Dust-free transportation carrier for high-precision plastic mold optical component

Abstract

The invention relates to the field of dust-free transportation, in particular to a dust-free transportation carrier for a high-precision plastic mold optical component. Comprising a shell, a bearing cavity, a tray positioning assembly, a cabin door, an acceleration sensor, a tilt angle sensor, a temperature and humidity sensor, a cabin door opening and closing sensor, a selectable particle sensing module and a control module. The trays are bound with batches and process routes through two-dimensional codes or radio frequency tags, when the carriers are transferred among the stations, the control module collects impact, postures, temperature and humidity and cabin door opening and closing states in real time, the transportation process is divided into a plurality of transportation sections, and the transportation sections are used for transporting the carriers. And calculating a corresponding transportation state index at the end of the interval, associating the transportation state index with batch information, and uploading the transportation state index and the batch information to an upper system, so as to realize monitoring and traceable management of the dust-free transportation process of the high-precision plastic optical component.

Description

Technical Field

[0001] This invention relates to the field of cleanroom transportation, specifically a cleanroom transportation vehicle for high-precision plastic mold optical components. Background Technology

[0002] High-precision plastic optical components, such as mobile phone lenses, automotive lenses, light guides, prisms, and lens arrays, are extremely sensitive to geometric stability, surface cleanliness, and appearance defects after molding. Production typically employs a process route of injection molding, annealing, coating, and assembly within a cleanroom, utilizing optical trays and cleanroom carts for material transfer between workstations. These trays generally have cavities cut to fit the shape of the parts, with covers or hoods to prevent particle accumulation; the carts are mostly made of stainless steel or clean plastic, meeting requirements for low dust generation and easy cleaning, and can transport materials between areas with different cleanliness levels. For transfers requiring cross-plant or cross-area transport, some companies also use sealed containers with desiccants or inert gases to reduce the risks associated with humidity and static electricity.

[0003] However, examining the existing technologies mentioned above from the perspective of quality control requirements for high-precision plastic optical components reveals that they primarily focus on providing a relatively clean physical space and reducing environmental pollution sources, while lacking the measurement and recording of actual working conditions during transportation. Existing pallets and trolleys generally lack online monitoring capabilities for impact, vibration, and tilting. Whether severe impacts, short-term drops, or prolonged tilting have occurred during transportation often relies solely on operator experience or post-event visual observation, making it impossible to quantify the impact of mechanical stress and posture instability on the shape and surface of optical components. If subsequent inspections reveal a decrease in yield or localized contamination in a particular batch, it is difficult to pinpoint the specific transportation section and abnormal event based on objective data; instead, one must deduce the suspected cause from multiple possible links, resulting in low problem diagnosis efficiency.

[0004] From an environmental perspective, while existing cleanroom carts and enclosed enclosures can structurally reduce the impact of external particles and humidity on optical components, most only meet cleanliness compatibility requirements on a macroscopic level. They do not provide real-time monitoring and recording of key factors such as internal temperature and humidity changes, door opening duration, actual exposure time, and sudden changes in particle concentration. During transportation, existing technologies generally cannot provide objective, quantifiable data to support any abnormal situations such as prolonged high humidity, doors left open for extended periods, frequent door openings during transport, or localized dust generation. To mitigate these risks on the production floor, the only recourse is to increase sampling rates and cleaning frequency, which, while ensuring quality, leads to increased costs and production cycle fluctuations. Furthermore, existing transport vehicles are often viewed as passive handling tools, and cleaning and maintenance strategies are typically developed based on calendar cycles or simple usage experience, lacking a direct correlation with actual impact intensity, environmental exposure, and door opening and closing frequency. This approach can easily lead to situations where some vehicles are used beyond their limits under high load and harsh conditions without timely maintenance, increasing potential quality risks; on the other hand, some vehicles are over-cleaned under milder conditions, causing unnecessary downtime and resource waste. Overall, while existing pallet and cleanroom trolley systems are relatively mature in terms of physical protection for cleanroom transportation, they lack a technical means to quantify, record, and correlate the mechanical and environmental conditions during transportation with batches and transport vehicles. Therefore, existing technologies for the cleanroom transportation of high-precision plastic optical components have the following objective shortcomings: First, the transportation process lacks quantitative monitoring and recording of impacts, vibrations, and attitude instability, making it difficult to identify and trace abnormal transportation events in a timely manner; second, there is a lack of online measurement of environmental exposure factors such as internal temperature and humidity of the vehicle, door opening and closing behavior, and particle mutations, making it impossible to accurately assess the specific impact of environmental anomalies on quality; and third, batch quality risks and vehicle maintenance strategies are disconnected from the actual working conditions of the transportation process, making it difficult to form data-driven risk classification and maintenance decisions. Summary of the Invention

[0005] The purpose of this invention is to provide a dust-free transport vehicle for high-precision plastic mold optical components, so as to solve the technical problems mentioned in the background art.

[0006] Based on the above ideas, the present invention provides the following technical solution: A cleanroom transport vehicle for high-precision plastic mold optical components includes: The system comprises a housing, a carrying cavity disposed within the housing, a pallet positioning assembly for carrying and positioning a pallet, a door for closing the carrying cavity, an acceleration sensor, a tilt sensor, a temperature and humidity sensor, a door opening / closing sensor, a particle sensing module, and a control module electrically connected to the sensors and the particle sensing module. The control module is communicatively connected to a host system and is configured to execute a control flow including the following when the cleanroom transport vehicle is used for handling high-precision plastic optical components: S1. Place the tray loaded with high-precision plastic optical components on the tray positioning assembly, and bind the tray with the corresponding batch number and process route information by means of the QR code or radio frequency tag set on the tray; S2. After the pallet is loaded, close the hatch to position the pallet in the positioning and clamping state within the bearing cavity. The handling equipment drives the cleanroom transport vehicle to transport the pallet between multiple stations of injection molding, cleaning, annealing, coating and testing. S3. When the cleanroom transport vehicle is parked at any workstation, the operator is allowed to open the door and pick up and put down the pallet to complete the corresponding process. After the process is completed, the pallet is put back and the door is closed to realize the multiple turnover of high-precision plastic optical components between multiple workstations. S4. During the transportation and docking process of the cleanroom transport vehicle, the instantaneous composite acceleration along at least two orthogonal directions is collected using an acceleration sensor, the attitude deflection angle of the pallet plane relative to the horizontal plane is collected using an tilt sensor, and the opening and closing status of the hatch is recorded using a hatch opening and closing sensor. At the end of each transportation section, the impact risk index characterizing the mechanical stress and attitude stability of the transportation section is calculated based on the composite acceleration, attitude deflection angle and hatch opening time. S5. During the transportation and docking process of the clean transport vehicle, the relative humidity in the bearing cavity and the duration of exceeding the preset upper limit are collected by the temperature and humidity sensor. Combined with the proportion of time the hatch is open and the docking time, and optionally combined with the particle concentration change event detected by the particle sensing module, the environmental exposure index characterizing the risk of optical surface exposure pollution in the transportation section is calculated. S6. When the cleanroom transport vehicle stops at any target workstation, the impact risk index, environmental exposure index, and corresponding basic sensor data within the transport section are uploaded to the host system via wired or wireless means. The host system then classifies and marks the corresponding batches according to the relationship between the impact risk index and environmental exposure index and preset thresholds. Based on the accumulated impact risk index, environmental exposure index, number of door openings and closings, total transport time, and other data across multiple transport sections, the cleaning and maintenance strategy for the cleanroom transport vehicle is updated. By integrating a carrying chamber, pallet positioning components, hatches, and various status sensors into a cleanroom transport vehicle, and having the control module work in conjunction with the upper-level system, the vehicle is upgraded from a passive material container into an active unit capable of sensing, recording, uploading, and managing. This way, every turn of the optical components between different workstations creates a complete transport status record, allowing for tracing back to the specific transport section and abnormal operating condition when yield anomalies occur. Simultaneously, the upper-level system can classify batches according to risk level, concentrating stricter cleaning and re-inspection on high-risk batches, reducing overall quality control costs. For the vehicle itself, cleaning and maintenance cycles can be dynamically adjusted based on historical usage, making cleanroom transport capabilities more stable and economical throughout its entire lifecycle.

