A modular integration method for a cosmetic workshop ingredient dispensing system

Abstract

This invention relates to the field of cosmetic production automation technology, and more particularly to a modular integration method for a cosmetic workshop ingredient dispensing system, comprising the following steps: dividing the ingredient dispensing system into multiple modules, configuring electronic identification tags and binding them to integrated standard interfaces, and generating initial binding data; parsing the initial binding data through a master station and establishing distributed real-time communication links with the slave controllers of each module; constructing reconfigurable pipeline topology data through a path planning algorithm and generating independent closed-loop cleaning path data; decomposing the ingredient formula data into the smallest process atomic units and generating sequence data for dynamic matching and allocation to each corresponding slave controller; executing the dispensing operation and transmitting real-time dispensing execution data back, with the master station self-calibrating and compensating for the timing and metering parameters of multi-module collaboration. This invention achieves intelligent integration of cosmetic ingredient dispensing through modular encapsulation and quantitative planning, ensuring accurate and hygienic dispensing, and adapting to small-batch production of multiple product categories.

Description

Technical Field

[0001] This invention relates to the field of cosmetic production automation technology, and in particular to a modular integration method for a cosmetic workshop ingredient dispensing system. Background Technology

[0002] The cosmetics industry is rapidly developing towards multi-category, small-batch, and refined production. Consumers are demanding higher standards for the diversity of cosmetic formulas and the stability of quality. As a core process in cosmetic production, the efficiency, accuracy, and hygiene control of the ingredient preparation process directly determine product quality. Therefore, the industry's need for flexible adaptation, automated control, and standardized integration of ingredient preparation systems is increasing. Modular integration technology, with its advantages of flexible functional decomposition, convenient equipment maintenance, and rapid adaptation to multi-category production, has become a core technology direction for upgrading and transforming cosmetic ingredient preparation systems. Currently, the industry generally decomposes ingredient preparation systems into several independent modules according to their functions, and centrally controls each module through a main station. Some technical solutions configure basic identification tags for modules, attempt to combine algorithms for pipeline path design, and decompose ingredient formulas into simple process units and assign them to each module for execution, while also making basic calibration and adjustment of metering parameters to improve the operational flexibility of the ingredient preparation system.

[0003] However, the existing modular integration method of the ingredient preparation system in cosmetic workshops still has many technical defects. The overall level of standardization, automation and intelligence is low, which cannot meet the requirements of refined cosmetic production. Among them, the defects in the collaborative control of module integration are particularly prominent. There is a lack of efficient distributed communication link construction scheme between the master station and each module. It is impossible to realize the automatic loading and configuration of control logic based on the module's own parameters. The collaborative execution between modules lacks a precise parameter correction mechanism. The timing and measurement deviations of multi-module collaboration can only be subject to simple manual intervention or basic correction. It is impossible to realize dynamic and accurate self-correction compensation based on actual production operation data. This results in poor synchronization of multi-module collaborative execution and low ingredient accuracy. In addition, there are problems such as the lack of unified binding specifications for module interfaces, the lack of scientific basis for pipeline path planning, the lack of standardized criteria for formula process disassembly, and the lack of independent closed-loop process for module cleaning. These problems further affect the operating efficiency and ingredient quality of the ingredient preparation system, making it difficult to meet the hygiene requirements and batch consistency requirements of cosmetic production. Summary of the Invention

[0004] To address the technical deficiencies in the background technology, this invention proposes a modular integration method for a cosmetic workshop ingredient preparation system, which solves the aforementioned technical problems and meets practical needs. The specific technical solution is as follows: A modular integration method for a cosmetic workshop ingredient dispensing system includes the following steps: The batching system is broken down and independently encapsulated into multiple modules in a closed loop. Electronic identification tags are configured and identity and technical parameters are pre-stored. Integrated standard interfaces are bound to generate initial binding data. Based on the electronic identity tag, the main station parses the initial binding data and automatically loads the corresponding control logic and communication variables, and builds a distributed real-time communication link with the slave controllers of each module to generate basic data for online operation. Based on online operational data, reconfigurable pipeline topology data is constructed using a path planning algorithm to generate independent closed-loop cleaning path data. Receive ingredient formula data, break it down into the smallest process atomic unit and generate sequence data, and dynamically match and allocate it to each corresponding slave controller in combination with online operation basic data; Each slave station controller executes the batching operation and sends back real-time batching execution data to the master station. The master station performs self-calibration and compensation for the timing and metering parameters of the multi-module collaboration.

[0005] Furthermore, the specific steps for generating the initial binding data are as follows: The batching system is divided into a raw material storage module, a metering module, a micro-feeding module, a mixing and homogenizing module, and a conveying and distribution module according to the closed-loop characteristics of the functions, thus completing the functional independent packaging of each module; Configure a unique electronic identity tag for each packaged module. Pre-write and store the module type, material compatibility type, hygiene level, IO point table, control algorithm library, calibration parameters, and load compatibility range identity and technical parameters in the electronic identity tag to generate a module electronic identity dataset. An integrated standard interface is configured for the physical connection end of each module. The integrated standard interface includes a physical mechanical interface, a sanitary pipeline interface, and a dual-redundant real-time communication interface. The three types of interfaces are standardized and bound to generate an interface standardization matching dataset. The module electronic identity dataset is uniquely associated and bound with the interface standardized matching dataset and the module hardware ontology to form the initial binding data that integrates the module hardware and electronic data, thus completing the basic configuration of each module.

[0006] Furthermore, the specific steps for generating the basic data for online operation are as follows: With the central dispatch master station as the control core, the master station scans and identifies the electronic identity tags of each module through an integrated standard interface, retrieves the corresponding initial binding data through the tags and transmits it to the data parsing unit of the master station to generate the original identity data of the module. The main station's data parsing unit parses and extracts the original data of the module identity to obtain the core parsing data of the module type, control requirements, and communication adaptation parameters, and transmits the core parsing data to the main station's logic matching unit. The logic matching unit retrieves the control logic program, interlocking protection logic, and communication variable table that match the core parsing data from the module control logic database built into the main station, completes the automatic loading of control logic and communication variables, and generates the module configuration completion signal; After receiving the module configuration completion signal, the master station establishes a distributed real-time communication link with the built-in slave controllers of each module, and sets up a communication status detection mechanism in the link to realize bidirectional interaction between the master station scheduling command data and the slave station execution feedback data. The main station aggregates and integrates the configuration data, communication link status data, and basic module operation data of each module to generate online operation basic data that includes the operating status of all online modules.

[0007] Furthermore, the specific steps for generating independent closed-loop cleaning path data are as follows: The main station extracts the identity information, material compatibility type, and hygiene level data of each module from the online operation basic data, and uses them as input parameters for the path planning algorithm, which are then transmitted to the path planning unit of the main station. The path planning unit outputs valve position control commands to the sanitary adaptive isolation valve group of the pipeline node between modules through the path planning algorithm. By dynamically adjusting the valve position, a reconfigurable pipeline topology structure without blind pipes and dead angles is constructed, and corresponding reconfigurable pipeline topology data is generated. Based on reconfigurable pipeline topology data, the main station combines the module's operating status and material residue characteristics to plan independent cleaning processes for individual modules, single pipeline segments, or single material pathways, generating independent closed-loop cleaning path data that covers the cleaning path, cleaning range, and cleaning connection logic.

[0008] Furthermore, the specific steps for dynamically matching and allocating data to the corresponding slave controllers based on online operational data are as follows: The main station receives complete data of cosmetic ingredient formulas to be executed, extracts core formula information such as material composition, process execution requirements, precision control standards, and sequence of processes, and generates a core formula information set. Based on the closed-loop characteristics of the material handling process of cosmetic ingredients, the core information set of the formula is decomposed into the smallest independent and individually executable process atomic units. Each process atomic unit is configured with corresponding execution parameters to generate a parameterized process atomic unit dataset. Based on the process execution logic of the formula, the parameterized process atomic unit dataset is arranged in a time sequence, the connection triggering conditions of adjacent process atomic units are set, and continuous process atomic unit sequence data is generated. The master station extracts real-time load status, metering accuracy level, and material compatibility data of each module from the online operation basic data, dynamically matches the process atomic unit sequence data, and assigns each process atomic unit to the module slave controller with compatible functions to complete the distribution and distribution of sequence data.

[0009] Furthermore, the specific steps for the main station to self-calibrate and compensate for the metering parameters of the multi-module collaboration are as follows: The main station sets metering deviation trigger thresholds that are compatible with each material type based on the metering characteristics of different material types, and generates a material metering deviation threshold dataset. The actual measurement values ​​of the process atomic units of each module are extracted from the real-time batching execution data returned by each slave station controller. The actual measurement values ​​are compared with the theoretical measurement values ​​corresponding to the batching formula data to obtain the actual measurement deviation value of each module and generate a real-time measurement deviation dataset of the module. The values ​​in the real-time metering deviation dataset of the module are compared with the corresponding thresholds in the material metering deviation threshold dataset. When the metering deviation value of a certain module exceeds the corresponding trigger threshold, a metering correction start signal is generated to start the metering parameter self-correction compensation process. After receiving the metering calibration start signal, the master station retrieves the factory calibration parameters from the initial binding data, and at the same time extracts the real-time operating environment data and material characteristic data from the online operating basic data, and transmits them to the metering coefficient correction unit of the master station. The measurement coefficient correction unit dynamically corrects the measurement coefficients based on the factory calibration parameters and real-time operation data, generates the corrected measurement coefficient data, and sends it to the slave controller of the corresponding module. The slave controller adjusts its own metering execution parameters based on the corrected metering coefficient data, re-executes the metering operation, and sends the new metering execution data back to the master station. The master station detects the new metering deviation value until the metering deviation value is lower than the corresponding trigger threshold, completes the self-calibration compensation of the metering parameters, and the master station synchronously updates the real-time batching execution data.

[0010] Furthermore, the specific steps for the master station to self-correct and compensate for the timing parameters of multi-module collaboration are as follows: Based on the updated real-time batching execution data, the main station extracts the actual execution time data, theoretical preset time data, and preset total execution time data of the process atomic unit sequence of each module, analyzes the timing deviation of the process atomic unit executed by multiple modules, generates a timing correction start signal, and starts the timing parameter self-correction compensation process of multi-module collaboration. Based on the timing correction start signal, the master station calculates the timing correction compensation value for each module, taking into account the total number of modules participating in the execution of the process atomic unit sequence and the viscosity characteristics of the material. The compensation value is then sent to the corresponding slave controller. The slave controller adjusts its own start and stop times for executing the corresponding process atomic unit according to the compensation value, thus completing the self-correction compensation of timing parameters for multi-module collaboration and achieving timing synchronization of process atomic units executed by multiple modules.

