Energy-saving drying equipment and drying method

Abstract

The application discloses an energy-saving drying equipment and drying method, and belongs to the field of food drying. The equipment comprises a device body, a rotary heating mechanism, a vacuum pumping mechanism and a cooperative control system. The cooperative control system comprises: a food state analysis module that outputs a food state coefficient based on food thickness, humidity and fat content; a motor state analysis module that evaluates motor failure risks through temperature, voltage and current fluctuations; a motor load pressure analysis module that calculates an overload coefficient in combination with vibration amplitude, output torque and power; a temperature-rotational speed cooperativity analysis module that dynamically analyzes the matching degree of drying temperature, drum rotational speed and food state, and generates a temperature-rotational speed cooperativity coefficient; and a vacuum degree optimization module that outputs a target vacuum degree according to the temperature-rotational speed cooperativity coefficient and the current vacuum degree. The application can dynamically optimize temperature, rotational speed and vacuum degree according to food characteristics, improve drying uniformity, and reduce motor overload risks and energy consumption.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of food drying, and particularly relates to an energy-saving drying device and a drying method. BACKGROUND

[0002] Food drying is a key link in food processing, and its quality directly affects the taste of food. Traditional food drying equipment mainly controls the drying environment through constant temperature heating, ventilation and dehumidification or vacuum dehydration, but has the following significant defects:

[0003] The existing equipment adopts preset temperature, humidity and vacuum degree parameters, and cannot adjust the drying strategy in real time according to the characteristics of food (such as thickness, moisture content, fat content), resulting in uneven drying.

[0004] The motor load fluctuates with the change of food state, but the traditional method lacks real-time monitoring and protection mechanism for the state of motor temperature, vibration and current, which is easy to cause overload failure.

[0005] The temperature, speed, vacuum degree and other parameters interact with each other, and the existing technology is difficult to coordinate the relationship among the three. For example, high temperature and high speed may aggravate the motor load, and the change of vacuum degree needs to adjust the temperature and humidity synchronously to maintain the drying efficiency.

[0006] The static control strategy is easy to cause energy waste (such as excessive heating) or insufficient drying (such as humidity not timely removed), especially for high-fat content or large-thickness food.

[0007] Although some researches introduce sensor monitoring technology at present, they are still limited to single-parameter feedback control and cannot build a multi-dimensional collaborative optimization model. Therefore, an integrated control system capable of dynamically integrating food characteristics, equipment state and environmental parameters is needed to realize efficient, safe and self-adaptive food drying. SUMMARY

[0008] In view of the deficiencies of the prior art, the present application provides an energy-saving drying device and a drying method, which solves the above problems.

[0009] To achieve the above purpose, the present application realizes the following technical scheme: an energy-saving drying device, comprising a device body, a sealing door hinged to the device body and a vacuum pumping mechanism, further comprising:

[0010] A rotary heating mechanism is used to cooperate with the device body to heat and process food, comprising a drying cylinder rotatably installed on the device body and a motor driving the drying cylinder to rotate;

[0011] A collaborative control system is used to control the heating state of food, comprising:

[0012] The food state analysis module constructs a food state model based on the thickness, humidity and fat content of the food, and outputs a food state coefficient;

[0013] The motor state analysis module constructs a motor state model based on the motor temperature, voltage fluctuation value and current fluctuation value, and outputs a motor state coefficient;

[0014] The motor load pressure analysis module constructs a motor load pressure model based on the motor vibration amplitude, motor output shaft torque and motor power under the motor state coefficient, and outputs a motor load pressure coefficient;

[0015] The temperature-rotation speed synergy analysis module constructs a temperature-rotation speed synergy model based on the drying space temperature, drying space humidity and drying cylinder rotation speed under the motor load pressure coefficient and the food state coefficient, and outputs a temperature-rotation speed synergy coefficient;

[0016] The vacuum degree optimization module constructs a vacuum degree optimization model based on the temperature-rotation speed synergy coefficient and the current vacuum degree, and outputs a target vacuum degree.

[0017] On the basis of the above technical solutions, the application further provides the following optional technical solutions:

[0018] A further technical solution is that the vacuum degree optimization model is represented as:

[0019]

[0020]

[0021] wherein, the target vacuum degree is represented as V, the current vacuum degree is represented as Vcur, the vacuum degree adjustment amount is represented as AV, the adjustment amplitude is represented as A, the response sensitivity is represented as S, the temperature-rotation speed synergy coefficient is represented as Sy, the synergy threshold value is represented as Syth, the vacuum degree adjustment reference is represented as Vref, and the vacuum degree response index is represented as I.

[0022] A further technical solution is that the working steps of the temperature-rotation speed synergy analysis module are:

[0023] The drying space temperature, drying space humidity and drying cylinder rotation speed are subjected to maximum-minimum normalization processing to obtain a drying space temperature index, a drying space humidity index and a drying cylinder rotation speed index;

[0024] A temperature deviation degree model is constructed based on the drying space temperature index, the food state coefficient, and the drying space humidity index to output a temperature deviation degree, which is represented as:

[0025]

[0026]

[0027] wherein, represents the temperature deviation degree, represents the drying space temperature index, represents an ideal drying space temperature index, represents the food state coefficient, represents the drying space humidity index, represents a food state index weight coefficient of the ideal drying space temperature index;

[0028] A rotation speed deviation degree model is constructed based on the drying drum rotation speed index, the food state coefficient, and the motor load pressure coefficient to output a rotation speed deviation degree, which is represented as:

