Method and system for on-line detection of oil deterioration degree in the frying process of instant noodles

Abstract

The application discloses a method and system for on-line detection of oil deterioration degree in instant noodle frying process, and relates to the field of oil quality detection. The method comprises the following steps: taking the instant noodle attribute information on the instant noodle frying production line as a search constraint, and collecting oil deterioration spatial distribution data set; performing oil deterioration state stratification analysis based on the oil deterioration spatial distribution data set, and constructing stratification decision; configuring stratification bypass oil sample analysis module for stratification deterioration detection of the frying tank on the instant noodle frying production line by taking the stratification decision, and generating stratification deterioration trend; and calling a preset oil change constraint mechanism to make oil change decision of full oil change and partial oil update based on the stratification deterioration trend. The technical problem that the judgment of oil deterioration state is not accurate and timely in the existing instant noodle frying process is solved, the oil deterioration degree is accurately and timely mastered, and the technical effect of reasonable oil change is achieved.

Description

Technical Field

[0001] This application relates to the field of oil quality testing, and in particular to an online detection method and system for the degree of oil deterioration during the frying process of instant noodles. Background Technology

[0002] Accurate detection and timely oil replacement during the frying process of instant noodles are crucial for ensuring product quality, reducing production costs, and improving production line efficiency. Currently, the main method to address this issue is to periodically sample and test relevant indicators of the frying oil to assess its deterioration level and determine when to replace it. However, this method cannot obtain real-time and comprehensive information on the spatial distribution of oil deterioration throughout the frying production line, leading to inaccurate and untimely assessments of oil degradation. This can result in premature oil replacement, wasting resources, or delayed replacement, impacting product quality.

[0003] Currently, the detection of oil degradation during the frying process of instant noodles has technical problems, namely, the inaccuracy and timeliness of judging the state of oil degradation. Summary of the Invention

[0004] This application provides an online detection method and system for the degree of oil degradation during the frying process of instant noodles. It employs several techniques, including first collecting a spatial distribution dataset of oil degradation based on the instant noodle's attribute information, then performing hierarchical analysis to construct hierarchical decision-making, configuring modules according to the hierarchical decision-making to perform hierarchical degradation detection on the frying tank to generate trends, and finally calling a preset mechanism to make oil replacement decisions based on the hierarchical degradation trends. This addresses the technical problems of inaccurate and untimely judgment of oil degradation status in existing instant noodle frying detection methods, achieving a more accurate and timely grasp of the degree of oil degradation, thereby enabling reasonable oil replacement.

[0005] This application provides an online detection method for the degree of oil degradation during the frying process of instant noodles, comprising: collecting a spatial distribution dataset of oil degradation using the attribute information of instant noodles on the instant noodle frying production line as a retrieval constraint; performing a hierarchical analysis of oil degradation status based on the spatial distribution dataset of oil degradation to construct a hierarchical decision; configuring a hierarchical bypass oil sample analysis module with the hierarchical decision to perform hierarchical degradation detection on the frying tanks of the instant noodle frying production line and generating a hierarchical degradation trend; and calling a preset oil replacement constraint mechanism to make an oil replacement decision of full oil replacement or partial oil replacement based on the hierarchical degradation trend.

[0006] In a possible implementation, the following processing is performed: Each layer of the bypass oil sample analysis module includes a sampling port, a filter structure, a constant temperature control structure, and a degradation detection area. The sampling port, filter structure, and degradation detection area are connected through an oil pipeline. The degradation detection area includes sensors for detecting various degradation indicators.

[0007] In a possible implementation, the layered bypass oil sample analysis module is configured using the layered decision to perform layered deterioration detection on the frying tank of the instant noodle frying production line, generating a layered deterioration trend, and performing the following processing: determining the sampling port distribution position corresponding to each layer of bypass oil sample analysis module based on the layered decision, and configuring the layered bypass oil sample analysis module; constructing the oil path topology between the frying tank and each layer of bypass oil sample analysis module in the layered bypass oil sample analysis module; analyzing the temperature loss index distribution topology of the oil sample reaching the deterioration detection area based on the oil path topology; controlling the isothermal control structure to perform temperature loss compensation based on the temperature loss index distribution topology, activating the sensor in the deterioration detection area to detect the degree of oil sample deterioration, and mapping and associating it with the corresponding distribution level to generate the layered deterioration trend.

[0008] In a possible implementation, based on the analysis of the temperature loss index distribution topology of the oil sample reaching the deterioration detection area according to the topology of each oil passage, the following processing is performed: heat dissipation characteristic analysis is performed on each oil passage topology to construct heat dissipation parameters for each oil passage; oil sample attribute information is determined, including oil volume information, flow velocity information in the oil passage, and oil temperature information at the time of sampling; oil passage heat dissipation index analysis is performed by combining the heat dissipation parameters of each oil passage with the oil sample attribute information to generate the temperature loss index distribution topology.

[0009] In a possible implementation, the following process is performed: before compensating for temperature loss by controlling the isothermal control structure based on the temperature loss index distribution topology, the valve of the sampling port is opened at the detection time based on the detection cycle, the oil sample is sampled according to the preset sampling oil volume, and then the valve is closed. Then, the oil sample is filtered, isothermal controlled, and deterioration detected by the stratified bypass oil sample analysis module.

