Digital filling system for medical iodophor cotton swab based on closed-loop control

Abstract

The present application belongs to the technical field of automatic filling and management and control of disposable consumables, and discloses a digital filling system for medical iodophor cotton swabs based on closed-loop control, which comprises: obtaining a physical trigger signal of a feeding station, generating a space-time ID of a single cotton swab, activating an exclusive physical storage space, collecting initial production data and binding; synchronously driving filling action execution and data collection, performing real-time deviation determination, triggering hierarchical closed-loop correction action when deviation is detected; positioning corresponding cross-station data, generating a feedforward correction instruction, integrating multi-station data, and forming a full-link cross-station data chain; screening filling quality verification indicators, verifying filling quality status, triggering reverse correction routing and precise rejection action for unqualified products, forming a closed-loop data flow and a reverse correction instruction set; archiving the full-process production data of a single cotton swab, generating a single production file, performing basic statistics during non-production period, dynamically optimizing control benchmarks, and forming full-process closed-loop iterative management and control.

Description

Technical Field

[0001] This invention relates to the field of automated filling and control technology for disposable consumables, and more specifically, to a digital filling system for medical iodine swabs based on closed-loop control. Background Technology

[0002] With the increasing demand for medical disinfection consumables and the upgrading of medical device compliance requirements, the production of disposable iodine swabs has placed stringent requirements on the consistency of quality of individual products and the traceability of the entire process. However, traditional automated filling systems mostly take batch as the core control unit, making it difficult to achieve precise control of the entire process at the individual product level, and thus failing to meet the needs of production efficiency, quality control and compliance traceability.

[0003] Most existing automated iodine swab filling solutions still have some problems. Existing solutions mostly use batch-level fixed parameter production, resulting in data fragmentation between workstations. They cannot achieve real-time deviation closed-loop correction at the individual swab level, which easily leads to batch defects and poor quality consistency. They can only achieve batch-level traceability, and individual swab identification is easy to tamper with, and data and products are easy to misalign. They cannot meet the compliance requirements for tamper-proof traceability of individual sterile consumable products throughout their entire life cycle. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a digital filling system for medical iodine swabs based on closed-loop control, comprising: Spatiotemporal ID Anchoring Module: Used to acquire physical trigger signals from the feeding station, generate a unique spatiotemporal ID for each cotton swab, activate the dedicated physical storage space with the spatiotemporal ID as the anchor point, synchronously collect initial production data and bind it with the spatiotemporal ID to form the original data unit; The filling closed-loop control module is used to synchronously drive the filling action and data acquisition based on the original data units, perform real-time deviation judgment on the collected filling process data, and trigger a hierarchical closed-loop correction action when a deviation is detected, forming a synchronous filling dataset with correction marks. Cross-workstation feedforward correction module: It is used to locate the corresponding cross-workstation data based on the synchronous filling dataset, generate data-driven feedforward correction instructions, and then integrate multi-workstation data and bind it with spatiotemporal ID to form a full-link cross-workstation data chain. Quality verification and handling module: It is used to screen filling quality verification indicators based on cross-workstation data chain, verify and determine the quality status of single cotton swab filling process, generate standardized quality labels and bind them with spatiotemporal IDs; for non-conforming products, it synchronously triggers reverse correction routing and precise rejection actions to form a closed-loop data flow and reverse correction instruction set; Data archiving and iteration module: It is used to archive the entire production data of a single cotton swab based on the closed-loop data flow and reverse correction instruction set, and generate a single production file; and to perform basic statistics based on qualified product data during non-production periods, dynamically optimize control benchmarks, and form a closed-loop iterative management and control of the entire process.

[0005] Furthermore, the method for generating a unique spatiotemporal ID for each cotton swab includes: The system synchronously collects physical trigger signals from the feeding station fixture arrival, cotton swab physical detection, and encoder absolute position. Then, through a fixed hardware logic circuit, the three types of physical trigger signals are combined into a fixed-format ID, which serves as a unique and tamper-proof spatiotemporal ID for each cotton swab.

[0006] Furthermore, the method of forming the original data unit includes: Using the spatiotemporal ID as the unique physical access address and spatiotemporal anchor point for the entire process, the dedicated physical storage space corresponding to a single cotton swab holder is located and activated. The initial production data of the material loading station is collected synchronously through the same source triggering mechanism, natively bound to the spatiotemporal ID, and written into a dedicated physical storage space, which is then encapsulated into a raw data unit with a unique identifier.

[0007] Furthermore, the synchronous drive filling action execution and data acquisition methods include: Based on the raw data unit, when the turntable fixture bound to the target spatiotemporal ID arrives at the filling station, it triggers the start of the filling station, driving the execution of actions at each stage of the filling process and synchronously collecting corresponding sensor data. The collected sensor data is bound to a spatiotemporal ID and used as filling process data, which is then written to the corresponding dedicated physical storage space in real time according to the process sequence.

[0008] Furthermore, the method for forming the synchronized filling dataset with correction tags includes: The system presets process compliance thresholds and performs real-time comparison and judgment of deviations in the filling process data. If a deviation is found, it locks the process deviation type, deviation value, deviation process and timestamp to form a deviation record. Deviation control levels are divided according to deviation records. Hardware parameter correction actions are executed in stages by matching corresponding correction strategies through hierarchical closed-loop correction rules. By integrating deviation records, correction strategies, and correction execution results, correction tags are generated and bound to spatiotemporal IDs, filling process data, and process sequence in three layers. Then, they are integrated according to the filling stage sequence to form a synchronous filling dataset with correction tags.

[0009] Furthermore, the methods for forming a full-link cross-workstation data chain include: Based on the synchronous filling dataset, when a cotton swab product bound to a spatiotemporal ID is transferred to the downstream workstation, cross-workstation data addressing is performed using the spatiotemporal ID as the unique index, forming data pairing between upstream and downstream workstations. Retrieve the core filling features in the synchronous filling dataset that affect downstream processes, analyze the degree of impact on the processing quality of downstream processes based on deviation records through cross-workstation linkage logic, determine the feedforward correction requirements, and generate feedforward correction instructions accordingly. The upstream synchronous filling dataset, feedforward correction instructions, and downstream workstation pending processing data are collected to form upstream and downstream multi-workstation data, which are bound to spatiotemporal IDs and encapsulated into a full-link cross-workstation data chain according to the process sequence.

[0010] Furthermore, the method for generating standardized quality labels and binding them to spatiotemporal IDs includes: Based on the dedicated full-chain cross-workstation data chain for each cotton swab, filling quality verification indicators are screened according to quality feature extraction rules. Based on filling quality verification indicators, the filling process quality of a single cotton swab is subject to multi-dimensional compliance verification and graded judgment. A standardized quality label containing quality grade and deviation information is generated and bound to spatiotemporal ID and full-link cross-workstation data chain to form a single-swab quality judgment dataset.