[0011] Furthermore, the timing correction compensation value for each module is calculated using a timing self-correction formula, as follows: , in, For the i-th module, execute the timing correction compensation value for the corresponding process atomic unit. The preset total execution time is denoted by n, where n is the total number of modules participating in the execution of this process atomic unit sequence. The theoretically preset execution time for the corresponding process atomic unit of the j-th module. This is a timing deviation correction factor, dynamically adjusted based on the viscosity characteristics of the ingredients. The actual deviation time for executing the corresponding process atomic unit for the j-th module.

[0012] Furthermore, when generating the corresponding reconfigurable pipeline topology data, an optimal cleaning path quantification evaluation formula is used to achieve cleaning path planning with no blind pipes and low flow resistance. The optimal cleaning path quantification evaluation formula is as follows: , in, The optimal comprehensive evaluation value for the cleaning path is given by m, where m is the total number of candidate pipe segments in the pipe topology. Let x be the actual pipe length of the x-th pipe segment. Let x be the material residue risk coefficient for the x-th pipeline segment. Let x be the flow resistance coefficient of the x-th pipeline segment; The main station will use module identity information, material compatibility type, and hygiene level data extracted from the online operation basic data as input conditions to process each candidate pipeline segment. and Dynamically assign values ​​and substitute them into the optimal quantitative evaluation formula for cleaning paths to calculate the corresponding topologies of all candidate pipelines. Select The pipeline topology corresponding to the minimum value is the target reconfigurable pipeline topology, and the corresponding reconfigurable pipeline topology data is generated.

[0013] Furthermore, after generating the independent closed-loop cleaning path data, the process also includes performing independent closed-loop cleaning based on the independent closed-loop cleaning path data, specifically including the following steps: The main station generates a dataset of cleaning path and process parameter combinations based on the generated independent closed-loop cleaning path data, combined with the module's hygiene level and material residue characteristics, and matches the corresponding cleaning process parameters. The combined dataset of cleaning path and process parameters is sent to the slave controller of the corresponding module through a distributed real-time communication link. After receiving the data, the slave controller parses the cleaning path and process parameters and generates the module cleaning execution command. The slave controller controls the execution components of the module to perform independent closed-loop cleaning operations according to the cleaning path and process parameters, based on the module cleaning execution instructions. After the cleaning operation is completed, the module's built-in residual detection sensor collects data on the material residue in the pipeline; The residual material data is compared with the preset residual material threshold. If the residual detection data is lower than the preset threshold, the cleaning is deemed qualified and a cleaning qualified signal is generated. At the same time, the cleaning completion status of the module is associated with and stored with the cleaning-related data. If the residual detection data is higher than the preset threshold, the cleaning is deemed unqualified and a secondary cleaning instruction is generated and sent to the slave controller to re-execute the cleaning operation until the residual detection data is lower than the preset threshold.

[0014] Compared with the prior art, the modular integration method of the cosmetic workshop ingredient dispensing system provided by the present invention has the following beneficial effects: This invention decomposes and encapsulates the batching system into functional closed-loop modules and binds them with integrated standard interfaces. Each module is assigned a unique electronic identification tag, and unified initial binding data is generated. Relying on the main station, it achieves automatic module identification, automatic loading of control logic, and efficient construction of distributed real-time communication links. This generates online operational foundation data containing the full operational status of each module. Simultaneously, it achieves reconfigurable pipeline topology planning with no blind pipes and low flow resistance through quantitative evaluation formulas. Furthermore, it generates independent closed-loop cleaning path data by combining module operational status and material characteristics. The invention also decomposes the batching formula into standardized minimum process atomic units, and based on the online operational foundation data, it achieves dynamic matching and precise allocation of process atomic unit sequence data, further enhancing its functionality. A systematic self-calibration compensation scheme was established, realizing precise self-calibration of timing and metering parameters during multi-module collaborative execution. It is also equipped with an independent closed-loop cleaning execution process, effectively solving the problems of low standardization of module integration, insufficient automation of communication link construction, unreasonable pipeline planning, lack of dynamic logic in formula process allocation, low accuracy of multi-module collaborative calibration, and material residue and cross-contamination in existing technologies. It significantly improves the automation, intelligence and standardization level of modular integration of the ingredient system in cosmetic workshops, ensuring the hygiene, accuracy and stability of cosmetic ingredient production. It perfectly adapts to the refined production needs of multiple categories and small batches of cosmetics, and significantly improves the overall operating efficiency of the ingredient system and the consistency of production batches. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a modular integration method for a cosmetic workshop ingredient preparation system according to the present invention. Detailed Implementation

[0016] In the description of this invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "middle," and "inner," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, it should be noted that unless otherwise explicitly specified and limited, the terms "installed," "connected," and "joined" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention through specific circumstances.

[0017] The embodiments of the present invention will be described below with reference to the accompanying drawings and related examples. The embodiments of the present invention are not limited to the following examples, and the present invention relates to the relevant necessary components in this technical field, which should be regarded as well-known technology in this technical field and can be known and mastered by those skilled in this technical field.

[0018] See Figure 1 This invention provides a modular integration method for a cosmetic workshop ingredient preparation system, characterized by the following steps: Step S100: Disassemble the batching system and encapsulate it into multiple independent closed-loop modules, configure electronic identification tags and pre-store identity and technical parameters, bind integrated standard interfaces, and generate initial binding data; The ingredient mixing system is a process system used to complete the proportioning, conveying, and mixing of cosmetic raw materials. It is an integrated system encompassing functional units such as material storage, metering, conveying, mixing, and cleaning, used to achieve precise, stable, and hygienic ingredient mixing operations in cosmetic production. A module is a physical and control closed-loop unit formed by independently encapsulating a complete functional unit of the ingredient mixing system. It is used to improve the flexibility of system reconfiguration and ease of maintenance, supporting rapid switching between multiple product categories. An electronic identification tag is a non-volatile electronic identification device bound to the module, used to store the module's unique identity information and technical parameters, enabling automatic identification and parameter reading upon module access. Technical parameters are a set of quantitative indicators describing the module's physical performance and operational capabilities, used as the basic input data for control logic configuration and path planning. An integrated standard interface is a composite interface structure integrating unified specifications for mechanical connections, fluid channels, electrical signals, and communication protocols, used to ensure plug-and-play compatibility between different modules at the structural and communication levels. Initial binding data is an initialization data packet jointly generated by the electronic identification tag and the integrated standard interface when the module accesses the system, used as the basis for the master station to perform control logic matching and communication variable mapping.

[0019] Step S200: Based on the electronic identity tag, the main station parses the initial binding data and automatically loads the corresponding control logic and communication variables, and builds a distributed real-time communication link with the slave station controllers of each module to generate basic data for online operation. The master station is the central control unit in the batching system, responsible for global scheduling, data integration, and collaborative control decisions, enabling centralized monitoring and dynamic control of multiple modules. Slave controllers are local control units deployed within each module, executing specific process actions and providing operational status feedback, ensuring the module's autonomous operation while accepting unified scheduling from the master station. The distributed real-time communication link is a low-latency, highly reliable data transmission channel established between the master station and each slave controller, ensuring the synchronous transmission of control commands and status data. Online operational basic data is a set of module statuses uploaded in real-time by each slave station during system operation, reflecting the system's current operating status and serving as dynamic input for path planning and task allocation.

[0020] Step S300: Based on the online operation data, construct reconfigurable pipeline topology data through path planning algorithm to generate independent closed-loop cleaning path data; The path planning algorithm is a mathematical calculation method based on constraints to optimize material transport paths, generating reconfigurable pipeline connection schemes that meet the requirements of no blind pipes and low flow resistance. Reconfigurable pipeline topology data is a dynamic data structure describing the material flow paths between modules, used to guide the switching combinations of solenoid valve groups to form the optimal material transport path. Independent closed-loop cleaning path data is dedicated to describing isolated fluid circulation paths during the cleaning process, ensuring that the cleaning fluid completely covers and wets the surface without connecting to other production pipelines, preventing cross-contamination.

[0021] Step S400: Receive the ingredient formula data, break it down into the smallest process atomic unit and generate sequence data, and dynamically match and allocate it to each corresponding slave controller in combination with the online operation basic data. Ingredient formulation data is a set of data defining the types, proportions, addition order, and process conditions of raw materials required for the product, used as the original input instructions for the ingredient formulation task. The smallest process atomic unit is an indivisible, standardized process unit derived from the ingredient formulation, used to achieve standardized expression of processes and flexible scheduling across modules. Sequence data is a combination of the smallest process atomic units arranged in chronological or logical order, used to represent the execution order of the complete ingredient formulation process. Sequence data is dynamically matched and allocated to the corresponding slave controllers for execution.

[0022] In step S500, each slave station controller executes the batching operation and sends back real-time batching execution data to the master station. The master station performs self-correction and compensation for the timing and metering parameters of the multi-module collaboration.

[0023] Real-time batching execution data consists of actual operating parameters transmitted back from the slave station controller during the execution of process atomic units. These parameters are used to verify execution accuracy and support subsequent correction calculations. Timing and metering parameters are key control variables describing the time sequence and material quantity in the collaborative execution of multiple modules. They are used to determine the start-up timing and feeding accuracy of each module.

[0024] Taking the rapid switchover of multi-category emulsion production lines as an example, the application method of this invention is as follows: When the production line needs to switch from type A emulsion to type B essence, the operator replaces the corresponding weighing and mixing module. The main station automatically reads the electronic identification tag of the new module, parses its technical parameters, and loads the appropriate control logic. Based on the current module layout and material characteristics, the system recalculates the blind pipe-free conveying path and generates a dedicated cleaning circuit for oily raw materials. After receiving the new formula, the main station breaks it down into process atomic units such as "precise refueling" and "constant temperature emulsification," and dynamically allocates tasks according to the real-time load of each module. During execution, the main station monitors the feeding sequence and flow deviation of each module and automatically adjusts the start-up delay and pump speed setting for the next cycle to ensure that the final mixing ratio is accurate and consistent.

[0025] This invention forms a complete modular integration technology system for cosmetic workshop ingredient systems through five core steps, characterized by data-driven, closed-loop control. From the standardized binding of basic module configurations, to the automatic identification, configuration, and construction of distributed real-time communication links for modules by the master station, to scientific pipeline topology planning and independent closed-loop cleaning path design based on online operational data, followed by the standardized decomposition of formulas and dynamic allocation based on real-time module status, and finally, the self-calibration and compensation of multi-module collaborative timing and metering parameters, each step progresses progressively with bidirectional data flow. This achieves full-process standardization, automation, and intelligence of the cosmetic ingredient system modular integration, from hardware to software, and from basic configuration to collaborative execution. It effectively solves the core problems of traditional modular integration methods, such as low standardization, poor master-slave station communication coordination, lack of scientific basis for pipeline planning, lack of dynamic logic in formula process allocation, and insufficient accuracy of multi-module collaborative parameter calibration. It significantly improves the collaborative execution efficiency between modules and the metering accuracy of cosmetic ingredients. Simultaneously, the design of independent closed-loop cleaning paths ensures the hygiene of ingredient production, improving the overall batch consistency of cosmetic ingredient production and perfectly adapting to the multi-category, small-batch, and refined production development needs of the cosmetic industry.

