Pear juice production system and intelligent control method thereof

Abstract

The invention relates to the technical field of food processing and intelligent control, and discloses a pear juice production system and an intelligent control method thereof.The system comprises a raw material processing module used for recognizing, sorting and cleaning pears entering a production line based on a visual sensor, image recognition equipment and automatic cleaning equipment; the juicing control module is used for controlling the working state of the juicer according to the real-time data; the invention further discloses a method which comprises the following steps: acquiring raw material information: acquiring physical characteristic data of types, maturity and hardness of pears entering a production line through a sensor and an image recognition technology, and classifying and preprocessing the pears; by combining an optimal control theory, a robust control algorithm, a system identification and adaptive adjustment algorithm and a deep learning and big data analysis technology, real-time optimization and adjustment of operating parameters of the juicer are realized, changes of different pear varieties and maturity are automatically adapted, potential problems are found in advance, and early warning is provided.

Description

Technical Field

[0001] The 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, as a fruit juice product deeply loved by consumers, is widely used in the beverage industry due to its refreshing taste and rich nutrition. The traditional pear juice production process usually includes multiple links such as pear selection, cleaning, juicing, blending, sterilization, and filling. The operation accuracy and control quality in each link have an important impact on the juice yield and taste stability of the final product. However, with the continuous growth of production demand and the improvement of consumer requirements for product quality, the traditional manual operation and experience-dependent 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 accurately control every link in the production process has become a problem that needs to be solved urgently in current technology.

[0003] At present, most pear juice production lines still use the traditional method based on manual adjustment. The key parameters of the juicer, such as the speed and pressure, are often set by the operator based on experience. Under this operation mode, the production effect and taste stability of pear juice are heavily dependent on the technical level and experience of the operator, resulting in consistency differences in the production process. Especially when the physical properties of 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 a flexible real-time adjustment mechanism when dealing with different pear varieties and changes in fruit maturity, and cannot achieve the best production effect.

[0004] In addition, traditional production systems lack efficient fault diagnosis and prediction mechanisms. The health of equipment usually relies on manual inspection and regular maintenance, and real-time monitoring and fault warning are not possible. If equipment failure occurs during the production process, it can often only be handled after the failure occurs, resulting in production stagnation or reduced efficiency. Most existing fault diagnosis technologies are based on manual experience or simple equipment status monitoring, and fail to effectively use big data and deep learning technologies to comprehensively monitor and warn equipment, increasing the instability and downtime risks of production lines.

[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 into industrial production. By combining optimal control theory, robust control algorithms, system identification, and deep learning, the pear juice production system can achieve more efficient production process control, more accurate taste blending, and real-time monitoring of equipment health. The intelligent pear juice production system can not only dynamically adjust the operating parameters of the juicer according to the physical characteristics of the pear, but also meet the taste requirements of different consumers through intelligent blending, thereby improving production efficiency and ensuring product quality. Summary of the invention

[0006] In view of the deficiencies in the prior art, 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: The raw material processing module is used to identify, sort and clean the pears entering the production line based on visual sensors, image recognition equipment and automated cleaning equipment; A juice extraction control module, which is 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 that automatically adjusts the sweetness, acidity and concentration of pear juice based on market demand and consumer preference data, as well as the pear juice production plan, to achieve personalized taste blending of the finished pear juice; Wherein, the pear juice production system also includes: 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.

[0008] 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 comprises the following steps: Obtaining raw material information: Using sensors and image recognition equipment to obtain the physical property data of pears entering the production line, and classifying and pre-processing the pears. The physical property data includes type, maturity, hardness, size, and surface defects; Raw material sorting and cleaning: Based on the physical property data obtained, the pears are accurately classified using automated sorting equipment, and then surface impurities and pesticide residues are removed using 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 according to the monitoring data to optimize the juice yield and taste. The operating parameters of the juicer include speed, pressure, temperature and squeezing time; Data collection and consumer preference analysis: Market demand and consumer preference data are collected through the data collection and processing unit, and consumer taste preferences are analyzed based on the collected data to provide decision support for the subsequent pear juice taste preparation; 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 achieve automatic blending of the pear juice taste; System optimization and optimal control: Combining optimal control theory and robust control algorithm, real-time calculation and optimization of juicer operating parameters ensures that juice yield is improved and taste is stable under different pear classification and maturity conditions; Real-time feedback and adaptive adjustment in 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 uncertainty of different physical properties and production environments; Big data analysis and equipment failure prediction: Through big data analysis technology, various data in the production process are monitored in real time, the pear juice production plan is predicted and optimized, and large model technology is used to diagnose and warn 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.

