Aseptic pipeline evaluation and commissioning method for food and beverage processing

Abstract

The present invention relates to a method for evaluating and commissioning a sterilized pipeline for food and beverage processing. By introducing a standardized sterile gas into a closed pipeline system, a slightly positive pressure environment is established; a micro-pressure difference sensing module is used to detect the pressure change curve of each node in the system in real time; the position and size of the micro-leakage points are deduced; a dynamic particle imaging system PIV is used to record the movement trajectories of the particles in the pipeline; through trajectory analysis, the flow dead zones, stagnant zones and potential pollution accumulation zones are identified, and the pollution accumulation probability of each section of the pipeline is marked in a quantitative manner; a simulation liquid is prepared, and biological marker particles are added to make the viscosity and fluidity close to those of the target beverage; according to the identified potential pollution accumulation zones, local directional perfusion is carried out; the whole process can realize automatic data collection, model judgment and debugging instruction issuance, greatly reducing manual intervention, improving the system stability, and being applicable to various food and beverage production scenarios.

Description

Technical Field

[0001] The invention relates to the field of beverage processing, and in particular to a method for evaluating and debugging a sterilized pipeline for food and beverage processing. Background Art

[0002] Currently in the food and beverage processing industry, the evaluation and debugging of sterile piping systems generally adopt traditional static pressure maintenance tests, regular disassembly inspections, full-line CIP (cleaning in place) and SIP (sterilization in place) followed by colony sampling and culture. Although these methods established basic industry standards in the early days, in the context of the modern food industry's demand for high speed, high precision and intelligence, they have exposed a number of unavoidable technical shortcomings and application drawbacks, which are specifically reflected in insufficient detection sensitivity, delayed response, high dependence on manual labor, uncontrollable debugging and poor system closed-loop. First, the traditional static pressure maintenance method mainly determines whether there is a leak by recording the pressure drop over a long period of time after pressurizing and sealing the pipeline system. This method can only provide a macro judgment of "yes / no leak" in essence, and cannot accurately locate the specific location or scale of the leak. It shows extremely low sensitivity to trace leaks in the pipeline, especially in high humidity or micro-crack areas. The problem of "no leaks in system detection, but repeated contamination of product batches" often occurs, indicating that it can no longer meet the requirements for micro-leakage identification in high-cleanliness level scenarios; secondly, the traditional evaluation process relies heavily on manual operation and human experience judgment. For example, the operator needs to complete sample collection, inoculation, constant temperature culture and visual counting after 24 hours for colony culture. It is time-consuming, has many interferences and large errors, and cannot achieve early identification and predictive control of pollution trends. Especially in high-speed production lines or multi-category alternating operation environments, it is very easy to form a reaction lag, which makes it difficult to trace the source of pollution.

[0003] Again, the traditional evaluation system lacks the integration ability with modern information systems, and cannot achieve online data collection, real-time processing, and intelligent judgment. The evaluation results often show static single-time characteristics, lacking dynamic change records, and it is also difficult to form a closed-loop feedback during the debugging process. For example, although the whole line can be sterilized after detecting excessive colonies in a certain section, it is impossible to form a quantitative evaluation on whether the sterilization effectiveness matches the pollution source and whether it reaches the key nodes, resulting in the debugging process still relying on the redundant method of "experience + full coverage", causing a large amount of energy and time waste. In addition, for complex internal structures of pipelines such as elbows, reducers, dead ends, etc., traditional means are difficult to accurately analyze the pollution accumulation risk in the fluid state because of the lack of effective flow visualization tools and quantitative tracking mechanisms, making the cleaning of dead ends, identification of cross-contamination paths, etc. rely on empirical settings and conventional conservative solutions for a long time, ultimately resulting in the polarization problem of over-cleaning or insufficient coverage. In terms of microbial pollution risk control, traditional methods generally use the culture results of end samples as the basis for compliance, which is completely a "result-oriented" control rather than a "process prediction" type of debugging. This means that once a colony exceeds the standard, it often indicates that the pollution has occurred and the debugging lags behind the actual problem, seriously affecting the product consistency and market compliance ability of enterprises. Furthermore, in modern high-precision and highly automated beverage processing lines, production switches are frequent and batches are short, and the time cost and labor cost of traditional debugging methods increase sharply. A round of debugging may take several hours or even half a day, greatly reducing the flexibility and operation efficiency of the production line. In addition, most of these traditional methods cannot automatically archive and reuse historical data, lacking traceability, analytical, and systematic support, unable to implement a standardized debugging process, and not suitable for intelligent deployment or industrial Internet of Things expansion in a large-scale production environment. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating and debugging a sterilized pipeline for food and beverage processing, so as to solve some of the drawbacks and deficiencies pointed out in the background technology.

