An ice cream machine blade monitoring method

Abstract

The application discloses an ice cream machine cutter head monitoring method, comprising the following steps: according to an initial distribution state, filtering distance data by using a signal processing module to remove noise interference and determine a stable spacing value between the cutter head and the container wall; comparing the stable spacing value with a preset threshold range, if the spacing value is detected to be out of the threshold range, triggering an abnormal signal to determine that the cutter head position is deviated; for the abnormal signal, acquiring current cutter head operation parameters, calculating an adjustment amount by using a pre-established correction model to obtain a correction instruction of the cutter head position; continuously collecting the spacing change trend between the cutter head and the container wall by using the adjusted position state, using a time series analysis method to determine whether the spacing change tends to be stable; for the final stable data, recording the association log of the cutter head position and the spacing change, saving to a system database through a data storage module for subsequent production batch reference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ice cream machine monitoring, in particular to an ice cream machine cutter head monitoring method. BACKGROUND

[0002] The research and optimization of ice cream machines are of great significance in the field of food processing equipment, as they directly affect the taste, quality, and production efficiency of the product. As a popular dessert in daily life, the stirring process plays a decisive role in the uniformity and fineness of the final product. Therefore, ensuring the stability and accuracy of stirring has become a key issue that cannot be ignored in this field.

[0003] However, the current ice cream machines on the market still have obvious deficiencies in stirring control. Many devices lack real-time monitoring and dynamic adjustment capabilities for the position of the stirring cutter head, which can cause deviations in the distance between the cutter head and the container wall during long-term operation or production of different formulations, affecting the stirring effect and even causing equipment wear or unstable product quality.

[0004] In-depth analysis of this problem reveals that the core challenge lies in the coordination between accurate perception and dynamic adjustment of the cutter head position. First, due to the lack of high-precision monitoring means, small changes in the distance between the cutter head and the container wall are often difficult to capture in time, which directly leads to the inability to accurately determine whether the cutter head is in the best working state. This problem further causes the adjustment mechanism to be lagging, making it difficult for existing devices to quickly correct the cutter head position through automated means even if the distance is found to be abnormal, resulting in uneven stirring or decreased production efficiency. These two technical factors are interrelated, with the lack of perception limiting the timeliness of adjustment, and the lag in adjustment exacerbating the uncertainty in the production process, forming a technical problem that needs to be solved urgently. SUMMARY

[0005] The present application provides an ice cream machine cutter head monitoring method to solve the above technical problems.

[0006] The technical solution of the present application is as follows:

[0007] An ice cream machine cutter head monitoring method, comprising

[0008] The sensor array is arranged on the inner side of the ice cream machine container wall to obtain real-time distance data between the cutter head and the container wall, and each group of data is sampled and processed to obtain the initial distribution state of the cutter head position;

[0009] According to the initial distribution state, the distance data is filtered to remove noise interference and determine the stable distance value between the cutter head and the container wall;

[0010] By comparing the stable interval value with the preset threshold range, if it is detected that the interval value exceeds the threshold range, an abnormal signal is triggered, and it is judged that the position of the tool head deviates;

[0011] For the abnormal signal, the current tool head running parameter is obtained, the correction model established in advance is used to calculate the adjustment amount, and the correction instruction of the tool head position is obtained;

[0012] According to the correction instruction, the axial or radial position of the tool head is adjusted, the distance data between the tool head and the container wall is updated in real time, and the adjusted position state is determined.

[0013] Further, the initial distribution state of the tool head position comprises:

[0014] Through the sensor array, the real-time distance data between the tool head and the container wall is continuously collected inside the container wall, and the signal preprocessing technology is used to denoise the collected original data to obtain the filtered distance data set;

[0015] According to the filtered distance data set, time series segmentation is performed for each data set, the position change characteristics of the tool head in different time periods are obtained, and the preliminary distribution state of the tool head motion trajectory is determined.

[0016] Further, the initial distribution state of the tool head position further comprises:

[0017] If there is an abnormal point in the preliminary distribution state of the tool head motion trajectory, the abnormal data exceeding the threshold is removed through the preset threshold to obtain the corrected motion trajectory data;

[0018] According to the corrected motion trajectory data, the support vector machine model is used to classify and process the tool head position distribution, the contact area distribution characteristics of the tool head inside the container wall are obtained, and the main contact area range is determined;

[0019] Through the main contact area range, combined with the real-time distance data, the frequency and intensity of the contact between the tool head and the container wall are analyzed, and the dynamic contact mode of the tool head during running is obtained;

[0020] For the dynamic contact mode, combined with the position distribution analysis, the running stability index of the tool head in different regions is obtained, and it is determined whether the initial state of the tool head position distribution meets the expected running condition;

[0021] If the initial state of the tool head position distribution does not meet the expected running condition, the collection parameters of the sensor array are adjusted through historical data comparison, more accurate real-time distance data is obtained, and the optimized distribution state is determined.

[0022] Further, the stable interval value between the tool head and the container wall comprises:

[0023] The distance data in the initial distribution state is filtered to remove noise interference, and a stable distance value between the cutter head and the container wall is obtained.

[0024] Based on the stable spacing value, data analysis tools were used to extract the distribution characteristics of the cutter head position near the container wall, obtain the spacing fluctuation of the cutter head in different regions, and determine the preliminary results of the spacing analysis.

[0025] If the fluctuation exceeds the preset threshold based on the preliminary results of the spacing analysis, the distance data is processed a second time using data smoothing technology to obtain adjusted and stable spacing data.

[0026] Furthermore, determining the stable distance value between the cutter head and the container wall also includes:

[0027] Based on the adjusted stable spacing data and combined with the real-time collected cutter head position information, the degree of proximity between the cutter head and the container wall at different time periods is analyzed to determine the dynamic change characteristics of the position monitoring.

[0028] By analyzing the dynamic changes in position monitoring, a support vector machine model is used to classify the distribution patterns of the cutter head position, obtain the main activity area of ​​the cutter head near the container wall, and determine the characteristic values ​​of the area distribution.

[0029] Based on the characteristic values ​​of regional distribution, the long-term stability of the cutter head position is evaluated, and the stability index of the distance between the cutter head and the container wall is obtained.

[0030] Regarding the spacing stability index, if the index value deviates from the preset range, the distance data is optimized by adjusting the parameters collected in real time to obtain a more accurate distribution of the cutter head position.

[0031] Furthermore, the determination that the cutter head position is deviated includes:

[0032] By acquiring stable spacing data and comparing it with a preset threshold range, if the comparison result shows that the spacing value exceeds the threshold range, an abnormal signal is triggered, and a preliminary judgment of the tool head position offset is obtained.

[0033] Based on the triggering of abnormal signals, the tool head position data is collected in real time to obtain the specific distribution characteristics of the position offset and determine the detailed basis for offset judgment.

[0034] Furthermore, the determination of a deviation in the cutter head position also includes:

[0035] By analyzing the distribution characteristics of position offset, the dynamic changes in the tool head position are continuously monitored to obtain time-series data on position offset changes.

[0036] Based on the time-series variation data, a support vector machine model is used to classify the offset patterns of the tool head position, obtain the main categories of the offset distribution, and determine the classification results of the offset features.

[0037] Based on the classification results of the offset features, a secondary analysis is performed on the frequency and intensity of the abnormal signal to obtain the updated status of the abnormal signal.

