Food crushing homogenizer and control method thereof

Abstract

The invention belongs to the technical field of food sampling, and particularly relates to a food crushing homogenizer and a control method thereof. The invention provides a food crushing homogenizer. The food crushing homogenizer comprises a base; the plurality of groups of first crushing and homogenizing components are mounted on the base, each group of first crushing and homogenizing component comprises a first driver, a first cutter and a first container, the output end of the first driver is connected with the first cutter, and the first cutter is positioned in the container; the second crushing and homogenizing assemblies are installed on the base, each second crushing and homogenizing assembly comprises a second cutter and a second container, the output end of the second motor is connected with the second cutter, and the second cutter is located in the second container; the capacity of the first container is greater than that of the second homogenizing container; and the control device is electrically connected with each first crushing and homogenizing assembly and each second homogenizing assembly. The food crushing and homogenizing device can remarkably improve the food crushing and homogenizing efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of food sampling, in particular to a food crushing homogenizer and a control method thereof. Background Art

[0002] According to GB2763-2021, "National Food Safety Standard - Maximum Residue Limits of Pesticides in Food," samples of foods such as fruits, vegetables, and dried fruits must be collected and then tested. Since sprayed pesticides often leave uneven residues on the surfaces of fruits and vegetables, this can easily lead to testing errors. Traditional household blenders can be used to crush fruit and vegetable samples. However, due to the small size of the blender's container, its efficiency is low, and the homogenization effect is less than ideal. Summary of the Invention

[0003] In view of this, the present invention provides a food crushing homogenizer and a control method thereof, which are used to solve the technical problem of low crushing efficiency of the wall breaking machine in the prior art: The food crushing and homogenizing machine provided by the present invention comprises: base; A plurality of first crushing and homogenizing components are mounted on the base, each of which includes a first driver, a first cutter, and a first container, wherein an output end of the first driver is connected to the first cutter, and the first cutter is located inside the container; a plurality of second crushing and homogenizing assemblies mounted on the base, the second crushing and homogenizing assemblies comprising a second cutter and a second container, the output end of the second motor being connected to the second cutter, and the second cutter being located inside the second container; The capacity of the first container is greater than the capacity of the second homogenizing container; A control device is electrically connected to each of the first crushing and homogenizing components and the second homogenizing components.

[0004] Preferably, the first crushing and homogenizing component further includes a transparent window cover, which is provided above the first container and / or the second crushing and homogenizing component further includes a transparent window cover, which is provided above the second container.

[0005] Preferably, the first knife and / or the second knife are four-leaf steel knives.

[0006] Preferably, the first crushing and homogenizing assembly and / or the second crushing and homogenizing assembly further comprises a tilting device, and the tilting device is installed on the base.

[0007] Preferably, the tilting device includes a support seat, a rotating shaft and a rocker, the rotating shaft is connected to the driver, both ends of the rotating shaft are respectively rotatably connected to the support seat, the rocker is connected to the rotating shaft, and the rocker is located on the outside of the support seat.

[0008] Preferably, the tilting device further comprises a limiting member, wherein the limiting member is provided with a plurality of positioning grooves spaced apart along the circumferential direction, the limiting member is mounted on the supporting seat, and the rocker is inserted into the limiting member.

[0009] Preferably, the tilting device further comprises a latch, at least a portion of which is inserted into the limiting member and abuts against the rocker.

[0010] Preferably, a buckle is further installed on the first container and / or the second container, and a limit switch is provided on the buckle, and the limit switch is electrically connected to the control device.

[0011] Preferably, it further comprises a cleaning waste collection drainage trough, which is installed on the base, and the first crushing and homogenizing assembly and the second crushing and homogenizing assembly are arranged in a row along the extension direction of the cleaning waste collection drainage trough.

[0012] In a second aspect, the present invention further provides a method for controlling a food crushing and homogenizing crusher, which is used to control the food crushing and homogenizing crusher according to the first aspect, the method comprising: S1: Obtain crushing and homogenization parameters according to the type of sample to be crushed; S2: According to the crushing and homogenizing parameters, controlling the first crushing and homogenizing component or the second crushing and homogenizing component at each station to crush the food sample; S3: During the crushing process, obtaining real-time status parameters of the first crushing and homogenizing component or the second crushing and homogenizing component at each station; S4: Adjusting the crushing and homogenizing parameters according to the real-time state parameters.

[0013] Beneficial effects: The food crushing and homogenizing machine and the control method thereof of the present invention can adaptively crush and homogenize large samples of fruits and vegetables and small samples of nuts at the same time by setting several groups of first crushing and homogenizing components and second crushing and homogenizing components. Multi-station parallel operation significantly improves processing efficiency, meets the needs of rapid processing of diversified samples, and avoids the problem of low efficiency of single container of traditional wall breaking machine. Since the capacity of the first container is larger than that of the second container, the large-capacity design ensures that the fruit and vegetable samples are fully mixed during the crushing process, reducing the detection error caused by uneven surface residue; the small-capacity container accurately adapts to the homogenization requirements of dried fruit samples, and improves the overall homogenization uniformity. The control device is uniformly electrically connected to each crushing and homogenizing component, and the user can centrally start, stop, and adjust different station equipment through a single control panel, simplifying the operating process, reducing the complexity of independent control of multiple devices, and improving the level of automation. The base integrates multiple groups 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work, and these are all within the scope of protection of the present invention.

