An intelligent digital-based grease production control method and system

Abstract

The present invention relates to the technical field of oil production, and particularly to an oil production control method and system based on intelligent digitization. The method includes the following steps: obtaining oil material data, and analyzing processing requirements based on the oil material data to obtain oilseed processing requirement data; obtaining a hydraulic press model; inputting the oilseed processing requirement data into the hydraulic press model, performing pressing simulation, and counting the low oil extraction rate for the pressing simulation result to obtain the low oil extraction rate; analyzing oilseed impurities of the oil material according to the low oil extraction rate to generate oilseed impurity data; analyzing the accumulation of filter grease in the hydraulic press model according to the oilseed impurity data to generate filter grease accumulation data; obtaining the pressing data of a screw press and performing analysis of the oil residue content to obtain high oil residue content data. The present invention improves the oil extraction rate and production efficiency based on oil production technology.

Description

Technical Field

[0001] The present invention relates to the technical field of oil production, and particularly relates to an oil production control method and system based on intelligent digitization. Background Art

[0002] Traditional methods rely on manual operation and empirical judgment, lacking intelligent real-time data feedback and automatic adjustment. As a result, control parameters such as temperature and pressure during the production process cannot accurately match the processing requirements of oil materials, thus affecting the oil extraction rate and production efficiency. Due to the lack of intelligent oil production scheduling, energy and material waste often occur during the production process. Especially in the oil processing and pressing processes, various parameters are not optimized according to real-time data, resulting in high production costs. In the traditional oil production process, the control of temperature and pressure depends on preset rules or experience, and cannot be adjusted individually according to the actual needs of different batches of oil materials, easily leading to unstable oil quality and affecting the quality of the final product. During the oil production process, the problem of oil accumulation in the filter is often not monitored and controlled in a timely manner, resulting in equipment blockage and affecting the continuity and efficiency of production. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an oil production control method and system based on intelligent digitization to solve at least one of the above technical problems.

[0004] To achieve the above object, an oil production control method based on intelligent digitization includes the following steps:

[0005] Step S1: Obtain oil material data, and conduct a processing requirement analysis based on the oil material data to obtain oil material processing requirement data;

[0006] Step S2: Obtain a hydraulic press model; input the oil material processing requirement data into the hydraulic press model and conduct a pressing simulation, and conduct a low oil extraction rate statistics on the pressing simulation results to obtain a low oil extraction rate; conduct an oil material impurity analysis on the oil material according to the low oil extraction rate to generate oil material impurity data; conduct a filter oil accumulation analysis on the hydraulic press model according to the oil material impurity data to generate filter oil accumulation data;

[0007] Step S3: Obtain the pressing data of a screw oil press and conduct an oil residue content analysis to obtain high oil residue content data; conduct an intelligent temperature control for pressing according to the high oil residue content data to obtain intelligent temperature control data for pressing;

[0008] Step S4: Conduct an intelligent production scheduling according to the oil material processing requirement data and the filter oil accumulation data to obtain intelligent production data; upload the intelligent production data and the intelligent temperature control data for pressing to an intelligent control system to execute the oil production control task.

[0009] By obtaining grease material data and conducting processing requirement analysis, the present invention can achieve accurate identification of the characteristics of grease materials, provide a data basis for the optimization of subsequent process parameters, help formulate a more reasonable processing plan according to the material characteristics, and reduce unnecessary resource waste. Using a hydraulic press model for pressing simulation, statistical analysis can be carried out on the oil extraction rate under different pressing conditions to quickly discover the problem of low oil extraction rate, and generate oil impurity data through a detailed analysis of oil impurities, providing a targeted improvement direction for further optimizing the pressing process. Combining the analysis of grease accumulation in the filter can accurately monitor the grease accumulation inside the filter, so as to carry out maintenance and cleaning in a timely manner, avoid equipment blockage problems caused by accumulation, and ensure the continuity and efficiency of the production process. Obtaining the pressing data of the screw oil press and analyzing the oil residue content can effectively evaluate the change in the residue content during the oil extraction process, dynamically adjust the intelligent temperature control during the pressing process according to the analysis results, further improve the oil extraction efficiency and reduce energy consumption. At the same time, by realizing intelligent production scheduling and combining the oil material processing requirement data with the grease accumulation data in the filter, the allocation of production tasks and resource utilization can be optimized, avoiding equipment overload or idle problems, and significantly improving the overall production efficiency. In addition, uploading the intelligent production data and the intelligent temperature control data for pressing to the intelligent control system, through the automatic control of the system, ensures the efficiency and accuracy of the production process, and further improves the quality of the grease product and the economy of production.

[0010] Optionally, step S1 is specifically as follows:

[0011] Step S11: Obtain grease material data and conduct spectral analysis to obtain grease spectral data;

[0012] Step S12: Evaluate the grease content based on the grease spectral data to obtain grease content data;

[0013] Step S13: Measure the moisture content of the grease content data to obtain moisture content data;

[0014] Step S14: Analyze the impurity content based on the moisture content data to obtain impurity content data;

[0015] Step S15: Analyze the oil material processing requirements based on the impurity content data to obtain oil material processing requirement data.

[0016] The present invention obtains spectral data of oil materials through spectral analysis, which can accurately judge the composition and quality characteristics of oils, avoiding the disadvantages of relying on manual experience judgment in traditional methods and providing more accurate raw material information. This process provides a scientific basis for subsequent oil content evaluation, can monitor the true oil content in real time, ensures more efficient use of raw materials in the production process, and avoids waste or shortage caused by inaccurate oil content. By measuring the moisture content of the oil content data, the moisture state of the oil materials can be further judged, providing an important reference for subsequent impurity content analysis, helping to accurately identify the moisture content in the materials, and avoiding the influence of excessive or insufficient moisture on the pressing process. On this basis, based on the moisture content data for impurity content analysis, the impurity content of the oil materials can be effectively evaluated, thus providing a basis for the demand analysis of oilseed processing. Avoiding the neglect of impurity content in traditional methods makes the oilseed processing requirements more accurate, reducing the phenomenon of reduced oil extraction rate or production efficiency due to impurity problems. Through intelligent analysis, the processing parameters can be adjusted in a timely manner according to the actual needs of the oilseeds, effectively improving the oil extraction rate and production efficiency, reducing energy and material waste, and ensuring the high efficiency, stability and sustainability of the production process.

[0017] Optionally, step S11 is specifically as follows:

[0018] Step S111: Obtain oil material data and perform near-infrared spectroscopy sampling to obtain near-infrared spectral data of the oil;

[0019] Step S112: Perform baseline correction on the near-infrared spectral data of the oil to obtain corrected near-infrared spectral data of the oil;

[0020] Step S113: Obtain oil absorption peak data;

[0021] Step S114: Perform position identification on the corrected near-infrared spectral data of the oil according to the oil absorption peak data to obtain oil absorption peak position data;

[0022] Step S115: Perform intensity statistics on the oil absorption peak position data to obtain oil absorption peak intensity data;

[0023] Step S116: Construct an oil content prediction model according to the oil absorption peak intensity data to obtain an oil content prediction model;

[0024] Step S117: Predict the oil content of the oil material data according to the oil content prediction model to obtain oil content data;

[0025] Step S118: Perform spectral measurement on the oil content data to obtain oil spectral data.

[0026] The present invention can accurately capture the chemical properties of fats and oils by acquiring data of fat and oil materials based on near-infrared spectroscopy sampling, avoiding the limitations of relying on manual experience in traditional methods and ensuring high-precision data acquisition. Baseline correction is performed on the near-infrared spectroscopy data of fats and oils to eliminate the interference caused by equipment or environmental changes, improving the reliability and accuracy of the spectroscopy data and providing a clearer basis for subsequent analysis. By acquiring the absorption peak data of fats and oils and identifying their positions, the key component characteristic peaks in fats and oils can be accurately located, further improving the precision and pertinence of fat and oil analysis. The statistics of the absorption peak intensity provide important data support for establishing an accurate fat and oil content prediction model, enabling the model to be optimized based on actual measurement results, thereby improving the accuracy of fat and oil content prediction. By applying the fat and oil content prediction model, the actual content of fat and oil materials can be accurately predicted, reducing the errors in manual measurement and providing a scientific basis for the subsequent processing of fats and oils. At the same time, based on the accurate results of the fat and oil content prediction model, spectral measurement can further verify the accuracy of the prediction, ensuring the efficiency and stability of the finally obtained fat and oil spectroscopy data. Through intelligent analysis and real-time data feedback, various operations in the production process are optimized, effectively avoiding the disadvantages of being unable to accurately adjust parameters in traditional methods, improving the fat and oil extraction rate, production efficiency and reducing energy and material waste.

[0027] Optionally, step S112 is specifically as follows:

[0028] Identify the region without absorption peaks in the near-infrared spectroscopy data of fats and oils to obtain the data of the region without absorption peaks;

[0029] Select baseline points according to the data of the region without absorption peaks to obtain baseline point data;

[0030] Obtain a cubic polynomial fitting model;

[0031] Perform polynomial baseline fitting on the baseline point data according to the cubic polynomial fitting model to generate polynomial fitting baseline data;

[0032] Perform baseline correction on the near-infrared spectroscopy data of fats and oils according to the polynomial fitting baseline data to obtain the corrected near-infrared spectroscopy data of fats and oils.

[0033] By identifying the absorption - free peak regions in the near - infrared spectral data of oils and fats, the present invention can effectively exclude unnecessary data caused by noise or interference factors in the spectral data, improving the accuracy of data analysis. The acquisition of absorption - free peak region data provides a scientific basis for further baseline point selection, avoiding the fuzzy judgment relying on manual experience in traditional methods and ensuring more accurate selection of baseline points. Through the application of a cubic polynomial fitting model, accurate polynomial baseline fitting can be performed on the baseline point data, thus avoiding the errors caused by simple linear fitting and improving the accuracy and stability of baseline correction. Based on this accurately polynomial - fitted baseline data, further baseline correction of the near - infrared spectral data of oils and fats is carried out, making the final spectral data of oils and fats more accurate after removing baseline drift and other interference factors, providing a reliable data basis for subsequent oil and fat component analysis. It not only optimizes the data - processing process in traditional methods, avoiding human operation errors, but also can be automatically adjusted according to real - time data, improving the processing efficiency of oil and fat materials in the production process, avoiding the defects that control parameters such as temperature and pressure in traditional methods cannot be accurately matched, and thus improving the oil extraction rate, production efficiency and the quality of the final product.