[0010] In a possible implementation, a preset oil change constraint mechanism is invoked to make oil change decisions for full oil change or partial oil replacement based on the layered degradation trend. The following processes are performed: based on the layered degradation trend, a degradation propagation analysis is performed vertically from the bottom layer to the top layer to generate degradation propagation features; real-time degradation indicators for each layer are extracted from the layered degradation trend; the real-time degradation indicators for each layer are matched with the preset oil change constraint mechanism to determine the oil change decision, wherein the preset oil change constraint mechanism includes a joint distribution constraint of layered degradation corresponding to full oil change and partial oil replacement; when the oil change decision is partial oil replacement, partial oil change decision optimization is performed based on the degradation propagation features.

[0011] In a possible implementation, the following processing is performed: the preset oil change constraint mechanism includes a stratified deterioration joint distribution constraint for different oil change amounts corresponding to partial oil replacement, and the stratified deterioration joint distribution constraint is the oil deterioration index threshold for each layer.

[0012] In a possible implementation, when the oil change decision is a partial oil replacement, partial oil change decision optimization is performed based on the degradation propagation characteristics, and the following processes are performed: based on the degradation propagation characteristics, degradation propagation rate analysis is performed between adjacent layers starting from the bottom layer to determine the target adjacent layer whose degradation propagation rate is greater than a preset threshold; the difference between the degradation index of the lower layer in the target adjacent layer and the corresponding safety degradation index is analyzed. If the difference is less than a preset difference, a forced oil change boundary is generated for the lower layer in the target adjacent layer; and partial oil change decision optimization is performed based on the forced oil change boundary.

[0013] In a possible implementation, based on the oil degradation spatial distribution dataset, a stratified analysis of oil degradation status is performed to construct a stratified decision, and the following processing is executed: each data point in the oil degradation spatial distribution dataset is subjected to inter-stratified consistent clustering of degradation status in vertical space according to a preset degradation consistency deviation, generating several inter-stratified clustering results; the stratified results are clustered using the several inter-stratified clustering results, and the stratified clustering result with the largest clustering coefficient is determined to generate the stratified decision.

[0014] This application also provides an online detection system for the degree of oil deterioration during the frying process of instant noodles, including: an oil deterioration spatial distribution dataset acquisition module, used to collect an oil deterioration spatial distribution dataset using the attribute information of instant noodles on the instant noodle frying production line as a retrieval constraint; an oil deterioration state hierarchical analysis module, used to perform hierarchical analysis of oil deterioration state based on the oil deterioration spatial distribution dataset and construct hierarchical decisions; a hierarchical deterioration detection module, used to configure a hierarchical bypass oil sample analysis module with the hierarchical decisions to perform hierarchical deterioration detection on the frying tanks of the instant noodle frying production line and generate hierarchical deterioration trends; and an oil replacement decision module, used to call a preset oil replacement constraint mechanism to make oil replacement decisions for full oil replacement and partial oil replenishment based on the hierarchical deterioration trends.

[0015] The proposed online detection method and system for the degree of oil degradation during the frying process of instant noodles first uses the attribute information of instant noodles on the frying production line as a retrieval constraint to collect a spatial distribution dataset of oil degradation. Then, based on this dataset, a hierarchical analysis of the oil degradation state is performed to construct a hierarchical decision. Next, a hierarchical bypass oil sample analysis module is configured using the hierarchical decision to perform hierarchical degradation detection on the frying tanks of the instant noodle frying production line, generating hierarchical degradation trends. Finally, a preset oil replacement constraint mechanism is invoked to make oil replacement decisions (full replacement or partial replenishment) based on the hierarchical degradation trends. This achieves a more accurate and timely grasp of the degree of oil degradation, thereby realizing the technical effect of reasonable oil replacement. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating the online detection method for the degree of oil deterioration during the frying process of instant noodles, as provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the structure of the online detection system for the degree of oil deterioration during the frying process of instant noodles provided in this application embodiment.

[0019] Figure labeling: Oil deterioration spatial distribution dataset acquisition module 10, oil deterioration state stratification analysis module 20, stratified deterioration detection module 30, oil change decision module 40. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides an online detection method for the degree of oil deterioration during the frying process of instant noodles, such as... Figure 1 As shown, the method includes:

[0024] Step S100: Using the instant noodle attribute information on the instant noodle frying production line as a retrieval constraint, collect the spatial distribution dataset of oil deterioration.

[0025] Specifically, instant noodle attribute information refers to the relevant characteristics of instant noodles during the frying process, such as frying time, frying temperature, and water content. This information is used as a constraint when collecting oil degradation data to more accurately pinpoint the state of oil degradation. The oil degradation spatial distribution dataset refers to the collection of oil degradation-related data collected at different locations within the frying tank (such as different depths or horizontal positions). This data can reflect the spatial differences in oil degradation.

[0026] In the frying tank of the instant noodle frying production line, multiple sensor nodes are evenly arranged along the depth and horizontal directions of the tank. These sensors include temperature sensors, conductivity sensors, optical sensors (such as spectral sensors), and viscosity sensors, used to monitor the physical and chemical properties of the oil in real time. A central data acquisition module (such as an industrial-grade data acquisition card or PLC controller) aggregates and performs preliminary processing on the data collected by each sensor. The data acquisition module can read sensor data at set time intervals (such as per second or per minute) and convert it into digital signals. The collected data is stored in a local industrial computer or cloud server, and simultaneously transmitted to the subsequent analysis system via industrial Ethernet or wireless communication modules (such as Wi-Fi, 4G / 5G).

[0027] For example, in a frying tank that is 5 meters long, 2 meters wide, and 1 meter deep, a layer of sensors is arranged every 0.2 meters along the depth direction. Each layer contains 4 temperature sensors, 2 conductivity sensors, and 1 spectral sensor. The data acquisition module reads the data from all the sensors every 30 seconds and stores it on a local server.

[0028] Step S200: Based on the oil degradation spatial distribution dataset, perform a hierarchical analysis of oil degradation status and construct a hierarchical decision.