[0038] Based on the update status of the abnormal signal, if the update status shows that the offset continues to exist, the data acquisition parameters are adjusted to obtain more accurate tool head position data and determine the final trend of the position offset.

[0039] By analyzing the final trend of positional offset and combining it with a preset threshold range, the stability of the tool head position is tracked over a long period to obtain comprehensive data for stability assessment.

[0040] Furthermore, the correction command for obtaining the cutter head position includes:

[0041] In response to abnormal signals, collect the tool head parameters and combine them with the operating status information to determine the current working status data of the tool head;

[0042] Based on the working status data, the parameter analysis method is used to extract features from the tool head parameters and obtain the key change trends in the parameters;

[0043] By analyzing key trends and combining them with a pre-built calibration model, adjustment calculations are performed to obtain preliminary adjustment values ​​for the tool head position.

[0044] If the initial adjustment value exceeds the preset threshold range, the abnormal signal is verified a second time to determine the reliability of the adjustment value.

[0045] Based on the results of the secondary verification and combined with the positional deviation information, a support vector machine model was used to optimize the adjustment value and determine the final correction amount.

[0046] Based on the final correction amount, a correction instruction for the tool head position is generated, and a specific plan for adjustment execution is obtained;

[0047] Based on the specific implementation plan, the position of the cutting head is dynamically updated to obtain the corrected operating status data.

[0048] Furthermore, determining the adjusted position state includes:

[0049] By using correction commands, axial or radial adjustments are performed on the tool head position, and real-time position data is obtained during the adjustment process.

[0050] Based on real-time location data, a data acquisition tool is used to synchronously process the distance information between the cutter head and the container wall to obtain the current distance change value.

[0051] If the change in spacing exceeds the preset threshold range, the data will be verified through the information processing stage to determine whether a position anomaly signal is triggered.

[0052] Based on the verified position anomaly signals, a support vector machine model is used to optimize the adjustment parameters and determine the final position adjustment scheme.

[0053] The position of the cutter head is dynamically updated based on the final position adjustment scheme, and the updated position status data is obtained.

[0054] Based on the updated location status data and the spacing information processed synchronously, the stability of the location status is verified using a data comparison tool to obtain the final status confirmation result.

[0055] Furthermore, this method also includes:

[0056] By adjusting the position, the distance between the cutter head and the container wall is continuously collected, and time series analysis is used to determine whether the distance change tends to stabilize.

[0057] For spacing changes that have not stabilized, the tool head operating parameters are reacquired, and combined with historical adjustment data, new correction commands are calculated to determine the secondary correction result of the tool head position.

[0058] Based on the secondary calibration results, update the automated drive parameters, obtain the final stable data of the blade position, and determine whether the stirring operation status meets expectations;

[0059] For the final stable data, record the correlation logs of the changes in the cutter head position and spacing, and save them to the database for reference in subsequent production batches.

[0060] Compared with the prior art, the present invention has the following advantages:

[0061] 1. This invention uses a sensor array arranged inside the container wall of an ice cream machine to acquire real-time distance data between the blade and the container wall. Signal preprocessing techniques are used to denoise the data. Simultaneously, the filtered data is segmented into time series to analyze the blade's positional changes over different time periods, determining the initial distribution of the blade's trajectory. Furthermore, abnormal data is removed through threshold filtering, and a support vector machine model is used to classify the blade's positional distribution, obtaining contact area distribution characteristics and dynamic contact patterns. This provides a more comprehensive understanding of the blade's operating status, offering accurate data for subsequent adjustments and solving the problem of traditional equipment's difficulty in timely capturing minute distance changes.

[0062] 2. By comparing the stable spacing value with a preset threshold range, an abnormal signal is immediately triggered once the spacing value exceeds the threshold range, indicating a deviation in the cutter head position. Subsequently, the current cutter head operating parameters are acquired, and the adjustment amount is calculated using a pre-established correction model to obtain the correction command for the cutter head position. Based on the correction command, the axial or radial position of the cutter head is adjusted, and the spacing data between the cutter head and the container wall is updated in real time to determine the adjusted position state. Throughout the monitoring and adjustment process, the cutter head position data is updated in real time, and the stability of the cutter head position is tracked and evaluated over a long period using data analysis tools and models to ensure that the cutter head is always in optimal working condition, thereby solving the problem of adjustment lag in existing equipment.

[0063] 3. This invention ensures the uniformity and fineness of the mixing process by precisely monitoring and dynamically adjusting the blade position, thereby improving the taste and quality of ice cream products. At the same time, it reduces problems such as uneven mixing or equipment wear caused by blade position deviation, thereby reducing equipment failure rate and maintenance costs, and improving production efficiency and equipment lifespan. Attached Figure Description

[0064] Figure 1 This is a flowchart of an ice cream machine blade monitoring method according to Example 1;

[0065] Figure 2 This is a flowchart of an ice cream machine cutter head monitoring method in Example 2. Detailed Implementation

[0066] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Example

[0067] like Figure 1 As shown, this embodiment provides a method for monitoring the blade of an ice cream machine, including...

[0068] By arranging a sensor array inside the container wall of the ice cream machine, real-time distance data between the blade and the container wall is obtained. Each set of data is sampled and processed to obtain the initial distribution state of the blade position.

[0069] Based on the initial distribution state, the distance data is filtered to remove noise interference and determine the stable distance value between the cutter head and the container wall.

[0070] By comparing the stable spacing value with a preset threshold range, if the spacing value is detected to exceed the threshold range, an abnormal signal is triggered to determine that there is a deviation in the position of the cutter head;

[0071] In response to abnormal signals, the current operating parameters of the tool head are obtained, and the adjustment amount is calculated using a pre-established correction model to obtain the correction command for the tool head position.

[0072] According to the correction instructions, adjust the axial or radial position of the cutter head, update the distance data between the cutter head and the container wall in real time, and determine the adjusted position status.

[0073] Furthermore, the initial distribution state of the obtained cutter head position includes:

[0074] The sensor array continuously collects real-time distance data between the cutter head and the container wall on the inside of the container wall. The raw data is denoised using signal preprocessing technology to obtain filtered distance data sets.

[0075] Based on the filtered distance data sets, time series segmentation is performed on each data set to obtain the position change characteristics of the cutter head in different time periods, and to determine the preliminary distribution state of the cutter head motion trajectory.

[0076] Furthermore, obtaining the initial distribution state of the cutter head position also includes:

[0077] If there are abnormal points in the initial distribution of the cutter head's motion trajectory, the abnormal data exceeding the preset threshold will be filtered out to obtain the corrected motion trajectory data.

[0078] Based on the corrected motion trajectory data, a support vector machine model is used to classify the distribution of the cutter head position, obtain the distribution characteristics of the contact area of ​​the cutter head on the inner side of the container wall, and determine the range of the main contact area.

[0079] By analyzing the main contact area range and combining real-time distance data, the frequency and intensity of the contact between the cutter head and the container wall are obtained, thus revealing the dynamic contact mode of the cutter head during operation.

[0080] For dynamic contact mode, combined with position distribution analysis, the operational stability index of the cutter head in different regions is obtained to determine whether the initial state of the cutter head position distribution meets the expected operating conditions;

[0081] If the initial state of the cutter head position distribution does not meet the expected operating conditions, the sensor array acquisition parameters are adjusted by comparing historical data to obtain more accurate real-time distance data and determine the optimized distribution state.