[0005] The technical solutions adopted by the present invention to solve the above technical problems are as follows: A method for evaluating and debugging a sterilized pipeline for food and beverage processing includes: introducing a standardized sterile gas into a closed pipeline system to establish a slightly positive pressure environment; using a differential pressure sensing module to detect the pressure change curve of each node in the system in real time; calculating the position and size of a micro-leakage point according to the differential pressure change rate formula;

[0006] On the basis of confirming no leakage, introducing calibrated particle sizes of particles including polystyrene microspheres; using a dynamic particle imaging system to record the movement trajectory of the particles in the pipeline; through trajectory analysis, identifying flow dead ends, potential pollution accumulation areas in stagnant areas, and quantitatively marking the pollution accumulation probability of each section of the pipeline;

[0007] Prepare a simulated liquid, add biomarker microparticles including latex particles encapsulated with fluorescent dyes, and make it close to the viscosity and fluidity of the target beverage; according to the identified potential pollution accumulation areas, perform local directional perfusion;

[0008] Collect the effluent sample of the local simulated liquid for rapid microbial culture; combine with an AI-assisted growth curve prediction system, and calculate the potential reproduction risk index based on the initial colony growth rate; for the reproduction risk index value area, trigger a local sterilization instruction.

[0009] Furthermore, the method for calculating the position and size of the micro leakage point includes:

[0010] In a closed pipeline system, create a controllable slightly positive pressure environment, form a unidirectional diffusion flow from inside to outside at the potential leakage point, introduce sterile gas filtered through particles, and at the same time adopt a flow management strategy with controlled disturbance. By setting the initial flow rate and introducing time-increasing disturbances, construct a dynamically changing gas injection mode. The gas flow rate change is described as:

[0011] ;

[0012] Where:

[0013] represents the sterile gas flow rate at time , is the initial gas injection flow rate, is the flow disturbance adjustment coefficient, used to control the amplitude of the flow rate increase rate, is the time variable after the start of gas injection, is the non-linear increasing exponent, used to adjust the curve shape of the gas flow rate increase.

[0014] Furthermore, the method for calculating the position and size of the micro leakage point includes:

[0015] Distributively install micro differential pressure sensing modules at key nodes including bends and turns inside the pipeline system, collect high-frequency data on the changes of local pressure at each node over time and position, use the spatial gradient and time acceleration characteristics of the pressure changes between nodes for multi-dimensional analysis, and construct a dynamic pressure change function. The local pressure change relationship can be described as:

[0016] ;

[0017] Where:

[0018] represents the local pressure change rate at position and time , represents the instantaneous pressure value of position and time, represents the first-order derivative of pressure along the spatial direction, that is, the rate of change of pressure at different positions, represents the second-order rate of change of pressure over time, that is, the acceleration of pressure change over time, It is the sensitivity adjustment factor of the time second-order derivative, which improves the ability to capture tiny transient leakage responses by moderately amplifying the time acceleration component.

[0019] Furthermore, the method for estimating the location and size of trace leakage points includes: establishing an integral evaluation system for the phenomenon of asynchronous pressure changes between nodes based on the pressure change curve collected by each sensor node, quantifying the risk of tiny leakage at different locations, and calculating the cumulative value of potential leakage risk by accumulating leakage signs through time integration.

[0020] Furthermore, the method for calculating the potential reproductive risk index includes:

[0021] The simulated liquid outlet samples at each node of the pipeline are collected and used for rapid microbial culture. The culture process adopts a temperature-controlled rapid culture system, and the imaging equipment is used to regularly collect colony images to obtain the early growth behavior data of the colony. By identifying the dynamic changes of the initial colony number growth, a growth trend function is constructed to fit the initial growth rate. The growth trend model is defined as:

[0022] ;

[0023] in:

[0024] represents the total number of colonies at time $t$; is the initial colony number; is the quantity growth response coefficient, which is a dimensionless coefficient and is used to control the response speed of colony quantity growth to time changes; is the disturbance gain factor, which determines the base amplitude of the growth acceleration; To cultivate time; It is a nonlinear growth index used to adjust the nonlinear steepness of the early growth curve.

[0025] Furthermore, the method for calculating the potential reproductive risk index includes:

[0026] Track the growth of the number, extract the area and morphological expansion of the colony in real time, extract the edge contour of the colony through image processing algorithm and calculate the radius change of each colony; based on the average expansion behavior of all colonies, define the local expansion potential energy function as follows:

[0027] ;

[0028] in:

[0029] Represents the average expansion rate of local colonies per unit time and is used to measure the activity of morphological expansion; Is the expansion weight adjustment coefficient and is used to regulate the contribution of the area change rate to the risk assessment; Is the time The total number of colonies identified at the moment; Is the Equivalent radius of the th colony; Is the arithmetic mean of the squares of the areas of all colonies; Represents the derivative operation with respect to time and extracts the dynamic change of the expansion rate.