[0029] Specifically, the collected oil degradation data is cleaned and normalized to remove noise and outliers. For example, a moving average filtering algorithm is used to smooth the temperature data to eliminate instantaneous fluctuations from the sensor. Features related to oil degradation, such as the rate of temperature change, the rate of change in conductivity, and changes in spectral absorption peaks, are extracted from the preprocessed data. These features serve as inputs for hierarchical analysis, employing machine learning algorithms (such as cluster analysis and decision trees) or rule-based algorithms to perform hierarchical analysis of the oil in the frying tank. Based on the differences in the physical and chemical properties of the oil, the oil in the frying tank is divided into multiple layers, each corresponding to a different degree of degradation.

[0030] For example, using clustering analysis algorithms, the oil in the frying tank is divided into three layers: the upper layer (higher temperature, lower degree of degradation), the middle layer (moderate temperature, moderate degree of degradation), and the lower layer (lower temperature, higher degree of degradation). The boundaries of each layer are determined based on the rate of change of temperature and conductivity.

[0031] In one possible implementation, based on the oil deterioration spatial distribution dataset, a hierarchical analysis of oil deterioration state is performed to construct a hierarchical decision. Step S200 further includes step S210, which involves performing vertical spatial inter-layer consistency clustering of deterioration state for each data point in the oil deterioration spatial distribution dataset according to a preset deterioration consistency deviation, generating several inter-layer clustering results. Specifically, based on the experimental results of oil deterioration, a deterioration consistency deviation threshold is set for each deterioration index to determine whether the deterioration state of oil is consistent between different layers. For example, if the difference between deterioration indices (such as acid value) at two layers is less than the threshold, their deterioration states are considered consistent. According to the preset deterioration consistency deviation, vertical spatial inter-layer consistency clustering of deterioration state is performed on the data at different depths within the frying tank. The specific method is as follows:

[0032] For each pair of adjacent data levels, calculate the difference in degradation indices between them. For example, calculate the difference in acid values. If the inter-level distance is less than a preset degradation consistency deviation, the two levels are grouped into the same cluster; otherwise, they are divided into different clusters. Starting from the bottom level, clustering is performed layer by layer upwards until all levels are grouped into different clusters. Inter-level consistent clustering is performed for each degradation index, generating several inter-level clustering results, each corresponding to one degradation index.

[0033] For example, assuming the frying tank is 1 meter deep, a sensor layer is arranged every 0.2 meters, for a total of 5 layers. The preset degradation consistency deviation is: acid value difference less than 0.1 mg / g, temperature difference less than 2°C, and spectral absorption peak difference less than 0.05. The interlayer clustering results based on acid value are layer scheme 1: {1,2}, {3}, {4}, {5}; the interlayer clustering results based on temperature are layer scheme 2: {1,2}, {3}, {4}, {5}; and the interlayer clustering results based on spectral absorption peaks are layer scheme 3: {1,2}, {3,4,5}.

[0034] Step S220 involves clustering the stratification results using the aforementioned inter-layer clustering results, determining the stratification clustering result with the largest clustering coefficient, and generating the stratification decision. Specifically, features are extracted from each inter-layer clustering result generated in step S210. These features include the size of each cluster, the mean of the degradation index, and the standard deviation of the degradation index. The rationality of the stratification is evaluated by combining the stratification results of multiple degradation indices. Clustering coefficients (such as silhouette coefficients) can be used to evaluate the internal consistency and external separability of each stratification scheme. The stratification scheme with the largest clustering coefficient is selected as the final stratification decision.

[0035] For example, among the three inter-layer clustering results generated above, the clustering coefficient of stratification scheme 1 based on acid value is 0.65, the clustering coefficient of stratification scheme 2 based on temperature is 0.70, and the clustering coefficient of stratification scheme 3 based on spectral absorption peaks is 0.68. Comparing the clustering coefficients of the three stratification schemes, stratification scheme 2 (clustering coefficient of 0.70), which has the largest clustering coefficient, is selected as the final stratification decision. This is because it has the largest silhouette coefficient, indicating that this stratification method performs best in terms of the distinguishability and consistency of the deterioration state. The final stratification decisions are: {1,2}, {3}, {4}, {5}. This implementation method, by comprehensively considering multiple deterioration indicators and evaluating the internal consistency and external separation of each stratification scheme, selects the optimal stratification scheme, thereby more accurately reflecting the deterioration state of the oil in the frying tank and providing a more reliable basis for subsequent oil change decisions.

[0036] Step S300: Using the layered decision configuration layered bypass oil sample analysis module, perform layered degradation detection on the frying tank of the instant noodle frying production line and generate layered degradation trends.

[0037] Specifically, a layered bypass oil sample analysis module is a device used to sample and analyze oil from different layers within a frying tank. It includes sampling pipes, sampling devices, and analysis units, enabling real-time analysis of oil samples from each layer and detection of oil degradation indicators. In one possible implementation, each layer of the layered bypass oil sample analysis module includes a sampling port, a filtration structure, a temperature control structure, and a degradation detection area. The sampling port, filtration structure, and degradation detection area are connected via oil pipes. The degradation detection area includes sensors for detecting various degradation indicators. Specifically, each bypass oil sample analysis module targets a specific layer within the frying tank, and the sampling port is used to collect oil samples from that specific layer. The collected oil samples flow into the filtration structure through the oil pipes. The filtration structure removes impurities and particulate matter from the oil samples, ensuring the accuracy of the detection. The filtration structure can employ multi-stage filtration, such as coarse filtration and fine filtration, to remove impurities of different particle sizes. During testing, the oil sample needs to be kept at a constant temperature to ensure the stability and repeatability of the test results. The temperature control structure maintains the oil sample temperature within a set range using heating or cooling devices. For example, a circulating water bath or electric heater can be used for temperature control. After filtration and temperature control, the oil sample enters the deterioration detection area, which is equipped with various sensors to detect multiple deterioration indicators of the oil. These indicators include acid value, peroxide value, polar substance content, and spectral absorption peak intensity. The sampling port, filtration structure, and deterioration detection area are connected sequentially via oil pipelines. The pipeline materials are selected to be high-temperature resistant and corrosion-resistant, such as stainless steel or polytetrafluoroethylene (PTFE).