[0015] Figure 1 It is a cross-sectional view of the food crushing and homogenizing machine of the present invention; Figure 2 Schematic diagram of the three-dimensional structure of the first crushing and homogenizing component in the present invention; Figure 3 This is a schematic diagram of the structure of the crushing tool in the container of the present invention; Figure 4 It is a flow chart of the food crushing homogenizer control method of the present invention.

[0016] Parts and their numbers in the picture: Base 1, first crushing and homogenizing assembly 2, transparent window cover 21, first container 22, buckle 221, first cutter 231, tilting device 24, support base 241, rotating shaft 242, rocker 243, stopper 244, positioning slot 2441, latch 245, second crushing and homogenizing assembly 3, second container 31, control device 4, control panel 41, control box 42. Drainage trough for cleaning waste 5, stainless steel universal casters with brakes 6. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in 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 article, 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 orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like is based on the orientation or position relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. Moreover, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, elements defined by the phrase "comprising..." do not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the elements. The embodiments of the present invention and the features thereof may be combined with each other if there is no conflict, and all are within the scope of protection of the present invention.

[0018] Example 1 like Figure 1 As shown, this embodiment provides a food crushing and homogenizing machine, which mainly includes a base 1, several groups of first crushing and homogenizing components 2, several groups of second crushing and homogenizing components 3 and a control device 4.

[0019] The base 1 serves as a mounting base for the 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 place, the brakes on the casters can be released to keep the food crusher and homogenizer in place.

[0020] In this embodiment, a plurality of first crushing and homogenizing assemblies 2 are mounted on the base 1. The number of the first crushing and homogenizing assemblies 2 is greater than or equal to 2. In this embodiment, the first crushing and homogenizing assemblies 2 are used to crush and homogenize fruits and vegetables with a large crushing amount.

[0021] like Figure 2 and Figure 3As shown, each set of first crushing and homogenizing components 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. The first driver drives the first cutter 231 to rotate. During rotation, the first cutter 231 crushes and homogenizes the fruits and vegetables placed in the container. The first driver can be a motor, such as a servo motor or a stepper motor. Other types of drivers capable of outputting rotational motion can also be used, without limitation. In this embodiment, a protective cover can also be provided on the exterior of the driver to protect the driver.

[0022] Several sets of second crushing and homogenizing assemblies 3 are mounted on the base 1. The second crushing and homogenizing assemblies 3 include 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 assemblies 3 are used to crush and homogenize nut samples. The number of first crushing and homogenizing assemblies 2 is greater than or equal to one.

[0023] In this embodiment, the capacity of the first container 22 is greater than the capacity of the second homogenizing container; In this embodiment, a first homogenizing component 2 with a larger capacity container is used to crush and homogenize fruit and vegetable samples, which can process more fruit and vegetable samples at one time, thereby improving the processing efficiency. In addition, the homogenization effect of the sample is further improved after the capacity of the first homogenizing component container is increased. For nut samples with a smaller crushing amount, a second homogenizing component 3 with a smaller capacity is used for crushing and homogenizing. Since the present application adopts the method of multiple first crushing and homogenizing components 2, different fruit and vegetable samples can be crushed and homogenized simultaneously at multiple stations, thereby further improving the sampling efficiency of fruit and vegetable samples.

[0024] The control device 4 of this embodiment is electrically connected to each of the first crushing and homogenizing components 2 and the second homogenizing components, so that the control device 4 can be used to control the operation of the first crushing and homogenizing components 2 and the second crushing and homogenizing components 3.

[0025] The control device 4 in this embodiment includes a control panel 41 and a control box 42. The user can input control commands through the control panel 41 and the control box 42 to control each crushing and homogenizing component.

[0026] In this embodiment, the first crushing and homogenizing component 2 further includes a transparent window cover 21, which is provided on top of the first container 22 and / or the second crushing and homogenizing component 3 further includes a transparent window cover 21, which is provided on top of the second container 31.

[0027] The transparent window cover 21 is placed over 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. Since this embodiment uses the transparent window cover 21, the user can clearly observe the crushing and homogenization status of the sample in the first container 22 or the second container 31 through the transparent window cover 21 without opening the transparent window cover 21, so that the driver parameters can be adjusted according to the crushing and homogenization status, thereby improving the homogenization efficiency and effect.

[0028] In this embodiment, the first cutter 231 and / or the second cutter are four-leaf steel cutters. The four-leaf steel cutters can crush the sample more efficiently.

[0029] The first crushing and homogenizing assembly 2 and / or the second crushing and homogenizing assembly 3 further include a tilting device 24 mounted on the base 1. When crushing and homogenizing a sample using the first crushing and homogenizing assembly 2 and / or the second crushing and homogenizing assembly 3, the tilting device 24 can be rotated to a position where the container is vertically facing upward. When the sample needs to be removed after crushing, the tilting device 24 can be rotated to a position where the container is tilted, lowering the top opening of the container, allowing the crushed and homogenized sample to be removed from the container.

[0030] In this embodiment, the tilting device 24 includes a support base 241, a rotating shaft 242, and a rocker 243. The rotating shaft 242 is connected to the driver, with both ends of the rotating shaft 242 rotatably connected to the support base 241. The rocker 243 is connected to the rotating shaft 242 and is located outside the support base 241. A user can operate the rocker 243 to rotate the rotating shaft 242, which in turn rotates the driver and the container mounted therewith. In this embodiment, the rotating shaft 242 is rotatably connected to the support base via a bearing. The rocker 243 can be threadedly connected to the rotating shaft 242.

