Starch syrup quality tracing method and system based on time series data analysis

Abstract

The present application relates to the technical field of fluid dynamics data processing, and more particularly to a starch syrup quality tracing method and system based on time series data analysis, which comprises: synchronously collecting absolute temperature, flow rate and quality data in a conveying pipeline, evaluating fluid dynamic viscosity by using an Arrhenius-type exponential model, constructing a flow pattern compensation operator to represent the leading effect of central flow rate relative to average flow rate in combination with Reynolds number, and then calculating dynamic transmission delay by a discrete cumulative algorithm of effective displacement flow rate, performing phase translation on process parameter sequences, and realizing accurate time domain alignment of multi-source time series data and downstream quality data. The present application solves the technical problem that time series signals and physicochemical indexes are misaligned in space-time mapping due to nonlinear fluctuation of starch syrup viscosity-temperature characteristics in the prior art, and can establish a high-precision quality tracing chain under complex variable temperature conditions.

Description

Technical Field

[0001] This invention relates to the field of fluid dynamics data processing technology, and in particular to a method and system for tracing the quality of starch syrup based on time-series data analysis. Background Technology

[0002] Starch syrup, as a product of deep processing of fluid-form agricultural products, involves multiple continuous stages in its production, including liquefaction, saccharification, refining, and concentration. The material is in a continuous flow state within long-distance pipelines and large-capacity reactors, leading to complex evolution of its physicochemical properties over time. Given that starch syrup production is a continuous industrial process, physically mapping the process parameters acquired by distributed sensors to specific production batches is a fundamental step in establishing a quality traceability chain.

[0003] Chinese patent application CN117195483A discloses a spatiotemporal data matching method for the entire hot rolling process of seamless steel pipes. This application calculates the stretching ratio of the material before and after processing based on material velocity and the time points of entry and exit from the equipment, combined with the principle of constant volume. It then matches the recorded production process data to various length positions of the material. This method represents the common logic for mapping continuous production line data in the current industrial field. It establishes a displacement model of the material on the production line to map process parameters to a spatial dimension, providing data support for subsequent quality traceability.

[0004] Considering the non-Newtonian fluid properties of starch syrup, significant fluctuations in flow resistance and uneven velocity distribution occur during pipeline transportation. Due to the velocity gradient and diffusion effect within the fluid, the physical boundary of the material exhibits a dynamic distribution that widens over time when traversing long pipelines. Existing spatiotemporal matching logic is primarily based on displacement calculations at steady velocities, failing to account for the nonlinear shift in starch syrup viscosity caused by fluctuations in absolute temperature. This leads to a misalignment between the time-series signals acquired at sampling points and the actual physicochemical properties of the downstream finished product on the time axis. This increases the probability of inaccurate correlation of quality characteristics, making it difficult to establish high-precision time-series data alignment logic under complex variable-temperature conditions. Summary of the Invention

[0005] To address the technical problem of misalignment between time-series signals and physicochemical indicators in spatiotemporal mapping caused by the nonlinear fluctuations in the viscosity-temperature characteristics of starch syrup, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for tracing the quality of starch syrup based on time-series data analysis, the method comprising the steps of: The physical characteristic data of the conveying pipeline is acquired, and the process parameter data of the starch syrup at several moments in the conveying pipeline are collected simultaneously. The physical characteristic data includes the inner diameter and cross-sectional area of ​​the conveying pipeline, and the process parameter data includes the absolute temperature data, flow rate data, quality data, and fluid density of the starch syrup. Based on the absolute temperature data of the starch syrup, the dynamic viscosity of the starch syrup at each moment is calculated using an Arrhenius exponential model. The average flow rate of the starch syrup is calculated based on the cross-sectional area of ​​the conveying pipeline. Based on the average flow rate, fluid density, and other parameters of the starch syrup at each moment... The dynamic viscosity of the starch syrup and the inner diameter of the delivery pipeline are used to calculate the Reynolds number of the starch syrup at each time point. The flow pattern compensation operator of the starch syrup at each time point is obtained, and the flow pattern compensation operator is positively correlated with the reciprocal of the Reynolds number at the corresponding time point. The effective displacement velocity of the starch syrup is calculated, and the effective displacement velocity is accumulated by a discrete accumulation algorithm to obtain the dynamic transfer delay. The dynamic transfer delay is used to perform phase shift on the time series composed of the absolute temperature data and flow rate data to align it with the time series composed of the quality data in the time domain, thereby obtaining a quality traceability dataset.