[0007] Preferably, step S4, when calculating the impact risk index, further includes: The instantaneous synthetic acceleration signal collected by the accelerometer within a single transport section is frequency filtered and envelope processed to obtain the impact acceleration signal of that transport section; the number of effective impacts with peak impact acceleration exceeding a preset impact threshold is counted; the attitude deviation angle time curve of the pallet plane is obtained based on the output of the tilt sensor, and the proportion of attitude instability time when the attitude deviation angle exceeds a preset attitude threshold is calculated; the control module uses the impact acceleration signal, the number of effective impacts, and the proportion of attitude instability time as input quantities to comprehensively characterize the mechanical stress risk and attitude instability degree of that transport section.

[0008] By performing frequency filtering and envelope processing on the acceleration signal, and introducing the proportion of effective impact counts and attitude instability time, the mechanical state within a single transport interval is refined from the absence of vibration to the magnitude of the impact, the number of times it occurs, and the attitude under which it occurs. This processing method can, on the one hand, distinguish the different effects of a single large impact and multiple medium impacts on optical components and the pallet structure, reflecting the cumulative effect of fatigue; on the other hand, it can explicitly quantify attitude anomalies such as prolonged tilting or swaying, avoiding slow displacement and stress concentration of optical components caused by attitude deviations of the pallet that are difficult to detect with the naked eye, thereby reducing misjudgments and omissions and improving the accuracy of mechanical risk identification.

[0009] Preferably, the method for calculating the impact risk index is as follows: Within a single transport interval, the instantaneous composite acceleration after frequency filtering is collected at a sampling period Δt, and the composite acceleration at the i-th sampling point is denoted as a. i The duration of this transport section is T, the number of sampling points is N, and the equivalent impact acceleration is defined as: ; Where m is a real number greater than 1; The impact risk index X is calculated using the following formula: ; in, Used to characterize the amplification effect of equivalent impact energy relative to a reference level. Used to characterize the fatigue damage risk caused by multiple impacts. It is used to characterize the comprehensive risk of being subjected to gravity and impact under attitude instability, and reflects the overall mechanical risk level of the transportation section to the high-precision plastic optical components in the pallet through the multiplicative coupling of the three. Effective impact count b is the number of peak values ​​of the combined acceleration exceeding the impact judgment threshold within the transport section; attitude instability time percentage c is the percentage of time within the transport section where the pallet plane attitude deflection angle exceeds the attitude threshold; a ref b ref c refThese are the pre-calibrated reference equivalent impact acceleration, reference impact number, and reference attitude instability time percentage, respectively; k0, λ1, λ2, α, β1, and β2 are coefficients preset according to the allowable mechanical stress level of high-precision plastic optical components.

[0010] By combining equivalent impact acceleration, effective impact count, and the proportion of attitude instability time in a nonlinear, multiplicative manner to construct an impact risk index, this index more closely reflects the actual mechanism of material stress and fatigue damage. The power-law form of the equivalent impact acceleration reflects the amplification effect of impact energy on sensitive components; the power-law form of multiple impact counts amplifies the cumulative damage caused by frequent impacts; and the multiplicative introduction of the proportion of attitude instability time emphasizes the danger of impacts under non-horizontal attitudes. These three factors, through their coupling relationship, jointly determine the magnitude of the risk, clearly highlighting the most concerning extreme conditions such as large impacts, high impact counts, and attitude instability. Furthermore, each parameter and coefficient can be calibrated experimentally, facilitating matching and optimization across different product lines.

[0011] Preferably, when calculating the environmental exposure index, step S5 further includes: Within a single transport interval, the time average relative humidity in the carrying cavity and the percentage of time the relative humidity exceeds the target humidity limit are calculated using temperature and humidity sensors. The percentage of time the hatch is open is calculated using a door opening and closing sensor. Optionally, the number of abrupt changes in particle concentration relative to the baseline value is calculated using a particle sensing module. The exposure and contamination risk of the optical components in this transport interval is characterized by the time average relative humidity, the percentage of time the humidity exceeds the limit, the percentage of time the hatch is open, and the number of abrupt changes in particle concentration.

[0012] By combining metrics such as the temporal average of relative humidity, the percentage of time exceeding humidity limits, the percentage of time the door was opened, and the number of sudden changes in particle concentration, previously vague environmental perceptions are transformed into calculable environmental exposure risks. This not only distinguishes between short-term, occasional humidity fluctuations and long-term, chronic humidity exceeding limits, but also visually reflects irregular practices such as prolonged door opening. Simultaneously, it records particle concentration fluctuations caused by events such as localized dust generation, improper wiping, or the instantaneous introduction of outside air. This transforms both humidity and particle issues from experience-based judgments into data-driven indicators, facilitating refined management on the production line.

[0013] Preferably, the cleaning load index is calculated according to the following equation: ; Among them, h ex h represents the average extent to which relative humidity exceeds the target upper limit. ex =max(0,RH avg RH ref ), RHavg The relative humidity (RH) inside the cavity is the time-averaged value. ref The upper limit of the target relative humidity is preset based on the hygroscopic properties of the optical component materials; τ h For relative humidity exceeding RH ref The percentage of duration; τ o d represents the percentage of time the hatch is open; d represents the number of abrupt changes in particle concentration detected by the particle sensing module relative to the baseline value; q0, η, μ1, μ2, μ3, and γ1 are preset coefficients based on the sensitivity of the high-precision plastic optical component surface to humidity and particles. exp(η h ex This is used to simulate the exponential amplification effect of excessive relative humidity on the risk of moisture absorption and coating failure. Used to amplify the chronic cumulative effect of prolonged excessive humidity. The method is used to simulate the trend of the diffusion path of water vapor and particles increasing with the square root of time under the condition of open hatch. ln(1+d) is used to reflect the marginal diminishing effect of the number of particle mutation events on pollution risk. The overall environmental exposure risk Y is formed by the multiplicative coupling of the above multifactors.

