Food product breaking homogenizer and control method thereof

Abstract

This invention belongs to the field of food sampling technology, specifically a food crushing and homogenizing machine and its control method. The food crushing and homogenizing machine provided by this invention includes: a base; several sets of first crushing and homogenizing components mounted on the base, each set of first crushing and homogenizing components including a first driver, a first cutter, and a first container, the output end of the first driver being connected to the first cutter, the first cutter being located inside the container; several sets of second crushing and homogenizing components mounted on the base, each second crushing and homogenizing component including a second cutter and a second container, the output end of a second motor being connected to the second cutter, the second cutter being located inside the second container; the capacity of the first container being greater than the capacity of the second container; and a control device electrically connected to each of the first and second crushing and homogenizing components. This invention can significantly improve the efficiency of food crushing and homogenizing.

Description

Technical Field

[0001] This invention belongs to the field of food sampling technology, specifically a food crushing and homogenizing machine and its control method. Background Technology

[0002] According to GB2763-2021, the National Food Safety Standard for Maximum Residue Limits of Pesticides in Food, samples of fruits, vegetables, dried fruits, and other food products need to be taken and then tested. Because pesticide residues on the surface of fruits and vegetables are often uneven, this can easily lead to testing errors. A traditional household blender can be used to break down the fruit and vegetable samples. However, due to the small container of the blender, its efficiency is low, and the homogenization effect is not ideal. Summary of the Invention

[0003] In view of this, the present invention provides a food crushing and homogenizing machine and its control method, which solves the technical problem of low crushing efficiency in existing wall-breaking machines:

[0004] The food crushing and homogenizing machine provided by this invention includes:

[0005] Base;

[0006] Several sets of first crushing and homogenizing components are installed on the base. Each set of first crushing and homogenizing components includes a first driver, a first cutter, and a first container. The output end of the first driver is connected to the first cutter, and the first cutter is located inside the container.

[0007] Several sets of second crushing and homogenizing components are installed on the base. The second crushing and homogenizing components include a second cutter and a second container. The output end of the second motor is connected to the second cutter, and the second cutter is located inside the second container.

[0008] The capacity of the first container is greater than the capacity of the second container;

[0009] A control device is electrically connected to each of the first crushing and homogenizing components and the second crushing and homogenizing components.

[0010] Preferably, the first crushing and homogenizing component further includes a transparent viewing cover, which is disposed above the first container and / or the second crushing and homogenizing component further includes a transparent viewing cover, which is disposed above the second container.

[0011] Preferably, the first and / or second cutting tools are four-bladed steel blades.

[0012] Preferably, the first crushing and homogenizing component and / or the second crushing and homogenizing component further include a tilting device, which is mounted on the base.

[0013] Preferably, the tilting device includes a support base, a rotating shaft, and a rocker arm. The rotating shaft is connected to the first driver and / or the second motor. Both ends of the rotating shaft are rotatably connected to the support base. The rocker arm is connected to the rotating shaft and is located outside the support base.

[0014] Preferably, the tilting device further includes a limiting member, which has a plurality of positioning grooves spaced apart along the circumferential direction. The limiting member is installed on the support base, and the rocker arm is inserted into the limiting member.

[0015] Preferably, the tilting device further includes a pin, at least a portion of which is inserted into the limiting member and abuts against the rocker arm.

[0016] Preferably, the first container and / or the second container are further equipped with a buckle, the buckle being provided with a limit switch, and the limit switch being electrically connected to the control device.

[0017] Preferably, it also includes a waste collection and drainage trough for cleaning residue, which is installed on the base, and the first crushing and homogenizing component and the second crushing and homogenizing component are arranged in a row along the extension direction of the waste collection and drainage trough for cleaning residue.

[0018] In a second aspect, the present invention also provides a method for controlling a food crushing and homogenizing machine, for controlling the food crushing and homogenizing machine described in the first aspect, the method comprising:

[0019] S1: Obtain the crushing homogenization parameters according to the type of sample to be crushed;

[0020] S2: Based on the crushing and homogenization parameters, control the first or second crushing and homogenization component at each station to crush the food sample.

[0021] S3: During the crushing process, obtain the real-time status parameters of the first or second crushing homogenizing component at each station.

[0022] S4: Adjust the crushing and homogenization parameters according to the real-time status parameters.

[0023] Beneficial Effects: The food crushing and homogenizing machine and its control method of the present invention, by setting up several sets of first and second crushing and homogenizing components, can simultaneously perform adaptive crushing and homogenizing for large samples of fruits and vegetables and small samples of nuts. Multi-station parallel operation significantly improves processing efficiency, meets the rapid processing needs of diverse samples, and avoids the problem of low efficiency of single containers in traditional blenders. Since the capacity of the first container is larger than that of the second container, the large-capacity design ensures that fruit and vegetable samples are fully mixed during the crushing process, reducing detection errors caused by uneven surface residues; the small-capacity container precisely adapts to the homogenization needs of dried fruit samples, improving overall homogenization uniformity. The control device is uniformly electrically connected to each crushing and homogenizing component, allowing users to centrally start, stop, and adjust different station equipment through a single control panel, simplifying the operation process, reducing the complexity of independent control of multiple devices, and improving the level of automation. The base integrates multiple sets of crushing components, and the modular layout facilitates equipment expansion and maintenance; each crushing and homogenizing component adopts an independent drive design to ensure that each station does not interfere with each other, reducing the risk of failure. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0025] Figure 1 This is a cross-sectional view of the food crushing and homogenizing machine of the present invention;

[0026] Figure 2 This is a three-dimensional structural schematic diagram of the first crushing and homogenizing component in this invention;

[0027] Figure 3 This is a schematic diagram of the structure of the crushing blade in the container in this invention;

[0028] Figure 4 This is a schematic flowchart of the food crushing and homogenizing machine control method of the present invention.