[0007] In the long-distance transportation of high-viscosity non-Newtonian fluids such as starch syrup, this invention fully considers the nonlinear mutation of fluid viscosity caused by industrial ambient temperature fluctuations and the resulting advance effect of micro-clusters in the pipeline center. By integrating the Arrhenius model and Reynolds number characteristics, a dynamic flow pattern compensation mechanism is constructed, which then calculates the dynamic transmission delay that reflects the actual transportation state of the material. Under complex temperature variation conditions, the spatiotemporal misalignment between upstream time-series process signals and downstream finished product quality data is eliminated, improving the correlation accuracy of multi-source heterogeneous data in continuous production lines. This lays a reliable data foundation for high-precision product quality traceability and accurate diagnosis of abnormal process parameters.

[0008] Preferably, the step of calculating the dynamic viscosity of the starch syrup at each moment using an Arrhenius exponential model based on the absolute temperature data of the starch syrup includes: obtaining a preset reference absolute temperature, a fluid flow activation energy parameter, and a reference viscosity of the starch syrup; calculating the reciprocal difference between the absolute temperature data at each moment and the reference absolute temperature; multiplying the fluid flow activation energy parameter by the reciprocal difference to obtain an exponential term; and multiplying the power of the exponential term (with the natural constant as the base) by the reference viscosity of the starch syrup to obtain the dynamic viscosity of the starch syrup at each moment.

[0009] This invention introduces a classical fluid dynamics physical model and performs constant transformation extraction, transforming the evolution of the microscopic motion energy barrier of fluids into a mapping logic that depends only on the temperature difference of external macroscopic sensors. This allows for the capture of changes in the ability of high-viscosity syrups to overcome molecular barriers under heater start-up and shutdown or environmental heat dissipation conditions without the need for expensive online microscopic viscosity detection equipment. This improves the engineering feasibility and calculation accuracy of online evaluation of dynamic viscosity parameters in variable temperature scenarios.

[0010] Preferably, the step of calculating the average flow rate of starch syrup based on the cross-sectional area of ​​the conveying pipeline includes: dividing the flow rate data of starch syrup at each time point by the cross-sectional area of ​​the conveying pipeline to obtain the average flow rate of starch syrup at each time point.

[0011] Preferably, the calculation of the Reynolds number of the starch syrup at each time step includes: multiplying the average flow rate, fluid density, and inner diameter of the delivery pipeline at each time step to obtain a first product term; and dividing the first product term at each time step by the dynamic viscosity of the starch syrup at the corresponding time step to obtain the Reynolds number of the starch syrup at each time step.

[0012] This invention integrates engineering parameters such as dynamic viscosity, pipe inner diameter, and flow velocity into physical dimensions using dimensionless classical criterion numbers. This allows for the characterization of the dynamic interplay between inertial and viscous forces as starch syrup flows through a pipe, thereby sensing the perturbation of the flow field morphology by the viscosity-temperature effect. This provides a basis for accurately determining whether the fluid is in a laminar or transitional flow state.

[0013] Preferably, the step of obtaining the flow pattern compensation operator of starch syrup at each time step includes: obtaining a preset profile evolution constant; dividing the profile evolution constant by the Reynolds number at the corresponding time step to obtain a compensation term; and adding the preset constant to the compensation term to obtain the flow pattern compensation operator of starch syrup at each time step.

[0014] This invention introduces a profile evolution constant and combines it with the reciprocal of the Reynolds number to adaptively and dynamically compensate for the non-uniformity caused by the early arrival of central material due to the sharp parabolic distribution of the velocity profile under low Reynolds number conditions at the physical calculation level. It corrects the overall flow rate to an effective flow state characteristic that can reflect the movement of the center of gravity of the material cluster, and eliminates the phase shift error that is unavoidable in the traditional rigid body displacement calculation model in non-Newtonian fluid transport scenarios.

[0015] Preferably, the calculation of the effective displacement velocity of the starch syrup includes: multiplying the average velocity of the starch syrup at each time moment with the flow pattern compensation operator at the corresponding time moment to obtain the effective displacement velocity of the starch syrup at each time moment.

