Methods, apparatus, systems, and media for conditioning mixed juice

Abstract

This invention discloses a method, equipment, system, and medium for adjusting mixed fruit juices, relating to the field of fruit juice production technology. The method includes: reading multiple fruit and vegetable raw materials for a target mixed fruit juice; performing a risk analysis of the negative effects of mixed juicing on the multiple fruit and vegetable raw materials, establishing a first classification map; performing an equipment adaptability analysis of mixed juicing on the multiple fruit and vegetable raw materials, establishing a second classification map; performing flavor intensity analysis and clustering on the multiple fruit and vegetable raw materials, establishing a third classification map; and fusing the first, second, and third classification maps to classify the multiple fruit and vegetable raw materials, identifying at least two raw material categories, performing independent pretreatment to obtain corresponding intermediate products, and then mixing them. This invention solves the technical problem in existing technologies where direct mixing and juicing of multiple fruit and vegetable raw materials easily leads to negative interactions and affects the quality of the juice, achieving the technical effect of reducing the negative effects of mixed juicing and improving the stability of the mixed fruit juice quality.

Description

Technical Field

[0001] This invention relates to the field of fruit juice production technology, and more specifically to methods, equipment, systems and media for adjusting mixed fruit juices. Background Technology

[0002] In the production of mixed fruit juice, multiple fruit and vegetable raw materials are usually crushed and juiced simultaneously. After the cells of different fruit and vegetable raw materials are broken, they release their own enzymes, polyphenols and flavor precursors. Due to the differences in their biochemical and physical properties, adverse reactions can easily occur during the blending process, such as intensified enzymatic browning, abnormal color, flavor masking or deterioration, and decreased system stability. As a result, it is difficult to maintain the stability of the color, taste and overall quality of the mixed fruit juice, affecting product consistency. Summary of the Invention

[0003] This application provides a method, apparatus, system, and medium for adjusting mixed fruit juices, which addresses the technical problem that direct mixing and juicing of multiple fruit and vegetable raw materials in the prior art easily leads to negative interactions and affects the quality of the juice.

[0004] In view of the above problems, this application provides a method, apparatus, system and medium for adjusting mixed fruit juices.

[0005] A first aspect of this application provides a method for adjusting mixed fruit juice, the method comprising: The process involves: reading multiple fruit and vegetable raw materials for the target mixed juice; conducting a risk analysis of the negative effects of mixed juicing on the multiple fruit and vegetable raw materials and establishing a first classification map; conducting an equipment adaptability analysis on the multiple fruit and vegetable raw materials for mixed juicing and establishing a second classification map; analyzing and clustering the flavor intensity of the multiple fruit and vegetable raw materials and establishing a third classification map; integrating the first, second, and third classification maps to classify the multiple fruit and vegetable raw materials into at least two raw material categories; performing independent pretreatment on the raw materials of at least two categories to obtain corresponding intermediate products before mixing.

[0006] A second aspect of this application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the adjustment method for mixing fruit juice provided in this application when executing the executable instructions stored in the memory.

[0007] A third aspect of this application provides a conditioning system for mixed fruit juices, the system comprising: The system comprises the following modules: an information reading module for reading multiple fruit and vegetable raw materials for the target mixed juice; a risk analysis module for analyzing the negative effects of mixing and juicing the multiple fruit and vegetable raw materials and establishing a first partitioning map; an adaptability analysis module for analyzing the equipment adaptability of mixing and juicing the multiple fruit and vegetable raw materials and establishing a second partitioning map; an analysis and clustering module for analyzing and clustering the flavor intensity of the multiple fruit and vegetable raw materials and establishing a third partitioning map; and a processing module for fusing the first, second, and third partitioning maps to classify the multiple fruit and vegetable raw materials, identifying at least two raw material categories, performing independent preprocessing on the raw materials of the at least two categories to obtain corresponding intermediate products before mixing.

[0008] A fourth aspect of the embodiments of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the adjustment method for mixed fruit juice provided in this application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application reads multiple fruit and vegetable raw materials for a target mixed juice; performs a risk analysis of the negative effects of mixed juicing on the multiple fruit and vegetable raw materials, establishing a first classification map; performs an equipment adaptability analysis on the multiple fruit and vegetable raw materials for mixed juicing, establishing a second classification map; performs flavor intensity analysis and clustering on the multiple fruit and vegetable raw materials, establishing a third classification map; integrates the first, second, and third classification maps to classify the multiple fruit and vegetable raw materials, identifying at least two raw material categories; and performs independent pretreatment on the raw materials of at least two categories, obtaining corresponding intermediate products before mixing. This invention solves the technical problem in existing technologies where direct mixing and juicing of multiple fruit and vegetable raw materials easily leads to negative interactions and affects juice quality. By rationally classifying the fruit and vegetable raw materials and implementing independent pretreatment before mixing, the technical effect of reducing the negative effects of mixed juicing and improving the stability of mixed juice quality is achieved. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of the process for adjusting mixed fruit juice provided in an embodiment of this application; Figure 2This is a schematic diagram of the structure of an exemplary electronic device of this application; Figure 3 This is a schematic diagram of the adjustment system for mixing fruit juice provided in an embodiment of this application.

[0012] Explanation of reference numerals in the attached drawings: Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus Interface 305, Information Reading Module 11, Risk Analysis Module 12, Adaptive Analysis Module 13, Analysis and Clustering Module 14, Processing Module 15. Detailed Implementation

[0013] This application provides a method, equipment, system, and medium for adjusting mixed fruit juices. It addresses the technical problem that direct mixing and juicing of multiple fruit and vegetable raw materials can easily lead to negative interactions and affect the quality of the juice. By rationally dividing the fruit and vegetable raw materials and implementing independent pretreatments that isolate them from each other before mixing, the technical effect of reducing the negative effects of mixed juicing and improving the stability of the quality of the mixed fruit juice is achieved.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0016] Example 1, as Figure 1 As shown, this application provides a method for adjusting mixed fruit juice, the method comprising: Step S100: Read the various fruit and vegetable ingredients of the target mixed juice.

[0017] In this embodiment of the application, during the adjustment of the mixed fruit juice, the formula information corresponding to the target mixed fruit juice is read. The formula information pre-records the composition scheme of the target mixed fruit juice. Based on the composition scheme, the various fruit and vegetable raw materials participating in the preparation are obtained and determined one by one from the formula information.

[0018] Step S200: Conduct a risk analysis of the negative effects of mixed juicing of the various fruit and vegetable raw materials, and establish a first partition map.