[0014] The environmental exposure index employs non-linear calculations such as exponential, power, square root, and logarithmic methods to ensure that the contribution of humidity exceedance magnitude, duration of exceedance, door opening exposure behavior, and particle mutation events to overall risk more closely aligns with actual physical and diffusion process laws. The humidity exceedance magnitude is amplified through an exponential function, forcing the system to more strictly control critical humidity ranges; the duration of humidity exceedance is processed using a power function to highlight the risk of long-term, mild exceedances, preventing it from being diluted by simple averaging; the door opening time is incorporated into the calculation in square root form, more closely reflecting the characteristics of water vapor and particle diffusion over time; and the number of particle mutations is represented by a logarithmic function to reflect the diminishing marginal effect of a significant impact from zero to significant impact, and the limited impact of multiple occurrences. Overall, this index demonstrates high sensitivity to critical environmental anomalies while avoiding overreaction to minor, transient disturbances.

[0015] Preferably, after receiving impact risk indicators and environmental exposure indicators from multiple transportation sections, S6 specifically performs management steps including the following: The impact risk index of each transportation section is compared with the preset impact risk threshold, and the corresponding transportation section is marked as the normal impact risk range or the impact risk exceeding the limit range; the environmental exposure index of each transportation section is compared with the preset environmental exposure threshold, and the corresponding transportation section is marked as the normal environmental exposure range or the environmental exposure exceeding the limit range. For transport sections that are simultaneously marked as exceeding the limits for impact risk and environmental exposure, statistics are compiled. Batches containing these transport sections are marked as batches requiring key re-inspection or high-risk batches. Color or icon prompts are displayed on the operating terminal at the destination workstation to instruct operators to perform stricter cleaning or testing operations on these batches. The impact risk indicators, environmental exposure indicators, number of door openings and closings, and total transportation time of the same cleanroom transport vehicle in multiple transport zones are accumulated to generate corresponding vehicle usage status records. When the accumulated results exceed the maintenance threshold set for the vehicle, the production plan restricts the vehicle from being assigned new tasks and prompts the vehicle to be sent to the cleaning or maintenance station.

[0016] By comparing thresholds, classifying zones, and accumulating cross-zone risks for mechanical and environmental risks across multiple transportation zones in the upper-level system, a two-tiered risk control and maintenance decision-making system for both batches and vehicles is achieved. For batches, batches containing high-risk zones can be automatically marked as requiring key re-inspection or as high-risk batches, prompting operators to add cleaning or testing procedures, thereby concentrating resources on the highest-risk materials. For vehicles, risk information accumulated during long-term operation is converted into usage status records to determine whether preset maintenance or shutdown thresholds have been reached. This allows cleaning and maintenance to be dynamically executed based on actual load and risk levels, rather than relying on subjective experience and fixed schedules, thus improving the overall controllability and consistency of the production line.

[0017] Preferably, the comprehensive optimization index Z is calculated using the following formula: ; The above formula is a comprehensive optimization index based on the impact risk index and the environmental exposure index, which is used to conduct a unified assessment of different batches and different cleanroom transport vehicles. Among them, X n =X / X ref The impact risk index X for this transportation section is relative to the reference impact level X. ref The normalized value; Y n =Y / Y ref , which is the environmental exposure index Y of the transportation section relative to the reference environmental exposure level Y. ref The normalized value; α z β z κ and λ are the tradeoff coefficients and nonlinear exponents that are greater than 0; ρ is the coupling coefficient characterizing the degree of coupling between shock risk and environmental exposure risk; The comprehensive risk value of a batch is obtained by weighted summation or taking the maximum value of the comprehensive optimization indicators obtained in multiple transportation sections for the same batch; the comprehensive load value of a cleanroom transport vehicle is obtained by weighted summation or taking the maximum value of the comprehensive optimization indicators obtained in multiple transportation sections for the same cleanroom transport vehicle. The comprehensive risk value of the batch is used to determine whether the batch should be subject to stricter cleaning or random inspection, and the comprehensive load value of the cleanroom transport vehicle is used to determine the cleaning and maintenance cycle and shutdown conditions of the cleanroom transport vehicle.

[0018] By normalizing mechanical and environmental risks and constructing a comprehensive optimization index, these two different dimensions of risk are mapped to a unified evaluation scale. Nonlinearity and coupling terms are introduced, enabling the comprehensive index to reflect both significant increases in a single risk and the synergistic failure risk when both mechanical and environmental factors deteriorate simultaneously. The comprehensive index employs an exponential mapping, compressing the risk energy from multiple factors into a fixed range, facilitating the setting of unified batch release and vehicle maintenance thresholds. Furthermore, by weighted summation or maximization of the comprehensive index across multiple transportation intervals, the overall risk level of a batch throughout the entire transportation process and the comprehensive load level of a vehicle throughout its entire service life can be obtained. This provides a unified, intuitive, and quantifiable decision-making basis for quality control and equipment management.

[0019] The technical solution of the present invention may include the following beneficial effects: This invention integrates multiple status sensors, control modules, and communication mechanisms with a host system into a cleanroom transport vehicle, transforming the originally passive pallets and trolleys into active, perceptible, recordable, and traceable vehicles. This achieves transparency and information closure for the status of high-precision plastic optical components throughout the entire turnover process. On one hand, key operating conditions such as impact, tilting, door opening and closing, temperature and humidity, and particle events are continuously collected and recorded in a structured manner according to transport intervals. When abnormal yields or local contamination are detected, the corresponding interval and abnormality type can be accurately traced, avoiding the crude approach of relying on experience to guess the process and large-scale re-cleaning. On the other hand, batch information is linked to transport health records, shifting the location of quality problems from post-event statistics to process monitoring, significantly improving the controllability and interpretability of the production process. Furthermore, by constructing impact risk indicators and environmental exposure indicators with physical meaning and nonlinear coupling characteristics, this invention comprehensively quantifies multiple factors such as mechanical stress, attitude instability, cumulative impacts, excessive humidity, exposure duration, door opening behavior, and particle mutation events into a calculable risk measure. Compared to simple threshold judgment or linear weighting, the risk indicators of this invention can amplify the impact of extreme working conditions and long-term mild anomalies, highlighting the most unfavorable scenarios for optical components, such as large impacts, multiple impacts, attitude instability, continuous high humidity, long-term door opening accompanied by particle mutations, etc., making risk assessment more consistent with the actual mechanisms of material fatigue, moisture absorption, and pollution diffusion. By normalizing and comprehensively optimizing the risk indicators for different transportation sections, different batches, and different vehicles, this invention provides a unified, calibrable, and adjustable quantitative tool for quality control and equipment management. By leveraging the aforementioned risk indicators, a coordinated decision-making process at the batch and vehicle levels is achieved in the upper-level system: For batches, the system determines whether to implement stricter cleaning or add testing items based on the comprehensive risk classification, ensuring precise allocation of testing resources to high-risk batches; for vehicles, the cleaning and maintenance cycle is dynamically adjusted based on the long-term accumulated risk load and operating conditions, automatically restricting the vehicle from continuing production and triggering maintenance procedures when a set threshold is reached. Thus, the original manual judgment based on experience and fixed schedules is replaced by objective decision-making based on data and rules, reducing unnecessary over-cleaning and over-inspection costs, and minimizing batch quality incidents caused by maintenance delays. This ensures higher reliability and economy in the cleanroom transportation of high-precision plastic optical components throughout their entire lifecycle. Attached Figure Description