[0034] Optionally, step S2 is specifically as follows:

[0035] Step S21: Obtain a hydraulic press model;

[0036] Step S22: Input the oil - processing requirement data into the hydraulic press model, perform pressing simulation, and conduct low - oil - extraction rate statistics on the pressing simulation results to obtain the low - oil - extraction rate;

[0037] Step S23: Screen the oil and fat materials according to the low - oil - extraction rate to obtain low - oil - content materials;

[0038] Step S24: Conduct impurity mass spectrometry analysis on the low - oil - content materials to generate an impurity mass spectrometry diagram;

[0039] Step S25: Identify high - impurity oil and fat materials from the low - oil - content materials according to the impurity mass spectrometry diagram to obtain high - impurity oil and fat materials;

[0040] Step S26: Conduct filter oil accumulation analysis on the hydraulic press model according to the high - impurity oil and fat materials to generate filter oil accumulation data.

[0041] The present invention can accurately simulate the processing process of oil materials by obtaining a hydraulic press model and inputting oil processing requirement data into the model for pressing simulation, ensuring the optimization of oil extraction rate and production efficiency during the pressing process. The statistics of low oil extraction rate in the pressing simulation results help to quickly identify low oil materials at the initial stage of production and eliminate them, avoiding inefficient materials from participating in subsequent processing and improving the overall oil extraction effect. Screening low oil materials further improves the selectivity of oil materials and ensures the quality of materials in the subsequent processing. Impurity mass spectrometry can accurately detect the impurity components in oil materials, generate detailed impurity mass spectra, provide more accurate data support, and help to effectively identify high-impurity oil materials. This process can timely detect and screen out high-impurity oil materials, thus avoiding production interruption or equipment damage caused by impurity problems. By identifying high-impurity oil materials and combining with the analysis of oil accumulation in the filter, it is possible to predict and pre-process the oil accumulation problem in the filter in advance, avoid equipment blockage, and keep the production line running smoothly. Finally, these steps optimize the oil production process through intelligent analysis and processing, reduce manual intervention, significantly improve production efficiency, reduce production costs, and ensure the stability and quality of oil quality.

[0042] Optionally, step S24 is specifically as follows:

[0043] Step S241: Directly perform mass spectrometry on the low oil material using a mass spectrometer to obtain low oil material mass spectrometry data;

[0044] Step S242: Identify the parent ions from the low oil material mass spectrometry data to obtain parent ion data;

[0045] Step S243: Obtain a parent ion spectral library;

[0046] Step S244: Compare the parent ions of the emulsion residues with the parent ion data according to the parent ion spectral library to obtain emulsion residue data;

[0047] Step S245: Identify the mass-to-charge ratio from the low oil material mass spectrometry data according to the emulsion residue data to obtain mass-to-charge ratio data;

[0048] Step S246: Identify the intensity peaks from the emulsion residue data according to the emulsion residue data to obtain intensity peak data;

[0049] Step S247: Draw an impurity mass spectrum according to the mass-to-charge ratio data and the intensity peak data to obtain an impurity mass spectrum.

[0050] Through direct mass spectrometry analysis of low-fat materials using a mass spectrometer, the present invention can accurately obtain the mass spectrometry data of the materials, further providing detailed information for subsequent analysis. Identifying precursor ions in the mass spectrometry data helps quickly determine the main components in the materials, laying a foundation for the next component analysis. By obtaining a precursor ion spectral library and comparing it with the data, emulsified residue can be accurately identified, thereby deeply understanding the impurity components in the fat materials. Obtaining emulsified residue data can specifically identify potential problems in the fat materials and provide a strong basis for optimizing the production process. On this basis, identifying the mass-to-charge ratio of the mass spectrometry data of low-fat materials can help extract the mass and charge information of substances, thereby further analyzing the component distribution and structural characteristics in the materials. Identifying intensity peaks helps quantify the distribution and concentration of impurities, providing specific data support for subsequent impurity treatment. Finally, by combining the mass-to-charge ratio data with the intensity peak data to draw an impurity mass spectrometry diagram, the impurity components in the materials can be comprehensively displayed, providing accurate visual data for fat purification and quality control in the production process, ensuring the stability and high-quality output of fat materials during the processing. It effectively reduces manual intervention, improves the analysis accuracy, ensures the efficient, accurate and low-cost operation of the production process, and at the same time guarantees the stability of fat quality.

[0051] Optionally, step S26 is specifically as follows:

[0052] Step S261: Put the high-impurity fat material into a hydraulic press model and conduct filtration simulation to generate hydraulic press filtration data;

[0053] Step S262: Conduct efficiency statistics on the hydraulic press filtration data to obtain low-efficiency hydraulic press filtration data;

[0054] Step S263: Obtain filter data and a filter blockage threshold;

[0055] Step S264: According to the low-efficiency hydraulic press filtration data, conduct real-time monitoring of surface accumulation of the filter data to obtain filter surface accumulation data;

[0056] Step S265: Calculate the accumulation rate of the filter surface accumulation data to obtain the filter accumulation rate;

[0057] Step S266: According to the filter blockage threshold, conduct blockage classification on the filter surface accumulation data to obtain filter surface blockage data;

[0058] Step S267: Conduct rate statistics on the filter accumulation rate to obtain high-rate filter accumulation data;

[0059] Step S268: Conduct blockage degree statistics on the filter surface blockage data to obtain filter surface severe blockage data;

[0060] Step S269: Perform an intersection operation on the grease accumulation data based on the data accumulated by the high-rate filter and the data indicating severe clogging on the filter surface to generate filter grease accumulation data.

[0061] By putting high-impurity grease materials into a hydraulic press model and conducting filtration simulation, the present invention can obtain accurate hydraulic press filtration data, providing a scientific basis for subsequent operations and optimizations. Conducting efficiency statistics on these data can identify low-efficiency filtration situations, thereby further optimizing the production process and reducing resource waste. By obtaining filter data and setting a filter clogging threshold, an early warning can be provided for clogging problems occurring in actual production. Conducting real-time monitoring of the surface accumulation of low-efficiency hydraulic press filtration data helps to track the grease accumulation situation of the filter in real time, providing dynamic feedback on problems during the filtration process. Calculating the rate of the accumulation data can accurately evaluate the speed of grease accumulation, thereby helping to predict in advance and take preventive measures to avoid the occurrence of clogging problems. Dividing the accumulation data based on the filter clogging threshold enables accurate identification of the areas causing equipment clogging in actual operations, providing effective guidance for equipment maintenance. Conducting statistical analysis on the accumulation data of high-rate filters can promptly detect filters with excessive accumulation and take effective measures to reduce equipment wear. At the same time, by conducting statistics on the clogging degree of the filter surface clogging data, the operating state of the equipment can be accurately evaluated, thereby helping to arrange cleaning and maintenance in a timely manner to ensure the stability of the production process. Finally, through the intersection operation of grease accumulation, the overall accumulation situation of the filter can be comprehensively evaluated, providing a scientific decision for improving filtration efficiency and equipment life, ensuring the efficient and stable operation of equipment during the grease production process, reducing production costs, and enhancing grease extraction efficiency and product quality. By making full use of data analysis and real-time monitoring, the grease treatment and pressing processes are optimized, realizing intelligent and automated management, reducing manual intervention, and enhancing the sustainability and stability of production.

[0062] Optionally, step S3 is specifically as follows:

[0063] Step S31: Obtain the pressing data of the screw press and collect grease samples of the screw press based on the pressing data of the screw press to obtain grease samples of the screw press;

[0064] Step S32: Use a Soxhlet extractor to extract oil residues from the grease samples of the screw press to obtain oil residue samples;

[0065] Step S33: Conduct statistics on the oil residue content based on the oil residue samples to obtain high oil residue content data;

[0066] Step S34: Conduct intelligent temperature control for pressing based on the high oil residue content data to obtain intelligent temperature control data for pressing.

[0067] By acquiring the pressing data of a screw oil press and collecting oil samples based on these data, the present invention can accurately obtain representative samples, providing a basis for subsequent oil quality analysis. Using a Soxhlet extractor to extract oil residues from the oil samples can effectively separate the oil from the oil residues, ensuring that the extracted oil is purer, further improving the extraction rate and quality of the oil. Conducting a statistical analysis of the oil residue content in the oil residue samples can accurately understand the situation of the oil residue content, thereby providing data support for subsequent process optimization and avoiding excessive oil residues from affecting the oil quality. By implementing intelligent temperature control for pressing according to the high oil residue content data, the temperature control during the pressing process can be dynamically adjusted according to the actual oil residue content, ensuring that the temperature matches the actual requirements of the oil material, thereby improving the extraction efficiency of the oil and the quality of the final product. By introducing an intelligent temperature control system, various parameters in the production can be adjusted in real time, avoiding energy waste and fluctuations in oil quality caused by inaccurate temperature control, reducing the dependence on manual operations, and enhancing the stability and efficiency of production. It effectively combines real-time data feedback and automatic adjustment, not only optimizing the production process, reducing production costs, but also enhancing the overall extraction efficiency of the oil, ensuring the high efficiency, energy conservation, and sustainability of the production process.

[0068] Optionally, step S34 is specifically as follows:

[0069] Step S341: Analyze the pressing conditions based on the high oil residue content data to obtain pressing condition data;

[0070] Step S342: Evaluate the temperature adjustment requirements for the pressing condition data to obtain temperature adjustment requirement data;

[0071] Step S343: Design an intelligent temperature control algorithm based on the temperature adjustment requirement data to generate temperature control algorithm data;

[0072] Step S344: Conduct a simulation of intelligent temperature control for pressing according to the temperature control algorithm data to obtain intelligent temperature control data for pressing.

[0073] Through the analysis of the pressing conditions based on the high oil residue content data, the present invention can accurately understand the processing requirements of different materials, and then obtain the optimal conditions required during the pressing process, which provides a scientific basis for the subsequent optimization of the pressing process. By evaluating the temperature adjustment requirements for the pressing condition data, the processing requirements of various materials at different temperatures can be quantified, providing data support for formulating a precise temperature control plan and effectively avoiding the problem of inaccurate temperature control caused by manual operation. Based on these requirement data, an intelligent temperature control algorithm is designed, which can automatically generate the most suitable temperature control plan according to the real-time temperature adjustment requirements, so as to ensure a more precise matching between the temperature and the oil materials, improve the oil extraction rate and reduce the production cost. By simulating the temperature control algorithm, its effect can be verified and further optimized, and finally a reliable temperature control plan is generated to ensure that in actual production, the temperature control can always meet the processing requirements of different oil materials, avoid the instability of the oil quality caused by temperature fluctuations, improve the production efficiency at the same time, reduce the waste of energy and materials, and thus realize an intelligent, energy-saving and efficient production process.