[0011] Furthermore, the method for forming a closed-loop data flow and a reverse correction instruction set includes: For single cotton swabs with non-compliant standardized quality labels, non-compliant products are classified into remedial general non-compliance and irremedial severe non-compliance based on a dedicated single-swab quality judgment dataset. For general non-conforming products, the filling deviation type is extracted, the source is traced and located, a reverse correction instruction is generated and routed to the upstream workstation; at the same time, severely non-conforming products are precisely rejected according to their spatiotemporal ID. By integrating the full-chain cross-workstation data of a single cotton swab, the single-swab quality judgment dataset, and the handling of non-conforming products, reverse correction instructions, rejection information, and deviation traceability information, a closed-loop data flow with quality conclusions is formed, and similar reverse correction instructions are collected to form a reverse correction instruction set.

[0012] Furthermore, the methods for generating individual production files for each cotton swab from the complete production process include: Based on the closed-loop data flow and reverse correction instruction set of a single cotton swab, the data of the entire production process of a single cotton swab is collected and locked according to the latching rules, and a unique production file for each swab is generated according to a preset standard structure with spatiotemporal ID as the index.

[0013] Furthermore, the method of dynamically optimizing the control benchmark based on qualified product data during non-production periods includes: Based on the unique production file of each cotton swab, the product data of qualified single cotton swabs are selected during non-production periods on the production line for basic process statistical analysis. Based on the statistical results, the control benchmark of the entire filling process is dynamically optimized and archived for iteration, forming a closed-loop iterative management and control of the entire process.

[0014] The technical effects and advantages of the digital filling system for medical iodine swabs based on closed-loop control in this invention are as follows: This invention focuses on the aseptic filling production scenario of medical iodine swabs, strictly adheres to the requirements of medical device production quality management standards, and realizes hardware-level closed-loop control of the entire process of a single product from material feeding, filling, cross-workstation linkage, quality control to process iteration, reducing the defect rate and improving production efficiency and compliance.

[0015] First, by generating a unique and immutable spatiotemporal ID for each product through hard-wired connections, production data is natively bound to the hardware level of each product, meeting the compliance requirements for immutable traceability throughout the entire lifecycle of each product. Second, by using spatiotemporal ID as an anchor point, we can achieve real-time closed-loop correction of filling, cross-workstation feedforward linkage, accurate verification of single-unit quality and accurate rejection of unqualified products, eliminate deviations from the source, and greatly improve product quality consistency and finished product qualification rate. Third, by archiving data throughout the entire process and using data-driven automatic process iteration, we can build a closed-loop management and control system for the entire process, continuously improve process stability and production efficiency, and adapt to the large-scale compliant production of various medical disinfection consumables.

[0016] This invention addresses the pain points of traditional iodine swab filling systems, such as poor traceability, low quality consistency, high defect rate, and insufficient compliance, through hardware-level identity anchoring, real-time closed-loop correction throughout the entire process, cross-workstation feedforward linkage, and full data quality verification. It is applicable to the automated filling and production of various disposable medical disinfection consumables, such as medical iodine swabs and alcohol swabs, and significantly improves the compliance of the production process, product quality stability, production efficiency, and system robustness. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the digital filling system for medical iodine swabs based on closed-loop control according to the present invention. Figure 2 This is a schematic diagram of the graded closed-loop correction operation process for the filling process of a single cotton swab in the digital filling system for medical iodine swabs based on closed-loop control of the present invention. Figure 3 This is a schematic diagram of the digital filling method for medical iodine swabs based on closed-loop control according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0019] Please see Figure 1 and Figure 2 As shown in this embodiment, the digital filling system for medical iodine swabs based on closed-loop control includes: Spatiotemporal ID Anchoring Module: Used to acquire physical trigger signals from the feeding station, generate a unique spatiotemporal ID for each cotton swab, activate the dedicated physical storage space with the spatiotemporal ID as the anchor point, synchronously collect initial production data and bind it with the spatiotemporal ID to form the original data unit; The filling closed-loop control module is used to synchronously drive the filling action and data acquisition based on the original data units, perform real-time deviation judgment on the collected filling process data, and trigger a hierarchical closed-loop correction action when a deviation is detected, forming a synchronous filling dataset with correction marks. Cross-workstation feedforward correction module: It is used to locate the corresponding cross-workstation data based on the synchronous filling dataset, generate data-driven feedforward correction instructions, and then integrate multi-workstation data and bind it with spatiotemporal ID to form a full-link cross-workstation data chain. Quality verification and handling module: It is used to screen filling quality verification indicators based on cross-workstation data chain, verify and determine the quality status of single cotton swab filling process, generate standardized quality labels and bind them with spatiotemporal IDs; for non-conforming products, it synchronously triggers reverse correction routing and precise rejection actions to form a closed-loop data flow and reverse correction instruction set; Data archiving and iteration module: It is used to archive the entire production data of a single cotton swab based on the closed-loop data flow and reverse correction instruction set, and generate a single production file; and to perform basic statistics based on qualified product data during non-production periods, dynamically optimize control benchmarks, and form a closed-loop iterative management and control of the entire process.

[0020] Methods for generating a unique spatiotemporal ID for a single cotton swab include: After the cotton swab tube is pushed to the target fixture on the turntable through the material handling station, the fixture rotates to the designated physical position at the loading station. The physical trigger signals of the fixture's arrival at the loading station, the physical detection of the cotton swab, and the absolute position of the encoder are collected synchronously through a hard wire direct connection. Among them, the fixture positioning trigger signal is the rising edge trigger signal output by the fixture positioning limit switch. The signal is connected to the PTP master clock synchronization mark unit to synchronously generate a trigger timestamp with nanosecond precision. The cotton swab detection signal is the rising edge trigger signal output by the through-beam fiber optic sensor after it detects that the cotton swab is fully in place in the fixture. It is synchronously connected to the PTP master clock marker module to generate a nanosecond-level trigger timestamp. The encoder absolute position signal is a 24-bit binary unique position value synchronously acquired from the absolute encoder of the rotary spindle corresponding to the current fixture. This value is the fixed physical address of the fixture and has no repetition or jump throughout its entire life cycle. By using pre-fixed hardware logic circuits (deterministic digital circuits composed of digital logic devices such as gate circuits and flip-flops, capable of real-time combination, encoding, and synchronous output of input physical signals), three types of physical trigger signals are combined into a unique spatiotemporal ID with a fixed format (encoding structure: 24-bit absolute position value of the encoder - 32-bit nanosecond-level timestamp of the fixture arrival trigger - 32-bit nanosecond-level timestamp of the fiber optic sensor trigger). This ensures that the spatiotemporal ID possesses a triple, tamper-proof physical anchoring attribute (the encoder's absolute position value ensures position anchoring, the dual nanosecond-level timestamps ensure time anchoring, and the fiber optic sensor trigger signal ensures physical anchoring), serving as a unique and tamper-proof spatiotemporal ID for each cotton swab. The generated spatiotemporal ID is broadcast in parallel via hardwired to the physical trigger bus, physical routing matrix, physical storage array, and hardware control unit of each workstation throughout the entire production chain, serving as the unique anchor point for subsequent data binding, addressing, and routing throughout the entire process; For newly generated spatiotemporal IDs, they need to be compared with the values ​​in the historical spatiotemporal ID latches to confirm that no duplicate spatiotemporal IDs have been generated. If a duplicate is found, the spatiotemporal ID regeneration process is immediately triggered, and an encoder abnormality alarm is set.