[0082] In one embodiment, the step of obtaining the initial distribution state of the cutter head position can be as follows:

[0083] A sensor array is arranged inside the container wall of an ice cream machine to obtain real-time distance data between the blade and the container wall. In the subsequent processing, 10 ultrasonic sensors are evenly distributed inside the container wall. Each sensor covers an angle range of about 36 degrees to ensure 360-degree full coverage. The distance data between the blade and the wall is collected in real time. The sampling frequency is set to 100 times per second to obtain the initial data stream. For example, in a certain sampling, the 10 sensors measured the distances as 5.2cm, 5.1cm, 4.9cm, 5.3cm, 5.0cm, 4.8cm, 5.4cm, 5.2cm, 4.7cm, and 5.1cm respectively.

[0084] Next, each set of data is sampled and processed using a sliding window averaging filter algorithm with a window size of 5 sampling points. The smoothed distance value of each sensor is calculated. For example, the 5 consecutive sampled values ​​of 5.2cm, 5.3cm, 5.1cm, 5.0cm, and 5.2cm of the first sensor are processed to obtain a smoothed value of (5.2+5.3+5.1+5.0+5.2) / 5=5.16cm, in order to reduce noise interference.

[0085] Subsequently, based on the smoothed data, the three-dimensional position of the cutter head was calculated using a triangulation algorithm combined with the sensor position coordinates (distributed on a circle with a radius of 10cm and the center of the container as the origin). Assuming that the smoothed distance data of 10 sensors at a certain moment were substituted into the algorithm, the position of the cutter head was obtained as (x=2.3cm, y=1.8cm, z=5.0cm).

[0086] Finally, multiple sets of position data (e.g., 1000 sets) are collected, and the initial distribution of the tool head position is analyzed by Gaussian distribution fitting. The mean and variance of the position distribution are calculated. For example, the mean is (x=2.5cm, y=2.0cm, z=5.1cm), and the variance is (0.2, 0.15, 0.1). This allows us to determine whether the tool head deviates from the center position. If the deviation exceeds a set threshold (e.g., 0.5cm), the tool head motor parameters are automatically adjusted to correct the position and ensure machining accuracy.

[0087] The above process forms a closed-loop logic, from data collection to processing, distribution analysis, and correction, relying on information technology to achieve automation and ensure the stability of ice cream processing.

[0088] Furthermore, determining the stable distance value between the cutter head and the container wall includes:

[0089] The distance data in the initial distribution state is filtered to remove noise interference, and a stable distance value between the cutter head and the container wall is obtained.

[0090] Based on the stable spacing value, data analysis tools were used to extract the distribution characteristics of the cutter head position near the container wall, obtain the spacing fluctuation of the cutter head in different regions, and determine the preliminary results of the spacing analysis.

[0091] If the fluctuation exceeds the preset threshold based on the preliminary results of the spacing analysis, the distance data is processed a second time using data smoothing technology to obtain adjusted and stable spacing data.

[0092] Furthermore, determining the stable distance value between the cutter head and the container wall also includes:

[0093] Based on the adjusted stable spacing data and combined with the real-time collected cutter head position information, the degree of proximity between the cutter head and the container wall at different time periods is analyzed to determine the dynamic change characteristics of the position monitoring.

[0094] By analyzing the dynamic changes in position monitoring, a support vector machine model is used to classify the distribution patterns of the cutter head position, obtain the main activity area of ​​the cutter head near the container wall, and determine the characteristic values ​​of the area distribution.

[0095] Based on the characteristic values ​​of regional distribution, the long-term stability of the cutter head position is evaluated, and the stability index of the distance between the cutter head and the container wall is obtained.

[0096] Regarding the spacing stability index, if the index value deviates from the preset range, the distance data is optimized by adjusting the parameters collected in real time to obtain a more accurate distribution of the cutter head position.

[0097] In one embodiment, the step of determining a stable distance value between the cutter head and the container wall can be described as follows:

[0098] In the process of acquiring the distance data between the blade and the wall inside the ice cream machine container, the initial distribution state is filtered to remove noise interference and determine a stable spacing value. The specific implementation method is to achieve automated processing through information technology.

[0099] First, distance data is acquired from eight infrared sensors on the inside of the container wall. Assuming that the distance values ​​collected in a certain instance are 6.1cm, 6.3cm, 5.9cm, 6.0cm, 6.2cm, 5.8cm, 6.4cm, and 5.7cm, and the sampling frequency is 50 times per second, an initial data stream is formed.

[0100] Next, the median filtering algorithm is used to process these data. Taking a sampling point with a window size of 3 as an example, the median value of 6.1cm, 6.2cm and 6.0cm of the first sensor are sorted and the median value of 6.1cm is taken as the filtering result. All sensor data are processed in turn to smooth the fluctuations.

[0101] Subsequently, a weighted average was calculated on the filtered data. Assuming that the weights of each sensor were set to 0.12, 0.13, 0.11, 0.12, 0.13, 0.10, 0.14, and 0.11 according to their installation position accuracy, the overall stable spacing value was calculated to be 6.05 cm.

[0102] To further analyze data reliability, standard deviation calculation is introduced. Assuming the standard deviation of a certain set of filtered data is 0.25cm, if it exceeds the preset threshold of 0.3cm, a data anomaly warning is triggered, and the system automatically switches to the backup sensor group to collect data to ensure continuity.

[0103] Finally, the stable spacing value is compared with historical data. Assuming the average spacing over the past 10 minutes is 6.08cm, the deviation of the current value of 6.05cm is 0.03cm, which is below the tolerance range of 0.1cm. This is considered normal, and the data is stored in the database for subsequent processing parameter optimization, forming a closed-loop logic from acquisition to filtering to stability analysis, ensuring the accuracy of the blade position data during ice cream processing.

[0104] Furthermore, the determination that the cutter head position is deviated includes:

[0105] By acquiring stable spacing data and comparing it with a preset threshold range, if the comparison result shows that the spacing value exceeds the threshold range, an abnormal signal is triggered, and a preliminary judgment of the tool head position offset is obtained.

[0106] Based on the triggering of abnormal signals, the tool head position data is collected in real time to obtain the specific distribution characteristics of the position offset and determine the detailed basis for offset judgment.

[0107] Furthermore, the determination of a deviation in the cutter head position also includes:

[0108] By analyzing the distribution characteristics of position offset, the dynamic changes in the tool head position are continuously monitored to obtain time-series data on position offset changes.

[0109] Based on the time-series variation data, a support vector machine model is used to classify the offset patterns of the tool head position, obtain the main categories of the offset distribution, and determine the classification results of the offset features.

[0110] Based on the classification results of the offset features, a secondary analysis is performed on the frequency and intensity of the abnormal signal to obtain the updated status of the abnormal signal.

[0111] Based on the update status of the abnormal signal, if the update status shows that the offset continues to exist, the data acquisition parameters are adjusted to obtain more accurate tool head position data and determine the final trend of the position offset.

[0112] By analyzing the final trend of positional offset and combining it with a preset threshold range, the stability of the tool head position is tracked over a long period to obtain comprehensive data for stability assessment.