[0029]

[0030]

[0031] wherein, represents the rotation speed deviation degree, represents the drying drum rotation speed index, represents an ideal drying drum rotation speed index, represents the motor load pressure coefficient, represents the food state coefficient, represents an inhibition intensity of the food state on the ideal rotation speed;

[0032] A temperature-rotation speed conflict factor model is constructed based on the drying space temperature index, the drying space humidity index, and the drying drum rotation speed index to output a conflict factor, which is represented as:

[0033]

[0034] wherein, represents the temperature-rotation speed conflict factor, represents the drying space temperature index, represents a drying space optimal working point reference temperature index, represents the drying drum rotation speed index, represents a drying drum optimal working point reference rotation speed index, represents the drying space humidity index, represents a temperature-rotation speed interaction conflict gain coefficient, represents a humidity conflict alleviation coefficient;

[0035] A temperature-rotation speed synergy model is constructed based on the temperature deviation degree, the rotation speed deviation degree and the temperature-rotation speed conflict factor, and is expressed as:

[0036]

[0037] wherein, represents a temperature-rotation speed synergy coefficient, represents a temperature deviation degree, represents a rotation speed deviation degree, represents a temperature-rotation speed conflict factor, represents a weight and , the and the greater the value, the better the synergy between temperature and rotation speed;

[0038] The current temperature deviation degree, the current rotation speed deviation degree and the current temperature-rotation speed conflict factor are introduced into the temperature-rotation speed synergy model to output the temperature-rotation speed synergy coefficient.

[0039] Further technical solutions: the working steps of the food state analysis module are:

[0040] The food thickness, the food humidity and the food fat content are maximum-minimum normalized to obtain a thickness index, a food humidity index and a fat content index;

[0041] A food state model is constructed based on the thickness index, the food humidity index and the fat content index, and is expressed as:

[0042]

[0043] wherein, represents a food state coefficient, represents a thickness index, represents a food humidity index, represents a fat content index, represents a weight and , the and the greater the value, the higher the drying difficulty;

[0044] The current thickness index, the current food humidity index and the current fat content index are introduced into the food state model to obtain the current food state coefficient.

[0045] Further technical solutions: the working steps of the motor load pressure analysis module are:

[0046] The motor vibration amplitude, motor output shaft torque and motor power are maximum-minimum normalized to obtain a vibration amplitude index, an output shaft torque index and a motor power index;

[0047] A motor load pressure model is constructed based on the vibration amplitude index, the output shaft torque index and the motor power index under the motor state coefficient, and the motor load pressure model is represented as:

[0048]

[0049] wherein, represents a motor load pressure coefficient, represents a vibration amplitude index, represents an output shaft torque index, represents a motor power index, represents a motor state coefficient, represents an interaction weight coefficient, and the The greater the value is, the greater the overload risk is.

[0050] The current motor state coefficient, the current vibration amplitude index, the current output shaft torque index and the current motor power index are introduced into the motor load pressure model to obtain a current motor load pressure coefficient.

[0051] Further technical solutions: the working steps of the motor state analysis module are:

[0052] The motor temperature, voltage fluctuation value and current fluctuation value are maximum-minimum normalized to obtain a motor temperature index, a voltage fluctuation index and a current fluctuation index.

[0053] A motor state model is constructed based on the motor temperature index, the voltage fluctuation index and the current fluctuation index, and the motor state model is represented as:

[0054]

[0055] wherein, represents a motor state coefficient, represents a voltage fluctuation index, represents a current fluctuation index, represents a motor temperature index, represents a weight and , and the The greater the value is, the greater the failure probability is.

[0056] The current motor temperature index, the current voltage fluctuation index and the current current fluctuation index are introduced into the motor state model to obtain a current motor state coefficient.

[0057] Further technical solutions: the rotating heating mechanism further comprises heating resistors, mounting grooves and a toothed belt pair, a plurality of heating resistors are annularly and uniformly embedded in the drying cylinder, a plurality of mounting grooves are annularly and uniformly formed on the drying cylinder, the motor is detachably mounted on the device body, and the output shaft of the motor is in transmission connection with the drying cylinder through the toothed belt pair.

[0058] Further technical solutions: the vacuumizing mechanism comprises a vacuumizing device and a one-way valve, the vacuumizing device is detachably mounted on the upper end of the device body and the air suction pipeline thereof is inserted into the through hole formed on the device body, and the one-way valve is detachably mounted in the through hole.

[0059] A drying method is adopted to dry the above-mentioned energy-saving drying equipment.

[0060] The present application provides an energy-saving drying equipment and a drying method, which have the following beneficial effects compared with the prior art:

[0061] 1. The present application can calculate the food state coefficient in real time through the food state analysis module (fusion thickness, humidity, fat content), and dynamically adjust the drying space temperature;

[0062] 2. The present application can work cooperatively with the motor state analysis module and the motor load pressure analysis module to output the motor state coefficient and the load pressure coefficient in real time, and then dynamically adjust the drying cylinder speed based on the two coefficients and the food state coefficient;

[0063] 3. The present application can obtain the temperature-speed coordination degree based on the obtained drying space temperature and drying cylinder speed, and dynamically adjust the vacuum degree of the drying space based on the temperature-speed coordination degree, so as to realize the real-time adjustment of the vacuum degree according to the food state change, and avoid the problem of dry efficiency reduction caused by parameter mismatch;

[0064] 4. The present application can dynamically adjust the drying temperature, the drying cylinder speed and the vacuum degree, especially for high-fat content or large-thickness food, which can automatically reduce the drying intensity and avoid surface carbonization. BRIEF DESCRIPTION OF DRAWINGS

[0065] Fig. 1 It is a flowchart of the cooperative control system of the present application.