[0026] In one embodiment of the present invention, the specific steps for generating the initial binding data are as follows: Step S101: The batching system is divided into raw material storage module, metering module, micro-feeding module, mixing and homogenizing module, and conveying and distributing module according to the functional closed-loop characteristics, thus completing the functional independent packaging of each module. Based on the closed-loop characteristics of material handling in the cosmetic ingredient preparation process, the overall ingredient preparation system is divided into five major functional modules: raw material storage module, metering module, micro-feeding module, mixing and homogenizing module, and conveying and distributing module. The division of each module follows the principle that "each module can complete its own complete material handling process," ensuring that each module has clear functional boundaries, independent operating logic, and can independently realize the corresponding ingredient preparation process operation. Furthermore, there is no functional overlap between modules, and the process connection is smooth. The five modules are then independently encapsulated in a closed loop. The encapsulation process ensures the integrity of the module's hardware structure, operational independence, and ease of maintenance, completing the functional independence encapsulation of each module and providing a physical carrier for subsequent standardized configuration.

[0027] Step S102: Configure a unique electronic identity tag for each packaged module. Pre-write and store the module type, material compatibility type, hygiene level, IO point table, control algorithm library, calibration parameters, and load compatibility range identity and technical parameters in the electronic identity tag to generate a module electronic identity dataset. Each functionally independent encapsulated module is assigned a unique electronic identification tag to ensure a one-to-one correspondence between the tag and the module's hardware, with no duplication or omission. In accordance with the modular control requirements of the cosmetic ingredient system, a complete set of identity and technical parameters, including module type, material compatibility type, hygiene level, I / O point table, control algorithm library, calibration parameters, and load compatibility range, are pre-written and stored within the electronic identification tag. All parameters are highly matched to the module's actual functions, operating characteristics, and compatibility requirements. All parameters stored within the tag are then structurally integrated to form a dedicated module electronic identification dataset. This dataset serves as the module's digital identity identifier and is the core electronic data basis for subsequent main station identification and configuration.

[0028] Step S103: Configure integrated standard interfaces for the physical connection ends of each module. The integrated standard interfaces include physical mechanical interfaces, sanitary pipeline interfaces and dual redundant real-time communication interfaces. Standardize and bind the three types of interfaces respectively to generate an interface standardization matching dataset. An integrated standard interface is uniformly configured at the physical connection ends of each module. This interface is an integrated structure containing three core sub-interfaces: physical mechanical interfaces, sanitary piping interfaces, and dual-redundant real-time communication interfaces. It covers all dimensions of docking needs between modules, including hardware splicing, material piping connections, and data communication. A unified standardized binding specification is implemented for each of the three types of sub-interfaces. A unified splicing and fixing standard is established for the physical mechanical interfaces, a connection and sealing standard that meets the hygiene requirements of cosmetic production is established for the sanitary piping interfaces, and a unified communication protocol and data transmission standard is established for the dual-redundant real-time communication interfaces. The standardized binding parameters and matching rules of the three types of sub-interfaces are structurally integrated to generate a standardized interface matching dataset, ensuring that the interface docking of each module has universality and consistency.

[0029] Step S104: Uniquely associate and bind the module electronic identity dataset with the interface standardized matching dataset and the module hardware ontology to form the initial binding data that integrates the module hardware and electronic data, thus completing the basic configuration of each module.

[0030] The module electronic identity dataset and the interface standardization matching dataset are integrated, and then the integrated dataset is uniquely associated with the module hardware. The binding process ensures a one-to-one correspondence between electronic data and hardware, realizing the three-in-one fusion of "physical hardware-electronic identity-interface standard" for the module. After binding, the initial binding data of the module hardware and electronic data is formed, which contains all the basic information of the module's physical characteristics, electronic identity, and interface standard. After completing the above configuration operations for all modules, the standardized basic configuration of all modules of the cosmetic workshop ingredient system is realized, providing unified and complete basic data for the subsequent scanning and recognition, data parsing, and logic configuration of the main station.

[0031] This invention, through refined and standardized design of the initial binding data generation steps, achieves the scientific decomposition and independent encapsulation of cosmetic ingredient system modules according to their functional closed-loop characteristics. This ensures the independence of module functions and the smoothness of process connections. Simultaneously, each module is equipped with a unique electronic identity tag and pre-stores all identity and technical parameters, constructing a module-specific digital identity identifier. Furthermore, through the standardized binding of integrated standard interfaces, it achieves standardized connection across all dimensions of module hardware splicing, pipeline connection, and data communication. Finally, through the unique association binding of electronic data and the hardware itself, it forms initial binding data that integrates "hardware-identity-interface," completely solving the problems of inconsistent interfaces, lack of dedicated identity recognition data, and disconnect between electronic data and the hardware itself in traditional cosmetic ingredient system module integration. This achieves standardization, normalization, and digitization of the module's basic configuration, providing a unified, complete, and reliable hardware and data foundation for subsequent automatic scanning and identification of modules by the main station, automatic loading of control logic, and construction of distributed real-time communication links. It fundamentally improves the standardization of modular integration of the cosmetic ingredient system, laying a solid foundation for the automated, intelligent, and collaborative control of the entire system.

[0032] In one embodiment of the present invention, the specific steps for generating the basic data for online operation are as follows: Step S201: Using the central dispatch master station as the control core, the master station scans and identifies the electronic identity tags of each module through the integrated standard interface, retrieves the corresponding initial binding data through the tag and transmits it to the data parsing unit of the master station to generate the original identity data of the module. The central dispatch master station is the core control unit in the modular integration of the cosmetic workshop's ingredient preparation system. It serves as the center for issuing instructions, receiving data, parsing and processing data, and configuring logic for the entire system. It is responsible for scanning and identifying each module, automatically configuring it, establishing communication links, and performing all core control operations such as path planning, formula allocation, and parameter calibration. The data parsing unit is the core data processing unit built into the central dispatch master station. Its main functions are to format, verify, and parse the initially bound data from the modules, removing redundant data and extracting core information such as module type, control requirements, and communication adaptation parameters.

[0033] The central dispatch master station serves as the control core of the entire batching system. Through the integrated standard interface of the module physical connection end, the master station continuously and automatically scans all modules in the system that have completed basic configuration, identifying the unique electronic identity tag of each module. Based on the identified electronic identity tag, the master station accurately retrieves the initial binding data corresponding to each module from the data storage unit, and transmits the retrieved initial binding data completely to the data parsing unit built into the master station. The data parsing unit first performs format regularization and data verification on the received initial binding data to ensure that the data is complete and error-free, and then generates the original module identity data without parsing processing, laying the foundation for subsequent core information extraction.

[0034] Step S202: The data parsing unit of the main station parses and extracts the original data of the module identity to obtain the core parsing data of module type, control requirements and communication adaptation parameters, and transmits the core parsing data to the logic matching unit of the main station. The main station's data parsing unit performs targeted parsing and information extraction on the original module identity data, eliminating redundant data and accurately obtaining three core types of information: module type, control requirements, and communication adaptation parameters. Among these, the module type determines the main station's logical matching direction, the control requirements clarify the module's operational control standards, and the communication adaptation parameters provide a basis for subsequent communication link construction. The data parsing unit then structurally integrates the three types of core information to generate core module parsing data. This core parsing data is then transmitted completely and in real-time to the main station's logical matching unit through the main station's internal data transmission channel, preparing for the automatic loading of control logic and communication variables.

[0035] Step S203: The logic matching unit retrieves the control logic program, interlocking protection logic, and communication variable table that match the core parsing data from the module control logic database built into the main station, completes the automatic loading of control logic and communication variables, and generates the module configuration completion signal. The module control logic database refers to the database built into the central dispatch master station. It stores control logic programs, interlock protection logic, and communication variable tables corresponding to various batching function modules. It is the core data carrier for the master station to achieve automatic module configuration. The logic matching unit refers to the core logic configuration unit built into the central dispatch master station. Its main function is to retrieve the matching control logic and communication variables from the module control logic database based on the core parsing data extracted by the data parsing unit, and automatically load them into the master station. The control logic program refers to the dedicated operation control program for different functional modules, tailored to the module's functional characteristics and production requirements. It is the core logical basis for the module to achieve automated operation and complete its dedicated batching process. The interlock protection logic refers to the logic protection rules designed to ensure module operation safety and system production stability. When module operating parameters exceed safety thresholds or the system experiences abnormal operating conditions, this logic automatically triggers protection actions, such as module shutdown and link disconnection, to prevent equipment damage or production accidents. The communication variable table records data transmission variables between the master and slave stations. It includes information such as variable name, variable type, variable range, and transmission frequency. It serves as the core parameter basis for establishing a communication link and enabling bidirectional data interaction between the master and slave stations. The module configuration completion signal is a trigger signal generated after the master station completes the control logic and automatic loading of communication variables for a specific module. It is a core condition for the master station to initiate and establish a distributed real-time communication link with that module, indicating that the module's control and communication configuration is complete.

[0036] After receiving the core parsed data, the logic matching unit automatically retrieves the control logic program, interlocking protection logic, and communication variable table that perfectly match the module from the module control logic database built into the main station, based on the module type as the index. This ensures that the retrieved logic and variables conform to the module's functional characteristics, operational requirements, and communication standards. The logic matching unit loads the retrieved control logic program and interlocking protection logic into the control channel of the corresponding module on the main station, and simultaneously imports the communication variable table into the communication configuration unit of the main station, completing the fully automated configuration of module control and communication. The entire process requires no manual intervention. After configuration, the main station automatically generates a module configuration completion signal as a trigger condition for subsequent communication link construction.

[0037] Step S204: After receiving the module configuration completion signal, the master station establishes a distributed real-time communication link with the built-in slave controller of each module, sets up a communication status detection mechanism in the link, and realizes bidirectional interaction between the master station scheduling command data and the slave station execution feedback data. The distributed real-time communication link refers to the real-time data transmission channel established between the central dispatch master station and the slave station controllers of each module, adopting a distributed architecture. The communication channels of each module are independent of each other; a communication failure in one module will not affect the normal communication between other modules and the master station, ensuring the stability of system communication. The communication status detection mechanism refers to the data monitoring rules set in the distributed real-time communication link. It can monitor the link's connectivity, data transmission rate, and data integrity in real time. When abnormalities such as communication interruption, data packet loss, or transmission delays occur, it will promptly report the abnormal information to the master station.