[0113] In one embodiment, the step of determining that there is a deviation in the cutter head position can be described as follows:

[0114] In ice cream processing equipment, information technology is used to automatically monitor and detect anomalies in the distance data between the cutter head and the container wall to ensure the stability of the processing.

[0115] First, real-time spacing data is acquired from six high-precision laser sensors installed on the inner side of the container wall. Assuming that the spacing values ​​collected in a certain instance are 5.5cm, 5.7cm, 5.4cm, 5.6cm, 5.3cm, and 5.8cm, and the sampling frequency is 30 times per second, a continuous data stream is formed.

[0116] Next, these data are compared with the preset threshold range. Assuming the normal spacing threshold range is set to 5.2cm to 5.9cm, the algorithm calculates the average value of the current data to be 5.55cm, which is within the threshold range. However, one sensor data point of 5.3cm is close to the lower limit. This data point is automatically marked and further analysis is performed.

[0117] Subsequently, a deviation analysis algorithm was used to calculate the difference between each sensor data and the average value, and the deviation values ​​were 0.05cm, 0.15cm, 0.25cm, 0.05cm, 0.25cm and 0.25cm respectively. The deviation threshold was set to 0.3cm. It was found that the deviation of three data points was close to the threshold, and a potential risk warning was automatically generated and recorded in the log.

[0118] Meanwhile, based on historical trend analysis, assuming the average interval over the past 5 minutes is 5.6cm, the current average of 5.55cm has an offset of 0.05cm, which is below the allowable offset range of 0.2cm. Therefore, it is determined to be temporarily stable, but continuous monitoring is still required.

[0119] Finally, if the average spacing value exceeds the threshold range at a certain moment, for example, reaching 6.0cm, an abnormal signal will be triggered, automatically transmitting the abnormal information to the controller, adjusting the cutting head operating parameters in conjunction with the controller, and uploading the data to the cloud database for subsequent analysis, forming a complete closed loop from data acquisition to abnormal judgment to information feedback, ensuring the continuity and reliability of the processing process.

[0120] Furthermore, the correction command for obtaining the cutter head position includes:

[0121] In response to abnormal signals, collect the tool head parameters and combine them with the operating status information to determine the current working status data of the tool head;

[0122] Based on the working status data, the parameter analysis method is used to extract features from the tool head parameters and obtain the key change trends in the parameters;

[0123] By analyzing key trends and combining them with a pre-built calibration model, adjustment calculations are performed to obtain preliminary adjustment values ​​for the tool head position.

[0124] If the initial adjustment value exceeds the preset threshold range, the abnormal signal is verified a second time to determine the reliability of the adjustment value.

[0125] Based on the results of the secondary verification and combined with the positional deviation information, a support vector machine model was used to optimize the adjustment value and determine the final correction amount.

[0126] Based on the final correction amount, a correction instruction for the tool head position is generated, and a specific plan for adjustment execution is obtained;

[0127] Based on the specific implementation plan, the position of the cutting head is dynamically updated to obtain the corrected operating status data.

[0128] The process of establishing the pre-built correction model is as follows:

[0129] ① Data collection:

[0130] Historical operation data collection: Collect historical data of the ice cream machine under different operating conditions, including blade position, operating parameters (such as speed, torque, stirring time, etc.), actual distance data between the blade and the container wall, and corresponding adjustment records;

[0131] Data annotation: Annotate the collected historical data to clarify which data corresponds to the normal operating state and which data corresponds to the abnormal state of the cutter head position deviation;

[0132] ② Data preprocessing:

[0133] Data cleaning: removing noise and outliers from historical data to ensure data accuracy and reliability;

[0134] Feature extraction: Extract key features from historical data, such as the rate of change of the tool head position, speed fluctuation, and torque change. These features will be used as input variables for the model.

[0135] ③ Model building:

[0136] A calibration model was constructed using weighted regression analysis. By assigning different weights to each data point, the model's fitting accuracy to important data points was improved.

[0137] Each data point is assigned a weight based on its importance and reliability. For example, data points with larger distance deviations can be assigned higher weights because these data points are more likely to reflect abnormalities in the tool tip position.

[0138] ④ Model Training: The weighted regression model is trained using historical data, and the model parameters are optimized by minimizing the weighted sum of squared residuals.

[0139] ,in, It is the target value of the i-th data point (such as the tool head position adjustment amount). It is the feature vector of the i-th data point. These are model parameters. It is the weight of the i-th data point;

[0140] ⑤ Model Validation:

[0141] The model is validated using a subset of data not used in training to evaluate its accuracy and generalization ability. Methods such as cross-validation are used to ensure that the model performs consistently across different datasets.

[0142] In one embodiment, the step of obtaining the correction command for the cutter head position can be described as follows:

[0143] During the operation of ice cream processing equipment, when an abnormal signal is detected, the cutter head position correction process will be automatically initiated, and the process will be fully automated through information technology.

[0144] First, extract the current operating parameters. Assume that the obtained tool head speed is 800 rpm, the tilt angle is 3.2 degrees, and the axial offset is 0.4 cm.

[0145] Subsequently, these parameters are input into a pre-established calibration model, which is built based on historical operating data and machine learning algorithms. Using a weighted regression analysis method, the weight of the influence of rotational speed on position is calculated to be 0.6, the weight of the influence of angle is 0.3, and the weight of axial offset is 0.1. The adjustment amount is calculated by formula as follows: the rotational speed needs to be reduced by 50 rpm, the angle needs to be adjusted to 3.0 degrees, and the axial offset needs to be corrected to 0.2 cm.

[0146] Next, a correction instruction is generated based on the calculation results. The instruction contains specific adjustment parameters and is transmitted to the tool head drive controller through an internal communication protocol. The drive controller automatically parses the instruction and updates the operating parameters. At the same time, it records the parameter comparison data before and after the adjustment to the log. For example, the rotation speed is reduced from 800 rpm to 750 rpm, the angle is adjusted from 3.2 degrees to 3.0 degrees, and the offset is reduced from 0.4 cm to 0.2 cm.

[0147] To ensure the stability after adjustment, a real-time monitoring module will be invoked to collect the tool head position data at a frequency of 50 times per second within 10 seconds after adjustment. Assuming that the average position offset collected is 0.25cm, which is lower than the preset allowable offset threshold of 0.3cm, the correction is deemed effective. The relevant data of this correction process will be uploaded to the cloud analysis platform for subsequent model optimization, forming a complete automated logic chain from anomaly detection to parameter adjustment and effect verification.

[0148] If the offset value is still higher than the threshold after correction, a second correction process will be automatically triggered to ensure the accuracy of the processing.

[0149] Furthermore, determining the adjusted position state includes:

[0150] By using correction commands, axial or radial adjustments are performed on the tool head position, and real-time position data is obtained during the adjustment process.

[0151] Based on real-time location data, a data acquisition tool is used to synchronously process the distance information between the cutter head and the container wall to obtain the current distance change value.

[0152] If the change in spacing exceeds the preset threshold range, the data will be verified through the information processing stage to determine whether a position anomaly signal is triggered.

[0153] Based on the verified position anomaly signals, a support vector machine model is used to optimize the adjustment parameters and determine the final position adjustment scheme.

[0154] The position of the cutter head is dynamically updated based on the final position adjustment scheme, and the updated position status data is obtained.