[0031] In this embodiment, the tilting device 24 further includes a stopper 244 having a plurality of positioning slots 2441 spaced apart along the circumferential direction. The stopper 244 is mounted on the support base 241, and the rocker 243 is inserted into the stopper 244. In this embodiment, the tilting device 24 further includes a latch 245, at least a portion of which is inserted into the stopper 244.

[0032] Before the first crushing and homogenizing assembly 2 and / or the second crushing and homogenizing assembly 3 performs the crushing and homogenizing operation, the tilting device 24 is rotated to a position where the container is vertically upward, and then the latch 245 is inserted into the limit piece 244, and the latch 245 is pressed against the rocker 243. In this way, the rocker 243 is locked on the limit piece 244, so that the rotating shaft 242 and the container are maintained at the current angle and cannot rotate arbitrarily.

[0033] In this embodiment, a buckle 221 is mounted on the first container 22 and / or the second container 31. A limit switch is provided on the buckle 221, which is electrically connected to the control device 4. After the fruit and vegetable samples are placed in the container, the transparent window cover 21 is placed over the container, and the buckle 221 is then engaged with the transparent window cover 21. To remove the transparent window cover 21 from the container, the buckle 221 can be released. Once the buckle 221 is released, the limit switch is triggered, and the control device 4 prohibits the device from starting, thereby improving operational safety.

[0034] The food crushing and homogenizing machine of this embodiment also includes a cleaning waste collection and drainage trough 5, which is installed on the base 1, and 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 cleaning waste collection and drainage trough 5.

[0035] When the first container 22 and / or the second container 31 need to be cleaned, the waste residue and waste water generated during the cleaning process can be collected in the cleaning waste residue collection drainage trough 5 and discharged uniformly through the interface at the bottom of the drainage trough.

[0036] Taking the 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 food crushing and homogenizing machine in this embodiment are as follows: 1. The volume of each container: A: 2500mm high x 200mm diameter; B, C stations: 200mm high x 200mm diameter; D station: 85mm high x 165mm diameter; 2. Crushing power: ABC station: 1KW / station; D station: 550W 3. Total power: 4.0KW; 4. Rated voltage: AC220V; 5. Speed: Station ABC: 0 ~ 3000r / min, can be set arbitrarily; Station D: 35000r / min; 5. Rated voltage: AC220V, total power 4KW; 6. Workstation settings: A, B, C, 3 fruit and vegetable stations; D, 1 nut station.

[0037] Example 2 This embodiment provides a method for controlling a food crushing and homogenizing machine, wherein the food crushing and homogenizing machine includes a plurality of workstations, each of which includes a first crushing and homogenizing component or a second crushing and homogenizing component. The method includes: S1: Obtain crushing and homogenization parameters according to the type of sample to be crushed; 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 crushing and homogenization parameter set corresponding to this type from the parameter library, including speed, time, stage structure, etc., to guide the operation of subsequent workstations.

[0038] S2: According to the crushing and homogenizing parameters, controlling the first crushing and homogenizing component or the second crushing and homogenizing component at each station to crush the food sample; Based on the parameter set obtained in S1, the system sends the parameters to the corresponding crushing components (the first crushing and homogenizing component is used for fruits and vegetables, and the second crushing and homogenizing component is used for nuts), starts each workstation synchronously or in batches according to the set speed and time, performs crushing and homogenization operations, and realizes efficient sample pre-processing.

[0039] S3: During the crushing process, obtaining real-time status parameters of the first crushing and homogenizing component or the second crushing and homogenizing component at each station; During operation, the system continuously monitors the core status parameters of each workstation, such as motor speed, current, vibration signals, etc., collects data at a preset sampling frequency, and generates a status data sequence for each workstation, which is used to dynamically judge its operating load and stability.

[0040] S4: Adjusting the crushing and homogenizing parameters according to the real-time state parameters.

[0041] 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 rotation speed or extending the stage duration, to achieve dynamic closed-loop control of processing consistency and operational safety among multiple workstations.

[0042] In this embodiment, S3: during the crushing process, obtaining the real-time status parameters of the first crushing and homogenizing component or the second crushing and homogenizing component at each station includes: S31: According to the number of each workstation and the preset sampling frequency, the state parameters of each workstation are collected in real time to obtain the original state data sequence of each workstation, wherein the state parameters include the motor speed and current; This step synchronously collects the motor speed and current at each station in real time, based on the station number and the preset sampling frequency, to generate a raw state data sequence with a timestamp. Motor speed reflects the actual operating speed of the cutter, while current reflects the load changes experienced by the equipment. As key indicators of the crushing process, these two parameters accurately reveal the dynamic behavior of each station during processing. The station state parameters refer to the real-time state parameters of the first or second crushing and homogenizing assembly used to crush the sample at that station.

[0043] S32: Based on the original state data sequence of each workstation, a sliding window algorithm is used to perform statistical processing on the data within a specified time window to obtain statistical features of each workstation within the current window, wherein the statistical features include mean, standard deviation, and extreme value; This step uses a sliding window algorithm to segment the raw data for each workstation and extract the statistical features within the current window, including the mean, standard deviation, and extreme values. These statistical features effectively filter out high-frequency disturbances and extract stable operating trends, allowing for a structured representation and analysis of the operating status of each workstation over different time periods.