[0009] The present invention provides a pear juice production system and an intelligent control method thereof, which have the following beneficial effects: 1. The present invention realizes real-time optimization and adjustment of the operating parameters of the juicer by adopting a technical solution combining optimal control theory and robust control algorithm. Through this technical solution, the rotation speed, pressure, time and other parameters of the juicer can be accurately adjusted according to the changes in different pear varieties, maturity and production conditions to ensure the best balance between the juice yield and taste of each batch of pear juice. Compared with the solutions of fixed parameters or simple manual adjustment in the prior art, the present invention effectively overcomes the instability problem caused by raw material differences and significantly improves production efficiency and product consistency.

[0010] 2. The present invention introduces a system identification and adaptive adjustment algorithm, models the production process through real-time data analysis, and dynamically optimizes the control strategy. This solution ensures that the production process can automatically adapt and optimize control parameters when faced with uncertain pear species and maturity. Compared with the manual adjustment or preset control scheme commonly used in traditional technologies, the present invention eliminates the dependence on operators and avoids errors caused by human intervention by updating the control strategy in real time, greatly improving the flexibility and stability of production.

[0011] 3. The present invention realizes fault prediction and equipment health management in the production process by integrating deep learning and big data analysis technology. Through intelligent analysis of equipment operation data, the system can predict potential faults in advance and issue early warnings in time. 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

[0012] Figure 1 It is a framework diagram of the system of the present invention; Figure 2 is a schematic diagram of a raw material processing module of the present invention; Figure 3 is a schematic diagram of a juice extraction control module of the present invention; Figure 4 is a schematic diagram of the intelligent deployment module of the present invention; Figure 5 A schematic diagram of a system optimization control module of the present invention; Figure 6 It is a flow chart of the intelligent control method of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0014] Please refer to the attached Figure 1-Figure 5 The embodiment of the present invention provides a pear juice production system, comprising: The raw material processing module is used to identify, sort and clean the pears entering the production line based on visual sensors, image recognition equipment and automated cleaning equipment; The raw material processing module of this embodiment is mainly responsible for sorting and cleaning the pears entering the pear juice production line. This module is the first process in the entire pear juice production system, which directly affects the effect of the subsequent juicing process and the quality of the pear juice. Its function is to preliminarily screen the quality of the pears through automated means, remove unqualified raw materials, and remove surface impurities and pesticide residues through cleaning, thereby ensuring the safety of the raw materials and the efficiency of production.

[0015] In the above system, the raw material processing module is closely connected with the optimization control of the juicing process and the subsequent formula adjustment. The sorted pears will enter the juicing process according to certain standards, and the cleaned pears provide higher quality raw materials for subsequent operations.

[0016] In this embodiment, the raw material sorting unit mainly uses visual sensors to automatically identify the pears entering the production line. The unit uses high-definition visual sensors installed above the production line and combines image recognition technology to detect the size, maturity, appearance and other characteristics of each pear in real time. Specifically, the visual sensor obtains the appearance information of the pear through lighting and image acquisition equipment, and analyzes the image data through image processing algorithms to identify the physical characteristics of the pear.

[0017] In some embodiments, the visual sensor uses a deep learning-based image classification algorithm, such as a convolutional neural network (CNN), to identify different varieties, sizes, and maturity of pears. Through multi-level processing of images, the system can distinguish different types and maturity of pears and classify the pears into multiple categories for subsequent juicing processes.

[0018] In specific implementation, the system establishes a standardized classification model to accurately classify pears according to their size, maturity and other characteristics. For example, pears are divided into multiple categories such as "ripe pears", "unripe pears" and "overripe pears", ensuring that the raw materials entering the juicing process meet production requirements, thereby reducing the impact of poor raw materials on juicing quality.

[0019] The formula for the sorting process is as follows: ; in: represents the classification results of pears, including variety, size and maturity category; is the physical property vector of the pear: ,in, is the diameter, is the weight, is the morphological factor, is the surface color distribution; Characteristics of pear maturity: ,in, For sugar content, is the hardness, is the maturity index, is the reflectance of the peel; Indicates control parameters: ,in, For the sorting strategy, is the color classification threshold, is the size classification weight, is the damage detection coefficient.

[0020] 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: ; in, , is the experience adjustment coefficient, It is the optimized classification index of pear maturity for the sorting unit. is the Sigmoid adjustment term.