[0155] Based on the updated location status data and the spacing information processed synchronously, the stability of the location status is verified using a data comparison tool to obtain the final status confirmation result.

[0156] In one embodiment, the step of determining the adjusted position state may be as follows:

[0157] During the automated control process of ice cream processing equipment, after receiving a correction command, the automated controller is driven to precisely adjust the position of the cutter head and update relevant data in real time to ensure processing accuracy.

[0158] First, the correction command is parsed into specific control signals. Assuming the command requires the axial position of the cutter head to be adjusted to 2.5cm from the container wall and the radial position to be adjusted to 0.8cm off the center line, the controller calculates the number of step pulses required for axial movement as 1200 steps and the number of radial adjustment pulses as 800 steps through the built-in servo motor drive algorithm. The algorithm uses the proportional-integral-derivative control method to ensure the smoothness of the movement process and control the error within 0.05cm.

[0159] Subsequently, during the adjustment process, a high-precision laser sensor was used to collect the distance data between the cutter head and the container wall at a frequency of 100 times per second. Assuming the initial distance was 3.0cm, it stabilized at 2.5cm after adjustment, and the radial deviation decreased from 1.0cm to 0.8cm. This data was stored in the local database in real time, and a smooth curve of the distance change was calculated using a time series analysis algorithm to determine whether there were any abnormal fluctuations during the adjustment process. If the fluctuation value was less than the preset threshold of 0.1cm, the adjustment process was considered to be stable.

[0160] Finally, based on the collected final position data and the preset target range (axial 2.4-2.6cm, radial 0.7-0.9cm), the cutter head position status is automatically analyzed to confirm that the adjusted position meets the requirements. At the same time, the status data is transmitted to the central control through the internal communication interface to form a linkage logic with other processing parameters such as stirring speed, ensuring the coordination of subsequent processing steps. The entire process is driven by information technology and requires no manual intervention. Example

[0161] like Figure 2 As shown, this embodiment provides a method for monitoring the blade of an ice cream machine, including...

[0162] By arranging a sensor array inside the container wall of the ice cream machine, real-time distance data between the blade and the container wall is obtained. Each set of data is sampled and processed to obtain the initial distribution state of the blade position.

[0163] Based on the initial distribution state, the distance data is filtered to remove noise interference and determine the stable distance value between the cutter head and the container wall.

[0164] By comparing the stable spacing value with a preset threshold range, if the spacing value is detected to exceed the threshold range, an abnormal signal is triggered to determine that there is a deviation in the position of the cutter head;

[0165] In response to abnormal signals, the current operating parameters of the tool head are obtained, and the adjustment amount is calculated using a pre-established correction model to obtain the correction command for the tool head position.

[0166] According to the correction instructions, adjust the axial or radial position of the cutter head, update the distance data between the cutter head and the container wall in real time, and determine the adjusted position status.

[0167] Furthermore, the initial distribution state of the obtained cutter head position includes:

[0168] The sensor array continuously collects real-time distance data between the cutter head and the container wall on the inside of the container wall. The raw data is denoised using signal preprocessing technology to obtain filtered distance data sets.

[0169] Based on the filtered distance data sets, time series segmentation is performed on each data set to obtain the position change characteristics of the cutter head in different time periods, and to determine the preliminary distribution state of the cutter head motion trajectory.

[0170] Furthermore, obtaining the initial distribution state of the cutter head position also includes:

[0171] If there are abnormal points in the initial distribution of the cutter head's motion trajectory, the abnormal data exceeding the preset threshold will be filtered out to obtain the corrected motion trajectory data.

[0172] Based on the corrected motion trajectory data, a support vector machine model is used to classify the distribution of the cutter head position, obtain the distribution characteristics of the contact area of ​​the cutter head on the inner side of the container wall, and determine the range of the main contact area.

[0173] By analyzing the main contact area range and combining real-time distance data, the frequency and intensity of the contact between the cutter head and the container wall are obtained, thus revealing the dynamic contact mode of the cutter head during operation.

[0174] For dynamic contact mode, combined with position distribution analysis, the operational stability index of the cutter head in different regions is obtained to determine whether the initial state of the cutter head position distribution meets the expected operating conditions;

[0175] If the initial state of the cutter head position distribution does not meet the expected operating conditions, the sensor array acquisition parameters are adjusted by comparing historical data to obtain more accurate real-time distance data and determine the optimized distribution state.

[0176] Furthermore, determining the stable distance value between the cutter head and the container wall includes:

[0177] The distance data in the initial distribution state is filtered to remove noise interference, and a stable distance value between the cutter head and the container wall is obtained.

[0178] Based on the stable spacing value, data analysis tools were used to extract the distribution characteristics of the cutter head position near the container wall, obtain the spacing fluctuation of the cutter head in different regions, and determine the preliminary results of the spacing analysis.

[0179] If the fluctuation exceeds the preset threshold based on the preliminary results of the spacing analysis, the distance data is processed a second time using data smoothing technology to obtain adjusted and stable spacing data.

[0180] Furthermore, determining the stable distance value between the cutter head and the container wall also includes:

[0181] Based on the adjusted stable spacing data and combined with the real-time collected cutter head position information, the degree of proximity between the cutter head and the container wall at different time periods is analyzed to determine the dynamic change characteristics of the position monitoring.

[0182] By analyzing the dynamic changes in position monitoring, a support vector machine model is used to classify the distribution patterns of the cutter head position, obtain the main activity area of ​​the cutter head near the container wall, and determine the characteristic values ​​of the area distribution.

[0183] Based on the characteristic values ​​of regional distribution, the long-term stability of the cutter head position is evaluated, and the stability index of the distance between the cutter head and the container wall is obtained.

[0184] Regarding the spacing stability index, if the index value deviates from the preset range, the distance data is optimized by adjusting the parameters collected in real time to obtain a more accurate distribution of the cutter head position.

[0185] Furthermore, the determination that the cutter head position is deviated includes:

[0186] By acquiring stable spacing data and comparing it with a preset threshold range, if the comparison result shows that the spacing value exceeds the threshold range, an abnormal signal is triggered, and a preliminary judgment of the tool head position offset is obtained.

[0187] Based on the triggering of abnormal signals, the tool head position data is collected in real time to obtain the specific distribution characteristics of the position offset and determine the detailed basis for offset judgment.

[0188] Furthermore, the determination of a deviation in the cutter head position also includes:

[0189] By analyzing the distribution characteristics of position offset, the dynamic changes in the tool head position are continuously monitored to obtain time-series data on position offset changes.

[0190] Based on the time-series variation data, a support vector machine model is used to classify the offset patterns of the tool head position, obtain the main categories of the offset distribution, and determine the classification results of the offset features.

[0191] Based on the classification results of the offset features, a secondary analysis is performed on the frequency and intensity of the abnormal signal to obtain the updated status of the abnormal signal.

[0192] Based on the update status of the abnormal signal, if the update status shows that the offset continues to exist, the data acquisition parameters are adjusted to obtain more accurate tool head position data and determine the final trend of the position offset.

[0193] By analyzing the final trend of positional offset and combining it with a preset threshold range, the stability of the tool head position is tracked over a long period to obtain comprehensive data for stability assessment.

