Intelligent speed regulation method and system for a mixer

Abstract

The application discloses a kind of intelligent speed regulation methods of mixer, comprising: according to initial speed regulation parameter configuration, motor speed fluctuation data is collected using real-time sensor, when speed fluctuation exceeds preset threshold, it is judged whether it deviates from reference value range, and speed deviation data is obtained;For speed deviation data, the difference between current speed fluctuation and reference value is calculated by preset speed correction algorithm, and the corrected speed adjustment value is determined;For the uniformity evaluation result that does not reach preset standard, then the combination of speed fluctuation frequency and amplitude is recalculated by short-period dynamic adjustment mechanism, to determine new speed regulation parameter;According to new speed regulation parameter, update motor control instruction, continuously monitor the mixing state of food material in mixing process, obtain the latest uniformity feedback data;According to the optimized control parameter, continuously execute motor speed adjustment and uniformity detection cycle until the preset mixing uniformity standard is reached, to determine the final mixing control scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of edible mixer control, in particular to a mixer intelligent speed regulation method and system. BACKGROUND

[0002] The intelligent speed regulation technology of food mixers is an important research direction in the field of kitchen appliances, and it is of key significance to improve the efficiency of food processing and user experience. With the increasing demand of consumers for food processing refinement and personalization, intelligent speed regulation technology has become one of the core driving forces for industry innovation, and its importance is self-evident.

[0003] However, the speed regulation methods of most mixers on the current market still remain at the level of simple speed adjustment, lacking targeted processing of different food material characteristics. This single speed regulation method often fails to adapt to the mixing needs of complex food materials, resulting in uneven stirring effect and even affecting the taste and quality of the final food.

[0004] In this context, the intelligent speed regulation field faces significant technical challenges. The first and foremost is how to accurately control the motor speed fluctuation to adapt to the physical characteristics of different food materials, such as batter which requires a larger stirring amplitude, while nuts require a more meticulous processing method. Due to the lack of control accuracy of speed fluctuation, it is difficult to form effective regular changes in the stirring process, which further leads to another problem, i.e., it is impossible to break the static structure between food materials through short-period dynamic adjustment, thereby achieving more uniform mixing effect. These two problems are interrelated, the former is the basis of technical implementation, and the latter is the key link directly affecting user experience, which together constitute the bottleneck of intelligent speed regulation technology breakthrough. SUMMARY

[0005] The present application provides a mixer intelligent speed regulation method and system to solve the above technical problems.

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

[0007] A mixer intelligent speed regulation method, comprising:

[0008] Pre-establishing a food material characteristic database, determining the speed fluctuation range and frequency reference value for food materials with different physical characteristics, and obtaining initial speed regulation parameter configuration;

[0009] According to the initial speed regulation parameter configuration, collecting motor speed fluctuation data, and when the speed fluctuation exceeds the preset threshold, determining whether it deviates from the speed reference value range to obtain speed deviation data;

[0010] For the speed deviation data, a preset speed correction algorithm is used to calculate the difference between the current speed fluctuation and the speed reference value, and determine the corrected speed adjustment value;

[0011] According to the corrected speed adjustment value, the speed fluctuation is monitored in real time, the speed curve is dynamically optimized, the speed fluctuation frequency and amplitude are adjusted in a short period, and the adjusted real-time speed data is obtained.

[0012] Further, the obtaining of the initial speed regulation parameter configuration comprises:

[0013] By means of the pre-constructed food material characteristic database, the type information of the food material currently processed is obtained, the food material is classified according to different physical characteristics, and a preliminary type classification result is determined;

[0014] According to the type classification result, the physical characteristic data related to different food materials is extracted, the corresponding speed fluctuation range is matched, and a preliminary speed configuration scheme is obtained;

[0015] If the speed configuration scheme deviates from the frequency reference value, the speed fluctuation range is adjusted by comparing the historical frequency data in the database, and a corrected speed parameter is generated;

[0016] For the corrected speed parameter, the frequency reference value associated therewith is obtained, it is judged whether the frequency reference value conforms to a preset threshold range, and a verified frequency parameter is output;

[0017] According to the verified frequency parameter, the physical characteristic data in the food material characteristics are combined, a preset speed regulation model is used to determine the final initial speed regulation parameter configuration, and a speed regulation parameter set is generated.

[0018] Further, the obtaining of the speed deviation data comprises:

[0019] By means of the real-time collected motor data, the sensor is used to continuously monitor the speed fluctuation, it is judged whether the fluctuation exceeds a preset threshold, and preliminary fluctuation state data is obtained;

[0020] According to the preliminary fluctuation state data, the speed fluctuation is compared and analyzed with respect to the reference range, if the speed fluctuation exceeds the reference range, the deviation information is extracted, and the specific deviation degree data is determined.

[0021] Further, the obtaining of the speed deviation data further comprises:

[0022] By means of the deviation degree data, the speed regulation parameter in the initial configuration is combined, the deviation information is processed by using a preset mapping rule, and an adjusted parameter correction value is obtained;

[0023] According to the adjusted parameter correction value, the motor data is updated in real time, it is judged by using a logical verification method whether the correction value conforms to a preset threshold, and verified parameter data is obtained;

[0024] Through the parameter data after the check, combined with the result of the fluctuation judgment, the speed fluctuation is analyzed twice, if the fluctuation still exceeds the reference range, the parameter is fine-tuned through the historical database record to determine the final speed regulation scheme;

[0025] According to the final speed regulation scheme, the real-time collected sensor values are continuously compared, the support vector machine algorithm is used to optimize the speed regulation scheme, and the control parameters suitable for the current environment are obtained;

[0026] Through the control parameters suitable for the current environment, the results of data detection are dynamically updated, whether the range comparison requirements are met is judged, and stable running state data is obtained.

[0027] Further, the determination of the corrected speed adjustment value comprises:

[0028] Through the speed deviation data, the preset correction algorithm is used to analyze the difference between the current speed and the speed reference value, calculate the specific fluctuation degree, and obtain the preliminary correction parameter;

[0029] According to the preliminary correction parameter, the speed fluctuation data is compared and processed, if the fluctuation degree exceeds the preset threshold, the parameter is corrected through the reference comparison logic, and the adjusted correction value is determined;

[0030] Through the adjusted correction value, combined with the speed data, the current speed is monitored in real time, whether the correction value meets the expected range is judged by using the fluctuation analysis method, and the verified correction data is obtained.

[0031] Further, the determination of the corrected speed adjustment value further comprises:

[0032] According to the verified correction data, the fluctuation degree is checked twice, if the fluctuation degree still exceeds the reference comparison range, the historical speed data is compared and analyzed to determine a further adjustment scheme;

[0033] Through the further adjustment scheme, combined with the real-time state of the current speed, the support vector machine algorithm is used to optimize the adjustment scheme, and the control parameters suitable for the environment are obtained;

[0034] According to the control parameters suitable for the environment, the speed fluctuation data is dynamically updated, if the updated fluctuation degree still does not meet the reference comparison condition, the parameter is fine-tuned through the preset logical verification tool, and the final correction result is determined;

[0035] Through the final correction result, the speed deviation data is continuously tracked, the correction result is verified by using the data calculation method, and the stable running parameter is obtained.

[0036] Further, the acquisition of the adjusted real-time speed data comprises:

[0037] For the corrected speed adjustment value, an adaptive intelligent algorithm is used to monitor the speed fluctuation value in real time, and by analyzing the fluctuation monitoring value and comparing it with the preset threshold range, the fluctuation state data is obtained;

[0038] According to the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, the frequency adjustment value in the speed curve graph is preliminarily corrected by a dynamic adjustment method, and a preliminary frequency parameter is obtained;

[0039] For the preliminary frequency parameter, if the preliminary frequency parameter does not match the target frequency range, the amplitude adjustment value is processed synchronously, the matching degree of the amplitude adjustment value and the frequency adjustment value is analyzed by an information comparison tool, and the corrected amplitude parameter is determined;

[0040] Through the corrected amplitude parameter, combined with real-time data flow, the optimized speed value is updated in a short time period, and a data verification tool is used to determine whether the updated speed value meets the preset range, and the verified speed data is obtained.