[0044] S33: Based on the statistical features of all workstations in the same time window, a time series alignment method is used to synchronize data to obtain a feature alignment matrix between different workstations in the same crushing stage.

[0045] This step performs temporal 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. In practice, the startup time or processing progress of each workstation may vary slightly. Temporal alignment ensures that each workstation has a consistent time reference across feature dimensions, improving comparison accuracy and logical consistency.

[0046] S34: Calculate the feature distance or similarity index between each workstation based on the feature alignment matrix to obtain the consistency evaluation result between the workstations.

[0047] This step calculates similarity or distance metrics between workstations based on the time-aligned feature matrix, such as the degree of deviation in rotational speed and current, to generate a consistency evaluation result. This evaluation quantifies the degree of consistency in the current crushing state of each workstation, providing an objective basis for the system to determine whether synchronization adjustment is necessary.

[0048] S35. Based on the consistency evaluation results, cluster analysis or threshold discrimination method is used to identify abnormalities in all workstations to obtain abnormal workstations.

[0049] Based on the consistency evaluation results, this step uses cluster analysis or threshold-based discrimination methods to identify abnormal stations whose processing status deviates significantly from other stations. This step enables early detection of potential overload, underload, or operational anomalies at individual stations, and provides clear targets for subsequent parameter fine-tuning and compensation, thereby ensuring the consistency of multi-station crushing results, sample representativeness, and reliability of test data.

[0050] The step S34: calculating the feature distance or similarity index between each workstation according to the feature alignment matrix, and obtaining the consistency evaluation result between the workstations includes: 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 the workstations. The distance measurement methods include Euclidean distance, Manhattan distance, and Pearson correlation coefficient.

[0051] This embodiment uses a variety of distance measurement methods to calculate the statistical characteristics between each pair of workstations, including Euclidean distance, Manhattan distance, and Pearson correlation coefficient. Euclidean distance and Manhattan distance can reflect the numerical differences in different dimensions and are suitable for consistency analysis of continuous variables such as speed and current; while the Pearson correlation coefficient can be used to measure the consistency of the changing trends of the characteristics of two workstations and is suitable for determining whether the processing rhythm is synchronized. By introducing multiple measurement methods, 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.

[0052] S342: Based on the multi-index distance matrix, perform weighted summation on the distance between each workstation and the group mean workstation to obtain a characteristic deviation score for each workstation.

[0053] This step calculates the multi-metric distances between each workstation and all other workstations, summing these values ​​using preset weighting coefficients to produce a characteristic deviation score for that workstation. To mitigate evaluation bias caused by uneven data distribution, the system incorporates the group mean workstation as a reference. This means a virtual workstation representing the overall operating status is constructed by aggregating the characteristic means of all workstations. This serves as a benchmark for measuring the relative deviation of individual workstations. This score intuitively reflects the gap between the current state of a workstation and the group average, providing a quantitative basis for subsequent anomaly identification and adjustment strategies.

[0054] S343: Based on the feature deviation score, combined with the historical threshold or adaptive threshold method, determine whether there is significant deviation at each workstation and obtain preliminary results of the consistency evaluation.

[0055] This step is based on the characteristic deviation score results of the workstation, combined with the fixed threshold set by historical experience or the adaptive threshold dynamically generated according to the fluctuation range of real-time data, to determine whether there is significant deviation in each workstation. Fixed thresholds are suitable for equipment scenarios with large samples and long-term stable operation, while adaptive thresholds are suitable for use in the initial stage of operation or situations where there are large differences in sample characteristics between batches, which helps to improve the adaptability and generalization ability of the adjustment algorithm. This step completes the conversion from feature difference to state recognition and is a key transition link from data analysis to control execution in the control process. The adaptive threshold can be dynamically adjusted according to the overall distribution characteristics of the current operating state, making the discrimination result more robust and adaptable.

[0056] S344: Based on the preliminary consistency evaluation results, a trend analysis method is used to fit the changes in the characteristic deviation of each workstation over time to obtain a list of workstations with potential abnormal trends.

[0057] Based on the preliminary assessment results, this step performs a fitting analysis of the time-varying trends in the characteristic deviations of each workstation. This identifies potentially abnormal workstations whose scores, while not currently crossing the threshold, are showing trends of sustained growth or increasing deviation. This trend analysis allows for early detection of potential problem workstations, enabling early warning and avoiding intervention and adjustments after the situation deteriorates. This mechanism shifts the control strategy from passive response to active intervention, thereby improving the stability and consistency of the entire multi-station control system.

[0058] The preliminary consistency evaluation result refers to the workstation consistency judgment information obtained by calculating the feature similarity or deviation between workstations and comparing it with the set threshold. It reflects the relative closeness of the processing status between the current workstations and is used to identify whether there is a trend of operation deviation or a problem workstation.

[0059] Trend analysis methods refer to methods that fit, model, or make directional judgments on time series data (such as the change in the deviation of workstation characteristics over time). Common methods include linear regression, polynomial fitting, and sliding average trend lines. They are used to identify whether the deviation is an occasional fluctuation or continuous growth, and then determine whether the anomaly is persistent and developing.

[0060] The change of characteristic deviation over time refers to the time series composed of characteristic deviation values ​​calculated in multiple continuous sliding windows for each station, which reflects the change process of the operating stability of the station during the entire crushing process cycle and is the basic data for evaluating abnormal trends.

