Food processing method and food processor
By optimizing mixing parameters using food processing models and reinforcement learning algorithms, the problem of low mixing efficiency in existing equipment has been solved, achieving efficient and uniform food mixing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-03
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing food processing equipment involves relatively few movement trajectories when reciprocating to transport mixtures, resulting in low and insufficient mixing efficiency. Current technology struggles to coordinate the actions of multiple mixing mechanisms to improve the mixing effect.
A food processing model is used to predict the mixing state and adjust the speed, duration, and direction of the reciprocating auger. Combined with reinforcement learning algorithms, the mixing parameters are automatically adjusted to optimize the mixing process.
It improves the mixing effect, saves time, increases the uniformity of mixing, and reduces the frequency of operation and equipment energy consumption, thus achieving high-efficiency production.
Smart Images
Figure CN121775720A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of food processing machinery technology, and more specifically, to a food processing method and a food processing machine. Background Technology
[0002] Food processing is the process of mixing raw materials and shaping them into new food products. In food processing, the mixing effect of food materials can, to a certain extent, determine the taste and quality of the finished food.
[0003] Existing technology publication CN108465413B discloses a novel food processing equipment and method. This device rapidly and thoroughly mixes edible water and powdered ingredients in a column cavity under the action of rectangular agitator blades. The rotation of the auger shaft, combined with the agitation of the mixing rod, ensures that the ingredients in the food channel are always uniformly mixed in the liquid. Furthermore, the constantly rotating mixing rod's injection holes ensure that the injection process in the food channel remains uniform, ultimately achieving dynamic equilibrium in the food processing cylinder, resulting in a consistently consistent fluid food product discharged from the outlet. This device processes and mixes through reciprocating conveying, but the movement trajectory of the mixture is limited. Although it runs continuously, the mixing efficiency is low, leading to incomplete mixing. Improving the mixing effect by adding a stirring mechanism requires addressing how to coordinate the actions of multiple stirring mechanisms to maximize the efficiency of the food processing equipment. Therefore, we propose a food processing method and a food processing machine. Summary of the Invention
[0004] 1. The technical problems to be solved.
[0005] The purpose of this application is to provide a food processing method that solves the technical problem in the above-mentioned background technology where the device processes and mixes the mixture by reciprocating conveying, resulting in limited movement of the mixture trajectory and low mixing efficiency, even though it runs continuously. By setting up a food processing model, the mixing state and condition of different mixtures can be predicted, and parameters such as the speed and duration of the stirring rod and the direction of movement of the reciprocating auger can be adjusted to improve the mixing effect and save time.
[0006] 2. Technical solution.
[0007] This application provides a food processing method applied to a food processing machine, comprising the following steps.
[0008] S1. Collect food processing data, including data on the mixing state during the food processing mixing process and data on the operation of the food processing machine.
[0009] S2. Food processing data preprocessing to obtain the training dataset.
[0010] S3. Based on the training dataset, a food processing model is established using a recurrent neural network. The input of the food processing model is the processed food processing data, and the output is the prediction result of the mixed state.
[0011] S4. Input the real-time collected food processing data into the food processing model to predict the food mixing state at future moments.
[0012] S5. Adjust the operating parameters of the food processor based on the prediction results of the food processing model to optimize the mixing process and improve efficiency.
[0013] By adopting the above technical solution, the mixing state and conditions of different mixtures are predicted using a food processing model, and the operating parameters of the food processor, such as the running speed and duration of the stirring rod, are adjusted based on the prediction results, thereby improving the mixing effect and saving time.
[0014] As an optional solution to the technical solution of this application, in step S1, the mixing state data during the food processing mixing process is obtained by a refractometer, and the operating data of the food processing machine includes the speed data of the stirring rod and the reciprocating auger.