[0194] Furthermore, the correction command for obtaining the cutter head position includes:

[0195] In response to abnormal signals, collect the tool head parameters and combine them with the operating status information to determine the current working status data of the tool head;

[0196] Based on the working status data, the parameter analysis method is used to extract features from the tool head parameters and obtain the key change trends in the parameters;

[0197] By analyzing key trends and combining them with a pre-built calibration model, adjustment calculations are performed to obtain preliminary adjustment values ​​for the tool head position.

[0198] If the initial adjustment value exceeds the preset threshold range, the abnormal signal is verified a second time to determine the reliability of the adjustment value.

[0199] Based on the results of the secondary verification and combined with the positional deviation information, a support vector machine model was used to optimize the adjustment value and determine the final correction amount.

[0200] Based on the final correction amount, a correction instruction for the tool head position is generated, and a specific plan for adjustment execution is obtained;

[0201] Based on the specific implementation plan, the position of the cutting head is dynamically updated to obtain the corrected operating status data.

[0202] Furthermore, determining the adjusted position state includes:

[0203] By using correction commands, axial or radial adjustments are performed on the tool head position, and real-time position data is obtained during the adjustment process.

[0204] Based on real-time location data, a data acquisition tool is used to synchronously process the distance information between the cutter head and the container wall to obtain the current distance change value.

[0205] If the change in spacing exceeds the preset threshold range, the data will be verified through the information processing stage to determine whether a position anomaly signal is triggered.

[0206] Based on the verified position anomaly signals, a support vector machine model is used to optimize the adjustment parameters and determine the final position adjustment scheme.

[0207] The position of the cutter head is dynamically updated based on the final position adjustment scheme, and the updated position status data is obtained.

[0208] Based on the updated location status data and the spacing information processed synchronously, the stability of the location status is verified using a data comparison tool to obtain the final status confirmation result.

[0209] Furthermore, this method also includes:

[0210] By adjusting the position, the distance between the cutter head and the container wall is continuously collected, and time series analysis is used to determine whether the distance change tends to stabilize.

[0211] For spacing changes that have not stabilized, the tool head operating parameters are reacquired, and combined with historical adjustment data, new correction commands are calculated to determine the secondary correction result of the tool head position.

[0212] Based on the secondary calibration results, update the automated drive parameters, obtain the final stable data of the blade position, and determine whether the stirring operation status meets expectations;

[0213] For the final stable data, record the correlation logs of the changes in the cutter head position and spacing, and save them to the database for reference in subsequent production batches.

[0214] Furthermore, determining whether the spacing change tends to stabilize includes:

[0215] By adjusting the position, the distance change data between the cutter head and the container wall is continuously acquired. The data change data is recorded in real time using a data acquisition tool to obtain a preliminary distance change dataset.

[0216] For the preliminary spacing change dataset, time series analysis was used to process the change trend, extract trend feature information, and determine the fluctuation pattern of spacing change;

[0217] Based on the fluctuation pattern of the spacing change, the comparison results of the fluctuation characteristics and the preset threshold are obtained. If the fluctuation characteristics exceed the preset threshold range, the data is verified through the information processing stage to determine whether the fluctuation belongs to an abnormal state.

[0218] Based on the judgment result of whether the fluctuation belongs to an abnormal state, the classification information of the abnormal state is obtained, and the classification information is filtered by data comparison tools to obtain the key time points of the abnormal fluctuation.

[0219] Based on the key time points of abnormal fluctuations, the distance data between the corresponding cutter head position and the container wall is obtained. The position state is optimized and adjusted using a support vector machine model to determine the adjusted position parameters.

[0220] By adjusting the position parameters, the drive controller dynamically updates the position of the cutter head, obtains the updated position status data, and determines whether the position status meets the stability standard.

[0221] Based on the judgment result of whether the location status meets the stability standard, if the preset standard is not met, a new adjustment instruction is generated through the information processing stage, and the data acquisition process is restarted.

[0222] In one embodiment, the step of determining whether the spacing change tends to stabilize can be described as follows:

[0223] By adjusting the position of the cutting head, the distance between the cutting head and the container wall is continuously monitored to ensure stability during the processing.

[0224] First, a high-precision ultrasonic sensor is used to collect spacing data at a frequency of 50 times per second. Assuming the initial adjusted spacing is 2.2cm, the target stability range is 2.1 to 2.3cm.

[0225] Next, a time series analysis method was used, specifically a moving average algorithm was used to smooth 250 data points in the past 5 seconds, and the trend curve of the spacing change was calculated. Assuming that the spacing fluctuation value was detected at 0.08cm at the 3rd second, and then the fluctuation value dropped to 0.03cm at the 5th second, the spacing change was judged to be stable by comparing the fluctuation value with the preset threshold of 0.05cm.

[0226] Meanwhile, the autoregressive model was used to predict the spacing change in the next 2 seconds. The prediction results showed that the spacing would stabilize at 2.15cm, which is within the target range.

[0227] To further ensure stability, the analysis results were correlated with the vibration frequency data of the processing equipment. If the vibration frequency was lower than the predetermined value of 0.2 Hz, it was considered that the environmental conditions had no significant interference with the spacing.

[0228] All data and analysis processes are stored in a cloud database. If the spacing is stable and the temperature exceeds the preset range of 35-40 degrees Celsius, the cooling mechanism is automatically triggered to ensure the overall stability of the processing environment. The entire process is completed automatically.

[0229] Furthermore, the secondary correction result for determining the cutter head position includes:

[0230] Through the data processing stage, relevant information on the current spacing change is extracted from the tool head operating parameters. The extracted data is then preliminarily organized using an information comparison tool to obtain a set of operating parameters in an unstable state.

[0231] Based on the set of operating parameters in the unstable state, combined with historical adjustment records, data analysis tools are used to compare the correlation between the two to determine the matching characteristics between the operating parameters and historical adjustments.

[0232] If the matching features meet the preset threshold range, a preliminary correction instruction is generated through the information processing stage to obtain the adjustment basis data for the cutter head position;

[0233] If the matching features exceed the preset threshold range, the set of operating parameters will be checked a second time through the information processing stage to determine whether there are abnormal fluctuations and obtain the checked parameter features.

[0234] Based on the verified parameter characteristics, a support vector machine model is used to optimize the tool head position and determine the correction command parameters required for secondary correction.

[0235] By correcting the command parameters, the drive controller dynamically adjusts the position of the cutter head and obtains the adjusted position correction data;

[0236] Based on the adjusted position correction data, the data is compared with the preset standard through the information processing stage to determine whether a stable state has been reached and to determine the final correction result.

[0237] In one embodiment, the step of determining the secondary correction result of the cutter head position can be as follows:

[0238] In the intelligent control of ice cream processing equipment, when the distance between the cutter head and the container wall is detected to be unstable, a series of processing procedures will be automatically initiated to optimize the position of the cutter head.

[0239] First, reacquire the real-time operating data of the cutting head, for example, the current rotation speed is 800 revolutions per minute and the cutting head deflection angle is 0.5 degrees;

[0240] Subsequently, the historical adjustment records stored in the local database are retrieved, the average offset of the past 10 adjustments is extracted as 0.3cm, and combined with the current parameters, a weighted linear regression algorithm is used to calculate the new correction instruction, resulting in the instruction value that the cutter head needs to be adjusted inward by 0.2cm.