[0029] The components and their numbers shown in the picture:

[0030] 1. Base; 2. First crushing and homogenizing component; 3. Transparent viewing window cover; 4. First container; 5. Buckle; 6. First cutter; 7. Inclining device; 8. Support seat; 9. Rotating shaft; 10. Rocker arm; 11. Limiting component; 12. Positioning groove; 13. Pin; 14. Second crushing and homogenizing component; 15. Second container; 16. Control device; 17. Control panel; 18. Control box; 19. Waste collection and drainage trough; 20. Stainless steel universal casters with brakes; 21. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, the element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Where there is no conflict, embodiments of the present invention and the various features thereof can be combined with each other, all of which are within the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1 As shown, this embodiment provides a food crushing and homogenizing machine, which mainly includes a base 1, several sets of first crushing and homogenizing components 2, several sets of second crushing and homogenizing components 3, and a control device 4.

[0034] The base 1 serves as the mounting foundation for other components of the food crusher and homogenizer. Stainless steel universal casters 6 with brakes can also be installed at the bottom of the base 1 to facilitate the movement of the food crusher and homogenizer. Once the food crusher and homogenizer is in position, the brakes on the casters can be released to keep the machine in a fixed position.

[0035] In this embodiment, several sets of first crushing and homogenizing components 2 are installed on the base 1. The number of first crushing and homogenizing components 2 is greater than or equal to 2. In this embodiment, the first crushing and homogenizing components 2 are used to crush and homogenize fruits and vegetables with a large amount of breakage.

[0036] like Figure 2 and Figure 3As shown, each first crushing and homogenizing component 2 includes a first driver, a first cutter 231 and a first container 22. The output end of the first driver is connected to the first cutter 231, and the first cutter 231 is located inside the container.

[0037] The first driver drives the first cutter 231 to rotate, and the first cutter 231 crushes and homogenizes the fruits and vegetables placed inside the container during rotation. The first driver can be a motor, such as a servo motor or a stepper motor. Other types of first drivers capable of outputting rotational motion can also be used; no limitation is made here. In this embodiment, a protective cover can also be provided outside the first driver to protect it.

[0038] Several sets of second crushing and homogenizing components 3 are installed on the base 1. Each second crushing and homogenizing component 3 includes a second cutter and a second container 31. The output end of the second motor is connected to the second cutter, which is located inside the second container 31. In this embodiment, the second crushing and homogenizing components 3 are used to crush and homogenize nut samples. The number of first crushing and homogenizing components 2 is greater than or equal to 1.

[0039] In this embodiment, the capacity of the first container 22 is greater than the capacity of the second container;

[0040] In this embodiment, a first crushing and homogenizing component 2 with a larger capacity container is used for crushing and homogenizing fruit and vegetable samples, allowing for the processing of more samples at once, thus increasing processing efficiency. Furthermore, the increased capacity of the first crushing and homogenizing component container further improves the homogenization effect. For nut samples with smaller crushing volumes, a second crushing and homogenizing component 3 with a smaller capacity is used. Because this application employs multiple first crushing and homogenizing components 2, different fruit and vegetable samples can be crushed and homogenized simultaneously at multiple workstations, further improving the sampling efficiency of fruit and vegetable samples.

[0041] In this embodiment, the control device 4 is electrically connected to each of the first crushing and homogenizing components 2 and the second crushing and homogenizing components, so that the operation of the first crushing and homogenizing components 2 and the second crushing and homogenizing components 3 can be controlled by the control device 4.

[0042] In this embodiment, the control device 4 includes a control panel 41 and a control box 42. Users can input control commands through the controllers in the control panel 41 and control box 42 to control the various crushing and homogenizing components.

[0043] In this embodiment, the first crushing and homogenizing component 2 further includes a transparent viewing cover 21, which is disposed above the first container 22 and / or the second crushing and homogenizing component 3 further includes a transparent viewing cover 21, which is disposed above the second container 31.

[0044] A transparent viewing window 21 covers the first container 22 or the second container 31 to prevent the sample from being thrown out of the container during the crushing and homogenization process. Because this embodiment uses a transparent viewing window 21, the user can clearly observe the crushing and homogenization of the sample in the first container 22 or the second container 31 through the transparent viewing window 21 without opening it. This allows for targeted adjustment of the parameters of the first driver and / or the second motor based on the crushing and homogenization situation, thereby improving homogenization efficiency and effect.

[0045] In this embodiment, the first cutting tool 231 and / or the second cutting tool are four-bladed steel blades. Using four-bladed steel blades allows for more efficient crushing of the sample.

[0046] The first crushing and homogenizing component 2 and / or the second crushing and homogenizing component 3 further include a tilting device 24, which is mounted on the base 1. When crushing and homogenizing the sample using the first crushing and homogenizing component 2 and / or the second crushing and homogenizing component 3, the tilting device 24 can be rotated to a position where the container is vertically upward. When it is necessary to remove the sample after crushing, the tilting device 24 can be rotated to tilt the container so that the top opening of the container is lowered, so that the crushed and homogenized sample can be removed from the container.

[0047] In this embodiment, the tilting device 24 includes a support base 241, a rotating shaft 242, and a rocker arm 243. The rotating shaft 242 is connected to the first driver and / or the second motor, and both ends of the rotating shaft 242 are rotatably connected to the support base 241. The rocker arm 243 is connected to the rotating shaft 242 and is located outside the support base 241. The user can operate the rocker arm 243 to rotate the rotating shaft 242. During rotation, the rotating shaft 242 drives the first driver and / or the second motor, as well as the container mounted with the first driver and / or the second motor, to rotate. In this embodiment, the rotating shaft 242 can be rotatably connected to the support base via bearings. The rocker arm 243 can be threadedly connected to the rotating shaft 242.

