A production process control system for functional foods

Abstract

The present invention discloses a production process control system for functional foods, which belongs to the technical field of production equipment control systems, and specifically includes: a parameter acquisition module, which is used to collect raw material characteristic parameters of functional food raw materials, and the raw material characteristic parameters include rheological characteristic parameters, thermal stability parameters and structural strength parameters; a process adaptation module, which is used to construct a dynamic process adaptation model and map the raw material characteristic parameters into a set of equipment control instructions; divide the energy demand hierarchy according to the raw material processing stage, and generate an on-demand energy supply tree structure; an equipment adjustment module, which is used to monitor the operating status data of the production equipment in real time; trigger dynamic compensation instructions for process parameters based on the equipment operating status data; an equipment feedback module, which is used to compare the deviation between the actual output characteristics of the production line and the target characteristics through a closed-loop verification unit, and iteratively optimize the dynamic process adaptation model; the present invention realizes the precise production and energy saving and efficiency improvement of functional foods through multi-module collaborative technology.

Description

Technical Field

[0001] The present invention relates to the technical field of production equipment control systems, and in particular to a production process control system for functional foods. Background Art

[0002] With consumers' growing health awareness, the functional food market is experiencing explosive growth. Functional foods achieve specific health benefits through the addition of active ingredients, and their production process involves complex biological, chemical, and physical changes. Traditional functional food production relies on experience-driven manual control and simple automated equipment, which struggles to meet the requirements for product quality uniformity, active ingredient stability, and production efficiency. With the advancement of industrial automation and intelligent manufacturing technologies, some manufacturers have begun to introduce sensor monitoring and PLC control systems to achieve preliminary automated collection and control of production parameters, providing a data foundation for production process optimization.

[0003] Existing functional food production process control systems have made progress in parameter acquisition, process adaptation, and equipment coordination. Some systems use temperature, humidity, and pressure sensors to monitor production environment parameters in real time, enabling simple equipment control based on pre-set rules. Regarding process adaptation, some companies have established basic process parameter databases, setting fixed equipment operating parameters for specific product types. Some advanced production lines are also experimenting with inter-device communication via Industrial Ethernet, establishing a preliminary framework for equipment linkage control.

[0004] However, when it comes to process adaptation, traditional systems use fixed process parameter templates and are unable to dynamically adjust equipment control instructions based on raw material characteristics. This results in significant fluctuations in product quality and significant loss of active ingredients when producing different batches of raw materials. Regarding energy management, existing systems lack the ability to intelligently adjust energy requirements during the production process. Each process's equipment operates independently, preventing energy from being shared across processes and resulting in energy waste. Summary of the Invention

[0005] The purpose of the present invention is to provide a production process control system for functional foods to solve the following technical problems:

[0006] In terms of process adaptation, traditional systems use fixed process parameter templates and are unable to dynamically adjust equipment control instructions based on raw material characteristics. This results in significant fluctuations in product quality and significant loss of active ingredients when producing different batches of raw materials. Regarding energy management, existing systems lack the ability to intelligently adjust energy requirements during the production process. Each process's equipment operates independently, and energy cannot be shared across processes, resulting in energy waste.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A production process control system for functional food, comprising:

[0009] A parameter acquisition module is used to acquire raw material characteristic parameters of functional food raw materials, wherein the raw material characteristic parameters include rheological characteristic parameters, thermal stability parameters and structural strength parameters;

[0010] The process adaptation module is used to build a dynamic process adaptation model, mapping raw material characteristic parameters into a set of equipment control instructions; dividing energy demand levels according to the raw material processing stage and generating an on-demand energy supply tree structure;

[0011] Equipment adjustment module, used to monitor the operating status data of production equipment in real time; trigger dynamic compensation instructions for process parameters based on equipment operating status data;

[0012] The equipment feedback module is used to compare the deviation between the actual output characteristics of the production line and the target characteristics through a closed-loop verification unit, and iteratively optimize the dynamic process adaptation model.

[0013] As a further solution of the present invention: in the parameter acquisition module, the method for extracting the characteristic parameters of the raw material is:

[0014] The complete curve of the viscosity of the raw material as a function of shear rate is measured by a rotational rheometer, and the viscosity values ​​at different shear rates are recorded. The non-Newtonian fluid index is extracted from the complete curve as the core characterization indicator of the rheological characteristic parameters;

[0015] A differential scanning calorimeter was used to obtain complete data on the phase transition temperature range of the raw materials during the programmed temperature increase process, and the starting point temperature of the thermal decomposition reaction was calculated as the key quantitative characteristic of the thermal stability parameter;

[0016] The elastic deformation response curve of the raw material particles under continuous pressure loading is detected using a micro-indentation test device, and the standard deviation of the elastic modulus distribution data is extracted as the final quantitative expression of the structural strength parameter;

[0017] Establish a permanent binding relationship database between raw material characteristic parameters and raw material batch codes, which includes parameter collection timestamps, instrument calibration records and operator identification information.

