A pear juice production system and intelligent control method thereof

Abstract

The present invention relates to the field of food processing and intelligent control technology, and discloses a pear juice production system and its intelligent control method. The system includes: a raw material processing module for identifying, sorting, and cleaning pears entering the production line using visual sensors, image recognition equipment, and automated cleaning equipment; a juicing control module for controlling the operating state of the juicer based on real-time data; and a method comprising the following steps: obtaining raw material information: using sensors and image recognition technology to obtain physical property data such as the type, maturity, and hardness of the pears entering the production line, and then classifying and pre-processing the pears. By combining optimal control theory, robust control algorithms, system identification and adaptive adjustment algorithms, as well as deep learning and big data analysis technologies, the system achieves real-time optimization and adjustment of the juicer's operating parameters, automatically adapting to changes in different pear types and maturity levels, and detecting potential problems in advance and providing early warnings.

Description

Technical Field

[0001] The present invention relates to the technical field of food processing and intelligent control, and in particular to a pear juice production system and an intelligent control method thereof. Background Art

[0002] Pear juice, a popular juice product among consumers, is widely used in the beverage industry for its refreshing taste and rich nutrition. The traditional pear juice production process typically includes multiple steps, such as pear selection, cleaning, juicing, blending, sterilization, and filling. The operational precision and control quality in each step have a significant impact on the juice yield and taste stability of the final product. However, with the continuous growth of production demand and the increasing demand for product quality from consumers, traditional manual operations and experience-based production methods can no longer meet the high efficiency and high quality requirements of modern production. Therefore, how to improve the level of automation in pear juice production and precisely control every step of the production process has become a pressing issue that needs to be addressed in current technology.

[0003] Currently, most pear juice production lines still use the traditional method based on manual adjustment. Key parameters such as the juicer's speed and pressure are often set by the operator based on experience. Under this operating mode, the production effect and taste stability of the pear juice are heavily dependent on the operator's technical level and experience, resulting in consistency differences in the production process. Especially when the physical properties of the pears, such as type, maturity, and hardness, vary greatly, manual operation is often difficult to accurately control, thus affecting the juice yield and taste. Although some automated equipment has entered the market, these devices still lack flexible real-time adjustment mechanisms when dealing with different pear varieties and changes in fruit maturity, and cannot achieve optimal production results.

[0004] Furthermore, traditional production systems lack efficient fault diagnosis and prediction mechanisms. Equipment health often relies on manual inspections and regular maintenance, failing to provide real-time monitoring and fault warnings. Equipment failures during production are often addressed only after they occur, leading to production stagnation and reduced efficiency. Existing fault diagnosis technologies are mostly based on manual experience or simple equipment status monitoring, failing to effectively leverage big data and deep learning technologies for comprehensive equipment monitoring and early warning, increasing production line instability and the risk of downtime.

[0005] With the development of technologies such as artificial intelligence, the Internet of Things, and big data, automated control and intelligent management have gradually penetrated industrial production. By combining optimal control theory, robust control algorithms, system identification, and deep learning, pear juice production systems can achieve more efficient production process control, more accurate flavor adjustment, and real-time monitoring of equipment health. This intelligent pear juice production system not only dynamically adjusts the operating parameters of the juicer based on the physical characteristics of the pear, but also intelligently adjusts the juicer to meet the taste requirements of different consumers, thereby improving production efficiency and ensuring product quality. Summary of the Invention

[0006] In response to the deficiencies of the existing technology, the present invention provides a pear juice production system and an intelligent control method thereof, which solve the problems of inaccurate manual operation, low production efficiency and insufficient equipment fault diagnosis in the traditional pear juice production process.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A pear juice production system, comprising:

[0008] A raw material processing module, which is used to identify, sort, and clean the pears entering the production line based on visual sensors, image recognition equipment, and automated cleaning equipment;

[0009] Juicing control module, used to dynamically adjust the operating parameters of the juicer according to the hardness, moisture content and maturity of the pears and the pear juice production plan to optimize the juice yield;

[0010] An intelligent blending module automatically adjusts the sweetness, acidity, and concentration of pear juice based on market demand, consumer preference data, and pear juice production plans to achieve personalized taste blending for the finished pear juice.

[0011] Wherein, the pear juice production system also includes:

[0012] The system optimization control module is used to obtain and analyze production data, equipment status data and market feedback data, and generate and output an optimized pear juice production plan in real time based on the analysis results. At the same time, it performs fault diagnosis and prediction on the operating status of the pear juice production equipment, issues equipment operation warning information, and adjusts the operating status of the pear juice production equipment in real time.

[0013] The present invention also discloses an intelligent control method for pear juice production, which is used in conjunction with the pear juice production system, and includes the following steps:

[0014] Obtaining raw material information: Sensors and image recognition equipment are used to obtain the physical property data of pears entering the production line, and the pears are classified and pre-processed. The physical property data includes type, maturity, firmness, size, and surface defects.

[0015] Raw material sorting and cleaning: Based on the acquired physical property data, pears are accurately classified using automated sorting equipment. Surface impurities and pesticide residues are then removed through automated cleaning equipment to ensure that the raw materials entering the juicing process meet production standards.

[0016] Real-time monitoring and juicer adjustment: The real-time monitoring system monitors the working status of the juicer, adjusts the operating parameters of the juicer, and dynamically adjusts the control parameters of the juicer based on the monitoring data to optimize the juice yield and taste. The operating parameters of the juicer include speed, pressure, temperature, and pressing time.

[0017] Data collection and consumer preference analysis: The data collection and processing unit collects market demand and consumer preference data, and analyzes consumer taste preferences based on the collected data to provide decision support for subsequent pear juice taste formulation;

[0018] Intelligent blending and taste optimization: Based on consumer preference data and production data, deep learning algorithms are used to optimize the sweetness, acidity, and concentration of pear juice in real time to automatically adjust the taste of pear juice.