[0048] In this embodiment, the tilting device 24 further includes a limiting member 244, which has a plurality of positioning grooves 2441 spaced apart along the circumferential direction. The limiting member 244 is mounted on the support base 241, and the rocker arm 243 is inserted into the limiting member 244. In this embodiment, the tilting device 24 also includes a pin 245, at least a portion of which is inserted into the limiting member 244.

[0049] Before the first crushing and homogenizing component 2 and / or the second crushing and homogenizing component 3 perform crushing and homogenizing operations, the tilting device 24 is rotated to a position where the container is vertically upward. Then, the pin 245 is inserted into the limiting member 244 and the pin 245 is locked against the rocker arm 243. In this way, the rocker arm 243 is locked on the limiting member 244, so that the rotating shaft 242 and the container are kept at the current angle and will not rotate arbitrarily.

[0050] In this embodiment, a buckle 221 is also installed on the first container 22 and / or the second container 31. The buckle 221 is equipped with a limit switch, which is electrically connected to the control device 4. After fruit and vegetable samples are placed in the container, the transparent window cover 21 is placed on top of the container, and then the buckle 221 is secured to the transparent window cover 21. When it is necessary to remove the transparent window cover 21 from the container, the buckle 221 can be released. After the buckle 221 is released, the limit switch is triggered, and the control device 4 prevents the equipment from starting, thereby improving operational safety.

[0051] The food crushing and homogenizing machine in this embodiment also includes a washing waste collection and drainage trough 5, which is installed on the base 1. The first crushing and homogenizing component 2 and the second crushing and homogenizing component 3 are arranged in a row along the extension direction of the washing waste collection and drainage trough 5.

[0052] When the first container 22 and / or the second container 31 need to be cleaned, the waste residue and wastewater generated during the cleaning process can be collected in the cleaning waste residue collection and drainage tank 5 and discharged uniformly through the interface at the bottom of the drainage tank.

[0053] Taking a food crushing and homogenizing machine with three first crushing and homogenizing components 2 (stations A, B, and C) and one second crushing and homogenizing component 3 (station D) as an example, the main technical parameters of the machine in this embodiment are as follows:

[0054] 1. Volume of each container: A: 2500mm high x 200mm diameter; B and C stations: 200mm high x 200mm diameter;

[0055] Station D: 85mm high x 165mm diameter;

[0056] 2. Crushing power: ABC stations: 1KW / station; D station: 550W

[0057] 3. Total power: 4.0KW;

[0058] 4. Rated voltage: AC220V;

[0059] 5. Rotation speed: Stations ABC: 0 ~ 3000 r / min, can be set arbitrarily; Station D: 35000 r / min;

[0060] 5. Rated voltage: AC220V, total power: 4KW;

[0061] 6. Workstation setup: A, B, and C, 3 fruit and vegetable workstations; D, 1 nut workstation.

[0062] Example 2

[0063] This embodiment provides a control method for a food crushing and homogenizing machine. The food crushing and homogenizing machine includes multiple workstations, each workstation including a first crushing and homogenizing component or a second crushing and homogenizing component. The method includes:

[0064] S1: Obtain the crushing homogenization parameters according to the type of sample to be crushed;

[0065] This step is used to establish a targeted processing strategy. The system determines the sample type (such as apple, carrot, etc.) through user input, recognition algorithm or preset template, and calls the corresponding crushing and homogenization parameter set from the parameter library, including rotation speed, time, stage structure, etc., to guide the operation of subsequent work stations.

[0066] S2: Based on the crushing and homogenization parameters, control the first or second crushing and homogenization component at each station to crush the food sample.

[0067] Based on the parameter set obtained in S1, the system sends the parameters to the corresponding crushing components (the first crushing and homogenizing component for fruits and vegetables, and the second crushing and homogenizing component for nuts), and starts each station to run synchronously or in batches according to the set speed and time to perform crushing and homogenizing operations, thereby achieving efficient sample pretreatment.

[0068] S3: During the crushing process, obtain the real-time status parameters of the first or second crushing homogenizing component at each station.

[0069] During operation, the system continuously monitors the core status parameters of each workstation, such as motor speed, current, and vibration signals. It collects data at a preset sampling frequency to generate a status data sequence for each workstation, which is used to dynamically determine its operating load and stability.

[0070] S4: Adjust the crushing and homogenization parameters according to the real-time status parameters.

[0071] The system analyzes the real-time status data of each workstation to determine whether its processing load deviates from the set target, and automatically corrects its operating parameters based on the degree of deviation, such as adjusting the speed or extending the stage duration, so as to achieve dynamic closed-loop control of processing consistency and operational safety among multiple workstations.

[0072] In this embodiment, S3: During the crushing process, acquiring the real-time status parameters of the first or second crushing homogenizing component at each station includes:

[0073] S31: Based on the number of each workstation and the preset sampling frequency, the status parameters of each workstation are collected in real time to obtain the original status data sequence of each workstation. The status parameters include motor speed and current.

[0074] This step involves real-time synchronous acquisition of motor speed and current at each workstation based on its station number and preset sampling frequency, forming a raw state data sequence including timestamps. Motor speed reflects the actual operating speed of the cutting tool, while current reflects the load changes borne by the equipment. Both are key indicators of the crushing process and can accurately reveal the dynamic behavior of each workstation during processing. The state parameters of a workstation refer to the real-time state parameters of the first or second crushing homogenizing component used to crush the sample at that workstation.

[0075] S32: Based on the original state data sequence of each workstation, the sliding window algorithm is used to perform statistical processing on the data within a specified time window to obtain the statistical characteristics of each workstation within the current window. The statistical characteristics include the mean, standard deviation, and extreme values.