[0018] As a further solution of the present invention: in the process adaptation module, the process of generating the equipment control instruction set is:

[0019] Define the nonlinear mapping function relationship expression between the rheological characteristic parameters and the speed setting value of the stirring equipment,

[0020] Configure multi-stage stepped speed control curve parameters for high non-Newtonian fluid index raw materials;

[0021] Establish dynamic association rule constraints between thermal stability parameters and sterilization equipment temperature control values, and activate the segmented gradient temperature rise control mode parameters of the sterilization equipment for raw materials with low thermal decomposition critical points;

[0022] Set the constraint boundary function relationship between the structural strength parameters and the pressure setting value of the tableting equipment, and enable the real-time pressure feedback compensation algorithm logic of the tableting equipment for raw materials with high hardness variation coefficient;

[0023] Generate a device control instruction set, which includes a coordinated adjustment strategy combination of a speed setting value, a temperature control value, and a pressure setting value.

[0024] As a further solution of the present invention: in the process adaptation module, the generation process of the on-demand energy supply tree structure is:

[0025] Identify key energy-consuming process nodes in the production process, including raw material pretreatment nodes, mixing reaction nodes, molding processing nodes, and sterilization processing nodes; assign a basic energy consumption level value to each process node, which is calculated based on the equipment's rated power and the standard process duration;

[0026] Dynamically modify the energy consumption level allocation plan based on raw material characteristic parameters, and increase the energy consumption priority weight of the sterilization node when the thermal stability parameter is lower than the safety threshold; construct a tree-like energy topology structure, with the root node connected to the total energy input port, and the child nodes establishing hierarchical link channels according to process dependencies;

[0027] A two-way energy sharing channel is set up between adjacent sub-nodes, allowing the energy of idle nodes to be dynamically allocated and transmitted to high-load nodes; emergency energy reserve capacity is configured for key energy-consuming process nodes, and the reserve capacity is dynamically adjusted according to real-time production needs.

[0028] As a further solution of the present invention: the bidirectional energy sharing channel is specifically:

[0029] Continuously monitor the changing trends of the real-time energy consumption demand levels of each process node; when the mixing reaction node needs to increase power due to high-viscosity raw materials, send an energy quota allocation request to the idle pre-processing node; the energy allocation and transmission process follows the on-demand energy supply tree topology path for directional transmission, and direct energy transmission operations across levels are prohibited; emergency energy reserve capacity is reserved for key sterilization processing nodes, and the reserve capacity is calculated and determined in real time based on the thermal stability parameters of the current batch of raw materials.

[0030] As a further solution of the present invention: in the equipment adjustment module, the process of real-time monitoring of the operating status data of the production equipment is:

[0031] A multispectral imaging probe array device is installed on the inner wall of the mixing reaction equipment chamber to capture the thickness distribution image sequence of the material adhesion layer on the surface of the stirring blade in real time and obtain the average value of the material adhesion thickness;

[0032] The three-axis amplitude spectrum data of the crushing equipment bearing is monitored through a vibration sensor network to obtain the bearing amplitude peak frequency;

[0033] Deploy a distributed temperature sensor array on the outer wall of the heat exchange pipe of the sterilization equipment to collect real-time data streams of the axial temperature gradient distribution on the heat exchanger surface and obtain the temperature gradient extremes;

[0034] The mean material adhesion thickness, bearing amplitude peak frequency, and temperature gradient range are integrated into an equipment condition monitoring dataset, which is stored in a real-time monitoring database by production batch and time series.

[0035] As a further solution of the present invention: In the equipment adjustment module, the process of triggering the dynamic compensation instruction of the process parameters based on the equipment operation status data is as follows:

[0036] When the average thickness of the material adhesion exceeds the process allowable threshold, a speed increase control instruction for the mixing equipment is generated, and the specific value of the speed compensation amount is calculated based on the current rheological characteristic parameters of the raw material;

[0037] When the peak frequency of the bearing amplitude exceeds the safe operating range of the equipment, a load balancing control instruction for the crushing equipment is generated, and the compensation amplitude of the crushing pressure setting value is adjusted according to the raw material structure strength parameters;

[0038] When the temperature gradient range exceeds the process standard limit, a temperature field reconstruction control instruction for the sterilization equipment is generated, and the slope parameter and platform time parameter of the temperature control curve are reset according to the thermal stability parameters of the raw materials;

[0039] All compensation operations generate detailed execution log records, which include trigger time, compensation parameters and execution result data.

[0040] As a further solution of the present invention: in the device feedback module, the closed-loop verification unit specifically includes:

[0041] Perform rapid component detection and analysis experiments on the final output of the production line to obtain quantitative indicator data such as active ingredient retention rate and particle uniformity. Calculate the absolute value of the deviation rate between the detection indicator data and the preset target characteristic value. When the absolute value of the deviation rate exceeds the optimization trigger threshold, trace back the complete corresponding chain between the process parameter adjustment instructions and the material status data.