[0019] In this embodiment, when analyzing the negative effects of mixed juicing of multiple fruit and vegetable raw materials, a biochemical characteristic database of the raw materials is first established, recording the key enzymes and their activity levels, polyphenol content and oxidizability, and the types of flavor precursors for each raw material. Then, based on the biochemical characteristic database, under a simulated environment of cell rupture and contact, negative effects are predicted for any two or more raw materials from the multiple fruit and vegetable raw materials, constructing a negative effect matrix. Subsequently, based on the negative effect index corresponding to each raw material combination in the negative effect matrix, raw material combinations with negative effect indices higher than a preset index are marked as risk-exclusive groups. Finally, based on the risk-exclusive groups, the negative effect risk relationships among multiple fruit and vegetable raw materials are divided, generating a first partitioning map.

[0020] Furthermore, the method provided in the application embodiment, which includes a risk analysis of the negative effects of mixed juicing of the various fruit and vegetable raw materials and the establishment of a first partitioning map, further includes: A biochemical characteristic database of fruit and vegetable raw materials is established. This database records at least the key enzymes and their activity levels, polyphenol content and oxidizability, and the types of flavor precursors for each raw material. Based on this database, negative effects are predicted for any two or more raw materials in a simulated environment of cell rupture and contact, and a negative effect matrix is ​​established. Based on this negative effect matrix, combinations of raw materials with negative effect indices higher than a preset indices are marked as risk-exclusive groups. The first partitioning map is generated based on these risk-exclusive groups.

[0021] In this embodiment, when establishing a database of the biochemical characteristics of fruit and vegetable raw materials, standardized sampling of various fruit and vegetable raw materials is first performed. Representative edible parts are selected, and pre-treatment processes such as washing, impurity removal, and homogenization are completed under controlled conditions to ensure the comparability of subsequent test results. Subsequently, biochemical component analysis is performed on each fruit and vegetable raw material sample under uniform testing conditions. Key enzymes in each fruit and vegetable raw material are identified and measured using enzymatic analysis, and their activity intensity is characterized by the catalytic rate per unit mass of raw material per unit time, for example, expressing the activity level of polyphenol oxidase or peroxidase in U / g. Simultaneously, the content of polyphenols in each fruit and vegetable raw material is determined using physicochemical detection methods, and its oxidizability is assessed by combining oxygen exposure experiments or oxidation rate parameters, for example, measuring polyphenol content in mg / 100g and reflecting oxidation sensitivity in conjunction with oxidation induction time. Furthermore, flavor precursors in fruit and vegetable raw materials are identified and classified using component analysis methods. They are labeled according to categories such as alcohols, aldehydes, esters, and organic acid precursors to reflect their potential pathways in flavor generation or transformation during processing. After collecting biochemical characteristic parameters such as key enzymes and their activity intensity, polyphenol content and oxidizability, and types of flavor precursors, the parameters are standardized and processed using unified dimensions. They are then stored in a structured manner according to the types of fruit and vegetable raw materials, thus forming a biochemical characteristic database to describe the biochemical reaction potential of different fruit and vegetable raw materials after cell rupture.

[0022] Next, based on a biochemical characteristic database, negative effects are predicted for any two or more raw materials from various fruits and vegetables under simulated conditions of cell rupture and contact. In this process, characteristic parameters corresponding to each fruit and vegetable raw material are extracted from the biochemical characteristic database. A digital reaction environment is constructed based on these parameters to characterize the blending state of the raw materials. A multiphysics simulation model is then run within this digital reaction environment to calculate enzymatic reactions, non-enzymatic browning, color fusion contamination, and changes in colloidal stability occurring under blending conditions for any two or more raw materials, obtaining corresponding change indices. Subsequently, the negative effects of different raw material combinations are comprehensively quantified based on these change indices, establishing a negative effect matrix to characterize the degree of negative effects from combinations of various fruits and vegetables.

[0023] Subsequently, the negative effect indices corresponding to each raw material combination in the negative effect matrix were compared with the preset effect indices. Raw material combinations with negative effect indices higher than the preset effect indices were identified and marked, forming risk exclusion groups. These risk exclusion groups were used to limit raw material combinations that posed a significant risk of negative effects under cell rupture and contact conditions and were unsuitable for simultaneous mixed juicing. This process determined the mutual exclusion relationships of negative effects among various fruit and vegetable raw materials.

[0024] After determining the risk exclusion groups, the risk relationships of negative effects among various fruit and vegetable raw materials are constrained based on the risk exclusion groups. Fruit and vegetable raw materials with risk exclusion relationships are divided into different raw material units, and the mutual exclusion relationships between each raw material unit are integrated and expressed, thereby generating the first partition map to characterize the risk distribution and mutual exclusion relationships of various fruit and vegetable raw materials at the level of negative effects of mixed juicing.

[0025] Furthermore, the method provided in the application embodiment, based on the biochemical characteristic database, predicts the negative effects of any two or more of the various fruit and vegetable raw materials under a simulated environment of cell rupture and contact, and establishes a negative effect matrix, further includes: Characteristic parameters of each of the various fruit and vegetable raw materials are extracted from the biochemical characteristic database. Based on the characteristic parameters of each raw material, a digital reaction environment simulating the blending system is constructed. In the digital reaction environment, a multiphysics simulation model is run. The multiphysics simulation model is used to calculate the change index of enzymatic reaction, non-enzymatic browning, color fusion contamination, and colloidal stability for any two or more raw materials. The negative effect matrix is ​​calculated based on the change index.

[0026] In this embodiment of the application, when extracting characteristic parameters of each raw material from a variety of fruits and vegetables from a biochemical characteristic database, the pH value, key enzyme activity concentration, polyphenol concentration, and oxidation rate constant of each raw material are read one by one according to the corresponding data records. The read characteristic parameters are then processed to unify the units and standardize the values ​​so that each characteristic parameter is within a range that can be directly compared and calculated. For example, the polyphenol concentration is uniformly converted to mg / L and the oxidation rate is uniformly expressed as the oxidation ratio per unit time, thereby completing the preparation of the basic reaction parameters of each fruit and vegetable raw material.