[0019] System optimization and optimal control: Combining optimal control theory and robust control algorithms, the operating parameters of the juicer are calculated and optimized in real time to ensure that the juice yield is improved and the taste is stable under different pear classifications and maturity conditions;

[0020] Real-time feedback and adaptive adjustment during the production process: Based on real-time production data, the production process is dynamically modeled through system identification algorithms, and the system control strategy is automatically adjusted to cope with the uncertainties of different physical characteristics and production environments;

[0021] Big data analysis and equipment failure prediction: Using big data analysis technology, we monitor various data in the production process in real time, predict and optimize pear juice production plans, and use large model technology to diagnose and warn of equipment failures, so that maintenance measures can be taken in advance to prevent production interruptions.

[0022] Optimize production scheduling and maintenance plans: Intelligently optimize production scheduling and equipment maintenance plans based on the equipment health assessment model obtained during the production process.

[0023] The present invention provides a pear juice production system and an intelligent control method thereof, which have the following beneficial effects:

[0024] 1. The present invention achieves real-time optimization and adjustment of the juicer's operating parameters by adopting a technical solution that combines optimal control theory with a robust control algorithm. This technical solution can accurately adjust parameters such as the juicer's speed, pressure, and time according to different pear varieties, maturity, and changes in production conditions to ensure the optimal balance between juice yield and taste for each batch of pear juice. Compared with the existing solutions that use fixed parameters or simple manual adjustments, the present invention effectively overcomes the instability caused by raw material differences and significantly improves production efficiency and product consistency.

[0025] 2. This invention incorporates a system identification and adaptive adjustment algorithm, modeling the production process through real-time data analysis and dynamically optimizing the control strategy. This approach ensures that the production process can automatically adapt and optimize control parameters when faced with uncertainties in pear species and maturity. Compared to the manual adjustment or preset control schemes common in traditional technologies, this invention eliminates operator dependence by updating the control strategy in real time, avoiding errors caused by human intervention and significantly improving production flexibility and stability.

[0026] 3. The present invention integrates deep learning and big data analysis technologies to achieve fault prediction and equipment health management in the production process. Through intelligent analysis of equipment operation data, the system can predict potential faults in advance and issue early warnings. Compared with the existing technology that relies on manual inspection and regular maintenance, this technical solution greatly improves the reliability of equipment and the continuous operation time of the production line, avoids production interruptions and maintenance costs caused by equipment failures, and improves production efficiency and equipment service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a framework diagram of the system of the present invention;

[0028] Figure 2 is a schematic diagram of a raw material processing module of the present invention;

[0029] Figure 3 is a schematic diagram of a juice extraction control module of the present invention;

[0030] Figure 4 Schematic diagram of the intelligent deployment module of the present invention;

[0031] Figure 5 A schematic diagram of the system optimization control module of the present invention;

[0032] Figure 6 Flowchart of the intelligent control method of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] Please see the attached Figure 1-Figure 5 The embodiment of the present invention provides a pear juice production system, comprising:

[0035] A raw material processing module, which is used to identify, sort, and clean the pears entering the production line based on visual sensors, image recognition equipment, and automated cleaning equipment;

[0036] The raw material processing module in this embodiment is primarily responsible for sorting and cleaning pears entering the pear juice production line. This module is the first step in the entire pear juice production system and directly impacts the subsequent juicing process and the quality of the pear juice. Its function is to perform preliminary screening of pear quality through automated means, removing substandard raw materials and cleaning to remove surface impurities and pesticide residues, thereby ensuring raw material safety and efficient production.

[0037] In this system, the raw material processing module is closely linked to the optimized control of the juicing process and subsequent recipe adjustments. Sorted pears are then sent to the juicing process according to specific standards, while cleaned pears provide higher-quality raw materials for subsequent operations.

[0038] In this embodiment, the raw material sorting unit primarily uses visual sensors to automatically identify pears entering the production line. This unit uses high-definition visual sensors installed above the production line, combined with image recognition technology, to detect the size, maturity, and appearance of each pear in real time. Specifically, the visual sensors use lighting and image acquisition equipment to capture information about the pear's appearance. Image processing algorithms analyze the image data to identify the pear's physical characteristics.

[0039] In some embodiments, the vision sensor uses a deep learning-based image classification algorithm, such as a convolutional neural network (CNN), to identify different pear varieties, sizes, and ripeness. Through multi-level image processing, the system can distinguish different pear types and ripeness levels, classifying the pears into multiple categories for subsequent juicing.

[0040] In practice, the system establishes a standardized classification model to accurately categorize pears based on characteristics such as size and maturity. For example, pears are divided into multiple categories, such as "ripe pears," "unripe pears," and "overripe pears." This ensures that raw materials entering the juicing process meet production requirements, thereby reducing the impact of poor quality raw materials on juicing quality.

[0041] The formula for the sorting process is as follows:

[0042] ;

[0043] in:

[0044] represents the classification results of pears, including variety, size and maturity category;

[0045] is the physical property vector of the pear: ,in, is the diameter, is weight, is the morphological factor, is the surface color distribution;

[0046] Characteristics of pear maturity: ,in, For sugar content, is the hardness, is the maturity index, is the reflectance of the peel;

[0047] Indicates control parameters: ,in, For the sorting strategy, is the color classification threshold, is the size classification weight, is the damage detection coefficient.

[0048] In some embodiments, the classification criteria of the sorting unit can be further refined, such as calculating the optimal sorting criteria for different types of pears based on the maturity index of the pears:

[0049] ;

[0050] in, , is the experience adjustment coefficient, It is the optimized classification index of pear maturity for the sorting unit. is the Sigmoid adjustment term.

[0051] These sorting results will be transmitted as signals to the subsequent juicing process to ensure that different types of pears are handled appropriately. After sorting, the pears enter the washing unit, which uses high-pressure water flow, ultrasonic cleaning and temperature-controlled spray systems to deeply clean the surface of the pears.

[0052] The cleaning unit uses automated cleaning equipment to remove impurities, dust, and pesticide residue from the pear surface, ensuring the hygienic safety of the raw materials. In one possible implementation, the cleaning unit uses high-pressure water flow, a spray system, and a temperature control system to automatically remove impurities from the pear surface.

[0053] During operation, the cleaning unit incorporates an automated control system, using sensors to monitor the pressure, temperature, and flow of the cleaning water in real time to ensure uniformity and effectiveness. By adjusting parameters such as water flow, pressure, and cleaning time, the system effectively removes impurities and pesticide residues adhering to the pear surface, preventing these substances from affecting subsequent juicing and the quality of the final product.