[0016] Preferably, the step of accumulating the effective displacement velocity using a discrete accumulation algorithm to obtain the dynamic transmission delay includes: obtaining the length of the delivery pipeline and a preset sampling period; taking the sampling start time as the start time and performing time stepping according to the sampling period; multiplying the effective displacement velocity corresponding to each step time with the sampling period and accumulating the results; when the accumulated sum is not less than the length of the delivery pipeline for the first time, obtaining the number of steps as the minimum discrete step count; and multiplying the minimum discrete step count with the sampling period to obtain the dynamic transmission delay.

[0017] This invention employs discrete numerical calculations to dynamically transfer delays through iterative accumulation over time. This allows for fine-grained inclusion of flow rate variations caused by sudden temperature drops and flow rate adjustments within each sampling period into the cumulative travel process. This ensures a high degree of physical consistency between the calculated estimated arrival time of the material and the actual characteristic value captured by the online quality analyzer at the end of the pipeline, even under complex and variable industrial conditions.

[0018] Preferably, the step of using the dynamic transmission delay to perform phase shifting on the time series composed of the absolute temperature data and flow data to align it in the time domain with the time series composed of the quality data to obtain a quality traceability dataset includes: shifting the time series composed of the absolute temperature data and flow data along the time axis by the length of the dynamic transmission delay, so as to reorganize the absolute temperature data and flow data with the quality data in the time dimension and construct a quality traceability dataset anchored to the production batch.

[0019] Preferably, the profile evolution constant is positively correlated with the inner wall roughness of the conveying pipeline.

[0020] In a second aspect, the present invention provides a starch syrup quality traceability system based on time-series data analysis. The starch syrup quality traceability system based on time-series data analysis includes a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the starch syrup quality traceability method based on time-series data analysis of the first aspect of the present invention is implemented.

[0021] By adopting the above technical solution, the starch syrup quality traceability method based on time-series data analysis of the first aspect of the present invention is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0022] The beneficial effects of this invention are as follows: This invention targets non-Newtonian fluids such as starch syrup, integrating the Arrhenius exponential model and the Reynolds number criterion to construct an adaptive flow pattern compensation operator for variable temperature conditions. It achieves comprehensive optimization and physical compensation of fluid dynamic parameters from microscopic molecular motion barriers to macroscopic velocity profile gradients within pipes. Dynamic transmission delay is calculated through discrete integration, eliminating the misalignment of multi-source heterogeneous data caused by viscous nonlinear fluctuations. This enables accurate time-domain alignment between upstream sensor process signals and downstream physicochemical indicators, establishing a high-precision digital traceability chain for deep fluid processing. Attached Figure Description

[0023] Figure 1 A flowchart of a starch syrup quality traceability method based on time-series data analysis provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the misalignment of process parameters and quality data before phase compensation, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram showing the alignment of phase-compensated process parameters and quality data provided in an embodiment of the present invention. Figure 4 The structural block diagram of the starch syrup quality traceability system based on time-series data analysis provided in the embodiments of the present invention is shown. Detailed Implementation

[0024] The first aspect of this invention provides a method for tracing the quality of starch syrup based on time-series data analysis, such as... Figure 1 As shown, the method includes steps S1-S4: Step S1: Collect process parameter data of starch syrup in the delivery pipeline.

[0025] It should be noted that the flow state of starch syrup during transportation is affected by temperature and instantaneous flow rate. If the parameter acquisition does not have temporal collinearity, subsequent physical quantity coupling will result in calculation deviations. Considering the high viscosity of starch syrup and the flow delay within the pipeline, this step constructs a unified time reference sampling window to obtain a basic raw dataset reflecting the current physical environment of the fluid, thereby providing a benchmark for the subsequent dynamic correlation of cross-domain parameters.

[0026] Specifically, multiple sensors deployed at the nodes of the starch syrup delivery pipeline continuously collect real-time data at several points within the same sampling time window according to a preset sampling frequency. Before data collection, the physical characteristics of the delivery pipeline section are pre-acquired and configured, including the inner diameter of the pipeline obtained by consulting construction specifications or actual measurements, the pipeline cross-sectional area determined based on the inner diameter, and the fluid density of the current batch of starch syrup measured by concentration calibration or an online mass flow meter.