[0241] Next, the instruction is sent to the cutter head drive controller via the internal control protocol. The drive controller automatically completes the position correction according to the instruction, and the corrected spacing data is fed back in real time. Assuming that the adjusted spacing is 2.4cm, it is consistent with the target range of 2.0 to 2.5cm.

[0242] To ensure the reliability of the correction results, the parameter change trends before and after correction were further analyzed, and the calculated deviation angle fluctuation decreased from 0.5 degrees to 0.1 degrees, confirming the effectiveness of the correction.

[0243] At the same time, the calibration data is correlated with the equipment's operating load data. If the load value is lower than the preset threshold of 500 watts, it is determined that the calibration process has not caused any additional burden on the overall operation of the equipment. All analysis results are automatically uploaded to the cloud for backup, forming a complete data chain to ensure that subsequent adjustments are based on evidence. The entire process is completed autonomously.

[0244] Furthermore, determining whether the stirring operation meets expectations includes:

[0245] Based on the secondary calibration results, the drive parameters in the controller are updated, and the preliminary data after parameter adjustment is extracted through the information processing stage to obtain the updated record of the cutter head position.

[0246] For the update records of the cutter head position, an information comparison tool is used to match and analyze the data with a preset standard to obtain the deviation characteristics between the position data and the standard, and to determine whether the deviation is within an acceptable range.

[0247] If the deviation characteristics exceed the preset standard range, the location data will be deeply analyzed through the information processing stage to extract potential abnormal fluctuation information and obtain the distribution characteristics of abnormal fluctuations.

[0248] Based on the distribution characteristics of abnormal fluctuations, the tool head position is optimized and adjusted using a support vector machine model to generate correction instructions for the drive parameters and obtain the adjusted parameter set.

[0249] By adjusting the set of parameters, the drive controller dynamically adjusts the operating status of the stirring, extracts the adjusted status information, and determines whether it meets the preset standard.

[0250] If the adjusted status information still does not meet the preset standard, the operating status will be continuously monitored through the information processing stage to obtain trend data of status changes and determine the direction of further adjustments.

[0251] Based on the trend data of state changes and combined with historical operation records, an optimization plan for stirring is generated through information comparison tools to obtain the final stable operating parameters.

[0252] In one embodiment, the step of determining whether the stirring operation meets expectations can be described as follows:

[0253] In the automated control of ice cream processing equipment, the processing flow based on the secondary correction results is fully automated through information technology to ensure the final stability of the cutter head position and the normal operation of the mixing.

[0254] First, the tool head position data after secondary calibration, such as a spacing value of 2.6cm, is compared with the preset target value of 2.5cm through the built-in dynamic parameter adjustment algorithm. The offset that needs to be fine-tuned is calculated to be 0.1cm, and the position control coefficient in the drive parameters is automatically updated from the original 1.2 to 1.25 to optimize the subsequent running accuracy. The updated parameters are stored in the local cache in real time.

[0255] Subsequently, the final stable data of the cutter head position was collected through a high-precision sensor network. Assuming that the real-time spacing was 2.51 cm and the cutter head vibration frequency was recorded as 3.5 times per second, the position fluctuation over the past 5 minutes was evaluated using a time series analysis algorithm. The standard deviation of the fluctuation was found to be 0.02 cm, which is less than the preset threshold of 0.05 cm, indicating that the position has reached a stable state.

[0256] To further determine whether the stirring operation meets expectations, the collected blade speed data, such as 750 revolutions per minute, is matched with the standard range of 700-780 revolutions per minute. At the same time, the stirring resistance value is analyzed. Assuming the current resistance is 320 Newtons, which is lower than the upper limit of 400 Newtons, the running stability index is calculated to be 92% through a comprehensive evaluation algorithm, which is higher than the expected threshold of 85%, confirming that the stirring operation is normal.

[0257] The current motor temperature was detected to be 45.3 degrees Celsius, which is below the safe value of 50 degrees Celsius. An operating status report was automatically generated and uploaded to the cloud log to ensure that subsequent maintenance has data support. The entire process was completed autonomously.

[0258] Furthermore, the associated log recording the changes in the cutter head position and spacing includes:

[0259] The records of changes in the position and spacing of the cutter head are organized to obtain a preliminary set of position information;

[0260] Based on the initially compiled set of position information, an information comparison tool was used to extract features from the correlation information between the position and spacing of the cutter head, and to determine the variation pattern between the two.

[0261] Based on the characteristics of the changing patterns, in-depth analysis is performed through information processing. If the analyzed feature values ​​deviate from the preset threshold, the location information set is then corrected a second time to obtain the corrected location data set.

[0262] Based on the corrected position data set, the correlation information between the tool head position and spacing change is classified using a support vector machine model to obtain the data category distribution after classification.

[0263] Based on the distribution of the categorized data, the stability of each data category is verified through information processing. If the verification result shows that the fluctuation of a certain data category exceeds the preset range, the data category is marked to obtain a subset of marked abnormal data.

[0264] For the labeled subset of abnormal data, obtain the difference features between the subset of abnormal data and the historical records;

[0265] Based on the differences, an adjustment strategy for the abnormal data subset is generated through information processing, the adjusted data storage scheme is determined, and it is saved to the database for reference in production batches.

[0266] Specifically, in the automated control of ice cream processing equipment, for the processing of the final stable data, information technology is used to realize the correlation log recording of the changes in the position and spacing of the cutter head, and save it to the database for reference in subsequent production batches;

[0267] First, the high-precision sensor array is used to collect the cutter head position data in real time. Assuming the current stable position is 3.2cm, compared with 3.25cm in the previous cycle, the spacing change value is 0.05cm. The built-in linear regression algorithm is used to analyze the spacing change trend in the past 10 minutes and calculate the average change rate as 0.03cm / minute, which is lower than the preset warning value of 0.1cm / minute, indicating that the position adjustment is within the controllable range.

[0268] Next, the location data is associated with the spacing change value to form a log record with a timestamp of 2023-10-15 14:30:00, which includes specific values ​​and change rate analysis results. Distributed storage technology is used to ensure data redundancy backup and prevent loss.

[0269] At the same time, the batch association algorithm is automatically invoked to bind the current log with the production batch number (such as Batch_20231015_001) and generate a unique identifier code for easy traceability later.

[0270] To form a complete logical chain, the current raw material viscosity data was collected at 280 Pa·s, and its potential impact on the change of the cutter head position was analyzed. The correlation coefficient was found to be 0.15, which is lower than the impact threshold of 0.3, confirming that the raw material factor did not interfere with the position stability.

[0271] Ultimately, the database automatically updates its indexes to ensure that log data can be queried within one second. All processes are completed autonomously, providing data support for subsequent production optimization.

[0272] The specific embodiments of the invention have been described in detail above, but these are merely examples. The invention is not limited to the specific embodiments described above. Those skilled in the art should understand that the embodiments and descriptions in the specification are only illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the blade of an ice cream machine, characterized in that, include: By arranging a sensor array inside the container wall of the ice cream machine, real-time distance data between the blade and the container wall is obtained. Each set of data is sampled and processed to obtain the initial distribution state of the blade position. Based on the initial distribution state, the distance data is filtered to remove noise interference and determine the stable distance value between the cutter head and the container wall. By comparing the stable spacing value with a preset threshold range, if the spacing value is detected to exceed the threshold range, an abnormal signal is triggered to determine that there is a deviation in the position of the cutter head; In response to abnormal signals, the current operating parameters of the tool head are obtained, and the adjustment amount is calculated using a pre-established correction model to obtain the correction command for the tool head position. According to the correction instructions, adjust the axial or radial position of the cutter head, update the distance data between the cutter head and the container wall in real time, and determine the adjusted position status.