[0015] As an optional solution to the technical solution of this application, in step S3, a food processing model is established based on the training dataset using a recurrent neural network. The architecture of the recurrent neural network includes: an input layer: a fusion array of the processed mixed state data and the running data; a hidden layer: using a long short-term memory network as a hidden layer to capture the long-term dependence in the food mixing process, consisting of multiple LSTM units superimposed; and an output layer: outputting the predicted future mixed state.
[0016] As an optional solution to the technical solution of this application, in step S5, a reinforcement learning algorithm is introduced to automatically adjust the speed and duration of the stirring rod and the forward and reverse rotation control of the reciprocating auger in the food processor based on the input features of the food processing model.
[0017] Based on the above scheme, a deep reinforcement learning algorithm is used in reinforcement learning. The fused array of mixed state data and running data is used as the state of reinforcement learning. The actions are the speed and duration of the stirring rod and the forward and reverse rotation of the auger. The reward function is the comprehensive minimization of the time predicted by the food processing model to reach the set uniform mixing state and the number of adjustment actions.
[0018] Wherein, the reward function R t for.
[0019]
[0020] Among them, T mix (t) represents the time required to reach the set mixing uniformity state within time step t, as predicted by the food processing model. A(t) represents the number of operations performed within time step t. E(t) represents the energy consumption within time step t. α, β and γ are the corresponding weighting factors used to balance the effects of mixing uniformity, number of adjustments and energy consumption.
[0021] The technical solution of this application also provides a food processing machine equipped with a real-time monitoring and control system, including...
[0022] A food mixing and processing box, which is divided into a processing chamber and a collection chamber. The processing chamber is located on the upper side of the collection chamber and is connected to it through an electrically controlled valve.
[0023] The lid is attached to one side of the food mixing and processing box.
[0024] The discharge port is located on one side of the food mixing and processing box and is connected to the collection chamber.
[0025] The processing mechanism is located inside the processing chamber and performs reciprocating processing on the food.
[0026] As an optional solution to the technical solution of this application, the processing mechanism and processing chamber are provided in multiple ways. The processing mechanism includes a reciprocating auger, one end of which is fixedly provided with an auger head. Multiple auger heads are synchronously driven to perform synchronous reciprocating motion and convey in different directions through different directions.
[0027] Based on the above scheme, the synchronous drive includes a synchronous gear fixedly installed at the outer end of the reciprocating auger. One of the synchronous gears is meshed with the drive gear. A first motor is coaxially fixedly installed on one side of the drive gear. A co-directional gear is meshed with the outer wall of one of the synchronous gears. The co-directional gear is meshed with the adjacent synchronous gear. The other synchronous gear is driven by another co-directional gear. The multiple synchronous gears do not mesh with each other.
[0028] Based on the above scheme, the reciprocating auger has a hollow structure in the middle, multiple feed channels are opened at the outer end of the reciprocating auger, multiple discharge channels are opened at the inner end, and a stirring rod is rotatably installed inside the reciprocating auger.
[0029] 3. Beneficial effects.
[0030] One or more technical solutions provided in this application have at least the following technical effects or advantages.
[0031] 1. This application, through the setting of a food processing model, can predict the mixing state and conditions of different mixtures, thereby improving the mixing effect and saving time by adjusting parameters such as the speed and duration of the stirring rod and the direction of motion of the reciprocating auger.
[0032] 2. This application introduces a reinforcement learning algorithm to enable the food processing machine to automatically adjust the speed and duration of the stirring rod and the forward and reverse rotation of the auger, optimizing the food mixing process with the optimal combination of actions. Through precise state representation and reasonable reward design, this system can not only improve mixing uniformity but also reduce operation frequency and equipment energy consumption, ultimately achieving the goal of high-efficiency production.
[0033] 3. This application, through the setting of the processing mechanism, can process the mixture through multiple chambers and carry out synchronous processing through synchronous drive, so that the mixture can be conveyed and mixed by reciprocating screw conveyor, and the stirring rod can be repeatedly processed to improve efficiency and effect.