[0041] Further, the obtained adjusted real-time speed data further comprises:

[0042] According to the verified speed data, if the verified speed data still has deviation, data comparison and analysis are performed in combination with historical fluctuation monitoring values, further adjustment parameters are obtained through a pre-established mapping relationship table;

[0043] For the further adjustment parameters, a support vector machine algorithm is used to comprehensively analyze the speed fluctuation value and the optimized speed value, and under the support of real-time monitoring method, the final speed control parameter is determined;

[0044] Through the final speed control parameter, the speed curve graph is dynamically updated, the fluctuation monitoring value is continuously tracked in combination with real-time data flow, whether the updated fluctuation state is stable is judged, and stable control data is obtained.

[0045] An intelligent speed regulation system of a mixer, comprising:

[0046] A parameter configuration and deviation detection module is used to obtain the current food material type information through a pre-established food material characteristic database, determine the corresponding speed fluctuation range and frequency reference value for different physical characteristics, and obtain the initial speed regulation parameter configuration; According to the initial speed regulation parameter configuration, real-time sensor is used to collect motor speed fluctuation data, and when the speed fluctuation exceeds the preset threshold, it is judged whether it deviates from the speed reference value range, and the speed deviation data is obtained;

[0047] The rotation speed correction and optimization module is used for calculating the difference between the current rotation speed fluctuation and the rotation speed reference value through a preset rotation speed correction algorithm for the rotation speed deviation data, determining the corrected rotation speed adjustment value, introducing an adaptive intelligent algorithm according to the corrected rotation speed adjustment value, monitoring the rotation speed fluctuation in real time, dynamically optimizing the rotation speed curve, adjusting the rotation speed fluctuation frequency and amplitude in a short period, and obtaining the adjusted real-time rotation speed data.

[0048] The uniformity detection and adjustment module is used for analyzing the structural change state of the food material in the stirring process by using a hybrid uniformity detection module for the adjusted real-time rotation speed data, judging whether the preset uniformity standard is reached, obtaining the uniformity evaluation result, and recalculating the rotation speed fluctuation frequency and amplitude combination through a short-period dynamic adjustment mechanism for the uniformity evaluation result that does not reach the preset standard, and determining new speed regulation parameters.

[0049] The control instruction and strategy adjustment module is used for updating the motor control instruction according to the new speed regulation parameters, continuously monitoring the food material mixing state in the stirring process, obtaining the latest uniformity feedback data, and adjusting the dynamic structure breaking strategy by increasing the rotation speed fluctuation frequency in a short period for the latest uniformity feedback data if the static structure is not broken, and obtaining the optimized control parameters.

[0050] The cycle control module is used for continuously executing the motor rotation speed adjustment and uniformity detection cycle according to the optimized control parameters until the preset mixing uniformity standard is reached, and determining the final stirring control scheme.

[0051] Compared with the prior art, the present application has the following beneficial effects:

[0052] 1、The present application establishes a database containing the physical characteristics of various food materials in advance, obtains the current food material type information through a sensor, and retrieves the corresponding rotation speed fluctuation range and frequency reference value from the database, so that the blender can adjust the rotation speed according to the physical characteristics of different food materials, solving the problem that the single speed regulation mode in the prior art cannot adapt to different food materials.

[0053] 2、In the stirring process, the present application collects motor rotation speed fluctuation data in real time, and when the rotation speed fluctuation exceeds the preset threshold, judges whether it deviates from the rotation speed reference value range to obtain the rotation speed deviation data, calculates the difference between the current rotation speed fluctuation and the rotation speed reference value through a preset rotation speed correction algorithm for the rotation speed deviation data, determines the corrected rotation speed adjustment value, ensures that the blender always maintains the best rotation speed fluctuation when processing different food materials, and further improves the adaptability and accuracy of the stirring effect.

[0054] 3、The application introduces an adaptive intelligent algorithm, monitors the speed fluctuation in real time according to the corrected speed adjustment value, and dynamically optimizes the speed curve; the frequency and amplitude of the speed fluctuation are adjusted in a short period, and through this dynamic adjustment strategy, the static structure between the food materials is broken; the adaptive intelligent algorithm can automatically adjust the regularity of the speed fluctuation according to the real-time monitoring data, so that a more uniform mixing effect is realized.

[0055] 4、The application adopts a mixing uniformity detection module for the adjusted real-time speed data, analyzes the structural change state of the food materials in the stirring process, judges whether the preset uniformity standard is reached, and if not, recalculates the combination of the speed fluctuation frequency and amplitude, determines new speed regulation parameters, and the feedback optimization mechanism based on the mixing uniformity ensures that the mixer can gradually optimize the stirring effect after each adjustment, and finally reaches the ideal mixing uniformity standard. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 A flowchart of an embodiment 1 intelligent speed regulation method of a mixer;

[0057] Figure 2 A flowchart of an embodiment 2 intelligent speed regulation method of a mixer;

[0058] Figure 3 A framework diagram of an embodiment 3 intelligent speed regulation system of a mixer. DETAILED DESCRIPTION

[0059] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Embodiment 1

[0060] As shown in Figure 1 , the present embodiment provides an intelligent speed regulation method of a mixer, comprising:

[0061] A food material characteristic database is pre-established, the speed fluctuation range and frequency reference value are determined for food materials with different physical characteristics, and initial speed regulation parameter configuration is obtained;

[0062] According to the initial speed regulation parameter configuration, the motor speed fluctuation data is collected, and when the speed fluctuation exceeds the preset threshold, it is judged whether the speed reference value range is deviated, and the speed deviation data is obtained;

[0063] For the rotational speed deviation data, a preset rotational speed correction algorithm is used to calculate the difference between the current rotational speed fluctuation and the rotational speed reference value, and to determine the corrected rotational speed adjustment value;

[0064] According to the corrected rotational speed adjustment value, the rotational speed fluctuation is monitored in real time, the rotational speed curve is dynamically optimized, the rotational speed fluctuation frequency and amplitude are adjusted in a short period, and the adjusted real-time rotational speed data is obtained.

[0065] Further, the obtained initial speed regulation parameter configuration includes:

[0066] Through the pre-constructed food material characteristic database, the current processing food material type information is obtained, the different physical characteristics of food materials are classified and processed, and the preliminary type classification result is determined;

[0067] According to the type classification result, the physical characteristic data related to different food materials is extracted, the corresponding rotational speed fluctuation range is matched, and the preliminary rotational speed configuration scheme is obtained;

[0068] If the rotational speed configuration scheme deviates from the frequency reference value, the rotational speed fluctuation range is adjusted by comparing the historical frequency data in the database, and the corrected rotational speed parameter is generated;

[0069] For the corrected rotational speed parameter, the frequency reference value associated therewith is obtained, it is judged whether the frequency reference value conforms to the preset threshold range, and the verified frequency parameter is output;

[0070] According to the verified frequency parameter, the physical characteristic data in the food material characteristics are combined, a preset speed regulation model is used to determine the final initial speed regulation parameter configuration, and a speed regulation parameter set is generated;

[0071] Through the speed regulation parameter set, the current processing food material type information is combined, and a support vector machine algorithm is used to optimize the parameter set, so as to obtain an optimized speed regulation configuration result;

[0072] If the optimized speed regulation configuration result does not match the actual processing environment, a secondary calibration is performed through the historical processing records in the database, and a final adaptive speed regulation parameter scheme is output.