[0038] After receiving the module configuration completion signal, the central dispatch master station immediately triggers the communication link construction program. Based on the loaded communication variable table, it establishes distributed real-time communication links with the built-in slave controllers of each module. The links adopt a distributed architecture design, and the communication channels between each module and the master station are independent of each other. At the same time, a communication status detection mechanism is set in each communication link to monitor the communication connectivity, data transmission rate and data integrity in real time. After the link is built, the master station can send dispatch command data to the slave controller through the link. The slave controller can transmit the module's running status, execution feedback and other data back to the master station in real time, realizing stable and efficient two-way data interaction between the master station and the slave station.

[0039] Step S205: The main station summarizes and integrates the configuration data, communication link status data, and basic operation data of each module to generate online operation basic data containing the operation status of all online modules.

[0040] Module configuration data refers to the control and communication configuration information of each module formed after the master station completes the control logic and communication variable configuration for each module. It is the core data reflecting the module configuration status. Communication link status data refers to the information collected through the communication status detection mechanism that reflects the operating status of the communication link between each module and the master station, including link connectivity, transmission rate, and anomaly records. Module basic operation data refers to the information collected in real time by the slave controllers of each module that reflects the basic operating status of the module itself, including module start / stop status, current load baseline value, material adaptation status, and hardware operating status.

[0041] The main station comprehensively summarizes and structurally integrates the data of all modules that have completed configuration and established communication links within the system. The integrated data includes three parts: first, the configuration data of each module, namely the configuration information of control logic, interlocking protection logic, and communication variables; second, the communication link status data of each module, namely the link connectivity, transmission rate, detection results, and other information; and third, the basic operating data of each module, namely the module's real-time start / stop status, load baseline value, material adaptation status, and other information. The main station classifies and associates the above three types of data by module to form a complete and real-time dataset, namely the online operating basic data. This data is updated synchronously in real time and comprehensively reflects the operating status of all online modules within the system.

[0042] This invention, through refined and automated design of the steps for generating online operational basic data, achieves automatic scanning and identification of modules by the central dispatch master station, accurate parsing of initial binding data, and extraction of core information. Relying on the module control logic database, it completes the automatic configuration of control logic and communication variables without manual intervention, constructs a distributed real-time communication link with a status detection mechanism, and ensures the stability and efficiency of bidirectional data interaction between the master and slave stations. Finally, it aggregates and integrates to form online operational basic data containing module configuration, communication link, and full basic operational information. This completely solves the problems of low efficiency in manual configuration, lack of unified detection of communication links, and scattered and non-real-time module operation data in the modular integration of traditional cosmetic ingredient systems. It achieves full automation of master-slave station configuration and communication, constructs a stable master-slave station collaborative control communication foundation, and provides accurate, real-time, and comprehensive core data support for subsequent pipeline topology planning, dynamic allocation of process atomic unit sequence data, multi-module collaborative timing, and self-calibration compensation of metering parameters. This significantly improves the automation and intelligence level of modular integration of cosmetic ingredient systems and lays a solid communication and data foundation for the collaborative control of the entire system.

[0043] In one embodiment of the present invention, the specific steps for generating independent closed-loop cleaning path data are as follows: Step S301: The main station extracts the identity information, material compatibility type and hygiene level data of each module from the online operation basic data, and uses them as input parameters for the path planning algorithm, and transmits them to the path planning unit of the main station. The main station extracts three core data categories from the generated online operational data, according to preset parameter extraction rules: module identity information, material compatibility type, and hygiene level. Module identity information is used to locate the physical location of the module and its connection to the pipeline; material compatibility type is used to match the material transportation requirements of the pipeline; and hygiene level is used to define the cleaning standards of the pipeline. The main station performs validity verification and structured processing on the extracted three types of data, eliminating invalid, abnormal, and duplicate data. The standardized data is then categorized and associated by module to form a dedicated input parameter set for the path planning algorithm. Subsequently, this input parameter set is transmitted to the dedicated path planning unit within the main station via its high-speed data transmission channel, providing accurate and complete data for subsequent pipeline topology planning.

[0044] Step S302: The path planning unit outputs valve position control commands to the sanitary adaptive isolation valve group of the inter-module pipeline node through the path planning algorithm. By dynamically adjusting the valve position, a reconfigurable pipeline topology structure without blind pipes and dead angles is constructed, and corresponding reconfigurable pipeline topology data is generated. After receiving the input parameter set, the path planning unit loads the preset path planning algorithm and starts the operation. The algorithm performs directional valve position planning for the sanitary adaptive isolation valve groups of the pipeline nodes between modules based on the module's location, material compatibility type, and sanitary level. The path planning unit converts the algorithm's calculation results into standardized valve position control commands, which are then sent to the sanitary adaptive isolation valve groups of each pipeline node through a distributed real-time communication link. After receiving the commands, the valve groups dynamically adjust the corresponding valve positions. Through the combination and adjustment of valve positions, a reconfigurable pipeline topology structure that adapts to the current production needs and meets the requirements of no blind pipes and no dead zones is constructed. The path planning unit performs digital modeling and data recording of the adjusted pipeline topology structure. The recorded content includes core information such as pipeline connection relationships, valve position status, pipeline segment ownership, and material conveying direction. Finally, the corresponding reconfigurable pipeline topology data is generated and synchronously stored in the main station's database.

[0045] Step S303: Based on the reconfigurable pipeline topology data, the main station plans an independent cleaning process for a single module, a single pipeline segment, or a single type of material path, taking into account the module's operating status and material residue characteristics, and generates independent closed-loop cleaning path data that covers the cleaning path, cleaning range, and cleaning connection logic.

[0046] The main station retrieves the generated reconfigurable pipeline topology data from the database and, combined with two key pieces of information extracted from the online operation data—the real-time operating status of the modules and the residual material characteristics of the corresponding pipelines—initiates the cleaning path planning program. Following the principle of "cleaning on demand, dedicated closed loop," the program plans independent cleaning processes for individual modules, pipeline segments, or material pathways. Module operating status is used to determine the timing and scope of cleaning, while residual material characteristics are used to match the closed-loop logic of the cleaning path. The planning program precisely designs the cleaning start point, cleaning path direction, cleaning end point, and connection sequence of each pipeline segment for each cleaning object, ultimately generating digital cleaning process data covering the cleaning path, cleaning scope, and cleaning connection logic—that is, independent closed-loop cleaning path data. This data is uniquely associated with the corresponding module and pipeline and synchronously stored in the main station database, providing direct data support for subsequent cleaning operations.

[0047] In one embodiment of the present invention, the specific steps of dynamically matching and allocating data to the corresponding slave controllers based on online operating basic data are as follows: Step S401: The main station receives the complete data of the cosmetic ingredient formula to be executed, extracts the core formula information such as material composition, process execution requirements, precision control standards, and sequence of processes from the formula, and generates a formula core information set. The central dispatch master station receives complete data of cosmetic ingredient formulas to be executed through the system data interaction interface. This data includes all information such as the formula's bill of materials, process parameters, precision requirements, process sequence, and material handling specifications. The master station performs format verification and validity screening on the received complete formula data, eliminating invalid, duplicate, and conflicting redundant data. Then, according to preset extraction rules, it accurately extracts four types of core formula information: ingredient composition, process execution requirements, precision control standards, and process sequence. Among them, the ingredient composition clarifies the types and proportions of raw materials, the process execution requirements define the operational standards for each process, the precision control standards determine the metering and processing accuracy thresholds of the ingredients, and the process sequence standardizes the process execution logic of the ingredients. The master station integrates and classifies the extracted four types of core information into a standardized set of core formula information, providing clear and accurate data basis for subsequent formula process breakdown.

[0048] Step S402: Based on the closed-loop characteristics of the material handling process of cosmetic ingredients, the core information set of the formula is decomposed into the smallest process atomic units that are independent of each other and can be executed individually. The corresponding execution parameters are configured for each process atomic unit to generate a parameterized process atomic unit dataset. Based on the core information set of the formula, the main station strictly follows the closed-loop characteristics of material handling procedures in cosmetic ingredients, breaking down the overall formula process into several independent and individually executable minimum process atomic units. The principle of decomposition is that each unit completes only one exclusive material handling operation and can be executed independently without relying on other units. For example, the metering of a single raw material, raw material preheating, single-stage mixing and homogenization, and the addition of trace ingredients are all independent minimum process atomic units. The main station configures exclusive execution parameters for each decomposed minimum process atomic unit that match its operation type. These parameters include process execution temperature, execution time, operation accuracy, material handling volume, equipment operation requirements, etc., ensuring that each unit has clear and implementable execution basis. The main station structurally integrates all minimum process atomic units with exclusive execution parameters, classifying them by operation type to form a parameterized process atomic unit dataset, providing the basic units for subsequent time-series arrangement.

[0049] It should be noted that the "independence" of the "smallest process atomic unit" in this invention mainly refers to its independent and complete process unit when defined, configured, and assigned to a single module for execution. However, when performing timing optimization and global efficiency correction for multi-module collaboration at the system level, the master station needs to integrate the execution data of all participating process atomic units for global calculation and compensation to achieve optimal collaboration across the entire production sequence. This "independence of process definition" and "globality of system correction" together form the basis for the flexible and precise collaboration of this invention.

[0050] Step S403: According to the process execution logic of the formula, the parameterized process atomic unit dataset is arranged in a time sequence, the connection triggering conditions of adjacent process atomic units are set, and continuous process atomic unit sequence data is generated. Based on the sequence of processes in the core information of the formula, the main station arranges each unit in the parameterized process atomic unit dataset in an ordered and time-sequential manner, restoring the complete process execution logic of cosmetic ingredients. At the same time, it sets clear connection trigger conditions for adjacent process atomic units. These conditions are the basis for determining the start of the next unit, including the completion signal of the previous unit, the material arrival detection signal, the equipment status ready signal, and the process parameter compliance signal, to ensure smooth and uninterrupted execution of each unit. The main station then integrates all the time-sequentially arranged process atomic units with configured connection trigger conditions into a continuous and sequentially executable process atomic unit sequence data. This data completely restores the process execution flow of the formula, and each step has clear execution standards and connection rules.

[0051] Step S404: The master station extracts the real-time load status, metering accuracy level, and material compatibility data of each module from the online operation basic data, dynamically matches the process atomic unit sequence data, and assigns each process atomic unit to the module slave controller with compatible functions, thus completing the allocation and distribution of sequence data.