[0042] Modify the core mapping rule parameters in the dynamic process adaptation model. When the active ingredient loss rate exceeds the standard, strengthen the constraint weight coefficient of the thermal stability parameter on temperature control. When the particle uniformity is insufficient, increase the sensitivity coefficient value of the structural strength parameter to pressure compensation.

[0043] The updated model parameter set is compiled into device executable code in real time and sent to the production equipment control terminal via industrial Ethernet.

[0044] As a further solution of the present invention: it also includes cross-process equipment linkage control, the process is:

[0045] When the raw material pretreatment equipment detects abnormal fluctuations in the structural strength parameters, it sends an early warning signal data frame containing particle hardness data to the crushing equipment control unit, and the crushing equipment control unit presets a dynamic pressure compensation parameter combination based on the early warning signal;

[0046] When the real-time particle uniformity data received by the sterilization equipment is lower than the process standard requirements, the duration control parameters of the sterilization equipment operation phase are automatically adjusted, and a real-time data exchange communication channel is established between the mixing equipment and the sterilization equipment;

[0047] Configure the transmission protocol parameters and verification mechanism parameters of the data exchange communication channel; transmit the status optimization results of the upstream process equipment to the downstream process control unit in real time.

[0048] Beneficial effects of the present invention:

[0049] The present invention addresses the problems of insufficient process adaptation and energy waste in the production of traditional functional foods, and achieves precise production and energy conservation and efficiency improvement through multi-module collaborative technology. The parameter acquisition module uses professional equipment such as rotational rheometers and differential scanning calorimeters to accurately extract raw material characteristic parameters such as raw material rheology, thermal stability and structural strength, and establishes a database bound to raw material batches to provide core data support for process adaptation. The process adaptation module constructs a dynamic process adaptation model based on the raw material characteristic parameters through nonlinear mapping functions, dynamic association rules, etc., and maps it into a set of equipment control instructions containing speed, temperature, and pressure set values ​​to achieve dynamic adjustment of equipment parameters; at the same time, it generates an on-demand energy supply tree structure based on the production process and raw material characteristics, and uses a two-way energy sharing channel to achieve dynamic energy allocation across processes to avoid energy waste. The equipment adjustment module monitors the equipment operating status in real time through multispectral imaging probes, vibration sensors, etc., and triggers dynamic compensation instructions for process parameters based on equipment status data to ensure a stable production process. The equipment feedback module compares the deviation between the output and the target characteristics through a closed-loop verification unit, iteratively optimizes the dynamic process adaptation model, and ensures product quality. In addition, the cross-process equipment linkage control mechanism enables each device to deeply coordinate based on the raw material characteristics and production status, ultimately achieving multiple benefits in the production process of functional foods, including improved product quality uniformity, reduced loss of active ingredients, and efficient use of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below with reference to the accompanying drawings.

[0051] Figure 1 It is a module schematic diagram of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] See also Figure 1 As shown, the present invention is a production process control system for functional foods, comprising:

[0054] The parameter acquisition module serves as the data cornerstone of the system. It uses professional testing equipment to conduct in-depth analysis of the core raw material characteristic parameters of functional food ingredients. A rotational rheometer is used to apply different shear rates to the raw materials to obtain viscosity change curves, from which the non-Newtonian fluid index is extracted to accurately characterize the rheological properties of the raw materials. A differential scanning calorimeter is used to simulate the programmed heating process, lock the starting temperature of the raw material's thermal decomposition, and quantify its thermal stability. A micro-indentation test device is used to apply pressure to the raw material particles, and the structural strength is evaluated through the standard deviation of the elastic deformation response curve. At the same time, the module establishes a comprehensive data management mechanism, permanently binding the collected raw material characteristic parameters to the raw material batch code, recording the parameter collection timestamp, instrument calibration records, and operator identification information to ensure data traceability and reliability.

[0055] The process adaptation module constructs a dynamic process adaptation model based on data from the parameter acquisition module. By defining nonlinear mapping functions, establishing dynamic association rules, and constraining boundary functions, it accurately converts raw material characteristic parameters into a set of equipment control instructions containing set values ​​such as the speed of the mixing equipment, the temperature of the sterilization equipment, and the pressure of the tableting equipment, thereby achieving dynamic adjustment of production parameters. Furthermore, based on the raw material processing stage, the module identifies key energy-consuming processes, divides energy demand levels, and generates an on-demand energy supply tree structure. Through a two-way energy sharing channel, it achieves dynamic cross-process energy allocation and improves energy efficiency.

[0056] The equipment adjustment module uses multispectral imaging probes, vibration sensors, distributed temperature sensors, and other devices to collect real-time data on production equipment operating status, such as material adhesion thickness, bearing amplitude spectrum, and heat exchanger temperature gradients. If abnormal data is detected, the system triggers dynamic compensation instructions for process parameters based on preset rules to ensure stable production operations.