[0021] These sorting results will be transmitted as signals to the subsequent juicing process to ensure that different types of pears can be properly processed. 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.

[0022] The cleaning unit uses an automated cleaning device, which is mainly used to remove impurities, dust and pesticide residues on the surface of the pears to ensure the hygiene and safety of the raw materials. In a possible implementation, the cleaning unit uses high-pressure water flow, a spray system and a temperature control system to automatically remove impurities on the surface of the pears.

[0023] During the implementation process, the cleaning unit will be combined with an automated control system to monitor the pressure, temperature and flow of the cleaning water in real time through sensors to ensure the uniformity and effectiveness of the cleaning process. By adjusting parameters such as water flow, pressure and cleaning time during the cleaning process, the system can effectively remove impurities and pesticide residues attached to the surface of the pears, preventing these substances from affecting the subsequent juicing and final product quality.

[0024] The cleaning process can be described by the following formula: ; in: The state of the pear after washing: ,in, is the residual rate of surface pollutants, is the water absorption rate of pears, is the peel damage rate; Control parameters for cleaning: ,in, is the water pressure, For cleaning time, is the water temperature, is the ultrasonic frequency; For external environment parameters: ,in, is the water temperature, is the flow rate, Type of surface contaminant.

[0025] In one possible implementation, the cleaning effect can be modeled by the following differential equation: ; in, , , , is the cleaning effect coefficient determined by the experiment, Represents the dissolution rate of contaminants on the pear surface.

[0026] In some embodiments, in order to further optimize the cleaning effect, the system may introduce an adaptive control strategy: ; in, is the maximum water pressure, is the regulating factor, is the control threshold of pollutant residual rate.

[0027] The cleaning effect will be evaluated according to the set standards, and qualified pears will enter the juicing stage, while unqualified pears will be automatically rejected to ensure the quality of the raw materials during the juicing process.

[0028] Generally speaking, 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 enter the juicing stage, while the unqualified pears are sorted and removed.

[0029] As an option, the washing unit can also be integrated with a drying system, which removes excess moisture from the pear surface through hot air circulation to improve the subsequent juicing efficiency.

[0030] In this embodiment, the raw material sorting unit and the cleaning unit work closely together to complete the preliminary processing of the raw materials. First, the pears are sorted by the visual sensor to identify the pears of different types and maturity. Then, all the sorted pears enter the cleaning unit and are cleaned by high-pressure water flow and temperature control system to remove impurities and pesticide residues on the surface of the pears.

[0031] After washing, the pears will enter the next stage - juicing. Through real-time feedback and monitoring systems, the control parameters of the juicing process are dynamically adjusted to optimize the juice yield and ensure product consistency. The raw material sorting and cleaning module ensures the high quality of raw materials in the juicing stage, making the subsequent intelligent blending and juicing process more efficient and stable.

[0032] In one embodiment, the raw material sorting unit uses a deep learning-based convolutional neural network (CNN) to sort pears. The image processing module can quickly analyze the color, surface characteristics and shape of the pears, and classify the pears into different categories through the trained classification model. By adjusting the sorting algorithm in real time, the system can make adaptive adjustments according to the real-time characteristics of the pears during the production process.

[0033] 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.

[0034] In the cleaning unit, the system uses an automatic temperature-controlled water flow system, which monitors the pressure, flow rate and water temperature of the water flow in real time to ensure that the cleaning process is efficient and uniform. After each cleaning, the system will conduct a quality inspection to check whether the residual substances (such as pesticides, dust, etc.) on the surface of the pears meet the standard requirements. Pears that do not meet the standards will be removed, and qualified pears will continue to enter the subsequent juicing process.

[0035] A juice extraction control module, which is 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; The juicing control module in this embodiment is responsible for adjusting the working state of the juicer based on real-time data to maximize the juice yield, thereby increasing the yield of pear juice and ensuring the consistency of its taste. This module adjusts key parameters such as the speed, pressure and time of the juicer through a real-time feedback mechanism to ensure that the juice yield during the juicing process is optimized. The juicing control module is closely connected with the aforementioned raw material processing module to ensure that the processed pears have been pre-processed according to the optimal conditions, thereby providing high-quality raw materials for subsequent production and ensuring juicing efficiency and juice quality.

[0036] 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 the automation system to ensure that the juicer can maximize the juice yield in each production cycle and maintain the stability of the product taste. In general, the system automatically adjusts the operating parameters of the juicer through the control algorithm according to the type, hardness, moisture content and other characteristics of the pears, so as 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 excessive squeezing and poor taste.