[0052] The main station accurately extracts three key operational data points from the online operational data: real-time load status, metering accuracy level, and material adaptability. Real-time load status reflects the module's current workload, preventing task allocation from causing module overload. Metering accuracy level matches the precision control requirements of the process atomic units, and material adaptability ensures the module can handle the raw material types of the corresponding processes. The main station loads a pre-set dynamic matching algorithm for process tasks, performing multi-dimensional matching between each unit in the process atomic unit sequence data and the aforementioned three types of operational data from each module. The matching principle is "accuracy adaptability, reasonable load, and material matching." For example, a process unit for high-precision micro-addition is assigned to the micro-feeding module, a process unit for large-dose raw material metering is assigned to the metering module, and a process unit for paste homogenization is assigned to the mixing and homogenizing module. After completing the matching, the main station accurately assigns each process atomic unit to the corresponding module slave controller and distributes the sequence data through a distributed real-time communication link, ensuring that the tasks received by each module are highly compatible with its functions, capabilities, and operational status.

[0053] This invention achieves efficient parsing and extraction of core information from complete cosmetic ingredient formulation data through refined, standardized, and intelligent design of dynamic matching and allocation steps for process atomic unit sequence data. Based on the closed-loop characteristics of material processing procedures, the formulation is broken down into independent, executable minimum process atomic units and configured with dedicated execution parameters. Combined with the formulation process logic, a temporal arrangement is completed, and connection trigger conditions for adjacent units are set, ensuring the continuity and standardization of ingredient process execution. Simultaneously, based on real-time module load, metering accuracy, and material adaptability data extracted from online operational data, a dynamic matching algorithm achieves precise adaptation between process tasks and module functions and operating states. This avoids module overload or wasted precision resources and solves the problems of lack of standardized decomposition, dynamic matching basis, and clear rules for process connection in traditional cosmetic ingredient allocation. It achieves automation and intelligence in formulation process task allocation, laying a precise and reasonable foundation for subsequent multi-module collaborative ingredient execution. This significantly improves the production flexibility and process execution efficiency of the cosmetic ingredient system, perfectly adapting to the refined production needs of the cosmetic industry for multiple categories and small batches.

[0054] In one embodiment of the present invention, the specific steps of the master station for self-calibration compensation of metering parameters of multi-module collaboration are as follows: Step S501: The main station sets a metering deviation trigger threshold that is compatible with each material type according to the metering characteristics of different material types, and generates a material metering deviation threshold dataset. Based on the physicochemical properties of different material types in cosmetic ingredients, such as liquids, powders, and high-viscosity pastes, and considering the difficulties and accuracy requirements of metering operations for each type of material, the main station sets appropriate metering deviation trigger thresholds for different materials. The threshold setting follows the principle that "the higher the difficulty of material metering, the more accurate the adaptability of the deviation threshold." The main station associates each material type with its corresponding metering deviation trigger threshold, performs structured integration and classification storage of the data, and generates a standardized material metering deviation threshold dataset. This dataset serves as the core basis for the main station to determine whether there are abnormal deviations in the metering of the subsequent modules, and can be flexibly adjusted according to production needs.

[0055] Step S502: Extract the actual measurement value of each module's execution process atomic unit from the real-time batching execution data returned by each slave station controller, compare the actual measurement value with the theoretical measurement value corresponding to the batching formula data, calculate the actual measurement deviation value of each module, and generate a module real-time measurement deviation dataset. The master station extracts the actual metering values ​​of each module when executing the corresponding process atomic unit from the real-time batching execution data returned by the slave station controllers of each module through a distributed real-time communication link. The master station calculates the difference between the extracted actual metering values ​​and the corresponding theoretical metering values ​​in the batching formula data to obtain the actual metering deviation value of each module when performing different material metering operations. At the same time, it records the module number, material type, and process atomic unit information corresponding to the deviation value. The master station integrates all actual metering deviation values ​​and related information in a structured manner to generate a real-time metering deviation dataset for each module, which intuitively reflects the actual metering deviation of each module.

[0056] Step S503: Compare the values ​​in the real-time metering deviation dataset of the module with the corresponding thresholds in the material metering deviation threshold dataset. When the metering deviation value of a certain module exceeds the corresponding trigger threshold, generate a metering correction start signal and start the metering parameter self-correction compensation process. The main station compares each measurement deviation value in the real-time measurement deviation dataset of the modules with the trigger threshold of the corresponding material type in the material measurement deviation threshold dataset. If the measurement deviation value of a module does not exceed the corresponding threshold, the measurement accuracy of the module is determined to meet the requirements and no correction is required. If the measurement deviation value of a module exceeds the corresponding trigger threshold, the main station immediately generates a measurement correction start signal. This signal serves as a trigger instruction to start the self-correction and compensation process of the measurement parameters specific to that module, while locking the subsequent measurement task allocation for that module to prevent the deviation from continuously affecting the batching accuracy.

[0057] Step S504: After receiving the metering calibration start signal, the master station retrieves the factory calibration parameters from the initial binding data, and at the same time extracts the real-time operating environment data and material characteristic data from the online operating basic data, and transmits them to the metering coefficient correction unit of the master station. After receiving the metrology calibration start signal, the master station accurately retrieves the factory calibration parameters from the initial binding data of the module according to the module number corresponding to the signal. These parameters are the basic metrology coefficients calibrated at the factory, ensuring the benchmark of metrology correction. At the same time, the master station extracts the real-time operating environment data and material characteristic data of the module from the online operation basic data. The real-time operating environment data includes environmental factors that affect metrology accuracy, such as production environment temperature and humidity, while the material characteristic data includes material intrinsic factors such as material viscosity and flowability. The master station integrates and verifies the retrieved factory calibration parameters with the extracted real-time operation-related data to ensure that the data is complete and valid. Then, it transmits the data to the metrology coefficient correction unit built into the master station, providing comprehensive and accurate data support for the dynamic correction of the metrology coefficients.

[0058] Step S505: The measurement coefficient correction unit dynamically corrects the measurement coefficients based on the factory calibration parameters and real-time operation data, generates the corrected measurement coefficient data, and sends it to the slave controller of the corresponding module. After receiving the factory calibration parameters and real-time operation data, the metrology coefficient correction unit loads a preset dynamic correction algorithm for metrology coefficients. The algorithm dynamically corrects the factory calibration parameters based on the influence of environmental factors and material characteristics on metrology accuracy, eliminating metrology deviations caused by environmental and material factors. After correction, the metrology coefficient correction unit generates the corrected metrology coefficient data for the module and records the correction basis, correction range, and other related information. The master station accurately sends the corrected metrology coefficient data to the corresponding module's slave controller through a distributed real-time communication link, providing a new metrology basis for the module to re-execute metrology operations.

[0059] Step S506: The slave station controller adjusts its own metering execution parameters based on the corrected metering coefficient data, re-executes the metering operation, and sends the new metering execution data back to the master station. The master station detects the new metering deviation value until the metering deviation value is lower than the corresponding trigger threshold, completes the self-calibration compensation of the metering parameters, and the master station synchronously updates the real-time batching execution data.

[0060] After receiving the corrected metering coefficient data from the master station, the slave controller of the corresponding module immediately adjusts its own metering execution parameters, integrating the new metering coefficients into the metering operation logic. Subsequently, the slave controller re-executes the corresponding material metering operation according to the adjusted metering execution parameters and transmits the new metering execution data back to the master station in real time. The master station performs deviation calculation and threshold comparison on the transmitted new metering execution data. If the new metering deviation value is lower than the corresponding trigger threshold, it determines that the metering parameter self-correction compensation is complete and unlocks the subsequent task allocation of the module. If the new metering deviation value still exceeds the threshold, the above correction process is repeated until the metering deviation value is lower than the threshold. At the same time, the master station synchronously updates the corrected metering execution data to the real-time batching execution data to ensure the real-time performance and integrity of the data.

[0061] The self-calibration compensation of the metering parameters can be configured in two modes according to production requirements: The first is an online real-time fine-tuning mode, suitable for scenarios with minor deviations where the correction can be compensated for within the current batch through subsequent processes (such as mixing and homogenization). The master station quickly calculates and issues correction coefficients, which are immediately applied by the slave station controller in subsequent metering cycles. The second is an inter-batch learning optimization mode, suitable for scenarios with larger deviations or where materials are not traceable. The master station records the deviation and correction value to optimize the initial metering parameters for the next production batch, while the current batch may be handled according to procedures. The system defaults to the inter-batch learning optimization mode to ensure the highest level of quality consistency.

[0062] It should be noted that the specific steps of the master station's self-correction and compensation of timing parameters for multi-module collaboration are as follows: Step S507: Based on the updated real-time batching execution data, the main station extracts the actual execution time data, theoretical preset time data, and preset total execution time data of the process atomic unit sequence of each module, analyzes the timing deviation of the process atomic unit executed by multiple modules in collaboration, generates a timing correction start signal, and starts the timing parameter self-correction compensation process of multi-module collaboration. The main station uses real-time batching execution data updated after self-calibration and compensation of metering parameters as its data foundation. Following preset data extraction rules, it extracts three types of core duration data: actual execution time of each module's smallest process atomic unit, theoretical preset execution time of the corresponding process atomic unit, and preset total execution time of the entire process atomic unit sequence. The main station integrates these three types of duration data by module and process atomic unit. Using a built-in timing deviation analysis algorithm, it compares the difference between the actual execution time and the theoretical preset execution time, combined with the preset total execution time of the entire sequence, to quantitatively analyze the timing deviations occurring during multi-module collaborative execution and determine whether these deviations affect process connection and overall production rhythm. If the analysis results show that the timing deviation exceeds the preset collaborative adaptation range, the main station immediately generates a timing correction start signal. This signal serves as a trigger command to initiate the multi-module collaborative timing parameter self-calibration and compensation process, while temporarily storing the execution trigger commands for subsequent process atomic units to prevent further deviation expansion.

[0063] Step S508: Based on the timing correction start signal, the master station calculates the timing correction compensation value for each module, taking into account the total number of modules participating in the execution of the process atomic unit sequence and the viscosity characteristics of the material. The compensation value is then sent to the slave controller of the corresponding module. The slave controller adjusts its own start and stop time for executing the corresponding process atomic unit according to the compensation value, thereby completing the self-correction compensation of timing parameters for multi-module collaboration and realizing the timing synchronization of multi-module execution of process atomic units.

[0064] After receiving the timing correction start signal, the master station first extracts the total number of modules participating in the execution of the process atomic unit sequence. Simultaneously, it retrieves the material viscosity characteristics data of the current batch from the online operational baseline data. These two types of data are the core factors affecting the timing compensation value calculation. The master station loads the preset timing compensation value calculation logic, combines the total number of modules, material viscosity characteristics, and the analyzed timing deviation data, and accurately calculates the timing correction compensation value for each module executing the corresponding process atomic unit. The master station accurately distributes the timing correction compensation values ​​of each module to the corresponding slave controllers via a distributed real-time communication link. The slave controllers of each module... After receiving the compensation value, the device immediately adjusts the start and stop times of the corresponding process atomic units according to the value. If the compensation value is positive, the start time is delayed or the execution connection time is extended. If the compensation value is negative, the start time is advanced or the execution connection time is shortened. The slave controller executes the process according to the adjusted start and stop times. The master station monitors the execution rhythm of each module in real time, confirms that the execution progress of the process atomic units of all modules tends to be consistent, completes the self-correction compensation of the timing parameters of multi-module collaboration, and finally realizes the timing synchronization of the process atomic units executed by multiple modules. Then the master station releases the temporarily stored process atomic unit execution trigger instructions and restores the normal process connection.