[0034] 4. This application incorporates a stirring mechanism inside the reciprocating auger. After stirring, the auger can not only stir again, but also output power through different rotation directions, enabling it to actively output power and thus preventing blockages and improving functionality. Attached Figure Description
[0035] Figure 1 This is a flowchart illustrating the food processing method disclosed in this application.
[0036] Figure 2 This is a schematic diagram of the overall structure of the food processing machine disclosed in this application.
[0037] Figure 3 This is a cross-sectional view of the overall structure of the food processing machine disclosed in this application.
[0038] Figure 4 This is an exploded view of the processing mechanism structure of the food processing machine disclosed in this application.
[0039] Figure 5 This is an exploded view of the internal structure of the reciprocating auger of the food processing machine disclosed in this application.
[0040] Figure 6 This is a schematic diagram showing the structural relationship between the reciprocating auger and the processing chamber of the food processing machine disclosed in this application.
[0041] The following are the labels in the diagram: 1. Food mixing and processing box; 11. Processing chamber; 12. Collection chamber; 2. Box cover; 3. Discharge port; 4. Processing mechanism; 41. Reciprocating auger; 42. Auger head; 43. Synchronous gear; 44. Rotary trough; 45. Mechanical chamber; 46. Drive gear; 47. First motor; 48. Co-directional gear; 401. Feed trough; 402. Discharge trough; 403. Stirring rod; 404. Machine chamber; 405. Second motor. Detailed Implementation
[0042] The present application will be further described in detail below with reference to the accompanying drawings.
[0043] Reference Figure 1 This application provides a food processing method applied to a food processing machine, comprising the following steps.
[0044] S1. Collect food processing data, including data on the mixing state during the food processing mixing process and data on the operation of the food processing machine.
[0045] S2. Food processing data preprocessing to obtain the training dataset.
[0046] S3. Based on the training dataset, a food processing model is established using a recurrent neural network. The input of the food processing model is the processed food processing data, and the output is the prediction result of the mixed state.
[0047] S4. Input the real-time collected food processing data into the food processing model to predict the food mixing state at future moments.
[0048] S5. Adjust the operating parameters of the food processor based on the prediction results of the food processing model to optimize the mixing process and improve efficiency.
[0049] By setting up a food processing model, the mixing state and conditions of different mixtures can be predicted, and the operating parameters of the food processor can be adjusted based on the prediction results. For example, by adjusting the speed and duration of the stirring rod 403 and the rotation direction of the reciprocating auger 41 in the food processor, the mixing effect can be improved and time can be saved.
[0050] Reference Figure 1 and Figure 2This application provides a food processing method. In step S1, a refractometer is used to acquire mixing state data during the food processing mixing process. The operating data of the food processor includes the speed data of the stirring rod 403 and the reciprocating auger 41. A speed sensor, such as a Hall effect sensor, is used to measure the rotor speed by detecting changes in the magnetic field. When the rotor rotates, the resulting change in the magnetic field causes a change in the output signal of the Hall effect element to detect the rotor's rotation speed and obtain data. After the data acquisition is completed, the data can be subjected to multiple diversification processing, including: (1) calculating the average value of each data item in the dataset to describe the central tendency of the data. The formula for the average value is:
[0051] in, This represents the mean. Let represent the i-th data point, and n represent the size of the dataset.
[0052] (2) Variance and standard deviation: used to describe the dispersion of data, and the calculation formulas are respectively.
[0053] in, Represents variance. It represents the standard deviation.
[0054] (3) Correlation coefficient: used to measure the strength and direction of the linear relationship between two variables. The formula for calculating the correlation coefficient is:
[0055] in, Let X represent the correlation coefficient between variables X and Y, and Cov(X,Y) represent the covariance between X and Y. and Let X and Y represent the standard deviations, respectively.