2. The method for monitoring the blade of an ice cream machine according to claim 1, characterized in that, The initial distribution state of the obtained cutter head position includes: The sensor array continuously collects real-time distance data between the cutter head and the container wall on the inside of the container wall. The raw data is denoised using signal preprocessing technology to obtain filtered distance data sets. Based on the filtered distance data sets, time series segmentation is performed on each data set to obtain the position change characteristics of the cutter head in different time periods, and to determine the preliminary distribution state of the cutter head motion trajectory.

3. The method for monitoring the blade of an ice cream machine according to claim 2, characterized in that, The initial distribution state of the cutter head position also includes: If there are abnormal points in the initial distribution of the cutter head's motion trajectory, the abnormal data exceeding the preset threshold will be filtered out to obtain the corrected motion trajectory data. Based on the corrected motion trajectory data, a support vector machine model is used to classify the distribution of the cutter head position, obtain the distribution characteristics of the contact area of ​​the cutter head on the inner side of the container wall, and determine the range of the main contact area. By analyzing the main contact area range and combining real-time distance data, the frequency and intensity of the contact between the cutter head and the container wall are obtained, thus revealing the dynamic contact mode of the cutter head during operation. For dynamic contact mode, combined with position distribution analysis, the operational stability index of the cutter head in different regions is obtained to determine whether the initial state of the cutter head position distribution meets the expected operating conditions; If the initial state of the cutter head position distribution does not meet the expected operating conditions, the sensor array acquisition parameters are adjusted by comparing historical data to obtain more accurate real-time distance data and determine the optimized distribution state.

4. The method for monitoring the blade of an ice cream machine according to claim 1, characterized in that, The determination of the stable distance value between the cutter head and the container wall includes: The distance data in the initial distribution state is filtered to remove noise interference, and a stable distance value between the cutter head and the container wall is obtained. Based on the stable spacing value, data analysis tools were used to extract the distribution characteristics of the cutter head position near the container wall, obtain the spacing fluctuation of the cutter head in different regions, and determine the preliminary results of the spacing analysis. If the fluctuation exceeds the preset threshold based on the preliminary results of the spacing analysis, the distance data is processed a second time using data smoothing technology to obtain adjusted and stable spacing data.

5. The method for monitoring the blade of an ice cream machine according to claim 4, characterized in that, The determination of the stable distance value between the cutter head and the container wall also includes: Based on the adjusted stable spacing data and combined with the real-time collected cutter head position information, the degree of proximity between the cutter head and the container wall at different time periods is analyzed to determine the dynamic change characteristics of the position monitoring. By analyzing the dynamic changes in position monitoring, a support vector machine model is used to classify the distribution patterns of the cutter head position, obtain the main activity area of ​​the cutter head near the container wall, and determine the characteristic values ​​of the area distribution. Based on the characteristic values ​​of regional distribution, the long-term stability of the cutter head position is evaluated, and the stability index of the distance between the cutter head and the container wall is obtained. Regarding the spacing stability index, if the index value deviates from the preset range, the distance data is optimized by adjusting the parameters collected in real time to obtain a more accurate distribution of the cutter head position.

6. The method for monitoring the blade of an ice cream machine according to claim 1, characterized in that, The determination that the cutter head position is deviated includes: By acquiring stable spacing data and comparing it with a preset threshold range, if the comparison result shows that the spacing value exceeds the threshold range, an abnormal signal is triggered, and a preliminary judgment of the tool head position offset is obtained. Based on the triggering of abnormal signals, the tool head position data is collected in real time to obtain the specific distribution characteristics of the position offset and determine the detailed basis for offset judgment.

7. The method for monitoring the blade of an ice cream machine according to claim 6, characterized in that, The determination of a deviation in the cutter head position also includes: By analyzing the distribution characteristics of position offset, the dynamic changes in the tool head position are continuously monitored to obtain time-series data on position offset changes. Based on the time-series variation data, a support vector machine model is used to classify the offset patterns of the tool head position, obtain the main categories of the offset distribution, and determine the classification results of the offset features. Based on the classification results of the offset features, a secondary analysis is performed on the frequency and intensity of the abnormal signal to obtain the updated status of the abnormal signal. Based on the update status of the abnormal signal, if the update status shows that the offset continues to exist, the data acquisition parameters are adjusted to obtain more accurate tool head position data and determine the final trend of the position offset. By analyzing the final trend of positional offset and combining it with a preset threshold range, the stability of the tool head position is tracked over a long period to obtain comprehensive data for stability assessment.

8. The method for monitoring the blade of an ice cream machine according to claim 1, characterized in that, The correction instructions for obtaining the cutter head position include: In response to abnormal signals, collect the tool head parameters and combine them with the operating status information to determine the current working status data of the tool head; Based on the working status data, the parameter analysis method is used to extract features from the tool head parameters and obtain the key change trends in the parameters; By analyzing key trends and combining them with a pre-built calibration model, adjustment calculations are performed to obtain preliminary adjustment values ​​for the tool head position. If the initial adjustment value exceeds the preset threshold range, the abnormal signal is verified a second time to determine the reliability of the adjustment value. Based on the results of the secondary verification and combined with the positional deviation information, a support vector machine model was used to optimize the adjustment value and determine the final correction amount. Based on the final correction amount, a correction instruction for the tool head position is generated, and a specific plan for adjustment execution is obtained; Based on the specific implementation plan, the position of the cutting head is dynamically updated to obtain the corrected operating status data.

9. The method for monitoring the blade of an ice cream machine according to claim 1, characterized in that, The determination of the adjusted position state includes: By using correction commands, axial or radial adjustments are performed on the tool head position, and real-time position data is obtained during the adjustment process. Based on real-time location data, a data acquisition tool is used to synchronously process the distance information between the cutter head and the container wall to obtain the current distance change value. If the change in spacing exceeds the preset threshold range, the data will be verified through the information processing stage to determine whether a position anomaly signal is triggered. Based on the verified position anomaly signals, a support vector machine model is used to optimize the adjustment parameters and determine the final position adjustment scheme. The position of the cutter head is dynamically updated based on the final position adjustment scheme, and the updated position status data is obtained. Based on the updated location status data and the spacing information processed synchronously, the stability of the location status is verified using a data comparison tool to obtain the final status confirmation result.

10. A method for monitoring the blade of an ice cream machine according to claim 1, characterized in that, Also includes: By adjusting the position, the distance between the cutter head and the container wall is continuously collected, and time series analysis is used to determine whether the distance change tends to stabilize. For spacing changes that have not stabilized, the tool head operating parameters are reacquired, and combined with historical adjustment data, new correction commands are calculated to determine the secondary correction result of the tool head position. Based on the secondary calibration results, update the automated drive parameters, obtain the final stable data of the blade position, and determine whether the stirring operation status meets expectations; For the final stable data, record the correlation logs of the changes in the cutter head position and spacing, and save them to the database for reference in subsequent production batches.

Images