[0027] During the data collection process, a synchronous pulse triggering mechanism ensures that absolute temperature data, flow rate data, and quality data all have the same time stamp at each sampling moment. The length of the sampling time window is set according to the maximum expected residence time of the material in the conveying pipeline section, ensuring that the collected data sequence can completely cover the entire physical process of the same batch of material flowing from the sampling point to the traceability point.

[0028] The data acquisition process specifically includes: using a resistance temperature detector (RTD) sensor arranged on the inner wall of the conveying pipeline to obtain the Celsius temperature of the fluid, and adding the Celsius temperature to the absolute zero constant 273.15 to convert it into absolute temperature data; using an electromagnetic flow meter arranged coaxially or adjacent to the RTD sensor to obtain the flow rate data of the pipeline cross section; and using an online quality analyzer installed at the outlet of the conveying pipeline or on the detection branch to simultaneously obtain quality data reflecting the characteristics of the material.

[0029] Thus, the absolute temperature time series, flow rate time series, and quality data series were obtained.

[0030] Step S2: Calculate the dynamic viscosity of the starch syrup at each time point.

[0031] It should be noted that, considering the high sensitivity of the internal friction of starch syrup to absolute temperature evolution, this invention preferably employs an Arrhenius exponential model to evaluate dynamic viscosity. This model is a classic physical model describing the viscosity-temperature characteristics of fluids. By establishing an exponential mapping relationship between viscosity and the reciprocal of absolute temperature, it can accurately characterize the nonlinear resistance evolution of high-viscosity fluids during temperature changes. This invention chooses this model precisely because the absolute temperature fluctuations of starch syrup during transportation due to heating or environmental heat dissipation directly cause a shift in viscosity by orders of magnitude. Utilizing its exponential mapping characteristics, it can more accurately capture changes in the fluid's ability to overcome molecular barriers at the microscopic level than linear interpolation, thus providing a robust physical property benchmark for subsequent flow regime identification.

[0032] Specifically, absolute temperature data for each moment is extracted from the absolute temperature sequence to obtain the dynamic viscosity at each moment. The dynamic viscosity satisfies the following relationship: ; in, yes The dynamic viscosity of the starch syrup at any given time; This is the activation energy parameter for fluid flow, which reflects the sensitivity of viscosity to changes in absolute temperature. yes Absolute temperature data at any given time; It is a reference absolute temperature; It is the reference viscosity at a reference absolute temperature, usually taken from the syrup calibration value at the reference absolute temperature.

[0033] This relationship originates from the Arrhenius exponential model in fluid mechanics that describes the viscosity-temperature characteristics of non-Newtonian fluids. Within the framework of molecular kinetic theory, fluid flow requires overcoming the intermolecular energy barrier, the fundamental physical expression of which is... .in, A constant term reflecting the microstructure characteristics of the fluid; It is the apparent activation energy of fluid flow, which characterizes the energy threshold that a molecule must cross to undergo displacement; The gas molar constant; This refers to absolute temperature. The goal is to eliminate microscopic constants that are difficult to measure directly online during the practical evaluation process. This invention introduces a reference state for ratio coupling and defines an equivalent flow activation energy parameter. At the reference absolute temperature, the reference viscosity satisfies... ;exist Absolute temperature data at any given time Below, the dynamic viscosity satisfies By performing a ratio operation on the relationship between the two states mentioned above, the constant term is effectively eliminated. The ratio relationship is obtained as follows: According to the rules of exponentiation, move the denominator term to the numerator and extract the common factor. The above dynamic viscosity evaluation model can then be derived: .

[0034] This relationship transforms the evolution of the fluid's microscopic energy barrier into a mapping logic that depends solely on the difference in external macroscopic absolute temperature, ensuring the physical accuracy of the model in assessing viscosity shifts under variable temperature conditions. Starch syrup, as a high-viscosity fluid, requires overcoming intermolecular potential barriers during its macroscopic flow process. According to statistical mechanics, the kinetic energy of molecular thermal motion varies with absolute temperature. When the absolute temperature deviates from the reference absolute temperature, the energy state required for molecules to overcome the potential barrier changes, leading to a nonlinear evolution of the internal friction force between fluid layers.