[0073] Specifically, in some embodiments, the following can be described:

[0074] Through the pre-constructed food material characteristic database, the current processing food material type information can be automatically obtained, for example, assuming that the current processing food material is yogurt, the database stores the physical characteristic data of yogurt, including the key parameters of viscosity 1500 centipoise and acidity 120 °T, the characteristic record of yogurt is automatically matched through the query interface, and the corresponding processing requirement is extracted;

[0075] Then, for the viscosity characteristics of the yogurt, the speed fluctuation range is determined according to the built-in algorithm, and the formula is: speed fluctuation range = basic speed × (viscosity coefficient / 1000), wherein the basic speed is set to 1800 rpm, the viscosity coefficient is 1500 / 1000 = 1.5, and the speed fluctuation range is 1800 × 1.5 = 2700 rpm, i.e. the speed can fluctuate between 900 and 3600 rpm, and at the same time, the influence of acidity 120 °T is combined, the frequency reference value is set to 45 Hz by analyzing the influence of acidity on speed frequency, and the calculation method is: frequency reference value = basic frequency + (acidity-100) × 0.25, wherein the basic frequency is 40 Hz, and the acidity adjustment value is (120-100) × 0.25 = 5, and 45 Hz is obtained as the reference;

[0076] Subsequently, if the oatmeal food material is processed, the database shows that its hardness is 4 levels (full level 10) and the fiber content is 10%, according to the positive correlation between hardness and speed, the formula speed fluctuation range = hardness × 400 is used to calculate the fluctuation range, which is 4 × 400 = 1600 rpm, i.e. the speed is between 1200 and 2800 rpm, and the frequency reference value is set to 60 Hz by the hardness linear mapping algorithm, to ensure the mixing efficiency of high-fiber food materials;

[0077] Finally, the initial speed regulation parameter configuration is generated according to the above calculation results, for example, the configuration of the yogurt is speed range 900-3600 rpm, frequency 45 Hz, and the configuration of the oatmeal is speed range 1200-2800 rpm, frequency 60 Hz.

[0078] Further, the obtained speed deviation data includes:

[0079] Through real-time acquisition of motor data, the sensor continuously monitors the speed fluctuation, judges whether the fluctuation exceeds the preset threshold, and obtains the preliminary fluctuation state data;

[0080] According to the preliminary fluctuation state data, the speed fluctuation is compared and analyzed with the reference range, if the speed fluctuation exceeds the reference range, the deviation information is extracted, and the specific deviation degree data is determined.

[0081] Further, the obtained speed deviation data further includes:

[0082] Through the deviation degree data, the speed regulation parameters in the initial configuration are combined, and the deviation information is processed by using the preset mapping rule to obtain the adjusted parameter correction value;

[0083] According to the adjusted parameter correction value, the motor data is updated in real time, and the logical verification method is used to judge whether the correction value meets the preset threshold, and the verified parameter data is obtained;

[0084] Through the parameter data after the check, combined with the result of the fluctuation judgment, the speed fluctuation is analyzed twice, if the fluctuation still exceeds the reference range, the parameter is fine-tuned through the historical database record to determine the final speed regulation scheme;

[0085] According to the final speed regulation scheme, the real-time collected sensor values are continuously compared, the support vector machine algorithm is used to optimize the speed regulation scheme, and the control parameters suitable for the current environment are obtained;

[0086] Through the control parameters suitable for the current environment, the results of data detection are dynamically updated to determine whether the range comparison requirements are met to obtain stable running state data.

[0087] Specifically, in some embodiments, the following can be described:

[0088] According to the initial speed regulation parameter configuration, the motor speed fluctuation data is collected and analyzed by the real-time sensor to ensure the stability of the equipment operation.

[0089] For example, assuming that the speed range set in the initial configuration is 1500 to 3500 revolutions per minute, the reference value is 2500 revolutions per minute, and the high-precision sensor built-in collects 10 speed data per second, the current speed fluctuation value is 1480 revolutions per minute, which is lower than the preset lower limit of 1500 revolutions per minute;

[0090] Then start the deviation detection algorithm, calculate the deviation value: deviation value = reference value-current speed, i.e. 2500-1480=1020 revolutions per minute, and compare this deviation with the preset threshold value 800 revolutions per minute, and find that it exceeds the threshold range;

[0091] Next, automatically enter the deviation analysis module, combine the historical running data, judge whether the speed deviation is caused by load mutation, analyze and obtain the load increase coefficient of 1.2, adjust the value through the formula: adjustment value = deviation value / load increase coefficient, i.e. 1020 / 1.2=850 revolutions per minute, determine that the speed needs to be adjusted up by 850 revolutions per minute to approach the reference value. Then, the adjustment instruction is transmitted to the motor control unit, the speed is updated to 2330 revolutions per minute in real time, and the fluctuation data is continuously monitored to form a closed-loop feedback mechanism;

[0092] To ensure the logical integrity, the current fluctuation detection service is also associated, if the current value exceeds the safe range 1.5 amperes after the speed adjustment, the speed is further fine-tuned to the safe interval, for example, reduced to 2200 revolutions per minute, to avoid the risk of overload. Through the above series of automatic processing, the complete logical chain from data collection to deviation judgment to parameter adjustment is realized to ensure the stable operation of the equipment under complex working conditions.

[0093] Further, the determination of the corrected speed adjustment value comprises:

[0094] Through the rotational speed deviation data, the preset correction algorithm is used to analyze the difference between the current rotational speed and the rotational speed reference value, to calculate the specific fluctuation degree, to obtain the preliminary correction parameter;

[0095] According to the preliminary correction parameter, the rotational speed fluctuation data is compared and processed, if the fluctuation degree exceeds the preset threshold value, the parameter is corrected through the reference comparison logic, to determine the adjusted correction value;

[0096] Through the adjusted correction value, combined with the rotational speed data, the current rotational speed is monitored in real time, and the fluctuation analysis method is used to judge whether the correction value meets the expected range, to obtain the verified correction data.

[0097] Further, the determination of the corrected rotational speed adjustment value further comprises:

[0098] According to the verified correction data, the fluctuation degree is rechecked, if the fluctuation degree still exceeds the reference comparison range, the historical rotational speed data is compared and analyzed to determine a further adjustment scheme;

[0099] Through the further adjustment scheme, combined with the real-time state of the current rotational speed, the support vector machine algorithm is used to optimize the adjustment scheme, to obtain the control parameter adapted to the environment;

[0100] According to the control parameter adapted to the environment, the rotational speed fluctuation data is dynamically updated, if the updated fluctuation degree still does not meet the reference comparison condition, the parameter is fine-tuned through the preset logical verification tool, to determine the final correction result;

[0101] Through the final correction result, the rotational speed deviation data is continuously tracked, the correction result is verified by using the data calculation method, to obtain the stable operation parameter.

[0102] Specifically, in some embodiments, it can be as follows:

[0103] For the processing of the rotational speed deviation data, a series of automatic algorithms and analysis processes are used to complete the complete logical chain from deviation calculation to rotational speed correction;

[0104] Firstly, based on the preset reference rotational speed value 1800 rpm, combined with the real-time collected rotational speed data, for example, the current rotational speed is 1650 rpm, the difference between the two is calculated, that is, 1800-1650=150 rpm, as the initial deviation data;

[0105] Then, a speed correction algorithm is called to compare the deviation value with a historical fluctuation trend, and it is analyzed that the speed drop is likely to be related to the increase of ambient temperature, the temperature influence coefficient is 1.1, and then the preliminary adjustment amount is determined by the correction formula: adjustment value=deviation value*temperature influence coefficient, that is, 150*1.1=165 revolutions / minute, and the preliminary adjustment amount is determined as increasing by 165 revolutions / minute;

[0106] At the same time, the vibration frequency detection service is introduced as the association logic, and if the vibration frequency exceeds the standard value of 0.5 Hz, the adjustment value is further corrected. Assuming that the current vibration frequency is 0.6 Hz, the adjustment amplitude is automatically reduced by 10%, that is, 165*0.9=148.5 revolutions / minute, and the corrected speed adjustment value is finally determined as increasing by 148.5 revolutions / minute;

[0107] Then, the adjustment value is converted into a control signal and transmitted to the drive module to update the target speed to 1798.5 revolutions / minute, and the adjusted speed data and vibration frequency change are recorded in real time to form a data feedback chain. If the vibration frequency is still not returned to the normal range in subsequent detection, a secondary correction process will be automatically started to recalculate the adjustment value combined with the temperature and vibration double coefficients to ensure a closed loop logic.