[0057] The equipment feedback module uses a closed-loop verification unit to rapidly test the composition of the final product coming off the production line, comparing the deviation between actual and target characteristics. If the deviation exceeds a threshold, the system backtracks through the process parameter adjustment process, revising the core mapping rule parameters in the dynamic process adaptation model and immediately distributing the updated parameters to the production equipment, forming a closed-loop "detection-feedback-optimization" system to continuously improve product quality.

[0058] In a preferred embodiment of the present invention, in the parameter acquisition module, the method for extracting the raw material characteristic parameters is:

[0059] In terms of obtaining rheological characteristic parameters, the system uses a rotational rheometer to conduct comprehensive testing of raw materials. By setting multiple shear rates from low to high, the different stirring and conveying conditions that the raw materials may experience during the production process are simulated, and a complete curve of the raw material viscosity changing with shear rate is obtained. This curve intuitively shows the rheological behavior of the raw material under different stress states. On this basis, the system uses a curve fitting algorithm to accurately extract the non-Newtonian fluid index. As the core characterization indicator of rheological properties, the non-Newtonian fluid index can accurately reflect the viscosity change pattern of the raw material, providing a key basis for setting parameters for stirring, mixing and other links in the subsequent production process.

[0060] To determine the thermal stability parameters, the system conducts experiments with the help of a differential scanning calorimeter. During the programmed temperature rise process, the instrument heats the raw materials at a precisely controlled heating rate, and simultaneously records the heat changes of the raw materials at different temperatures in real time, thereby obtaining complete data on the phase change temperature range. Through in-depth analysis of the data, the system can accurately calculate the starting temperature value of the thermal decomposition reaction. This temperature value, as a key quantitative characteristic of thermal stability, is of great significance for determining the tolerance of the raw materials in subsequent processing, such as sterilization, baking and other high-temperature links, and effectively avoids the loss of active ingredients in the raw materials or quality degradation due to improper temperature control.

[0061] To test structural strength parameters, the system utilizes a microindentation tester to perform continuous pressure loading tests on raw material particles. As pressure gradually increases, the particles undergo elastic deformation, and the tester records this deformation process in real time, generating an elastic deformation response curve. The system statistically analyzes the curve data and extracts the standard deviation of the elastic modulus distribution data as the final quantitative expression of the structural strength parameter. This standard deviation reflects the degree of dispersion in structural strength between raw material particles, helping to determine whether uneven structural strength during processing steps such as crushing and tableting could lead to product quality fluctuations.

[0062] Furthermore, the parameter collection module establishes a comprehensive raw material characteristic parameter management system. Each collected raw material characteristic parameter is permanently linked to the raw material batch code. The database also records the parameter collection timestamp, instrument calibration records, and operator identification information. The timestamp ensures data timeliness, the instrument calibration records guarantee data accuracy and reliability, and the operator identification information clarifies the responsible party for data collection, facilitating data traceability and management.

[0063] In another preferred embodiment of the present invention, in the process adaptation module, the process of generating the equipment control instruction set is:

[0064] Based on the raw material characteristic parameters acquired by the parameter acquisition module, the system establishes a scientific and rigorous device control instruction generation mechanism. For the rheological characteristic parameters, the system defines a nonlinear mapping function relationship expression and establishes a corresponding relationship between it and the speed setting value of the stirring equipment. For raw materials with high non-Newtonian fluid exponents and significant viscosity changes, the system configures multi-stage stepped speed control curve parameters. In the initial mixing stage, a lower speed is used to initially disperse the raw materials to avoid excessive stirring resistance due to high viscosity. As the mixing process progresses, the speed is gradually increased to enhance the mixing effect and ensure uniform mixing of the raw materials.

[0065] Regarding the application of thermal stability parameters, the system establishes dynamic association rule constraints, closely linking thermal stability parameters to the sterilization equipment's temperature control values. For raw materials with low thermal decomposition critical points, the sterilization equipment's segmented gradient temperature control mode parameters are automatically activated. Preheating is initially performed at a lower temperature to gradually adapt the raw materials to the temperature change, and then the temperature is slowly increased in stages to the required sterilization level. This ensures sterilization effectiveness while minimizing the impact of sudden temperature increases on the raw material's thermal stability.

[0066] For the structural strength parameters, the system sets the constraint boundary function relationship to clarify its impact on the pressure setting value of the tableting equipment. For raw materials with a high hardness variation coefficient and large differences in structural strength, the real-time pressure feedback compensation algorithm logic of the tableting equipment is enabled. During the tableting process, the pressure sensor monitors the pressure changes in real time. Once the pressure fluctuation exceeds the preset range, the system automatically adjusts the pressure setting value immediately to ensure uniform tablet thickness and consistent hardness, effectively improving product quality. Finally, the system synergistically integrates parameters such as the speed setting value, temperature control value, and pressure setting value to generate a complete set of equipment control instructions, providing accurate and effective operation guidance for production equipment, and achieving perfect adaptation of production processes and raw material characteristics.