[0056] In step S2, the obtained food processing data is preprocessed, including data cleaning such as removing noise and outliers, and then data normalization. This normalization standardizes operational data of different dimensions (such as speed and duration) to ensure better convergence during model training. In this embodiment, the food processing data is scaled to a similar range to accelerate model convergence and improve training performance. This includes min-max scaling and Z-score standardization to ensure the data has a mean of 0 and a standard deviation of 1, or scaling the data to a specific range. Data preprocessing also includes serializing the data, such as arranging the data from the food processing process in a time-series format.
[0057] In step S3, a food processing model is established and trained based on the training dataset obtained after preprocessing and fusion in step S2. In this embodiment, a recurrent neural network is used for the architecture, and the architecture of the algorithm is as follows.
[0058] Input layer: Input the processed food processing data, that is, the fused array of input mixing state data and operation data. Input dimensions include operation data (speed, stirring time, auger control, etc.) and mixing state (such as uniformity, viscosity, etc.).
[0059] Hidden layer: Long Short-Term Memory (LSTM) network or Gated Recurrent Unit (GRU) network is used as the hidden layer. In this embodiment, LSTM is selected to capture long-term dependence in the food mixing process.
[0060] Multiple LSTM units are stacked to better learn complex mixed state changes.
[0061] Output layer: Outputs the predicted future mixing state, including key indicators such as uniformity and fluidity.
[0062] During training, supervised learning is employed, and the model parameters are adjusted by optimizing the loss function to make the model's predictions as close as possible to the actual monitoring results. After training, a food processing model is obtained, whose input is the processed food processing data, and whose output is the prediction results for the mixed state.
[0063] After training, the food processing model is validated and optimized. Cross-validation is used to divide the dataset into multiple subsets, with training and validation performed alternately on each subset to comprehensively evaluate model performance. This includes k-fold cross-validation, where the dataset is divided into k parts, with one part serving as the validation set and the remaining k-1 parts as the training set. The validation set is then rotated k times for training and validation, ultimately yielding the average of k model performance evaluation metrics. The value of k is chosen to be 5 or 10. Alternatively, each sample can be used as a separate validation set, with the remaining samples serving as the training set, for n training and validation iterations, where n is the total number of samples.
[0064] In step S4, after the food processing model is trained and optimized, the real-time collected food processing data is input into the food processing model to predict the food mixing state at future times. It can be set to make rolling predictions for every future time point (such as the next day or week).
[0065] In step S5, a reinforcement learning algorithm is introduced to automatically adjust the speed and duration of the stirring rod in the food processor, as well as the forward and reverse rotation control of the reciprocating auger, based on the input features of the food processing model.
[0066] In this embodiment, a deep reinforcement learning algorithm is used, taking the mixing state data and operational data during food processing as input, and optimizing the processing by adjusting the stirring rod speed, duration, and reciprocating auger control. The algorithm architecture is as follows.
[0067] Input Layer (State Space): The input states consist of a fused array, containing mixed state data and runtime data. State dimension = [number of mixed state features + number of runtime parameters] Hidden Layers: These are layers of a fully connected neural network used to learn the non-linear relationship between states and actions. They typically consist of two or three hidden layers, each using the ReLU activation function.
[0068] Output layer (Action Space): Outputs the Q-values of each action, including specific actions.
[0069] Adjust the stirring rod speed: increase, decrease, or keep it unchanged.
[0070] Adjust the stirring time: increase, decrease, or keep it unchanged.
[0071] Control the auger's forward and reverse rotation: forward, reverse, or remain unchanged.
[0072] The fusion array of mixed state data and operational data serves as the state for reinforcement learning. Actions include the speed and duration changes of the stirring rod and the forward and reverse rotation of the auger. The reward function is the combined minimization of the time predicted by the food processing model to reach the set uniform mixing state and the number of adjustment actions. The Q-value is used to measure the expected reward of each action in the current state. The agent selects actions based on these Q-values.
[0073] Specifically, the reward function R t for.