[0108] Through this series of automatic processing, the whole process intelligent management from deviation identification, influence factor analysis to parameter correction is realized.

[0109] Further, the obtaining of the adjusted real-time speed data comprises:

[0110] For the corrected speed adjustment value, an adaptive intelligent algorithm is used to monitor the speed fluctuation value in real time, and the fluctuation state data is obtained by analyzing and comparing the fluctuation monitoring value with the preset threshold range.

[0111] According to the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, the frequency adjustment value in the speed curve is preliminarily corrected by a dynamic adjustment method to obtain a preliminary frequency parameter.

[0112] For the preliminary frequency parameter, if the preliminary frequency parameter does not match the target frequency range, the amplitude adjustment value is processed synchronously, the matching degree of the amplitude adjustment value and the frequency adjustment value is analyzed by an information comparison tool, and a corrected amplitude parameter is determined.

[0113] Through the corrected amplitude parameter, the optimized speed value is updated in a short time period in combination with real-time data flow, and a data verification tool is used to determine whether the updated speed value meets the preset range to obtain verified speed data.

[0114] The preliminary correction of the frequency adjustment value in the speed curve comprises:

[0115] Real-time monitoring of speed fluctuation, generating an optimized curve to calculate the smoothing coefficient according to the fluctuation characteristics;

[0116] Optimized speed increment : ;

[0117] Introducing a load compensation factor: load fluctuation rate , load compensation factor , adjusted increment : ;

[0118] Update real-time speed data :

[0119] ;

[0120] Preliminary correction of frequency adjustment value :

[0121] ;

[0122] Synchronous processing of amplitude adjustment value, corrected amplitude parameter , if the preliminary frequency adjustment value is within the target frequency range, the current amplitude adjustment value is kept unchanged; if it exceeds the target frequency range, it is adjusted according to the matching degree of the frequency adjustment value and the target frequency range;

[0123] Updated optimized speed value , if the updated optimized speed value is within the preset range, the verification is passed; if it exceeds the preset range, it needs to be further adjusted.

[0124] Further, the obtaining of the adjusted real-time speed data further comprises:

[0125] According to the verified speed data, if the verified speed data still has deviation, data comparison and analysis are performed in combination with historical fluctuation monitoring values, further adjustment parameters are obtained through a pre-established mapping relationship table;

[0126] For further adjustment parameters, support vector machine algorithm is adopted to comprehensively analyze the speed fluctuation value and the optimized speed value, and under the support of real-time monitoring method, the final speed control parameter is determined;

[0127] Through the final speed control parameter, the speed curve graph is dynamically updated, the fluctuation monitoring value is continuously tracked in combination with real-time data flow, whether the updated fluctuation state is stable is judged, and stable control data is obtained.

[0128] Specifically, in some embodiments, it can be as follows:

[0129] For the corrected speed adjustment value, the complete process of real-time monitoring and dynamic optimization of the speed curve is realized through an adaptive intelligent algorithm.

[0130] Assuming that the corrected speed adjustment value has been determined to be increased by 120 rpm, first, the speed data is collected every second through the built-in sensor, and the current real-time speed is 1720 rpm, which is still 30 rpm lower than the target value of 1750 rpm.

[0131] Next, the adaptive algorithm is started, and the speed fluctuation frequency in the short period is analyzed. Assuming that the fluctuation frequency is 3 times per minute, and the amplitude is ±10 rpm, the algorithm generates an optimization curve based on the fluctuation characteristics, calculates a smoothing coefficient of 0.8, and calculates the optimized speed increment through the formula: optimized speed increment = fluctuation amplitude * smoothing coefficient, i.e. 10 * 0.8 = 8 rpm, and the preliminary adjustment increment per period is determined to be 8 rpm.

[0132] Subsequently, combined with historical data analysis, it is found that the speed fluctuation is related to the load change, and the load fluctuation rate is 5%, so a load compensation factor of 1.05 is introduced, and the further adjustment increment is 8 * 1.05 = 8.4 rpm, ensuring that the adjusted speed is closer to the target value.

[0133] Finally, the adjusted real-time speed data is updated to 1728.4 rpm, and the fluctuation frequency and amplitude change are recorded to the database to form a closed-loop feedback.

[0134] At the same time, the power consumption monitoring service is automatically associated, and if the power fluctuation exceeds the standard value of 2%, the auxiliary algorithm is triggered to recalculate the optimization curve to ensure that the speed adjustment is balanced with energy consumption.

[0135] Through this series of automatic processing, dynamic management from fluctuation monitoring to curve optimization is realized. Embodiment 2

[0136] As shown in Figure 2 The present embodiment provides an intelligent speed regulation method for a blender, comprising:

[0137] A food material characteristic database is pre-established, and for food materials with different physical characteristics, the speed fluctuation range and frequency reference value are determined to obtain initial speed regulation parameter configuration;

[0138] According to the initial speed regulation parameter configuration, the motor speed fluctuation data is collected, and when the speed fluctuation exceeds the preset threshold, it is judged whether it deviates from the speed reference value range to obtain the speed deviation data.

[0139] For the speed deviation data, the difference between the current speed fluctuation and the speed reference value is calculated through the preset speed correction algorithm, and the corrected speed adjustment value is determined.

[0140] According to the corrected speed adjustment value, the speed fluctuation is monitored in real time, the speed curve is dynamically optimized, the speed fluctuation frequency and amplitude are adjusted in a short period, and the adjusted real-time speed data is obtained.

[0141] Further, the obtaining of the initial speed regulation parameter configuration comprises:

[0142] By means of the pre-constructed food material characteristic database, the type information of the food material currently processed is obtained, the different physical characteristics of the food material are classified and processed, and the preliminary type classification result is determined.

[0143] According to the type classification result, the physical characteristic data related to different food materials is extracted, the corresponding speed fluctuation range is matched, and the preliminary speed configuration scheme is obtained.

[0144] If the speed configuration scheme deviates from the frequency reference value, the speed fluctuation range is adjusted by comparing the historical frequency data in the database, and the corrected speed parameter is generated.

[0145] For the corrected speed parameter, the frequency reference value associated therewith is obtained, it is judged whether the frequency reference value conforms to the preset threshold range, and the verified frequency parameter is output.

[0146] According to the verified frequency parameter, the physical characteristic data in the food material characteristics are combined, the final initial speed regulation parameter configuration is determined through the preset speed regulation model, and a speed regulation parameter set is generated.

[0147] Further, the obtaining of the speed deviation data comprises:

[0148] By means of the real-time collected motor data, the sensor is used to continuously monitor the speed fluctuation, it is judged whether the fluctuation exceeds the preset threshold, and the preliminary fluctuation state data is obtained.

[0149] According to the preliminary fluctuation state data, the speed fluctuation is compared and analyzed with the reference range, if the speed fluctuation exceeds the reference range, the deviation information is extracted, and the specific deviation degree data is determined.

[0150] Further, the obtaining of the speed deviation data further comprises:

[0151] By means of the deviation degree data, the speed regulation parameter in the initial configuration is combined, the deviation information is processed by means of the preset mapping rule, and the adjusted parameter correction value is obtained.

[0152] According to the adjusted parameter correction value, the motor data is updated in real time, it is judged by means of the logical verification method whether the correction value conforms to the preset threshold, and the verified parameter data is obtained.

[0153] Through the parameter data after the check, combined with the result of the fluctuation judgment, the speed fluctuation is analyzed twice, if the fluctuation still exceeds the reference range, the parameter is fine-tuned through the historical database record to determine the final speed regulation scheme;

[0154] According to the final speed regulation scheme, the real-time collected sensor values are continuously compared, the support vector machine algorithm is used to optimize the speed regulation scheme, and the control parameters suitable for the current environment are obtained;

[0155] Through the control parameters suitable for the current environment, the results of data detection are dynamically updated, whether the range comparison requirements are met is judged, and stable running state data is obtained.