[0067] In another preferred embodiment of the present invention, in the process adaptation module, the generation process of the on-demand energy supply tree structure is:

[0068] The system first comprehensively analyzes the production process, accurately identifying key energy-consuming process nodes such as raw material pretreatment, mixing reaction, molding, and sterilization. For each process node, a basic energy consumption level is assigned based on the equipment's rated power and the standard process duration using a rigorous energy consumption calculation model. This value serves as a benchmark for initial energy allocation, ensuring that each process receives a stable energy supply under normal production conditions. For example, sterilization equipment is assigned a higher basic energy consumption level due to its high temperature and long operating time; raw material pretreatment equipment, on the other hand, is assigned a lower level due to its relatively low energy consumption.

[0069] On this basis, the system incorporates raw material characteristic parameters as dynamic correction factors. When a raw material's thermal stability parameter is detected to be below a safety threshold, the system automatically increases the energy consumption priority of the sterilization node. This is because raw materials with poor thermal stability require more precise and stable temperature control during the sterilization process to prevent excessive destruction of their active ingredients, thus requiring more energy to be allocated to the sterilization equipment. Based on the specific values ​​of the thermal stability parameters, the system dynamically adjusts the energy consumption level allocation according to a preset algorithm, ensuring that energy is prioritized to meet the needs of critical processes.

[0070] The system then constructs a tree-like energy topology. This structure uses the total energy input port as the root node, with each process node as a child node. Hierarchical links are established according to the sequence and dependencies of the production processes. This hierarchical topology design provides clear energy transmission paths and effectively avoids chaotic energy distribution. For example, after the raw material pretreatment process is completed, the raw materials enter the mixing reaction process, and energy is then transferred in an orderly manner from the pretreatment node to the mixing reaction node according to the hierarchical relationship of the tree structure.

[0071] In a preferred embodiment of this invention, the bidirectional energy sharing channel is specifically:

[0072] To further improve energy efficiency, the system sets up a two-way energy sharing channel between adjacent sub-nodes. This channel realizes dynamic energy allocation by real-time monitoring of the changing trend of energy consumption demand levels of each process node. When the mixing reaction node needs to increase the stirring power to enhance the mixing effect due to the processing of high-viscosity raw materials, the system will automatically detect the operating status of other process nodes. If the raw material pretreatment node is found to be idle, the mixing reaction node will send an energy quota allocation request to it. Energy allocation strictly follows the topological path of the on-demand energy supply tree for directional transmission, and prohibits direct energy transmission operations across levels to ensure the safety and stability of energy transmission.

[0073] For key energy-intensive process nodes, particularly sterilization, the system implements an emergency energy reserve capacity mechanism. This reserve capacity is not fixed but is calculated in real time based on the thermal stability parameters of the current batch of raw materials. The lower the thermal stability parameter, the less tolerant the raw materials are to sterilization temperature fluctuations. Consequently, the system reserves more emergency energy for the sterilization node to cope with unexpected situations such as temperature fluctuations or equipment failures. This dynamically adjusted emergency energy reserve strategy ensures the continued operation of key processes in the event of emergencies while avoiding excessive energy storage.

[0074] In another preferred embodiment of the present invention, in the equipment adjustment module, the process of real-time monitoring of the operating status data of the production equipment is:

[0075] For monitoring mixing reaction equipment, the system installs a multispectral imaging probe array on the inner wall of the chamber. This device integrates multi-band light sources, such as visible light and near-infrared, with a high-resolution camera. It can penetrate materials of varying transparency and capture a real-time image sequence of the thickness distribution of the material adhered to the surface of the mixing blade. An image segmentation algorithm is used to separate the blade area from the background, and a threshold method and morphological operations are used to extract the outline of the adhered material. The average adhered thickness per unit area is then calculated. For example, if the average adhered thickness of high-protein powder material exceeds 5mm (the process-allowed threshold), it indicates that the material's viscosity is too high, resulting in a decrease in mixing efficiency, and the speed compensation mechanism needs to be triggered.

[0076] For the crushing equipment, the system utilizes a distributed vibration sensor network, with accelerometers installed in the three orthogonal X, Y, and Z directions of the bearing housing to collect real-time triaxial amplitude spectrum data. A Fast Fourier Transform (FFT) is used to convert the time-domain vibration signal into a frequency-domain spectrum, extracting the frequency components corresponding to the peak amplitude. If the peak amplitude frequency of the bearing exceeds the equipment's safe operating range, such as exceeding 1.2 times the bearing's natural frequency, this indicates possible bearing wear or load imbalance, necessitating adjustment of the crushing pressure based on the raw material's structural strength parameters.