[0076] This step uses a sliding window algorithm to segment the raw data for each workstation, extracting statistical features within the current window, including the mean, standard deviation, and extreme values. These statistical features can effectively filter out high-frequency disturbances, extract stable operating trends, and allow the operating status of each workstation at different time periods to be represented and analyzed in a structured manner.

[0077] S33: Based on the statistical characteristics of all workstations within the same time window, a time-series alignment method is used to synchronize data, resulting in a feature alignment matrix between different workstations at the same crushing stage.

[0078] This step performs time-series alignment on the statistical features extracted from each workstation within the same time window, ensuring that all data points to the same stage in the crushing process. Since there may be slight differences in the start-up time or processing progress of each workstation in actual operation, time-series alignment ensures that each workstation has a unified time reference in terms of feature dimensions, thereby improving the accuracy and logical consistency of the comparison.

[0079] S34: Based on the feature alignment matrix, calculate the feature distance or similarity index between each workstation to obtain the consistency evaluation result between workstations.

[0080] This step calculates the similarity or distance indicators between workstations based on the time-aligned feature matrix, such as the degree of deviation in rotational speed and current, to obtain a consistency evaluation result between workstations. This evaluation result is used to quantify the consistency of the current crushing state of each workstation, providing an objective basis for the system to determine whether synchronous adjustment needs to be performed.

[0081] S35. Based on the consistency evaluation results, use cluster analysis or threshold discrimination methods to identify anomalies in all workstations and obtain the abnormal workstations.

[0082] Based on the consistency evaluation results, this step uses cluster analysis or threshold-based discrimination methods to identify abnormal workstations whose processing status significantly deviates from other workstations. This step enables early detection of potential overload, underload, or operational anomalies at individual workstations and provides a clear target for subsequent parameter fine-tuning and compensation, thereby ensuring the overall consistency of multi-workstation crushing results, sample representativeness, and reliability of test data.

[0083] S34: Based on the feature alignment matrix, calculate the feature distance or similarity index between each workstation to obtain the consistency evaluation result between workstations, including:

[0084] S341: Based on the feature alignment matrix, the statistical features of each pair of workstations are calculated using multiple distance measurement methods to obtain a multi-index distance matrix between workstations. The distance measurement methods include Euclidean distance, Manhattan distance, and Pearson correlation coefficient, etc.

[0085] This embodiment employs multiple distance metrics to calculate the statistical characteristics between each pair of workstations, including Euclidean distance, Manhattan distance, and Pearson correlation coefficient. Euclidean and Manhattan distances reflect numerical differences across different dimensions and are suitable for consistency analysis of continuous variables such as rotational speed and current. The Pearson correlation coefficient, on the other hand, measures the consistency of the changing trends of two workstation characteristics and is suitable for determining whether processing rhythms are synchronized. By introducing multiple metrics, the similarity between workstations can be comprehensively characterized from multiple dimensions, improving the robustness and accuracy of the evaluation results and avoiding misjudgments caused by a single indicator.

[0086] S342: Based on the multi-index distance matrix, the distance between each workstation and the group mean workstation is weighted and summed to obtain the characteristic deviation score of each workstation.

[0087] This step calculates the multi-indicator distance between each workstation and all other workstations, and sums these distances using preset weighting coefficients to obtain a characteristic deviation score for that workstation. To prevent evaluation bias caused by uneven data distribution, the system introduces a workstation representing the group average as a reference. Specifically, by aggregating the characteristic averages of all workstations, a virtual workstation representing the overall operational status is constructed, serving as a benchmark to measure the relative deviation of individual workstations. This score directly reflects the gap between the current status of a workstation and the group level, providing a quantitative basis for subsequent anomaly identification and adjustment strategies.

[0088] S343: Based on the feature deviation score, combined with historical threshold or adaptive threshold methods, determine whether there is a significant deviation in each workstation, and obtain the preliminary results of the consistency evaluation.

[0089] This step, based on the characteristic deviation score of each workstation, combines a fixed threshold set according to historical experience or an adaptive threshold dynamically generated based on real-time data fluctuations to determine whether there is a significant deviation at each workstation. The fixed threshold is suitable for large-sample, long-term stable equipment scenarios, while the adaptive threshold is suitable for situations in the early stages of operation or when there are significant differences in sample characteristics between batches, helping to improve the adaptability and generalization ability of the adjustment algorithm. This step completes the transformation from characteristic difference to state recognition, and is a key transitional link in the control process from data analysis to control execution. The adaptive threshold can be dynamically adjusted according to the overall distribution characteristics of the current operating state, making the judgment results more robust and adaptable.

[0090] S344: Based on the preliminary consistency evaluation results, the trend analysis method is used to fit the change of the characteristic deviation of each workstation over time to obtain a list of workstations with potential abnormal trends.

[0091] This step, based on the preliminary assessment results, performs a fitting analysis on the trend of characteristic deviation of each workstation over time. It identifies potentially abnormal workstations whose current scores have not yet exceeded the threshold, but which show a continuous increase or worsening of the deviation. By introducing trend analysis, workstations that may encounter problems can be identified in advance, enabling early warning identification and avoiding intervention and adjustments only after the situation deteriorates. This mechanism transforms the control strategy from passive response to proactive intervention, thereby improving the stability and processing consistency of the entire multi-workstation control system.

[0092] The preliminary consistency evaluation result refers to the consistency judgment information of the workstations obtained by calculating the feature similarity or deviation between workstations and comparing it with the set threshold. It reflects the relative closeness of the current processing status between each workstation and is used to identify whether there is a trend of operational deviation or a problem workstation.

[0093] Trend analysis methods refer to methods that fit, model, or determine the direction of time series data (such as the change of workstation characteristic deviation over time). Common methods include linear regression, multinomial fitting, and moving average trend lines. These methods are used to identify whether the deviation is an occasional fluctuation or a continuous increase, and then to determine whether the anomaly is persistent and progressive.