[0156] Further, the determination of the corrected speed adjustment value comprises:

[0157] Through the speed deviation data, the preset correction algorithm is used to analyze the difference between the current speed and the speed reference value, calculate the specific fluctuation degree, and obtain the preliminary correction parameter;

[0158] According to the preliminary correction parameter, the speed fluctuation data is compared and processed, if the fluctuation degree exceeds the preset threshold, the parameter is corrected through the reference comparison logic, and the adjusted correction value is determined;

[0159] Through the adjusted correction value, combined with the speed data, the current speed is monitored in real time, whether the correction value meets the expected range is judged by using the fluctuation analysis method, and the verified correction data is obtained.

[0160] Further, the determination of the corrected speed adjustment value further comprises:

[0161] According to the verified correction data, the fluctuation degree is checked twice, if the fluctuation degree still exceeds the reference comparison range, the historical speed data is compared and analyzed to determine a further adjustment scheme;

[0162] Through the further adjustment scheme, combined with the real-time state of the current speed, the support vector machine algorithm is used to optimize the adjustment scheme, and the control parameters suitable for the environment are obtained;

[0163] According to the control parameters suitable for the environment, the speed fluctuation data is dynamically updated, if the updated fluctuation degree still does not meet the reference comparison condition, the parameter is fine-tuned through the preset logical verification tool, and the final correction result is determined;

[0164] Through the final correction result, the speed deviation data is continuously tracked, the correction result is verified by using the data calculation method, and the stable running parameter is obtained.

[0165] Further, the acquisition of the adjusted real-time speed data comprises:

[0166] For the corrected speed adjustment value, an adaptive intelligent algorithm is used to monitor the speed fluctuation value in real time, and by analyzing the fluctuation monitoring value and comparing it with the preset threshold range, the fluctuation state data is obtained;

[0167] According to the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, the frequency adjustment value in the speed curve graph is preliminarily corrected by a dynamic adjustment method, and a preliminary frequency parameter is obtained;

[0168] For the preliminary frequency parameter, if the preliminary frequency parameter does not match the target frequency range, the amplitude adjustment value is processed synchronously, and the matching degree of the amplitude adjustment value and the frequency adjustment value is analyzed by an information comparison tool to determine the corrected amplitude parameter;

[0169] Through the corrected amplitude parameter, combined with real-time data flow, the optimized speed value is updated in a short time period, and a data verification tool is used to determine whether the updated speed value meets the preset range, and the verified speed data is obtained.

[0170] Further, the obtaining of the adjusted real-time speed data further comprises:

[0171] According to the verified speed data, if the verified speed data still has deviation, data comparison and analysis are performed in combination with historical fluctuation monitoring values, and further adjustment parameters are obtained through a pre-established mapping relationship table;

[0172] For the further adjustment parameters, a support vector machine algorithm is used to comprehensively analyze the speed fluctuation value and the optimized speed value, and under the support of real-time monitoring method, the final speed control parameter is determined;

[0173] Through the final speed control parameter, the speed curve graph is dynamically updated, the fluctuation monitoring value is continuously tracked in combination with real-time data flow, and whether the updated fluctuation state is stable is determined, and stable control data is obtained.

[0174] Further, the method further comprises:

[0175] For the adjusted real-time speed data, the structural change state of the food material in the stirring process is analyzed, and whether the preset uniformity standard is reached is determined, and a uniformity evaluation result is obtained;

[0176] For the uniformity evaluation result that does not meet the preset standard, the speed fluctuation frequency and amplitude combination are recalculated through a short-period dynamic adjustment mechanism, and a new speed regulation parameter is determined;

[0177] According to the new speed regulation parameter, the motor control instruction is updated, and the food material mixing state in the stirring process is continuously monitored to obtain the latest uniformity feedback data;

[0178] For the latest uniformity feedback data, if it is detected that the static structure has not yet been broken, the dynamic structure breaking strategy is adjusted by increasing the number of speed fluctuations in the short cycle, and the optimized control parameters are obtained;

[0179] According to the optimized control parameters, the motor speed adjustment and uniformity detection cycle is continuously executed until the preset mixing uniformity standard is reached, and the final stirring control scheme is determined.

[0180] Further, the obtaining of the uniformity evaluation result comprises:

[0181] The real-time speed data is preliminarily processed by the mixing module, the change of the state of the food material during stirring is analyzed, and preliminary structure change data is obtained;

[0182] According to the preliminary structure change data, the uniform state of the food material is continuously monitored by using a detection analysis tool, whether it is close to the preset standard is judged, and monitoring data of the uniform state is obtained;

[0183] For the monitoring data of the uniform state, if the monitoring data does not reach the preset standard, the speed data during stirring is compared and analyzed through the information processing link to determine the adjustment direction data;

[0184] According to the adjustment direction data, real-time speed fluctuation information during stirring is obtained, which is compared with the preset threshold range to obtain reference data for speed adjustment;

[0185] For the reference data for speed adjustment, a support vector machine algorithm is used to comprehensively analyze the structure change and the uniform state, and optimized speed control parameters are obtained;

[0186] Through the optimized speed control parameters, the speed data during stirring is dynamically updated, and whether the target data of the uniform state is reached is judged in combination with real-time monitoring information;

[0187] According to the target data of the uniform state, if the target data still does not meet the preset standard, information comparison is performed in combination with the structure change data in the historical stirring process to determine the final speed adjustment parameter.

[0188] Specifically, in some embodiments, the following can be described:

[0189] For the adjusted real-time speed data, the structure change state of the food material during stirring is analyzed by the mixing uniformity detection module to determine whether the preset uniformity standard is reached, and an evaluation result is generated;

[0190] Firstly, the high-precision image sensor collects the food material distribution image data in the stirring container every 5 seconds. Assuming that the current image analysis shows that the particle size distribution range of the food material is 2.5 to 8.3 mm, and the preset uniformity standard requires that the particle size distribution range be within 3.0 to 5.0 mm, the current uniformity deviation value is calculated to be 35%;

[0191] Subsequently, the built-in uniformity analysis algorithm is started, the image data is converted into a gray value matrix, the variance of the gray distribution is calculated, and assuming that the variance value is 12.7 and the standard variance threshold is 5.0, it is indicated that the current mixing state has a high degree of dispersion;

[0192] Next, according to the variance value and the particle distribution range, a weighted calculation formula is used: uniformity score = 100-(variance value*2+deviation value*1.5), and the current uniformity score is obtained to be 100-(12.7*2+35*1.5)=22.1, which is far lower than the preset qualified score of 80;

[0193] Further, in combination with the stirring time data, assuming that the current stirring time is 120 seconds, historical data analysis shows that the uniformity score is positively correlated with the stirring time, and the correlation coefficient is 0.75, so the stirring time is automatically extended to 150 seconds, and the stirring resistance monitoring service is associated. If the resistance value exceeds the preset threshold of 10 Newton, the resistance compensation algorithm is triggered, and the stirring mode is adjusted to intermittent operation to reduce the load of the equipment;

[0194] Finally, the uniformity score, particle distribution range and adjusted stirring parameters are stored in the database to form a data feedback chain to provide a reference basis for subsequent batches of stirring;

[0195] Through this automatic process, the whole information processing from image acquisition to uniformity evaluation is realized.

[0196] Further, the determination of the new speed regulation parameter comprises:

[0197] Through the short-period adjustment mechanism, data collection is performed on the uniformity evaluation results, and the speed fluctuation related information is extracted therefrom to obtain preliminary distribution data of the fluctuation frequency and the fluctuation amplitude;

[0198] According to the preliminary distribution data, the speed fluctuation is classified and processed by using a preset threshold range to determine whether the fluctuation frequency and the fluctuation amplitude are within an acceptable range, and the classified fluctuation characteristic data is obtained;

[0199] For the classified fluctuation characteristic data, if the fluctuation frequency exceeds the preset threshold range, the historical speed data is compared and analyzed through the information processing link to obtain a reference value for frequency adjustment;

[0200] According to the reference value of frequency adjustment, combined with the fluctuation amplitude data, the support vector machine algorithm is used for comprehensive analysis to determine the preliminary combination scheme of the speed regulation parameter;

[0201] For the preliminary combination scheme of the speed regulation parameter, the real-time data acquisition tool is used to continuously monitor the speed fluctuation during the mixing process to obtain the latest fluctuation characteristic update data;

[0202] According to the latest fluctuation characteristic update data, if the fluctuation amplitude still does not reach the preset standard, the parameter combination is fine-tuned through the information processing link to determine the final speed regulation parameter scheme;

[0203] Through the final speed regulation parameter scheme, the speed fluctuation during the mixing process is dynamically adjusted, and combined with the real-time monitoring information, it is judged whether the related requirements of the uniformity evaluation are met.