[0074] Optionally, the present specification also provides an intelligent digital-based oil production control system for executing an intelligent digital-based oil production control method as described above. The intelligent digital-based oil production control system includes:

[0075] A processing requirement analysis module, configured to obtain oil material data and perform processing requirement analysis based on the oil material data, so as to obtain oil material processing requirement data;

[0076] An oil material impurity analysis module, configured to obtain a hydraulic press model; input the oil material processing requirement data into the hydraulic press model and perform pressing simulation, and perform low oil extraction rate statistics on the pressing simulation results to obtain a low oil extraction rate; perform oil material impurity analysis on the oil material according to the low oil extraction rate to generate oil material impurity data; perform filter oil accumulation analysis on the hydraulic press model according to the oil material impurity data to generate filter oil accumulation data;

[0077] A pressing intelligent temperature control module, configured to obtain screw press pressing data and perform oil residue content analysis, so as to obtain high oil residue content data; perform pressing intelligent temperature control according to the high oil residue content data to obtain pressing intelligent temperature control data;

[0078] An intelligent production scheduling module, configured to perform intelligent production scheduling according to the oil material processing requirement data and the filter oil accumulation data to obtain intelligent production data; upload the intelligent production data and the pressing intelligent temperature control data to the intelligent control system to execute the oil production control task.

[0079] An intelligent digital-based grease production control system of the present invention can implement any intelligent digital-based grease production control method of the present invention, which is a medium for coordinating operations and signal transmission between various modules to complete the intelligent digital-based grease production control method. The internal modules of the system cooperate with each other, thereby improving the grease extraction rate and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-restrictive embodiments with reference to the following drawings:

[0081] Figure 1 It is a schematic flowchart of the steps of the intelligent digital-based grease production control method of the present invention;

[0082] Figure 2 It is a detailed schematic flowchart of step S1 in the present invention;

[0083] Figure 3 It is a detailed schematic flowchart of step S2 in the present invention;

[0084] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0086] In addition, the drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0087] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0088] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an intelligent digital-based grease production control method, and the method includes the following steps:

[0089] Step S1: Obtain grease material data, and perform processing requirement analysis based on the grease material data to obtain oil material processing requirement data;

[0090] In this embodiment, the grease material data is obtained from the grease production line system, including but not limited to information such as the type, weight, grease content, moisture content, and impurity content of the grease material. The obtained raw data is collected through instruments such as spectrometers and near-infrared spectrometers, especially near-infrared spectral data, for analyzing the grease content. The grease content is analyzed by the absorption peak position and intensity of the spectral data. When specifically calculating, it is necessary to identify the characteristic wavelengths and corresponding absorption intensity values in the spectral data. According to the grease content data, oil material processing requirement analysis is performed through the established linear regression model to obtain the temperature, time, and pressure parameters required for oil material processing. These requirement data need to be represented in specific numerical forms. For example, if the grease content is higher than 85%, the processing temperature can be set to 55°C, and if the content is lower than 15%, the processing temperature can be set to 70°C.

[0091] Step S2: Obtain a hydraulic press model; input the oil material processing requirement data into the hydraulic press model, perform pressing simulation, and perform low grease extraction rate statistics on the pressing simulation results to obtain the low grease extraction rate; perform oil material impurity analysis on the grease material according to the low grease extraction rate to generate oil material impurity data; perform filter grease accumulation analysis on the hydraulic press model according to the oil material impurity data to generate filter grease accumulation data;

[0092] In this embodiment, first, a physical model of the hydraulic press is obtained, and the oil processing requirement data is input into this model. The input parameters of this model include oil type, oil content, impurity content, processing temperature, pressure, etc. These input data will be used to simulate the pressing process. During the simulation process, the hydraulic press model calculates the oil extraction rate according to the given pressure and temperature conditions. For the calculation of the extraction rate, a threshold standard needs to be set. For example, when the extraction rate is lower than 70%, it is considered that the oil extraction efficiency of this batch is unqualified. According to the pressing simulation results, a low oil extraction rate statistics is carried out. Specifically, during the statistics, the extraction rate of each processing batch needs to be statistically analyzed to obtain the average value and compared with the standard value. The low oil extraction rate results are used to analyze the impurities in the oil material. During the analysis process, based on mass spectrometry technology, a quantitative analysis of the impurity components in the oil material is carried out to generate oil material impurity data. Specifically in the operation, the mass spectrometry of the sample is obtained by a mass spectrometer, the main impurity substances are identified, and their contents are calculated. If the content of a certain impurity exceeds the set critical value (such as the impurity content exceeds 5%), further treatment or screening is carried out. According to the oil material impurity data, the analysis of the grease accumulation in the filter of the hydraulic press model is continued. This analysis requires simulating the accumulation process of grease in the filter to obtain the rate and degree of grease accumulation. During the simulation process, physical parameters such as the viscosity of the grease, the flow rate, and the pore size of the filter are used to simulate the deposition process of the grease on the filter surface. Finally, the filter grease accumulation data is generated, and this data is used for subsequent production scheduling decisions.

[0093] Step S3: Obtain the pressing data of the screw press and conduct an analysis of the oil residue content to obtain high oil residue content data; conduct intelligent temperature control for pressing based on the high oil residue content data to obtain intelligent temperature control data for pressing;

[0094] In this embodiment, the real-time pressing data of the screw press is obtained, and these data include the oil residue content, oil extraction rate, and other parameters related to the pressing efficiency during each pressing process. By using devices such as load cells and oil flow meters, the oil residue content of the output material of the oil press is monitored in real time. Through the analysis of the oil residue sample, the data of the high oil residue content is obtained. During the analysis, the oil residue content is confirmed by the laboratory sample test method. Usually, the Soxhlet extraction method is used to extract the oil in the oil residue to determine its oil residue content. Based on the high oil residue content data, the temperature control is adjusted according to the characteristics of different oil materials to determine the optimal temperature range for the pressing process. Usually, this range is set from 50°C to 80°C. Through this data analysis, the temperature control data that needs to be adjusted is obtained and enters the intelligent temperature control process for pressing.

[0095] Step S4: Perform intelligent production scheduling based on the oil treatment requirement data and the filter grease accumulation data to obtain intelligent production data; upload the intelligent production data and the pressing intelligent temperature control data to the intelligent control system to execute the oil production control task.

[0096] In this embodiment, by analyzing the obtained oil treatment requirement data (including oil type, quantity, material quality parameters, processing time limit requirements, etc.), a quantifiable production task description is generated. Based on the oil type, combined with the accumulation rate and blockage degree data provided in the filter grease accumulation data, the linear programming optimization algorithm is used to calculate the task allocation and processing priority of each device. The filter grease accumulation data should include the accumulation thickness, the percentage of filtration efficiency decline, and the deviation from the blockage threshold. Taking the accumulation thickness reaching 1.5 mm and the filtration efficiency declining by 20% as the boundaries, the devices with severe accumulation are preferentially excluded or the task load is reduced. The intelligent scheduling module uses an industrial control computing platform, calls historical operation data and real-time monitoring data, and comprehensively considers the device operation status, energy consumption level, current task urgency, and filter status. The generated intelligent production data includes specific data files such as device numbers, processing task allocation tables, temperature, pressure, and time parameters required for processing, and processing schedules, in XML or CSV format for subsequent steps to call. To ensure data integrity and scheduling efficiency, the data recording frequency is set to once per minute. Format and integrate the aforementioned generated intelligent production data with the pressing intelligent temperature control data. The pressing intelligent temperature control data includes a temperature control point list, time control parameters, real-time temperature change records, and adjustment thresholds. The temperature control point list should specifically list temperature control solutions such as "75°C ± 1°C for 15 minutes" or "85°C ± 2°C for 10 minutes"; the time parameters need to clearly define the moment of control strategy switching to ensure that adjustments are immediately executed when the temperature fluctuates by more than ±5°C. The integrated data is uploaded to the intelligent control system through the industrial Ethernet protocol (such as Modbus TCP / IP). The system needs to have real-time monitoring and feedback functions and support the OPC UA protocol to ensure seamless cross-platform data exchange. The system will parse and generate operation instructions based on the uploaded data, including device start / stop instructions, temperature control device adjustment instructions, and alarm trigger logic in case of abnormalities. For example, if the uploaded accumulation data indicates that the filter blockage rate reaches more than 50%, the system will trigger the instruction to enable the standby filter and prompt the replacement requirement on the display terminal. During the system operation, real-time status feedback is performed through sensors (temperature sensors, pressure sensors, filter screen accumulation sensors, etc.) installed at key parts of the devices. The sensor data acquisition frequency is 100 Hz to ensure real-time response ability during production. The uploaded data should have complete timestamp information for system log recording and subsequent analysis.

[0097] Optionally, step S1 is specifically:

[0098] Step S11: Obtain oil material data and perform spectral analysis to obtain oil spectral data;

[0099] In this embodiment, it is necessary to obtain the spectral data of the oil material through a near-infrared spectrometer. When using the near-infrared spectrometer, set the wavelength range for spectral acquisition to be from 1100 nm to 2500 nm, and adjust the light source intensity and sampling time of the instrument to ensure that the spectral data of each sample has sufficient accuracy. The treatment of the oil sample needs to be carried out in a uniform state to avoid the influence of material non-uniformity on the spectral data. Each sample needs to be measured independently at least three times, and the average value is taken as the final spectral data. This data reflects the spectral characteristics of molecular absorption in the oil, and the absorption peaks at specific wavelengths reflect the main components of the oil, such as fatty acids, triglycerides, etc. The measured spectral data is processed through software to obtain the specific spectral values of each oil sample, which serve as the basic data for subsequent analysis.

[0100] Step S12: Evaluate the oil content based on the oil spectral data to obtain oil content data;

[0101] In this embodiment, based on the oil spectral data, the regression analysis method is used to evaluate the oil content. According to the previous experimental data, a quantitative analysis model between the oil spectral data and the oil content is established. This model is usually established through the partial least squares regression (PLSR) method, where the spectral characteristics at specific wavelengths are selected as input parameters. For each sample, by comparing the collected spectral data with the existing calibration model, the estimated value of the oil content is obtained using the regression formula. When evaluating, an error tolerance range needs to be set. Usually, an error less than ±0.5% is considered qualified. The obtained oil content data is usually presented in percentage form. For example, if the oil content of an oil sample is 78%, it means that the sample contains 78% of the oil component.