[0030] Furthermore, the method for calculating the potential reproduction risk index MGRI includes:

[0031] Unify the modeling of quantity growth and morphological expansion and conduct risk prediction. Introduce the reproduction risk index and comprehensively calculate the weighted sum of the colony quantity growth rate and the expansion potential energy per unit time. The constructed risk integral model is:

[0032] ;

[0033] Where:

[0034] : Represents the maximum potential reproduction risk index in the target pipeline area within the time interval ; Is the weight coefficient of the quantity growth risk and is used to adjust the contribution of colony quantity growth to the total risk; Is the weight coefficient of the expansion rate risk; Represents the change rate of colony quantity with time; Represents the power function of the expansion rate, Is the risk weighting index and is used to enhance the influence of the morphological expansion factor on the total risk value; Is the integral symbol and represents the cumulative risk calculation within the entire culture observation window.

[0035] Advantages of the present invention: This method breaks through the limitations of traditional single static detection means and constructs an intelligent evaluation and dynamic debugging system for aseptic pipelines integrating "gas perturbation leakage detection, particle behavior recognition, simulated liquid marking verification, rapid microbial culture, AI-assisted prediction, and local sterilization linkage", significantly improving the aseptic protection level and production efficiency in the food and beverage processing process. First, by introducing a perturbation gas injection model and differential pressure dynamic analysis method, high-sensitivity positioning of micro leakage points and quantitative evaluation of leakage degree are achieved, with the detection sensitivity improved by more than one order of magnitude compared with the traditional static pressure method. Second, using particle visual imaging technology and simulated liquid marking verification, flow dead corners and pollution accumulation areas inside the pipeline are accurately identified, filling the blank of the traditional method for identifying the blind area of internal fluidity risk in the system. Third, by integrating the two-dimensional modeling of the colony number growth curve and morphological expansion dynamics, and combining with the maximum growth risk index (MGRI) constructed by the AI system, the future pollution risk trend of colonies can be predicted in the early growth stage of colonies, triggering the local sterilization response mechanism in advance, realizing the transformation from "post-disinfection" to "pre-judgment and active intervention". Finally, this method can realize full-process automatic data collection, model judgment, and debugging instruction issuance, greatly reducing manual intervention, improving system stability, applicable to various food and beverage production scenarios, especially having significant application effects in low-temperature cold chain, high-sugar and high-acid products, or ultra-clean filling environments, and having broad industry promotion value and engineering application prospects. Description of the Drawings

[0036] Figure 1 It is a flowchart of a method for evaluating and debugging an aseptic pipeline for food and beverage processing according to the present invention;

[0037] Figure 2 It is a flowchart of a method for calculating the position and size of a micro leakage point according to the present invention;

[0038] Figure 3 It is a flowchart of a method for calculating the potential growth risk index (MGRI) according to the present invention. Detailed Embodiments

[0039] The following makes a detailed description of the specific embodiments of the present invention in conjunction with the drawings.

[0040] Combined with the attached Figure 1, a method for evaluating and debugging aseptic pipelines in food and beverage processing of the present invention, realizes highly sensitive identification of the leakage risk of the aseptic pipeline system by establishing a controlled slightly positive pressure environment; first, when the food and beverage processing pipeline system to be detected is in a fully enclosed state, introduce standardized aseptic gas filtered by high-efficiency particulate filtration (such as HEPA), slowly inject it into the pipeline interior, and control the internal gas pressure to stably maintain a slightly positive pressure state 10 to 30 mbar higher than the external atmospheric pressure. The purpose of setting this slightly positive pressure is to ensure that in the case of a small leak, the gas can only slowly escape from the pipeline interior to the outside, thus avoiding the risk of secondary contamination caused by the unsterilized gas from the outside pouring back into the system; at the same time, multiple high-precision differential pressure sensing modules are distributed in the system. These modules can record the pressure change data of each detection node during the gas injection process in real time at a high frequency. Especially during the pressure stabilization stage, by continuously monitoring and plotting the curve trend of pressure change over time, it can be judged whether there is an abnormal pressure drop signal in the system. If there is a small leak near a certain node, the pressure change rate at its location will show a descending slope that is out of sync with other normal areas; in order to further quantify and locate the leak point, the data analysis model built into the system is based on the differential pressure change rate formula, that is, by comparing and analyzing the pressure curves between different nodes in the spatial and time dimensions, combining the micro-perturbation flow model and the local gas loss behavior, the approximate location and the degree of leakage of the leak point are deduced, specifically including the estimation of the radius of the leak opening and the approximate value of the volume of leaked gas per unit time, so as to provide a precise decision-making basis for subsequent local sterilization, structural repair or re-verification operations.