[0065] It should be noted that the timing correction compensation values ​​for each module are calculated using a timing self-correction formula, as follows: , in, The timing correction compensation value for the corresponding process atomic unit of the i-th module is the core quantitative indicator that guides the adjustment of the module's start and stop time. It has no fixed unit, and its positive or negative value corresponds to the adjustment direction (positive means delayed execution, negative means early execution), and the magnitude of the value corresponds to the adjustment range.

[0066] The preset total execution time for a sequence of process atomic units, i.e., the planned total time for this group of process atomic units to complete all execution operations according to the formulation process requirements, is the core benchmark value for timing correction. Extracted from the core information set of the ingredient formulation, the main station, after receiving complete cosmetic ingredient formulation data, analyzes and extracts core information such as process execution requirements and the sequence of procedures from the formulation. Combined with the theoretical execution time of each process atomic unit, the preset total execution time for that process atomic unit sequence is pre-set and stored, and can be directly retrieved.

[0067] n is the total number of modules participating in the execution of the atomic unit sequence of this process. It is a positive integer representing the number of modules in this collaborative execution task and is the boundary value for summation calculation in the formula.

[0068] The theoretical preset execution time for the corresponding process atomic unit of the j-th module is the planned execution time of the j-th module according to the formula process standard for its dedicated process atomic unit. It serves as the time benchmark for the execution of a single module's process. Extracted from the parameterized process atomic unit dataset, the master station configures corresponding execution parameters for each atomic unit when decomposing the core formula information set into the smallest process atomic units. These parameters include the theoretical preset execution time for that atomic unit. After completing the process task allocation, the master station stores the theoretical preset execution time of the process atomic units corresponding to each module in a linked manner, allowing direct retrieval of the j-th module's time. Values.

[0069] K is a time-series deviation correction coefficient, dynamically adjusted based on the viscosity characteristics of the ingredients. It is a dimensionless parameter, and its core function is to dynamically correct overall time-series deviations based on the viscosity characteristics of cosmetic ingredients. The higher the material viscosity, the larger the K value, thus adapting to the process execution characteristics of different materials (e.g., high-viscosity pastes require longer homogenization times, resulting in greater deviation impact and necessitating a higher correction coefficient). The main station extracts the material characteristic data (including viscosity and flowability) of the current ingredients from the online operational baseline data. Then, based on the system's preset matching rules between material viscosity and correction coefficients, it dynamically matches the corresponding K value for the current ingredient scenario. The matching rules can be flexibly adjusted according to the actual process requirements of cosmetic production. As an example, and not a limitation, the time-series deviation correction coefficient K can be mapped to a range based on the average dynamic viscosity η (mPa·s) of the ingredients: when η < 100, K is 0.8~1.0; when 100 ≤ η < 1000, K is 1.0~1.2; when η ≥ 1000, K is 1.2~1.5. The specific values ​​can be optimized by the main site based on historical data, or preset by process engineers.

[0070] This represents the actual deviation time for the j-th module to execute the corresponding process atomic unit. It is the difference between the actual execution time of a single module and the theoretical preset time; its value, positive or negative, reflects the deviation type (positive indicates the actual execution time is greater than the theoretical preset time, negative indicates the actual execution time is less than the theoretical preset time). The master station extracts the actual execution time of the j-th module's process atomic unit from the real-time batching execution data returned by the slave station controllers of each module through a distributed real-time communication link, and then subtracts the corresponding theoretical preset time for that module from this actual execution time. The difference was calculated to obtain The specific value to be taken.

[0071] The timing self-calibration formula is a quantitative calculation formula specifically designed for multi-module collaborative scenarios in cosmetic workshop ingredient dispensing systems. Its core function is to accurately calculate the timing correction compensation value for each participating process atomic unit sequence execution module, providing a quantitative basis for the master station to issue start / stop time adjustment commands to the slave station controller. This formula uses the preset total execution time of the process atomic unit sequence as a benchmark, combined with the theoretical preset execution time and actual execution deviation time of each participating module. It also introduces a timing deviation correction coefficient adapted to the material viscosity characteristics to dynamically correct deviations, ultimately deriving the timing correction compensation value for a single module. The master station calculates this using this formula. It can directly guide the corresponding module to adjust the start and stop time of the process atomic unit. Positive compensation value corresponds to delayed start and stop of the module, and negative compensation value corresponds to early start and stop of the module, so as to offset the execution timing deviation between modules, realize the timing synchronization of multi-module collaborative execution, ensure the smooth connection of cosmetic ingredient process and the stability of production rhythm, and adapt to the process collaboration requirements of multi-category and small-batch production of cosmetics.

[0072] It should be noted that when generating the corresponding reconfigurable pipeline topology data, the optimal cleaning path quantification evaluation formula is used to achieve cleaning path planning with no blind pipes and low flow resistance. The optimal cleaning path quantification evaluation formula is as follows: , in, The optimal comprehensive evaluation value for the cleaning path is a dimensionless parameter and the core calculation result of the formula. It is used to determine the quality of the pipeline topology. The smaller the value, the better the overall performance of the corresponding pipeline topology in terms of residual risk, flow resistance, and path length.

[0073] m is the total number of pipeline segments to be selected in the pipeline topology. It is a positive integer and is the upper boundary value of the summation calculation in the formula, representing that the pipeline topology consists of m independent pipeline segments.

[0074] The actual pipeline length of the x-th pipeline segment is a fundamental physical parameter reflecting its actual spatial dimensions. It is directly extracted from the basic pipeline parameter database of the batching system. This database is a pre-stored basic engineering parameter database containing the actual length, inner diameter, bending angle, and other physical parameters of all pipeline segments in the batching system. The main station can accurately retrieve the corresponding x-th pipeline segment based on its unique identifier. Values.

[0075] Let be the material residue risk coefficient for the x-th pipeline segment. This is a dimensionless parameter that primarily reflects the degree of material residue risk in this pipeline segment due to the type of material being used and the required hygiene level. A higher residue risk indicates a higher risk. The larger the value (e.g., for pipeline sections that come into contact with high-viscosity pastes or have high hygiene requirements), the higher the coefficient value will be. The main station extracts the module identity information, material compatibility type, and hygiene level data under the current production scenario from the online operation basic data as input conditions, and dynamically assigns a value to the x-th pipeline section by combining the system's preset material residue risk coefficient matching rules.

[0076] Let be the flow resistance coefficient of the x-th pipeline segment; be a dimensionless parameter that primarily reflects the magnitude of fluid flow resistance caused by the structural characteristics of that pipeline segment. A higher flow resistance indicates greater resistance. The larger the value (e.g., smaller pipe inner diameter, larger bending angle, higher inner wall roughness, the higher the flow resistance coefficient value), the higher the flow resistance coefficient value will be. The main station extracts the structural parameters (pipe inner diameter, bending angle, inner wall roughness, etc.) of the x-th pipe segment from the basic parameter library of the batching system pipeline, and combines them with the system's preset flow resistance coefficient calculation / assignment rules to dynamically calculate or match the corresponding flow resistance coefficient for that pipe segment. Values.

[0077] The main station will use module identity information, material compatibility type, and hygiene level data extracted from the online operation basic data as input conditions to process each candidate pipeline segment. and Dynamically assign values ​​and substitute them into the optimal quantitative evaluation formula for cleaning paths to calculate the corresponding topologies of all candidate pipelines. Select The pipeline topology corresponding to the minimum value is the target reconfigurable pipeline topology, and the corresponding reconfigurable pipeline topology data is generated.

[0078] The dynamic assignment matching rules are based on the material characteristics and hygiene control requirements of cosmetic ingredient production. First, the material types suitable for pipeline sections are divided into three basic levels: low residual risk (e.g., aqueous liquids, low-viscosity alcohols), medium residual risk (e.g., powder raw materials, low-viscosity oil liquids), and high residual risk (e.g., high-viscosity pastes, wax raw materials, fragrances / active ingredients), assigned basic coefficient ranges of 0.8-1.0, 1.1-1.5, and 1.6-2.0 respectively. Then, the matching rules are combined with the corresponding production hygiene requirements of the pipeline section. For each pipeline segment (Class A: critical pipelines directly contacting finished products; Class B: pipelines contacting semi-finished products; Class C: auxiliary pipelines for raw material transport), adjust the base coefficients as follows: add 0.2 for Class A hygiene level, 0.1 for Class B, and no adjustment for Class C. Finally, considering historical material residue testing data and usage frequency for this pipeline segment, if the number of residue exceedances in the past 7 days is ≥1 or the daily usage frequency is ≥5 times, add an additional 0.1 to the adjusted coefficients. If there are no residue exceedances in the past 30 days and the daily usage frequency is ≤2 times, subtract an additional 0.1. The final value is... The assignment result, and after the assignment, it is necessary to ensure that α The value is controlled between 0.7 and 2.1, and the guarantee factor is highly compatible with the actual residual risk of the pipeline section.

[0079] The dynamic assignment matching rules are designed based on the fundamental principles of fluid mechanics and combined with the structural characteristics of cosmetic ingredient pipelines. First, the nominal inner diameter of the pipeline section is used as the basic dimension for assignment. The basic coefficient for straight pipe sections with an inner diameter ≥ 50mm is 0.8-1.0, for those with an inner diameter 30-50mm it is 1.1-1.3, and for those with an inner diameter < 30mm it is 1.4-1.6. Then, corrections are made for the pipe bending angle: 0.1 is added when the bending angle is < 45°, and 0.1 is added when the bending angle is 45° ≤ 45°. Add 0.2 for bends <90°, and 0.3 for bends ≥90°. No correction is needed for straight pipe sections without bends. Finally, adjust for pipe inner wall roughness: subtract 0.1 for inner wall roughness Ra≤0.8μm (high-gloss polishing), no correction for Ra between 0.8-1.6μm (conventional polishing), and add 0.1 for Ra>1.6μm (unpolished). Additionally, if the pipe section contains reducing joints, valves, or other resistance components, add an extra 0.2. The final calculated value is... The assignment result, after assignment, ensure The value is controlled within the range of 0.7-2.2 to accurately reflect the actual fluid flow resistance characteristics of the pipeline section.