[0066] Fig. 2 It is a three-dimensional structure diagram of the present application.

[0067] Fig. 3 It is a structure diagram of the rotating heating mechanism and the vacuumizing mechanism in the present application.

[0068] Reference signs annotation: 1, device body; 2, rotating heating mechanism; 201, drying cylinder; 202, heating resistance; 203, mounting groove; 204, motor; 205, toothed belt pair; 3, sealing door; 4, vacuumizing mechanism; 401, vacuumizing device; 402, one-way valve. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0070] The specific implementation of the present application is described in detail below in combination with specific examples.

[0071] Please refer to Figs. 1-3 For an embodiment of the present application, an energy-saving drying equipment is provided, which comprises a device body 1, a sealing door 3 hingedly connected to the device body 1, and a vacuumizing mechanism 4, and further comprises:

[0072] A rotating heating mechanism 2 is used to cooperate with the device body 1 to heat and process food, which comprises a drying cylinder 201 rotatably mounted on the device body 1 and a motor 204 driving the drying cylinder 201 to rotate;

[0073] A cooperative control system is used to control the heating state of food, which comprises:

[0074] A food state analysis module is used to construct a food state model based on the thickness, humidity and fat content of food to output a food state coefficient;

[0075] A motor state analysis module is used to construct a motor state model based on the motor temperature, voltage fluctuation value and current fluctuation value to output a motor state coefficient;

[0076] A motor load pressure analysis module is used to construct a motor load pressure model based on the motor vibration amplitude, motor output shaft torque and motor power under the motor state coefficient to output a motor load pressure coefficient;

[0077] A temperature-rotation speed cooperativity analysis module is used to construct a temperature-rotation speed cooperativity model based on the drying space temperature, drying space humidity and drying cylinder rotation speed under the motor load pressure coefficient and the food state coefficient to output a temperature-rotation speed cooperativity coefficient;

[0078] A vacuum degree optimization module is used to construct a vacuum degree optimization model based on the temperature-rotation speed cooperativity coefficient and the current vacuum degree to output a target vacuum degree.

[0079] By the technical scheme, the drying parameters are automatically adjusted according to the food properties, and the uneven drying and cracking are reduced. Through real-time monitoring of the motor state and the load pressure, the equipment failure rate is reduced. The parameter combination is optimized by using the temperature-rotation speed synergy model, and energy waste is avoided. For high-fat content or large-thickness food, the drying efficiency is improved by adjusting the dynamic vacuum degree.

[0080] Preferably, the rotating heating mechanism further comprises heating resistors 202, mounting grooves 203, and a toothed belt pair 205, a plurality of heating resistors 202 are annularly and uniformly embedded in the drying cylinder 201, a plurality of mounting grooves 203 are annularly and uniformly formed on the drying cylinder 201, the motor 204 is detachably mounted on the device body 1, and the output shaft of the motor 204 is drivingly connected with the drying cylinder 201 through the toothed belt pair 205. The purpose of this arrangement is to heat the food by using the heating resistors 202, and to drive the drying cylinder 201 to rotate relative to the device body 1 by using the motor 204 through the toothed belt pair 205, so as to make the food placed in the mounting grooves 203 rotate around the central axis of the drying cylinder 201, thereby achieving the technical effect of uniformly heating the food.

[0081] Preferably, the humidity of the food can be detected in real time by the humidity sensor.

[0082] Preferably, the vacuumizing mechanism 4 comprises a vacuumizing device 401 and a one-way valve 402, the vacuumizing device 401 is detachably mounted on the upper end of the device body 1, and the air suction pipeline thereof is inserted into a through hole (not labeled in the figure) formed on the device body 1, and the one-way valve 402 is detachably mounted in the through hole. The purpose of this arrangement is to perform vacuumizing treatment on the space in the device body 1.

[0083] Preferably, the device body 1 is provided with a temperature sensor and a humidity sensor for real-time monitoring of the temperature and humidity in the device body 1, the motor 204 is provided with a vibration sensor (acceleration sensor) and a torque sensor for corresponding real-time detection of the vibration amplitude of the motor 204 and the torque of the rotating shaft of the motor 204, and the device body 1 is provided with a vacuum degree sensor for detecting the vacuum degree in the device body 1.

[0084] Preferably, the working steps of the food state analysis module are as follows:

[0085] The food thickness, food humidity, and food fat content are subjected to maximum-minimum normalization processing to obtain a thickness index, a food humidity index, and a fat content index;

[0086] A food state model is constructed based on the thickness index, the food humidity index, and the fat content index, and the food state model is represented as:

[0087]

[0088] wherein, represents the food state coefficient, represents the thickness index, represents the food humidity index, represents the fat content index, represents the weight and , the and the greater the value, the higher the drying difficulty;

[0089] The current thickness index, the current food humidity index and the current fat content index are introduced into the food state model to obtain the current food state coefficient.

[0090] wherein, the max-min normalization processing refers to linearly transforming the original parameters to the [0, 1] interval, which is used to eliminate the dimensional difference and unify the parameter evaluation benchmark. The food state model refers to quantifying the influence of the comprehensive characteristics of the food on the drying difficulty through weighted linear combination, which can specifically adopt to set the weight coefficient , wherein the weight distribution is based on the contribution degree of different parameters to the moisture diffusion resistance, for example, when the fat content is high, a greater weight is given to reflect its inhibitory effect on drying efficiency.

