Method and system for monitoring turbidity in a pomelo juice pulping and filtering process

Abstract

The application relates to the field of food processing technology, and in particular to a honey pomelo juice beating and filtering process turbidity monitoring method and system.The method comprises the following steps: obtaining a pomelo pulp information set, determining normal turbidity characteristics caused by fruit tissue and abnormal turbidity characteristics caused by non-fruit tissue in the current pomelo pulp based on the pomelo pulp information set, and obtaining a turbidity characteristic information set; obtaining a device working condition information set, taking the turbidity characteristic information set as a judgment reference, inversely deducing the deviation of the beating and filtering process corresponding device working condition caused by the fluctuation of honey pomelo raw material difference, and obtaining a working condition traceability information set; according to the working condition traceability information set, taking maintaining continuous operation efficiency as a constraint condition, generating an adaptive control instruction with the least regulation frequency as an optimization target, and outputting a honey pomelo juice beating and filtering process turbidity monitoring log. The production oscillation caused by the fluctuation of raw materials is reduced, and the continuity of honey pomelo juice processing and the product quality consistency are improved.

Description

Technical Field

[0001] This application relates to the field of food processing technology, and in particular to a method and system for monitoring turbidity during the pulping and filtration process of pomelo juice. Background Technology

[0002] In the processing of pomelo juice, pulping and filtration are crucial steps that determine the final product's taste and quality. Turbidity, as an important indicator of the clarity and uniformity of pulp content in pomelo juice, directly affects the product's sensory evaluation and market acceptance. In existing industrial production systems, to monitor turbidity changes during production, online sensors are typically used to collect optical signals of the fluid within pipelines in real time, or this is combined with periodic offline sampling and testing to obtain relevant data.

[0003] However, in the long-term practice of large-scale production of pomelo juice, the natural fluctuations in factors such as the origin of raw materials, the maturity of varieties, and seasonal climate have led to differences in the physical properties of the pomelo raw materials entering the processing stage. This heterogeneity at the raw material end often causes abnormal fluctuations in the turbidity index at the end of the production line, making it difficult for existing monitoring and control methods to maintain product quality stability while ensuring continuous operation efficiency. Faced with the dynamic coupling relationship between the complex and ever-changing characteristics of raw materials and the operating conditions of equipment, how to effectively deal with the mismatch of process parameters caused by raw material differences, avoid production oscillations caused by frequent unnecessary interventions, and at the same time ensure that the final product meets the ideal standards in terms of turbidity composition and taste consistency has become a key bottleneck that is of widespread concern and urgently needs to be overcome in this technical field. Summary of the Invention

[0004] This application provides a method and system for monitoring turbidity during the pulping and filtration process of pomelo juice to solve the above-mentioned problems.

[0005] In a first aspect, this application provides a method for monitoring turbidity during the pulping and filtration process of pomelo juice, the method comprising: A set of pomelo pulp information is obtained. Based on this set, the normal turbidity characteristics caused by fruit pulp tissue and the abnormal turbidity characteristics caused by non-fruit pulp tissue in the current pomelo pulp are determined to obtain a turbidity feature information set. A set of equipment operating condition information is obtained. Based on this set, and using the turbidity feature information set as a judgment benchmark, the deviation of the equipment operating condition corresponding to the pulping and filtration process caused by the fluctuation of pomelo raw material differences is deduced to obtain an operating condition traceability information set. Based on the operating condition traceability information set, with the constraint of maintaining continuous operation efficiency, an adaptive control command with the minimum number of adjustments as the optimization objective is generated, and a turbidity monitoring log of the pomelo juice pulping and filtration process is output.

[0006] Optionally, the step of determining the normal turbidity characteristics caused by fruit pulp tissue and the abnormal turbidity characteristics caused by non-fruit pulp tissue in the current pomelo pulp based on the pomelo pulp information set, and obtaining the turbidity feature information set, includes: the pomelo pulp information set includes insoluble solids content, pomelo pulp viscosity, and pomelo pulp image information; based on the pomelo pulp image information, combined with the insoluble solids content and the pomelo pulp viscosity, analyzing the suspension characteristics of solid substances in the liquid in the current pomelo pulp to obtain pulp dispersion information; based on the pulp dispersion information, analyzing the light transmittance characteristics of the current pomelo pulp to identify the outlines of substances that obstruct light transmission; comparing the outlines of substances with the inherent morphology of fruit pulp tissue, determining that small integument fragments that are dispersed and whose size is within a preset normal range are the normal turbidity characteristics, and determining that integument fibers with a length exceeding a preset threshold, intertwined, or forming local aggregation areas are the abnormal turbidity characteristics, and summarizing to obtain the turbidity feature information set.

[0007] Optionally, the process of constructing the pulp dispersion information includes: based on the pomelo pulp image information, combined with the insoluble solids content and the pomelo pulp viscosity, identifying the settling characteristics of the pulping particles of pomelo seeds and stems in the liquid to obtain particle settling information; simultaneously, identifying the floating and stagnant characteristics of the pericarp in the liquid to obtain pericarp suspension information; based on the particle settling information and the pericarp suspension information, analyzing the distribution state of solid substances of different properties in the pulp, including their stratification and local aggregation, to obtain the pulp dispersion information used to distinguish between fruit pulp tissue and foreign matter.

[0008] Optionally, the process of analyzing the light transmittance characteristics of the current pomelo pulp based on the pulp dispersion information and identifying the material contours that obstruct light transmission includes: based on the pulp dispersion information, identifying the differences in sedimentation rates of pulp particles of different sizes in a static state and the local retention areas formed by the entanglement of the peel in the pomelo pulp to obtain pomelo pulp layer distribution information; based on the pomelo pulp layer distribution information, analyzing the occlusion characteristics when light passes vertically through each pulp layer, identifying the shadow boundaries formed after the light is occluded, and tracing the outer edge lines of the object whose light is occluded along the shadow boundaries to obtain the material contours.

[0009] Optionally, the step of using the equipment operating condition information set as a judgment benchmark and back-calculating the deviation of the corresponding equipment operating conditions in the pulping and filtration process caused by the fluctuation of pomelo raw material differences to obtain the operating condition traceability information set includes: the equipment operating condition information set includes pomelo feed flow rate information, pulper speed information, and filter screen pressure difference information; based on the pomelo feed flow rate information and the pulper speed information, analyzing the matching relationship between the pomelo processing volume per unit time and the pulping and crushing intensity in the current pulping process to obtain feed-pulping load matching information; based on the filter screen pressure difference information and the abnormal turbidity characteristics, analyzing the clogging characteristics on the screen surface caused by the accumulation of non-fruit pulp tissue to obtain screen clogging traceability information; based on the feed-pulping load matching information and the screen clogging traceability information, analyzing the pulping speed mismatch and screen clogging offset caused by changes in pomelo raw material batches and varieties to obtain the operating condition traceability information set.

[0010] Optionally, the process of constructing the screen blockage source information includes: based on the pressure difference information of the filter screen, identifying the pressure difference change trend over time to obtain the pressure difference fluctuation time sequence characteristics; based on the abnormal turbidity characteristics, comparing the pressure difference fluctuation time sequence characteristics with the appearance time of different forms of non-fruit pulp tissue in the grapefruit pulp in the abnormal turbidity characteristics, analyzing the staged increase and sudden increase phenomenon of pressure difference caused by the gradual accumulation of non-fruit pulp tissue on the screen surface, and obtaining the blockage accumulation process information; based on the blockage accumulation process information, distinguishing between gradual blockage caused by peel entanglement and sudden blockage caused by grapefruit seeds and stem jamming, and obtaining the screen blockage source information.

[0011] Optionally, based on the feed-pulping load matching information and combined with the screen blockage tracing information, the analysis of pulping speed mismatch and screen blockage deviation caused by changes in pomelo raw material batches and varieties, to obtain the working condition tracing information set, includes: based on the feed-pulping load matching information, analyzing whether the current pulping and crushing force is suitable for the current feed amount, to obtain pulping deviation information; based on the screen blockage tracing information, analyzing the location and degree of screen blockage changes, to obtain blockage change information; based on the pulping deviation information and the blockage change information, comparing the processing performance of the current batch and historical reference batches of raw materials, analyzing the physical differences in the quantity of pomelo seeds, stems, and peels in the current raw materials; based on the physical differences, analyzing the impact of changes in pomelo raw material batches and varieties on pulping load and screen flow capacity, to obtain the working condition tracing information set.

[0012] Optionally, the step of generating an adaptive control command with the minimum number of adjustments as the optimization objective, based on the working condition traceability information set and with the constraint of maintaining continuous operation efficiency, and outputting a turbidity monitoring log of the pomelo juice pulping and filtration process, includes: analyzing the fluctuations in the physical properties of pomelo seeds, stems, and peels caused by changes in raw material batches and varieties, and the degree of imbalance between the current pulping load and the screen flow capacity, based on the working condition traceability information set, to obtain raw material disturbance characteristics; based on the raw material disturbance characteristics, and with the constraint of maintaining continuous operation efficiency, analyzing the coverage of a single adjustment action in restoring the balance of the pulping and filtration working conditions, screening the adjustment combination that can simultaneously correct insufficient pulping and crushing strength and screen clogging trend, to obtain the optimal adjustment parameter combination; and based on the optimal adjustment parameter combination, generating the adaptive control command with the minimum number of adjustments as the optimization objective according to the priority of the adjustment range from large to small, and outputting the turbidity monitoring log of the pomelo juice pulping and filtration process.

[0013] Optionally, the process of constructing the optimal control parameter combination includes: based on the raw material disturbance characteristics, analyzing the effect of increasing the pulper speed to crush and refine the pomelo seeds and stems and the effect of cutting the pericarp on the current pomelo raw material, and obtaining pulping intensity adaptation information; based on the pulping intensity adaptation information, analyzing the sieve smoothness of the crushed pomelo seeds and stem fragments and the entanglement and hindrance characteristics of the cut pericarp, and obtaining pulping-filtration synergistic correction information; based on the pulping-filtration synergistic correction information, with the constraint of maintaining continuous operation efficiency, analyzing the synchronous correction amplitude of a single adjustment of the pulper speed on two types of deviations: insufficient pulping and crushing intensity and screen clogging, eliminating control actions that only improve the pulping effect but aggravate screen clogging, and obtaining a dual-effect control parameter range; based on the dual-effect control parameter range, screening parameter combinations that can maintain continuous operation without additional adjustment of screen conditions after control, and obtaining the optimal control parameter combination.