[0021] The ways to form raw data units include: Using the 24-bit encoder absolute position value in the spatiotemporal ID as the unique physical access address and the spatiotemporal anchor point of the entire process, the address is transmitted to the address decoding circuit of the physical storage array through the hard-wired address bus. The circuit directly locates the dedicated non-volatile memory chip corresponding to the clamp of the single cotton swab through fixed decoding logic. Then, a high-level activation signal is released to the target memory chip through the hardware logic circuit. The chip is powered on and enters the read and write state. At the same time, the chip address segment is written into the spatiotemporal ID of the production, completing the hardware-level binding between the memory chip and the ID of the single cotton swab, forming a dedicated physical storage space for the single cotton swab. Initial production data from the material loading station is collected synchronously through a shared triggering mechanism. Specifically: The trigger signal for the robotic arm's grasping action on the physical trigger bus is directly connected via a hard wire, synchronously completing two parallel drives, including: driving the three-axis robotic arm at the loading station to perform the complete action of grasping cotton swabs and releasing them into the fixture, and synchronously driving the high-precision grasping force sensor at the loading station and the three-axis position encoder of the robotic arm to start continuous sampling. The sampling frequency is completely synchronized with the entire action execution. Then, initial production data, including the continuous curve of gripping force, real-time position data of the robotic arm, and material unloading completion status signal, are collected. After analog-to-digital conversion, the data is directly written to the activated dedicated physical storage space via the hard-wired address bus. At the same time as writing to the dedicated physical storage space, a latched spatiotemporal ID and a corresponding timestamp are added to each set of collected initial production data to achieve hardware-level native binding between the data and the spatiotemporal ID of a single cotton swab at the moment of production, ensuring that the data and the spatiotemporal ID are inseparable and cannot be misaligned. The spatiotemporal ID information, initial production data, physical address of the storage space, and access permission rules within the dedicated physical storage space are encapsulated to form a raw data unit with a unique spatiotemporal ID identifier.

[0022] The methods for synchronously driving the filling action execution and data acquisition include: Based on the original data unit, when the turntable fixture bound to the target spatiotemporal ID (referring to the spatiotemporal ID existing in the original data unit) moves to the designated position of the filling station, the filling station is triggered to start. At the same time, the start time of the filling station is marked as a unified timing reference for all subsequent filling stage actions and data collection. The timestamp is synchronously written to the dedicated physical storage space of the spatiotemporal ID to complete the timing anchoring. After the filling station is started, the actions of each stage of the filling process are executed in parallel in stages, and the corresponding sensor data is collected synchronously. Specifically, the entire filling process can be broadly summarized as the needle descent stage, the initial filling stage, the constant filling stage, the pressure holding stage, and the back suction stage. During the needle descent phase, the needle lifting servo driver is driven to perform a precise needle descent action, simultaneously triggering the pressure sensor, force sensor, and other sensors to start continuous sampling. The collected data, such as pressure and contact force, during the needle descent process are temporarily stored and await synchronous writing instructions. During the initial filling phase, the solenoid valve of the filling valve is opened, the flow sensor starts sampling, and the pressure sensor is triggered to continuously sample. The dynamic data of flow and pressure collected during the initial filling stage are spliced ​​with the data during the needle descent stage to form the initial filling stage data. During the constant filling stage, the filling valve is kept open, the flow sensor continuously samples, and the pressure sensor monitors the filling pressure stability in real time. The collected stable flow and pressure data during the constant irrigation phase are temporarily stored in chronological order to ensure that the data and action execution are completely synchronized. During the pressure holding stage, the filling valve is closed, the pressure sensor continuously samples, and the force sensor monitors the static force on the needle and the swab. The pressure decay curve and force data collected during the pressure holding stage are temporarily stored. At the same time, the core feature of the cotton head adsorption rate of the cotton swab (obtained by calculation through decay rate) is extracted through hardware logic circuit and also temporarily stored. During the back suction phase, the needle tip lifting servo driver performs the back suction action, the flow sensor samples and monitors the back suction flow, and the pressure sensor monitors the back suction pressure. The dynamic data of flow and pressure collected during the back suction stage are temporarily stored, thereby completing the data collection of the entire filling process. After data collection is completed at each stage of the filling process, a latched spatiotemporal ID and a corresponding timestamp are added to each set of data (filling process data collected from a single cotton swab) in a synchronous manner. This enables the data to be natively bound to the spatiotemporal ID of a single cotton swab at the moment of collection. As filling process data, the data and the spatiotemporal ID are inseparable and cannot be misaligned. The temporarily stored filling process data bound with the time-space ID and timestamp is directly written to the dedicated physical storage space of the time-space ID and stored in the time sequence of the filling stage, thereby ensuring no cache transfer or software forwarding, and the write latency can be reduced to less than 100ns. After the writing is completed, the data segment of the original filling process data in the dedicated physical storage space is time-locked to ensure that subsequent workstations can only read and not modify the written synchronous data from the same source, and the lock state is irrevocable.