[0102] Step S13: Determine the moisture content of the oil content data to obtain moisture content data;

[0103] In this embodiment, prepare the sample solution and configure the solvent and reagents according to the standard operating procedures. Use 50 grams of oil for each sample experiment. Add the Karl Fischer reagent with a known concentration to the oil sample. Through the titration reaction until the moisture completely reacts, record the volume of the reagent consumed. Calculate the moisture content based on the ratio of the volume of the reagent consumed to the sample. At this time, the moisture content data is expressed in percentage form. If the result is 8%, it means that the sample contains 8% of moisture. The determination of the moisture content needs to be carried out at room temperature, and no other volatile substances are required to interfere during the experiment to ensure the measurement accuracy.

[0104] Step S14: Analyze the impurity content based on the moisture content data to obtain the impurity content data;

[0105] In this embodiment, based on the moisture content data, the impurity content analysis of the oil material is carried out. The content analysis of impurities is usually carried out by chemical precipitation method or solvent extraction method. In this step, first, the moisture content in the sample needs to be removed to ensure the dry state of the sample. Then, an appropriate solvent (such as n-hexane or alcohol solvent) is added for oil extraction. After dissolution, insoluble substances such as fine plant fibers and impurities are removed by filtration. The solvent in the filtered solution is evaporated to obtain the residue of oil and impurities. Finally, the mass of the residual impurities is weighed by a precision balance, and the ratio of it to the total weight of the sample is calculated. This ratio is the impurity content, usually expressed as a percentage. If the impurity content exceeds the set standard value (such as 5%), this batch of oil is regarded as unqualified.

[0106] Step S15: Analyze the oil material processing requirements based on the impurity content data to obtain the oil material processing requirement data.

[0107] In this embodiment, the key is to determine the optimal oil material processing process conditions according to the type and content of impurities. First, set the oil material processing standards under different impurity contents. For example, if the impurity content is less than 3%, the oil material can be directly pressed; if the impurity content is higher than 5%, pretreatment is required, such as using a separator to remove large particle impurities, or further cleaning through a sedimentation tank. In this process, through empirical formulas and experimental data, the optimal pretreatment time, temperature, and equipment parameters are calculated. The processing requirement data includes control parameters such as pressure, temperature, and time in each stage, and these data will be used for subsequent production scheduling and oil material processing processes.

[0108] Optionally, step S11 is specifically:

[0109] Step S111: Obtain the oil material data and perform near-infrared spectroscopy sampling to obtain the oil near-infrared spectroscopy data;

[0110] In this embodiment, during sampling, a near-infrared spectrometer (such as the FOSS XDS series) is used to scan the oil sample. First, select an appropriate wavelength range, usually from 1100nm to 2500nm, to capture the characteristic absorption peaks in the oil. The light source of the instrument should be set as a tungsten halogen lamp to ensure the stability of the light source and no fluctuations during use. Each oil sample is scanned by the instrument at least three times independently to ensure the accuracy and repeatability of the data. The scanning time during the acquisition process should be set to 5 seconds / time to ensure the measurement accuracy. All spectral data are recorded and stored through the data acquisition software to form the near-infrared spectroscopy data of the oil material.

[0111] Step S112: Perform baseline correction on the near-infrared spectral data of the oil to obtain the corrected near-infrared spectral data of the oil;

[0112] In this embodiment, baseline correction is performed on the collected near-infrared spectral data of the oil to eliminate the influence of instrument error and external environment on the spectrum. The baseline offset correction method is adopted, and linear regression is performed on each wavelength point through software to adjust the baseline level of the original spectral data to zero. In specific operations, a characteristic flat interval in the wavelength range (for example, the section between 1100 nm and 1300 nm) is selected, and the baseline offset within this interval is calculated. Then, each wavelength point of the entire spectral data is subtracted by this offset to obtain the corrected near-infrared spectral data of the oil after baseline correction. The corrected data should have no obvious offset or fluctuation to ensure the accuracy of subsequent analysis.

[0113] Step S113: Obtain the absorption peak data of the oil;

[0114] In this embodiment, by analyzing the corrected near-infrared spectral data of the oil, the absorption peak data in the oil sample is extracted. The spectral peak detection algorithm is used, and usually the second derivative method is selected to highlight the peaks and valleys. Calculate the second derivative of the spectral data and identify the positions where mutations occur in the data. These mutation points are the absorption peaks. According to the known characteristics of the oil components, the absorption peaks near 3000 cm-1 (such as CH vibration) and the C=O stretching vibration peak around 1700 cm-1 are mainly extracted. At each wavelength, record the position and intensity data of the absorption peak to form the absorption peak data of the oil, which serves as the basis for subsequent analysis.

[0115] Step S114: Perform position identification on the corrected near-infrared spectral data of the oil according to the absorption peak data of the oil to obtain the absorption peak position data of the oil;

[0116] In this embodiment, based on the wavelength information of the absorption peak, the position of each absorption peak is marked in the original spectral data. In specific operations, the wavelength position corresponding to the maximum absorption value of the absorption peak is selected as the accurate position of the absorption peak. Use the wavelength comparison function in the data processing software to compare the extracted absorption peak position with the known standard absorption peak position and determine its accurate coordinate position. During this process, the set threshold should ensure that the absorption peak error is less than 2 nm, so as to obtain accurate absorption peak position data of the oil.

[0117] Step S115: Perform intensity statistics on the absorption peak position data of the oil to obtain the absorption peak intensity data of the oil;

[0118] In this embodiment, by calculating the intensity of each absorption peak, that is, the absorbance value at the wavelength of the absorption peak in the spectral data. When performing intensity statistics, first calculate the absorbance values at each absorption peak, and statistically analyze these absorbance values to obtain the average intensity and standard deviation of each absorption peak. For example, for the absorption peak at 3000 cm-1, measure its absorbance value and calculate the average absorbance value among multiple samples. Through intensity statistical analysis, the relative intensity of each absorption peak can be obtained, and further, the stability of the absorbance can be judged based on the standard deviation, so as to obtain the grease absorption peak intensity data.

[0119] Step S116: Construct a grease content prediction model based on the grease absorption peak intensity data, so as to obtain the grease content prediction model;

[0120] In this embodiment, a suitable modeling method is selected, such as partial least squares regression (PLSR). First, use the sample data with known grease content (i.e., the grease content value measured experimentally) as the training set. Use the grease absorption peak intensity data as the input variable and establish a prediction model through a regression algorithm. During the modeling process, select key absorption peaks (such as the peaks at positions 3000 cm-1, 1730 cm-1, 1450 cm-1, etc.) as feature inputs to ensure that the regression model has the greatest contribution at these peaks. During the model training process, use the cross-validation method to adjust the regression parameters and set the error tolerance threshold to ±0.5%. Finally, the constructed grease content prediction model will be able to accurately predict the grease content based on the grease absorption peak intensity data.

[0121] Step S117: Predict the grease content of the grease material data according to the grease content prediction model, so as to obtain the grease content data;

[0122] In this embodiment, the absorbance value of the grease material (i.e., the absorbance in the spectral data) is used as the input, and the trained regression model is applied for prediction. In the specific steps, extract the key absorption peak values in the spectral data of the grease material, calculate the corresponding absorbance intensity, and input these values into the grease content prediction model for prediction. Through the prediction formula, the content data of the grease material is obtained, usually expressed in percentage form. This prediction process should ensure that the error of each sample is controlled within ±1% to ensure the accuracy and reliability of the prediction results.

[0123] Step S118: Perform spectral measurement on the grease content data to obtain the grease spectral data.

[0124] In this embodiment, spectral measurement verification is performed on the oil content data to ensure that the predicted oil content results have sufficient accuracy and reliability. This process first ensures that the measurement is carried out using the same near-infrared spectrometer as in step S111, which requires the equipment model, light source, detector, scanning range, and measurement settings to be exactly the same. To maintain the consistency and comparability of the spectral data, the calibration status of the instrument needs to be checked, and it is ensured that it has been correctly zero-calibrated and the light source stability has been verified. When performing spectral measurement, the sample preparation method should be the same as in step S111, that is, each oil sample should be evenly mixed and maintained under appropriate temperature and humidity conditions. The oil sample should be placed in a standard sample cell, and the light beam passes through the sample for scanning, and the wavelength range should be from 1100 nm to 2500 nm. During this process, ensure that the resolution setting of the spectrometer is set to high precision, such as 1 nm, and ensure that the scanning speed is not less than 5 seconds each time to improve the quality of the data. Each sample is subjected to at least three independent spectral measurements to ensure the repeatability and stability of the data. After each scan, the measured spectral data is recorded in real time and stored in a digital data format for subsequent analysis. After the measurement is completed, the key absorption peaks of each spectrum are extracted, especially the bands closely related to the oil content (such as 3000 cm-1, 1730 cm-1, etc.) for analysis. The data of these absorption peaks will be compared with the predicted oil content data in step S117, and by calculating the error between the predicted value and the actual value, the accuracy of the model is further evaluated. For the calculation of the error, first, the actually measured spectral data is corresponded one by one with the predicted data in step S117, the absolute error between each pair of data is calculated, and the average error of each sample is obtained. According to these error data, check whether the predetermined error standard is met, that is, the error is less than ±1%. If the error range in this process exceeds ±1%, the model needs to be adjusted to check whether there are deviations in the data acquisition process, baseline correction, absorption peak identification, etc., or whether the model needs to be retrained. Through comparison and analysis, the reliability of the oil content prediction can be ensured, thereby providing accurate data support for industrial applications.

[0125] Optionally, step S112 is specifically:

[0126] Identify the absorption peak-free region of the near-infrared spectral data of the oil to obtain the absorption peak-free region data;

[0127] In this embodiment, for the identification of the non-absorption peak region of the near-infrared spectrum of oils and fats, it is first necessary to identify the region without obvious absorption peaks in the spectrum by analyzing the absorption characteristics of the near-infrared spectrum of oils and fats. During the specific operation, by determining the wavelength range in the spectral data, select those wavelength segments with relatively stable absorbance values and no obvious absorption peaks. Use spectral data processing tools, such as the findpeaks function in MATLAB, to analyze the spectral curve and identify the regions near the peaks. If the change in absorbance in a certain wavelength region is less than the set threshold (such as 0.01), then this region is regarded as the non-absorption peak region. The identification data of the non-absorption peak region will include the starting and ending wavelengths of each non-absorption peak segment, as well as the absorbance range of this segment.