[0091] Specifically, the food thickness, humidity and fat content are collected in real time by laser ranging sensors, humidity sensors and spectral analyzers, and the collected data is converted into comparable indexes after normalization processing. The thickness index reflects the influence of the food volume on the heat conduction path, the humidity index represents the requirement of the initial moisture content on the drying time, and the fat content index is related to the internal water migration resistance. The three are fused into a single state coefficient through a weighted model, wherein the weight coefficient can be pre-set according to the food type or dynamically optimized through machine learning. For example, for high-fat content food, the system automatically increases the weight of to strengthen the evaluation of fat on drying difficulty. After the state coefficient is output, the coordinated control system adjusts the drying temperature, speed and vacuum degree based on the coefficient, for example, when the value increases, the system reduces the drying drum speed to avoid the hardening of the food surface caused by the melting of fat.

[0092] Compared with the prior art, the traditional method usually only sets the drying conditions according to a single parameter, for example, only adjusts the temperature according to the humidity, without considering the problem of uneven heat transfer caused by diameter difference. The prior art lacks a monitoring mechanism for fat content, and cannot predict the drying defects caused by changes in the softening temperature of fat. The present scheme models by fusing multiple parameters, converts the food characteristics into a quantifiable state coefficient, so that the drying strategy can dynamically adapt to the physical property differences of different batches or individual foods.

[0093] By the technical solution, the application solves the problems of uneven drying and cracking caused by static parameter setting of the traditional equipment, dynamically adjusts the drying intensity and time by real-time evaluation of the comprehensive state of the food, prevents surface carbonization of high-fat food caused by overheating, and avoids deformation of food with large thickness caused by internal water retention. The model further provides accurate input for subsequent temperature-rotation speed cooperative control to ensure stable operation of the equipment under complex working conditions.

[0094] Preferably, the working steps of the motor state analysis module are:

[0095] The motor temperature, voltage fluctuation value and current fluctuation value are maximum-minimum normalized to obtain a motor temperature index, a voltage fluctuation index and a current fluctuation index;

[0096] A motor state model is constructed based on the motor temperature index, the voltage fluctuation index and the current fluctuation index, and the motor state model is represented as:

[0097]

[0098] Among them, represents a motor state coefficient, represents a voltage fluctuation index, represents a current fluctuation index, represents a motor temperature index, represents a weight and , the and the greater the value, the greater the probability of failure;

[0099] The current motor temperature index, the current voltage fluctuation index and the current current fluctuation index are introduced into the motor state model to obtain the current motor state coefficient.

[0100] Among them, the maximum-minimum normalization processing refers to a standardization method of linearly transforming original data to a range of 0 to 1, which can be realized by using a linear scaling formula, and is used to eliminate the dimensional differences of motor temperature, voltage and current fluctuation values, so that different physical quantities can be weighted and calculated.

[0101] Among them, the motor state model refers to a mathematical model that maps the weighted combination of the square of the voltage fluctuation index, the square of the current fluctuation index and the square of the motor temperature index to the interval of 0 to 1 by using an exponential function, which can be realized by using a nonlinear decay function, and is used to strengthen the contribution of abnormal parameters to the probability of failure, for example, a sudden change in voltage will cause the exponential term to increase rapidly.

[0102] Among them, the weight coefficient is referred to as the relative importance of each parameter in the model. The specific parameters can be determined by empirical values or machine learning optimization methods, which are used to dynamically adjust the monitoring focus according to the actual working conditions, for example, increasing the weight of the temperature index in a high temperature environment.

[0103] Specifically, the motor temperature, voltage fluctuation value and current fluctuation value are first normalized to an index value in the range of 0 to 1 to eliminate dimensional differences and unify the parameter range. Subsequently, the abnormal fluctuations of the voltage fluctuation index, the current fluctuation index and the motor temperature index are amplified by squaring operation, for example, when the current suddenly changes, its square value will increase significantly. These square terms are input into the exponential decay function after weighted summation, and are converted into a motor state coefficient in the range of 0 to 1, wherein the weight coefficient can adjust the parameter priority according to different working conditions. When the motor state coefficient tends to 1, it indicates that the motor is in a high risk state, at which time a warning signal can be triggered or the load strategy can be adjusted.

[0104] Compared with the prior art, the traditional method usually only monitors a single parameter or uses a fixed threshold to judge the motor state, for example, only monitors whether the motor temperature exceeds the set threshold. However, the present scheme can identify potential risks earlier by fusing multi-dimensional parameters of voltage, current and temperature and dynamically evaluating the comprehensive failure probability by using a nonlinear model. For example, when the voltage fluctuation and the current fluctuation increase at the same time but the temperature has not yet exceeded the threshold, the traditional method may not be able to issue a warning, but the present scheme can reflect the abnormality in advance through the superposition effect of the square terms.

[0105] Through the above technical scheme, the present application realizes real-time comprehensive monitoring of motor temperature, voltage and current fluctuation, dynamically quantifies the failure probability through a nonlinear model, and solves the problem that the traditional method cannot accurately evaluate the motor health status. For example, in the case of frequent voltage fluctuations but not reaching the threshold, potential risks can still be identified through model calculation, providing a basis for subsequent load adjustment and avoiding equipment downtime due to motor overload.