[0023] Methods for creating synchronized fill datasets with correction tags include: Based on the production process standards, fixed process compliance thresholds (such as pressure peak, flow rate, force value, etc.) are preset, and the corresponding types of data in the filling process data are compared and judged in real time to confirm whether the data values ​​are within the compliance range of the production process standards specified by the process compliance thresholds. If it is within the compliance range, the production process corresponding to the filling process data is deemed to be qualified, and it is released directly to enter the subsequent marking and packaging stage. No deviation record is generated, and only the basic compliance mark is retained. If it is outside the compliance range, it is determined that there is a process deviation. The type of process deviation (such as excessive amplitude, abnormal rate, large steady-state deviation, etc.), the corresponding deviation value and deviation magnitude, the corresponding process stage and the timestamp of the deviation occurrence are promptly identified to form a deviation record. Deviation control levels are categorized based on deviation records, with a specific example of the categorization logic (divided into three levels): Level 1 deviation (minor deviation): The deviation range is ≤10% of the process compliance threshold, which only affects the fine-tuning of process parameters and does not affect the basic quality of the product; Level 2 deviation (moderate deviation): 10% of the process compliance threshold < deviation range ≤ 30% of the process compliance threshold, process parameters need to be adjusted to eliminate cumulative deviation; Level 3 deviation (severe deviation): Deviation range > 30% of process compliance threshold, which may lead to product quality failure and require process adaptation correction. Through hierarchical closed-loop correction rules, corresponding correction strategies are matched according to the control level. Level 1 deviation is matched with parameter fine-tuning correction strategy, Level 2 deviation is matched with process parameter adaptation correction strategy, and Level 3 deviation is matched with process stage rollback correction strategy. Then, based on the correction strategy, tiered correction actions are performed, specifically: Level 1 Deviation Correction: The parameter fine-tuning instructions are generated by the hierarchical closed-loop correction rules. Within the current filling process cycle, the filling valve opening, servo motor speed, etc. are fine-tuned within ±5%. The correction process is completed within 100ms without interrupting the production process. Secondary deviation correction: Generate process parameter adaptation instructions, adjust the process parameter benchmarks of the corresponding process stage (such as the flow benchmark of the constant filling stage and the pressure benchmark of the pressure holding stage), and fine-tune the correction range according to the deviation range (such as fine-tuning 10% if the deviation range is 15%, and the fine-tuning ratio needs to be designed according to the actual production situation). It is necessary to ensure that the deviation converges within the current cycle. Level 3 Deviation Correction: Generates a process stage rollback instruction, rolls back to the process node before the deviation occurred, re-executes the corresponding stage filling operation, and retains the deviation record to prevent unqualified data from flowing into subsequent stages; After the correction work is completed, the deviation status of the corresponding filling process data is checked in real time to confirm that the deviation has been reduced to the compliance range. If it is not met, the corresponding correction strategy is repeated. After the correction is completed, the deviation record, correction strategy and correction execution result are integrated to generate a correction mark (if there is no deviation data, a compliant and uncorrected blank mark is generated to ensure the uniformity of the dataset format), and a three-layer strong binding is performed with the spatiotemporal ID, filling process data and process sequence. Finally, the data is integrated according to the order of the filling stages to form a synchronous filling dataset of individual swabs with correction marks, ensuring that each set of data can be traced and corrected throughout the entire process, with no data or mark misalignment.