[0077] To monitor the sterilization equipment, the system deploys a distributed array of temperature sensors along the outer wall of the heat exchanger pipes, with a temperature measurement node set every 10 cm to form an axial temperature gradient monitoring network. Thermocouples or RTD sensors collect temperature data from each node in real time, calculate the temperature difference between adjacent nodes, and use the maximum difference as the temperature gradient range. If the axial temperature gradient range on the sterilization equipment heat exchanger surface exceeds 8°C (the process standard limit), it indicates an uneven temperature distribution and requires the temperature control curve to be reconstructed based on the raw material thermal stability parameters.

[0078] After preprocessing by the edge computing unit, this monitoring data is integrated into an equipment condition monitoring dataset containing indicators such as average material adhesion thickness, bearing amplitude peak frequency, and temperature gradient extremes. This dataset is structured and stored by production batch number and timestamp, and connected to a real-time monitoring database, supporting historical data query, trend analysis, and anomaly tracing.

[0079] In another preferred embodiment of the present invention, in the equipment adjustment module, the process of triggering the process parameter dynamic compensation instruction based on the equipment operation status data is:

[0080] When the average thickness of material adhesion in the mixing and reaction equipment exceeds the specified value, the model retrieves the current raw material's rheological properties, such as the non-Newtonian fluid exponent n, and calculates the speed compensation using a preset nonlinear function. For example, for a high-viscosity pectin raw material with n=0.3, the calculation shows that the speed needs to be increased by 20% to enhance shear force and break the material adhesion.

[0081] When the peak frequency of the crushing equipment's bearing vibration amplitude is abnormal, the system generates load-balancing control instructions based on raw material structural strength parameters, such as the standard deviation of the elastic modulus σ. Using a fuzzy control algorithm, the crushing pressure compensation amplitude is adjusted accordingly: when σ > 100 MPa, the pressure compensation amplitude is +15%; when 50 MPa < σ ≤ 100 MPa, the compensation amplitude is +10%, and so on. This strategy effectively alleviates equipment vibration overload caused by uneven raw material particle hardness.

[0082] To address temperature anomalies in the sterilization equipment, the system resets the temperature control curve based on the raw material's thermal stability parameters, such as the thermal decomposition starting temperature T0. For heat-sensitive raw materials with T0 < 120°C, the heating slope is adjusted from 5°C / min to 3°C / min, and the 120°C constant temperature platform duration is extended to 20 minutes, ensuring sterilization effectiveness while minimizing active ingredient degradation. A detailed log is generated during the execution of all compensation instructions, including trigger time, original parameters, compensated parameters, execution time, and result status. This log can be transmitted to a cloud management platform via industrial Ethernet for analysis and optimization by process engineers.

[0083] In another preferred embodiment of the present invention, in the device feedback module, the closed-loop verification unit specifically includes:

[0084] The closed-loop verification unit utilizes a systematic testing-analysis-optimization process. At the end of the production line, specialized equipment such as high-performance liquid chromatography and laser particle size analyzers conduct rapid component analysis on the final product. The instruments accurately measure the retention rate of core active ingredients in functional foods, such as probiotic counts and vitamin content. Simultaneously, image recognition technology and statistical methods are used to quantify particle uniformity indicators, such as the coefficient of variation of particle size distribution, to comprehensively assess product quality. The system compares the test data with pre-set target characteristic values ​​and calculates the absolute value of the deviation rate using the formula "deviation rate = |(measured value - target value) / target value| × 100%." ​​If this value exceeds a pre-set optimization trigger threshold, such as a deviation exceeding 5% in active ingredient retention or 8% in particle uniformity coefficient of variation, the system immediately initiates a backtracking mechanism. Leveraging comprehensive data logging throughout the production process, the system can trace the entire chain of events, from the collection of raw material characteristic parameters, the issuance of process parameter adjustment instructions, to changes in equipment operating status, accurately pinpointing the key links leading to quality deviations.

[0085] Based on the backtracking results, the system deeply optimizes the dynamic process adaptation model. If the loss rate of active ingredients exceeds the standard, it indicates that high-temperature processes such as sterilization have insufficient consideration of the thermal stability of the raw materials. At this time, the system uses a machine learning algorithm to strengthen the constraint weight coefficient of the thermal stability parameter in the temperature control mapping rule, such as increasing its weight from 0.3 to 0.5, so that the temperature control in subsequent production is more strictly adapted to the characteristics of the raw materials; when the particle uniformity is insufficient, the system increases the sensitivity coefficient of the structural strength parameter to the pressure compensation of processes such as tableting and crushing, such as adjusting it from 1.2 to 1.5, to ensure that the equipment pressure parameters can respond more sensitively to changes in the raw material structure. After completing the parameter correction, the system compiles the updated model parameter set into a control code that can be executed by the equipment, and sends it to the production equipment control terminal through the industrial Ethernet with a millisecond delay to achieve real-time optimization of the process parameters.