[0080] In the initial stage of system launch, α A transitional scheme of preset benchmark assignment + step-by-step iterative calibration is adopted: First, a fixed benchmark mapping table is established based on the material residual risk level and hygiene level. Low residual risk materials (such as aqueous liquids, low viscosity alcohols) + Class C hygiene level, medium residual risk materials (such as powder raw materials, low viscosity oil liquids) + Class B hygiene level, and high residual risk materials (such as high viscosity pastes, fragrances / active ingredients) + Class A hygiene level are preset to benchmark values ​​of 0.8, 1.2, and 1.8 respectively, which are directly used for the initial cleaning path assessment. After each cleaning and residual detection is completed, the system automatically modifies the detection results (residual amount, whether it exceeds the standard) with the current α value. The value is correlated. If the pipeline section passes the residue test three times in a row, the corresponding benchmark value will be lowered by 0.1. If the residue exceeds the standard once, it will be raised by 0.1. The benchmark value will be gradually corrected. When a combination of a certain type of material and hygiene grade accumulates 30 valid test data, it will automatically switch to the material residue risk coefficient matching rule based on historical data, and complete the smooth transition from the transition to the formal system.

[0081] In the initial stage of system launch, A transitional scheme employing theoretical calculations plus online differential pressure calibration is adopted: First, based on pipeline physical parameters and material viscosity, the basic flow resistance of the straight pipe section is calculated strictly according to Poiseuille's law. Then, pre-set structural correction rules are applied: Add 0.1 for bending angle < 45°, add 0.2 for 45° ≤ bending angle < 90°, add 0.3 for bending angle ≥ 90°; subtract 0.1 for inner wall roughness Ra ≤ 0.8μm, add 0.1 for Ra > 1.6μm; add an additional 0.2 for valves / reducing joints, forming an initially usable system. Value acquisition; After the system is running, the actual pressure drop data of the pipeline section is collected through the online differential pressure sensor and compared with the current value. The calculated theoretical pressure drop is compared with the actual pressure drop. If the actual pressure drop deviates from the theoretical value by more than 15%, the corresponding structural correction item is adjusted (the correction item is adjusted upward if the deviation is positive, and downward if the deviation is negative). When a certain type of pipeline structure and material viscosity combination accumulates 20 effective differential pressure detection data, it automatically switches to the flow resistance coefficient calculation / assignment rule based on experimental calibration, realizing a precise transition from theoretical benchmark to actual measurement.

[0082] The optimal cleaning path quantitative evaluation formula is a quantitative calculation formula specifically designed for pipeline topology planning in cosmetic workshop ingredient dispensing systems. Its core function is to calculate the comprehensive evaluation value of each candidate pipeline topology and select the optimal solution, providing a quantitative basis for the main station to construct a reconfigurable pipeline topology with no blind pipes and low flow resistance. This formula takes the actual physical properties of the pipeline segment, material compatibility characteristics, and fluid transport characteristics as the core consideration dimensions. It multiplies the pipeline segment length, material residue risk coefficient, and flow resistance coefficient, and then sums the results over all candidate pipeline segments. By finding the minimum value of this sum, the optimal pipeline topology is determined.

[0083] The formula's design logic aligns with the hygiene and fluid transport requirements of cosmetic production: the material residue risk coefficient adapts to the diverse material types in cosmetic ingredients (liquids, powders, high-viscosity pastes, etc., have different residue risks); the flow resistance coefficient ensures the efficiency of pipeline fluid transport; and the pipeline length takes into account the economic efficiency of the path. The product and minimum value of these three factors correspond to a pipeline topology with low residue risk, low fluid resistance, and a reasonable path. Furthermore, the formula's calculation process uses online operational data as input and can dynamically adjust parameters based on the module's real-time operating status and material compatibility type, ensuring that the planned pipeline topology is highly compatible with current production needs while meeting the hygiene requirements of cosmetic production, which requires no blind pipes and no dead zones.

[0084] In one embodiment of the present invention, after generating the independent closed-loop cleaning path data, the method further includes performing independent closed-loop cleaning based on the independent closed-loop cleaning path data, specifically including the following steps: Step S601: The main station generates a dataset of cleaning path and process parameter combinations by matching the corresponding cleaning process parameters with the generated independent closed-loop cleaning path data, combined with the module's hygiene level and material residue characteristics. The main station retrieves the generated independent closed-loop cleaning path data from the database. Simultaneously, it extracts two core data points from the online operational data: the hygiene level and material residue characteristics of the corresponding cleaning object (a single module, a single pipeline section, or a single material path). These serve as the basis for matching cleaning process parameters. Based on preset cleaning process parameter matching rules, the main station matches suitable cleaning process parameters for cleaning objects with different hygiene levels and material residue characteristics. Specifically, for CIP cleaning, it matches parameters such as cleaning medium type, cleaning medium temperature, cleaning medium flow rate, and cleaning cycle count; for SIP sterilization, it matches parameters such as sterilization medium temperature, sterilization pressure, sterilization holding time, and sterilization cooling rate. The main station then structurally integrates the independent closed-loop cleaning path data with the matched cleaning process parameters, uniquely associating them by cleaning object to generate a cleaning path and process parameter combination dataset. This dataset serves as the core data basis for executing the cleaning operation.

[0085] Step S602: The combined dataset of cleaning path and process parameters is sent to the slave controller of the corresponding module through a distributed real-time communication link. After receiving the data, the slave controller parses the cleaning path and process parameters and generates the module cleaning execution command. The master station accurately distributes the combined dataset of cleaning paths and process parameters to the corresponding module slave controllers through the established distributed real-time communication link, ensuring the integrity and real-time performance of the data during the distribution process. After receiving the dataset, the slave controllers analyze the cleaning path direction, cleaning range, cleaning connection logic, and cleaning process parameters one by one, transforming the digitized dataset into control instructions that the modules can recognize and execute. The instructions are then integrated according to the sequence of cleaning procedures to generate module cleaning execution instructions, which clearly specify the action sequence, operating parameters, and action requirements of each execution component.

[0086] Step S603: The slave station controller controls the execution components of the module to perform independent closed-loop cleaning operations according to the module cleaning execution instructions and the cleaning path and process parameters. The slave controller issues action commands to the built-in valves, pumps, heating devices, cleaning medium delivery devices, and other actuators according to the generated module cleaning execution commands. It controls each actuator to work collaboratively according to the preset cleaning path and process parameters. During the cleaning process, the independent closed-loop principle is strictly followed to ensure that the cleaning medium circulates only within the preset cleaning path and does not connect with other uncleaned modules, pipelines, or material passages to avoid cross-contamination. The slave controller monitors the operating status of each actuator and the execution of the cleaning process parameters in real time to ensure that the cleaning operation is carried out according to standards and completes the actual execution of independent closed-loop cleaning.

[0087] Step S604: After the cleaning operation is completed, the residual detection sensor built into the module collects the material residue data in the pipeline; Once the cleaning operation is completed according to the preset process parameters, the module's built-in residual detection sensor automatically starts the detection program to detect material residue on key parts such as the inner wall of the pipeline, interfaces, and valve groups within the cleaning path. The collected detection data includes core indicators such as material residue content, pipeline cleanliness, and cleaning medium residue. After the sensor standardizes and converts the collected material residue data, it transmits it to the module's slave controller in real time, providing a real and accurate detection basis for subsequent cleaning effect judgment.

[0088] Step S605: Compare the material residue data with the preset material residue threshold. If the residue detection data is lower than the preset threshold, determine that the cleaning is qualified and generate a cleaning qualified signal. At the same time, associate and store the cleaning completion status of the module with the cleaning-related data. The slave controller accurately compares the collected material residue data with the system's preset material residue threshold. This threshold is set according to the hygiene requirements and raw material characteristics of cosmetic production and is the core standard for determining whether the cleaning is qualified. If the material residue detection data is lower than the preset material residue threshold, the cleaning operation is deemed qualified, and the slave controller immediately generates a cleaning qualified signal and sends it back to the master station. At the same time, the master station integrates and correlates the module's cleaning completion status, cleaning path and process parameter combination dataset, cleaning execution process data, and material residue detection data, and stores them uniformly according to production batch and module identifier to form a complete cleaning record, meeting the traceability requirements of cosmetic production.

[0089] Step S606: If the residual detection data is higher than the preset threshold, the cleaning is deemed unqualified and a secondary cleaning instruction is generated and sent to the slave controller to re-execute the cleaning operation until the residual detection data is lower than the preset threshold.

[0090] If the residual material detection data exceeds the preset residual material threshold, the cleaning operation is deemed unqualified, and the slave controller immediately sends the unqualified cleaning information back to the master station. After receiving the information, the master station automatically generates a secondary cleaning instruction and, based on the residual detection data from the first cleaning, appropriately optimizes and adjusts the cleaning process parameters (such as increasing the number of cleaning cycles, increasing the temperature of the cleaning medium, etc.). The secondary cleaning instruction and the optimized process parameters are then sent to the corresponding slave controller via a distributed real-time communication link. The slave controller, based on the secondary cleaning instruction and optimized parameters, re-executes the independent closed-loop cleaning operation. After completion, it again collects residual material data through the residual detection sensor and compares it against the threshold. This process is repeated until the residual material detection data is lower than the preset threshold, ensuring that the cleaning effect fully meets the hygiene requirements of cosmetic production.

[0091] This invention constructs a data-driven, end-to-end closed-loop modular integration technology system. By decomposing and encapsulating functionally closed-loop modules of the batching system, configuring unique electronic identification tags and binding them to integrated standard interfaces, the standardization and digitization of basic module configurations are achieved. The master station automatically parses the initial bound data based on the electronic identification tags, loads the corresponding control logic and communication variables, and establishes a stable distributed real-time communication link with the slave station controllers. The generated online operational basic data provides accurate and real-time data support for all subsequent control operations. The master station constructs a reconfigurable pipeline topology without blind pipes or dead zones through path planning algorithms combined with quantitative evaluation formulas. It can also generate independent closed-loop cleaning paths for different cleaning objects and execute standardized closed-loop cleaning operations, effectively avoiding material residue and cross-contamination. At the same time, the batching formula is decomposed into the smallest process atomic units and sequence data is generated. Combined with the real-time operating status of the modules, dynamic matching and precise allocation of process tasks are completed, realizing the standardization of process execution. The intelligentization of task allocation and automation allows for self-correction and compensation of timing and metering parameters during multi-module collaborative execution based on real-time batching execution data returned from the slave station controller. This achieves timing synchronization of multiple modules and precise control of batching and metering. This method completely solves the problems of low standardization, insufficient automation and intelligence, poor master-slave station collaboration, lack of scientific basis for pipeline planning, lack of dynamic logic in formula allocation, low accuracy of multi-module collaborative parameter correction, and lack of closed-loop management for cleaning in the modular integration of traditional cosmetic ingredient systems. It significantly improves the overall automation and intelligence level of modular integration of cosmetic workshop ingredient systems, ensures the hygiene, accuracy, and batch consistency of cosmetic ingredient production, significantly enhances the production flexibility and equipment maintenance convenience of the ingredient system, and perfectly adapts to the multi-category, small-batch, and refined production development needs of the cosmetic industry. At the same time, the full-process production and operation data records also meet the hygiene standards and traceability requirements related to cosmetic production.

