A hard candy cooling machine optimization method based on roller frequency conversion speed control

Abstract

The present application relates to a kind of hard candy cooling machine optimization method based on roller frequency conversion speed control. Among them, the method includes: obtaining multidimensional cooling condition data, by cooling stage division and unique identification, obtain segmented cooling condition data;The corresponding data is input into the speed-cooling effect mapping model, and the mapping relationship of the roller speed hard candy cooling rate is obtained;The speed regulation interval of the corresponding stage is divided, and the linkage cam swing conveying collaborative mechanism is used to dynamically match the swing amplitude;By optimizing the corresponding model, the roller frequency conversion speed control strategy is obtained;The characteristic state of current hard candy is identified, and the control instruction of corresponding stage is output;The feedback data of roller speed is compared with control instruction threshold, and the cooling air volume is dynamically adjusted, and the cooling process data is output;Fine-tune swing amplitude and frequency, and obtain cooling execution result;Through deviation analysis, the hard candy cooling machine optimization method of self-iterative frequency conversion control is realized.

Description

Technical Field

[0001] This invention belongs to the field of food processing machinery and intelligent control technology, specifically relating to an optimization method for a hard candy cooler based on roller frequency conversion speed regulation control. Background Technology

[0002] Currently, hard candy cooling machines still have the following areas for improvement in actual production applications: In automated continuous production lines for hard candy, cooling and shaping are core processes that ensure product appearance, taste, and shelf life. The operational control level of the cooling machine directly determines production stability and product qualification rate. As food processing upgrades towards high speed, large scale, and intelligence, the control methods of traditional hard candy cooling machines are no longer suitable for the differentiated heat transfer patterns and shaping requirements of the candy blanks in the three stages of pre-cooling, homogenization, and shaping, becoming a key bottleneck restricting capacity improvement and quality enhancement.

[0003] Currently, most mainstream hard candy coolers adopt a fixed-frequency drive and fixed-speed roller operation mode, without establishing a quantitative mapping relationship between roller speed and cooling rate. This makes it impossible to dynamically adjust the speed based on factors such as feed output, initial temperature of the candy blank, and ambient temperature and humidity. This crude control easily leads to uneven cooling of the candy blank: excessively rapid cooling during the pre-cooling stage can cause surface cracks; insufficient heat exchange during the homogenization stage can cause internal softening; and unreasonable dwell time during the shaping stage can cause defects such as deformation and stickiness, seriously affecting product consistency.

[0004] The three main actuators of the cooling machine—airflow control, roller speed, and cam-driven oscillating conveyor—are independent of each other and lack a coordinated control mechanism. Airflow is mostly adjusted manually and cannot be matched in real time with changes in cooling stage and speed. The amplitude and frequency of the cam-driven oscillating mechanism are fixed and cannot be adaptively adjusted with the roller linear speed, which easily causes jamming, stacking, and deviation in the conveying of hard candies. This not only reduces production smoothness but also exacerbates uneven cooling and wastes electrical and cooling energy.

[0005] Existing equipment lacks multi-dimensional operating condition data acquisition, segmented cooling identification, and self-iterative optimization capabilities. It cannot collect key parameters such as roller speed, motor power, sugar blank temperature, cooling airflow, and feed rate in real time for time-series analysis. Cooling stage division relies on experience, lacking precise identification and feature extraction methods, and control parameters cannot be corrected in a closed-loop manner based on actual cooling effects. After long-term operation, the decrease in control accuracy due to component wear and operating condition drift is difficult to correct autonomously, requiring frequent shutdowns for debugging. The overall equipment efficiency is low, which is seriously inconsistent with the development trend of intelligent control, energy saving, and improved efficiency in modern food equipment.

[0006] To address the aforementioned issues, the industry urgently needs a control method for hard candy coolers based on roller frequency conversion speed regulation, multi-unit collaboration, segmented precise control, and self-iterative optimization. This method aims to solve the technical pain points of traditional equipment, such as uneven cooling, poor coordination, low level of intelligence, and high energy consumption, and to meet the needs of high-end candy industrial production. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides an optimization method for a hard candy cooler based on roller frequency conversion speed regulation control; The objective of this invention can be achieved through the following technical solutions: S1: Obtain multi-dimensional cooling condition data, divide the multi-dimensional cooling condition data into cooling stages and assign unique identifiers to obtain segmented cooling condition data. S2: Extract the segmented cooling condition data, input the corresponding data into the speed-cooling effect mapping model to obtain the mapping relationship between the roller speed and the hard candy cooling rate; combine the standard roller diameter parameters of the hard candy cooler to divide the speed adjustment range of the corresponding stage; use the linkage cam swing conveying coordination mechanism to dynamically match the swing amplitude; and obtain the roller frequency conversion speed regulation control strategy by optimizing the corresponding model. S3: Based on the roller frequency conversion speed regulation control strategy, identify the current characteristic state of the hard candy and output the corresponding stage control command; use the dynamic cooling collaborative control model to adjust the motor power in real time, compare the feedback data of the roller speed with the control command threshold; at the same time, link the air volume collaborative model to dynamically adjust the cooling air volume and output the cooling process data; combine the cam swing conveying parameters to fine-tune the swing amplitude and frequency to obtain the cooling execution result. S4: Based on the cooling execution results, perform deviation analysis to obtain the basis for adjusting control parameters and obtain an optimized scheme for the hard candy cooler with self-iterative frequency conversion control.