[0086] In another preferred embodiment of the present invention, cross-process equipment linkage control is also included, and the process is as follows:

[0087] The cross-process equipment linkage control mechanism overcomes the limitations of independent equipment operation in traditional production. When sensors in the raw material pretreatment equipment detect abnormal fluctuations in the raw material's structural strength parameters, such as a sudden increase in the standard deviation of the elastic modulus, indicating uneven hardness of the raw material particles, the equipment immediately generates a warning signal data frame containing the particle hardness data and transmits it to the pulverization equipment control unit via the Industrial Internet Protocol. Upon receiving the signal, the pulverization equipment, based on a built-in expert system and a preset dynamic pressure compensation strategy, preemptively adjusts parameters such as blade speed and grinding pressure. For example, it increases the initial pressure from 10 MPa to 12 MPa, effectively preventing a decrease in pulverization efficiency or equipment wear due to variations in raw material hardness.

[0088] During the sterilization process, when the sterilization equipment receives real-time particle uniformity data from the laser particle size analyzer that is lower than the process standard requirements, the system automatically triggers a linkage response. On the one hand, the duration control parameters of the sterilization equipment operation phase are dynamically adjusted according to the degree of deviation, such as extending the preheating time or increasing the constant temperature holding time to ensure that particles of different particle sizes can be effectively sterilized; on the other hand, a real-time data exchange communication channel based on the OPCUA protocol is established between the mixing equipment and the sterilization equipment. By configuring the transmission protocol parameters of the channel, such as data encryption method, transmission frequency, and CRC check mechanism parameters, the accuracy and stability of data transmission are guaranteed. Through this channel, the upstream process equipment transmits the state optimization results, such as the adjusted stirring speed and mixing time of the mixing equipment, to the downstream process control unit in real time, so that the subsequent process equipment can prepare for parameter adaptation in advance, realize the coordinated optimization of the entire production line, and effectively improve production efficiency and product quality consistency.

[0089] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A production process control system for functional foods, characterized in that: include: A parameter acquisition module is used to acquire raw material characteristic parameters of functional food raw materials, wherein the raw material characteristic parameters include rheological characteristic parameters, thermal stability parameters and structural strength parameters; The process adaptation module is used to build a dynamic process adaptation model, mapping raw material characteristic parameters into a set of equipment control instructions; dividing energy demand levels according to the raw material processing stage and generating an on-demand energy supply tree structure; Equipment adjustment module, used to monitor the operating status data of production equipment in real time; Triggering dynamic compensation instructions for process parameters based on equipment operating status data; The equipment feedback module is used to compare the deviation of the actual output characteristics of the production line with the target characteristics through a closed-loop verification unit, and iteratively optimize the dynamic process adaptation model; In the process adaptation module, the generation process of the on-demand energy supply tree structure is as follows: Identify key energy-consuming process nodes in the production process, including raw material pretreatment nodes, mixing reaction nodes, molding processing nodes, and sterilization processing nodes; assign a basic energy consumption level value to each process node, which is calculated based on the equipment's rated power and the standard process duration; Dynamically modify the energy consumption level allocation plan based on raw material characteristic parameters, and increase the energy consumption priority weight of the sterilization node when the thermal stability parameter is lower than the safety threshold; construct a tree-like energy topology structure, with the root node connected to the total energy input port, and the child nodes establishing hierarchical link channels according to process dependencies; A bidirectional energy sharing channel is set up between adjacent sub-nodes, allowing the energy of idle nodes to be dynamically allocated and transmitted to high-load nodes; emergency energy reserve capacity is configured for key energy-consuming process nodes, and the reserve capacity is dynamically adjusted according to real-time production needs; The bidirectional energy sharing channel is specifically: Continuously monitor the changing trends of the real-time energy consumption demand levels of each process node; when the mixing reaction node needs to increase power due to high-viscosity raw materials, send an energy quota allocation request to the idle pre-processing node; the energy allocation and transmission process follows the on-demand energy supply tree topology path for directional transmission, and direct energy transmission operations across levels are prohibited; emergency energy reserve capacity is reserved for key sterilization processing nodes, and the reserve capacity is calculated and determined in real time based on the thermal stability parameters of the current batch of raw materials.

2. A production process control system for functional foods according to claim 1, characterized in that: In the parameter acquisition module, the method for extracting the raw material characteristic parameters is: The complete curve of the viscosity of the raw material as a function of shear rate is measured by a rotational rheometer, and the viscosity values ​​at different shear rates are recorded. The non-Newtonian fluid index is extracted from the complete curve as the core characterization indicator of the rheological characteristic parameters; A differential scanning calorimeter was used to obtain complete data on the phase transition temperature range of the raw materials during the programmed temperature increase process, and the starting point temperature of the thermal decomposition reaction was calculated as the key quantitative characteristic of the thermal stability parameter; The elastic deformation response curve of the raw material particles under continuous pressure loading is detected using a micro-indentation test device, and the standard deviation of the elastic modulus distribution data is extracted as the final quantitative expression of the structural strength parameter; Establish a permanent binding relationship database between raw material characteristic parameters and raw material batch codes, which includes parameter collection timestamps, instrument calibration records and operator identification information.