[0038] Based on the results of the stratified analysis, a stratified bypass oil sample analysis module is configured. An automatic sampling device (such as a peristaltic pump or solenoid valve) collects oil samples from each stratum at set time intervals (e.g., every 5 minutes) and delivers them to the analysis unit (deterioration detection area). The analysis unit uses equipment such as chromatographs and spectrometers to perform real-time analysis of the oil samples, detecting deterioration indicators (such as acid value, peroxide value, and polar substance content). The deterioration indicator data detected by the analysis unit is processed to generate deterioration trend curves for each stratum. For example, linear regression or moving average algorithms are used to model the changes in deterioration indicators over time, thereby obtaining the deterioration trend.

[0039] For example, in the lower layer of the frying tank, oil samples are collected every 5 minutes, and the content of polar substances in the oil is detected using a spectrometer. The data of polar substance content detected each time is recorded, and a degradation trend curve is generated using a moving average algorithm. If the polar substance content shows an upward trend in three consecutive tests and exceeds a preset threshold, the oil in that layer is considered to have a high degree of degradation.

[0040] In one possible implementation, the stratified bypass oil sample analysis module is configured based on the stratified decision to perform stratified degradation detection on the frying tank of the instant noodle frying production line, generating a stratified degradation trend. Step S300 further includes step S310, determining the sampling port distribution location corresponding to each layer's bypass oil sample analysis module based on the stratified decision, and configuring the stratified bypass oil sample analysis module. Specifically, according to the generated stratified decision (e.g., {1,2}, {3}, {4}, {5}), the specific distribution location of the sampling port corresponding to each layer in the frying tank is determined. Each sampling port is specifically used to collect oil samples from the corresponding layer. According to the stratified decision, a stratified bypass oil sample analysis module is configured for each layer, and each stratified bypass oil sample analysis module includes a sampling port, a filter structure, a constant temperature control structure, and a degradation detection area.

[0041] Step S320: Construct the oil path topology between the frying tank and each layer of the bypass oil sample analysis module in the layered bypass oil sample analysis module. Specifically, design the oil path connection method between the frying tank and the layered bypass oil sample analysis module. Ensure that each sampling port is connected to the corresponding layered bypass oil sample analysis module through an independent oil path pipe.

[0042] Step S330: Analyze the temperature loss distribution topology of the oil sample reaching the degradation detection area based on the topology of each oil path. Specifically, analyze the temperature loss that may occur during the transport of the oil sample from the sampling port to the degradation detection area. Temperature loss is related to factors such as oil path length, pipe material, and ambient temperature. Construct a temperature loss distribution map to show the temperature loss from each sampling port to the degradation detection area.

[0043] Step S340: After temperature loss compensation is performed by the isothermal control structure based on the temperature loss index distribution topology, a sensor is activated in the deterioration detection area to detect the degree of oil sample deterioration and map it to the corresponding distribution level to generate the stratified deterioration trend. Specifically, according to the temperature loss index distribution topology, the temperature of the oil sample is compensated by the isothermal control structure to ensure that the temperature of the oil sample when it reaches the deterioration detection area meets the detection requirements. A sensor is activated in the deterioration detection area to detect the degree of deterioration of the compensated oil sample. The detection results are mapped to the corresponding distribution level to generate the stratified deterioration trend. This implementation method achieves accurate assessment of the deterioration state of oil in the frying tank through precise stratified sampling, temperature loss compensation, and real-time monitoring.

[0044] In one possible implementation, based on the analysis of the temperature loss index distribution topology of the oil sample reaching the deterioration detection area according to the topology of each oil passage, step S330 further includes step S331, performing heat dissipation characteristic analysis on each oil passage topology to construct heat dissipation parameters for each oil passage. Specifically, heat dissipation characteristic analysis is performed on each oil passage topology. Heat dissipation characteristics include the material, length, diameter, and ambient temperature of the oil passage, all of which affect the heat dissipation of the oil sample during transport. Based on the results of the heat dissipation characteristic analysis, heat dissipation parameters for each oil passage are constructed. These heat dissipation parameters can quantify the heat dissipation capacity of the oil passage, for example, by calculating thermal resistance.

[0045] Step S332: Determine the oil sample properties, including oil volume, flow rate within the oil path, and oil temperature at the time of sampling. Specifically, measure or calculate the volume or mass of the oil sample in each oil path. Measure the flow rate of the oil sample within the oil path; flow rate affects heat dissipation, and a higher flow rate can reduce the heat exchange time between the oil sample and the environment. Record the initial temperature of the oil sample at the time of sampling; this is the starting point for temperature loss analysis.