[0008] As a preferred technical solution of the present invention, the specific process of acquiring multi-dimensional cooling condition data includes: Obtain raw data on multi-dimensional cooling conditions, including: roller speed, motor power, sugar blank temperature, cooling air volume, and feed output data; The original data is deduplicated and outlier removed to obtain regularized data; The regularized data is aligned according to the time series, and the time deviation of the data is calibrated and corrected by combining the time series data to obtain multi-dimensional cooling condition data.

[0009] Specifically, the process of obtaining segmented cooling condition data includes: Based on the multi-dimensional cooling condition data, extract the temperature change gradient feature data of the sugar blank, filter the multi-dimensional cooling condition data, input the filtering results into the cluster analysis model, and obtain the cluster analysis results. The cluster analysis results include at least three cooling stages: pre-cooling, uniform cooling, and shaping. The operating data of the three cooling stages are uniquely identified. Based on the identified stage data, a feature importance ranking algorithm is used to extract features and screen the core features of the corresponding stages. The core features include at least: average speed and peak cooling rate. A segmented cooling condition sample library was constructed to obtain segmented cooling condition data.

[0010] Specifically, the process for obtaining the mapping relationship between the roller rotation speed and the cooling rate of the hard candy includes: Based on the segmented cooling condition data, the roller speed and corresponding sugar blank cooling rate data of the cooling stage are extracted; The extracted data is input into the rotation speed-cooling effect mapping model. The deviation between the predicted cooling rate and the actual cooling rate is compared to obtain the corresponding hard candy cooling rate, thus obtaining the mapping relationship between the roller rotation speed and the hard candy cooling rate.

[0011] Specifically, the process of dividing the speed adjustment range into corresponding stages includes: Based on the mapping relationship and the standard roller diameter parameters of the hard candy cooler, the threshold range of the cooling rate is calculated using the cooling rate-rotation speed reverse derivation algorithm. Based on the calculation results, the corresponding roller speed range is derived, and the speed thresholds are obtained, including at least the pre-cooling, uniform cooling and shaping stages.

[0012] Specifically, the process of dynamically matching the swing amplitude includes: Based on the speed adjustment range of the cooling stage, the roller running speed of the corresponding stage is obtained. Using speed-amplitude correlation analysis, the sway amplitude at the corresponding speed is measured to establish a basic matching relationship. The adjusting bolts of the cam swing mechanism are adjusted by utilizing the linkage cam swing conveying coordination mechanism. Construct a swing amplitude-speed correlation model, input the current roller speed into the swing amplitude-speed correlation model, and obtain the appropriate swing amplitude parameters; Based on the swing amplitude parameters, operational tests were conducted, and the swing amplitude adjustment bolts were fine-tuned according to the test results to dynamically adapt the swing amplitude and roller speed.

[0013] Specifically, the process of obtaining the roller frequency conversion speed control strategy includes: Input the speed adjustment range and sway amplitude matching parameters into the roller frequency conversion speed control model, and optimize the coordination and matching of speed adjustment and cam sway. The adaptability of the roller frequency conversion speed regulation control model was verified by simulating working conditions with different output levels. Based on the verification results, the roller frequency conversion speed regulation control model is fine-tuned, and the roller frequency conversion speed regulation control strategy for the corresponding stage is output.

[0014] Specifically, the process of identifying the current characteristic state of the hard candy includes: Preset the threshold values ​​of the sugar blank characteristic parameters for the corresponding stage, and obtain the characteristic data of the current hard candy; The filtered feature data is input into the cooling stage identification model to identify the current feature state of the hard candy. The feature state includes: pre-cooling state, uniform cooling state, and shaping state.

[0015] Specifically, the process of outputting the control instructions for the corresponding stage includes: Retrieve the control parameters for the corresponding stage and, in conjunction with the current operating status of the cooler, obtain real-time operating data; The speed control command and air volume control command are obtained through a dynamic command generation mechanism. The corresponding control commands are verified, and the command parameter deviations are corrected based on the verification results. The corresponding cooler control commands for the corresponding stage are then output.

[0016] Specifically, the process of comparing the feedback data of the roller speed with the control command threshold includes: The roller is driven to run according to the corresponding control commands through a soft start control mechanism, and the roller speed data is obtained. Extract the rotational speed threshold from the control command, compare the roller rotational speed data with the rotational speed threshold in real time, calculate the deviation between the feedback data and the threshold, and obtain the deviation result.