[0061] The list of workstations with potential abnormal trends refers to a collection of workstations identified through trend analysis that have not currently exceeded the abnormal threshold but have trends such as continued growth in characteristic deviation and amplified fluctuations. Such workstations may evolve into overt abnormalities in subsequent stages and are target objects that the control system needs to pay attention to and intervene in advance.

[0062] S35: Based on the consistency evaluation results, cluster analysis or threshold discrimination method is used to identify abnormalities in all workstations, and the abnormal workstations include: S351: Based on the characteristic deviation sequence of each workstation, the noise is smoothed using a sliding average method to obtain a smoothed deviation curve.

[0063] Based on the characteristic deviation score generated in the previous step, the deviation sequence for each workstation in multiple continuous time windows is extracted and smoothed using a sliding average method to obtain a smoothed deviation curve for each workstation. Because the original score may experience transient jumps or abnormal spikes due to the influence of operating condition fluctuations, current pulsation, etc., directly participating in anomaly identification is prone to misjudgment. Therefore, by using a sliding average method to reduce the noise of the deviation curve in the time dimension, it is possible to more accurately extract the stability trend of the workstation, enhance the anti-interference ability and continuity discrimination effect of the workstation status assessment.

[0064] S352: Based on the smoothed deviation curve, an unsupervised learning method such as density clustering (such as DBSCAN) or hierarchical clustering is used to group the workstations to obtain isolated workstations that deviate from the main group.

[0065] Based on the smoothed deviation curves of all workstations, representative feature values ​​are extracted at the current time point or within a specific time period to construct a workstation feature space. Unsupervised learning algorithms such as density clustering (such as DBSCAN) or hierarchical clustering are then used to group and identify workstations. This process eliminates the need to predefine the number of workstation categories. It automatically detects outliers based on the distribution of deviations between workstations, groups similar workstations into the same cluster, and identifies outliers if their distance from the main group is significantly greater than that of the main group. This method is more adaptable than traditional thresholding methods and can identify anomalous workstations in nonlinear and heterogeneous distributions, making it a key tool for improving the sensitivity and accuracy of anomaly detection.

[0066] S353: Perform cross-validation based on the clustering results and the threshold method results to filter out the workstations that are ultimately determined to be abnormal.

[0067] Aberrant workstations identified by density clustering are cross-validated with those identified using a deviation scoring threshold method. Workstations that meet both criteria are selected as the final aberrant workstations. This strategy combines the controllability of rule-based approaches with the adaptability of clustering methods, enhancing the ability to identify borderline workstations while maintaining interpretability and transparency. The resulting aberrant workstations serve as key targets for subsequent adaptive parameter adjustments, ensuring a clear, targeted, and operational control strategy, thereby enhancing the consistency of multi-workstation homogenization and the representativeness of test samples.

[0068] In this embodiment, the step S4: adjusting the crushing and homogenizing parameters according to the real-time state parameters includes: S41: Calculate the target adjustment coefficient of each workstation based on the abnormal workstation list and the corresponding feature deviation score, and obtain the parameter adjustment instruction for each workstation; The regular workstation list refers to the set of workstation numbers that deviate from the normal workstation group in terms of operating status, which is screened out through previous consistency analysis and anomaly identification steps (such as clustering or threshold judgment). It is the target workstation set that currently needs to be adjusted first. The feature deviation score is a quantitative score obtained based on the distance calculation results of each workstation in the statistical feature space (such as the degree of difference in speed and current), which is used to indicate the degree of deviation of the workstation from the main group state. The target adjustment coefficient refers to the proportional coefficient calculated by the system based on the degree of deviation of the workstation for adjusting parameters such as speed and duration. It is the basic factor for achieving personalized adjustment; parameter adjustment instruction: refers to the control parameter adjustment command calculated and generated by the system based on the target adjustment coefficient, usually including fields such as target speed and target processing duration, which are used to guide the equipment to perform differentiated operation control on each workstation.

[0069] This step is based on the list of abnormal workstations identified in the previous stage, and combined with the characteristic deviation scores of each abnormal workstation, it calculates the target adjustment coefficient of each workstation to quantify 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, which is usually obtained by standardizing the degree of deviation and mapping it proportionally with the benchmark workstation parameters. This step ensures that the parameter adjustment is targeted and accurate, and can be tailored and corrected according to preset rules to avoid excessive adjustments due to individual extreme deviations. After the adjustment coefficient is determined, the system further converts it into a structured parameter adjustment instruction, clearly specifying the speed increase or decrease of each workstation, the range of extension or shortening of the stage processing time, etc., to provide a direct control basis for subsequent execution.

[0070] S42: According to the parameter adjustment instruction of each workstation, the rotation speed and stage duration of the workstation are modified in real time to obtain a revised workstation operation parameter set.

[0071] 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 contain specific parameter values ​​used to adjust the operating status, such as the speed increase or decrease, the stage duration adjustment, etc. Stage duration refers to the preset running time of the current crushing process stage and is an important control variable that determines the duration of sample stress and the degree of homogenization; The workstation operation parameter set refers to a set of control parameters that are effective for each workstation in the current stage, including the corrected target speed and target stage duration, which serves as a reference for subsequent execution and continuous monitoring.

[0072] This step modifies the operating parameters of each abnormal station in real time based on the aforementioned adjustment instructions. Specifically, this includes updating the currently set tool speed and crushing phase duration. These modifications typically involve incrementing or decrementing the current operating values, while remaining within the equipment's permitted operating range and adjustment steps to ensure safety and mechanical response stability. This operation allows each station to resume the crushing process with more appropriate parameters. The goal is to proactively intervene to return the abnormal station to the mainstream processing state, achieving local consistency compensation.