[0014] Through the above solution, this application achieves the following beneficial effects: This method achieves a complete closed loop from turbidity feature identification to tracing the source of operating condition deviations, and then to adaptive control decision-making. This feature-based monitoring method can effectively distinguish between normal turbidity caused by raw materials and abnormal turbidity caused by impurities, and infer the specific deviation of equipment operating conditions accordingly. Furthermore, by generating the fewest number of collaborative control commands, it can quickly eliminate abnormal operating conditions while ensuring production continuity, thereby improving the level of automation control and product quality stability in the pomelo juice processing process.

[0015] Based on morphological comparison and matching of the standard pulp profile library, parameters such as aspect ratio and roundness are calculated to distinguish between cell membrane fragments, juice cells and hard impurities, determine normal turbidity and abnormal turbidity, summarize the determination confidence level and distribution data, and qualitatively analyze the turbidity components.

[0016] By utilizing the differences in the fluid dynamics of different substances, namely the difference between sedimentation and suspension, and combining multi-source images and physical property parameters, high-precision slurry dispersion information was constructed, enabling the analysis of the spatial distribution of non-flesh tissues.

[0017] Through a layered and focused processing mechanism, background noise can be effectively filtered out, and the outline of the target material that obstructs the passage of light can be extracted, providing high-fidelity geometric feature data to distinguish normal fruit pulp from abnormal impurities.

[0018] Feed-pulping load matching information provides a quantitative assessment of the front-end crushing capacity, while screen blockage traceability information provides a diagnosis of the back-end separation status. The two corroborate each other and jointly construct a full-process operating condition inversion logic from raw material input to finished product output, which can lock in the equipment parameters that need to be adjusted when raw material fluctuates.

[0019] By deeply integrating the time-series characteristics of differential pressure fluctuations with abnormal turbidity characteristics, a mapping relationship from microscopic impurity behavior to macroscopic equipment response was constructed, enabling the identification and tracing of screen blockage types. This monitoring mechanism based on time-series correlation analysis can effectively distinguish between two distinct physical processes: coating entanglement and hard jamming. It provides highly reliable state-aware input for the refined control of the pulping and filtration process, allowing the production system to eliminate potential equipment operation hazards with minimal control costs while maintaining continuous operation.

[0020] It clearly reveals that the root cause of the current production fluctuations lies in the change of raw material batches or varieties, rather than equipment failure, thus providing a direct logical starting point and data support for implementing adaptive control.

[0021] It can map the fluctuations in the physical properties of raw materials into quantitative imbalances in equipment operating conditions in real time, and use a control sequence with synergistic coverage to eliminate deviations in the pulping and filtration stages with the simplest control actions while ensuring continuous operation of the production line. This effectively solves the problems of frequent control and easy system oscillation in existing technologies, and ensures the stability of pomelo juice product quality.

[0022] By quantitatively analyzing the destructive effect of improved pulping intensity on different impurities and its kinetic behavior in the filtration process, the side effects of optimizing a single step are effectively avoided, truly achieving synergistic optimization throughout the entire process. By maintaining the production parameter combination without additional intervention in the screening screen, the safety and effectiveness of control actions are ensured, thereby improving the continuous operation efficiency of the production line while guaranteeing the quality of pomelo juice products.

[0023] Secondly, this application provides a turbidity monitoring system for the pomelo juice pulping and filtration process. The system includes: a turbidity feature module, used to acquire a pomelo pulp information set, and based on the pomelo pulp information set, to determine the normal turbidity features caused by fruit pulp tissue and the abnormal turbidity features caused by non-fruit pulp tissue in the current pomelo pulp, thereby obtaining a turbidity feature information set; an operating condition traceability module, used to acquire an equipment operating condition information set, and based on the equipment operating condition information set, using the turbidity feature information set as a judgment benchmark, to inversely deduce the deviation of the corresponding equipment operating condition in the pulping and filtration process caused by the fluctuation of pomelo raw material differences, thereby obtaining an operating condition traceability information set; and an adaptive control module, used to generate adaptive control instructions with the minimum number of adjustments as the optimization objective, based on the operating condition traceability information set and with the constraint of maintaining continuous operation efficiency, and output a turbidity monitoring log of the pomelo juice pulping and filtration process. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart of a method for monitoring turbidity during the pulping and filtration process of pomelo juice, provided in an embodiment of this application; Figure 3 This is a schematic diagram of a turbidity monitoring system for the pulping and filtration process of pomelo juice, provided as an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0029] The pulping and filtration processes of pomelo juice determine the final product's taste and quality. Turbidity reflects the clarity of the juice and the uniformity of the pulp. Current methods rely on online sensing and offline sampling to monitor turbidity. However, variations in raw material origin and ripeness lead to uneven raw material distribution and turbidity fluctuations. Existing control methods struggle to balance production efficiency with stable quality. The challenge of parameter adaptation under coupled raw material conditions urgently needs to be addressed. Based on this, this application provides a method and system for monitoring turbidity during the pulping and filtration process of pomelo juice. It constructs a complete closed loop of turbidity identification, operational condition tracing, and adaptive control. This system can distinguish between normal turbidity of raw materials and abnormal turbidity of impurities, locate equipment deviations, output the simplest collaborative control commands, stabilize production, eliminate abnormalities, and improve the automation level and quality stability of pomelo juice processing.

[0030] Figure 1 This application provides an illustration of an application scenario. In the turbidity monitoring process of pomelo juice pulping and filtration, the method provided in this application is applied to build a fully closed-loop intelligent control system based on turbidity characteristics. This system distinguishes different turbidities induced by raw materials and impurities, traces back equipment operating deviations, generates simplified collaborative control instructions, eliminates abnormalities under uninterrupted production, and optimizes the automatic control level and quality consistency of pomelo juice processing.

[0031] Specifically, the method provided in this application can be applied to any server. The server interacts with the online multi-parameter detection machine for pomelo pulp and the online sensor for production conditions to obtain the pomelo pulp information set provided by the online multi-parameter detection machine and the equipment condition information set provided by the online sensor for production conditions. It establishes an integrated closed-loop system for turbidity feature discrimination, condition traceability and adaptive control, identifies two types of turbidity signals: raw materials and impurities, locates equipment operation deviations, outputs the optimal control command, and outputs turbidity monitoring logs of the pomelo juice pulping and filtration process to food processing workers, ensuring continuous production of pomelo juice and simultaneously improving self-control capabilities and the stability of finished product quality.

[0032] The specific implementation method can be referred to in the following embodiments, wherein the data mentioned in the embodiments are only for reference and examples, so that relevant personnel can better understand them.

[0033] Figure 2 This is a flowchart illustrating a method for monitoring turbidity during the pulping and filtration process of pomelo juice, provided in one embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. (See also...) Figure 2 The process, specifically the implementation steps, are as follows: Example 1: S201. Obtain the grapefruit pulp information set. Based on the grapefruit pulp information set, determine the normal turbidity characteristics caused by fruit pulp tissue and the abnormal turbidity characteristics caused by non-fruit pulp tissue in the current grapefruit pulp, and obtain the turbidity characteristic information set.

[0034] The pomelo pulp information set is a comprehensive data collection used to characterize the physical and chemical state of pomelo pulp during the current pulping and filtration stage, with an online multi-parameter pomelo pulp analyzer as the data source. The process of acquiring this pomelo pulp information set includes, for example, acquiring real-time images of the pomelo pulp flowing through the pulper's outlet pipe using a high-speed industrial camera installed at the pulper's outlet pipe; simultaneously, using an online viscometer and component analyzer to acquire the current viscosity parameters and insoluble solids content data of the pomelo pulp. These images, viscosity, and component data together constitute the basic input for subsequent analysis.

[0035] Based on the aforementioned pomelo pulp information set, the system classifies and identifies the sources of turbidity in the current pomelo pulp, aiming to distinguish between normal turbidity characteristics caused by fruit pulp tissue and abnormal turbidity characteristics caused by non-fruit pulp tissue. Normal turbidity characteristics typically originate from broken tissues within the pomelo pulp itself (such as juice cells), and are an essential component of the product's sensory quality. Abnormal turbidity characteristics originate from non-fruit pulp tissues such as seeds and stems, and their presence usually indicates incomplete raw material processing or equipment malfunctions. By analyzing the morphology, edge texture, and suspension state of solid substances in the pomelo pulp image information, and combining this with the rheological properties reflected by the insoluble solids content and pomelo pulp viscosity, the system identifies the outlines of substances that obstruct light transmission. These outlines are then compared with preset inherent morphologies of fruit pulp tissue: if they match the morphological characteristics of fruit pulp tissue, they are determined to be normal turbidity characteristics; if they present as hard particles or long fibers, or other non-fruit pulp morphologies, they are determined to be abnormal turbidity characteristics. Finally, the system summarizes the classification results to generate a turbidity feature information set containing details of the current turbidity composition, thus achieving a qualitative assessment of the turbidity properties.

[0036] S202. Obtain the equipment operating condition information set. Based on the equipment operating condition information set, and using the turbidity characteristic information set as the judgment benchmark, reverse the deviation of the equipment operating condition corresponding to the pulping and filtration process caused by the difference in pomelo raw material, and obtain the operating condition traceability information set.

[0037] The equipment operating condition information set is a real-time data collection reflecting the current operating status of the pulping and filtering equipment, with online sensors for production conditions as the data source. The process of acquiring this equipment operating condition information set includes, for example, reading pomelo feed flow rate information, pulper speed information, and filter screen pressure difference information from the production line's programmable logic controller or distributed control system. Specifically, the pomelo feed flow rate information represents the volume of raw material entering the system per unit time, the pulper speed information reflects the rotational linear velocity of the pulping and crushing components, and the filter screen pressure difference information reflects the pressure difference across the screen and is directly related to the degree of screen clogging.