[0054] The cleaning process can be described by the following formula:

[0055] ;

[0056] in: The state of the pear after washing: ,in, is the residual rate of surface pollutants, is the water absorption rate of the pear, is the peel damage rate;

[0057] Control parameters for cleaning: ,in, is the water pressure, For cleaning time, For water temperature, is the ultrasonic frequency;

[0058] For external environment parameters: ,in, For water temperature, is the flow rate, Surface contamination type.

[0059] In one possible implementation, the cleaning effect can be modeled by the following differential equation:

[0060] ;

[0061] in, , , , is the cleaning effect coefficient determined by the experiment, Represents the dissolution rate of contaminants on the pear surface.

[0062] In some embodiments, to further optimize the cleaning effect, the system may introduce an adaptive control strategy:

[0063] ;

[0064] in, is the maximum water pressure, is the regulating factor, is the control threshold of pollutant residual rate.

[0065] The cleaning effect will be evaluated according to the set standards. Qualified pears will enter the juicing stage, while unqualified pears will be automatically rejected to ensure the quality of raw materials during the juicing process.

[0066] In general, the residual rate of pollutants in pears after washing should be lower than the set standard value If the value is exceeded, the system will adjust the water pressure and cleaning time to ensure that the pears that pass the cleaning process enter the juicing stage, while the unqualified pears are sorted and eliminated.

[0067] As an option, the washing unit can also be integrated with a drying system that removes excess moisture from the pear surface through hot air circulation to improve the subsequent juicing efficiency.

[0068] In this embodiment, the raw material sorting unit and the cleaning unit work closely together to complete the initial processing of the raw materials. First, the pears are sorted using a visual sensor to identify the pears by type and maturity. Next, all sorted pears enter the cleaning unit, where they are cleaned using high-pressure water and a temperature-controlled system to remove impurities and pesticide residues from the pears' surfaces.

[0069] After washing, the pears enter the next stage—juicing. Real-time feedback and monitoring systems dynamically adjust control parameters during the juicing process to optimize juice yield and ensure product consistency. The raw material sorting and cleaning modules ensure high-quality raw materials during the juicing process, making the subsequent intelligent blending and juicing processes more efficient and stable.

[0070] In one embodiment, the raw material sorting unit uses a deep learning-based convolutional neural network (CNN) to sort pears. The image processing module rapidly analyzes the color, surface characteristics, and shape of the pears and uses a trained classification model to classify the pears into different categories. By adjusting the sorting algorithm in real time, the system can adapt to the pears' real-time characteristics during the production process.

[0071] For example, a batch of pears may have some damage or irregular shapes on the surface due to different transportation and storage conditions during the production process. At this time, the image recognition algorithm can adjust the classification strategy in real time to reduce the impact on poor raw materials and ensure that unqualified pears entering the juicing process are minimized.

[0072] The system utilizes an automatic temperature-controlled water flow system in the washing unit, ensuring efficient and uniform washing by real-time monitoring of water pressure, flow rate, and temperature. After each wash, the system performs a quality inspection to check whether residual substances (such as pesticides and dust) on the pear surface meet standard requirements. Pears that do not meet these standards are discarded, while qualified pears continue into the subsequent juicing process.

[0073] Juicing control module, used to dynamically adjust the operating parameters of the juicer according to the hardness, moisture content and maturity of the pears and the pear juice production plan to optimize the juice yield;

[0074] The juicing control module in this embodiment is responsible for adjusting the juicer's operating state based on real-time data to maximize juice yield, thereby increasing pear juice production and ensuring consistent taste. This module uses a real-time feedback mechanism to adjust key parameters such as the juicer's speed, pressure, and time to ensure optimal juice yield during the juicing process. The juicing control module is closely integrated with the aforementioned raw material processing module to ensure that the processed pears are pre-processed according to optimal conditions, providing high-quality raw materials for subsequent production and ensuring juicing efficiency and juice quality.

[0075] In this embodiment, the juicer control unit is mainly responsible for adjusting the key operating parameters of the juicer, such as the speed, pressure, and time, based on real-time data. Its control goal is to optimize these parameters in real time through an automated system to ensure that the juicer can maximize the juice yield within each production cycle and maintain a stable product taste. Generally, the system automatically adjusts the operating parameters of the juicer through a control algorithm based on the type, hardness, moisture content and other characteristics of the pears to achieve the maximum juice yield. For example, for harder pears, the system will increase the speed and pressure of the juicer to extract juice more efficiently; for softer pears, the system will reduce the speed and pressure to prevent over-squeezing and poor taste.

[0076] Specifically, the adjustment of the juicer's speed, pressure, and time is achieved through the following optimization formula to ensure the maximum juice yield:

[0077] ;

[0078] in: represents the optimized juice yield, expressed as a percentage, which represents the ratio of the amount of pear juice extracted to the original mass of the pears; Physical properties of pears, including firmness , moisture content and peel thickness ,in: ,in, Indicates the hardness of the pear in Newton (N). Indicates the moisture content of the pear in percentage (%). is the peel thickness, in millimeters (mm); is the water release rate, which is defined as the mass of liquid released from the pulp cells per unit time, in grams per second (g / s). ,in, Indicates the quality of the juice during the juicing process; is the control parameter vector of the juicer, including the speed ,pressure and time , indicating the operating parameters of the juicer: ,in, is the speed of the juicer in revolutions per minute (rpm); is the pressure of the juicer in MPa; is the juicing time of the juicer, in seconds (s); is the temperature impact factor, which represents the difference between the pulp temperature and the optimal juicing temperature and is defined as: ,in, is the flesh temperature of the pear, in degrees Celsius (℃); is the optimal juicing temperature, in degrees Celsius (℃); is the temperature normalization coefficient; Represents the energy loss during the juicing process, and shows the impact of speed and pressure on energy consumption: ,in, and Respectively represent the influence weights of speed and pressure on energy consumption.