[0024] The methods for forming a full-link, cross-workstation data chain include: Based on the synchronous filling dataset of a single cotton swab, when the cotton swab bound with a spatiotemporal ID is transferred to the downstream workstation, its spatiotemporal ID is used as a unique index. According to the principle of product physical flow synchronization and data logical addressing synchronization, cross-workstation data addressing is performed to locate the storage node of the dedicated physical storage space of the upstream synchronous filling dataset corresponding to the spatiotemporal ID, and complete the data pairing of the upstream and downstream workstations of the cotton swab, so as to prevent data mismatch or omission. The core filling features that affect downstream processes are retrieved from the synchronous filling data of this cotton swab. Taking the sealing station as an example, the core filling features of the upstream process are filling volume (affecting sealing performance), steady-state filling pressure (affecting sealing fit), deviation records (reflecting process stability), and filling process sequence nodes (affecting processing rhythm matching). The process deviation types, deviation amplitudes, and deviation occurrence sequences corresponding to the core filling features are extracted synchronously to provide a basis for subsequent analysis of the degree of impact. Then, based on the cross-station linkage logic of a single cotton swab, the impact on the processing quality of downstream processes is analyzed through deviation records. Specifically: The cross-workstation linkage logic maps the relationship between the core characteristics of upstream filling and the control parameters of downstream processes, forming a mapping logic of upstream core filling characteristics - downstream process quality - control parameters, so as to clarify the transmission path of the impact of characteristic deviation on downstream processes. Example logic: Too much filling volume → too high liquid pressure inside the swab tube → easy leakage or deformation of the seal during sealing; Excessive fluctuation in filling pressure → uneven stress on the sealing surface during sealing → easy to cause sealing defects; Symbol → indicates logical pointer. Then, the core filling characteristics are compared with the preset linkage threshold (the linkage threshold is less than the process compliance threshold), and the correction requirement level is divided according to the degree of deviation. Specifically: No deviation level: The core features are within the compliance threshold range and the deviation is ≤ the linkage threshold (e.g., ±1%), and no feedforward correction is required for the judgment; Minor deviation level: The deviation range is between the linkage threshold and 10% of the process compliance threshold, and the deviation is an instantaneous fluctuation (not continuous). It is determined that a slight feedforward correction is required. Moderate deviation level: The deviation range is between 10% and 30% of the process compliance threshold, or the deviation will continue to be transmitted to the downstream and cause a slight quality impact. It is determined that moderate feedforward correction is required. Severe Deviation Level: Deviation range > 30% of compliance threshold, or deviation that would cause downstream processes to fail to meet standards (such as leakage, sealing failure), and is deemed to require severe feedforward correction; Based on the deviation type and corresponding correction requirements of the core filling characteristics, the feedforward control direction of a single cotton swab is matched. An example matching method: Upstream filling core characteristic deviation: Filling volume is too high (+5%), impact on downstream: excessive sealing pressure → leakage or deformation, control direction: reduce sealing pressure; Filling volume is too low (-3%), insufficient sealing pressure → poor sealing, control direction: increase sealing pressure; The direction of regulation must be strongly tied to the product specifications of a single cotton swab and the process characteristics of downstream workstations; it cannot be universal or adapted in batches. Furthermore, it is necessary to clearly define the execution boundaries of feedforward correction to prevent adjustments beyond the acceptable range from affecting production stability. The specific constraint setting logic is as follows: The amplitude constraint is that the parameter adjustment amplitude is less than or equal to the maximum adjustment range allowed by the downstream process (e.g., the sealing pressure adjustment amplitude is less than or equal to ±5%). The timing constraint is that the correction instruction must be issued within the processing timing window of the downstream workstation to avoid interrupting the normal flow of the production line. Specification constraints require that the correction strategy match the product specifications of a single cotton swab (e.g., different specifications of cotton swabs have different sealing pressure benchmarks). The stability constraint requires that the modified parameters remain within the process stability range and do not cause new process fluctuations. The constraints are tied to the actual working conditions of a single cotton swab, dynamically adapting to different production scenarios. Based on the correction demand level, control direction, and constraints, a specific correction strategy is matched and the parameter adjustment value for a single cotton swab is accurately calculated. A specific matching logic example is as follows: Slight deviation level matching parameter fine-tuning adaptation strategy: Only make small, risk-free fine-tuning adjustments to downstream parameters to quickly eliminate the impact of instantaneous deviations; The specific calculation logic for fine-tuning the parameters is as follows: Single-unit parameter adjustment value = basic adjustment coefficient × single-unit filling characteristic deviation range × level 1 constraint coefficient; Basic adjustment coefficient: The preset correlation coefficient between upstream filling core characteristics and downstream control parameters (without reverse coupling), determined by process verification and fixed for each specification (e.g., for every 1% deviation in the filling volume of iodine swabs, the sealing pressure needs to be adjusted by 0.5%, k=0.5). Single swab filling characteristic deviation range: The actual deviation value extracted from the synchronous filling dataset of this swab (e.g., if the filling volume is 5% too high, the single swab filling characteristic deviation range = +5%; if it is 2% too low, the single swab filling characteristic deviation range = -2%). Level 1 constraint coefficient: fixed at 1 (due to the small deviation, no limit is required), only need to meet the condition that the adjustment value is ≤ the downstream parameter fine-tuning threshold (e.g., ±2%), and if it exceeds, the upper or lower limit of the threshold is taken; Moderate deviation level matching and coupling compensation adaptation strategy: Combine the coupling relationship between upstream deviation and downstream process, calculate the compensation value and adjust multiple types of parameters simultaneously. Specifically, for continuous, moderate-amplitude filling deviations (such as filling volume ±5%~±10%), or deviations involving multiple characteristics (such as filling volume +8% + pressure fluctuation 2%), the coupling effect of the deviation is offset by the linkage compensation of main parameters and auxiliary parameters (avoiding insufficient adjustment of a single parameter). Specific calculation logic: The first step is to calculate the adjustment value of the main control parameter: Main parameter adjustment value = main coupling coefficient × core deviation amplitude × coupling correction coefficient × level 2 constraint coefficient; The second step is to calculate the auxiliary parameter adjustment value: Auxiliary parameter adjustment value = Auxiliary coupling coefficient × Main parameter adjustment value × Level 2 constraint coefficient; Among them, the main coupling coefficient is the strong coupling coefficient between the core filling characteristics (such as filling volume) and the downstream main control parameters (such as sealing pressure) (the default value is 0.6, which needs to be higher than the basic adjustment coefficient because the deviation is larger). Core deviation range: The most critical filling deviation value of this cotton swab (e.g., if the filling volume is 8% too high, the core deviation range = +8%). Coupling correction factor: This factor reflects the correction of minor filling characteristic deviations to the main parameters (e.g., if the filling pressure fluctuation is 2%, then the coupling correction factor = 1.1, which amplifies the adjustment range of the main parameters to offset the coupling effect). Auxiliary coupling coefficient: The linkage coefficient between the main parameter and the auxiliary parameter (e.g., if the sealing pressure is adjusted by 1%, the pressure holding time needs to be adjusted by 0.2%, then the auxiliary coupling coefficient = 0.2). Level 2 constraint coefficient: dynamically selected (0.8~1) to ensure that the overall process remains stable after the main and auxiliary parameters are adjusted (e.g., the adjustment range of the main parameter is ≤ ±4%, and the auxiliary parameter is ≤ ±1%). Practical example (single cotton swab): A cotton swab's filling volume is 8% higher than expected (core deviation = +8%) + filling pressure fluctuation is 2% (coupling correction coefficient = 1.1). The corresponding downstream sealing station is as follows: Calculation of main parameter (sealing pressure): Main parameter adjustment value = 0.6 × 8% × 1.1 × 1 = +5.28% → Since the constraint threshold is ±4%, take the main parameter adjustment value = +4% (limit). Calculation of auxiliary parameter (holding time): Auxiliary parameter adjustment value = 0.2 × 4% × 1 = +0.8%; Execution: The sealing pressure of this cotton swab is reduced by 4%, and the pressure holding time is extended by 0.8%. Through the linkage compensation of pressure and time, the coupled effect of the two deviations of filling volume and pressure is offset. Severe deviation level matching timing calibration + parameter compensation strategy: For large, high-risk filling deviations (such as filling volume > ±10%), or deviations that may cause downstream processing timing misalignment (such as filling delays causing mismatch in downstream processing rhythm), first calibrate the downstream processing timing, and then add parameter compensation to ensure that the deviation is completely offset (avoiding the generation of defective products). Specific calculation logic: The first step is to calculate the timing calibration value (correct the processing rhythm first): Timing calibration offset = timing deviation of filling process × timing coupling coefficient × timing constraint coefficient; The second step is to calculate the parameter compensation value (precise compensation after superimposed timing): Parameter compensation value = severe deviation coupling coefficient × (core deviation amplitude + timing offset correction value) × level 3 constraint coefficient; Among them, filling process timing deviation: the difference between the actual filling time of this cotton swab and the benchmark time (e.g., if the filling delay is 0.5s, the filling process timing deviation = +0.5s). Timing coupling coefficient: The influence coefficient of timing deviation on downstream processing (e.g., for every 0.1s delay in filling, the downstream sealing needs to be delayed by 0.08s, and the timing coupling coefficient = 0.8). Timing constraint coefficient: upper limit for timing adjustment (e.g., ≤ ±0.3s) to avoid excessive timing deviations that could disrupt the production line; Level 3 constraint coefficient: Due to excessive deviation, it is necessary to limit the large-scale compensation to avoid excessive parameter adjustment leading to process time and space. The dynamic value range is further narrowed, and the limit is stronger (e.g., 0.7~0.9). Severe deviation coupling coefficient: The enhanced value of the core coupling coefficient (severe deviation coupling coefficient = main coupling coefficient × 1.5, stronger compensation is required due to the large deviation amplitude). Timing offset correction value: Timing deviation is converted into equivalent deviation magnitude (e.g., a timing delay of 0.3s is equivalent to a 2% increase in filling volume, so the timing offset correction value is +2%). Practical example: A cotton swab's filling volume is 12% too high (core deviation = +12%) + filling time delay of 0.4s (filling process time deviation = +0.4s), corresponding to the downstream sealing station: Timing calibration calculation: Timing calibration offset = 0.4s × 0.8 × 0.75 (Level 3 constraint coefficient = 0.75, due to the constraint upper limit of 0.3s) = 0.24s → The sealing process of this cotton swab is delayed by 0.24s. Timing offset correction value: Timing offset correction value = +2% (0.24s timing delay is equivalent to 2% more filling volume); Parameter compensation calculation: Parameter compensation value = 0.9 (severity deviation coupling coefficient = 0.6 × 1.5) × (12% + 2%) × 0.9 (level 3 constraint coefficient = 0.9, amplitude limit ± 12%) = 0.9 × 14% × 0.9 = +11.34%; Execution: The swab first delays the sealing process by 0.24 seconds, then reduces the sealing pressure by 11.34%. Through the dual compensation of timing and parameters, the impact of severe filling deviation is completely offset. After the calculation is completed, check whether the adjusted values ​​meet the constraints. If they exceed the limits, limit the range according to the constraint threshold to ensure that the parameters are compliant. Information such as the correction requirements, correction strategies, and parameter adjustment values ​​for a single cotton swab is integrated into a feedforward correction instruction and precisely bound to the processing sequence of the downstream workstation. The synchronous filling dataset, feedforward correction instructions, and downstream workstation data to be processed for each swab are collected to form multi-workstation data, which is bound to spatiotemporal IDs and encapsulated into a single swab-specific full-link cross-workstation data chain according to the process sequence.

