150] The control adjustment terminal of the adjustable conductive polymer material is an interface or contact point on the material through which a control signal can be input to adjust the material's conductivity. This port is an electrode, contact point, or other input interface of the adjustable conductive polymer material used to receive electrical signals from the drive circuit.

[0151] Electrodes are conductive parts used to provide electrical connections, which are connected to the drive circuit to transmit signals.

[0152] Contacts or connectors are interfaces used to facilitate connection and disconnection, and are connected to the drive circuit via wires or contacts.

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

[0154] Step S4: Obtain the static electricity accumulation evaluation coefficients calculated for the flexible antistatic shell over the past M monitoring time periods, and perform correlation mapping on the output values ​​of these static electricity accumulation evaluation coefficients to obtain a set of mapping results;

[0155] The mapping result set is divided into two subsets in chronological order, and the superposition value of several mapping results in each subset is calculated separately. The superposition value of the two subsets is compared and analyzed with the corresponding preset thresholds to obtain the strategy adjustment coefficient. This strategy adjustment coefficient is used to provide calibration and optimization rules for the intelligent control strategy of the next monitoring period.

[0156] Further explanation: The output values ​​of the electrostatic accumulation evaluation coefficients for the M monitoring time periods are correlated and mapped as follows:

[0157]

[0158] Where i∈{1,2,…,M}, and M<10; if represents “if”;

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

[0160] The initial settings for the two subsets are as follows:

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

[0162] Computational subset The superposition value QZ2 and the preset threshold YS2 are respectively Indicates rounding up to the nearest integer;

[0163] The selection rules for YS1 and YS2 are as follows: Initially set PF i ∈{PF1,PF2,…,PF M} in PF i The value is always 1; that is, it means that the EAC value corresponding to each mapping result is within the interval [q1,q2], which is a case of moderate electrostatic accumulation level;

[0164] The formula for calculating the strategy adjustment coefficient is defined as follows:

[0165]

[0166] Where TZ is the strategy adjustment coefficient;

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

[0168] The calibration and optimization rule for the intelligent control strategy is as follows: In the next monitoring period, for the high target conductivity range [DE] of the first strategy data... bace +μ1,DE bace The following adjustments will be implemented for +μ2]:

[0169]

[0170] The explanation for adjusting the formula is as follows:

[0171] when When TZ > 0; since the electrostatic accumulation level is decreasing; therefore, the high target conductivity range [DE] of the first strategy data is required. bace +μ1,DE bace +μ2] Implement the reduction rule, through as well as The settings effectively reduced the output values ​​of μ1 and μ2, thereby reducing the range of high target conductivity; and The setting smooths the values ​​to prevent excessively large values ​​from significantly affecting the adjustment results; this adjustment aims to reduce the range of high target conductivity [DE]. bace +μ1,DE bace The sensitivity of +μ2] is thus avoided when the electrostatic accumulation level is decreasing, preventing the effective identification of high target conductivity ranges [DE]. bace +μ1,DE bace +μ2];

[0172] Furthermore, when When TZ = 0, it means that the ratio of the superposition value QZ1 of the previous subset to the preset threshold YS1 is equal to the ratio of the superposition value QZ2 of the subsequent subset to the preset threshold YS2; thus, it represents that the electrostatic accumulation level is in a stable state.

[0173] when When TZ < 0, it means that the ratio of the superposition value QZ1 of the first subset to the preset threshold YS1 is less than the ratio of the superposition value QZ2 of the second subset to the preset threshold YS2; thus, it represents that the electrostatic accumulation level is increasing.