[0128] Select baseline points according to the non-absorption peak region data, so as to obtain baseline point data;

[0129] In this embodiment, the selection of baseline points should be based on the spectral data within the non-absorption peak region. First, calculate the mean or median of the absorbance values within the non-absorption peak region and regard it as the baseline value. During the specific operation, for each non-absorption peak region, select several wavelength points within the interval whose absorbance values are closest to the mean or median as the baseline points. The selection criterion for the baseline points can be set that the fluctuation range of the absorbance value is lower than ±0.005 to ensure that these points have high stability and reliability when fitting the baseline. The selected baseline point data includes the wavelength coordinates and the corresponding absorbance values.

[0130] Obtain a cubic polynomial fitting model;

[0131] In this embodiment, obtain a cubic polynomial fitting model, which is used to fit the baseline points. Use existing spectral analysis software or programming tools (such as MATLAB, Python, etc.) to perform cubic polynomial fitting on the baseline point data. During the fitting process, the least squares method can be used for parameter estimation to ensure that the fitting result has the smallest error for the baseline point data. The form of the fitted cubic polynomial equation is:

[0132] y = ax 3 + bx 2 + cx + d;

[0133] Among them, a, b, c, and d are fitting parameters, which are determined by minimizing the sum of the squares of the errors. The cubic polynomial fitting model will provide a smooth fitting curve within the range of the baseline point data to represent the baseline trend of the spectrum.

[0134] Perform polynomial baseline fitting on the baseline point data according to the cubic polynomial fitting model to generate polynomial fitting baseline data;

[0135] In this embodiment, a cubic polynomial model is applied to the baseline point data, and the fitted values of each baseline point are obtained through fitting calculations. The specific process is as follows: For the wavelength of each baseline point, substitute this wavelength into the cubic polynomial equation to obtain the corresponding absorbance value, and compare it with the original baseline point data. If the error between the fitted value and the original data is less than the set threshold (such as 0.001), the fitting result is considered valid.

[0136] Perform baseline correction on the near-infrared spectral data of grease according to the polynomial-fitted baseline data, so as to obtain the corrected near-infrared spectral data of grease.

[0137] In this embodiment, the near-infrared spectral data of grease is corrected based on the polynomial-fitted baseline data, aiming to eliminate the baseline drift in the spectrum. By subtracting the absorbance value of each wavelength point in the near-infrared spectral data of grease from the corresponding polynomial-fitted baseline value, the corrected absorbance value is obtained. Specifically, when implementing, for each wavelength point, use the polynomial-fitted baseline value obtained in step S4 and perform the following operations:

[0138] A calibrated = A original - A fitted ;

[0139] where, A calibrated is the corrected absorbance, A original is the original absorbance, and A fitted is the baseline absorbance obtained by polynomial fitting. Through this correction process, the low-frequency noise and baseline drift in the spectrum are eliminated, and the corrected data of the near-infrared spectrum of grease are obtained. These corrected data will more accurately reflect the true spectral characteristics of the grease sample and provide more reliable basic data for subsequent analysis and prediction.

[0140] Optionally, step S2 is specifically as follows:

[0141] Step S21: Obtain the hydraulic press model;

[0142] In this embodiment, the physical and structural models of the hydraulic press are obtained. This model includes the working principle of the hydraulic press, the structural parameters of each component (such as the filter aperture, grease extraction pressure, volume, etc.), and the mathematical expressions of the oilseed processing process. The hydraulic press model should be modeled using the finite element analysis (FEA) method. Based on the actual working environment of the hydraulic press, select appropriate parameters, such as setting the pressing force to 5000 N, maintaining the working temperature at 50 °C, and the oilseed input amount at 5 kg / h. Through simulation software (such as ANSYS or ABAQUS), perform dynamic analysis on the structure of the hydraulic press to simulate the flow characteristics of the internal grease and the pressure distribution during the working process. Finally, according to the output data of the model, obtain important parameters such as the pressing efficiency and grease extraction rate.

[0143] Step S22: Input the oil processing requirement data into the hydraulic press model, conduct pressing simulation, and perform statistics on the low oil extraction rate of the pressing simulation results to obtain the low oil extraction rate;

[0144] In this embodiment, input the processing requirement data of the oil into the hydraulic press model. The processing requirement data includes the initial oil content of the oil (for example, the initial oil content of the oil is 30%) and the operating parameters of the hydraulic press (such as pressing time, pressure, temperature, etc.). After simulating the input data, conduct pressing simulation, and use finite element analysis software to perform numerical simulation on the pressing process of the oil in the hydraulic press. During the simulation process, set the pressing time to 30 minutes and the pressure to 3500 Pa. According to the simulation results, count the situation of the low oil extraction rate during the pressing process. The low oil extraction rate can be calculated by measuring the difference between the actual oil extraction amount and the theoretical maximum extraction amount. If the oil extraction rate is lower than the set threshold (such as lower than 20%), it is marked as a low oil extraction rate.

[0145] Step S23: Screen the oil materials according to the low oil extraction rate to obtain low oil materials;

[0146] In this embodiment, set the oil with an oil extraction rate lower than 20% as low oil materials. First, compare the oil extraction rates of all oil materials with the set threshold. For the oil with an oil extraction rate lower than 20%, classify it as low oil materials. During the screening process, an oil content measuring instrument (such as an oil analyzer) can be used to measure the oil content of each oil sample and compare it with the preset low oil extraction rate threshold. The screened low oil materials will enter the subsequent impurity mass spectrometry analysis step.

[0147] Step S24: Conduct impurity mass spectrometry analysis on the low oil materials to generate an impurity mass spectrum;

[0148] In this embodiment, first prepare a low oil material sample and dissolve it with an appropriate solvent to ensure that the impurity components can be completely dissolved. Use a gas chromatography - mass spectrometry (GC - MS) or liquid chromatography - mass spectrometry (LC - MS) to perform mass spectrometry analysis on the dissolved oil sample. Set the operating conditions of the instrument as follows: the injection volume is 1 μL, the separation column temperature is set to 70°C, the mass spectrometry scanning range is 50 - 500 m / z, and the acquisition time is 3 minutes. Through the mass spectrum, various impurity components (such as fatty acids, heavy metals, etc.) in the oil can be identified. The impurity mass spectrum will show the mass and relative abundance of the impurity components, providing a basis for the identification of high - impurity oil materials in the subsequent steps.

[0149] Step S25: Identify high - impurity oil materials from the low oil materials according to the impurity mass spectrum to obtain high - impurity oil materials;

[0150] In this embodiment, the standards for impurity components are set. For example, the content of fatty acids exceeds 5% or the content of heavy metals exceeds a certain set value (such as the lead content is greater than 0.05 mg / kg). Quantitative analysis is performed on each impurity component in the mass spectrum to calculate its relative abundance. If the abundance of certain components exceeds the set threshold, it is determined that the oil material is a high-impurity oil material. The detected components are compared using a standard mass spectrometry database (such as NIST or Wiley) to further confirm the types and concentrations of the impurity components. For high-impurity oil materials, they need to be marked and recorded for subsequent processing.

[0151] Step S26: Analyze the grease accumulation in the hydraulic press model based on the high-impurity oil material to generate grease accumulation data for the filter.

[0152] In this embodiment, the flow of the high-impurity oil material during the pressing process is simulated through the hydraulic press model, especially the accumulation situation in the filter part. According to the structural parameters of the filter (such as pore size, filtration rate, etc.) and the rheological properties of the oil material (such as viscosity, fluidity, etc.), the accumulation of grease in the filter is simulated. The filter pore size is set to 50 μm, and the viscosity of the grease is 0.35 Pa·s to simulate the grease accumulation rate in the filter. The simulation results will show the grease accumulation amount in the filter and the accumulation trend over time. Through this analysis, grease accumulation data for the filter is generated, including relevant parameters such as the accumulation amount and accumulation time.

[0153] Optionally, step S24 is specifically as follows:

[0154] Step S241: Perform direct mass spectrometry on the low-oil material using a mass spectrometer to obtain low-oil material mass spectrometry data;

[0155] In this embodiment, a low-oil material sample is prepared and dissolved in an appropriate solvent to ensure that the components of the grease and impurities are completely dissolved. Then, the sample is injected into the mass spectrometer for direct mass spectrometry analysis. The set parameters of the mass spectrometer should include: electrospray ionization source (ESI) or atmospheric pressure chemical ionization (APCI) mode, the analysis mode is positive ion mode, and the scanning range is set to 50 to 1000 m / z. The mass spectrometry data of the low-oil material is obtained through the mass spectrometer. The data includes ion peaks, their relative abundances, and mass-to-charge ratio (m / z) information in all samples. This data provides molecular information on the components in the low-oil material and serves as the basic data for subsequent analysis steps.

[0156] Step S242: Identify the precursor ions from the low-oil material mass spectrometry data to obtain precursor ion data;

[0157] In this embodiment, the identification of precursor ions is carried out through the ion peaks detected by a mass spectrometer. The specific method is to extract all the base peaks from the mass spectrum and identify their precursor ions. Usually, when selecting precursor ions, it is necessary to identify ion peaks with relatively high abundance and stability (such as greater than 10%). In the specific implementation, through software analysis tools (such as MassLynx or Xcalibur), the precursor ion analysis is performed on each ion peak. If the molecular weight of the precursor ion of a certain ion peak is relatively high and stable, its data is recorded, and precursor ion data is generated, including the m / z value and its intensity of the precursor ion.

[0158] Step S243: Obtain the precursor ion library;

[0159] In this embodiment, the precursor ion library is constructed through a database or manually, and it contains the m / z values, structural information, and corresponding chemical components of known precursor ions. The methods for obtaining the library include downloading relevant precursor ion spectrum data from public databases (such as NIST, HMDB), or obtaining the precursor ion data of standard chemical substances through experiments. To ensure the accuracy of the data, the library should include the precursor ion information of various common oil and fat components and their impurity components, such as fatty acids, triglycerides, and other impurity substances. The role of this library is to provide accurate reference data for subsequent precursor ion comparison and analysis.