3. A production process control system for functional foods according to claim 1, characterized in that: In the process adaptation module, the process of generating the equipment control instruction set is as follows: Define the nonlinear mapping function relationship expression between the rheological characteristic parameters and the speed setting value of the stirring equipment, Configure multi-stage stepped speed control curve parameters for high non-Newtonian fluid index raw materials; Establish dynamic association rule constraints between thermal stability parameters and sterilization equipment temperature control values, and activate the segmented gradient temperature rise control mode parameters of the sterilization equipment for raw materials with low thermal decomposition critical points; Set the constraint boundary function relationship between the structural strength parameters and the pressure setting value of the tableting equipment, and enable the real-time pressure feedback compensation algorithm logic of the tableting equipment for raw materials with high hardness variation coefficient; Generate a device control instruction set, which includes a coordinated adjustment strategy combination of a speed setting value, a temperature control value, and a pressure setting value.

4. A production process control system for functional foods according to claim 1, characterized in that: In the equipment adjustment module, the process of real-time monitoring of the operating status data of the production equipment is as follows: A multispectral imaging probe array device is installed on the inner wall of the mixing reaction equipment chamber to capture the thickness distribution image sequence of the material adhesion layer on the surface of the stirring blade in real time and obtain the average value of the material adhesion thickness; The three-axis amplitude spectrum data of the crushing equipment bearing is monitored through a vibration sensor network to obtain the bearing amplitude peak frequency; Deploy a distributed temperature sensor array on the outer wall of the heat exchange pipe of the sterilization equipment to collect real-time data streams of the axial temperature gradient distribution on the heat exchanger surface and obtain the temperature gradient extremes; The mean material adhesion thickness, bearing amplitude peak frequency, and temperature gradient range are integrated into an equipment condition monitoring dataset, which is stored in a real-time monitoring database by production batch and time series.

5. A production process control system for functional foods according to claim 1, characterized in that: In the equipment adjustment module, the process of triggering the dynamic compensation instruction of process parameters based on the equipment operation status data is as follows: When the average thickness of the material adhesion exceeds the process allowable threshold, a speed increase control instruction for the mixing equipment is generated, and the specific value of the speed compensation amount is calculated based on the current rheological characteristic parameters of the raw material; When the peak frequency of the bearing amplitude exceeds the safe operating range of the equipment, a load balancing control instruction for the crushing equipment is generated, and the compensation amplitude of the crushing pressure setting value is adjusted according to the raw material structure strength parameters; When the temperature gradient range exceeds the process standard limit, a temperature field reconstruction control instruction for the sterilization equipment is generated, and the slope parameter and platform time parameter of the temperature control curve are reset according to the thermal stability parameters of the raw materials; All compensation operations generate detailed execution log records, which include trigger time, compensation parameters and execution result data.

6. A production process control system for functional foods according to claim 1, characterized in that: In the device feedback module, the closed-loop verification unit specifically includes: Perform rapid component detection and analysis experiments on the final output of the production line to obtain quantitative indicator data such as active ingredient retention rate and particle uniformity. Calculate the absolute value of the deviation rate between the detection indicator data and the preset target characteristic value. When the absolute value of the deviation rate exceeds the optimization trigger threshold, trace back the complete corresponding chain between the process parameter adjustment instructions and the material status data. Modify the core mapping rule parameters in the dynamic process adaptation model. When the active ingredient loss rate exceeds the standard, strengthen the constraint weight coefficient of the thermal stability parameter on temperature control. When the particle uniformity is insufficient, increase the sensitivity coefficient value of the structural strength parameter to pressure compensation. The updated model parameter set is compiled into device executable code in real time and sent to the production equipment control terminal via industrial Ethernet.

7. A production process control system for functional foods according to claim 1, characterized in that: It also includes cross-process equipment linkage control, the process is: When the raw material pretreatment equipment detects abnormal fluctuations in the structural strength parameters, it sends an early warning signal data frame containing particle hardness data to the crushing equipment control unit, and the crushing equipment control unit presets a dynamic pressure compensation parameter combination based on the early warning signal; When the real-time particle uniformity data received by the sterilization equipment is lower than the process standard requirements, the duration control parameters of the sterilization equipment operation phase are automatically adjusted, and a real-time data exchange communication channel is established between the mixing equipment and the sterilization equipment; Configure the transmission protocol parameters and verification mechanism parameters of the data exchange communication channel; transmit the status optimization results of the upstream process equipment to the downstream process control unit in real time.

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