[0041] On the premise that the micro differential pressure method has been completed to confirm that there is no obvious leakage in the pipeline system, further visual fluid analysis is carried out on the flow dead corners and micro pollution residue areas inside the system to identify the distribution positions of aseptic hazards; in the pipeline system where a stable slightly positive pressure has been established and the sealing state has been confirmed, a flowing medium carrying visually calibrated particles is injected, and the calibrated microparticles used are polystyrene microspheres with stable physical properties. Such microspheres have a unified particle size range, excellent optical response characteristics and biocompatibility, and will not chemically react with pipeline materials or residues, so as to ensure that the motion state of the particles during the test truly reflects the hydrodynamic behavior; subsequently, a dynamic particle imaging system (Particle Image Velocimetry, abbreviated as PIV) is used to take high-frame-rate pictures of the motion trajectories of the calibrated particles in the fluid. The PIV system is based on the principles of laser illumination and high-speed photography, and can capture the displacement and velocity information of each microsphere changing with time in three-dimensional space. Through the image sequence processing algorithm, a particle velocity vector field and a fluid streamline diagram are constructed; the system then identifies the areas where flow stagnation or local turbulence occurs in specific pipeline structures (such as elbows, tees, slow flow sections) according to the distribution density of the microsphere trajectories, the distribution characteristics of the velocity vectors and the residence time differences. These areas are usually regarded as high-risk points for pollutant retention and microbial growth; to achieve quantitative expression, the system further calculates the pollution accumulation probability value of each small section of the pipeline based on the cumulative residence probability and local flow velocity decay rate of the particles at different spatial positions, and marks these probabilities on the pipeline structure diagram in the form of a two-dimensional or three-dimensional heat map, finally forming a set of accurate, quantifiable and visual pollution hazard distribution maps, providing a scientific basis for subsequent targeted cleaning, local sterilization or structural optimization.

[0042] Targeted verification and micro-pollution residue identification of potential contamination areas using biomarker simulation fluid; First, a set of simulation fluids with similar rheological properties are prepared according to the actual physical parameters (such as density, viscosity, surface tension, etc.) of the target food or beverage product. Biomarker particles composed of latex microspheres wrapped with fluorescent dyes are added to the simulation fluid. These latex particles have good traceability, particle size uniformity, and biodegradability. The fluorescent dyes coated on their exterior can emit stable fluorescence signals under the irradiation of a specific wavelength light source, facilitating subsequent trajectory tracking and residue detection. At the same time, the suspension characteristics of the latex particles are consistent with the fluid, ensuring that their particle attachment and retention behaviors during flow can fully simulate those of real products in pipelines; After completing the PIV trajectory analysis of polystyrene microspheres and identifying the structural dead ends or retention areas with pollutant residues in the pipeline, the system performs local directional perfusion operations on these high-risk areas according to the positioning results, that is, through a dedicated switching valve and branch system, guiding the simulation fluid to flow into the identified key areas instead of circulating throughout the system, thereby achieving more efficient and targeted local verification; During the perfusion process, the system continuously monitors the residence time and flow state of the simulation fluid in the local pipe section, and analyzes the fluorescence signals remaining on the pipeline wall after perfusion through an optical detection device to judge the particle retention degree and the formation trend of biofilm attachment in the local area, thereby realizing the quantitative evaluation and high-precision identification of aseptic weak points, compared with the traditional overall circulation method of simulation fluid.

[0043] Through the combination of rapid microbial culture and artificial intelligence-assisted analysis, quantitative assessment and dynamic intervention control of local pollution hazards are achieved; after the local directional perfusion of the simulated liquid and the detection of residues in the retention area are completed, samples of the simulated liquid at each local outlet are collected and immediately sent into a dedicated rapid microbial culture system for short-cycle incubation. This culture system uses a modified high-sensitivity culture medium and sets a constant temperature condition (such as 32°C ± 2°C), which can prompt early microorganisms to rapidly form recognizable colonies within 4 to 6 hours; during this culture process, the supporting imaging acquisition system will take pictures of the growth of colonies at fixed time intervals, and the image data will be synchronously uploaded to the AI-assisted growth curve prediction system. This system conducts real-time analysis based on the growth models constructed from a large number of known standard microbial samples. By extracting characteristic parameters such as the change trend of colony quantity, the growth rate of average diameter, and the amplitude of area expansion, it identifies the different stages of the current bacterial population in the latent period, logarithmic growth period, or stationary period, and further predicts its future reproduction trend and hazard potential; on this basis, the system comprehensively calculates indicators such as growth rate, expansion speed, and density change to calculate the maximum potential reproduction risk index MGRI. This index is a dimensionless evaluation value, which is used to reflect the maximum amplification level of microorganisms in the sample under the most favorable environmental conditions in the future. The higher the value, the greater the risk of local pollution diffusion; the system sets the MGRI risk threshold as the judgment criterion. Once the MGRI value calculated for a sample in a certain section exceeds this safety limit, it is determined that this section is a high-risk pollution area, and then a local sterilization instruction is triggered. The control system will accurately call the corresponding pulsed high-temperature steam sterilizer or supercritical carbon dioxide sterilization module according to the location of this section to implement rapid, efficient, and directional disinfection treatment on the target area, avoiding the spread of pollution to other pipe sections or production batches, thereby realizing the intelligent, self-closed-loop, and responsive debugging control of the aseptic pipeline system.