[0038] After acquiring the equipment operating condition information set, the aforementioned turbidity characteristic information set is used as a judgment benchmark to conduct a reverse analysis of operating condition deviations. This reverse analysis logic is based on the causal mapping relationship between raw material differences, equipment response, and turbidity performance. Specifically, when the turbidity characteristic information set shows an increase in the proportion of abnormal turbidity characteristics (such as a large number of unbroken pomelo seeds or long fibrous stems), the cause is analyzed in conjunction with the equipment operating condition information set: if the crushing intensity corresponding to the current pulper speed information is insufficient to handle the raw material hardness under the current feed flow rate, it is determined that there is a pulping speed mismatch; if the filter screen pressure difference information increases abnormally in a short period of time and is synchronized with the detection time of specific morphological impurities in the abnormal turbidity characteristics, it is determined that there is a screen blockage deviation. By comparing the data differences between the current operating conditions and historical standard operating conditions, the equipment operating condition deviations caused by the change of pomelo raw material batch or variety (such as thicker peel or increased seed hardness) are identified. Finally, the specific causes and degrees of deviation are quantified and summarized to form an operating condition traceability information set, providing a basis for subsequent regulation.

[0039] S203. Based on the working condition traceability information set, with the constraint of maintaining continuous operation efficiency, generate adaptive control instructions with the goal of minimizing the number of adjustments, and output the turbidity monitoring log of the pomelo juice pulping and filtration process.

[0040] Based on the aforementioned operational condition traceability information set, the system enters the control decision-making stage. The core objective of this stage is to find a control strategy that can correct the current operational condition deviation with the fewest adjustments, while maintaining continuous operation efficiency. Maintaining continuous operation efficiency as a constraint requires that the generated control commands avoid frequent start-ups and shutdowns or large fluctuations in the production line, ensuring a smooth transition in the production process. Minimizing the number of adjustments is the optimization objective, requiring the system to prioritize coordinated control actions that can simultaneously address multiple issues, rather than adjusting individual deviations one by one.

[0041] The process of generating adaptive control commands includes, for example, the following: The system first analyzes the weights and coupling relationships of various deviation factors in the operational condition traceability information set, and selects control combinations that can simultaneously correct insufficient pulping and crushing strength and screen clogging trends. For instance, appropriately increasing the pulper speed can not only enhance the crushing effect on hard raw materials, but may also improve screen throughput by reducing particle size, thereby simultaneously alleviating the problems of insufficient pulping and screen clogging. By calculating the coverage of different control combinations in restoring operational balance, the system determines the optimal control parameter sequence and encapsulates it into adaptive control commands, which are then sent to the actuators. Simultaneously, the system structurally records the grapefruit pulp information set, turbidity characteristic information set, equipment operational condition information set, operational condition traceability information set, and the finally generated control commands acquired during this monitoring process, according to time axis and event type, and outputs a turbidity monitoring log for the grapefruit juice pulping and filtration process. This log is used to trace the production status of historical batches and provides data support for subsequent process optimization and model training.

[0042] Example 2: In some embodiments, based on the grapefruit pulp information set, the normal turbidity characteristics caused by fruit pulp tissue and the abnormal turbidity characteristics caused by non-fruit pulp tissue in the current grapefruit pulp are determined to obtain the turbidity feature information set, specifically including the following steps: Step 1: The grapefruit pulp information set includes insoluble solids content, grapefruit pulp viscosity, and grapefruit pulp image information.

[0043] The insoluble solids content characterizes the mass fraction of suspended solids in pomelo pulp, used to quantify the proportion of components such as pulp fiber, peel fragments, and pomelo seed fragments in the overall concentration. Pomelo pulp viscosity reflects the internal friction characteristics of the fluid, measured in real-time by an online viscometer. Its value directly determines the resistance to movement and settling velocity of solid particles in the liquid medium, making it a key hydrodynamic parameter for analyzing particle suspension. Pomelo pulp image information is visual data acquired through a hyperspectral camera or industrial vision camera at the transparent window of the flow channel. It contains the spectral reflectance characteristics and texture information of the substances inside the pulp, providing an intuitive digital matrix for subsequent identification of the specific morphology and spatial distribution of the substances. The comprehensive acquisition of these three types of information constitutes a complete data perception foundation from macroscopic physical properties to microscopic visual morphology.

[0044] Step 2: Based on the image information of the pomelo pulp, combined with the content of insoluble solids and the viscosity of the pomelo pulp, analyze the suspension characteristics of solid substances in the liquid in the current pomelo pulp to obtain pulp dispersion information.

[0045] By fusing and analyzing multimodal data, the dynamic behavior of solid particles in a specific fluid environment is analyzed. First, the pomelo pulp image information is preprocessed. Optical flow or particle image velocimetry is used to track the displacement vectors of discrete particles in the image sequence, thereby obtaining the instantaneous velocity and direction of the particles. Simultaneously, the content of insoluble solids and the viscosity of the pomelo pulp are introduced into the suspension dynamics model to correct for visual occlusion errors and fluid viscous drag coefficients caused by changes in pulp concentration. Through this fusion calculation, the system can distinguish the motion differences of substances with different densities and shapes. For example, the rapid sinking trajectory of denser pomelo seed fragments, or the tendency of more flexible capsular fibers to linger and entangle. Finally, these dynamic suspension states are summarized into structured data containing descriptions of particle distribution concentration, velocity field, and aggregation morphology—that is, pulp dispersion information. This information not only records what is there but also how it moves, providing a dynamic behavioral basis for subsequent identification of the substance's nature.

[0046] Step 3: Based on the pulp flow information, analyze the light transmission characteristics of the current pomelo pulp and identify the outline of the material that obstructs the passage of light.

[0047] Based on the acquired pulp dispersion information, this study further explores the transmission characteristics of light in complex pulp systems. When a light source of a specific wavelength (such as visible or near-infrared light) illuminates flowing grapefruit pulp, the light-blocking effect differs between uniformly dispersed pulp tissue and clustered impurities. By analyzing the high-density aggregation regions of substances in the pulp dispersion information, high-obstruction points on the light transmission path are identified, and edge detection algorithms are used to extract shadow boundaries in backlit or sidelit images. By continuously tracking and fitting these boundaries, the outer edge geometry of the obstructing substances on the two-dimensional imaging plane is constructed, thereby identifying the contours of substances that obstruct light transmission. This process essentially completes the inverse reconstruction from light and shadow changes to solid morphology in a dynamic fluid background, transforming the originally invisible impurity boundaries mixed in the liquid into computable geometric feature data.

[0048] Step 4: Compare the outline of the material with the inherent morphology of the pulp tissue. Small capsule fragments that are dispersed and within the preset normal range in size are identified as normal turbidity features. Capsule fibers that exceed the preset threshold in length, are intertwined, or form localized aggregation areas are identified as abnormal turbidity features. The turbidity feature information set is then obtained.

[0049] Morphological comparison logic is used to determine the attributes of the extracted material contours. The system has a pre-set standard library characterizing the inherent morphological features of fruit pulp tissue. Among them, small, dispersed capsule fragments within a preset normal size range are considered normal tissue fragments; capsule fibers exceeding a preset threshold in length, intertwined, or forming localized aggregates are considered abnormal non-fruit pulp tissues and are used to determine abnormal turbidity characteristics. The material contours identified in the current step are matched with templates in this standard library for feature matching, such as calculating morphological parameters like aspect ratio, roundness, and edge roughness. If the geometric parameters of the material contour fall within the morphological threshold range of fruit pulp tissue, its origin is attributed to normal processing products and marked as a normal turbidity feature; conversely, if the material contour exhibits a high-hardness, high-density geometric shape (such as approximately round hard particles or long, strip-shaped woody fibers), it is determined that it does not conform to the morphology of fruit pulp tissue and is marked as an abnormal turbidity feature caused by non-fruit pulp tissue. Finally, the above judgment results and their corresponding confidence levels, distribution areas, and other data are aggregated to form a turbidity feature information set containing classification details and spatial location, thereby achieving qualitative and refined analysis of turbidity components.

[0050] Example 3: In some embodiments, the process of constructing the slurry dispersion information specifically includes the following steps: Step 1: Based on the image information of pomelo pulp, combined with the content of insoluble solids and the viscosity of pomelo pulp, identify the settling characteristics of the pulp particles of pomelo seeds and stems in the liquid, and obtain particle settling information.

[0051] For high-density solid impurities, images of the pomelo pulp are acquired using a hyperspectral camera or industrial camera deployed in the outlet pipe of the pulper. Optical flow or particle tracking velocimetry algorithms are then used to calculate the real-time displacement vector of each particle in the image sequence. This displacement vector serves as the basis for subsequent analysis, characterizing the trajectory of high-density substances in a viscous fluid.

[0052] Based on a variant of Stokes' Law, and combining the insoluble solids content reflecting pulp density and the pulp viscosity reflecting fluid resistance, the theoretical final settling velocity of particles of different sizes was calculated. The observed settling velocities were compared with the theoretical calculations, and the texture and morphological features of pomelo seeds and stems in the images—such as the dark, hard texture of the seeds and the elongated, woody structure of the stems—were used to segment particles with high settling tendency and matching morphological features from the background pulp.

[0053] The specific processing steps include: first, background subtraction is performed on the grapefruit pulp image information to extract the moving foreground region; then, the aspect ratio, roundness, and grayscale mean of each connected region in the foreground region are calculated to screen out candidate targets that meet the characteristics of hard particles; finally, Kalman filtering is used to perform temporal tracking of the candidate targets and record their rate of position change in the vertical direction. Based on the above processing, particle sedimentation information including sedimentation velocity, deposition location coordinates, and particle size distribution is constructed. This identification method based on the coupling of hydrodynamic and visual features can effectively distinguish between normal pulp sedimentation and abnormal hard impurity deposition, providing high-dimensional data support for subsequent foreign object identification.

[0054] Step 2: Simultaneously, identify the floating and retention characteristics of the capsule in the liquid to obtain capsule suspension information.