[0074] Among them, T mix (t) represents the time required to reach the set mixing uniformity state within time step t, as predicted by the food processing model. A(t) represents the number of operations performed within time step t. α and β are the corresponding weighting factors used to balance the influence of mixing uniformity and the number of adjustments.
[0075] After completing the algorithm architecture, reinforcement learning training is performed. The specific training process is as follows.
[0076] (1) Initialize Q network: Initialize the neural network weights of DQN and initialize the experience replay pool to store historical experience.
[0077] (2) ε-greedy strategy: In the early stage of training, the agent explores randomly with a probability of ε and selects the action with the largest current Q value with a probability of 1-ε, thereby balancing exploration and utilization.
[0078] (3) State transition: After performing an action, the system transitions from the current state S. t Transition to the next state S t+1 And record the corresponding rewards.
[0079] (4) Calculation of reward function: The reward function is calculated based on the two main objectives.
[0080] a. Improved mixing uniformity: If the mixing uniformity after stirring is close to the target value, a positive reward is given; if it is far from the target value, a negative reward is given.
[0081] b. Minimize the number of adjustments: Encourage agents to reach the target state with the fewest possible adjustments, reducing frequent operations to lower energy consumption.
[0082] (5) Experience replay and Q-value update.
[0083] Experience (S) t A t ,R t ,S t+1 The samples are stored in the experience replay pool, and then a batch of samples are randomly selected from the pool for training. The target network is used to calculate the target Q value to reduce instability.
[0084] .
[0085] The Q-network weights are updated using gradient descent, with the loss function being...
[0086] Where γ is the discount factor, and θ is the parameter of the current Q-network. - These are the parameters of the target network.
[0087] (6) Network parameter update: Periodically copy the parameters of the current Q network to the target network to improve the stability of training;
[0088] (7) After training, the agent learns to optimize the mixing uniformity and reduce the number of adjustment actions by dynamically adjusting the speed and duration of the stirring rod and the forward and reverse rotation of the auger.
[0089] By combining deep reinforcement learning, the food processing system can automatically adjust the speed and duration of the stirring rod and the forward and reverse rotation of the auger to optimize the food mixing process with the optimal combination of actions, achieving the best overall effect of improving mixing uniformity and reducing operation frequency.
[0090] Furthermore, based on the above embodiments, the reward function can be further optimized according to actual needs. For example, energy consumption parameters can be introduced. If it is desired to reduce the energy consumption of the processing equipment, the actual power consumption of the equipment can be added to the reward function.
[0091] Here, E(t) represents the energy consumption within time step t, and γ is the weighting factor for energy consumption. This reward design can incentivize the agent to save energy as much as possible while ensuring processing quality.
[0092] Through precise state representation and reasonable reward design, this system can not only improve mixing uniformity, but also reduce operation frequency and equipment energy consumption, ultimately achieving the goal of high-efficiency production.
[0093] Reference Figure 2 and Figure 3 This application provides a food processing machine including processing equipment, wherein the processing equipment includes...
[0094] Food mixing and processing box 1 is divided into processing chamber 11 and collection chamber 12. Processing chamber 11 is located on the upper side of collection chamber 12 and is connected to it through an electrically controlled valve.
[0095] Box lid 2 is attached to one side of food mixing and processing box 1.
[0096] The discharge port 3 is located on one side of the food mixing and processing box 1 and is connected to the collection chamber 12.
[0097] Processing mechanism 4 is located inside processing chamber 11 and can process food repeatedly.