[0204] Specifically, in some embodiments, the following can be described:

[0205] For the uniformity evaluation result that does not reach the preset standard, a series of information processing procedures are automatically started through the short-period dynamic adjustment mechanism to recalculate the speed fluctuation frequency and amplitude combination, and finally determine the new speed regulation parameter;

[0206] First, based on historical mixing data and current evaluation results, the initial value of the speed fluctuation frequency is extracted, assuming that the current fluctuation frequency is 15 times per minute and the amplitude is 10% of the speed, while the database analysis shows that the best fluctuation frequency range is 18 to 22 times per minute and the amplitude is 8% to 12% of the speed;

[0207] Through the built-in dynamic optimization algorithm, the frequency adjustment coefficient is calculated, the formula is: new frequency = current frequency + (target frequency median - current frequency) * 0.6, the new frequency is 15 + (20-15) * 0.6 = 18 times / minute, and the amplitude adjustment uses linear interpolation method, the new amplitude is 9.5%;

[0208] Next, the calculated new frequency and amplitude combination are input into the simulation analysis module to predict the adjusted mixing stability, assuming that the simulation result shows that the stability index is 0.85, which is lower than the preset threshold 0.9, and the load distribution monitoring service is automatically associated to detect that the current load peak is 8.2 kilowatts, which exceeds the standard value of 7.5 kilowatts, so the amplitude is further fine-tuned to 9.2% to balance the load and stability;

[0209] Finally, the newly determined speed regulation parameter, i.e. frequency 18 times / minute and amplitude 9.2%, is transmitted to the mixing control unit, and the load change data during the adjustment process is recorded in real time, assuming that the load peak after adjustment is reduced to 7.6 kilowatts, and the stability index is improved to 0.91, meeting the operation requirements;

[0210] The parameters and prediction data are stored in a log to form a closed-loop feedback mechanism to provide data support for subsequent adjustment;

[0211] Through this process, full automation dynamic adjustment from parameter calculation to stability verification is achieved.

[0212] Further, the obtaining of the latest uniformity feedback data comprises:

[0213] By updating the speed regulation parameters, the motor control instructions are reconfigured to obtain adjusted instruction data;

[0214] According to the adjusted instruction data, the mixing state of the food materials during the stirring process is monitored in real time to obtain the current mixing state information;

[0215] For the current mixing state information, if the uniformity value does not reach the preset threshold range, the feedback information is analyzed through the information processing link to determine the parameter direction that needs to be adjusted;

[0216] According to the determined parameter direction, a pre-established regression model is used to predict the parameter adjustment amplitude to obtain an optimized speed regulation parameter combination;

[0217] Through the optimized speed regulation parameter combination, the motor control instructions are updated, and the state monitoring is continuously performed to obtain the latest uniformity feedback information;

[0218] If the latest uniformity feedback information still shows that the mixing state is not up to standard, the historical state data is compared through the information processing link to determine whether there is a periodic fluctuation characteristic;

[0219] According to the periodic fluctuation characteristic obtained by the determination, the control instruction frequency in the stirring process is adjusted to obtain the final stable mixing state data.

[0220] Specifically, in some embodiments, the following can be described:

[0221] According to the newly calculated speed regulation parameters, the motor control instructions are automatically generated and updated, and the mixing state of the food materials during the stirring process is continuously monitored through information means, and the latest uniformity feedback data is obtained to optimize the operation effect;

[0222] Firstly, set the new speed parameter, such as the speed setting of 1200 rpm, the fluctuation range is controlled within ± 5%, and the specific motor control instruction is converted into the corresponding voltage adjustment value through the built-in instruction conversion algorithm. Assuming that the current voltage reference value is 24 volts, the algorithm formula is: target voltage = reference voltage * (1 + fluctuation ratio * 0.8), and the target voltage is 24*(1+0.05*0.8)=24.96 volts. Then the instruction is transmitted to the motor drive module through the data interface to ensure accurate speed execution;

[0223] Next, start the real-time monitoring program, use the multi-point sensor array installed in the mixing tank to collect food material particle distribution data every 2 seconds, and automatically analyze that the mixing state has local aggregation phenomenon, assuming that the current collected distribution variance value is 0.12, and the standard uniformity variance threshold is 0.08.

[0224] To solve this problem, associate the flow field simulation service in the mixing tank, predict the food material particle movement trajectory through the flow field analysis algorithm, calculate that the current mixing paddle angle deviates from the best value by 3.5 degrees, causing uneven flow field, and immediately generate a temporary adjustment instruction to fine-tune the paddle angle to a deviation of 2.0 degrees, and predict that the distribution variance after adjustment can be reduced to 0.09.

[0225] Finally, upload all monitoring data and adjustment records to the cloud analysis platform, compare the current uniformity feedback with the historical data, assuming that the historical average variance is 0.10 and the current value is 0.09, indicating that the mixing state has improved, automatically update the monitoring frequency to every 3 seconds to reduce resource occupation, and store the analysis results in the database to form a data closed loop, providing a reference for subsequent optimization;

[0226] Through this series of automatic processing, the whole process information management from instruction update to state monitoring is realized.

[0227] Further, the obtained optimized control parameter includes:

[0228] By analyzing the uniformity feedback data, it is determined whether the static structure is still broken, and the detailed characteristic information of the current mixing state is obtained;

[0229] According to the obtained mixing state characteristic information, the frequency of speed fluctuation is adjusted for the case that the static structure is not broken, and the fluctuation frequency change range in the short period is determined;

[0230] The adjusted fluctuation frequency range is used to dynamically configure the speed fluctuation in the short period to obtain the preliminary adjustment result of the dynamic structure;

[0231] For the preliminary adjustment result of the dynamic structure, if it is detected that the uniformity feedback data still does not reach the preset threshold value, the difference between the feedback data and the historical data is analyzed through the information processing link to determine whether there is a persistent static structure feature;

[0232] If it is judged that there is a persistent static structure feature, the control parameters are optimized and predicted through a pre-established regression model to obtain an optimized control parameter combination;

[0233] According to the optimized control parameter combination, the specific execution mode of the breaking strategy is adjusted to obtain updated dynamic structure state information;

[0234] For the updated dynamic structure state information, whether the final mixed state meets the preset standard is determined by continuously monitoring the uniformity feedback data.

[0235] Specifically, in some embodiments, the following can be described:

[0236] According to the latest uniformity feedback data, it is assumed that the currently detected distribution variance is 0.15, which is much higher than the target threshold value 0.07, indicating that the static structure has not been effectively broken, and the dynamic structure breaking strategy needs to be adjusted;

[0237] First, through the built-in uniformity analysis algorithm, combined with historical data records, the aggregation index of the static structure under the current mixed state is calculated as 0.22, which is higher than the normal range 0.10, and it is determined that the number of speed fluctuations in the short period needs to be increased to enhance the disturbance effect;

[0238] Next, the speed fluctuation optimization module is called, the short period is set to 30 seconds, the initial speed fluctuation frequency is set to 3 times per cycle, and through the algorithm formula: fluctuation frequency adjustment value = aggregation index * 10, the adjustment value is obtained as 0.22 * 10 = 2.2, and the new fluctuation frequency is determined as 5 times per cycle after rounding up, and the speed fluctuation amplitude is set to plus or minus 8% to ensure the disturbance intensity;

[0239] Subsequently, the optimized parameters are converted into control instructions, assuming that the basic speed is 1000 revolutions per minute, and the fluctuation speed range is 920 to 1080 revolutions per minute, which is transmitted to the drive unit for execution through the data bus;

[0240] Then, the dynamic structure analysis business is associated, and the adjusted mixed state data is collected in real time, assuming that the distribution variance decreases to 0.09 after 5 minutes, which is close to the target threshold value, indicating that the strategy is effective, but still needs to be fine-tuned;

[0241] Further analysis of the relationship between the fluctuation frequency and the uniformity reduction rate shows that each increase of 1 fluctuation can reduce the variance by 0.01, and a prediction value is obtained, so a secondary optimization instruction is generated to increase the fluctuation frequency to 6 times per cycle, and the predicted variance can be further reduced to 0.08;

[0242] Finally, all parameter adjustment records are synchronized with feedback data to the background analysis to form a dynamic strategy optimization archive to provide data support for subsequent similar scenarios, and to ensure that the entire adjustment process is automatically completed through information means.