[0044] Example 1:

[0045] Combined with the attached Figure 2 , in this example, on a sterile beverage filling production line of a food enterprise, a set of SUS304 stainless steel food-grade pipeline system with a total length of about 180 meters is installed, which is used to transport the prepared fruit juice beverage from the mixing tank to multiple filling heads. The pipeline is designed as a fully enclosed aseptic structure and has CIP and SIP cleaning functions. Due to the recent situation of individual colony exceeding the standard in the filling batches, the enterprise decides to conduct a complete aseptic evaluation and debugging of the entire aseptic pipeline system.

[0046] After the system completes CIP cleaning, it enters the aseptic detection stage. First, connect this closed system to the aseptic gas supply module, and the gas type is 0.22 Sterile air processed by a Class HEPA high-efficiency filter. To ensure that gas at any tiny leakage point during detection can only escape from the inside of the pipeline to the outside, the system maintains the internal pressure at a slightly positive pressure state about 25 mbar above atmospheric pressure. At this time, a dynamic perturbation flow mode is started to inject sterile air into the pipeline, aiming to increase the gas escape rate at the tiny leakage points through perturbation, making its signal easier to be monitored.

[0047] The initial gas injection flow rate is set to , and the system designs a flow perturbation model with hourly growth, using the function:

[0048] ;

[0049] where the set parameters are:

[0050] , this coefficient is adjusted within the range of 0.01 - 0.05 and is used to reflect the perturbation amplification ability;

[0051] , its set range is between 1 - 2, and it is optimized according to the experience of the leakage point response sensitivity;

[0052] The time variable t is in minutes, and data for continuous testing for 10 minutes is selected for sample analysis.

[0053] Substitute for calculation example: When = 5 min, there is

[0054] ;

[0055] That is, at the 5th minute, the gas flow rate has increased from the initial 15 liters per minute to about 18.25 liters per minute. This controlled perturbation can significantly enhance the gas escape signal at the leakage port, making it easier for the differential pressure sensing module to capture subtle pressure changes.

[0056] During the entire gas injection process, a total of 12 highly sensitive differential pressure sensing modules are set on the pipeline, with an average layout spacing of about 15 meters, and the measurement frequency is set to 5 times per second. By comparing the real-time pressure curves at each node under the perturbation flow rate, the system finds that there are abnormal fluctuations in the pressure response curves between the 8th and 9th sensors, showing a local non-synchronous downward trend; further fitting its pressure drop rate model, the system infers that there is a tiny leakage point in this section, approximately between the 120th and 132nd meters of the pipeline.

[0057] To further estimate the size of the leak, the system collects the pressure drop rate per unit time in this section, and combines it with the total gas flow change under the disturbance model. It is concluded that under continuous disturbance, about 0.45 liters of gas per minute cannot maintain a stable pressure, and the equivalent leak aperture is converted to a pinhole leak with a diameter of about 0.35 mm. This aperture is far lower than the detection sensitivity limit of traditional static pressure testing, but this method can achieve accurate identification through the flow disturbance amplification effect and the local pressure gradient difference.

[0058] Finally, the maintenance personnel dismantled the place and found that a section of the elbow seal was slightly displaced, and there was indeed a pinhole leak. After replacing the seal and re-injecting gas to verify, the pressure changes at all nodes were consistent, the leakage index returned to a safe level, and the system judged that there was no leakage.

[0059] The perturbation gas injection flow model was successfully used After accurately identifying a tiny leak between 120 and 132 meters, the company's technical team initiated the next phase of operations, which was to conduct a detailed quantitative analysis of the leak location and extent based on the spatial-temporal response behavior of pressure changes. In order to improve the accuracy of leak location and the ability to capture dynamic responses, distributed sensors with denser spacing were installed at all key structural nodes (such as elbows, tees, and reducers) based on the original micro-pressure differential sensing modules, shortening the spacing between the 120-meter to 132-meter sections from 15 meters to 3 meters, forming a high-density monitoring grid; these sensing modules collect instantaneous pressure values ​​at their respective locations at a high frequency of 20 times per second. and synchronize the data to the central analysis system in real time.

[0060] According to this dynamic pressure model, the system calculates the local pressure change rate of each node , where the pressure change function is:

[0061] ;

[0062] In the formula, It represents the pressure gradient per unit length. In the measured data, this item shows an obvious discontinuous jump near the 123-meter node, which is about -1.85 Pa / m, indicating that this is the local pressure drop center. The acceleration component of pressure over time. The maximum acceleration detected at the 123-meter period is 0.12 Pa / s², which is much higher than the average value of other sections of the pipeline (<0.01 Pa / s²). The sensitivity adjustment factor set by the system Select 3.0 (the recommended value range is 1.5 to 5.0, the higher the value, the more inclined to short-term response amplification). Under this parameter, the total response peak value of the pressure anomaly at the 123-meter node is obtained by comprehensive calculation. , far exceeding the safety and stability threshold (usually set at 0.5 - 0.8 Pa), the system determines that this point is a suspected position of first-level risk leakage.