[0017] Specifically, the process of dynamically adjusting the cooling airflow using the linked airflow coordination model includes: Based on the cooling stage identification results, the air volume range parameters of the corresponding stage are retrieved, and the air volume-temperature correlation optimization is performed to obtain the match between the air volume parameters and the cooling requirements. Based on the comparison deviation between the speed feedback data and the control command threshold, the coordinated matching of air volume and speed is obtained; Input the real-time operating data of the cooler into the air volume coordination model to obtain the air volume adjustment parameters; Acquire data on sugar blank temperature changes, verify the airflow adjustment results, and dynamically adjust the cooling airflow based on the verification results to control the sugar blank temperature fluctuation range.

[0018] Specifically, the process of performing deviation analysis includes: Based on the cooling execution results, the number of qualified samples of hard candy after cooling is extracted, and the actual operation data of the cooling process is exported. The actual operating data is compared with the preset parameters in the roller frequency conversion speed regulation control strategy, and the deviation value of the corresponding parameter is calculated by using the deviation quantization calculation algorithm. Attribution analysis is performed based on the deviation values ​​to pinpoint the causes of the deviations. At the same time, the causes of the deviations are graded and evaluated to obtain the basis for adjusting the control parameters.

[0019] The beneficial effects of this invention are as follows: By collecting multi-dimensional cooling condition data and precisely dividing the cooling process into segments, the hard candy cooling process is clearly divided into three key stages: pre-cooling, uniform cooling, and shaping, and each stage is uniquely identified and managed. Combined with the speed-cooling effect mapping model, a stable correspondence between roller speed and cooling rate is established. The optimal speed range can be intelligently matched according to the needs of different cooling stages, so that the candy blank is heated evenly and cooled smoothly throughout the entire cooling process. This fundamentally solves the problems of deformation, cracking, and surface roughness of hard candy caused by the fixed speed and uneven cooling of traditional coolers.

[0020] By adopting the roller frequency conversion speed regulation and linkage cam swing conveyor coordinated control technology, the swing amplitude and frequency can be dynamically adjusted according to the real-time speed. At the same time, the linkage air volume coordination model automatically adapts to the cooling air volume, realizing the synchronous coordination and dynamic self-adaptation of the three key parameters of speed, conveying and air volume, so that the cooler can maintain the best operating state under different production conditions of hard candy of different output and specifications.

[0021] By forming a closed-loop control system through real-time data feedback, threshold comparison, and deviation self-iterative optimization, the system can monitor the roller operating status in real time and correct control command deviations, automatically complete parameter fine-tuning and strategy iteration upgrades, reduce wear on core components, extend equipment life, promote the upgrading of hard candy cooling production lines towards intelligence, automation, high efficiency and energy saving, and improve the overall economic benefits and market competitiveness of the production line. Attached Figure Description

[0022] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0023] Figure 1 This is a schematic flowchart of an optimization method for a hard candy cooler based on roller frequency conversion speed regulation control according to the present invention. Figure 2 This is a structural block diagram of the coolant linkage control in this invention. Detailed Implementation

[0024] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0025] Please see Figure 1-2 An optimization method for a hard candy cooler based on roller frequency conversion speed control includes: S1: Obtain multi-dimensional cooling condition data, divide the multi-dimensional cooling condition data into cooling stages and assign unique identifiers to obtain segmented cooling condition data. S2: Extract the segmented cooling condition data, input the corresponding data into the speed-cooling effect mapping model to obtain the mapping relationship between the roller speed and the hard candy cooling rate; combine the standard roller diameter parameters of the hard candy cooler to divide the speed adjustment range of the corresponding stage; use the linkage cam swing conveying coordination mechanism to dynamically match the swing amplitude; and obtain the roller frequency conversion speed regulation control strategy by optimizing the corresponding model. S3: Based on the roller frequency conversion speed regulation control strategy, identify the current characteristic state of the hard candy and output the corresponding stage control command; use the dynamic cooling collaborative control model to adjust the motor power in real time, compare the feedback data of the roller speed with the control command threshold; at the same time, link the air volume collaborative model to dynamically adjust the cooling air volume and output the cooling process data; combine the cam swing conveying parameters to fine-tune the swing amplitude and frequency to obtain the cooling execution result. S4: Based on the cooling execution results, perform deviation analysis to obtain the basis for adjusting control parameters and obtain an optimized scheme for the hard candy cooler with self-iterative frequency conversion control.

[0026] As a preferred technical solution of the present invention, the specific process of acquiring multi-dimensional cooling condition data includes: Obtain raw data on multi-dimensional cooling conditions, including: roller speed, motor power, sugar blank temperature, cooling air volume, and feed output data; The original data is deduplicated and outlier removed to obtain regularized data; The regularized data is aligned according to the time series, and the time deviation of the data is calibrated and corrected by combining the time series data to obtain multi-dimensional cooling condition data.