[0037] Specifically, the adjustment of the speed, pressure and time of the juicer is achieved through the following optimization formula to ensure the maximum juice yield: ; in: represents the optimized juice yield, expressed as a percentage, which is 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, expressed 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 megapascals (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 pulp 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 indicates the impact of speed and pressure on energy consumption: ,in, and Respectively represent the influence weights of speed and pressure on energy consumption.

[0038] This optimization formula comprehensively considers the relationship between the physical properties of pears, the operating parameters of the juicer, temperature effects and energy loss, and adjusts the juicer parameters through a nonlinear optimization method to maximize the juice yield.

[0039] In this embodiment, the real-time feedback monitoring unit monitors the working state of the juicer in real time through sensors, and transmits feedback signals to the central control system, thereby adjusting the operating parameters of the juicer. Specifically, the real-time feedback monitoring unit includes a pressure sensor, a speed sensor, a temperature sensor, etc. These sensors obtain the working state data of the juicer in real time and transmit it to the central control unit.

[0040] The working formula of the real-time feedback monitoring unit is as follows: ; in: To normalize the mapping function and ensure that the feedback signal is within a reasonable range: ; , , , , It is the feedback control weight that determines the contribution of different parameters to the feedback signal.

[0041] In this embodiment, through real-time feedback monitoring, the system can dynamically adjust the operating parameters of the juicer, 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.

[0042] The juice extraction control module of this embodiment adjusts the operating state of the juice extractor under different production conditions through real-time feedback mechanism and adaptive control algorithm, optimizes the juice extraction rate, and ensures the consistency of product quality. By real-time monitoring and adjusting control parameters, the system can quickly respond to any changes that may occur in the production process, ensuring that the optimal juice extraction effect is always maintained in each production cycle.

[0043] In one embodiment, the juicer control unit uses a regulation mechanism based on an adaptive control algorithm. This mechanism can dynamically adjust the speed and pressure of the juicer according to real-time parameters such as the type, hardness, and moisture content of the pears. For example, for harder pears, the system will automatically increase the speed and pressure of the juicer to efficiently extract juice; while for softer pears, the system will reduce the speed and pressure to avoid over-squeezing the pear flesh.

[0044] In terms of real-time feedback monitoring, the system uses temperature and pressure sensors to fully monitor the juicer. If the temperature or pressure is detected to be out of the set range, the system will immediately adjust the operating parameters of the juicer and issue an alarm to prevent equipment damage or product quality.

[0045] For example, if the system detects that the pressure of the juicer exceeds the preset value, the system 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 juice extraction.

[0046] An intelligent blending module that automatically adjusts the sweetness, acidity and concentration of pear juice based on market demand and consumer preference data, as well as the pear juice production plan, to achieve personalized taste blending of the finished pear juice; In this embodiment, the intelligent blending module is mainly used to automatically adjust the taste characteristics of pear juice, such as sweetness, acidity, and concentration, based on real-time data of market demand and consumer preferences to achieve personalized production. The module uses deep learning algorithms and optimization technology to dynamically adjust the formula parameters of pear juice in combination with changes in consumer tastes, ensuring that each batch of pear juice can meet the needs of specific markets and consumers and ensure consistency and high quality of taste.

[0047] 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 loop, automated blending control, and optimization objective function calculation. These links are interconnected and closely coordinated to ensure that the production process can respond to market demand and changes in consumer taste preferences.

[0048] In this embodiment, the data acquisition and processing unit is responsible for collecting information from market demand, consumer preferences, historical production data, etc., and processing it through data analysis. Specifically, the unit will obtain consumers' purchasing behavior, feedback, social media comments and other information in real time through various sensors, data interfaces or API interfaces to form a multi-dimensional data set.

[0049] As a possible implementation method, the data processing unit models and analyzes the collected data through data mining and statistical learning methods to identify potential trends in consumer tastes. The data analysis results provide a dynamically updated taste model for the intelligent blending control unit, providing a decision-making basis for subsequent recipe optimization.

[0050] The process of data analysis can be expressed as the following formula: ; in: Indicates at time A dataset of market demand or consumer preferences at a given moment; Characteristic functions of taste, usually including sweetness, sourness and other related factors; is the weight of each piece of data, indicating the influence of the data on the model; is the number of data samples.

[0051] Through this process, the system can dynamically extract valuable information from various data sources and provide an accurate basis for subsequent recipe adjustments.