[0106] Preferably, the working steps of the motor load stress analysis module are:

[0107] The motor vibration amplitude, motor output shaft torque and motor power are maximum-minimum normalized to obtain a vibration amplitude index, an output shaft torque index and a motor power index;

[0108] A motor load stress model is constructed based on the vibration amplitude index, the output shaft torque index and the motor power index under the motor state coefficient, and the motor load stress model is represented as:

[0109]

[0110] wherein, represents a motor load stress coefficient, represents a vibration amplitude index, represents an output shaft torque index, represents a motor power index, represents a motor state coefficient, represents an interaction weight coefficient, the and the greater the value, the greater the risk of overload;

[0111] The current motor state coefficient, the current vibration amplitude index, the current output shaft torque index, and the current motor power index are introduced into the motor load pressure model to obtain a current motor load pressure coefficient.

[0112] The maximum-minimum normalization processing refers to linearly transforming the original data to the [0, 1] interval, and can be realized by using the ratio of the measured value of the vibration amplitude, torque, and power measured by the sensor to the historical maximum and minimum value, for eliminating the dimensional differences of different parameters and facilitating unified calculation of the model.

[0113] The vibration amplitude index refers to a standardized index reflecting the mechanical vibration intensity of the motor, and can be realized by measuring the peak value or effective value of the vibration amplitude by an acceleration sensor and normalizing processing, for representing the mechanical stability of the motor during operation.

[0114] The output shaft torque index refers to a standardized index reflecting the load torque of the output shaft of the motor, and can be realized by measuring the real-time torque value by a torque sensor and normalizing processing, for quantifying the resistance when the motor drives the drying cylinder to rotate.

[0115] The motor power index refers to a standardized index reflecting the power consumption of the motor, and can be realized by measuring the input power of the motor by an electric parameter measurement method or a torque-speed method and normalizing processing, for evaluating the power consumption and efficiency of the motor.

[0116] The motor state coefficient refers to a comprehensive index reflecting the running health state of the motor itself, and can be calculated by monitoring the motor temperature, voltage fluctuation, and current fluctuation and constructing a state model, for representing the influence of the motor fault risk on the load pressure.

[0117] The interaction weight coefficient refers to the contribution weight of different parameter combinations in the model, and can be determined by an empirical value or machine learning optimization, for adjusting the proportion of the vibration-torque combination term and the power-state combination term in the load pressure evaluation.

[0118] Specifically, the real-time data of motor vibration amplitude, output shaft torque and power are collected and converted into comparable exponential form through normalization. The product term of vibration amplitude index and output shaft torque index reflects the direct pressure of mechanical load on the motor, and the product term of motor power index and motor state coefficient reflects the influence of motor operating state on power consumption. Through the interaction weight coefficient, the two types of parameter combinations are weighted and fused, and the load pressure coefficient output by the model can dynamically represent the comprehensive overload risk under the joint action of mechanical load and motor state. For example, when the vibration amplitude and torque increase at the same time, the mechanical load term significantly increases; if the motor power abnormally increases due to state deterioration at this time, the power-state term further aggravates the load pressure coefficient, thereby triggering the protection mechanism.

[0119] Compared with the prior art, the traditional method usually only monitors a single parameter such as current or temperature to determine overload, while the present scheme solves the problem that single parameter monitoring is easily disturbed and cannot reflect the comprehensive load by fusing multi-dimensional data of vibration, torque, power and motor state to construct a dynamic weighted evaluation model. The prior art lacks quantitative analysis of the interaction between mechanical load and motor state, while the present scheme realizes balanced evaluation of the two types of influencing factors by introducing an interaction weight coefficient, thereby improving the accuracy of overload prediction.

[0120] Through the above technical scheme, the present application can monitor the multi-dimensional operating parameters of the motor in real time during the food drying process, dynamically evaluate the comprehensive influence of mechanical load and motor state, and accurately identify the overload risk. In this way, motor damage caused by abnormal vibration or sudden increase in torque is avoided, and false judgments caused by power fluctuations or state deterioration are prevented, ensuring the stable operation of the drying equipment under complex working conditions.

[0121] Preferably, the working steps of the temperature-rotation speed synergy analysis module are:

[0122] The drying space temperature, drying space humidity and drying cylinder rotation speed are maximum-minimum normalized to obtain a drying space temperature index, a drying space humidity index and a drying cylinder rotation speed index;

[0123] A temperature deviation degree model is constructed based on the drying space temperature index, the food state coefficient and the drying space humidity index to output a temperature deviation degree, and the temperature deviation degree model is represented as:

[0124]

[0125]

[0126] wherein, the temperature deviation degree is represented as, the drying space temperature index is represented as, the ideal drying space temperature index is represented as, Represents the food state coefficient. This indicates the humidity index of the drying space. This indicates the weighting coefficient of the temperature index in the ideal drying space for food.