[0027] In this embodiment, by arranging corresponding sensors at different key locations in the cooler, synchronous data acquisition of roller speed, motor power, sugar blank temperature, cooling airflow, and feed output is achieved. This effectively avoids the data bias caused by a single sensor, while ensuring the comprehensiveness and real-time nature of the data acquisition. Each set of data accurately reflects the actual operating status of the cooler. After acquiring the raw data, targeted deduplication and outlier removal operations are performed. Through preset data filtering rules, abnormal data that exceeds the normal operating range is identified and deleted. The normalized data is aligned according to the time sequence, and combined with time-series data calibration technology, time deviation correction is applied to the data acquired by different sensors to avoid analytical bias caused by data acquisition time differences and improve data synchronization.

[0028] Specifically, the process of obtaining segmented cooling condition data includes: Based on the multi-dimensional cooling condition data, extract the temperature change gradient feature data of the sugar blank, filter the multi-dimensional cooling condition data, input the filtering results into the cluster analysis model, and obtain the cluster analysis results. The cluster analysis results include at least three cooling stages: pre-cooling, uniform cooling, and shaping. The operating data of the three cooling stages are uniquely identified. Based on the identified stage data, a feature importance ranking algorithm is used to extract features and screen the core features of the corresponding stages. The core features include at least: average speed and peak cooling rate. A segmented cooling condition sample library was constructed to obtain segmented cooling condition data.

[0029] In this embodiment, the temperature gradient characteristics of the sugar blank can reflect different stages of the cooling process. As the cooling process progresses, the temperature of the sugar blank will show different decreasing patterns. By extracting this characteristic data, the different stages of the cooling process can be clearly distinguished. A clustering analysis model is used to analyze and process the screened multi-dimensional cooling condition data to ensure the objectivity and accuracy of the division results. Finally, three cooling stages are obtained: pre-cooling, homogenization, and shaping, which meet the actual process requirements of hard candy cooling. The condition data of the three cooling stages are uniquely identified, and exclusive identification information is assigned to each stage. Based on the identified stage data, a feature importance ranking algorithm is used for feature extraction to screen out the core features of each stage, eliminate redundant data with low correlation to the cooling effect, and focus on key parameters such as the average rotation speed and peak cooling rate.

[0030] Specifically, the process for obtaining the mapping relationship between the roller rotation speed and the cooling rate of the hard candy includes: Based on the segmented cooling condition data, the roller speed and corresponding sugar blank cooling rate data of the cooling stage are extracted; The extracted data is input into the rotation speed-cooling effect mapping model. The deviation between the predicted cooling rate and the actual cooling rate is compared to obtain the corresponding hard candy cooling rate, thus obtaining the mapping relationship between the roller rotation speed and the hard candy cooling rate.

[0031] In this embodiment, based on segmented cooling condition data, the roller speed and corresponding sugar blank cooling rate data for each cooling stage are extracted one by one. These two sets of data are the core of constructing the correlation between speed and cooling effect. The extracted core data are input into the speed-cooling effect mapping model. Through multiple rounds of data calculation and fitting, the model obtains a preliminary mapping relationship between roller speed and hard candy cooling rate, reflecting the basic correlation between the two. The predicted cooling rate output by the model is compared with the actual cooling rate, and the deviation value of each set of data is recorded. Based on the magnitude and distribution of the deviation value, the core parameters of the model, such as weights and thresholds, are gradually corrected to optimize the model's fitting accuracy and accurately reflect the changing pattern of hard candy cooling rate under different roller speeds.

[0032] Specifically, the process of dividing the speed adjustment range into corresponding stages includes: Based on the mapping relationship and the standard roller diameter parameters of the hard candy cooler, the threshold range of the cooling rate is calculated using the cooling rate-rotation speed reverse derivation algorithm. Based on the calculation results, the corresponding roller speed range is derived, and the speed thresholds are obtained, including at least the pre-cooling, uniform cooling and shaping stages.

[0033] In this embodiment, when dividing the speed adjustment range for the corresponding stages, the mapping relationship between roller speed and hard candy cooling rate is fully combined, and the standard roller diameter parameter of the hard candy cooler is incorporated. Through parameter correlation calculation, the speed adjustment is matched with the equipment performance. A cooling rate-speed reverse derivation algorithm is adopted, with the required cooling effect for each cooling stage as the core objective. Combined with the process requirements of candy molding, the corresponding cooling rate threshold range is calculated in reverse, clarifying the reasonable range of cooling rate for each stage, ensuring the cooling effect meets the requirements of hard candy molding. Based on the calculated cooling rate threshold range, the corresponding roller speed range is further derived in reverse. Combining the different cooling requirements of the three cooling stages—pre-cooling, uniform cooling, and shaping—the speed threshold for each stage is obtained, allowing for differentiated speed control at different stages. Through reverse derivation, a precise correspondence between cooling requirements and speed parameters is achieved. The division of speed thresholds for different cooling stages meets the differentiated cooling requirements of each stage, avoiding problems such as uneven cooling and candy deformation caused by a single speed.