[0052] The intelligent blending control unit is the core of the module. It uses deep learning algorithms (such as deep neural networks (DNNs)) to adjust the sweetness, acidity, concentration and other taste characteristics of pear juice in real time based on market demand and consumer preference data. The deep learning model is trained using historical data and updated and optimized using real-time production data to ensure that the taste of each batch of pear juice meets consumer demand.

[0053] In one possible implementation, a deep learning model (such as a multi-layer perceptron, convolutional neural network, etc.) processes the input information from the data acquisition and processing unit and predicts the taste formula that consumers like best. Based on these predictions, the intelligent blending control unit automatically adjusts parameters such as sweetness, acidity, and concentration during the production process.

[0054] In the process of allocation control, the goal is to minimize the consumer's preference error. The objective function can be expressed as the following formula: , 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 value between the sweetness, acidity and concentration of the pear juice detected in real time and the target value, It is the smallest unit of each time change.

[0055] The goal of formula optimization is to minimize the error between the taste characteristics after formulation and consumer preferences while maintaining the stability of control parameters and production efficiency during the production process.

[0056] In the real-time optimization and adjustment phase, the system uses an optimization algorithm to adjust the taste characteristics of pear juice based on the real-time collected data on the physical properties of pears and consumer preference data. Common optimization algorithms such as gradient descent and genetic algorithms can be used to optimize taste parameters to ensure the consistency of taste of each batch of pear juice produced.

[0057] The goal of the optimization algorithm is to make each batch of pear juice accurately meet the taste requirements of consumers while maintaining efficiency in the production process. The formula for this optimization process is as follows: ; 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 taste characteristics; The optimization goal is to minimize the error between the taste characteristics of the blended juice and consumer preferences. Through this optimization process, the system can make real-time adjustments during the production of pear juice to ensure precise control of the taste.

[0058] The intelligent blending control unit uses deep neural networks (DNN) to perform deep learning in order to adjust recipe parameters in real time during the production process. The deep learning model continuously optimizes the adjustment strategy through feedback loops to respond to changes in consumer tastes.

[0059] In the deep learning and feedback loop, the system inputs historical data and real-time production data into the deep neural network for training, thus forming a feedback mechanism that continuously adjusts the blending parameters to ensure that the taste of each batch of pear juice is consistent with the changes in consumer preferences.

[0060] The feedback loop can be described by the following formula: ; in: For in time The taste parameters are optimized at all times; 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 in the adjustment process.

[0061] In this way, the feedback loop can respond to changes in consumer tastes in real time during each production process, ensuring personalized production of pear juice. After adjusting the optimized formula parameters, the system automatically transmits the adjusted taste formula to the production line and implements the adjustment through the intelligent control system. Through automated blending control, the system can efficiently apply the optimized taste parameters to the actual production process, ensuring that each batch of pear juice meets the taste requirements of consumers.

[0062] 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.

[0063] During the taste blending process, the following optimization formula is used for real-time adjustments to ensure that the taste characteristics meet the target requirements: ; This optimization formula is continuously used to adjust the taste characteristics such as sweetness, acidity, concentration, etc. during the production process in real time, ensuring the consistency of taste of each batch of pear juice and meeting personalized production needs.

[0064] The system optimization control module is used to obtain and analyze production data, equipment status data and market feedback data, generate and output an optimized pear juice production plan in real time based on the analysis results, perform fault diagnosis and prediction on the operating status of the pear juice production equipment, issue equipment operation warning information, and adjust the operating status of the pear juice production equipment in real time; The system optimization control module in this embodiment combines optimal control theory and robust control algorithm to adjust the operating parameters of the juicer in real time to ensure the efficiency of the production process and the stability of the system. The module optimizes the juice yield and ensures the consistency of the taste of the final pear juice by calculating and adjusting the key operating parameters of the juicer (such as speed, pressure, time, etc.). In particular, it can automatically adapt and make precise adjustments when facing uncertainties such as different pear species and different maturity.

[0065] This module is closely connected with the aforementioned raw material processing module, juice extraction control module and intelligent blending module to ensure that every link in the production process can be adjusted based on optimized control and real-time feedback. Through the optimized control of this module, not only the production efficiency of pear juice is improved, but also the consistency of product quality is improved.

[0066] The optimization control unit in this embodiment combines the optimal control theory with the robust control algorithm to achieve the optimal juice yield and taste stability by calculating and adjusting the operating parameters of the juicer in real time. The optimal control theory is based on a mathematical model, which optimizes the control parameters by analyzing the system status (such as the physical properties of pears, the operating status of production equipment, etc.) in real time, thereby ensuring that each production cycle can achieve the highest juice yield efficiency.