[0020] Figure 1 This is a flowchart of a dust-free transport vehicle for high-precision plastic mold optical components according to the present invention. Detailed Implementation

[0021] Example 1 like Figure 1 , A cleanroom transport vehicle for high-precision plastic mold optical components includes: The system comprises a housing, a carrying cavity disposed within the housing, a pallet positioning assembly for carrying and positioning a pallet, a door for closing the carrying cavity, an acceleration sensor, a tilt sensor, a temperature and humidity sensor, a door opening / closing sensor, a particle sensing module, and a control module electrically connected to the sensors and the particle sensing module. The control module is communicatively connected to a host system and is configured to execute a control flow including the following when the cleanroom transport vehicle is used for handling high-precision plastic optical components: S1. Place the tray loaded with high-precision plastic optical components on the tray positioning assembly, and bind the tray with the corresponding batch number and process route information by means of the QR code or radio frequency tag set on the tray; S2. After the pallet is loaded, close the hatch to position the pallet in the positioning and clamping state within the bearing cavity. The handling equipment drives the cleanroom transport vehicle to transport the pallet between multiple stations of injection molding, cleaning, annealing, coating and testing. S3. When the cleanroom transport vehicle is parked at any workstation, the operator is allowed to open the door and pick up and put down the pallet to complete the corresponding process. After the process is completed, the pallet is put back and the door is closed to realize the multiple turnover of high-precision plastic optical components between multiple workstations. S4. During the transportation and docking process of the cleanroom transport vehicle, the instantaneous composite acceleration along at least two orthogonal directions is collected using an acceleration sensor, the attitude deflection angle of the pallet plane relative to the horizontal plane is collected using an tilt sensor, and the opening and closing status of the hatch is recorded using a hatch opening and closing sensor. At the end of each transportation section, the impact risk index characterizing the mechanical stress and attitude stability of the transportation section is calculated based on the composite acceleration, attitude deflection angle and hatch opening time. S5. During the transportation and docking process of the clean transport vehicle, the relative humidity in the bearing cavity and the duration of exceeding the preset upper limit are collected by the temperature and humidity sensor. Combined with the proportion of time the hatch is open and the docking time, and optionally combined with the particle concentration change event detected by the particle sensing module, the environmental exposure index characterizing the risk of optical surface exposure pollution in the transportation section is calculated. S6. When the cleanroom transport vehicle stops at any target workstation, the impact risk index, environmental exposure index, and corresponding basic sensor data within the transport section are uploaded to the host system via wired or wireless means. The host system then classifies and marks the corresponding batches according to the relationship between the impact risk index and environmental exposure index and preset thresholds. Based on the accumulated impact risk index, environmental exposure index, number of door openings and closings, total transport time, and other data across multiple transport sections, the cleaning and maintenance strategy for the cleanroom transport vehicle is updated. In one optional embodiment, the shell of the cleanroom transport vehicle is specifically formed by bending and welding 304 stainless steel plate, with external dimensions of approximately 800mm × 600mm × 900mm, forming a through-cavity. A pull-out tray guide rail assembly is fixed to the bottom of the cavity, and adjustable limit blocks are provided on both sides to limit the tray's translational and rotational freedom in the horizontal plane. A door with a transparent observation window is installed at the front of the shell. The door is connected to the shell by two hinges and is secured to the shell door frame by a locking handle. A silicone rubber sealing strip is affixed to the door frame contact point to ensure the cabin's airtightness. The door opening / closing sensor is a microswitch with a roller, installed inside the door frame. When the door is fully closed, the door body presses down the microswitch to output a closing signal; when the door deviates from the closed position by more than 5°, the microswitch automatically pops up to output an opening signal. The tray carrying high-precision plastic optical components is injection molded from anti-static PC material. An RFID tag or QR code is embedded in the bottom of the tray, with a unique identifier pre-written on the tag. An industrial-grade QR code camera assembly or RFID reader module is installed at the top inside the carrier. After the tray is inserted into the guide rail and positioned, the camera illuminates the bottom of the tray with LED lighting and captures the QR code image. The tray number is then decoded by a local decoding program; alternatively, the RFID reader reads the tag information. Upon receiving the tray number, the control module binds it to the batch number and process route number entered or scanned by the operator at the workstation terminal, generating an associated record in the local storage unit corresponding to step S1. The carrier is supported by four cleanroom-specific casters, two of which are equipped with foot brakes. The carrier can be manually pushed or towed by an automated guided vehicle (AGV) to facilitate transfer from the injection molding station to different stations such as cleaning, annealing, coating, and testing, specifically corresponding to S2. Each station is equipped with an operating terminal with wireless communication capabilities. Once the carrier is in position, the operator releases the door clamping handle and opens the door, extracting the pallet from the carrying chamber to complete the loading and unloading operation. The pallet is then pushed back and the door closed, specifically corresponding to S3. A control board is fixed to the upper inner side of the vehicle. This board integrates a microcontroller, analog-to-digital converter, wireless communication module, and local Flash memory. The control board collects output signals from the accelerometer, tilt sensor, temperature and humidity sensor, door opening / closing sensor, and particle sensing module via shielded cables. The control board samples the acceleration signal at a frequency of 200Hz, the tilt and temperature / humidity signals at 10Hz, and polls the door opening / closing status and particle sensor counts at 1Hz. The sampled data is stored in timestamp order as a "transportation interval buffer." When the vehicle is detected to switch from a moving state to a stationary state for more than 30 seconds, the previous buffer segment is marked as a complete transportation interval. The impact risk index and environmental exposure index for this interval are calculated locally and uploaded wirelessly to the host system, corresponding to steps S4 to S6. The mechanical structure module, pallet identification module, status acquisition module, and data upload module have clearly defined functions, allowing those skilled in the art to implement the system directly.

[0022] By integrating a carrying chamber, pallet positioning components, hatches, and various status sensors into a cleanroom transport vehicle, and having the control module work in conjunction with the upper-level system, the vehicle is upgraded from a passive material container into an active unit capable of sensing, recording, uploading, and managing. This way, every turn of the optical components between different workstations creates a complete transport status record, allowing for tracing back to the specific transport section and abnormal operating condition when yield anomalies occur. Simultaneously, the upper-level system can classify batches according to risk level, concentrating stricter cleaning and re-inspection on high-risk batches, reducing overall quality control costs. For the vehicle itself, cleaning and maintenance cycles can be dynamically adjusted based on historical usage, making cleanroom transport capabilities more stable and economical throughout its entire lifecycle.

[0023] Specifically, S4, when calculating the impact risk index, also includes: The instantaneous synthetic acceleration signal collected by the accelerometer within a single transport section is frequency filtered and envelope processed to obtain the impact acceleration signal of that transport section; the number of effective impacts with peak impact acceleration exceeding a preset impact threshold is counted; the attitude deviation angle time curve of the pallet plane is obtained based on the output of the tilt sensor, and the proportion of attitude instability time when the attitude deviation angle exceeds a preset attitude threshold is calculated; the control module uses the impact acceleration signal, the number of effective impacts, and the proportion of attitude instability time as input quantities to comprehensively characterize the mechanical stress risk and attitude instability degree of that transport section.