[0035] It should be noted that the activation energy parameter of the flow... The value needs to be adjusted according to the actual syrup concentration. Considering that high-concentration syrups have a higher degree of hydrogen bond association and a larger molecular kinetic energy barrier, their viscosity is more sensitive to absolute temperature fluctuations; therefore, a higher flow activation energy benchmark needs to be selected. As a preferred implementation method, for mass concentrations of... Starch syrup within the range, the flow activation energy parameter The value range can be set from 4500K to 6000K; for mass concentrations within... Low-concentration syrups within the range, the The value range can be adjusted down to 3000K to 4000K.

[0036] At this point, the dynamic viscosity at each moment was obtained.

[0037] Step S3: Calculate the flow pattern compensation operator for starch syrup at each time point.

[0038] It should be noted that the flow velocity of starch syrup during pipeline transportation is not uniformly distributed across the pipe cross-section. Due to viscous resistance, the flow velocity is lower at the pipe wall and higher at the center. This velocity gradient causes a time-domain broadening effect in material agglomerations during transport, meaning that the material in the center arrives at the trace point earlier than predicted by the average flow velocity. To quantitatively assess this transport deviation caused by hydrodynamic characteristics, this invention introduces a flow pattern compensation operator, which identifies the current flow pattern and provides a nonlinear compensation benchmark for subsequent time alignment.

[0039] It should be further noted that, for the identification of fluid flow states, this invention preferably uses the Reynolds number to construct the evaluation index. The Reynolds number is a classical dimensionless criterion number in fluid mechanics that characterizes the flow characteristics of fluids. By comprehensively measuring the ratio of inertial force to viscous force, it can objectively reflect the threshold of fluid transition between laminar and turbulent flow. This invention chooses the Reynolds number precisely because the high viscosity of starch syrup makes it prone to being in the laminar or transition region with low Reynolds numbers. At this time, the velocity gradient distribution of the flow profile is more sensitive to viscosity fluctuations. Using the Reynolds number as a medium, the dynamic viscosity and real-time flow rate obtained in the previous steps can be normalized to physical dimensions, thereby accurately capturing the flow field morphology drift caused by the viscosity-temperature effect and avoiding the transmission time deviation caused by calculation based on a single flow rate dimension.

[0040] Specifically, the inner diameter of the delivery pipeline, the fluid density of the starch syrup, the average flow velocity of the fluid in the delivery pipeline, and the dynamic viscosity are substituted into the Reynolds number formula. Calculate the Reynolds number at each time step, where, yes The Reynolds number at time t; It is the fluid density of the starch syrup; yes The average flow velocity of the fluid in the delivery pipeline at any given time, through The flow rate data at any given time is calculated by dividing the cross-sectional area of ​​the delivery pipeline. It is the inner diameter of the delivery pipeline; yes The dynamic viscosity of the starch syrup at any given time.

[0041] It should be noted that, due to the nonlinear viscosity of starch syrup under varying temperature conditions, traditional constant velocity displacement models cannot characterize the leading effect of fluid clusters at the tube axis center. This invention constructs a flow pattern compensation operator, establishing a rate-adjustment operator that dynamically evolves with the flow state. The flow pattern compensation operator senses the degree of suppression of the velocity profile by viscous drag in real time through the Reynolds number, correcting the macroscopic volumetric flow rate to an effective flow pattern characteristic that reflects the movement of the center of gravity of the material clusters, thereby eliminating the phase shift caused by the velocity gradient at the physical level.

[0042] Based on the above logic, the manifold compensation operator satisfies the following relation: ; in, yes A flow pattern compensation operator for time-varying starch syrup is used to characterize the degree to which the central velocity leads the average velocity. It is the profile evolution constant, and its value depends on the roughness of the inner wall of the pipeline and the geometric characteristics of the pipeline; yes The Reynolds number at a given time reflects the ratio of inertial forces to viscous forces in the fluid. It is a preset first tiny value to avoid the denominator being zero, and the preferred value range is... .