[0243] Further, the determining the final stirring control scheme comprises:

[0244] By acquiring the current control parameter data, the motor speed is preliminarily configured, the initial adjustment frequency is set, and a preliminary speed configuration result is obtained;

[0245] According to the preliminary speed configuration result, a uniformity detection process is synchronously executed, real-time information of the mixing state is obtained from the detection link, and it is determined whether the current state is close to the preset standard;

[0246] If the current state does not reach the preset standard, the deviation between the real-time information and the historical data is analyzed through the information processing link, it is determined whether there is a persistent mixing unevenness feature, and a deviation analysis result is obtained;

[0247] According to the deviation analysis result, a regression model established in advance is used to dynamically correct the control parameters, obtain a corrected parameter combination, and determine a new adjustment frequency range;

[0248] According to the corrected parameter combination and the adjustment frequency range, the motor speed is adjusted in a loop and the state monitoring is continuously executed, and updated mixing state data is obtained;

[0249] Through the updated mixing state data, the preset standard is verified, if it still does not meet the standard, the deviation analysis of the information processing link is repeated, and further optimization parameters are obtained;

[0250] According to the further optimization parameters, the execution mode of the stirring scheme is adjusted, the state monitoring process is synchronously updated, and the final stirring control scheme is determined.

[0251] Specifically, in some embodiments, the following can be described:

[0252] According to the optimized control parameters, the cycle process of motor speed adjustment and uniformity detection is automatically started, the goal is to reach the preset mixing uniformity standard value 0.05, and the stirring control scheme is finally determined;

[0253] First, the current optimization parameters are extracted from the database, assuming that the basic speed is set to 1200 rpm, the speed fluctuation range is plus or minus 5%, the fluctuation period is 20 seconds, and the fluctuation frequency is 4 times per period, and these parameters are converted into control signals through the data bus and sent to the motor drive module for adjustment;

[0254] At the same time, activate the uniformity detection sensor, collect the distribution data of the mixed material every 2 minutes, assuming that the uniformity index of the first detection is 0.12, which is much higher than the target value, calculate the uniformity deviation value 0.07 through the built-in analysis algorithm, and deduce the correlation formula of the number of speed fluctuation and the uniformity index according to the historical trend data: index decrease value = fluctuation frequency increase value * 0.02, according to which the number of fluctuation needs to be increased by 3.5 times, and after rounding, it is adjusted to 7 times per cycle;

[0255] Subsequently, the control instruction is automatically updated, the new parameters are issued to the drive unit, and the uniformity index is obtained as 0.08 in the next round of detection, and the deviation is reduced to 0.03, indicating that the adjustment direction is correct, but still needs to be optimized;

[0256] Further call the material viscosity analysis module, combine the current viscosity value 1.5 Pa s, calculate that the speed fluctuation amplitude needs to be increased to plus or minus 7% to enhance the mixing disturbance effect, and record the adjusted data to the cloud log in real time;

[0257] Finally, when the uniformity index is detected to be reduced to 0.05, the current parameter combination is automatically locked, including the speed 1200 rpm, the fluctuation amplitude plus or minus 7%, the fluctuation period 20 seconds, and the fluctuation frequency 7 times, to form the final stirring control scheme, and the information interface is synchronized to the production management to provide a reference basis for subsequent batches. The whole process does not need manual intervention and is completed by relying on algorithm and data driving. Embodiment 3

[0258] As shown in Figure 3 , the embodiment provides an intelligent speed regulation system of a mixer for realizing an intelligent speed regulation method of a mixer, and the system comprises:

[0259] A parameter configuration and deviation detection module is configured to obtain the type information of the food material currently processed through a pre-established food material characteristic database, determine the corresponding speed fluctuation range and frequency reference value for different physical characteristics, and obtain the initial speed regulation parameter configuration; according to the initial speed regulation parameter configuration, collect the motor speed fluctuation data by using a real-time sensor, and when the speed fluctuation exceeds a preset threshold, judge whether the speed reference value range is deviated, and obtain the speed deviation data;

[0260] A speed correction and optimization module is configured to calculate the difference value between the current speed fluctuation and the speed reference value according to the speed deviation data through a preset speed correction algorithm, determine the corrected speed adjustment value; according to the corrected speed adjustment value, introduce an adaptive intelligent algorithm, monitor the speed fluctuation in real time, dynamically optimize the speed curve, adjust the speed fluctuation frequency and amplitude in a short period, and obtain the adjusted real-time speed data;

[0261] The uniformity detection and adjustment module is configured to analyze the structural change state of the food material in the stirring process by using a hybrid uniformity detection module for the adjusted real-time rotating speed data, to determine whether the preset uniformity standard is reached, and to obtain a uniformity evaluation result. For the uniformity evaluation result that does not reach the preset standard, the rotating speed fluctuation frequency and amplitude combination are recalculated through a short-period dynamic adjustment mechanism to determine new speed regulation parameters.

[0262] The control instruction and strategy adjustment module is configured to update the motor control instruction according to the new speed regulation parameters, to continuously monitor the food material mixing state in the stirring process, to obtain the latest uniformity feedback data, and to adjust the dynamic structure breaking strategy by increasing the rotating speed fluctuation frequency in the short period to obtain optimized control parameters if the static structure is still not broken for the latest uniformity feedback data.

[0263] The cycle control module is configured to continuously execute the motor rotating speed adjustment and uniformity detection cycle according to the optimized control parameters until the preset mixing uniformity standard is reached to determine the final stirring control scheme.

[0264] The specific embodiments of the application are described in detail above, but they only serve as examples. The application is not limited to the specific embodiments described above. Those skilled in the art should understand that the above examples and descriptions in the specification only illustrate the principles of the application. Without departing from the spirit and scope of the application, various changes and improvements can be made to the application. These changes and improvements fall within the scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent speed control of a mixer, characterized in that, include: A database of food characteristics is established in advance. For foods with different physical properties, the range of rotation speed fluctuation and the reference value of frequency are determined to obtain the initial speed regulation parameter configuration. Based on the initial speed regulation parameter configuration, motor speed fluctuation data is collected. When the speed fluctuation exceeds the preset threshold, it is determined whether it deviates from the speed reference value range, and speed deviation data is obtained. For the speed deviation data, the difference between the current speed fluctuation and the speed reference value is calculated using a preset speed correction algorithm, and the corrected speed adjustment value is determined. Based on the corrected speed adjustment value, speed fluctuations are monitored in real time, the speed curve is dynamically optimized, the frequency and amplitude of speed fluctuations are adjusted within a short period, and the adjusted real-time speed data is obtained. Based on the adjusted real-time rotation speed data, the structural changes of the ingredients during the stirring process are analyzed to determine whether the preset uniformity standard has been met, and the uniformity evaluation result is obtained. For uniformity evaluation results that do not meet the preset standards, a short-cycle dynamic adjustment mechanism is used to recalculate the combination of speed fluctuation frequency and amplitude to determine new speed regulation parameters.