[0094] The change of characteristic deviation over time refers to the time series of characteristic deviation values ​​calculated for each station in multiple continuous sliding windows. It reflects the change in the operational stability of the station throughout the entire crushing process and is the basic data for assessing abnormal trends.

[0095] The list of potentially abnormal trend workstations refers to a set of workstations identified through trend analysis that, although currently not exceeding the abnormal threshold, show a trend of continuously increasing deviation and amplified fluctuations. These workstations may evolve into explicit anomalies in subsequent stages and are target objects that the control system needs to pay attention to and intervene in in advance.

[0096] S35: Based on the consistency evaluation results, cluster analysis or threshold discrimination methods are used to identify anomalies in all workstations, resulting in the following abnormal workstations:

[0097] S351: Based on the characteristic deviation sequence of each workstation, the noise is smoothed using the moving average method to obtain the smoothed deviation curve.

[0098] Based on the feature deviation score results generated in the previous step, the deviation sequence of each workstation under multiple continuous time windows is extracted, and smoothed using a moving average method to obtain a smoothed deviation curve for each workstation. Since the original score may exhibit instantaneous jumps or abnormal peaks due to factors such as operating condition fluctuations and current pulsations, directly using it for anomaly identification can easily lead to misjudgments. Therefore, using a moving average method to reduce noise in the deviation curve over time can more accurately extract the stability trend of the workstation, enhancing the anti-interference capability and continuous discrimination effect of the workstation status assessment.

[0099] S352: Based on the smoothed deviation curve, unsupervised learning methods such as density clustering (e.g., DBSCAN) or hierarchical clustering are used to group the workstations and obtain isolated workstations that deviate from the main group.

[0100] Based on the smoothed deviation curves of all workstations, representative feature values ​​are extracted at the current time point or within a certain time period to construct a workstation feature space. Unsupervised learning algorithms such as density clustering (e.g., DBSCAN) or hierarchical clustering are then used to group and identify the workstations. This process does not require pre-setting the number of workstation categories and can automatically identify outliers based on the distribution of deviation degrees between workstations. Workstations with similar states are grouped into the same cluster, while workstations significantly farther from the main group are identified as isolated points. Compared to traditional thresholding methods, this method is more adaptable and can identify anomalous workstations under nonlinear and heterogeneous distributions, making it an important means to improve the sensitivity and accuracy of anomaly identification.

[0101] S353; Cross-validate the clustering results with the threshold method results to screen the workstations that are ultimately judged as abnormal.

[0102] Cross-validation was performed on the abnormal workstations obtained from density clustering and those determined based on deviation score thresholds. Workstations that simultaneously met both criteria were selected as the final abnormal workstations. This strategy combines the controllability of rule-based methods with the adaptability of clustering, enhancing the ability to identify boundary-state workstations while maintaining interpretability and transparency. The final selected abnormal workstations will serve as the key targets for subsequent adaptive parameter adjustments, ensuring that the control strategy is explicit, targeted, and operable, thus guaranteeing improved consistency in multi-workstation homogenization processing and representativeness of test samples.

[0103] In this embodiment, step S4: adjusting the crushing and homogenization parameters according to the real-time state parameters includes:

[0104] S41: Based on the list of abnormal workstations and the corresponding feature deviation scores, calculate the target adjustment coefficient for each workstation to obtain the parameter adjustment instructions for each workstation;

[0105] The abnormal workstation list refers to the set of workstation numbers that deviate from the normal workstation group in terms of operation status, which are selected through the preceding consistency analysis and abnormal identification steps (such as clustering or threshold judgment). It is the target workstation set that needs to be adjusted first.

[0106] Feature deviation score refers to a quantitative score obtained based on the distance calculation results (such as the degree of difference in rotation speed and current) of each workstation in the statistical feature space, which is used to represent the degree of deviation of the workstation from the main group state;

[0107] The target adjustment coefficient refers to the proportional coefficient calculated by the system based on the deviation of the workstation, which is used to adjust parameters such as speed and duration. It is the basic factor for realizing personalized adjustment. Parameter adjustment command: refers to the control parameter adjustment command calculated and generated by the system based on the target adjustment coefficient. It usually includes fields such as target speed and target processing time, and is used to guide the equipment to perform differentiated operation control for each workstation.

[0108] This step, based on the list of abnormal workstations identified in the previous stage, and combined with the characteristic deviation scores of each abnormal workstation, calculates the target adjustment coefficient for each workstation. This coefficient quantifies the degree of difference between the current state and the ideal state. The target adjustment coefficient reflects the intensity of adjustment required for each workstation, typically obtained by standardizing the deviation and mapping it proportionally to the baseline workstation parameters. This step ensures that parameter adjustments are targeted and accurate, while allowing for tailoring and correction according to preset rules to avoid over-adjustment due to individual extreme deviations. After the adjustment coefficients are determined, the system further transforms them into structured parameter adjustment instructions, explicitly specifying the range of speed increase or decrease, stage processing time extension or reduction, etc., for each workstation, providing direct control basis for subsequent execution.

[0109] S42: Based on the parameter adjustment instructions for each station, the rotation speed and stage duration of the station are modified in real time to obtain the corrected set of station operation parameters.

[0110] Parameter adjustment instructions refer to the set of control instructions generated by the system in the previous step based on the degree of deviation of the status of each workstation. They include specific parameter values ​​used to adjust the operating status, such as the speed increase or decrease and the adjustment amount of the stage duration.

[0111] The stage duration refers to the preset running time of the current crushing and processing stage, which is an important control variable that determines the duration of sample stress and the degree of homogeneity.