[0024] By performing frequency filtering and envelope processing on the acceleration signal, and introducing the proportion of effective impact counts and attitude instability time, the mechanical state within a single transport interval is refined from the absence of vibration to the magnitude of the impact, the number of times it occurs, and the attitude under which it occurs. This processing method can, on the one hand, distinguish the different effects of a single large impact and multiple medium impacts on optical components and the pallet structure, reflecting the cumulative effect of fatigue; on the other hand, it can explicitly quantify attitude anomalies such as prolonged tilting or swaying, avoiding slow displacement and stress concentration of optical components caused by attitude deviations of the pallet that are difficult to detect with the naked eye, thereby reducing misjudgments and omissions and improving the accuracy of mechanical risk identification.

[0025] The signal processing for calculating impact risk is handled by the control board firmware. The accelerometer is a triaxial MEMS-based accelerometer with a range of ±16g, mounted on a metal bracket near the tray positioning assembly to minimize measurement errors caused by structural resonance. The microcontroller reads the triaxial acceleration a from the accelerometer. x (t),a y (t),a z (t), calculate the instantaneous composite acceleration at each sampling point: ; To suppress high-frequency electrical noise and structural self-vibration, the firmware first passes the specific a(t)a(t)a(t)a(t) through a specific 5th order IIR low-pass filter and a specific 2nd order high-pass filter to form a specific 1Hz to 200Hz bandpass, retaining only the components related to transportation shock.

[0026] The filtered acceleration signal is sent to the envelope detection module, where a smooth impact envelope curve is obtained by calculating the square root average within a sliding window. The firmware uses a peak detection algorithm to identify local peaks. When a peak exceeds a preset impact threshold (e.g., 1.5g) and the interval between adjacent peaks is greater than 50ms, the peak is counted as a valid impact event and accumulated as the number of valid impacts. Simultaneously, the tilt sensor outputs the pitch and roll angles of the pallet plane relative to the horizontal plane. The firmware calculates the composite attitude deflection angle based on the two-axis tilt angles. When the composite deflection angle exceeds a preset attitude threshold (e.g., 3°), this moment is recorded as an attitude instability state, and the proportion of attitude instability time in the total transport interval is calculated to obtain the attitude instability time percentage. The impact envelope curve, the number of valid impacts, and the attitude instability time percentage are used as inputs for the next step of risk formula calculation.

[0027] Specifically, the shock risk index is calculated as follows: Within a single transport interval, the instantaneous composite acceleration after frequency filtering is collected at a sampling period Δt, and the composite acceleration at the i-th sampling point is denoted as a.i The duration of this transport section is T, the number of sampling points is N, and the equivalent impact acceleration is defined as: ; Where m is a real number greater than 1; The shock risk index X is calculated using the following formula: ; in, Used to characterize the amplification effect of equivalent impact energy relative to a reference level. Used to characterize the fatigue damage risk caused by multiple impacts. It is used to characterize the comprehensive risk of being subjected to gravity and impact under attitude instability, and reflects the overall mechanical risk level of the transportation section to the high-precision plastic optical components in the pallet through the multiplicative coupling of the three. Effective impact count b is the number of peak values ​​of the combined acceleration exceeding the impact judgment threshold within the transport section; attitude instability time percentage c is the percentage of time within the transport section where the pallet plane attitude deflection angle exceeds the attitude threshold; a ref b ref c ref These are the pre-calibrated reference equivalent impact acceleration, reference impact number, and reference attitude instability time percentage, respectively; k0, λ1, λ2, α, β1, and β2 are coefficients preset according to the allowable mechanical stress level of high-precision plastic optical components.

[0028] The calculation of the impact risk index is performed by the control board after each transport interval. The control board first reads the synthesized acceleration sequence |a, which has been bandpass filtered and absolute value processed, from the buffer of this interval. i |, i is the sampling sequence number, and the sampling period Δt is automatically counted by the system clock, for example, 5ms, which corresponds to a sampling frequency of 200Hz.

[0029] The effective number of impacts is given by the aforementioned peak detection module, and the percentage of attitude instability time is given by the tilt angle statistics module. The reference equivalent impact acceleration, reference number of impacts, and reference percentage of attitude instability time are obtained through test calibration during the equipment manufacturing or process introduction stage: for example, a set of typical transportation conditions that have been verified not to cause damage to optical components are selected, and the three indicators calculated under these conditions are used as reference values.

[0030] The calculation results are recorded as the impact risk index for this transportation section. Mechanistically, the ratio of equivalent acceleration to the reference value reflects the overall impact energy level; the power factor amplifies the energy increase numerically. The ratio of the number of impacts to the reference number describes the impact frequency; the power factor causes the contribution of multiple impacts to fatigue damage to exceed linear superposition. The ratio of the proportion of attitude instability to the reference value describes the duration of impact on the pallet under adverse posture; the multiplicative term amplifies the combination of high impact and attitude instability numerically. Those skilled in the art can adjust the parameters according to the impact resistance limit of the actual product to ensure the formula matches the measured damage data.

[0031] By combining equivalent impact acceleration, effective impact count, and the proportion of attitude instability time in a nonlinear, multiplicative manner to construct an impact risk index, this index more closely reflects the actual mechanism of material stress and fatigue damage. The power-law form of the equivalent impact acceleration reflects the amplification effect of impact energy on sensitive components; the power-law form of multiple impact counts amplifies the cumulative damage caused by frequent impacts; and the multiplicative introduction of the proportion of attitude instability time emphasizes the danger of impacts under non-horizontal attitudes. These three factors, through their coupling relationship, jointly determine the magnitude of the risk, clearly highlighting the most concerning extreme conditions such as large impacts, high impact counts, and attitude instability. Furthermore, each parameter and coefficient can be calibrated experimentally, facilitating matching and optimization across different product lines.

[0032] Specifically, when S5 calculates the environmental exposure index, it also includes: Within a single transport interval, the time average relative humidity in the carrying cavity and the percentage of time the relative humidity exceeds the target humidity limit are calculated using temperature and humidity sensors. The percentage of time the hatch is open is calculated using a door opening and closing sensor. Optionally, the number of abrupt changes in particle concentration relative to the baseline value is calculated using a particle sensing module. The exposure and contamination risk of the optical components in this transport interval is characterized by the time average relative humidity, the percentage of time the humidity exceeds the limit, the percentage of time the hatch is open, and the number of abrupt changes in particle concentration.