[0079] This optimization formula comprehensively considers the relationship between the physical properties of pears, juicer operating parameters, temperature effects, and energy loss, and adjusts the juicer parameters through a nonlinear optimization method to maximize the juice yield.

[0080] In this embodiment, the real-time feedback monitoring unit uses sensors to monitor the juicer's operating status in real time and transmits feedback signals to the central control system, which then adjusts the juicer's operating parameters. Specifically, the real-time feedback monitoring unit includes pressure sensors, speed sensors, temperature sensors, etc. These sensors acquire real-time operating status data of the juicer and transmit it to the central control unit.

[0081] The working formula of the real-time feedback monitoring unit is as follows:

[0082] ;

[0083] in: To normalize the mapping function and ensure that the feedback signal is within a reasonable range: ;

[0084] , , , , It is the feedback control weight that determines the contribution of different parameters to the feedback signal.

[0085] In this embodiment, through real-time feedback monitoring, the system can dynamically adjust the juicer's operating parameters such as speed, pressure, and time to adapt to the characteristics of different types of pears (such as hardness, water content, maturity, etc.) and changes in the production environment.

[0086] The juice extraction control module in this embodiment uses a real-time feedback mechanism and adaptive control algorithm to adjust the juicer's operating state under different production conditions, optimizing juice yield and ensuring consistent product quality. By monitoring and adjusting control parameters in real time, the system can quickly respond to any changes that may occur during the production process, ensuring optimal juice extraction results throughout each production cycle.

[0087] In one embodiment, the juicer control unit utilizes a regulation mechanism based on an adaptive control algorithm. This mechanism dynamically adjusts the juicer's speed and pressure based on real-time parameters such as the pear type, firmness, and moisture content. For example, for firmer pears, the system automatically increases the speed and pressure to efficiently extract juice; for softer pears, the system reduces the speed and pressure to avoid over-squeezing the pear flesh.

[0088] In terms of real-time feedback monitoring, the system uses temperature and pressure sensors to comprehensively monitor the juicer. If the temperature or pressure is detected outside the set range, the system will immediately adjust the juicer's operating parameters and issue an alarm to prevent equipment damage or product quality.

[0089] For example, if the system detects that the pressure of the juicer exceeds the preset value, it will automatically reduce the pressure to avoid over-squeezing and ensure the stability of the taste of the final product. Conversely, if the pressure is insufficient, the system will increase the pressure to ensure efficient juicing.

[0090] An intelligent blending module automatically adjusts the sweetness, acidity, and concentration of pear juice based on market demand, consumer preference data, and pear juice production plans to achieve personalized taste blending for the finished pear juice.

[0091] In this embodiment, the intelligent blending module is primarily used to automatically adjust the taste characteristics of pear juice, such as sweetness, acidity, and concentration, based on real-time data on market demand and consumer preferences, enabling personalized production. Using deep learning algorithms and optimization techniques, the module dynamically adjusts pear juice recipe parameters based on evolving consumer tastes, ensuring that each batch of pear juice meets specific market and consumer needs while maintaining consistent and high-quality taste.

[0092] The design and implementation of the intelligent blending module relies on the following steps: data collection and processing, real-time optimization and adjustment, deep learning and feedback loops, automated blending control, and calculation of the optimization objective function. These interconnected and closely coordinated steps ensure that the production process can respond to market demand and evolving consumer preferences.

[0093] In this embodiment, the data acquisition and processing unit is responsible for collecting information such as market demand, consumer preferences, and historical production data, and processing it through data analysis. Specifically, this unit uses various sensors, data interfaces, or APIs to obtain real-time information such as consumer purchasing behavior, feedback, and social media comments, forming a multi-dimensional dataset.

[0094] As one possible implementation, the data processing unit models and analyzes the collected data using data mining and statistical learning methods to identify potential trends in consumer tastes. The data analysis results provide the intelligent blending control unit with a dynamically updated taste model, providing a basis for subsequent recipe optimization decisions.

[0095] The process of data analysis can be expressed as the following formula:

[0096] ;

[0097] in: Indicates time A dataset of market demand or consumer preferences at a given moment; Characteristic functions of the taste, usually including sweetness, sourness and other related factors; is the weight of each piece of data, indicating the degree of influence of the data on the model; is the number of data samples.

[0098] Through this process, the system can dynamically extract valuable information from various data sources and provide an accurate basis for subsequent formula adjustments.

[0099] The intelligent blending control unit is the core of this module. Based on market demand and consumer preference data, it uses deep learning algorithms (such as deep neural networks (DNNs)) to adjust the pear juice's taste characteristics, including sweetness, acidity, and concentration, in real time. The deep learning model is trained using historical data and updated and optimized using real-time production data, ensuring that each batch of pear juice meets consumer taste expectations.

[0100] In one possible implementation, a deep learning model (such as a multilayer perceptron or convolutional neural network) processes input from the data acquisition and processing unit and predicts the flavor profile most favored by consumers. Based on these predictions, the intelligent blending control unit automatically adjusts parameters such as sweetness, acidity, and concentration during the production process.

[0101] In the allocation control process, the goal is to minimize the consumer's preference error. The objective function can be expressed as the following formula:

[0102] , where For the operating parameters of the juicer that need to be adjusted, 、 and are weight coefficients, which are dynamically adjusted by the particle swarm optimization algorithm. For time period The pear juice yield rate, For juicer in time period Energy consumption within The Euclidean distance calculation result of the sweetness, acidity and concentration of the pear juice detected in real time and the target value is calculated. It is the smallest unit of each time change.

[0103] The goal of this formula optimization is to minimize the error between the taste characteristics of the blended product and consumer preferences while maintaining the stability of control parameters and production efficiency during the production process.

[0104] During real-time optimization and adjustment, the system uses an optimization algorithm to adjust the taste characteristics of pear juice based on real-time data collected on the physical properties of pears and consumer preferences. Common optimization algorithms, such as gradient descent and genetic algorithms, can be used to optimize taste parameters to ensure consistent taste across each batch of pear juice.