[0027] After extracting the characteristic parameters, a digital reaction environment simulating the blending system is constructed based on the characteristic parameters of each raw material. This digital reaction environment is built by simultaneously introducing the characteristic parameters of two or more fruit and vegetable raw materials involved in the blending into the same computational space. It is used to simulate the state of cell rupture, full release of internal components, and contact reaction of fruit and vegetable raw materials after crushing and juicing. During the construction process, the overall acid-base conditions of the blending system are calculated and determined according to the pH value of each raw material. For example, if the pH of raw material A is 3.8 and the pH of raw material B is 4.2, the overall pH of the blending system can be taken as the midpoint between the two. At the same time, the concentrations of key enzyme activities and polyphenols are used as the initial concentration inputs for the reaction, and the oxidation rate constant is used as a control parameter describing the rate of oxygen participation in the reaction. The reaction temperature, oxygen exposure intensity, and reaction duration are uniformly set, thereby forming a digital reaction environment that can reflect the main reaction conditions during the blending and juicing process.

[0028] When running a multiphysics simulation model in a digital reaction environment, the multiphysics simulation model calculates the changes of various negative effects in a time-step manner. For non-enzymatic browning, the degree of oxidation of polyphenols under oxygen-involved conditions is calculated step by step over time. Specifically, the initial concentration of polyphenols and their corresponding oxidation rate constants for each fruit and vegetable raw material in the blend system are first read, and a uniform reaction time step is set, for example, 1 minute as a time step. Assuming that the initial concentration of polyphenols in a certain raw material combination is 100 mg / L and the oxidation rate constant corresponds to 5% oxidation per minute, then the amount of polyphenols oxidized in the first time step is 5 mg / L, and the remaining polyphenol concentration is 95 mg / L. In the second time step, the calculation continues with 95 mg / L as the new starting concentration, and the amount of polyphenols oxidized is 4.75 mg / L. The calculation is repeated in each time step in the above manner, and the amount of polyphenols oxidized in each time step is accumulated. For example, the accumulated oxidation amount in 10 minutes is about 40 mg / L. Finally, the accumulated oxidation amount is normalized with a preset reference value. For example, with 50 mg / L as the full scale, the change index of non-enzymatic browning is 0.8.

[0029] In the calculation process for color blending contamination, within a digital reaction environment, the amount of pigment released by each fruit and vegetable raw material per unit time after crushing and juicing is first determined and expressed as an equivalent pigment concentration or absorbance change value. For example, in the first time step, if raw material A releases 10 pigment units and raw material B releases 6 pigment units, the comprehensive pigment value obtained under blending conditions is 16 pigment units. If the pigment value of raw material A in the corresponding time step is 10 pigment units when it exists alone, the color shift in that time step is 6 pigment units. In subsequent time steps, the newly released pigment amount of each raw material is calculated separately, and the process of superposition and comparison is repeated. For example, in the second time step, the comprehensive pigment value is 14 pigment units and the single raw material is 9 pigment units, so the shift is 5 pigment units. The color shifts generated in each time step are accumulated. For example, the accumulated shift in 10 time steps is 48 pigment units, and this accumulated shift is normalized with a preset maximum shift value to obtain the color blending contamination change index.

[0030] In the calculation process for changes in colloidal stability within a digital reaction environment, the initial dispersion state of colloidal particles in the blend system is first determined, for example, using an average particle size of 1.0 micrometers as the initial marker of a stable state. In the first time step, based on the concentration changes of colloidal components such as pectin, protein, or fiber in each fruit and vegetable raw material, the average particle size after aggregation is calculated, for example, increasing from 1.0 micrometers to 1.1 micrometers. In the second time step, the calculation continues with 1.1 micrometers as the new starting state, increasing the average particle size to 1.25 micrometers. The particle size change is continuously calculated in each time step in the same manner, and the increment of particle size change from a dispersed state to an aggregated state is accumulated, for example, an accumulated increase of 0.8 micrometers over 10 time steps. This accumulated change is compared with a preset stability threshold and normalized, for example, using 1.0 micrometers as the upper limit of instability, resulting in a colloidal stability change index of 0.8.

[0031] After obtaining the change indices for enzymatic reaction, non-enzymatic browning, color fusion contamination, and colloidal stability, each change index was normalized to ensure that different types of change indices were within the same numerical range. The normalized change indices corresponding to the same raw material combination were integrated to form a comprehensive index to characterize the degree of negative effects of the raw material combination under mixed juicing conditions. Finally, the comprehensive indexes corresponding to different raw material combinations were arranged according to the relationship between the raw material combinations to obtain the negative effect matrix.

[0032] Step S300: Perform equipment adaptability analysis on the mixed juicing of the various fruit and vegetable raw materials, and establish a second classification map.

[0033] In this embodiment, when performing equipment adaptability analysis for mixed juicing of various fruit and vegetable raw materials, physical property parameters such as hardness, fiber strength, juice yield, and pectin content of the various fruit and vegetable raw materials are first collected to determine the crushing and juicing equipment used in the target mixed juice production workshop and establish a corresponding twin crushing model. Then, in the twin crushing model, based on the collected multiple sets of physical property parameters, the processing of any two or more fruit and vegetable raw materials under unified equipment parameter conditions is simulated and evaluated. The processing efficiency loss and quality loss caused by differences in the physical properties of the raw materials are quantified, forming an equipment mismatch degree. Finally, based on the comparison results between the equipment mismatch degree and a preset mismatch threshold, raw material combinations with high equipment mismatch degrees are distinguished, and a second partitioning map is generated to characterize the matching relationship between various fruit and vegetable raw materials and the crushing and juicing equipment.

[0034] Furthermore, the method provided in the application embodiments, which involves analyzing the equipment adaptability of the mixed juicing of the various fruit and vegetable raw materials and establishing a second partition map, further includes: Multiple sets of physical property parameters of various fruit and vegetable raw materials are collected, including hardness, fiber strength, juice yield, and pectin content; the crushing and juicing equipment in the production workshop of the target mixed juice is determined, and a twin crushing model is established; based on the multiple sets of physical property parameters, for any two or more raw materials in the twin crushing model, the efficiency loss and quality loss of processing different raw materials under the same equipment parameters are evaluated, and the loss is quantified as equipment mismatch degree; raw material combinations with equipment mismatch degree higher than a preset mismatch threshold are distinguished, and a second classification map is generated for different categories of raw materials.