[0025] Methods for generating standardized quality labels and binding them to spatiotemporal IDs include: Based on the dedicated full-chain cross-workstation data chain for a single cotton swab, filling quality verification indicators are screened according to quality feature extraction rules. Specifically, the filling quality verification indicators consist of two parts: core mandatory inspection indicators and auxiliary reference indicators. Among them, the core mandatory inspection indicators include the deviation of filling volume, sealing parameters, the liquid level of iodine solution after filling, and the execution result of feedforward correction; Auxiliary reference indicators include steady-state filling pressure, deviations in process timing, and details of correction records; Based on the filling quality verification indicators, the quality of the filling process of a single cotton swab is verified for compliance from multiple dimensions. Specifically: First, use the core mandatory inspection indicators for veto verification: Based on expert experience, the quality threshold ranges corresponding to each core mandatory inspection indicator are preset. Each indicator has a severe non-conformance threshold range, a general deviation threshold range, and a qualified threshold range. For core mandatory inspection indicators such as filling volume deviation and sealing parameters, precise comparison with quality thresholds is performed: If any core mandatory inspection indicator exceeds the threshold for severe non-compliance, it will be directly judged as non-compliant and trigger a veto. If the core mandatory inspection indicators are within the general deviation threshold range, they are marked as minor quality deviations and are not directly rejected. All core mandatory test indicators are within the acceptable threshold range, and the core indicators are deemed to be qualified. Then, auxiliary reference indicators are used for correlation verification: By combining auxiliary reference indicators, the causes of deviations in core mandatory inspection indicators are verified and the types of filling deviations (such as filling deviation, insufficient sealing correction, and timing misalignment) are recorded to provide a traceability basis for subsequent quality labeling and handling of non-conforming products. Based on the results of the quality verification, a detailed quality verification report for each unit is generated, recording the original values ​​of each indicator, the threshold comparison results, the deviation range, and the filling deviation type, which serves as the direct basis for quality judgment. Based on the individual quality verification details, each cotton swab is graded and judged. The entire process is carried out independently, without batch merging. The specific judgment logic is as follows: Based on the individual quality verification details, the filling quality status of a single cotton swab is divided into three levels: Pass: All core mandatory inspection indicators are within the pass threshold, with no serious deviations. General deviation: The core mandatory inspection indicators are within the allowable deviation range, and there are only slight deviations in auxiliary indicators, which do not affect the performance of the product. Non-compliant: Any core mandatory inspection indicator exceeds the severe threshold, or there are two or more general deviations that overlap, affecting product quality and safety of use; Integrate the individual quality verification details and filling quality status to generate a standardized quality label containing quality grade and deviation information, including spatiotemporal ID, quality judgment grade, verification results of core mandatory inspection indicators, filling deviation type (if there is no deviation, it is marked as compliant) and judgment timestamp. Standardized quality labels are bound to spatiotemporal IDs, individual quality verification details, and cross-workstation data chains across the entire supply chain, and then encapsulated according to the process sequence to form an individual quality judgment dataset.

[0026] The methods for forming a closed-loop data flow and a reverse correction instruction set include: For single cotton swabs with non-compliant standardized quality labels, based on a dedicated single-swab quality judgment dataset, non-compliant products are divided into remediable general non-compliance (non-compliant products with two or more superimposed general deviations) and irremediable severe non-compliance (non-compliant products with any core mandatory inspection indicator exceeding the severe threshold). For general non-conforming products, the filling deviation type is extracted from the single-unit quality judgment dataset, and the source is traced back to the filling process and feedforward correction link to locate the specific process node and parameter cause of the deviation. The logic for locating specific process nodes is as follows: Based on the production process principle of iodine swab filling, a mapping rule is preset for filling deviation type → responsible process node. For example, filling volume and pressure deviation → locate to filling station; feedforward correction not offset deviation → locate to feedforward correction link; sealing parameter mismatch → locate to cross-station linkage parameter. Then, according to the preset rules and the specific type of filling deviation, the location is determined to the specific workstation or process step; The way to locate the cause of the parameter is: Preset mapping rules for filling deviation types → suspicious parameter causes, for example: High filling volume → Cause: Filling flow threshold is too high or filling servo speed is too fast; Low filling volume → Cause: Filling flow threshold is too low or filling servo speed is too slow; Large filling pressure fluctuation → Cause: Pressure steady-state threshold is too wide; Insufficient feedforward correction → Cause: Coupling coefficient is too small or correction range upper limit is too low; Sealing adaptation deviation → Cause: Sealing reference pressure does not match filling volume; Then, under the already located process nodes, according to the deviation type and the corresponding workstation, the corresponding parameter causes are matched according to the rules; Based on the specific process node and parameter triggers identified by the location, the parameter type that needs to be modified is directly determined through a fixed correspondence rule between the parameter trigger and the upstream parameter type that needs to be modified. An example of the fixed correspondence rule is as follows: High filling volume → High flow threshold → High filling flow threshold; Low filling volume → Low flow threshold → Filling flow threshold; Large pressure fluctuations → excessively wide steady-state threshold → filling pressure steady-state threshold; Insufficient feedforward correction → small coupling coefficient → upstream and downstream coupling coefficient; Mismatched sealing pressure → Inappropriate reference pressure → Sealing reference pressure; Then, using the principle of reverse compensation where the deviation direction is opposite to the correction direction, the correction direction of the reverse correction is determined. Example: Filling volume is too high → Correction direction: reduce the filling flow rate threshold or rotation speed; Low filling volume → Correction direction: Increase the filling flow rate threshold or rotation speed; Excessive pressure fluctuations → Correction direction: Narrow the steady-state pressure threshold; Insufficient feedforward correction → Correction direction: Increase coupling coefficient; Sealing pressure is too high → Correction direction: Reduce the sealing reference pressure; Next, the types, directions, and target workstations of the correction parameters are integrated to form the correction requirements corresponding to the filling deviation of a single cotton swab. This leads to the formation of reverse correction instructions for upstream process optimization, including: spatiotemporal ID, root cause of filling deviation, target correction workstation, parameter correction direction and suggested range, and applicable process range. The recommended adjustment range is based on the actual filling deviation of a single cotton swab (the larger the deviation, the larger the recommended adjustment range; the smaller the deviation, the smaller the adjustment range), and is calculated according to the fixed correction ratio preset in the process (e.g., 1% filling volume deviation corresponds to 0.5% parameter correction). The adjustment range must not exceed the safety limit of the workstation and must not exceed the range of correction. The applicable process scope refers to the production scenarios to which this reverse correction instruction is effective. Specifically, it is only applicable to iodine swabs of the same specification as the currently non-conforming product (such as the same tube capacity and the same iodine filling volume); it is only applicable to the same group of filling or sealing stations that caused the deviation, and should not be blindly applied across stations; it is only applicable to the same type of filling deviation (such as only effective for "filling volume is too high", and not applicable to pressure fluctuations or sealing deviations); it is only applicable to the same production batch and the same process benchmark, excluding scenarios with changes in operating conditions such as product change, material change, or equipment restart. After generating the reverse correction instruction, it is routed to the corresponding upstream process such as filling and feedforward correction, serving as the basis for subsequent process pre-adjustment and threshold optimization of products of the same specification, thereby achieving upstream closed-loop blocking of filling deviation. Meanwhile, for severely defective products, the spatiotemporal ID is used as a unique identifier to match the physical flow sequence of the product, accurately locking down the severely defective products that need to be rejected. When the defective products arrive at the rejection station, the rejection action is triggered simultaneously, diverting them to the defective product area. General deviation products are normally transferred to the subsequent processes. The rejection action is completely synchronized with the product flow. It integrates the full-chain cross-workstation data of a single cotton swab, the single-swab quality judgment dataset, and the handling records of non-conforming products, reverse correction instructions, rejection information and deviation traceability information, and strongly binds them with spatiotemporal IDs to form a closed-loop data flow with quality conclusions; Then, based on the deviation type and target station, reverse correction instructions of the same type are collected, duplicate and redundant instructions are removed, and a reverse correction instruction set is formed for batch optimization of upstream processes.