[0073] S43: Based on the corrected workstation operation parameter set, continuously collect the state data of the next sliding window and recalculate the feature deviation to obtain a consistency recovery evaluation result; To continuously evaluate the effectiveness of parameter adjustments, this step re-collects status data and recalculates characteristic deviations for the adjusted workstations, using the same method as steps S31–S34. By continuously collecting data such as speed and current within the adjusted sliding window, the system dynamically calculates new deviation scores and compares them with previous statuses to determine whether each workstation has returned to normal. This process forms a feedback loop within the control system, providing real-time perception of adjustment results and helping to prevent blind, repetitive adjustments or prolonged operation in suboptimal conditions.

[0074] S44: Based on the consistency recovery evaluation result, a progressive recovery strategy is adopted to gradually call back the adjusted parameters according to a preset step, and a process curve of smoothly recovering the station operation parameters to the target set values ​​is obtained.

[0075] If the assessment indicates that the station's status has returned to within the consistency standard, the system initiates a gradual recovery mechanism, gradually adjusting the operating parameters back to the initially set target values ​​in small, phased steps. This strategy effectively avoids system instability caused by oscillating parameter adjustments, especially when processing marginal samples, ensuring a smooth transition from regulation to stability. The recovery process typically sets a maximum single-step change limit, such as a maximum speed change of 3% per phase, to ensure gentle and easy-to-track control.

[0076] S45: updating the adaptive parameter record table according to the adjustment effect data accumulated during the gradual recovery process to obtain an optimized parameter recommendation library.

[0077] The gradual recovery process refers to the process in which the control system gradually restores the previously adjusted parameters (such as speed and duration) to the initial set values ​​according to the set steps after the status of each workstation stabilizes, ensuring that the adjusted system can smoothly return to the target state; The accumulated adjustment effect data refers to the operating status feedback information after each parameter adjustment during the entire recovery process, including the speed, current, operating stability, consistency score before and after the adjustment, etc., which is used to evaluate whether the adjustment is effective; The adaptive parameter record table refers to the parameter change trajectory and corresponding operation 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; The optimized parameter recommendation library refers to a set of parameter recommendations that is compiled and refined by analyzing the sample characteristics, initial value settings, adjustment process and final consistency performance in the historical record table. It can be quickly called by the system when processing similar samples in the future to improve operating efficiency and the accuracy of initial settings.

[0078] This step archives the parameter changes, status responses, and final consistency results throughout the adjustment and recovery process, forming a structured parameter record table. This record is then used to update a parameter recommendation library for the next batch. This recommendation library reflects historical experience with parameter adjustments for different samples and operating conditions during actual operation, providing sustainable optimization capabilities. Through continuous accumulation and refinement, this library can provide more accurate initial parameter settings for future similar batches of samples, improving operational efficiency and processing consistency, ultimately establishing an intelligent control foundation for large-scale food sample crushing scenarios.

[0079] In this embodiment, the step S41 of calculating the target adjustment coefficient of each workstation based on the abnormal workstation list and the corresponding characteristic deviation score to obtain the parameter adjustment instruction for each workstation includes: S411: Based on the characteristic deviation scores of each workstation, the workstation with the largest score is selected as the benchmark workstation; To precisely adjust the operating status of abnormal workstations, the system first selects the workstation with the highest score based on the characteristic deviation score of each workstation as the benchmark. This workstation typically represents the most challenging and significant deviation. Using this as a benchmark for parameter calibration ensures that all workstations achieve the most demanding processing conditions, thus avoiding overloading some workstations or affecting consistency due to insufficient processing due to uniform parameter increases. The system then extracts the characteristic deviation value of this benchmark workstation to obtain a quantitative reference.

[0080] S412: extracting the characteristic deviation value of the quasi-station to obtain a reference deviation; After determining the benchmark workstation, this step extracts the feature deviation score of the workstation in the current time window as a quantitative reference standard to measure the difference between other workstations and the benchmark workstation.

[0081] S413: Determine the relative deviation ratio of the remaining workstations based on the reference deviation and the characteristic deviation values ​​of the remaining workstations, and obtain the relative deviation coefficient of each workstation.

[0082] The relative deviation ratio refers to the proportional relationship between the deviation of each workstation and the benchmark deviation, which is used to describe the relative severity of the deviation of the workstation; The relative deviation coefficient refers to the numerical coefficient determined according to the relative deviation ratio. The larger the relative deviation coefficient, the more significant the gap between the workstation and the group average state.

[0083] This step establishes a clear baseline deviation, providing a unified scale for subsequent proportional adjustments, so that the adjustment operation has a clear reference value. In specific implementation, the normalized proportional mapping method can be used to determine the relative deviation coefficient of each station.

[0084] By comparing each station's deviation with the baseline deviation, the relative deviation percentage of each station relative to the baseline is calculated, generating a list of relative deviation coefficients. The relative deviation coefficient characterizes the degree of closeness of the station's current state to the baseline. A larger coefficient indicates a greater deviation from the baseline and a greater need for adjustment. Compared to directly using the raw score, the deviation percentage is more standardized and adaptable, making it suitable for comparison and adjustment across different samples and run batches.