[0055] The study focuses on identifying the retention behavior of lightweight, fibrous materials, analyzing the temporal variation characteristics of grapefruit pulp images. Because the coating density is close to or slightly less than the pulp density, and it possesses a large aspect ratio and flexibility, it exhibits complex random motion and retention tendencies in fluids. The coating suspension information not only reflects the spatial location of the coating but also implies its dynamic characteristics of entanglement and fluid obstruction.

[0056] A method combining texture analysis and motion vector field statistics was employed for processing. First, the inter-frame difference method was used to extract motion-blurred or slowly moving regions in the image. Due to its floating properties, the fibrous membrane often forms a translucent flocculent texture in the image. Second, by combining fluid environment parameters provided by the insoluble solids content and pulp viscosity, the motion trajectory of these flocculent regions was analyzed to determine whether they exhibit a reciprocating pattern of stagnation-drift-re-stagnation, rather than rapidly passing through with the mainstream.

[0057] By analyzing the rate of change of the rotation angle of the membrane in the flow field, its spatial distribution density, and the presence of a network-like entanglement structure, parameters characterizing its buoyancy stability are extracted. When multiple filamentous materials are detected intertwining in a local area and their rate of position change is lower than a preset suspension threshold, membrane aggregation is identified. Finally, membrane suspension information including the membrane distribution area, floating residence time, and entanglement index is generated. This step, through refined capture of the suspended morphology of lightweight impurities, compensates for the limitation of a single turbidimeter in detecting fibrous impurities, achieving effective monitoring of membrane-like foreign matter.

[0058] Step 3: Based on particle sedimentation information and capsule suspension information, analyze the distribution of solid substances with different properties in the slurry, including their stratification and local aggregation, to obtain slurry dispersion information used to distinguish between fruit pulp tissue and foreign matter.

[0059] Slurry dispersion information is a comprehensive description that integrates the vertical gravity sorting effect and the horizontal flow aggregation effect, aiming to quantify the non-uniformity of the slurry.

[0060] In practice, particle settling information is mapped to the bottom layer in three-dimensional space, while coating suspension information is mapped to the middle and upper layers or local eddy zones. By statistically analyzing the density distribution of materials on slices at different depths, a thermal map of solid phase concentration distribution along the radial and axial directions of the pipe is generated. For example, if the particle settling information in the bottom layer shows a large amount of high-hardness, large-particle-size aggregates, while the coating suspension information in the middle and upper layers shows a high-density fiber network, then it is determined that the current slurry is in a severely non-uniform mixing state, posing a significant risk of clogging.

[0061] Using a data fusion algorithm, discrete sedimentation trajectory points and suspended region pixels are aggregated into a unified flow field distribution grid, and the mixing entropy values ​​of different material types within the grid are calculated. The non-uniformity of the slurry is quantified by analyzing the clarity of the upper and lower layers (the stratification index) and the area proportion of local aggregation zones. The final generated slurry dispersion information includes an overall stratified structure diagram of the slurry, the coordinates of key aggregation regions, and the proportion and weight of various foreign matter. This information set provides direct data support for subsequent steps to distinguish between fruit pulp tissue and foreign matter. Fruit pulp tissue is usually uniformly distributed, while foreign matter such as seeds and pericarps are stratified or aggregated, thus improving the accuracy of identifying the source of turbidity anomalies.

[0062] Example 4: In some embodiments, the process of analyzing the light transmittance characteristics of the current grapefruit pulp and identifying the outlines of substances that obstruct light transmission based on the pulp dispersion information specifically includes the following steps: Step 1: Based on the pulp flow information, identify the differences in sedimentation rate of pulp particles of different sizes under static conditions and the local retention areas formed by the entanglement of the peel in the pomelo pulp, and obtain the pomelo pulp layer distribution information.

[0063] When the slurry flow tends to be stable or in a brief state of stillness, the dynamic behavior of solid materials is governed by their own physical properties. Slurry stratification information is used to characterize the non-uniform distribution state in the vertical direction within the slurry system. This state is not a simple vertical stratification, but rather reflects the thermodynamically stable distribution of substances of different densities and forms in a liquid medium. The aforementioned slurry stratification information can be constructed by analyzing the movement trends of solid particles in the slurry dispersion information.

[0064] Specifically, pulp particles of different sizes exhibit differentiated settling rates according to Stokes' law. Larger, higher-density pulp particles experience greater gravitational force than fluid resistance, tending to settle rapidly to the bottom, thus forming the bottom sedimentation zone. Smaller pulp particles with a density close to that of fruit juice remain suspended under the influence of fluid turbulence, forming an intermediate suspension layer. Meanwhile, the capsid material, due to its unique fibrous structure and flexibility, readily becomes physically entangled upon contact. This entanglement increases its equivalent volume and reduces its average density, causing the capsid to exhibit floating or retention characteristics in the grapefruit pulp, thus forming localized retention areas near the liquid surface or in the slower-moving reflux zone. By tracking the spatiotemporal trajectories of each material unit in the pulp dispersion information and identifying the aforementioned differences in settling rates and localized retention areas, a stratified distribution of grapefruit pulp, including the upper capsid retention zone, the middle pulp suspension zone, and the bottom heavy sedimentation zone, can be constructed, providing a spatial positioning reference for subsequent optical analysis.

[0065] Step 2: Based on the layered distribution information of the pomelo pulp, analyze the occlusion characteristics when light passes vertically through each pulp layer, identify the shadow boundaries formed after the light is occluded, and trace the outer edge lines of the object whose light is occluded along the shadow boundaries to obtain the material outline.

[0066] Based on the acquired information on the layered distribution of grapefruit pulp, the attenuation and occlusion patterns of light as it penetrates different pulp layers can be analyzed in a targeted manner. The occlusion characteristics of light as it passes perpendicularly through each pulp layer are the key basis for determining the continuity of the optical path between the light source and the sensor, directly reflecting the geometry and location of obstacles. In complex pulp environments, light undergoes a combination of scattering, absorption, and diffraction to form a distribution of varying intensity, rather than being completely blocked.

[0067] For example, this process can be achieved using backlight imaging: a parallel light source is placed on one side of the pulp to be tested, and an image acquisition device is placed on the other side. When light passes through the pulp, substances in a suspended or stagnant state will block part of the light, forming a projection with strong contrast between light and dark at the receiving end. By analyzing these projected images, the locations where the light intensity changes abruptly can be identified, i.e., the shadow boundaries formed after the light is blocked. This shadow boundary is a mapping of the geometric edge of the material blocking the light onto the imaging plane, containing information about the cross-sectional shape of the object being measured.

[0068] Subsequently, contour tracking is performed along the shadow boundary using an edge detection algorithm to extract continuous, closed lines as the outer edge lines of the object whose light is blocked, ultimately obtaining the material contour. This process incorporates information on the layered distribution of pomelo pulp, pre-emptively eliminating interference from gradual light changes caused by uneven overall pulp concentration, focusing on sharp shadow boundaries generated by the presence of solid objects, rather than performing simple binarization. For example, for areas where the capsule membrane is retained, the focus is on detecting shadow contours with irregular edges and gradual internal grayscale changes; for large deposited particles, the focus is on detecting circular or irregular contours with clear edges and high contrast.

[0069] Example 5: In some embodiments, based on the equipment operating condition information set and using the turbidity characteristic information set as a judgment benchmark, the deviation of the equipment operating condition corresponding to the pulping and filtration process caused by the fluctuation of pomelo raw material differences is deduced to obtain the operating condition traceability information set, which specifically includes the following steps: Step 1: The equipment operating information set includes pomelo feed flow rate information, pulper speed information, and filter screen pressure difference information.

[0070] The equipment operating condition information set serves as the data foundation for inferring operating condition deviations, encompassing three key parameters reflecting the core operating status of the pulping system. Specifically, the pomelo feed flow rate represents the volume of raw material entering the pulping system per unit time, typically collected in real-time by an electromagnetic flowmeter or Coriolis flowmeter installed on the feed pipe; the pulper rotation speed reflects the linear velocity of the pulping blades, determining the frequency of mechanical impact and shearing of the pomelo pulp per unit time, with data sourced from the encoder feedback signal of the pulping drive motor; and the filter screen pressure difference reflects the pressure difference between the upstream and downstream sides of the screen, directly related to the screen's flow resistance, generally monitored and obtained through a high-precision differential pressure transmitter. By synchronously collecting these parameters, an input dataset that comprehensively maps the current operating status of the pulping and filtration system is constructed, providing multi-dimensional data support for subsequent operating condition tracing analysis.

[0071] Step 2: Based on the pomelo feed flow rate information and the pulper speed information, analyze the matching relationship between the pomelo processing volume per unit time and the pulping and crushing intensity during the current pulping process to obtain the feed-pulping load matching information.

[0072] Feed-pulping load matching information is used to quantify the balance between the pulper's load and processing capacity. Its construction logic is based on the coupled analysis of material throughput and mechanical work intensity. The pomelo feed flow rate information and pulper speed information are time-series aligned and normalized to evaluate the dynamic matching degree between the two. When the pomelo processing volume per unit time increases but the pulper speed does not increase synchronously, the average shear force on a single fruit decreases, resulting in insufficient pulping and crushing strength, forming a mismatch state of high feed and low speed. Conversely, if the feed rate decreases sharply while the speed remains high, it may lead to dry-pulping or over-crushing.

[0073] To quantify this matching relationship, a pulping load factor is introduced as an evaluation index, which is calculated using the following formula:

[0074] In the formula, This indicates the pomelo feed flow rate, in units of... ; This indicates the average density of grapefruit pulp, in units of... ; This indicates the rotational speed of the pulper, in units of... ; This represents the real-time output torque of the pulping motor, in units of... ; This indicates the mechanical efficiency of the transmission system. The value reflects the amount of material load that needs to be processed per unit of mechanical work.

[0075] By calculating the pulping load factor in real time This information is then compared with a preset ideal working range to generate feed-pulping load matching information. If the calculated... If the value is consistently higher than the upper limit of the preset range, it is determined that the feed is overloaded or the rotation speed is insufficient, indicating that the current pulping and crushing intensity is unable to cope with the high flow rate of raw material input, thus identifying potential operating condition deviations.

[0076] Step 3: Based on the pressure difference information of the filter screen and combined with the abnormal turbidity characteristics, analyze the clogging characteristics on the screen surface caused by the accumulation of non-fruit pulp tissue to obtain the source information of screen clogging.