[0034] Specifically, the process of dynamically matching the swing amplitude includes: Based on the speed adjustment range of the cooling stage, the roller running speed of the corresponding stage is obtained. Using speed-amplitude correlation analysis, the sway amplitude at the corresponding speed is measured to establish a basic matching relationship. The adjusting bolts of the cam swing mechanism are adjusted by utilizing the linkage cam swing conveying coordination mechanism. Construct a swing amplitude-speed correlation model, input the current roller speed into the swing amplitude-speed correlation model, and obtain the appropriate swing amplitude parameters; Based on the swing amplitude parameters, operational tests were conducted, and the swing amplitude adjustment bolts were fine-tuned according to the test results to dynamically adapt the swing amplitude and roller speed.

[0035] In this embodiment, the core of dynamically matching the sway amplitude is to achieve coordinated adaptation between the roller speed and the sway amplitude. Based on the speed adjustment range of each cooling stage, the roller running speed of the corresponding stage is obtained in real time through a speed sensor. Through speed-amplitude correlation analysis, the sway amplitude corresponding to different roller speeds is measured and multiple sets of test data are recorded using field testing to establish a basic matching relationship between the two. Utilizing the linkage cam sway conveying coordination mechanism, the eccentricity of the cam is changed by manually adjusting the adjusting bolt of the cam sway mechanism, thereby achieving preliminary adjustment of the sway amplitude and ensuring that the sway mechanism can flexibly adjust according to the roller speed. A sway amplitude-speed correlation model is constructed. The roller speed of the current stage is input into the model. The model combines the established basic matching relationship and outputs sway amplitude parameters adapted to the current speed through algorithm calculation, thus achieving sway amplitude matching. The operation test is conducted based on the output swing amplitude parameters to simulate the hard candy conveying scenario in the actual production process. The conveying status of the hard candy is continuously observed, and abnormal situations such as deviation and collision during the conveying process are recorded. The swing amplitude adjustment bolt is finely adjusted according to the test results to dynamically and adaptively adjust the swing amplitude and roller speed to ensure that the two always maintain the best fit and avoid problems such as deviation, collision and adhesion during the conveying of hard candy.

[0036] Specifically, the process of obtaining the roller frequency conversion speed control strategy includes: Input the speed adjustment range and sway amplitude matching parameters into the roller frequency conversion speed control model, and optimize the coordination and matching of speed adjustment and cam sway. The adaptability of the roller frequency conversion speed regulation control model was verified by simulating working conditions with different output levels. Based on the verification results, the roller frequency conversion speed regulation control model is fine-tuned, and the roller frequency conversion speed regulation control strategy for the corresponding stage is output.

[0037] In this embodiment, when acquiring the roller frequency conversion speed control strategy, the previously determined speed adjustment range and sway amplitude matching parameters are input one by one into the roller frequency conversion speed control model to optimize the coordinated matching of speed adjustment and cam sway. By setting a coordinated response mechanism, the adjustment of roller speed and the change of cam sway amplitude can be synchronously linked. Multi-condition simulation tests are conducted on operating data of different output levels to simulate cooling scenarios under different feed outputs in actual production, covering various common operating conditions such as low, medium, and high output. The model's operating status and control effect are observed to verify its adaptability to different production scenarios, proving that the model is applicable to various production conditions. Based on the verification results of the simulation tests, problems such as adaptation deviation and adjustment lag that occur during model operation are investigated. The parameters of the roller frequency conversion speed control model are fine-tuned to gradually optimize the model's control accuracy and response speed, ensuring that the model can quickly respond to changes in operating conditions. After parameter fine-tuning, the roller frequency conversion speed control strategy for the corresponding stage is output.

[0038] Specifically, the process of identifying the current characteristic state of the hard candy includes: Preset the threshold values ​​of the sugar blank characteristic parameters for the corresponding stage, and obtain the characteristic data of the current hard candy; The filtered feature data is input into the cooling stage identification model to identify the current feature state of the hard candy. The feature state includes: pre-cooling state, uniform cooling state, and shaping state.