[0063] To further quantify this risk and exclude error interference, based on the phenomenon of asynchronous pressure changes on the time axis of each node, the system constructs an integral-type leakage assessment model to cumulatively evaluate minor leakage signs and calculates the leakage risk integral value L(x) at each position x. The calculation process is as follows: Integrate the instantaneous pressure derivative changes at each position point in this section within 10 minutes (600 seconds), where the system performs cumulative processing after exponentially weighting the absolute value derivative (i.e., the pressure change rate). For actual substitution and analysis, the cumulative risk integral value at the 123-meter point within 10 minutes is:

[0064] ;

[0065] While the average integral value of other normal sections of the pipeline is approximately 30 - 40 , indicating that the leakage behavior in this section is a high-probability event, and the system marks it as a key intervention target.

[0066] After technical personnel conducted on-site inspection by opening the cover and confirmed, the 123-meter node is indeed a return elbow, and there are aging micro-cracks at its connecting flange. The leaked gas escapes along the sealing ring gap. Subsequently, the system sterilizes this node with local high-temperature steam and replaces the sealing components. After reusing the disturbance airflow and dynamic pressure model for retesting, the pressure curves of all nodes return to synchronization, and the integral value drops below 20. It is evaluated and confirmed that the aseptic pipeline state has reached the standard.

[0067] Example 2:

[0068] Combined with the appendix Figure 3 , in this embodiment, the enterprise further uses rapid microbial assessment and intelligent debugging technology to identify whether there are potential microbial contamination hazards and evaluate the potential expansion ability of the contamination risk, so as to achieve precise verification and control of the asepticity of the entire pipeline system. The total length of this pipeline system is 180 meters. Leakage has been excluded and cleaning has been completed in the early stage. Subsequently, fluorescent-labeled simulation liquid is injected into each key node, and liquid output samples are collected from 10 key nodes (such as the feed end, middle section elbow, tail end return point, etc.), and dynamic culture analysis is carried out using a rapid microbial culture system. The culture environment is set at 32°C ± 1°C, a modified TSA rapid culture medium is used, and colony image data is collected every 20 minutes with an imaging device. The entire culture time is set at 6 hours, and a total of 18 groups of images are recorded for each sample point. The AI analysis system extracts the colony quantity and area through image recognition algorithms, and extracts the colony growth trend of each group of samples in the initial stage, and substitutes it into the growth trend function for non-linear fitting:

[0069]

[0070] In the sample detected at Node No. 6 (located at the 135-meter backflow dead zone), the system initially detected = 5 colonies, and then observed that the number of colonies increased rapidly within 1 hour. After the AI system fitted the curve of this sample, it was confirmed that: = 1.35 (the recommended value range is 0.5 - 2.0, used to represent the growth reaction intensity), = 0.4 (the typical range is 0.1 - 0.6, controlling the growth acceleration), = 1.8 (non-linear growth adjustment factor, usually set between 1.2 - 2.5, used to describe the early growth slope).

[0071] Substitute the above parameters into the model to calculate the number of colonies at this point after 3 hours of cultivation (i.e., t = 3), and we get:

[0072]

[0073] That is, after 3 hours of cultivation, the total number of colonies in this sample has increased from 5 to approximately 14, with an increase close to three times. The system judges that its growth rate and acceleration are much higher than those in the normal area (for example, the number of colonies at Node No. 2 only increased from 4 to 6.2 after 3 hours of growth). It is initially evaluated as a "high potential reproduction risk area". Subsequently, the AI system compared the growth trend of this node with other nodes for modeling, and weighted the risk level in combination with three dimensions: growth rate, acceleration, and density. Finally, the maximum potential reproduction risk index MGRI value of this node was calculated to be 72.4 (the standard sets the MGRI safety threshold at 40, and the normal range of MGRI is 10 - 35). Since this value significantly exceeds the standard, the system triggers the local response program, executes the local pulsed high-temperature steam sterilization process for the 135-meter backflow section, and re-samples and verifies after sterilization. The number of colonies returns to zero, confirming that this risk point has been controlled.