[0105] The goal of the optimization algorithm is to ensure that each batch of pear juice accurately meets the taste requirements of consumers while maintaining efficiency in the production process. The formula for this optimization process is as follows:

[0106] ;

[0107] in: For in time Always optimize the pear juice blending control parameters (such as sweetness, acidity, concentration, etc.); Real-time market demand or consumer preference data; By controlling the parameters calculated mouthfeel characteristics;

[0108] The optimization goal is to minimize the difference between the taste characteristics of the blended juice and consumer preferences. Through this optimization process, the system can make real-time adjustments during the pear juice production process to ensure precise control of taste.

[0109] The intelligent blending control unit uses deep neural networks (DNNs) for deep learning, enabling real-time adjustments to recipe parameters during the production process. This deep learning model continuously optimizes and adjusts strategies through feedback loops to respond to changing consumer tastes.

[0110] In a deep learning and feedback loop, the system feeds historical data and real-time production data into a deep neural network for training, creating a feedback mechanism that continuously adjusts blending parameters to ensure the taste of each batch of pear juice remains consistent with evolving consumer preferences.

[0111] The feedback loop can be described by the following formula:

[0112] ;

[0113] in: For in time Constantly optimized taste parameters; is the learning rate, which controls the step size of each adjustment; is the objective function The gradient of the control parameters indicates the optimal direction during the adjustment process.

[0114] This feedback loop enables real-time response to consumer taste changes during each production process, ensuring personalized pear juice production. After adjusting the optimized recipe parameters, the system automatically transmits the adjusted taste formula to the production line and implements the adjustments through the intelligent control system. Through automated batching control, the system can efficiently apply the optimized taste parameters to the actual production process, ensuring that each batch of pear juice meets consumer taste requirements.

[0115] During this process, the control system dynamically adjusts the operating parameters of the production line by performing automated tasks, automatically adjusting the taste characteristics such as sweetness and sourness, and ensuring that every link in the production process can be precisely controlled.

[0116] During the taste blending process, real-time adjustments are made using the following optimization formula to ensure that the taste characteristics meet the target requirements:

[0117] ;

[0118] This optimization formula is continuously used to adjust the taste characteristics such as sweetness, acidity, and concentration during the production process in real time, ensuring the consistency of taste of each batch of pear juice and meeting personalized production needs.

[0119] The system optimization control module is used to obtain and analyze production data, equipment status data, and market feedback data. Based on the analysis results, it generates and outputs an optimized pear juice production plan in real time. It also diagnoses and predicts the operating status of the pear juice production equipment, issues equipment operation warning information, and adjusts the operating status of the pear juice production equipment in real time.

[0120] The system optimization control module in this embodiment combines optimal control theory with robust control algorithms to adjust the juicer's operating parameters in real time, ensuring efficient production and system stability. By calculating and adjusting key juicer operating parameters (such as speed, pressure, and time), the module optimizes juice yield and ensures consistent taste across the final pear juice. It automatically adapts and makes precise adjustments to address uncertainties such as varying pear varieties and maturity.

[0121] This module is closely integrated with the aforementioned raw material processing module, juicing control module, and intelligent blending module, ensuring that every step in the production process is adjusted based on optimized control and real-time feedback. This module's optimized control not only improves pear juice production efficiency but also enhances product quality consistency.

[0122] The optimization control unit in this embodiment combines optimal control theory with robust control algorithms to achieve optimal juice yield and flavor consistency by calculating and adjusting the juicer's operating parameters in real time. Optimal control theory, based on mathematical models, optimizes control parameters by analyzing system conditions (such as the physical properties of pears and the operating status of production equipment) in real time, ensuring maximum juice extraction efficiency throughout each production cycle.

[0123] In practice, the Optimization Control Unit collects real-time data on raw materials, equipment status, and production, and uses this data to calculate the optimal control strategy. During this real-time optimization process, the control unit adjusts parameters such as the juicer's speed, pressure, and time to ensure that each pear juice produced achieves the optimal balance between yield and taste.

[0124] The optimization control process can be expressed by the following formula:

[0125] ,in: Indicates the taste characteristics of pear juice through the physical properties of pears and adjustment parameters Calculated, is the operating parameter of the juicer, is the desired value of the deployment parameter, is the expected value of market demand or consumer preference, and is the newly added adjustment coefficient, is the time derivative of the juicer's operating parameters, is the optimization target value in the deployment process, Real-time data on market demand or consumer preferences, 、 and is the adjustment coefficient, It is the smallest unit of each time change, and the taste characteristics include sweetness, sourness and concentration.

[0126] By optimizing the objective function, the system can accurately control various parameters of the juicer to maximize production efficiency and product taste stability.

[0127] The adaptive adjustment unit in this embodiment uses a system identification algorithm to model production data in real time and automatically adjust the control strategy to account for the uncertainties introduced by pear species and maturity. Pear species and maturity significantly influence the juicing process. Typically, different pear species or maturity levels impose different operating requirements on the juicer, requiring the system to adjust operating parameters (such as speed, pressure, and time) in real time to account for these changes.

[0128] The adaptive adjustment unit uses system identification techniques, such as recursive least squares (RLS) or Kalman filtering, to continuously update the parameter model of the production process. By analyzing real-time information such as raw material characteristics and equipment operating data, the system can adjust the control strategy to ensure consistent juice yield and taste for each batch of pear juice.

[0129] The adaptive adjustment process can be expressed by the following formula:

[0130] ;

[0131] in: For the moment The estimated values of the system parameters represent the control characteristics of the juicer (such as speed, pressure, etc.); is the covariance matrix, which represents the uncertainty of the model parameters at the previous moment; For the moment production data, including raw material characteristics, equipment operating status and other information; The actual output data collected (such as actual juice yield or taste characteristics); is the estimated value of the control parameter at the previous moment; It is a newly added adjustment coefficient used to control the impact of historical data; Raw material characteristics The time derivative of represents the rate of change of raw material characteristics over time, further improving the system's adaptability to uncertainty; for The transpose of .

[0132] Through this formula, the system can dynamically adjust the control strategy based on real-time collected data and feedback information to ensure that each batch of pear juice in the production process can achieve the best juice yield and stable taste.

[0133] The system optimization control module works closely with the raw material processing module, the juicing control module, and the intelligent blending module. First, the raw material processing module classifies the pears by type and maturity, providing real-time data to the system. The juicing control module adjusts the juicer's operating parameters based on this data and real-time feedback.