[0039] In this embodiment, when identifying the current characteristic state of the hard candy, preset threshold values ​​for sugar blank characteristic parameters corresponding to each cooling stage are established. Combining the cooling requirements of each stage with the sugar blank forming pattern, the judgment criteria for characteristic parameters such as temperature and hardness of the sugar blank in the three stages of pre-cooling, homogenization, and shaping are clarified. Characteristic data of the current hard candy, including core parameters such as sugar blank temperature and hardness, are acquired through corresponding high-precision sensors. After data acquisition, the characteristic data is filtered using a digital filtering algorithm to effectively eliminate the influence of external interference factors such as ambient temperature and equipment operating noise, significantly improving the accuracy of characteristic data acquisition and ensuring that the data input to the model can truly and accurately reflect the actual state of the current hard candy. The filtered characteristic data is input into the cooling stage identification model. The model, combined with preset characteristic parameter thresholds, performs a group-by-group comparative analysis of the current hard candy's characteristic data. Through similarity calculation, the current cooling stage of the hard candy is determined, and the corresponding characteristic state is identified.

[0040] Specifically, the process of outputting the control instructions for the corresponding stage includes: Retrieve the control parameters for the corresponding stage and, in conjunction with the current operating status of the cooler, obtain real-time operating data; The speed control command and air volume control command are obtained through a dynamic command generation mechanism. The corresponding control commands are verified, and the command parameter deviations are corrected based on the verification results. The corresponding cooler control commands for the corresponding stage are then output.

[0041] In this embodiment, when outputting control commands for the corresponding stage, the control parameters for the corresponding stage are precisely retrieved from the roller frequency conversion speed control strategy based on the currently identified hard candy characteristic state. These control parameters cover core indicators such as rotational speed and airflow, ensuring that the control parameters meet the needs of the current cooling stage and remain consistent with the process requirements for candy cooling. Combining the current operating status of the cooler, real-time operating data such as motor power, roller speed, and cooling airflow are acquired through various types of sensors. The retrieved control parameters are correlated with the real-time operating data to adapt to the current operating status of the cooler. Through a dynamic command generation mechanism, corresponding rotational speed control commands and airflow control commands are automatically generated based on the retrieved control parameters and real-time operating data. During the generation process, the control commands are adapted to changes in the cooler's operating status in real time, achieving flexible adaptation and dynamically adjusting command parameters according to the cooler's operating status. After generating the control commands, a comprehensive verification is performed on the corresponding control commands. Each command parameter is checked to ensure it conforms to the cooler's equipment specifications and operating requirements, identifying and addressing issues such as parameter anomalies and command conflicts. Based on the verification results, command parameter deviations are corrected, and the corresponding cooler control commands for the stage are output.

[0042] Specifically, the process of comparing the feedback data of the roller speed with the control command threshold includes: The roller is driven to run according to the corresponding control commands through a soft start control mechanism, and the roller speed data is obtained. Extract the rotational speed threshold from the control command, compare the roller rotational speed data with the rotational speed threshold in real time, calculate the deviation between the feedback data and the threshold, and obtain the deviation result.

[0043] In this embodiment, when comparing the feedback data of the roller speed with the control command threshold, a soft-start control mechanism is used to drive the roller operation. This mechanism smoothly starts the roller, avoiding excessive current during startup that could impact the motor and roller. The roller operates smoothly according to the corresponding control commands. Roller speed data is acquired in real time through a speed sensor, which continuously collects speed information during roller operation; the collected speed data is the speed feedback data. A preset speed threshold is extracted from the control commands, set according to the speed adjustment range of each cooling stage. The real-time collected roller speed data is compared with the extracted speed threshold using a real-time data comparison algorithm. Each set of feedback data is compared with the threshold, and the deviation between the feedback data and the threshold is calculated to obtain a clear deviation result.

[0044] Specifically, the process of dynamically adjusting the cooling airflow using the linked airflow coordination model includes: Based on the cooling stage identification results, the air volume range parameters of the corresponding stage are retrieved, and the air volume-temperature correlation optimization is performed to obtain the match between the air volume parameters and the cooling requirements. Based on the comparison deviation between the speed feedback data and the control command threshold, the coordinated matching of air volume and speed is obtained; Input the real-time operating data of the cooler into the air volume coordination model to obtain the air volume adjustment parameters; Acquire data on sugar blank temperature changes, verify the airflow adjustment results, and dynamically adjust the cooling airflow based on the verification results to control the sugar blank temperature fluctuation range.

[0045] In this embodiment, when the coordinated airflow model dynamically adjusts the cooling airflow, it accurately retrieves the corresponding airflow range parameters from a preset parameter library based on the cooling stage identification results. These airflow range parameters are set in conjunction with the cooling requirements of each stage. Through airflow-temperature correlation optimization technology, the relationship between airflow parameters and sugar blank cooling temperature is analyzed. By fitting multiple sets of data, the airflow parameters are matched with the cooling requirements, ensuring that the airflow adjustment aligns with the temperature control requirements of the current cooling stage. Based on the comparison deviation between the previously obtained speed feedback data and the control command threshold, the deviation information is integrated into the airflow adjustment logic, establishing a correlation between speed deviation and airflow adjustment. This achieves coordinated matching of airflow and speed, allowing the airflow adjustment to synchronize with changes in roller speed, avoiding uneven cooling caused by a disconnect between airflow and speed. Real-time operating data of the cooling machine, including roller speed, sugar blank temperature, and motor power, are input into the coordinated airflow model. The model processes this data through algorithms, combining preset airflow range parameters with speed deviation data, to output airflow adjustment parameters adapted to the current operating conditions. Temperature sensor data is used to obtain sugar blank temperature change data. This data is used to verify the rationality of the air volume adjustment results and determine whether the current air volume can meet the cooling requirements. Based on the verification results, the cooling air volume is dynamically fine-tuned to ensure that the sugar blank temperature fluctuation is controlled within a reasonable range, ensuring the stability and uniformity of the cooling effect and avoiding sugar blank forming defects caused by improper air volume.