[0046] Step S333: Combine the heat dissipation parameters of each oil path with the oil sample attribute information to perform oil path heat dissipation index analysis, generating the temperature loss index distribution topology. Specifically, the heat dissipation parameters of the oil path are combined with the oil sample attribute information for comprehensive analysis. Based on the heat dissipation parameters and oil sample attribute information, the temperature loss of each oil path is calculated. The temperature loss can be estimated using the heat transfer formula: ,in, R is temperature loss, Q is thermal resistance, and Q is the heat flow rate of the oil sample. This refers to the specific heat capacity of the oil. The calculated temperature loss results are organized into a distribution topology map, showing the temperature loss of each oil path. This approach, through precise heat dissipation characteristic analysis, determination of oil sample property information, and calculation of temperature loss indices, achieves accurate assessment of oil sample temperature changes, improving the accuracy and reliability of temperature compensation.

[0047] In one possible implementation, step S300 further includes step S350, which involves controlling the isothermal control structure to compensate for temperature loss based on the temperature loss index distribution topology before opening the valve of the sampling port at the detection time based on the detection cycle, sampling according to the preset sampling oil volume and then closing the valve, and then using the layered bypass oil sample analysis module to filter, control the temperature and detect the deterioration of the oil sample.

[0048] Specifically, based on a preset detection cycle, the sampling port valve is automatically opened at the detection time. The detection cycle can be set according to production needs and the rate of oil deterioration, for example, sampling every 10 or 30 minutes. After the valve opens, a preset sampling oil volume is collected. The sampling oil volume can be set according to detection needs and the capacity of the oil sample analysis module, for example, 100 mL per sample. After sampling is completed, the sampling port valve is automatically closed to prevent oil leakage and contamination. The collected oil sample is passed through a filtration structure to remove impurities and particulate matter. Based on the temperature loss index distribution topology, a constant temperature control structure is used to compensate for the temperature of the oil sample, ensuring that the temperature of the oil sample when it reaches the deterioration detection area meets the detection requirements. In the deterioration detection area, the sensor is activated to detect the degree of deterioration of the oil sample, and the detection results are mapped and correlated with the corresponding distribution level to generate a stratified deterioration trend. This implementation method, by automatically controlling the opening and closing of the sampling port valve based on the detection cycle, can accurately collect a preset amount of oil sample. This precise sampling control ensures that the amount of oil sample collected each time is consistent, improving the repeatability and reliability of the detection results.

[0049] Step S400: Invoke the preset oil change constraint mechanism to make an oil change decision on full oil change or partial oil replacement based on the described stratified deterioration trend.

[0050] Specifically, the oil replacement constraint mechanism consists of a set of preset oil replacement conditions and rules, used to determine whether to perform a full oil replacement or partial oil replenishment based on the degree of oil deterioration. These constraint mechanisms can be set according to the oil's deterioration indicators, deterioration trends, and production process requirements. A full oil replacement refers to replacing all the oil in the frying tank with new oil, used when the oil's deterioration level is high and its quality cannot be restored through partial oil replenishment. Partial oil replenishment refers to replacing only the portion of oil in the frying tank with the most deteriorated oil and adding new oil. This method can save resources to some extent while ensuring oil quality during the frying process. For example, when the oil deterioration level at a certain level exceeds a preset threshold (e.g., polar substance content exceeds 25%), a full oil replacement decision is triggered; when the oil deterioration level at a certain level is close to the threshold but not exceeded, a partial oil replenishment decision is triggered.

[0051] The system includes an oil change actuator, comprising an oil change valve at the bottom of the frying tank, a waste oil recovery system, and a new oil replenishment system. The oil change valve can be controlled by a PLC controller or an automated control system to achieve precise oil change operations. Based on the oil change decision, the automated control system controls the opening and closing of the oil change valve, discharging the deteriorated oil into the waste oil pool and replenishing the frying tank with new oil through the new oil replenishment system. For a full oil change, all oil in the frying tank is drained; for partial oil replenishment, only the most deteriorated layers of oil are drained, and the corresponding amount of new oil is added.

[0052] For example, when a high level of oil degradation is detected in the lower layer of the fryer, with a polar substance content reaching 28%, a full oil replacement decision is triggered. The automated control system opens the oil replacement valve at the bottom of the fryer, draining all the oil into the waste oil tank, and then replenishes the fryer with new oil through the new oil supply system. For partial oil replacement, only the corresponding lower layer oil replacement valve is opened to drain the lower layer oil and replenish it with new oil.

[0053] In one possible implementation, a preset oil change constraint mechanism is invoked to make oil change decisions for full oil change or partial replacement oil replenishment based on the layered deterioration trend. Step S400 further includes step S410, which performs a vertical deterioration transmission analysis from the bottom layer to the top layer based on the layered deterioration trend, generating deterioration transmission characteristics. Specifically, based on the layered deterioration trend, the transmission of the deterioration state of the oil in the frying tank from the bottom layer to the top layer in the vertical direction is analyzed, and the propagation path and scope of the deterioration state are identified. Based on the results of the deterioration transmission analysis, deterioration transmission characteristics are generated, including the rate of change of deterioration indicators, propagation speed, and scope of influence.

[0054] For example, suppose the stratified degradation trend shows that the oil at the bottom layer is highly degraded, and the degradation gradually propagates upwards. The generated degradation propagation characteristics include: degradation index change rate: the degradation index at the bottom layer increases by 0.2 mg / g (acid value) per hour; propagation speed: the degradation propagates upwards by 0.1 meters per hour; and the scope of influence: the degradation has affected the bottom layer and the adjacent upper layer.

[0055] Step S420: Extract real-time degradation indicators for each layer from the stratified degradation trend. Specifically, extract real-time degradation indicators for each layer from the stratified degradation trend, such as acid value, peroxide value, and polar substance content.