[0077] The purpose of tracing the source of screen clogging is to reveal the root cause of increased screen resistance, especially to distinguish specific clogging patterns caused by differences in raw materials. First, the temporal characteristics of pressure difference changes are extracted from the pressure difference information of the filter screen, including the rate of increase in pressure difference, fluctuation frequency, and the presence of abrupt changes. Then, these temporal characteristics are correlated with the type and distribution of non-fruit pulp tissue in abnormal turbidity characteristics.

[0078] If the pressure difference of the filter screen shows a slow, linear upward trend, and a large number of long strips or filaments of the filter cake are detected in the abnormal turbidity characteristics, then the current clogging characteristic is determined to be filter cake fiber entanglement clogging. The mechanism is that the flexible filter cake overlaps with each other on the screen surface to form a dense filter cake layer. If the pressure difference of the filter screen increases sharply in a short period of time, and a large number of high-hardness, large-diameter grapefruit seeds or fruit stem fragments are present in the abnormal turbidity characteristics, then it is determined to be particle jamming clogging. The mechanism is that rigid particles are directly embedded in the screen pores.

[0079] By coupling analysis of differential pressure time series and abnormal morphology, the screen blockage source information not only records the severity of the blockage (such as the increase in differential pressure), but also clarifies the material composition of the blockage (such as the outer layer and the core) and the physical morphology (such as the entanglement layer and the jamming point), thus providing a precise basis for the subsequent formulation of targeted unblocking strategies.

[0080] Step 4: Based on the feed-pulping load matching information and combined with the screen blockage traceability information, analyze the pulping speed mismatch and screen blockage offset caused by changes in pomelo raw material batches and varieties, and obtain the working condition traceability information set.

[0081] The operational condition traceability information set establishes a causal mapping relationship between raw material characteristic fluctuations and equipment operating deviations. After acquiring the feed-pulping load matching information, it first identifies whether there is an abnormal increase in the pulping load coefficient due to increased raw material hardness (such as increased density of pomelo seeds in a certain batch), which directly points to a pulping speed mismatch problem. At the same time, based on the screen blockage traceability information, it determines whether the current decrease in screen flow capacity is caused by specific raw material components (such as tough capsules), thereby determining the type of screen blockage deviation.

[0082] By comprehensively analyzing the above two aspects of information, and eliminating interference from equipment malfunctions, the root cause of the abnormal operating conditions was identified as raw material differences. For example, the operating condition traceability information set could output the following conclusion: the current batch of raw material has a 15% higher pulp hardness than the standard value, resulting in insufficient crushing at the current pulping speed (speed mismatch), and the excessive coarse fibers generated cause the formation of a flexible filter cake layer on the screen surface (clogging and displacement). This operating condition traceability information set fully records the evolution chain from raw material characteristic variations to equipment parameter deviations, providing a foundation for the subsequent generation of adaptive control commands.

[0083] Example 6: In some embodiments, the process of constructing the screen blockage tracing information specifically includes the following steps: Step 1: Based on the pressure difference information of the filter screen, identify the trend of pressure difference change over time and obtain the time series characteristics of pressure difference fluctuation.

[0084] The differential pressure information of the filter screen is a series of pressure data collected in real time by a differential pressure transmitter installed between the inlet and outlet of the screen, reflecting the changes in resistance as the fluid flows through the screen. The time series characteristics of differential pressure fluctuation are statistical indicators extracted from this pressure data series that can characterize the dynamics of blockage development. It is not a single instantaneous differential pressure value, but a multi-dimensional time series feature vector that includes the rate of change, trend direction, fluctuation amplitude, and abrupt change points.

[0085] First, the collected raw differential pressure data undergoes noise reduction preprocessing to eliminate high-frequency fluctuations caused by pumping pulses or sensor zero-point drift. Then, time series analysis algorithms, such as moving averages or sliding window differencing, are used to calculate the first and second derivatives of the differential pressure to capture the slope and curvature of pressure changes. By analyzing the distribution of these derivatives within a continuous time window, the steady rise phase, accelerated rise phase, and abrupt inflection points of the differential pressure are identified, thus forming a temporal characteristic of differential pressure fluctuations that includes both trend and local morphological features. Robust signals reflecting the nature of screen blockage are extracted from the high-frequency pressure data, providing a data foundation for subsequent correlation analysis.

[0086] Step 2: Based on the abnormal turbidity characteristics, compare the timing of pressure difference fluctuations with the occurrence times of different forms of non-fruit pulp tissue in the grapefruit pulp in the abnormal turbidity characteristics. Analyze the phased increase and sudden rise of pressure difference caused by the gradual accumulation of non-fruit pulp tissue on the screen surface to obtain information on the blockage accumulation process.

[0087] Abnormal turbidity characteristics are descriptive information generated from preceding steps, including the morphology, size, and timing of different types of non-flesh tissues (such as pericarp, seeds, and stems). Information on the clogging accumulation process can refer to a logical record describing the causal relationship between pressure differential changes and the time when specific types of impurities enter the screen, used to reveal the dynamic evolution of clogging formation.

[0088] The timeline of differential pressure fluctuation characteristics is synchronized with the timeline of abnormal turbidity characteristics identified by the visual monitoring system. For each stage of increase in differential pressure fluctuation characteristics, the abnormal turbidity characteristics within a preset time window prior to the start of that stage are traced back to check for the presence of non-flesh tissue of a specific form. If a large amount of filamentous or flaky material (i.e., the capsule) is continuously detected passing through, and the differential pressure shows a slow, continuous linear or exponential upward trend, it is determined to be a gradual accumulation process. If the appearance of hard, large particles (i.e., grapefruit seeds or stems) highly coincides with the instantaneous jump (sudden increase) in differential pressure, it is determined to be an instantaneous blockage impact. Through this causal mapping in the time dimension, the simple change in differential pressure is transformed into a physically meaningful description of the blockage process, obtaining information on the blockage accumulation process. The originally abstract pressure fluctuations are given a clear material attribution, clarifying the intrinsic relationship between impurity behavior and equipment resistance response.

[0089] Step 3: Based on the information on the blockage accumulation process, distinguish between gradual blockage caused by the tangled fruit peel and sudden blockage caused by the stuck fruit seeds or stems, and obtain the source information of the screen blockage.

[0090] Screen blockage source information provides the final qualitative conclusion on the current blockage status of the screen, clearly indicating the type, cause, and severity of the blockage, and is a key basis for generating subsequent control instructions. Based on the pressure difference change pattern and associated impurity types in the blockage accumulation process information, execution mode classification logic is applied.

[0091] When the clogging accumulation process information shows a long-term, gradual upward trend in differential pressure, and the main associated substance in the abnormal turbidity characteristics is celluloid, the current clogging type is determined to be progressive clogging formed by the interweaving and adsorption of celluloid fibers onto the screen pores. This type of clogging typically develops slowly, allowing for predictive maintenance over a longer time span. Conversely, when the clogging accumulation process information shows a rapid, non-linear increase in differential pressure within a very short time, and the main associated substance in the abnormal turbidity characteristics is grapefruit seeds or stems, the current clogging type is determined to be sudden clogging formed by the mechanical jamming of hard particles in the screen pores. This type of clogging is sudden and destructive, typically requiring immediate shutdown or emergency backwashing. Through the above classification, the generated screen clogging source information not only includes a description of the clogging state but also implicitly explains the physical mechanism of the clogging. This allows the control system to adopt differentiated response strategies for different clogging types. For example, it can dynamically adjust the backwashing frequency for progressive clogging and immediately trigger a shutdown protection mechanism for sudden clogging, effectively avoiding the misoperation or delayed response caused by traditional alarms based solely on differential pressure thresholds.

[0092] Example 7: In some embodiments, based on the feed-pulping load matching information and combined with screen blockage traceability information, the pulping speed mismatch and screen blockage offset caused by changes in pomelo raw material batches and varieties are analyzed to obtain the working condition traceability information set, specifically including the following steps: Step 1: Based on the feed-pulping load matching information, analyze whether the current pulping and crushing force is suitable for the current feed rate, and obtain pulping deviation information.

[0093] Pulping deviation information is used to quantify the degree of imbalance between the mechanical work capacity of the pulper and the current material input. From the acquired feed-pulping load matching information, the current pulper speed and pomelo feed flow rate per unit time are extracted to calculate the implicit theoretical crushing load requirement. The current energy input per unit volume of the pulper is further calculated and compared with the target energy threshold required to completely crush standard pomelo raw materials at the current feed rate. If the calculated actual energy input value is lower than the target energy threshold, and this low value persists for more than a preset time window, the current pulping crushing force is determined to be insufficient, thus generating pulping deviation information including the deviation direction (e.g., negative deviation) and deviation percentage (e.g., less than 15%). This deviation information objectively reflects the processing capacity gap caused by fluctuations in raw material hardness or feed rate during the pulping process.

[0094] Step 2: Based on the source information of screen blockage, analyze the location and degree of screen blockage changes to obtain blockage change information.

[0095] Clogging change information is a refined deconstruction of the dynamic state of the filtration process. Physical coordinates or grid areas of clogging occurrence are extracted from the screen clogging source information to pinpoint the specific location of the clogging, such as identifying that clogging is mainly concentrated in the middle section of the screen or the discharge port area. Simultaneously, the rate of change in clogging severity is analyzed to distinguish between a linear, slow, cumulative process and an exponentially worsening, stagnant process. The location information and severity change information are then integrated to form multi-dimensional clogging change information. This information not only indicates the severity of the current screen flow obstruction but also reveals the dynamic trend of clogging development, providing data support for subsequent judgment of the clogging cause (such as filter cake layer thickening or large particle jamming).

[0096] Step 3: Based on the pulping deviation information and blockage change information, compare the processing performance of the current batch with that of historical reference batches of raw materials, and analyze the physical differences in the quantity of grapefruit seeds, stems and peels in the current raw materials.