[0046] Specifically, the process of performing deviation analysis includes: Based on the cooling execution results, the number of qualified samples of hard candy after cooling is extracted, and the actual operation data of the cooling process is exported. The actual operating data is compared with the preset parameters in the roller frequency conversion speed regulation control strategy, and the deviation value of the corresponding parameter is calculated by using the deviation quantization calculation algorithm. Attribution analysis is performed based on the deviation values ​​to pinpoint the causes of the deviations. At the same time, the causes of the deviations are graded and evaluated to obtain the basis for adjusting the control parameters.

[0047] In this embodiment, during deviation analysis, based on the cooling execution results, a comprehensive collection of cooled hard candy products is performed. The condition of each product is checked individually, distinguishing between qualified and unqualified samples. The number of qualified samples after cooling is extracted, directly reflecting the actual cooling effect. Simultaneously, all actual operating data during the cooling process is exported from the cooling machine control system. This data covers all core operating parameters such as roller speed, cooling airflow, motor power, and candy blank temperature. The exported actual operating data is compared one by one with the preset parameters in the roller frequency conversion speed control strategy. A deviation quantification calculation algorithm is used to quantify the actual values ​​and preset values ​​of the core parameters, accurately determining the deviation values ​​for each parameter. These deviation values ​​clearly reflect the gap between the actual operating state and the preset requirements. Based on the calculated deviation values, an attribution analysis model is used for in-depth analysis, systematically investigating the specific causes of the deviations and accurately locating their root causes. The causes of deviations include various possible factors such as speed adjustment deviations, improper airflow matching, and insufficient sway amplitude adaptation. At the same time, the causes of deviations are graded and evaluated, and the severity of deviations is classified according to their impact. Deviation factors that have a greater impact on the cooling effect are given priority, so as to obtain a clear and accurate basis for adjusting the control parameters.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An optimization method for a hard candy cooler based on roller frequency conversion speed regulation control, characterized in that, include: S1: Obtain multi-dimensional cooling condition data, divide the multi-dimensional cooling condition data into cooling stages and assign unique identifiers to obtain segmented cooling condition data. S2: Extract the segmented cooling condition data, input the corresponding data into the speed-cooling effect mapping model to obtain the mapping relationship between the roller speed and the hard candy cooling rate; combine the standard roller diameter parameters of the hard candy cooler to divide the speed adjustment range of the corresponding stage; use the linkage cam swing conveying coordination mechanism to dynamically match the swing amplitude; and obtain the roller frequency conversion speed regulation control strategy by optimizing the corresponding model. S3: Based on the roller frequency conversion speed regulation control strategy, identify the current characteristic state of the hard candy and output the corresponding stage control command; use the dynamic cooling collaborative control model to adjust the motor power in real time, compare the feedback data of the roller speed with the control command threshold; at the same time, link the air volume collaborative model to dynamically adjust the cooling air volume and output the cooling process data; combine the cam swing conveying parameters to fine-tune the swing amplitude and frequency to obtain the cooling execution result. S4: Based on the cooling execution results, perform deviation analysis to obtain the basis for adjusting control parameters and obtain an optimized scheme for the hard candy cooler with self-iterative frequency conversion control.

2. The method according to claim 1, characterized in that, The specific process for acquiring multi-dimensional cooling condition data includes: Obtain raw data on multi-dimensional cooling conditions, including: roller speed, motor power, sugar blank temperature, cooling air volume, and feed output data; The original data is deduplicated and outlier removed to obtain regularized data; The regularized data is aligned according to the time series, and the time deviation of the data is calibrated and corrected by combining the time series data to obtain multi-dimensional cooling condition data.

3. The method according to claim 1, characterized in that, The specific process for obtaining segmented cooling condition data includes: Based on the multi-dimensional cooling condition data, extract the temperature change gradient feature data of the sugar blank, filter the multi-dimensional cooling condition data, input the filtering results into the cluster analysis model, and obtain the cluster analysis results. The cluster analysis results include at least three cooling stages: pre-cooling, uniform cooling, and shaping. The operating data of the three cooling stages are uniquely identified. Based on the identified stage data, a feature importance ranking algorithm is used to extract features and screen the core features of the corresponding stages. The core features include at least: average speed and peak cooling rate. A segmented cooling condition sample library was constructed to obtain segmented cooling condition data.