[0112] The workstation operation parameter set refers to a set of control parameters that are effective for each workstation at the current stage. It includes the corrected target speed and target stage duration and serves as a reference for subsequent execution and continuous monitoring.

[0113] This step, based on the aforementioned adjustment instructions, modifies the operating parameters of each abnormal workstation in real time. Specifically, it updates the currently set cutter speed and crushing stage duration. Modifications are typically made by increasing or decreasing relative to the current operating values, while adhering to the equipment's permissible operating range and adjustment increments to ensure safety and smooth mechanical response. Through this operation, each workstation will continue the crushing process with more suitable parameters. The aim is to proactively intervene and bring the abnormal workstations back to the processing state of the main group, achieving local consistency compensation.

[0114] S43: Based on the corrected workstation operation parameter set, continuously collect the status data of the next sliding window and recalculate the characteristic deviation to obtain the consistency recovery assessment result;

[0115] To continuously evaluate the effectiveness of parameter adjustments, this step re-acquires status data and calculates characteristic deviations for the adjusted workstations, in the same manner as steps S31–S34. By continuously acquiring data such as rotational speed and current within the adjusted sliding window, the system can dynamically calculate new deviation scores and compare them with previous states to determine whether each workstation has approached a normal state. This process constitutes a feedback loop for the control system, enabling real-time perception of adjustment results and helping to prevent blind, repeated adjustments or prolonged operation in a suboptimal state.

[0116] S44: Based on the consistency recovery assessment results, a gradual recovery strategy is adopted to gradually restore the adjusted parameters at a preset step size, thereby obtaining a process curve in which the station operating parameters are smoothly restored to the target set value.

[0117] If the evaluation results indicate that the workstation status has returned to the consistency standard range, the system will initiate a gradual recovery mechanism, progressively reverting the operating parameters back to the initially set target values ​​in stages and small increments. This strategy effectively avoids system instability caused by oscillating parameter adjustments, especially when handling samples in edge states, ensuring a smooth transition from the adjusted state to a stable state. The recovery process typically sets a maximum single-step change limit, for example, the rotational speed change per stage does not exceed 3%, ensuring a gentle and easily trackable control process.

[0118] S45: Based on the accumulated adjustment effect data during the gradual recovery process, update the adaptive parameter record table to obtain the optimized parameter recommendation library.

[0119] The gradual recovery process refers to the process by which the control system gradually restores the previously adjusted parameters (such as speed and duration) to their initial set values ​​according to a set step size after the state of each workstation has stabilized, so as to ensure that the adjusted system can smoothly return to the target state.

[0120] The cumulative adjustment effect data refers to the operational status feedback information after each parameter adjustment during the entire recovery process, including the speed, current, operational stability, consistency score, etc. before and after the adjustment, which is used to evaluate whether the adjustment is effective.

[0121] The adaptive parameter recording table refers to the parameter change trajectory and corresponding operating results automatically recorded by the system during each round of adjustment, which is used to form an empirical mapping relationship between sample processing and parameter adjustment;

[0122] The optimized parameter recommendation library refers to a set of recommended parameters compiled and refined by analyzing the sample characteristics, initial value settings, adjustment process, and final consistency performance in the historical records. This set of parameters is used by the system to quickly call upon when processing similar samples in the future, thereby improving operating efficiency and the accuracy of initial settings.

[0123] This step archives the parameter changes, status responses, and final consistency results throughout the adjustment and recovery process, forming a structured parameter record table. Based on this table, a parameter recommendation library is updated for use in subsequent batches. This library reflects historical experience in parameter adjustment under different samples and operating conditions during actual operation, possessing sustainable optimization characteristics. Through continuous accumulation and refinement, this library can provide more accurate initial parameter settings for similar batches of samples in the future, improving operational efficiency and processing consistency, ultimately building an intelligent control foundation for large-scale food sample breakage scenarios.

[0124] In this embodiment, step S41: Calculating the target adjustment coefficient for each workstation based on the abnormal workstation list and the corresponding feature deviation score, and obtaining the parameter adjustment instruction for each workstation includes:

[0125] S411: Based on the characteristic deviation score of each workstation, select the workstation with the highest score as the benchmark workstation;

[0126] To achieve precise adjustment of abnormal workstation operating states, the system first selects the workstation with the highest characteristic deviation score as the benchmark workstation based on the characteristic deviation score of each workstation. This workstation typically represents the state with the greatest processing difficulty and the most significant deviation. Using it as the benchmark for parameter calibration ensures that all workstations approach the most stringent conditions in terms of processing effectiveness, thereby avoiding overloading some workstations or affecting consistency due to insufficient processing caused by uniformly increasing parameters. Next, the system extracts the characteristic deviation value of the benchmark workstation to obtain its quantitative benchmark for reference.

[0127] S412: Extract the characteristic deviation value of the reference station to obtain the reference deviation;

[0128] After determining the baseline workstation, this step extracts the characteristic deviation score of that workstation within the current time window, which serves as a quantitative reference standard to measure the difference between other workstations and the baseline workstation.

[0129] S413: Based on the baseline deviation and the characteristic deviation values ​​of the other workstations, determine the relative deviation ratio of the remaining workstations and obtain the relative deviation coefficient of each workstation.

[0130] The relative deviation ratio refers to the ratio between the deviation of each workstation and the baseline deviation, which is used to describe the relative severity of the deviation at that workstation.

[0131] The relative deviation coefficient is a numerical coefficient determined based on the relative deviation ratio. The larger the relative deviation coefficient, the more significant the difference between the workstation and the group average.

[0132] This step, by establishing a clear baseline deviation, provides a unified standard for subsequent proportional adjustments, giving the adjustment operations a clear reference. In practice, a normalized proportional mapping method can be used to determine the relative deviation coefficient for each workstation.