[0134] During this process, the system optimization control module receives data input from the juicing control module. Based on this input, it uses an optimal control algorithm to calculate the optimal control parameters. Simultaneously, the adaptive adjustment unit dynamically optimizes the control strategy based on real-time data. For example, if the system detects a high moisture content in the pears, it automatically reduces the speed or pressure to avoid over-squeezing. If the pears are detected to be firm, it increases the speed or pressure to improve the juice yield.

[0135] In the intelligent blending module, the system automatically adjusts pear juice's taste parameters, such as sweetness and sourness, based on evolving consumer tastes and fluctuations in market demand. The system's optimization control module combines optimal and robust control to ensure that each batch of pear juice is not only efficient and stable, but also adaptable to changing production conditions.

[0136] In one embodiment, the system optimization control module automatically adjusts the juicer's control parameters by analyzing the pear's physical properties, production data, and market demand in real time. For example, during the production process, the system automatically adjusts the juicer's pressure and speed by monitoring the pear's firmness and moisture content. If the pear's moisture content is high, the system reduces the pressure to prevent over-squeezing; if the pear's firmness is high, the system increases the speed to improve juice yield.

[0137] The adaptive adjustment unit updates model parameters using real-time data to account for variations in pear variety. For example, if the system detects changes in juice yield due to different pear varieties, the model, based on the system identification algorithm, optimizes the control strategy in real time and adjusts the juicer's operation to ensure consistent quality for each batch of pear juice.

[0138] The data analysis and prediction module is used to perform real-time analysis of data in the production process, optimize production plans, and provide intelligent early warning of equipment operation through fault diagnosis and prediction.

[0139] The data analysis and prediction module in this embodiment optimizes production plans through real-time analysis of various production process data, and provides intelligent early warnings for equipment operation through fault diagnosis and prediction. Based on deep learning, optimization algorithms, and big data analysis technologies, this module processes key data from the production process to improve production efficiency and quality, predict potential equipment failures, and implement proactive maintenance measures to avoid production interruptions or equipment damage.

[0140] The core mission of the data analysis and prediction module is to provide solutions for optimizing the production process through in-depth analysis of real-time data, combined with the dynamic changes in production equipment and market demand. It also utilizes large-scale modeling technology to predict faults and diagnose potential equipment problems in advance. This information is crucial for efficient, stable, and precise production control.

[0141] The data analysis unit in this embodiment collects various data from the production process (such as raw material data, production data, and market demand data) and uses statistical learning and machine learning techniques to perform real-time analysis and prediction on this data. The goal of the data analysis unit is to generate reasonable production adjustment plans based on the collected data, enabling continuous optimization of the production process and achieving optimal production efficiency.

[0142] The core task of the data analysis unit is to integrate and analyze raw material data, production process data, and market demand data to provide decision support for optimizing production plans. For example, by analyzing data such as raw material hardness and moisture content in real time, the system can adjust the operating parameters of the juicer in real time to maximize juice yield.

[0143] The data analysis process can be expressed by the following formula:

[0144] ;

[0145] in: is the weight coefficient of each data point, indicating the contribution of the data point to the production plan optimization; It is an analytical function for each data point, which indicates the impact of raw material data (such as hardness, moisture content, etc.), production data and market demand data on production process optimization; , , They represent the raw material characteristics, production data, and market demand data collected at a given moment in time; and They represent the gradients of raw material characteristics and production data, respectively, reflecting their changes over time; , , is the adjustment coefficient to control the influence of various factors on the optimization process; The rate of change of market demand data over time, reflecting the impact of market demand fluctuations on production plans

[0146] Through this analysis process, the data analysis unit can predict the best production adjustment plan based on real-time data, thereby optimizing various tasks in the production process, ensuring maximum juice yield and flexibility in production planning.

[0147] The fault diagnosis and prediction unit utilizes deep learning and big data analytics technologies, leveraging historical equipment operating data and real-time sensor data to intelligently monitor equipment during production. This unit can predict equipment failure risks in real time, issue early warnings, and, through intelligent fault diagnosis modules, provide operators with timely maintenance and adjustment recommendations.

[0148] Specifically, the fault diagnosis and prediction unit uses a deep neural network (DNN) model to learn and predict equipment status data in real time. This data includes equipment operating status (such as pressure, speed, temperature), historical fault records, and workload. Based on this input data, the system can identify potential equipment failure risks and predict the likely time of occurrence and type of failure.

[0149] The fault diagnosis and prediction process can be described by the following formula:

[0150] ;

[0151] in: The weighting coefficient for each device status data point indicates the degree of influence of the data on the fault prediction result; It is a function that processes equipment operation status data and fault prediction parameters and outputs equipment status assessment results; The device status data collected at any given moment (such as pressure, temperature, speed, etc.); are the parameters of the equipment failure prediction model; and It is a newly added adjustment coefficient used to control the rate of change of equipment status and the nonlinear impact of equipment failure; It is the time derivative of the equipment status data, reflecting the rate of change of the equipment operating status; is the gradient of the device state data, reflecting the spatial change of the device state.

[0152] Through this prediction mechanism based on big data technology and deep learning, the system can identify potential equipment failures in advance and provide production line managers with intelligent early warning and diagnostic information to prevent equipment failures from affecting production efficiency and product quality.

[0153] The Data Analysis and Forecasting Module is closely integrated with the aforementioned Raw Material Processing Module, Juicing Control Module, and Intelligent Blending Module. First, the Data Analysis Unit obtains real-time data from these modules, such as raw material characteristics, real-time data from the production process (such as the juicer's status), and market demand, and conducts a comprehensive analysis.

[0154] After acquiring real-time data, the data analysis and prediction module generates optimization plans based on the production plan and dynamically adjusts the production process. In particular, during the juicing process, the data analysis unit analyzes changes in raw materials and fluctuations in market demand, adjusting juicer operating parameters (such as speed and pressure) in real time to ensure consistent juice yield and taste.