[0174] The calibration and optimization rule for the intelligent control strategy is as follows: In the next monitoring period, for the low target conductivity range [DE] of the second strategy data... bace +μ3,DE bace The following adjustments will be implemented with +μ4]:

[0175]

[0176] The explanation for adjusting the formula is as follows:

[0177] when When TZ < 0; since the electrostatic accumulation level is increasing; therefore, the low target conductivity range [DE] of the second strategy data is required. bace +μ3,DE bace +μ4] Implement the added rule, through as well as The settings effectively increased the output values ​​of μ3 and μ4, thereby improving the low target conductivity range; this adjustment aims to improve the low target conductivity range [DE]. bace +μ3,DE bace The sensitivity of +μ4] is thus avoided in situations where the electrostatic accumulation level is increasing, preventing the effective identification of low target conductivity ranges [DE]. bace +μ3,DE bace +μ4];

[0178] In the next monitoring period, the adjusted intelligent control strategy is received and the conductivity of the flexible antistatic shell is dynamically adjusted through the static dissipation unit to achieve static dissipation.

[0179] Example 2:

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

[0181] The experimental steps are as follows:

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

[0183] The expert panel used fuzzy hierarchical analysis (FAHP) to determine the adjustment threshold range for the electrostatic accumulation evaluation coefficient as [q1, q2], where q1 = 0.3 and q2 = 0.6. This range reasonably reflects the assessment and control of 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, μ2=0.1.

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

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

[0188] During the experiment, the static electricity accumulation of each flexible antistatic shell was evaluated over the past M=6 monitoring time periods, and the EAC value corresponding to each time period was recorded; the recorded EAC values ​​were 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 its corresponding PF.i The value is used to obtain the mapping result set PF. i ∈{PF1,PF2,…,PF M}

[0193] The mapping result set is divided into two subsets in chronological order:

[0194] The first subset is {PF1,PF2,PF3}, and the second subset is {PF4,PF5,PF6}.

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

[0196]

[0197] Strategy adjustment coefficient calculation:

[0198]

[0199] Adjust the high or low target conductivity range based on the TZ value:

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

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

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

[0203] The conductivity adjustment results and static dissipation efficiency were compared between those with and without calibration optimization rules.

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

[0205] Table 1. Comparative Study of Calibration and Optimization Rules for Intelligent Control Strategies:

[0206]

[0207] Data Analysis:

[0208] As shown in Table 1, the intelligent control strategy with calibration optimization rules significantly improved static dissipation efficiency in all models of flexible antistatic housings. Specifically, the improvement rates were 6.25% for Model A, 6.02% for Model B, and 4.65% for Model C, with an average improvement of 5.64%. In contrast, the traditional strategy without calibration optimization rules did not improve static dissipation efficiency, remaining at 80%, 83%, and 86% respectively.

[0209] Based on the above data, it can be concluded that the "intelligent control strategy" in this invention, through calibration and optimization rules, effectively improves the static dissipation efficiency of the flexible antistatic shell, demonstrating significant innovation and technological advantages. The average improvement rate reached 5.64%, fully proving the effectiveness of the calibration and optimization rules in controlling static accumulation.

[0210] The following is additional information:

[0211] Electrostatic Accumulation Evaluation Coefficient (EAC): The electrostatic accumulation evaluation coefficient for each model in the experiment, ensuring consistent experimental conditions.

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

[0213] Adjusted conductivity (S / m): The change in conductivity after calibration and optimization rules are applied according to the intelligent control strategy.

[0214] Static dissipation efficiency (%): The adjusted static dissipation efficiency shows that the calibrated and optimized group performs better.

[0215] Dissipation efficiency improvement (%): The percentage improvement in efficiency compared to the uncalibrated optimization group.

[0216] Average improvement percentage (%): The average dissipation efficiency improvement percentage for all models reached 5.64% overall.