[0133] By comparing the deviation of each workstation with the baseline deviation, the deviation ratio of each workstation relative to the baseline is calculated, generating a list of relative deviation coefficients. The relative deviation coefficient characterizes how close the current state of the workstation is to the baseline; a larger coefficient indicates a greater difference from the baseline state, and a higher necessity for adjustment. Compared to directly using the original score, the deviation ratio is more normalized and adaptable, and can be applied to comparisons and adjustments under different samples and different batches of operations.

[0134] S414: Based on the initial adjustment coefficient list and the current set speed of the reference station, calculate the speed adjustment amount of each station in a proportional mapping manner to obtain the speed adjustment amount list;

[0135] The initial adjustment coefficient list refers to a set of normalized proportional coefficients calculated based on the comparison between the current state of each workstation and the deviation of the reference workstation. It is used to quantify the adjustment range that each workstation should be given during adjustment. Each coefficient in the list is usually normalized by dividing the characteristic deviation of a workstation by the deviation of the reference workstation and combining it with preset upper and lower limits. It reflects the adjustment priority and intensity of each workstation relative to the reference workstation in parameters such as speed or duration. It is a key intermediate variable for subsequently mapping specific adjustment amounts (such as speed adjustment values).

[0136] Proportional mapping refers to multiplying the initial adjustment coefficient of each station by the current set speed of the reference station, and then multiplying by a preset adjustment factor (such as the maximum allowable adjustment ratio) to obtain the speed adjustment amount of that station. For example, the maximum adjustment ratio can be set to 10%, then the speed adjustment amount of a certain station is equal to the relative deviation coefficient of that station × the current speed of the reference station × 10%. Positive values ​​indicate upward adjustment, and negative values ​​indicate downward adjustment. The mapping process can be tailored to the allowable range of the equipment.

[0137] After determining the relative deviation coefficient of each workstation, the system proportionally maps it to the current set speed of the reference workstation, calculating the required speed adjustment value for each workstation. This adjustment can be positive (increased) or negative (decreased), depending on the direction of deviation for each workstation. This method enables flexible control of the operating speed, allowing each workstation to gradually approach the reference state through reasonable speed changes, thereby reducing overall processing differences and improving homogeneity.

[0138] S415: Based on the initial adjustment coefficient list and the remaining time of the current stage of the benchmark station, and in accordance with the rule of maintaining the consistency of the stage processing effect, calculate the stage duration adjustment amount for each station, so that the target duration and target speed of each station compensate for each other, and obtain the stage duration adjustment amount list.

[0139] Besides rotational speed, stage runtime is also a crucial factor affecting crushing results. This step, based on the same deviation coefficient as S414 and combined with the remaining time of the current stage at the benchmark station, uses a control strategy to ensure a complementary relationship between the processing time and rotational speed of each station. For example, if a station needs to increase its rotational speed, its runtime can be slightly shortened, and vice versa. Through dual-variable adjustment of rotational speed and runtime, the processing intensity can be balanced without adding extra processing cycles. The stage runtime adjustment list refers to the set of target runtime adjustment values ​​calculated for each station based on the initial adjustment coefficient of each station and the remaining processing time of the current stage at the benchmark station, combined with the control principle of compensating for rotational speed differences through time changes. Each adjustment represents the increase or decrease in runtime required for the current stage of that station, calculated by multiplying the relative deviation coefficient by the current remaining time, and then trimmed according to the preset maximum adjustable range to generate a safe and executable time adjustment value that can both compensate for differences in processing intensity and ensure synchronous completion. This value is used to guide modifications to the stage processing time in parameter adjustment instructions.

[0140] S416: Based on the speed adjustment list and the stage duration adjustment list, and combined with the allowable variation range of the equipment, the range is trimmed to obtain the trimmed adjustment parameter table;

[0141] This step ensures that all calculated adjustment values ​​operate within the safe limits allowed by the equipment. The system performs upper and lower limit checks on each parameter change; for example, the rotational speed must not exceed the rated range, and the duration must not be less than the minimum processing time. Any deviations will be truncated to the boundary values, resulting in a trimmed adjustment parameter table. This trimming mechanism ensures stable equipment operation and prevents adjustments from failing due to exceeding hardware capabilities; it is a crucial interface connecting theoretical calculations and actual execution in the adjustment strategy.

[0142] Example 3

[0143] This embodiment provides a food sampling method. The method uses the food crushing and homogenizing machine described in Embodiment 1 for sampling. The method includes:

[0144] S01: After positioning the food crusher and homogenizer, lower the foot cup, level the machine platform, ground the equipment and connect the power supply.

[0145] Before sampling, the equipment must be prepared. Leveling the base and grounding the power supply are important tasks in this step.

[0146] S02: Place the prepared sample into the container at the corresponding workstation;

[0147] If the sample is too large, it must be cut into smaller pieces before being placed in the container.

[0148] S03: Cover the transparent window cover 21 and use the buckle 221 to lock the transparent window cover 21 onto the container;

[0149] S04: Control the rotation of the first cutter 231 and / or the second cutter to homogenize the product;

[0150] Before performing homogenization, you can first enter the human face interface operation homepage and clear the previous data by using the zeroing button in this workstation box.

[0151] If the fruit and vegetable samples are placed in the first crushing and homogenizing component 2, the first blade 231 can be manually rotated forward to cut the samples into small pieces with a maximum size of 20mm before the actual homogenization process. If the samples are nuts placed in the second crushing and homogenizing component 3, it is not necessary to cut them into small pieces first.

[0152] S05: After homogenization, load the sample into the sample container.

[0153] After the sample has been crushed and homogenized, open the transparent viewing window cover 21, remove the positioning pin, and insert it into the placement hole.

[0154] Then the operator holds the handle of the joystick 243, removes the pin 245 to unlock, and slowly pours the broken sample into the sample container.