[0098] Reference Figure 4 The processing mechanism 4 and processing chamber 11 are provided with multiple processing mechanisms. The processing mechanism 4 includes a reciprocating auger 41, with a auger head 42 fixedly installed at one end of the reciprocating auger 41. The processing chamber 11 is adapted to the size of the reciprocating auger 41 and the auger head 42. Multiple auger heads 42 are synchronously driven to reciprocate synchronously, conveying in different directions through different directions. The synchronous drive includes a synchronous gear 43 fixedly installed at the outer end of the reciprocating auger 41. The synchronous gear 43 is rotatably installed inside the rotating groove 44 opened in the inner wall of the processing chamber 11. Multiple mechanical chambers 45 are opened inside the food mixing and processing box 1. A drive gear 46 is rotatably installed inside the mechanical chamber 45. The drive gear 46 is meshed with one of the synchronous gears 43. A first motor 47 is coaxially fixedly installed on one side of the drive gear 46. A co-rotating gear 48 is meshed with the outer wall of one of the synchronous gears 43. The co-rotating gear 48 is meshed with the adjacent synchronous gear 43. The other synchronous gears 43 are meshed and driven through another co-rotating gear 48. The multiple synchronous gears 43 do not mesh with each other.
[0099] The rotation of the output shaft of the first motor 47 drives the drive gear 46 to rotate, and the drive gear 46 meshes with one of the synchronous gears 43, thereby driving the synchronous gear 43 to rotate. At this time, the synchronous gear 43 rotates and meshes with the same direction gear 48, and the same direction gear 48 meshes with another synchronous gear 43, thereby driving multiple reciprocating screw conveyors 41 to rotate. Through the setting of the processing mechanism 4, the food mixture can be processed through multiple cavities, and synchronous mixing can be achieved through synchronous drive, so that the mixture can be conveyed and mixed through the reciprocating screw conveyors 41.
[0100] Reference Figure 5 and Figure 6 The reciprocating auger 41 has a hollow structure in the middle. Multiple feed channels 401 are opened at the outer end of the reciprocating auger 41, and multiple discharge channels 402 are opened at the inner end. A stirring rod 403 is rotatably installed inside the reciprocating auger 41. An organic cavity 404 is opened inside the auger head 42 of the reciprocating auger 41. A second motor 405 is installed inside the organic cavity 404. The output shaft of the second motor 405 is coaxially and fixedly connected to the stirring rod 403.
[0101] The rotation of the output shaft of the second motor 405 drives the stirring rod 403 to rotate, thereby enabling the stirring rod 403 to stir the mixture inside the reciprocating auger 41. By setting the stirring rod 403 inside the reciprocating auger 41, the mixture can be stirred again by the reciprocating auger 41 after stirring. While the reciprocating auger 41 is conveying and mixing the mixture, the stirring rod 403 is used to repeatedly process it, improving efficiency and effectiveness. At the same time, the output can be generated by the reciprocating auger 41 in different rotation directions, so that it can actively output, thereby avoiding the blockage of the feed and discharge inside the reciprocating auger 41 and improving its functionality.
[0102] When food processing is required, firstly, data on the mixing state during food processing is collected, including the speed data of the stirring rod 403 and the reciprocating auger 41. Using a speed sensor such as a Hall effect sensor, the rotor speed is measured by detecting changes in the magnetic field. When the rotor rotates, the resulting change in the magnetic field causes a change in the output signal of the Hall effect element to detect the rotor's rotational speed and obtain rotational speed data. After the data acquisition is completed, the data is subjected to majority sampling processing. A food processing model is established using the food processing data and trained using the food processing data. The trained food processing model is applied to the real-time monitoring and control system to receive and process data in real time. Based on the prediction results of the food processing model, the speed and duration of the stirring rod 403 and the forward and reverse rotation control of the reciprocating auger 41 are adjusted to optimize the mixing process and improve efficiency. After prediction, the output shaft of the second motor 405 rotates, driving the stirring rod 403 to rotate, so that the stirring rod 403 can stir the mixture inside the reciprocating auger 41. The output shaft of the first motor 47 rotates, driving the drive gear 46 to rotate. The drive gear 46 meshes with one of the synchronous gears 43, thereby driving the synchronous gear 43 to rotate. At this time, the synchronous gear 43 meshes with the same direction gear 48, and the same direction gear 48 meshes with another synchronous gear 43, so that multiple reciprocating augers 41 rotate for processing. After completion, the solenoid valve is opened to allow the mixture to be collected in the collection chamber 12.