[0160] Step S244: Compare the precursor ion data with the precursor ion library for the precursor ions of the emulsion residue to obtain the emulsion residue data;

[0161] In this embodiment, the comparison is carried out between the precursor ion data and the precursor ion library. This comparison process uses the standardized library data to match each precursor ion. If the m / z value of the precursor ion matches the precursor ion data related to the emulsion residue in the library, it is confirmed that this precursor ion belongs to the component of the emulsion residue. During the implementation process, through the automated comparison function of the mass spectrometry software, the precursor ions related to the emulsion residue are screened out. For example, for common phospholipids or surfactants in the emulsion, the m / z values of their precursor ions will match the data in the library. Finally, the relevant data of the emulsion residue is obtained, including the types, concentrations, and relative abundances of the matching precursor ions.

[0162] Step S245: Identify the mass-to-charge ratio of the low-oil material mass spectrometry data according to the emulsion residue data, so as to obtain the mass-to-charge ratio data;

[0163] In this embodiment, the ion peaks in the mass spectrum are analyzed for the mass-to-charge ratio (m / z). The identification process focuses on specific ion peaks in the emulsion residue, and their m / z values are usually closely related to the chemical composition of the emulsion. Software (such as OpenMS or MZmine) is used to automatically extract the m / z data related to the emulsion and perform data filtering to remove noise and irrelevant peaks. According to the data of the emulsion residue, the corresponding m / z values and their intensities are recorded.

[0164] Step S246: Identify the intensity peaks from the emulsion residue data based on the emulsion residue data, so as to obtain the intensity peak data;

[0165] In this embodiment, the ion peaks related to the emulsion residue are screened out from the mass spectrometry data of the emulsion residue. These ion peaks usually represent the chemical components in the emulsion. The core objective of intensity peak identification is to determine the ion peaks with relatively high signal intensities in the mass spectrum, and the intensities of these peaks are usually related to the relative abundance of the emulsion. By using mass spectrometry data analysis software (such as MassHunter, SpectraSchool, MZmine, etc.), the signal of the entire mass spectrum is first processed to filter out the background noise and identify the ion peaks. Next, each peak is further analyzed to determine its m / z value and intensity value. For each qualified peak, its m / z value is recorded, and its relative intensity is calculated, usually expressed as the percentage of the maximum signal intensity of the peak relative to the total signal intensity. The identification of intensity peaks also needs to be filtered according to the threshold set by the standard, and the threshold is usually at least greater than 5% of the relative abundance. For each identified intensity peak, its m / z value and signal intensity data are recorded in detail, and these data will provide basic information for the subsequent drawing of the impurity mass spectrum.

[0166] Step S247: Draw the impurity mass spectrum based on the m / z data and the intensity peak data to obtain the impurity mass spectrum.

[0167] In this embodiment, by combining the mass-to-charge ratio data and the intensity peak data, an impurity mass spectrum of the low-fat material is plotted. Specifically, first, the mass-to-charge ratio data obtained in step S245 is combined with the intensity peak data identified in step S246 to establish a complete two-dimensional mass spectrum data set. Using mass spectrometry data processing software (such as ProteoWizard, OpenMS, or other relevant software), all valid mass-to-charge ratio values (m / z) and corresponding intensity data are imported into the software and data processing is performed. The software will first sort these data to ensure that the mass-to-charge ratio is arranged from smallest to largest. Then, using the data visualization function, a two-dimensional graph is plotted with the mass-to-charge ratio (m / z) as the X-axis and the signal intensity as the Y-axis. In the graph, each point represents an intensity peak corresponding to a mass-to-charge ratio, and these points are presented as curves or bar graphs. This graph will show the distribution of impurities in the low-fat material, and different mass-to-charge ratios correspond to different impurity components. Through this impurity mass spectrum, the relative abundances of each component can be clearly observed, and its content and influence in the sample can be analyzed. This graph not only provides a basis for the screening of fat materials but also provides visual data support for subsequent quality analysis.

[0168] Optionally, step S26 is specifically as follows:

[0169] Step S261: Place the high-impurity fat material into a hydraulic press model and perform a filtration simulation to generate hydraulic press filtration data;

[0170] In this embodiment, it is necessary to place the high-impurity fat material into a hydraulic press model for simulation. For this purpose, a numerical simulation tool of the hydraulic press model is required, such as computational fluid dynamics simulation software (such as ANSYS Fluent or COMSOL Multiphysics). This simulation tool can accurately reproduce the physical behavior of the fat material during the filtration process under specific working conditions.

[0171] Input the relevant parameters of the high-impurity fat material into the simulation system, including data such as the viscosity, flow rate, and particle size of the material. The parameters of the fat material can be obtained through laboratory analysis. For example, the viscosity of the fat material is measured using a viscometer, and the particle size is measured using a particle size analyzer. Then, define the filtration conditions of the hydraulic press, such as pressure, flow rate, and the pore size of the filter screen. During the simulation process, a mathematical model is used to calculate the flow and filtration process of the fat material in the filter. According to the characteristics of the material and the resistance of the filter screen, data generated during the filtration process are simulated, including filtration efficiency, pressure loss, flow rate changes, etc. Finally, complete hydraulic press filtration data are generated, including various physical quantity data and performance data during the filtration process.

[0172] Step S262: Perform efficiency statistics on the hydraulic press filtration data to obtain low-efficiency hydraulic press filtration data;

[0173] In this embodiment, a special analysis tool, such as MATLAB or Excel, is required to analyze the various data obtained during the filtration process of the hydraulic press. First, calculate the filtration efficiency of the hydraulic press. The filtration efficiency can be obtained by calculating the change in the total amount of grease material before and after filtration. For example, subtract the amount of grease flowing through the filter after filtration from the total amount of grease material before filtration, and then calculate the ratio with the amount of grease before filtration to obtain the percentage of the filtration efficiency. According to the simulation data obtained in step S261, select the low-efficiency filtration cases, which usually correspond to higher pressure losses or lower filtration effects. Set a threshold for low-efficiency filtration efficiency, such as when it is lower than 80%, it is considered low-efficiency filtration. By comparing all the filtration data, screen out the data that meet the low-efficiency filtration criteria, and finally obtain the low-efficiency hydraulic press filtration data.

[0174] Step S263: Obtain the filter data and the filter blockage threshold;

[0175] In this embodiment, relevant data of the filter and the blockage threshold need to be obtained. The data of the filter usually includes the pore size of the filter, the filter element material, the working pressure range, etc., and these data can be obtained through the technical manual of the filter or laboratory tests. The setting of the blockage threshold depends on the design standard of the filter, and usually when the surface resistance of the filter element reaches a certain level, it is defined as blocked. For example, it is set that when the pressure loss of the filter exceeds a predetermined value (such as 20%), it is considered that the filter is blocked. This threshold can be obtained through experiments or set according to the design regulations of the filter.

[0176] Step S264: Perform real-time monitoring of the surface accumulation of the filter data according to the low-efficiency hydraulic press filtration data, so as to obtain the filter surface accumulation data;

[0177] In this embodiment, according to the low-efficiency hydraulic press filtration data, it is necessary to perform real-time monitoring of the grease accumulation on the surface of the filter. Use pressure sensors, flow meters and surface sensors (such as laser scanners or infrared sensors) to monitor the surface state of the filter in real time. By combining with the low-efficiency data obtained from the simulation of the hydraulic press, analyze the degree of grease material accumulated on the surface of the filter. The data recorder and sensors will detect the situation of grease accumulation on the filter surface in real time and transmit the data to the data processing platform. At this time, the monitoring data will be recorded and statistically analyzed according to the real-time accumulation amount, and the filter surface accumulation data will be output.

[0178] Step S265: Calculate the accumulation rate of the filter surface accumulation data to obtain the filter accumulation rate;

[0179] In this embodiment, the calculation of the filter accumulation rate is based on the change rate of the grease accumulation amount on the filter surface. Using the time series analysis method, the hourly calculation of the filter surface accumulation data obtained in real time is carried out. By recording the accumulation amount at each time point and calculating the difference between it and the accumulation amount at the previous time point, the accumulation rate can be obtained. The calculation formula for the accumulation rate is:

[0180]

[0181] In this step, it is necessary to perform periodic statistics on the accumulation rate, set a time interval (for example, every 10 minutes), and calculate the accumulation rate within each time period. The final result will show the grease accumulation rate of the filter.

[0182] Step S266: Perform clogging classification on the filter surface accumulation data according to the filter clogging threshold, so as to obtain the filter surface clogging data;

[0183] In this embodiment, first, it is necessary to set the clogging threshold of the filter. The setting of the clogging threshold is based on the design standard and experimental data of the filter, and is usually defined by the standard value of the accumulation rate or the accumulation amount. For example, when the accumulation rate exceeds a certain value (such as 10 g / min) or the cumulative accumulation amount exceeds a certain limit value (such as 5 g / cm 2 ), it is considered that the filter is in a clogged state. At the same time, the setting of the clogging threshold can also be adjusted according to the working environment of the filter such as pressure loss and flow rate change. For example, when the pressure loss of the filter exceeds 15%, it can also be considered that the filter is severely clogged. Next, it is necessary to use real-time monitoring devices (such as pressure sensors, flow meters, and surface sensors, etc.) to obtain the accumulation data on the filter surface. These data record the change of the grease accumulation amount on the filter surface, including the accumulation rate or accumulation amount at each time point. These data are obtained through sensors and calculation algorithms. The measurement method of the accumulation amount can be based on physical sensors and fluid mechanics models, while the accumulation rate is the change amount of the accumulation amount per unit time. After obtaining the real-time accumulation data, compare these data with the set clogging threshold. When the accumulation rate or cumulative accumulation amount in the real-time data exceeds the preset threshold, mark this moment as the filter clogging moment. At this time, the accumulation data of the filter is considered to be clogging data, and will include information such as the accumulation rate, accumulation amount, pressure change, and flow rate change at this moment. Finally, all the moments and related data that meet the clogging standard will be recorded in the database and a clogging report will be generated. The report details the clogging time, clogging degree, accumulation rate, pressure loss, etc. of each filter. These data can not only help identify the clogging state of the current filter, but also be used as the basis for subsequent maintenance and optimization decisions, predict the clogging trend of the filter, and optimize the use and replacement cycle of the filter.