[0127] A speed deviation model is constructed based on the drying drum speed index, food state coefficient, and motor load pressure coefficient to output the speed deviation, which is expressed as:

[0128]

[0129]

[0130] in, Indicates the deviation of rotational speed. Indicates the rotational speed index of the drying drum. This indicates the ideal drying drum rotation speed index. Indicates the motor load pressure coefficient. Represents the food state coefficient. This indicates the degree to which the food condition inhibits the ideal rotational speed;

[0131] A temperature-rotation speed conflict factor model is constructed based on the drying space temperature index, drying space humidity index, and drying drum rotation speed index to output conflict factors. The temperature-rotation speed conflict factor model is expressed as follows:

[0132]

[0133] in, This indicates the temperature-speed conflict factor. This indicates the temperature index of the drying space. This indicates the reference temperature index for the optimal operating point of the drying space. Indicates the rotational speed index of the drying drum. This indicates the reference speed index for the optimal operating point of the drying drum. This indicates the humidity index of the drying space. This represents the gain coefficient for temperature-speed interaction conflict. Indicates the mitigation factor of humidity on the conflict;

[0134] A temperature-speed synergy model is constructed based on temperature deviation, speed deviation, and temperature-speed conflict factor. This temperature-speed synergy model is expressed as follows:

[0135]

[0136] in, Indicates the temperature-speed compatibility coefficient. Indicates the degree of temperature deviation. Indicates the deviation of rotational speed. represents a temperature-rotation speed conflict factor, represents a weight and , the and the greater the value, the better the synergy between temperature and rotation speed;

[0137] The current temperature deviation, the current rotation speed deviation, and the current temperature-rotation speed conflict factor are introduced into the temperature-rotation speed synergy model to output a temperature-rotation speed synergy coefficient.

[0138] Preferably, the temperature deviation and the rotation speed deviation are compared with the corresponding preset threshold values, and the drying space temperature and the drying cylinder rotation speed are adjusted until the temperature deviation and the rotation speed deviation are within the corresponding threshold values.

[0139] wherein the max-min normalization processing refers to a standardization process of linearly mapping original parameters to a range of 0 to 1, for eliminating the interference of parameters with different dimensions on model operation. The temperature deviation model refers to reflecting temperature control deviation by dynamically calculating the difference between an actual temperature index and an ideal temperature index, wherein the ideal temperature index is generated by weighting a food state coefficient and a humidity index, for dynamically adjusting a temperature target value according to food characteristics. The rotation speed deviation model refers to reflecting rotation speed control deviation by calculating the difference between an actual rotation speed index and an ideal rotation speed index, wherein the ideal rotation speed index is determined by a motor load pressure coefficient and a food state suppression factor, for balancing motor load and rotation speed demand. The temperature-rotation speed conflict factor model refers to identifying parameter synergy imbalance risk by quantifying the interactive influence of temperature and rotation speed deviation from an optimal benchmark, combined with the adjustment of conflict intensity by a humidity parameter, for dynamically evaluating the matching degree of temperature and rotation speed. The temperature-rotation speed synergy model refers to outputting a coefficient reflecting the overall synergy state of the system by integrating temperature deviation, rotation speed deviation, and conflict factor through an exponential function, for guiding vacuum degree optimization adjustment.

[0140] Specifically, during the drying process, the temperature, humidity and rotation speed of the drying space are first normalized and converted into standardized indexes to provide a unified input benchmark for subsequent models. In the temperature deviation model, the ideal temperature index is dynamically adjusted according to the food state coefficient and the current humidity index. For example, when the food state coefficient is high, the ideal temperature index deviates towards the direction of the food characteristic requirements, avoiding the cracking of the food caused by a fixed temperature threshold. In the rotation speed deviation model, the ideal rotation speed index is calculated in combination with the motor load pressure coefficient and the food state suppression factor. When the motor load pressure increases or the food drying difficulty is high, the rotation speed benchmark is automatically reduced to prevent motor overload. In the temperature-rotation speed conflict factor model, the square of the deviation of the temperature and rotation speed from the optimal benchmark reflects the strength of the interaction conflict between the two, and the humidity index is used as the denominator to alleviate the conflict degree. For example, in a high humidity environment, the calculated value of the conflict factor is reduced, reducing the parameter adjustment amplitude. Finally, the temperature-rotation speed synergy model fuses the deviation and the conflict factor into a synergy coefficient within the range of 0 to 1 through an exponential function. The higher the synergy coefficient, the better the matching degree of the temperature and rotation speed, providing a dynamic adjustment basis for the vacuum degree optimization.

[0141] Compared with the prior art, the prior art usually uses preset temperature and rotation speed parameters, which cannot be adjusted in real time according to the changes in food state and motor load fluctuations, resulting in low drying efficiency and easy equipment overload. The present scheme eliminates the dimensional differences through normalization processing, constructs dynamic deviation models and conflict factor models, and calculates the synergy relationship of temperature, rotation speed and equipment state in real time to achieve adaptive matching of parameters. For example, in the prior art, the rotation speed is only set according to experience, while in the present scheme, the motor load pressure coefficient and the food state suppression factor are introduced to optimize the rotation speed while ensuring the safety of the motor; in the prior art, the temperature and rotation speed are adjusted independently of each other, while in the present scheme, the interaction between the two is quantified through the conflict factor, and the conflict strength is dynamically adjusted in combination with the humidity parameter to avoid parameter adjustment conflicts.

[0142] Through the above technical scheme, the present application can adjust the synergy relationship of temperature and rotation speed in real time according to the food state and motor load, solving the problems of uneven drying and equipment overload caused by traditional static parameter setting. By dynamically calculating the temperature deviation and rotation speed deviation, adaptive matching of the target values of temperature and rotation speed is achieved, avoiding drying defects caused by changes in food characteristics. The interaction between temperature and rotation speed is quantified through the conflict factor model, and the conflict strength is dynamically adjusted in combination with the humidity parameter to reduce parameter adjustment conflicts. The synergy coefficient integrates multi-dimensional parameter deviations to provide a dynamic basis for vacuum degree optimization, improving drying efficiency and equipment operation stability.