[0097] Physical differences are a key analytical result that maps macroscopic deviations at the equipment operating condition level back to the microscopic properties of raw materials. Using historical batches as reference benchmarks for pulping deviation and blockage changes during normal processing, the currently acquired pulping deviation and blockage change information are projected onto these benchmarks for deviation analysis. If the current pulping deviation information shows a higher degree of insufficient crushing force than the historical average, and the blockage change information exhibits numerous sudden jamming characteristics, it is inferred that there is an abnormal increase in the quantity or size of hard, difficult-to-crush grapefruit seeds and stems in the current raw material. Conversely, if the blockage change information mainly shows a gradual entanglement characteristic, it is inferred that the proportion of tough, easily intertwined integument in the current raw material is high. Through this type of correlation analysis, abstract operating condition data is transformed into a concrete description of the physical morphology and quantity distribution of specific components such as grapefruit seeds, stems, and integument in the raw material, thus revealing physical differences.

[0098] Step 4: Based on physical differences, analyze the impact of changes in pomelo raw material batches and varieties on pulping load and screen flow capacity to obtain a set of working condition traceability information.

[0099] The operational condition traceability information set is the final comprehensive decision-making basis used to guide subsequent control. Based on the physical differences obtained in the preceding steps, the specific impact effects of changes in raw material properties on each link of the production system are quantified. According to the component characteristics identified in the physical differences, their load impact coefficients on the pulper and filter screen are calculated respectively. For example, for the physical difference of increased pomelo seed hardness, the increase in its impact resistance on the pulper cutter head is calculated to assess the increase in pulping load; for the physical difference of increased capsule toughness, its interlacing probability on the screen surface is simulated to assess the degree of reduction in the effective flow passage diameter of the screen. The above independent impact effects are aggregated to form an operational condition traceability information set that includes pulping load overload rate, screen flow capacity reduction rate, and expected equipment wear hotspots.

[0100] Example 8: In some embodiments, based on the working condition traceability information set and with the constraint of maintaining continuous operation efficiency, an adaptive control command is generated with the goal of minimizing the number of adjustments, and a turbidity monitoring log of the pomelo juice pulping and filtration process is output. Specifically, this includes the following steps: Step 1: Based on the working condition traceability information set, analyze the fluctuations in the physical properties of pomelo seeds, stems and peels caused by changes in raw material batches and varieties, and obtain the raw material disturbance characteristics by assessing the degree of imbalance between the current pulping load and the screen flow capacity.

[0101] Raw material disturbance characteristics are specific quantitative indicators that characterize the deviation of the production system from the ideal operating state caused by changes in the physical properties of raw materials. This characteristic is not a simple single value, but a multi-dimensional data structure that includes pulping load deviation rate, screen flow capacity reduction index, and key interference factor identification results. Its formation process is based on the operating condition traceability information set obtained in the aforementioned steps, which has already identified the causal chain between abnormal turbidity characteristics and equipment operating condition deviations. From the acquired operating condition traceability information set, data on the fluctuations in the physical properties of grapefruit seeds, stems, and pericarps are extracted, such as the increase in grapefruit seed hardness, the increase in the lignification degree of stems, and the distribution changes in pericarp fiber length. These changes in physical properties are mapped into a model of pulping load and screen flow capacity, calculating the deviation between the current required torque and the rated torque of the pulper, as well as the difference between the current filtration efficiency and the benchmark filtration efficiency, thereby determining the degree of imbalance. For example, if the hardness of the pomelo seeds in a batch increases by 20%, it will lead to a heavy-load imbalance in the pulping process. At the same time, due to the increase in large fruit stems, the screen flow capacity will be moderately blocked. The final raw material disturbance characteristics include the type of imbalance (such as overload or flow obstruction), the imbalance value, and the dominant substance identifier, which are used to guide subsequent control decisions.

[0102] Step 2: Based on the characteristics of raw material disturbance, and with the constraint of maintaining continuous operation efficiency, analyze the coverage of a single control action on restoring the balance of pulping and filtration conditions, screen the control combination that can simultaneously correct insufficient pulping and crushing strength and screen clogging trend, and obtain the optimal control parameter combination.

[0103] The optimal control parameter combination is a set of parameter adjustment schemes selected to address the imbalance problem described by the raw material disturbance characteristics. The core of this sequence construction lies in analyzing the coverage capability of a single control action, i.e., evaluating whether a parameter adjustment (such as increasing the pulper speed) can simultaneously improve pulping and crushing effects and alleviate screen clogging. A candidate set containing all potential control actions is established, such as adjusting the pulper speed, adjusting the feed pump frequency, and triggering screen backwashing. For each candidate action, simulation models or historical data are used to predict its dual corrective effect on insufficient pulping and crushing strength and screen clogging trend under the constraint of maintaining continuous operation efficiency (i.e., no downtime and production capacity fluctuations within a preset range). For example, while increasing the pulper speed can enhance the crushing ability of hard grapefruit seeds (correcting insufficient pulping strength), if the speed is too high, causing the peel to be broken into finer fibers, it may exacerbate screen clogging, resulting in a lower coverage capability score for this action. Conversely, finding a specific rotational speed range that can moderately crush the grapefruit seeds while simultaneously cutting the long-fiber capsule, or coordinating with specific feed rate adjustments, to address both issues positively at the same time, will result in a combination labeled as having high coverage capability. The system selects the control combination with the highest overall score that can simultaneously correct both types of problems as the optimal control parameter combination. This sequence limits the specific value range and execution order of the control parameters, ensuring that the overall working condition is restored with the fewest intervention steps.

[0104] Step 3: Based on the optimal combination of control parameters, and according to the priority of the control range from large to small, generate an adaptive control command with the goal of minimizing the number of control operations, and output a turbidity monitoring log of the pomelo juice pulping and filtration process.

[0105] Adaptive control commands are a set of specific control signals generated based on the optimal combination of control parameters to drive the actions of field actuators. Their generation follows a priority principle from largest to smallest, prioritizing control measures with a wide impact on the entire process and the ability to quickly correct major deviations, followed by fine-tuning. The system analyzes each control combination within the optimal control parameter set and sorts them according to their impact on the system state. For example, adjusting the pulper speed directly affects the crushing effect of the entire pulping section and the feeding state of the subsequent filtration section; its impact is greater than adjusting the backwashing time of a specific screen, therefore, speed adjustment is given higher priority. The system generates speed adjustment control commands accordingly. If, after speed adjustment, the raw material disturbance characteristics show that the imbalance has decreased to within acceptable limits, then no further fine-tuning commands are needed, thus achieving the optimization goal of minimizing the number of control operations. The final generated adaptive control commands are then distributed to a programmable logic controller (PLC) or distributed control system (DCS) via the industrial network. Simultaneously, the aforementioned raw material disturbance characteristic analysis results, the selection logic for the optimal control parameter combination, the final control command parameters, and the expected recovery prediction of the operating conditions are structured and encapsulated to generate a turbidity monitoring log for the pomelo juice pulping and filtration process. This log, indexed by timestamps, records in detail the entire process from raw material fluctuation identification to adaptive control, providing production managers with complete traceability data and accumulating historical experience data for the processing of similar raw materials in subsequent batches.

[0106] Example 9: In some embodiments, the process of constructing the optimal combination of control parameters specifically includes the following steps: Step 1: Based on the characteristics of raw material disturbance, analyze the effect of increasing the pulping speed of the pulper on the crushing and refining effect on pomelo seeds and stems, as well as the effect on cutting the peel, to obtain pulping intensity matching information.

[0107] The pulping intensity adaptation information is used to quantify the effectiveness of pulper speed adjustments on non-flesh tissues with different physical properties. Its foundation lies in the current physical characteristics of the raw materials as reflected by the raw material disturbance features. By monitoring the changes in the impact torque generated on hard materials such as grapefruit seeds and stems during the increase in pulper speed, the degree of breakage and refinement of these hard particles is assessed. Simultaneously, combined with the changes in shear force generated on fibrous materials such as the integument by the speed increase, the length reduction effect after the integument is cut is evaluated. This analysis process can be fitted using discrete element simulation or regression models based on historical operating data to establish the functional mapping relationship between the speed increment and the breakage rate and cutting rate. For example, if the current raw material disturbance characteristics indicate that the grapefruit seeds are hard, the pulping intensity adaptation information will reflect the high sensitivity of speed increase to grapefruit seed breakage, meaning that a small speed increase can achieve a particle refinement effect; conversely, if the integument is extremely tough, a higher speed is required to achieve effective cutting. Through this processing, the mechanical energy input of the pulper is converted into a quantitative indicator of its destructive ability against specific impurities, providing data support for subsequent collaborative correction.

[0108] Step 2: Based on the pulping strength adaptation information, analyze the sieving smoothness of the crushed pomelo seeds and stem fragments, as well as the entanglement and hindrance characteristics of the cut pericarp, to obtain pulping-filtration synergistic correction information.

[0109] The pulping-filtration synergistic correction information is used to characterize the coupled impact of product morphology changes in the pulping stage on the performance of subsequent filtration stages. Specifically, it assesses the ease with which crushed particles pass through the screen and the risk level of clogging caused by the cut capillaries. Based on the obtained pulping intensity adaptation information, the particle size distribution of the crushed and refined grapefruit seed and stem fragments is further analyzed to determine whether it falls within the allowable range of the filter screen aperture, thereby predicting its screening smoothness. Simultaneously, the length of the cut capillaries fibers is assessed to determine whether it is smaller than the screen aperture or the critical entanglement length, in order to evaluate its entanglement and hindrance characteristics on the screen surface. This process establishes a correlation model between pulping output and filtration input, which differs from traditional single-stage optimization. For example, if the pulping intensity matching information shows that increasing the rotation speed refines the seed particles, causing the particle size to change from larger than the sieve aperture to smaller than the sieve aperture, then the sieving smoothness improves, exhibiting a positive synergistic effect. Conversely, if increasing the rotation speed, while cutting off the seed coat, generates a large number of flexible fragments with a size close to the sieve aperture, it may instead form a dense filter cake layer on the sieve surface, leading to a deterioration of the entanglement and hindrance characteristics, exhibiting a negative synergistic effect. By comprehensively quantifying these two aspects, the true impact path of adjusting the pulping conditions on filtration capacity can be identified, thereby selecting the parameter range that can improve pulping performance without worsening filtration conditions.