[0067] In actual application, the optimal control unit collects raw material data, equipment status and production data in real time, and calculates the optimal control strategy based on these data. During the real-time optimization process, the control unit adjusts the speed, pressure, time and other parameters of the juicer to ensure that the pear juice produced each time can achieve the best balance in juice yield and taste.

[0068] The process of optimization control can be expressed by the following formula: ,in: Indicates the taste characteristics of pear juice, through the physical properties of pears and adjustment parameters Calculated, are the operating parameters 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 operating parameters of the juicer, 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.

[0069] By optimizing the objective function, the system can accurately control various parameters of the juicer to maximize production efficiency and product taste stability.

[0070] 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 cope with the uncertainty caused by the pear species and maturity. The type and maturity of pears have a great influence on the juicing process. Usually, pears of different species or maturity have different operating requirements for the juicer, which requires the system to adjust the working parameters of the juicer (such as speed, pressure, time, etc.) in real time to cope with the changes.

[0071] The adaptive adjustment unit continuously updates the parameter model of the production process through system identification technology, such as recursive least squares (RLS) or Kalman filtering algorithms. By real-time analysis of raw material characteristics, equipment operation data and other information, the system can adjust the control strategy to ensure the stable juice yield and taste of each batch of pear juice.

[0072] The process of adaptive adjustment can be expressed by the following formula: ; in: For the moment The estimated values ​​of 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 collected output data (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 .

[0073] 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.

[0074] The system optimization control module works closely with the raw material processing module, the juice extraction control module, and the intelligent blending module. First, the raw material processing module classifies the types and maturity of the pears and provides real-time data to the system. The juice extraction control module adjusts the working parameters of the juicer based on this data and real-time feedback.

[0075] During this process, the system optimization control module receives data input from the juice extraction control module, calculates the best control parameters based on these inputs through the optimal control algorithm, and dynamically optimizes the control strategy according to real-time data through the adaptive adjustment unit. For example, when the system detects that the water content of the pear is high, it automatically reduces the speed or pressure to avoid over-squeezing; if it detects that the pear is hard, it increases the speed or pressure to increase the juice yield.

[0076] In the intelligent blending module, the system will also automatically adjust the sweetness, sourness and other taste parameters of pear juice according to changes in consumer tastes and fluctuations in market demand. The system optimization control module combines optimal control with robust control to ensure that the production process of each batch of pear juice is not only efficient and stable, but also able to adapt to changing production conditions.

[0077] In one embodiment, the system optimization control module automatically adjusts the control parameters of the juicer by analyzing the physical properties of the pears, production data, and market demand in real time. For example, during the production process, the system automatically adjusts the pressure and speed of the juicer by detecting the hardness and moisture content of the pears in real time. If the moisture content of the pears is high, the system will reduce the pressure to prevent over-squeezing; if the hardness of the pears is high, the system will increase the speed to increase the juice yield.

[0078] The adaptive adjustment unit updates the model parameters through real-time data to cope with the changes in different pear varieties. For example, when the system detects that the juice yield changes due to different pear varieties, the model based on the system identification algorithm will optimize the control strategy in real time and adjust the operation mode of the juicer to keep the quality of each batch of pear juice stable.

[0079] 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.

[0080] The data analysis and prediction module in this embodiment is dedicated to optimizing the production plan by analyzing various data in the production process in real time, and providing intelligent early warning for the operation of the equipment through fault diagnosis and prediction. Based on deep learning, optimization algorithms and big data analysis technology, this module processes various key data in the production process to improve production efficiency and quality, predict possible equipment failures, take maintenance measures in advance, and avoid production interruptions or equipment damage.

[0081] The core task 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 of production equipment and market demand, and to use large model technology to predict faults and diagnose potential equipment problems in advance. This information is crucial for the efficiency, stability and precise control of production.

[0082] The data analysis unit in this embodiment collects various data from the production process (such as raw material data, production data, market demand data, etc.) and uses statistical learning and machine learning techniques to perform real-time analysis and prediction on these data. The goal of the data analysis unit is to generate a reasonable production adjustment plan based on the collected data, so that the production process can be continuously optimized to achieve optimal production efficiency.

[0083] 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 the optimization of production plans. For example, by real-time analysis of raw material hardness, moisture content and other data, the system can adjust the operating parameters of the juicer in real time to maximize the juice yield.