[0043] This relationship originates from the pipe velocity profile theory in fluid mechanics and the analytical approximation of the Navier-Stokes equations. Under actual transport conditions, the axial velocity distribution of the fluid evolves with the flow state: in the laminar region at low Reynolds numbers, the velocity profile exhibits a parabolic distribution, with the central velocity reaching up to twice the average velocity; while in the turbulent region at high Reynolds numbers, the profile tends to be flat. To evaluate the impact of this non-uniformity on time shift, this invention constructs a compensation term based on the reciprocal of the Reynolds number: when... As the dynamic viscosity increases at a given time, leading to a decrease in the Reynolds number, the denominator in the equation decreases accordingly, causing the second term to increase and thus boosting the flow pattern compensation operator. This physically corresponds precisely to the dynamic process of the velocity profile evolving from a flat to a conical shape. By introducing a profile evolution constant, this invention can adaptively reflect the broadening intensity of the internal velocity gradient of the fluid under different pipeline environments.

[0044] It should be noted that the value of the profile evolution constant is positively correlated with the physical characteristics of the inner wall of the pipeline. Specifically, the value of the profile evolution constant should be determined based on the roughness of the pipeline's inner wall. Segmentation is performed. As a preferred implementation: when the pipeline is made of finely polished sanitary stainless steel and the inner wall roughness is... When the profile evolution constant is set to a range of 0.4 to 0.6, it represents a relatively flat velocity profile. When the pipeline is a standard industrial-grade pipeline, or when slight fouling occurs due to long-term operation leading to increased inner wall roughness... exist to When the velocity profile evolution constant is between 0.8 and 1.2, it is used to characterize the sharpening of the velocity profile caused by increased resistance. In this embodiment, tracer pulse experiments are performed on pipelines of different materials and service durations to obtain different... The mean residence time deviation of fluids in the environment is used to establish a mapping table between roughness and profile evolution constants. In actual production, the corresponding profile evolution constants are dynamically retrieved from this mapping table based on the current pipeline's calibrated roughness, thus achieving accurate configuration of the flow pattern compensation operator.

[0045] At this point, the manifold compensation operators for each time step have been obtained.

[0046] Step S4: Perform phase compensation based on manifold compensation operator to achieve time-domain alignment.

[0047] It is important to note that the transfer of starch syrup in pipelines is not a simple rigid body displacement. Due to the dynamic shift in viscosity with temperature, the velocity profile within the fluid is constantly evolving nonlinearly. This results in a significant phase difference between the theoretical delay obtained by dividing the length of the traditional conveying pipeline by the average flow velocity and the actual arrival time of the material. This phase difference is the physical cause of the misalignment between the process parameter sequence and the quality data sequence on the time axis, leading to a decrease in traceability accuracy. This step aims to utilize the previously calculated flow pattern compensation operator to perform microscopic compensation for the flow velocity, establishing a dynamic alignment logic based on flow pattern evolution.

[0048] Specifically, this invention first calculates the effective displacement velocity at each time point. The effective displacement velocity satisfies the following relationship: ; in, for The effective displacement velocity of the starch syrup at any given time; for The average velocity of the fluid across the pipe cross-section at any given moment; for The flow pattern compensation operator for time-varying starch syrup.

[0049] It should be noted that the reason for introducing the effective displacement velocity is that in the transportation of non-Newtonian fluids such as starch syrup, what truly determines the quality characteristic transmission cycle is not the overall volumetric flow rate, but the dominant fluid micro-clusters with a leading effect at the center of the pipe. By applying the flow pattern compensation operator as a rate multiplier to the average flow velocity, the macroscopic flow rate can be converted into the actual physical rate characterizing the movement of the material's center of gravity, thereby providing an accurate velocity reference for phase alignment under different temperature zones and viscosity conditions.

[0050] Based on the aforementioned effective displacement velocity, this invention calculates the dynamic transfer delay of material from the sampling point to the traceability point using a discrete accumulation algorithm. The calculation process includes: obtaining the preset sampling period and the pipeline length between the sampling point and the tracing point. From the start time of sampling Begin by searching for the minimum discrete step number that satisfies the following integration criterion. : ; in, Based on the sampling start time At the starting time, in the... The effective displacement velocity at each sampling step point; It is the length of the delivery pipeline; It is the preset sampling period; It is the minimum displacement step count that satisfies the displacement accumulation criterion; in obtaining the minimum displacement step count Then, the dynamic propagation delay is calculated by multiplying the minimum discrete step count by the sampling period. The dynamic propagation delay satisfies the following conditions: .