[0035] In this embodiment, multiple sets of physical property parameters of various fruit and vegetable raw materials are first collected based on a unified physical testing method. Hardness is obtained through compression testing. For example, under the same loading rate, the maximum force when the structure of an apple is damaged is about 50N, and that of a carrot is about 120N. Similarly, the corresponding hardness values ​​can be obtained for raw materials such as pears and beets. Fiber strength is obtained through shear testing. For example, the fiber strength of an apple is about 20N, and that of a carrot is about 45N. High-fiber raw materials such as celery show higher fiber strength. Juice yield is obtained by measuring the ratio of juice mass to raw material mass after processing a unit mass of raw material under fixed crushing and juicing conditions. For example, the juice yield of an apple is about 70%, that of a carrot is about 45%, and that of citrus raw materials is usually higher. Pectin content is obtained through chemical extraction and quantitative analysis methods. For example, the pectin content of an apple is about 1.1g / 100g, that of a carrot is about 0.4g / 100g, and that of citrus raw materials is usually higher.

[0036] After collecting multiple sets of physical property parameters, the actual crushing and juicing equipment used in the target mixed fruit juice production workshop was determined. A twin crushing model was then established based on the structural and operational parameters of this equipment, for example, using a rotation speed of 3000 rpm, a pressing pressure of 2.5 MPa, a screen aperture of 0.5 mm, and a processing time of 30 s as input parameters. The twin crushing model parametrically describes the compressive, shear, and extrusive forces applied to the fruit and vegetable raw materials by the equipment under the aforementioned operating conditions. It is used to reproduce the stress and crushing process of different fruit and vegetable raw materials such as apples, carrots, pears, and oranges within the crushing chamber in a simulation environment. This model is also applicable to other fruit and vegetable raw materials with different physical properties.

[0037] In the twin crushing model, based on the aforementioned multiple sets of physical property parameters, the crushing and juicing process of any two or more fruit and vegetable raw materials under uniform equipment parameters is simulated and evaluated. Uniform equipment parameters refer to maintaining constant rotation speed, pressure, screen aperture, and processing time throughout the evaluation process. In the efficiency loss assessment, the simulated crushing time required to achieve effective crushing is first calculated based on the raw material hardness and fiber strength. For example, under the same equipment parameters, the simulated crushing time for apples alone is approximately 20 seconds, for carrots alone it is approximately 40 seconds, and for pears alone it is approximately 18 seconds. When apples and carrots are mixed, the simulated crushing time is approximately 35 seconds. Therefore, the increase in crushing time compared to apples alone is (35−20) / 20=0.75, which characterizes the degree of decrease in crushing efficiency. Subsequently, the change in juice output of the mixed treatment is calculated based on the juice yield parameter. For example, the juice yield of apples alone is 70%, and the simulated juice yield of apples and carrots is about 55%. The juice yield decrease ratio is (70−55) / 70≈0.21, which is used to characterize the loss of juicing efficiency. Similarly, the same calculation method can be used for other combinations such as apples and pears, oranges and carrots.

[0038] In the process of quality loss assessment, the changes in juice quality after juicing are simulated based on the pectin content parameter. For example, the simulated juice viscosity of apples alone is about 1.2 Pa·s, while the simulated juice viscosity of apples and carrots mixed together is about 1.8 Pa·s. The viscosity shift ratio is (1.8−1.2) / 1.2=0.5. This ratio is used to characterize the quality shift caused by differences in pectin content and changes in particle structure. For other fruit and vegetable combinations, such as oranges and apples, pears and beets, the corresponding quality change ratios can also be obtained through the same calculation method.

[0039] After obtaining the quantitative results of efficiency and quality losses, the results are processed to unify dimensions. The percentage increase in crushing time, the percentage decrease in juice yield, and the percentage change in viscosity are used as equipment adaptability evaluation indicators. Preset weighting coefficients are assigned to each of these parameters. The adverse effects of mixed processing on efficiency and quality are comprehensively quantified as equipment mismatch. The equipment mismatch reflects the degree of compatibility between any two or more fruit and vegetable raw materials and the equipment under unified crushing and juicing parameters. The value of the equipment mismatch is limited to between 0 and 1; a value closer to 0 indicates better equipment adaptability, and a value closer to 1 indicates poorer equipment adaptability.

[0040] After completing the above determination, raw material combinations with equipment mismatch higher than the preset mismatch threshold are distinguished so that they are not classified into the same processing category, while raw material combinations with equipment mismatch lower than the mismatch threshold can be classified into the same processing category. Finally, based on the above distinction results, various fruit and vegetable raw materials are classified and organized to generate a second classification map that reflects the adaptability relationship between various fruit and vegetable raw materials and crushing and juicing equipment.

[0041] Furthermore, in the method provided in the application embodiments, after determining the crushing and juicing equipment in the production workshop of the target mixed fruit juice, it further includes: If the production workshop contains multiple crushing and juicing equipment, multiple twin crushing models are established. For any two or more raw materials, the efficiency loss and quality loss of processing different raw materials under the same equipment parameters are evaluated using the multiple twin crushing models. Only when the equipment mismatch of multiple crushing and juicing equipment is higher than the preset mismatch threshold, the corresponding raw material combination is distinguished. Otherwise, the corresponding raw material combination is divided into a group and the equipment type is marked.

[0042] In this embodiment, when a variety of crushing and juicing equipment is configured in the production workshop of the target mixed fruit juice, such as a screw press, a high-speed centrifuge, and an ultrafine grinder, a twin crushing model is first established for each type of crushing and juicing equipment. The establishment of the twin crushing model is based on the method of analyzing equipment structural parameters and modeling operating parameters. For the screw press, the screw structure dimensions, pressing channel morphology, screw speed, and axial pressure are parametrically modeled to simulate the crushing and juicing process of the raw material under continuous extrusion and shearing conditions. For the high-speed centrifuge, the drum radius, speed, centrifugal acceleration, and material residence time are parametrically modeled to simulate the crushing, separation, and juicing process of the raw material under the centrifugal force generated by high-speed rotation. For the ultrafine grinder, the grinding media gap, shear frequency, energy density, and processing time are parametrically modeled to simulate the particle refinement and juice release process of the raw material under high-frequency shearing and grinding conditions, thereby forming twin crushing models that can reflect the processing mechanism of different crushing and juicing equipment.

[0043] After establishing multiple twin crushing models, the processing of any two or more fruit and vegetable raw materials under unified equipment parameters was evaluated based on these models. Unified equipment parameters refer to keeping key operating parameters constant within the same type of crushing and juicing equipment to eliminate interference from equipment parameter adjustments. During the evaluation, the twin crushing models were used to simulate and calculate the crushing time, energy consumption changes, juice yield, and juice particle state of different combinations of fruit and vegetable raw materials in screw presses, high-speed centrifuges, and ultrafine grinders. By comparing the mixed processing results with the single-raw-material processing baseline, efficiency and quality losses were calculated in each type of equipment, and these losses were converted into corresponding equipment mismatch degrees, thus obtaining multiple sets of equipment mismatch degrees for the same raw material combination under different crushing and juicing equipment conditions.