[0084] The data analysis process can be expressed by the following formula: ; in: is the weight coefficient of each data point, indicating the contribution of the data point to the optimization of the production plan; It is an analytical function of each data point, which indicates the influence of raw material data (such as hardness, moisture content, etc.), production data and market demand data on the optimization of production process; , , They represent raw material characteristics, production data, and market demand data collected at a given moment in time, respectively; and They represent the gradients of raw material characteristics and production data, respectively, reflecting their changes over time; , , To adjust the coefficient, 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 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.

[0085] The fault diagnosis and prediction unit uses deep learning and big data analysis technology to intelligently monitor equipment in the production process using historical equipment operation data and real-time sensor data. The unit can predict the risk of equipment failure in real time, issue early warnings, and provide operators with timely maintenance and adjustment suggestions through intelligent fault diagnosis modules.

[0086] Specifically, the fault diagnosis and prediction unit uses a deep neural network (DNN) model to learn and predict the equipment status data in real time. These data include the equipment's operating status (such as pressure, speed, temperature, etc.), historical fault records, workload, etc. Based on these input data, the system can identify the potential failure risks of the equipment and predict the possible occurrence time and type of failure.

[0087] The fault diagnosis and prediction process can be described by the following formula: ; in: The weighting coefficient for each equipment status data point indicates the degree of influence of the data on the fault prediction result; It is a function for processing equipment operation status data and fault prediction parameters, and outputs equipment status assessment results; The device status data collected at any time (such as pressure, temperature, speed, etc.); 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; It is the gradient of the device state data, reflecting the spatial change of the device state.

[0088] Through this prediction mechanism based on big data technology and deep learning, the system can identify potential equipment failures in advance and provide intelligent early warning and diagnostic information to production line managers to prevent equipment failures from affecting production efficiency and product quality.

[0089] The data analysis and prediction module is closely connected with the aforementioned raw material processing module, juice extraction 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 in the production process (such as the status of the juicer), market demand, etc., and conducts a comprehensive analysis.

[0090] After acquiring real-time data, the data analysis and prediction module generates an optimization plan based on the production plan and dynamically adjusts the production process. In particular, in the juicing process, the data analysis unit can adjust the operating parameters of the juicer (such as speed, pressure, etc.) in real time by analyzing the changes in raw materials and fluctuations in market demand to ensure the consistency of juice yield and taste.

[0091] The fault diagnosis and prediction unit plays a vital role in equipment status monitoring. By monitoring the operating status of equipment such as juicers, the system can detect potential faults in a timely manner and make predictions. Through intelligent diagnosis, the system can warn of equipment problems in advance, avoid production downtime, and improve the reliability of the production line.

[0092] In one embodiment, the data analysis and processing unit uses a hybrid model based on regression analysis and classification algorithms to analyze the data of each production link in real time. The model can combine raw material data, production equipment data and market demand to generate a production optimization plan in real time. For example, if the hardness of the raw material is too high, the system will predict that the pressure and speed of the juicer need to be increased; if the market demand changes, the system will automatically adjust the production plan to ensure that the new market demand is met.

[0093] During the implementation process, the fault diagnosis and prediction unit uses historical operation data and real-time monitoring data to intelligently monitor the juicer. Through the deep learning model, the system can predict the possibility of equipment failure when the pressure or temperature of the juicer is abnormal, and give early warning information. For example, if the sensor detects that the temperature of the juicer is too high, the system will sound an alarm and predict that it may cause equipment failure, reminding the staff to check or repair it.

[0094] In order to cope with the impact of equipment failure on production plan, the data analysis and prediction module introduces a dynamic interaction mechanism between equipment failure and production plan. This mechanism is expressed by the following formula: ; in: Output the 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.

[0095] 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.

[0096] The intelligent control method for pear juice production described below and the pear juice production system described above can be referred to each other.

[0097] Please refer to the attached Figure 6 , a pear juice production intelligent control method, used in conjunction with a pear juice production system, comprising the following steps: Obtaining raw material information: Using sensors and image recognition equipment to obtain the physical property data of pears entering the production line, and classifying and pre-processing the pears. The physical property data includes type, maturity, hardness, size, and surface defects; Raw material sorting and cleaning: Based on the physical property data obtained, the pears are accurately classified using automated sorting equipment, and then surface impurities and pesticide residues are removed using 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 according to the monitoring data to optimize the juice yield and taste. The operating parameters of the juicer include speed, pressure, temperature and squeezing time; Data collection and consumer preference analysis: Market demand and consumer preference data are collected through the data collection and processing unit, and consumer taste preferences are analyzed based on the collected data to provide decision support for the subsequent pear juice taste preparation; 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 achieve automatic blending of the pear juice taste; System optimization and optimal control: Combining optimal control theory and robust control algorithm, real-time calculation and optimization of juicer operating parameters ensures that juice yield is improved and taste is stable under different pear classification and maturity conditions; Real-time feedback and adaptive adjustment in 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 uncertainty of different physical properties and production environments; Big data analysis and equipment failure prediction: Through big data analysis technology, various data in the production process are monitored in real time, the pear juice production plan is predicted and optimized, and large model technology is used to diagnose and warn 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.