[0033] The temperature and humidity sensor uses an integrated digital temperature and humidity chip, installed on the top side wall of the carrying chamber. The probe is exposed to the airflow inside the chamber through a small opening. The control board reads the relative humidity output by the sensor at a frequency of 10Hz and accumulates the humidity values ​​for each transport interval. The opening and closing status of the hatch is given by a microswitch signal. When the switch is in the "open" state, the firmware accumulates this time period as the hatch opening time; when the switch is in the "closed" state, it accumulates this time period as the hatch closing time. The sum of the two is the total time of the interval. During the process implementation phase, the system sets a target relative humidity upper limit, such as 40%, based on the moisture absorption sensitivity of the optical component materials and coatings. After each transport interval, the firmware calculates the time-averaged relative humidity for that interval, as well as the proportion of time the humidity exceeds the upper limit, and the proportion of time the hatch is open. The particle sensing module can use a light-scattering particle counter or a simple dust monitoring sensor. When the particle count per unit time increases by more than a set multiple relative to a baseline value over a period of time, this event is counted as a mutation event, and the firmware accumulates the number of mutations within that interval. The calculated average humidity, the proportion of time the humidity exceeds the limit, the proportion of time the hatch is open, and the number of particle mutations are used as inputs to the environmental exposure formula.

[0034] By combining metrics such as the temporal average of relative humidity, the percentage of time exceeding humidity limits, the percentage of time the door was opened, and the number of sudden changes in particle concentration, previously vague environmental perceptions are transformed into calculable environmental exposure risks. This not only distinguishes between short-term, occasional humidity fluctuations and long-term, chronic humidity exceeding limits, but also visually reflects irregular practices such as prolonged door opening. Simultaneously, it records particle concentration fluctuations caused by events such as localized dust generation, improper wiping, or the instantaneous introduction of outside air. This transforms both humidity and particle issues from experience-based judgments into data-driven indicators, facilitating refined management on the production line.

[0035] Specifically, the cleaning load index is calculated according to the following equation: ; Among them, h ex h represents the average extent to which relative humidity exceeds the target upper limit. ex =max(0,RH avg RH ref ), RH avg The relative humidity (RH) inside the cavity is the time-averaged value. ref The upper limit of the target relative humidity is preset based on the hygroscopic properties of the optical component materials; τ h For relative humidity exceeding RH ref The percentage of duration; τ o d represents the percentage of time the hatch is open; d represents the number of abrupt changes in particle concentration detected by the particle sensing module relative to the baseline value; q0, η, μ1, μ2, μ3, and γ1 are preset coefficients based on the sensitivity of the high-precision plastic optical component surface to humidity and particles. exp(η h ex This is used to simulate the exponential amplification effect of excessive relative humidity on the risk of moisture absorption and coating failure. Used to amplify the chronic cumulative effect of prolonged excessive humidity. The method is used to simulate the trend of the diffusion paths of water vapor and particles increasing with the square root of time under the condition of open hatch. ln(1+d) is used to reflect the marginal diminishing effect of the number of particle mutation events on pollution risk. The overall environmental exposure risk Y is formed by the multiplicative coupling of the above multifactors.

[0036] At the end of the interval, the control panel calculates environmental exposure indicators based on the aforementioned statistics. First, the time-average relative humidity (RH) is calculated from the humidity statistics. avg Target humidity upper limit RH ref Stored in the configuration table, for example, 40%. If the average value is less than or equal to the upper limit, the excess range h is... ex Set to 0; if it is greater than the upper limit, then h ex It equals the difference between the two. The percentage of time the humidity exceeds the standard is determined by the ratio of the time exceeding the standard to the total time; the percentage of time the hatch is open is determined by the ratio of the opening time to the total time; the number of particle mutations is given by the particle sensor count.

[0037] The coefficients were determined through comparative experiments during the process implementation phase. For example, a series of different combinations of humidity and door opening time were selected, and indicators such as surface haze of optical components and water absorption and expansion of the coating layer were observed. Based on this, a set of coefficients was fitted to make the risk value highly correlated with the actual defect probability. Mechanistically, the magnitude of humidity exceeding the standard was amplified through an exponential function to simulate the sensitivity of the material's moisture absorption rate to the humidity driving force; the duration of humidity exceeding the standard was included in the calculation in a power-law form to enhance the contribution of long-term mild exceeding the standard; the door opening time was included in the square root form to reflect the sublinear growth characteristics of water vapor and particle diffusion over time; the number of particle mutations was included through a logarithmic function to capture the change from nothing to something without excessively amplifying the subsequent cumulative number of times. In this embodiment, the calculation was completed on the control board, and only the environmental exposure results and necessary statistics were uploaded to the upper system to reduce the amount of wireless data transmission.

[0038] The environmental exposure index employs non-linear calculations such as exponential, power, square root, and logarithmic methods to ensure that the contribution of humidity exceedance magnitude, duration of exceedance, door opening exposure behavior, and particle mutation events to overall risk more closely aligns with actual physical and diffusion process laws. The humidity exceedance magnitude is amplified through an exponential function, forcing the system to more strictly control critical humidity ranges; the duration of humidity exceedance is processed using a power function to highlight the risk of long-term, mild exceedances, preventing it from being diluted by simple averaging; the door opening time is incorporated into the calculation in square root form, more closely reflecting the characteristics of water vapor and particle diffusion over time; and the number of particle mutations is represented by a logarithmic function to reflect the diminishing marginal effect of a significant impact from zero to significant impact, and the limited impact of multiple occurrences. Overall, this index demonstrates high sensitivity to critical environmental anomalies while avoiding overreaction to minor, transient disturbances.

[0039] Specifically, after receiving impact risk indicators and environmental exposure indicators from multiple transportation sections, S6 performs management steps including the following: The impact risk index of each transportation section is compared with the preset impact risk threshold, and the corresponding transportation section is marked as the normal impact risk range or the impact risk exceeding the limit range; the environmental exposure index of each transportation section is compared with the preset environmental exposure threshold, and the corresponding transportation section is marked as the normal environmental exposure range or the environmental exposure exceeding the limit range. For transport sections that are simultaneously marked as exceeding the limits for impact risk and environmental exposure, statistics are compiled. Batches containing these transport sections are marked as batches requiring key re-inspection or high-risk batches. Color or icon prompts are displayed on the operating terminal at the destination workstation to instruct operators to perform stricter cleaning or testing operations on these batches. The impact risk indicators, environmental exposure indicators, number of door openings and closings, and total transportation time of the same cleanroom transport vehicle in multiple transport zones are accumulated to generate corresponding vehicle usage status records. When the accumulated results exceed the maintenance threshold set for the vehicle, the production plan restricts the vehicle from being assigned new tasks and prompts the vehicle to be sent to the cleaning or maintenance station.

[0040] The upper-level system is implemented by the risk management module on the manufacturing execution system server. This module receives impact risk indicators, environmental exposure indicators, and associated pallet numbers and batch numbers uploaded by each cleanroom transport vehicle according to the transport interval. The system maintains a transport interval record table with fields including: vehicle number, pallet number, batch number, process route number, start time, end time, impact risk indicators, environmental exposure indicators, percentage of time with door open, and percentage of time with humidity exceeding the limit.

[0041] During batch risk assessment, the system aggregates all relevant transportation intervals by batch number, compares the impact risk indicators of each interval with preset impact thresholds, and marks intervals exceeding the thresholds as impact exceedance intervals. Similarly, it compares environmental exposure indicators with preset environmental thresholds, and marks intervals exceeding the thresholds as environmental exceedance intervals. If a batch contains any environmental exceedance or impact exceedance interval, it is marked as a batch requiring key re-inspection. If multiple impact exceedances and multiple environmental exceedances exist simultaneously, it is marked as a high-risk batch, and additional testing procedures are automatically inserted for this batch in the scheduling plan of the testing station.