4. The method according to claim 1, characterized in that, The specific process for obtaining the mapping relationship between the roller rotation speed and the hard candy cooling rate includes: Based on the segmented cooling condition data, the roller speed and corresponding sugar blank cooling rate data of the cooling stage are extracted; The extracted data is input into the rotation speed-cooling effect mapping model. The deviation between the predicted cooling rate and the actual cooling rate is compared to obtain the corresponding hard candy cooling rate, thus obtaining the mapping relationship between the roller rotation speed and the hard candy cooling rate.

5. The method according to claim 1, characterized in that, The specific process of dividing the speed adjustment range into corresponding stages includes: Based on the mapping relationship and the standard roller diameter parameters of the hard candy cooler, the threshold range of the cooling rate is calculated using the cooling rate-rotation speed reverse derivation algorithm. Based on the calculation results, the corresponding roller speed range is derived, and the speed thresholds are obtained, including at least the pre-cooling, uniform cooling and shaping stages.

6. The method according to claim 1, characterized in that, The specific process of dynamically matching the swing amplitude includes: Based on the speed adjustment range of the cooling stage, the roller running speed of the corresponding stage is obtained. Using speed-amplitude correlation analysis, the sway amplitude at the corresponding speed is measured to establish a basic matching relationship. The adjusting bolts of the cam swing mechanism are adjusted by utilizing the linkage cam swing conveying coordination mechanism. Construct a swing amplitude-speed correlation model, input the current roller speed into the swing amplitude-speed correlation model, and obtain the appropriate swing amplitude parameters; Based on the swing amplitude parameters, operational tests were conducted, and the swing amplitude adjustment bolts were fine-tuned according to the test results to dynamically adapt the swing amplitude and roller speed.

7. The method according to claim 1, characterized in that, The specific process of obtaining the roller frequency conversion speed control strategy includes: Input the speed adjustment range and sway amplitude matching parameters into the roller frequency conversion speed control model, and optimize the coordination and matching of speed adjustment and cam sway. The adaptability of the roller frequency conversion speed regulation control model was verified by simulating working conditions with different output levels. Based on the verification results, the roller frequency conversion speed regulation control model is fine-tuned, and the roller frequency conversion speed regulation control strategy for the corresponding stage is output.

8. The method according to claim 1, characterized in that, The specific process for identifying the current characteristic state of the hard candy includes: Preset the threshold values ​​of the sugar blank characteristic parameters for the corresponding stage, and obtain the characteristic data of the current hard candy; The filtered feature data is input into the cooling stage identification model to identify the current feature state of the hard candy. The feature states include: pre-cooling state, uniform cooling state, and shaping state.

9. The method according to claim 1, characterized in that, The specific process of outputting the control command corresponding to the stage includes: Retrieve the control parameters for the corresponding stage and, in conjunction with the current operating status of the cooler, obtain real-time operating data; The speed control command and air volume control command are obtained through a dynamic command generation mechanism. The corresponding control commands are verified, and the deviation of the command parameters is corrected based on the verification results. The corresponding cooler control commands for the corresponding stage are then output.

10. The method according to claim 1, characterized in that, The specific process of comparing the feedback data of the roller speed with the control command threshold includes: The roller is driven to run according to the corresponding control commands through a soft start control mechanism, and the roller speed data is obtained. Extract the rotational speed threshold from the control command, compare the roller rotational speed data with the rotational speed threshold in real time, calculate the deviation between the feedback data and the threshold, and obtain the deviation result.

11. The method according to claim 1, characterized in that, The specific process of dynamically adjusting the cooling airflow using the linked airflow coordination model includes: Based on the cooling stage identification results, the air volume range parameters of the corresponding stage are retrieved, and the air volume-temperature correlation optimization is performed to obtain the match between the air volume parameters and the cooling requirements. Based on the comparison deviation between the speed feedback data and the control command threshold, the coordinated matching of air volume and speed is obtained; Input the real-time operating data of the cooler into the air volume coordination model to obtain the air volume adjustment parameters; Acquire data on sugar blank temperature changes, verify the airflow adjustment results, and dynamically adjust the cooling airflow based on the verification results to control the sugar blank temperature fluctuation range.

12. The method according to claim 1, characterized in that, The specific process of performing deviation analysis includes: Based on the cooling execution results, the number of qualified samples of hard candy after cooling is extracted, and the actual operation data of the cooling process is exported. The actual operating data is compared with the preset parameters in the roller frequency conversion speed regulation control strategy, and the deviation value of the corresponding parameter is calculated by using the deviation quantization calculation algorithm. Attribution analysis is performed based on the deviation values ​​to pinpoint the causes of the deviations. At the same time, the causes of the deviations are graded and evaluated to obtain the basis for adjusting the control parameters.

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