[0143] Preferably, the vacuum degree optimization model is represented as:

[0144]

[0145]

[0146] wherein, represents the target vacuum degree, represents the current vacuum degree, represents the vacuum degree adjustment amount, represents the adjustment amplitude, represents the response sensitivity, represents the temperature-rotation cooperativity coefficient, represents the cooperativity threshold, represents the vacuum degree adjustment reference, represents the vacuum degree response index.

[0147] wherein, represents the target vacuum degree, which can be calculated by superimposing the adjustment amount on the real-time measurement of the current vacuum degree by the vacuum degree sensor, for dynamic optimization of the drying environment pressure, represents the vacuum degree adjustment amount, which can be generated by a nonlinear function based on the temperature-rotation cooperativity coefficient, for adaptive adjustment of the vacuum degree according to the device operating state, represents the adjustment amplitude, which can be determined by preset parameters or historical data training, for controlling the overall intensity of the vacuum degree change, represents the response sensitivity, which can be set by experience or through the slope parameter setting of the exponential function, for adjusting the sensitivity of the system to the cooperativity deviation, represents the temperature-rotation cooperativity coefficient, for representing the matching degree of the heating and rotation parameters, represents the cooperativity threshold, which can be set based on the critical value of the safe operation range of the device, for judging whether to trigger the vacuum degree adjustment, represents the vacuum degree adjustment reference (decision reference point of the vacuum degree adjustment direction), which can be set by experience or calibrated by the reference offset under normal working conditions through experiments, for maintaining the stability during regular operation and determining the direction of the vacuum degree adjustment, represents the vacuum degree response index, which can be dynamically adjusted according to the remaining adjustment space of the vacuum degree, for preventing over-adjustment when the vacuum degree approaches the limit value.

[0148] Specifically, the model realizes dynamic optimization of vacuum degree through phased calculation. First, the temperature-rotation speed synergy coefficient is compared with the synergy threshold based on the sigmoid function. When the temperature-rotation speed synergy coefficient is far below the threshold, the function output tends to zero, and the vacuum degree is adjusted positively. By introducing an adjustment reference parameter, the reference vacuum degree is maintained under normal working conditions, and significant adjustment is only started in abnormal states. At the same time, in combination with the current vacuum degree and the response index, the adjustment amplitude is automatically reduced when the vacuum degree is close to the limit value, avoiding overloading of the equipment due to over-adjustment. The synergistic effect of the adjustment amplitude, the response sensitivity and the response index makes the vacuum degree optimization process not only quickly respond in abnormal working conditions, but also maintain stability in critical states.

[0149] Compared with the prior art, the traditional vacuum degree control uses fixed parameters or single-factor adjustment, which cannot coordinate the coupling relationship of temperature, rotation speed and vacuum degree. The scheme quantifies the temperature-rotation speed synergy into a calculable coefficient by constructing a multi-parameter fusion optimization model, and dynamically generates the vacuum degree adjustment amount based on the coefficient. Compared with the static control method, the model can adjust the vacuum degree in real time according to the state change of the food, avoiding the decrease of drying efficiency caused by parameter mismatch. At the same time, by introducing a nonlinear response mechanism and a dynamic constraint term, the over-adjustment phenomenon is effectively inhibited under the premise of ensuring the adjustment sensitivity, reducing the risk of equipment overload.

[0150] Through the above technical scheme, the application can dynamically optimize the vacuum degree parameter according to the synergy state of temperature and rotation speed in the food drying process. When the matching degree of temperature and rotation speed decreases, the vacuum degree adjustment amount is automatically increased to improve the drying efficiency; when the vacuum degree is close to the limit value, the adjustment amplitude is automatically attenuated to prevent equipment overload. This dynamic adjustment mechanism not only maintains the drying quality, but also significantly reduces energy waste and equipment failure rate, especially suitable for complex drying conditions of high-fat content or large-thickness food.