[0155] The fault diagnosis and prediction unit plays a crucial role in equipment status monitoring. By monitoring the operating status of equipment like juicers, the system promptly identifies and predicts potential faults. Through intelligent diagnosis, the system provides early warning of equipment issues, avoiding production downtime and improving production line reliability.

[0156] In one embodiment, the data analysis and processing unit uses a hybrid model based on regression analysis and classification algorithms to analyze data from each production link in real time. This model can combine raw material data, production equipment data, and market demand to generate production optimization plans in real time. For example, if the raw material is too hard, the system will predict the need to increase the pressure and speed of the juicer; if market demand changes, the system will automatically adjust the production plan to ensure that the new market demand is met.

[0157] During implementation, the fault diagnosis and prediction unit intelligently monitors juicers using historical operating data and real-time monitoring data. Using deep learning models, the system can predict the possibility of equipment failure when the juicer's pressure or temperature is abnormal, providing early warning information. For example, if sensors detect that the juicer's temperature is too high, the system will issue an alarm and predict a possible equipment failure, prompting staff to conduct inspections or repairs.

[0158] To address the impact of equipment failures on production plans, the data analysis and prediction module introduces a dynamic interaction mechanism between equipment failures and production plans. This mechanism is expressed by the following formula:

[0159] ;

[0160] in: Output of production plan after fault diagnosis and prediction adjustment; It is the equipment failure prediction result, reflecting the impact of equipment failure on production plan; is the impact coefficient of equipment failure on production plan; It is the time derivative of the equipment status data, describing the dynamic impact of equipment failure on the production plan.

[0161] Through this interactive model, the system can quickly adjust the production plan when equipment fails to ensure the continuity and efficiency of the production process.

[0162] The intelligent control method for pear juice production described below and the pear juice production system described above can be used in correspondence with each other.

[0163] Please see the attached Figure 6 A pear juice production intelligent control method, used in conjunction with a pear juice production system, comprises the following steps:

[0164] Obtaining raw material information: Sensors and image recognition equipment are used to obtain the physical property data of pears entering the production line, and the pears are classified and pre-processed. The physical property data includes type, maturity, firmness, size, and surface defects.

[0165] Raw material sorting and cleaning: Based on the acquired physical property data, pears are accurately classified using automated sorting equipment. Surface impurities and pesticide residues are then removed through automated cleaning equipment to ensure that the raw materials entering the juicing process meet production standards.

[0166] Real-time monitoring and juicer adjustment: The real-time monitoring system monitors the working status of the juicer, adjusts the operating parameters of the juicer, and dynamically adjusts the control parameters of the juicer based on the monitoring data to optimize the juice yield and taste. The operating parameters of the juicer include speed, pressure, temperature, and pressing time.

[0167] Data collection and consumer preference analysis: The data collection and processing unit collects market demand and consumer preference data, and analyzes consumer taste preferences based on the collected data to provide decision support for subsequent pear juice taste formulation;

[0168] Intelligent blending and taste optimization: Based on consumer preference data and production data, deep learning algorithms are used to optimize the sweetness, acidity, and concentration of pear juice in real time to automatically adjust the taste of pear juice.

[0169] System optimization and optimal control: Combining optimal control theory and robust control algorithms, the operating parameters of the juicer are calculated and optimized in real time to ensure that the juice yield is improved and the taste is stable under different pear classifications and maturity conditions;

[0170] Real-time feedback and adaptive adjustment during the production process: Based on real-time production data, the production process is dynamically modeled through system identification algorithms, and the system control strategy is automatically adjusted to cope with the uncertainties of different physical characteristics and production environments;

[0171] Big data analysis and equipment failure prediction: Using big data analysis technology, we monitor various data in the production process in real time, predict and optimize pear juice production plans, and use large model technology to diagnose and warn of equipment failures, so that maintenance measures can be taken in advance to prevent production interruptions.

[0172] Optimize production scheduling and maintenance plans: Intelligently optimize production scheduling and equipment maintenance plans based on the equipment health assessment model obtained during the production process.

[0173] The method of this embodiment can be used to execute the above system embodiment. Its principles and technical effects are similar and will not be described in detail here.

[0174] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A pear juice production system, characterized in that: The pear juice production system comprises: A raw material processing module, which is used to identify, sort, and clean the pears entering the production line based on visual sensors, image recognition equipment, and automated cleaning equipment; Juicing control module, used to dynamically adjust the operating parameters of the juicer according to the hardness, moisture content and maturity of the pears and the pear juice production plan to optimize the juice yield; An intelligent blending module automatically adjusts the sweetness, acidity, and concentration of pear juice based on market demand, consumer preference data, and pear juice production plans to achieve personalized taste blending for the finished pear juice. Wherein, the pear juice production system further comprises: The system optimization control module is used to obtain and analyze production data, equipment status data, and market feedback data. Based on the analysis results, it generates and outputs an optimized pear juice production plan in real time. It also diagnoses and predicts the operating status of the pear juice production equipment, issues equipment operation warning information, and adjusts the operating status of the pear juice production equipment in real time. The system optimization control module includes an optimization control unit and an adaptive adjustment unit, wherein: An optimization control unit combines optimal control theory with robust control to calculate and adjust the operating parameters of the juicer in the pear juice production plan in real time using an objective function to achieve optimal juice yield, energy consumption, and taste stability. An adaptive adjustment unit, which uses a system identification algorithm to build a model based on real-time production data and automatically adjusts the control strategy based on a Kalman filter to eliminate the impact of sensor noise on the control strategy and cope with the uncertainty of different pear varieties and maturity; Wherein, the objective function is: ； Where, For the operating parameters of the juicer that need to be adjusted, 、 and are weight coefficients, which are dynamically adjusted by the particle swarm optimization algorithm. For time period The pear juice yield rate, For juicer in time period Energy consumption within The Euclidean distance calculation result of the sweetness, acidity and concentration of the pear juice detected in real time and the target value is calculated. It is the smallest unit of each time change; The optimization control unit in the system optimization control module performs real-time optimization calculations using the following formula: in: Indicates the taste characteristics of pear juice through the physical properties of pears and adjustment parameters Calculated, is the operating parameter of the juicer, is the desired deployment parameter value, is the expected value of market demand or consumer preference, and is the newly added adjustment coefficient, is the time derivative of the juicer's operating parameters, is the optimization target value in the deployment process, Real-time data on market demand or consumer preferences, 、 and is the adjustment coefficient, It is the smallest unit of each time change, and the taste characteristics include sweetness, sourness and concentration.