[0027] The methods for archiving the entire production process data of a single cotton swab and generating a single-swab production file include: Based on the closed-loop data flow and reverse correction instruction set of a single cotton swab, the spatiotemporal ID of a single cotton swab is used as an index to collect data of the entire process and production links of a single cotton swab, including filling process data, cross-workstation linkage and feedforward correction data, filling quality judgment data, non-conforming product handling and rejection data, deviation tracing and reverse correction instruction data, etc. Then, according to the production process sequence of filling, cross-workstation linkage, quality judgment, non-conformance handling, and reverse correction, the collected data is time-series regularized to ensure that the data logic is consistent with the actual production process. According to the latching rules, the data of the entire production process of a single cotton swab is fixed and latched. The specific logic of the latching rule is as follows: all collected data is subject to read-only latching that is immutable, deletable, and overwriteable, ensuring the originality and authenticity of the archived data and eliminating the risk of data tampering; After data latching is completed, the latched status is automatically marked, a latching certificate is generated, and it is bound to the spatiotemporal ID for storage; Finally, using the spatiotemporal ID as an index, a unique production file for each swab is generated according to a preset standard structure. The file structure includes: basic information of the single cotton swab filling process, full-process locked production data, quality judgment conclusion, non-conforming disposal record, and reverse correction information. Each swab is individually documented and its corresponding time-space ID is deeply linked to ensure that each swab corresponds to a unique production file.

[0028] During non-production periods, basic statistics are conducted based on qualified product data, and methods for dynamically optimizing control benchmarks include: Based on the individual production record of each cotton swab, during non-production periods on the production line, the product data of qualified single cotton swabs with qualified filling quality labels, no filling deviation, and normal parameters throughout the process are selected as qualified statistical samples. Based on the selected qualified samples, a basic statistical analysis of the process is performed to extract the optimal process range. The specific logic is as follows: We conducted dimensional statistical analysis of key parameters throughout the entire filling process for qualified products, including core filling parameters, linkage control parameters, and quality compliance parameters. Among them, the core filling parameters are: average filling flow rate, steady-state range of filling pressure, and compliance range of filling time deviation and liquid level height. Linkage control parameters: coupling coefficient of feedforward correction, matching degree of upstream and downstream timing, and efficiency of parameter adjustment; Quality compliance parameters: zero-deviation threshold for filling, optimal value for sealing fit; Finally, the mean, standard deviation and stable operating range of the process parameters of the qualified samples are calculated to form standardized statistical results; By comparing the statistical results of this iteration with the baseline data of the previous iteration, we can analyze the fluctuation trend of process parameters, identify the shortcomings and optimization points of the current process, and thus clarify the optimization direction. Based on the statistical results, the control benchmarks for various processes throughout the filling process are dynamically optimized in a targeted manner. The specific operation logic is as follows: By comparing the stable operating range of qualified samples, the control benchmarks that need to be optimized are matched, including filling flow rate benchmark, filling pressure steady-state benchmark, feedforward correction coupling coefficient benchmark, sealing parameter benchmark, etc. Following the principle of small steps and steady improvement, the benchmark value is adjusted according to the statistical mean of qualified samples: if the filling volume of qualified samples is concentrated in the ±1% deviation range, the original benchmark is narrowed to this range; if the feedforward correction efficiency is too low, the coupling coefficient benchmark is adjusted appropriately to ensure that the optimization fits the optimal state of actual production. Verify whether the optimized baseline meets the process safety range and eliminate parameters that exceed equipment operating limits and affect product quality; Based on the product specifications of iodine swabs (tube capacity, iodine infusion volume, type, etc.), the optimized control benchmarks are bound to the corresponding specifications to form a single-specification exclusive process formula, ensuring accurate adaptation of production change parameters; Replace outdated and inefficient process parameters in the formula library, enter the new optimized formula, mark the update time and iteration batch, and retain historical formulas for easy retrospective verification; By conducting small-batch simulated trial production during non-production periods, the feasibility of the optimized control benchmark and formula is verified. Once the verification is successful, the new version of the process control benchmark and production change formula are locked and simultaneously pushed to the corresponding workstations and processes to replace the old parameters. Iteration records are archived, ultimately forming a closed-loop iterative control system for the entire process of production, quality judgment, data archiving, iterative optimization, and re-production. Example

[0029] Please see Figure 3 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A digital filling method for medical iodine swabs based on closed-loop control is provided, including: S1: Obtain the physical trigger signal of the feeding station, generate a unique spatiotemporal ID for each cotton swab, activate the exclusive physical storage space with the spatiotemporal ID as the anchor point, synchronously collect the initial production data and bind it with the spatiotemporal ID to form the original data unit; S2: Based on the original data unit, synchronously drive the filling action execution and data acquisition, perform real-time deviation judgment on the acquired filling process data, and trigger the hierarchical closed-loop correction action when a deviation is detected, forming a synchronous filling dataset with correction marks. S3: Based on the synchronous filling dataset, locate the corresponding cross-workstation data, generate data-driven feedforward correction instructions, and then integrate multi-workstation data and bind it with spatiotemporal ID to form a full-link cross-workstation data chain; S4: Based on cross-workstation data chain, filter filling quality verification indicators, verify and determine the quality status of a single cotton swab filling process, generate standardized quality labels and bind them with spatiotemporal IDs; for unqualified products, synchronously trigger reverse correction routing and precise rejection actions to form a closed-loop data flow and reverse correction instruction set. S5: Based on closed-loop data flow and reverse correction instruction set, archive the entire production data of a single cotton swab and generate a single production file; and during non-production periods, perform basic statistics based on qualified product data, dynamically optimize control benchmarks, and form a closed-loop iterative management and control throughout the entire process. Example

[0030] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the aforementioned closed-loop control-based digital filling system for medical iodine swabs.

[0031] Since the electronic device described in this embodiment is the electronic device used to implement the digital filling method for medical iodine swabs based on closed-loop control in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the digital filling method for medical iodine swabs based on closed-loop control described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any electronic device used by those skilled in the art to implement the digital filling method for medical iodine swabs based on closed-loop control in the embodiments of this application falls within the scope of protection of this application.