[0051] It should be added that the above cumulative calculation logic not only considers traffic fluctuations, but also... The flow regime changes caused by temperature fluctuations are taken into account in real time. In this embodiment, when the temperature drop in the pipeline causes a decrease in dynamic viscosity... When the Reynolds number increases sharply, Decrease As the flow rate increases accordingly, the calculated effective flow rate will adaptively compensate for the lead caused by the sharpening of the flow rate profile, thereby ensuring that the calculated arrival time remains physically consistent with the time when the online quality analyzer captures the material characteristic value under complex temperature variations.

[0052] Finally, this invention utilizes the calculated dynamic transmission delay to perform phase shifting on the absolute temperature time series and flow rate time series, and reconstructs the process parameters of the sampling points with the corresponding quality data in the time domain to construct a quality traceability dataset uniquely anchored to each production batch. Thus, this invention not only achieves accurate alignment of multi-source time series data under variable temperature conditions, but also establishes a high-precision traceability chain from upstream process fluctuations to downstream product physicochemical indicators through physical-level phase compensation, providing reliable data support for optimizing the starch syrup production process and diagnosing quality anomalies.

[0053] like Figure 2 As shown in the figure, this is a schematic diagram illustrating the misalignment between process parameters and quality data before phase compensation. In the figure, the horizontal axis represents time, and the vertical axis, from top to bottom, represents the absolute temperature sequence, flow rate sequence, and quality data, respectively. Looking at the key time periods in the figure: in the initial sampling stage when the upstream material just enters the pipeline, a distinct characteristic peak appears on the left side of both the absolute temperature and flow rate sequences, reflecting the initial disturbance of the upstream material. However, due to the complex flow delay during the high-viscosity transport of starch syrup, the corresponding quality data sequence at the end of the pipeline does not change synchronously; the physicochemical characteristic peak of this batch of material is significantly shifted to the right in the quality data sequence in the figure, with a significant time span between it and the upstream process parameter peak, i.e., a dynamic transmission delay. Without phase compensation, the process parameters and quality indicators at the same time point physically belong to different batches of material, leading to inaccurate data correlation and making it difficult to establish an effective traceability chain.

[0054] like Figure 3 As shown in the figure, this diagram illustrates the alignment of process parameters and quality data after phase compensation. In the figure, the horizontal axis represents time, and the vertical axis, from top to bottom, represents the absolute temperature sequence, flow rate sequence, and quality data, respectively. This invention utilizes dynamic transfer delay to shift the process parameter sequence of the sampling points along the time axis by a corresponding length. Figure 3 From the perspective of the recombination effect: after phase compensation, the peak positions of the absolute temperature and flow rates accurately coincide with the quality data on the time axis. This means that the temperature and flow fluctuations captured by the upstream sensors are completely aligned in the time domain with the physicochemical characteristics of the batch of materials captured by the downstream quality analyzer. This alignment eliminates the signal misalignment caused by the nonlinear shift of viscosity-temperature characteristics, demonstrating the reliability and accuracy of the invention in constructing a high-precision quality traceability dataset under complex temperature variations.

[0055] The second aspect of this embodiment provides a starch syrup quality traceability system based on time-series data analysis, such as... Figure 4As shown, the starch syrup quality traceability system based on time-series data analysis includes a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the starch syrup quality traceability method based on time-series data analysis of the first aspect of the present invention is implemented.