Claims
1. A food processing method, applied to a food processing machine, characterized in that: Includes the following steps: S1. Collect food processing data, including data on the mixing state during the food processing mixing process and data on the operation of the food processing machine; S2. Food processing data preprocessing to obtain the training dataset; S3. Based on the training dataset, a food processing model is established using a recurrent neural network. The input of the food processing model is the processed food processing data, and the output is the prediction result of the mixed state. S4. Input the real-time collected food processing data into the food processing model to predict the food mixing state at future moments; S5. Adjust the operating parameters of the food processor based on the prediction results of the food processing model to optimize the mixing process and improve efficiency.
2. The food processing method according to claim 1, characterized in that: In step S1, mixing state data during the food processing mixing process is acquired using a refractometer; The operating data of the food processing machine includes the speed data of the stirring rod and the reciprocating auger.
3. The food processing method according to claim 1, characterized in that: In step S3, a food processing model is built using a recurrent neural network based on the training dataset. The architecture of the recurrent neural network includes: Input layer: A fused array of processed mixed state data and runtime data; Hidden layers: Long Short-Term Memory (LSTM) networks are used as hidden layers to capture long-term dependencies in the food mixing process; multiple LSTM units are stacked together. Output layer: Outputs the predicted future mixed state.
4. The food processing method according to claim 1, characterized in that: In step S5, a reinforcement learning algorithm is introduced to automatically adjust the speed and duration of the stirring rod in the food processor, as well as the forward and reverse rotation control of the reciprocating auger, based on the input features of the food processing model.
5. The food processing method according to claim 4, characterized in that: Using deep reinforcement learning algorithms, the fused array of mixed state data and running data is used as the state for reinforcement learning, and the actions are the speed and duration changes of the stirring rod and the forward and reverse rotation of the reciprocating auger.
6. The food processing method according to claim 5, characterized in that, The reward function is the combined minimization of the time it takes for the food processing model to reach a set homogeneous mixing state and the number of adjustment actions.
7. A food processing machine, equipped with a real-time monitoring and control system, and applying the food processing method as described in claims 1-6 to the system, characterized in that, Food processing machines include: A food mixing and processing box, which is divided into a processing chamber and a collection chamber. The processing chamber is located on the upper side of the collection chamber and is connected to it through an electrically controlled valve. A lid, which is snapped onto one side of the food mixing and processing box; The discharge port is located on one side of the food mixing and processing box and is connected to the collection chamber; The processing mechanism is located inside the processing chamber and performs reciprocating processing on the food.
8. The food processing machine according to claim 7, characterized in that: The processing mechanism and processing chamber are provided in multiple ways. The processing mechanism includes a reciprocating auger. One end of the reciprocating auger is fixedly provided with an auger head. Multiple auger heads are synchronously driven to perform synchronous reciprocating motion and convey in different directions through different directions.
9. The food processing machine according to claim 8, characterized in that: The synchronous drive includes a synchronous gear fixedly installed at the outer end of the reciprocating auger. One of the synchronous gears is meshed with the drive gear. A first motor is coaxially fixedly installed on one side of the drive gear. A co-directional gear is meshed with the outer wall of one of the synchronous gears. The co-directional gear is meshed with the adjacent synchronous gear. The other synchronous gear is driven by meshing with another co-directional gear. The multiple synchronous gears do not mesh with each other.
10. The food processing machine according to claim 8, characterized in that: The reciprocating auger has a hollow structure in the middle. Multiple feed channels are opened at the outer end of the reciprocating auger, and multiple discharge channels are opened at the inner end. A stirring rod is rotatably installed inside the reciprocating auger.
Citation Information
Patent Citations
A food processing equipment and method
CN108465413B