[0032] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0033] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for users of ordinary technical skills, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A digital filling system for medical iodine swabs based on closed-loop control, characterized in that, include: Spatiotemporal ID Anchoring Module: Used to acquire physical trigger signals from the feeding station, generate a unique spatiotemporal ID for each cotton swab, activate the dedicated physical storage space with the spatiotemporal ID as the anchor point, synchronously collect initial production data and bind it with the spatiotemporal ID to form the original data unit; The filling closed-loop control module is used to synchronously drive the filling action and data acquisition based on the original data units, perform real-time deviation judgment on the collected filling process data, and trigger a hierarchical closed-loop correction action when a deviation is detected, forming a synchronous filling dataset with correction marks. Cross-workstation feedforward correction module: It is used to locate the corresponding cross-workstation data based on the synchronous filling dataset, generate data-driven feedforward correction instructions, and then integrate multi-workstation data and bind it with spatiotemporal ID to form a full-link cross-workstation data chain. Quality verification and handling module: It is used to screen filling quality verification indicators based on cross-workstation data chain, verify and determine the quality status of single cotton swab filling process, generate standardized quality labels and bind them with spatiotemporal IDs; for non-conforming products, it synchronously triggers reverse correction routing and precise rejection actions to form a closed-loop data flow and reverse correction instruction set; Data archiving and iteration module: It is used to archive the entire production data of a single cotton swab based on the closed-loop data flow and reverse correction instruction set, and generate a single production file; and to perform basic statistics based on qualified product data during non-production periods, dynamically optimize control benchmarks, and form a closed-loop iterative management and control of the entire process.

2. The digital filling system for medical iodine swabs based on closed-loop control according to claim 1, characterized in that, The methods for generating a unique spatiotemporal ID for a single cotton swab include: The system synchronously collects physical trigger signals from the feeding station fixture arrival, cotton swab physical detection, and encoder absolute position. Then, through a fixed hardware logic circuit, the three types of physical trigger signals are combined into a fixed-format ID, which serves as a unique and tamper-proof spatiotemporal ID for each cotton swab.

3. The digital filling system for medical iodine swabs based on closed-loop control according to claim 2, characterized in that, The methods for forming the original data unit include: Using the spatiotemporal ID as the unique physical access address and spatiotemporal anchor point for the entire process, the dedicated physical storage space corresponding to a single cotton swab holder is located and activated. The initial production data of the material loading station is collected synchronously through the same source triggering mechanism, natively bound to the spatiotemporal ID, and written into a dedicated physical storage space, which is then encapsulated into a raw data unit with a unique identifier.

4. The digital filling system for medical iodine swabs based on closed-loop control according to claim 3, characterized in that, The methods for synchronously driving the filling action and data acquisition include: Based on the raw data unit, when the turntable fixture bound to the target spatiotemporal ID arrives at the filling station, it triggers the start of the filling station, driving the execution of actions at each stage of the filling process and synchronously collecting corresponding sensor data. The collected sensor data is bound to a spatiotemporal ID and used as filling process data, which is then written to the corresponding dedicated physical storage space in real time according to the process sequence.

5. The digital filling system for medical iodine swabs based on closed-loop control according to claim 4, characterized in that, The methods for forming the synchronized filling dataset with correction tags include: The system presets process compliance thresholds and performs real-time comparison and judgment of deviations in the filling process data. If a deviation is found, it locks the process deviation type, deviation value, deviation process and timestamp to form a deviation record. Deviation control levels are divided according to deviation records. Hardware parameter correction actions are executed in stages by matching corresponding correction strategies through hierarchical closed-loop correction rules. By integrating deviation records, correction strategies, and correction execution results, correction tags are generated and bound to spatiotemporal IDs, filling process data, and process sequence in three layers. Then, they are integrated according to the filling stage sequence to form a synchronous filling dataset with correction tags.

6. The digital filling system for medical iodine swabs based on closed-loop control according to claim 5, characterized in that, The methods for forming a full-link cross-workstation data chain include: Based on the synchronous filling dataset, when a cotton swab product bound to a spatiotemporal ID is transferred to the downstream workstation, cross-workstation data addressing is performed using the spatiotemporal ID as the unique index, forming data pairing between upstream and downstream workstations. Retrieve the core filling features in the synchronous filling dataset that affect downstream processes, analyze the degree of impact on the processing quality of downstream processes based on deviation records through cross-workstation linkage logic, determine the feedforward correction requirements, and generate feedforward correction instructions accordingly. The upstream synchronous filling dataset, feedforward correction instructions, and downstream workstation pending processing data are collected to form upstream and downstream multi-workstation data, which are bound to spatiotemporal IDs and encapsulated into a full-link cross-workstation data chain according to the process sequence.

7. The digital filling system for medical iodine swabs based on closed-loop control according to claim 6, characterized in that, The methods for generating standardized quality labels and binding them to spatiotemporal IDs include: Based on the dedicated full-chain cross-workstation data chain for each cotton swab, filling quality verification indicators are screened according to quality feature extraction rules. Based on filling quality verification indicators, the filling process quality of a single cotton swab is subject to multi-dimensional compliance verification and graded judgment. A standardized quality label containing quality grade and deviation information is generated and bound to spatiotemporal ID and full-link cross-workstation data chain to form a single-swab quality judgment dataset.

8. The digital filling system for medical iodine swabs based on closed-loop control according to claim 7, characterized in that, The methods for forming a closed-loop data stream and a reverse correction instruction set include: For single cotton swabs with non-compliant standardized quality labels, non-compliant products are classified into remedial general non-compliance and irremedial severe non-compliance based on a dedicated single-swab quality judgment dataset. For general non-conforming products, the filling deviation type is extracted, the source is traced and located, a reverse correction instruction is generated and routed to the upstream workstation; at the same time, severely non-conforming products are precisely rejected according to their spatiotemporal ID. By integrating the full-chain cross-workstation data of a single cotton swab, the single-swab quality judgment dataset, and the handling of non-conforming products, reverse correction instructions, rejection information, and deviation traceability information, a closed-loop data flow with quality conclusions is formed, and similar reverse correction instructions are collected to form a reverse correction instruction set.

9. The digital filling system for medical iodine swabs based on closed-loop control according to claim 8, characterized in that, The methods for generating a single cotton swab's production file from the archived full-process production data include: Based on the closed-loop data flow and reverse correction instruction set of a single cotton swab, the data of the entire production process of a single cotton swab is collected and locked according to the latching rules, and a unique production file for each swab is generated according to a preset standard structure with spatiotemporal ID as the index.

10. The digital filling system for medical iodine swabs based on closed-loop control according to claim 9, characterized in that, The method of dynamically optimizing the control benchmark based on qualified product data during non-production periods includes: Based on the unique production file of each cotton swab, the product data of qualified single cotton swabs are selected during non-production periods on the production line for basic process statistical analysis. Based on the statistical results, the control benchmark of the entire filling process is dynamically optimized and archived for iteration, forming a closed-loop iterative management and control of the entire process.

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