[0044] After obtaining the equipment mismatch degree corresponding to the same raw material combination under various crushing and juicing equipment conditions, the multiple sets of equipment mismatch degrees are compared with the preset mismatch threshold one by one. Only when the equipment mismatch degree of the raw material combination is higher than the preset mismatch threshold under various crushing and juicing equipment such as screw press, high-speed centrifuge and ultrafine grinder, it is determined that the raw material combination does not have equipment adaptability under the current production workshop conditions, and the raw material combination is treated separately. Conversely, when the equipment mismatch degree of the raw material combination is lower than the preset mismatch threshold under at least one crushing and juicing equipment, it is determined that the raw material combination has acceptable equipment adaptability under this type of equipment condition, the raw material combination is divided into the same treatment group, and the equipment type matching the treatment group is marked, thereby completing the adaptability judgment and classification of fruit and vegetable raw material combinations under multiple equipment conditions.

[0045] Step S400: Analyze and cluster the flavor intensity of the various fruit and vegetable raw materials to establish a third partitioning map.

[0046] In this embodiment, when analyzing and clustering the flavor intensity of various fruit and vegetable raw materials, the various fruit and vegetable raw materials are first sampled, and the flavor substances in the sampled raw materials are qualitatively and quantitatively analyzed. The quantitative results are then assigned to the corresponding flavor intensity values ​​of each fruit and vegetable raw material. Subsequently, cluster analysis is performed on the various fruit and vegetable raw materials based on the flavor intensity values, so that the fruit and vegetable raw materials with significant dominant flavor characteristics are classified into independent categories, thereby generating a third partition map used to characterize the differences and distribution relationships of the flavor intensity of various fruit and vegetable raw materials.

[0047] Furthermore, the method provided in the application embodiments, which analyzes and clusters the flavor intensity of the various fruit and vegetable raw materials to establish a third partitioning map, also includes: The various fruit and vegetable raw materials are sampled, and qualitative and quantitative analysis of flavor substances is performed to assign flavor intensity values. The various fruit and vegetable raw materials are clustered according to the flavor intensity values, wherein the clustering follows the principle of separate division of dominant flavors to generate the third division map.

[0048] In this embodiment of the application, during the sampling and qualitative and quantitative analysis of flavor substances of various fruit and vegetable raw materials, edible parts of each fruit and vegetable raw material are first selected and sampled, with a uniform sampling weight of 100g. After cleaning, the samples are cut and homogenized with 100mL of deionized water to fully break down the cell structure and release flavor substances. The homogenized liquid is then divided into a volatile flavor substance detection part and a non-volatile flavor substance detection part. For the volatile flavor substance detection part, 10mL of homogenized liquid is added with 2g of sodium chloride and equilibrated at 40℃ for 10min to promote the release of volatile substances. For the non-volatile flavor substance detection part, the supernatant is collected after centrifugation at 8000rpm for 10min for subsequent determination, thereby obtaining test samples that can be directly compared under uniform pretreatment conditions.

[0049] In the qualitative analysis of flavor compounds, gas chromatography-mass spectrometry (GC-MS) is used to analyze volatile flavor compounds. After sample injection, multiple peaks are separated according to chromatographic retention time, and mass spectrometric fragment signals are obtained for each peak. The names of the substances are determined by comparing the retention time and fragment signals with those of standards, thus completing the qualitative identification of flavor compounds. For example, ethyl butyrate and hexanal can be identified in apple samples, β-pinene in carrot samples, and limonene in citrus samples. The identification results are recorded as substance names and retention times. High-performance liquid chromatography (HPLC) is used to analyze non-volatile flavor compounds. Organic acids and sugars are qualitatively identified by comparing retention times with standards. For example, malic acid, citric acid, glucose, and fructose can be identified. The identification results are also recorded as substance names and retention times, thus obtaining the flavor compound composition information for each fruit and vegetable raw material.

[0050] In the quantitative analysis of flavor compounds, a standard curve is established for the qualitatively identified target flavor compounds. Specifically, standard solutions with multiple concentration gradients are prepared and their peak areas are measured under the same instrument conditions. The correlation between peak area and concentration is used to form a calibration curve. Subsequently, the peak area of ​​the corresponding flavor compound in the sample is measured and substituted into the calibration curve to obtain the concentration of the compound in the sample, which is then converted into a content value per unit mass of sample. Taking ethyl butyrate as an example, the standard curve example shows five gradients: 0.1, 0.5, 1, 5, and 10 mg / L. The concentration of ethyl butyrate in the apple sample was measured to be 2.0 mg / L. Combining the total pretreatment volume of 100 mL and the sample mass of 100 g, the content was calculated to be 2.0 mg / L × 0.1 L / 0.1 kg = 2.0 mg / kg. Taking limonene as an example, the concentration of limonene in a certain citrus sample was measured to be 15 mg / L. The content was calculated to be 15 mg / L × 0.1 L / 0.1 kg = 15 mg / kg. Taking β-pinene as an example, the concentration of β-pinene in a certain carrot sample was measured to be 0.4 mg / L. The content was calculated to be 0.4 mg / L × 0.1 L / 0.1 kg = 0.4 mg / kg. The same quantitative conversion is performed for sugars and organic acids. For example, if the soluble sugar content of an apple sample is measured to be 110 g / L, the sugar content is calculated as 110 g / L × 0.1 L / 0.1 kg = 110 g / kg. If the total acid content is measured to be 4.5 g / L, the acid content is calculated as 4.5 g / L × 0.1 L / 0.1 kg = 4.5 g / kg. The above values ​​are used to illustrate the quantitative conversion process and do not constitute a limitation on the range of raw materials or parameters.