[0184] Step S267: Conduct rate statistics on the filter accumulation rate to obtain high-rate filter accumulation data;

[0185] In this embodiment, a detailed analysis of the filter accumulation rate is carried out to identify which filters are in a high-rate accumulation state. First, based on the existing filter accumulation rate data, a high-rate threshold is set. Usually, when the rate exceeds 10 g / min, it is considered a high-rate accumulation. The setting of this threshold is based on the filter design specifications, working conditions, and the common accumulation rates in actual operations. Next, all the filter accumulation rate data is screened to find all the records with a rate exceeding 10 g / min. These records represent that the accumulation rate of the filter is relatively high at a certain moment and belongs to the high-rate accumulation state. By summarizing and statistically analyzing the qualified data, a high-rate filter accumulation data set is generated. This data set includes each qualified filter, its accumulation rate, and the corresponding time information. These data can help identify the filters in an abnormal accumulation state and provide a basis for subsequent optimization and maintenance decisions.

[0186] Step S268: Conduct blockage degree statistics on the filter surface blockage data to obtain severe filter surface blockage data;

[0187] In this embodiment, the standard for the blockage degree needs to be defined. The blockage degree is usually measured by the amount of accumulated grease or the pressure increment caused by the blockage. For grease accumulation, the blockage degree can be calculated based on the amount of accumulated grease. If the accumulation amount exceeds a certain standard value (such as 5 g / cm 2 ), it is considered a high blockage degree; for the pressure increment, if the pressure loss of the filter reaches a certain proportion (such as exceeding 80%), it is considered severely blocked. During the statistical process, a cumulative statistical method is adopted to record the blockage state of the filter in each time period. For each monitoring time point, calculate the blockage degree at that moment. If the blockage degree exceeds the set severe blockage standard (for example, the blockage degree exceeds 90%), then classify the blockage data at that moment as severe blockage data. Finally, generate the severe filter surface blockage data, recording the blockage degree of each filter during the monitoring period and the moment when severe blockage occurs, as reference data for subsequent analysis and maintenance.

[0188] Step S269: Conduct an intersection operation on the high-rate filter accumulation data and the severe filter surface blockage data to generate filter grease accumulation data.

[0189] In this embodiment, first, an intersection operation needs to be performed on the two sets of data obtained in steps S267 and S268. The first set of data is the accumulation data of high-rate filters, which records the filters with an accumulation rate exceeding a set threshold (such as 10 g / min) and their related data; the second set of data is the data of severely blocked filter surfaces, which records the filters with a blockage degree exceeding the severe standard. The purpose of the intersection operation is to find the filters that have both high-rate accumulation and severe blockage. The specific operation is to compare these two sets of data and select the records that meet both conditions (i.e., high-rate accumulation and severe blockage). These records represent the filters with a relatively high grease accumulation rate and an accumulation amount sufficient to cause blockage. Through set operations (such as intersection operations), the grease accumulation data of the filters is finally obtained. This data set not only records the basic information of the high-rate accumulation filters but also includes their blockage status, providing important data support for subsequent analysis of the grease accumulation trend, optimization of filter design, and prediction of maintenance cycles.

[0190] Optionally, step S3 is specifically as follows:

[0191] Step S31: Obtain the pressing data of the screw oil press and collect the grease sample of the screw oil press according to the pressing data of the screw oil press to obtain the grease sample of the screw oil press;

[0192] In this embodiment, first, the operating data of the screw oil press needs to be obtained through a data acquisition system, including parameters such as pressing pressure, rotational speed, material temperature, and oil yield. This data can be obtained in real time through sensors installed on the oil press, such as pressure sensors, temperature sensors, rotational speed sensors, etc. After obtaining the real-time pressing data of the screw oil press, the grease sample is collected. When collecting the grease sample, the collection time interval and collection amount need to be set. The collected sample needs to be judged based on the outflow of grease during the operation of the press. For example, set to collect the grease sample every 100 minutes to ensure the representativeness and accuracy of the sample. The quality of the grease sample can be monitored by weighing regularly to ensure the effectiveness of the sample. Finally, the collected grease sample is the grease sample of the screw oil press, providing basic data for subsequent extraction and analysis.

[0193] Step S32: Use a Soxhlet extractor to extract the oil residue from the grease sample of the screw oil press to obtain the oil residue sample;

[0194] In this embodiment, the oil sample collected in step S31 is placed in a Soxhlet extractor for processing. The Soxhlet extractor is a commonly used experimental device for oil extraction, which extracts the oil components in the oil sample through continuous solvent reflux and penetration. The specific operation is to mix the oil sample with an appropriate solvent (such as petroleum ether or ether), heat the solvent to boiling and let it evaporate, and the solvent condenses into a liquid in the condenser and flows back into the extractor. The oil components dissolve in the solvent and are extracted. This process usually lasts about 6 - 8 hours to ensure complete oil extraction. After extraction, a separating funnel is used to separate the oil residue and the oil, and the obtained oil residue sample is the solid residue sample after removing the oil components from the oil. The mass of the oil residue sample can be measured by the weighing method to ensure the efficiency of the extraction process.

[0195] Step S33: Conduct a statistical analysis of the oil residue content based on the oil residue sample to obtain high oil residue content data;

[0196] In this embodiment, the oil residue sample needs to be dried to ensure that there is no moisture in the sample. Drying is usually carried out at 105°C until the mass of the oil residue sample no longer changes. Then, the weighing method is used to record the mass of the oil residue sample, and the oil residue content is calculated. The oil residue content refers to the proportion of the mass of the solid residue in the oil residue sample to the mass of the oil sample, usually expressed as a percentage. A threshold for the oil residue content is set. For example, when the oil residue content exceeds 30%, it is considered that the oil residue content is high. By statistically analyzing multiple oil residue samples, high oil residue content data can be obtained. These data can be used to evaluate the oil residue situation under different raw materials or different operating conditions and provide a basis for subsequent oil pressing control.

[0197] Step S34: Conduct intelligent temperature control for pressing based on the high oil residue content data to obtain intelligent temperature control data for pressing.

[0198] In this embodiment, it is necessary to analyze the high oil residue content data to identify the oil samples with a relatively high oil residue content. Based on the high oil residue content data, the pressing conditions are analyzed with the aim of determining how to adjust the temperature conditions of the press under the condition of high oil residue. This analysis includes evaluating the influence of different oil residue contents on the oil pressing effect, especially on the oil recovery rate and the oil quality. According to the analysis results, an adaptive temperature range is set. When the oil residue content reaches or exceeds the preset standard, the temperature of the press will be adjusted within this range to optimize the oil pressing effect and the oil residue removal efficiency. Further, an assessment of the temperature adjustment requirement is carried out to determine the specific temperature adjustment range. For example, when the oil residue content exceeds 35%, the temperature needs to be increased to 80°C to increase the fluidity of the oil and improve the pressing effect. Based on the temperature adjustment requirement data, a temperature control algorithm is designed, which automatically controls the heating system of the press according to the real-time oil residue data and the temperature adjustment requirement. The temperature control algorithm data will be used to simulate the pressing process under different temperature conditions to optimize the pressing efficiency and reduce the pressure problem caused by excessive oil residue content, ensuring the intelligent control and efficiency of oil pressing.

[0199] Optionally, step S34 is specifically as follows:

[0200] Step S341: Analyze the material pressing conditions according to the high oil residue content data to obtain the pressing condition data;

[0201] In this embodiment, first, it is necessary to analyze the samples with different oil residue contents according to the high oil residue content data. First, a standard interval of the oil residue content is set. For example, the oil residue content is divided into three grades: low (below 20%), medium (20% - 30%), and high (above 30%). Then, the material pressing conditions are analyzed according to the oil residue content grade. This analysis includes setting appropriate factors such as pressing pressure, rotation speed, heating temperature, etc. The specific operation is as follows: for the samples with low oil residue content, the pressing pressure can be set to 10 MPa and the rotation speed to 30 revolutions per minute; for the samples with medium oil residue content, the pressing pressure can be set to 15 MPa and the rotation speed to 40 revolutions per minute; for the samples with high oil residue content, the pressing pressure can be set to 20 MPa and the rotation speed to 50 revolutions per minute. On this basis, experiments are carried out by adjusting the temperature and pressure to analyze the oil yield and the remaining amount of oil residue under different pressing conditions, so as to obtain the pressing condition data applicable to different oil residue content data.

[0202] Step S342: Evaluate the temperature adjustment requirement for the pressing condition data to obtain the temperature adjustment requirement data;

[0203] In this embodiment, it includes analyzing the temperature requirements under different oil residue content conditions. For example, in the case of a high oil residue content (above 30%), the oil is more viscous, so the temperature needs to be increased to enhance the fluidity of the oil. At this time, the temperature is set to 80°C; for samples with a medium oil residue content (20%-30%), the temperature is set to 70°C; for samples with a low oil residue content, the temperature is set to 60°C. Through the oil pressing experiments on samples with different oil residue contents, the fluidity and pressure changes of the oil during the pressing process are measured, and the relationship between the fluidity of the oil and the temperature is further analyzed. Finally, the temperature adjustment requirement data is determined. These data will provide parameter support for the subsequent design of the intelligent temperature control algorithm.

[0204] Step S343: Design an intelligent temperature control algorithm based on the temperature adjustment requirement data to generate temperature control algorithm data;

[0205] In this embodiment, during the design process, first, the correlation between different oil residue contents and temperature needs to be considered, and a functional relationship between the oil residue content and temperature control is established through a mathematical model. For example, when the oil residue content reaches 30%, the temperature is adjusted to 80°C. If the oil residue content drops to 20%, the temperature can be adjusted to 70°C. The relationship between the temperature change and the oil residue content change can be established through a formula and further optimized through a data fitting algorithm. The system response time also needs to be considered during the design of the temperature control algorithm to ensure that the temperature adjustment can be achieved in a short time to avoid affecting the pressing efficiency due to temperature lag. According to the temperature adjustment requirement data, through the design of the control algorithm, temperature control algorithm data corresponding to each oil residue content is generated to ensure that the temperature can be accurately adjusted during actual operation.

[0206] Step S344: Conduct a simulation of the intelligent temperature control for pressing based on the temperature control algorithm data to obtain the intelligent temperature control data for pressing.

[0207] In this embodiment, during the simulation process, the real pressing data is input into the temperature control algorithm system, including parameters such as the pressing pressure, rotation speed, and oil residue content. Then, according to the designed temperature control algorithm, the heating temperature is adjusted based on the real-time oil residue content data. For example, during actual operation, when the real-time monitored oil residue content is 28%, the system automatically adjusts the heating temperature to 75°C according to the temperature control algorithm data and provides real-time feedback on the temperature change inside the press. The simulation system should record the pressing effects at different temperatures, including the oil collection rate and the oil residue remaining rate, to evaluate the accuracy of the temperature control and its impact on the pressing efficiency. Through multiple simulations and adjustment of the algorithm parameters, a set of stable temperature control strategies is finally determined to obtain the intelligent temperature control data for pressing, and these data can be used to implement the temperature control system during the actual production process.