[0042] In vehicle maintenance decision-making, the system aggregates all transport segments participated in by the vehicle number, and performs simple weighting or cumulative summation of the impact risk indicators and environmental exposure indicators for each segment using a comprehensive indicator (described later) to form a cumulative risk curve that grows over time. Simultaneously, it tracks the cumulative number of hatch openings and closings and the total transport time for the vehicle. When any of the cumulative risk value, number of openings and closings, or total transport time exceeds the configured maintenance threshold, the system marks the vehicle as requiring maintenance in the production schedule, prohibits it from being assigned new transport tasks, and generates a cleaning or repair work order at the maintenance terminal.

[0043] By comparing thresholds, classifying zones, and accumulating cross-zone risks for mechanical and environmental risks across multiple transportation zones in the upper-level system, a two-tiered risk control and maintenance decision-making system for both batches and vehicles is achieved. For batches, batches containing high-risk zones can be automatically marked as requiring key re-inspection or as high-risk batches, prompting operators to add cleaning or testing procedures, thereby concentrating resources on the highest-risk materials. For vehicles, risk information accumulated during long-term operation is converted into usage status records to determine whether preset maintenance or shutdown thresholds have been reached. This allows cleaning and maintenance to be dynamically executed based on actual load and risk levels, rather than relying on subjective experience and fixed schedules, thus improving the overall controllability and consistency of the production line.

[0044] Specifically, the comprehensive optimization index Z is calculated using the following formula: ; The above formula is a comprehensive optimization index based on the impact risk index and the environmental exposure index, which is used to conduct a unified assessment of different batches and different cleanroom transport vehicles. Among them, X n =X / X ref The impact risk index X for this transportation section is relative to the reference impact level X. ref The normalized value; Y n =Y / Y ref , which is the environmental exposure index Y of the transportation section relative to the reference environmental exposure level Y. ref The normalized value; α z β z κ and λ are the tradeoff coefficients and nonlinear exponents that are greater than 0; ρ is the coupling coefficient characterizing the degree of coupling between shock risk and environmental exposure risk; The comprehensive risk value of a batch is obtained by weighted summation or taking the maximum value of the comprehensive optimization indicators obtained in multiple transportation sections for the same batch; the comprehensive load value of a cleanroom transport vehicle is obtained by weighted summation or taking the maximum value of the comprehensive optimization indicators obtained in multiple transportation sections for the same cleanroom transport vehicle. The comprehensive risk value of the batch is used to determine whether the batch should be subject to stricter cleaning or random inspection, and the comprehensive load value of the cleanroom transport vehicle is used to determine the cleaning and maintenance cycle and shutdown conditions of the cleanroom transport vehicle.

[0045] The risk management module of the upper-level system normalizes the impact risk and environmental exposure risk and calculates a comprehensive optimization index. The system configures reference impact levels and reference environmental exposure levels, for example, based on the quantile value corresponding to 80% of normal operating conditions in long-term statistics. For each transportation section, the system divides the impact risk index by the reference impact level to obtain the normalized impact risk, and divides the environmental exposure index by the reference exposure level to obtain the normalized environmental risk.

[0046] This yields the comprehensive optimization index. Parameter α z ,β z The parameters κ, λ, and ρ can be determined by fitting the statistical relationship between the defect rate of historical batches and the corresponding X and Y combinations, so that the comprehensive index has a high correlation with the actual defect rate. The system takes the maximum value of the comprehensive index obtained for each batch across all transportation intervals or accumulates it over time as the comprehensive risk for that batch; it also accumulates the comprehensive index of each vehicle across all transportation intervals over time or averages it over the number of intervals as the comprehensive load for that vehicle. When making decisions, the system compares the comprehensive risk of the batch with the preset batch release threshold and the comprehensive load of the vehicle with the maintenance threshold, thereby automatically determining whether the batch needs stricter processing and whether the vehicle needs to enter the maintenance process. Those skilled in the art can use common data processing and statistical analysis libraries to complete the relevant calculations and interface display.

[0047] By normalizing mechanical and environmental risks and constructing a comprehensive optimization index, these two different dimensions of risk are mapped to a unified evaluation scale. Nonlinearity and coupling terms are introduced, enabling the comprehensive index to reflect both significant increases in a single risk and the synergistic failure risk when both mechanical and environmental factors deteriorate simultaneously. The comprehensive index employs an exponential mapping, compressing the risk energy from multiple factors into a fixed range, facilitating the setting of unified batch release and vehicle maintenance thresholds. Furthermore, by weighted summation or maximization of the comprehensive index across multiple transportation intervals, the overall risk level of a batch throughout the entire transportation process and the comprehensive load level of a vehicle throughout its entire service life can be obtained. This provides a unified, intuitive, and quantifiable decision-making basis for quality control and equipment management.

Claims

1. A dust-free transport vehicle for high-precision optical components of plastic molds, characterized in that, include: The system comprises a housing, a carrying cavity disposed within the housing, a pallet positioning assembly for carrying and positioning a pallet, a door for closing the carrying cavity, an acceleration sensor, a tilt sensor, a temperature and humidity sensor, a door opening / closing sensor, a particle sensing module, and a control module electrically connected to the sensors and the particle sensing module. The control module is communicatively connected to a host system and is configured to execute a control flow including the following when the cleanroom transport vehicle is used for handling high-precision plastic optical components: S1. Place the tray loaded with high-precision plastic optical components on the tray positioning assembly, and bind the tray with the corresponding batch number and process route information by means of the QR code or radio frequency tag set on the tray; S2. After the pallet is loaded, close the hatch to position the pallet in the positioning and clamping state within the bearing cavity. The handling equipment drives the cleanroom transport vehicle to transport the pallet between multiple stations of injection molding, cleaning, annealing, coating and testing. S3. When the cleanroom transport vehicle is parked at any workstation, the operator is allowed to open the door and pick up and put down the pallet to complete the corresponding process. After the process is completed, the pallet is put back and the door is closed to realize the multiple turnover of high-precision plastic optical components between multiple workstations. S4. During the transportation and docking process of the cleanroom transport vehicle, the instantaneous composite acceleration along at least two orthogonal directions is collected using an acceleration sensor, the attitude deflection angle of the pallet plane relative to the horizontal plane is collected using an tilt sensor, and the opening and closing status of the hatch is recorded using a hatch opening and closing sensor. At the end of each transportation section, the impact risk index characterizing the mechanical stress and attitude stability of the transportation section is calculated based on the composite acceleration, attitude deflection angle and hatch opening time. S5. During the transportation and docking process of the clean transport vehicle, the relative humidity in the bearing cavity and the duration of exceeding the preset upper limit are collected by the temperature and humidity sensor. Combined with the proportion of time the hatch is open and the docking time, and optionally combined with the particle concentration change event detected by the particle sensing module, the environmental exposure index characterizing the risk of optical surface exposure pollution in the transportation section is calculated. S6. When the cleanroom transport vehicle stops at any target workstation, the impact risk index, environmental exposure index, and corresponding basic sensor data within the transport section are uploaded to the host system via wired or wireless means. The host system then classifies and marks the corresponding batches according to the relationship between the impact risk index and environmental exposure index and preset thresholds. Based on the accumulated impact risk index, environmental exposure index, number of door openings and closings, total transport time, and other data across multiple transport sections, the cleaning and maintenance strategy for the cleanroom transport vehicle is updated.