[0051] In assigning flavor intensity values, the quantitative results are converted into flavor intensity values ​​on a uniform scale. The aroma contribution and basic flavor contribution are calculated separately and then normalized and summarized. For the aroma contribution, the qualitatively and quantitatively identified volatile flavor compounds are selected, and their contents are divided by the corresponding odor thresholds to obtain monomer contribution values. The contribution values ​​of each monomer are then summed to obtain the aroma contribution. For example, if the ethyl butyrate content in an apple sample is 2.0 mg / kg and the ethyl butyrate odor threshold is 0.02 mg / kg, the monomer contribution value is 2.0 / 0.02 = 100. If the hexanal content is 0.3 mg / kg and the hexanal odor threshold is 0.05 mg / kg, the monomer contribution value is 0.3 / 0.05 = 6. The sum of the two values ​​gives an aroma contribution of 106. For the contribution of basic flavor, the sugar-acid ratio is calculated based on sugar and acid content as a basic flavor balance index. For example, if an apple sample has a sugar content of 110 g / kg and an acid content of 4.5 g / kg, the sugar-acid ratio is 110 / 4.5 ≈ 24.4. If a citrus sample has a sugar content of 95 g / kg and an acid content of 8.0 g / kg, the sugar-acid ratio is 95 / 8.0 = 11.9. Subsequently, the aroma contribution and sugar-acid ratio are converted into normalized values ​​of 0 to 1 for combined calculation. During normalization, the maximum and minimum values ​​among all raw materials to be analyzed are used as mapping boundaries. For example, if the aroma contribution of this batch of samples is at least 20 and at most 200, then the normalized value when the apple aroma contribution is 106 is (106−20) / (200−20)=86 / 180≈0.48. If the sugar-acid ratio is at least 8 and at most 30, then the normalized value when the apple sugar-acid ratio is 24.4 is (24.4−8) / (30−8)=16.4 / 22≈0.75. Averaging the two normalized values, the apple flavor intensity value is (0.48+0.75) / 2≈0.62. Similarly, the flavor intensity values ​​of raw materials such as carrots, pears, beets, and citrus can be calculated. The above values ​​are used to illustrate the calculation steps and do not constitute a limitation on the range of values.

[0052] In the process of clustering various fruit and vegetable raw materials according to their flavor intensity values, the flavor intensity value corresponding to each fruit and vegetable raw material is used as the clustering input, and the distance metric is used to classify raw materials with similar flavor intensities into the same category. For example, if the calculated flavor intensity values ​​are 0.60 for pear, 0.62 for apple, 0.35 for carrot, 0.38 for beet, and 0.90 for citrus, then 0.60 to 0.62 can be classified into one category, 0.35 to 0.38 into another category, and 0.90 into a third category. During the clustering process, the principle of separating dominant flavors is followed. To avoid flavor masking, individuals with dominant flavors that are significantly stronger than other ingredients are separated. That is, before clustering, the difference between the flavor intensity value of a certain ingredient and the maximum flavor intensity value of the remaining ingredients is calculated. When the difference exceeds a preset difference range, the ingredient is directly classified into an independent category. For example, if the flavor intensity value of citrus is 0.90, while the maximum value of the remaining ingredients is 0.62, the difference is 0.28, which is a case where the flavor intensity is significantly stronger than the other ingredients. The citrus ingredient is separated into an independent category, thereby reducing the risk of it masking the flavor of other ingredients in the subsequent blending and juicing.

[0053] After completing the above clustering and separate classification of dominant flavors, the flavor categories to which each fruit and vegetable raw material belongs and the corresponding flavor intensity ranges of each category are organized and expressed to form a third classification map for characterizing the flavor intensity distribution and category relationships of various fruit and vegetable raw materials.

[0054] Step S500: Combine the first partition map, the second partition map and the third partition map to partition the various fruit and vegetable raw materials, and divide them into at least two raw material categories. Perform independent preprocessing on the raw materials of the at least two raw material categories to obtain the corresponding intermediate products, and then mix them.

[0055] In this embodiment, when classifying various fruit and vegetable raw materials by integrating the first, second, and third classification maps, the first, second, and third classification maps are first used as parallel classification criteria to align the correspondence of the same fruit and vegetable raw material in the three classification maps. Then, a consistency comparison is performed on the classification results of any two or more fruit and vegetable raw materials in the three classification maps. If a combination of raw materials is classified into different categories in any one classification map, the combination is determined to be unclassifiable. If a combination of raw materials is classified into the same category in all three classification maps, the combination is determined to be classifiable into the same raw material category. After completing the above consistency comparison, the various fruit and vegetable raw materials are grouped according to the raw material combination relationships that can be classified into the same raw material category, ensuring that the raw material categories remain distinct in the classification results corresponding to the three classification maps, thereby classifying the various fruit and vegetable raw materials into at least two raw material categories.

[0056] Next, at least two raw material categories undergo independent pretreatment to obtain corresponding intermediate products before being mixed. Specifically, firstly, core vulnerability analysis is performed on at least two raw material categories during the crushing and juicing process. Then, based on the core vulnerability results of each raw material category, the environmental parameters for crushing and juicing are adjusted to ensure that different raw material categories are crushed and juiced under their respective suitable environmental parameter conditions, and the corresponding primary juices are obtained separately through isolation. After obtaining at least two types of primary juices, the primary juices are sterilized separately, and after sterilization, each primary juice is transferred to an intermediate storage tank filled with inert gas for temporary storage to inhibit oxidation and quality deterioration. Finally, after all raw material categories of primary juices are prepared, the final mixing process is carried out.

[0057] Furthermore, the method provided in the application embodiments, which involves performing independent pretreatments on at least two raw material categories to obtain corresponding intermediate products before mixing, further includes: Core vulnerability analysis is performed on at least two raw material categories during crushing and juicing. Based on the core vulnerability, environmental matching for crushing and juicing is performed. Under the matched environmental parameters, the raw materials of at least two categories are crushed and juiced in isolation to obtain at least two types of primary juice. After sterilization, the at least two types of primary juice are transferred to an intermediate storage tank filled with inert gas for temporary storage, awaiting final mixing.

[0058] In this embodiment, a core vulnerability analysis is first performed on at least two raw material categories during crushing and juicing. The sensitivity to processing conditions is determined by comparing the color changes, flavor changes, and juice stability of different raw materials under the same crushing conditions. For example, during small-scale crushing at the same rotation speed and processing time, some raw materials show significant browning or flavor reduction within a short time, while others show little change. The former is then determined to be more sensitive to oxygen exposure and shear stress during crushing and juicing, thus obtaining the vulnerability characteristics of each raw material category during the crushing and juicing stage.