2. A pear juice production system according to claim 1, characterized in that: The modules in the pear juice production system interact with each other in real time through the industrial Internet of Things platform. The system optimization control module outputs an optimized pear juice production plan and sends the optimized pear juice production plan to the juicing control module and the intelligent blending module. The juicing control module dynamically adjusts the operating parameters of the juicer according to the received pear juice production plan, and the intelligent blending module automatically adjusts the sweetness, acidity, and concentration of the pear juice according to the received pear juice production plan. The raw material processing module includes a raw material sorting unit and a cleaning unit, wherein: The raw material sorting unit is used to automatically identify the size, maturity and surface defects of pears through visual sensors and image recognition equipment, and classify the pears according to variety and maturity based on the recognition results; The cleaning unit is used to clean the surface of the sorted pears through automated cleaning equipment to remove impurities and pesticide residues, and control the conveying equipment to convey the cleaned pears to the juicer.

3. A pear juice production system according to claim 1, characterized in that: The juice extraction control module includes a juice extraction control unit, a real-time feedback monitoring unit and a juice extraction control module, wherein: a real-time feedback monitoring unit, which uses sensors to collect the hardness, moisture content, and maturity of the pears in the juicer in real time, and simultaneously monitors and collects operating status data of the juicer, and generates a feedback signal based on the hardness, moisture content, and maturity of the pears and the operating status data of the juicer, and sends the feedback signal to the juicer control unit in real time, wherein the operating status data of the juicer includes speed, pressure, and squeezing time; The juicer control unit is used to receive and analyze feedback signals, obtain and adjust the juicer's speed, pressure and pressing time in real time based on the hardness, moisture content and maturity data of the pears to optimize the juice yield.

4. A pear juice production system according to claim 1, characterized in that: The intelligent allocation module includes a data acquisition and processing unit and an intelligent allocation control unit, wherein: A data acquisition and processing unit is used to collect market demand and consumer preference data, perform data analysis and processing on the market demand and consumer preference data, and receive the pear juice production plan output by the system optimization control module; The intelligent blending control unit is used to automatically adjust the sweetness, acidity and concentration of pear juice through a deep learning algorithm based on the processing and analysis results of market demand and consumer preference data, as well as the pear juice production plan.

5. A pear juice production system according to claim 1, characterized in that: The system optimization control module also includes a data analysis unit and a fault diagnosis and prediction unit, wherein: Data analysis unit, used to analyze equipment status data in the production process in real time and predict optimization and adjustment plans in production; The fault diagnosis and prediction unit is used to build an equipment health assessment model based on the Transformer architecture. It uses the equipment health assessment model and real-time analysis results of equipment status data to diagnose the operating status of the pear juice production equipment. It also predicts potential fault types and occurrence probabilities through vibration spectrum analysis and temperature trends, and issues equipment operation warning information to provide early diagnosis and warning.

6. A pear juice production system according to claim 4, characterized in that: The intelligent allocation control unit in the intelligent allocation module performs real-time adjustment through the following steps: Data processing and analysis: Deep learning algorithms are used to train and model market demand, consumer preference data, and historical production data to generate a personalized pear juice taste model; Real-time optimization and adjustment: Based on the real-time collected data on the physical properties of pears, an optimization algorithm is used to adjust the taste characteristics of sweetness, acidity, and concentration to ensure the consistency of taste of each batch of pear juice; Deep learning and feedback loops: Through deep neural network feedback mechanisms, recipe parameters are adjusted in real time during the production process, dynamically optimizing taste characteristics to adapt to changing consumer tastes; Automated blending control: The adjusted taste formula is automatically transmitted to the production line and adjusted through an intelligent control system; Optimization objective function calculation: During the taste adjustment process, real-time adjustments are made through optimization formulas.

7. An intelligent control method for pear juice production, characterized in that: Used in conjunction with a pear juice production system according to any one of claims 1 to 6, comprising the following steps: Obtaining raw material information: Sensors and image recognition equipment are used to obtain the physical property data of pears entering the production line, and the pears are classified and pre-processed. The physical property data includes type, maturity, firmness, size, and surface defects. Raw material sorting and cleaning: Based on the acquired physical property data, pears are accurately classified using automated sorting equipment. Surface impurities and pesticide residues are then removed through automated cleaning equipment to ensure that the raw materials entering the juicing process meet production standards. Real-time monitoring and juicer adjustment: The real-time monitoring system monitors the working status of the juicer, adjusts the operating parameters of the juicer, and dynamically adjusts the control parameters of the juicer based on the monitoring data to optimize the juice yield and taste. The operating parameters of the juicer include speed, pressure, temperature, and pressing time. Data collection and consumer preference analysis: The data collection and processing unit collects market demand and consumer preference data, and analyzes consumer taste preferences based on the collected data to provide decision support for subsequent pear juice taste formulation; Intelligent blending and taste optimization: Based on consumer preference data and production data, deep learning algorithms are used to optimize the sweetness, acidity, and concentration of pear juice in real time to automatically adjust the taste of pear juice. System optimization and optimal control: Combining optimal control theory and robust control algorithms, the operating parameters of the juicer are calculated and optimized in real time to ensure that the juice yield is improved and the taste is stable under different pear classifications and maturity conditions; Real-time feedback and adaptive adjustment during the production process: Based on real-time production data, the production process is dynamically modeled through system identification algorithms, and the system control strategy is automatically adjusted to cope with the uncertainties of different physical characteristics and production environments; Big data analysis and equipment failure prediction: Using big data analysis technology, we monitor various data in the production process in real time, predict and optimize pear juice production plans, and use large model technology to diagnose and warn of equipment failures, so that maintenance measures can be taken in advance to prevent production interruptions. Optimize production scheduling and maintenance plans: Intelligently optimize production scheduling and equipment maintenance plans based on the equipment health assessment model obtained during the production process.

Images