[0151] It should be noted that, in this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0152] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. An energy-saving drying apparatus comprising an apparatus body, a sealing door hinged to the apparatus body, and a vacuum-pumping mechanism, characterized in that, Also comprising: A rotating heating mechanism for heating the food in cooperation with the device body, comprising a drying cylinder rotatably installed on the device body and a motor for driving the drying cylinder to rotate; A cooperative control system for controlling the heating state of the food, comprising: A food state analysis module for constructing a food state model based on the thickness, humidity and fat content of the food to output a food state coefficient; A motor state analysis module for constructing a motor state model based on the motor temperature, voltage fluctuation value and current fluctuation value to output a motor state coefficient; A motor load pressure analysis module for constructing a motor load pressure model based on the motor vibration amplitude, motor output shaft torque and motor power under the motor state coefficient to output a motor load pressure coefficient; A temperature-rotation speed cooperativity analysis module for constructing a temperature-rotation speed cooperativity model based on the drying space temperature, drying space humidity and drying cylinder rotation speed under the motor load pressure coefficient and the food state coefficient to output a temperature-rotation speed cooperativity coefficient; A vacuum degree optimization module for constructing a vacuum degree optimization model based on the temperature-rotation speed cooperativity coefficient and the current vacuum degree to output a target vacuum degree; The vacuum degree optimization model is represented as: wherein, represents a target vacuum degree, represents a current vacuum degree, represents a vacuum degree adjustment amount, represents an adjustment range, represents a response sensitivity, represents a temperature-rotation speed cooperativity coefficient, represents a cooperativity threshold value, represents a vacuum degree adjustment reference, represents a vacuum degree response index; The working steps of the temperature-rotation speed cooperativity analysis module are: Performing maximum-minimum normalization processing on the drying space temperature, drying space humidity and drying cylinder rotation speed to obtain a drying space temperature index, a drying space humidity index and a drying cylinder rotation speed index; Constructing a temperature deviation degree model based on the drying space temperature index, food state coefficient and drying space humidity index to output a temperature deviation degree, the temperature deviation degree model is represented as: wherein, represents a temperature deviation degree, represents a drying space temperature index, represents an ideal drying space temperature index, represents a food state coefficient, represents a drying space humidity index, represents a food state coefficient in an ideal drying space temperature index weight coefficient; Constructing a rotation speed deviation degree model based on the drying cylinder rotation speed index, food state coefficient and motor load pressure coefficient to output a rotation speed deviation degree, the rotation speed deviation degree is represented as: wherein, represents a rotational speed deviation degree, represents a drying drum rotational speed index, represents an ideal drying drum rotational speed index, represents a motor load pressure coefficient, represents a food state coefficient, represents a food state suppression intensity on the ideal rotational speed; Constructing a temperature-rotation speed conflict factor model based on the drying space temperature index, drying space humidity index and drying cylinder rotation speed index to output a conflict factor, the temperature-rotation speed conflict factor model is represented as: wherein, represents a temperature-rotation speed conflict factor, represents a drying space temperature index, represents a drying space optimal working point reference temperature index, represents a drying cylinder rotation speed index, represents a drying cylinder optimal working point reference rotation speed index, represents a drying space humidity index, represents a temperature-rotation speed interaction conflict gain coefficient, represents a humidity alleviation coefficient for conflict; Constructing a temperature-rotation speed cooperativity model based on the temperature deviation degree, rotation speed deviation degree and temperature-rotation speed conflict factor, the temperature-rotation speed cooperativity model is represented as: wherein, represents the temperature-rotation speed synergy coefficient, represents the temperature deviation, represents the rotation speed deviation, represents the temperature-rotation speed conflict factor, represents the weight and , the and the greater the value the better the synergy between temperature and rotation speed; Importing the current temperature deviation degree, current rotation speed deviation degree and current temperature-rotation speed conflict factor into the temperature-rotation speed cooperativity model to output the temperature-rotation speed cooperativity coefficient.

2. The energy-saving drying apparatus according to claim 1, characterized by The working steps of the food state analysis module are: Performing maximum-minimum normalization processing on the food thickness, food humidity and food fat content to obtain a thickness index, a food humidity index and a fat content index; Constructing a food state model based on the thickness index, food humidity index and fat content index, the food state model is represented as: wherein, represents a food state coefficient, represents a thickness index, represents a food humidity index, represents a fat content index, represents a weight and , said and the greater the value the higher the drying difficulty; Importing the current thickness index, current food humidity index and current fat content index into the food state model to obtain the current food state coefficient.

3. The energy-efficient drying apparatus of claim 2, wherein, The working steps of the motor state analysis module are: Performing maximum-minimum normalization processing on the motor temperature, voltage fluctuation value and current fluctuation value to obtain a motor temperature index, a voltage fluctuation index and a current fluctuation index; A motor state model is constructed based on a motor temperature index, a voltage fluctuation index and a current fluctuation index, and is expressed as: wherein, represents a motor state coefficient, represents a voltage fluctuation index, represents a current fluctuation index, represents a motor temperature index, represents a weight and , said and the greater the value the greater the probability of failure; The current motor state coefficient is obtained by introducing the current motor temperature index, the current voltage fluctuation index and the current current fluctuation index into the motor state model.

4. The energy-saving drying apparatus according to claim 3, characterized by The working steps of the motor load pressure analysis module are as follows: The motor vibration amplitude, the motor output shaft torque and the motor power are subjected to maximum-minimum normalization processing to obtain a vibration amplitude index, an output shaft torque index and a motor power index; A motor load pressure model is constructed based on the vibration amplitude index, the output shaft torque index and the motor power index under the motor state coefficient, and is expressed as: wherein, represents a motor load pressure coefficient, represents a vibration amplitude index, represents an output shaft torque index, represents a motor power index, represents a motor status coefficient, represents an interaction weight coefficient, said and the greater the value the greater the risk of overload; The current motor load pressure coefficient is obtained by introducing the current motor state coefficient, the current vibration amplitude index, the current output shaft torque index and the current motor power index into the motor load pressure model.

5. The energy-saving drying apparatus according to claim 1, wherein The rotating heating mechanism further comprises heating resistors, mounting grooves and a toothed belt pair, a plurality of the heating resistors are evenly embedded in the drying cylinder in a ring shape, a plurality of the mounting grooves are evenly formed on the drying cylinder in a ring shape, the motor is detachably mounted on the device body, and the output shaft of the motor is in driving connection with the drying cylinder through the toothed belt pair.

6. The energy-efficient drying apparatus of claim 1, wherein, The vacuumizing mechanism comprises a vacuumizing device and a one-way valve, the vacuumizing device is detachably mounted on the upper end of the device body and its air suction pipeline is inserted into the through hole formed on the device body, and the one-way valve is detachably mounted in the through hole.

7. A drying method, characterized by, The energy-saving drying equipment of any one of claims 1-6 is adopted for drying.

Images