[0059] After completing the above analysis, the environmental conditions for crushing and juicing were adjusted based on the vulnerability characteristics exhibited by each raw material category. Specifically, for raw material categories prone to browning or flavor loss, the crushing speed was reduced or the processing time was shortened, and air ingress during feeding and crushing was minimized. For raw material categories with a harder structure and difficult juice extraction, the crushing intensity was appropriately increased or the crushing time was extended to ensure sufficient juice extraction. For example, for raw materials prone to browning, the speed was reduced from 3000 rpm to 2000 rpm and the processing time was controlled within 20 seconds, while for raw materials with more fiber, the speed was maintained at 3000 rpm and the processing time was extended to 40 seconds, thus allowing different raw material categories to be processed under their more suitable conditions.

[0060] Under well-matched environmental conditions, at least two categories of raw materials undergo separate crushing and juicing processes, meaning that different categories of raw materials are not crushed simultaneously in the same batch, but rather crushed and juiced separately, preventing direct contact between the raw materials during processing. After separate crushing and juicing, at least two primary juices corresponding to the raw material categories are obtained; for example, one type of primary juice is obtained from the first type of raw material, and another type of primary juice is obtained from the second type of raw material, thus avoiding adverse interactions during the crushing and juicing stage.

[0061] After obtaining at least two types of primary juices, each primary juice is sterilized separately using methods commonly used in fruit and vegetable juice processing, such as heat sterilization at 85°C for 30 seconds, to reduce microbial content and stabilize juice quality. After sterilization, each primary juice is transferred to an intermediate storage tank for temporary storage. Before transfer, inert gases such as nitrogen are introduced into the intermediate storage tank to replace the air inside, creating a low-oxygen environment and reducing the risk of oxidation and quality degradation during temporary storage. Once all primary juices are prepared, final mixing is performed.

[0062] In summary, the embodiments of this application have at least the following technical effects: This application reads multiple fruit and vegetable raw materials for a target mixed juice; performs a risk analysis of the negative effects of mixed juicing on the multiple fruit and vegetable raw materials, establishing a first classification map; performs an equipment adaptability analysis on the multiple fruit and vegetable raw materials for mixed juicing, establishing a second classification map; performs flavor intensity analysis and clustering on the multiple fruit and vegetable raw materials, establishing a third classification map; integrates the first, second, and third classification maps to classify the multiple fruit and vegetable raw materials, identifying at least two raw material categories; and performs independent pretreatment on the raw materials of at least two categories, obtaining corresponding intermediate products before mixing. This invention solves the technical problem in existing technologies where direct mixing and juicing of multiple fruit and vegetable raw materials easily leads to negative interactions and affects juice quality. By rationally classifying the fruit and vegetable raw materials and implementing independent pretreatment before mixing, the technical effect of reducing the negative effects of mixed juicing and improving the stability of mixed juice quality is achieved.

[0063] Example 2: Based on the inventive concept of the method for adjusting mixed fruit juice in the foregoing embodiments, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of any of the methods described in Example 1 above.

[0064] Figure 2 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 2 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0065] Example 3, based on the same inventive concept as the adjustment method for mixed fruit juice in the foregoing examples, such as... Figure 3 As shown, this application provides a conditioning system for mixing fruit juices. The system and method embodiments in this application are based on the same inventive concept. The system includes: The information reading module 11 is used to read multiple fruit and vegetable raw materials of the target mixed juice; the risk analysis module 12 is used to perform a risk analysis of the negative effects of mixed juicing of the multiple fruit and vegetable raw materials and establish a first partition map; the adaptability analysis module 13 is used to perform an equipment adaptability analysis of mixed juicing of the multiple fruit and vegetable raw materials and establish a second partition map; the analysis and clustering module 14 is used to perform flavor intensity analysis and clustering of the multiple fruit and vegetable raw materials and establish a third partition map; the processing module 15 is used to merge the first partition map, the second partition map and the third partition map to classify the multiple fruit and vegetable raw materials, classify them into at least two raw material categories, perform independent preprocessing of the raw materials in at least two raw material categories to obtain corresponding intermediate products before mixing.

[0066] Furthermore, the system is also used to implement the following functions: A biochemical characteristic database of fruit and vegetable raw materials is established. This database records at least the key enzymes and their activity levels, polyphenol content and oxidizability, and the types of flavor precursors for each raw material. Based on this database, negative effects are predicted for any two or more raw materials in a simulated environment of cell rupture and contact, and a negative effect matrix is ​​established. Based on this negative effect matrix, combinations of raw materials with negative effect indices higher than a preset indices are marked as risk-exclusive groups. The first partitioning map is generated based on these risk-exclusive groups.

[0067] Furthermore, the system is also used to implement the following functions: Characteristic parameters of each of the various fruit and vegetable raw materials are extracted from the biochemical characteristic database. Based on the characteristic parameters of each raw material, a digital reaction environment simulating the blending system is constructed. In the digital reaction environment, a multiphysics simulation model is run. The multiphysics simulation model is used to calculate the change index of enzymatic reaction, non-enzymatic browning, color fusion contamination, and colloidal stability for any two or more raw materials. The negative effect matrix is ​​calculated based on the change index.

[0068] Furthermore, the system is also used to implement the following functions: Multiple sets of physical property parameters of various fruit and vegetable raw materials are collected, including hardness, fiber strength, juice yield, and pectin content; the crushing and juicing equipment in the production workshop of the target mixed juice is determined, and a twin crushing model is established; based on the multiple sets of physical property parameters, for any two or more raw materials in the twin crushing model, the efficiency loss and quality loss of processing different raw materials under the same equipment parameters are evaluated, and the loss is quantified as equipment mismatch degree; raw material combinations with equipment mismatch degree higher than a preset mismatch threshold are distinguished, and a second classification map is generated for different categories of raw materials.

[0069] Furthermore, the system is also used to implement the following functions: If the production workshop contains multiple crushing and juicing equipment, multiple twin crushing models are established. For any two or more raw materials, the efficiency loss and quality loss of processing different raw materials under the same equipment parameters are evaluated using the multiple twin crushing models. Only when the equipment mismatch of multiple crushing and juicing equipment is higher than the preset mismatch threshold, the corresponding raw material combination is distinguished. Otherwise, the corresponding raw material combination is divided into a group and the equipment type is marked.

[0070] Furthermore, the system is also used to implement the following functions: The various fruit and vegetable raw materials are sampled, and qualitative and quantitative analysis of flavor substances is performed to assign flavor intensity values. The various fruit and vegetable raw materials are clustered according to the flavor intensity values, wherein the clustering follows the principle of separate division of dominant flavors to generate the third division map.