[0208] Optionally, this specification also provides an intelligent digital-based oil production control system for implementing an intelligent digital-based oil production control method as described above. The intelligent digital-based oil production control system includes:

[0209] A processing requirement analysis module for obtaining oil material data and performing processing requirement analysis based on the oil material data to obtain oilseed processing requirement data;

[0210] An oilseed impurity analysis module for obtaining a hydraulic press model; inputting the oilseed processing requirement data into the hydraulic press model, performing pressing simulation, and conducting low oil extraction rate statistics on the pressing simulation results to obtain a low oil extraction rate; analyzing the oil material for oilseed impurities based on the low oil extraction rate to generate oilseed impurity data; and performing filter oil accumulation analysis on the hydraulic press model according to the oilseed impurity data to generate filter oil accumulation data;

[0211] A pressing intelligent temperature control module for obtaining screw press pressing data and performing oil residue content analysis to obtain high oil residue content data; and performing pressing intelligent temperature control based on the high oil residue content data to obtain pressing intelligent temperature control data;

[0212] An intelligent production scheduling module for performing intelligent production scheduling based on the oilseed processing requirement data and the filter oil accumulation data to obtain intelligent production data; and uploading the intelligent production data and the pressing intelligent temperature control data to an intelligent control system to execute the oil production control task.

[0213] An intelligent digital-based oil production control system of the present invention can implement any intelligent digital-based oil production control method of the present invention. It is a medium for coordinating the operations and signal transmissions between various modules to complete the intelligent digital-based oil production control method. The internal modules of the system cooperate with each other, thereby improving the oil extraction rate and production efficiency.

[0214] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0215] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A grease production control method based on intelligent digitization, characterized in that, It includes the following steps: Step S1: Obtain oil material data, and conduct a processing requirement analysis based on the oil material data to obtain oil processing requirement data; among them, the oil processing requirement data includes temperature, time, and pressure parameters required for oil processing. Step S2: Obtain a hydraulic press model; input the oil processing requirement data into the hydraulic press model, conduct a pressing simulation, and perform a low oil extraction rate statistics on the pressing simulation results to obtain a low oil extraction rate; conduct an oil material impurity analysis on the oil material based on the low oil extraction rate to generate oil material impurity data; conduct a filter oil accumulation analysis on the hydraulic press model based on the oil material impurity data to generate filter oil accumulation data; among them, the low oil extraction rate is an oil extraction rate lower than 70%; the filter oil accumulation analysis is to simulate the accumulation rate of oil in the filter. Step S3: Obtain screw press pressing data, and conduct an oil residue content analysis to obtain high oil residue content data; conduct an intelligent temperature control for pressing based on the high oil residue content data to obtain intelligent temperature control data for pressing; among them, the high oil residue content data is oil residue content data exceeding 30%. Step S4: Conduct an intelligent production scheduling based on the oil processing requirement data and the filter oil accumulation data to obtain intelligent production data; upload the intelligent production data and the intelligent temperature control data for pressing to an intelligent control system to execute an oil production control task.

2. The intelligent digital-based grease production control method according to claim 1, wherein Specifically, step S1 is as follows: Step S11: Obtain oil material data, and conduct a spectral analysis to obtain oil spectral data. Step S12: Conduct an oil content evaluation based on the oil spectral data to obtain oil content data. Step S13: Measure the moisture content of the oil content data to obtain moisture content data. Step S14: Conduct an impurity content analysis based on the moisture content data to obtain impurity content data. Step S15: Conduct an oil processing requirement analysis based on the impurity content data to obtain oil processing requirement data.

3. The intelligent digital-based grease production control method according to claim 2, wherein, Specifically, step S11 is as follows: Step S111: Obtain oil material data, and conduct a near-infrared spectrum sampling of the oil to obtain near-infrared spectrum data of the oil. Step S112: Conduct a baseline correction on the near-infrared spectrum data of the oil to obtain corrected near-infrared spectrum data of the oil. Step S113: Obtain oil absorption peak data. Step S114: Conduct a position identification on the corrected near-infrared spectrum data of the oil based on the oil absorption peak data to obtain oil absorption peak position data. Step S115: Conduct an intensity statistics on the oil absorption peak position data to obtain oil absorption peak intensity data. Step S116: Construct an oil content prediction model based on the oil absorption peak intensity data to obtain an oil content prediction model. Step S117: Conduct an oil content prediction on the oil material data based on the oil content prediction model to obtain oil content data. Step S118: Conduct a spectral measurement on the oil content data to obtain oil spectral data.

4. The intelligent digital-based grease production control method according to claim 3, characterized in that Specifically, step S112 is as follows: Conduct an identification of the non-absorption peak region on the near-infrared spectrum data of the oil to obtain non-absorption peak region data. Select baseline points according to the data in the absorption peak - free region to obtain baseline point data; Obtain a cubic polynomial fitting model; Perform polynomial baseline fitting on the baseline point data according to the cubic polynomial fitting model to generate polynomial - fitted baseline data; Perform baseline correction on the near - infrared spectrum data of oil according to the polynomial - fitted baseline data to obtain corrected near - infrared spectrum data of oil.

5. The intelligent digital-based grease production control method according to claim 1, wherein Step S2 is specifically as follows: Step S21: Obtain a hydraulic press model; Step S22: Input the oil - processing requirement data into the hydraulic press model, perform pressing simulation, and conduct statistics on the low oil extraction rate of the pressing simulation results to obtain the low oil extraction rate; Step S23: Screen the oil materials according to the low oil extraction rate to obtain low - oil - content materials; Step S24: Perform impurity mass spectrometry analysis on the low - oil - content materials to generate an impurity mass spectrometry diagram; Step S25: Identify high - impurity oil materials from the low - oil - content materials according to the impurity mass spectrometry diagram to obtain high - impurity oil materials; Step S26: Analyze the grease accumulation in the filter of the hydraulic press model according to the high - impurity oil materials to generate filter grease accumulation data.

6. The intelligent digital-based grease production control method according to claim 5, wherein Step S24 is specifically as follows: Step S241: Perform direct mass spectrometry on the low - oil - content materials using a mass spectrometer to obtain low - oil - content material mass spectrometry data; Step S242: Identify the parent ions from the low - oil - content material mass spectrometry data to obtain parent ion data; Step S243: Obtain a parent ion spectral library; Step S244: Compare the parent ions of the emulsified residue according to the parent ion spectral library for the parent ion data to obtain emulsified residue data; Step S245: Identify the mass - to - charge ratio from the low - oil - content material mass spectrometry data according to the emulsified residue data to obtain mass - to - charge ratio data; Step S246: Identify the intensity peaks from the emulsified residue data according to the emulsified residue data to obtain intensity peak data; Step S247: Draw an impurity mass spectrometry diagram according to the mass - to - charge ratio data and the intensity peak data to obtain the impurity mass spectrometry diagram.

7. The method for controlling oil production based on intelligent digitization according to claim 5, characterized in that, Step S26 is specifically as follows: Step S261: Put the high - impurity oil materials into the hydraulic press model and perform filtration simulation to generate hydraulic press filtration data; Step S262: Conduct efficiency statistics on the hydraulic press filtration data to obtain low - efficiency hydraulic press filtration data; Step S263: Obtain filter data and the filter blockage threshold; Step S264: Monitor the surface accumulation of the filter data in real - time according to the low - efficiency hydraulic press filtration data to obtain filter surface accumulation data; Step S265: Calculate the accumulation rate of the filter surface accumulation data to obtain the filter accumulation rate; Step S266: Divide the filter surface accumulation data according to the filter blockage threshold to obtain filter surface blockage data; Step S267: Conduct rate statistics on the filter accumulation rate to obtain high - rate filter accumulation data; Step S268: Conduct blockage degree statistics on the filter surface blockage data to obtain severe filter surface blockage data; Step S269: Perform an intersection operation on the grease accumulation based on the high - rate filter accumulation data and the severe filter surface blockage data to generate filter grease accumulation data.

8. The method for controlling oil production based on intelligent digitization according to claim 1, wherein Step S3 is specifically as follows: Step S31: Obtain the pressing data of the screw oil press, and collect the grease sample of the screw oil press according to the pressing data of the screw oil press to obtain the grease sample of the screw oil press; Step S32: Use a Soxhlet extractor to extract oil residues from the grease sample of the screw oil press to obtain an oil residue sample; Step S33: Statistically analyze the oil residue content based on the oil residue sample to obtain high oil residue content data; Step S34: Perform intelligent temperature control for pressing based on the high oil residue content data to obtain intelligent temperature control data for pressing.

9. The intelligent digital-based grease production control method according to claim 8, wherein Step S34 is specifically as follows: Step S341: Analyze the pressing conditions based on the high oil residue content data to obtain pressing condition data; Step S342: Evaluate the temperature adjustment requirements for the pressing condition data to obtain temperature adjustment requirement data; Step S343: Design an intelligent temperature control algorithm based on the temperature adjustment requirement data to generate temperature control algorithm data; Step S344: Perform a simulation of intelligent temperature control for pressing according to the temperature control algorithm data to obtain intelligent temperature control data for pressing.

10. An intelligent digital-based grease production control system, characterized in that, For executing a grease production control method based on intelligent digitization as described in claim 1, the intelligent digitization-based grease production control system includes: A processing requirement analysis module, configured to obtain grease material data, and perform processing requirement analysis based on the grease material data to obtain oil material processing requirement data; An oil material impurity analysis module, configured to obtain a hydraulic press model; input the oil material processing requirement data into the hydraulic press model, perform a pressing simulation, and statistically analyze the low grease extraction rate of the pressing simulation result to obtain a low grease extraction rate; analyze the oil material impurities of the grease material according to the low grease extraction rate to generate oil material impurity data; analyze the grease accumulation in the filter of the hydraulic press model according to the oil material impurity data to generate filter grease accumulation data; A pressing intelligent temperature control module, configured to obtain the pressing data of the screw oil press, and perform oil residue content analysis to obtain high oil residue content data; perform intelligent temperature control for pressing according to the high oil residue content data to obtain intelligent temperature control data for pressing; An intelligent production scheduling module, configured to perform intelligent production scheduling according to the oil material processing requirement data and the filter grease accumulation data to obtain intelligent production data; upload the intelligent production data and the intelligent temperature control data for pressing to the intelligent control system to execute the grease production control task.

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