[0217] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, 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 computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the 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 sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0219] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, 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 are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within 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 are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A big data-driven antistatic optimization design method for cleanroom cable chains, applied to the optimization design of flexible antistatic shells on cleanroom cable chains, characterized in that... A piezoelectric energy harvesting module and an electrostatic dissipation unit are installed on the flexible antistatic housing. The piezoelectric energy harvesting module is connected to the input terminal of the energy storage management unit. The specific steps include: Step S1: The mechanical vibration energy generated during the movement of the cleanroom cable chain is converted into electrical energy through the piezoelectric energy harvesting module, and the converted electrical energy is intelligently regulated and stored using the energy storage management unit. Step S2: Collect static electricity accumulation data of the flexible antistatic shell in real time and input it into the big data analysis platform. The big data analysis platform analyzes the static electricity accumulation data within the current monitoring period to generate a static electricity accumulation evaluation coefficient. The static electricity accumulation evaluation coefficient is used to generate an intelligent control strategy for the energy storage management unit. The data on static charge accumulation includes static charge, voltage, and current indices for the current monitoring period; as well as a hysteresis coefficient to describe the hysteresis characteristics of static charge accumulation. The hysteresis coefficient is used to evaluate the degree of hysteresis effect of the charge release rate and charge accumulation rate on the cleanroom cable chain under different movement speeds and environmental indicators. Define the lag effect coefficient The calculation formula is as follows: in, It is the charge release rate of the flexible antistatic shell during the current monitoring period; It is the rate of charge accumulation in the flexible antistatic shell during the current monitoring period; It is related to the average movement speed v and contact area change of the cleanroom cable chain during the current monitoring period. Temperature changes Related comprehensive correction factors; When the average velocity v is in the high-speed range At that time, This indicates that the rate of charge release lags behind the rate of charge accumulation, and the actual static electricity accumulation data will continue to increase. When the average velocity v is in the middle velocity range, and At that time, This indicates that the rate of charge release is synchronized with the rate of charge accumulation, and the electrostatic accumulation is in a state of maintaining charge balance. When the average speed v is in the low-speed range, and At that time, This indicates that the rate of charge accumulation lags behind the rate of charge release, and the level of electrostatic accumulation is low. The electrostatic accumulation evaluation coefficient is defined as EAC, and the expression for EAC is as follows: Among them, the function Defined as: EAC is used to quantify the static electricity accumulation status of the flexible antistatic shell during the current monitoring period, with a value range of (0,1). It is a static charge index obtained after uniform dimensionless processing, representing the static charge level of the flexible antistatic shell measured during the current monitoring period. It is a voltage index obtained after uniform dimensionless processing, representing the voltage level measured during the current monitoring period; It is a current index obtained after uniform dimensionless processing, representing the current intensity level measured within the current monitoring period. It is the lag effect coefficient; e is the natural constant; , and The output values ​​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. 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 interval (0,1), and b1+b2+b3+b4=1; The intelligent control strategy of the energy storage management unit specifically includes: The adjustment threshold range for the electrostatic accumulation evaluation coefficient EAC is set to [q1, q2], and 0.23 ≤ q1 < q2 ≤ 0.73 is set. When EAC is greater than q2, it indicates a high level of static electricity accumulation, requiring an increase in the conductivity of the flexible antistatic shell to enhance static electricity dissipation. Specifically, the first strategy data for increasing conductivity is generated. The first strategy data includes the requirement that the flexible antistatic housing needs to achieve a high target conductivity range; The range of high target conductivity is denoted as . , ; It is the basic conductivity of the flexible antistatic shell; and These are the first volatility factor and the second volatility factor, respectively. When EAC is less than q1, it indicates a low level of static electricity accumulation; the conductivity of the flexible antistatic shell needs to be reduced to decrease static electricity dissipation, and specific data for a second strategy to reduce conductivity is generated. The second strategy data includes the requirement that the flexible antistatic housing needs to achieve a low target conductivity range. The range of low target conductivity is denoted as . , ;and ; and These are the third volatility factor and the fourth volatility factor, respectively. When the EAC value is within the range [q1, q2], it indicates a moderate level of static electricity accumulation; there is no need to adjust the conductivity of the flexible antistatic shell. Step S3: Receive the intelligent control strategy and dynamically adjust the conductivity of the flexible antistatic shell through the static dissipation unit to achieve static dissipation; Step S4: Obtain the static electricity accumulation evaluation coefficients calculated for the flexible antistatic shell over the past M monitoring time periods, and perform correlation mapping on the output values ​​of these static electricity accumulation evaluation coefficients to obtain a set of mapping results; The mapping result set is divided into two subsets in chronological order, and the superposition value of several mapping results in each subset is calculated separately. The superposition value of the two subsets is compared and analyzed with the corresponding preset thresholds to obtain the strategy adjustment coefficient. This strategy adjustment coefficient is used to provide calibration and optimization rules for the intelligent control strategy of the next monitoring period. The output values ​​of the electrostatic accumulation evaluation coefficients for the M monitoring time periods are correlated and mapped as follows: ； in, And M < 10; It is a representation of "if"; Define the mapping result set as ; It is the correlation mapping value corresponding to the electrostatic accumulation evaluation coefficient of the i-th monitoring time period; The initial settings for the two subsets are as follows: and ; Pre-compute subset superposition value and preset threshold They are respectively ; Computational subset superposition value and preset threshold They are respectively ; Indicates rounding up to the nearest integer; in and The selection rules are: initial settings middle The value is always 1; that is, it means that the EAC value corresponding to each mapping result is within the interval [q1,q2], which is a case of moderate electrostatic accumulation level; The formula for calculating the strategy adjustment coefficient is defined as follows: in, It is the strategy adjustment coefficient.