[0071] Furthermore, the system is also used to implement the following functions: Core vulnerability analysis is performed on at least two raw material categories during crushing and juicing. Based on the core vulnerability, environmental matching for crushing and juicing is performed. Under the matched environmental parameters, the raw materials of at least two categories are crushed and juiced in isolation to obtain at least two types of primary juice. After sterilization, the at least two types of primary juice are transferred to an intermediate storage tank filled with inert gas for temporary storage, awaiting final mixing.

[0072] Example 4: Based on the same inventive concept as the adjustment method for mixed fruit juice in the foregoing examples, this application also provides a computer-readable storage medium storing a computer program that, when executed, implements the steps of any of the methods described in Example 1 above.

[0073] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for conditioning mixed fruit juice, characterized by, include: Read the various fruit and vegetable ingredients of the target mixed juice; A risk analysis of the negative effects of mixed juicing of the aforementioned fruit and vegetable raw materials was conducted, and a first partitioning map was established. Equipment adaptability analysis was conducted on the mixed juicing of the aforementioned fruit and vegetable raw materials, and a second classification map was established; Flavor intensity analysis and clustering were performed on the various fruit and vegetable raw materials to establish a third partitioning map; The first, second, and third classification maps are combined to classify the various fruit and vegetable raw materials, resulting in at least two raw material categories. The raw materials of the at least two categories are then subjected to independent pretreatment to obtain corresponding intermediate products before being mixed.

2. The adjustment method for mixed juice according to claim 1, characterized by, A risk analysis of the negative effects of mixed juicing of the aforementioned fruit and vegetable raw materials was conducted, and a first partition map was established, including: Establish a biochemical characteristic database of fruit and vegetable raw materials. The biochemical characteristic database shall record at least the key enzymes and their activity intensity, the content and oxidizability of polyphenols, and the types of flavor precursors contained in each raw material. Based on the biochemical characteristic database, negative effects are predicted for any two or more of the various fruit and vegetable raw materials under a simulated environment of cell rupture and contact, and a negative effect matrix is ​​established. Based on the negative effect matrix, raw material combinations with negative effect indices higher than the preset effect index are marked as risk-exclusive groups; The first partition map is generated based on the mutually exclusive risk groups.

3. The conditioning method for mixed juice as claimed in claim 2, wherein, Based on the aforementioned biochemical characteristic database, negative effects are predicted for any two or more of the various fruit and vegetable raw materials under simulated conditions of cell rupture and contact, and a negative effect matrix is ​​established, including: Extract the characteristic parameters of each of the various fruit and vegetable raw materials from the biochemical characteristic database. Based on the characteristic parameters of each raw material, a digital reaction environment for simulating the blending system is constructed. In the digital reaction environment, a multiphysics simulation model is run. The multiphysics simulation model is used to calculate the change index of enzyme-catalyzed reaction, non-enzymatic browning, color fusion contamination, and colloidal stability for any two or more raw materials. The negative effects matrix is ​​calculated based on the change index.

4. The adjustment method for mixed juice according to claim 1, wherein Equipment adaptability analysis was conducted on the mixed juicing of the aforementioned multiple fruit and vegetable raw materials, and a second classification map was established, including: Multiple sets of physical property parameters of the various fruits and vegetables were collected, including hardness, fiber strength, juice yield and pectin content; Identify the crushing and juicing equipment within the production workshop of the target mixed fruit juice and establish a twin crushing model; Based on the aforementioned multiple sets of physical property parameters, in the twin crushing model, for any two or more raw materials, the efficiency loss and quality loss of processing different raw materials under unified equipment parameters are evaluated, and the loss is quantified as equipment mismatch. The raw material combinations with equipment mismatch exceeding a preset mismatch threshold are distinguished, and a second classification map is generated for different categories of raw materials.

5. The conditioning method for mixed juice as claimed in claim 4, wherein, After determining the crushing and juicing equipment in the production workshop for the target mixed fruit juice, the following is also included: If the production workshop contains multiple crushing and juicing equipment, establish multiple twin crushing models; Using the aforementioned twin crushing models, the efficiency and quality losses of processing different raw materials under unified equipment parameters are evaluated for any two or more raw materials. Only when the equipment mismatch of multiple crushing and juicing equipment is higher than the preset mismatch threshold, the corresponding raw material combination is distinguished; otherwise, the corresponding raw material combination is grouped together and the equipment type is marked.

6. The method for adjusting mixed juice according to claim 1, wherein Flavor intensity analysis and clustering were performed on the various fruit and vegetable raw materials to establish a third partitioning map, including: The various fruit and vegetable raw materials were sampled, and qualitative and quantitative analysis of flavor substances was performed to assign flavor intensity values. The various fruit and vegetable raw materials are clustered according to the flavor intensity values, wherein the clustering follows the principle of dividing the dominant flavor separately, and the third division map is generated.

7. The method for adjusting mixed juice according to claim 1, wherein The process involves independently pre-processing raw materials from at least two different categories to obtain corresponding intermediate products, followed by mixing, including: Core vulnerability analysis is performed on at least two raw material categories during crushing and juicing. Based on the core vulnerability, environmental matching for crushing and juicing is performed. Under the matched environmental parameters, the raw materials of at least two raw material categories are crushed and juiced in isolation to obtain at least two types of primary juice. After sterilizing at least two types of primary juices, they are transferred to intermediate storage tanks filled with inert gas for temporary storage, awaiting final mixing.

8. An electronic device, comprising: The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the method for adjusting mixed fruit juice according to any one of claims 1-7.

9. Conditioning system for mixed fruit juices, characterized by The system is used to perform the adjustment method for mixing fruit juice as described in any one of claims 1-7, the system comprising: The information reading module is used to read the various fruit and vegetable ingredients of the target mixed juice; The risk analysis module is used to analyze the negative effects of mixing and juicing the various fruit and vegetable raw materials and to establish a first partitioning map. An adaptability analysis module is used to perform equipment adaptability analysis on the mixed juicing of the various fruit and vegetable raw materials and to establish a second partitioning map; The clustering analysis module is used to analyze and cluster the flavor intensity of the various fruit and vegetable raw materials and establish a third partitioning map. The processing module is used to merge the first partition map, the second partition map, and the third partition map to partition the various fruit and vegetable raw materials, divide them into at least two raw material categories, perform independent preprocessing on the raw materials of at least two raw material categories to obtain corresponding intermediate products, and then mix them.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the adjustment method for mixed fruit juice as described in any one of claims 1-7.

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