[0085] S414: Calculate the speed adjustment value of each workstation in a proportional mapping manner based on the initial adjustment coefficient list and the current set speed of the reference workstation to obtain a speed adjustment value list; The initial adjustment coefficient list refers to a set of normalized proportional coefficients calculated based on the comparison results of the current state of each workstation and the degree of deviation from the benchmark workstation, which is used to quantify the adjustment range that should be given to each workstation during adjustment; each coefficient in the list is usually obtained by dividing the characteristic deviation of a certain workstation by the deviation of the benchmark workstation, and normalizing it with preset upper and lower limits, reflecting the adjustment priority and strength of each workstation relative to the benchmark workstation in parameters such as speed or duration, and is a key intermediate variable for subsequent mapping of specific adjustment amounts (such as speed adjustment values).

[0086] Proportional mapping means multiplying the initial adjustment coefficient of each station by the current set speed of the reference station, and then multiplying it by a preset adjustment factor (such as the maximum allowable adjustment ratio) to obtain the speed adjustment amount of the 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 the station × the current speed of the reference station × 10%. A positive value indicates an increase, and a negative value indicates a decrease. The mapping process can be tailored according to the allowable range of the equipment.

[0087] After determining the relative deviation coefficient for each station, the system proportionally maps it to the current set speed of the reference station and calculates the speed adjustment required for each station. This adjustment can be positive (increase) or negative (decrease), depending on the deviation direction of each station. This approach enables flexible control of operating speed, allowing each station to gradually approach the reference state through reasonable speed changes, thereby reducing overall processing differences and improving homogeneity consistency.

[0088] S415: Based on the initial adjustment coefficient list and the remaining time of the current stage of the benchmark workstation, and in accordance with the rule of maintaining consistent stage processing effects, the stage duration adjustment amount is calculated for each workstation, so that the target duration and target speed of each workstation compensate each other, and a stage duration adjustment amount list is obtained.

[0089] In addition to speed, stage run time is also a significant factor influencing crushing results. This step uses the same deviation coefficient as S414 and, in conjunction with the remaining time of the benchmark station's current stage, a control strategy ensures that the processing time of each station and the speed complement each other. For example, if a station's speed needs to be increased, its run time can be slightly shortened, and vice versa. By adjusting the speed and run time in a dual variable, processing intensity can be balanced without adding additional processing cycles. The stage run time adjustment list is a set of target run time adjustment values ​​calculated for each station based on the initial adjustment coefficient of each station and the remaining run time of the benchmark station's current stage, combined with the control principle of compensating for speed differences through time changes. Each adjustment represents the required increase or decrease in run time for the station's current stage. It is calculated by multiplying the relative deviation coefficient by the current remaining run time and then tailored to the preset maximum adjustable range. This generates a safe and executable time adjustment value that compensates for differences in processing intensity while ensuring synchronous completion. This adjustment value is used to guide the modification of stage run times in parameter adjustment instructions.

[0090] S416: Based on the speed adjustment value list and the stage duration adjustment value list, and in combination with the allowed variation range of the equipment, interval clipping is performed to obtain a clipped adjustment parameter table; This step ensures that all calculated adjustment values ​​remain within the device's safe range. The system applies upper and lower limits to each parameter change, ensuring, for example, that the speed must not exceed the rated range and that the duration must not fall below the minimum processing time. Exceeding limits are truncated to the boundary values, forming a trimmed adjustment parameter table. This trimming mechanism ensures stable device operation while preventing adjustments from failing due to exceeding hardware capabilities. It serves as the key interface between theoretical calculations and actual execution within the adjustment strategy.

[0091] Example 3 This embodiment provides a food sampling method, which uses the food crushing and homogenizing machine described in Example 1 to perform sampling, and the method includes: S01: After positioning the food crusher and homogenizer, put down the foot cup, level the machine, ground the equipment and connect the power supply; This step first prepares the equipment before sampling. Leveling the base and grounding the power supply are important tasks in this step.

[0092] S02: Place the prepared sample into the container of the corresponding station; If the sample is too large, cut it into small pieces before adding it.

[0093] S03: Cover the transparent window cover 21 and lock the transparent window cover 21 on the container using the buckle 221; S04: Control the first tool 231 and / or the second tool to rotate and homogenize the product; Before performing homogenization, you can first enter the human face interface operation homepage and clear the last data by clicking the reset button in the work station box.

[0094] If the fruit or vegetable sample is placed in the first crushing and homogenizing component 2, the first cutter 231 can be controlled to rotate forward at the manual speed to cut the sample into small pieces of up to 20 mm before the actual homogenization process. If the nut sample is placed in the second crushing and homogenizing component 3, it is not necessary to cut it into small pieces first.

[0095] S05: After homogenization is completed, the sample is placed in a sample container.

[0096] After the sample is crushed and homogenized, the transparent window cover 21 is opened, the positioning pin is pulled out, and inserted into the placement hole.

[0097] The operator then holds the handle of the rocker 243, removes the latch 245 to unlock it, and slowly pours the crushed sample into the sample container.

[0098] After the sample is taken out, the container is cleaned and air-dried. After the container is air-dried, the container position is reset and the latch is inserted into the limiter 244 to lock the position of the rotating shaft 242, and then the transparent window cover 21 is covered for next use.

[0099] The above description is only a specific embodiment of the present invention. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention.