[0056] Step S430: Match the real-time degradation indicators of each layer with the preset oil change constraint mechanism to determine the oil change decision. The preset oil change constraint mechanism includes layered degradation joint distribution constraints corresponding to full oil change and partial oil replacement. Specifically, the layered degradation joint distribution constraints in the preset oil change constraint mechanism correspond to different amounts of replacement oil for partial oil replacement, and the layered degradation joint distribution constraints are the oil degradation indicator thresholds for each layer.

[0057] Specifically, the preset oil change constraint mechanism includes full oil change conditions and partial replacement oil conditions. The full oil change condition is based on a stratified degradation joint distribution constraint. A full oil change is triggered when degradation indicators at multiple levels simultaneously meet preset full oil change thresholds. These thresholds are set considering the joint distribution of degradation indicators at each level. The preset oil change constraint mechanism defines stratified degradation joint distribution constraints for different replacement oil volumes corresponding to partial replacement oil. These constraints are set based on degradation indicator thresholds at each level and are used to determine when to perform partial replacement oil and the specific amount of oil to be replaced.

[0058] The real-time degradation indicators at each level are compared with preset degradation indicator thresholds. If the degradation indicators at multiple levels simultaneously meet the full oil change threshold, a full oil change is triggered. If the degradation indicators at some levels are within the threshold range for partial oil replacement, the specific amount of replacement oil is determined based on the joint distribution constraints of the layered degradation.

[0059] Step S440: When the oil replacement decision is partial oil replacement, the partial oil replacement decision is optimized based on the degradation propagation characteristics. Specifically, the partial oil replacement decision is further optimized based on degradation propagation characteristics (such as the rate of change of degradation indicators, propagation speed, and scope of influence). For example, if the degradation state is mainly concentrated in the bottom layer and the propagation speed is fast, the oil in the bottom layer can be replaced first, and the amount of replacement oil in the upper layer can be appropriately increased. This implementation achieves accurate assessment of the degradation state of the oil in the frying tank and scientific oil replacement decision by matching real-time degradation indicators with preset layered degradation joint distribution constraints and optimizing the partial oil replacement decision based on degradation propagation characteristics.

[0060] In one possible implementation, when the oil change decision is a partial oil replacement, partial oil change decision optimization is performed based on the degradation propagation characteristics. Step S440 further includes step S441, which involves performing degradation propagation rate analysis between adjacent layers starting from the bottom layer based on the degradation propagation characteristics, and determining target adjacent layers whose degradation propagation rate is greater than a preset threshold. Specifically, based on the degradation propagation characteristics, the degradation propagation rate between adjacent layers is analyzed layer by layer, starting from the bottom layer. The degradation propagation rate can be determined by calculating the rate of change of degradation indicators of adjacent layers. The calculated degradation propagation rate is compared with a preset threshold. If the degradation propagation rate is greater than the preset threshold, then this pair of adjacent layers is marked as a target adjacent layer.

[0061] Step S442: Analyze the difference between the degradation index and the corresponding safety degradation index of the lower layer in the target adjacent layer. If the difference is less than a preset difference, a forced oil change boundary is generated for the lower layer in the target adjacent layer. Specifically, for each lower layer in the target adjacent layer, calculate the difference between its degradation index and the corresponding safety degradation index. The safety degradation index refers to the maximum allowable degradation index value of the oil within the normal operating range. If the difference is less than the preset difference, the lower layer is marked as a forced oil change boundary. The preset difference can be set according to actual production needs.

[0062] Step S443 involves optimizing the partial oil change decision based on the mandatory oil change boundary. Specifically, the partial oil change decision is adjusted according to the mandatory oil change boundary, increasing or decreasing the amount of replacement oil based on the boundary, and the oil change operation is performed according to the adjusted amount of replacement oil. This implementation adjusts the predetermined partial oil change decision based on the mandatory oil change boundary, ensuring the scientific and economical nature of the oil change operation. It can dynamically adjust the partial oil change decision according to the actual degradation situation, avoiding insufficient or excessive oil changes.

[0063] This application's embodiments employ technical means such as first collecting a spatial distribution dataset of oil degradation based on instant noodle attribute information, then performing hierarchical analysis to construct hierarchical decisions, then configuring modules according to hierarchical decisions to conduct hierarchical degradation detection of the frying tank to generate trends, and finally calling a preset mechanism to make oil replacement decisions based on the hierarchical degradation trends. This solves the technical problem of inaccurate and untimely judgment of oil degradation status in existing instant noodle frying process detection, achieving a more accurate and timely grasp of the degree of oil degradation, thereby realizing the technical effect of reasonable oil replacement.

[0064] In the above text, refer to Figure 1 A method for online detection of oil deterioration during the frying process of instant noodles according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes an online detection system for the degree of oil deterioration during the frying process of instant noodles, according to an embodiment of the present invention.

[0065] The online detection system for oil degradation during the frying process of instant noodles according to an embodiment of the present invention addresses the technical problems of inaccurate and untimely judgment of oil degradation status in existing instant noodle frying detection methods. It achieves more accurate and timely monitoring of oil degradation, thereby enabling the rational replacement of oil. The online detection system for oil degradation during the frying process of instant noodles includes: an oil degradation spatial distribution dataset acquisition module 10, an oil degradation status hierarchical analysis module 20, a hierarchical degradation detection module 30, and an oil replacement decision module 40.

[0066] The oil deterioration spatial distribution dataset acquisition module 10 is used to collect the oil deterioration spatial distribution dataset using the attribute information of instant noodles on the instant noodle frying production line as a retrieval constraint; the oil deterioration state hierarchical analysis module 20 is used to perform oil deterioration state hierarchical analysis based on the oil deterioration spatial distribution dataset and construct hierarchical decisions; the hierarchical deterioration detection module 30 is used to perform hierarchical deterioration detection on the frying tanks on the instant noodle frying production line using the hierarchical decision configuration hierarchical bypass oil sample analysis module and generate hierarchical deterioration trends; the oil replacement decision module 40 is used to call the preset oil replacement constraint mechanism to make oil replacement decisions for full oil replacement and partial oil replacement based on the hierarchical deterioration trends.