[0110] Step 3: Based on the pulping-filtration synergistic correction information, and with the constraint of maintaining continuous operation efficiency, analyze the synchronous correction range of two types of deviations: insufficient pulping and crushing strength and screen clogging, by adjusting the pulping speed once. Eliminate the control actions that can only improve the pulping effect but aggravate screen clogging, and obtain the dual-effect control parameter range.

[0111] The dual-effect control parameter range refers to a set of parameters that can simultaneously correct insufficient pulping and crushing strength and screen clogging tendency. Its determination process is based on the rigid constraint of maintaining continuous operation efficiency. Based on the synergistic correction information of pulping and filtration, the correction ranges for insufficient pulping and crushing strength and screen clogging tendency are calculated under different pulper speed settings. Maintaining continuous operation efficiency is introduced as a constraint, such as limiting the maximum allowable downtime or the minimum throughput threshold, and parameter combinations are screened under this constraint. The key is to eliminate those control actions that are counterproductive—that is, parameter points that, while improving crushing strength and solving the problem of insufficient pulping, exacerbate screen clogging due to poor particle morphology or increased coating fragments. The parameter range that remains is the dual-effect control parameter range. Any parameter combination within this range can ensure that the improvement of crushing capacity in the pulping stage and the improvement of flow capacity in the filtration stage occur simultaneously, thereby achieving positive system-level benefits. For example, this step may determine that when the rotation speed is increased to between 100 rpm and 150 rpm, it can effectively break up hard grapefruit seeds without producing a large number of capsule fragments that clog the screen. However, above 150 rpm, the risk of clogging increases sharply. Therefore, 100 rpm to 150 rpm is determined to be the dual-effect control parameter range.

[0112] Step 4: Based on the dual-effect control parameter range, select the parameter combination that can maintain continuous operation without additional adjustment of the screen working conditions after control, and obtain the optimal control parameter combination.

[0113] The optimal control parameter combination is the final output set of control strategies, with the core objective of completing the operational condition correction with the fewest possible adjustments. After determining the range of dual-effect control parameters, further screening is conducted within this range to select parameter combinations that meet the requirements of not needing additional adjustments to the screen's operating conditions. This means that after the selected parameter combination is applied to the pulper, the resulting changes in particle morphology and rheological properties enable the screen to automatically restore or maintain normal flow capacity under existing operating conditions without triggering additional maintenance actions such as backwashing, vibration cleaning, or screen replacement. The screening process can be prioritized according to the control range from large to small, energy consumption from low to high, or response speed from fast to slow. Through this screening, the optimal control parameter combination typically contains several preferred parameter points. These parameter points represent the best solutions under different priorities or scenarios, ensuring that the production line can maintain continuous and stable operation after any parameter adjustment, thereby maximizing production efficiency and reducing operational complexity.

[0114] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0115] Figure 3 This is a schematic diagram of the structure of a turbidity monitoring system for the pulping and filtration process of pomelo juice, provided in an embodiment of this application. Figure 3 As shown, the turbidity monitoring system 300 for the pulping and filtration process of pomelo juice in this embodiment includes: a turbidity feature module 301, a working condition traceability module 302, and an adaptive control module 303.

[0116] The turbidity feature module 301 is used to acquire a grapefruit pulp information set. Based on the grapefruit pulp information set, it determines the normal turbidity features caused by fruit pulp tissue and the abnormal turbidity features caused by non-fruit pulp tissue in the current grapefruit pulp, and obtains a turbidity feature information set. The operating condition traceability module 302 is used to acquire a set of equipment operating condition information. Based on the set of equipment operating condition information and using the turbidity feature information set as a judgment benchmark, it reverse-calculates the deviation of the equipment operating condition corresponding to the pulping and filtration process caused by the fluctuation of grapefruit raw material differences, and obtains an operating condition traceability information set. The adaptive control module 303 is used to generate an adaptive control command with the minimum number of adjustments as the optimization objective, based on the operating condition traceability information set and with the constraint of maintaining continuous operation efficiency, and outputs a turbidity monitoring log of the grapefruit juice pulping and filtration process.

[0117] Optionally, when the turbidity feature module 301 determines the normal turbidity features caused by fruit pulp tissue and the abnormal turbidity features caused by non-fruit pulp tissue in the current pomelo pulp based on the pomelo pulp information set, and obtains the turbidity feature information set, the following is specifically used: the pomelo pulp information set includes insoluble solids content, pomelo pulp viscosity, and pomelo pulp image information; based on the pomelo pulp image information, combined with the insoluble solids content and the pomelo pulp viscosity, the suspension characteristics of solid substances in the current pomelo pulp in the liquid are analyzed to obtain pulp dispersion information; based on the pulp dispersion information, the light transmittance characteristics of the current pomelo pulp are analyzed to identify the outlines of substances that obstruct light transmission; the outlines of substances are compared with the inherent morphology of fruit pulp tissue, and small integument fragments that are dispersed and whose size is within a preset normal range are determined as the normal turbidity features, and integument fibers that exceed a preset threshold in length, are intertwined, or form local aggregation areas are determined as the abnormal turbidity features, and the turbidity feature information set is obtained by summarizing them.

[0118] Optionally, the turbidity feature module 301, during the construction of the pulp dispersion information, is specifically used to: based on the pomelo pulp image information, combined with the insoluble solids content and the pomelo pulp viscosity, identify the settling characteristics of the pulping particles of pomelo seeds and stems in the liquid to obtain particle settling information; simultaneously, identify the floating and stagnant characteristics of the pericarp in the liquid to obtain pericarp suspension information; based on the particle settling information and the pericarp suspension information, analyze the distribution state of solid substances of different properties in the pulp, including their stratification and local aggregation, to obtain the pulp dispersion information used to distinguish between fruit pulp tissue and foreign matter.

[0119] Optionally, when the turbidity feature module 301 analyzes the light transmittance characteristics of the current pomelo pulp based on the pulp dispersion information and identifies the outline of the material that obstructs light transmission, it is specifically used to: identify the difference in sedimentation rate of pulp particles of different sizes in a static state and the local retention area formed by the entanglement of the peel in the pomelo pulp based on the pulp dispersion information, to obtain the pomelo pulp layer distribution information; analyze the occlusion characteristics when light passes vertically through each pulp layer based on the pomelo pulp layer distribution information, identify the shadow boundary formed after the light is occluded, and trace the outer edge line of the object whose light is occluded along the shadow boundary to obtain the material outline.

[0120] Optionally, when the operating condition traceability module 302 obtains the operating condition traceability information set by using the turbidity feature information set as a judgment benchmark to inversely deduce the deviation of the corresponding equipment operating conditions in the pulping and filtration process caused by the fluctuation of pomelo raw material differences, the specific usage of the operating condition traceability information set is as follows: The equipment operating condition information set includes pomelo feed flow rate information, pulper speed information, and filter screen pressure difference information; based on the pomelo feed flow rate information and the pulper speed information, analyze the matching relationship between the pomelo processing volume per unit time and the pulping and crushing intensity in the current pulping process to obtain feed-pulping load matching information; based on the filter screen pressure difference information and the abnormal turbidity feature, analyze the clogging characteristics on the screen surface caused by the accumulation of non-fruit pulp tissue to obtain screen clogging traceability information; based on the feed-pulping load matching information and the screen clogging traceability information, analyze the pulping speed mismatch and screen clogging offset caused by changes in pomelo raw material batches and varieties to obtain the operating condition traceability information set.

[0121] Optionally, the working condition traceability module 302, during the construction of the screen blockage traceability information, is specifically used for: identifying the pressure difference change trend over time based on the filter screen pressure difference information, and obtaining the pressure difference fluctuation time sequence characteristics; comparing the pressure difference fluctuation time sequence characteristics with the appearance time of different forms of non-fruit pulp tissue in the grapefruit pulp in the abnormal turbidity characteristics, analyzing the staged increase and sudden increase phenomenon of pressure difference caused by the gradual accumulation of non-fruit pulp tissue on the screen surface, and obtaining the blockage accumulation process information; and distinguishing between gradual blockage caused by peel entanglement and sudden blockage caused by grapefruit seeds and stem jamming, based on the blockage accumulation process information, and obtaining the screen blockage traceability information.

[0122] Optionally, when the working condition traceability module 302 analyzes the mismatch in pulping speed and screen blockage deviation caused by changes in pomelo raw material batches and varieties based on the feed-pulping load matching information and the screen blockage traceability information to obtain the working condition traceability information set, it is specifically used for: analyzing whether the current pulping and crushing force is suitable for the current feed amount based on the feed-pulping load matching information to obtain pulping deviation information; analyzing the location and degree of screen blockage change characteristics based on the screen blockage traceability information to obtain blockage change information; comparing the processing performance of the current batch and historical reference batch raw materials with the pulping deviation information and the blockage change information to analyze the physical differences in the quantity of pomelo seeds, stems, and peels in the current raw materials; and analyzing the impact of changes in pomelo raw material batches and varieties on pulping load and screen flow capacity based on the physical differences to obtain the working condition traceability information set.

[0123] Optionally, when the adaptive control module 303 generates an adaptive control instruction with the minimum number of adjustments as the optimization objective based on the working condition traceability information set and with the constraint of maintaining continuous operation efficiency, and outputs the turbidity monitoring log of the pomelo juice pulping and filtration process, it is specifically used to: analyze the fluctuations in the physical properties of pomelo seeds, stems, and peels caused by changes in raw material batches and varieties, based on the working condition traceability information set, and determine the degree of imbalance between the current pulping load and the screen flow capacity to obtain raw material disturbance characteristics; based on the raw material disturbance characteristics, and with the constraint of maintaining continuous operation efficiency, analyze the coverage ability of a single adjustment action to restore the balance of the pulping and filtration working conditions, screen the adjustment combination that can simultaneously correct insufficient pulping and crushing strength and screen clogging trend, and obtain the optimal adjustment parameter combination; based on the optimal adjustment parameter combination, according to the priority from large to small adjustment range, generate the adaptive control instruction with the minimum number of adjustments as the optimization objective, and output the turbidity monitoring log of the pomelo juice pulping and filtration process.