2. The dust-free transport vehicle for high-precision plastic mold optical components according to claim 1, characterized in that, The S4 method, when calculating the impact risk index, also includes: The instantaneous synthetic acceleration signal collected by the accelerometer within a single transport section is frequency filtered and envelope processed to obtain the impact acceleration signal of that transport section; the number of effective impacts with peak impact acceleration exceeding a preset impact threshold is counted; the attitude deviation angle time curve of the pallet plane is obtained based on the output of the tilt sensor, and the proportion of attitude instability time when the attitude deviation angle exceeds a preset attitude threshold is calculated; the control module uses the impact acceleration signal, the number of effective impacts, and the proportion of attitude instability time as input quantities to comprehensively characterize the mechanical stress risk and attitude instability degree of that transport section.

3. The dust-free transport vehicle for high-precision plastic mold optical components according to claim 2, characterized in that, The method for calculating the aforementioned shock risk index is as follows: Within a single transport interval, the instantaneous composite acceleration after frequency filtering is collected at a sampling period Δt, and the composite acceleration at the i-th sampling point is denoted as a. i The duration of this transport section is T, the number of sampling points is N, and the equivalent impact acceleration is defined as: ； Where m is a real number greater than 1; The impact risk index X is calculated using the following formula: ； in, Used to characterize the amplification effect of equivalent impact energy relative to a reference level. Used to characterize the fatigue damage risk caused by multiple impacts. It is used to characterize the comprehensive risk of being subjected to gravity and impact under attitude instability, and reflects the overall mechanical risk level of the transportation section to the high-precision plastic optical components in the pallet through the multiplicative coupling of the three. Effective impact count b is the number of peak values ​​of the combined acceleration exceeding the impact judgment threshold within the transport section; attitude instability time percentage c is the percentage of time within the transport section where the pallet plane attitude deflection angle exceeds the attitude threshold; a ref b ref c ref These are the pre-calibrated reference equivalent impact acceleration, reference impact number, and reference attitude instability time percentage, respectively; k0, λ1, λ2, α, β1, and β2 are coefficients preset according to the allowable mechanical stress level of high-precision plastic optical components.

4. The dust-free transport vehicle for high-precision plastic mold optical components according to claim 3, characterized in that, When S5 calculates the environmental exposure index, it also includes: Within a single transport interval, the time average relative humidity in the carrying cavity and the percentage of time the relative humidity exceeds the target humidity limit are calculated using temperature and humidity sensors. The percentage of time the hatch is open is calculated using a door opening and closing sensor. Optionally, the number of abrupt changes in particle concentration relative to the baseline value is calculated using a particle sensing module. The exposure and contamination risk of the optical components in this transport interval is characterized by the time average relative humidity, the percentage of time the humidity exceeds the limit, the percentage of time the hatch is open, and the number of abrupt changes in particle concentration.

5. The dust-free transport vehicle for high-precision plastic mold optical components according to claim 4, characterized in that, The cleaning load index is calculated according to the following equation: ； Among them, h ex h represents the average extent to which relative humidity exceeds the target upper limit. ex =max(0,RH avg RH ref ), RH avg The relative humidity (RH) inside the cavity is the time-averaged value. ref The upper limit of the target relative humidity is preset based on the hygroscopic properties of the optical component materials; τ h For relative humidity exceeding RH ref The percentage of duration; τ o d represents the percentage of time the hatch is open; d represents the number of abrupt changes in particle concentration detected by the particle sensing module relative to the baseline value; q0, η, μ1, μ2, μ3, and γ1 are preset coefficients based on the sensitivity of the high-precision plastic optical component surface to humidity and particles. exp(η h ex This is used to simulate the exponential amplification effect of excessive relative humidity on the risk of moisture absorption and coating failure. Used to amplify the chronic cumulative effect of prolonged excessive humidity. The method is used to simulate the trend of the diffusion path of water vapor and particles increasing with the square root of time under the condition of open hatch. ln(1+d) is used to reflect the marginal diminishing effect of the number of particle mutation events on pollution risk. The overall environmental exposure risk Y is formed by the multiplicative coupling of the above multifactors.

6. The dust-free transport vehicle for high-precision plastic mold optical components according to claim 5, characterized in that, After receiving impact risk indicators and environmental exposure indicators from multiple transportation sections, S6 performs the following management steps: The impact risk index of each transportation section is compared with the preset impact risk threshold, and the corresponding transportation section is marked as the normal impact risk range or the impact risk exceeding the limit range; the environmental exposure index of each transportation section is compared with the preset environmental exposure threshold, and the corresponding transportation section is marked as the normal environmental exposure range or the environmental exposure exceeding the limit range. For transport sections that are simultaneously marked as exceeding the limits for impact risk and environmental exposure, statistics are compiled. Batches containing these transport sections are marked as batches requiring key re-inspection or high-risk batches. Color or icon prompts are displayed on the operating terminal at the destination workstation to instruct operators to perform stricter cleaning or testing operations on these batches. The impact risk indicators, environmental exposure indicators, number of door openings and closings, and total transportation time of the same cleanroom transport vehicle in multiple transport zones are accumulated to generate corresponding vehicle usage status records. When the accumulated results exceed the maintenance threshold set for the vehicle, the production plan restricts the vehicle from being assigned new tasks and prompts the vehicle to be sent to the cleaning or maintenance station.

7. The dust-free transport vehicle for high-precision plastic mold optical components according to claim 6, characterized in that, The comprehensive optimization index Z is calculated using the following formula: ； The above formula is a comprehensive optimization index based on the impact risk index and the environmental exposure index, which is used to conduct a unified assessment of different batches and different cleanroom transport vehicles. Among them, X n =X / X ref The impact risk index X for this transportation section is relative to the reference impact level X. ref The normalized value; Y n =Y / Y ref , which is the environmental exposure index Y of the transportation section relative to the reference environmental exposure level Y. ref The normalized value; α z β z κ and λ are the tradeoff coefficients and nonlinear exponents that are greater than 0; ρ is the coupling coefficient characterizing the degree of coupling between shock risk and environmental exposure risk; The comprehensive risk value of a batch is obtained by weighted summation or taking the maximum value of the comprehensive optimization indicators obtained in multiple transportation sections for the same batch; the comprehensive load value of a cleanroom transport vehicle is obtained by weighted summation or taking the maximum value of the comprehensive optimization indicators obtained in multiple transportation sections for the same cleanroom transport vehicle. The comprehensive risk value of the batch is used to determine whether the batch should be subject to stricter cleaning or random inspection, and the comprehensive load value of the cleanroom transport vehicle is used to determine the cleaning and maintenance cycle and shutdown conditions of the cleanroom transport vehicle.

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