[0056] The starch syrup quality traceability system based on time-series data analysis also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0057] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for tracing the quality of starch syrup based on time-series data analysis, characterized in that, include: S1: Acquire physical characteristic data of the conveying pipeline and simultaneously collect process parameter data of starch syrup at several moments in the conveying pipeline; the physical characteristic data includes the inner diameter and cross-sectional area of ​​the conveying pipeline, and the process parameter data includes the absolute temperature data, flow rate data, quality data and fluid density of the starch syrup. S2: Based on the absolute temperature data of the starch syrup, the dynamic viscosity of the starch syrup at each moment is calculated using an Arrhenius exponential model, including: obtaining a preset reference absolute temperature, fluid flow activation energy parameter, and reference viscosity of the starch syrup, wherein the reference viscosity is the reference viscosity at the reference absolute temperature, and the reference viscosity is taken from the syrup calibration value at the reference absolute temperature; calculating the reciprocal difference between the absolute temperature data at each moment and the reference absolute temperature; multiplying the fluid flow activation energy parameter by the reciprocal difference to obtain the exponential term; and multiplying the power of the exponential term (with the natural constant as the base) by the reference viscosity of the starch syrup to obtain the dynamic viscosity of the starch syrup at each moment. S3: Calculate the average flow velocity of the starch syrup based on the cross-sectional area of ​​the delivery pipeline. Based on the average flow velocity of the starch syrup, the fluid density of the starch syrup, the dynamic viscosity of the starch syrup at each time moment, and the inner diameter of the delivery pipeline, calculate the Reynolds number of the starch syrup at each time moment. Obtain the flow pattern compensation operator of the starch syrup at each time moment, including: obtaining a preset profile evolution constant; dividing the profile evolution constant by the Reynolds number at the corresponding time moment to obtain a compensation term; adding the preset constant to the compensation term to obtain the flow pattern compensation operator of the starch syrup at each time moment, wherein the flow pattern compensation operator is positively correlated with the reciprocal of the Reynolds number at the corresponding time moment. S4: Calculate the effective displacement velocity of the starch syrup, accumulate the effective displacement velocity using a discrete accumulation algorithm to obtain the dynamic transfer delay, and use the dynamic transfer delay to perform phase shift on the time series composed of the absolute temperature data and flow rate data to align it with the time series composed of the quality data in the time domain, thereby obtaining a quality traceability dataset.

2. The starch syrup quality traceability method based on time-series data analysis according to claim 1, characterized in that, The calculation of the average flow rate of starch syrup based on the cross-sectional area of ​​the conveying pipeline includes: Divide the flow rate of starch syrup at each time point by the cross-sectional area of ​​the delivery pipeline to obtain the average flow rate of starch syrup at each time point.

3. The starch syrup quality traceability method based on time-series data analysis according to claim 1, characterized in that, The calculation of the Reynolds number of the starch syrup at each time point includes: The first product term is obtained by multiplying the average flow rate of the starch syrup, the fluid density, and the inner diameter of the delivery pipeline at each time point. Dividing the first product term at each time point by the dynamic viscosity of the starch syrup at the corresponding time point yields the Reynolds number of the starch syrup at each time point.

4. The starch syrup quality traceability method based on time-series data analysis according to claim 1, characterized in that, The calculation of the effective displacement velocity of the starch syrup includes: The effective displacement velocity of the starch syrup at each time point is obtained by multiplying the average flow velocity of the starch syrup at the corresponding time point by the flow pattern compensation operator.

5. The starch syrup quality traceability method based on time-series data analysis according to claim 1, characterized in that, The step of accumulating the effective displacement velocity using a discrete accumulation algorithm to obtain the dynamic transmission delay includes: Obtain the length of the delivery pipeline and the preset sampling period; Using the sampling start time as the starting time, time steps are performed according to the sampling period; Multiply the effective displacement velocity corresponding to each step time by the sampling period and accumulate them; When the cumulative sum is not less than the length of the delivery pipeline for the first time, the number of steps is obtained as the minimum number of steps. The dynamic propagation delay is obtained by multiplying the minimum discrete step count by the sampling period.

6. The starch syrup quality traceability method based on time-series data analysis according to claim 1, characterized in that, The step of using the dynamic transmission delay to perform phase shifting on the time series composed of the absolute temperature data and flow data to align it in the time domain with the time series composed of the quality data, thereby obtaining a quality traceability dataset, includes: The time series composed of the absolute temperature data and flow rate data is shifted along the time axis by the length of the dynamic transmission delay, so as to reorganize the absolute temperature data and flow rate data with the quality data in the time dimension and construct a quality traceability dataset anchored to the production batch.

7. The starch syrup quality traceability method based on time-series data analysis according to claim 1, characterized in that, The profile evolution constant is positively correlated with the inner wall roughness of the delivery pipeline.

8. A starch syrup quality traceability system based on time-series data analysis, characterized in that, The starch syrup quality traceability system based on time-series data analysis includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the starch syrup quality traceability method based on time-series data analysis according to any one of claims 1-7.

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