Claims

1. Food crushing homogenizer, characterized in that, include: base; A plurality of first crushing and homogenizing components are mounted on the base, each of which includes a first driver, a first cutter, and a first container, wherein an output end of the first driver is connected to the first cutter, and the first cutter is located inside the container; a plurality of second crushing and homogenizing assemblies mounted on the base, the second crushing and homogenizing assemblies comprising a second cutter and a second container, the output end of the second motor being connected to the second cutter, and the second cutter being located inside the second container; The capacity of the first container is greater than the capacity of the second homogenizing container; A control device is electrically connected to each of the first crushing and homogenizing components and the second homogenizing components.

2. The food crushing homogenizer according to claim 1, characterized in that: The first crushing and homogenizing component further includes a transparent window cover, which is arranged above the first container and / or the second crushing and homogenizing component further includes a transparent window cover, which is arranged above the second container.

3. The food crushing homogenizer according to claim 2, characterized in that: The first knife and / or the second knife are four-leaf steel knives.

4. The food crushing homogenizer according to claim 1, characterized in that: The first crushing and homogenizing assembly and / or the second crushing and homogenizing assembly further includes a tilting device, which is installed on the base.

5. The food crushing homogenizer according to claim 4, characterized in that: The tilting device includes a support seat, a rotating shaft and a rocker, the rotating shaft is connected to the driver, both ends of the rotating shaft are respectively rotatably connected to the support seat, the rocker is connected to the rotating shaft, and the rocker is located outside the support seat.

6. The food crushing homogenizer according to claim 1, characterized in that: The tilting device further comprises a limiting member, wherein the limiting member is provided with a plurality of positioning grooves spaced apart along a circumferential direction, the limiting member is mounted on the supporting seat, and the rocker is inserted into the limiting member.

7. Food crushing homogenizer control method, characterized in that, The method for controlling the food crushing homogenizer according to any one of claims 1 to 6 comprises: S1: Obtain crushing and homogenization parameters according to the type of sample to be crushed; S2: According to the crushing and homogenizing parameters, controlling the first crushing and homogenizing component or the second crushing and homogenizing component at each station to crush the food sample; S3: During the crushing process, obtaining real-time status parameters of the first crushing and homogenizing component or the second crushing and homogenizing component at each station; S4: Adjusting the crushing and homogenizing parameters according to the real-time state parameters.

8. The food crushing homogenizer control method according to claim 7, characterized in that: S3: During the crushing process, obtaining the real-time status parameters of the first crushing and homogenizing component or the second crushing and homogenizing component at each station includes: S31: According to the number of each workstation and the preset sampling frequency, the state parameters of each workstation are collected in real time to obtain the original state data sequence of each workstation, wherein the state parameters include the motor speed and current; S32: Based on the original state data sequence of each workstation, a sliding window algorithm is used to perform statistical processing on the data within a specified time window to obtain statistical features of each workstation within the current window, wherein the statistical features include mean, standard deviation, and extreme value; S33: Based on the statistical features of all workstations in the same time window, a time series alignment method is used to synchronize data to obtain a feature alignment matrix between different workstations in the same crushing stage; S34: Calculate the feature distance or similarity index between each workstation based on the feature alignment matrix to obtain the consistency evaluation result between the workstations; S35. Based on the consistency evaluation results, cluster analysis or threshold discrimination method is used to identify abnormalities in all workstations to obtain abnormal workstations.

9. The food crushing homogenizer control method according to claim 7, characterized in that: The step S4: adjusting the crushing and homogenizing parameters according to the real-time state parameters includes: S41: Calculate the target adjustment coefficient of each workstation based on the abnormal workstation list and the corresponding feature deviation score, and obtain the parameter adjustment instruction for each workstation; S42: According to the parameter adjustment instruction of each workstation, the speed and stage duration of the workstation are modified in real time to obtain a revised workstation operation parameter set; S43: Based on the corrected workstation operation parameter set, continuously collect the state data of the next sliding window and recalculate the feature deviation to obtain a consistency recovery evaluation result; S44: Based on the consistency recovery evaluation result, a progressive recovery strategy is adopted to gradually adjust the adjusted parameters according to a preset step size, so as to obtain a process curve in which the station operation parameters are smoothly restored to the target set values; S45: updating the adaptive parameter record table according to the adjustment effect data accumulated during the gradual recovery process to obtain an optimized parameter recommendation library.

10. The food crushing homogenizer control method according to claim 9, characterized in that: S41: Calculate the target adjustment coefficient of each workstation based on the abnormal workstation list and the corresponding feature deviation score, and obtain the parameter adjustment instructions for each workstation, including: S411: Based on the characteristic deviation scores of each workstation, the workstation with the largest score is selected as the benchmark workstation; S412: extracting the characteristic deviation value of the quasi-station to obtain a reference deviation; S413: Determine the relative deviation ratio of the remaining workstations based on the reference deviation and the characteristic deviation values ​​of the remaining workstations, and obtain the relative deviation coefficient of each workstation; S414: Calculate the speed adjustment value of each workstation in a proportional mapping manner based on the initial adjustment coefficient list and the current set speed of the reference workstation to obtain a speed adjustment value list; S415: Based on the initial adjustment coefficient list and the remaining time of the current stage of the reference station, and in accordance with the rule of maintaining consistent stage processing effects, the stage duration adjustment value is calculated for each station so that the target duration and target speed of each station compensate each other, thereby obtaining a stage duration adjustment value list; S416: According to the speed adjustment amount list and the stage duration adjustment amount list, interval clipping is performed in combination with the variation range allowed by the equipment to obtain a clipped adjustment parameter table.

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