[0092] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A modular integration method for a cosmetic workshop ingredient dispensing system, characterized in that, Includes the following steps: The batching system is broken down and independently encapsulated into multiple modules in a closed loop. Electronic identification tags are configured and identity and technical parameters are pre-stored. Integrated standard interfaces are bound to generate initial binding data. Based on the electronic identity tag, the main station parses the initial binding data and automatically loads the corresponding control logic and communication variables, and builds a distributed real-time communication link with the slave station controllers of each module to generate basic data for online operation. Based on online operational data, reconfigurable pipeline topology data is constructed using a path planning algorithm to generate independent closed-loop cleaning path data. Receive ingredient formula data, break it down into the smallest process atomic unit and generate sequence data, and dynamically match and allocate it to each corresponding slave controller in combination with online operation basic data; Each slave station controller executes the batching operation and sends back real-time batching execution data to the master station. The master station performs self-calibration and compensation for the timing and metering parameters of the multi-module collaboration.

2. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 1, characterized in that, The specific steps for generating the initial binding data are as follows: The batching system is divided into a raw material storage module, a metering module, a micro-feeding module, a mixing and homogenizing module, and a conveying and distribution module according to the closed-loop characteristics of the functions, thus completing the functional independent packaging of each module; Configure a unique electronic identity tag for each packaged module. Pre-write and store the module type, material compatibility type, hygiene level, IO point table, control algorithm library, calibration parameters, and load compatibility range identity and technical parameters in the electronic identity tag to generate a module electronic identity dataset. An integrated standard interface is configured for the physical connection end of each module. The integrated standard interface includes a physical mechanical interface, a sanitary pipeline interface, and a dual-redundant real-time communication interface. The three types of interfaces are standardized and bound to generate an interface standardization matching dataset. The module electronic identity dataset is uniquely associated and bound with the interface standardized matching dataset and the module hardware ontology to form the initial binding data that integrates the module hardware and electronic data, thus completing the basic configuration of each module.

3. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 1, characterized in that, The specific steps for generating the basic data for online operation are as follows: With the central dispatch master station as the control core, the master station scans and identifies the electronic identity tags of each module through an integrated standard interface, retrieves the corresponding initial binding data through the tags and transmits it to the data parsing unit of the master station to generate the original identity data of the module. The main station's data parsing unit parses and extracts the original data of the module identity to obtain the core parsing data of the module type, control requirements, and communication adaptation parameters, and transmits the core parsing data to the main station's logic matching unit. The logic matching unit retrieves the control logic program, interlocking protection logic, and communication variable table that match the core parsing data from the module control logic database built into the main station, completes the automatic loading of control logic and communication variables, and generates the module configuration completion signal; After receiving the module configuration completion signal, the master station establishes a distributed real-time communication link with the built-in slave controllers of each module, and sets up a communication status detection mechanism in the link to realize bidirectional interaction between the master station scheduling command data and the slave station execution feedback data. The main station aggregates and integrates the configuration data, communication link status data, and basic module operation data of each module to generate online operation basic data that includes the operating status of all online modules.

4. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 1, characterized in that, The specific steps for generating independent closed-loop cleaning path data are as follows: The main station extracts the identity information, material compatibility type, and hygiene level data of each module from the online operation basic data, and uses them as input parameters for the path planning algorithm, which are then transmitted to the path planning unit of the main station. The path planning unit outputs valve position control commands to the sanitary adaptive isolation valve group of the pipeline nodes between modules through the path planning algorithm. By dynamically adjusting the valve position, a reconfigurable pipeline topology structure without blind pipes and dead angles is constructed, and corresponding reconfigurable pipeline topology data is generated. Based on reconfigurable pipeline topology data, combined with the module's operating status and material residue characteristics, the main station plans an independent cleaning process for a single module, a single pipeline segment, or a single type of material path, generating independent closed-loop cleaning path data that covers the cleaning path, cleaning range, and cleaning connection logic.

5. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 1, characterized in that, The specific steps for dynamically matching and allocating data to the corresponding slave controllers based on online operational data are as follows: The main station receives complete data of cosmetic ingredient formulas to be executed, extracts core formula information such as material composition, process execution requirements, precision control standards, and sequence of processes, and generates a core formula information set. Based on the closed-loop characteristics of the material handling process of cosmetic ingredients, the core information set of the formula is decomposed into the smallest independent and individually executable process atomic units. Each process atomic unit is configured with corresponding execution parameters to generate a parameterized process atomic unit dataset. Based on the process execution logic of the formula, the parameterized process atomic unit dataset is arranged in a time sequence, the connection triggering conditions of adjacent process atomic units are set, and continuous process atomic unit sequence data is generated. The master station extracts real-time load status, metering accuracy level, and material compatibility data of each module from the online operation basic data, dynamically matches the process atomic unit sequence data, and assigns each process atomic unit to the module slave controller with compatible functions to complete the distribution and distribution of sequence data.

6. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 5, characterized in that, The specific steps for the main station to self-calibrate and compensate for the metering parameters of multiple modules are as follows: The main station sets metering deviation trigger thresholds that are compatible with each material type based on the metering characteristics of different material types, and generates a material metering deviation threshold dataset. The actual measurement values ​​of the process atomic units of each module are extracted from the real-time batching execution data returned by each slave station controller. The actual measurement values ​​are compared with the theoretical measurement values ​​corresponding to the batching formula data to obtain the actual measurement deviation value of each module and generate the module real-time measurement deviation dataset. The values ​​in the real-time metering deviation dataset of the module are compared with the corresponding thresholds in the material metering deviation threshold dataset. When the metering deviation value of a certain module exceeds the corresponding trigger threshold, a metering correction start signal is generated to start the metering parameter self-correction compensation process. After receiving the metering calibration start signal, the master station retrieves the factory calibration parameters from the initial binding data, and at the same time extracts the real-time operating environment data and material characteristic data from the online operating basic data, and transmits them to the metering coefficient correction unit of the master station. The measurement coefficient correction unit dynamically corrects the measurement coefficients based on the factory calibration parameters and real-time operation data, generates the corrected measurement coefficient data, and sends it to the slave controller of the corresponding module. The slave controller adjusts its own metering execution parameters based on the corrected metering coefficient data, re-executes the metering operation, and sends the new metering execution data back to the master station. The master station detects the new metering deviation value until the metering deviation value is lower than the corresponding trigger threshold, completes the self-calibration compensation of the metering parameters, and the master station synchronously updates the real-time batching execution data.

7. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 6, characterized in that, The specific steps of the master station's self-correction and compensation of timing parameters for multi-module collaboration are as follows: Based on the updated real-time batching execution data, the main station extracts the actual execution time data, theoretical preset time data, and preset total execution time data of the process atomic unit sequence of each module, analyzes the timing deviation of the process atomic unit executed by multiple modules, generates a timing correction start signal, and starts the timing parameter self-correction compensation process of multi-module collaboration. Based on the timing correction start signal, the master station calculates the timing correction compensation value for each module, taking into account the total number of modules participating in the execution of the process atomic unit sequence and the viscosity characteristics of the material. The compensation value is then sent to the corresponding slave controller. The slave controller adjusts its own start and stop times for executing the corresponding process atomic unit according to the compensation value, thus completing the self-correction compensation of timing parameters for multi-module collaboration and achieving timing synchronization of process atomic units executed by multiple modules.

8. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 7, characterized in that, The timing correction compensation value for each module is calculated using a timing self-correction formula, as follows: ， in, For the i-th module, execute the timing correction compensation value for the corresponding process atomic unit. The preset total execution time is denoted by n, where n is the total number of modules participating in the execution of this process atomic unit sequence. The theoretically preset execution time for the corresponding process atomic unit of the j-th module. This is a timing deviation correction factor, dynamically adjusted based on the viscosity characteristics of the ingredients. The actual deviation time for executing the corresponding process atomic unit for the j-th module.

9. The modular integration method for a cosmetic workshop ingredient dispensing system according to claim 4, characterized in that, When generating the corresponding reconfigurable pipeline topology data, the optimal cleaning path quantification evaluation formula is used to achieve cleaning path planning with no blind pipes and low flow resistance. The optimal cleaning path quantification evaluation formula is as follows: ， in, The optimal comprehensive evaluation value for the cleaning path is given by m, where m is the total number of candidate pipe segments in the pipe topology. Let x be the actual pipe length of the x-th pipe segment. Let x be the material residue risk coefficient for the x-th pipeline segment. Let x be the flow resistance coefficient of the x-th pipeline segment; The main station will use module identity information, material compatibility type, and hygiene level data extracted from the online operation basic data as input conditions to process each candidate pipeline segment. and Dynamically assign values ​​and substitute them into the optimal quantitative evaluation formula for cleaning paths to calculate the corresponding topologies of all candidate pipelines. Select The pipeline topology corresponding to the minimum value is the target reconfigurable pipeline topology, and the corresponding reconfigurable pipeline topology data is generated.

10. A modular integration method for a cosmetic workshop ingredient dispensing system according to claim 4, characterized in that, After generating the independent closed-loop cleaning path data, the process also includes performing independent closed-loop cleaning based on the independent closed-loop cleaning path data, specifically including the following steps: The main station generates a dataset of cleaning path and process parameter combinations based on the generated independent closed-loop cleaning path data, combined with the module's hygiene level and material residue characteristics, and matches the corresponding cleaning process parameters. The combined dataset of cleaning path and process parameters is sent to the slave controller of the corresponding module through a distributed real-time communication link. After receiving the data, the slave controller parses the cleaning path and process parameters and generates the module cleaning execution command. The slave controller, based on the module cleaning execution command, controls the execution components of the module to perform independent closed-loop cleaning operations according to the cleaning path and process parameters; After the cleaning operation is completed, the module's built-in residual detection sensor collects data on the material residue in the pipeline; The residual material data is compared with the preset residual material threshold. If the residual detection data is lower than the preset threshold, the cleaning is deemed qualified and a cleaning qualified signal is generated. At the same time, the cleaning completion status of the module is associated with and stored with the cleaning-related data. If the residual detection data is higher than the preset threshold, the cleaning is deemed unqualified and a secondary cleaning instruction is generated and sent to the slave controller to re-execute the cleaning operation until the residual detection data is lower than the preset threshold.

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