[0074] After the high-risk point at the 135-meter backflow dead zone was confirmed as a potential hot spot for microbial reproduction and the MGRI value was calculated to be 72.4 through the colony number growth model, the system further conducted a more in-depth assessment of the morphological expansion characteristics of this point to determine whether the actual spatial diffusion ability of the colonies also reached the warning level, so as to support the comprehensive judgment of the MGRI index. At this stage, the system not only tracked the change in the number of colonies but also real-time extracted the change in the area and equivalent radius of the colonies at each moment. By using the image recognition module to obtain the edge contours of all colonies in each image, the system calculated the equivalent radius based on the closed path of the colony edge and averaged the squares of the radii of all colonies. Finally, a dynamic evaluation was carried out using the expansion potential energy function, and this function is:

[0075] ;

[0076] Among them, D(t) represents the colony expansion rate per unit time (i.e., the area growth trend), which is used to reflect the ability of the colony to form potential cross - contamination on the surface of the culture medium. is the expansion weight adjustment coefficient, and the actual value range is recommended to be set as 0.5 - 1.5. The value taken this time is = 1.2 to increase the influence weight of area change on the overall risk calculation; M(t) represents the total number of colonies effectively identified at time t. In the image data at the 3rd hour, the system identified M(3)=14 colonies, which is consistent with the calculated value of the quantity model; the equivalent radius of each colony is in millimeters. After calibration by image calibration, the system obtains the change in the square value of the radius of each colony from the initial to 3 hours. For example, the of the 1st colony at t = 3h is 1.9mm, the 2nd is = 1.7mm, and the value range of the remaining colonies is 1.3 - 2.0mm. The calculated average square value is:

[0077] ;

[0078] The average square value calculated at the same node at t = 2.5h is 2.68. Therefore, the growth in unit time (0.5h) is:

[0079] ;

[0080] Substitute into the original formula for calculation:

[0081] ;

[0082] This expansion rate is much higher than the set threshold of 0.6 mm² / h (the system experience threshold range is 0.4 - 0.7 mm² / h), and it is judged that the morphological expansion is extremely active. Combining the known colony number growth rate and this expansion speed in the front, after the AI risk assessment system recalculates the comprehensive MGRI value (adding the weight of the D(t) component) for this node, it is corrected to 79.2, further confirming that it is a "very high - risk" area. It should be noted that adjacent colonies of the colonies at this node have shown a tendency to merge in the image, indicating that its transmission potential already has a real risk of causing local cross - contamination. At this time, the system immediately sets the 135 - meter section as "forced closure" through the set response rule, suspends the material operation of this section, and starts the double - section staggered sterilization mechanism (i.e., sterilizing two adjacent sections simultaneously) to prevent the remaining colonies at the boundary.

[0083] After confirming that the 135-meter recirculation dead zone area is an abnormally active point of morphological expansion and completing the independent modeling of quantity growth and expansion potential, the food and beverage enterprise further utilized a comprehensive risk assessment mechanism to integrate the colony quantity growth rate and morphological expansion ability into a multi-factor prediction model, constructing the Maximum Potential Reproduction Risk Index (MGRI) as the key decision-making parameter for whether to trigger local sterilization and containment isolation. The specific formula is as follows:

[0084] ;

[0085] where the integration interval is set as = 1.0 h to = 3.0 h, corresponding to the core window for continuous tracking starting from the sampling of the simulated liquid; is the weight coefficient of the colony quantity growth rate. The empirically recommended value is 1.0 - 3.0. In this experiment, it is set as = 2.2 to emphasize the dominant role of rapid proliferation in system risk; is the weight coefficient of the morphological expansion rate part, with a value range of 0.5 - 2.5. In this example, it is set as = 1.8 to enhance the feedback sensitivity of morphological abnormalities to the model; the exponential weighting factor is used to non-linearly amplify the impact of the expansion behavior. The empirically recommended value is 1.5 - 2.5. In this case, it is set as = 2.0, that is, for every doubling of the expansion speed, the risk impact increases by four times.

[0086] According to the above calculations, at the 3rd hour, the colony quantity growth rate is approximately:

[0087] ;

[0088] Meanwhile, the expansion rate has been calculated as , substituting it into the model, the instantaneous risk intensity at t = 3 hours is obtained as:

[0089] ;

[0090] Approximately integrating the above values using the trapezoidal method within the 2-hour observation window, calculating the MGRI intensity for each 30-minute interval and summing them up, the cumulative MGRI estimate is obtained as:

[0091] ;

[0092] However, due to the significant exponential growth of the expansion rate in the last hour, the system predicts according to the AI model that without intervention, the MGRI will exceed 75 at the 4th hour, reaching the "extremely high risk" classification (system warning classification criteria: low risk < 20, medium risk 20 - 50, high risk 50 - 70, extremely high risk > 70). Therefore, before the system outputs a warning, the AI active control logic immediately triggers the "active lockdown + bilateral local sterilization" instruction for this section, simultaneously implementing the combined sterilization of superheated steam + dry gas on the 135-meter section and its adjacent sections of 120 - 135 meters and 135 - 150 meters for a duration of 12 minutes, and synchronously executing the verification program of the simulated liquid after sterilization to verify whether the aseptic reconstruction status meets the standards.