2. The big data-driven antistatic optimization design method for cleanroom cable chains according to claim 1, characterized in that: The piezoelectric energy harvesting module includes a piezoelectric element, a mechanical coupling structure, and a rectification and voltage regulation circuit. The piezoelectric element is a thin sheet or a multilayer structure; the mechanical coupling structure is a spring or a flexible support. The piezoelectric element is attached to the high-vibration part of a flexible antistatic shell via a mechanical coupling structure. The energy storage management unit includes a supercapacitor and a smart energy management chip; the rectification and voltage regulation circuit adopts a full-bridge rectifier circuit and a voltage regulator.

3. The big data-driven antistatic optimization design method for cleanroom cable chains according to claim 2, characterized in that: The static dissipation unit includes an adjustable conductive polymer material, a low-power drive circuit, and a grounding system. The specific logic for improving the conductivity of the flexible antistatic shell includes: The microcontroller (MCU) of the electrostatic dissipation unit generates a high conductivity control signal for the tunable conductive polymer material based on the received first strategy data. A low-power drive circuit is used to transmit a high conductivity control signal to the control adjustment terminal of the tunable conductive polymer material, so as to improve the conductivity of the tunable conductive polymer material to a high target conductivity range; The specific logic for reducing the conductivity of the flexible antistatic shell includes: The microcontroller (MCU) of the electrostatic dissipation unit generates a low conductivity control signal for the tunable conductive polymer material based on the second strategy data. A low-conductivity control signal is transmitted to the control adjustment terminal of the tunable conductive polymer material using a low-power drive circuit, so as to reduce the conductivity of the tunable conductive polymer material to a low target conductivity range.

4. The big data-driven antistatic optimization design method for cleanroom cable chains according to claim 3, characterized in that: when hour, ; indicates the sum of the values ​​of the preceding subsets. and preset threshold The ratio is greater than the sum of the values ​​of the subsequent subsets. and preset threshold The ratio represents the level of static electricity accumulation, indicating a decreasing state. The calibration and optimization rule for the intelligent control strategy is as follows: In the next monitoring period, for the high target conductivity range of the first strategy data... The following adjustments will be implemented: when hour, ; indicates the sum of the values ​​of the preceding subsets. and preset threshold The ratio is equal to the sum of the values ​​of the later subsets. and preset threshold The ratio of these values ​​represents the level of static electricity accumulation required to maintain a stable state. when hour, ; indicates the sum of the values ​​of the preceding subsets. and preset threshold The ratio is less than the sum of the values ​​of the later subsets. and preset threshold The ratio of these values ​​represents the increasing level of electrostatic accumulation. The calibration and optimization rule for the intelligent control strategy is as follows: In the next monitoring period, for the low target conductivity range of the second strategy data... The following adjustments will be implemented: In the next monitoring period, the adjusted intelligent control strategy is received and the conductivity of the flexible antistatic shell is dynamically adjusted through the static dissipation unit to achieve static dissipation.

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