[0155] After removing the sample, clean the container and allow it to air dry. Once the container is dry, reset its position and insert the locking pin into the limiting piece 244 to lock the position of the rotating shaft 242. Then, cover it with the transparent viewing window cover 21 for future use.

[0156] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A control method for a food crushing and homogenizing machine, characterized in that, For controlling a food crushing and homogenizing machine, the food crushing and homogenizing machine includes: Base; Several sets of first crushing and homogenizing components are installed on the base. Each set of first crushing and homogenizing components includes a first driver, a first cutter, and a first container. The output end of the first driver is connected to the first cutter, and the first cutter is located inside the first container. Several sets of second crushing and homogenizing components are installed on the base. The second crushing and homogenizing components include a second cutter and a second container. The output end of a second motor is connected to the second cutter, and the second cutter is located inside the second container. The capacity of the first container is greater than the capacity of the second container; A control device, which is electrically connected to each of the first crushing and homogenizing components and the second crushing and homogenizing components respectively; The method includes: S1: Obtain the crushing homogenization parameters according to the type of sample to be crushed; S2: Based on the crushing and homogenization parameters, control the first or second crushing and homogenization component at each station to crush the food sample. S3: During the crushing process, obtain the real-time status parameters of the first or second crushing homogenizing component at each station. S4: Adjust the crushing and homogenization parameters according to the real-time status parameters; S3: During the crushing process, acquiring the real-time status parameters of the first or second crushing homogenizing component at each workstation includes: S31: Based on the number of each workstation and the preset sampling frequency, the status parameters of each workstation are collected in real time to obtain the original status data sequence of each workstation. The status parameters include motor speed and current. S32: Based on the original state data sequence of each workstation, the sliding window algorithm is used to perform statistical processing on the data within a specified time window to obtain the statistical characteristics of each workstation within the current window. The statistical characteristics include the mean, standard deviation, and extreme values. S33: Based on the statistical characteristics of all workstations within the same time window, a time-series alignment method is used to synchronize data, resulting in a feature alignment matrix between different workstations at the same crushing stage. S34: Based on the feature alignment matrix, calculate the feature distance or similarity index between each workstation to obtain the consistency evaluation result between workstations; S35. Based on the consistency evaluation results, cluster analysis or threshold discrimination method is used to identify anomalies in all workstations to obtain the abnormal workstations. Step S4: Adjusting the crushing homogenization parameters according to the real-time state parameters includes: S41: Based on the list of abnormal workstations and the corresponding feature deviation scores, calculate the target adjustment coefficient for each workstation to obtain the parameter adjustment instructions for each workstation; S42: Based on the parameter adjustment instructions of each workstation, the rotation speed and stage duration of each abnormal workstation are modified in real time to obtain the corrected workstation operation parameter set; S43: Based on the corrected workstation operation parameter set, continuously collect the status data of the next sliding window and recalculate the characteristic deviation to obtain the consistency recovery assessment result; S44: Based on the consistency recovery assessment results, a gradual recovery strategy is adopted to gradually restore the adjusted parameters at a preset step size, so as to obtain the process curve of the workstation operating parameters smoothly recovering to the target set value; S45: Based on the accumulated adjustment effect data during the gradual recovery process, update the adaptive parameter record table to obtain the optimized parameter recommendation library.

2. The control method for a food crushing and homogenizing machine according to claim 1, characterized in that, S41: Based on the list of abnormal workstations and the corresponding feature deviation scores, calculate the target adjustment coefficient for each workstation to obtain the parameter adjustment instructions for each workstation, including: S411: Based on the characteristic deviation score of each workstation, select the workstation with the highest score as the benchmark workstation; S412: Extract the characteristic deviation value of the reference station to obtain the reference deviation; S413: Based on the baseline deviation and the characteristic deviation values ​​of the other workstations, determine the relative deviation ratio of the remaining workstations and obtain the relative deviation coefficient of each workstation. S414: Based on the initial adjustment coefficient list and the current set speed of the reference station, calculate the speed adjustment amount of each station in a proportional mapping manner to obtain the speed adjustment amount list; S415: Based on the initial adjustment coefficient list and the remaining time of the current stage of the benchmark station, and in accordance with the rule of maintaining the consistency of the stage processing effect, calculate the stage duration adjustment amount for each station, so that the target duration and target speed of each station compensate for each other, and obtain the stage duration adjustment amount list. S416: Based on the speed adjustment list and the stage duration adjustment list, and combined with the allowable variation range of the equipment, the interval is trimmed to obtain the trimmed adjustment parameter table.

3. The control method for a food crushing and homogenizing machine according to claim 1, characterized in that, The first crushing and homogenizing component further includes a transparent viewing cover, which is disposed above the first container and / or the second crushing and homogenizing component further includes a transparent viewing cover, which is disposed above the second container.

4. The control method for a food crushing and homogenizing machine according to claim 3, characterized in that, The first and / or second cutting tools are four-bladed steel blades.

5. The control method for a food crushing and homogenizing machine according to claim 1, characterized in that, The first crushing and homogenizing component and / or the second crushing and homogenizing component further include a tilting device mounted on the base.

6. The control method for a food crushing and homogenizing machine according to claim 5, characterized in that, The tilting device includes a support base, a rotating shaft, and a rocker arm. The rotating shaft is connected to the first driver and / or the second motor. Both ends of the rotating shaft are rotatably connected to the support base. The rocker arm is connected to the rotating shaft and is located outside the support base.

7. The control method for a food crushing and homogenizing machine according to claim 6, characterized in that, The tilting device also includes a limiting member, which has a plurality of positioning grooves spaced apart along the circumferential direction. The limiting member is installed on the support base, and the rocker arm is inserted into the limiting member.

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