[0098] The method of this embodiment can be used to execute the above system embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0099] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present 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: The raw material processing module is used to identify, sort and clean the pears entering the production line based on visual sensors, image recognition equipment and automated cleaning equipment; A juice extraction control module, which is 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 that automatically adjusts the sweetness, acidity and concentration of pear juice based on market demand and consumer preference data, as well as the pear juice production plan, to achieve personalized taste blending of the finished pear juice; Wherein, the pear juice production system also includes: 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.

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 juice extraction control module and the intelligent blending module. The juice extraction 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 an automated cleaning device to remove impurities and pesticide residues, and to control a conveying device to convey the cleaned pears to a 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 is used to collect the hardness, moisture content and maturity of the pears in the juicer in real time through sensors, monitor and collect the running status data of the juicer at the same time, and generate a feedback signal based on the hardness, moisture content and maturity of the pears and the running status data of the juicer, and send the feedback signal to the juicer control unit in real time, wherein the running status data of the juicer includes rotation speed, pressure and squeezing time; The juicer control unit is used to receive and analyze feedback signals, obtain and adjust the speed, pressure and pressing time of the juicer 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, used to collect market demand and consumer preference data, perform data analysis and processing on the market demand and consumer preference data, and also used to 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 includes an optimization control unit and an adaptive adjustment unit, wherein: The optimization control unit is used to combine the optimal control theory with robust control, and to calculate and adjust the operating parameters of the juicer in the pear juice production plan in real time through the objective function to achieve the optimal juice yield, energy consumption and taste stability; An adaptive adjustment unit is used to build a model based on real-time production data through a system identification algorithm, and automatically adjust the control strategy based on a Kalman filter to eliminate the impact of sensor noise on the control strategy to cope with the uncertainty of different pear types 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 is For juicer in time period Energy consumption within The Euclidean distance calculation result value between 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.

6. 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 the equipment status data in the production process in real time and predict the optimization adjustment plan in production; The fault diagnosis and prediction unit is used to build an equipment health assessment model based on the Transformer architecture, and use 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 the potential fault type and probability of occurrence through vibration spectrum analysis and temperature trend, and issues equipment operation warning information to provide early diagnosis and warning.

7. 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: Use deep learning algorithms to train and model the collected 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 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 in the production process are adjusted in real time to dynamically optimize taste characteristics to adapt to changes in consumer tastes; Automated blending control: The adjusted taste formula is automatically transmitted to the production line and adjusted through an intelligent control system; Optimize objective function calculation: During the taste adjustment process, real-time adjustments are made through optimization formulas.

8. A pear juice production system according to claim 5, characterized in that: 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 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 operating parameters of the juicer, 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.

9. 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 8, comprising the following steps: Obtaining raw material information: Using sensors and image recognition equipment to obtain the physical property data of pears entering the production line, and classifying and pre-processing the pears. The physical property data includes type, maturity, hardness, size, and surface defects; Raw material sorting and cleaning: Based on the physical property data obtained, the pears are accurately classified using automated sorting equipment, and then surface impurities and pesticide residues are removed using 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 according to the monitoring data to optimize the juice yield and taste. The operating parameters of the juicer include speed, pressure, temperature and squeezing time; Data collection and consumer preference analysis: Market demand and consumer preference data are collected through the data collection and processing unit, and consumer taste preferences are analyzed based on the collected data to provide decision support for the subsequent pear juice taste preparation; 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 achieve automatic blending of the pear juice taste; System optimization and optimal control: Combining optimal control theory and robust control algorithm, real-time calculation and optimization of juicer operating parameters ensures that juice yield is improved and taste is stable under different pear classification and maturity conditions; Real-time feedback and adaptive adjustment in 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 uncertainty of different physical properties and production environments; Big data analysis and equipment failure prediction: Through big data analysis technology, various data in the production process are monitored in real time, the pear juice production plan is predicted and optimized, and large model technology is used to diagnose and warn 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.

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