[0124] Optionally, the adaptive control module 303, during the construction of the optimal control parameter combination, is specifically used for: analyzing the effect of increasing the pulper speed to crush and refine the pomelo seeds and stems and the effect of cutting the pericarp on the current pomelo raw material, based on the raw material disturbance characteristics, to obtain pulping intensity adaptation information; analyzing the sieve smoothness of the crushed pomelo seeds and stem fragments and the entanglement and hindrance characteristics of the cut pericarp, based on the pulping intensity adaptation information, to obtain pulping-filtration collaborative correction information; based on the pulping-filtration collaborative correction information, with the constraint of maintaining continuous operation efficiency, analyzing the synchronous correction amplitude of a single adjustment of the pulper speed on two types of deviations: insufficient pulping and crushing intensity and screen blockage, eliminating control actions that only improve the pulping effect but aggravate screen blockage, to obtain a dual-effect control parameter range; based on the dual-effect control parameter range, selecting parameter combinations that can maintain continuous operation without additional adjustment of screen conditions after control, to obtain the optimal control parameter combination.

[0125] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A method for monitoring turbidity during the pulping and filtration process of pomelo juice, characterized in that, include: Obtain a grapefruit pulp information set; based on the grapefruit pulp information set, determine the normal turbidity characteristics caused by fruit pulp tissue and the abnormal turbidity characteristics caused by non-fruit pulp tissue in the current grapefruit pulp, and obtain a turbidity characteristic information set. Obtain the equipment operating condition information set; based on the equipment operating condition information set, and using the turbidity feature information set as the judgment benchmark, reverse the deviation of the equipment operating condition corresponding to the pulping and filtration process caused by the difference in pomelo raw material, and obtain the operating condition traceability information set. Based on the aforementioned working condition traceability information set, and with the constraint of maintaining continuous operation efficiency, an adaptive control command is generated with the goal of minimizing the number of adjustments, and a turbidity monitoring log of the pomelo juice pulping and filtration process is output.

2. The method according to claim 1, characterized in that, Based on the grapefruit pulp information set, the normal turbidity characteristics caused by fruit pulp tissue and the abnormal turbidity characteristics caused by non-fruit pulp tissue in the current grapefruit pulp are determined to obtain a turbidity feature information set, including: The pomelo pulp information set includes insoluble solids content, pomelo pulp viscosity, and pomelo pulp image information; Based on the grapefruit pulp image information, combined with the insoluble solids content and grapefruit pulp viscosity, the suspension characteristics of solid substances in the current grapefruit pulp in the liquid are analyzed to obtain pulp dispersion information; Based on the pulp dispersion information, the light transmittance characteristics of the current pomelo pulp are analyzed, and the outlines of substances that obstruct light transmission are identified; The outline of the substance is compared with the inherent morphology of the pulp tissue. Small capsule fragments that are dispersed and within the preset normal range in size are identified as normal turbidity features. Capsule fibers that exceed the preset threshold in length, are intertwined, or form local aggregates are identified as abnormal turbidity features. The turbidity feature information set is then obtained by summarizing these features.

3. The method according to claim 2, characterized in that, The process of constructing the slurry dispersion information includes: Based on the grapefruit pulp image information, combined with the insoluble solids content and the grapefruit pulp viscosity, the settling characteristics of the pulp particles of grapefruit seeds and stems in the liquid are identified to obtain particle settling information; Simultaneously, the floating and retention characteristics of the capsule membrane in the liquid are identified to obtain the capsule membrane suspension information; Based on the particle sedimentation information and the capsule suspension information, the distribution of solid substances with different properties in the slurry, including their stratification and local aggregation, is analyzed to obtain the slurry dispersion information used to distinguish between fruit pulp tissue and foreign matter.

4. The method according to claim 2, characterized in that, The process of analyzing the light transmittance characteristics of the current grapefruit pulp based on the pulp dispersion information and identifying the outlines of substances that obstruct light transmission includes: Based on the pulp dispersion information, the sedimentation rate differences of pulp particles of different sizes under static conditions and the local retention areas formed by the entanglement of the peel in the grapefruit pulp are identified, and the stratified distribution information of the grapefruit pulp is obtained. Based on the layered distribution information of the pomelo pulp, the occlusion characteristics when light passes vertically through each pulp layer are analyzed, the shadow boundary formed after the light is occluded is identified, and the outer edge line of the object whose light is occluded is traced along the shadow boundary to obtain the outline of the substance.

5. The method according to claim 2, characterized in that, The step of using the equipment operating condition information set as a judgment benchmark and the turbidity characteristic information set as a criterion to infer the deviation of the equipment operating conditions in the pulping and filtration process caused by the fluctuation of pomelo raw material differences, and obtaining the operating condition traceability information set, includes: The equipment operating information set includes pomelo feed flow rate information, pulper speed information, and filter screen pressure difference information; Based on the pomelo feed flow rate information and the pulper rotation speed information, the matching relationship between the pomelo processing volume per unit time and the pulping and crushing intensity during the current pulping process is analyzed to obtain the feed-pulping load matching information. Based on the pressure difference information of the filter screen and the abnormal turbidity characteristics, the clogging characteristics caused by the accumulation of non-fruit pulp tissue on the screen surface are analyzed to obtain the clogging source information of the screen. Based on the feed-pulping load matching information and the screen blockage traceability information, the pulping speed mismatch and screen blockage offset caused by changes in pomelo raw material batches and varieties are analyzed to obtain the working condition traceability information set.

6. The method according to claim 5, characterized in that, The process of constructing the screen blockage source information includes: Based on the pressure difference information of the filter screen, the changing trend of pressure difference over time is identified, and the time series characteristics of pressure difference fluctuation are obtained. Based on the abnormal turbidity characteristics, the timing of the pressure difference fluctuation is compared with the time of appearance of different forms of non-fruit pulp tissue in the grapefruit pulp in the abnormal turbidity characteristics. The phased increase and sudden rise of pressure difference caused by the gradual accumulation of non-fruit pulp tissue on the screen surface are analyzed to obtain information on the blockage accumulation process. Based on the information on the blockage accumulation process, the progressive blockage caused by the tangled fruit peel and the sudden blockage caused by the stuck fruit seeds or stems are distinguished, and the source information of the screen blockage is obtained.

7. The method according to claim 5, characterized in that, Based on the feed-pulping load matching information and the screen blockage tracing information, the analysis of pulping speed mismatch and screen blockage offset caused by changes in pomelo raw material batches and varieties yields the working condition tracing information set, including: Based on the feed-pulping load matching information, analyze whether the current pulping and crushing force is suitable for the current feed rate, and obtain pulping deviation information; Based on the screen blockage source information, the location and degree of screen blockage changes are analyzed to obtain blockage change information; Based on the pulping deviation information and the blockage change information, compare the processing performance of the current batch and the historical reference batch of raw materials to analyze the physical differences in the quantity of grapefruit seeds, fruit stems and peels in the current raw materials; Based on the physical differences, the impact of changes in pomelo raw material batches and varieties on pulping load and screen flow capacity was analyzed, and the working condition traceability information set was obtained.

8. The method according to claim 7, characterized in that, Based on the aforementioned operational condition traceability information set, and with the constraint of maintaining continuous operation efficiency, an adaptive control command is generated with the goal of minimizing the number of adjustments, and a turbidity monitoring log of the pomelo juice pulping and filtration process is output, including: Based on the aforementioned working condition traceability information set, the physical property fluctuations of pomelo seeds, fruit stalks, and pericarps caused by changes in raw material batches and varieties are analyzed, and the degree of imbalance between the current pulping load and the screen flow capacity is assessed to obtain the raw material disturbance characteristics. Based on the raw material disturbance characteristics, and with the constraint of maintaining continuous operation efficiency, the coverage of a single control action on restoring the balance of pulping and filtration conditions is analyzed. Control combinations that can simultaneously correct insufficient pulping and crushing strength and screen clogging trend are screened to obtain the optimal control parameter combination. Based on the optimal combination of control parameters, and according to the priority of control range from large to small, the adaptive control command with the goal of minimizing the number of control operations is generated, and the turbidity monitoring log of the pomelo juice pulping and filtration process is output.

9. The method according to claim 8, characterized in that, The process of constructing the optimal combination of control parameters includes: Based on the raw material disturbance characteristics, the effects of increasing the pulping speed on the crushing and refining of pomelo seeds and stems and the cutting effect on the peel of the current pomelo raw material are analyzed, and pulping intensity adaptation information is obtained. Based on the pulping strength adaptation information, the smoothness of sieving of crushed pomelo seeds and stem fragments, as well as the entanglement and hindrance characteristics of the cut pericarp, are analyzed to obtain pulping-filtration synergistic correction information. Based on the aforementioned pulping-filtration collaborative correction information, and with the constraint of maintaining continuous operation efficiency, the synchronous correction range of two types of deviations—insufficient pulping and crushing strength and screen clogging—by a single adjustment of the pulping machine speed is analyzed. Control actions that can only improve the pulping effect but aggravate screen clogging are eliminated, and the range of dual-effect control parameters is obtained. Based on the aforementioned dual-effect control parameter range, the optimal control parameter combination is obtained by screening and controlling the parameters that allow continuous operation without additional adjustments to the screen conditions.

10. A turbidity monitoring system for the pulping and filtration process of pomelo juice, characterized in that, The method applied to any one of claims 1-9 includes: The turbidity feature module is used to acquire a grapefruit pulp information set. Based on the grapefruit pulp information set, it determines the normal turbidity features caused by fruit pulp tissue and the abnormal turbidity features caused by non-fruit pulp tissue in the current grapefruit pulp, and obtains a turbidity feature information set. The working condition traceability module is used to acquire the equipment working condition information set. Based on the equipment working condition information set, and using the turbidity characteristic information set as the judgment benchmark, it reverses the deviation of the equipment working condition corresponding to the pulping and filtration process caused by the difference in pomelo raw material fluctuations, and obtains the working condition traceability information set. The adaptive control module is used to generate adaptive control instructions with the goal of minimizing the number of adjustments, based on the working condition traceability information set and with the constraint of maintaining continuous operation efficiency, and outputs turbidity monitoring logs of the pomelo juice pulping and filtration process.

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