[0067] The specific configuration of the stratified degradation detection module 30 will be described in detail below. As mentioned above, the stratified degradation detection module 30 may further include: each layer of the stratified bypass oil sample analysis module includes a sampling port, a filter structure, a constant temperature control structure, and a degradation detection area. The sampling port, filter structure, and degradation detection area are connected through an oil pipeline. The degradation detection area includes sensors for detecting various degradation indicators.

[0068] The layered decision-making configuration of the layered bypass oil sample analysis module performs layered deterioration detection on the frying tank of the instant noodle frying production line, generating a layered deterioration trend. The layered deterioration detection module 30 may further include: a layered bypass oil sample analysis module configuration unit for determining the sampling port distribution position corresponding to each layered bypass oil sample analysis module based on the layered decision, and configuring the layered bypass oil sample analysis module; an oil path topology construction unit for constructing the oil path topology between the frying tank and each layered bypass oil sample analysis module in the layered bypass oil sample analysis module; a temperature loss index distribution topology analysis unit for analyzing the temperature loss index distribution topology of the oil sample reaching the deterioration detection area based on the oil path topology; and a layered deterioration trend generation unit for controlling the constant temperature control structure to perform temperature loss compensation based on the temperature loss index distribution topology, activating the sensor in the deterioration detection area to detect the degree of oil sample deterioration, and mapping and associating it with the corresponding distribution level to generate the layered deterioration trend.

[0069] The temperature loss index distribution topology analysis unit, which analyzes the temperature loss index distribution topology of the oil sample reaching the degradation detection area based on the topology of each oil path, may further include: a heat dissipation characteristic analysis subunit for analyzing the heat dissipation characteristics of each oil path topology and constructing heat dissipation parameters for each oil path; an oil sample attribute information determination subunit for determining oil sample attribute information, including oil volume information, flow velocity information within the oil path, and oil temperature information at the time of sampling; and an oil path heat dissipation index analysis subunit for combining the heat dissipation parameters of each oil path with the oil sample attribute information to perform oil path heat dissipation index analysis and generate the temperature loss index distribution topology.

[0070] The stratified degradation detection module 30 may further include: a degradation detection unit that, before performing temperature loss compensation based on the temperature loss index distribution topology control of the isothermal control structure, controls the valve of the sampling port to open at the detection time based on the detection cycle, performs sampling according to the preset sampling oil volume, closes the valve, and then uses the stratified bypass oil sample analysis module to filter, control the temperature and perform degradation detection on the oil sample.

[0071] The specific configuration of the oil change decision module 40 will be described in detail below. As mentioned above, the oil change decision module 40 can further include: a degradation propagation analysis unit for performing degradation propagation analysis from the bottom layer to the top layer in the vertical direction based on the layered degradation trend, generating degradation propagation features; a real-time degradation index extraction unit for extracting real-time degradation indices for each layer in the layered degradation trend; an oil change decision determination unit for matching the real-time degradation indices for each layer with the preset oil change constraint mechanism to determine the oil change decision, wherein the preset oil change constraint mechanism includes the layered degradation joint distribution constraint corresponding to full oil change and partial oil change; and a partial oil change decision optimization unit for performing partial oil change decision optimization based on the degradation propagation features when the oil change decision is partial oil change.

[0072] The oil change decision determination unit may further include: a layered deterioration joint distribution constraint in the preset oil change constraint mechanism for different oil change amounts corresponding to partial oil replenishment, wherein the layered deterioration joint distribution constraint is the oil deterioration index threshold of each layer.

[0073] Wherein, when the oil change decision is a partial oil replacement, the partial oil change decision optimization is performed based on the degradation propagation characteristics. The partial oil change decision optimization unit may further include: a degradation propagation rate analysis subunit for performing degradation propagation rate analysis between adjacent layers starting from the bottom layer based on the degradation propagation characteristics, and determining the target adjacent layer whose degradation propagation rate is greater than a preset threshold; a difference analysis subunit for analyzing the difference between the degradation index of the lower layer in the target adjacent layer and the corresponding safety degradation index, and if the difference is less than a preset difference, a forced oil change boundary is generated based on the lower layer in the target adjacent layer; and a partial oil change decision optimization execution subunit for performing partial oil change decision optimization based on the forced oil change boundary.

[0074] The specific configuration of the oil degradation state stratification analysis module 20 will be described in detail below. As mentioned above, based on the oil degradation spatial distribution dataset, the oil degradation state stratification analysis is performed to construct a stratification decision. The oil degradation state stratification analysis module 20 may further include: a degradation state inter-stratification consistent clustering unit used to perform vertical spatial inter-stratification consistent clustering of each data in the oil degradation spatial distribution dataset according to a preset degradation consistency deviation, generating several inter-stratification clustering results; and a stratification decision generation unit used to cluster the stratification results using the several inter-stratification clustering results, determine the stratification clustering result with the largest clustering coefficient, and generate the stratification decision.