[0093] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating and commissioning a sterile pipeline for food and beverage processing, characterized in that: It includes the following steps: Establish a slightly positive pressure environment by introducing a standardized sterile gas into a closed pipeline system; Utilize a differential pressure sensing module to detect the pressure change curves of each node in the system in real time; According to the differential pressure change rate formula, deduce the position and size of the micro leakage point; On the basis of confirming no leakage, introduce calibrated particle sizes of particles including polystyrene microspheres; Use a dynamic particle imaging system to record the movement trajectories of the particles in the pipeline; Through trajectory analysis, identify flow dead zones, potential pollution accumulation areas in stagnant zones, and quantitatively label the pollution accumulation probability of each section of the pipeline; Prepare a simulation liquid, add biological marker particles including latex particles encapsulated with fluorescent dyes to make it close to the viscosity and fluidity of the target beverage; According to the identified potential pollution accumulation areas, perform local directional perfusion; Rapidly culture microorganisms from the collected local effluent samples of the simulation liquid; Combine with an AI-assisted growth curve prediction system, and calculate the potential reproduction risk index based on the initial colony growth rate; For areas with high reproduction risk index values, trigger local sterilization instructions.

2. The aseptic pipeline evaluation and commissioning method for food and beverage processing according to claim 1, wherein: The method for deducing the position and size of the micro leakage point includes: In a closed pipeline system, form a controllable slightly positive pressure environment, form a unidirectional outward diffusion flow at the potential leakage point, introduce a sterile gas filtered through particles, and at the same time adopt a flow management strategy with controlled disturbance. By setting the initial flow rate and introducing a time-increasing disturbance, construct a dynamically changing gas injection mode.

3. A method for evaluating and commissioning a sterilized pipeline for food and beverage processing according to claim 2, characterized in that: The method for deducing the position and size of the micro leakage point includes: Distributively install differential pressure sensing modules at key nodes with bends and turns inside the pipeline system, collect high-frequency data on the changes in local pressure at each node over time and position, and use the spatial gradient and time acceleration characteristics of the pressure changes between nodes for multi-dimensional analysis to construct a dynamic pressure change function.

4. A method for evaluating and commissioning a sterilized pipeline for food and beverage processing according to claim 3, characterized in that: The method for deducing the position and size of the micro leakage point includes: Based on the pressure change curves collected by each sensing node, establish an integral evaluation system for the asynchronous phenomenon of pressure changes between nodes, quantify the micro leakage risks at different positions, and calculate the potential leakage risk cumulative value by integrating the leakage signs over time.

5. A method for evaluating and commissioning a sterile pipeline for food and beverage processing according to claim 1, characterized in that: The method for calculating the potential reproduction risk index includes: Collect the effluent samples of the simulation liquid from each node of the pipeline and use them for rapid microorganism culture; The culture process uses a temperature-controlled rapid culture system and is supplemented by an imaging device to regularly collect colony images to obtain early growth behavior data of the colonies; By identifying the dynamic changes in the initial colony number growth, construct a growth trend function to fit the initial growth rate; The growth trend model is defined as: ； Where: represents the total number of colonies at time t; is the initial number of colonies; is the quantity growth response coefficient, with the unit of dimensionless coefficient, used to control the response speed of the colony quantity growth to the time change; is the perturbation gain factor, determining the reference amplitude of the growth acceleration; is the culture time; is the non-linear growth index, used to adjust the non-linear steepness of the early growth curve.

6. A method for evaluating and debugging a sterile pipeline for food and beverage processing according to claim 5, characterized in that: The method for calculating the potential reproduction risk index includes: Track the number growth, extract the area and morphological expansion of the colonies in real time, extract the colony edge contours through image processing algorithms and calculate the radius changes of each colony; Based on the average expansion behavior of all colonies, define the local expansion potential energy function as follows: ； Where: It represents the average expansion rate of local colonies per unit time and is used to measure the activity degree of morphological expansion; is the expansion weight adjustment coefficient and is used to regulate the contribution degree of the area change rate to the risk assessment; is the time The total number of colonies identified at the moment; is the equivalent radius of the nth colony; is the arithmetic mean of the squares of the areas of all colonies; represents the derivative operation with respect to time and extracts the dynamic change of the expansion rate.

7. A method for evaluating and commissioning a sterilized pipeline for food and beverage processing according to claim 6, characterized in that: The method for calculating the potential reproduction risk index includes: Unify the modeling of quantity growth and morphological expansion for risk prediction, introduce a reproduction risk index, and comprehensively calculate the weighted sum of the colony quantity growth rate and expansion potential energy per unit time. The constructed risk integral model is as follows: ； Where: : represents the maximum potential reproduction risk index of the target pipeline area within the time interval ; is the weight coefficient of the quantity growth risk, used to adjust the contribution of the colony quantity growth to the total risk; is the weight coefficient of the expansion rate risk; represents the change rate of the colony quantity over time; represents the power function of the expansion rate, is the risk weighted index, used to enhance the influence of the morphological expansion factor on the total risk value; is the integral symbol, indicating the cumulative risk calculation within the entire culture observation window.

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