2. The intelligent speed control method for a mixer according to claim 1, characterized in that: The initial speed regulation parameter configuration includes: By using a pre-built food characteristic database, information on the types of food currently being processed is obtained, and the food is classified according to its different physical characteristics to determine the preliminary classification results. Based on the type classification results, physical property data related to different ingredients are extracted, and the corresponding speed fluctuation range is matched to obtain a preliminary speed configuration scheme. If there is a deviation between the speed configuration scheme and the frequency reference value, the speed fluctuation range is adjusted by comparing the historical frequency data in the database, and the corrected speed parameters are generated. For the corrected rotational speed parameters, obtain the associated frequency reference value, determine whether the frequency reference value meets the preset threshold range, and output the verified frequency parameters; Based on the verified frequency parameters and the physical property data of the ingredients, the final initial speed regulation parameter configuration is determined through a preset speed regulation model, and a speed regulation parameter set is generated.

3. The intelligent speed control method for a mixer according to claim 1, characterized in that: The obtained speed deviation data includes: By collecting motor data in real time, sensors are used to continuously monitor speed fluctuations, determine whether the fluctuations exceed preset thresholds, and obtain preliminary fluctuation status data. Based on the preliminary fluctuation data, the speed fluctuation is compared and analyzed with the reference range. If the speed fluctuation exceeds the reference range, the deviation information is extracted to determine the specific degree of deviation.

4. The intelligent speed control method for a mixer according to claim 3, characterized in that: The obtained speed deviation data also includes: By using deviation data and combining it with the speed regulation parameters in the initial configuration, the deviation information is processed using a preset mapping rule to obtain the adjusted parameter correction value. Based on the adjusted parameter correction values, the motor data is updated in real time, and a logical verification method is used to determine whether the correction values ​​meet the preset threshold, thus obtaining the verified parameter data. By combining the verified parameter data with the results of the fluctuation judgment, a second analysis of the speed fluctuation is performed. If the fluctuation still exceeds the reference range, the parameters are fine-tuned by using historical database records to determine the final speed regulation scheme. Based on the final speed regulation scheme, the real-time sensor values ​​are continuously compared, and the speed regulation scheme is optimized using the support vector machine algorithm to obtain control parameters that are suitable for the current environment. By adapting the control parameters to the current environment, the data detection results are dynamically updated to determine whether the range comparison requirements are met, thus obtaining stable operating status data.

5. The intelligent speed control method for a mixer according to claim 1, characterized in that: The determination of the corrected speed adjustment value includes: By using the speed deviation data, a preset correction algorithm is used to analyze the difference between the current speed and the speed reference value, calculate the specific degree of fluctuation, and obtain preliminary correction parameters. Based on the initial calibration parameters, the speed fluctuation data is compared and processed. If the fluctuation exceeds the preset threshold, the parameters are corrected through the benchmark comparison logic to determine the adjusted calibration value. By combining the adjusted correction value with the speed data, the current speed is monitored in real time. The fluctuation analysis method is used to determine whether the correction value meets the expected range, and the verified correction data is obtained.

6. The intelligent speed control method for a mixer according to claim 5, characterized in that: The determination of the corrected speed adjustment value also includes: Based on the verified correction data, a second check is conducted on the degree of fluctuation. If the degree of fluctuation still exceeds the benchmark comparison range, a comparative analysis is performed using historical speed data to determine further adjustment plans. By further adjusting the scheme and combining it with the real-time status of the current rotational speed, the support vector machine algorithm is used to optimize the adjustment scheme and obtain control parameters that are adapted to the environment. Based on the control parameters of the adapted environment, the speed fluctuation data is dynamically updated. If the updated fluctuation level still does not meet the benchmark comparison conditions, the parameters are fine-tuned through the preset logic verification tool to determine the final correction result. Based on the final correction results, the speed deviation data is continuously tracked, and the correction results are verified using data calculation methods to obtain stable operating parameters.

7. The intelligent speed control method for a mixer according to claim 1, characterized in that: The process of obtaining the adjusted real-time speed data includes: For the corrected speed adjustment value, an adaptive intelligent algorithm is used to monitor the speed fluctuation value in real time. By analyzing the fluctuation monitoring value and comparing it with the preset threshold range, fluctuation status data is obtained. Based on the acquired fluctuation data, if the fluctuation data exceeds the preset threshold range, the frequency adjustment value in the speed curve is initially corrected by the dynamic adjustment method to obtain the preliminary frequency parameters. If the initial frequency parameters do not match the target frequency range, the amplitude adjustment value is processed synchronously. The matching degree between the amplitude adjustment value and the frequency adjustment value is analyzed by information comparison tools to determine the corrected amplitude parameters. By combining the corrected amplitude parameters with real-time data streams, the optimized speed value is updated within a short time period. A data verification tool is then used to determine whether the updated speed value meets the preset range, thus obtaining the verified speed data.

8. The intelligent speed control method for a mixer according to claim 7, characterized in that: The process of obtaining the adjusted real-time speed data also includes: If the verified speed data still shows a deviation, then the historical fluctuation monitoring values ​​are compared and analyzed, and further adjustment parameters are obtained through a pre-established mapping relationship table. For further parameter adjustments, a support vector machine algorithm is used to comprehensively analyze the speed fluctuation value and the optimized speed value. With the support of real-time monitoring, the final speed control parameters are determined. The speed curve is dynamically updated using the final speed control parameters. The fluctuation monitoring value is continuously tracked in conjunction with the real-time data stream to determine whether the updated fluctuation state is stable, thus obtaining stable control data.

9. The intelligent speed control method for a mixer according to claim 1, characterized in that: Also includes: Based on the new speed adjustment parameters, update the motor control commands, continuously monitor the mixing state of the ingredients during the stirring process, and obtain the latest uniformity feedback data; If the static structure is still not broken according to the latest uniformity feedback data, the dynamic structure breaking strategy is adjusted by increasing the number of speed fluctuations in a short period of time, and the optimized control parameters are obtained. Based on the optimized control parameters, the motor speed adjustment and uniformity detection cycles are continuously executed until the preset mixing uniformity standard is reached, and the final stirring control scheme is determined.

10. A smart speed control system for a mixer, used to implement the aforementioned smart speed control method for a mixer, characterized in that, include: The parameter configuration and deviation detection module is used to obtain the type information of the food being processed through a pre-established food characteristic database, determine the corresponding speed fluctuation range and frequency reference value for different physical characteristics, and obtain the initial speed regulation parameter configuration; based on the initial speed regulation parameter configuration, the module uses real-time sensors to collect motor speed fluctuation data, and when the speed fluctuation exceeds a preset threshold, it determines whether it deviates from the speed reference value range and obtains the speed deviation data; The speed correction and optimization module is used to calculate the difference between the current speed fluctuation and the speed reference value based on the speed deviation data using a preset speed correction algorithm, and determine the corrected speed adjustment value. Based on the corrected speed adjustment value, an adaptive intelligent algorithm is introduced to monitor the speed fluctuation in real time, dynamically optimize the speed curve, adjust the speed fluctuation frequency and amplitude within a short period, and obtain the adjusted real-time speed data. The uniformity detection and adjustment module is used to analyze the structural changes of the ingredients during the mixing process based on the adjusted real-time speed data, and determine whether the preset uniformity standard is met, thus obtaining the uniformity evaluation result. For uniformity evaluation results that do not meet the preset standard, the speed fluctuation frequency and amplitude combination is recalculated through a short-cycle dynamic adjustment mechanism to determine new speed adjustment parameters. The control command and strategy adjustment module is used to update the motor control command according to the new speed adjustment parameters, continuously monitor the mixing state of the ingredients during the stirring process, and obtain the latest uniformity feedback data. If the static structure is still not broken according to the latest uniformity feedback data, the dynamic structure breaking strategy is adjusted by increasing the number of speed fluctuations in a short period of time to obtain optimized control parameters. The cycle control module is used to continuously execute motor speed adjustment and uniformity detection cycles based on optimized control parameters until the preset mixing uniformity standard is reached, and then determine the final stirring control scheme.

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