[0075] The online detection system for the degree of oil deterioration during the frying process of instant noodles provided in this embodiment of the invention can execute the online detection method for the degree of oil deterioration during the frying process of instant noodles provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0076] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0077] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for on-line detection of the degree of oil deterioration in the frying process of instant noodles, characterized in that, include: Using the attribute information of instant noodles on the instant noodle frying production line as a retrieval constraint, a dataset of spatial distribution of oil deterioration was collected. Based on the aforementioned spatial distribution dataset of oil degradation, a hierarchical analysis of oil degradation status is performed to construct a hierarchical decision-making mechanism. The stratified decision-making configuration stratified bypass oil sample analysis module is used to perform stratified degradation detection on the frying tank of the instant noodle frying production line and generate stratified degradation trends. The oil change decision, which involves invoking a preset oil change constraint mechanism based on the described stratified degradation trend, includes: Based on the aforementioned hierarchical degradation trend, a degradation propagation analysis is performed vertically from the bottom layer to the top layer to generate degradation propagation characteristics; Extract real-time degradation indicators for each layer from the layered degradation trend. The real-time degradation indicators of each layer are matched with the preset oil change constraint mechanism to determine the oil change decision. The preset oil change constraint mechanism includes the layered degradation joint distribution constraint corresponding to full oil change and partial oil replacement. When the oil change decision is a partial oil replacement, the partial oil change decision optimization is performed based on the degradation propagation characteristics. In the preset oil change constraint mechanism, some oil replacement corresponds to different replacement oil amounts, and the layered deterioration joint distribution constraint is the oil deterioration index threshold of each layer. When the oil change decision is a partial oil replacement, the partial oil change decision optimization is performed based on the degradation propagation characteristics, including: Based on the degradation propagation characteristics, degradation propagation rate analysis is performed between adjacent layers starting from the bottom layer to determine the target adjacent layer whose degradation propagation rate is greater than a preset threshold. Analyze the difference between the degradation index of the lower layer in the target adjacent layer and the corresponding safety degradation index. If the difference is less than the preset difference, a forced oil change boundary is generated in the lower layer in the target adjacent layer. Perform partial oil change decision optimization based on the aforementioned forced oil change boundary; The step of using the stratified decision-configuration stratified bypass oil sample analysis module to perform stratified degradation detection on the frying tanks of the instant noodle frying production line and generate stratified degradation trends includes: Based on the hierarchical decision, the sampling port distribution location corresponding to each layer of bypass oil sample analysis module is determined, and the hierarchical bypass oil sample analysis module is configured. Construct the oil path topology between the frying tank and each layer of the bypass oil sample analysis module in the layered bypass oil sample analysis module; Based on the topology of each oil passage, the distribution topology of temperature loss index of oil sample reaching the deterioration detection area is analyzed. After temperature loss compensation is performed based on the temperature loss index distribution topology control isothermal control structure, the sensor is activated in the deterioration detection area to detect the degree of oil sample deterioration, and it is mapped and associated with the corresponding distribution level to generate the layered deterioration trend. The analysis of the temperature loss index distribution topology of the oil sample reaching the deterioration detection area based on the topology of each oil passage includes: The heat dissipation characteristics of each oil circuit topology are analyzed, and the heat dissipation parameters of each oil circuit are constructed. Determine the oil sample properties, including oil volume, flow rate within the oil path, and oil temperature at the time of sampling; By combining the heat dissipation parameters of each oil circuit with the oil sample property information, the heat dissipation index of the oil circuit is analyzed, and the distribution topology of the temperature loss index is generated. Wherein, according to the heat dissipation parameters and the oil sample attribute information, the temperature loss of each oil circuit is calculated.

2. The method for on-line detection of the degree of oil deterioration in the frying process of instant noodles according to claim 1, characterized in that, Each layer bypass oil sample analysis module in the layered bypass oil sample analysis module includes a sampling port, a filtering structure, a constant temperature control structure and a deterioration detection area, the sampling port, the filtering structure and the deterioration detection area are connected through an oil circuit pipeline, and the deterioration detection area includes sensors for detecting various deterioration indicators.

3. The method for on-line detection of the degree of oil deterioration in the frying process of instant noodles according to claim 1, characterized in that, Before the temperature loss compensation of the constant temperature control structure is controlled based on the temperature loss index distribution topology, the valve of the sampling port is opened at the detection time based on the detection cycle, the valve is closed after sampling according to the preset sampling oil amount, and the oil sample is filtered, constant temperature controlled and deteriorated detected by the layered bypass oil sample analysis module.

4. The method for on-line detection of the degree of oil deterioration in the frying process of instant noodles according to claim 1, characterized in that, Based on the oil deterioration space distribution data set, oil deterioration state layering analysis is performed to construct a layering decision, including: According to the preset deterioration consistent deviation, the deterioration state interlayer consistent clustering of each data in the oil deterioration space distribution data set under the vertical space is performed to generate a plurality of interlayer clustering results; The layering results are clustered based on the plurality of interlayer clustering results to determine the layering clustering result with the maximum clustering coefficient, and the layering decision is generated.

5. The system for on-line detection of the degree of oil deterioration in the frying process of instant noodles, characterized in that, The system is used to implement the oil deterioration degree online detection method in the instant noodle frying process according to any one of claims 1-4, and the system comprises: An oil deterioration space distribution data set acquisition module is used to acquire the oil deterioration space distribution data set by taking the instant noodle attribute information on the instant noodle frying production line as a retrieval constraint; An oil deterioration state layering analysis module is used to perform oil deterioration state layering analysis based on the oil deterioration space distribution data set to construct a layering decision; A layered deterioration detection module is used to configure a layered bypass oil sample analysis module for layered deterioration detection of the frying tank on the instant noodle frying production line based on the layering decision to generate a layered deterioration trend; An oil change decision module is used to call a preset oil change constraint mechanism to make an oil change decision of full oil change and partial oil update based on the layered deterioration trend.

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